A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data

Edoardo Arnaudo,Claudio Rossi,Alessandro Farasin

doi:10.3390/app10134587

Edoardo Arnaudo, Claudio Rossi + Show 1 more

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https://doi.org/10.3390/app10134587

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Abstract

Air pollution in urban regions remains a crucial subject of study, given its implications on health and environment, where much effort is often put into monitoring pollutants and producing accurate trend estimates over time, employing expensive tools and sensors. In this work, we study the problem of air quality estimation in the urban area of Milan (IT), proposing different machine learning approaches that combine meteorological and transit-related features to produce affordable estimates without introducing sensor measurements into the computation. We investigated different configurations employing machine and deep learning models, namely a linear regressor, an Artificial Neural Network using Bayesian regularization, a Random Forest regressor and a Long Short Term Memory network. Our experiments show that affordable estimation results over the pollutants can be achieved even with simpler linear models, therefore suggesting that reasonably accurate Air Quality Index (AQI) measurements can be obtained without the need for expensive equipment.

Highlights

Nowadays, air pollution represents a major environmental problem, increasingly worsening and affecting more people every year
The high incidence of deaths caused by air pollution can be explained by the fact that more than 90% of the world population lives in places where the air quality exceeds the guideline limits established by the World Health
In this work we propose to combine meteorological and traffic-related features with recent machine learning and deep learning models to obtain a robust estimate of both pollutants and Air Quality Index (AQI)

Summary

Introduction

Air pollution represents a major environmental problem, increasingly worsening and affecting more people every year. This is especially true in urban environments, where the majority of the industries and traffic reside, releasing into the air alarmingly large quantities of pollutants and particulate matter that become a severe health risk from exposure [1]. These figures appear surprisingly high when compared to other common death causes such as car crashes, which is approximately three times lower [3]. The high incidence of deaths caused by air pollution can be explained by the fact that more than 90% of the world population lives in places where the air quality exceeds the guideline limits established by the World Health. Air pollution is one of the major factors contributing to climate change, especially in terms of global warming

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