Soil again

Ascension Day gave me the opportunity to visit the Veluwe and take a series of soil samples. Traveling from Harderwijk to Stroe some stops were made and soil samples collected (none of the water bodies were accessible – all in protected areas, therefore no water samples could be taken).

Solse gat

Previously I took several samples in an area of ten square metres and combined them in one bottle. This time I had a couple of larger bottles, but several smaller ones. After starting out with the combined samples in the large ones I had to put individual samples in the small bottles for the other locations. This was actually a blessing in disguise, because impressive differences came up even for short distances.

As expected, most samples showed rather acidic values (around 4) and a low conductivity (the majority lower than 50 µS/cm). Two were a little bit higher (58 and 91). One sample was a real outlier for both pH and conductivity. Values were 7.05 and 224 µS/cm. The way of working was as usual – I mixed the fresh soil with an equal weight of demineralised water. Conductivity values were not corrected for temperature, because all measurements were done at home at the same time and the slurry temperature was around 18^oC.

I was curious about potential relationships between pH, conductivity and moisture content (as a percentage of the fresh weight after 48 hours of drying with a little help from LED-bars, warming the soil gently). The soil was rather dry from the start and percentages varied between 2% and 28%. Without the outlier, average pH values ranged from 3.85 to 5.55 and the average conductivity per sample was between 9 and 91. Unfortunately the outlier was spoiling the relationships.

Boeschoter gat

In the past we were only allowed to remove outliers (documented and motivated!) if they were more than three standard errors away from the average – both calculated with the outlier in the set. Here this was only 2.84 times, so I didn’t feel free to leave it out after all.

The interesting part was that the outlier was only one of three samples, taken probably 10 metres apart in the surroundings of the “Solse Gat”. It was from the lowest point, the other two from higher up. Now the question was whether the soil types were different. Wikipedia offers a nice soil determination scheme, based on mechanical behaviour and “feel”. The result will be one of the soil types presented in the triangle with percentages of sand, silt and clay.

All soil types were either sand, loamy sand or sandy loam, except for the outlier: it clearly held some clay. Rather unusual for the Veluwe! An information board however, explained that the location was an old loam quarry, so it made sense after all.

The relationships were not very impressive anyway – with or without outlier. Below are some scatter plots with correlations.

A higher conductivity at a lower pH is not surprising. The mineral content is low, so the H⁺ ions will be very influential!

I noticed that most samples with a lower pH had visible pieces of organic matter. A higher percentage of weight loss was certainly related to the organic matter content, but for some it just wasn’t visible, except for a darker colour.

Be aware that in this photograph the samples were still drying

That’s why I came up with my hypothesis:  “the larger organic fraction (withheld by a sieve) with have a lower pH than the fraction passing the sieve”, but that’s for the next time.

For now I can tell you that neither ammonia nor nitrate could be detected. It was a bit hard, because the filtrates from the slurry were brownish. First I was impressed by the deep orange colours observed in the ammonia test, until I realised it had to be green! Probably all the nitrogen was either strongly adsorbed or taken up by organisms.

The pH meter was checked before the measurements started (with fresh buffer “4.00” the measured value was 4.01). The standard error (sample) was usually 0.01 for the pH, sometimes higher but only once as high as 0.04. For the conductivity the error was between 0 and 15% of the µS/cm value, so let’s presume it’s + or – 15% of the presented value. The moisture percentages were based on 50 grams of fresh soil and an error of 2 grams will affect the very low moisture percentages more than the higher ones. Below the table with my results is presented.

The samples with visible organic fragments (except for Solse gat 1 – the outlier) are marked in red

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A deep dive in shallow water

Let me explain the title first. The shallow water is one of the small ponds investigated last time. More precisely, it’s the one in our Beatrixpark. This one deserves an extra post, because the results were rather unexpected. Not the weakly buffered water, almost lacking all minerals. On the contrary! The conductivity was quite high and I planned to have a look at the bottom, hence the deep dive.

Despite being at the bottom of a former sea, our central lake “Weerwater” is very standard an I can use it as a reference. Probably that’s because it’s not really the bottom of the former Zuyderzee (Sourthern Sea), but the result of massive excavation (removing the orginal bottom). Indeed when taking soil samples from the Weerwater it was loamy sand, with larger pieces of stone and some organic matter, certainly not clay at all.

The soil below the pond in the Beatrixpark however, was sandy loam, silty clay loam and clay. I refer to the pond, but actually it’s two ponds – a small one and an even smaller one with a path in between. To different locations of the small one provided very different samples. An organic layer was present, but those parts were washed away easily from the sampling tube. Poking deeper, some clay and sand came up. The very small one yielded pure clay – a really oily substance! The water content was about one third (it took several days to get the water out – even with gentle warmth from a LED-bar; the same one used in the lab experiment). The water content (moist) was about 35% but for the Weerwater it was more like 75%.

Fresh water samples were also taken, to be sure the situation was not very different from two weeks before and (two measurements for pH and two for conductivity at two samples) showed values of 7.64 (standard error of the samples 0.05 but make it 0.1 because of some drift of the meter) for the small pond – with conductivity slightly above 2200 µS/cm (give or take 5% – corrected to 25^oC it was the same value as two weeks earlier: around 2500). For the even smaller one, it was 7.81 with a conductivity of nearly 2450 (2700 at 25^oC).

As a reference for this park I also sampled a larger pond, more like a small lake, connected to canals. There the pH was 8.23 with a conductivity of less than 1400, but the Weerwater, sampled an hour before showed a pH of 8,42 and a conductivity of about only 970! Nevertheless the small lake was much, much more like the Weerwater and didn’t resemble those ponds, only about 100 – 200 metres away.

A higher conductivity could mean a higher buffering capacity (alkalinity) and indeed with the Weerwater being 8 this time (DH – equivalent to 175 mg/l bicarbonate), the other lake was between 11 and 12 (about 250 mg/l). The very small pond showed the same level, but the other small pond had a KH value of even 16 (DH – about 350 mg/l bicarbonate).

After drying, the soil was sieved and mixed with water again. Taking a 1:1 ratio was partly restoring the original situation, but the 1:2 would be similar to mixing the fresh soil with an equal amount of water (except for the Weerwater sample). By the way, I had to dry the samples first to be able to homogenise them. When still wet it was impossible to mix clay, loam, sand and other parts.

The picture below shows clearly which one was clay (the other holding a lot of sand).

The pH values of the slurry were around 7.6 (measured in the slurry and it didn’t matter whether the water ratio to dry soils was 1:1 or 1:2. Surprisingly diluting more – 1:5 – the pH went up! to nearly 8). Conductivity was a little harder, because the more quartz (sand) gets between the electrodes, the lower the conductivity will be. Therefore measurements were done above the sandy layer at the bottom. Yet the results were nowhere near the values of the water samples and only hovered around 1000 µS/cm (at 23^oC – so correction to 25^oC wouldn’t make the difference).

Finally I checked the soil samples from the smallest pond for Nitrate (around 3 mg/kg of fresh soil), Nitrite (not detectable), Ammonia (also not detectable) and Phosphate (less than 1 mg/kg fresh soil).

The conclusion is clear: our Flevoland ponds are not the acidic, weakly buffered ones like in other areas. The rich soil provides a lot of minerals and the decaying organic material will probably release even more.

The ponds seemed neither rich in algae nor animals, but I observed a nice Daphnia (water flea – released in the Weerwater afterwards).

Working in the lab – a retrospective

Although the last time I worked in a professional biochemistry lab was probably in 1984, I still know that it’s less romantic than in the documentaries where a whole lifetime of research is compressed in less than an hour. In reality it’s just hard labour and my series of experiments became a routine at first (measuring the values for pH, conductivity and temperature usually two to four times a day). I wasn’t willing to wake up early in the weekends, but that was not a real issue, because the patterns became visible anyway.

Photo by Kindel Media

After the routine, I had to process the data. Since I registered those in a way less prone to errors (following the positions of the bins and using a notebook and pencil), the preparation of the graphs was a lot of work. It was only later that I thought about sharing the results in a standard format, so I didn’t profit from my own restructuring activity! Processing the data was an intensive process. For hours I was busy pivoting small chunks of data and checking whether no mistakes were made.

In finance a famous dataset is used quite often. It’s called after its providers Fama & French.

My dataset is less extensive, but I also wanted it to be available to everybody who wants to work with it. It’s just the data, because everybody will be able to derive averages, standard errors, to test significance and so on and so forth. My dataset is available at the website of Charles Warter (the artist) in a special subdomain anrep3d (still my name on Twitter). Both an integrated Excel and a zipped set of .csv–s are available.

The trouble with science is that it’s never ready and the experiments I did could be improved, to be able to draw more reliable conclusions. One of the issues is that I used urine and we can’t be sure that the effects were caused by the phosphate in it (only). Adding either pure phosphate, urea or other components, would make things more transparent.

I also didn’t check that it was the plants taking up the phosphate. It could have been bacteria, archaea, fungi or algae (and from bin 1 we know algae seem to love phosphate, although there it could be bacteria, archaea and fungi as well).

Then there is the evaporation. I left the bins alone and did not refill. Of course not, because adding water would influence the circumstances, but so does evaporation.

The pH meter was pretty stable and reliable and checked with the calibration buffers (calibrated if necessary) at a regular basis, but the conductivity shows some issues. It’s clear that the conductivity is very sensitive to temperature (my measurements showed 2.1% per degree) and during the day the temperature would move up a couple of degrees. During the period of an experiment, the temperature also changed. Some slightly warmer and colder periods were observed. That’s not a big issue, but the temperature was measured only once (pH and conductivity twice), because the meter adjusted really slowly and I had to wait at least a minute to get a stable value and even then it sometimes changed later on. So in the end the temperature is not too reliable and will have an error margin of probably half a degree at some moments. Then there was this issue with the battery running low, the electrodes getting dirty and so on. In every experiment the combined graph for conductivity recalculated to 25^oC showed a strange correlated pattern of fluctuations for the three bins, although trends were still present.

In the end it was a lot of work, but now I know for sure that plants are capable of pushing up the pH to values over 10. This was already mentioned to me by ChatGPT, but I needed more detail.

The bins were really shallow and the volume was low, so in a lake the pH will change less during the day and not get over 10, although the values observed in recreational areas were close after all. A light strength of 10000 lux is not exceptional, although often it won’t last for 16 hours. Yet in the second half of March 2023 and the beginning of April 2023, I measured “around noon” peaks of 80 000 and 100 000 lux (but also values around 5000 on overcast days). The total amount of energy provided during the summer outside will be much higher than in my experiment. The total daily light energy in my experiment was at 4.6 MJ/m² but during summer it can easily go up to five or six times this value.

What did I learn from this experiment? Apart from having support for my urine hypothesis, the most important lesson is that under conditions with bright light (sunny days in summer) the pH will change more over a day than expected. The daily pattern I observed earlier was for May and I should repeat it somewhere in July or August. Looking at all my reference measurements in the Weerwater, the real outlier was sampled in the summer, late in the afternoon. All others were in other seasons and usually in the morning or around noon.

I think I can put my lux meter to rest now and go on with different types of measurements in the field!

More ponds in (different) woods

Until now the pattern seemed to be clear. Lakes and rivers would be alkaline – usually around pH 8 or higher – and a pond in a wood more acidic (pH below 7). The latter I mostly read in literature, because I only sampled a single one myself – until now.

This meant it was time to do some additional investigations. However, the reason why I didn’t take more samples yet, was that we don’t have many of those ponds in the neighbourhood. At least there are not too many accessible ones, because a lot will be in privately owned areas. So instead of searching for named water bodies, I started to look at Google maps. Now the difficulty was that in the satellite-view it is very hard to see those tiny waters. Trees will cover them and make them invisible and even if in the open, to me the colour is very similar to a pasture or meadow. Working with the plain view, another surprise came up: small water bodies are not marked on the map at all! Some I knew about and indeed in satellite view I was able to detect them, but switching off this layer, no blue spot appeared! After several hours I found some candidates, using a mix of websites, on- and offline maps.

Then it was time to go! The first obstacle I hit was a road (Weg over Anna’s Hoeve) being blocked, without any indication about how to get to the other side of it. I took a small road perpendicular to it and suddenly I noticed a pond. My car’s navigation didn’t show a blue spot either and then it was surrounded by a fence.

The small blue spot was added by me!

Fortunately the fence was not meant to keep me out, but should just keep the cattle in. I parked my car, went to the small gate and I visited the wood. There was a less used path close to the pond, but timber was stacked all around, to keep visitors out. Nevertheless I managed to get two samples and started measuring. Although the conductivity was really, really low (31 – which would be only 33 at 25 ^oC, meaning it was hardly buffered at all) the pH was not extreme. An average of 6.33 (with a sample error of 0.03, but later the pH meter showed 3.9 for the 4.00 buffer, so the total error was probably up to 0.15). Acidic yes, but only moderate.

Pond close to Anna’s Hoeve

The next day I determined the concentration of several minerals and the KH (hardness or alkalinity). The KH was special, because even the first drop didn’t show a hint of blue, not even for a moment. It went orange immediately, so the KH was 0 (DH). Not a surprise of course, when the conductivity is really low.

Although I wanted to sample the “Laarder Wasmeer” as well, I noticed it was completely fenced and this time it was meant to keep me out. No samples there!

The samples had a lot of small black dots. Looking at the samples under my microscope, I noticed that those were animals. Actually the animals were ton-like creatures, swimming very fast and clearly using their cilia (visible at higher magnification). Even with the help of Google Lens I could not determine what those creatures might be, but digging deep, deep in my memory (in 1975 I started out with a year of classical biology, learning a lot about invertebrates) I remembered some larvae (Annelida?) are looking like this. Later I released them in the Weerwater.

Animal in pond water – 100x enlarged.

After the first one, I went for another pond in Vuursche – close to the Vuursesteeg. The area is called Nonnenland. It wasn’t an easy location, but with the help of Google Streetview (by the way: I’m neither  sponsored by Google nor Alphabet) I managed to spot a place to park my car and from there it was a fifteen minutes’ walk. Again there was a surprise, because I encountered a pond at a places where no blue spot was visible at the map. This was the kind of small water body I was looking for. Shallow, the bottom covered with leafs. There the pH was 4.53 (standard error of the sample 0.02 but again, the meter was 0.1 towards a lower pH, so it could have been 4.65 as well). Nevertheless clearly acidic. The conductivity was slightly higher: 55 (70 when recalculated to 25^oC). The water samples were really yellow! Here the alkalinity (KH) was 0 too.

Yellow water samples from Nonnenland

Pond at Nonnenland

Two days later I went to a pond in our Beatrixpark (Almere). I was reluctant to look at a pond in Flevoland, because then we’re looking at a former seabed. For a reference however, having samples three ponds in total in the old land now, it would be interesting.

Pond in Beatrixpark

The big surprise was not the pH – 7.40 (the pH meter was calibrated before) but the conductivity: 1852 at 12.7 ^oC (the standard error only being 14 for the samples, so even close to 2500 at 25^oC).

Very different indeed, although the Weerwater is very similar to other lakes, but there a huge amount of soil, clay and sand was dug out and probably no trace of the former sea was left there.

The alkalinity was of the pond’s water 12 – corresponding (for this pH) to over 250 mg/l of (bi-)carbonates. Probably I’ll have to take some soil samples from the bottom, with the device I created for this purpose.

The regular pollutants (ammonia, nitrite, nitrate and phosphate were determined, but not alarming).

The locations for the three ponds are shown at the maps below.

The big surprise

The last step in the sequence of laboratory experiments was really simple: the cover was removed from bin 2.

You will remember that bin 2, the one in the middle, was permanently covered with aluminium foil, so basically no light was received by the plants in it. It was in the middle on purpose, because then the temperature would be close as possible to the temperature of the other two, despite the lack of light energy coming in. Of course bin 2 and 3 were switched a while ago, but after a short while their behaviour was according to their number, like it was before. The dark bin 2 didn’t show a strong daily pattern whereas bin 3 clearly did.

It would be reasonable to expect bin 2, after the removal of the cover, to catch up with bin 3: both getting at the high pH level observed for bin 3 that is.

What actually happened was the increase of the pH in both bins – even (after additional calibration of the pH meter!) over pH 10! (Actually both reached 10.22 at the end of the last day of this experiment).

Like ChatGPT already told me, plants are able to push the pH up to 10 and above under certain conditions. I wanted to know how difficult or easy this would be. Well, it happened at a rather high light intensity, shallow water and a large amount of plants.

In the graph below I drew the trend lines myself for the left part. To the right Excel was able to do it for me.

In the combined pH graph it is easier to see how bin 2 got closer to bin 2 during the experiment and eventually got the same pH. The bright red lines show the trend for the daily highs (just before the light switched off). It’s impressive that it took bin 2 only a week, after taking off the cover, to get at the same pH level.

The conductivity went up as a result of evaporation. In bin 1 it was really strong and that’s why this bin was removed from the experiment. The animals (Cyclops) went to our pond.

Phosphate is plant candy!

At the end of laboratory experiment 4, where the light and dark bin (both holding plants) were switched, the pH in bin 3 was pretty high. Not as high as at the end of experiment 3, where nearly 9.7 was reached in the previous bin 3 (now covered and called bin 2), but still around 9.0 in the new bin 3 (previously covered and called bin 2). Now the question was whether another donation of phosphate would have any impact. This time I was prepared and determined the phosphate concentration immediately after the addition of urine (used as the phosphate source again).

Bin 1 was left alone this time, but bin 2 and 3 received 2 ml of urine each. Before we go to those, I want to mention the little water fleas (Cyclops) appearing in bin 1. Nice little creatures, released into our pond later on. The two sacks with eggs can be recognised in the picture.

Bin 3, which had been covered for a long time, had a larger volume than bin 2 (only covered since the switch) and therefore it’s not a surprise that its phosphate concentration became a little bit lower: 1.8 mg/l for bin 2 and 1.4 mg/l for bin 3.

The next morning the levels were already down to around 1.0 and 0.7 mg/l PO₄^3- respectively. The day after, the levels were even hardly detectable: 0.15 and 0.075 mg/l PO₄^3- , indicating that plants really love phosphate (as do algae). Bin 1 showed 0 mg/l all the time, as expected. The interesting part is that the phosphate uptake is not related to the amount of light received.

Now the question was what happened to the pH. Well, without light (bin 2) not so much. During the four days of the experiment the pH went up from 7.3 to 7.5 at the end of the day. However, in bin 3, the pH went up from the original 9.0 to 9.9 which is certainly an interval far outside any potential error of the meter (usually 0.05 of and never more than 0.2 in very special circumstances). It is even above the values I observed in the recreational areas. At night the pH dropped even more, to 8.2 making the daily swing even more impressive.

This finding seems to support our “urine hypothesis”. Although I look at the phosphate, we have to be careful, because urine is a mix, also offering nitrogen and other chemicals, but it’s for sure that the phosphate is consumed! The temperature and the shallowness of the water will also be influential.

Below the same graph patterns is presented as for the other experiments.

Be aware that the scale had to be stretched again and again (not only for the pH, but also for the conductivity).

By now we are used to the strangely correlated movements of the conductivity.

Yet the trend is clear. Bin 1 keeps losing volume and because the surface is constant, the relative decrease will go up and so will the conductivity. For bin 2 we don’t expect a lot of impact from evaporation, but bin 3 should certainly go up if nothing else was influencing it.

Indeed the trend-line for bin 1 is going up and the one for bin 2 too, but not as steep. The surprise (or perhaps not by now) is bin 3, where the trend-line is more or less flat. This means that the concentration as a result of evaporation and the withdrawal of ions are keeping pace.

In the combined graph we can still see the correlated movements, but the trends are diverging after all.

Trading places: deterioration and recovery

The title would be a bit boring if I called it Part IV, but this is still about another step in the longer running experiment. This time it was really just about the influence of light, because bin 2 – the dark one – was switched with bin 3, which received a lot of light and held plants for the previous eight days. The difference between the two was very clear. The covered bin (2) never showed an average pH above 8.0 (rounded), but the bin (3) receiving light for 16 hours a day had its pH going up to 9.3 and after the application of urine even nearly 9.7! That’s quite a difference.

The three bins with the LED-bars taken off (plants are present in the middle one)

Now the question was what would happen to the dark bin (2) if it started to receive light and the other way round for bin 3. Because the number depended on the position, the bins also switched numbers, so the previous bin 3 went to the middle and became bin 2 and the old bin 2 moved to the right and became bin 3.

The results were really surprising. It was already clear that during the dark period the pH would drop by about 1.5 point in the illuminated bin (3) to somewhere around 8.2 – slightly above the rather stable value of the covered bin (2). The switch was done just after the end of the daily light period, so we could expect a more or less equal start in the morning, when the light switched on.

Indeed the new dark bin didn’t show any rise of the pH during the day. The value got around 7.5 whilst the new bin 3 had its pH going up to 9.0 This was not as high as for the old bin 3 but during the dark period it dropped to around 8.3 – like the old bin 3 did (now covered and called bin 2). Somehow the light was not as effective, but probably this long period in the dark took its toll.

It’s very clear that light really influences the pH when plants are present in the water. Or, more accurate, plants are able to increase the pH in the presence of light.

In the graphs below the change of the pH pattern is clearly visible. The daily patters is present for bin 1 and bin 3, but much stronger in bin 3. The graphs to the right show all daily patterns overlapping with a 24 hours scale.

In between bin 1, meant as a reference, kept switching from a pH around 8.0 at the start of the light to somewhere around 8.5 at the end of the lighting period. The algae were active and the water was really green and so were the walls of the bin. In the picture at the start of this post the green is probably not too clear, but the lumps in the water are visible. The combined pH-graph emphasises the differences between the three bins. The daily pattern for bin 3 is much stronger than for bin 1 and bin 2 is just stabilising at a base level.

Like I did with the water of the Tjeukemeer, I investigated the water of bin 1 under my microscope. Before talking about the conductivity I will show a couple of pictures (taken with my cell phone through the microscope, so not a high quality but it’ll do to give you an impression).

Although I have an idea what some of these creatures might be, I won’t mention names. As a biochemist I’m not an expert on this and my guess might be as good as yours.

Now we still need to look at the conductivity (again recalculated to the 25^oC value in uS/cm). The experiment with the switched bins lasted for four days and the evaporation went on, especially for bins 1 and 3, so there we could expect an increase. For bin 2 this evaporation-effect would be much smaller, but actually the slope is even a bit steeper than for bin 1. Somehow minerals must have been released, probably from dying parts of the plants.

For bin 3 – where evaporation would also have some impact – the slope is negative, so it seems the plants are taking up the available minerals in the presence of light.

Again the fluctuations over time are rather synchronised, pointing to some common (unknown – temperature? measuring device?) influence (see combined graph below), so I wasn’t reluctant to put straight trend-lines in this time.

Intermezzo: two other lakes in the North revisited

Previously I revisited two lakes in the North, but I had no time to sample the other ones in a different season. Until now!

The first one was a small lake in a suburb of Groningen (Kardinger Plas or Zilvermeer – after Karl May) was visited the second week of June last year, so the end of the spring, but now it was the beginning of the next spring. Back then no conductivity was measured, so that part was not for comparison, but just a surprise. The pH was 8.30 (with a small standard error of 0.02).

This time I took three samples at slightly different locations (close to the original one). The average pH was 8.10 still with a standard error of 0.04 for the three samples, measured twice (the meter moved down first, but went up again after a while. Sometimes this will happen, especially in windy conditions, I noticed – pressure?). The conductivity was  510 µS/cm, with a standard error of 7. Not a surprise that the pH was a little bit lower after the winter period, although the nice weather had started already. The pH-drop was either small or the pH was already rising.

The conductivity was not very high, but in comparison with the two other lakes (Hoornse Plas and Hoornse Meer – only to the other side of the municipality of Groningen) not really low. Those lakes were at 193 & 223 µS/cm, although we have to correct for temperature. Still those values would only be 292 and 338 at 25 ^oC. The water temperature of the Kardingerplas was 13 ^oC, so the corrected value for 25 ^oC would be even higher:  682 – twice as high as for the other lakes!

To know more about the minerals I determined the NO₂^–, NO₃^–, NH₄⁺ and the PO₄^3- concentrations and found no Nitrite- and no Ammonium-ions, but some Nitrate (0.5 mg/l) and a trace of Phosphate (0.05 mg/l – there was a slight bluish discoloration, indicating a detectable amount). The hardness was quite normal:  6 (German dH units – a unit corresponding with 22 mg/l of bicarbonate, so around 130 mg/l).

The other lake was the Tjeukemeer in Fryslân. It’s not allowed to walk in the reed bed, so I could only go to the location underneath the A6 highway, but that was the place where I sampled nearly a year ago.

The previous visit (May 21, 2022) gave me a pH value of 8.19. This time it was 8.18 with a standard error for the samples of 0.03 (three samples, measured three times). Here the previous conductivity was also unknown, but this time it was 341 (with a standard error of 3). The temperature was only 11 ^oC now (end of the day), so the calculated value for 25 ^oC would be 483 – somewhere in between the lakes around the city of Groningen. Much lower than the Weerwater, with probably still holds traces of salt from the former sea (I have to determine the Chloride concentration, some day).

However, that’s not all. The water was really green and I expected a lot of algae. Looking at the water I noticed some foam was present. Somehow water was dripping from draining pipes in the highway above. Thinking about all the traffic, I was curious whether an elevated level of Nitrate would be found. The next day (samples kept in the dark) I determined the NO₂^–, NO₃^–, NH₄⁺ and the PO₄^3- concentrations for a sample and indeed:

Although Ammonium and Phosphate were not detectable, the Nitrite was 0.05 mg/l and the Nitrate even 3 mg/l! The hardness (KH) was only 4 (German dH units).

Limnology is not only about physical and chemical parameters and because of the very green colour of the samples, I took my microscope and looked for plankton. At first I saw a lot of debris, but then magnifying 400x I discovered fresh water diatoms (Asterionella) and several other interesting creatures, like Scenedesmus quadricauda, Volvox and Pediastrium.

It’s not that I’m that good at recognising plankton. Actually I only recognised the diatoms and a Volvox. For the others I got help from Google lens! The photos were taken with a regular cellular phone. It’s not easy to get sharp pictures (the software gets confused), but the ones below will do for recognition (although not for a contest).

A longer running experiment, Part III

After the completion of the second experiment, it was time to test the “urine hypothesis“. Phosphate is useful for plants, but will it change the pH of the water they are in? After the last day of experiment two, when the light switched off, 1 ml of urine was added to each one of the three bins.

Photo by Milenqqa on Pixabay

The phosphate concentration of the urine was determined (in a 1:1000 dilution) and turned out to be 1.5 g/l, a rather normal level. Since 1 ml would hold 1.5 mg and still about 4 litres of water were present in a bin, the final concentration would be around 0.4 mg/l. The pH of the urine was 5.75, but did not show a strong buffering capacity, because diluting 1:1000 with tap water (pH 7.75) did not show any change of the pH of the tap water.

Then I made a mistake. I really underestimated the plants’ hunger for phosphate and didn’t actually check the concentration of phosphate in the bins. The next no phosphate was detectable in any of the bins. This may seem unlikely, because bin 1 didn’t hold (higher) plants, but the algae really flourished!

In a later experiment I would perform a similar check and noticed that relatively high levels of phosphate can be consumed within hours.

However, the question was whether the pH would change as a result of the addition of urine and it did, but the effect was not very strong. During the days of experiment 2 the end of day pH was around 9.30 but after the addition of the urine the pH in bin 3 went up to 9.65. Yet it was the highest value observed until then and similar to the values measured in the areas with a lot of leisure activity. Bin 1 showed the same weak pH fluctuations during the day and bin 2 showed an almost flat curve. To the left the full period is shown and to the right the days are combined to show the hourly pattern throughout a day.

Presenting the period’s patterns in a combined graph makes the differences even more clear.

Then we have the conductivity.

The trend lines are flat for bin 1 and 2, but for bin 3 the trend is upward. Don’t forget that all the phosphate was already gone when the first measurement was done! The evaporation was higher in bin 3 than in bin 2, so we have to take that influence into account. On the other hand, we would expect the same for bin 1, because neither of the two were covered and reached the same temperatures (roughly between 12 and 16 ^oC).

The combined graph shows the same correlated pattern we already saw for experiment 2. It’s still not clear whether it’s a time-related error of the instrument or probably some other shared influence, like an imperfect correction for temperature.

A longer running experiment, Part II

The second laboratory experiment was the logical successor to the first one and very similar. Plants and light were the combination which increased the pH in experiment 1 (but the algae in the bin without plants were also active!). So the question was what plants will do without the light.

Half of the plants from the third bin were moved to the second one in the middle. The one in the middle was still covered with aluminium foil and there the plants would hardly receive any light, but the temperature was similar (although usually one degree Celsius lower than the bins to the left and the right, receiving light). The situation is shown below, with the LED-bars removed.

The picture below shows the bins in position with the LED-bars right on top.

The aluminium foil is a bit wrinkled, because the instruments went in up to four times a day!

The results were rather clear. The graphs below present the pH values over time.

Bin 1, illuminated without plants (but holding an increasing amount of algae!) showed a weak daily pattern, but during the four days of the experiment the trend was flat.

Bin 2, holding plants but not illuminated showed a gradual decrease of the pH.

Bin 3 however exhibited a strong daily pattern, yet without a long term trend.

The graphs to the right show the daily patterns combined and we can see that every day was more or less the same (the trend for bin 2 is really weak).

The graph below combines the three pH graphs. We can see how the lines are closer in the morning, right after the start of the light period and deviating most just before the onset of darkness.

The plants in bin 3 are able to increase the pH with more than a full point. The brave algae in bin 1 push the pH with a couple of tenths only. During the dark period the pH drops more in bin 3, actually to the level of bin 2 where the night is permanent. Bin 2 is quite stable.

It’s probably not a surprise that plants which don’t receive light don’t increase the pH. But what about the conductivity? The error for those values is much larger and we have to correct for temperature influences, so the values are recalculated to 25^oC (2.1% shift per degree deviating from this temperature).

The trends are almost completely flat, but some daily fluctuations are visible. Almost like minerals are “borrowed” during the day and given back during the night.

Looking at the combined graph, the fluctuations are too correlated to believe and probably it’s either the error of the measuring instrument or some temperature influence we didn’t capture in the correction.

What we can tell is that the conductivity in bin 3 is lower, which makes sense because the plants will need minerals when working hard on photosynthesis. In bin 2 the plants are rather powerless and don’t need as much minerals, but still some. Bin 1 has the highest conductivity, indicating that minerals are still in the solution. The 25^oC reference value in the Weerwater (sample taken right before the start of experiment 2) was 1067 with a standard error of 10. This value is similar to bin 2, but certainly lower than bin 1. That’s a surprise. We don’t know the composition of the materials contributing to the conductivity, but because the lake is at – or actually in – the bottom of a former sea, some NaCl (sodium chloride) will still be in. It’s rather useless for plants and evaporation will increase the concentration.

A longer running experiment, part I

In a previous post I explained how I set up an experiment with three plastic bins (30 x 20 cm), filled with about 4.5 litres of “Weerwater” (from our lake). The measured water level was 7.5 cm. Actually it was a number of experiments, performed sequentially. At the start the bins were just water, to allow the water to adjust to the situation. The left was without plants, the middle one also without plants, but covered with Aluminium-foil and the one to the right received (water-) plants the next day. Three rather strong LED-bars were crossing over the three bins, providing a light strength between 8000 (bottom) and 16000 (top) with an average of approximately 10 000. The energy consumption of the LED-bars was 36 Watt, but calculations showed that 40% of it was converted to light and 60% to heat, just raising the temperature of the water. The covered bin was to determine the difference between light and temperature.

The set-up with three bins, all covered by three LED-bars

Method details

During the five weeks of daily measurements, the water vaporised gradually and was also lost because of spilling (a little bit went out with the meters every time). At the last day (more than a month later) the water level was only 3.3 cm, so more than half of the water was lost!

The light was on between 7 a.m. and 11 p.m. (7:00 – 23:00, with some fluctuations in start and end time because of the timer being accurate at a quarter of an hour, but always 16 hours of light and 8 hours of dark).

The experiment ran for a full week (actually 8 days – after the addition of the plants to the third bin that is).

If possible the pH, conductivity and temperature were measured four times a day (after the light switching on, around noon, at the end of the day and around the onset of the dark period an hour before midnight). Sometimes I was elsewhere and measurements were only done at the start or the end of the day. This was the pattern throughout the whole period of five weeks when all the experiments were done.

During every measurement moment, two pH values and two conductivity values (µS/cm) were determined per bin and  just one temperature value per bin (this took a minute and values were slightly different, the covered bin always being about one degree cooler that the others!). Determining the pH would take half a minute, because the electrode had to adjust to the environment. The conductivity was quicker, but clearly had an error of 1%. The pH fluctuated up to 5 hundredths of a point between measurements (random error), but the drift (checked on a regular basis with the standard buffers) could even be 0.1 (systematic error). Yet we can be rather sure that the value of the pH shown will never be more than 0.2 beside the real value.

During all the experiments, the bins were in an unheated room, ventilated during the day and I slept in the adjacent room during the night, certainly providing additional CO₂. The air temperature was quite stable – between 14 and 17 ^oC. During the light period the water temperature usually went up 2 ^oC (less for the covered bin in the middle). For the pH the temperature is not too influential (the error margin is larger), but for the conductivity all values were recalculated to the level for 25 ^oC.

Results

And now for the results! Below the graphs are presented for the three bins during the nine (actually eight – the first day was the adjustment and serves as a reference) days of experiment 1.

In the fourth graph the three lines are combined, so it’s easier to see the difference.

Bin 2 is the real reference, because no light and no plants were present. Bin 1 didn’t hold plants during experiment 1, but algae and bacteria were certainly present! Although invisible, eggs of small animals must have been in as well, because several weeks later some Cyclops appeared. They won’t have been brought in by the wind, although one never knows, but generatio spontanea is not fashionable since Louis Pasteur.

During the five weeks of experimenting, the left bin became greener and greener, showing that algae were present indeed. This will account for the (small difference) between bin 1 (light) and bin 2 (covered). Although bin 2 was usually 1 degree Celsius lower than the other ones. There was a clear effect of the light in all three bins and during the day the water became warmer than the environment.

Despite the small fluctuations in bin 1 and 2, the covered one (bin 2) was more or less stable, although the pH went up with 0.2 points during the nine days of the experiment, with daily fluctuations of 0.1. For bin 1 this was 0.4 points during the whole period and 0.2 during the day.

However, for bin 3, holding plants and getting light, the pH went up from 7.9 to 9.2 during the experiment and daily fluctuations grew until they were nearly a whole pH-point, e.g. from 8.2 to 9.1!

To visualise the daily pattern (getting stronger and stronger from day 2 to day 9) the days for bin 3 were plotted at an hourly level, ignoring the day.

The X-axis presents the fraction of the daily 24 hours – trend-line is for day 9.

The trend-line is for day 9. It is clear that after switching off the light, the pH drops quickly and goes up again when the light switches on again. After an initial increase of the early morning value, from day 3 the pH starts (round 7:00) around 8.2 but the value at the end of the day kept going up from 8.7 to 9.2.

At the end of this experiment, February 19 at 10:30, I took some reference samples from the Weerwater. The water temperature was only  9 ^oC and the pH was 8.00 (average of three samples, each measured three times, with a standard Error of the population of 0.02). By then bBin 3 was already above 8.33 and would be 9.2 at the end of the day (the Weerwater doesn’t change a lot during the day).

In this lab experiment it was clearly shown that (intensive) light is able to help plants raise the pH to a rather high level – as high as 9.2. The light outside was usually less than 10 000 lux at the water surface and then the plants in the lake are not 7 cm below the surface, but more like a metre. There light strength will be much lower.

Then there is still the conductivity. The values have to be corrected for temperature and the graph below shows recalculated values for 25 ^oC, but there is another complication.

While measuring around noon at day 5, the values for conductivity dropped dramatically during measurement. Every next minute the value was much lower, so I decided to clean the electrodes with an acidic buffer (and replace the battery). After that, the values were suddenly higher than usual – at least 5%. This is clearly visible in the graph below.

Because of this “jump” I present the graph for days 5 – 9, with trend-lines for the three bins below:

X-axis shows days of experiment, Y-axis is conductivity in µS/cm

For bin 1 the conductivity values increased gradually. This was probably influenced by evaporation and because bin 2 was covered with aluminium foil, there the impact was much lower. Despite the evaporation, the conductivity went down for bin 3. This is consistent with earlier observations that pH and conductivity are correlated negatively. Surely the plants have to withdraw minerals during photosynthesis.

The daily pattern is less clear than for the pH and it seems the conductivity is higher at noon than during the start or the end of the period of light.

This post is only the start, because several experiments would follow, changing light conditions and also testing the “urine” theory. Then I am planning to put all the measurements for all experiments in a large table, so everybody will be able to work with them, but this will only be done after completion of the whole series of posts about the lab experiments.

Revisiting the “high pH” lake in the North

Previously I wrote about my phosphate hypothesis, but I was only able to check the IJmeer during the winter to obtain a baseline. Yet there was this other lake (Hoornse Plas in Groningen/Drenthe), but I wasn’t in the neighbourhood until last week. Then, of course, I planned to take some water samples. It was rather cold (about 5^oC) and the wind was blowing really hard, but everything went well. The water temperature was slightly higher than the air temperature – about 8^oC. Next to the lake Hoornse Plas, confined with a dam, is the larger lake Hoornse Meer. Previously it served as a reference and its pH values were rather close to the ones observed for the Weerwater in the summer (well, formally it was the end of the spring) of 2022. Hoornse Meer was  pH 8.17 and the Weerwater pH values in the afternoon, the week before were around 8.11.  The Hoorse Plas however, showed an average pH of 9.57 – similar to the pH measured at Muiderberg the week after: pH 9.7. The Hoornse Meer could serve as a reference again this winter and its pH values were slightly lower: 7.80 but the winter measurements for the Weerwater were also lower: 7.75. For both the drop was less than 0.4. Both lakes – 150 kilometres apart have their pH values well synchronised, but their neighbours were very, very different during the summer.  The IJmeer at Muiderberg, in winter, had its pH dropped from 9.70 to 8.74 –  almost a full point. The Hoornse Plas was even more volatile with its winter value of 7.91 – a drop of more than 1.6 from  the previous pH 9.7. 

_{Hoornse Plas}

This time I had my conductivity meter with me and determined the conductivity (in µS/cm) as well. The usual values observed for rivers and lakes are – roughly speaking – between 700 and 1200 (let’s forget about the brackish North Sea Canal and the pond in the wood, obviously lacking minerals). For Hoornse Plas and Hoornse Meer the values were much lower:  193 and 223 with a rather small error. Of course we have to correct to the standard temperature of 25^oC to be able to compare values throughout the year (2.1% change per degree is a lot), but then the values would be 292 and 338. Really low and very surprising. 

The dam, separating the Hoornse Plas (to the right) and the Hoornse Meer (left).

To be honest I forgot about the samples and let them be at 15^oC in the dark. After a week pH and conductivity (this time at 16^oC, but converted to 16^oC again) were completely unchanged. Then I determined the phosphate level and no (free) phosphate was present at all! At least we have a baseline now, but I’m not that patient, so I used my laboratory experiment (running for several weeks) to check some influences. Next posts will tell you about those results.For the ones who like to have a structured overview, there is a table below with the pH values.  

A light intermezzo

Sorry, this won’t be a mellow piece of text. It’s just about light – light and pH, to be more specific. You will probably remember that I wondered what the relationship is between pH and light energy. Once I measured the pH during a full day and indeed some fluctuations came up. Then, during several days I observed both pH and the light strength, hoping to find a relationship. During the period November 2022 until the start of February, the illuminance (in lux) fluctuated, but the trend line was almost flat. The trend line comes from a series of light measurements (10 minutes usually between 12 and 13 ‘o clock when the sunshine is as bright as possible – unless it’s e.g. a rainy day and the sun only comes in later of course).

Only when bringing in new data from the period after, the line went up and so did the energy of course. It’s not too meaningful, because I didn’t measure every day!  (The formula I use to calculate the daily energy includes the length of the day and February has slightly longer days than November, so for the energy the slope will be a little bit steeper.)

During the same time I also sampled at the Weerwater lake, Maastrichtkwartier, because that’s my reference location. The pH fluctuates as well, although not strongly and then the systematic error will be 0.05 at most and the standard error of the samples set (three samples measured three times each) is usually not above 0.02, so in total we can be sure a total error over 0.1 is very unlikely.

^{X-axis illuminance in 1000 lux, Y-axis pH value}

The difference between 7.75 and 8.00 is marginal, when the error is included and on combining pH and lux, the results remained inconclusive – even worse, the higher light strength does not show a higher pH.

^{X-axis MJ of light energy per square metre of water surface, Y-axis pH value}

Probably the short term effect is too weak and then it’s not only light but also temperature. During the winter everything is more or less in dormancy.

If the longer term (seasons!) is relevant, then there was not much of a chance to see a clear effect. That’s why I decided to set up a longer running laboratory experiment.

Three bins were filled with about 4.5 litres of fresh water from the reference location (again Weerwater, Maastrichtkwartier). The middle one was covered with aluminium foil and then a group of three strong LED-bars (total consumed power 36 Watt) was placed very close to the water surface and kept on during 16 hours a day (leaving 8 ours of darkness). The illuminance was about 16000 lux at the top and 8000 at the bottom level, but because this was a measurement in air, the estimated level is 10000 lux on average. The dark one would stay more or less at the same temperature (usually one degree lower), but received no light at all.

After a day I put some water plants in the bin to the right and measured pH and conductivity 2-4 times a day during more than a week. Then I changed some things for the next experiments.

The bins were 20×30 cm so the received light energy would be 0.3MJ per day for a single bin. The light energy would hypothetically raise the temperature with 14 ^oC), but of course most of the heat would be lost to the rather cool environment (usually between 14 and 16 ^oC). The actual delta with the environment went up from zero (light switching on) to two degrees (light switching off).

Then, to check whether the energy calculation is realistic: 0.82 MJ received by the three bins during a day. Power uptake 36*3600*16 = 2.1 MJ means that the efficiency of the LEDs would be 8.82/2.1 = 40% and that’s possible. This site mentions 80% light, but for my simple LED-bars it would be half of it.

In upcoming posts I hope to tell you about the results of this longer running experiment. The data are available by now, but a lot of analysis still has to be done.

This pond in a wood is different indeed!

It was still my plan to take water samples from pond in forests, woods, heathland or moorland. From long ago I remembered the Dutch word “ven” which probably means the same as “fen” in English. To be sure I started looking for the definition of a “ven” and got confused. An additional problem was that in my environment (let’s say a radius of 50 km) no “ven” would be present. Clearly not all small, isolated ponds (without a brook or river feeding them, I mean) are called “ven”. In the end I decided to ask ChatGPT for help and got this answer:

“The Dutch word “ven” can refer to a few different things depending on the context, but it typically means “pond” or “small lake.” It can also refer to a fen, which is a type of wetland habitat.”

Probably the latter meaning is what we don’t have in our area, but we do have small ponds in woods. Until now all water samples had a pH value above 7 and actually always close to 8 or (much) higher. Ponds in woods would be more acidic and I really wanted to see this for myself.

Walking in “Hoge Vuursche” in the province of Utrecht I was looking for the “Kruisvijver” (Cross Pond), but actually found a small pond close to it.

_{(this screenshot was not taken from Google maps, but from https://mapcarta.com/ )}

Not surprising was the thick layer of leaves and other organic material at the bottom. The water was quite clear and hardly any duckweed was present.

As usual I took three samples and measured them three times each. The water temperature was 8^oC  – close to the air temperature of 9^oC, which was nice. The conductivity was the first surprise: the overall average was only 45 (only forty five!) with a standard error (not of the mean, but for the population) of 2. This indicates a lack of minerals, so the water would be poor in nutrients (although it’s not only nutrients increasing the conductivity, because in Flevoland there is still salt present in the soil).

Then the pH was below 7 for the first time – and it was really below: an average of 6.22 with a standard error of 0.11. Afterwards the pH meter was checked (only a small deviation of a couple of hundreds, as usual) and calibrated for the next time. This was my first encounter with nutrient poor, acidic water since the start of this blog.

Of course I wanted to be sure about the minerals, so 40 ml water was taken from each bottle and pooled. From these combined samples I took two new samples for every analysis. Below is an overview of the results.

The alkalinity test was a weird one this time. Usually I start adding drops until the colour changes. The first drop will give a dark blue colour and then drops have to be added until the colour changes (to orange). I forgot about the blue, because nothing happened after the first drop and only after a lot of them I realised that no colour-switch would happen this time! The blue didn’t appear so it didn’t have to go either. The alkalinity was 0 for this acidic water.

The water samples had an orange/pink colour. Again, JBL is not my sponsor, but their tests compensate for it, because a reference bottle is placed over the coloured spots, indicating certain concentrations.

Of course more ponds will be sampled, but I think this was a nice first step.

A hypothesis

First I’ll give an update on the device, for which the design was shared in the previous post. It was finished last week and although I didn’t test it yet, it should allow me to take water samples close to the bottom. The total length of the stick attached to it can go up to four metres and that will do for most water bodies in the Netherlands.

Now for the hypothesis! From the moment I got the high values for the pH, I’ve been wondering why. The only thing I noticed was that a lot of people are swimming or wading through the water as those areas were meant for recreation. My first idea (that it would be sun lotions, deodorants, shampoo or the like) wasn’t valid. Most of those substances are pH neutral. Then I learnt that plants are able to raise the pH up to 10 after all, as a result of photosynthesis, but why only in those areas?

One day I was reading in Wetzel’s “Limnology” again (second edition), which always offers me a lot of background information. On page 288 I saw a picture of a lake, more or less divided in two parts, with one part full of algae, but the other part quite clear. Nitrogen and carbon levels were the same, but the former part had a higher level of Phosphorus (most likely present as phosphate, PO₄^3- ). Suddenly I wondered whether the Phosphorus would be limiting the growth of algae and plants in water bodies in general and whether raising the level a little bit would increase the level of photosynthesis.

The human body excretes small amounts of Phosphorus in sweat, but much higher levels are present in urine – about 1.2 grams per litre! Most people won’t urinate in a swimming area, but suppose that 1% would do?

_{The Hoornse Plas (to the right) is separated from the Hoornse & Paterwoldse Meer (Lake) by a dam.}

The Hoornse Plas, in the Northern part of the Netherlands, receives over 10 000 visitors at a busy day, I read. If only one per cent of them is peeing in the water, that would make at least 100 times let’s say 300 ml of urine (holding 0.4 grams of phosphate) and during ten days this would make 400 grams of phosphate. The Hoornse Plas is 20 hectares and its depth is probably 2 metres, making the volume 40 000 cubic metres. The increase of the phosphate could be: 400/4 0000 = 0.01 g/m3 or 10 µg/l. Although this doesn’t sound very impressive, the base level of phosphate is about 100 µg/l, so it’s a 10% increase at least. Probably more, because my estimates are rather conservative. It’s certainly not at the level causing algae to bloom, but it could stimulate the plants present. Then the swimming area is rather limited, so the phosphate level could be higher at some locations.

If all of the 10 000 bodies would add a ml of sweat each, this would only add 10 000 ml of sweat, holding about 4 µg of phosphate per ml. This could add 40 mg of phosphate on a daily basis (or 400 mg in ten days), which is negligible in comparison to the 400 grams potentially coming from urine.

When visiting the Hoornse Plas during the summer, I didn’t see any plants in the water, but at the bottom Stonewort would be rather abundant.

Since I live in the middle of the Netherlands, I wasn’t able to take a winter sample from the Hoornse meer (the lake being documented rather thoroughly). I will be there in the near future and hope to be able to take some samples. For now I noticed that an extensive document (unfortunately in Dutch) shows summer values for the pH going up to 10!

However, I was able to take some samples from my reference point at the Weerwater (Maastrichtkwartier) and the locations with a higher pH during the summer: the IJmeer close to Marinazand and the beach at Muiderberg (a very busy bathing place during summer).

My chemical analysis kit didn’t hold a phosphate test, so I ordered one. I’m not sponsored by JBL, but I really like their kits! The colour indicating the phosphate level ranges from grey to deep blue.

Analysing the samples I got 0.1 mg/l for the Maastrichtkwartier and Ijmeer, but 0.2 mg/l for the beach at Muiderberg (Muiderzand). Although it’s not the beach season, the phosphate is still higher in this part of the lake (actually the same as the IJmeer, but to the other side).

I also tested two drops of urine (about 100 µl) in 10 ml of water. Within seconds the colour went of the scale. Not a surprise when the phosphate concentration is 1.2 grams per litre: diluting 100 times would still mean 12 mg/litre, with a scale only going up to 1.2!

It’s very hard to tell how the phosphate will distribute over the larger lake (the beach at Muiderberg is adjacent to the IJmeer, which is actually a part of the same water body as the Markermeer. The area is 70 000 ha and the depth is about 4 metres, so we are talking about nearly 3 billion cubic metres, but water doesn’t mix instantaneously and at Muiderberg the water is really shallow. Let’s see whether the local phosphate levels are higher during the summer. This would still not be proof, but it would certainly support my hypothesis!

Plans and preparations

It’s rather cold outside and I’ve been busy. Yet my silence here (at this blog) doesn’t mean I wasn’t involved in water and soil activities. The plans started to expand quicker than I could handle, so I just wrote them down.

One of them was taking soil samples at the bottom of water bodies (lakes, rivers, ponds, canals). To be able to do so I needed a device similar to my soil sampler (picture), but with a tall stick attached to be able to reach some depth.

Our lake is not very deep, but at some points still a couple of metres and then I won’t be able to get close to the water surface, so another metre will be lost. Then some other lakes and rivers or canals will be deeper and I decided to go for about 4 metres – including “the lost one” – allowing me to reach the bottom of most waters. Yet the device had to be transportable, so a modular one would be the solution. The solution is shown below (both unpacked and packed).

Then I also planned to take water samples at a deeper level. Until now all my measurements were about the surface, but from the start my plan was to differentiate. The only issue was that I didn’t know how to do it. It has to be practical and simple to prepare. Last month I designed a bottle holder, where an ordinary suction cup will close the bottle (pressured by a spring), until released. Then the bottle will fill itself with water (air bubbles coming up will be an indicator) and be closed again because of the spring, so the sample can be brought up safely (without significant contamination that is). The device is not completed yet, but the drawing will give an impression.

Another experiment I really want to perform is to determine the influence of light. I have this set of LED-bars (15000 lux at 10 cm, which will be the distance to the water surface). It’s not like a bright sunny day in summer of course, but above the lower values in winter (during November and December my measurements (around noon) showed averages between 2800 and 54000 lux. The days were short (9 hours of light, but only 5 of reasonable strength), so the total amount of energy was not very high. Using 15000 lux for e.g. 7:00 to 23:00 will provide about three times as much energy during a day (during summer it will be 20,000 to 100,000 lux during 16 hours – probably 30000 on average so twice as much). Of course temperature is important and I will use a dark set with the same temperature as a baseline.

The plan to sample our canals will have to wait for the summer. Sampling ponds in the woods (would be acidic) is still on the list as well.

Finally the riddle of the high pH got my attention again and I came to a hypothesis, but I will save that for the next post. And of course the regular sampling goes on, with one location at the Weerwater serving as a reference.

Short update

At this moment it is cold and rainy and a lot of other things were going on. I was even interviewed by our regional broadcast about my measurements. The light measurements are going on, but the effect has to be observed over a longer period of time. The measurements performed until now (November 2022 to January 2023) show a very volatile pattern.

The energy provided per square metre of water surface is estimated between 0.4 and 8.4 with the average somewhere around 3.7 MJ/m² (of course the graph is not presenting the real truth with its straight lines, because the measurements were only performed at the days with a dot or actually a small square, but this presentation reads nicer than without lines).

Then I took a series of reference samples from  the Weerwater (lake) at three locations. As usual three samples, measured three times each. In total this provided me with 27 pH values and the same number of conductivities. Because the temperature dependence of the conductivity is rather strong, I measured again after 2 and 6 hours. Correcting the conductivity to a standard temperature showed that the values were close to each other after two hours, but after six hours the conductivity was still the same, despite the increase of another two degrees of temperature.

The average pH for all 27 values was 7.92 with a standard error of only 0.07, despite of the samples being taken at three locations! After 4 hours it was 7.98, with the same standard error (0.07) and after six hours it was 7.94 and 0.05 respectively. The conductivity was 682, but the water was only 9 ^oC so at 25^oC this would be 911 (standard error of 13, so the error can be set at 4% at most).

During the last couple of weeks the weather was mostly rainy, with a temperature between 0 and 10^oC. It’s not a surprise that the pH-value graph is rather flat for this period (the graph shows the number of days since January 2022, so look at the part to the far right).

The alkalinity (KH) was slightly higher than previously (rising from 6 to about 7.5) and the nitrate was clearly at 1 mg/l – also slightly higher than during the summer, but then minerals were probably part of the organic matter (which I don’t see, since I focus on inorganic Nitrogen). During the winter a lot of water plants are decaying and this will add all kinds of ions to the water. In the end I don’t think it’s too spectacular, although it is interesting after all.

The plans I had are still waiting for better conditions: to measure several spots at the web of our canals and taking samples from ponds in forests (those would be more acidic). I also want to perform some lab experiments to check the impact of light in a more controlled environment. Then I would like to tale soil samples from river beds and the bottom of lakes. Finally I would like to investigate the difference between upper and lower water layers, but for this I need to prepare some smart devices first.

A terrible discovery

Terrible is strong word and we should interpret it from a scientific point of view. No buildings collapsed and nobody got hurt, but in the end a hypothesis was damaged badly! It is important to mention that all the values presented in this post are from one single location (Weerwater Almere at Maastrichtkwartier).

What happened? I was working on light and temperature as potential factors influencing the pH. We already know there probably is a (weak?) relationship between light and pH, but measuring over a longer period we will know more. The data for light are still inconclusive (range is still too small), but I was also creating graphs for pH and conductivity against temperature. That was where the shock came in. The pH has a weird pattern, but the suspicion is still that temperature is related to pH because both are the result of the input of sunlight (warmth and photosynthesis respectively). For conductivity I knew that temperature was influential, but I never paid much attention to it. This time, when adding a linear trend line to the graph, I could not deny what I saw.

Searching for the relationship between conductivity and temperature I found this website, telling me the change is 2% per degree according to the industry standard. That’s quite a lot when we are talking about a range of nearly 25 degrees (4 – 27^oC!)

Although the measurements are not at a straight line, the 17.1 in its formula, divided by 900 (more or less the value for 25^oC) is close to 2% indeed.

Now the issue is, that these samples were collected during a long period, between July and December 2022. A lot of variables changed over time, so I decided to take fresh (very cold!) samples and keep them at room temperature, measuring both temperature and conductivity (and pH) now and then. Three samples were measured only one time each for a specific temperature level, because with additional measurements the temperature would have changed too much already. The temperature went up rather quickly, but the “room” temperature in my room at the top of our house is only 16^oC. For the final step I placed the bottles next to the (warm) airflow from my laptop fan and got a slightly higher temperature, completing my series. The standard error for the conductivity of the samples was between 1 and 15, with temperatures  in the mid-range being closer to each other (hence a low error). Not surprising, because the volumes were different and the change was quicker at the low and high range. Time to switch from one bottle to the other was influential, especially because the measurements were done with the same device and it takes some time to adapt to low temperatures. For each temperature level I took the average. Despite all those uncertainties and errors, the resulting graph is impressive:

The slope of the trend line is 20.1 and dividing it by 1000 (conductivity expected for 25^oC). This would be 2.1%, close to the industry standard!

Would this mean that the conductivity values were just caused by temperature and nothing else? Well, you probably noticed the difference between the graph with data from half a year, based on a series of fresh samples and the one based on only three samples measured within two hours after getting them. The latter is a neat straight line, but the former actually isn’t. I did two things. First I calculated the conductivity values from July to December back to 25^oC, the standard.

Doing so, the resulting graph did not show a horizontal line, but a trend line with a slightly negative slope.

Then I went back to the original results and instead of a linear trend line, I used a polynomial one (second degree).

The fit is better and now we can see that some of the conductivity (actually ions) was lost during the summer (personally I even see and S-shaped curve with a jump between 10 and 15^oC). This is very different from the trend line for the set of samples from mid-December, measured at different temperatures, so there is hope after all! What I learnt from this is that the conductivity should be corrected for temperature.

During the conductivity analysis, I also encountered a strange effect for the pH. The temperature also influences the pH, but not very strongly compared to the range observed in our samples. Then the trend should be downward for this slightly alkaline region, not up. During the year, between May and December, the graph looks like this:

Between 10^oC and 25^oC and a pH above 7, we expect the pH to drop about 0.3 points, so corrected for their baseline, the summer values would even be higher! The surprise however, was the bended curve. For lower temperatures the pH is as expected. The pH meter may vary a little bit, but is calibrated on a regular basis and was usually checked before and after the measurements. The values shown are based on 2-4 samples, measured 2-4 times, leaving us with a standard error of around 0.02 which is small compared to the differences in pH measured. The samples taken for a water temperature around 4 or 5 ^oC were taken in the presence of ice. Despite the top layer of the water being 0^oC, the lower layers of the water were still slightly warmer (2-3^oC and during the measurement the temperature measured was even 4-5^oC, probably because the instruments were still warm and influenced the samples). At 25^oC degrees the pH should have been nearly 0.4 points lower than at 4^oC, but the values were higher. Looking at the recent set of samples, measured at different temperatures within two hours, the curve was also bended!

The fresh samples show a decrease in pH between 5^oC and 10^oC according to this formula. It’s also a temperature where not a lot of biological activity is expected. Then between 11^oC and 20^oC the pH is going up 0.15 instead of down 0.2. Are we probably waking up some organisms?

Of course the effect is weak (the Y-scales are very different) and the pH has an uncertainty of about 0.02 so the curve is not conclusive. Yet we stumbled upon some interesting effects!

Even more fun with light calculations

Should I give a “nerd alert”? Don’t be afraid of the formulas, I’m just taking you with me on my journey, but after getting complicated things will be rather easy! Of course it’s still approximations of what’s really happening, but that’s not an issue. My quest was about finding the light integral – the total amount of energy received by a water body per square metre!

Previously I explained how I was able to get reasonable estimates of the moments of dawn and dusk and – what’s more important – estimates of the length of a day throughout the year. A sine-equation did pretty well, but then I started looking for the pattern during the day. Although this is not a pure sine-function, again good estimates can be obtained when applying another sine-function.

Photo by Tony Cordaro on Pixabay

The principle works fine if it’s a purely overcast day or a nearly cloudless sunny day. However, if clouds come and go, things will be a bit more complicated. I started out to take the highest lux value observed during the day. The early measurements were done throughout the day, often taking several sets of 50 measurements with one minute intervals. Averages were taken for periods assigned to the starting hour (e.g. a series covering a period between 11:50 and 12:39 was cut into two sets : 11:50 – 11:59 and 12:00 – 12:39, providing averages for both 11 and 12). Actually there was no need to have so many observations, but it was the way I set up my light metering device and with those settings I measured several days. It turned out that ten measurements with one minute intervals will do, but it was only later that I adjusted the settings.

Plotting the Lux-values for the resulting series, I added a sine function (cut-off before dawn and after dusk) multiplied with the highest Lux-average observed during the day. Although for stable days this will be somewhere around 12:30 (13:30 during daylight saving time) for the centre of the Netherlands, cloudy and sunny periods can shift this peak to other moments. However, I’m looking for the full-day light integral and not for single estimates for hourly values.

Although I thought I was working with dusk and dawn values, from the table I shared earlier the times provided were actually sunrise and sunset. That’s fine, because the periods between the start of dawn and sunrise and between sunset and the end of dusk don’t really contribute to the total daily energy and can be ignored. The formula used to obtain the calculated values in the graphs is:

max(lux) * sine (π * (hour-sunrise)/(sunset-sunrise))

The value hour minus sunrise will be between zero and total day length, but because it is divided by sunset minus sunrise, the result will be between 0 and 1 and after multiplying by π the sine will cover half a circle will go from 0 to 1 back to 0 only. Because this result is multiplied by the highest average Lux-value measured during the day, it will go from zero (sunrise) to the highest lux value (around noon) and back to zero (sunset).

Then I went the wrong way! Probably not from a mathematical point of view, but I surely missed my purpose (still being in need of the light energy integral for a day). I substituted my sine-function for the calculation of sunrise and sunset in this formula and then went for its integral. After some reductions I got something like this in Excel:

=SIN((hour-[6,82-2,16*SIN((month-3)/1,9)])/([2,16*SIN((month-3)/1,9)+19,18]-[6,82-2,16*SIN((month-3)/1,9)])*PI()

Because I was looking for daily values, I only had to integrate for the variable “hour”, basically meaning the other parts would be constants, only depending on the month. I realised the integral would be a minus cosine, but then I thought: what am I doing?

Photo by Robin Higgins on Pixabay

Coming up with an integral depending only on the month-number? Days and seasons are not changing that fast. There is a table with sunrise and sunset values and I took the values for the 15^th of every month. Why don’t I just come up with a daily integral and fill out those constants for the 12 months? No estimates for sunrise and sunset, just twelve day-integrals, only in need of the highest amount of lux measured during a day?

After this brainwave the integral was not too complicated. For the full day I only need the total integral of the minus cosine, being –cosine(π) – – cosine(0) = 2. However, the part inside of the sine will also be part of the integral, because ʃ sin (a.x) = – 1/a cos (a.x). The inside was π/(sunset – sunrise) (the hour minus sunrise only being an irrelevant phase-shift) hence (sunset – sunrise)/ π will go to the front, together with the 2. But then we got hours of lux-values and we want to get to total Joules.

Lux-values can be converted to Watt by multiplying them by 0.0079 and hours will be converted to seconds by multiplication with 3600. So 3600 * 0.0079 * 2 *  (sunset – sunrise)/ π = 18.11 * (sunset – sunrise).

The estimates for monthly length of the day (sunset minus sunrise) is a simple table:

And combining those values with the 18.11 we get:

All I have to do now is multiply those factors with the highest average value of lux observed during the day and we will know the total number of Joules received by a square metre of the water surface. Let’s try:

For a sunny day in June it will be up to 120 000 Lux *302 = 36 MJ

For an overcast day in December, like we have now, it will be 3000 Lux * 146 = less than 0.5 MJ

 A huge difference. Of course it could be an overcast day in June and a sunny day in December and we would get 1 MJ versus 17 MJ. In the end the length of a day is far less influential than the amount of Lux, because the former is only a factor 2, but the latter is more like a factor 100. Yet it’s good to have this table and the formula.

Why did I do all this? Well, I already told you I needed to get hold of all the variables influencing the total amount of energy received by a square metre of a water body. Sampling ten Lux values with one minute intervals around 13:00 will provide me with an average, to be multiplied with the conversion factor I obtained to get the total energy for that day. Often it will be 50% off probably, but who cares if the actual variation (clouds!) during the month can be like 10000%?

The formula’s outcome is consistent with the yield of solar panels. A single panel is about 0.55 m2 and will convert about 20% of the light to electricity, so for the sunny day in June it will be 0.55 * 20% * 36 MJ = 3.96 MJ or 1.1 kWh – at most! A quarter to half of this value is more realistic of course. Indeed those are the values reported by this website and another one.

With the new approach I proudly present some curves based on only a few measured values (the Y-axis is the same for all). Yet the total energy for the day derived with our new formula will be sufficient for the next steps.

Fun with light calculations

The light meter is really great, but what I need to know is the total amount of energy a water body is receiving throughout a day. I knew about the angle with the sun, changing during the year. It’s causing different lengths of the days and then we have the difference between a sunny and a cloudy day. What I wasn’t aware of were the huge fluctuations from day to day and even within a day. Actually, the full light (or energy) integral for a day is the relevant information I need.

One way to go would be to buy something like a Raspberry Pi and create a light integrator. I did some research and although it’s not too hard, I prefer to go with a theoretical approach and do my calculations first. After all, a rough estimate will do.

That’s why I looked for more detailed information about the sun angle and the length of the day. I really love trigonometry (seriously!) and when I looked at the angle with the sun throughout the year (61.5 in summer, 14.5 in winter and 38 in between), I  realised it had to be a sine-function. Dividing the month’s number by 1.9 to get more or less 2π, shifting the top of the function to June by subtracting 3 from the month, multiplying by 24 to get the amplitude and adding 38 for the base-level, I got my formula: 38+24*SIN((month-3)/1.9)

Then I wondered what the relationship with the average length of the day would be and plotted the monthly averages obtained from a table  (link is in Dutch, but it’s mostly numbers) against my calculated angles. The result was almost a straight line (actually a very narrow ellipse, but that’s fine). Excel came up with a formula and after a lot of attempts I realised I couldn’t beat Excel’s parameters.

The next step was to substitute my sine-function into Excel’s linear regression formula. It was a struggle with all the brackets, but after simplifying I got: 4.32*SIN((D44-3)/1.9))+12.36 providing me with the number of hours (with decimals). Comparing the calculated values with the ones from the table, the error is between -4% and +5%. That’s certainly sufficient for my purpose!

The next step is to convert the hours to energy. Of course the energy-level depends largely on the number of Lux (Candela per m²), but the Lux can be converted. Apparently it’s 1 Lux = 0,0079 W/m² for the solar spectrum, but we are dealing with sunlight.

Then the angle of the sun is influential of course. If the sun is high in the sky in June, the light will be stronger than in the winter, when the angle is rather small. Yet we don’t have to think about this, because the Lux is about energy per square meter, so we only have to know the amount of Lux. In December or January, we can expect about a quarter to a third of the number of Lux measured during June, according to the sine-values (trigonometry!) of the angle at which we see the sun. This graph is helpful, I think (it shows the surface beneath the lower curve is nearly ten times smaller than it is for the red one):  

This one is a bit more sophisticated, but very informative (although the summer/winter ratio for the total energy – about 3.5 – doesn’t apply to the Netherlands):

It’s the combination of the light level and the day length (in December the length of a day is about 47% of June and the illumination is around 27%, leaving us in December with only 13% of the energy of June) . Beware: I live in the Northern hemisphere in Western Europe. For Australia or Northern Africa things will be very different!

Several sources mention values for “cloudy” and “sunny” days, but in summer this will be very different from winter, because of this “sun angle”. Then my measurements show that in November a “overcast” hour is more like 2000 Lux. For sunny hours it’s about eight times higher. In summer those values will be much higher, but I still have to do my measurements. Using the ratios of the sine-values for the summer and winter angles I came up with an estimate for the real energy values for cloudy and sunny days for every month. I’m rather sure about the autumn, but the other periods are not completely sure and therefore printed in grey. Yet the values don’t differ too much from the graphs shown above.

In the future limited samples, taken around noon, will provide an estimate of the light intensity for that day. No need for complex devices.

Warning! These are all calculations and estimates fit for my purpose (pH prediction), but don’t think it’s the truth. Then it’s only meant for the North-West of Europe, but even then you won’t be able to predict the yield from your PV-panels accurately, using these numbers!

Photo by Aserov24 on Pixabay

A new parameter: light

Yes, I was silent. After the retrospective, the pattern throughout the Netherlands was rather clear. Larger water bodies have a clear range for the pH above 7 and our woods are rather acid – mostly around pH 4 – with the level of Nitrate being too high. Conductivity is an interesting addition, partly related to salt, but also correlated with the pH. Nevertheless following the same path would be boring, because the results would be more or less the same.

That’s why I was thinking about two possible extensions. One is to include canals (connected to lakes) and ponds in woods (the latter would be more acidic). It took me a while to understand how the major lakes and ponds in Almere are connected and I worked out a map with several sample points. I won’t show it right now, because values have to come in. Why didn’t I sample yet, you may think? Well, I could answer it has been too busy to do so, but it’s not the complete truth. It’s cold, dark and rainy most of the time and my field work should be fun. In the summer it was a bit of a holiday, travelling around and taking samples. Now it’s different.

That’s still not everything. During the period of sampling I reported changes in pH and the idea is that there is a relationship with the plants. Indeed the values are fluctuating during the day and for the plants (including algae and probably adding cyanobacteria, holding chlorofyl although not plants) light must be an important factor (together with the temperature).

Knowledge about light, I gathered during the period of the old-fashioned chemical photography. Nothing was automated and we either had to make an estimate of the light or used light meters. When studying I also learnt about light and energy and I knew that the human eye is able to adjust over a huge range of intensities, varying between roughly 1 and 100 000 lux. As a result we don’t see much of a difference between a sunny and a cloudy day, but the actual energy received by plants will vary a lot. That’s why I bought a simple device to measure the light (Lux meter).

First I had to learn how to use it. It’s really nice, because it is able to sample 50 times, with an interval of a minute (other options, like less samples or shorter intervals are available as well, but this setting is what I needed). During couple of days I sampled from morning to evening (days are short in the second half of November!).

Despite all the theory and experience with old fashioned photography (mostly forgotten probably), I never realised how the light intensity can change from moment to moment during the day. Clouds are very, very influential and so is rain. No nice curves, but a collection of spikes was what I got.

What we really need is the light integral, presenting the total amount of energy received during the day. That’s not easy, but it is possible to get an estimate for a couple of days, by sampling at a couple of moments. Not a large number, like fifty, but certainly a couple to prevent outliers influencing the curve the wrong way. The period around sunrise and sunset is not very informative, although the patterns are really nice (see graphs below).

Looking at the min and max value during the day, it’s better to measure around noon, but we can also see that the differences are still impressive.  The graph below shows the lowest and highest values for the light intensity (lux) in the periods between 11:00 to 14:00. The 120000 during the first day was a one-off. The clouds went away for a moment and I saw the value spiking. That’s why the second highest value was added. It’s more realistic. For all the other days the min and max values are realistic, with ratios between 5 and 12.

Let’s see how we can get the best estimate of the light strength during a couple of days. Probably a couple of samples around noon will do. It’s also possible to take more light samples during the same day the water samples are collected.

I already took three reference samples at the Maastrichtkwartier (Weerwater, Almere) after this dark period (at November 25, 6 PM).

The conductivity (average: 645) was the lowest since I started the measurements (mid of August, 2022), but conductivity is very sensitive to temperature.

Strictly speaking the pH (average:  7.75) was not the lowest since the start of the measurements (beginning of May 2022), because mid-September the value was also 7.75, but only because one of the values was low. Now all values were rather low, but the temperature was only about 10^oC, so it’s hard to compare.

Water and soil samples – a retrospective

Until now we took samples from different, larger open waters in the Netherlands. Mostly lakes, but also some rivers. Then we sampled soil, mainly in woods. Values for pH and conductivity were determined and in some cases also the inorganic Nitrogen content, primarily Nitrate (NO₃^–).

Now it’s a good moment to step back and have a look at the ranges of the values we observed. Of course it has not been a full year, because the sampling only started in May 2022 and conductivity and Nitrogen came in even later. During the winter additional samples will be taken and then we will know if things are different in cold and or dark circumstances. Most likely we will recognise an annual cycle, but we already know it’s not that simple. Rain or high temperatures are very influential.

The values plotted on maps as shown in this post, are not too accurate. The purpose is just to provide an overview. The first set of maps is for the water samples. Although Nitrate and some other inorganic Nitrogen compounds (like Nitrite – NO₂^– and Ammonia – NH₄⁺) were measured, the values were very low and never exceeded the 1 mg/l. Alkaline hardness (or alkalinity) was also determined a couple of times, but was always about 6, so here we only show pH and conductivity (in µS/cm) on the maps.

pH of surface water

The range of observed values is 7.7 – 9.7 but we know that the values above 9 are rare and the ones over 9.7 are real exceptions. It would still be interesting to know what is causing those high values.

Conductivity of surface water (samples from North and East undetermined)

The conductivity is probably more interesting than the pH. The North Sea Canal is a very special location, of course, with its diluted sea water (brackish). It’s a pity that I didn’t have my conductivity meter yet, when I travelled to the North. We still have to fill in the gaps in the East and the question is: “will the conductivity increase as we get closer to the sea?”. The range in the overview (top left) is in milliSiemens per cm, rather than microSiemens per cm, because the highest levels take five positions whilst the accuracy is not very high (an error of 1-2% is no exception).

For soil the inorganic Nitrogen is more interesting, although Nitrate is the main contributor. Nitrite and Ammonia were in the error margins of the Nitrate, but let’s start first with the pH and Conductivity.

pH in soil from woods (mixed with demineralised water)

The pH is the woods is much, much lower than expected. It’s not healthy at all and it confirms the stories we hear about acidification of the woods and the damage of nature in general. The range says 7.7 is the upper value, but actually that’s only a wood in Flevoland. It’s an artificial wood, started a couple of decades ago at the bottom of a former sea. When drained, the water was already turned into a fresh water lake, but the soil is still a former seabed. If we leave this one out, all remaining values had a pH of less than 4! Very acidic.

Conductivity in soil from woods (mixed with demineralised water)

The conductivity of the soil samples is low, but that’s a dangerous one. We already observed that the sand in the slurry can lower the conductivity and – very different from a simple salt solution – adding water can actually increase the conductivity. Flevoland is an exception again, as there is still a lot of salt in the former seabed. The surface water in Flevoland has rather high conductivity values. Be aware that the picture shows values in mS/m. To get the µS/cm values presented throughout this blog, the numbers should be multiplied by 10!

Finally we get to the Nitrogen. The values vary, but in general they are too high for a wood. The ranges are broad, because the colorimetric determination was done without the help of a spectrophotometer. This introduces an uncertainty expressed by taking a range from half to twice the observed value. This makes the ranges different from the ones in the maps above. There the low and high values were actually observed for a series of locations. Here the range expresses the uncertainty of the value for a single location. Nevertheless even the lowest values are too high in most cases.

Nitrate content of soil from woods

For details see the posts about the different locations.

Nitrogen content of forgotten samples.

Recently I was creating some maps with locations where I measured soil and water samples. For one I was entering the nitrogen concentration in the soil and got as far as below.

Nitrate concentrations measured.

A lot of values seemed to be lacking, so I had a look in my spreadsheets at the tabs for Slinge, Holterberg and Almere, but could not find anything. Then I looked at my blog-posts and still didn’t find any information on Nitrogen for those locations either. Finally I went through my original notes on paper. It took a while before I understood that I had kept the air-dried samples, to be able to determine the Nitrogen content at some moment in the future. The measurements were never done in the first place!

Holterberg

It would not be a big deal, but I was really interested in the Eastern part of the Netherlands, so I decided to take the combined samples from the Holterberg and the ones taken close to the river Slinge (near Winterswijk). Then I added the combined samples for the wood near Austerlitz and also the sample-set I took in Almere in a wood close to an agricultural area.

By now the standard method I use is to take 20 grams of air-dried soil sample and mix it with 100 grams (100 ml) of demineralised water. Two slurries per location were prepared. Shaking gently now and then, I let them soak and mix for an hour and then filtered the slurries (using a standard coffee filter in a funnel). The filtrates were investigated for Nitrate (NO₃^–), because by now we know that Nitrite (NO₂^–) and Ammonia – or actually the Ammonium ion (NH₄⁺) do not really contribute to the total amount of Nitrogen.

The concentrations in the filtrates were calculated back to the sample, to 1 kg of dry soil and then – using the soil moisture content – finally back to the concentration of Nitrate (NO₃^–) in mg/kg of fresh soil. The largest influence on the error is the visual determination of the colour, since a colorimetric approach was used. That’s why I am careful with the values obtained at this moment. For the future it might be useful to set up a simple spectrophotometer, but then I will also need a range of nitrate concentrations for calibration. All were rounded to whole milligrams per kg and then the range could still be between half and one and a half of the actually observed value, although both slurries for a location gave the same results without an exception. That’s a good sign.

Just as a check, Nitrite (NO₂^–) and Ammonium (NH₄⁺) were measured for one of the slurries for the Slinge. As expected the Nitrogen content from those compounds were completely in the error margin of the Nitrate.

The concentrations themselves were not a real surprise, because we know the Netherlands have an extremely high concentration of Nitrogen in the soil in woods. For agriculture Nitrogen is very important to obtain a high yield, but in the woods it’s reducing biodiversity. Because of this conflict we are importing a lot of fertiliser, having a surplus of manure at the same time!

Nitrate concentrations in combined soil samples from four woods

The samples taken in the woods next to the “van Wagtendonkpad” in Almere show extremely high concentrations, but it makes perfectly sense. It is an artificial wood, less than fifty years old, growing at a former seabed. Originally, after draining this sea (which became a fresh water lake in between, because a large dyke, the Afsluitdijk was put in place, closing of the former sea), it was meant to be agricultural soil. That’s why it was sown with seeds of Nitrogen-binding plants. Instead of harvesting, those were ploughed under. Only later some locations became (recreational or production oriented?) woods, whilst other areas got an agricultural destination after all. Actually we should ignore this set of samples when looking at woods in the Netherlands.

The area around the river Slinge is in one of the endangered zones and indeed the Nitrogen concentration is rather high. Even when taking the lowest value from the error margin, the value is twice as high as what we find in references shared previously (see post about the Echobos ). At least the map with Nitrogen values is more complete now!

Analysis of stored samples – last time I will do this!

In previous posts I gave updates about samples kept for a while (days to weeks). This time I kept the samples taken from Vecht and IJmeer (and the references from the Weerwater). As usual the samples were kept in closed bottles, at room temperature, in the dark. All samples were kept this time, so not only the second or third one. This is nice, because the behaviour of different samples doesn’t have to be the same. A grain of sand holding bacteria, a piece of a plant, some algae, a small animal – things can be different for the individual samples and influence the change of pH and conductivity over time.

All kinds of processes (aerobic and anaerobic) will be able to change both pH and Conductivity).

Location with still the highest pH, despite being a full point lower now

Spoiler: the Standard Error for both pH and conductivity was still very small (two measurements at two samples for most locations – except samples three for the Weerwater –  led to four values per location for each of both physical quantities). For the pH, the Standard Error (population – not the mean) was nearly always below 0.10 – with one exception being 0.12. For the conductivity it was usually around 1% of the value – again with one outlier being 2.1%. This means the behaviour of the different samples from the same location was not that different after all.

During the two weeks between sampling and measuring again (actually 13 days), the drop in the average pH value was between 0.24 and 0.83, where in most cases a higher initial pH showed a larger drop. For conductivity it was the other way round: the values went up, but for most cases a larger conductivity showed a smaller increase! A huge difference with previous re-measurements after a long time was the temperature during the storage period. Previously temperatures were rather high – up to 27 ^oC but now the bottles were stored in a room where the temperature was only 17 ^oC. During the measurement the temperature went up a little bit (to 19^oC) and as a result most values obtained in the second round of this session, (both pH and conductivity) were slightly higher. The effect is rather small, as indicated by the Standard Error.

Again the correlation between pH and conductivity was confirmed. Even with the decrease of the pH and the increase of the conductivity, the correlation remained at 0.88 – the same as for the fresh samples (see the previous post, where I rounded the value to “nearly 0.9”). Below three graphs are shown: conductivity (as always in µS/cm against the pH, pH drop against original pH and conductivity increase against original conductivity. 

Difference between original pH and value two weeks later,
against the original pH. Correlation 0.92

Difference between original conductivity and value two weeks later,
against the original conductivity (all µS/cm as usual). Correlation -0.56

New conductivity against new pH (Correlation still 0.88)

By now the effect is so clear, that it’s not necessary to go on and keep samples for a long time after the initial measurements. For those who want to do an in-depth analysis, the original spreadsheet with all the individual measurements is available. Just contact me at Twitter (@AnRep3D – alias Scientassist) or mail your request at anrep3d@gmail.com – putting “Request for Data” in the subject line.

IJmeer and Vecht – mystery still unsolved!

For those who remember:  of couple of months ago I wrote a post about the IJmeer, where very high pH values could be observed. Especially at Muiderberg the an average value of 9.70 was observed. Looking at the map I noticed that the river Vecht discharges into the IJmeer and I wondered what the pH (and conductivity) would be there. The situation is a little bit unusual, because for some reason the river is extended by two dams of about 400 metre in length. This means the water is not mixing right away. Yet it would be interesting to take some samples.

As a reference, because we had several showers and weeks of moderate temperatures, I sampled the Weerwater first. During the hot, dry period the average pH at the “Maastrichtkwartier” went up to 8.97 but now it was only 8.07!

The location at Muiderberg, close to a small beach, had a lower pH too. Not 9.70 like before, but  8.74 which makes sense, because the Weerwater also dropped nearly a whole point. Nevertheless, it would still be worthwhile to investigate the Vecht, but first I sampled a bit closer (halfway) to the outflow. Surprisingly the value there was much lower: 8.06. This was an indication that the Vecht was probably not pushing the pH to higher levels.

The two dams, extending the Vecht into the IJmeer

It was not possible to reach the water between the dams (the “extended river”), so I took a sample from the IJmeer outside of the left dam. There it was 8.37, but because of the dams this water was a bit in the “shadow” of the river’s flow. The water there was very shallow. So shallow that it was hard to fill my little bottle! The water was very clear, will a lot of crushed shells at the bottom.

Finally I was able to sample the river itself. Going upstream, the pH went down! It made me wonder what is causing the increase of the pH when following the flow. Is it the plants? Or is something put in the water? Despite the increase of the pH, the water doesn’t reach the level of the small beach in Muiderberg. This means it’s very unlikely that the river Vecht is pushing the pH of the IJmeer to higher values, so the mystery of the extremely high pH (for a normal lake, that is), remains unsolved.

The pH is plotted at a map, for a better overview.

Of course the conductivity was also determined, but conductivity is much more sensitive to temperature than pH. When starting the air temperature and the temperature of the water sampled was around 14^oC, but the last sample taken (upstream Vecht) was about 19^oC. However, the location at Muiderberg was a bit of an exception. Getting there shortly after taking a reference sample from the Weerwater (within half an hour), the temperature was 18^oC already. A couple of kilometres further, the IJmeer was only 16^oC! Yet the difference in temperature was not so big that the conductivities can’t be compared. The conductivity is plotted at the same map.

Conductivity in µS/cm

After completing both maps I was curious whether the negative correlation between pH and conductivity would still hold. Indeed it was still there and nearly 0.9

The graph with Excel’s formula is shown below.

Echobos Muiderberg: inorganic nitrogen

For our superficial and deep samples we would like to know the inorganic Nitrogen content. The organic part will be mainly in (decaying remains of) plants, but the inorganic part, like NO₂^– , NO₃^–  and NH₄⁺  will be solved when water is present. With our 1:5 soil to water slurry, the soluble Nitrogen will get into the excess of water and leave the soil. Again the filtrate (see previous post) was pink.

I wanted to be sure that the coffee filters did not add any Nitrate, so I soaked ten of those – 18 grams together in 90 grams of water (also 1:5). We can’t be sure that the Nitrate will spread evenly over the water, because a lot is absorbed by the filters, but waiting for some time will get the equilibrium closer.  The measurement said 1 mg/l and for 90 ml that is only 90 µg for ten filters, so the content of a single one is negligible – even if the actual content would be ten times higher.

This road chapel was placed as a reminder of the former Boskapel (wood chapel).
The building is still there, but became a regular home.

It won’t be a surprise that the surface held more inorganic Nitrogen than the deeper layers. The surface soil collected close to the entry near the “Boskapel” held (calculated back from the slurry) 42 mg  NO₃^–  per kg fresh soil on average (the actual range is broad, because the colour has to be determined visually: 35 – 50 mg/kg).

For the location near the Soccer club the Nitrate concentration was lower: about 19 mg/kg (10 – 30 mg/l). Probably at the side of the Boskapel, being close to the village, more people will walk their dogs and leave the dog poo in the wood and its Nitrogen will accumulate in the soil. The deep layers held lower concentrations which makes sense.

Nitrogen is not just Nitrate, so Ammonia and Nitrite were also determined for the location “Boskapel”. The Nitrite concentration was very low: less than 0.15 mg/kg fresh soil. This is much less than the error margin of the Nitrate, so the Nitrite (NO₂^–) can be ignored.

Ammonia (NH₄⁺ ) wasn’t impressive either. A mix of the two slurries for the surface showed 0.5 mg/kg fresh soil. We have to take into account that a molecule of NO₃^–  is nearly three times as heavy than a molecule of NH₄⁺  (because of the three O’s) but it’s still in the error margin of the Nitrate and therefore Ammonia will also be ignored.

A bar-graph was prepared for the Nitrate. Now the question is what’s normal for a wood. For agricultural soil it seems to be 10-50 mg/kg. The levels will differ, here is another reference from Sri Lanka.

For forests and woods it’s much lower:  Denmark mentions a mean of 1.5 mg/kg, but with a very broad range. Germany (calculations have to be made from kg/ha to mg/kg), indicates  more or less the same magnitude. This means that our Echobos is extremely rich in Nitrogen, like so many places in the Netherlands.

Echobos: confirming previous observations

After drying and weighing, the samples from the Echobos at Muiderberg were stored for further investigation, including inorganic Nitrogen. The Nitrogen will be in the next post, because first we will go into pH and conductivity of the dry samples. Although my (generally accepted) approach for fresh soil is now to mix a certain weight with an equal amount of water, this doesn’t work for the dry ones. Think about soil samples losing 50% of their weight on drying – meaning half of the original weight was just water. By adding the same amount of water again, we would just restore the original situation, with all the water being in the soil. To be safe – especially for the conductivity – five times the amount of water was added to a dry sample. In a previous post we showed that at this ratio the conductivity will be more realistic. This was a short explanation why a for dry soil samples a sample to water ratio 1:5 is used.

Road chapel ‘Mary Queen of Peace’ in front of the former “Boskapel” (Wood chapel).

pH

Now we know that the pH for acidic samples will go up upon dilution. We can calculate the expected increase, but it is tricky. For the fresh samples taken in the Echobos near the “Boskapel” and close to the Soccer Club, the pH values were obtained from a 1:1 slurry. The moisture content was around 50%, so adding 100 grams of water to 20 grams of dry soil, means that 20 grams of water are needed t0 restore the original 40 gram and the other 40 grams of water are just able to mimic the 1:1 slurry. Then 20 grams of water are left of the initial 100 grams, so actually the dilution compared to the previous measurement is 1.5 and (if no buffering capacity is present and no acid was lost upon drying) the pH would only go up log(1.5) = 0.18 Those calculations can be performed on all samples, obtaining predictions about the pH (and I did). Then there is also the standard error of the measurements of course. In the end for all samples an increase of 0.1 to 0.4 was observed (rounded – it was 0.14 – 0.36) but standard errors were between 0.01 and 0.08. In the end we can be sure the acidity was still present in the dry samples.

The deep sample-set taken near the Boskapel showed an odd behaviour: every time the slurry was shaken, the pH would be around 3.9 of 4.0 and then start creeping up to 4.0 or 4.1. It’s quite normal that it takes about twenty seconds to get a stable pH, but this was happening after every shaking! Does somebody know what might be causing this?

Conductivity

The conductivity depends on both acids and salts in the sample. By applying the 1:5 sample to water ratio we encounter the same situation as with the pH: we can calculate the conductivity in the slurry. From previous observations we know that solid matter like sand can lower the conductivity and adding more water will not reduce the conductivity (expected from dilution), but make it go up. Indeed all values measured were lower because of the dilution, but not as low as calculated, which makes sense.

In the end this provides us with confirmation of what we observed when processing the Oranjewoud samples.

Filtering – impact on pH and conductivity

Then, because the inorganic N was to be determined, the slurries were filtered (standard coffee filters, not rinsed in advance). For filtering we also made a strange observation in the past: the pH goes up after filtering, sometimes even a full point. Back then I didn’t understand the phenomenon, but later I discovered that pushing the pH meter deeper in the slurry will provide a lower pH than keeping it in the fluid on top of the thicker layer. It seems filtering keeps out the more acid fraction. Of course this is a risk for the Nitrogen analysis, because keeping the acid out could also hold back some Nitrogen. However, looking at the conductivity we don’t see a lot of change, so the effect is probably related to the H⁺ rather than the salts. For these four samples (eight slurries; for each of the two locations with their superficial and deep samples, two slurries were prepared). After filtering, the pH for the filtrate was up to a full point higher than for the original slurry.

In the end we got all kinds of observations confirmed with samples from a very different location. Next time I will tell about the Nitrogen.

The table below (boring, sorry) shows some details, although I left out all individual values. I can send them by email if you like!

Values for dry samples (20 grams with 100 grams of demineralised water.
To the right the values for the filtrate are presented.

Probably you wondered why the pasture was left out. Well, dealing with eight slurries was a lot of work and I didn’t want to get confused. The soil in the wood was what I came for and the pasture was more of a reference. After the next post on Nitrogen, we might be back for the grassland.

The Weerwater over time

After a long period of drought, we finally got some showers. During the dry period the pH went up, but I wouldn’t know about the conductivity, because I got this device much later, so not many data points are available.

Then the pH data were scattered over different blog posts, corresponding to different tabs in my Excel. Yet I managed to glue some together for the boat-like jetty at the “Maastrichtkwartier” and a location just outside the harbour in “Stedenwijk” (see map at the bottom for GPS-locations).

Large patches of duckweed (clustered by the wind) came up during the hot period

After some heavy showers I collected some additional samples, because those would be the most interesting ones.

Both locations are only a couple of hundreds of metres apart and their values do not differ a lot. For the Maastrichtkwartier more values were present and for those I added a polygonal trend-line.  This is not what really happened in the water (or perhaps it was, but we don’t know), but it helps to guide the eye.

For the pH a baseline of 7 was taken, because that’s neutral. Although it makes the graph less spectacular, the conductivity-graph is ranging from 0 to 1000.

Weerwater during the hot, dry and sunny period and in the cooler, rainy period after.

Looking at the graph it is clear that the pH went up during the long hot and dry period. Of course this could be the result of increased photosynthesis, but when the showers came in the pH suddenly went down. An interesting event, because the volume of the rain is relatively small compared to the volume of the lake. September 10, 2022 the water temperature was still 23^oC, but seven days later it was only 18^oC. On August 18, day with the highest pH, the temperature was even 27^oC!

The conductivity drop is around 10% (for both locations)

The conductivity went down as well. Previously we noticed a negative correlation between pH and conductivity, but that was mainly from location to location and for isolated samples. Now we are talking about changes over time. The 10% drop can’t just be dilution, because – again – the volume of the lake is too large compared with the volume brought by the showers. The rain was only a couple of centimetres and the lake is at least a metre, so we are talking about 1 or 2 % but not 10%.

When the showers came, the light dimmed and the temperature dropped. All those factors may have been influential, just as the long sunny and hot period may have brought up the pH. It wasn’t like the lake was running dry because of evaporation!

For those who are interested the GPS-locations are shown in the map

Soil from the Echobos Muiderberg, sampled at two depths.

This time I didn’t go very far, but I still left our “new land” and crossed the water to visit the province of North Holland. In Muiderberg, a small, yet well-known village (Count Floris V was killed in this area)  there is a nice wood called “Echobos”. This is not just a name. Centuries ago an echo was discovered in the centre, which seems to be related to a structure under the ground.

Of course I just went there to take soil samples and this time I took them at two depths. First I collected some superficial samples as usual (0-10 cm), but then I also went deeper and collected samples at a depth of 35-50 cm, to see the difference. The same hole was used for both and the layer in between was discarded. About five samples, taken in an area of about 5×5 metre were combined for a location. The first location was near the former chapel called “Boskapel” and the other set to the farthest end, close to the entry of the soccer club. There the adjacent pasture was also sampled, but only at two spots, as a reference. Although the upper layers held humus and were sandy, deeper down clay was present in the wood at the soccer club side and the pasture.

At home the samples were weighed and spread out to dry, but parts of the fresh samples were used for preliminary investigations (pH and conductivity). Between 20 and 41 grams of soil were taken and mixed with an equal amount of water (only one slurry per location). Then both pH and conductivity were measured three times. The conductivity is not easy, because the value depends on the amount of water available and the values were very different, depending on the position of the meter. For the pH values were much closer to each other. Of course the pH meter was checked with the buffers before and after.

Some values were even more extreme than encountered in previous samples. For the location near the Boskapel, the pH measured for the upper layer (0-10 cm) was 3.08, 3.11 and 3.09. The average is 3.09 with a standard error (for the population) of 0.02. Detailed values are in the table and graphs below, but this one is highlighted, because now we are at the level of (sour) grapes or a grapefruit. (This link provides a table with the pH of some fruits, but scroll down to see it.)

The pH for deeper layers was higher than for the upper layers, which makes sense. Acidification is mainly caused by deposits and in lower layers the impact will be smaller, partly because of processes in the soil.

The conductivity was lower in the deeper layer which also makes sense, because plants will use the minerals present and therefore remove them from the soil. Less minerals means a lower conductivity and of course also less H+ from the acid is present when the pH is closer to 7.

On drying the upper layers of the soil in the wood lost a lot of weight, because the moisture content was high. For the pasture it was different. The lower layer, silty clay as tough as nougat, held slightly more water than the top layer.

In the next post additional analyses will be performed. For now I provide an table and graphs with information about the fresh soil.

_{The standard error for the population (stdev.s) is much larger for the conductivity, because of the difficulty of the measurement. Values for the pH were close together because the position of the meter is not influential.}

^{For details, see table above. Be aware that some values for the conductivity of a sample were very different (hence the high standard error).}

Some standards for Dutch water and soil quality

Looking for standards, declaring the boundaries between good, mediocre and poor water quality, I had to be really persistent. At first a lot of rather graphs showed how many waters were OK and not OK, but nowhere any quantitative boundaries were mentioned. Finally a document came up, which was about the implementation of the EU “Water Framework Directive”. The directive itself is rather qualitative and doesn’t mention thresholds. The Dutch implementation does, but for those who can’t read Dutch it won’t be useful, so let me present some highlights.

At first I was surprised about all the codes for different water bodies, like M12, M21, R4 or K3. Then I realised those codes are derivatives of the European categories:

Rivers, Lakes, Transitional waters, Coastal waters, Artificial and heavily modified surface water bodies. In Dutch, Lake is “Meer” explaining the M-codes. Rivers will keep the R (Rivier in Dutch). Transition will be “Overgang”, hence getting an O and Coastal becomes Kust, getting a K.

The funny part is that “Artificial Waters” are not in the Dutch list, but at the same time I realised our lake the “Weerwater” is artificial! It only exists because after draining a part of the former IJsselmeer, to create a polder, this area was excavated (sand was needed for construction) and filled itself with water. “Weerwater” literally means “Water Again”.

It was not the only surprise, because canals are artificial as well, in my opinion. However, the North Sea Canal was mentioned and got an M-code, meaning it’s in the “lake” category. Of course it’s completely irrelevant what the code is, as long as the right standards are applicable.

The huge list of different water bodies was a bit overwhelming, until I noticed that for a lot of them the same pH values came up in the tables. This makes sense, because many different aspects are specific, but pH and conductivity are the more generic. In the end I was happy to get a table showing what was good and what would be “not so good” for weak brackish waters, like the North Sea Canal.

English classification added above the Dutch ones. Temperature, Chloride concentration and pH for samples taken are marked with green boxes.

At the day of my measurements, the temperature was around 24^oC. That’s good (English translation of the qualification is added in green). The salt concentration was between 8.5 and 10.3. Calculating back from NaCl to mg. of Chloride ions (multiplying by 35.5/58.5) we get (rounded) 6300 mg/l for Velsen North. At the end of the canal (NDSM pier, because the other locations were formally “the IJ”) it went down to (rounded) 5200 mg/l. That’s moderate and it seems that a low concentration is worse than a higher one, because there is no upper limit (although in practice it won’t get beyond the North Sea level of 21000 Cl^– mg/l). Finally the pH should be between 6.0 and 9.0 for a “very good” qualification and the individual values observed were between 7.64 and 8.20, so it was very good indeed when sampled. Unfortunately the lowest value will count and because of the drought the salt level pushed the water quality down. Over ten grams of salt per litre is not really “weak brackish” and therefore below par.

For other lakes the tables are slightly different. The Tjeukemeer and Zuidlaardermeer (see previous posts) were mentioned as examples of type M14 lakes, saving me a lot of thinking! For those lakes the table is shown below.

Here the green box marks the pH standards. The Zuidlaardermeer was over pH 8.5 only at one location, the others being lower. The Tjeukemeer was closer to 8.

Some of the other lakes, like IJsselmeer and Markermeer are of type M21. The question is whether the IJmeer (showing very high pH values – even over 9.5), actually a part of the greater Markermeer-area, is of the same type and has the same thresholds for “poor quality”. I’m not sure, but it’s certainly not “Good”.

Again the pH values are emphasised by the green box. Some samples in the Markermeer were above 8.5 (ignoring the IJmeer-part). The IJsselmeer samples were below 8.5

What struck me most is poor quality being indicated by a pH over 9.5 (for both types M20 and M21). That’s high, but photosynthesis seems to be able to raise the pH even up to 10!

The salt concentration is expressed in mg Cl^–/litre. Most of the salt in our samples won’t be chlorides but (bi)carbonates. Even if the conductivity was caused by NaCl, the Chloride concentration would be under 200 mg/litre in most cases, so that’s OK! Total Nitrogen I don’t know, because I only checked the inorganic part.

Of course my measurements are very limited and my blog is primarily meant to raise curiosity. For those who’d like a concise introduction to limnology, this youtube video could be a nice start.

Now what about soil pH? That’s a very different subject, but I also managed to get some information about those standards. Personally I think they are not very ambitious. The link is to a Dutch text, but I put a part of it into a Google translate box, saving myself a lot of time.

“National calculations of the pH since 1967 show a decrease in pH of sometimes more than one pH unit, for example in the Veluwe. This is a significant decrease for the more acidic sandy soils and this will also be reflected in the occurrence of species. Although the pH is decreasing, almost all of the acreage of dry heath and dry forests still has a pH of 4 or more. These pH values fall within the requirements that these types place on their environment and are therefore still assessed as ‘good’. If acidification continues, the number of sites with too low pH will increase and will be rated as moderate or poor; the conditions are not suitable for achieving the desired natural quality.”

Still a pH of 4? Well, some of the samples I took were definitely below 4, but rounding is nice of course. It’s not 4.0, but 4 meaning it could also be 3.6. Then the graph behind the link starts in 2000  instead of 1967 and shows a decrease of nearly 1.5 point for the pH! The comment says that the pH values were “derived from the vegetation”. This suggests no actual measurements were done.

In general it seems that for soil pH 4 is still acceptable, whilst open water should not exceed pH 9.

Let’s keep that in mind!

Some laboratory investigations: conductivity versus concentration

The adventures with the brackish water of the North Sea Canal made me curious about the exact relationship between salt concentration and conductivity. First I took a dilution of CaCl2 (somewhere around 0.5% or 45 mM), having a conductivity of 7738 µS/cm. I diluted the 5 ml of the solution by adding 5ml of demineralised water again and again, until the conductivity was around 1100. Of course this doesn’t provide data points which are evenly distributed, but it’s a rather reliable way of working. Then I took a standard solution of 0.5% NaCl (about 85 mM) with a conductivity of 8185 and followed the same procedure. Finally I prepared a solution of NaCl, with a little bit of CaCl2, because seawater has this combination (among other salts, like the ones of Magnesium). The pictures may be a bit boring. Sorry for that!

Of course we have to realise that CaCl₂ dissociates in a an ion with 2 units of charge (Ca²⁺) and two with a single unit Cl^–), making it 4 charges in total, whereas it’s only 2 for NaCl (Na⁺ and Cl^–). That’s why we only need half of the molarity for CaCl to get the same conductivity. Yet, because CaCl₂ is about twice as heavy as NaCl, the number of grams per litre is more or less the same.

The curves were clearly convex, meaning the curve bent down towards higher concentrations. This makes sense, because the ions will start to hinder each other, adding less conductivity than expected from the increase in concentration.

The curve could be approximated by a negative quadratic component and I used Excel to derive the parameters of the best fitting curve. I didn’t work with absolute concentration, but set the original concentration to 1, deriving relative values (0.5, 0,33 and so on) for the dilutions. This allowed me to combine the three sets in a single graph and have a look at their curves.

A couple of days later I wanted to know the real concentrations as well, and repeated the measurements. This time the combination of CaCl₂ and NaCl was 50/50, diluting them to half their concentrations, but keeping the conductivity (more or less) the same.  I got parameters to calculate the conductivity from the concentration, but that wasn’t what I wanted to know. Yet I will show the graphs below, with the formulas of their best fitting curves.

The conductivity can be calculated from the molarity, using Excel’s parameters (the “bump” in the graph for NaCl is most likely caused by an error – bottles were switched)

To check the difference of the best fitting curves, I even applied the parameters of the first series of experiments to the data of the second and the other way round. Despite the parameters being different, even those curves were only a little bit off. Combining all values for the second series in one single graph, using relative concentrations, showed how much the three curves (CaCl₂, NaCl and both combined) were alike.

The second series of measurements, a couple of days later, was done with known concentrations. Yet the relative values were used to be able to map the curves. Conductivities were close.

To really be able to compare all the curves for all measurements at once, I decided to stretch all conductivities of the undiluted solutions a little bit to 8000 µS/cm, stretch all other values of the series using the same factor and plot them against the relative concentration. This way I was able to combine the three series of the most recent measurement in one graph and compare the shape of their curves.

Finally I combined all those values to a large single series (still using relative concentrations), to be able to obtain the parameters for a best fitting curve.

Combining all measurements from series one and two – both for NaCl, CaCl₂ and a mix – in one large set, after stretching conductivities and using relative concentrations.

By the way: we know that the conductivity of pure demineralised water is negligible and therefore all curves were set to cross the origin.

I won’t bother you with the sums of the squared errors, but I did check. The square roots are not very different for the parameters from the series (about a factor 2). This indicates that the best fitting curve parameters are doing well for these salts and this range of concentrations.

What did we learn from these experiments? Firstly, the curves are convex after all. Doubling the concentration does not provide a conductivity value twice as high – at least not in the range of 1-100 mM. Secondly, that the number of charges matters indeed, when molarity is being considered. Thirdly that when aligning starting conductivities to 8000, the other values accordingly and using relative concentrations, all curves are pretty much the same shape.

The real salinity of the North Sea Canal

In the previous post we showed the conductivity of water samples in the North Sea Canal. My conductivity meter offers TDS values (Total Dissolved Solids – actually salts) as well, but the conversion factor is just 0.47 times the conductivity. This was a useful option for the high conductivity values, which were not shown in the display. By taking the TDS value (in ppm by the way) and dividing it by 0.47, the invisible conductivity was still made visible.

To be sure, I decided to look at a 1:1 (50%) dilution of the water samples and expected to get 50% of the conductivity for the undiluted samples. Multiplying by 2 would confirm the original value. It was just a simple check, but the results were not what I expected!

Ferry at Assendelft

Of course I’m familiar with the fact that conductivity-curves are not linear when concentrations get higher, but since similar graphs on the Internet showed linear relationships I expected it to be a good approximation (the conductivity indicated concentrations around 100 mM (roughly 6 grams of NaCl per litre).

The conductivity derived from the 1:1 dilution (using 5 ml of sample water with 5 ml of demineralised water (having a conductivity of 0 μS/cm) provided values nearly 20% higher than half the conductivity value of the undiluted sample! Of course this makes sense, because at higher concentrations the ions will start to hinder each other, but I expected the effect to be negligible at these concentrations far below 1 M. For the samples taken in Schellingwoude the error was only a couple of per cents indeed (4% observed).

Of course the conductivity can’t be disputed. It’s what the meter registered, but the conversion to salt concentration is a different story. Working with the conductivity of the 1:1 diluted samples, we get better (higher) results for the salt concentration and then it also turns out the 0.47 is not applicable to NaCl. Although Wikipedia is not always right, this article holds a lot of details and says:

^{“The conversion of conductivity to the total dissolved solids depends on the chemical composition of the sample and can vary between 0.54 and 0.96. Typically, the conversion is done assuming that the solid is sodium chloride; 1 μS/cm is then equivalent to about 0.64 mg of NaCl per kg of water.”}

Seawater is not just NaCl in water, but it’s most abundant salt, so let’s pretend it is for now. Going with the conductivity of the diluted samples and the 0.64 mg per µS/cm, the calculated salt concentrations in the original samples are much higher than indicated by the device. That’s why I previously showed the “measured” conductivity values in the map, rather than salt concentrations.

Now it’s time to show “grams of NaCl per litre” along the canal. Because even after dilution all samples were still in the region where the relationship between concentration and conductivity is curved, we can presume the total salt content is still slightly underestimated. This means the error leaves room for other salts than NaCl, but our concentration will be close to the real NaCl concentrations.

Looking at Velsen Noord, now we can see that the water is holding about 30% of sea water.

All the water in the canal is formally brackish but in the IJ the concentration is only 1/7^th of the level at Velsen Noord. Only 4% of seawater is present in the fresh water after the lock at Schellingwoude (Oranjesluizen).

Keep in mind that the only way to be completely sure about concentrations would be a titration on NaCl. Here we only present circumstantial evidence (still providing a strong indication).

Salinity of the North Sea Canal

After a long period of high temperatures and hardly any rain, Western Europe experiences a serious drought. In the Netherlands the rivers don’t bring enough fresh water to push back the seawater.

The North Sea Canal is close to where I live and I presumed that this canal would suffer from the same issue. It has locks (the IJmuiden floodgates) and every time the locks open, a certain amount of sea water will come in. The level of the canal is usually a little bit below sea level (actually I didn’t know – I read about it after sampling), so there will always be an influx. The impact will be different for ebb and flow , but if enough water is present the seawater will be diluted anyway. And of course the lower the level of the canal, the more salt water will enter, although it’s not really simple to tell. Seawater is heavier than fresh water and will creep under the fresh water – even during ebb – making the calculation harder.

The heavy clouds didn’t bring rain

Of course I didn’t calculate, but decided to take samples and different places along the canal. By determining the conductivity of the samples, I would get a good impression about the salinity. My conductivity meter also provides TDS (often referred to as Total Dissolved Solids, but that’s nonsense because e.g. sugar won’t be detected – Total Dissolved Salts is closer to the truth). The value is simply 0.47 times the conductivity in µS/cm, although it’s not a very accurate approach. In an upcoming post I will talk about that, for now it’s just about the North Sea Canal.

The map with list is shown below (and for the exact locations I added GPS-coordinates).

Sampling locations: addresses and GPS coordinates

Starting in Velsen-Noord, I expected a rather high conductivity, because it is close to the sea. Walking towards the canal I already noticed that the vegetation is a bit different, most likely because of the salt. Until now we saw conductivity values for fresh water roughly between 700 and 1100, but here I expected values probably to be five times higher than the highest value. Actually 1100 is already quite high, because (using the 0.47 conversion ratio) it means 500 ppm or 500 mg of salts (and acids or alkalis) per litre. That’s half a gram per litre, but most of the anions will be (bi-) Carbonates, not Chloride and the cations Calcium. Here, the higher conductivity would be caused by mainly Sodium and Chloride.

Putting my conductivity in the bottle holding the first sample, I got a display with “- – – -“, meaning the value was too high! Then I thought of the ppm-option, providing values 0.47 lower. After switching the value was 6466 and for the second sample it was 6557 (the accuracy is lower in the high regions and therefore these are adjacent values – the error being nearly 2%).

Back at home I would calculate back to the conductivity. It was just a matter of the display not having enough positions for it.

For the next location, a couple of kilometres towards Amsterdam, the conductivity was still too high to be displayed. Actually this applied to the entire canal. The first location showing a value for the conductivity in the display was the NDSM-pier and formally that’s at the IJ. I used to think that the North Sea Canal was connected to the IJ by the Oranjesluizen (locks), but the boundary seems to be somewhere around the Coentunnel.

The final samples were taken before and after the Oranjesluizen (so formally it was the IJ) and there the difference was impressive. Before the locks (coming from the side of the North Sea), the average conductivity was around 5500 µS/cm and after the locks (Markermeer side) it was slightly above 2100 µS/cm.

From previous measurements we know that for the IJmeer /Markermeer it’s more like 1100. Because of the high values, I decided to present the averages in mS/cm, rather than µS/cm.

It’s hard to tell how the water is flowing in the North Sea Canal. Not much water will come from pumps in the polders now and the IJssel won’t provide a lot of water to the IJsselmeer either. (And I don’t even know how much water will be allowed to flow from IJsselmeer to the Markermeer/IJmeer and therefore to the IJ).

If we go back from conductivity to salt concentrations again, then even in Assendelft the canal still holds over 6 grams per litre (in the next post we will show why it’s even more). The famous physiological salt solution used in health care (and sold in pharmacies) holds 9 grams per litre, so it’s pretty close! Of course we have to realise that seawater holds 35 grams per litre, so the floodgates are still able to keep the level six times lower in the canal.

By the way: the average pH-values (measured together with the conductivity of the samples) varied from 7.7 to 8.1. Lowest individual value was 7.64 and the highest one 8.20.

Now the question remains what the conductivity (and pH) levels will be after heavy rainfall or during winter.

Inorganic Nitrogen in the Oranjewoud samples

Working with the soil samples collected in Oranjewoud, I thought it would be nice to look at the inorganic Nitrogen as well. My measuring kit holds several fluid reagents. The approach with reagents seems more reliable than the strips, but still the interpretation of the colour had to be done visually. In the past, I built a simple spectrophotometer using a two or three coloured LED. It would be nice to do it again, but such an approach asks for regular calibration, which isn’t easy (and expensive too). That’s why I went with the provided approach: compare the bottles with a card with colour levels for the different concentrations.

Quick recapitulation: two sets of samples were collected in Oranjewoud, near Heerenveen in the Netherlands. One was in the vicinity of the Lollius Ademalaan (name of a street) and the other was close to Hotel Tjaarda. For details see the previous post. Samples were dried (losing 16% and 56% of weight respectively). Slurries were made from the dried soil – two for each location.

Oranjewoud, summer 2022

Despite negative experiences in previous attempts, I filtered the slurries and combined the filtrates of the two representing the same location. Be aware that this doubles the weight of the sample, but the same goes for the amount of water in the combined slurries.

Comparing the colour of the fluid in the bottle with a colour-card is not really accurate, but then there will be an error range anyway.. It took me a very long time to determine the right colours, but in the end the results were available.

Since the test is meant for aquarium water, the risk was that the concentrations would be too high. On the other hand, I started with the samples from the North, because lower Nitrogen concentrations were expected over there and then the range of the test is quite impressive (Ammonia up to 5 mg/l, for Nitrite it is 1 mg/l and for Nitrate even 200 mg/l, which is a lot). On top of this, the slurries were more diluted than the soil, because the soil to water ratio was 1:2 and 1:3.

Instead to doing a double check, I repeated the measurements with 1:5 dilutions of the filtrates. This was because the original filtrates were rather pink. Although the kit has a correction mechanism for the colour (using a reference bottle which is placed over the reference colours), pink is a bad combination with green.

The Nitrite I left out after the first check, because it was really low (0.05 mg/l) for the Lollius Adema location. The question was about the total inorganic Nitrogen and Nitrite would not really contribute as the value would be in the error margin of the concentration of the other compounds.

Knowing the concentration in the filtrate (and assuming the molecules were distributed evenly over the water in the slurry, making the filtrate a good representative!) we can calculate the total amount of ammonia, nitrate and nitrate in the total amount of water added. To avoid confusion, I took the values for both water and soil of the combined samples of a location.

Because this total came from the samples originally, we can calculate back to 1 kg of dried sampled soil. Then we have to correct for the lost water during drying (the original samples before drying were heavier, hence concentrations were lower). Then we will know the concentrations in mg/kg of fresh soil.

For NH₄⁺  and NO₃^– the results were calculated the same way, of course.  

The values seem rather low, but that’s from an agricultural point of view. We have to consider that the molar mass of the NH₄⁺ ion (18) is much lower than for NO₃^– (62), so the Nitrogen contribution is more or less equal for both.

Because of the pink colour of the filtrate, the values derived from the 1:5 dilution of the filtrate may be more reliable. Those values are also closer for both locations, although different soil types could have different affinities for these substances.

In the end it is very hard to tell what to think about these levels. A lot of blackberry plants were present, but no virtually no nettles suggesting the inorganic Nitrogen levels were not too high.

For agricultural soil the levels of nitrate (relevant for the plant’s growth) the level would be 10-50 mg/kg, but for forests it’s certainly lower. It’s hard to find any information on nitrate in the soil of European forests, but I found something about Denmark mentioning a mean of about 1 mg/kg (calculated, because they work with litres), but with a very broad range and another about Germany (where calculations have to be made from kg/ha to mg/kg), indicating more or less the same magnitude: 1-2 mg/kg (depending on the assumptions). For ammonia I didn’t find any relevant references.

Soil from the North. Is it different?

It isn’t the first time that I was looking at soil, yet the situation was different now. Previous samples were mostly sandy soil or clay, but the ones discussed here were more like peat.

When I was a young boy, I lived in Fryslân (Friesland) for a couple of years. During my recent vacation I visited the forest “Oranjewoud”, where I had been so often back then and of course I took some soil samples. At two locations – a couple of kilometres apart – I collected between five and ten superficial samples (5-15 cm, after scraping off the humus top layer) in a range of ten metres and combined them.

The samples were taken close to the point where we (I took a stroll in the wood together with my youngest son) entered the wood and then close to hotel Tjaarda. The maps below show the locations. Although the coordinates seem very precise, I don’t know the exact locations. It’s just somewhere in neighbourhood of the marker. Although the first marker is closer to another road, I called it “Lollius Ademalaan” because for me that’s a familiar location. The other one is called “Tjaarda” for obvious reasons. I remember the parents of a classmate were the owners or managers of the hotel, but those days are long gone.

Location called “Lollius Adema (lane), but actually closer to the Koningin Wilhelminaroad

Location near Hotel Tjaarda

At home I spread out the samples on cardboard to dry as usual, but because the soil looked different (lot of peat) I decided to do preliminary measurements at the fresh soil as well. This was mainly to check what the impact of drying would be. By the way, the pH values for the fresh Lollius Adema samples (soil to water 1:1) were 3.24, 3.36 and 3.44 with conductivity 148, 157 and 144. For Tjaarda it was 3.14, 3.23, 3.19 with conductivity 238, 206 and 248.

In the end the pH and conductivity were not very different after drying, but some complications came up.

Because the soil was weighed before and after drying, the water content could be determined. The Lollius Adema sample-set held some peat but was rather sandy and the moist percentage was only 16% (not surprising after a long period of drought with probably two small showers). For Tjaarda however, it was 56% (= 44% of dry stuff) and there is the complication: if a dry sample is mixed with the same weight of (demineralised) water, it still holds less water than the fresh sample! Nevertheless it was possible to determine the pH of something looking more like normal peat rather than slurry. For the conductivity the lack of water is very influential, so for Tjaarda additional measurements were done with soil to water ratios 1:2 and 1:3.

We know that the pH is rather predictable for the dilutions, so the values were only checked once for the two Tjaarda-slurries : 3.60 and 3.70 (really, no rounding!). This is more or less the expected log(2) = 0.3 increase.

Adding additional water will lower the conductivity if the slurry contained enough water already and indeed for Lollius Adema (LA) the conductivity readings for the 1:2 ratio were about half of the previous values (because the water-volume was doubled). The 1:1 LA slurry showed values 268, 280 and 221 for slurry 1 µS/cm and 191, 180 and 202 µS/cm for slurry 2. (The difference shows that it’s hard to get a representative sample for mixed soils.) For the 1:2 LA slurry 1 is was 123 and 155 and for LA slurry 2 is was 95 and 97 (only two values, because it was just a check.

For the Tjaarda peat it was quite different! Adding more water increased the conductivity. It became more of a slurry and the dissolved salts could do their job now. The original 1:1 values for Tjaarda (Tj) “slurry” 1 were 59, 63 and 76 and for “slurry” 2 it was 55, 87 and 63 (be aware that three rounds of measuring are presented for the same slurry, showing that the error is rather large, although the difference between these two slurries was much smaller, because this soil was more homogeneous.

After adding (demineralised, of course) water again, the values were 82 and 119 for slurry 1 and even 261 and 276 for slurry 2. Obviously the soil was less homogeneous than presumed, because the response to additional water was quite different!

Finally the soil to water ratio was increased to 1:3 by adding the same amount of water for the third time (25 grams of soil were now mixed with 75 grams of water). Slurry 1 went up and came close to the LA values now. 229 and 272 – all µS/cm, because that’s our standard unit. Slurry 2 was over the top and went down again: 210 and 214.

For those who like tables or want to calculate averages, standard errors and the like the Tjaarda information is provided in a table.

Tjaarda	condutivity in µS/cm
Soil to water ratio in mix:	1:1
“Slurry” 1	59	63	76
“Slurry” 2	55	87	63
Soil to water ratio in mix:	1:2
Slurry 1	123	155
Slurry 2	95	97
Soil to water ratio in mix:	1:3
Slurry 1	82	119
Slurry 2	261	276

This time it’s a real table, so the values can be copied and pasted.

To be more complete, a table of the pH measurements before and after drying (only for the 1:1 soil to water ratio) is added below for both locations.

Lollius Adema	pH before drying
Soil to water ratio in mix:	1:1
One slurry	3,24	3,36	3,44
Lollius Adema	pH after drying
Soil to water ratio in mix:	1:1
Slurry 1	3,43	3,44	3,51
Slurry 2	3,52	3,51	3,51
Tjaarda	pH before drying
Soil to water ratio in mix:	1:1
One slurry	3,14	3,23	3,19
Tjaarda	pH after drying
Soil to water ratio in mix:	1:1
Slurry 1	3,28	3,20	3,29
Slurry 2	3,61	3,51	3,44

The message is not that the pH will go up a little bit after drying, but that again the soil is as acidic as a (sour) pineapple.

We’re not done yet, but the next post will tell more about the nitrogen

Extended measurements for the Weerwater: Alkalinity and Nitrogen

By now the picture for water is clear. Nearly all the samples we took at different locations in the Netherlands had pH-values above 8, with the lower exceptions being very close to 8. The extremes on the high side were sometimes over 9.5, but those were also very rare and bound to specific locations. The conductivity was roughly between 700 and 1100, also depending on the location.

The central lake of Almere, the Weerwater, previously showed values between 8 and 8.5 – even throughout the day, but after four weeks of nearly complete drought en high temperatures (25 – 35^oC) things could be different and they were!

By now I was also more familiar with my water analysis kit, so I decided to take samples again and look at soluble Nitrogen: NH₃/NH₄⁺, NO₂^– and NO₃^–  (the inorganic Nitrogen, not being part of plants or animals in e.g. amino acids and proteins).

Another interesting test was the Carbonate Hardness, basically the resistance of the pH to acid, also called “alkalinity”.

Three samples were taken at two different locations each and every sample was measured twice. Measuring twice is to exclude errors in the meter or the reading, taking three samples is because of potential local heterogeneity  and two different locations because different flows and plants can influence the values locally. This time a lot of duckweed was present in the water, but not everywhere. It seems like the wind had trapped green sheets in protrusions of the lake, like the harbour of Stedenwijk. In the evening the temperature was still 27^oC

All values were very close. The two locations differed by 0.2 points for the pH only:  8.97 versus 9.17 on average (standard deviations between 0.01 and 0.03). Again the conductivity was slightly higher at the location with a lower pH: 934 and 921, also on average (standard deviations all around 5).

Checking the carbonate hardness or alkalinity, 5, 7, 6, 6, 6 and 6 drops were needed for the six samples respectively (every drop represents a German degree of hardness). The first two should probably have been 6 as well, but some drops were not very clear. In the end the average value was 6, meaning the hardness in German degrees (dH) is 6, with each degree representing 0.36 mM or 21.8 mg/l for the carbonate concentration, according to the manual. For six degrees the carbonate concentration would be 2.1 mM or 131 mg/l.

Combining the pH with this value, the concentration of CO₂ can be derived. However, I had to extend the table (delivered with the analysis kit) a little bit first, because the pH values presented were only one point around neutral and the samples were 2 points away from neutral. After some optimisation I found a formula which fitted the whole table quite well (correlation was > 0.9996).

The CO₂ would be less than 0.25 mg/l! Now the negative correlation between conductivity and pH makes more sense. The CO₂, dissociates partly into H⁺ and HCO₃^– or even 2 H⁺ and CO₃^2- thus lowering the pH and increasing the conductivity. On removing the CO₂ (creating organic matter and O₂) the pH will go up, but the conductivity will go down (as neither organic matter nor O₂ will contribute). Although this is not a direct proof, it’s very likely that these effects are caused by the CO₂ (and its ions) concentration in the water after all.

Now the question was about the nitrogen. To be sure the kit was still fresh enough, I prepared a dilution of standard ammonia, measured the pH and used the pK_b to calculated the concentration. The kit worked well, because the right shade of green came up.

Example of colorimetric determination of NH₃/NH₄⁺ (photo is a collage).

Testing the samples however (the three for one location were combined in a ratio 1:1:1) hardly any colour could be observed.  This simply meant that not a lot of soluble nitrogen was present in the water. For both locations the values were as shown below.

NH4+	< 0.05 mg/l
NO2–	< 0.01 mg/l
NO3–	< 0.5 mg/l

Anorganic nitrogen concentrations in the Weerwater (August 18, 2022)

Most likely the plants used nearly all the nitrate available in the lake. It’s good to know that the high pH is not combined with a high level of ammonia. After all the Weerwater seems to be quite healthy and indeed fish (and humans) are swimming around and plants are growing both at the bottom and at the surface.

Of course the pH meter was checked with the standard buffers before and after. (Clean) bottles were rinsed with the lake water before sampling and this time the conductivity meter was also checked with a 0.01 M CaCl₂ solution. Samples were remeasured at home within an hour, showing basically the same values. All details were written down.

Three acidic forests

The title looks simple, but at the same time it’s a difficult one. Of course I haven’t been looking at the trees and other plants, because it’s all about the soil. Then I wondered whether I should talk about woods or forests. To me a limited area with trees won’t be qualified as a forest, but the FAO seems to be quite tolerant.

Some time ago I did several experiments with soil from the Spanderswoud (near Bussum), which turned out to be very acidic (pH 4 or less). This time I visited the forest to the North of Austerlitz in the Netherlands, a modest wood near Winterswijk, close to the river Slinge and a spot at the Holterberg. To get an impression about the soil I collected samples from an area of about ten metres along a path (actually two different ones for Austerlitz) and combined them in a bottle. Again the samples were superficial – between 5 and 10 cm deep. Later on we’ll probably look at deeper soil.

Photos are only meant to give an impression – not taken at the sampling locations
(left one manipulated, because people could be recognised).

Although the woods in Almere (formally also forests) were rather basic, this was probably because the soil is heavy clay. The three locations investigated this time, all had sandy soil mixed with humus. The top layer of dead organic material was scraped of before sampling, but still a lot of organic material was in. As learnt from previous experiments the soil was air-dried first and investigated without sieving (except for removal of stones and large pieces of wood).

Spoiler alert: all pH values were below 4!

For each sample-set (remember that between five and ten samples over a distance of ten metres were combined in a single bottle) two slurries were made out of 30 grams of soil with 30 grams of demineralised water.

Although sometimes the slurry was really compact, it was possible to measure both pH and conductivity. Each slurry was measure three times. The first round was for the pH, then one for conductivity and so on. Taking two slurries is a necessary approach, because not all the samples were very homogeneous. It’s not easy to have the same balanced combination the sandy and organic fraction every time. For the pH this is not very important, it seems, but the conductivity is very sensitive to different compositions. It was really interesting to see that a rather solid slurry could have a higher conductivity than a more fluid one.

This time I will present the actual values measured. The pH meter was checked with the buffers before and after. If the deviation became too large (e.g. over 0.05), a calibration was performed (beforehand of course, not afterwards). Usually some drift is observed – also depending on the temperature – but the values stay close to the real values, so the measurements are reliable.

Overview of all single measurements and some derived values

The Spanderswoud is left out, because there we used a 1:2 mixture of soil and water and the measurements were only done twice. The latter is not an issue, but different dilutions will lead to different values for both pH and conductivity.

Doing statistics, there is a complication. The three measurements for the same slurry will be correlated closely, but the two slurries derived from a single sample-set are correlated as well. We can’t simply assume that we are dealing with six different observations of equal weight. Yet the pH values were very close together – even so close that we present an average, minimum and maximum for the total values of all the locations. The individual values are also shown in a bar-graph.

All single results for pH combined in a graph

For the conductivity the pattern is less consistent. When a sample-set has both sandy soil and organic matter it is hard to take a representative sample from it. As a result the two slurries made out of the same sample-set got very different conductivities. Then different attempts to determine the conductivity in a single slurry can also differ a lot. This can be seen very clearly in the bar-graph.

All single results for conductivity (µS/cm) combined in a graph

Stepping back, the conclusion is that the three locations have a very low pH, meaning a lot of acid is present. This is something the Netherlands are struggling with indeed. The conductivity is less uniform, meaning the content of soluble salts is quite different for the locations. A lot of nettles and blackberries were present, especially in Winterswijk, near the Slinge.

Non-agricultural soil as a reference

Previously a series of samples were taken from a field near the “van Wagtendonkpad” in Almere. It is a nice circumstance that the field lays in between two recreational woods. Although some forestry will be done, those areas are not intensively managed, so we have nice references!

We already know that the field was rather homogeneous (apart from one strip with elevated conductivity). Therefore sampling only at the start and the end of the field would do. In the end four strips were sampled. The strips were about 200 metres long and every 20-50 metres superficial samples (5-10 cm, because it was all heavy clay) were taken and combined in a single bottle per strip. Thus four bottles with mixed samples were obtained. For locations 3 and 4 the sampling was done both to left and right of the path (alternating, at least 1 metre from the path).

The way of working for the analysis was rather similar to the approach for the agricultural field. First the samples were spread out on cardboard and left to dry. Larger lumps were crushed. After 2 hours, when most of the water was gone, the first slurries were prepared by taking 20 gram of soil and mixing it with 20 gram of (demineralised) water.

Only the addition of CaCl₂ was left out, because it only seems to lower the pH and for conductivity measurements it isn’t useful at all.

After additional drying, 24 hours after sampling, the remaining soil samples were crushed another time and sieved (again coarse net, openings about 2 mm). Then the procedure was repeated, mixing 20 grams of soil with an equal weight of water.

Because weighing was done at every step, the percentage of lost water could be determined after 2 and 24 hours. It varied for the locations, but in the first step the weight went down between 11% and 19% and after the second step the (calculated) total loss of weight was between 15% and 24%.

In the end the sieving removed about 1/3^rd of the weight (lumps, shells, pieces of wood). To be sure the removed parts did not have a completely different composition, the discarded fractions were put together, mixed thoroughly and then a single sample of 30 grams was taken out. After mixing with an equal amount of water, both pH and conductivity were measured several times.

Now it’s time for the results and some conclusions. This time, because we are looking at the non-agricultural areas, the reference is the agricultural field (next to the van Wagtendonkpad – the Lawsonpad field is ignored).

Let’s have a look at the pH first.

the order of the locations was changed, because 1 and 4 are close to each other, as are 2 and 3

Of course we can’t tell the significance from the graph, but the Standard Error of the Mean for the pH of the agricultural samples is very low, looking at the measurements made after 2 hours of drying and without sieving. Therefore the difference with even the closest value from the non-agricultural sample-set is still significant (99%).

For the additionally dried and sieved samples, the pH did not change a lot and the differences are still significant – even for the closest values. It’s safe to say that the agricultural soil is slightly more basic, but despite the significance it doesn’t seem too relevant. It can’t be the shells, because those were present in all the soil everywhere. By the way: the order of the locations was changed, because 1 and 4 are close to each other, as are 2 and 3 (see map).

Then it’s the conductivity’s turn.

We can clearly see that the conductivity is not the same for the four locations. It’s interesting to see that the higher value (location 2), is close to the place where the samples in the field also showed a higher conductivity (next to Homeruspad). Perhaps there is a strip of land with different properties running through both the agricultural and non-agricultural area?

Mainly because of the outliers, the differences are not really significant, although in general the conductivity is higher for the non-agricultural soil. That’s not what we expected! Is the soil exhausted after the harvest and waiting for fertiliser?

Rather than doing all kinds of statistical tricks, it’s interesting to put all conductivity observations together and sort them (basically a  non-parametrical approach). The result is shown below.

It’s very remarkable that the order flips after 24 hours and the suspicion is that this could be caused by the sieving. For the non-agricultural samples the discarded fraction was kept and measured as well. The conductivity of this slurry is much lower, but (knowing that about 1/3^rd was kept out) the weighted average of the sieved fractions and the fractions to be discarded is very close to the original overall average! In the end there is a risk that the conducting materials are kept, whilst discarding the fraction with a lower conductivity. The real risk is that the split won’t be done equally for all kinds of samples.

Drying is inevitable, because without drying it’s nearly impossible to get a homogeneous mix of the sub-samples (unless it is all turned into slurry). Yet sieving is a risk, especially when a relevant part of the total sample is being kept out. Sieving the slurry could be a good compromise.

For now the conclusion is that drying is OK, but sieving of dry material is not. The CaCl₂ doesn’t seem to add value at all. Let’s see where the new approach will bring us!

An unplanned river.

In the past I used to travel to the Hague five days a week (for a previous assgnment), but since the lockdowns, working from home became the standard. Nowadays I travel to the Hague only once a week and I thought it would be nice to take advantage of it for water sampling. Looking around at the map I spotted two lakes, near Voorschoten: Vlietland and Starrevaart.

Entering the road next to the lakes in my navigation system, I expected to get close to the water, but unfortunately the road was cut in two disconnected parts and I ended up in the wrong part. There was no time for a walk and sample for half an hour at least, so I went back to my car, but then I realised that the (bicycle) bridge I crossed went over the river “the Vliet”.

It’s not a matter of principle that I would stick to lentic systems (standing water like lakes) and ignore the lotic ones (streams, like rivers). It was just that the lakes were easily spotted at the map whereas the Vliet was hardly visible and I really had to look for it. It’s a small river, but worth sampling and so I did. This time I took four samples around the bridge, kept them and did the measurements in the car again. In between the series a boat passed by, probably mixing the water a little bit.

Blue circles indicate the sampling locations

Usually I present graphs, but a table is shown first. Values for the samples are pretty close. The systematic change in conductivity is not exceptional. Perhaps a small particle was covering a part of an electrode, changing the values by 1%.

The averages per measuring round are to check whether a systematic error can be observed. Averages per sample (only two measurements, but used to check unexpected differences between the samples) are not very informative and left out.

The graph won’t be very exciting, so this is a good opportunity to compare a couple of locations and also see what the behaviour of the samples is over time (kept in the dark at – still rather high – room temperature).

Earlier, I noticed that the pH will be lower after a couple of days, with the conductivity being increased. Then, by accident, I forgot about some samples (Blocq van Cuffeler, IJmeer) only to rediscover them nearly two weeks later. For the Merwede and the Vliet I thought one week would be nice. The graphs presented below cannot be compared without some hesitation, because for some locations several samples were kept (Merwede, Vliet) and for others it was only the third one, but it’s still informative in a qualitative way.

As expected from earlier observations, the pH goes down and the conductivity increases after keeping the samples for a week.

The pH goes down but the conductivity is quite stable. It’s only five days

The forgotten one, hence a long period (13 days) and a moment in between. Again pH going down, conductivity getting higher.

pH going down, like for all the others, but conductivity decreasing as well.

In the end it is clear that the pH will get lower over time (under the conditions as stated). Other observations showed the same trend, but the ones above were combined with conductivity.

Initial pH values were between 8 and 8.5 and conductivity is often close to 1000 µS/cm, except for the Merwede. Probably this is because of the large volume of fresh water coming in from its source, containing less soluble salts.

Dried sieved and combined soil samples – two days later

Two days later, crushing the remaining lumps again after an additional drying period, I sieved the dry soil and combined the samples of the “van Wagtendonk” field, because of the similarity of the values (the five samples themselves were already a combination of ten sub-samples each, taken along the sampling strip).

From the combination, 20 gram of soil was remixed with an equal amount of (demineralised) water and measured again. The same was done for the “Lawson” potato-field.

As usual duplicate measurements were taken, but because of the huge differences between the first and second values for conductivity (and then the change being even in different directions for the two fields), six measurements were taken this time – all about five minutes apart. This could reveal a time-related shift, if present. For completeness the pH values were determined four additional times as well, although no surprises were expected there.

The measurements were alternated for the two fields and the instruments were rinsed and dried between every measurement. In between the samples were stirred. As always the pH-meter was checked with the buffers, to be sure the values were not more off than a couple of hundreds (if so, calibration would be performed).

The measurements 3, 4 5 and 6 for conductivity mainly confirmed the second one, making the first one an outlier. The values for the “Lawson” field were actually close to the ones observed in the previous session. The “van Wagtendonk” field however, showed much higher conductivity values!

The pH was not too different from the range observed earlier (just a little bit lower), but the conductivity had changed a lot for the “van Wagtendonk” field. The “Lawson” potato-field was more or less unchanged. Of course the additional drying may have increased the concentration of salts present, but this can’t explain the 60% increase, because the humidity of the soil was rather low in the first place (as will be quantified in another post).

Several explanations are possible. It is unlikely that the fraction kept out during sieving contained less salt, but it is possible that particles present in there (and present in the slurry created with the un-sieved samples lowered the conductivity). It’s also possible that the finer particles will release the salt easier than the larger lumps. Of course one of the original samples had a high value for conductivity, but even this value was about 20% lower than the combined values.

In the end the real question is whether the rather high conductivity (even the values for the first round before sieving were ten times higher than the values of the previous samples, taken from a forest) is specific for the agricultural soil. So what we should do is take samples from the neighbouring wood and see if there is any difference.

Agricultural soil (at the bottom of a former sea)

Before presenting additional water samples, I really wanted to present another set of soil samples. The very acidic soil (pH as low as 3.5) in the wood (Spanderswoud, near Hilversum) was a surprise and I wanted to check the agricultural soil in our “new land”. New land basically means that it’s the bottom of a former sea. When the “polder” was created by pumping the water out of the area, the salt had already been washed out, because the “Afsluitdijk” was put in place first, changing the former sea into a freshwater lake. Therefore I didn’t expect a high conductivity as a result of the seawater once flowing here, although fertiliser might be influential.

In Almere there is a very elongated field next to the “van Wagtendonkpad”. The field is over 1.5 km in length, but rather narrow (about 25 metres wide). At one place it is suddenly broader, nearly 90 metres (I measured the sizes at Google maps to be sure).

Because of the very long stretch it seemed a nice field to investigate. Most likely it belongs to one farmer of at least it’s worked by the only one farmer. Previously onions were bred. I decided to sample at start, end and some spots in between, where paths were crossing. The land was freshly ploughed by the look of it and looked like heavy clay, with lumps of half a metre! The sample drill was of no use, because I could get it in only 10 cm and then the sample would not come out, so I started collecting smaller lumps (3-5cm) perpendicular to the length, from front to rear at each single location. This worked rather well.

Red rectangle is the field. Blue lines are sampling strips.

At home I investigated the soil and it seems to be silty clay.

The large lump is about half a metre!

As a reference I cycled a little bit further to the north and collected samples in a potato-field. This time I used my sampling drill, but only superficial (soil was still very compact) and in the between the potato beds as much as possible. Although the structure of the field looked quite different, the soil turned out to be clay after all.

At home I dried the lumps to the air and crushed them all to smaller pieces (3-5 mm) and powder. The five sample locations were investigated separately. The soil from the potato-field was already fine-grained. The samples were (air) dried for about 2 hours, but not sieved yet.

(After several hours of additional air-drying, samples from the same field were sieved, mixed thoroughly and kept in a bottle. Two days later an additional set of measurements was performed on the samples of the two fields.)

Sample before and after crushing

Taking 40 grams of all the samples, I decided to add only 40 grams of water, obtaining a 1:1 mixture. It was possible to determine both pH and conductivity in the slurry.

After two rounds of measurements, a single drop of a 0.755 M CaCl₂ solution was added to the slurries. This could cause the CaCl₂ concentration to get up to 1 mM and I was curious to see what would happen to the pH. Then I added 0.5 ml of the same stock-solution, raising the CaCl₂ concentration close to 0.01 M (10mM), to see whether the pH would drop with half a point indeed (as stated in the literature quoted previously) .

(I also determined the conductivity to see whether it would be close to the value of the 0.01 M solution, but is was lower than calculated and much more different than the error of less than 10% would explain. It’s not clear why).

To cut a long story short: throughout the field the pH and conductivity values were very, very similar.

The 99% confidentiality interval is for the samples of the “van Wagtendonk” field.

Adding 0.5 ml of 0.755 M CaCl₂ solution to the slurry holding 40 grams of water, caused the pH to drop about half a point indeed (range 7.47 – 7.52)! Those values are not shown in the graph.

The 99% confidentiality interval is for the samples of the “van Wagtendonk” field.

The conductivity values presented are for the 1:1 soil water slurry. Of course the values after adding Calcium Chloride are left out, because then the conductivity was increased artificially (to the range 1531 – 1868).

Actually the pH and conductivity values were a surprise, as they were completely different from the ones for the soil samples taken in the wood.

Here all pH values were above 7, meaning they were at the alkaline side rather than acid. I already noticed a lot of shells were in the soil. Not surprising for the bottom of a former sea of course, but the calcium compound might be able to absorb acid. Then the conductivity was much higher than for the samples from the wood.  The 99% confidentiality interval for the population was 239 – 383 The range was mainly this broad because one location was an outlier. Why? I wouldn’t know (I certainly was not eating crisps while working with the samples!). The range is still very different from the low values in the woods (those were twenty times lower, for the sandy soil with humus). The values were also very different from the lakes in Flevoland (three times higher), but that’s comparing apples and oranges of course.

The potato-field, despite the very different macroscopic structure had more or less the same values for pH and conductivity.

Location of the potato field, used as a reference with sampling area (red).

The question is how influential the use of fertilisers may be. For comparison samples can be taken from the recreational wood to the other side of the van Wagtendonkpad. But the next update will be about the samples after additional drying, sieving and combining all samples at field-level. Then we will present another river.

The Merwede, yet another river

Some time ago I wrote that it would be good to get some samples outside in the South, to be sure that we are not looking at local anomalies. Although the Maas would have been better, I was happy to stop at Gorinchem to get some samples from the Merwede – the extension of the Waal, actually a side river of the Rhine. It’s still the Rhine-IJssel system, of course, but not too close to Almere.

It was a sunny afternoon and the temperature was 26^oC.

Usually I take three samples and measure them, keeping only the third to check at home and to see what happens to the pH and conductivity over time, but not this time.

Gorinchem

To get close to the water I had to walk at the landing stage of the ferry and a jetty nearby. Walking back and forth would take time and it was rather busy at the quay, so I took three samples at once from the landing stage and then three additional ones a bit closer to the river itself. Then I walked back to my car and started measuring, taking advantage of the parking fee I paid.

The results were not surprising at all, because by now we know what to expect!

The samples differed a little bit, but only within a rather narrow bandwidth: 0.2 or 0.3 for the pH and 10-30 µS/cm for the conductivity. The two locations were not too different either, with their averages close to each other, although the standard errors were quite different (much lower for location 2).

Earlier measurements were also done during warm weather and looking at the range of all pooled samples gathered earlier (see: “What if .. all lakes were actually at the same pH?”) we predicted (based on the samples collected back then) that 99% of the future samples would be in the range 7.66 – 8.78 and indeed the six Merwede samples, each measured twice, all fit in this range.  So there is nothing unexpected about the Merwede’s pH.

Conductivity we didn’t have back then, so I took some recent observations, pooled and sorted them and created a graph. The Merwede samples have the lowest conductivity until now.

At home, about four hours later the measurements were repeated (the bottles had been closed at “a summer’s room” temperature and were kept in the dark). The pH went down for most of the individual samples (paired measurements, both duplicate readings), but the effect was weak and smaller than the measuring error. The conductivity however, was really higher for every sample (paired measurements, both duplicate readings). The increase was between 27 and 32 µS/cm.

For both moments the averages of two measurements per sample (1-6) were taken.

Because of the high temperature hardly any additional CO₂ will have been dissolved and this will probably explain the rather stable pH. But what could have caused the increased conductivity?

Finally, soil samples: the results

The previous post was about the methodology. This time the results will be presented. Let’s start with some graphs.

Conductivity is in µS/cm

The different approaches of the measurements allowed me to do a lot of calculations and see the differences between them. As expected the 0.01 M CaCl₂ solution lowered the pH, but even more than expected – in some cases up to a whole point instead of a half. Of course there are no conductivity values for this case.

Drying and sieving increases the pH a little and reduces the conductivity – roughly between 20% and 30%, although the 21B value looks like an outlier. When measuring the pH in the slurry rather than in the fluid above, the difference is smaller. However, for the conductivity it’s the opposite, because all the sand in the slurry will increase the electrical resistance – hence showing a lower conductivity.

In general these soil samples were rather acidic (somewhere in the range of an orange, pineapple or an apple, but not quite getting to the level of a lime (see this picture for a visualisation of the pH values of fruit), although the mixture with the CaCl₂ solution (0.01 M) got below pH 3!

This is unlike what we observed for the water in lakes, with pH values usually above 8 and even over 9.5 in some situations!

Diluting the 2:1 mixture to a 5:1 ratio increases the pH as expected with 0.3 or 0.4. No need to do that, because it was only meant to be able to filter the slurry, but filtering seems to be a bad idea anyway. It’s not just the pH being pushed up, but the impact seems to be very unpredictable (remember that we used rinsed filters). The same goes for the conductivity, becoming unpredictable after filtering. In general the dilution from 1:2 to 1:5 reduces the conductivity, between 25% and 45% as expected. We better stick to the 2:1 approach in the future as it is the best alternative for 1:1.

Drying and sieving is a good idea anyway, because it also allows us to mix several samples from the same area. This will provide a more stable picture of an area, when e.g. ten samples from an area are combined. So next time we will go for dried and sieved samples, being mixed with twice their weight of water. Measuring of the pH will be done in the slurry, but for the conductivity it should be the fluid above.

Perhaps the usage of 0.01 M CaCl₂ 2:1 will be investigated again, to see whether the impact will become more predictable for larger amounts of samples.

Still one question left: is there a correlation between the pH and the conductivity like we observed before in lake water? The answer is yes.

Although the impact is not too strong, the correlation is clear. Especially when looking within the sample values rather high correlations were observed. Below there are two tables after all. Sorry!

Looking within the samples a clear correlation is observed, but it could be the method!

The dried and sieved samples also show a clear correlation, but for the others it’s rather weak.

In the end the conclusion is that lake and river water and soil samples seem to be opponents. High pH versus low pH and rather high conductivity versus rather low values. Actually I didn’t expect soils samples to have such a low conductivity, although I knew the soil is getting rather acidic in the Netherlands as a result of the ammonia and nitrite/nitrate deposits. In the future I will try and determine those concentrations as well, with my new kit.

Finally, soil samples

After a long range of posts about water samples, I gathered my first set of soil samples in the wood.

This picture is from another moment. To the right is only a partial sample.

Water samples are so much easier than soil samples, because they can be measured right away and then water is mixed well, although it’s not completely homogeneous as we saw earlier.

Now it was time to practice with soil samples and investigate all the different methods used in labs all over the world. Of course I’m still concentrating on pH and conductivity. The analysis of salts will come later.

In the Southern part of the “Spanderswoud in the middle of the Netherlands, I took two samples at two locations each, close to two different posts indicating a walking route (numbers 10 and 21). Four samples in total and although this sounds like duplicate sampling, soil does not really mix, so the results can be very different and actually it is better to mix a large number of samples for one location to avoid incidental anomalies. In this case I wanted to know the similarities and differences, so I kept the four apart and split the samples for different kinds of treatments.

First, I split each sample into halves. One half was left to dry in the air for about two hours and later they were sieved though a rather coarse net (openings about 2 mm). The remaining pieces were discarded. By the way, I used a kitchen scale to obtain the right portions.

Air drying and sieving. Bottom right the remainder is shown

During the drying process the other half was split again (except for on sample, which turned out to be rather small).

The two quarters were treated differently. The first ones were mixed with an equal weight of water, but then I noticed the slurry was too thick to put even an instrument in, so I switched to a ratio of 2:1 adding the same amount of water again. This time it was more fluid, but my attempt to filter it was a bit pathetic, because nothing happened. The filter, although rather porous, was clogged immediately! Fortunately I only tried to filter the first one and still I managed to measure the slurry in the filter once, but most likely it was already compromised by the filter as I discovered later on.

Determining tare, weighing total and adding water 2:1 to obtain a slurry

The 2:1 water to soil sample slurries were measured in two rounds, both for pH and conductivity. I also noticed that the fluid on top had a higher pH value than the slurry at the bottom of the cup. That’s probably why it is advised to put the pH meter in the slurry [page 14]. At the same time the conductivity would go down, probably because a lot of sand is present in the slurry, increasing the electrical resistance! Apart from the two normal pH measurements I also did a single observation pushing the pH meter into the slurry.

The second quarter of the sample was mixed with a 0.01 M CaCl₂ solution. In a previous post I told about the pH being completely unchanged and it was still the same as demineralised water: slightly acidic, with a pH of about 5.85. However, mixing it with the soil sample in the same 2:1 ratio, the pH of the mixture turned out to be much lower than for the demineralised water. This is a known effect [the addition of the salt does lower the pH by
about 0.5 pH units compared to soil pH in water (Schofield and Taylor 1955; Courchesne et al. 1995)] Of course the conductivity wasn’t measured now, because the CaCl₂ would mask everything.

Then I went on with the dried and sieved samples, mixing them with demineralised water only (2:1 weight/weight again). Again the pH and conductivity were measured in two rounds and then I doubled the weight by adding more (demineralised) water, basically creating a 5:1 mixture.

Two more rounds of measurements followed – again with an additional series of pH measured in the slurry. If no buffering capacity was present in the soil, the addition of more water would increase the pH by ¹⁰log(2) or  ¹⁰log (2.5), depending on how the soil itself will count (as a volume or not – probably in between). This would be an increase in the pH of 0.3 – 0.4.

Measuring pH and conductivity in the slurry

Finally I tried and filtered the 5:1 slurry and took additional measurements for pH and conductivity. Neither filtering nor measuring the filtrate were a great success. Although I used a rinsed filter, the pH still went up. Later I started thinking that it’s probably not some kind of substance in the filter, but a property of the paper ad- or absorbing (H+) ions. For now that’s all about the methodology. Next time I will present the results and my conclusions.

Does filter paper influence pH or conductivity?

It may sound like a weird question, but I’m planning to investigate soil samples and then it will be better to filter the slurry before measuring the pH and conductivity. Of course those values should not be influenced largely by the filter paper. Laboratory filters are rather expensive and then I’m not talking about the glass filters (those will be completely neutral for both pH and conductivity), but about paper filters. A cheap and sufficient alternative is to use coffee filters like the ones sold in supermarkets. When testing the latter I noticed that both conductivity and pH went up when passing demineralised water through it. How would this be for professional filter paper?

For my birthday I got (among several other things) an analysis kit for nitrate, nitrite and ammonia and professional filter paper from the Braumarkt. The analysis kit will be very useful for the next series of samples, but first I focused at the filtering, to be sure the measurements will not be biased.

Coffee filter to the left, Braumarkt filter to the right. Funnel used below.

For this post I used the data of four professional paper filters and four simple coffee filters. Five times (at first ten, but the second series of five is not really informative) I passed a new amount (probably 10 ml) of demineralised water through the filter and tested both pH and conductivity.

In the graphs I present the results for the professional filters (four experiments) and the coffee filters (also four experiments).

Numbers below show the number of times an amount of demineralised water was passed through.

Numbers as indicated above.

The conclusions are simple: the first sample of water passing through the filter will have a much higher conductivity and pH, so it is better to rinse once before use.

The pH change is not very relevant, because the demineralised water is somewhere between 5.5 and 6.00 because of the CO₂ solved in it. No minerals are in (that’s the meaning of demineralised water) and therefore there is no buffering capacity. Moving towards a pH of 7 is not impressive, but it should not be much higher.

The conductivity going up – it’s zero for demineralised water – is something else. Somehow an increase in conductivity means that a salt (or acid or base) is being added (non-ionic substances like e.g. sugar, don’t influence the conductivity). Now I had to cut the professional filter paper and touching it with my hands will have added some salt. For reference: dipping my finger in the water for a fraction of a second increased the conductivity to 10 µS/cm immediately. Dipping twice doubled the value. These values correspond roughly with 5 and 10 parts per million of salt respectively (TDS – Total Dissolved Salt). That is a not a lot at all. Five or ten grams of salt in a cubic metre of water and for the 10 ml we are talking about probably 50 or 100 micrograms!

We already observed the conductivity of lake water being around 1000 µS/cm, so the error introduced would be about 1%, but for soil samples it might be even less influential. The funny part is that the professional filter paper is thicker and denser, so the water will remain in contact longer. The thin and porous coffee filters allow the water to pass quickly. Those effects are reflected in the graphs as well. In the end it’s clear the cheap coffee filter will do, but all filters should be rinsed with demineralised water once before using them.

After some initial experiments I wanted to throw away the used filters, but then I realised those are the ones rinsed with demineralised water and I should keep them!

Water samples and pH – adding conductivity

Having this new device, the conductivity meter, it was time to broaden the activities for water samples. Since the lake “Weerwater” is close to my home in Almere, that’s where I started again. Now it is a couple of weeks later and summer came. No reason to expect the pH values to be the same, but the combination with conductivity could be interesting.

By the way, I purchased a book on limnology (“water science”) by Wetzel. It turned out to be a very thorough description of all aspects of water, but especially pH and conductivity are not discussed in depth. Yet some interesting information is in I will use.

To be able to compare the pH measurements with the original samples, two months ago, I went to four of the same locations: the long jetty near Schippersplein, the entry of the small harbour at Stedenwijk, the boat-like jetty at the Maastrichtkwartier in Stedenwijk and the end of a strip of land close to the “Phantasy beach”. Two days later I added the environment of the pumping station “Blocq van Cuffeler” and the “IJmeer, near Marinastrand Poort” again. You may remember that for the latter two I had some issues with the use of my pH meter, so the corrected values are shown here. The new pH values and the conductivity are added this for the most recent measurements (averages shown)

	2 months ago	July
	pH	pH	Conductivity
Jetty near Schipperpl.	8,30	8,34	914
Harbour Stedenw.	8,02	8,51	876
Boat Maastrichtkw.	8,16	8,46	914
Strip near Phantasy beach	8,29	8,48	929
Blocq van Cuffeler (“bridge”)	8,64	8,27	1074
Ijmeerdijk, near Godendreef	8,69	8,47	774

Again three different samples were measured and as always I kept the third sample for reference at home and for checking developments in time.

With only a small number of samples we cannot draw conclusions, but it seems like the pH and the conductivity are correlated in a negative way. All individual samples were combined for day one, but also for two days later. We already know the pH goes down in time (with samples kept in closed bottles, at room temperature in the dark), but the conductivity is going up at the same time. The effect will be weaker than the trend-line suggests, because of some outliers. Yet, if only the averages for day 1 are used the effect is still present.Funny enough the correlation with a small number of values (table above) is -0.73, but with all sample-values for day 1 and 3 it is only -0.48 Despite the limited number of values, both correlation values differ significantly from 0 (t-test).

The pH in Dutch lakes will be mainly related to Calcium and Carbonates. Somehow a lower pH means more solved salts or acids are present (the conductivity is an indication of the Total Dissolved Salts. Most likely the closed bottles in the dark will produce CO₂, solving in water and dissociating to H⁺ and CO₂^2- or HCO₂^– and this will reduce the pH. Some CaCO₃ might even solve again by turning into Ca²⁺ and 2 HCO₃^– but that’s just speculation.

We can see that adding the conductivity is really useful, because it provides us with more information to put the pieces of the puzzle together. Now the question is whether the extreme pH values, observed earlier, will also show a lower conductivity.

Why I’m a bit silent now: some other Citizen Science

During the last couple of weeks I posted on a regular basis, but now I’ll remain a bit silent. It’s for a good reason and I already told a little bit about it. Firstly I got my original blog site back (and posts had to be copied from the old to the new site) and secondly I was involved in a “Citizen Science” project concerning water quality in the Netherlands.

For this project I promised to visit fine locations and four were in Almere, so that was rather close to where I live. The fifth spot was further away and rather complicated. After all it wasn’t as easy as expected and this weekend it took me about eight hours in total (checking the locations, preparing, sampling, taking pictures, counting and filling out the online form, after a lot of preparation during the weekend before).

Because of this I wasn’t able to take my own samples. Originally the plan was to revisit some locations and look after both pH and conductivity, but this has to wait for another weekend now. The posts are usually about measurements done in the previous week or weekend and this time I’m  running dry because of a lack of time.

I’ll be back and hope to talk about the combination of pH and conductivity, moving on to soil samples as well.

Juggling with brine

No pH this time! I already mentioned the nice conductivity meter I purchased, but to be sure it was nice, I had to investigate the precision and accuracy. Precision is not an issue. It’s just repeatedly measuring salt solutions. Accuracy is different, because some calibration fluid has to be present. It wasn’t my plan to pay a lot for an overpriced bottle of brine and I was thinking about a way to make it myself. Although the kitchen salt I used holds a little bit of Iodine, it’s within the error margin. Only about 2.2 mg per 100 gram and even when the NaI will be more soluble, it won’t exceed the 10 mg in total (on 400 g of total salt)

Since at 26^o C 359 grams of kitchen salt can be dissolved in (demineralised) water, I decided to prepare a saturated solution by adding excess salt (400 grams) to the water (obtaining 1 litre in total) and leave it for a couple of days, mixing it now and then. Finally I mixed it again, waited for an hour or so and took 16.3 ml from what I expected to be a 6.14 M solution (M is mole per litre, so 6.14 * 58.4 grams in a litre – rounded that’s 359 gram).  16.3 ml would hold more or less a 0.1 mole and adding water to a total volume of 100 ml would make the result 1 M. From this stock,  I started to dilute 10 ml of solution in 100 ml of water or 5 ml of solution in 50 ml. After a couple of steps I obtained 0.1, 0.2 and 0.3 M and even 0.01, 0.02 and 0.03 M. By the way, for the small volumes I used an old 5 ml pipette (divided in tens of a ml, but 0.03 is possible to read) and for the larger volumes an 100 ml graduated cylinder.

To compensate for errors I took two different pathways and compared the results. The conductivity meter gave very consistent results and the values were on a straight line (more or less, as it is actually slightly bent, especially at higher concentrations).

_{Be aware that this graph is biased as explained below. The concentrations are 13% lower than shown on the X-axis}

Demineralised water had conductivity 0, like expected. The 1, 2 and 3 milli mole per litre were 10, 21 and 31 micro-Siemens/cm. Very precise, but when I prepared a 0.05% solution, with an expected conductivity of 1014 µS/cm the result was only 880 µS/cm. How could I end up with an error of 13%? I was very aware of the errors I introduced with my dilutions and it could go up to 6%, but it was more likely to be less, because those were random errors, annihilating each other most of the time.

Finally I decided to craft a very precise balance. It’s not too hard when the arms are long and a perpendicular is used (relying on gravity). Actually I used to have a real milligram laboratory balance myself in the past, but this would do for once in a decade.

_{The small piece of tape is for calibration! It’s very sensitive. Note that the weight to the bottom is taking care of the perpendicular.}

An old Dutch dime is 1.5 gram, so I obtained this amount of salt, added it to 100 ml of water (the volume of the salt is negligible) and diluted again 10 ml to 100 total volume.

The conductivity of the new solution was… 1010 µS/cm – as predicted from the literature.

Now I was really confused and I wanted to understand why my saturated NaCl solution would be 13% off. Finally I realised that – different from dilutions, always being about adding water to the final volume, solubility is about putting the salt into a litre of water. Doing it like that, the volume of the salt will be added and actually it’s not 359 grams to be solved in one litre of final solution, but 359 grams with a litre of water added. The density of NaCl is 2.170 g/ml and therefore the 359 grams will add a volume of 359/2,17 to the 1000 ml, making it a total of = 1165,5 ml holding those 359 grams.

This means the saturated solution was not 6.14 M, but only 5.27 M – an error of about 14%

Therefore the conductivity of my (supposedly) 0.05% solution (derived from the saturated solution) would not be 1014, but only 870 µS/cm! Now I had been able to prove the accuracy of the conductivity meter in two different ways.

Still being curious about the CaCl₂ solution I prepared a couple of posts ago (meant for soil samples to come), I checked the 0.01 M solution and indeed it was 2.4 mS/cm.

Tap water, by the way was about 415 µS/cm – rather rich in minerals, it seems.

For those who want to know more about the simple and inexpensive device doing so well: this is the Amazon-link.

The original scientassist.wordpress.com – it took some time…

For a while no new posts appeared. That’s because I got my original blog address back and had to do some work to get it up and running. The result is much better, because even I wasn’t able to remember the scientassist[large-number-looking-like-a-phone-number].wordpress.com!

However, it was not the only reason. In the Netherlands an NGO called “Natuur & Millieu” (Nature & Environment) was looking for Citizen Scientists to investigate water quality, so I took the training. I am a scientist and a citizen after all and the training only took a couple of hours. The measurements are one-offs and will take probably a day in total (five rather extensive investigations on different locations, sending samples to a lab as well). For the Dutch readers who are interested to join: here’s the URL. If you don’t have time, some money will also do 🙂 One of the more time consuming activities was the construction of a Secchi disk. A simple but elegant device to measure the transparency of the water. Mine is not the size of an old LP record, but a smaller one works well. Nowadays a CD/DVD is common, but I used the disk of an angle grinder.

Secchi disk

Then – and this will be discussed in the next post – I purchased a conductivity meter. A small Chinese device and I didn’t expect much of it, but it turned out to be very precise and accurate!

It was the one with a lot of positive reviews whilst the negative ones were hardly relevant to me (some received a broken or used one – that’s not the manufacturer and some others didn’t quite understand how to use it). It doesn’t look fancy, but after a several hours of measurements, I’m impressed. Next time I will tell you about my adventures with different concentrations of NaCl solved in water (brine, simply put).

More surprises: details along the dyke

Nearly two weeks ago I took samples along the dyke (Oostvaardersdijk) from Lelystad, via Almere to Zeewolde. The post about this set of samples showed a map with pH values. Somewhere at the beginning of the parking IJmeerdijk the pH value was rather high: 8.7 Previously the same high value also came up, so now I was looking for more detail. This time I planned to look at the other side of the bridge as well. We live in the “new land” (polder), but after crossing the bridge the “old land” is reached. Would things be different there?

To be honest, I didn’t think it would be possible to defeat the high pH score of the Hoornse Plas in the province of Groningen (9.57), but this time things were even more extreme. Again it was in an area where people are chilling out at the beach (a very small beach), SUP-ing (Stand-Up Paddle-boarding –  I didn’t know it was an abbreviation), rowing or swimming.

The cause of the high pH is probably not at the Almere side after all, because there the pH values over there were much lower. Here the top-score was 9.70 – soapy water again! I still don’t know if this is caused by human presence.

Yet it was a bit strange that about 750 metres away from the small beach the pH was still 9.63! Because the sampling was done at this point first, I wondered whether it could be the shells at the beach. After mixing a sample of those with some demineralised water, the pH I got was only 8.08. Surely not an explanation.

The good news is that – despite the high pH – a lot of little fish were swimming in all the Muiderberg-locations. At least this would mean the water was not toxic. Most likely the ammonia-concentration was low. Of course the pH will go up when the weather is hot, but now it was only around 25^oC

Getting samples was not easy this time and I had to walk at some of the locations, looking for a good spot (not at a beach or harbour itself, but closer to open water). Again the pH-values are plotted on a map.

Again all values are the average of three samples (third one kept) and again I measured the values of the third sample again when back at home – also checking the reference buffers before and after. Standard deviations (of the samples) were 0.015 or less. The reference values were never more than 0.03 off, so the measurements are reliable. Of course the values remeasured at home were about 0.05 – 0.2 lower, as explained in a previous post.

By now it is time to move on and investigate soil as well. Conductivity will also be added in the future and hopefully some other analytical values.