Integrated Environmental Assessment and Management最新文献

Soil mesofauna field studies conducted for ecological risk assessments of plant protection products (PPPs) require expertise for interpretation. The new statistical Closure Principle Computational Approach Test (CPCAT; introduced for Poisson-distributed count data) is proposed as alternative for established methods. However, the biological relevance of potential effects remains unclear. One aim was to investigate how to assess biological relevance. Biological Control Ranges (BCR) were calculated for each taxon and sampling time point within individual studies and it was compared whether the average abundance of a treatment group is in- or outside of the BCR. High control abundance variability of Collembola resulted in largely varying BCR values between studies. The second aim was to investigate performance of different statistical methods. Statistical analysis revealed that abundance data exhibited overdispersion in the majority of cases, instead of Poisson-distribution. Significant differences were observed in more than 50% of the comparisons using CPCAT, even before test item application happened in the field. Post-application, most statistical significances determined by CPCAT occurred despite the absence of a rate-response relationship and the arithmetic mean abundance values in the assigned treatment plots being within the BCR; thus, they should be considered as false positive results. In contrast, the Dunnett's test and Ranked Dunnett's test, either considering normally distributed data or being independent from the data distribution, barely detected a significant difference at pre-application when the abundance average values of assigned treatment plots were within the BCR pre- and post-application. In conclusion, pre-requisites for CPCAT (Poisson distribution, no over- or underdispersion of data) do not apply in the majority of the cases examined here. Holistic interpretation of field data needs to consider the ecological relevance of observations (via comparison with the BCR), presence of a rate-response-relationship, onset, scale, and pattern of the response, instead of solely focusing on statistics.

Developing countries are implementing strategies to mitigate the environmental impacts of municipal solid waste (MSW) in sustainable ways. Therefore, a holistic approach as municipal solid waste management (MSWM) has emerged to handle Sustainable Development Goals (SDG), in particular SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Production and Consumption). Increased MSW generation rates compel policymakers to develop feasible MSW management strategies to meet sustainable development goals. This study aims to assess the environmental and exergetic impacts of an integrated municipal solid waste management system by considering MSW collection and transportation, landfill site construction and operation, piping process, landfill gas (LFG) system, and electricity generation. Hotspots and major contributors within the system's sub-processes have been identified from the life cycle assessment (LCA) perspective. Results were normalized by one ton of disposed MSW and generated one kWh of electricity by LFG power plant. Global warming potential (GWP) and exergy consumption (CExD) for one ton of disposed MSW were calculated as 1.60E + 02 kg CO2-eq/ton and 2.45E + 3 MJ/ton, respectively. Furthermore, LFG power plant's impacts were calculated as 1.56E + 00 kg CO2-eq/kWh and 2.40E + 01 MJ/kWh, respectively. Results showed that hotspots of environmental and exergy impacts on overall system were accumulated in MSW collecting and transportation processes at 97.80% and 93.50%, respectively. This study highlighted that optimizing the waste truck routes, constructing transfer stations, and decreasing diesel use in waste trucks substantially influence the total life cycle performance of integrated MSWM systems.

The remediation of soils and aquifers contaminated by Light Non-Aqueous Phase Liquid (LNAPL) relies on a precise understanding of the LNAPL distribution above the water table. This study investigates the impact of groundwater table fluctuations and temperature change on LNAPL redistribution in a heterogeneous porous medium through laboratory-scale experiments. Experiments were conducted in a two-dimensional tank simulating aquifer condition, using diesel fuel as the LNAPL. The reservoir filled with coarse sand and fine sand low-permeability lenses, reproduced the subsurface heterogeneity. Following LNAPL infiltration from the top, controlled drainage and imbibition cycles simulated water table fluctuations. Experiments were conducted at 10 °C and 20 °C to characterize temperature effects. Fluid behavior was monitored using Time Domain Reflectometry (TDR) probes and high-resolution image analysis. TDR measurements provided quantitative dielectric permittivity data, which were converted to saturation profiles. Simultaneously, an image processing approach using the Biodock platform based on artificial intelligence and OpenCV was used to visualize the spatial distribution of LNAPL, water, and air. Applying the two methods allowed integrated methodology and a detailed understanding of the dynamics driving LNAPL migration. Results show that water table fluctuations significantly affect LNAPL redistribution, with each imbibition cycle leading to LNAPL entrapment in the capillary fringe due to wettability changes and capillary barriers. Higher temperature increased the mobility of LNAPL by reducing its viscosity, resulting in more efficient fluid displacement during drainage. This highlights the importance of studying the fate and transport of pollutants in the laboratory under temperature conditions relevant to the aquifers. Low-permeability lenses further modulated LNAPL migration, emphasizing subsurface heterogeneity critical role. Overall, the comprehensive experimental design combining TDR and advanced image analysis provides insight into the mechanisms of LNAPL behavior under dynamic environmental conditions and hints at further improvements for predictive models and remediation strategies in contaminated subsurface environments.

The intensive and repeated use of agrochemicals, including synthetic pesticides, herbicides, and fertilisers, has led to persistent contamination of agricultural soils, endangering soil health, ecosystem services, biodiversity, and sustainable food production. Soil microbiomes, with their remarkable metabolic versatility, represent a promising resource for in situ remediation of these pollutants. This review provides an integrated overview of the enzymatic and regulatory mechanisms underpinning microbial remediation, placing greater emphasis on enzymatic degradation as the central process driving pollutant breakdown. The biodegradation of soil pollutants is orchestrated by a network of microbial enzymes, including organophosphorus hydrolases, dehalogenases, oxidoreductases, dioxygenases, plastic-degrading and alkane-catabolising enzymes, that catalyse oxidation, hydrolysis, and dehalogenation reactions, transforming toxic compounds into less harmful intermediates that feed into metabolic pathways. Understanding the relationship between these enzymes, their encoding genes, and microbial hosts is crucial for designing robust bioremediation strategies. Complementing these biochemical processes, quorum sensing (QS) is discussed as a regulatory system that modulates microbial cooperation, biofilm formation, and catabolic gene expression during degradation. Emerging strategies, including microbial consortia design and synthetic biology-based engineering, are evaluated with a focus on the integration of QS-mediated interactions. Critical challenges, including soil heterogeneity, abiotic inhibition of QS signals, enzyme instability, biosafety concerns related to engineered strains, and horizontal gene transfer, are discussed. Future perspectives highlight enzyme engineering, QS-based biosensors, artificial intelligence-driven modelling, and synthetic QS circuits as tools to optimise bioremediation outcomes. Collectively, these insights outline pathways for advancing ecologically sound and sustainable approaches to the remediation of agrochemical-contaminated soils.

Digitalization in agriculture is rapidly progressing. Smart farming technology and usage of farm management information systems implementing detailed geospatial data are used more frequently. The authorization approach of plant protection products in Europe does not currently make use of these advances. A 90th percentile protection goal is currently often established based on a few scenarios representing a realistic worst case of agri-environmental conditions. Within this process the products receive authorization and mitigation requirements on the product label, which usually cover all fields, no matter if the field is very vulnerable or not. This is a pragmatic approach that may lead to sufficient protection of most fields while at the same time other fields are accepted as being under protected. To overcome the limitations of the current assessment based on a few worst-case scenarios, a transformation of the current risk assessment scheme towards a digital driven field specific risk management is proposed in three phases. The risk assessment procedure on European Union and member state level would remain in large parts as it is. All three phases make use of the availability of farm management information systems to distribute field specific restrictions and mitigation requirements. In phase 1 the mitigation requirements, based on standard regulatory scenarios (e.g., FOCUS (FOrum for Co-ordination of pesticide fate models and their USe) or PERSAM (Vito NV, 2016)), are transferred to the specific fields resembling the closest similarities of the environmental conditions. In phase 2, field specific modelling is performed where the standard parameterization can be adapted for local conditions. In phase 3, geospatial data are used to derive field specific parameterizations for the exposure and effect models. In all phases, each field receives application restrictions and mitigation requirements depending on the local situation which the farmers can fulfil by combining different mitigation options from a mitigation toolbox. The proposed scheme increases protection of biodiversity without compromising yield production.

Wastewater treatment plants (WWTPs) are recognized as major sources of organic micropollutants (OMPs) for aquatic environments. Yet, chemical monitoring alone may underestimate the ecological risks posed by complex OMP mixtures. Here, we combined an effect-based monitoring (EBM) approach with targeted chemical analysis to assess environmental risks of OMP mixtures in effluents from 16 WWTPs in Flanders, Belgium. Effluent sites were selected from a five-year regional monitoring dataset, prioritizing locations with high cumulative risk quotients. Bioassays using Microcystis aeruginosa (cyanobacteria growth inhibition) and Danio rerio (zebrafish larvae, light/dark locomotive assay) were conducted on effluent extracts. High-resolution mass spectrometry identified 130 compounds, with 26 OMPs quantified across classes, including pharmaceuticals, antibiotics, herbicides, and per- and polyfluoroalkyl substances. Median effective concentrations (EC50) and 10% effective concentrations (EC10) for cyanobacteria inhibition ranged between relative enrichment factors (REF) of 4.1-38 and 1.1-4.7, respectively. Iceberg modeling identified azithromycin and clarithromycin as the main drivers of cyanobacterial inhibition. Zebrafish behavioral responses were significantly affected in 8 of 16 samples (REF 1.25-20), but these differences could not be explained by the available chemical data. Only some suspect compounds were identified, including antidepressants and pesticides, and therefore this remains an interesting aspect for future investigations. Risk characterization for receiving surface waters using chemical-based risk quotients, margin of safety, and effect-based trigger values revealed ecological risk (RQ > 1) in 13 of 16 sites. This study highlights the added value of integrating EBM with chemical monitoring to explain mixture effects, identify key toxicants, and support improved regulatory frameworks for environmental management.

For several decades, the benchmark dose (BMD) methodology has been recommended for estimating safe exposure levels to toxic substances. When the response variable is continuous, the BMD and its lower confidence limit (BMDL) are often estimated using the hybrid method, which assumes a normal distribution to model the probability of an adverse response. Typically, this approach relies on a dose-response model with the assumption of constant standard deviation across all doses. However, when this assumption is violated, it can lead to biased estimates, and current implementations of the hybrid method do not account for this. In this paper, we introduce an extended class of dose-response models that allows for variation in the standard deviation across doses and adapt the hybrid method accordingly. We illustrate the proposed method using two data sets with two types of heteroscedasticity and show, through simulation, that addressing variance heterogeneity reduces bias and results in BMDL estimates with coverage closer to the nominal level.

Mesocosms can be used in higher tier aquatic risk assessments to assess the impact of Plant Protection Products (PPPs) on macrophytes. However, it is unclear whether these expensive and time consuming higher tier studies influence regulatory outcomes. This review highlights common shortcomings in the experimental design of mesocosm studies, with the aim of maximising the regulatory value of future mesocosm studies. Fourteen mesocosm studies, which have been submitted for the regulatory risk assessments for macrophytes in the EU or GB, were identified and reviewed. Results show that only five of the 14 mesocosm studies were deemed acceptable by the regulatory authorities, suggesting that mesocosm studies are not currently being used to their full potential. Issues with the submitted studies include not following a realistic PPP exposure profile (including incorrect dose timings and dilutions), only using one macrophyte morphology, not leaving enough time for the macrophytes to establish and a lack of replicates which increases variability within treatments. Glyceria maxima and Myriophyllum spicatum were frequently the most sensitive macrophyte species, whilst dry weight was often the most sensitive and least variable endpoint. Even though mesocosms provide the opportunity for recovery and community responses to be observed, such information has not been used by regulatory authorities. Future regulatory mesocosm studies can build upon the shortcomings highlighted here, providing a greater chance of regulatory impact.

Marine and coastal environments are facing unprecedented challenges due to the presence of litter, mesolitter and microplastics. This study investigated the characteristics and distribution of litter, mesolitter (2-25 mm) and microplastics (MPs; <5 mm) in beach sediment and MPs in seawater samples from Table Bay, Cape Town. Each "litter-category" was assessed and analyzed separately. Samples were collected from two sites: Woodbridge Island and Derdesteen. Litter and mesolitter were sampled along 100 meters of beach for 10 consecutive days during summer and autumn. A total of 11,179 litter items (average: 139.74 ± 20.69 SE items/100 m) and 1,428 mesolitter pieces (average: 4.46 ± 0.60 items/m) were collected, while 688 microplastics (MPs) were extracted from water and sediment samples. Plastic was the most abundant litter and mesolitter recorded. Plastics accounted for 90% by count and 48% by weight in collected litter, with foam (mainly polystyrene) being the most abundant plastic type found. Plastic pellets were the dominant mesolitter type, while fibrous MPs dominated the extracted MPs, which were mainly blue in color. Further analysis of the collected plastic mesolitter using a Spectrum Two Universal Attenuated Total Reflectance Infrared (UATR-IR) spectrometer showed polyethylene (PE: 60%) and polypropylene (PP: 27%) as the dominant polymers in meso-plastics. All the three categories of contaminants (litter, mesolitter and MPs) were higher at Woodbridge Island than Derdesteen, indicating the effects of anthropogenic inputs at the impact site. The anthropogenic inputs at the impacted site stem from beachgoers, residential and commercial inputs, maritime operations, recreational activities, and tide pooling activities at the site. Our study highlighted plastics as a significant component of marine litter, and the prevalence of polyethylene and pellets in mesolitter highlights the urgent need for preventive measures and sustainable clean-ups, to mitigate the short- and long-term impacts of plastics on the marine ecosystems and biodiversity.

Focusing on sustainable agriculture production, this study presents a thorough analysis of the environmental and economic aspects of maize grain cultivation across farm sizes. Primary data were collected from 210 maize farmers using multi-stage random sampling through direct interviews during the 2021-22 crop year, with rigorous pre-testing to ensure reliability. The study examines different environmental impact categories and economic performance, revealing the relationship between land size and financial outcomes. The Global Warming Potential per ha (GWPha) increased across farm size categories, with large farms emitting 2.600 t CO2e/ha, medium farms 2.364 t CO2e/ha and small farms 2.3049 t CO2e/ha, primarily due to more resource-intensive practices on larger farms. Other environmental impacts revealed that large farms had higher acidification and fresh eutrophication potentials. Economic analysis showed that large farms achieved higher gross returns, while small and medium farms recorded better net revenue and lower costs per unit of maize produced, reflecting higher economic efficiency. Carbon Efficiency (CE) and the Carbon Sustainability Index (CSI) further highlighted the advantage of small and medium farms in managing emissions while maintaining productivity. The study emphasizes the need for sustainable practices such as optimized fertilizer use, efficient irrigation and mechanization to reduce emissions and enhance profitability. These findings highlight the potential of small and medium-scale farms to lead sustainable agricultural production, suggesting that collaborative strategies promoting sustainable inputs and technologies can support both environmental and economic goals in maize farming.