THE EFFECTS OF BIOAVAILABLE CADMIUM,
NICKEL AND LEAD IN DUTCH
AGRICULTURAL SOILS WHEN PROCESSED
SEWAGE SLUDGE IS INTRODUCED AS A
BIO-BASED FERTILIZER
Jesse van Gent - 12378925
Amsterdam
30/05/2021 Primary supervisor: Dhr. dr. B. Jansen Secondary supervisor: Dhr. Dr. J.R. Parsons1.Abstract
Phosphorus (P) and nitrogen (N) are vital for crop growth, and often need to be added periodically through mineral fertilizers. Minable P and N reserves, used in these mineral fertilizers, are however quickly diminishing. Most of the P and N nutrients in mineral fertilizers eventually end up in
biowastes. However, biowastes can be reprocessed into bio-based fertilizer (BBF). These BBF’s reapply P and N to agricultural soils. BBF’s can thus reduce dependency of the agricultural sector on finite mineral N and P stocks.
Nonetheless, BBF’s often contain increased levels of heavy metals. These metals can hamper crop growth, and induce (neuro)toxic effects in humans. Especially Cd, Ni and Pb can cause serious effects. The severity of the effects of metals largely depend on the quality of the source material of the BBF and the reprocessing method. Metal concentration in BBF’s can thus differ regionally.
This paper has addressed the significance to examine the concentration of Cd, Ni and Pb in processed sewage sludge used as BBF on crop- and grassland in the Dutch South-Flevoland polder, and their effect on human health. To derive significant results, soil samples were taken from two agricultural fields in this polder. Bioavailable Cd, Ni and Pb, as well as relevant background parameters were then determined using the ICP-OES, auto-analyzer and TOC methods, and modelling using visual MINTEQ. Two sensitivity analyses were also included to test the robustness of the results. It was concluded that bioavailable Pb concentrations were at hazardous levels, while Cd and Ni concentrations remained below the safety limit. Cd and Ni levels could however eventually exceed safety limits, when processed sewage sludge is applied regularly.
2.Introduction
2.1 Relevance
Due to rapid population growth in the past 70 years, and further projected population growth in the next half century (UN, 2019), the agricultural sector had to expand massively (Ramankutty et al, 2008). This expansion was partly facilitated because of the application of mineral fertilizers. These fertilizers enhance soil fertility by adding essential plant nutrients (Herencia et al, 2007). Nutrients in these artificial fertilizers are however often extracted from non-renewable stocks. Especially the available phosphorus (P) and nitrogen (N) stocks are rapidly declining. In 2010, in Europe only, 10.4 Mt of N and 2.4 Mt of P was used artificial chemical fertilizer (Vaneeckhaute et al, 2013). Meanwhile, most added P and N stocks in agricultural soils are lost, because large parts of the nutrients in the crops end up in waste products (Schröder & Neeteson, 2008). Furthermore, usage of conventional fertilizer also lead to soil acidification (Schroder et al, 2011) and atmospheric nitrogen deposition (Holland et al, 2005).
One approach to ensure a more sustainable agricultural sector could be to use bio-based fertilizers (BBF’s) on agricultural soils. BBF’s are processed waste streams with high nutrient concentrations and can include for example, organic residues, animal biowaste or sewage sludge (Vaneeckhaute et al, 2013). By using BBF as a fertilizer, N and P will be reapplied to the soil. This will reduce the
dependency on mineral N and P and close agrichemical nutrient cycles (Vaneeckhaute et al, 2013; Bloem et al, 2020). By closing the nutrient cycle, BBF application also lead to reduced soil
nutrients are reused instead of discarded, these nutrients will not lead to nitrogen or acidic emissions (Vaneeckhaute et al, 2013).
Processed sewage sludge is exceptionally useful as a BBF as sewage sludge is exceptionally rich in crop nutrients (including P and N) (Singh & Agrawal, 2008). Sewage sludge is a by-product, from the sewage treatment process. Treated sewage has reduced concentrations of easily decomposable organic materials. The remaining insoluble solid residue is referred to as sewage sludge (Singh & Agrawal, 2008).
However, BBF in general, and sewage sludge especially may pose problems, as it may contain critical amounts of heavy metals (Wood, 2019; Bloem et al, 2020). The metal concentration in the sewage sludge depends on several factors such as: sewage origin, sewage treatment processes, and sludge treatment processes (Singh & Agrawal, 2008). There are 3 situations where these heavy metals can impose serious risks. Firstly, many heavy metals cause disturbances in the metabolism of plants. Relative small amounts (mg/kg) present in floral tissue can already lead to disturbances in the
collection, transport and assimilation of macro-and micronutrients (Fijałkowski et al, 2012). Secondly, heavy metals can also be toxic to soil fauna, which can hamper crop growth significantly (Vásquez-Murrieta, 2006). Lastly, heavy metals can bioaccumulate in humans. They are also often neurotoxic and/or critically hamper metabolism (Wood, 2019).
Although heavy metals do pose a risk, it is important to note that only the bioavailable fraction of the total metal content in soil negatively affects organisms (Kim et al, 2015). The bioavailable fraction, is the portion of metals that actually is transferred from the abiotic to the biotic environments
(Chojnacka et al,2005). Bioavailability is strongly influenced by the type of organism, exposure and metal speciation (Hund-Rinke & Koerdel, 2003; Kim et al, 2015). There are several physiochemical and physiological processes that influence bioavailability (Lanno et al, 2004; Kim et al, 2015). For this study, we mainly consider unbound and dissolved metallic cations bioavailable, as these metals can be taken up by crops the easiest (Kim et al, 2015).
This research will focus on Cadmium (Cd), Nickel (Ni) and led (Pb). These metals pose especially severe risks (Wood, 2019). Cd can pose health risks through crop exposure. It has a particularly severe effect on the Kidney and bones (Nawrot et al, 2010). Although Cd concentrations in European soils are mostly too low to have a major impact (with median values of 0.2 / 0.8 mg/kg, and a
maximum value of 2.5 mg/kg), Cd levels in soils are steadily rising (wood, 2019). There is also evidence that especially for children, the safe weekly Cd intake through crops could be exceeded (Wood, 2019). It should however be noted that BBF’s only modestly contribute to the total amount of Cd in the soil, compared to mainly atmospheric deposition (Wood, 2019). BBF’s however do significantly increase soil Cd (Wood, 2019).
Ni, on the other hand, poses a risk to human health based on mammalian developmental toxicity and secondary poisoning (Wood, 2019). The typical concentrations of Ni in BBF’s are 10 – 39 mg/kg, but concentrations are generally higher in processed sewage sludge. The highest concentrations found are approximately 250 mg/kg (Wood, 2019). The safe limit concentration of Ni when applied to agricultural land is 130 mg/kg (Wood, 2019). Most applied BBF’s will thus not lead to dangerous levels of Ni. However, some BBF’s that are particularly rich in Ni might pose serious risks. BBF’s contribute roughly 20% of the soil Ni (Wood, 2019).
Finally, Pb is a non-threshold neurotoxic substance. Hence there is no safe limit regarding Pb concentrations in the edible parts of crops (Wood, 2019). Typical Pb concentrations in BBF’s are between 2.5 and 91 mg/kg. Maximum Pb values found in BBF’s are around 230 mg/kg (Wood, 2019). Pb accumulation in BBF’s is exceptionally high when it is produced from liquid biowaste, like sewage
sludge (Wood, 2019). Pb is particularly immobile. It thus often stays in local soils. 95% of Pb is locally contained (Wood, 2019).
It is as of yet often unknown if and how severe the effects of dissolved metallic cations precisely are. This is mostly because the precise concentration of heavy metals like Cd, Ni and Pb in sewage sludge often differ significantly per region (Singh & Agrawal, 2008; Wood, 2019). Hence it is important to measure concentrations of these cations in BBF’s locally.
One organisation that aims to optimise BBF usage in agriculture in the EU is the LEX4BIO project (Lex4bio, n.d.). This research too is part of a larger LEX4BIO project that specifically examines the input of N and P when BBF’s are applied as well as the risks of metal contamination in the Netherlands. This larger project also includes a evaluation of commercial soil test kits.
There are already experiments with BBF deposition on agricultural fields in the Netherlands (Jansen, 2021). The Dutch agricultural sector exported products worth 95.6 billion euros (CBS, 2021). This is partly due to great soil quality and fertility (Reijneveld, 2013). Dutch soils are however also under pressure from the intensifying agriculture, Soil mismanagement already leads to significant soil degradation (Reijneveld, 2013). To prevent soil degradation, sustainable fertilizers like BBF’s are increasingly applied. It is however unclear whether Cd, Ni and Pb are contained in significant fraction in types of BBF’s, like (locally) processed sewage sludge, and what health- and harvest risks they comprise. Thus arises the proposed research subject What are the biochemical effects on grassland and sugar beets when increased bioavailable Cd, Ni and Pb content is introduced in the soil via biobased fertilizers processed from sewage sludge in two agricultural fields in the Dutch polder of South-Flevoland?
2.2 Research aims
To answer the proposed research question, several sub questions have been formulated. These sub questions will together discuss the main research subject.
1) How much does the concentration of Cd, Ni and Pb in the soil increase when processed sewage sludge is used as a fertilizer?
To estimate how much the concentration of Cd, Ni and Pb in the soil increase when processed sewage sludge is used as a fertilizer, zero measurements have to be taken. After all, atmospheric deposition can also increase Cd, Ni and Pb concentrations in the soil (Nicholson et al, 2006). It is thus expected that not all heavy metals in the soil are derived from processed sewage sludge.
2) What is the bioavailable concentration of Cd, Ni and Pb in the local soil?
To be able to address the potential hazardous effects of increased Cd, Ni and Pb concentrations, the bioavailable quantity of these elements in the soils in the South-Flevoland polder should be
calculated. As stated earlier, this research considers that the bioavailable fraction consists of the unbound ions.
3) What are the effects of the bioavailable fractions of Cd, Ni and Pb on crops and humans
After the bioavailable fractions of Cd, Ni and Pb have been quantified and their influence on the biochemical equilibria has been modelled, biochemical effects of the heavy metals on crops and humans can be examined. Scientific literature concerning the influence of the identified quantity of chemicals will be the primary source to determine these biochemical effects.
2.3 Hypothesis
It is expected that BBF in the Netherlands is delicately processed. The source material for the BBF is probably also of relative high quality, because Dutch biological waste processing maintains high standards (Krozer et al, 2010). All this would imply that heavy metal content in BBF will not be expected to be hazardous. Thus, bioavailable heavy metal content in the respective soils will not increase significantly due to BBF application. It is expected that during the chemical analyses, low concentrations of Cd, Ni and Pb are found. During the modelling phase, these low concentrations would lead to low bioavailable heavy metal content in the modelled soil.
3. Methods
Two datasets of Cd, Ni and Pb concentrations have been obtained. One set was derived from two agricultural fields which used a fertilizer regime without a BBF. Samples from these fields have functioned as an estimation of background levels of Cd, Ni and Pb. Other significant parameters, notably pH and EC values, as well as other relevant inorganic soil components (NO2-, NO3-, PO43-, NH4+
and K+), and total carbon were also derived from these agricultural fields.
A second dataset contained the maximum Cd, Ni and Pb limit for sewage sludge used in agricultural fields in the Netherlands. It has been used as an indication of how high Cd, Ni and Pb concentrations can get due to BBF input.
3.1 Fieldwork
Valid background levels of Cd, Ni and Pb were obtained by collecting soil samples from 2 agricultural fields in the Dutch South-Flevoland polder. These samples were collected in order to estimate cation concentration in fields with diverse land uses. One field consisted of grassland, and one field
consisted of crops (sugar beets). All samples have been gathered on a single day. From both fields, 20 samples were taken. A team of 5 people took the samples in 2 groups (of 2 and 3 people). Sample were collected from an average depth of 30 cm, with a standard deviation of approximately 2 cm. At this depth, BBF has a prominent biochemical influence on the soil. To ensure enough soil for
subsampling for chemical analysis, a minimum of 300g of soil was collected for each sample. Samples were gathered at random places in the fields. This was accomplished by beforehand setting up a random sample pattern in both fields using a collector app. Samples have primarily been obtained with augers, and shovels. Depth from where the soil samples needed to come from (30 cm), was measured with measuring tape. Sample mass (300g per sample and 30g per subsample) was measured with a scale. Sample bags, with coloured tape (to identify individual samples), were used to transport samples from the agricultural fields to the lab.
3.2 Sample preparation
80 water extracts were prepared. These water extracts were made from 40 duplicated subsamples of 30g (20 samples per field). To set up water extracts, all samples were dried in temperatures between 30° C and 40° C to separate the soil fraction of particles > 2 mm. Afterwards, water extracts were produced in the ratio 1:2:5, with 20g soil, and 50g water. The extracts were then placed in a shaker for 30 minutes to blend the water extracts. pH and EC measurements were taken using a Consort C831 pH and EC meter. The water extracts were afterwards placed in a centrifuge device for 20 minutes at 2000 rpm to separate the water extracts in a filtrate and a residue. The filtrate of each water extract was then poured over in prepared filtrating tubes placed in a filterbox. Filtrating tubes were prepared by placing filter membranes of 0.45 µm in filterholders. Filter tubes were placed on top of these filter holders. The filters have been tested by adding 3 mL of Milli-Q water. The filterbox was then pumped vacuum. The filters where milli-Q water ran through the filter (instead of filtrating slowly) should be replaced after placing the filterbox in normal air pressure again. Finally, the water extracts were carefully poured over in the filter tubes. Thereafter the filtration process begins when the filterbox was again placed in a vacuum state (van Hall, 2021).
After filtration, each filtrate was in divided in 3 tubes of each 10mL. One tube was utilized using the ICP method, one tube was utilized using the TOC method and one tube was utilized using the auto-analyser method.
3.3 Chemical analysis
Once the water extracts were prepared, they were analysed using the ICP, TOC and auto-analyser methods. The ICP OES method was used to measure cation concentrations. This method is thus useful to identify Cd2+, Ni2+ and pb2+ ions, as well as the K+ ions. Specifically a Optima-8000 ICP-OES
from Perkin Elmer, Waltham, U.S.A device was used. The procedure operated under suitable conditions and wavelengths for each element (214.439 nm for Cd, 213.857 nm for Zn and 220.350 nm for Pb) (Massadeh et al, 2016). The procedure is conducted using a nebulizer argon flow of approximately 12L/min, and an auxiliary argon flow rate of 0.6L/min (Massadeh et al, 2016). The TOC method is often used to measure carbon and inorganic carbon, which can be used to indicate organic matter content (organic carbon = total carbon – inorganic carbon). This is crucial for this research, as organic carbon content is important in determining bioavailability of heavy metals (De Vos et al, 2007). In this research, specifically a Vario TOC cube, elementar GmbH from
Langenselbord, Germany was used.
The auto-analyser method is used to asses anions. This method will be used to identify other significant parameters, especially PO43-, NO3-, NO2- and NH4+. These ions were used to give a more
precise understanding of the soil chemistry. For this method, specifically a skalar san++ device was used.
3.4 Modelling
Once fractions of Cd, Ni and Pb were known, together with the background parameters, the
bioavailable fraction of these cations were computed using the modelling program ‘Visual MINTEQ’. For this research, only the unbound dissolved cations of Cd2+, Ni2+ and Pb2+ were considered
bioavailable. Visual MINTEQ calculated metal speciation, solubility equilibria, chemical equilibria, sorption, etc. in soils using input data that the model user has to specify (Gustafsson, 2013). Input data for this research included dissolved ions Cd2+, Ni2+, Pb2+, K+, NO
3-, NO2-, PO43- and NH4+, dissolved
organic matter (DOM) (which includes 50 weight percent C and fulvic acids that function as
adsorption complexes), pH, ionic strength and temperature. Quantified input are the mean values of the parameters stated in appendix A. Temperature was set to 15°C. Ionic strength can be derived by multiplying the found EC value with 1.6*10-5.
The model is executed twice; once for each of the two datasets (containing different Cd, Ni and Pb concentrations). In the output, under the ‘view species distribution’ tab, the calculated percentage distribution of the different cations among dissolved and adsorbed species can be seen. Also, under the ‘equilibrated mass distribution’ tab the distribution of cations between dissolved, sorbed and precipitated phases as well as the amount of cations that is bound to DOM. With the help of the output under these two tabs, the bioavailable fraction of Cd, Ni and Pb can be calculated. As the bioavailable fraction consists of unbound cations, the bioavailable fraction can be calculated by dividing the unbound cations by the total cation ratio for Cd, Ni and Pb.
3.5 Sensitivity analyses
2 Sensitivity analyses have also been modelled with visual MINTEQ. These analyses were important, as they clarified how much parameters influence heavy metal bioavailable content in the soil. They also tested the robustness of the. To implement the sensitivity analyses in visual MINTEQ, sweep analyses under the Multi-problem / Sweep menu were selected. A pH sweep was introduced by clicking on ‘’pH’’ option. pH values have been increased or decreased up to 3 points compared with the pH value used in the main model runs. Increment steps of 0.1 have been taken. In a separate model run, a Al3+ sweep was introduced by clicking on the ‘’total concentration, any component’’
option, and then choosing Al3+. Al3+ has been introduced to simulate the effect of cation competition.
Al3+ concentrations were modelled from 0 up to 0.00074 millimolar It was hypothesised that
increased Al3+ content in the soil, will lead to increased cation competition, and thus to increased
concentration of unbound Cd, Ni and Pb cations. Al3+ was chosen, as it represents an excellent cation
competitor due to its relative large positive charge (Shi et al, 2013).
3.6 Determining the effects on crops, humans
Once heavy metal concentration in the soils and in processed sewage sludge applied as BBF have been calculated, toxic effects of these heavy metals can be determined. This will be done by comparing the found concentration values of Cd, Ni and Pb to the effect of these concentrations on soil fauna, crops and humans, based on scientific literature (Hudcova et al, 2019). For this, heavy metal concentrations values found using visual MINTEQ under normal pH and soil organic matter conditions will be used.
Figure 1; amount of Cd, Ni and Pb in mg/L found without application of processed sewage sludge as a BBF.
Figure 2; amount of Cd, Ni and Pb in mg/L found with application of processed sewage sludge as a BBF. 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 Cd Ni Pb Am o u n t o f t o ta l Cd , N i a n d Pb [m g/L] Type of metal [Cd / Ni / Pb] 0 2 4 6 8 10 12 14 16 18 Cd Ni Pb Am o u n t o f t o ta l Cd , N i a n d Pb [m g/L] Type of metal [Cd / Ni / Pb]
Figure 3; Species distribution of Cd2+, Ni2+ and Pb2+ without application of processed sewage sludge as
a BBF. FA1 and FA2 are different types of fulvic acids, M is metal, and represents Cd2+, Ni2+ and Pb2+
respectively, and D represents a diffuse Nica-Donnan layer.
Figure 4; Species distribution of Cd2+, Ni2+ and Pb2+ with application of processed sewage sludge as a
BBF. Where FA1 and FA2 are different types of folic acid, M is metal, and represents Cd2+, Ni2+ and
Pb2+, and D represents a diffuse Nica-Donnan layer. OH-, NO2-, NO3-, PO43- and H+ are other
chemicals. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Cd+2 Ni+2 Pb+2 Perc en ta ge o f th e t o ta l m eta l con ce n tra tio n [% ] Type of metal [Cd / Ni / Pb]
FA1-M(6)(aq) FA2-M(6)(aq) (6)M+2D(aq)
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Cd Ni Pb Perc en ta ge o f th e t o ta l m eta l con ce n tra tio n [% ] Type of metal [Cd / Ni / Pb]
M+2 FA1-M(6)(aq) FA2-M(6)(aq) MOH+ M(OH)2 (aq)
Figure 5; percentage of free Cd2+ and Ni2+ cations plotted against different amounts of Al3+ in a
without application of processed sewage sludge as a BBF.
Figure 6; percentage of free Pb2+ cations plotted against different amounts of Al3+ in a soil without
application of processed sewage sludge as a BBF. 0.000000% 0.000001% 0.000002% 0.000003% 0.000004% 0.000005% 0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 0.0008 Perc en ta ge o f fre e Cd 2+ cat ion s [% ] Amount of Al3+[millimolal]
Cd+2 Concentration [%] Ni+2 Concentration [%]
0.0000% 0.0005% 0.0010% 0.0015% 0.0020% 0.0025% 0.0030% 0.0035% 0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 0.0008 Perc en ta ge o f fre e Pb 2+ cat ion s [% ] Amount of Al3+[millimolal]
Figure 7; percentage of free Cd2+ cations plotted against different amounts of Al3+ in a soil with
application of processed sewage sludge as a BBF.
Figure 8; percentage of free Ni2+ cations plotted against different amounts of Al3+ in a soil without
application of processed sewage sludge as a BBF. 33.90% 33.92% 33.94% 33.96% 33.98% 34.00% 0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 0.0008 Perc en ta ge o f fre e Cd 2+ cat ion s [% ] Amount of Al3+[millimolal] 68.18% 68.19% 68.20% 68.21% 68.22% 68.23% 68.24% 68.25% 68.26% 68.27% 0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 0.0008 Perc en ta ge o f fre e N i 2+ cat ion s [% ] Amount of Al3+[millimolal]
Figure 9; percentage of free Pb2+ cations plotted against different amounts of Al3+ in a soil without
application of processed sewage sludge as a BBF.
Figure 10; percentage of free Cd2+ cations plotted against different pH valuesin a soil without
application of processed sewage sludge as a BBF. 21.50% 21.60% 21.70% 21.80% 21.90% 22.00% 22.10% 22.20% 22.30% 22.40% 0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 0.0008 Perc en ta ge o f fre e Pb 2+ cat ion s [% ] Amount of Al3+ [millimolal 0.00000% 0.00005% 0.00010% 0.00015% 0.00020% 0.00025% 4.5 5.5 6.5 7.5 8.5 9.5 10.5 Perc en ta ge o f fre e Cd 2+ cat ion s [% ] pH
Figure 11; percentage of free Ni2+ cations plotted against different pH valuesin a soil without
application of processed sewage sludge as a BBF.
Figure 12; percentage of free Pb2+ cations plotted against different pH valuesin a soil without
application of processed sewage sludge as a BBF. 0.00000% 0.00005% 0.00010% 0.00015% 0.00020% 0.00025% 0.00030% 0.00035% 0.00040% 0.00045% 4.5 5.5 6.5 7.5 8.5 9.5 10.5 Perc en ta ge o f fre e N i 2+ cat ion s [% ] pH 0.00% 0.05% 0.10% 0.15% 0.20% 0.25% 0.30% 4.5 5.5 6.5 7.5 8.5 9.5 10.5 Perc en ta ge o f fre e Pb 2+ cat ion s [% ] pH
Figure 13; percentage of free Cd2+, Ni2+ & Pb2+ cations plotted against different pH valuesin a soil with
application of processed sewage sludge as BBF.
5. Discussion
5.1 Implications of results
When comparing figure 2 with figure 3, the Cd, Ni and Pb levels were distinctly higher when processed sewage sludge was applied to the soil. Cd and Ni concentrations in figure 2 could not be included in the figure, as their values are lower than the detection limit. The concentration of Pb was measurable, and identified at 0.00728 mg/L. When processed sewage sludge was applied, Cd
concentration was projected to be 0.2066 mg/L. Ni concentration was projected to be 4.956 mg/L. Pb concentration was projected to be 16.5 mg/L (figure 3). Pb concentrations were approximately 2266 times higher when processed sewage sludge was applied to the soil (16.5 / 0.00728 ≈ 2266.48). Because Cd and Ni values were not measurable when no processed sewage sludge was present, a similar comparison for Cd and Ni was not possible.
Also, almost all (>99%) Cd, Ni and Pb in dataset 1 is bound to organic fulvic acids, and is thus not bioavailable. A significant fraction of the metals in dataset 2 however, were unbounded dissolved cations. This fraction is bioavailable, and can thus be taken up in crops. The modelled bioavailable fraction (in figure 5, the red part of the bar graphs) of Pb was about 21%, the bioavailable fraction of Cd was about 33% and the bioavailable fraction of Ni was about 68%. When combining this data with the total amount of respectively modelled cations, it is estimated that for Cd, Ni and Pb respectively 0.068 mg/L (0.2066*0.33), 3,37 mg/L (4.956*0.68) and 3.465 mg/L (16.5*0.21) becomes bioavailable after application of sewage sludge.
The modelled bioavailable Cd concentration seems to indicate that while 0.068 mg/L of Cd will be bioavailable, this is lower than the save limit threshold. Wood, 2019 indicates that the save limit threshold for Cd is at least 2.75 mg/kg, which is higher than the measured 0.068 mg/L bioavailable Cd. Cd levels would thus not pose immediate health risks. However, as 67% of the total Cd mass would remain within the local region (Wood, 2019), accumulation of Cd could occur when processed sewage sludge is regularly applied. This would lead to the eventual exceeding of the save limit
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 4.5 5.5 6.5 7.5 8.5 9.5 10.5 Perc en ta ge o f fre e Cd 2+, N i 2+ & P b 2+ cat ion s [% ] pH
threshold. Once this threshold is exceeded, Cd could lead to toxicity to kidney and bones through dietary intake (Wood, 2019).
Concerning Ni, Wood, 2019 stated that the save limit concentration is 130 mg/kg when it is applied through compost or digestate on agricultural land. As the modelled bioavailable Ni was much lower (3,37 mg/L), it can be assumed that the increased bioavailable Ni due to processed sewage sludge application will not pose immediate problems. However, bioavailable Ni concentrations do increase due to sewage sludge application. Wood, 2019 does also imply that there is relative low transfer of Ni over long distances due to the application of BBF’s. Thus Ni seems to accumulate in the soil when BBF’s are regularly applied. Eventually, save limit concentrations might then be exceeded. Regarding Pb, Wood, 2019 affirms that any bioavailable Pb can be a hazard, as it is already neurotoxic to man in as of yet undefined small amounts. The results suggest that background concentrations of Pb are low enough so that no Pb becomes bioavailable. When processed sewage sludge is applied however, Pb concentrations increase enough, that 21% of Pb, in total 3.465 mg/L, becomes bioavailable. Also, 95% of Pb by mass seems to remain in the local area after application. This might indicate a steady rise of bioavailable Pb when total Pb concentrations increase due to regular application of BBF’s. This would pose more exacerbated (neuro)toxic risks in the future. Wood, 2019 states that median values for Cd in BBF’s are approximately 0.2 to 0.8 mg/kg. For Ni in BBF’s, these values are 10 to 39 mg/kg. Pb median values in BBF’s are 2.5 to 91 mg/kg. These values overlap with the total values found in this research (0.2066 mg/L, 4.956 mg/L and 16.5 mg/L for Cd, Ni and Pb respectively). This suggests that the results are valid, as the found concentrations are confirmed by earlier research.
The results of the sensitivity analysis for Al3+ showed that there is only a small positive correlation
between Al3+ and Ni2+ or Pb2+, and that there was no correlation between Al3+ and Cd2. Ni2+ and Pb2+
only increased by a few tenths of a percentage points. Ni2+ increased almost linearly, while Pb2+
increases followed a flattening curve. Dissolved Cd2+ remained almost completely stable when Al3+
was introduced in the model. Concerning the sensitivity analysis when processed sewage sludge was not applied, Al3+ let to minute increases in Cd2+, Ni2+ and Pb2+. The correlation followed a flattening
curve.
Regarding the sensitivity analysis computed with differentiating values for pH, it was observed that when no processed sewage sludge was applied, Cd (figure 12), Ni (figure 13) and Pb (figure 14) sharply declined from pH 4.5 to pH 6.0. Cd, Ni and Pb tended to a zero value at pH levels between 6 and 10.5. In the sensitivity analysis where processed sewage sludge was introduced, Cd2+, Ni2+ and
Pb2+ concentrations decreased with increasing pH levels. At pH levels of 4.63, Cd2+ included 65% of all
Cd in the soil, Ni2+ includes 79% of all Ni in the soil, and Pb includes 62% of all Pb in the soil. At a pH of
10.63, these respective values were only 0.2%, 0.3% and 0.003%. Cd2+ levels decline relatively
moderately over the whole spectrum (pH 4.63 to 10.63), but does decrease more sharply between pH 4.63 and 6.5, and between pH 8 and 10. Ni2+ decline more sharply between pH 4.63 and 6, and
between pH 7.5 and 8.5. But Ni2+ values also decrease more slowly at other pH levels between 4.63
and 10.63 Pb2+ levels slowly decline between pH 4.63 and 8.5, and declines more sharply between pH
8.5 and 10.0.
These decreasing values seem to roughly confirm patterns from earlier research, although the sharp decline of Pb2+ cations seem to happen at somewhat higher pH values than expected (Houben et al,
2013). But the overall results of the sensitivity analysis seem to reasonably correspond with expected results based on scientific data. This would indicate that other results are also robust.
Regarding the implications of the results for LEX4BIO project, where this research is mainly devoted to, it seems that while BBF’s can reduce the dependency of the agricultural sector on mineral N and P resources, specifically processed sewage sludge can introduce (neuro)toxic quantities of Pb in
humans, through crop exposure.
5.2 Methodological assumptions
Results of this research are obtained by simulating chemical conditions in the soil, though modelling a simplified soil environment in Visual MINTEQ. Therefore, some parameter simplifications, might have significantly influenced the results. pH and EC values were measured during the chemical analysis, and thus were fairly precisely included in the research. However, average pH and EC values have been used during the model runs, while both pH and EC values showed some amount of divergence of the mean value. pH values were estimated between 7.31 and 7.88. EC values were estimated between 211 and 441 (See appendix A). The large discrepancies within the pH and EC values, might indicate that taking mean values would not have been precise. This problem has partly been addressed by providing a sensitivity analysis for pH however.
Also, the total amount of anions and cations were not known. Thus cation competition was not quantifiable. The only measured cations were Cd2+, Ni2+, Pb2+ and K+. This was partly resolved by
implementing series of Al3+ concentrations in a sensitivity analysis. Al3+ is an excellent cation
competitor. Cation competition seemed a rather important parameter, as, in consonance with the sensitivity analysis, it seemed that increased Al3+ and thus increased cation competition influenced
the bioavailability of Cd, Ni and Pb.
Furthermore Visual MINTEQ gives the option to calculate the concentration of fulvic acids from the input of organic carbon. But the ratio dissolved organic carbon to dissolved organic matter, and the percentage of dissolved organic matter that is fulvic acid in reality can both still deviate from what the organic matter sub model (here, NICA-Donnan) assumes. Quantifying the abundance of fulvic acids seems crucial, as any Cd, Ni or Pb that is bound to these organic acids, is not bioavailable. While all these issues do influence the amount of bioavailable Cd, Ni and Pb, it does probably not change the primary outcome of the results. It is expected that Pb will still be available in (neuro)toxic quantities, and Cd and Ni will still not be available in (neuro)toxic quantities. This is expected,
because even while changing parameters like Al3+ concentrations, the bioavailable percentage of Cd
did not change, and the bioavailable percentage of Ni and Pb only changed a few tenths of a percentage point. At different pH levels, the bioavailable share of Cd, Ni and Pb differed greatly. However, it is unlikely that measured pH values differed more than 0.5 points from pH values in reality. Within this 0.5 range, the bioavailable share of Cd, Ni and Pb were still quite similar compared to the model results. As the bioavailable fractions of Cd, Ni and Pb did not change greatly during these sensitivity analyses, it is expected that slight parametrical changes influences the outcome of the results minimally. Furthermore, it turned out that any bioavailable Pb can already be (neuro)toxic to humans, while toxic concentrations of Cd and Ni are tens of times higher than modelled
concentration. Thus only extreme, and unlikely, chemical changes can change these results.
However, slightly changed values of the soil parameters will influence how much bioavailable Cd, Ni and Pb will be released through fertilization with BBF. When BBF’s are applied regularly, it will thus influence how quick the cumulative rise of Cd, Ni and Pb will be. Hence, these different values of the soil parameters may have an important influence on how soon bioavailable Cd and Ni fractions can become available in hazardous quantities.
Furthermore, inorganic adsorption complexes, like clay minerals, have also not been taken into account. Inorganic adsorption complexes are hard to implement in Visual MINTEQ. It is expected that clay makes up a significant fraction of the soil. Thus the absence of inorganic adsorption complexes in this research might seriously underestimate the amount of Cd, Ni and Pb that is strongly bounded to the soil fraction. The bioavailable fraction of Cd, Ni and Pb might therefore by lower than what was found in this research.
Because water extracts were used, not all metals that were tightly absorbed to the soil will have been extracted. This will result in a underestimation of the total Cd, Ni and Pb concentration. However, it is expected that this fraction of the metals is not bioavailable, because it is often so tightly adsorbed to the soil. This will thus probably not result in a difference in the total
concentration of bioavailable Cd, Ni and Pb, but results might overestimate the portion of bioavailable metals, as not all metals tightly bound to the soil have been included.
It was assumed that Cd, Ni and Pb concentrations in processed sewage sludge would be in a similar range to the maximum allowed cations in sewage sludge applied to agricultural areas. When for example governmental oversight is inadequate or the maximum allowed concentration of Cd, Ni and Pb in processed sewage sludge is changed, this assumption will not hold.
While the results of this study can only be locally implemented, as soil parameters strongly differ per region, the methods used in this research can broadly be applied anywhere. The broad methods used (collecting soil samples, chemically analyse the amount of cations, as well as other notable
parameters, model the cations and soil parameters) are after all not site-specific. It is also expected that this research can be applied on cations other than Cd, Ni and Pb.
6. Conclusion
When no processed sewage sludge was used as a BBF, Cd and Ni concentrations were below the detection limit. Pb concentrations were 0.00728 mg/L. When processed sewage sludge was part of the fertilization regime, the Cd concentration was estimated to be 0.2066 mg/L, the Ni concentration was estimated to be 4.956 mg/L, and the Pb concentration was estimated to be 16.5 mg/L. Cd, Ni and Pb concentrations were thus found to be much higher when processed sewage sludge was used as a fertilizer. Moreover, the bioavailable portion of Cd, Ni and Pb were also much higher when processed sewage sludge was present in the soil. Free dissolved Cd2+, Ni2+ and Pb2+ made up <1% of
the total Cd, Ni or Pb present in the soil. Cd, Ni and Pb bound to fulvic acids made up >99%. When processed sewage sludge was used as a fertilizer, 33% of Cd (0.068 mg/L), 68% of Ni (3.37 mg/L) and 21% of Pb (3.465 mg/L) was bioavailable.
Concerning the Al3+ sensitivity analysis, the unbound dissolved fraction of Cd, Ni and Pb did increase
by a few tenths of a percentage point when more Al3+ was present. Thus the presence of Al3+ has had
no major impact on the results. Results thus seem robust to slight parametrical changes. The sensitivity analysis results for pH show that the estimated changes of bioavailable Cd, Ni and Pb as a function of pH are roughly in accordance with scientific data. This indicates that this research provided accurate results.
When no processed sewage sludge was used as a BBF, (bioavailable) Cd, Ni and Pb concentrations were not concerning. When Processed sewage sludge was used as a BBF, Pb concentrations are problematic. Pb is a non-threshold neurotoxic, and therefore 3.465 mg/L bioavailable Pb is hazardous. The 0.068 mg/L bioavailable Cd and 3.37 mg/L bioavailable Ni are lower than the save
threshold limits (2.75 mg/kg and 130 mg/kg for Cd and Ni respectively), and these metals thus do not pose immediate health risks. When processed fertilizer is applied regularly however, and bioavailable Cd and Ni concentrations increase, these metals can also become hazardous.
7. Reverences
Bloem, E., Kratz, S., Panten, K., Schnug, E. (2020). Achieving sustainable crop nutrition. CBS (2021). Landbouwexport blijft op de been. Retrieved from https://www.cbs.nl/nl-nl/nieuws/2021/03/landbouwexport-blijft-op-de-been.
Chojnacka, K., Chojnacki, A., Gorecka, H., & Górecki, H. (2005). Bioavailability of heavy metals from polluted soils to plants. Science of the total Environment, 337(1-3), 175-182.
De Vos, B., Lettens, S., Muys, B., & Deckers, J. A. (2007). Walkley–Black analysis of forest soil organic carbon: recovery, limitations and uncertainty. Soil Use and Management, 23(3), 221-229.
Fijałkowski, K., Kacprzak, M., Grobelak, A., & Placek, A. (2012). The influence of selected soil parameters on the mobility of heavy metals in soils. Inżynieria i Ochrona środowiska, 15, 81-92. Gustaffson (2013). Visual MINTEQ ver. 3.1. Retrieved from https://vminteq.lwr.kth.se/
Herencia, J. F., Ruiz‐Porras, J. C., Melero, S., Garcia‐Galavis, P. A., Morillo, E., & Maqueda, C. (2007). Comparison between organic and mineral fertilization for soil fertility levels, crop macronutrient concentrations, and yield. Agronomy Journal, 99(4), 973-983.
Holland, E. A., Braswell, B. H., Sulzman, J., & Lamarque, J. F. (2005). Nitrogen deposition onto the United States and Western Europe: synthesis of observations and models. Ecological
applications, 15(1), 38-57.
Hudcová, H., Vymazal, J., & Rozkošný, M. (2019). Present restrictions of sewage sludge application in agriculture within the European Union. Soil and Water Research, 14(2), 104-120.
Kim, R. Y., Yoon, J. K., Kim, T. S., Yang, J. E., Owens, G., & Kim, K. R. (2015). Bioavailability of heavy metals in soils: definitions and practical implementation—a critical review. Environmental
geochemistry and health, 37(6), 1041-1061.
Krozer, Y., Hophmayer-Tokich, S., van Meerendonk, H., Tijsma, S., & Vos, E. (2010). Innovations in the water chain–experiences in The Netherlands. Journal of Cleaner Production, 18(5), 439-446.
Lex4bio (n.d.). Lex4bio: optimising bio-based fertilisers in agriculture. Retrieved from https://www.lex4bio.eu.
Jansen (2021). ‘’What is the potential for pollution with heavy metals and possibly also organic pollutants via the application of biobased fertilizers in agricultural sites in The Netherlands, and how can this be assessed via a combination of modelling and in-situ field tests by farmers/citizens themselves?’’
Massadeh, A. M., Alomary, A. A., Mir, S., Momani, F. A., Haddad, H. I., & Hadad, Y. A. (2016). Analysis of Zn, Cd, As, Cu, Pb, and Fe in snails as bioindicators and soil samples near traffic road by ICP-OES. Environmental Science and Pollution Research, 23(13), 13424-13431.
Nawrot, T. S., Staessen, J. A., Roels, H. A., Munters, E., Cuypers, A., Richart, T., ... & Vangronsveld, J. (2010). Cadmium exposure in the population: from health risks to strategies of
prevention. Biometals, 23(5), 769-782.
Nicholson, F. A., Smith, S. R., Alloway, B. J., Carlton‐Smith, C., & Chambers, B. J. (2006). Quantifying heavy metal inputs to agricultural soils in England and Wales. Water and Environment Journal, 20(2), 87-95.
Ramankutty, N., Mehrabi, Z., Waha, K., Jarvis, L., Kremen, C., Herrero, M., & Rieseberg, L. H. (2018). Trends in global agricultural land use: implications for environmental health and food
security. Annual review of plant biology, 69, 789-815.
Reijneveld, J. A. (2013). Unravelling changes in soil fertility of agricultural land in the Netherlands. Wageningen UR.
Schroder, J. L., Zhang, H., Girma, K., Raun, W. R., Penn, C. J., & Payton, M. E. (2011). Soil acidification from long‐term use of nitrogen fertilizers on winter wheat. Soil Science Society of America
Journal, 75(3), 957-964.
Schröder, J. J., & Neeteson, J. J. (2008). Nutrient management regulations in The Netherlands. Geoderma, 144(3-4), 418-425.
Shi, Z., Allen, H. E., Di Toro, D. M., Lee, S. Z., & Harsh, J. B. (2013). Predicting PbII adsorption on soils: the roles of soil organic matter, cation competition and iron (hydr) oxides. Environmental
Chemistry, 10(6), 465-474.
Singh, R. P., & Agrawal, M. (2008). Potential benefits and risks of land application of sewage sludge. Waste management, 28(2), 347-358
United Nations (2019). Revision of world population prospects. Retrieved from https://population.un.org/wpp/
Vaneeckhaute, Céline, et al. "Ecological and economic benefits of the application of bio-based mineral fertilizers in modern agriculture." Biomass and Bioenergy 49 (2013): 239-248.
Violante, A., Cozzolino, V., Perelomov, L., Caporale, A. G., & Pigna, M. (2010). Mobility and
bioavailability of heavy metals and metalloids in soil environments. Journal of soil science and plant nutrition, 10(3), 268-292.
Van Hall (2021). ‘’Nadat je de monsters hebt meegenomen uit het veld kun je ze eerst even drogen op een lage temperatuur (30/40 graden) zodat je ze kunt zeven (<2 mm; de fijnaarde fractie van een bodem), maar waarschijnlijk is dit in jullie geval niet nodig aangezien er waarschijnlijk vrij weinig materiaal > 2mm aanwezig is. Van deze bodem maak je een (water?) extract. Normaliter doe ik dit in de verhouding 1:2.5 (dus equivalent van 20 gram droge bodem met 50 gram water) maar andere verhoudingen zijn ook mogelijk. Dit wordt gefiltreerd over een filter met poriegrootte (0.45 µm). Voor de autoanalyser vul je een buisje met ~10 ml van deze vloeistof’’
Vásquez-Murrieta, M. S., Migueles-Garduño, I., Franco-Hernández, O., Govaerts, B., & Dendooven, L. (2006). C and N mineralization and microbial biomass in heavy-metal contaminated soil. European journal of soil biology, 42(2), 89-98.
8. Appendices
Appendix A: Values of the parameters measured during lab analysis Sample
number
NO3 [µmol/l] NO2 [µmol/l] NH4 [µmol/l] PO4 [µmol/l] DON [µmol/l]
A01.1 81.4 1.7 < 20 11 142 A01.2 83.4 2.3 < 20 11.7 134 A02.1 91.5 1.6 < 20 6.2 63 A02.2 102 1.3 < 20 4.5 67 A03.1 68.1 < 1.0 7.5 2.46 85 A03.2 57.7 < 1.0 7.4 2.38 78 A04.1 73.4 1.3 < 20 4.1 54 A04.2 78.3 1.4 < 20 3.5 48 A05.1 78 < 1.0 8.5 1.56 103 A05.2 78.4 < 1.0 7.7 2.27 88 A06.1 80 1.4 < 20 1.3 58 A06.2 72.6 1.2 < 20 1.4 57 A07.1 66.7 1.3 < 20 4.2 60 A07.2 63.4 1.3 < 20 3 55 A08.1 51.5 1.4 < 20 3.8 60 A08.2 55.9 1.2 < 20 3.4 61 A09.1 A09.2 A10.1 61.4 2.3 < 20 2.1 144 A10.2 48.1 2.6 < 20 1.5 78 A11.1 69.3 1.2 < 20 1.6 66 A11.2 67.9 1.4 < 20 1.5 68 A12.1 319 < 1.0 8.3 7.2 99 A12.2 357 1.4 9.1 8.04 103 A13.1 91.4 2.4 < 20 1.1 68 A13.2 86.3 1.3 < 20 1.4 57 A14.1 69.3 1.8 < 20 1.6 51 A14.2 68.2 1.7 < 20 1.9 44 A15.1 61.3 < 1.0 8.2 1.02 95 A15.2 46.4 < 1.0 9.3 3.12 98 A16.1 77.8 2.3 < 20 2 58 A16.2 56.9 2 < 20 1.8 66 A17.1 60.1 < 1.0 7.5 5.16 67 A17.2 57.3 < 1.0 7.8 4.32 80 A18.1 66.5 1.4 < 20 1.7 160 A18.2 59.8 3.6 < 20 1.5 163 A19.1 78.5 1.5 < 20 1.9 67 A19.2 78.9 1.4 < 20 4.8 102
A20.1 92.4 1.4 < 20 2.8 59 A20.2 71.1 1.2 < 20 3.1 53 B01.1 934 1.6 < 20 4.5 105 B01.2 889 1.4 < 20 5.6 74 B02.1 461 < 1.0 8.4 6.19 72 B02.2 421 10 9.1 3.75 84 B03.1 1030 87.3 94 2 72 B03.2 832 1.2 < 20 4.6 76 B04.1 172 3.4 < 20 2.5 46 B04.2 297 1.3 < 20 2.5 55 B05.1 1203 1.5 < 20 3.5 28 B05.2 1288 1.7 < 20 5 56 B06.1 481 1.8 < 20 5.7 28 B06.2 510 1.4 < 20 0.7 37 B07.1 755 1.2 < 20 3.4 80 B07.2 952 59 < 20 3.5 44 B08.1 527 1.8 < 20 15.4 45 B08.2 524 1.5 < 20 8.2 48 B09.1 915 4.2 8.2 13.9 100 B09.2 991 39.3 33.4 7.59 141 B10.1 530 6.6 < 20 1.8 46 B10.2 295 1.6 < 20 0.9 43 B11.1 675 1.8 < 20 4.9 61 B11.2 993 1.6 < 20 6 13 B12.1 1277 71.3 47 4.7 40 B12.2 562 27.5 < 20 2.8 50 B13.1 851 22 9.7 5 113 B13.2 1183 48 35.1 17.1 219 B14.1 756 2.4 < 20 14.9 128 B14.2 827 1.9 < 20 12.8 156 B15.1 468 1.7 < 20 3.6 54 B15.2 469 1.2 < 20 3.7 46 B16.1 274 < 1.0 7.6 7.08 77 B16.2 340 < 1.0 10.5 7.06 134 B17.1 1368 1 < 20 5.6 60 B17.2 1179 1.2 < 20 6.3 57 B18.1 539 196 13.3 5.46 106 B18.2 520 85.5 7 4.97 36 B19.1 702 1.2 < 20 8.3 80 B19.2 711 1.2 < 20 14 83 B20.1 764 5.1 < 20 1.5 96 B20.2 1686 25.9 < 20 2.4 29 BL 1 4.2 < 1.0 6.6 0.79 < 15
BL 3 < 3.0 0.6 20 0.6 < 15 BL 4 < 3.0 0.6 21 0.6 < 15 K 766.490 (mg/L) Ni 231.604 (mg/L) P 213.617 (mg/L) Pb 220.353 (mg/L) Acidity [pH] EC [µs] 12.206 0.01 0.307 0.01 7.75 255 11.288 0.008 0.288 0.016 7.77 268 2.95 < LOD 0.125 0.009 7.72 246 2.794 < LOD 0.087 < LOD 7.72 242 2.016 < LOD 0.106 0.008 7.55 250 1.79 < LOD 0.097 0.014 7.59 256
1.137 < LOD < LOD < LOD 7.78 227
1.18 < LOD 0.065 < LOD 7.73 233
0.746 0.005 0.094 0.01 7.6 246
0.741 0.006 0.109 0.021 7.59 245
1.127 < LOD < LOD < LOD 7.72 245
1.106 < LOD < LOD < LOD 7.65 243
2.045 < LOD < LOD < LOD 7.67 250
2.003 < LOD < LOD < LOD 7.79 247
0.506 0.006 0.126 0.017 7.73 213 0.588 0.01 0.12 0.011 7.71 211 1.295 0.009 0.113 0.024 7.61 232 0.323 < LOD 0.114 0.017 7.6 224
1.648 < LOD < LOD < LOD 7.76 228
1.603 < LOD < LOD < LOD 7.81 213
10.084 < LOD < LOD 0.011 7.59 281
10.199 < LOD 0.099 0.008 7.59 301
0.98 < LOD < LOD < LOD 7.75 231
0.539 < LOD < LOD < LOD 7.77 225
0.497 < LOD < LOD < LOD 7.74 221
0.991 < LOD < LOD < LOD 7.77 221
0.167 < LOD 0.088 < LOD 7.64 231
0.935 < LOD 0.091 0.017 7.69 222
1.881 < LOD < LOD < LOD 7.78 227
1.888 < LOD < LOD < LOD 7.84 225
3.497 < LOD 0.097 0.015 7.54 242
3.888 0.007 0.084 < LOD 7.56 240
1.162 < LOD 0.103 0.016 7.63 252
2.171 < LOD 0.107 0.014 7.63 247
4.907 0.016 0.148 0.02 7.58 243
1.828 < LOD < LOD < LOD 7.82 236
1.791 < LOD < LOD < LOD 7.8 237
4.717 0.007 0.131 0.017 7.54 280 4.178 0.007 0.129 0.015 7.49 324 2.63 0.005 0.141 0.014 7.59 280 2.736 < LOD 0.087 0.017 7.57 290 5.38 < LOD 0.102 0.014 7.54 344 4.291 < LOD 0.1 0.021 7.59 315
1.855 < LOD < LOD < LOD 7.4 302
1.739 < LOD < LOD < LOD 7.66 307
3.518 < LOD 0.089 0.007 7.48 347
4.142 < LOD 0.124 0.033 7.46 345
1.8 < LOD < LOD < LOD 7.43 272
1.951 < LOD < LOD < LOD 7.46 273
1.999 < LOD < LOD < LOD 7.4 316
2.214 < LOD < LOD < LOD 7.38 344
7.118 < LOD 0.316 < LOD 7.41 311 6.427 < LOD 0.125 < LOD 7.45 306 16.98 < LOD 0.261 0.012 7.55 350 10.16 < LOD 0.165 0.014 7.53 346 3.224 0.007 0.096 0.017 7.52 336 1.953 < LOD 0.079 0.007 7.58 319 5.209 < LOD 0.085 0.013 7.88 342 9.739 < LOD 0.153 < LOD 7.51 364 4.907 < LOD 0.118 0.012 7.32 386
3.035 < LOD < LOD < LOD 7.47 310
1.34 < LOD 0.076 0.011 7.47 349
0.219 < LOD < LOD < LOD 7.47 364
8.626 < LOD 0.285 0.011 7.58 319
8.711 < LOD 0.262 < LOD 7.52 329
1.955 < LOD < LOD < LOD 7.55 280
3.004 < LOD < LOD < LOD 7.69 272
4.247 < LOD 0.143 0.013 7.48 319 4.678 0.005 0.157 0.013 7.53 316 2.04 < LOD 0.068 < LOD 7.31 369 1.597 < LOD 0.093 < LOD 7.36 344 1.354 < LOD 0.113 0.017 7.54 306 0.759 < LOD 0.105 0.012 7.58 285 5.261 0.008 0.701 < LOD 7.61 293 6.598 < LOD 0.17 < LOD 7.68 291
0.114 < LOD 0.116 0.007 8.03 32
0.07 0.007 0.173 0.024 7.25 7.25
0.109 < LOD < LOD < LOD 7.79 373
0.099 < LOD < LOD < LOD 7.76 337
Appendix B: Species distribution of Ni2+, Pb2+ and Cd2+ in dataset 1.
Component % of total concentration Species name
Ni+2 40.375 FA1-Ni(6)(aq) 59.624 FA2-Ni(6)(aq) Pb+2 80.938 FA2-Pb(6)(aq) 0.11 (6)Pb+2D(aq) 18.952 FA1-Pb(6)(aq) Cd+2 68.274 FA1-Cd(6)(aq) 31.726 FA2-Cd(6)(aq)
Appendix C: Saturation indices of several minerals containing Ni2+, Pb2+ and Cd2+ in dataset 1.
Mineral log IAP
Sat.
index Stoichiometry Kolom1 Kolom2 Kolom3 Kolom4 Kolom5
Cd(OH)2(s) -9.254 -23.473 1 Cd+2 2 H2O -2 H+1
Cd3(PO4)2(s) -94.174 -61.574 3 Cd+2 2 PO4-3
Hydroxylpyromorphite -90.385 -27.595 5 Pb+2 3 PO4-3 1 H2O
Litharge 1.847 -11.242 1 Pb+2 1 H2O -2 H+1
Massicot 1.847 -11.45 1 Pb+2 1 H2O -2 H+1
Ni(OH)2 (am) -9.339 -22.812 1 Ni+2 2 H2O -2 H+1
Ni(OH)2 (c) -9.339 -20.129 1 Ni+2 -2 H+1 2 H2O
Ni3(PO4)2(s) -94.428 -63.128 3 Ni+2 2 PO4-3
Pb(OH)2(s) 1.846 -6.659 -2 H+1 1 Pb+2 2 H2O
Pb2O(OH)2(s) 3.693 -22.497 2 Pb+2 3 H2O -4 H+1
Pb3(PO4)2(s) -60.872 -17.342 3 Pb+2 2 PO4-3
PbHPO4(s) -31.36 -7.555 1 Pb+2 1 H+1 1 PO4-3
PbO:0.3H2O(s) 1.846 -11.134 -2 H+1 1 Pb+2 1.33 H2O
Appendix D: Equilibrated mass distribution of Ni2+, Pb2+ and Cd2+ in dataset 1.
Component Dissolved inorganic Bound to DOM Total dissolved % dissolved
Cd+2 0 0 0 0
Ni+2 0 0 0 0
Component Total sorbed % sorbed Total precipitated % precipitated Cd+2 0 0 0 0 Ni+2 0 0 0 0 Pb+2 0 0 0 0
Appendix E: Species distribution of Ni2+, Pb2+ and Cd2+ in dataset 2.
Component % of total concentration Species name
Cd+2 33.961 Cd+2 0.043 CdOH+ 0.052 CdNO2+ 0.042 CdNO3+ 0.338 CdHPO4 (aq) 13.068 (6)Cd+2D(aq) 52.478 FA1-Cd(6)(aq) 0.015 FA2-Cd(6)(aq) Ni+2 68.194 Ni+2 0.144 NiOH+ 0.025 NiNO2+ 0.049 NiNO3+ 0.121 NiHPO4 (aq) 26.241 (6)Ni+2D(aq) 5.189 FA1-Ni(6)(aq) 0.02 FA2-Ni(6)(aq) Pb+2 21.611 Pb+2 43.905 FA2-Pb(6)(aq) 13.493 PbOH+ 0.238 Pb(OH)2 (aq) 0.014 Pb2OH+3 0.106 PbNO2+ 0.095 PbNO3+ 0.053 PbHPO4 (aq) 8.316 (6)Pb+2D(aq) 12.158 FA1-Pb(6)(aq)
Appendix F: Saturation indices of several minerals containing Ni2+, Pb2+ and Cd2+ in dataset 2.
Mineral log IAP
Sat.
index Stoichiometry Kolom5 Kolom6 Kolom7 Kolom8 Kolom9
Cd(OH)2(s) 8.932 -5.288 1 Cd+2 2 H2O -2 H+1
Cd3(PO4)2(s) -39.64 -7.04 3 Cd+2 2 PO4-3
Hydroxylpyromorphite -47.793 14.997 5 Pb+2 3 PO4-3 1 H2O
Litharge 10.372 -2.716 1 Pb+2 1 H2O -2 H+1
Ni(OH)2 (am) 10.896 -2.577 1 Ni+2 2 H2O -2 H+1
Ni(OH)2 (c) 10.896 0.106 1 Ni+2 -2 H+1 2 H2O
Ni3(PO4)2(s) -33.746 -2.446 3 Ni+2 2 PO4-3
Pb(OH)2(s) 10.372 1.866 -2 H+1 1 Pb+2 2 H2O
Pb2O(OH)2(s) 20.744 -5.446 2 Pb+2 3 H2O -4 H+1
Pb3(PO4)2(s) -35.319 8.211 3 Pb+2 2 PO4-3
PbHPO4(s) -22.846 0.959 1 Pb+2 1 H+1 1 PO4-3
PbO:0.3H2O(s) 10.372 -2.608 -2 H+1 1 Pb+2 1.33 H2O
Appendix G: Equilibrated mass distribution of Ni2+, Pb2+ and Cd2+ in dataset 2.
Component Dissolved inorganic Bound to DOM Total dissolved % dissolved
Cd+2 6.3301E-07 1.2051E-06 1.8381E-06 100
Ni+2 0.000057865 0.00002655 0.000084415 100
Pb+2 0.000028368 0.000051273 0.000079641 100
Total sorbed % sorbed Total precipitated % precipitated
0 0 0 0
0 0 0 0
0 0 0 0
Appendix H: Sensitivity analysis results from dataset 1 while using a range of Al3+ values.
Problem no. Al+3 Cd+2 Ni+2 Pb+2
Total dissolved Concentration [%] Concentration [%] Concentration [%]
1 1.0001E-16 4.0709E-09 3.3504E-09 1.46036E-06 2 7.4119E-06 5.5104E-09 5.4977E-09 2.6309E-06 3 0.000014826 6.8672E-09 7.3301E-09 3.6955E-06 4 0.000022239 8.0621E-09 8.8416E-09 4.5774E-06 5 0.000029651 9.1336E-09 1.0167E-08 5.35316E-06 6 0.000037064 1.0107E-08 1.1361E-08 6.05378E-06 7 0.000044476 1.1003E-08 1.2453E-08 6.69664E-06 8 0.000051888 1.1834E-08 1.3464E-08 7.29425E-06 9 0.000059301 1.2613E-08 1.441E-08 7.85458E-06 10 0.000066714 1.3347E-08 1.5301E-08 8.38418E-06 11 0.000074126 1.4044E-08 1.6146E-08 8.88702E-06 12 0.000081538 1.4709E-08 1.6951E-08 9.36739E-06 13 0.000088951 1.5345E-08 1.7722E-08 9.82783E-06 14 0.000096363 1.5955E-08 1.8462E-08 1.02709E-05 15 0.00010378 1.6544E-08 1.9175E-08 1.06992E-05 16 0.00011119 1.7113E-08 1.9864E-08 1.1113E-05 17 0.0001186 1.7663E-08 2.0531E-08 1.15142E-05 18 0.00012601 1.8198E-08 2.1178E-08 1.19044E-05 19 0.00013343 1.8717E-08 2.1807E-08 1.2284E-05 20 0.00014084 1.9223E-08 2.242E-08 1.2654E-05
21 0.00014825 1.9716E-08 2.3017E-08 1.30151E-05 22 0.00015566 2.0197E-08 2.36E-08 1.33682E-05 23 0.00016308 2.0667E-08 2.417E-08 1.37137E-05 24 0.00017049 2.1128E-08 2.4728E-08 1.40524E-05 25 0.0001779 2.1579E-08 2.5275E-08 1.43842E-05 26 0.00018531 2.2021E-08 2.5812E-08 1.47106E-05 27 0.00019273 2.2454E-08 2.6338E-08 1.50307E-05 28 0.00020014 2.288E-08 2.6854E-08 1.53449E-05 29 0.00020755 2.3299E-08 2.7362E-08 1.56542E-05 30 0.00021496 2.371E-08 2.7861E-08 1.59585E-05 31 0.00022238 2.4115E-08 2.8352E-08 1.62581E-05 32 0.00022979 2.4514E-08 2.8835E-08 1.65535E-05 33 0.0002372 2.4906E-08 2.9311E-08 1.68446E-05 34 0.00024461 2.5293E-08 2.9781E-08 1.71318E-05 35 0.00025203 2.5675E-08 3.0243E-08 1.74149E-05 36 0.00025944 2.6051E-08 3.07E-08 1.76944E-05 37 0.00026685 2.6422E-08 3.115E-08 1.79704E-05 38 0.00027426 2.6789E-08 3.1595E-08 1.82433E-05 39 0.00028168 2.7151E-08 3.2034E-08 1.85128E-05 40 0.00028909 2.7508E-08 3.2468E-08 1.87792E-05 41 0.0002965 2.7862E-08 3.2897E-08 1.90424E-05 42 0.00030391 2.8211E-08 3.3321E-08 1.93031E-05 43 0.00031133 2.8556E-08 3.3741E-08 1.95609E-05 44 0.00031874 2.8898E-08 3.4155E-08 1.98159E-05 45 0.00032615 2.9236E-08 3.4566E-08 2.00686E-05 46 0.00033356 2.9571E-08 3.4972E-08 2.03187E-05 47 0.00034098 2.9902E-08 3.5374E-08 2.05663E-05 48 0.00034839 3.0229E-08 3.5772E-08 2.08116E-05 49 0.0003558 3.0554E-08 3.6166E-08 2.10549E-05 50 0.00036321 3.0876E-08 3.6557E-08 2.12957E-05 51 0.00037063 3.1194E-08 3.6944E-08 2.15347E-05 52 0.00037804 3.151E-08 3.7328E-08 2.17715E-05 53 0.00038545 3.1823E-08 3.7708E-08 2.20063E-05 54 0.00039286 3.2133E-08 3.8085E-08 2.2239E-05 55 0.00040027 3.2441E-08 3.8458E-08 2.24701E-05 56 0.00040769 3.2746E-08 3.8829E-08 2.26992E-05 57 0.0004151 3.3048E-08 3.9196E-08 2.29266E-05 58 0.00042251 3.3348E-08 3.9561E-08 2.31522E-05 59 0.00042992 3.3646E-08 3.9923E-08 2.33762E-05 60 0.00043734 3.3941E-08 4.0282E-08 2.35985E-05 61 0.00044475 3.4234E-08 4.0641E-08 2.38207E-05 62 0.00045216 3.4525E-08 4.0995E-08 2.40398E-05 63 0.00045958 3.4814E-08 4.1346E-08 2.42575E-05 64 0.00046699 3.5101E-08 4.1694E-08 2.44738E-05 65 0.0004744 3.5386E-08 4.204E-08 2.46884E-05 66 0.00048181 3.5668E-08 4.2384E-08 2.49018E-05 67 0.00048923 3.5949E-08 4.2726E-08 2.51135E-05 68 0.00049664 3.6228E-08 4.3065E-08 2.53241E-05
69 0.00050405 3.6505E-08 4.3402E-08 2.55336E-05 70 0.00051146 3.678E-08 4.3736E-08 2.57413E-05 71 0.00051888 3.7054E-08 4.4069E-08 2.59482E-05 72 0.00052629 3.7326E-08 4.4399E-08 2.61537E-05 73 0.0005337 3.7596E-08 4.4728E-08 2.63577E-05 74 0.00054111 3.7864E-08 4.5054E-08 2.65609E-05 75 0.00054853 3.8131E-08 4.5379E-08 2.67627E-05 76 0.00055594 3.8396E-08 4.5701E-08 2.69636E-05 77 0.00056335 3.866E-08 4.6022E-08 2.71631E-05 78 0.00057076 3.8922E-08 4.6341E-08 2.73617E-05 79 0.00057817 3.9183E-08 4.6658E-08 2.75589E-05 80 0.00058559 3.9442E-08 4.6973E-08 2.77555E-05 81 0.000593 3.9699E-08 4.7287E-08 2.79508E-05 82 0.00060041 3.9956E-08 4.7598E-08 2.81451E-05 83 0.00060782 4.0211E-08 4.7908E-08 2.83384E-05 84 0.00061524 4.0464E-08 4.8217E-08 2.85316E-05 85 0.00062265 4.0716E-08 4.8524E-08 2.87223E-05 86 0.00063006 4.0967E-08 4.8829E-08 2.89129E-05 87 0.00063747 4.1217E-08 4.9133E-08 2.91036E-05 88 0.00064489 4.1465E-08 4.9435E-08 2.92914E-05 89 0.0006523 4.1712E-08 4.9736E-08 2.94792E-05 90 0.00065971 4.1958E-08 5.0035E-08 2.96642E-05 91 0.00066712 4.2203E-08 5.0332E-08 2.9852E-05 92 0.00067454 4.2446E-08 5.0629E-08 3.0037E-05 93 0.00068195 4.2689E-08 5.0924E-08 3.0222E-05 94 0.00068936 4.293E-08 5.1217E-08 3.04041E-05 95 0.00069677 4.317E-08 5.1509E-08 3.05862E-05 96 0.00070419 4.3409E-08 5.18E-08 3.07684E-05 97 0.0007116 4.3647E-08 5.209E-08 3.09505E-05 98 0.00071901 4.3883E-08 5.2378E-08 3.11298E-05 99 0.00072642 4.4119E-08 5.2665E-08 3.1309E-05 100 0.00073384 4.4354E-08 5.295E-08 3.14883E-05
Appendix I: Sensitivity analysis results from dataset 2 while using a range of Al3+ values.
Problem no. Al+3 Cd+2 Ni+2 Pb+2
Total
dissolved Concentration Concentration Concentration
1 1E-16 0.339595 0.681952 0.2161 2 7.41E-06 0.339595 0.681917 0.218034 3 1.48E-05 0.339595 0.681929 0.218486 4 2.22E-05 0.339595 0.68194 0.218812 5 2.97E-05 0.339595 0.68194 0.219064 6 3.71E-05 0.339595 0.681952 0.219277 7 4.45E-05 0.339595 0.681964 0.219453 8 5.19E-05 0.339595 0.681964 0.219616 9 5.93E-05 0.339595 0.681976 0.219767 10 6.67E-05 0.339595 0.681988 0.219905
11 7.41E-05 0.339595 0.681988 0.22003 12 8.15E-05 0.339595 0.682 0.220143 13 8.9E-05 0.339595 0.682011 0.220256 14 9.64E-05 0.339595 0.682011 0.220357 15 0.000104 0.339595 0.682023 0.220457 16 0.000111 0.339595 0.682035 0.220545 17 0.000119 0.339595 0.682035 0.220633 18 0.000126 0.339595 0.682047 0.220721 19 0.000133 0.339595 0.682047 0.220796 20 0.000141 0.339595 0.682059 0.220872 21 0.000148 0.339595 0.682071 0.220947 22 0.000156 0.339595 0.682071 0.221035 23 0.000163 0.339595 0.682083 0.221098 24 0.00017 0.339595 0.682083 0.221173 25 0.000178 0.339595 0.682094 0.221236 26 0.000185 0.339595 0.682106 0.221299 27 0.000193 0.339595 0.682106 0.221361 28 0.0002 0.339595 0.682118 0.221424 29 0.000208 0.339595 0.682118 0.221487 30 0.000215 0.339595 0.68213 0.221537 31 0.000222 0.339595 0.68213 0.2216 32 0.00023 0.339595 0.682142 0.22165 33 0.000237 0.339595 0.682154 0.2217 34 0.000245 0.339595 0.682154 0.221763 35 0.000252 0.339595 0.682165 0.221813 36 0.000259 0.339595 0.682165 0.221864 37 0.000267 0.339595 0.682177 0.221914 38 0.000274 0.339595 0.682177 0.221964 39 0.000282 0.339595 0.682189 0.222002 40 0.000289 0.339595 0.682201 0.222052 41 0.000297 0.339595 0.682201 0.222102 42 0.000304 0.339595 0.682213 0.222152 43 0.000311 0.339595 0.682213 0.22219 44 0.000319 0.339595 0.682225 0.22224 45 0.000326 0.339595 0.682225 0.222278 46 0.000334 0.339595 0.682237 0.222328 47 0.000341 0.339595 0.682237 0.222366 48 0.000348 0.339595 0.682248 0.222404 49 0.000356 0.339595 0.682248 0.222454 50 0.000363 0.339595 0.68226 0.222491 51 0.000371 0.339595 0.682272 0.222529 52 0.000378 0.339595 0.682272 0.222567 53 0.000385 0.339595 0.682284 0.222605 54 0.000393 0.339595 0.682284 0.222642 55 0.0004 0.339595 0.682296 0.22268 56 0.000408 0.339595 0.682296 0.222718 57 0.000415 0.339595 0.682308 0.222755 58 0.000423 0.339595 0.682308 0.222793
59 0.00043 0.339595 0.682319 0.222831 60 0.000437 0.339595 0.682319 0.222868 61 0.000445 0.339595 0.682331 0.222893 62 0.000452 0.339595 0.682331 0.222931 63 0.00046 0.339595 0.682343 0.222969 64 0.000467 0.339595 0.682355 0.222994 65 0.000474 0.339595 0.682355 0.223031 66 0.000482 0.339595 0.682367 0.223069 67 0.000489 0.339595 0.682367 0.223094 68 0.000497 0.339595 0.682379 0.223132 69 0.000504 0.339595 0.682379 0.223157 70 0.000511 0.339595 0.682391 0.223195 71 0.000519 0.339595 0.682391 0.22322 72 0.000526 0.339595 0.682402 0.223257 73 0.000534 0.339595 0.682402 0.223283 74 0.000541 0.339595 0.682414 0.22332 75 0.000549 0.339595 0.682414 0.223345 76 0.000556 0.339595 0.682426 0.223383 77 0.000563 0.339595 0.682426 0.223408 78 0.000571 0.339595 0.682438 0.223433 79 0.000578 0.339595 0.682438 0.223471 80 0.000586 0.339595 0.68245 0.223496 81 0.000593 0.339595 0.68245 0.223521 82 0.0006 0.339595 0.682462 0.223546 83 0.000608 0.339595 0.682473 0.223584 84 0.000615 0.339595 0.682473 0.223609 85 0.000623 0.339595 0.682485 0.223634 86 0.00063 0.339595 0.682485 0.223659 87 0.000637 0.339595 0.682485 0.223697 88 0.000645 0.339595 0.682497 0.223722 89 0.000652 0.339595 0.682497 0.223747 90 0.00066 0.339595 0.682509 0.223772 91 0.000667 0.339595 0.682509 0.223797 92 0.000675 0.339595 0.682521 0.223822 93 0.000682 0.339595 0.682533 0.223848 94 0.000689 0.339595 0.682533 0.223873 95 0.000697 0.339595 0.682545 0.22391 96 0.000704 0.339595 0.682545 0.223936 97 0.000712 0.339595 0.682556 0.223961 98 0.000719 0.339595 0.682556 0.223986 99 0.000726 0.339595 0.682568 0.224011 100 0.000734 0.339595 0.682568 0.224036
Appendix K: Sensitivity analysis results from dataset 1 while using a range of pH values.
pH Cd+2 Ni+2 Pb+2 Concentration [%] Concentration [%] Concentration [%]
4.63 2.08E-06 4.1319E-06 0.002549 4.73 1.62E-06 3.1714E-06 0.002005 4.83 1.26E-06 2.4445E-06 0.001575 4.93 9.90E-07 1.8916E-06 0.001236 5.03 7.79E-07 1.469E-06 0.000968 5.13 6.16E-07 1.1446E-06 0.000758 5.23 4.89E-07 8.9461E-07 0.000592 5.33 3.90E-07 7.012E-07 0.000462 5.43 3.11E-07 5.5106E-07 0.00036 5.53 2.50E-07 4.3415E-07 0.00028 5.63 2.01E-07 3.4282E-07 0.000218 5.73 1.62E-07 2.7128E-07 0.000169 5.83 1.31E-07 2.1508E-07 0.000132 5.93 1.07E-07 1.7081E-07 0.000102
6.03 8.69E-08 1.3586E-07 7.96E-05
6.13 7.10E-08 1.082E-07 6.18E-05
6.23 5.81E-08 8.6261E-08 4.81E-05
6.33 4.77E-08 6.8821E-08 3.74E-05
6.43 3.93E-08 5.4932E-08 2.91E-05
6.53 3.24E-08 4.3853E-08 2.26E-05
6.63 2.67E-08 3.5002E-08 1.76E-05
6.73 2.21E-08 2.7922E-08 1.37E-05
6.83 1.83E-08 2.2254E-08 1.07E-05
6.93 1.52E-08 1.7714E-08 8.32E-06
7.03 1.26E-08 1.4077E-08 6.49E-06
7.13 1.05E-08 1.1163E-08 5.06E-06
7.23 8.68E-09 8.8306E-09 3.94E-06
7.33 7.20E-09 6.9659E-09 3.08E-06
7.43 5.97E-09 5.4775E-09 2.4E-06
7.53 4.93E-09 4.292E-09 1.87E-06
7.63 4.07E-09 3.3504E-09 1.46E-06
7.73 3.35E-09 2.6048E-09 1.14E-06
7.83 2.75E-09 2.0166E-09 8.89E-07
7.93 2.24E-09 1.5544E-09 6.94E-07
8.03 1.83E-09 1.1928E-09 5.41E-07
8.13 1.48E-09 9.1128E-10 4.22E-07
8.23 1.19E-09 6.9309E-10 3.3E-07
8.33 9.51E-10 5.2486E-10 2.57E-07
8.43 7.56E-10 3.9579E-10 2.01E-07
8.53 5.96E-10 2.9726E-10 1.56E-07
8.63 4.67E-10 2.2241E-10 1.22E-07
8.73 3.64E-10 1.6582E-10 9.52E-08
8.83 2.81E-10 1.2323E-10 7.42E-08
8.93 2.16E-10 9.1304E-11 5.79E-08
9.03 1.64E-10 6.747E-11 4.52E-08
9.13 1.24E-10 4.9741E-11 3.52E-08
9.23 9.34E-11 3.6595E-11 2.75E-08
9.43 5.19E-11 1.9714E-11 1.67E-08
9.53 3.84E-11 1.4445E-11 1.3E-08
9.63 2.83E-11 1.0576E-11 1.02E-08
9.73 2.08E-11 7.7402E-12 7.94E-09
9.83 1.52E-11 5.6644E-12 6.21E-09
9.93 1.11E-11 4.1463E-12 4.85E-09
10.03 8.13E-12 3.0368E-12 3.8E-09
10.13 5.93E-12 2.2261E-12 2.98E-09
10.23 4.32E-12 1.6338E-12 2.33E-09
10.33 3.15E-12 1.2008E-12 1.83E-09
10.43 2.29E-12 8.8419E-13 1.44E-09
10.53 1.67E-12 6.5235E-13 1.14E-09
10.63 1.22E-12 4.8245E-13 8.98E-10
Appendix L: Sensitivity analysis results from dataset 2 while using a range of pH values.
pH Cd+2 Ni+2 Pb+2 Concentration [%] Concentration [%] Concentration [%] 4.63 0.652086 0.789409 0.624702 4.73 0.636853 0.784422 0.604937 4.83 0.621675 0.779624 0.585135 4.93 0.60655 0.77504 0.565434 5.03 0.591535 0.770633 0.546008 5.13 0.576737 0.766404 0.526985 5.23 0.562211 0.762353 0.508501 5.33 0.547957 0.758479 0.490658 5.43 0.5341 0.754771 0.473568 5.53 0.520657 0.751217 0.457307 5.63 0.507687 0.747829 0.4419 5.73 0.49524 0.744583 0.427396 5.83 0.483347 0.74148 0.41381 5.93 0.472047 0.738506 0.401102 6.03 0.461351 0.735663 0.389224 6.13 0.451265 0.732938 0.378149 6.23 0.441782 0.730309 0.367777 6.33 0.432893 0.727762 0.35802 6.43 0.424547 0.725274 0.348741 6.53 0.416713 0.722798 0.339825 6.63 0.409325 0.720322 0.331111 6.73 0.402307 0.717799 0.322434 6.83 0.395582 0.715169 0.313607 6.93 0.389054 0.712373 0.304453 7.03 0.382618 0.709353 0.294746 7.13 0.37616 0.706024 0.284312 7.23 0.369566 0.702316 0.272948
7.33 0.362717 0.698146 0.260529 7.43 0.355508 0.693408 0.246917 7.53 0.347827 0.688041 0.2321 7.63 0.339595 0.68194 0.216115 7.73 0.330755 0.67507 0.199113 7.83 0.321283 0.667358 0.18132 7.93 0.311185 0.658769 0.16305 8.03 0.300501 0.649316 0.144692 8.13 0.28931 0.638998 0.126623 8.23 0.2777 0.627791 0.109225 8.33 0.265785 0.61572 0.092843 8.43 0.25367 0.602713 0.077744 8.53 0.241445 0.588675 0.064123 8.63 0.229171 0.57337 0.052089 8.73 0.216865 0.55643 0.041676 8.83 0.204472 0.53725 0.032844 8.93 0.191839 0.514944 0.025497 9.03 0.178733 0.488349 0.019498 9.13 0.164801 0.456104 0.014688 9.23 0.149665 0.41694 0.010898 9.33 0.133045 0.370278 0.007963 9.43 0.114928 0.316993 0.005726 9.53 0.095816 0.259883 0.004052 9.63 0.076704 0.203305 0.00282 9.73 0.058881 0.151845 0.00193 9.83 0.043452 0.10877 0.0013 9.93 0.03101 0.075238 0.000861 10.03 0.021561 0.05063 0.000562 10.13 0.014715 0.033363 0.000361 10.23 0.009922 0.021636 0.000229 10.33 0.006645 0.013857 0.000143 10.43 0.00444 0.008779 8.84E-05 10.53 0.00297 0.005507 5.38E-05 10.63 0.001994 0.003418 3.23E-05
