AIMC Topic: Environmental Monitoring

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Assessing 3-D variability of ultrafine particle using a Geo-AI modelling approach: A case study in Zhunan-Miaoli, Taiwan.

Environmental pollution (Barking, Essex : 1987)
Previous air pollution modeling studies have predominantly emphasized horizontal distributions, overlooking the critical vertical variability of pollutant concentrations in urban environments. Therefore, the three-dimensional (3-D) behavior of air po...

Advanced spatiotemporal downscaling of MODIS land surface temperature: utilizing Sentinel-1 and Sentinel-2 data with machine learning technique in Qazvin Province, Iran.

Environmental monitoring and assessment
This study presents a spatiotemporal downscaling framework for MODIS land surface temperature (LST) using Sentinel-1 and Sentinel-2 data with machine learning techniques on the Google Earth Engine (GEE) platform. Random Forest regression was applied ...

Analysis of interactions of particle-associated oxidative potential sources using multilayer perceptron neural networks: A case study in Shenyang, China.

Environmental pollution (Barking, Essex : 1987)
The oxidative potential (OP) of particulate matter (PM) is a possible indicator for assessing the oxidative-imbalance risk caused by PM exposure. The OP contributions of different PM sources exhibit nonlinear relationships, and the specific patterns ...

Comparing the applicability of de facto population markers for spatiotemporal trend analysis in wastewater-based epidemiology.

Journal of hazardous materials
Wastewater-based epidemiology is an effective public health approach that enables early detection, monitoring, and assessment of community health trends by analysing human excretion products in wastewater. Here, accurate population normalization is e...

High pollution and health risk of antibiotic resistance genes in rural domestic sewage in southeastern China: A study combining national-scale distribution and machine learning.

Environmental pollution (Barking, Essex : 1987)
Rural domestic sewage has emerged as an important reservoir of antibiotic resistance genes (ARGs) under rapid urbanization, while the national-scale geographical patterns and risks of ARGs remaining unclear. We investigated ARG pollution in rural dom...

Machine learning-based source apportionment and source-oriented probabilistic ecological risk assessment of heavy metals in urban green spaces.

Ecotoxicology and environmental safety
Global urbanization has significantly contributed to soil contamination by heavy metals (HMs), posing serious ecological risks, particularly within urban green spaces (UGS). This study focused on UGS soils in Lanzhou, a major river-valley city in Chi...

Groundwater quality assessment and health risk evaluation for schoolchildren in Mujibnagar, Bangladesh: safe consumption guidelines using artificial neural network modeling.

Environmental geochemistry and health
Groundwater is a vital source of drinking water in Bangladesh, with tubewells commonly used, particularly in schools. This study assessed the quality of tubewell water in the southwest region, focusing on iron (Fe), arsenic (As), pH, electrical condu...

Distribution, sources, and ecological risks of heavy metal contamination at the sediment-water interface in the Dongjiang Basin based on in situ high-resolution measurements.

Environmental pollution (Barking, Essex : 1987)
As a critical drinking water source for over 40 million people in southern China, the Dongjiang River faces growing ecological threats from sediment-derived heavy metals (HMs: As, Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn). This pioneering study is the firs...

Racial and ethnic disparities in exposure to short-term NO air pollution in California during 1980-2022.

Journal of hazardous materials
Historical racial and ethnic disparities in short-term exposure to ambient nitrogen dioxide (NO) have rarely been investigated, primarily due to the lack of spatiotemporally resolved NO data covering the historical period. In this study, we used publ...

Unlocking urban soil secrets: machine learning and spectrometry in Berlin's heavy metal pollution study considering spatial data.

Environmental monitoring and assessment
Berlin has historically been impacted by heavy metal (HM) emissions, raising concerns about soil pollution. In this study, machine learning (ML) techniques were applied to predict HM concentrations across the Berlin metropolitan area. A dataset of 66...