AIMC Topic: Pesticides
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Assessing the environmental determinants of micropollutant contamination in streams using explainable machine learning and network analysis.
Chemosphere
Dec 31, 2024
Even at trace concentrations, micropollutants, including pesticides and pharmaceuticals, pose considerable ecological risks, and the increasing presence of synthetic chemical substances in aquatic systems has emerged as a growing concern. Moreover, l...
Predictive modeling of diazinon residual concentration in soils contaminated with potentially toxic elements: a comparative study of machine learning approaches.
Biodegradation
Dec 28, 2024
The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Eleme...
Using AI to prevent the insect apocalypse: toward new environmental risk assessment procedures.
Current opinion in insect science
Dec 27, 2024
Insect populations are declining globally, with multiple potential drivers identified. However, experimental data are needed to understand their relative contributions. We highlight the sublethal effects of pesticides at field-relevant concentrations...
Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach.
Environment international
Nov 19, 2024
Pesticides typically co-occur in agricultural surface waters and pose a potential threat to human and ecosystem health. As pesticide screening in global agricultural surface waters is an immense analytical challenge, a detailed risk picture of pestic...
Explainable machine learning models for predicting the acute toxicity of pesticides to sheepshead minnow (Cyprinodon variegatus).
The Science of the total environment
Nov 16, 2024
A quantitative structure-activity relationship (QSAR) study was conducted on 313 pesticides to predict their acute toxicity to Sheepshead minnow (Cyprinodon variegatus) by using DRAGON descriptors. Essentials accounting for a reliable model were all ...
Global meta-analysis and machine learning reveal the critical role of soil properties in influencing biochar-pesticide interactions.
Environment international
Nov 6, 2024
Biochar application in soils is increasingly advocated globally for its dual benefits in enhancing agricultural productivity and sequestering carbon. However, lingering concerns persist regarding its environmental impact, particularly concerning its ...
Machine learning-assisted laccase-like activity nanozyme for intelligently onsite real-time and dynamic analysis of pyrethroid pesticides.
Journal of hazardous materials
Sep 30, 2024
The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs) residues is critical for food safety analysis and environmental pollution traceability. Here, a fluorescent nanozyme Cu-ATP@ [Ru(bpy)] with...
Progress of machine learning-based biosensors for the monitoring of food safety: A review.
Biosensors & bioelectronics
Sep 12, 2024
Rapid urbanization and growing food demand caused people to be concerned about food safety. Biosensors have gained considerable attention for assessing food safety due to selectivity, and sensitivity but poor stability inherently limits their applica...
Machine learning-assisted melamine-Cu nanozyme and cholinesterase integrated array for multi-category pesticide intelligent recognition.
Biosensors & bioelectronics
Sep 4, 2024
Expanding target pesticide species and intelligent pesticide recognition were formidable challenges for existing cholinesterase inhibition methods. To improve this status, multi-active Mel-Cu nanozyme with mimetic Cu-N sites was prepared for the firs...
Elucidating and forecasting the organochlorine pesticides in suspended particulate matter by a two-stage decomposition based interpretable deep learning approach.
Water research
Aug 23, 2024
Accurately predicting the concentration of organochlorine pesticides (OCPs) presents a challenge due to their complex sources and environmental behaviors. In this study, we introduced a novel and advanced model that combined the power of three distin...
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