AIMC Topic: Environmental Monitoring

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Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.

The Science of the total environment
Chlorophyll-a (Chl-a) is a direct indicator used to evaluate the ecological state of a waterbody, such as algal blooms that degrade the water quality in lakes, reservoirs and estuaries. In this study, artificial neural network (ANN) and support vecto...

Environmental contamination, product contamination and workers exposure using a robotic system for antineoplastic drug preparation.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
Environmental contamination, product contamination and technicians exposure were measured following preparation of iv bags with cyclophosphamide using the robotic system CytoCare. Wipe samples were taken inside CytoCare, in the clean room environment...

Beyond model-specific biases: An explainable multifaceted approach for robust PM source apportionment.

Environmental research
Liu et al. (2025) present an innovative approach to PM source apportionment in urban environments by integrating Positive Matrix Factorization with machine learning (ML) models including XGBoost, Random Forest (RF), and Support Vector Machine (SVM). ...

Persistence after prohibition: Revealing the drivers of traditional and novel organochlorine pesticide residues in river sediments.

Environmental research
Legacy organochlorine pesticides (OCPs) persist as global environmental threats despite international bans, while novel OCPs have been widely adopted as alternatives; however, the spatiotemporal dynamics and regulatory drivers of both legacy and nove...

Soil and litter emission sources as important contributors to ozone production from volatile organic compounds in island tropical forests.

Environmental research
While studies have confirmed that volatile organic compounds (VOCs) emitted directly by tropical island forest vegetation significantly influence ozone (O) production and climate change through atmospheric oxidation processes, the environmental effec...

Spatiotemporal evolution and driver analysis of wastewater greenhouse gas emissions in Chinese mainland: Insights and future trends.

Environmental research
Wastewater greenhouse gas (GHG) emissions represent a complex system characterized by distinct spatial-temporal patterns influenced by various drivers. This study examined the spatiotemporal heterogeneity of wastewater GHG emission intensity and tota...

Identifying factors influencing trace metal concentrations in urban residential soil using an optimal parameter-based geographical detector model.

Environmental research
Australia's national citizen science program VegeSafe has collected and analysed over 26,000 residential garden soil samples for their trace metal concentrations, enabling a more comprehensive understanding of the factors influencing contamination. H...

Insights into the comparison of machine learning models on rice grain arsenic prediction: Interplay of rice cultivation systems and soil environmental factors.

Environmental pollution (Barking, Essex : 1987)
Arsenic (As) exposure to rice threatens food safety while transferring As to rice from paddy soils significantly impacts increasing As levels in rice. This study explores establishing an efficient model for predicting As accumulation in rice grain us...

Differentiating estuarine dissolved organic matter composition by unsupervised and supervised machine learning.

Water research
Differentiating the composition of Dissolved Organic Matter (DOM) in estuaries is a major environmental concern, as the DOM characteristics are closely linked to biogeochemical and ecological considerations (e.g. water properties and trophic cycling)...