Joint effects of air pollution and neighborhood socioeconomic status on cognitive decline - Mediation by depression, high cholesterol levels, and high blood pressure

Air pollution and neighborhood socioeconomic status (N-SES) are associated with adverse cardiovascular health and neuropsychiatric functioning in older adults. This study examines the degree to which the joint effects of air pollution and N-SES on the cognitive decline are mediated by high cholesterol levels, high blood pressure (HBP), and depression. In the Emory Healthy Aging Study, 14,390 participants aged 50+ years from Metro Atlanta, GA, were assessed for subjective cognitive decline using the cognitive function instrument (CFI). Information on the prior diagnosis of high cholesterol, HBP, and depression was collected through the Health History Questionnaire. Participants' census tracts were assigned 3-year average concentrations of 12 air pollutants and 16 N-SES characteristics. We used the unsupervised clustering algorithm Self-Organizing Maps (SOM) to create 6 exposure clusters based on the joint distribution of air pollution and N-SES in each census tract. Linear regression analysis was used to estimate the effects of the SOM cluster indicator on CFI, adjusting for age, race/ethnicity, education, and neighborhood residential stability. The proportion of the association mediated by high cholesterol levels, HBP, and depression was calculated by comparing the total and direct effects of SOM clusters on CFI. Depression mediated up to 87 % of the association between SOM clusters and CFI. For example, participants living in the high N-SES and high air pollution cluster had CFI scores 0.05 (95 %-CI:0.01,0.09) points higher on average compared to those from the high N-SES and low air pollution cluster; after adjusting for depression, this association was attenuated to 0.01 (95 %-CI:-0.04,0.05). HBP mediated up to 8 % of the association between SOM clusters and CFI and high cholesterol up to 5 %. Air pollution and N-SES associated cognitive decline was partially mediated by depression. Only a small portion (<10 %) of the association was mediated by HBP and high cholesterol.

Multi-omic responses of fish exposed to complex chemical mixtures in the Shenandoah River watershed

To evaluate relationships between different anthropogenic impacts, contaminant occurrence, and fish health, we conducted in situ fish exposures across the Shenandoah River watershed at five sites with different land use. Exposure water was analyzed for over 500 chemical constituents, and organismal, metabolomic, and transcriptomic endpoints were measured in fathead minnows. Adverse reproductive outcomes were observed in fish exposed in the upper watershed at both wastewater treatment plant (WWTP) effluent- and agriculture-impacted sites, including decreased gonadosomatic index and altered secondary sex characteristics. This was accompanied with increased mortality at the site most impacted by agricultural activities. Molecular biomarkers of estrogen exposure were unchanged and consistent with low or non-detectable concentrations of common estrogens, indicating that alternative mechanisms were involved in organismal adverse outcomes. Hepatic metabolomic and transcriptomic profiles were altered in a site-specific manner, consistent with variation in land use and contaminant profiles. Integrated biomarker response data were useful for evaluating mechanistic linkages between contaminants and adverse outcomes, suggesting that reproductive endocrine disruption, altered lipid processes, and immunosuppression may have been involved in these organismal impacts. This study demonstrated linkages between human-impact, contaminant occurrence, and exposure effects in the Shenandoah River watershed and showed increased risk of adverse outcomes in fathead minnows exposed to complex mixtures at sites impacted by municipal wastewater discharges and agricultural practices.

Metals and per- and polyfluoroalkyl substances mixtures and birth outcomes in the New Hampshire Birth Cohort Study: Beyond single-class mixture approaches

We aimed to investigate the joint, class-specific, and individual impacts of (i) PFAS, (ii) toxic metals and metalloids (referred to collectively as "metals"), and (iii) essential elements on birth outcomes in a prospective pregnancy cohort using both established and recent mixture modeling approaches. Participants included 537 mother-child pairs from the New Hampshire Birth Cohort Study. Concentrations of 6 metals and 5 PFAS were measured in maternal toenail clippings and plasma, respectively. Birth weight, birth length, and head circumference at birth were abstracted from medical records. Joint, index-wise, and individual associations of the metals and PFAS concentrations with birth outcomes were evaluated using Bayesian Kernel Machine Regression (BKMR) and Bayesian Multiple Index Models (BMIM). After controlling for potential confounders, the metals-PFAS mixture was associated with a larger head circumference at birth, which was driven by manganese. When using BKMR, the difference in the head circumference z-score when changing manganese from its 25th to 75th percentiles while holding all other mixture components at their medians was 0.22 standard deviations (95% posterior credible interval [CI]: -0.02, 0.46). When using BMIM, the posterior mean of index weight estimates assigned to manganese for head circumference z-score was 0.72 (95% CI: 0, 0.99). Prenatal exposure to the metals-PFAS mixture was not associated with birth weight or birth length by either BKMR or BMIM. Using both traditional and new mixture modeling approaches, prenatal exposure to manganese was associated with a larger head circumference at birth after accounting for exposure to PFAS and multiple toxic and essential metals.

Using non-parametric Bayes shrinkage to assess relationships between multiple environmental and social stressors and neonatal size and body composition in the Healthy Start cohort

BACKGROUND

Both environmental and social factors have been linked to birth weight and adiposity at birth, but few studies consider the effects of exposure mixtures. Our objective was to identify which components of a mixture of neighborhood-level environmental and social exposures were driving associations with birth weight and adiposity at birth in the Healthy Start cohort.

METHODS

Exposures were assessed at the census tract level and included air pollution, built environment characteristics, and socioeconomic status. Prenatal exposures were assigned based on address at enrollment. Birth weight was measured at delivery and adiposity was measured using air displacement plethysmography within three days. We used non-parametric Bayes shrinkage (NPB) to identify exposures that were associated with our outcomes of interest. NPB models were compared to single-predictor linear regression. We also included generalized additive models (GAM) to assess nonlinear relationships. All regression models were adjusted for individual-level covariates, including maternal age, pre-pregnancy BMI, and smoking.

RESULTS

Results from NPB models showed most exposures were negatively associated with birth weight, though credible intervals were wide and generally contained zero. However, the NPB model identified an interaction between ozone and temperature on birth weight, and the GAM suggested potential non-linear relationships. For associations between ozone or temperature with birth weight, we observed effect modification by maternal race/ethnicity, where effects were stronger for mothers who identified as a race or ethnicity other than non-Hispanic White. No associations with adiposity at birth were observed.

CONCLUSIONS

NPB identified prenatal exposures to ozone and temperature as predictors of birth weight, and mothers who identify as a race or ethnicity other than non-Hispanic White might be disproportionately impacted. However, NPB models may have limited applicability when non-linear effects are present. Future work should consider a two-stage approach where NPB is used to reduce dimensionality and alternative approaches examine non-linear effects.

The complex relationship of air pollution and neighborhood socioeconomic status and their association with cognitive decline

BACKGROUND

Air pollution and neighborhood socioeconomic status (nSES) have been shown to affect cognitive decline in older adults. In previous studies, nSES acts as both a confounder and an effect modifier between air pollution and cognitive decline.

OBJECTIVES

This study aims to examine the individual and joint effects of air pollution and nSES on cognitive decline on adults 50 years and older in Metro Atlanta, USA.

METHODS

Perceived memory and cognitive decline was assessed in 11,897 participants aged 50+ years from the Emory Healthy Aging Study (EHAS) using the cognitive function instrument (CFI). Three-year average air pollution concentrations for 12 pollutants and 16 nSES characteristics were matched to participants using census tracts. Individual exposure linear regression and LASSO models explore individual exposure effects. Environmental mixture modeling methods including, self-organizing maps (SOM), Bayesian kernel machine regression (BKMR), and quantile-based G-computation explore joint effects, and effect modification between air pollutants and nSES characteristics on cognitive decline.

RESULTS

Participants living in areas with higher air pollution concentrations and lower nSES experienced higher CFI scores (beta: 0.121; 95 % CI: 0.076, 0.167) compared to participants living in areas with low air pollution and high nSES. Additionally, the BKMR model showed a significant overall mixture effect on cognitive decline, suggesting synergy between air pollution and nSES. These joint effects explain protective effects observed in single-pollutant linear regression models, even after adjustment for confounding by nSES (e.g., an IQR increase in CO was associated with a 0.038-point lower (95 % CI: -0.06, -0.01) CFI score).

DISCUSSION

Observed protective effects of single air pollutants on cognitive decline can be explained by joint effects and effect modification of air pollutants and nSES. Researchers must consider nSES as an effect modifier if not a co-exposure to better understand the complex relationships between air pollution and nSES in urban settings.

The challenge of micropollutants in surface water of the Yangtze River

The Yangtze River, the third largest river and supporting nearly one-third of Chinese population, has been severely polluted in recent decades. Among the numerous pollutants, organic micropollutants, as one kind of important emerging contaminants, are currently key contaminants of concern. However, few studies have focused on their mixture environmental impacts, especially for the complex environmental mixtures. In the current study, four categories of organic micropollutants, including 16 polycyclic aromatic hydrocarbons (PAHs), 32 polychlorinated biphenyls (PCBs), 27 organochlorine pesticides (OCPs) and 20 pharmaceutical and personal care products (PPCPs) are analyzed in 10 study sites on the Yangtze River. Subsequently, comprehensive risk assessment for micropollutant mixtures was conducted by risk quotient based on the sum of PEC/PNEC values (RQ_MEC/PNEC) and risk quotient based on the toxic units (RQ_STU). The mixture risk evaluation based on the detected environmental concentrations indicates that micropollutant mixtures in surface water of the Yangtze River exhibited relative high risks for aquatic organisms. The observed results revealed that mixture risk assessments have to consider the complexity of environmental samples; PCBs dominated main mixture risks in the upper stream; PAHs contributed major comprehensive risks in the middle stream; and OCPs were the key micropollutants in the downstream. The outcomes of the present study here can serve for pollution control in the Yangtze River, which provide the scientific underpinnings and regulatory reference for risk management and river protection.

Interdisciplinary data science to advance environmental health research and improve birth outcomes

Rates of preterm birth and low birthweight continue to rise in the United States and pose a significant public health problem. Although a variety of environmental exposures are known to contribute to these and other adverse birth outcomes, there has been a limited success in developing policies to prevent these outcomes. A better characterization of the complexities between multiple exposures and their biological responses can provide the evidence needed to inform public health policy and strengthen preventative population-level interventions. In order to achieve this, we encourage the establishment of an interdisciplinary data science framework that integrates epidemiology, toxicology and bioinformatics with biomarker-based research to better define how population-level exposures contribute to these adverse birth outcomes. The proposed interdisciplinary research framework would 1) facilitate data-driven analyses using existing data from health registries and environmental monitoring programs; 2) develop novel algorithms with the ability to predict which exposures are driving, in this case, adverse birth outcomes in the context of simultaneous exposures; and 3) refine biomarker-based research, ultimately leading to new policies and interventions to reduce the incidence of adverse birth outcomes.

Prenatal environmental exposures and associations with teen births

BACKGROUND

Children's prenatal exposure to multiple environmental chemicals may contribute to subsequent deficits in impulse control, predisposing them to risk-taking.

OBJECTIVE

Our goal was to investigate associations between prenatal exposure mixtures and risk of teen birth, a manifestation of high-risk sexual activity, among 5865 girls (1st generation) born in southeast Massachusetts from 1992-1998.

METHODS

Exposures included prenatal modeled polychlorinated biphenyls (PCBs), ρ,ρ'-dichlorodiphenyl dichloroethylene (DDE), hexachlorobenzene (HCB), lead (Pb), and mercury (Hg). We fit adjusted generalized additive models with multivariable smooths of exposure mixtures, 1st generation infant's birth year, and maternal age at 1st generation birth. Predicted odds ratios (ORs) for teen birth were mapped as a function of joint exposures. We also conducted sensitivity analyses among 1st generation girls with measured exposure biomarkers (n = 371).

RESULTS

The highest teen birth risk was associated with a mixture of high prenatal HCB, Hg, Pb, and PCB, but low DDE exposure, with similar associations in sensitivity analyses. The highest OR predicted for girls born in 1995 to mothers of median age (26 years) was at the 95th percentile of the HCB and PCB exposure distributions (OR = 3.09; 95% confidence interval: 0.29, 32.4). Additionally, girls born earlier in the study period or to teen mothers were at increased risk of teen birth.

SIGNIFICANCE

Prenatal environmental chemical exposures and sociodemographic characteristics may interact to substantially increase risk of teen births.

Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps

PURPOSE OF REVIEW

The goal of this article is to review the use of machine learning (ML) within studies of environmental exposures and children's health, identify common themes across studies, and provide recommendations to advance their use in research and practice.

RECENT FINDINGS

We identified 42 articles reporting upon the use of ML within studies of environmental exposures and children's health between 2017 and 2019. The common themes among the articles were analysis of mixture data, exposure prediction, disease prediction and forecasting, analysis of complex data, and causal inference. With the increasing complexity of environmental health data, we anticipate greater use of ML to address the challenges that cannot be handled by traditional analytics. In order for these methods to beneficially impact public health, the ML techniques we use need to be appropriate for our study questions, rigorously evaluated and reported in a way that can be critically assessed by the scientific community.

A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes

BACKGROUND

Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposures into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility.

METHODS

We presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003-2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models.

RESULTS

PLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene having the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects.

CONCLUSIONS

We presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.

Evaluating associations between early pregnancy trace elements mixture and 2nd trimester gestational glucose levels: A comparison of three statistical approaches

OBJECTIVE

Studies have shown that individual trace element levels might be associated with abnormal glycemic status, with implications for diabetes. Few studies have considered these trace elements as a mixture and their impact on gestational glucose levels. Comparing three statistical approaches, we assessed the associations between essential trace elements mixture and gestational glucose levels.

METHODS

We used data from 1720 women enrolled in the Eunice Kennedy Shriver National Institute of Child Health and Human Development's Fetal Growth Study, for whom trace element concentrations (zinc, selenium, copper, molybdenum) were measured by inductively coupled plasma mass spectrometry (ICP-MS) using plasma collected during the 1st trimester. Non-fasting glucose levels were measured during the gestational diabetes mellitus (GDM) screening test in the 2nd trimester. We applied (1) Bayesian Kernel Machine Regression (BKMR); (2) adaptive Least Absolute Shrinkage and Selection Operator (LASSO) in a mutually adjusted linear regression model; and (3) generalized additive models (GAMs) to evaluate the joint associations between trace elements mixture and glucose levels adjusting for potential confounders.

RESULTS

Using BKMR, we observed a mean 2.7 mg/dL higher glucose level for each interquartile increase of plasma copper (95% credible interval: 0.9, 4.5). The positive association between plasma copper and glucose levels was more pronounced at higher quartiles of zinc. Similar associations were detected using adaptive LASSO and GAM. In addition, results from adaptive LASSO and GAM suggested a super-additive interaction between molybdenum and selenium (both p-values = 0.04).

CONCLUSION

Employing different statistical methods, we found consistent evidence of higher gestational glucose levels associated with higher copper and potential synergism between zinc and copper on glucose levels.

An overview of methods to address distinct research questions on environmental mixtures: an application to persistent organic pollutants and leukocyte telomere length

BACKGROUND

Numerous methods exist to analyze complex environmental mixtures in health studies. As an illustration of the different uses of mixture methods, we employed methods geared toward distinct research questions concerning persistent organic chemicals (POPs) as a mixture and leukocyte telomere length (LTL) as an outcome.

METHODS

With information on 18 POPs and LTL among 1,003 U.S. adults (NHANES, 2001-2002), we used unsupervised methods including clustering to identify profiles of similarly exposed participants, and Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) to identify common exposure patterns. We also employed supervised learning techniques, including penalized, weighted quantile sum (WQS), and Bayesian kernel machine (BKMR) regressions, to identify potentially toxic agents, and characterize nonlinear associations, interactions, and the overall mixture effect.

RESULTS

Clustering separated participants into high, medium, and low POP exposure groups; longer log-LTL was found among those with high exposure. The first PCA component represented overall POP exposure and was positively associated with log-LTL. Two EFA factors, one representing furans and the other PCBs 126 and 118, were positively associated with log-LTL. Penalized regression methods selected three congeners in common (PCB 126, PCB 118, and furan 2,3,4,7,8-pncdf) as potentially toxic agents. WQS found a positive overall effect of the POP mixture and identified six POPs as potentially toxic agents (furans 1,2,3,4,6,7,8-hxcdf, 2,3,4,7,8-pncdf, and 1,2,3,6,7,8-hxcdf, and PCBs 99, 126, 169). BKMR found a positive linear association with furan 2,3,4,7,8-pncdf, suggestive evidence of linear associations with PCBs 126 and 169, and a positive overall effect of the mixture, but no interactions among congeners.

CONCLUSIONS

Using different methods, we identified patterns of POP exposure, potentially toxic agents, the absence of interaction, and estimated the overall mixture effect. These applications and results may serve as a guide for mixture method selection based on specific research questions.

Complex Mixtures, Complex Analyses: an Emphasis on Interpretable Results

PURPOSE OF REVIEW

The purpose of this review is to outline the main questions in environmental mixtures research and provide a non-technical explanation of novel or advanced methods to answer these questions.

RECENT FINDINGS

Machine learning techniques are now being incorporated into environmental mixture research to overcome issues with traditional methods. Though some methods perform well on specific tasks, no method consistently outperforms all others in complex mixture analyses, largely because different methods were developed to answer different research questions. We discuss four main questions in environmental mixtures research: (1) Are there specific exposure patterns in the study population? (2) Which are the toxic agents in the mixture? (3) Are mixture members acting synergistically? And, (4) what is the overall effect of the mixture? We emphasize the importance of robust methods and interpretable results over predictive accuracy. We encourage collaboration with computer scientists, data scientists, and biostatisticians in future mixture method development.

Approaches for incorporating environmental mixtures as mediators in mediation analysis

Mediation analysis offers an essential and rapidly expanding tool in environmental health studies to investigate the contribution of environmental factors towards observed associations between risk factors and health outcomes. When evaluating environmental factors, there may be particular interest in quantifying the impact of exposure to environmental mixtures on human health. In this context, evaluating the joint effect of multiple chemicals or pollutants, rather than individual examination, allows accurate identification of risk factors, assessment of interactions, and ultimately development of more targeted public health interventions. While mediation analysis has been extended to incorporate several methodological complexities specific to environmental factors, little attention has been given to integrating the analysis of environmental mixtures. The aim of this review is to present some of the available methods for environmental mixtures, and discuss how these methods can be integrated within a mediation analysis framework. By incorporating these methods into a mediation framework, investigators will be able to evaluate the contribution of environmental mixtures as mediators of exposure-outcome associations, based on methodologies that are currently available. While standard regression-based methods for multiple mediators can be used, these can easily become unstable as the number of mixture components increases. Summary and classification methods, or hierarchical modeling, can reduce the number of mediators by creating scores or possibly uncorrelated subgroups. This approach allows retrieving indirect effects due to the mixture or to a specific subgroup, but makes identification of component-specific effects and interactions complicated. Finally, one can use various approaches for analyzing mixtures in a two-stage fashion, selecting relevant mediators to be included in the final model. We focused this review on techniques that have been presented to the environmental health community and that can be conducted with major statistical software. We encourage researchers to move beyond the evaluation of one environmental factor at a time to the assessment of the joint effects of environmental mixtures when a mediation model is of interest. Available methods target different aspects related to environmental mixtures and the choice of the suitable approach will depend on data structures and the research question of interest.

Description of exposure profiles for seven environmental chemicals in a US population using recursive partition mixture modeling (RPMM)

Biomonitoring studies have shown that humans are exposed to numerous environmental chemicals. Previous work provides limited insights into the dynamic relationship between different chemicals within a population. The objective of this study is to develop an analytical method identifying exposure profiles of seven common environmental chemicals and determine how exposure profiles differ by sociodemographic groups and National Health and Nutrition Examination Survey (NHANES) 2003-2012 cycle year. We used recursive partition mixture modeling (RPMM) to define classes of the population with similar exposure profiles of lead, cadmium, 2,4-dichlorophenol, 2,5-dichlorophenol, bisphenol A (BPA), triclosan, and benzophenone-3 in individuals aged ≥6 years. Additionally, quasibinomial logistic regression was used to examine the association between each class and selected demographic characteristics. Eight exposure profiles were identified. Individuals who clustered together and had the highest chemical exposures were more likely to be older, to be Non-Hispanic Black (NHB) or Other Hispanic (OH), more likely to live below the poverty line, more likely to be male, and more likely to have participated in the earlier NHANES cycle (2003-2004). The developed method described the dynamic relationship between chemicals and shows that this relationship is different for subpopulations based on their sociodemographic characteristics.

Hepatic gene expression profiles of a non-model cyprinid (Barbus plebejus) chronically exposed to river sediments

In this study, we characterized the gene expression responses of the Padanian barbel (Barbus plebejus), a native benthivorous cyprinid with a very compromised presence within the fish community of the River Po. Barbel juveniles were exposed in the laboratory to two river sediments reflecting an upstream/downstream gradient of increasing contamination and collected from one of the most anthropized tributaries of the River Po. After 7months of exposure, hepatic transcriptional changes that were diagnostic of sediment exposure were assessed. We investigated a set of 24 genes involved in xenobiotic biotransformation (cyp1a, gstα, ugt), antioxidant defense (gpx, sod, cat, hsp70), trace metal exposure (mt-I, mt-II), DNA repair (xpa, xpc), apoptosis (bax, casp3), growth (igf2), and steroid (erα, erβ1, erβ2, ar, vtg) and thyroid (dio1, dio2, trα, trβ, nis) hormone signaling pathways. In a consistent overall picture, the results showed that long-term sediment exposure mainly increased the levels of mRNAs encoding proteins involved in xenobiotic metabolism, oxidative stress defense, repair of DNA damage and activation of the apoptotic process. Transcript up-regulation of three receptor genes (erβ2, ar, trβ), likely representing compensatory responses to antagonistic/toxic effects, was also observed, confirming the exposure to disruptors of the reproductive and thyroidal axes. In contrast to expectations, a few genes showed no response (e.g., casp3) or even downregulation (vtg), further suggesting that the timing of exposure/assessment, potential compensatory effects or post-transcriptional modifications interact to modify the gene expression profiles, particularly during exposure to mixtures of contaminants.

Passive sampling reversed: coupling passive field sampling with passive lab dosing to assess the ecotoxicity of mixtures present in the marine environment

This study presents a new approach in aquatic toxicity testing combining passive sampling and passive dosing. Polydimethylsiloxane sheets were used to sample contaminant mixtures in the marine environment. These sheets were subsequently transferred to ecotoxicological test medium in which the sampled contaminant mixtures were released through passive dosing. 4 out of 17 of these mixtures caused severe effects in a growth inhibition assay with a marine diatom. These effects could not be explained by the presence of compounds detected in the sampling area and were most likely attributable to unmeasured compounds absorbed to the passive samplers during field deployment. The findings of this study indicate that linking passive sampling in the field to passive dosing in laboratory ecotoxicity tests provides a practical and complimentary approach for assessing the toxicity of hydrophobic contaminant mixtures that mimics realistic environmental exposures. Limitations and opportunities for future improvements are presented.