Enabling forecasts of environmental exposure to chemicals in European agriculture under global change

Quantifying the influence of climate change on pesticide risks in drinking water

Climate change can influence pesticide contamination and resulting human health risks due by altering weather conditions that drive pesticide fate and transport. However limited research has examined these effects, leaving regulatory frameworks and adaptation strategies unable to address future pesticide risks. This study develops a novel probabilistic model to quantify climate change impacts on pesticide-related human health risks under two different climate scenarios, using study locations in the north-east and south-west of Ireland. Results indicate that pesticide concentrations in drinking water are projected to exceed legal limits more frequently, and by greater amounts, under all climate scenarios, with associated health risks increasing by an average of 18 % under RCP 4.5 (2050) and 38 % under RCP8.5 (2100). The model results also indicate significant regional variation in health risk, with risk 48 % higher in the south-west than the north-east under baseline conditions. Climate change effects intensify these regional variances with risk up to 70 % higher under RCP4.5 (2050), and 85 % higher under RCP8.5 (2100). Despite these increases, overall pesticide human health risks are likely to remain low in Ireland under future climates. This study presents a probabilistic framework that may be applied internationally to quantify the impact of climate change on human health risk at a local-scale and may be adapted for different site conditions and climate projections to suit users' needs. This approach can inform future pesticide management programmes by identifying vulnerable areas and key pesticides under changing climate conditions, emphasizing the importance of incorporating climate change into pesticide risk mitigation and public health strategies.

Research on a hybrid model for flood probability prediction based on time convolutional network and particle swarm optimization algorithm

Accurate flood forecasting in advance is crucial for planning and implementing watershed flood prevention measures. This study developed the PSO-TCN-Bootstrap flood forecasting model for the Tailan River Basin in Xinjiang by integrating the particle swarm optimisation (PSO) algorithm, temporal convolutional network (TCN), and Bootstrap probability sampling method. Evaluated on 50 historical flood events from 1960 to 2014 using observed rainfall-runoff data, the model showed, under the same lead time conditions, a higher Nash efficiency coefficient, along with lower root mean square and relative peak errors in flood forecasting. These results highlight the PSO-TCN-Bootstrap model's superior applicability and robustness for the Tailan River Basin. However, when the lead time exceeds 5 h, the model's relative peak error remains above 20%. Future work will focus on integrating flood generation mechanisms and enhancing machine learning models' generalisability in flood forecasting. These findings provide a scientific foundation for flood management strategies in the Tailan River Basin.

Impacts of warming and acidification on pesticide toxicity in continental aquatic environments: A scientometric and systematic map

Carbon dioxide emissions are altering aquatic ecosystems by causing water acidification and temperature increases, and these environments are also facing pesticide contamination. We present a scientometric and systematic map of these impacts in continental aquatic environments, aiming to provide an overview of research investigating the effects of temperature and acidification on pesticide toxicity. Our findings reveal a significant increase in research output on this topic, especially over the past seven years, with the United States leading due to high pesticide use and rigorous environmental monitoring. International collaborations remain low. High-impact journal publications underscore the importance of this topic. The primary focus is on temperature-pesticide interactions, highlighting the need for studies on pesticide-acidification interactions driven by climate change. The most studied class of pesticides is insecticides, particularly chlorpyrifos. Animals such as fish and crustaceans are the most frequently used organisms in ecotoxicological tests, indicating the need for broader assessments of impacts on other aquatic groups. Synergistic effects in interactions were prevalent, stressing the importance of an integrated approach in considering the interplay between temperature, pH, and pesticides. The information presented in this study directs and encourages studies in areas that have not yet addressed this topic.

Impacts of an organophosphate pesticide and water restriction on physiology and immunity in the passerine Zonotrichia capensis

Climate change, specifically rising temperatures and increased frequency of droughts will increase the level of exposure of organisms to chemical pollution. Notably, the impact of increased frequency and duration of drought events and subsequent dehydration on pesticide toxicity remains largely unknown. We evaluated the combined effects of exposure to the pesticide Chlorpyrifos (CPF) and water restriction (WR) on the enzymatic activities of three plasma esterases (acetylcholinesterase, butyrylcholinesterase and carboxylesterase), basal metabolic rate (BMR), leukocyte profile (proportion of heterophils to lymphocytes; H:L ratio), and bactericidal capacity of plasma in the rufous-collared sparrow (Zonotrichia capensis), a common passerine bird in Chile. The activity of the enzyme carboxylesterase decreased in birds exposed only to CPF and birds exposed to both the CPF and WR. In the group exposed to both stressors, the butyrylcholinesterase activity was also reduced. The BMR exhibited an increase in both groups exposed to CPF, with a higher rise observed in the group exposed to both CPF and WR, while the group subjected to WR showed no change. The bactericidal capacity of the plasma decreased significantly in birds exposed to CPF and in those exposed to both the pesticide and WR, while a marginal effect was observed in the group only subjected to WR. The H:L ratio increased in all groups, with the most pronounced effect in birds exposed to both stressors. These results suggest a shift in the energy budget of Z. capensis, favoring the detoxification of the pesticide through esterase activities, at the expense of reduced immunocompetence.

Modelling the impacts of climate change on agrochemical fate and transport by water on a catchment scale

The export of agrochemicals and their transformation products (TPs) following their application in the agricultural fields poses a threat to water quality. Future changes in climatic conditions (e.g. extreme weather events such as heavy rainfall or extended dry periods) could alter the degradation and mobility of agrochemicals. In this research, we use an integrated modelling framework to understand the impact of extreme climate events on the fate and transport of the agrochemical S-Metolachlor and two of its TPs (M-OXA, Metolachlor Oxanilic Acid and M-ESA, Metolachlor Ethyl Sulfonic Acid). This is done by coupling climate model outputs to the Zin-AgriTra agrochemical reactive transport model in four simulation scenarios. 1) Reference (2015-2018), 2) Very dry (2038-2041), 3) Very wet (2054-2057) and 4) High temperature (2096-2099) conditions of a selected RCP8.5 based regional climate scenario. The modelling framework is tested on an agricultural catchment, Wulka, in Burgenland, Austria. The model results indicate that 13-14 % of applied S-Metolachlor is retained in the soil, and around 85 % is degraded into TPs in the different scenarios. In very dry and high-temperature scenarios, degradation is higher, and hence, there is less S-Metolachlor in the soil. However, a large share of formed M-OXA and M-ESA are retained in the soil, which is transported via overland and groundwater flow, leading to a build-up effect in M-OXA and M-ESA river concentrations over the years. Though a small share of S-Metolachlor and TPs are transported to rivers, their river export is affected by the intensity and amount of rainfall. The very wet and high-temperature scenarios show higher S-Metolachlor and TP concentrations at the catchment outlet due to higher river discharge. The reference scenario shows higher river peak concentrations associated with higher overland flow caused by measured hourly rainfall compared to disaggregated daily precipitation data in the other scenarios.

Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting

Accurate multi-step ahead flood forecasting is crucial for flood prevention and mitigation efforts as well as optimizing water resource management. In this study, we propose a Runoff Process Vectorization (RPV) method and integrate it with three Deep Learning (DL) models, namely Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Transformer, to develop a series of RPV-DL flood forecasting models, namely RPV-LSTM, RPV-TCN, and RPV-Transformer models. The models are evaluated using observed flood runoff data from nine typical basins in the middle Yellow River region. The key findings are as follows: Under the same lead time conditions, the RPV-DL models outperform the DL models in terms of Nash-Sutcliffe efficiency coefficient, root mean square error, and relative error for peak flows in the nine typical basins of the middle Yellow River region. Based on the comprehensive evaluation results of the train and test periods, the RPV-DL model outperforms the DL model by an average of 2.82%-22.21% in terms of NSE across nine basins, with RMSE and RE reductions of 10.86-28.81% and 36.14%-51.35%, respectively. The vectorization method significantly improves the accuracy of DL flood forecasting, and the RPV-DL models exhibit better predictive performance, particularly when the lead time is 4h-6h. When the lead time is 4-6h, the percentage improvement in NSE is 9.77%, 15.07%, and 17.94%. The RPV-TCN model shows superior performance in overcoming forecast errors among the nine basins. The research findings provide scientific evidence for flood prevention and mitigation efforts in river basins.

Large reductions in nutrient losses needed to avoid future coastal eutrophication across Europe

Rapid technological development in agriculture and fast urbanization have increased nutrient losses in Europe. High nutrient export to seas causes coastal eutrophication and harmful algal blooms. This study aims to assess the river exports of nitrogen (N) and phosphorus (P), and identify required reductions to avoid coastal eutrophication in Europe under global change. We modelled nutrient export by 594 rivers in 2050 for a baseline scenario using the new MARINA-Nutrients model for Europe. Nutrient export to European seas is expected to increase by 13-28% under global change. Manure and fertilizers together contribute to river export of N by 35% in 2050. Sewage systems are responsible for 70% of future P export by rivers. By 2050, the top ten polluted rivers for N and P host 42% of the European population. Avoiding future coastal eutrophication requires over 47% less N and up to 77% less P exports by these polluted rivers.

Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems

Global climate change will significantly impact the biodiversity of freshwater ecosystems, both directly and indirectly via the exacerbation of impacts from other stressors. Pesticides form a prime example of chemical stressors that are expected to synergize with climate change. Aquatic exposures to pesticides might change in magnitude due to increased runoff from agricultural fields, and in composition, as application patterns will change due to changes in pest pressures and crop types. Any prospective chemical risk assessment that aims to capture the influence of climate change should properly and comprehensively account for the variabilities and uncertainties that are inherent to projections of future climate. This is only feasible if they probabilistically propagate extensive ensembles of climate model projections. However, current prospective risk assessments typically make use of process-based models of chemical fate that do not typically allow for such high-throughput applications. Here, we describe a Bayesian network model that does. It incorporates a two-step univariate regression model based on a 30-day antecedent precipitation index, circumventing the need for computationally laborious mechanistic models. We show its feasibility and application potential in a case study with two pesticides in a Norwegian stream: the fungicide trifloxystrobin and herbicide clopyralid. Our analysis showed that variations in pesticide application rates as well as precipitation intensity lead to variations in in-stream exposures. When relating to aquatic risks, the influence of these processes is reduced and distributions of risk are dominated by effect-related parameters. Predicted risks for clopyralid were negligible, but the probability of unacceptable future environmental risks due to exposure to trifloxystrobin (i.e., a risk quotient >1) was 8%-12%. This percentage further increased to 30%-35% when a more conservative precautionary factor of 100 instead of 30 was used. Integr Environ Assess Manag 2024;20:384-400. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Incorporation of climate change into a multiple stressor risk assessment for the Chinook salmon (Oncorhynchus tshawytscha) population in the Yakima River, Washington, USA

One outcome of the 2022 Society of Environmental Toxicology and Chemistry Pellston Workshop on incorporating climate change predictions into ecological risk assessments was the key question of how to integrate ecological risk assessments that focus on contaminants with the environmental alterations from climate projections. This article summarizes the results of integrating selected direct and indirect effects of climate change into an existing Bayesian network previously used for ecological risk assessment. The existing Bayesian Network Relative Risk Model integrated the effects of two organophosphate pesticides (malathion and diazinon), water temperature, and dissolved oxygen levels on the Chinook salmon population in the Yakima River Basin (YRB), Washington, USA. The endpoint was defined as the entity, Yakima River metapopulation, and the attribute was defined as no decline to a subpopulation or the overall metapopulation. In this manner, we addressed the management objective of no net loss of Chinook salmon, an iconic and protected species. Climate change-induced changes in water quality parameters (temperature and dissolved oxygen levels) used models based on projected climatic conditions in the 2050s and 2080s by the use of a probabilistic model. Pesticide concentrations in the original model were modified assuming different scenarios of pest control strategies in the future, because climate change may alter pest numbers and species. Our results predict that future direct and indirect changes to the YRB will result in a greater probability that the salmon population will continue to fail to meet the management objective of no net loss. As indicated by the sensitivity analysis, the key driver in salmon population risk was found to be current and future changes in temperature and dissolved oxygen, with pesticide concentrations being not as important. Integr Environ Assess Manag 2024;20:419-432. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach

The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near- and long-term ecological risks, developed in collaboration with climate scientists. State-of-the-art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate. Integr Environ Assess Manag 2024;20:367-383. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Environmental management cycles for chemicals and climate change, EMC4 : A new conceptual framework contextualizing climate and chemical risk assessment and management

The environmental management cycles for chemicals and climate change (EMC4 ) is a suggested conceptual framework for integrating climate change aspects into chemical risk management. The interaction of climate change and chemical risk brings together complex systems that are imperfectly understood by science. Making management decisions in this context is therefore difficult and often exacerbated by a lack of data. The consequences of poor decision-making can be significant for both environmental and human health. This article reflects on the ways in which existing chemicals management systems consider climate change and proposes the EMC4 conceptual framework, which is a tool for decision-makers operating at different spatial scales. Also presented are key questions raised by the tool to help the decision-maker identify chemical risks from climate change, management options, and, importantly, the different types of actors that are instrumental in managing that risk. Case studies showing decision-making at different spatial scales are also presented highlighting the conceptual framework's applicability to multiple scales. The United Nations Environment Programme's development of an intergovernmental Science Policy Panel on Chemicals and Waste has presented an opportunity to promote and generate research highlighting the impacts of chemicals and climate change interlinkages. Integr Environ Assess Manag 2024;20:433-453. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Wooded biocorridors substantially improve soil properties in low-altitude rural benchlands

This study examines soil properties in 30- and 60-year-old agricultural biocorridors and provides a comparative overview with neighbouring farmland. Both mixed and undisturbed soil samples were collected from six farmland/biocorridor study areas to assess a wide spectrum of physical, hydrophysical, chemical and biological soil properties. Biocorridor soils were characterised by higher water retention capacities, porosity, aeration and soil carbon stock, the latter increasing with depth. On the other hand, biocorridor bulk density under forest vegetation cover was lower, indicating progressive soil restoration. Slightly lower soil reactions in biocorridor soils disproved the hypothesis that nutrient-rich soils under biocorridors would form substrates with a high base cation content, leading to soil acidification. Biological activity, expressed through respiration coefficients, was generally low due to unfavourable physical conditions (clayey or silty-clay substrates), with the lowest levels in biocorridors. Nevertheless, biocorridor soil microbiota displayed more effective utilisation of organic matter as a carbon and nitrogen source, with lighter-textured soils tending to show more effective organic matter utilisation after excluding the influence of land use. Our results confirm biocorridors as an important landscape component, contributing to both soil stability and local revitalisation of soil environments and further emphasising their potential as climate-change mitigation tools in their role as carbon sinks.

The role of biochar and green compost amendments in the adsorption, leaching, and degradation of sulfamethoxazole in basic soil

The fate of the antibiotic sulfamethoxazole in amended soils remains unclear, moreover in basic soils. This work aimed to assess the adsorption, leaching, and biodegradation of sulfamethoxazole in unamended and biochar from holm oak pruning (BC)- and green compost from urban pruning (CG)-amended basic soil. Adsorption properties of the organic amendments and soil were determined by adsorption isotherms of sulfamethoxazole. The leachability of this antibiotic from unamended (Soil) and BC- (Soil + BC) and GC- (Soil + GC) amended soil was determined by leaching columns using water as solvent up to 250 mL. Finally, Soil, Soil + BC, and Soil + GC were spiked with sulfamethoxazole and incubated for 42 days. The degradation rate and microbial activity were periodically monitored. Adsorption isotherms showed poor adsorption of sulfamethoxazole in unamended basic soil. BC and CG showed good adsorption capacity. Soil + BC and Soil + GC increased the sulfamethoxazole adsorption capacity of the soil. The low sulfamethoxazole adsorption of Soil produced quick and intense sulfamethoxazole leaching. Soil + BC reduced the sulfamethoxazole leaching, unlike to Soil + GC which enhanced it concerning Soil. The pH of adsorption isotherms and leachates indicate that the anion of sulfamethoxazole was the major specie in unamended and amended soil. CG enhanced the microbial activity of the soil and promoted the degradability of sulfamethoxazole. In contrast, the high adsorption and low biostimulation effect of BC in soil reduced the degradation of sulfamethoxazole. The half-life of sulfamethoxazole was 2.6, 6.9, and 11.9 days for Soil + GC, Soil, and Soil + BC, respectively. This work shows the benefits and risks of two organic amendments, BC and GC, for the environmental fate of sulfamethoxazole. The different nature of the organic carbon of the amendments was responsible for the different effects on the soil.

Computational models to confront the complex pollution footprint of plastic in the environment

The threat posed by plastic in the environment is poorly characterized due to uncertainties and unknowns about sources, transport, transformation and removal processes, and the properties of the plastic pollution itself. Plastic creates a footprint of particulate pollution with a diversity of composition, size and shape, and a halo of chemicals. In this Perspective, we argue that process-based mass-balance models could provide a platform to synthesize knowledge about plastic pollution as a function of its measurable intrinsic properties.

Extending shared socio-economic pathways for pesticide use in Europe: Pest-Agri-SSPs

While pesticides are essential to agriculture and food systems to sustain current production levels, they also lead to significant environmental impacts. The use of pesticides is constantly increasing globally, driven mainly by a further intensification of agriculture, despite stricter regulations and higher pesticide effectiveness. To further the understanding of future pesticide use and make informed farm-to-policy decisions, we developed Pesticide Agricultural Shared Socio-economic Pathways (Pest-AgriSSPs) in six steps. The Pest-Agri-SSPs are developed based on an extensive literature review and expert feedback approach considering significant climate and socio-economic drivers from farm to continental scale in combination with multiple actors impacting them. In literature, pesticide use is associated with farmer behaviour and practices, pest damage, technique and efficiency of pesticide application, agricultural policy and agriculture demand and production. Here, we developed PestAgri-SSPs upon this understanding of pesticide use drivers and relating them to possible agriculture development as described by the Shared Socio-economic Pathways for European agriculture and food systems (Eur-Agri-SSPs).The Pest-AgriSSPs are developed to explore European pesticide use in five scenarios representing low to high challenges to mitigation and adaptation up to 2050. The most sustainable scenario (Pest-Agri-SSP1) shows a decrease in pesticide use owing to sustainable agricultural practices, technological advances and better implementation of agricultural policies. On the contrary, the Pest-Agri-SSP3 and Pest-Agri-SSP4 show a higher increase in pesticide use resulting from higher challenges from pest pressure, resource depletion and relaxed agricultural policies. Pest-Agri-SSP2 presents a stabilised pesticide use resulting from stricter policies and slow transitions by farmers to sustainable agricultural practices. At the same time, pest pressure, climate change and food demand pose serious challenges. Pest-Agri-SSP5 shows a decrease in pesticide use for most drivers, influenced mainly by rapid technological development and sustainable agricultural practices. However, Pest-Agri-SSP5 also presents a relatively low rise in pesticide use driven by agricultural demand, production, and climate change. Our results highlight the need for a holistic approach to tackle pesticide use, considering the identified drivers and future developments. The storylines and qualitative assessment provide a platform to make quantitative assumptions for numerical modelling and evaluating policy targets.