AIMC Topic: Environmental Monitoring

Clear Filters Showing 211 to 220 of 1335 articles

Robust S3Former deep learning model for the direct diagnosis and prediction of natural organic matter (NOM) from three-dimensional excitation-emission-matrix (3D-EEM) data.

Water research
The non-destructive, three-dimensional excitation-emission matrix (3D-EEM) based on fluorescence spectroscopy has been widely used in natural organic matter (NOM) monitoring in aquatic environments. However, the direct recognition of the species and ...

Decoding PM oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning.

Journal of hazardous materials
PM oxidative potential (OP), a key driver of health risks, was investigated in Ningbo, China, using dual dithiothreitol (DTT) and ascorbic acid (AA) assays combined with machine learning (ML). This approach accounts for the complexity of interactions...

QSAR Model Development for the Environmental Risk Limits and High-Risk List Identification of Phenylurea Herbicides in Aquatic Environments.

Journal of agricultural and food chemistry
Due to the extensive residues of phenylurea herbicides (PUHs) in the environment, it is important for the ecological risk assessment of PUHs to determine their environmental risk limits and identify the high-risk PUHs. This study derived the environm...

Machine learning classifiers to detect data pattern change of continuous emission monitoring system: A typical chemical industrial park as an example.

Environment international
Continuous Emission Monitoring Systems (CEMS) are critical for real-time pollutant measurement, widely deployed to supervise industrial emissions and ensure regulatory compliance. Despite their utility, CEMS data face challenges of data fabrications,...

Unsupervised deep clustering of high-resolution satellite imagery reveals phenotypes of urban development in Sub-Saharan Africa.

The Science of the total environment
Sub-Saharan Africa and other developing regions have urbanized extensively, leading to complex urban features with varying presence and types of roads, buildings and vegetation. We use a novel hierarchical deep learning framework and high-resolution ...

Urban-rural inequality in soil heavy metal health risks: Insights from Baoding, China.

Ecotoxicology and environmental safety
Soil heavy metal contamination poses serious health risks, but few studies have quantitatively assessed disparities in these risks between urban and rural populations. To address this gap, we introduce a novel framework integrating machine learning a...

Exploring Aerosol Vertical Distributions and Their Influencing Factors: Insight from MAX-DOAS and Machine Learning.

Environmental science & technology
Understanding aerosol vertical distribution is crucial for aerosol pollution mitigation but is hindered by limited observational data. This study employed multiaxis differential optical absorption spectroscopy (MAX-DOAS) technology with a coupled rad...

Assessing the transfer of Cd and As from co-contaminated soil to peanut (Arachis hypogaea L.): prediction models and soil thresholds.

Environmental pollution (Barking, Essex : 1987)
In China, the co-contamination of soil with cadmium (Cd) and arsenic (As) is one of the most severe forms of combined pollution. Modeling the transfer of Cd and As from co-contaminated soil to crops has not been thoroughly studied. In this study, fiv...

Enhancing particulate matter prediction in Delhi: insights from statistical and machine learning models.

Environmental monitoring and assessment
This study advances our approach to modeling particulate matter levels-specifically, PM and PM-in Delhi's dynamic urban environment through an extensive evaluation of traditional time series models (ARIMAX, SARIMAX) and machine learning models (RF, S...

Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO via machine learning.

Environmental pollution (Barking, Essex : 1987)
The Geostationary Environment Monitoring Spectrometer (GEMS) has revolutionized air quality monitoring with hourly resolution from geostationary Earth orbit (GEO). However, satellite-derived air quality data often face limitations and biases due to m...