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

Huixiang Liu,Dongbing Yu,Yu Gu,Qing Li

doi:10.3390/app9194069

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.

Highlights

Many environmental crises currently confront us: global warming, hazardous waste, resource depletion, air pollution, and many more [1,2,3,4]
Random Forest (RF) were used to build prediction models for the Air Quality Index (AQI) of Beijing and the r, andcity, R2 were used to evaluate the performance of the regression models
Accurate air quality forecasting has important theoretical and practical value for the public; without it, neither the government nor the public can effectively avoid the health damage caused by air pollution or improve the emergency response capability of heavy pollution days

Summary

Introduction

Many environmental crises currently confront us: global warming, hazardous waste, resource depletion, air pollution, and many more [1,2,3,4]. Millions of people die every year from diseases caused by exposure to outdoor air pollution [5]. In China, the health risk from overexposure to particles is becoming an important public health concern [6]. China’s capital, Beijing, has experienced rapid development in economy and energy consumption. Along with this rapid development, hazy weather caused by air pollution has become increasingly serious in Beijing. Air-pollution-weather occurs often and lasts for a long time, it has seemed better in the last two years [7]

Methods

Results

Conclusion