AIMC Topic: Environmental Monitoring

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Exploring the carbon rebound effect of agriculture and policy response: Lessons from zero growth of fertiliser action.

Environmental research
Agricultural technological progress is theoretically regarded as the core driving force to curb carbon emissions, but its possible rebound effect often leads to systematic offsetting of emission reduction. Based on the panel data of 267 cities in Chi...

Evaluating the transferability of low-cost sensor calibration using ANFIS: a field study in Putrajaya, Malaysia.

Environmental monitoring and assessment
This study evaluates the robustness of a previously developed calibration model for low-cost ozone sensors, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). The model was deployed at a different site without retraining. It was tested in Pu...

Optimization of spatio-temporal ozone (O) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm.

Ecotoxicology and environmental safety
The future of ozone (O) pollution presents significant environmental and public health challenges worldwide. High O levels can harm respiratory health, exacerbating conditions such as asthma and increasing the risk of cardiovascular diseases. Address...

Integrating machine learning for enhanced spatial prediction and risk assessment of soil heavy metal(loid)s.

Environmental pollution (Barking, Essex : 1987)
Accurately predicting the concentrations and spatial distribution of soil heavy metal(loid)s is crucial for effective environmental management and human health risk assessment. However, existing studies are often limited by poor model accuracy, featu...

Mapping hotspots and identifying drivers of lead bioaccumulation in Oryza sativa L. in tropical agroecosystems.

Journal of hazardous materials
Tropical rice systems exhibit high annual rates of heavy metal accumulation, requiring accurate identification of accumulation drivers in rice-growing ecosystems to ensure regional food security. Therefore, we collected 229 paired soil and rice sampl...

Quantitative evaluation of hydrocarbon contamination in soil using hyperspectral data-a comparative study of machine learning models.

Environmental monitoring and assessment
This study aims to evaluate the applicability of existing machine learning and deep learning techniques for the rapid prediction of hydrocarbon contamination in soils using hyperspectral data. Soil samples of three types, i.e., clayey, silty, and san...

Predicting arsenic bioaccessibility: A global data-driven machine learning approach and its implication for reducing carbon emissions.

Journal of hazardous materials
Site-specific arsenic (As) bioaccessibility data can improve the accuracy of health risk assessments, but direct measurements are costly and time-consuming. Even when available, measured values such as the mean still yield remediation targets below n...

A critical review on the application of environmental DNA (eDNA) metagenomics in monitoring and assessing biological communities post marine oil spills.

The Science of the total environment
Oil spills pose a serious threat to marine communities, and there is an urgent need for an effective technique to monitor and assess the impacts on biological communities. While traditional methods with low sensitivity, being time-consuming and limit...

An automated machine learning-based framework for predicting groundwater quality with sensor data.

Journal of environmental management
Groundwater quality monitoring stands as a critical aspect of groundwater management, necessitating real-time and accurate measurement technologies. In this study, we introduce an automated framework for predicting NH-N in groundwater using multipara...

Machine Learning-Driven Dynamic Measurement of Environmental Indicators in Multiple Scenes and Multiple Disturbances.

Environmental science & technology
Digital city water management systems require extensive data sensing for various environmental indicators, yet measurement accuracy often falls short under diverse extreme conditions. This study proposes a chemical oxygen demand (COD) measurement met...