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Environmental Monitoring

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Integrating bioassay and machine learning data for ecological risk assessments of herbicide use on Ulva australis.

Marine pollution bulletin
Herbicide contamination of aquatic ecosystems poses a critical risk to biodiversity. Bioassays provide useful ecological insights on responses to herbicides; however, they require a model organism. Ulva australis is an ideal candidate for herbicide t...

Water quality parameters retrieval and nutrient status evaluation based on machine learning methods and Sentinel- 2 imagery: a case study of the Hongjiannao Lake.

Environmental monitoring and assessment
A timely and accurate understanding of lake water quality is significant for maintaining ecological balance, ensuring water resource security, and promoting regional sustainable development. However, due to the varying numerical ranges and characteri...

Decoding the Plastic Patch: Exploring the Global Microplastic Distribution in the Surface Layers of Marine Regions with Interpretable Machine Learning.

Environmental science & technology
The marine environment is grappling with microplastic (MP) pollution, necessitating an understanding of its distribution patterns, influencing factors, and potential ecological risks. However, the vast area of the ocean and budgetary constraints make...

Variability analysis of soil organic carbon content across land use types and its digital mapping using machine learning and deep learning algorithms.

Environmental monitoring and assessment
Soil organic carbon (SOC) plays a crucial role in carbon cycle management and soil fertility. Understanding the spatial variations in SOC content is vital for supporting sustainable soil resource management. In this study, we analyzed the variability...

Exploring multivariate machine learning frameworks to parallelize PM simultaneous estimations across the continental United States.

Environmental pollution (Barking, Essex : 1987)
Fine particulate matter (PM2.5) comprises diverse chemical components, including elemental carbon (EC), silicon (SI), sulfate (SO), and calcium (CA), each linked to varied health and environmental impacts. Accurately estimating these components' spat...

Spatial and temporal classification and prediction of aspen probability in boreal forests using machine learning algorithms.

Environmental monitoring and assessment
Mapping and classifying the probability of occurrence of Populus tremula L. (aspen) in boreal forests is a complex task for sustainable forest management and biodiversity conservation. As a key broadleaved species in the taiga region, aspen supports ...

A method for delineating traffic low emission control zone based on deep learning and multi-objective optimization.

Environmental monitoring and assessment
Current methods for defining traffic low emission control zones (TLEZ) often face limitations that hinder their widespread implementation and effectiveness. This study addresses these challenges by employing a comprehensive approach to analyze PM con...

Spatiotemporal dynamics of cyanobacterial blooms: Integrating machine learning and feature selection techniques with uncrewed aircraft systems and autonomous surface vessel data.

Journal of environmental management
Cyanobacterial blooms pose significant threats to aquatic ecosystems and public health due to their ability to release harmful toxins, degrade water quality, disrupt aquatic habitats, and endanger human and animal health through contact or consumptio...

Applications of machine learning in potentially toxic elemental contamination in soils: A review.

Ecotoxicology and environmental safety
Soil contamination by potentially toxic elements (PTEs) poses substantial risks to the environment and human health. Traditional investigational methods are often inadequate for large-scale assessments because they are time-consuming, costly, and hav...

Advancing low-cost air quality monitor calibration with machine learning methods.

Environmental pollution (Barking, Essex : 1987)
Low-cost monitors for measuring airborne contaminants have gained popularity due to their affordability, portability, and ease of use. However, they often exhibit significant biases compared to high-cost reference instruments. For optimal accuracy, t...