AIMC Topic: Soil

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Digital soil mapping in support of voluntary carbon market programs in agricultural land.

PloS one
Voluntary carbon market (VCM) programs in agriculture depend on accurate measurements of soil organic carbon (SOC) that can be deployed at scale efficiently, but barriers are preventing widespread adoption. To overcome these challenges, we developed ...

A data-intensive framework for evaluating ecological and human health impacts of soil potentially toxic elements (PTEs) in the mining-endemic region of Singida, Tanzania.

Environmental geochemistry and health
Uncontrolled soil contamination by potentially toxic elements (PTEs) poses serious threats to environmental and public health in mining-intensive regions. Against this background, this study assessed the distribution, sources, ecological impact, and ...

Influence of sample size and machine learning algorithms on digital soil nutrient mapping accuracy.

Environmental monitoring and assessment
The objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, viz., multi-layer perceptron (MLP), random forest (RF), extra trees regressor (ETR), CatBoost, and gradient boost (GB), considering ...

Study on the effect of light distribution on the greenhouse environment in Chinese solar greenhouse.

PloS one
Solar greenhouse is a primary agricultural facility in northern China during winter, providing a certain level of security for the demand for vegetables and melons in the northern regions. However, there remains a lack of uniformity between crop requ...

Prevalence, associated risk factors and satellite imagery analysis in predicting soil-transmitted helminth infection in Nakhon Si Thammarat Province, Thailand.

Scientific reports
Soil-transmitted helminth (STH) infections remain a significant public health concern in rural areas, often leading to nutritional and physical impairment, particularly in children. This study aimed to assess the prevalence and associated factors of ...

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...

Predicting the Sorption Capacity of Perfluoroalkyl and Polyfluoroalkyl Substances in Soils: Meta-Analysis and Machine Learning Modeling.

Environmental science & technology
Predicting the soil sorption capacity for perfluoroalkyl and polyfluoroalkyl substances (PFAS) is pivotal for environmental risk assessment. However, traditional experimental methods are inefficient, necessitating computational model development. We ...