The Beijing-Tianjin-Hebei region is facing a very serious air pollution problem. To obtain the future trend of air quality, the GM(1,1) model with the fractional order accumulation (FGM(1,1)) is used to predict the average annual concentrations of PM2.5, PM10, SO2, NO2, 8-h O3, ...
To predict air quality (PM2.5 concentrations, et al), many parametric regression models have been developed, while deep learning algorithms are used less often. And few of them takes the air pollution emission or spatial information into consideration or predict them in hour scale. In this paper...
Prediction of air quality is a topic of great interest in air quality research due to direct association with health effect. The prediction provides pre-information to the overall population of the area about the status of pollution on w... J Sadhasivam,V Muthukumaran,JT Raja,... - 《Journ...
With the rapid economic growth, air quality continues to decline. High-intensity pollution emissions and unfavorable weather conditions are the key factors for the formation and development of air heavy pollution processes. Given that research into air q
Monitoring and preserving air quality has become one of the most essential activities in many industrial and urban areas today. The quality of air is adversely affected due to various forms of pollution caused by transportation, electricity, fuel uses etc. The deposition of harmful gases is creatin...
(MLR) model4,5proposed an algorithm to assess the pollution level of air quality parameters and create a new air quality index based on the fuzzy reasoning system to predict air quality parameters by AR model. Zhang et al.6examines two different approaches to model development, including GAM ...
Air quality prediction using optimal neural networks with stochastic variables Ana Russo a , Frank Raischel b , Pedro G. Lind b,c,d a Center for Geophysics , IDL, University of Lisbon 1749-016 Lisboa, Portugal b Center for Theoretical and Computational Physics, University of Lisbon, Av. ...
A critical component within EIA that influences its quality is the impact prediction (IP) stage, which is a crucial part of EIA reports [5]. IP applies to all the natural environmental elements such as air, water, biodiversity, noise, social, and health that are anticipated to change with ...
In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter (PM2.5) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model...
This paper investigates the effects of vertical eddy diffusivities derived from the 3 different planetary boundary layer (PBL) schemes on predictions of chemical components in the troposphere of East Asia. Three PBL schemes were incorporated into a regional air quality model (RAQM) to represent vertic...