AIMC Topic: Environmental Monitoring

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Machine learning-based forecasting of air quality index under long-term environmental patterns: A comparative approach with XGBoost, LightGBM, and SVM.

PloS one
Air pollution is a global problem that threatens environmental sustainability and severely affects public health. Monitoring air quality and predicting future pollution levels are critical for creating effective environmental policies and enabling in...

Integrating artificial intelligence with microbial biotechnology for sustainable environmental remediation.

Environmental monitoring and assessment
This narrative review examines the significant advances of artificial intelligence (AI) in enhancing the identification and microbial degradation of environmentally persistent compounds, addressing major issues in pollution monitoring and management....

An earth observation and explainable machine learning approach for determining the drivers of invasive species - a water hyacinth case study.

Environmental monitoring and assessment
Invasive species management is often constrained by limited resources and complicated by ecological and socio-economic variability across landscapes, leading to inconsistent outcomes. We use water hyacinth (Pontederia crassipes) in South Africa as a ...

Automatic detection of harmful cyanobacterial genera using deep CNN models and artemisinin optimization.

Scientific reports
Concerns over the spread of Cyanobacteria, which can lead to dangerous blooms that harm drinking water quality and, therefore, the health of plants and animals, are being raised by global warming. Traditional methods for assessing the amount of toxic...

Mathematical models for predicting the toxicity of micropollutant mixtures in water.

Arhiv za higijenu rada i toksikologiju
Water pollution caused by micropollutants has been a global issue for decades, prompting the scientific community and industry professionals to develop new and effective wastewater treatment methods. Understanding the interactions of these compounds ...

Prediction of regional cropland soil organic carbon content and distribution using deep learning: a case study of the Northeast China Plain.

Environmental monitoring and assessment
Soil organic carbon (SOC) is a critical component of soil fertility and plays a significant role in global carbon sequestration. The decline in SOC content across global croplands poses significant challenges to both agricultural productivity and env...

Advancing Air Pollution Exposure Models with Open-Vocabulary Object Detection and Semantic Segmentation of Street-View Images.

Environmental science & technology
Mobile monitoring campaigns combined with land use regression (LUR) models effectively capture fine-scale spatial variations in urban air pollution. However, traditional predictor variables often fail to capture the nuances of the built environment a...

Conceptual development and implementation of a digital twin model for managing saltwater intrusion of an island coastal aquifer.

Environmental monitoring and assessment
Saltwater intrusion (SWI) poses a significant environmental challenge for coastal aquifers in Pacific Island nations, including Port Vila, Vanuatu. This study utilised a 3D numerical simulation model to evaluate SWI in the Tagabe coastal aquifer unde...

Evaluating contaminated land and the environmental impact of oil spills in the Niger Delta region: a remote sensing-based approach.

Environmental monitoring and assessment
The Niger Delta region of Nigeria is a major oil-producing area which experiences frequent oil spills that severely impacts the local environment and communities. Effective environmental monitoring and management remain inadequate in this area due to...

Water resource assessment in data-scarce hydrological regions based on limited underwater survey points.

Environmental monitoring and assessment
Lake topography, which serves as a crucial basis for water resource monitoring, has been extensively applied in hydrological and geomorphological research. However, monitoring lake dynamics in data-scarce regions remains challenging due to the limite...