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

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.

[1]  Yu Gu,et al.  Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection , 2018, Sensors.

[2]  Pavlos A Kassomenos,et al.  Development of an aggregate Air Quality Index for an urban Mediterranean agglomeration: relation to potential health effects. , 2007, Environment international.

[3]  S. De Vito,et al.  Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction , 2012, IEEE Sensors Journal.

[4]  P. Vitousek.  Beyond Global Warming: Ecology and Global Change , 1994 .

[5]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[6]  Lixin Fu,et al.  Characterization of personal exposure concentration of fine particles for adults and children exposed to high ambient concentrations in Beijing, China. , 2010, Journal of environmental sciences.

[7]  2 Machine Learning Algorithms adapted in , .

[8]  Yuanyuan Wang,et al.  Daily air quality index forecasting with hybrid models: A case in China. , 2017, Environmental pollution.

[9]  Jorge Reyes,et al.  Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile , 2000 .

[10]  S. D. Vito,et al.  CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization , 2009 .

[11]  Samuel D. Lightstone,et al.  Comparing CMAQ Forecasts with a Neural Network Forecast Model for PM2.5 in New York , 2017 .

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  Marcella Busilacchio,et al.  Recursive neural network model for analysis and forecast of PM10 and PM2.5 , 2017 .

[14]  Gary W. Fuller,et al.  An empirical approach for the prediction of daily mean PM10 concentrations , 2002 .

[15]  Gabriel Ibarra-Berastegi,et al.  From diagnosis to prognosis for forecasting air pollution using neural networks: Air pollution monitoring in Bilbao , 2008, Environ. Model. Softw..

[16]  J. Gibbins,et al.  Coal Characterisation for NOx Prediction in Air-Staged Combustion of Pulverised Coals , 2005 .

[17]  G. Mudd,et al.  Unresolved Complexity in Assessments of Mineral Resource Depletion and Availability , 2018, Natural Resources Research.

[18]  Ni Sheng,et al.  The first official city ranking by air quality in China — A review and analysis , 2016 .

[19]  E. Massera,et al.  On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario , 2008 .

[20]  Ulku Yetis,et al.  Hazardous waste management system design under population and environmental impact considerations. , 2017, Journal of environmental management.

[21]  John Kaiser Calautit,et al.  A review of artificial neural network models for ambient air pollution prediction , 2019, Environ. Model. Softw..

[22]  John Kaiser Calautit,et al.  Hybrid artificial neural network models for effective prediction and mitigation of urban roadside NO2 pollution , 2017 .

[23]  M. Brauer,et al.  Transboundary health impacts of transported global air pollution and international trade , 2017, Nature.

[24]  Yunhao Liu,et al.  Big Data: A Survey , 2014, Mob. Networks Appl..

[25]  Giorgio Corani,et al.  Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning , 2005 .

[26]  Cardona Alzate,et al.  Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .

[27]  Jiming Hao,et al.  Air pollution and control action in Beijing , 2016 .

[28]  P. Smirniotis,et al.  Impact of nitrogen oxides on the environment and human health: Mn-based materials for the NOx abatement , 2016 .

[29]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.