Integrated Environmental Assessment and Management最新文献

Analysing datasets from ecotoxicological studies conducted under field or semi-field (i.e.,, enclosure) conditions to evaluate the risks posed by chemicals can be challenging due to the inherent variability of natural systems, complex interactions between environmental factors, presence of non-linear dynamics, and difficulties working with free-ranging wildlife. Regression-based statistical approaches, including generalized linear and additive effect models (GLM, GAM) and their "mixed" counterparts (GLMM, GAMM) have a long tradition in ecology to find signals in noisy data by disentangling the influence of multiple factors. They have gained attention in analysing ecotoxicological data for risk assessment of chemicals. Nevertheless, GLMM and GAMM are often perceived as complex, leading to hesitation to account for their results in regulatory evaluations. To enhance the understanding and uptake of GLMM and GAMM, we present a framework to demystify the development (i.e.,, calibration, internal validation, and selection) of GLMM and GAMMs within the context of ecotoxicological field study data. An initial data exploration; an evaluation of the smallest level of significant difference that the model can discriminate (Minimum Detectable Difference, MDD); and a final model interpretation and visualization complete the framework in a total of six steps which enable significance of treatment-related effects to be checked at two independent stages. The framework is exemplified with a case study on common voles exposed to a fungicide under field conditions. The case study demonstrated the advantages of GLMM and GAMM in obtaining most out of valuable ecotoxicological field data, namely, their flexibility to different data types (e.g.,, counts, proportions, continuous data) recorded as study endpoints, ability to incorporate all data within a single analysis while considering the repeated sampling within the same fields (i.e.,, avoiding pseudoreplication), the potential non-linear dynamics of the endpoints over time, and the multiple influencing factors of direct and indirect interest to the study interpretation.

An important limitation of concentration-response (C-R) modeling of toxicity test data is the imposition of a shape for this relationship that can deviate from the underlying true relationship, thereby biasing estimates of effect concentrations (ECp's). In particular, the imposed mathematical model is often symmetric, whereas considerable asymmetry might be present in the underlying relationship that is either not evident or not addressed in standard toxicity tests with limited numbers of treatments. To evaluate asymmetry and its implications for ECp estimation, six simultaneous tests of NaCl chronic toxicity to Ceriodaphnia dubia were conducted, providing extensive information on inter-replicate variability and on the shape of the C-R relationship. An asymmetric C-R relationship derived from this large data set was used to simulate data sets with a more typical, smaller configuration, which were subject to C-R analysis using both symmetric and asymmetric models. Both models resulted in substantial uncertainties for estimating ECp's at low p values for this test configuration. There is a need for more work and care regarding the use of C-R analysis in developing effects assessments for low levels of effect.

Pistachio is an important agricultural commodity, and Iran is among the world's leading manufacturers, exporters, and consumers. However, extensive pesticide application in pistachio groves is a source of concern regarding residual contamination in edible nuts. In the present study, 25 pistachio samples collected from Sirjan orchards were analyzed for pesticide residues by the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) procedure followed by Gas Chromatography-Mass Spectrometry quantification. Five pesticides from three priority chemical classes-organophosphates (dichlorvos, dimethoate, ethion), organochlorines (DDT), and neonicotinoids (thiacloprid)-were present in concentrations ranging from 0.015 to 0.064 mg/kg. Residue concentrations were assessed against national and European Maximum Residue Limits (MRLs). Probabilistic health risk was assessed by a Monte Carlo Simulation to estimate non-carcinogenic and carcinogenic risks for children and adults. The 95th percentile of total hazard quotient was 1.471 in adults and 10.644 in children, indicating moderate to high risk of non-carcinogenicity, particularly in children due to their low body weight and high intake-to-weight ratio. Carcinogenic risk calculation of dichlorvos and DDT also exceeded the U.S. The EPA threshold level is 1 × 10-6, while children's and adults' total carcinogenic risk could reach 7.4 × 10-4 and 1.2 × 10-4, respectively. Pesticide concentration was identified as the greatest predictor of risk in a sensitivity analysis. The results urge the implementation of Good Agricultural Practices, frequent residue monitoring, and more stringent pesticide regulations to improve food safety and safeguard vulnerable populations, particularly children.

The revised 2023 European Food Safety Authority (EFSA) guidance on the risk assessment of plant protection products on bees introduced a major change in the statistical evaluation of higher tier studies, replacing difference testing with the equivalence testing approach. This paper evaluates several statistical models for equivalence testing of colony strength endpoints in honey bee semi-field studies, including a t-test, a two-way ANOVA, and a linear-mixed-effects model incorporating an autoregressive (AR) structure. Using a range of simulated scenarios, model performance was compared to determine suitability and the likely level of replication needed to conclude a low risk of a test substance with a true effect size of <10% reduction in colony strength. The linear mixed-effects model with AR structure and baseline adjustment offered the highest statistical power among the tested approaches. In all simulated scenarios, achieving 80% power to conclude equivalence required substantially more replication than the minimum of three replicates recommended in the EPPO (2010) test guideline. Under the best-case scenario, a minimum of seven replicates was needed when the true effect size was 0, whereas effects close to the equivalence margin (a true 9% reduction) required extremely large sample sizes, up to 612 replicates, to achieve sufficient power. Potential modifications to the study design to reduce replication needs were also explored. Reducing initial inter-colony variability alone did not meaningfully decrease required sample sizes, whereas increasing temporal correlation among repeated observations improved power and lowered replication requirements. Nevertheless, it is questioned whether the large numbers of replicates illustrated here are manageable in a practical study setup. Caution is needed during the implementation of the equivalence approach for regulatory evaluation until applicants and regulatory bodies better understand if such studies can be feasibly designed and conducted to demonstrate acceptable risk against the specific protection goals.

The utilization of fertilizers in contemporary agriculture is of paramount importance, given their capacity to enhance crop productivity. However, fertilizer manufacturing is a very energy-consuming activity that also emits harmful substances, thereby impacting the environment. In this article, we propose the integration of Lean Six Sigma and Life-Cycle Assessment methodologies to guide fertilizer manufacturers towards a more sustainable fertilizer production. We applied this approach in a case study with a Spanish fertilizer producer. A comparative study of its environmental performance was undertaken to quantify the benefit of applying Lean Six Sigma principles to achieve reduced variability in product compositions, optimized raw material use, and improved process performance. We also assessed the environmental impact of four waste treatment scenarios for the wastewater and solid waste generated in the manufacturing process. Our results showed that the improvements obtained by the application of Lean Six Sigma resulted in a reduction of the environmental impact by 8.6%, mostly driven by a lower use of copper sulphate. The best waste treatment scenario was determined to be composting of the solid waste and wastewater recovery. Human carcinogenic toxicity accounted for nearly half of the total environmental impact of fertilizer production. Furthermore, the reaction process was found to contribute the most to the overall environmental impact due to the use of ethylenediaminetetraacetic acid. It is expected that this study will motivate fertilizer manufacturers, as well as the chemical industry in general, to adopt methodologies like Lean Six Sigma and Life-Cycle Assessment to make their processes more sustainable.

Normalization strategies in reporter gene in vitro assays may influence the interpretation of chemical bioactivity, yet their implications are rarely systematically assessed. This study evaluates how different normalization approaches-subtraction versus division by baseline controls, and normalization to positive controls-affect assay outcomes across three receptor-based systems: Aryl hydrocarbon Receptor (AhR), Estrogen Receptor agonist (ERago), and Nuclear factor erythroid 2-related factor 2 (Nrf2). Analyses are done on > 400 wastewater samples tested in 34-54 microplates including reference chemical concentration-response curves, 1% methanol, 1% ethanol, evaporated solvents and media controls. Results demonstrate that even low solvent concentrations can significantly alter receptor responses. Evaporation of solvents mitigated adverse effects but may introduce biases due to loss of volatile or hydrophobic compounds in samples. Variability in assay signals was predominantly driven by systematic factors such as e.g. cell growth and reader sensitivity, rather than random noise. Normalization to both baseline and positive controls consistently yielded the most reproducible EC-values across plates and campaigns. The findings underscore the importance of rigorous control validation and normalization in in vitro bioassays, particularly when applied to complex environmental samples. Recommendations include minimizing solvent concentrations, validating solvent effects per assay, and using dual normalization strategies. The study also highlights the need for further research into time-dependent toxicity dynamics and signal quenching by environmental matrices to enhance in vitro assay robustness and comparability.

Sulfate is a major ion commonly found in natural environments but excess amounts of it can be harmful to aquatic life. Effects of sulfate on freshwater organisms have been explored previously while its effects on brackish water biota are poorly known. The elevating human-induced sulfate load to the Baltic Sea necessitates understanding how brackish water species tolerate the additional sulfate. In this study, we conducted chronic sulfate toxicity tests on species from coastal areas of the Baltic Sea. Species of both freshwater and marine origin in the Baltic Sea were tested. Species sensitivity distribution (SSD) with a model-averaging approach was used to derive the 5th percentile hazardous concentration (HC5). For comparison, HC5s were also derived from three alternative single-distribution models. Sulfate sensitivity varied notably between the test species. The most sensitive species were found to be common water moss Fontinalis antipyretica and European whitefish Coregonus lavaretus. The chronic HC5 with 95% confidence interval was 1 204 (472-2429) mg/L for the coastal Baltic Sea areas with a salinity range from 2 to 6‰. To account for SSD uncertainties, the HC5 is divided by the assessment factor (AF) to derive the predicted no effect concentration (PNEC). Different AF scenarios were evaluated through deriving the percentages of species affected under each PNEC. The PNEC that would protect over 95% of species with a 95% probability was 469 mg/L, corresponding to an AF of 2.57, which accounted for the statistical uncertainties from the SSD modeling. Compared to species inhabiting freshwater ecosystems in general, the Baltic Sea species showed significantly higher tolerance to sulfate. The toxicity tests performed here on a wide range of Baltic Sea species significantly contribute to the understanding of sensitivity of brackish water species to sulfate and advanced the development of its risk assessment in brackish water.

Regulatory risk assessment in ecotoxicology relies heavily on statistical endpoints such as No Observed Effect Concentrations (NOECs) and Effective Concentrations (ECxs). Current practices, as outlined in the Organisation for Economic Co-operation and Development (OECD) Guidance Document No. 54 (2006) and related guidelines, typically employ decision flowcharts to guide statistical analysis. The flowcharts for NOEC calculations use pre-tests for normality and variance homogeneity to select appropriate statistical tests from pre-defined options, including Williams, Dunnett, step-down Jonckheere-Terpstra, and other tests. While these flowcharts aim to ensure appropriate test selection, our analysis reveals critical limitations in their application to real ecotoxicological data, particularly when dealing with heterogeneous variances and small sample sizes. Through simulation studies and practical data examples, we demonstrate that commonly recommended approaches, including nonparametric tests, can produce misleading results under realistic conditions. Our findings show that robust methods, particularly Welch-type Dunnett's tests, consistently outperform standard approaches when handling heterogeneous variances. Importantly, we emphasize that the goal of statistical analysis should not be to maximize test power for specific data characteristics, but rather to ensure reliable inference across diverse experimental conditions. We propose practical improvements to current guidelines, including: (1) prioritizing robust statistical methods that perform reliably under various conditions, (2) incorporating visual inspections and comprehensive sanity checks, and (3) establishing a public repository for benchmark data and methods. These recommendations aim to enhance the reliability and transparency of statistical analyses in regulatory ecotoxicology, ultimately improving the quality of environmental risk assessments.

This study documented short-term spatial patterns of the neotropical river otter (Lontra longicaudis annectens) during hydroelectric dam construction in the Grijalva River, Chiapas, Mexico. Weekly systematic surveys were conducted in two zones during 2023-2024 (104 surveys per zone): a construction-Disturbed Zone and a less altered Riparian Zone. Methodological constraints-including absence of pre-construction baselines and a disturbance-gradient rather than control-impact design-limit causal inferences; findings represent preliminary observations temporally correlated with construction activities. Habitat use indices were higher in the Disturbed Zone (0.76 records/km) than the Riparian Zone (0.54 records/km). However, spatial analyses (Kernel density, Getis-Ord Gi*, Ripley's K function) revealed a 2.14 km southeast shift in activity hotspots toward the Riparian Zone during 2024, consistent with potential displacement patterns as construction intensified. Results provide preliminary insights for hydroelectric project management, emphasizing the need for robust pre-construction baseline monitoring and connected riparian corridors to facilitate species movement.

New approach methodologies (NAMs) are emergent tools and methods that are increasingly being viewed by the regulatory and scientific community as ones that can support or even replace traditional (ie, conventional) approaches for use in chemical hazard, exposure, and risk assessments. New approaches ultimately promise to improve the efficiency, cost-effectiveness, resource requirements, and ethical challenges associated with conventional assessment approaches. Despite escalating activity in the field, less common in the 'NAMs' discourse is the consideration of the specific priorities and needs of Environmental Justice (EJ) communities. These communities are racialized, marginalized, and low-income communities who face disproportionate health impacts related to environmental contamination. Traditional risk, exposure, and hazard assessment methods have been critiqued as generic, institutionalized, and failing to capture the unique and disproportionate health risks of importance to communities themselves. Despite an understanding of the need to include EJ communities in chemicals management, there remains little to no information on their inclusion in the design of NAMs (which are expected to eventually be adopted for regulatory use). This commentary argues that "now" is the time to ensure that EJ considerations are designed into NAMs, and that EJ communities are meaningfully involved. We separate the paper into sections on exposure, hazard and risk assessment. For each section, we provide some comments on the challenges with conventional methods that have been documented in EJ community contexts, followed by our perspectives on opportunities to build new approaches. We conclude by ideating future directions in three areas: the role of regulatory systems, the design of NAMs for EJ community contexts, and the building of capacity for researchers and EJ communities to collaborate on NAMs design and implementation. With the ultimate goal of more equitable chemicals management, these perspectives and ideas hope to inform the development of EJ community-relevant NAMs.