AIMC Topic: Environmental Monitoring

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Seasonal total coliform dynamics in a drinking water reservoir.

Water research
Maintaining high-quality drinking water supply reservoirs is important for protecting public health. Despite extensive watershed protection efforts, reservoirs can still experience seasonal, elevated total coliform bacteria concentrations, indicator ...

Exploring hydrochemical drivers of drinking water quality in a tropical river basin using self-organizing maps and explainable AI.

Water research
Groundwater quality assessment is essential for ensuring sustainable water resource management, particularly in regions heavily dependent on groundwater for domestic and agricultural needs. This study aims to investigate the hydrochemical characteris...

Diffusive gradient in thin films combined with machine learning to discern the accumulation characteristics and driving factors of Cd and Cu in soil-rice systems.

Journal of hazardous materials
The dietary exposure risk of cadmium (Cd) in rice is significantly higher than that of copper (Cu), while the co-migration of Cd and Cu in the soil-crop system may enhance the bioavailability of pollution, thus making rapid and accurate prediction of...

How can machine learning inform about chemical risks in circular textiles?

Integrated environmental assessment and management
Hazardous chemicals in textiles represent a serious health issue. This is mainly due to missing data on the used chemicals and/or on their hazard, which prevents proper chemical risk assessment. Although identifying and filling these data gaps is cru...

Prediction of bioconcentration factors (BCFs) and bioaccumulation factors (BAFs) for per- and polyfluoroalkyl substances (PFASs) using Read-Across and q-RASPR.

The Science of the total environment
Per- and polyfluoroalkyl substances (PFASs) contamination poses an environmental concern due to their ability to bioaccumulate in aquatic species and adversely impact human health. Experimental bioconcentration factor (log BCF) data of freshwater fis...

Optimizing machine learning methods for groundwater quality prediction: Case study in District Bagh, Azad Kashmir, Pakistan.

Ecotoxicology and environmental safety
Groundwater quality monitoring is crucial for protecting the environment and human health. Machine learning (ML) offers substantial potential for enhancing groundwater quality prediction, classification, and identification of pollution indicators. Th...

Microplastics assessment in the lower stretch of the Ganga River sediment from East Indian region: Influence of land use and rainfall patterns.

Chemosphere
Microplastic (MP) pollution is increasingly viewed as a serious threat to waterways. However, little is known about the effects of land use and rainfall patterns on the occurrence and distribution of MPs in the river sediments. Herein, the MP polluti...

Unveiling sources of organophosphate esters in marine environments utilizing multi-factor multi-modal high-dimensional clustering algorithm.

Water research
In marine environments, the sources of organophosphate esters (OPEs), particularly emerging OPEs (eOPEs) remain primarily unclear and present significant challenges for accurate source tracing. Here, we developed an unsupervised machine learning fram...

Spatiotemporal variations in Pearl River plume dispersion over the last decade based on VIIRS-derived sea surface salinity.

Marine pollution bulletin
A river plume indicates the dispersion and transport path of pollutants from runoff, monitoring the spatiotemporal variation of river plume distribution from space is crucial for marine environmental governance. This study focuses on the Pearl River ...

Algal bloom forecasting leveraging signal processing: A novel perspective from ensemble learning.

Water research
Accurate forecasting of algal blooms is essential for implementing timely control measures. However, given their inherent complex time-frequency characteristics, capturing the dynamics of algal blooms remains an ongoing challenge in standalone models...