Bioconcentration of Organotin Cations during Molting Inhibits Heterocypris incongruens Growth

Abstract

The densely populated North Sea region encompasses catchments of rivers such as Scheldt and Meuse. Herein, agricultural, industrial, and household chemicals are emitted, transported by water, and deposited in sediments, posing ecological risks. Though sediment monitoring is often costly and time-intensive, modeling its toxicity to biota has received little attention. Due to high complexity of interacting variables that induce overall toxicity, monitoring data only sporadically validates current models. Via a range of concepts, we related bio-physicochemical constituents of sediment in Flanders to results from toxicity bioassays performed on the ostracod Heterocypris incongruens. Depending on the water body, we explain up to 90% of the variance in H. incongruens growth. Though variable across Flanders’ main water bodies, organotin cations and ammonia dominate the observed toxicity according to toxic unit (TU) assessments. Approximately 10% relates to testing conditions/setups, species variabilities, incoherently documented pollutant concentrations, and/or bio-physicochemical sediment properties. We elucidated the influence of organotin cations and ammonia relative to other metal(oxides) and biocides. Surprisingly, the tributylin cation appeared ∼1000 times more toxic to H. incongruens as compared to “single-substance” bioassays for similar species. We inferred indirect mixture effects between organotin, ammonia, and phosphate. Via chemical speciation calculations, we observed strong physicochemical and biological interactions between phosphate and organotin cations. These interactions enhance bioconcentration and explain the elevated toxicity of organotin cations. Our study aids water managers and policy makers to interpret monitoring data on a mechanistic basis. As sampled sediments differ, future modeling requires more emphasis on characterizing and parametrizing the interactions between bioassay constituents. We envision that this will aid in bridging the gap between testing in the laboratory and field observations.

1. Introduction

Despite decreasing emissions,^1,2 persistent chemicals can remain in the aquatic ecosystem for decades (legacy pollutants)³ because they bind physicochemically to sediment.⁴ Changing bio-physicochemical conditions can render pollutants bioavailable again to let them re-initiate adverse effects on aquatic organisms.^5,6 Thus, sediment “quality” is crucial to the health of an aquatic ecosystem.^7,8

As a combination of species from different trophic levels and effect endpoints characterizes an ecosystem’s resilience,⁹⁻¹¹ bioassays/test batteries for daphnia, algae, midge larvae, or bacteria^12,13 are common to establish a weight of evidence approaches for sediment quality. Toxic effects vary not only between organisms and life stages^14,15 but also due to differences in exposure regimes and sediment properties: one can use the solution as extracted from sediment¹⁶ to evaluate “dissolved” contaminants or exposure to whole sediment to elucidate more integrated effects.

A variety of chemometric methods attempt to explain the observed effects¹⁷⁻¹⁹ and have shown that toxicity often does not relate to dry weight pollutant concentration.²⁰ Establishing dose–effect relationships with data from field studies is more difficult than that from spiking with single chemicals. Testing may not agree with field observations at all.^21,22 Many interactions between sediment constituents can attenuate or amplify toxic effects, more so than for surface water, which hampers selection of a proper dose metric.²³ Relations between bioassays and physicochemical constituents of sediment in the field are scarce, and quantitative predictions based thereon are elusive.

Among recent progress,²⁴⁻²⁷ insight into the extent of additional available binding capacity via sediment properties allowed correct identification of toxicity.²⁸ Both the apparent bioavailability and toxicity relate to chemical speciation. Speciation is the quantitative distribution of all chemical forms of a compound over all possible phases, as a function of bio-physicochemical properties of the endemic solution: pH, hardness, redox conditions, and natural catalysts (clays, microorganisms). These factors vary geographically.^29,30

The North Sea region has a documented history in pollution linked to industrial (heavy metals, acidification³¹) and agricultural (e.g., eutrophication) activities.³²⁻³⁴ An extensive measurement campaign by the Flemish Environmental Agency (VMM)³² has existed in Flanders for approximately 20 years, gathering information on pollutants in 1000+ sampling locations (e.g., metals, polycyclic hydrocarbons), in conjunction with characteristics known to affect speciation/bioavailability. Built hereon, models linked >60% of the observed variance in Hyalella azteca mortality bioassays to heavy metals and NH₃/NH₄⁺.³⁵

Given the amount of pollutants and sediments to be evaluated and monitored, bioassay(s) should entail limited redundancy so as to capture the bulk of the potential effects with minimum effort and cost.³⁶ Given their sensitivities, Chlorella vulgaris and Ceriodaphnia dubia may be suitable to evaluate metals,³⁷ whereas Daphnia magna and Raphidocelis subcapitata may be more applicable to metallic/metal oxide nanomaterials.³⁸ For Flanders, the question would be: which causal relationships between constituents of polluted sediment and ecotoxicological effects allow us to prioritize or complement bioassays?

Ostracod microcrustaceans are keystone meiofauna species in production and community metabolism of micro- or mesocosm freshwater beds.³⁹ Present in diverse freshwater benthic habitats (ponds, lakes) in all continents, ostracods prove to be bioindicators when other species fall short.⁴⁰ Many campaigns entail tests with Heterocypris incongruens (seed shrimp).⁴¹⁻⁴⁴H. incongruens is substantially more sensitive than midge larvae⁴⁵ and comparably sensitive (to heavy metals, NH₄⁺) with H. azteca,⁴¹ but H. incongruens growth is particularly sensitive.³⁹ As Flanders is an urbanized coastal (maritime) and agricultural region, concentrations of organotins (antifouling endocrine disruptors)⁴⁶⁻⁴⁸ and phosphate^49,50 could be elevated and particularly relevant for growth.

Given the potentially higher sensitivity of H. incongruens growth to sediments in Flanders and its dissimilarity with other bioassays, we set out to build a quantitative model for predicting ostracod growth inhibition. Using extensive monitoring data in Flanders, we accounted for bioavailability and chemical speciation to develop a predictive model based on toxic unit (TU)⁵¹ calculations linking concentration to effect. We predict ostracod growth inhibition with appreciable certainty, ∼90%. Our method underpins that ostracods complement other bioindicators and supplements the existing suite of models.

2. Materials and Methods

In the current study, we quantitatively predict the growth inhibition of H. incongruens as would be observed when exposed to sediment. As an empirical exercise, we utilized the results from bioassays as obtained from our Flemish VMM monitoring campaign. Bioassays are detailed in Section 2.6 and elsewhere.⁴⁵

Input variables for our predictions were dry weight concentrations of various chemicals. Following a recent study,³⁵ we considered heavy metals, nitrogen, and phosphorus. Given exposure history, we also hypothesized local toxic effects by organotins. As variables, we also used common sediment characteristics: pH, organic carbon, clay, and the nitrogen status. Physicochemical sampling of sediments and analytical methods are detailed in the Supporting Information (Section S0) and elsewhere.^13,32 We describe the parametrization below (Sections 2.1–2.5).

2.1. Estimation of Ammonia and Phosphate in Sediment

Data on aqueous concentrations of NH₄⁺ and are either scattered, unrepresentative, or their uncertainties are fairly high.^13,32,35 This is partially due to their high mobilities. Therefore, we estimate and from the total phosphorus, P_T

1

with f being a ratio applied to total P to distinguish for available fractions: a typical natural water body (P_T ∼23 or ∼30 mg/m³) constitutes 3 mg/m³ soluble + and usual ( + )/P_T ratios of 1:10. Hence, f_P=0.1.⁵²

In turn, we estimate NH₄⁺ and NH₃ concentrations from the Kjeldahl nitrogen (KjN) and eq 2

2

wherein f_N is taken as the fraction of the KjN that is [NH₄⁺] + [NH₃], taken to be 0.5(±0.1).^53,54 Exclusive for the data in West Flanders (s = 1, else s = 0), we applied a sinoid term with maximal amplitude (peak) in mid-march (d = 65)^55,56 to account for local seasonal (agricultural) fluctuations^57,58 (S1). If the data adhering to negative growth inhibition (Section 2.6) did not entail KjN determination, we applied the log-average of samples analyzed throughout Flanders. To prevent bias, we excluded those sediments that we already selected from curation (Section 2.6).

2.2. Bioavailability

Toxicity is most often associated with the fraction of the chemical that is freely bioavailable. Though test vessels contained sediment,^13,59 we had no information on burrowing behavior. We assumed that all ostracods are exposed to chemicals in the water phase. From our campaign, we obtained only the total sediment concentrations. Since those sediments are applied to the (aqueous) bioassay, we estimate in situ water concentrations from the total in sediment.

We converted sediment (sed) concentrations to water (aq) concentrations via phase partitioning^60,61

3

wherein 0.2 represents the sediment to test volume ratio (1:5⁴⁵). Considering that the test medium is prescribed as moderately hard,¹³ we took the value for the solids-to-water partitioning coefficient K_SW, local for NH₄⁺/NH₃ and H₂PO₄^–/HPO₄^2– to be 6.0(±3.0)⁶²⁻⁶⁵ and 100 L/kg,^66,67 respectively, for field concentrations⁶⁴⁻⁶⁶ representable for Flanders^13,32,35 and pH = 7.4.

The binding of metals is stronger in sediments with increasing clay and organic carbon (OC),^68,69 creating a natural “bias”. Thus, we calculated K_SW,local based on %clay and %OC content, analogous to Bruijn and Denneman⁷⁰

4

wherein we obtained the solid-water partition coefficient (K_SW) from the literature,^60,71,72 representing a mix of speciation states. K_SW values were assumed to represent 11% clay and 5% OS.^13,68a and b are the concentrations (mg/kg dissolved substances) of metals per %clay and %OS, respectively, based on (log–log) multiple linear regression (MLR) for the entire VMM (Flanders) data set (N = 1762):

5

Considering their hydrophobicity, we estimated K_SW,local for organotins from their organic carbon partition coefficients (K_OC) and the %OC content (varying 10 ≳ %OC ≳ 0.1^13,32):

6

Sediment and biota affect the in situ ionic composition of the medium,^73,74 though the influence of salinity^75,76 on organotin sorption is conflicting (salting out vs. cation exchange). We also lacked information on the relevant OC type.⁷⁵ Hence, we did not consider influences of salinity and pH on K_OC. Instead, we applied log-normal averages for K_OC values reported for fresh and marine sediments (Table 1).

Table 1. Sorption Equilibrium Partitioning Coefficients.

chemical	log KOC75	log KSW	ref
tetrabutyltin	5.5(±0.1)	4.2(±0.1)a	(75)
tributyltin	5.3(±0.1)	4.0(±0.1)a	(75)
dibutyltin	5.2(±0.2)	3.9(±0.2)a	(75)
monobutyltin	5.0(±0.2)	3.7(±0.2)a	(75)
triphenyltin	4.9(±0.1)	3.6(±0.2)a	(75)
diphenyltin	4.7(±0.2)	3.4(±0.2)a	(75)
monophenyltin	4.4(±0.3)	3.1(±0.2)a	(75)
NH4+/NH3	n/a	0.8b	(62, 65)
H2PO4–/HPO42–	n/a	2.0(±0.5)b	(66)
Cd	n/a	2.1a	(60)
Cr	n/a	2.5a	(60)
Cu	n/a	1.7a	(60)
Hg	n/a	2.2a	(60)
Ni	n/a	0.9a	(60)
Pb	n/a	2.8a	(60)
Zn	n/a	2.0a	(60)

^aTaken to represent sediments in Flanders with 5% OC and 11% and clay.

^bTaken for all sediments in Flanders.

2.3. Speciation

Toxicity is often the result of a specific speciation state of a chemical; heavy metals, organotins, phosphate, and ammonia are subject to adopt different speciation. Thus, we describe explicitly the influence of the (bio)chemical properties of the endemic solution.

Phosphate may act both as a toxicant⁴⁹ and a nutrient,⁵⁰ whereas organotins and ammonium are toxicants. Prominently, the fraction of nonionized ammonium often relates to observed toxicity.⁷⁷ The species (de)protonate according to

7

8

9

We thus determine their speciation via Henderson–Hasselbalch⁷⁸

10

11

wherein pK_a(XH^n + 1 = NH₄⁺) = 9.25 and pK_a(XH^n + 1 = ) = 7.2. We took the pK_a values for organotins from the literature (Table 2).

Table 2. Proton Dissociation Constant (pK_a) Values^a.

name	formula	pKa	ref
tetrabutyltinhydroxide	Sn(By)4	n/a	n/a
tributyltinhydroxide cation	(Sn(OH2))+(By)3	6.25	(79−81)
dibutyltinhydroxide cation	(Sn(OH2)1)+(OH)1(By)2	5.1(±0.2)	(82)
monobutyltinhydroxide cation	(Sn(OH2)1)+(OH)2(By)1	5.9(±0.1)	(82)
triphenyltinhydroxide cation	Sn(OH2)+(Ph)3	5.2	(79, 81, 83)
diphenyltinhydroxide cation	(Sn(OH2)1)+(OH)1(Ph)2	4.0	(84)b
monophenyltinhydroxide cation	(Sn(OH2)1)+(OH)2(Ph)1	4.8	**c
dihydrogenphosphate	H2PO4–	7.2	n/a
ammonia	NH4+	9.25	n/a

^aSee Table S2 for full details. By = butyl; Ph = phenyl.

^bIn 75% dioxane–water solution. pK_a values of ligands in 75% dioxane–water solutions are higher than those reported in water.⁸⁴

^cDouble asterisks mean an estimation assuming the substitution of butyl by phenyl has a constant effect on pK_a: pK_a MPT = pK_a MBT – ((pK_a TBT – pK_a TPT) + ( pK_a DBT – pK_a DPT)/2).

Cr toxicity relates to the presence of CrO₄^2–85,86 via microbial (nitrification,⁸⁷⁻⁸⁹ eutrophication³³) and redox processes:⁹⁰ [CrO₄^2–] correlates with [NO₃^–] and pH⁹¹ due to (bio)chemical reactions

12

13

Because K_SW(Cr³⁺) ≫ K_SW(), the total sediment Cr concentration is dominated by [Cr³⁺], i.e.,

Thus, based on eqs 12 and 13, we characterized the ratio between and Cr³⁺ empirically via³⁵

14

wherein we take on average to be 4.13% of total Cr⁹¹ and c, d, e, and f are regression coefficients³⁵ (Figure S4). Since K_SW of Cr^VI (<1–50 L/kg) ≫ Cr^lll (850–5600 L/kg),⁹² ∼90% of aqueous Cr is .

2.4. Median Effect Concentrations (EC50)

With the exception of Cr and Sn, we assumed that the total concentration of metals is equal to a single free valent (uncomplexed) M²⁺ oxidation state, as the sole contributor to the observed toxicity. We assume that Cr expresses toxicity only via the CrO₄^2– form (Table 3).

Table 3. H. incongruens Effect Level Concentrations of Chemicals from the Open Literature^a.

chemical	effect (EC50) concentration (ng/L)	effect (EC50) concentration (ng/L cation)b	ref
tetrabutyltin	50	n/a	Table S3
tributyltinhydroxide	50	3.5	Table S3
dibutyltinhydroxide	50	0.3	Table S3
monobutyltinhydroxide	50	1.6	Table S3
triphenyltinhydroxide	50	0.3	Table S3
diphenyltinhydroxide	50	0.02	Table S3
monophenyltinhydroxide	50	0.1	Table S3
Cu2+	950,000	n/a	(95)
Ni2+	2500,000	n/a	(95)
Hg2+	400,000	n/a	(95)
Zn2+	14,775,880	n/a	(95)
Cd2+	70,000	n/a	(95)
CrO42–c	4,310,000	n/a	(95)
Pb2+	39,200,000	n/a	(95)
NH3	2000	140,000d	(96, 97)
HPO42–/H2PO4–	3,905,460,000	2,000,000,000e	(49)

^aThe values for organotins are the means from hatching, developmental, growth, and mortality experiments with varying durations (4 to 28 days); details are shown in Table S3.

^bAssuming pH = 7.4.

^cSince dichromate hydrolyzes in water (Cr₂O₇^2– + H₂O ⇌ 2 CrO₄^2– + 2H⁺), we multiplied EC50 by 2.

^dRepresents NH_3.

^eRepresents HPO₄^2–.

In contrast to uncomplexed heavy metals (Table 3), to our knowledge, there are no 6 day EC50 values available for H. incongruens growth when exposed to organotins, NH₃, or H₂PO₄^–/HPO₄^2–. Since we assumed all H. incongruens to reside in the water layer, we screened the literature for aqueous phase EC50 values for related species/studies. Though tributylated organotin is generally more toxic than other organotins,^93,94 to our knowledge, there is no information on the (relative) H. incongruens toxicity due to substituents.

Based on Table S3, we initially estimated (based on log-averages) the EC50 of (neutral) organotins, NH₃, and HPO₄^2– for H. incongruens growth to be 50, 2000, and 2,000,000,000 ng/L (Table 3), respectively.

2.5. Toxic Unit (TU)

2.5.1. Initial Approximation

The TU estimates mixture-toxicity risks to both terrestrial and aquatic communities.⁸ We assessed the total exposure from the sediment by summing the exposures for all of the contaminants (eq 15)⁹⁸

15

wherein n reflects the number of pollutants i, taken as heavy metals, organotins, NH₃, and H₂PO₄^–/HPO₄^2–.

For the current purpose, it was not feasible to parametrize a priori the toxic interaction between chemicals based on their EC50 values only (i.e., without SSD shape parameters).^51,99 Taking γ_i = 1 reflects all chemicals to have a similar mode of action (MoA) and negligible interaction between chemicals. As the simple response addition (RA) method does not necessarily produce better results than concentration addition (CA),³⁵eq 15 produces an “educated guess” for the TU of H. incongruens.

2.5.2. Calibration

Assuming a logistic distribution of sensitivities toward the polluted sediment, we described the percentage of growth inhibited as a function of exposure via a cumulative logistic distribution function⁹⁸

16

wherein β is a steepness function and TU is initially the ratio of the measured (bioavailable) chemical concentration [C]_aq to its effect concentration [EC50]_aq.¹⁰⁰ Fitting mortalities with these TU values (Section 2.5.1) produced suboptimal results (Figure 3A; Figures S5, S7).

Figure 3.

H. incongruens growth inhibition (%) versus the toxic unit. GI = growth inhibition. (A) EC50 for organotins (cations) represented 3 ng/L; pH assumed to be 7.5. R² = 0.26. (B) EC50 for organotins (cations) represented 0.003 ng/L (eq 17); pH assumed to be 7.5. R² = 0.82. (C) EC50 for organotins (cations) represented 0.003 ng/L(eq 17); pH taken for the porewater. R² = 0.90.

To acquire statistically better predictions based on the explained variance, we allowed variation in EC50 values to fit the growth inhibition data (Section 2.6) via eq 16. This was done via the factor γ_i in eq 15. The preselected range for γ_i was 1000. The fitting implies the average TU fixed at the exposure for which 50% of the growth is inhibited. We envisioned potential divergences between values for γ_iEC50 and EC50 to be the result of unknown testing procedures and conditions altering speciation, bioavailability (e.g., metals as oxides or organometals), or MoAs. Final γ_iEC50 values are given in Figure 2.

Figure 2.

H. incongruens growth inhibition (%) versus the toxic unit. EC50 for organotin cations was taken to be 0.003 ng/L, representing By₃SnOH₂⁺. Simulation was performed using β = 4.5 (unitless). The far-left triangle denotes the weighted average of values with log TU < −1. Squares denote data wherefore KjN was unknown and taken as a regional average (Section 2.1). The table provides corresponding uncertainties/ranges. For EC50 values, the errors are those carried over from errors in bioavailabilities.

2.6. Bioassay Description and Data Curation

We calibrate and test our methods (eqs 1–16) using data on whole-sediment toxicity assessed by VMM via laboratory bioassays performed on the ostracod H. incongruens, a small benthic freshwater crustacean. The data represents a 6 day growth inhibition test (ISO 14371¹⁰¹) with freshly (≤52 h old) hatched cysts (i.e., neonates, with lengths of 150–250 μm) incubated at 25(±1) °C, in darkness. Details are shown elsewhere.^45,102 The percentage % of growth inhibition μ because of exposure to sediment is defined⁴⁵ as

17

where the lengths are the averages of a total of 60 organisms distributed across six replicates. The reference refers to a standard sand. For the reference, length_{day6, reference} ≥ 1.5·length_day0 and mortality is ≤20%. To enlarge our modeling domain, we specifically included also bioassays reporting negative growth inhibition (i.e., growth stimulation).

Since the bioassay is aerobic, it should represent aerobic sediments in the field. This implies minimizing speciation-related changes due to in situ anaerobic–aerobic transformations. Also, previous O₂ deprivation in the field may create bias for germination/growth (chemical cues) at the start of the bioassay: crustaceans are sensitive to O₂ deprivation;^103,104 low O₂ induces dormancy (HOEC = 5.5 mg O₂/L).¹⁰⁴ Taking 44 ± 4 mg/L and 0.21 as solubility (T ≈ 20 °C) and partial pressure, respectively, this amounts to 60 ± 5% O₂. H. incongruens does not tolerate acidic conditions.³⁹

Therefore, we exclude all assays for which the field porewater O₂ < 65%, O₂ ≥ 100%, and pH ≤ 6.5, which we consider nonrepresentable sediments.

3. Results

We performed analyses to elucidate the toxicity of sediments in Flanders to H. incongruens. According to initial TU results (Figures S5, S7), NH₃ (TU = 0.1) contributed most to H. incongruens growth inhibition. Other contributors were Ni and Cu (TUs ≤ 0.1), whereas the individual TUs of Zn, Cd, CrO₄, Hg, and Pb were ≤0.01. Predicting the % growth inhibition via initial TU using initial (standard) EC50 values (Table 3, Section 2.5.1) yielded maximal explained variance lower than 30%. Initially, speciation calculations (eqs 11, 14) did not appear to improve these results. In contrast to Figure S7 (wherein there is a low contribution), organotins are among the species most highly correlating with H. incongruens % growth inhibition (Figure S9).

We then performed “semiempirical” analyses to further elucidate the toxicity of sediments by letting γ_iEC50 values fit the growth inhibition data (Section 2.5.2). This “calibrating” procedure allowed a robust TU analysis for H. incongruens. According to the semiempirical analysis, too, NH₃ dominates the TU (TU = 0.3). Additional toxic pressure arises from organotins (as represented by tributyltin, TU = 0.3). Then, the contributions from Ni and Cu appear minor (≤0.1) (Figure 1). Inclusion of chemicals other than NH₃, HPO₄^2–, and By₃SnOH₂ only marginally improved the fit. Instead, we found a positive influence by HPO₄^2–, i.e., growth-promoting (TU = −0.3).

Figure 1.

Toxic units for H. incongruens ([C]_aq/EC50_aq) for individual pollutants in sediments from water bodies in Flanders. Table 3 denotes EC50_aq values for free metals; Figure 2 denotes EC50 values for NH₃, By₃SnOH₂⁺, and H₂PO₄/HPO₄, contrasting ranges in Table 3. Hence, the TUs may be both underestimations/overestimations. Error bars denote variability throughout Flanders. For MFT (PhSn(OH)₂OH₂⁺), assuming porewater (pH = 7.5), 99% of samples were below the detection limit (DL); negative errors are 10-fold the SD including the values set at the DL.

After calibration, we found that speciation calculations (eq 11) significantly improved the results (Figure 3B,C). Then, NH₃, organotins, and HPO₄^2– explain up to 90% of the observed variance in the % growth inhibition for water bodies throughout Flanders. We established γ_iEC50 values for NH₃, HPO₄^2–, and TBT cation (By₃SnOH₂⁺) at 150,000–39,000 and 0.003 ng/L. The relative importance of the toxicants differs between water bodies. In general, there is a higher toxic pressure in West Flanders than in inland regions (Table S4). In general, predictions for H. incongruens growth inhibition are more precise for “uniform” (e.g., large rivers and canals) water bodies than for small “nonuniform” water bodies.

4. Discussion

For sediments in Flanders, we report a maximum growth of H. incongruens of 56% (Supporting Information). The curated data depict a leveling (for low toxicant concentrations) between 0 and −40% (Figure 2). In comparison, H. incongruens grows in unpolluted soft and hard water (pH = 7) with 0.063(±0.003) and 0.072(±0.003) mm/day, respectively.¹⁰⁵ Assuming linear growth, this amounts to 84(±42)% in 6 days, similar to sediments in Flanders with “low” toxicant concentrations. This indicates toxic pressure to H. incongruens in the majority of bioassays on sediments from Flanders.

We explain up to 90% of the variance in H. incongruens growth. The standard error of model prediction (Figure 2) is lower than that of a similar model for H. azteca.³⁵ This is likely related to a better defined testing system and lower physical or metabolic complexity of the organism.^13,45 Predictions appear more precise for neonate growth than for survival (Figures S9, S13), similar to earlier observations.⁹⁶ A coefficient of variation of 6–20% was reported using parallel mortality assays.¹⁰⁶ Thus, the contribution of other toxicants, erroneous speciation/bioavailability, or mixture effects to the residual variance (∼10%, Figure 2) is limited. 10-fold intraspecies uncertainty factors are widely used;¹⁰⁷ our prediction errors are well within this margin.

The average toxic pressure for H. incongruens is comparable to that for H. azteca.³⁵ Both initial and calibrated TU analyses support our hypotheses that NH₃, along with heavy metals, dominates the effects on H. incongruens. Meanwhile, in contrast to H. azteca,³⁵ CrO₄^2– did not appear to contribute to the apparent toxicity. The analysis also shows that the mixture of organotins, NH₃, and HPO₄^2– acts distinctly toward H. incongruens. We interpreted this partially as a bioconcentration effect, which we will explain stepwise below. From its distinct sensitivity and the 90% explained variance, we deem the H. incongruens sediment contact test to be a complementary bioassay to evaluate sediment and water quality.

4.1. Organotins

Ni and Cu seemed to only marginally inhibit growth (TUs ≤ 0.1), though heavy metal loading as low as 100 μg/kg affecting aquatic species is generally accepted.¹⁰⁸ Organotins, as characterized by TBT, appear significantly more toxic to H. incongruens than to similar aquatic species in single-substance tests (Figure 2, Table S3). This is on average a factor of 1000, irrespective of speciation. In other words, the term γ_TBT in eq 15 is ∼1 × 10^–3. Metals react with receptors, enzymes, DNA, protein and lipids or produce structural and functional changes resulting in toxic damage. The toxicant can be a derivative of the parent metal or reactive species (ROS or RNS) from (bio)transformations.

The correlation between TBT cation concentrations and growth inhibition is stronger than for the neutral TBT (Figure 3B,C; Figure S10). Since H. incongruens is small, the internal (e.g., cytoplasmic) pH may be correlated to medium/porewater pH.^109,110 This indicates that growth inhibition is associated with the cation. In contrast, organotin toxicity toward yeast increases with cations < hydroxides < chlorides.¹¹¹ From pH 5.8 to 8.0, the K_OW of TBT and TPT increased.^112,113 As it partitions into the lipid fraction of the biomembrane, the neutral (hydrophobic) form is more bioavailable:¹¹⁴ uptake and bioconcentration of TBT are higher at pH 8.0 (where neutral TBTOH predominates) than at pH 5.0–6.0.¹¹⁵

However, biomembrane–water distribution of the TBT cation exceeds liposome–water distribution, and distribution of the TBT cation exceeds that of the neutral hydroxo-complex. This relates to the cation complexing with ligands and proteins.¹¹⁴ This indicates that the high sensitivity of H. incongruens growth on organotin as compared to other aquatic species (Figure 2; Table S2) does not arise from nonpolar (baseline) narcosis.¹¹⁶ In turn, this implies interactions between the TBT cation and proteins with growth regulation activity.¹¹⁷

Endocrine activity of TBT in crustaceans refers to the interactions with the ecdysteroid receptor–retinoid-X receptor dimer (CrcEcR-CrcRXR) complex:^48,117,118 TBT binds in the electrostatic and neutral areas of CrcRXR.¹¹⁷ The ecdysone (nuclear) receptor is responsible for transcriptional regulation of molting,¹¹⁹ which is critical for growth of crustaceans accumulating (i.e., “feeding on”) phosphate and carbonate. Interaction with vitellogenin and cuticular proteins may cause a second endocrine disruption on the Y-organ–ecdysteroidal endocrine axis and impair growth.¹¹⁷

4.2. Phosphate

In contrast to toxicity (γ_PO4 > 0), phosphate appears to promote H. incongruens growth (γ_PO4 < 0) (eq 15). The apparent effect level is ∼0.04 mg/L, far lower than levels causing toxicity (∼2000 mg/L⁴⁹). Given the aqueous concentration of phosphate in the bioassays, 0.02–2 mg/L, and an LC50 value of approximately 3900 mg/L,⁴⁹ toxicity of HPO₄^2– to H. incongruens is negligible. H. incongruens is one of the only two ostracods wherefore Ca²⁺ positively correlates with its natural abundance; Ca²⁺ associates with phosphate.¹²⁰ P content of the carapace differs geographically.³⁹

Prior to molting, ostracods store CaHPO₄ (apatite) granules and chitin precursors in epidermal cells. The granules expulse their content, which is transformed into platelets of CaCO₃, disintegrated to amorphous calcite and, ultimately, crystals to build the carapace.¹²¹ Note that HCO₃^– is not a limiting factor (relatively constant) since it is added via the synthetic medium.⁴⁵ The carapace may need ∼7 days to complete its calcification, via up to nine growth stages (instars).⁵⁰

We find a higher correlation for HPO₄^2– than for H₂PO₄^– (Figure 3B,C; Figure S11). Indeed, an alkaline environment (high pH) accelerates tissue calcification.^122,123 Moreover, calcium phosphate crystallinity decreases with an increase in reaction pH,^123,124 and solubilities decrease with increasing pH.¹²⁵ The combined results indicate that phosphate in bioassays performed with sediments from Flanders, in the HPO₄^2– form, promotes H. incongruens growth.

4.3. Ammonia

Whereas we initially assigned an EC50 value for NH₃ of 0.002 mg/L, we found an empirical value for γ_NH3EC50 of 0.15 mg/L (implying γ_NH3 > 1) (eq 15). In analogy, the LC50 value for H. azteca exposed to NH₃ is 2–5 mg/L,^78,126 whereas in bioassays, it appears empirically to be 5000 mg/L.³⁵ The average TU of NH₄⁺/NH₃ is 0.3 across Flanders, and NH₃ explains 30% of the variance in growth inhibition. We noted a relatively high toxicity of sediments from West Flanders (e.g., Yser river) collected during (fertilization⁵⁶ in) spring; levels of NH₄⁺/KjN in the Yser are structurally high (Table S4); anaerobic conditions are more likely for those sediments.

We found a slightly higher correlation for NH₃ than for NH₄⁺ (Figure 3B,C; Figure S12). Aquatic ecotoxicity studies in the 1990s identified NH₄⁺/NH₃ (esp. NH₃) as a contributing cause.¹²⁷ More so than NH₄⁺, NH₃ can pass through biological surfaces into tissue fluid. When pH of the tissue fluid is lower than that of the surrounding water, NH₃ can disrupt ionoregulatory functions.^49,128 In addition, NH₃ may activate N-methyl-d-aspartate receptors in the brain,¹²⁹ leading to intoxication.

4.4. Mixture Effects

The γ_iEC50_i values for HPO₄^2–, NH₃, and organotin used for the analysis differed from the initial estimation (Table 3; Figure 2). With respect to literature EC50 values, much variation exists (Table S3); the values obtained represent extreme values. Thus, the extreme γ_iEC50_i values may not only represent a direct mode of toxic action from HPO₄^2–, NH₃, and organotin individually but also suggest toxicity as the result of interaction(s) between them, i.e., γ_i ≠ 1 (see eq 15).

There are many documented interactions between P, N, and metals.¹³⁰ However, models (e.g., the TU, eq 15) often describe toxicity additively or independently,⁹⁸ i.e., not including synergistic/antagonistic mixture effects.¹³¹ Mixture toxicity entails detailed description of the interactions between sediment fractions and chemicals: depending on numerous conditions, metals form organic complexes or oxides, each with its own characteristic influence on the intoxication pathway.

4.4.1. Phosphorus–Tin Interaction

Though phosphorus can be toxic,¹³² it can diminish metal toxicity via precipitation.¹³³ Phosphate is a polydentate ligand forming negatively charged complexes with triorganotins mono- and dimers;^73,134 organotin is 10²–10⁵ times more selective toward phosphate than toward other oxoanions¹³⁵ and aids in its extraction.¹³⁶ In fact, extraction of organotins from sediment co-extracts phosphorus compounds.¹³⁷ Plants absorb phosphate; ostracods feed on dead/living plants. Thus, the influence of sediment’s organic phosphate on the distribution of TBT in ostracods may relate to dietary assimilation via periphyton.^120,130 Acute toxicity of TPT on ostracods may be supplemented by elimination of food.¹³⁸

Prominently, H. incongruens accumulates HPO₄^2–, hence TBT, via molting (Section 4.2). Additionally, the TPT⁺ cation sorbs onto liposomes via complex formation with phospholipids.¹¹⁴ Guanosine-5-monophosphate (G-5-MP) most efficiently forms a competitive organotin complex;¹³⁹ as an essential enzyme for cell growth, G-5-MP kinase converts GMP to GDP. Combined, the interactions with phosphate compounds indicate an effective bioconcentration larger than based on the thermodynamic (hydrophobic) maximum ((1) in Figure 4). Reported BCFs for species (e.g., bivalves) capable of accumulating phosphate are ∼1000 times larger than those without the capability.^81,140 This value matches with 1/γ_TBT, insinuating a similar TMoA:

18

Figure 4.

Simplified schematic of interactions between sediment constituents affecting bioassay toxicity. Green and red arrows denote positive and negative effects on H. incongruens growth, respectively. According to de Deckere et al.,¹³ O₂ ought to remain ≥60% during bioassays.

4.4.2. Nitrogen–Tin Interaction

Since the bioassays are aerobic,^45,102 NH₃/NH₄ may be converted to NO₂^–/NO₃^– (Figure 4), which would be enhanced by bioturbation.⁷⁴ Conversion to NO₃^–35 could contribute to the high apparent γEC50 value of NH₃ (Figure 2). Though affecting ostracods,^141−143 NO₃^– is likely not directly toxic.^35,144 Instead, metal-nitrates are well-soluble, hence bioavailable.¹⁴⁵ Heavy metals may influence nitrification.^88,146 In turn, nitrification and volatilization (NH₄⁺ → NH₃(↑) + H⁺) affect pH. Changes in pH in sediment/ecotoxicity tests are reported;^35,147 we cannot assume that buffering was consistent.

Surplus H⁺ increases the cation concentration and interactions of the TBT metal-type behavior by complex formation with ligands in phospholipids and proteins,^48,148 illustrated by (2) in Figure 4. NO₃^– and SO₄^2– (also in the EPA medium) may solubilize organotins. In addition, the relative concentrations of NH₄ and metals may influence uncoupling of mitochondrial oxidative phosphorylation.¹⁴⁹

4.5. Limitations, Recommendations, and Outlook

Sorption to varying organic carbon did not appear to strengthen correlations (eq 6). Apart from those considered here, other sediment constituents (e.g., Al, Mg, Ca, S) affect speciation and bioavailability.^30,150 Prominently, acid-volatile sulfide (AVS)^20,69 relates to invertebrate toxicity.^85,86,151 We can determine bioavailability more comprehensively by distinguishing sediment fractions (e.g., humic acid⁷⁵) and binding mechanisms,¹⁵² pending available data.^75,153Section S3 contains a more complete discussion of limitations. Despite increased monitoring,¹³ it is laborious to measure all such relevant parameters, which depend on the sediment/organism. Even if we have such direct detailed information, it is difficult to describe all interactions explicitly.^154−156 We therefore ought to consider also (semi)empirical- and evidence-based approaches.

Based on the current study, we argue for an approach based on (bio)chemical interactions between NH₄⁺, organotins, and HPO₄^2–. Incomplete monitoring data for N or P might obscure or underestimate ostracod toxicities.³⁵ Since phosphate was not present in reference sediments, this may obscure the toxic effect on the ostracods. Thus, we propose to standardize total phosphorus (i.e., phosphate) in the sediment bioassays. Prediction of ostracod growth is probably more straightforward than that of mortality, which relates to, e.g., complexity of the toxic mode of action. Due to the higher sensitivity of juveniles, we recommend using the neonate test or making age-based extrapolations. BCF tests should elucidate the validity of eq 18.

Pollutants are not always (directly) measured, which would be important for remote (small) water bodies where an “exotic” chemical may skew local toxicity. More direct data for NH₄/PO₄ may help to solidify the relationships.^35,157,158 Large, uniform water bodies are less susceptible to pressures from point releases of chemicals (dilution). Higher temperature and drought (concentrating pollutants) may influence (bio)transformation, O₂ uptake, and elevate toxicity.¹⁵⁹ This determines parametrization, representability, and implementability of models. We may optimize via identifying “hotspot” locations, e.g., bridges/harbors.¹⁶⁰ Concentrations of organotins appear to decrease over time in sediment in Flanders. Removal mechanisms include leaching into sea or biodegradation (Figure S3; Figure 5), which need to be elucidated.

Figure 5.

First-order disappearance rate constant for TBT in Flemish sediment (x, in year^–1) versus salinity and concentration (y1 and y2). Symbols denote distinct sampling locations, having a uniform temporal trend in concentration (continuously increasing or continuously decreasing).

In this study, we have applied explicit descriptions to characterize the speciation and bioavailability of pollutants in ostracod bioassays performed using sediments from Flanders. Semiempirical toxic unit analyses elucidated the effects of heavy metals (esp. organotin) and ammonia inhibiting H. incongruens growth. Based on standard approaches, 26% of the variance in the observed effect could be explained. Yet, by acknowledging speciation and interactions between bioassay constituents, we can explain up to 90%. Organotin and ammonia appear more and less toxic, respectively, than what is reported in the existing literature for controlled (single-substance) laboratory tests. We can attribute this to a cascade of bio-physicochemical interactions affecting speciation and transformations of the chemicals in situ. Prominently, organotins appear to bioaccumulate during the molting process of H. incongruens. Future modeling ought to focus on the factors controlling the interactions, via more explicit measurement and monitoring of speciation and bioavailability. This is likely to help in bridging the gap between laboratory and field tests so as to enable a more reliable risk assessment for sediments. Despite the need for further study, we deem our model a worthwhile complementary method to evaluate sediments in Flanders. Applying the approach to more spatiotemporal and geochemical settings^11,161 will evaluate its broader applicability. Thus, we deem it a starting point for more integrated ecotoxicity modeling in general.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.0c02855.

• Figure S1: VMM monitoring network for sediment quality assessment. Table S1: Mean characteristics and chemical concentration of sediment samples in Flanders and its three main rivers. Figure S2: Trend of the monthly maximum ammonia concentration. Figure S3: Concentrations of ammonia throughout Flanders. Table S2: Proton dissociation constants (pK_a) values. Table S3: Experimental H. incongruens effect concentrations (EC) of chemicals. Figure S4: Total concentration of chromium in sediment (as predicted from metal concentrations, so to prevent bias) versus MLR results using %clay, %OS, KjN, O₂, P_T, and pH as input. Figure S5: % growth inhibition of H. incongruens versus the TU before empirical calibration. Figure S6: % growth inhibition of H. incongruens versus the TU after empirical calibration. Figure S7: H. incongruens toxic units for different chemicals. Figure S8: H. incongruens toxic units for different chemicals. Table S4: Aqueous concentrations of chemicals and corresponding toxic units (TUs) calculated. Figure S9: Correlation coefficients between chemicals, bioassays, and sediment characteristics Figure S10: Calculation of TBT cation concentration at pH 7.5. Figure S11: Calculation of HPO₄^2– concentrations at pH 7.5. Figure S12: Calculation of the NH₃ concentration at pH 7.5. Figure S13: Ostracod mortality (%) versus ostracod growth inhibition (PDF)

The authors declare no competing financial interest.