Environmental Risk Assessment Research Articles

Development of a probabilistic risk model for pharmaceuticals in the environment under population and wastewater treatment scenarios.

Preparing for future environmental pressures requires projections of how relevant risks will change over time. Current regulatory models of environmental risk assessment (ERA) of pollutants such as pharmaceuticals could be improved by considering the influence of global change factors (e.g., population growth) and by presenting uncertainty more transparently. In this article, we present the development of a prototype object-oriented Bayesian network (BN) for the prediction of environmental risk for six high-priority pharmaceuticals across 36 scenarios: current and three future population scenarios, combined with infrastructure scenarios, in three Norwegian counties. We compare the risk, characterized by probability distributions of risk quotients (RQs), across scenarios and pharmaceuticals. Our results suggest that RQs would be greatest in rural counties, due to the lower development of current wastewater treatment facilities, but that these areas consequently have the most potential for risk mitigation. This pattern intensifies under higher population growth scenarios. With this prototype, we developed a hierarchical probabilistic model and demonstrated its potential in forecasting the environmental risk of chemical stressors under plausible demographic and management scenarios, contributing to the further development of BNs for ERA. Integr Environ Assess Manag 2024;00:1-21. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Contamination and pesticide multiresidue analysis in cotton production systems in Benin

High quantity of chemical pesticides spraying in conventional cotton production systems may affect negatively natural ecosystems components. This study examined the contamination levels of soil in Benin's cotton production system in West Africa by residues of pesticides used in cotton farming. One hundred and fourteen soil samples from six conventional and organic cotton production systems were collected and analysed. Multi-class pesticide residues were extracted from the soil samples, and analysed using respectively modified QuEChERS extraction and LC–MS/MS chromatography methods. Results showed an overall soil contamination to emamectin benzoate (18.5 μg/kg), imidacloprid (116.3 μg/kg), profenofos (12.7 μg/kg), acetamiprid (10.8 μg/kg), triasophos (12.6 μg/kg), abamectin (14.3 μg/kg), and deltamethrin (10.4 μg/kg). Especially, emamectin benzoate was detected with a high contamination in Banikoara district (18.5 μg/kg). Nevertheless, these contaminations were not deemed threatening from a toxicological perspective. Soils in conventional cotton cultivation displayed lower organic matter levels but higher concentrations of pesticides, whereas soils in organic cotton cultivation were less degraded and contaminated. Consequently, it is imperative to conduct environmental risk assessments and monitor key pesticide metabolites to establish sustainable cotton production systems in Benin.

Risk assessment of land subsidence based on GIS in the Yongqiao area, Suzhou City, China.

This study focuses on the Yongqiao District in Suzhou City, Anhui Province, China, aiming to analyze the current situation of ground settlement and its influencing factors in the area. The selected risk indices include settlement rate, cumulative settlement amount, groundwater level drop funnel, thickness of loose sediment layer, thickness of soft soil layer, and the number of groundwater extraction layers. Additionally, vulnerability indices such as population density, building density, road traffic, and functional zoning are considered. An evaluation index system for assessing land Subsidence risk was established. The risk evaluation of land Subsidence was conducted using the Hierarchical analysis-composite index method and ArcGIS spatial analysis, The evaluation results show that the area of higher risk area is about 2.82 km2, accounting for 0.96% of the total area, mainly distributed in the area of Jiuli village, Sanba Street. The middle risk area is distributed around the higher area, with an area of about 9.18 km2, accounting for 3.13% of the total area. The lower risk areas were distributed in most of the study area, covering an area of 222.24 km2, accounting for 75.82% of the total area. The low risk assessment area is mainly distributed in Bianhe Street and part of Zhuxianzhuang Town, with an area of about 58.88 km2, accounting for 20.09% of the total area. The findings of this study are not only crucial for informing local policies and practices related to land use planning, infrastructure development, and emergency response but also enhance our understanding of the complexities of land Subsidence processes and their interactions with human activities, informing future research and practice in environmental risk assessment and management.

Integrated framework to assess soil potentially toxic element contamination through 3D pollution analysis in a typical mining city

Soil potentially toxic elements (PTEs) pollution of contaminated sites has become a global environmental issue. However, given that previous studies mostly focused on pollution assessment in surface soils, the current status and environmental risks of potentially toxic elements in deeper soils remain unclear. The present study aims to cognize distribution characteristics and spatial autocorrelation, pollution levels, and risk assessment in a stereoscopic environment for soil PTEs through 3D visualization techniques. Pollution levels were assessed in an integrated manner by combining the geoaccumulation index (Igeo), the integrated influence index of soil quality (IICQs), and potential ecological hazard index. Results showed that soil environment at the site was seriously threatened by PTEs, and Cu and Cd were ubiquitous and the predominant pollutants in the study area. The stratigraphic models and pollution plume simulation revealed that pollutants show a decreasing trend with the deepening of the soil layer. The ranking of contamination soil volume is as follows: Cu > Cd > Zn > As > Pb > Cr > Ni. According to the IICQs evaluation, this region was subject to multiple PTE contamination, with more than 60% of the area becoming seriously and highly polluted. In addition, the ecological hazard model revealed the existence of substantial ecological hazards in the soils of the site. The integrated potential ecological risk index (RI) indicated that 45.7%, 10.13%, and 4.15% of the stereoscopic areas were in considerable, high, and very high risks, respectively. The findings could be used as a theoretical reference for applying multiple methods to integrate evaluation through 3D visualization analysis in the assessment and remediation of PTE-contaminated soils.

Phosphorus Environmental Risk Assessment in Wetland Soil

Phosphorus Environmental Risk Assessment in Wetland Soil

Regulatory framework for the assessment of the impacts of plant protection products on biodiversity: review of strengths and limits.

The placing of plant protection products (PPPs) on the market in the European Union is governed by numerous regulations. These regulations are among the most stringent in the world, however they have been the subject of criticisms especially because of the decline in biodiversity. The objectives of this work were to review (1) the functioning and actors involved in the PPP framework processes, (2) the construction of the environmental risk assessment focused on biodiversity, and (3) the suggested ways to respond to the identified limits. Both literature from social sciences and ecotoxicology were examined. Despite the protective nature of the European regulation on PPPs, the very imperfect consideration of biodiversity in the evaluation process was underlined. The main limits are the multiplicity of applicable rules, the routinization of the evaluation procedures, the lack of consideration of social data, and the lack of independence of the evaluation. Strengths of the regulation are the decision to integrate a systemic approach in the evaluation of PPPs, the development of modeling tools, and the phytopharmacovigilance systems. The avenues for improvement concern the realism of the risk assessment (species used, cocktail effects…), a greater transparency and independence in the conduct of evaluations, and the opening of the evaluation and decision-making processes to actors such as beekeepers or NGOs. Truly interdisciplinary reflections crossing the functioning of the living world, its alteration by PPPs, and how these elements question the users of PPPs would allow to specify social actions, public policies, and their regulation to better protect biodiversity.

An enhanced fourier neural operator surrogate for radioactive plume transport forecasting

AbstractAccurate real-time forecasts of atmospheric plume behavior are crucial for effective management of environmental release incidents. However, the computational demands of weather simulations and particle transport codes limit their applicability during emergencies. In this study, we employ a U-Net enhanced Fourier Neural Operator (U-FNO) to statistically emulate the calculations of the WSPEEDI dose forecasting numerical simulator, using pre-calculated ensemble simulations. The developed emulator is capable of effectively simulating any radioactive-release scenario and generating the time series of dose distribution in the environment 4000 times faster than the numerical simulator, while still maintaining high accuracy. It predicts the plume direction, extent, and dose-rate magnitudes using initial- and boundary-condition meteorological data as input. The speed and efficiency of this framework offers a powerful tool for swift decision-making during emergencies, facilitating risk-informed protective actions, evacuation execution, and zone delineation. Its application extends to various contaminant release and transport problems, and can be instrumental in engineering tasks requiring uncertainty quantification (UQ) for environmental risk assessment.

A regressive analysis of the main environmental risk factors of human echinococcosis in 370 counties in China.

Echinococcosis is a natural focal, highly prevalent disease in China. Factors influencing the spread of echinococcosis are not only related to personal exposure but also closely related to the environment itself. The purpose of this study was to explore the influence of environmental factors on the prevalence of human echinococcosis and to provide a reference for prevention and control of echinococcosis in the future. Data were collected from 370 endemic counties in China in 2018. By downloading Modis, DEM and other remote-sensing images in 2018. Data on environmental factors, i.e., elevation, land surface temperature (LST) and normalized difference vegetation index (NDVI) were collected. Rank correlation analysis was conducted between each environmental factor and the prevalence of echinococcosis at the county level. Negative binomial regression was used to analyze the impact of environmental factors on the prevalence of human echinococcosis at the county level. According to rank correlation analysis, the prevalence of human echinococcosis in each county was positively correlated with elevation, negatively correlated with LST, and negatively correlated with NDVI in May, June and July. Negative binomial regression showed that the prevalence of human echinococcosis was negatively correlated with annual LST and summer NDVI, and positively correlated with average elevation and dog infection rate. The prevalence of human cystic echinococcosis was inversely correlated with the annual average LST, and positively correlated with both the average elevation and the prevalence rate of domestic animals. The prevalence of human alveolar echinococcosis was positively correlated with both NDVI in autumn and average elevation, and negatively correlated with NDVI in winter. The prevalence of echinococcosis in the population is affected by environmental factors. Environmental risk assessment and prediction can be conducted in order to rationally allocate health resources and improve both prevention and control efficiency of echinococcosis.

Enhancing Workplace Safety through Personalized Environmental Risk Assessment: An AI-Driven Approach in Industry 5.0

This paper describes an integrated monitoring system designed for individualized environmental risk assessment and management in the workplace. The system incorporates monitoring devices that measure dust, noise, ultraviolet radiation, illuminance, temperature, humidity, and flammable gases. Comprising monitoring devices, a server-based web application for employers, and a mobile application for workers, the system integrates the registration of workers’ health histories, such as common diseases and symptoms related to the monitored agents, and a web-based recommendation system. The recommendation system application uses classifiers to decide the risk/no risk per sensor and crosses this information with fixed rules to define recommendations. The system generates actionable alerts for companies to improve decision-making regarding professional activities and long-term safety planning by analyzing health information through fixed rules and exposure data through machine learning algorithms. As the system must handle sensitive data, data privacy is addressed in communication and data storage. The study provides test results that evaluate the performance of different machine learning models in building an effective recommendation system. Since it was not possible to find public datasets with all the sensor data needed to train artificial intelligence models, it was necessary to build a data generator for this work. By proposing an approach that focuses on individualized environmental risk assessment and management, considering workers’ health histories, this work is expected to contribute to enhancing occupational safety through computational technologies in the Industry 5.0 approach.

DivFolio: a Shiny application for portfolio divestment in green finance wealth management

Abstract This paper introduces DivFolio, a multiperiod portfolio selection and analytic software application that incorporates automated and user-determined divestment practices accommodating Environmental Social Governance (ESG) and portfolio carbon footprint considerations. This freely available portfolio analytics software tool is written in R with a GUI interface developed as an R Shiny application for ease of user experience. Users can utilize this software to dynamically assess the performance of asset selections from global equity, exchange-traded funds, exchange-traded notes, and depositary receipts markets over multiple time periods. This assessment is based on the impact of ESG investment and fossil-fuel divestment practices on portfolio behavior in terms of risk, return, stability, diversification, and climate mitigation credentials of associated investment decisions. We highlight two applications of DivFolio. The first revolves around using sector scanning to divest from a specialized portfolio featuring constituents of the FTSE 100. The second, rooted in actuarial considerations, focuses on divestment strategies informed by environmental risk assessments for mixed pension portfolios in the US and UK.

Environmental Risk Identification and Green Finance Development Based on Multi-scale Fusion Recognition Network

AbstractThis paper aims to enhance the resilience of financial enterprises against environmental risks by leveraging financial data analysis tools. The approach involves designing environmental risk assessment indicators and rating criteria. The study utilizes a convolutional neural network model extended by a multi-scale feature fusion module to analyze environmental risk information in the industry. The proposed model achieves impressive results with accuracy (Acc), precision (P), recall (R), and F1 scores reaching 99.09, 96.31, 95.32, and 95.64, respectively. These metrics outperform those of comparison models. The success of this model is anticipated to pave the way for the transformation of green finance through automated industry-level environmental risk assessment. Furthermore, the method’s adaptability extends beyond environmental risks, offering a scalable solution for identifying and assessing environmental risks in various contexts.

Distribution patterns and environmental risk assessments of microplastics in the lake waters and sediments from eight typical wetland parks in Changsha city, China

The quality of water in urban parks is closely related to people’s daily lives, but the pollution caused by microplastics in park water and sediments has not been comprehensively studied. Therefore, eight typical parks in the urban area of Changsha, China, were selected, and Raman spectroscopy was used to explore the spatial distributions and compositions of the microplastics in the water and sediments, analyze their influencing factors, and evaluate their environmental risks. The results showed that the abundances of surface water microplastics in all parks ranged from 150 to 525 n L−1, and the abundances of sediment microplastics ranged from 120 to 585 n kg−1. The microplastics in the surface water included polyethylene terephthalate (PET), chlorinated polyethylene (CPE), and fluororubber (FLU), while those in the sediments included polyvinyl chloride (PVC), wp-acrylate copolymer (ACR), and CPE. Regression analyses revealed significant positive correlations between human activities and the abundances of microplastics in the parks. Among them, the correlations of population, industrial discharge and domestic wastewater discharge with the abundance of microplastics in park water were the strongest. However, the correlations of car flow and tourists with the abundance of microplastics in park water were the weakest. Based on the potential ecological risk indices (PERI) classification assessment method, the levels of microplastics in the waters and sediments of the eight parks were all within the II-level risk zone (53–8,549), among which the risk indices for Meixi Lake and Yudai Lake were within the IV risk zone (1,365–8,549), which may have been caused by the high population density near the park. This study provides new insights into the characteristics of microplastics in urban park water and sediment.

Effects of Pesticides Use on Pesticides Residues and Its Environmental Risk Assessment in Xingkai Lake（China）

Xingkai Lake, located in Heilongjiang Province, is an important fishery and agricultural base and is seriously polluted by agricultural non-point sources. To clarify the residual status of many pesticides in the surface water of Xingkai Lake, 27 types of pesticides, herbicides, and their degradation products were analyzed in rice paddy, drainage, and surface water around Xingkai Lake (China) during the rice heading and maturity periods. The results showed that all 27 types of pesticides, herbicides, and their degradation products were detected during the rice heading period, and the total concentration ranged from 247.97 to 6 094.49 ng·L-1. Additionally, 25 species were detected during the rice maturity period, and the total concentration ranged from 485.36 to 796.23 ng·L-1. In comparison, more pesticides, herbicides, and derived degradation products were detected during the heading period, and their total concentration was higher as well. During the rice heading period, atrazine, simetryn, and paclobutrazol were the main detected pesticides, atrazine and isoprothiolane were the main pesticides detected during the maturity period. The distribution characteristics of pesticides and herbicides in the surface water around Xingkai Lake (China) was similar to that in drainage, so they were probably imported from the drainage and rice paddy. The average risk quotient (RQ) values of atrazine, simetryn, prometryn, butachlor, isoprothiolane, and oxadiazon were higher than 0.1 in drainage and Xingkai Lake (China), which showed a potential risk to aquatic organisms.

An investigation into the effect of UV irradiation and biofilm colonization on adsorption and desorption behavior of polyurethane (PU) microplastics for bisphenol A (BPA)

Recent studies have demonstrated that microplastics (MPs) can act as a transport vector for pollutants in the environment. However, most studies have focused on carbon-carbon backbone polymers, with little known about heteroatom backbone polymers. In this study, polyurethane (PU) MPs were modified by UV irradiation and microorganism colonization to assess the different aging treatments on the interaction behavior (adsorption/desorption) between PU MPs and bisphenol A (BPA). The results of the adsorption kinetics experiment illustrated that the adsorption process of BPA could be described well by a pseudo-second order kinetic equation on both fresh and aged PU MPs. Although fresh PU MPs revealed the highest saturated adsorption capacity, at low BPA exposure concentrations (≤ 100 μg/L) the adsorption capacities for UV aged and biofilm colonized PU MPs were very similar to fresh MPs or even slightly higher than for fresh MPs, the main adsorption mechanisms including H-bonding interactions and π–π interactions. Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) investigations suggested obvious physicochemical property changes (e.g., generation of new functional groups, erosion, etc.) after UV irradiation and biofilm colonization. This study also showed that different exposure environments (ultrapure water, artificial seawater, simulated gastric fluid) could affect BPA adsorption/desorption via hydrophobic and electrostatic interaction on PU surfaces. These results further clarify the adsorption mechanisms of BPA on PU MPs that have undergone different aging treatments, and provides a theoretical basis for the assessment of environmental behavior and exposure risks when PU MPs and BPA coexist.

Emerging Technology Integration - Artificial Intelligence (AI) and Machine Learning (ML) for Predictive Analysis for Safety and Toxicity Assessment in Environmental Toxicology

Environmental toxicology is facing unprecedented challenges due to the escalating complexity of environmental issues. Traditional methodologies for safety and toxicity assessment are struggling to keep pace with the rapid evolution of pollutants and their impacts on ecosystems and human health. In response to this pressing need, there has been a paradigm shift in the field towards the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. This research article delves into the transformative potential of AI and ML in environmental toxicology, elucidating how these advanced computational tools offer promising solutions for predictive analysis. By leveraging vast datasets and sophisticated algorithms, AI and ML enable more efficient, accurate, and timely assessment of environmental risks. They empower researchers and policymakers to anticipate and mitigate potential hazards before they escalate into crises. This paper provides a comprehensive overview of the diverse applications of AI and ML in environmental toxicology, ranging from predictive modeling of chemical hazards to early detection of environmental threats. Furthermore, it highlights the potential of AI and ML to revolutionize decision-making processes in environmental management and policy formulation. By synthesizing vast amounts of data and identifying complex patterns and correlations, these technologies offer invaluable insights for crafting evidence-based policies and strategies to safeguard the environment and public health. This research article underscores the pivotal role of AI and ML in addressing the multifaceted challenges of environmental toxicology. Through their integration, we have the opportunity to enhance our understanding of environmental risks, optimize resource allocation, and ultimately, pave the way towards a more sustainable and resilient future.

Multi-strategy enhanced snake optimizer for quantitative structure-activity relationship modeling

Quantitative structure-activity relationship modeling techniques are widely employed in diverse areas of chemistry, including environmental risk assessment of compounds and virtual screening of drugs. The modeling process entails optimizing the hyper parameters of the model and selecting appropriate molecular descriptors. It is a typical optimization problem with high-dimensional, nonlinear, continuous and discrete variables coexisting. To address this challenge, this paper proposes an enhanced snake optimizer with multiple strategies. First, new food emergence and temperature change mechanisms are introduced to balance the exploration and exploitation. Second, a death and regeneration mechanism is employed to enhance the algorithm's ability to escape from local optima. Third, new formulas for food searching, fighting, mating and eating are designed to simplify the complexity of the algorithm and improve its search efficiency and accuracy. Fourth, the performance of the proposed algorithm is evaluated using the CEC2017 benchmark functions, eight engineering cases with continuous variables, and six feature selection cases with discrete variables. Experimental results demonstrate that the algorithm significantly outperforms comparison algorithms. Finally, the algorithm was applied to quantitative structure-activity relationship modeling for environmental toxicity prediction. Compared with other algorithms, the algorithm can better configure hyper-parameters and select fewer and more effective features for environmental toxicity prediction model. It will be an effective tool to improve the performance of quantitative structure-activity relationship models.

Which distribution to choose for deriving a species sensitivity distribution? Implications from analysis of acute and chronic ecotoxicity data

Species sensitivity distributions (SSDs) estimated by fitting a statistical distribution to ecotoxicity data are indispensable tools used to derive the hazardous concentration for 5 % of species (HC5) and thereby a predicted no-effect concentration in environmental risk assessment. Whereas various statistical distributions are available for SSD estimation, the fundamental question of which statistical distribution should be used has received limited systematic analysis. We aimed to address this knowledge gap by applying four frequently used statistical distributions (log-normal, log-logistic, Burr type III, and Weibull distributions) to acute and chronic SSD estimation using aquatic toxicity data for 191 and 31 chemicals, respectively. Based on the differences in the corrected Akaike’s information criterion (AICc) as well as visual inspection of the fitting of the lower tails of SSD curves, the log-normal SSD was generally better or equally good for the majority of chemicals examined. Together with the fact that the ratios of HC5 values of other alternative SSDs to those of log-normal SSDs generally fell within the range 0.1–10, our findings indicate that the log-normal distribution can be a reasonable first candidate for SSD derivation, which does not contest the existing widespread use of log-normal SSDs.

Predicted concentrations of antineoplastic drugs in the aquatic environment: The case of Ría de Vigo (NW, Spain)

The European Medicines Agency (EMA) mandates Environmental Risk Assessments (ERAs) since 2006 to determine potential risks of new marketed medicines. Drugs with a Predicted Environmental Concentration (PEC) in inland surface waters exceeding 0.01 μg L−1 require further environmental risk assessment. PEC may be refined based on prevalence data and/or based on the treatment regimen. In this study, based on EMA regulations, refined PEC of 108 antineoplastic drugs in coastal waters were determined based on the consumption in a coastal health area during 2021, identifying six drugs with potential environmental risk in surface waters (hydroxyurea, capecitabine, abiraterone, ibrutinib, imatinib and 5-fluorouracil) and two in marine ecosystem (hydroxyurea and capecitabine). Comparison of these refined PECs with data from marketing laboratories revealed significant disparities, suggesting the need for regular updates, especially with changes in drug indications or financing. Notably, the identified drugs are not yet on the main reference lists of emerging contaminants.

Pharmacokinetic and Environmental Risk Assessment of Prime-2-CoV, a Non-Replicating Orf Virus-Based Vaccine against SARS-CoV-2

Viral vector vaccines represent a substantial advancement in immunization technology, offering numerous benefits over traditional vaccine modalities. The Orf virus (ORFV) strain D1701-VrV is a particularly promising candidate for vaccine development due to its distinctive attributes, such as a good safety profile, the ability to elicit both humoral and cellular immunity, and its favorable genetic and thermal stability. Despite ORFV’s theoretical safety advantages, such as its narrow host range and limited systemic spread post-inoculation, a critical gap persists between these theoretical benefits and the empirical evidence regarding its in vivo safety profile. This discrepancy underscores the need for comprehensive preclinical validations to bridge this knowledge gap, especially considering ORFV’s use in humans. Our research introduces Prime-2-CoV, an innovative ORFV-based vaccine candidate against COVID-19, designed to elicit a robust immune response by expressing SARS-CoV-2 Nucleocapsid and Spike proteins. Currently under clinical trials, Prime-2-CoV marks the inaugural application of ORFV in human subjects. Addressing the aforementioned safety concerns, our extensive preclinical evaluation, including an environmental risk assessment (ERA) and detailed pharmacokinetic studies in rats and immunocompromised NOG mice, demonstrates Prime-2-CoV’s favorable pharmacokinetic profile, negligible environmental impact, and minimal ERA risks. These findings not only affirm the vaccine’s safety and efficacy but also pioneer the use of ORFV-based therapeutics, highlighting its potential for wider therapeutic applications.

Occurrence and ecological risks assessment of representative herbicides in a plateau alkali lake: Implications water management in prairie lakes

Herbicide residues in surface waters pose a potential hazard to aquatic ecosystems. Moreover, there is a scarcity of data regarding the occurrence of herbicide residues in lakes predominantly surrounded by non-agricultural land. This study aimed to monitor and assess seasonal variations in four commonly used herbicides (i.e., acetochlor, pretilachlor, butachlor, and atrazine) in the surface waters of the Hulun Lake Basin in China. The results indicated that the target herbicides were widely distributed throughout the study area.The residues of the four herbicides in surface water ranged from non-detectable levels to 1,978.60 ng L−1. Among the herbicides, atrazine had the highest detection rate (90.48 %). When considering the temporal factor, the median concentration of the selected herbicides in the lake was significantly higher during summer (102.68 ng L−1) than that during spring (97.59 ng L−1) and autumn (6.78 ng L−1). Furthermore, there were spatial variations in the surface waters of the Hulun Lake Basin. The residual levels in the lake exceeded those found in the inlet river, and the concentrations of herbicides in the estuary of the Kerulen River and central area of Hulun Lake were higher. The environmental risk assessment results indicated that the selected herbicides posed acceptable risks to fish in the aquatic environment of Hulun Lake. However, the risk quotient (RQ) values for acetochlor and atrazine in relation to algae exceeded 0.10. Similarly, the RQ values for butachlor in relation to algae and crustaceans exceeded 0.10 during the summer and autumn seasons and even surpassed 1.00 in certain samples. This can potentially endanger the survival of aquatic organisms. The findings of this study offer valuable insights to support efforts in pollution control, environmental safety assessments, and decision-making regarding water resource management in the Hulun Lake Basin.