Environmental Health

Evaluation of machine learning and deep learning models for daily air quality index prediction in Delhi city, India.

The air quality index (AQI), based on criteria for air contaminants, is defined to provide a shared vision of air quality. As air pollution continues to rise in global cities due to urbanization and climate change, air pollution monitoring and forecasting models for effective air quality monitoring that gather and forecast information about air pollution concentration are essential in every city. ...

High-resolution spatiotemporal prediction of PM concentration based on mobile monitoring and deep learning.

Obtaining the high-resolution distribution characteristics of urban air pollutants is crucial for effective pollution control and public health. In order to fulfill it, mobile monitoring offers a novel and practical approach compared to traditional fixed monitoring methods. However, the sparsity of mobile monitoring data still makes it a challenge to recover the high-resolution pollutant concentra...

hERGBoost: A gradient boosting model for quantitative IC prediction of hERG channel blockers.

The human ether-a-go-go-related gene (hERG) potassium channel is pivotal in drug discovery due to its susceptibility to blockage by drug candidate mol...

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 (Cypr...

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 strate...

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...

Accurate and efficient prediction of atmospheric PM, PM, PM, and O concentrations using a customized software package based on a machine-learning algorithm.

Particulate matter (PM) and ozone (O) pollution have been attracting increasing attention recently due to their severe harm to human health. PM and O ...

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 ...