Integrated Environmental Assessment and Management最新文献

The European Union's (EU's) Green Deal initiatives, including the Farm to Fork Strategy and the Chemicals Strategy for Sustainability, emphasize the need for developing plant protection products (PPPs) that meet safety and sustainability goals. In the EU, PPPs are regulated under Regulation (EC) No. 1107/2009, which sets approval criteria to ensure human health and environmental safety. This legislation is complemented by the sustainable use of pesticides (Directive 2009/128), which aims to achieve sustainable pesticide use by minimizing risks to human health and the environment, while promoting use of integrated pest management and nonchemical alternatives. Although both legislations address the conditions of placing PPPs on the market and their use, neither directly addresses the broader aspects of sustainability, such as the life cycle impacts, resource efficiency during design and manufacture, and the socioeconomic dimensions of sustainability. The safe- and sustainable-by-design framework of the EU Commission's Joint Research Centre offers a holistic approach to chemical product innovation, minimizing risks and maximizing sustainability throughout a chemical's life cycle. This framework, combined with existing safety regulations, can advance the sustainability of PPPs in line with the European Green Deal and the Chemicals Strategy for Sustainability. Agrochemical manufacturers have embedded into their innovation pipelines practices that align with the safe- and sustainable-by-design framework, but approaches tend to be company specific and lack standardized metrics. Incorporating well-defined sustainability criteria and incentives for manufacturers would accelerate the development of PPPs that contribute to long-term agricultural sustainability, safeguard human health and the environment, and ensure food security in line with sustainable development goals.

Pesticide residues in soil samples were analyzed by a gas chromatograph-mass spectrometer. To evaluate the ecological risks posed to significant soil organisms, including earthworms (Eisenia fetida), springtails (Folsomia candida), and nitrogen-mineralizing microorganisms, we employed toxicity exposure ratios and risk quotients to assess potential impacts. The rank order of pesticides in soil based on their concentration was p,p'-DDE (dichlorodiphenyldichloroethylene) (M ± SD; 37.48 ± 16.76 μg kg-1) > propoxur (23.94 ± 10.71 μg kg-1) > propargite (22.09 ± 9.88 μg kg-1) > α-endosulfan (11.56 ± 5.17 μg kg-1) > malathion (10.22 ± 4.57 μg kg-1) > fenthion (7.78 ± 3.48 μg kg-1) > … > fenitrothion (0.11 ± 0.05 μg kg-1). Exposed soil organisms of F. candida and nitrogen mineralization are at risk due to chronic exposure to chlorpyrifos and fenthion, with TERmax toxicity exposure ratios of 0.86 and 1.20, respectively. The cumulative risk quotient of 5.3 indicates that the detected pesticide mixtures would pose significant ecological risks in traditional farming study areas. Therefore, to preserve soil quality and mitigate the risks associated with pesticide use, it is crucial to develop targeted integrated crop protection strategies.

The response mechanisms and quantitative analysis of runoff pollution reduction, accumulation effect of pollutants in media, and plant physiological characteristics for bioretention systems remain inadequately investigated. In this study, we constructed pot-scale systems with a mixture of [soil + sand + leaf litter compost + Ophiopogon japonicus], and designed the inflow condition by L39 orthogonal combinations under the impact conditions of runoff pollution load from different functional zones/underlying surface. The indicators of inflow/outflow, media, and plant aboveground and underground parts were tested. The results showed that the bioretention systems under different pollutants load impacts had a significant effect on runoff ammonium nitrogen (NH4+-N), total phosphorus (TP), and chemical oxygen demand (COD), with load reduction rate of 11.9%-71.8%, 8.3%-43.7%, 5.1%-30.1%, respectively. Significant leaching was observed with nitrate nitrogen (NO3--N) and total nitrogen (TN). The results of multiple linear regression analysis showed that the load reduction of carbon, nitrogen, and phosphorus (C, N, and P) in runoff is positively correlated with their inflow concentrations (R2 > 0.8), and the interaction between them is not significant. Under different inflow load impacts, the variation ranges for chlorophyll, malondialdehyde, and biomass are 63.4%, 77.4%, and 59.8%, respectively. The growth of plants is mainly influenced by the inflow concentration of C and N. The research results contribute to a deeper understanding of the impact of incoming pollutant concentrations on bioretention systems, and are helpful for the long-term management of bioretention systems and the assessment of potential risks.

Understanding the removal of a chemical in a wastewater treatment plant (WWTP) is important when performing chemical risk assessments. Chemicals undergoing assessment often have limited experimental measurements of physicochemical properties, biodegradation rates, and WWTP removal efficiencies. Models available to risk assessors to predict WWTP removal efficiencies are best used with high-quality input data and knowledge of plant conditions, information often unavailable when performing chemical risk assessments. In this work, we outline the development of the Canadian A.I. Removal Rate Estimator (CAIRRE), an artificial intelligence (A.I.) model suite designed to estimate removal efficiencies from secondary WWTPs. CAIRRE was trained on median experimental removal efficiencies for 161 chemicals across 59 secondary WWTPs in Canada, the USA (California), and various other locations curated from literature. The CAIRRE regression model has a validation Pearson R2 of 0.81 based on leave-one-out-validation (LOOV) results. When used to predict effluent concentrations for a test set containing 53 chemicals not seen during model training, CAIRRE was able to reproduce experimental observations with a Pearson R2 of 0.91. The CAIRRE model outperformed existing mechanistic and fugacity WWTP models which rely on physical-chemistry and biodegradation data provided by the user. This work demonstrates that the A.I. modeling approach taken in the development of CAIRRE is a promising strategy for predicting removal efficiencies of chemicals from secondary WWTPs.

Climate change-induced environmental alterations are significantly accelerating the development and dissemination of antimicrobial resistance (AMR) in the environment through multiple interconnected pathways. Rising global temperatures facilitate bacterial adaptation and mutation rates, with studies demonstrating that even small temperature increases can enhance bacterial resistance gene stability and horizontal transfer efficiency. Extreme weather events such as flooding and droughts disrupt sanitation infrastructure, leading to increased pathogen transmission and subsequent antimicrobial use, while also creating conditions that promote the mixing of resistant bacteria from different environmental compartments. Climate-induced changes in precipitation patterns and ecosystem disruption further contribute to AMR spread by altering microbial community dynamics and increasing exposure to heavy metals and pollutants that co-select for antibiotic resistance genes. Current management strategies remain fragmented, with the UK's 2024-2029 National Action Plan emphasizing the need for improved waste management, wastewater treatment, and stewardship initiatives to reduce environmental AMR dissemination, though implementation faces significant technical and financial barriers. Critical knowledge gaps persist regarding the quantitative relationships between environmental factors and AMR development, with insufficient surveillance data from environmental matrices, limited understanding of resistance gene transmission pathways, and inadequate standardized methodologies for environmental AMR monitoring. Perhaps most concerning is the lack of comprehensive government policies specifically addressing climate-AMR interactions, with most countries lacking integrated frameworks that connect climate adaptation strategies with AMR mitigation efforts, despite growing recognition that both challenges share common drivers and require coordinated responses under the One Health approach. The absence of robust environmental AMR surveillance systems particularly in low- and middle-income countries creates substantial data gaps that hinder evidence-based policy development, while regulatory frameworks remain primarily focused on clinical settings rather than addressing the broader environmental dimensions of resistance emergence and spread.

Growing concerns over global warming and environmental degradation emphasize the need for sustainable waste management and renewable energy solutions. This study conducts a comprehensive multicriteria decision-making (MCDM) assessment of manure from six livestock and poultry types in Turkey-dairy cow, buffalo, beef cattle, sheep, goat, and chicken-focusing on their carbon footprint and environmental impacts. Fourteen criteria were used for evaluation, including greenhouse gas emissions, biogas potential, volatile solids, and nutrient composition. To determine the most sustainable manure source, three MCDM methods were applied: analytic hierarchy process, technique for order preference by similarity to ideal solution, and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR - Multi-Criteria Optimization and Compromise Solution). The analytic hierarchy process provided the criteria weights through pairwise comparisons, while the other two methods ranked alternatives based on proximity to the ideal solution. All methods consistently identified beef cattle manure as the optimal option. This integrated MCDM framework offers insights for policy makers to improve manure management strategies balancing environmental impact reduction and renewable energy production.

Understanding how sublethal contaminant effects scale up to impact fish populations remains a key challenge in ecotoxicology. In this exploratory study, we propose a conceptual approach linking behavioral impairment to population dynamics using ivermectin (IVM) exposure in freshwater migratory neotropical characiform Prochilodus lineatus. Juveniles of P. lineatus were exposed for 15 days to environmentally relevant concentrations of IVM (0.5 and 1.5 µg·L-1), and escape performance was assessed via maximum swimming speed (MSS) during a predator avoidance response. A significant reduction in MSS was detected at 0.5 µg·L-1. A logistic model was then used to estimate predator capture probability from normalized MSS, and this relationship was integrated into a virtual population analysis by adjusting natural mortality. Under this scenario, population trajectories showed an accelerated decline, particularly in early years, as compared with a no-IVM baseline. While simplified, this framework demonstrates how behavioral endpoints can be used to generate first-order approximations of demographic consequences. Rather than offering a formal risk assessment, our goal is to illustrate a transferable and hypothesis-driven method that connects individual-level toxicological effects with ecological outcomes. Given the widespread use of IVM in floodplain cattle production and the ecological relevance of P. lineatus, this case highlights the potential of sublethal contaminants to alter fish population and stocks. The framework may serve as a starting point for integrating behavioral biomarkers into population-level models in support of environmental assessment and decision making, especially in data-limited contexts.

Climate change is triggering a systemic crisis in global agriculture by simultaneously eroding its fundamental pillars: the area of cultivable land, the yield per unit of land, and the stability of annual production. This "triple threat" manifests through the progressive loss of productive croplands, significant declines in crop yields, and increasingly volatile food supplies under climate change. Consequently, the combined risks to food security are far more severe than assessments focusing solely on declining average yields suggest. Although autonomous adaptation can moderate these impacts, substantial residual damages persist. Securing future food supplies therefore demands an integrated policy strategy that concurrently safeguards cropland, boosts climate-resilient productivity, and manages systemic volatility through targeted interventions by governments, the private sector, and international bodies.