Environmental Health

Explainable machine learning models for predicting the acute toxicity of pesticides to sheepshead minnow (Cyprinodon variegatus).

A quantitative structure-activity relationship (QSAR) study was conducted on 313 pesticides to predict their acute toxicity to Sheepshead minnow (Cyprinodon variegatus) by using DRAGON descriptors. Essentials accounting for a reliable model were all considered carefully, giving full consideration to the OECD (Organization for Economic Co-operation and Development) principles for QSAR acceptability...

Predicting green technology innovation in the construction field from a technology convergence perspective: A two-stage predictive approach based on interpretable machine learning.

The construction industry, as a major global energy consumer and carbon emitter, plays a crucial role in achieving global sustainability. A key strategy for the green transformation of this industry-without compromising development-involves fostering green technology innovation. Nevertheless, existing studies exhibit a notable gap in identifying and evaluating potential green technology innovation...

Identifying and quantifying multiple pollution sources in estuaries using fluorescence spectra and gradient-based deep learning.

This study developed an intelligent method for identifying and quantifying water pollution sources in estuarine areas. It characterized the excitation...

Unravelling integrated groundwater management in pollution-prone agricultural cities: A synergistic approach combining probabilistic risk, source apportionment and artificial intelligence.

Groundwater is vital for agricultural cities, but intensive farming and fertilizer use have increased contamination risks, particularly for non-carcin...

Why we should talk about institutional (dis)trustworthiness and medical machine learning.

The principle of trust has been placed at the centre as an attitude for engaging with clinical machine learning systems. However, the notions of trust...

Water environment risk prediction method based on convolutional neural network-random forest.

The accelerated processes of urbanization and industrialization globally have resulted in an increased risk to aquatic environments, posing a signific...

Microplastics and Trash Cleaning and Harmonization (MaTCH): Semantic Data Ingestion and Harmonization Using Artificial Intelligence.

With the rapid expansion of microplastic research and reliance on semantic descriptors, there is an increasing need for plastic pollution data harmoni...

Improving the construction and prediction strategy of the Air Quality Health Index (AQHI) using machine learning: A case study in Guangzhou, China.

Effectively capturing the risk of air pollution and informing residents is vital to public health. The widely used Air Quality Index (AQI) has been cr...

Statistical inference and neural network training based on stochastic difference model for air pollution and associated disease transmission.

A polluted air environment can potentially provoke infections of diverse respiratory diseases. The development of mathematical models can study the me...

Can urban digital intelligence transformation promote corporate green innovation? Evidence from China.

Can urban digital intelligence transformation (DIT) facilitate corporate green innovation (CGI)? This research uses the staggered difference-in-differ...

Mining soil heavy metal inversion based on Levy Flight Cauchy Gaussian perturbation sparrow search algorithm support vector regression (LSSA-SVR).

Soil heavy metal pollution in mining areas poses severe challenges to the ecological environment. In recent years, machine learning has been widely us...

Assessment of groundwater quality variation characteristics and influencing factors in an intensified agricultural area: An integrated hydrochemical and machine learning approach.

The decline in groundwater quality in intensive agricultural areas in recent years, driven by environmental change and intensified human activity, pos...

Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM in 22 cities in China.

Many studies have confirmed that PM exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM in indoo...

Nordic environmental resilience: balancing air quality and energy efficiency by applying artificial neural network.

Maintaining public health and environmental safety in the Nordic nations calls for a strict plan to define exact benchmarks on air quality and energy ...

Machine Learning Framework for Conotoxin Class and Molecular Target Prediction.

Conotoxins are small and highly potent neurotoxic peptides derived from the venom of marine cone snails which have captured the interest of the scient...

High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning model.

Ambient carbon monoxide (CO) is a primary air pollutant that poses significant health risks and contributes to the formation of secondary atmospheric ...

Significant spatiotemporal changes in atmospheric particulate mercury pollution in China: Insights from meta-analysis and machine-learning.

PM bound mercury (PBM) in the atmosphere is a major component of total mercury, which is a pollutant of global concern and a potent neurotoxicant when...

Topic-sentiment analysis of citizen environmental complaints in China: Using a Stacking-BERT model.

Environmental complaints serve as a crucial means for citizens to participate in environmental regulation, providing precise insights for real-time id...

Impacts of micro/nano plastics on the ecotoxicological effects of antibiotics in agricultural soil: A comprehensive study based on meta-analysis and machine learning prediction.

Micro/nano plastics (M/NPs) and antibiotics, as widely coexisting pollutants in environment, pose serious threats to soil ecosystem. The purpose of th...

Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos.

Water pollution poses a significant risk to the environment and human health, necessitating the development of innovative detection methods. In this s...