Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms

Abstract

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping

“…In the field of environmental research air quality, information about the quality (i.e., clean or polluted) of air is reported as an air quality index (AQI) [28]. The AQI tracks six major air pollutants: inhalable particulate matter (PM 10 ), fine particulate matter (PM 2.5 ), ozone (O 3 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and carbon monoxide (CO) [24].…”

Air Pollutant Analysis and AQI Prediction Based on GRA and Improved SOA-SVR by Considering COVID-19

“…Withing the literature [10,11] SVR has been applied to predict short-term electrical load. Mauro Castelli et al used an SVR model to predict AQI in California [12]. In the literature [13], an SVR model was used to predict the AQI value of Beijing and NOx values in India.…”

Survey on the indexes of health equity in the physical environment and infrastructures of Kermanshah province, Iran

“…These levels indicate air pollution overall effects on human health and decent reference for people’s outdoor activities in a numerical pattern. A low number means good air quality, while an increasing number means worsening air quality, for example values over 300 represent hazardous air quality which is the health warning of emergency condition and everyone is more likely to be affected [ 11 ]. The present study aimed to survey the indexes of health equity in the physical environment and infrastructures of Kermanshah province based on the national indexes.…”