Environmental Risk Assessment Research Articles

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.

Constructing an Ecological Network Based on Heat Environment Risk Assessment: An Optimisation Strategy for Thermal Comfort Coupling Society and Ecology

Constructing an Ecological Network Based on Heat Environment Risk Assessment: An Optimisation Strategy for Thermal Comfort Coupling Society and Ecology

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.

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.

Quinoline derivatives as corrosion inhibitors for mild steel in acid medium: Deeper insights from experimental, ab initio density functional theory modeling, and in silico ecotoxicity investigations

Two (E)-2-(((2-((7-chloroquinolin-4-yl)amino)ethyl)imino)methyl)-4-R-phenol Schiff bases derivatives CMN (R = NO2) and CMP (R = H) were successfully prepared. Then, CMN and CMP were evaluated as corrosion inhibitors for 1020 mild steel in 1.0molL–1 HCl using different methods. Gravimetric experiments have shown that CMN and CMP reach efficiencies of 80 and 91% at 1.45mmolL–1. Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM) measurements have indicated the formation of a protective adsorbed film on the mild steel surface. Electrochemical Impedance Spectroscopy (EIS), Linear Polarization Resistance (LPR), Potentiodynamic Polarization (PP), and Electrochemical Frequency Modulation (EFM) have also shed some light on electrochemical behavior of compounds. These data have depicted better polarization resistance and lower corrosion current density in the presence of both organic molecules, also proving that the process is controlled by charge transfer and that CMN and CMP are mixed-type corrosion inhibitors. ab initio Density Functional Theory (DFT) investigated how two quinoline-based molecules (CMN and CMP) prevent corrosion on iron surfaces. Simulations revealed CMP bonds more strongly to iron than CMN, leading to better protection. Simulations also showed that these inhibitor molecules form a protective layer on the iron, slowing down the movement of corrosive substances. Environment risk assessment through QSAR-based models have indicated that the corrosion inhibitors do not have the potential to bioaccumulate in fish. Still, the acute and chronic toxicity values at the different trophic levels investigated call for attention for environmentally friendly structural optimization.

Planning scale flood risk assessment and prediction in ultra-high density urban environments: The case of Hong Kong

Climate change has significantly increased the risks associated with urban flooding. However, most research on flood risk assessment focuses on large-scale climate changes and impacts, leaving a research gap in the high spatial resolution of flood risk assessment in inter-urban areas. This gap makes it difficult to guide regional planning for the government. Therefore, this study aims to explore the risks of floods in ultra-high-density cities under climate change at the planning scale, using Hong Kong as a case study. We comprehensively assessed the flood risk index (FRI) in the built environment in 211 tertiary planning units (TPUs) from the three dimensions of vulnerability, exposure, and hazard from 2006 to 2021. We also employed a prediction model to forecast the spatial–temporal patterns of FRI in the next 5, 10, and 15 years and evaluated the uneven distribution of flood risks. The results show that the FRI of TPUs increased yearly, which poses higher threats to agglomerative areas of transportation and functional facilities. Additionally, future FRI will further impact coastal TPUs in western Hong Kong, resulting in more negative impacts on high-building areas. Therefore, urban planning should prioritize integrating flood management and risk mitigation measures.

Exposomics in practice: Multidisciplinary perspectives on environmental health and risk assessment.

Exposomics in practice: Multidisciplinary perspectives on environmental health and risk assessment.

Acute toxicity of binary mixtures for quorum sensing inhibitors and sulfonamides against Aliivibrio fischeri: QSAR investigations and joint toxic actions

Quorum sensing inhibitors (QSIs), as a kind of ideal antibiotic substitutes, have been recommended to be used in combination with traditional antibiotics in medical and aquaculture fields. Due to the co-existence of QSIs and antibiotics in environmental media, it is necessary to evaluate their joint risk. However, there is little information about the acute toxicity of mixtures for QSIs and antibiotics. In this study, 10 QSIs and 3 sulfonamides (SAs, as the representatives for traditional antibiotics) were selected as the test chemicals, and their acute toxic effects were determined using the bioluminescence of Aliivibrio fischeri (A. fischeri) as the endpoint. The results indicated that SAs and QSIs all induced S-shaped dose-responses in A. fischeri bioluminescence. Furthermore, SAs possessed greater acute toxicity than QSIs, and luciferase (Luc) might be the target protein of test chemicals. Based on the median effective concentration (EC50) for each test chemical, QSI-SA mixtures were designed according to equitoxic (EC50(QSI):EC50(SA) = 1:1) and non-equitoxic ratios (EC50(QSI):EC50(SA) = 1:10, 1:5, 1:0.2, and 1:0.1). It could be observed that with the increase of QSI proportion, the acute toxicity of QSI-SA mixtures enhanced while the corresponding TU values decreased. Furthermore, QSIs contributed more to the acute toxicity of test binary mixtures. The joint toxic actions of QSIs and SAs were synergism for 23 mixtures, antagonism for 12 mixtures, and addition for 1 mixture. Quantitative structure–activity relationship (QSAR) models for the acute toxicity QSIs, SAs, and their binary mixtures were then constructed based on the lowest CDOCKER interaction energy (Ebind-Luc) between Luc and each chemical and the component proportion in the mixture. These models exhibited good robustness and predictive ability in evaluating the toxicity data and joint toxic actions of QSIs and SAs. This study provides reference data and applicable QSAR models for the environmental risk assessment of QSIs, and gives a new perspective for exploring the joint effects of QSI-antibiotic mixtures

Comparative Toxicology of Algal Cell Extracts and Pure Cyanotoxins: Insights into Toxic Effects and Mechanisms of Harmful Cyanobacteria Raphidiopsis raciborskii

Ongoing research on cyanotoxins, driven by the socioeconomic impact of harmful algal blooms, emphasizes the critical necessity of elucidating the toxicological profiles of algal cell extracts and pure toxins. This study comprehensively compares Raphidiopsis raciborskii dissolved extract (RDE) and cylindrospermopsin (CYN) based on Daphnia magna assays. Both RDE and CYN target vital organs and disrupt reproduction, development, and digestion, thereby causing acute and chronic toxicity. Disturbances in locomotion, reduced behavioral activity, and weakened swimming capability in D. magna have also been reported for both RDE and CYN, indicating the insufficiency of conventional toxicity evaluation parameters for distinguishing between the toxic effects of algal extracts and pure cyanotoxins. Additionally, chemical profiling revealed the presence of highly active tryptophan-, humic acid-, and fulvic acid-like fluorescence compounds in the RDE, along with the active constituents of CYN, within a 15-day period, demonstrating the chemical complexity and dynamics of the RDE. Transcriptomics was used to further elucidate the distinct molecular mechanisms of RDE and CYN. They act diversely in terms of cytotoxicity, involving oxidative stress and response, protein content, and energy metabolism, and demonstrate distinct modes of action in neurofunctions. In essence, this study underscores the distinct toxicity mechanisms of RDE and CYN and emphasizes the necessity for context- and objective-specific toxicity assessments, advocating nuanced approaches to evaluate the ecological and health implications of cyanotoxins, thereby contributing to the precision of environmental risk assessments.

Seismic Risk Assessment of Built Environment in the Himalayan Foothill City of Dehradun, Uttarakhand

Seismic Risk Assessment of Built Environment in the Himalayan Foothill City of Dehradun, Uttarakhand

Microplastic in Dredged Sediments: From Databases to Strategic Responses.

Microplastics (MPs) accumulate in sediments, yet guidelines for evaluating MP risks in dredged sediments are lacking. The objective of this study was to review existing literature on MPs in sediments to improve fundamental knowledge of MP exposures and develop a publicly available database of MPs in sediments. Twelve percent of the reviewed papers (nine studies) included sediment core samples with MP concentrations generally decreasing with depth, peaking in the top 15cm. The remaining papers evaluated surficial grab samples (0 to 15cm depth) from various water bodies with MPs detected in almost every sample. Median MP concentrations (items/kg dry sediment) increased in this order: lakes and reservoirs (184), estuarine (263), Great Lakes nearshore areas and tributaries (290), riverine (410), nearshore marine areas (487), dredge activities (817), and harbors (948). Dredging of recurrent shoaling sediments could be expected to contain MPs at various depths with concentrations influenced by the time elapsed since the last dredging event. These results offer key insights into the presence and variability of MPs in dredged sediments, informing environmental monitoring and risk assessment strategies.

Different Sensitivity of Flower-Visiting Diptera to a Neonicotinoid Insecticide: Expanding the Base for a Multiple-Species Risk Assessment Approach

Insects play an essential role as pollinators of wild flowers and crops. At the same time, pollinators in agricultural environments are commonly exposed to pesticides, compromising their survival and the provision of pollination services. Although pollinators include a wide range of species from several insect orders, information on pesticide sensitivity is mostly restricted to bees. In addition, the disparity of methodological procedures used for different insect groups hinders the comparison of toxicity data between bees and other pollinators. Dipterans are a highly diverse insect order that includes some important pollinators. Therefore, in this study, we assessed the sensitivity of two hoverflies (Sphaerophoria rueppellii, Eristalinus aeneus) and one tachinid fly (Exorista larvarum) to a neonicotinoid insecticide (Confidor®, imidacloprid) following a comparative approach. We adapted the standardized methodology of acute contact exposure in honey bees to build dose–response curves and calculate median lethal doses (LD50) for the three species. The methodology consisted in applying 1 µL of the test solution on the thorax of each insect. Sphaerophoria rueppelli was the most sensitive species (LD50 = 10.23 ng/insect), and E. aeneus (LD50 = 18,176 ng/insect) the least. We then compared our results with those available in the literature for other pollinator species using species sensitivity distribution (SSD). Based on the SSD curve, the 95th percentile of pollinator species would be protected by a safety factor of 100 times the Apis mellifera endpoint. Overall, dipterans were less sensitive to imidacloprid than most bee species. As opposed to most bee species, oviposition and fecundity of many dipteran species can be reliably assessed in the laboratory. We measured the number of eggs laid following exposure to different insecticide doses and assessed the potential trade-off between oviposition and survival through the sublethal sensitivity index (SSI). Exposure to imidacloprid had a significant effect on fecundity, and SSI values indicated that oviposition is a sensitive endpoint for the three dipteran species tested. Future studies should integrate this information related to population dynamics in simulation models for environmental risk assessment.