Accurate prediction of air pollution is essential for public health protection. Air quality,
however, is difficult to predict due to the complex dynamics, and its accurate forecast still …

In recent years, ozone (O3) has gradually become the primary pollutant plaguing urban air
quality. Accurate and efficient ozone prediction is of great significance to the prevention and …

A transformer-based method was firstly developed to predict the hourly PM 2.5 concentration
at 12 monitoring stations in Beijing. Convolutional neural network-long short-term memory …

Air pollution in the environment is growing daily as a result of urbanization and population
growth, which causes numerous health issues. Information about air quality and …

Recently, air quality has become a major concern for the protection of the environment and
the well-being of people. Air pollution is a key proxy of the quality of life in any city and is …

In this paper, a prediction method based on the KNN-Prophet-LSTM hybrid model is
established by using the daily pollutant concentration data of Wuhan from January 1, 2014 …

The direct acquisition of the permeability of porous media by digital images helps to
enhance our understanding of and facilitate research into the problem of subsurface flow. A …

Ozone pollution is an important environmental issue in many countries. Accurate forecasting
of ozone concentration enables relevant authorities to enact timely policies to mitigate …

Purpose The aim of this study is to offer valuable insights to businesses and facilitate better
understanding on transformer-based models (TBMs), which are among the widely employed …

Abstract The Air Quality Index (AQI) is a crucial indicator for assessing the degree of
atmospheric pollution. Accurately forecasting AQI is notably challenging due to the …