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Air pollution as a result of the development of motorization

Paper Topic: 
Air Quality
 
Volume: 
 
Issue: 
 

Pages :
220 - 230

Corresponing Author: 
mzabochnicka@is.pcz.czest.pl
 
Authors: 
Renata Czech Magdalena Zabochnicka-Świątek Mateusz K. Świątek
Paper ID: 
gnest_03021
Paper Status: 
Published
Date Paper Accepted: 
04/03/2020
Paper online: 
18/06/2020
Visual abstract: 
Abstract: 

The problem of air pollution is one of the most important problems faced by the world in the context of large urban agglomerations. Numerous activities are being undertaken all over the world to both counteract existing pollution and prevent the emergence of another one. Manufacturing is one of the activities that have a significant negative impact on the natural environment. Starting from the production itself, through the life of vehicles, and ending with the problems of storage and recycling of used means of transport, at every stage there are negative effects on the environment. There is no doubt that in all types of pro-ecological activities, it may be extremely important to predict the future state based on various determinants occurring in the analysed or preceding period of time. To this end, various types of mathematical and statistical tools are being used. Recently, you can also notice interest in all types of methods aided by artificial intelligence. Such methods include artificial neural networks, fuzzy logic, or evolutionary algorithms. The article presents the problems of environmental pollution as a result of the development of motorization. The main components of exhaust gases from car engines are discussed, which are particularly harmful to the natural environment. Methods for reducing the emission of pollutants emitted from internal combustion engines are described, and the monitoring of hazards related to air pollution is discussed. The possibilities of using artificial neural networks as a tool for modelling the state of air pollution have also been discussed.

Keywords: 
air pollution, automotive, modelling, neural networks