Chemical Monitoring Data Research Articles

Case studies of assessment of human health risks after the dam failures of the Córrego do Feijão Mine and Fundão in Brazil

ABSTRACT In recent years, there has been an increase in environmental catastrophes involving mining dam failures. This study assessed the potential risks of iron ore tailings in riparian soils to human health after the worst dam collapses recorded in Brazil: from Córrego do Feijão Mine-CFM and Fundão. Chemical monitoring data from the three-year time interval since the collapses were used to estimate human risk indexes for trace elements such as arsenic, cadmium, chromium, mercury, nickel, lead, and zinc. In both case studies, the adult group indicated a propensity for the development of non-carcinogenic diseases through ingestion, inhalation and dermal pathways for all contaminated areas. Overall, children showed an exacerbated sensitivity to possible cancerous and non-cancerous effects, especially in the areas surrounding the dams and in Barra Longa, São José do Goiabal, Córrego Novo, Veneza, Periquito, Governador Valadares, Tumiritinga, Conselheiro Pena, Honório Fraga and Linhares (from the Fundão dam) and in all study areas from the CFM dam. These assessments demonstrate that in regions with mining activities, the cumulative effects of trace elements over time are harmful and expected. Therefore, there is a need for greater supervision by competent government bodies of conservation practices that enable environmental recovery or mitigation of their impacts, ensuring appropriate health and well-being conditions for local residents and workers.

Predictive value of the ToxCast/Tox21 high throughput toxicity screening data for approximating in vivo ecotoxicity endpoints and ecotoxicological risk in eco- surveillance applications

Ecotoxicology has long relied on assessing the hazard potential of chemicals through traditional in vivo testing methods to understand the possible risk exposure could pose to ecological taxa. In the past decade, the development of non-animal new approach methods (NAMs) for assessing chemical hazard and risk has quickly grown. These methods are often cheaper and faster than traditional toxicity testing, and thus are amenable to high-throughput toxicity testing (HTT), resulting in large datasets. The ToxCast/Tox21 HTT programs have produced in vitro data for thousands of chemicals covering a large space of biological activity. The relevance of these data to in vivo mammalian toxicity has been much explored. Interest has also grown in using these data to evaluate the risk of environmental exposures to taxa of ecological importance such as fish, aquatic invertebrates, etc.; particularly for the purpose of estimating the risk of exposure from real-world complex mixtures. Understanding the relationship and relative sensitivity of NAMs versus standardized ecotoxicological whole organism models is a key component of performing reliable read-across from mammalian in vitro data to ecotoxicological in vivo data. In this work, we explore the relationship between in vivo ecotoxicity data from several publicly available databases and the ToxCast/Tox21 data. We also performed several case studies in which we compare how using different ecotoxicity datasets, whether traditional or ToxCast-based, affects risk conclusions based on exposure to complex mixtures derived from existing large-scale chemical monitoring data. Generally, predictive value of ToxCast data for traditional in vivo endpoints (EPs) was poor (r ≤ 0.3). Risk conclusions, including identification of different chemical risk drivers and prioritized monitoring sites, were different when using HTT data vs. traditional in vivo data.

Lake water chemistry and its relationship to shoreline residential development and natural landscape features in Algonquin Provincial Park, Ontario, Canada

There is a scarcity of long-term chemical monitoring data for lakes in Algonquin Provincial Park (APP), with minimal understanding of the impacts of cottage-leases (e.g., cottage lots, campgrounds, and commercial leases) on lake water chemistry. We examine spatial patterns in water chemistry and landscape features of 32 reference and 22 cottage-lease lakes in APP. Multivariate techniques were used to examine differences in water chemistry, and to identify the subset of landscape features that best explain this variability. Breakpoint analysis was used to examine the relationship between gradients of water chemistry and specific landscape features. Lakes were separated along a primary gradient of ions and pH and a secondary gradient of nutrients and colour. These gradients were best explained by a combination of six landscape features (wetlands, elevation, lake depth, road length, and coniferous trees). Except for chloride, there was no statistically significant difference in water chemistry between cottage-lease and reference lakes. A roughly west-to-east gradient in catchment vegetation and lake chemistry was related to the location of the Algonquin Dome, a natural geological feature in APP, and the park’s glacial history. These results emphasize the importance of the park’s topography in influencing regional water chemistry.

Statistical methods for handling nondetected results in food chemical monitoring data to improve food risk assessments.

Chemical risk assessment is important for risk management, and estimates of chemical exposure must be as accurate as possible. Chemical concentrations in food below the limit of detection are known as nondetects and result in left-censored data. During statistical analysis, the method used for handling values below the limit of detection is important. Many risk assessors employ widely used substitution methods to treat left-censored data, as recommended by international organizations. The National Institute of Food and Drug Safety Evaluation of South Korea also recommends these methods, which are currently used for chemical exposure assessments. However, these methods have statistical limitations, and international organizations recommend more advanced alternative statistical approaches. In this study, we assessed the validity of currently used statistical methods for handling nondetects. To identify the most suitable statistical method for handling nondetection, we created virtual data and conducted simulation studies. Based on both simulation and case studies, the Maximum Likelihood Estimation (MLE) and Robust Regression on Order Statistics (ROS) methods were found to be the best options. The statistical values obtained from these methods were similar to those obtained from the commonly used 1/2 Limit of Detection (LOD) substitution method for nondetection treatment. In three case studies, we compared the various methods based on the root mean squared error. The data for all case studies were from the same source, to avoid heterogeneity. Across various sample sizes and nondetection rates, the mean and 95th percentile values for all treatment methods were similar. However, "lognormal maximum likelihood estimation" method was not suitable for estimating the mean. Risk assessors should consider statistical processing of monitoring data to reduce uncertainty. Currently used substitution methods are effective and easy to apply to large datasets with nondetection rates <80%. However, advanced statistical methods are required in some circumstances, and national guidelines are needed regarding their use in risk assessments.

Harmonized approach to estimate the burden of disease of dietary exposure to four chemical contaminants - A French study

Exposure to chemical contaminants found in foods has been associated with various adverse health effects. Burden of disease studies are increasingly used to estimate the public health impact of such exposures. The aims of this study were to estimate the burden of disease due to dietary exposure to four chemicals in France in 2019 (lead (Pb), cadmium (Cd), methylmercury (MeHg), and inorganic arsenic (i-As)), and to develop harmonized methods that can be applied for other chemicals and countries. We used national food consumption data from the third French national food consumption survey, chemical food monitoring data from the Second French Total Diet Study (TDS), dose-response data and disability weights from scientific literature, and disease incidence and demographics from national statistics. We adopted a risk assessment approach to estimate disease burden, incidence, mortality, and Disability-Adjusted Life Years (DALYs) attributable to dietary exposure to the chemicals. In all models, we harmonized food classification and exposure assessment. We propagated uncertainty through the calculations using Monte Carlo simulation. We estimated that, among these chemicals, i-As and Pb were responsible for the highest disease burden. i-As was estimated to cause 820 DALYs, or approximately 1.25 DALYs per 100,000 inhabitants. The estimated burden of Pb was 1834 to 5936 DALYs, or 2.7 (lower bound) to 8.96 (upper bound) DALYs/100,000. The burden of MeHg (192 DALYs), and Cd (0 DALY) was substantially lower. The foods contributing most to disease burden was drinks (30 %), “other foods” (mostly composite dishes) (19 %), and fish and seafood (7 %). Interpretation of estimates needs to consider all underlying uncertainties, linked with data and knowledge gaps. The harmonized models are the first to make use of data from TDS, which are available in several other countries. Thus, they can be applied to estimate the burden and to rank food-associated chemicals at national level.

Chemical monitoring reporting guidance: 2023 data collection

This document should be used for the reporting to EFSA of samples analysed for chemicals. It provides guidance on how to use the Standard Sample Description (SSD2) data model for submission to the EU of analytical results of food and feed samples taken during control activities carried out to monitor residues of pesticides and veterinary medicinal products, contaminants, food additives and chemicals from food contact materials (FCM). This document does not replace – but complements and updates some aspects of – the general EFSA Guidance on Standard Sample Description (SSD2) and Guidance on Data Exchange (GDE2). It is meant to provide guidance on the specific technical and legislative requirements as well as clarity on data quality validation for chemical monitoring (ChemMon) data at national and EU levels.

Statistical Methods for Handling Nondetected Results in Food Chemical Monitoring Data to Improve Food Risk Assessments

Chemical risk assessment is important for risk management, and estimates of chemical exposure must be as accurate as possible. Chemical concentrations in food below the limit of detection are known as nondetects and result in leftcensored data. During statistical analysis, the method used for handling values below the limit of detection is important. Many risk assessors employ widely used substitution methods to treat left-censored data, as recommended by international organizations. The National Institute of Food and Drug Safety Evaluation of South Korea also recommend these methods, which are currently used for chemical exposure assessments. However, these methods have statistical limitations, and international organizations recommend more advanced alternative statistical approaches. In this study, we assessed the validity of currently used statistical methods for handling nondetects. The best method was determined based on a simulation study. In three case studies, we compared the various methods based on the root mean squared error. The data for all case studies were from the same source, to avoid heterogeneity. Across various sample sizes and nondetection rates, the mean and 95th percentile values for all treatment methods were similar. However, “log normal maximum likelihood estimation” method was not suitable for estimating the mean. Risk assessors should consider statistical processing of monitoring data to reduce uncertainty. Currently used substitution methods are effective and easy to apply to large data sets with nondetection rates &lt; 80%. However, advanced statistical methods are required in some circumstances, and national guidelines are needed regarding their use in risk assessments.

Risk from unintentional environmental mixtures in EU surface waters is dominated by a limited number of substances

Unintentional environmental mixtures happen when multiple chemicals co-occur in the environment. A generic mixture assessment factor (MAF), has been proposed to account for this. The MAF is a number by which safe exposure levels for single chemicals are divided to ensure protection against combined exposures to multiple chemicals. Two key elements to judge the appropriateness of a generic MAF are (1) defining the scope of mixtures that need to be addressed by a MAF (i.e.: simple mixtures vs complex mixtures), and (2) the existence of common risk drivers across large spatial scales. Simple mixtures with one to three risk drivers can easily be addressed by chemical-by-chemical regulatory action. Our work provides evidence on the prevalence and complexity of cumulative risk in EU freshwaters based on chemical monitoring data from one of the largest databases in the EU. With 334 chemicals being monitored, low complexity mixtures (one to 3 three risk drivers) dominated. After excluding metals, only 15 out of 307 chemicals (5 %) were most frequent chemical risk drivers. When these 15 chemicals were excluded from the analysis, 95 % of all monitoring site – year combinations did not pose a concern for cumulative risk. Most of these 15 chemicals are already banned or listed in various priority lists, showing that current regulatory frameworks were effective in identifying drivers of single chemical and cumulative risk. Although the monitoring data do not represent the entirety of environmental mixtures in the EU, the observed patterns of (1) limited prevalence of truly complex mixtures, and (2) limited number of overall risk drivers, argue against the need for implementing a generic MAF as a regulatory tool to address risk from unintentional mixtures in EU freshwaters.

Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data

Reliable long-horizon PM2.5 forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM2.5 forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM2.5 forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM2.5 simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM2.5 forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM2.5 forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.

Reservoir Dynamics Monitoring in A Liquid Dominated Geothermal Field Based on Surveillance Data and Tracer Flow Test

Two common characteristics changes in the liquid-dominated reservoir because of exploitation are a decrease in temperature in the liquid reservoir zone (due to injection breakthrough) and the development of a steam zone (because of lower liquid water level). Both phenomena are observed by using the tracer flow test (TFT) technique. The comprehensive analysis using enthalpy from TFT data, combined with the data of chloride in brine, NCG in steam, and subsurface temperature (from PT logging), can be used to identify the dynamics of the reservoir processes. The application of the comprehensive analysis is selected for this study by taking those various monitoring of surveillance programs from a liquid-dominated geothermal system. In the selected geothermal field, which the operation began in 1994, an injection breakthrough occurred in the western part of the field. On the other side, the development of a steam reservoir was observed in the eastern part of the field. TFT monitoring is carried out every three months in each active production well as stated in the surveillance program. Other chemical monitoring and (PT) log data are also carried out periodically. Two wells from the studied field are selected, in which one well experience with injection breakthrough and the other one is in steam cap development. Comprehensive analysis results from TFT data, geochemical monitoring data (NCG and Cl), and subsurface temperature are used to understand the dynamics in geothermal reservoirs. Injection breakthrough in production well is indicated by changes in chloride content, decrease in enthalpy and NCG in the steam. Whereas the formation of steam cap will be characterized by an increase in enthalpy and NCG. With a thorough understanding of the changing conditions in the reservoir, recommendations for appropriate surveillance strategies can be formulated to maintain an optimal and sustainable generation process.

Emerging contaminant exposure to aquatic systems in the Southern African Development Community.

The growing production and use of chemicals and the resultant increase in environmental exposure is of particular concern in developing countries where there is rapid industrialization and population growth but limited information on the occurrence of emerging contaminants. Advances in analytical techniques now allow for the monitoring of emerging contaminants at very low concentrations with the potential to cause harmful ecotoxicological effects. Therefore, we provide the first critical assessment of the current state of knowledge about chemical exposure in waters of the Southern African Developmental Community (SADC). We achieved this through a comprehensive literature review and the creation of a database of chemical monitoring data. Of the 59 articles reviewed, most (n = 36; 61.0%) were from South Africa, and the rest were from Botswana (n = 6; 10.2%), Zimbabwe (n = 6; 10.2%), Malawi (n = 3; 5.1%), Mozambique (n = 3; 5.1%), Zambia (n = 2; 3.4%), Angola (n = 1; 1.7%), Madagascar (n = 1; 1.7%), and Tanzania (n = 1; 1.7%). No publications were found from the remaining seven SADC countries. Emerging contaminants have only been studied in South Africa and Botswana. The antiretroviral drug ritonavir (64.52 µg/L) was detected at the highest average concentration, and ibuprofen (17 times) was detected most frequently. Despite being the primary water source in the region, groundwater was understudied (only 13 studies). High emerging contaminant concentrations in surface waters indicate the presence of secondary sources of pollution such as sewage leakage. We identify research gaps and propose actions to assess and reduce chemical pollution to enable the SADC to address the Sustainable Development Goals, particularly Goal 3.9, to reduce the deaths and illnesses from hazardous chemicals and contamination. Environ Toxicol Chem 2022;41:382–395. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Burden of Disease of Dietary Exposure to Four Chemical Contaminants in Denmark, 2019

Exposure to chemical contaminants found in foods has been associated with diverse adverse health effects. The aim of this study was to estimate the burden of disease associated with dietary exposure to four chemicals in Denmark in 2019: lead (Pb), cadmium (Cd), methylmercury (MeHg), and inorganic arsenic (i-As). We collected national food consumption and chemical food monitoring data from Danish databases, dose-response and severity data from the scientific literature, and disease incidence and population numbers from national statistics. We adopted a risk assessment approach to estimate disease burden, quantifying incidence, mortality, and Disability-Adjusted Life Years (DALYs) attributable to dietary exposure to the chemicals. In all models, we propagated uncertainty around the input parameters through the calculations using Monte Carlo simulation. We estimated that, among these chemicals, Pb and MeHg were responsible for the highest disease burden. MeHg led to the loss of nearly 600 healthy life years, or approximately 10 DALYs per 100,000 inhabitants. Dietary exposure to Pb was estimated to cause 383 to 1,261 DALYs, corresponding to 6.6 (lower bound) to 22 (upper bound) DALYs/100,000 per year. The foods contributing most to disease burden were fish (MeHg) and sweets, fruit, and bread and cereals (Pb). The burden attributed to dietary exposure to i-As (5 DALYs) and Cd (0.04 DALY) was substantially lower. Interpretation of estimates needs to consider all underlying uncertainties, linked with data and knowledge gaps. These estimates are useful supplements to traditional risk assessment to guide food safety interventions and inform dietary guidelines for different population groups.

Long-term PM2.5 source analyses in New York City from the perspective of dispersion normalized PMF

Receptor models like positive matric factorization (PMF) have been widely used in atmospheric studies for source identification and quantification. However, one of the issues with ambient air pollution data used in those analyses is that the concentrations result from a combination of emission/formation rates and meteorological dispersion. Dispersion normalized PMF (DN-PMF) was recently developed to reduce the influence of dilution on source resolution. Studies have shown its capability to better reflect actual emission rates and yield more interpretable results for hourly integrated ambient data for particulate matter and particle number size distributions. In this study, conventional and DN-PMF were used to deconvolute long-term 24-h integrated speciated PM2.5 data sets, for the first time, for three urban sites in the NYC metropolitan area. DN-PMF using 24-h integrated data does not provide much improvement over DN-PMF using hourly integrated data since averaging over 24 h loses the strong diel dispersion patterns. However, it provides improved seasonal patterns for different sources that are lost with conventional PMF results. DN-PMF implements necessary corrections to the conventional PMF approach, and makes better use of the chemical monitoring data that generally have lower time resolution.

Chemical monitoring reporting guidance: 2022 data collection

This document should be used for the reporting to EFSA of samples analysed for chemicals. It provides guidance on how to use the Standard Sample Description (SSD2) data model for submission to the EU of analytical results of food and feed samples taken during control activities carried out to monitor residues of pesticides and veterinary medicinal products, contaminants, food additives and chemicals from food contact materials (FCM). This document does not replace – but complements and updates some aspects of – the general EFSA Guidance on Standard Sample Description (SSD2) and Guidance on Data Exchange (GDE2). It is meant to provide guidance on the specific technical and legislative requirements as well as clarity on data quality validation for chemical monitoring (ChemMon) data at national and EU levels.

Biological effect and chemical monitoring of Watch List substances in European surface waters: Steroidal estrogens and diclofenac - Effect-based methods for monitoring frameworks.

Three steroidal estrogens, 17α-ethinylestradiol (EE2), 17β-estradiol (E2), estrone (E1), and the non-steroidal anti-inflammatory drug (NSAID), diclofenac have been included in the first Watch List of the Water Framework Directive (WFD, EU Directive 2000/60/EC, EU Implementing Decision 2015/495). This triggered the need for more EU-wide surface water monitoring data on these micropollutants, before they can be considered for inclusion in the list of priority substances regularly monitored in aquatic ecosystems. The revision of the priority substance list of the WFD offers the opportunity to incorporate more holistic bioanalytical approaches, such as effect-based monitoring, alongside single substance chemical monitoring. Effect-based methods (EBMs) are able to measure total biological activities (e.g., estrogenic activity or cyxlooxygenase [COX]-inhibition) of specific group of substances (such as estrogens and NSAIDs) in the aquatic environment at low concentrations (pg/L). This makes them potential tools for a cost-effective and ecotoxicologically comprehensive water quality assessment. In parallel, the use of such methods could build a bridge from chemical status assessments towards ecological status assessments by adressing mixture effects for relevant modes of action. Our study aimed to assess the suitability of implementing EBMs in the WFD, by conducting a large-scale sampling and analysis campaign of more than 70 surface waters across Europe. This resulted in the generation of high-quality chemical and effect-based monitoring data for the selected Watch List substances. Overall, water samples contained low estrogenicity (0.01-1.3ng E2-Equivalent/L) and a range of COX-inhibition activity similar to previously reported levels (12-1600ng Diclofenac-Equivalent/L). Comparison between effect-based and conventional analytical chemical methods showed that the chemical analytical approach for steroidal estrogens resulted in more (76%) non-quantifiable data, i.e., concentrations were below detection limits, compared to the EBMs (28%). These results demonstrate the excellent and sensitive screening capability of EBMs.

Ecological and spatial variations of legacy and emerging contaminants in white-tailed sea eagles from Germany: Implications for prioritisation and future risk management

The increasing use of chemicals in the European Union (EU) has resulted in environmental emissions and wildlife exposures. For approving a chemical within the EU, producers need to conduct an environmental risk assessment, which typically relies on data generated under laboratory conditions without considering the ecological and landscape context. To address this gap and add information on emerging contaminants and chemical mixtures, we analysed 30 livers of white-tailed sea eagles (Haliaeetus albicilla) from northern Germany with high resolution-mass spectrometry coupled to liquid and gas chromatography for the identification of >2400 contaminants. We then modelled the influence of trophic position (δ15N), habitat (δ13C) and landscape on chemical residues and screened for persistent, bioaccumulative and toxic (PBT) properties using an in silico model to unravel mismatches between predicted PBT properties and observed exposures. Despite having generally low PBT scores, most detected contaminants were medicinal products with oxfendazole and salicylamide being most frequent. Chemicals of the Stockholm Convention such as 4,4′-DDE and PCBs were present in all samples below toxicity thresholds. Among PFAS, especially PFOS showed elevated concentrations compared to other studies. In contrast, PFCA levels were low and increased with δ15N, which indicated an increase with preying on piscivorous species. Among plant protection products, spiroxamine and simazine were frequently detected with increasing concentrations in agricultural landscapes. The in silico model has proven to be reliable for predicting PBT properties for most chemicals. However, chemical exposures in apex predators are complex and do not solely rely on intrinsic chemical properties but also on other factors such as ecology and landscape. We therefore recommend that ecological contexts, mixture toxicities, and chemical monitoring data should be more frequently considered in regulatory risk assessments, e.g. in a weight of evidence approach, to trigger risk management measures before adverse effects in individuals or populations start to manifest.

Use of large datasets of measured environmental concentrations for the ecological risk assessment of chemical mixtures in Italian streams: A case study

A method to evaluate the ecological risk of chemical mixtures in water bodies is here presented. In the first phase, the approach considered routine chemical monitoring data (MEC: measured environmental concentrations) obtained from the Italian National Institute for Environmental Protection and Research, which were georeferenced to a single coordinate system for each monitoring station. The overall mixture toxicity were then evaluated for three representative aquatic organisms (algae, Daphnia, fish) using the concentration addition model to combine exposure with ecotoxicological data (from different databases). A database management system was used to facilitate the creation, organisation, and management of the large datasets of this study. The outputs were obtained as GIS-based mixture risk maps and tables (listing the toxic unit of mixtures and individual substances) useful for further analysis. The method was applied to an Italian watershed (Adda River) as a case study. In the first phase, the mixture toxicity was calculated using two scenarios: best- and worst-case; wherein the former included only those compounds that were be detected, while the latter involved also substances with concentrations below the limit of quantification. The ratio between the two scenarios indicated the range within which mixture toxicity should ideally vary. The method demonstrates that these ratios were very small when the calculated toxicity using the best case indicated a potential risk and vice versa, indicating that the worst-case scenario could not be appropriate (extremely conservative). Consequently, in the successive phase, we focused exclusively on the best-case scenario. Finally, this approach allowed the priority mixture identification (those most likely occurring in the analysed water samples), algae as the organism at the highest risk, and the substances that contributed the most to the overall mixture toxicity (terbuthylazine and s-metolachlor for algae, and chlorpyrifos and chlorpyrifos-CH3 for Daphnia and fish).

A Bayesian Approach to Incorporating Spatiotemporal Variation and Uncertainty Limits into Modeling of Predicted Environmental Concentrations from Chemical Monitoring Campaigns.

Environmental monitoring studies provide key information to assess ecosystem health. Results of chemical monitoring campaigns can be used to identify the exposure scenarios of regulatory concern. In environmental risk assessment (ERA), measured concentrations of chemicals can be used to model predicted environmental concentrations (PECs). As the PEC is, by definition, a predicted variable, it is highly dependent on the underlying modeling approach from which it is derived. We demonstrate the use of Bayesian distributional regression models to derive PECs by incorporating spatiotemporal conditional variances, and limits of quantification (LOQ) and detection (LOD) as de facto data censoring. Model accuracies increase when incorporating spatiotemporal conditional variances, and the inclusion of LOQ and LOD results in potentially more robust PEC distributions. The methodology is flexible, credibly quantifies uncertainty, and can be adjusted to different scientific and regulatory needs. Posterior sampling allows to express PECs as distributions, which makes this modeling procedure directly compatible with other Bayesian ERA approaches. We recommend the use of Bayesian modeling approaches with chemical monitoring data to make realistic and robust PEC estimations and encourage the scientific debate about the benefits and challenges of Bayesian methodologies in the context of ERA.

Groundwater quality trend and trend reversal assessment in the European Water Framework Directive context: an example with nitrates in Italy

Groundwater resources are of utmost importance in sustaining water related ecosystems, including humans. The long-lasting impacts from anthropogenic activities require early actions, owing to the natural time lag in groundwater formation and renewal. The European Union (EU) policy, within the implementation of the Water Framework Directive (WFD), requires Member States to identify and reverse any significant and sustained upward trend in the concentration of pollutants, defining specific protection measures to be included in the River Basin Management Plans (RBMP). In Italy, official guidelines for trend and trend reversal assessment have been published recently. Statistical methods, such as the Mann-Kendall test for trend analysis and the Sen’s method for estimating concentration scenarios, should be applied at the fixed terms stated by the WFD implementation cycles to identify upward trends, while the Pettitt test is proposed for the identification of trend reversal. In this paper, we present an application of a slightly modified version of the Italian Guidelines to a groundwater body in Northern Italy featuring nitrate pollution and discuss its advantages and limitations. In addition to Pettitt test, for the trend reversal analysis, we apply the Mann-Kendall test in two sections and compare the results. We conclude that this method seems more reliable than Pettitt test to identify a reversal point in quality time series. The overall procedure can be easily applied to any groundwater body defined at risk across Europe, for the assessment of the upward trends of pollutants and their reversal, even with little chemical monitoring data. Although focused on the EU legislative framework, this procedure may be relevant for a wider context, allowing to individuate upward trend as early warning for contamination processes in an integrated water resources management context.

Use of Postregistration Monitoring Data to Evaluate the Ecotoxicological Risks of Pesticides to Surface Waters: A Case Study with Chlorpyrifos in the Iberian Peninsula.

Chemical monitoring data sets such as those provided by the implementation of the Water Framework Directive (WFD) offer opportunities to evaluate the ecological risks of pesticides under large spatiotemporal scales and to evaluate the protectiveness of the current prospective risk-assessment framework. As a case study, we used the monitoring data set for the insecticide chlorpyrifos to perform a probabilistic risk assessment for Iberian surface-water ecosystems. The specific objectives of the study were 1) to assess the occurrence of chlorpyrifos in relation to different agricultural production land uses, 2) to assess the spatiotemporal variation in the exceedance of the European WFD short- and long-term environmental quality standards (maximum allowable concentration environmental quality standard [MAC-EQS] and annual average [AA] EQS), and 3) to perform a probabilistic risk assessment for freshwater invertebrates. A database that contains chlorpyrifos concentrations from 14 600 surface water samples taken between 2012 and 2017 in the Iberian Peninsula (Spain and Portugal) was analyzed, and chlorpyrifos was detected in 21% of these samples. The MAC-EQS was exceeded in 2% of the cases, whereas the AA-EQS was exceeded in 18% of the cases. The majority of the exceedances took place in the littoral areas of the eastern and southeastern parts of the Iberian Peninsula, particularly in areas with dominant citrus production during late spring, late summer, and autumn. The present study indicates unacceptable risks posed by chlorpyrifos to Iberian surface waters over the study period, although it was approved for use in Europe. The present study supports the need to perform further postregistration monitoring assessments with other pesticides following similar approaches, which can help to identify possible pesticide-misuse practices and improvements of the prospective risk-assessment framework. Environ Toxicol Chem 2021;40:500-512. © 2020 SETAC.