Work,Year,Case Study,Prediction Target,Dataset Type,Data Rate,Period (Days),Open Data,Algorithm,Time Granularity,Evaluation Metric
"Just, A.C.; Arfer, K.B.; Rush, J.; Dorman, M.; Shtein, A.; Lyapustin, A.; Kloog, I. Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM2.5) using satellite data over large regions. Atmospheric Environment 2020, 239,  117649",2020,USA,PM2.5,"Spatial, Temporal, AOD, PBL Height",Daily,5779,No,Hybrid,24h,"RMSE, SD, R2"
"Xu, J.; Wang, A.; Schmidt, N.; Adams, M.; Hatzopoulou, M.  A gradient boost approach for predicting near-road ultrafine particle concentrations using detailed traffic characterization. Environmental Pollution 2020, p. 114777.",2020,Canada,UFP,"MET, Traffic, Land Use, BEV",N/S,120,No,Ensemble,,"RMSE, R2"
"Chang, Y.S.;  Abimannan, S.;  Chiao, H.T.;  Lin, C.Y.;  Huang, Y.P.   An ensemble learning based hybrid model and framework for air pollution forecasting. Environmental Science and Pollution Research2020, 27, 38155–38168",2020,Taiwan,"PM2.5, PM10",MET,N/S,2192,No,Hybrid,8h,"RMSE, MAE"
"Li,  Z.;  Yim,  S.H.L.;  Ho,  K.F.   High  temporal  resolution  prediction  of  street-level  PM2.5  and  Nox concentrations using machine learning approach. Journal of Cleaner Production 2020, p. 121975.",2020,China,"PM2.5, NOx","MET, Traffic",Hourly,731,No,"Regression, Ensemble",1h,"RMSE, ME, NRMSE, NME, POD, POF, R2"
"Ma, J.; Ding, Y.; Cheng, J.C.; Jiang, F.; Gan, V.J.; Xu, Z.  A Lag-FLSTM deep learning network based on Bayesian Optimization for multi-sequential-variant PM2. 5 prediction. Sustainable Cities and Society 2020, 60, 102237",2020,USA,PM2.5,"MET, Temporal",Hourly,730,No,NN,,"RMSE, MAE, MAPE"
"Shah, J.; Mishra, B. Analytical Equations based Prediction Approach for PM2. 5 using Artificial Neural Network.arXiv preprint  arXiv:2002.11416 2020",2020,India,PM2.5,MET,Hourly,1230,No,NN,,"RMSE, R2"
"Castelli, M.; Clemente, F.M.; Popovi?c, A.; Silva, S.; Vanneschi, L. A Machine Learning Approach to Predict Air Quality in California. Complexity 2020, 2020",2020,USA,AQI,MET,Hourly,851,Yes,Regression,1h,"RMSE, MAE, NRMSE, R"
"Bozda  ?g, A.; Dokuz, Y.; Gökçek, Ö.B.  Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey. Environmental Pollution 2020, p. 114635",2020,Turkey,PM10,"Spatial, Land Use",N/S,3652,No,"Regression, Ensemble, NN",,"RMSE, MAE, R2"
"Feng, R.; Gao, H.; Luo, K.; Fan, J.r.  Analysis and accurate prediction of ambient PM2. 5 in China using Multi-layer Perceptron. Atmospheric Environment 2020, p. 117534",2020,China,PM2.5,MET,Hourly,31,Yes,NN,1h,"RMSE, R"
"Zheng, H.; Cheng, Y.; Li, H. Investigation of model ensemble for fine-grained air quality prediction. China Communications 2020, 17, 207–223",2020,China,"AQHI, IAQL","MET, Temporal",Hourly,730/1826,Yes,Ensemble,12h,"Acc, MSE, WP, WR, WF"
"Guo, Q.; He, Z.; Li, S.; Li, X.; Meng, J.; Hou, Z.; Liu, J.; Chen, Y.; others.  Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions. Aerosol and Air Quality Research 2020, 20, 1429–1439",2020,China,PM10,MET,Daily,1096,No,NN,24h,"RMSE, ME, R, EOp"
"Masmoudi, S.; Elghazel, H.; Taieb, D.; Yazar, O.; Kallel, A. A machine-learning framework for predicting multiple air pollutants’ concentrations via multi-target regression and feature selection. Science of The Total Environment 2020, 715,  136991",2020,"Tunisia, Italy",,"MET, Temporal",Hourly,1461/366,No,Ensemble,1week,"aRRMSE, aRMSE, R2, aCC, MSE, aRE, RP"
"Zhang, Y.; Zhang, R.; Ma, Q.; Wang, Y.; Wang, Q.; Huang, Z.; Huang, L. A feature selection and multi-model fusion-based approach of predicting air quality. ISA transactions 2020,100, 210–220",2020,China,PM2.5,MET,N/S,46,Yes,Ensemble,24h,"RMSE, MAE, SMAPE"
"Zhang, B.; Zhang, H.; Zhao, G.; Lian, J. Constructing a PM2. 5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks. Environmental Modelling & Software 2020, 124, 104600",2020,China,PM2.5,MET,Hourly,1825,No,NN,1week,RMSE
"Pak, U.; Ma, J.; Ryu, U.; Ryom, K.; Juhyok, U.; Pak, K.; Pak, C.  Deep learning-based PM2. 5 prediction considering the spatiotemporal correlations: A case study of Beijing, China.Science of The Total Environment 2020, 699, 133561",2020,China,PM2.5,MET,N/S,1096,Yes,NN,24h,"RMSE, MAE, MAPE"
"Mo, Y.; Li, Q.; Karimian, H.; Fang, S.; Tang, B.; Chen, G.; Sachdeva, S.  A novel framework for daily forecasting of ozone mass concentrations based on cycle reservoir with regular jumps neural networks. Atmospheric Environment 2020, 220, 117072",2020,China,O3,"MET, UV Index",Daily,1491,Yes,Hybrid,1week,"RMSE, MAE, MAPE, IA"
"Yang, G.; Lee, H.; Lee, G. A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea. Atmosphere 2020, 11, 348",2020,South Korea,"PM2.5, PM10",MET,Hourly,1461,Yes,Hybrid,15days,"RMSE, MAE"
"Fong, I.H.;  Li, T.;  Fong, S.;  Wong, R.K.;  Tallón-Ballesteros, A.J.   Predicting concentration levels of air pollutants by transfer learning and recurrent neural network. Knowledge-Based Systems 2020, 192, 105622",2020,China,"PM2.5, PM10, NO2, NO, CO",MET,Daily,4656,No,NN,24h,MSE
"Lee, M.; Lin, L.; Chen, C.Y.; Tsao, Y.; Yao, T.H.; Fei, M.H.; Fang, S.H. forecasting Air Quality in taiwan by Using Machine Learning. Scientific Reports 2020, 10, 1–13",2020,Taiwan,PM2.5,"MET, Spatial, Temporal",Hourly,365,Yes,Ensemble,24h,"RMSE, NRMSE, R2"
"Danesh Yazdi, M.; Kuang, Z.; Dimakopoulou, K.; Barratt, B.; Suel, E.; Amini, H.; Lyapustin, A.; Katsouyanni, K.; Schwartz, J.  Predicting Fine Particulate Matter (PM2. 5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods. Remote Sensing 2020,12, 914",2020,UK,PM2.5,"MET, Spatial, Temporal, AOD, Land Use",Daily,3287,Partially,Ensemble,24h,"RMSE, MSE, R2"
"Zalakeviciute, R.; Bastidas, M.; Buenaño, A.; Rybarczyk, Y. A Traffic-Based Method to Predict and Map Urban Air Quality. Applied Sciences 2020,10, 2035",2020,Ecuador,PM2.5,"MET, Spatial, Temporal, Traffic",5s,4,No,,,Acc
"Gu, K.; Xia, Z.; Qiao, J. Stacked selective ensemble for PM 2.5 forecast. IEEE Transactions on Instrumentation and Measurement 2019, 69, 660–671",2020,China,PM2.5,MET,Hourly,365,No,Ensemble,48h,"MSE, IA, NMGE, R2"
"Ma, J.; Yu, Z.; Qu, Y.; Xu, J.; Cao, Y.; others.  Application of the xgboost machine learning method in pm2. 5 prediction: A case study of shanghai. Aerosol and Air Quality Research 2020,20, 128–138",2020,China,PM2.5,MET,Hourly,1461,No,Ensemble,24h,"RMSE, MB, ME, R"
"Zhang, L.; Li, D.; Guo, Q. Deep Learning From Spatio-Temporal Data Using Orthogonal Regularizaion Residual CNN for Air Prediction. IEEE Access 2020, 8, 66037–66047",2020,China,AQI,MET,Hourly,2192,No,NN,48h,"RMSE, Acc"
"Zhang, K.; Thé, J.; Xie, G.; Yu, H. Multi-step ahead forecasting of regional air quality using spatial-temporal deep neural networks:  A case study of Huaihai Economic Zone. Journal of Cleaner Production 2020, 277, 123231",2020,China,AQI,MET,Hourly,730,Yes,NN,24h,"RMSE, MAE, R2, FB"
"Zhang, D.; Woo, S.S.  Real Time Localized Air Quality Monitoring and Prediction Through Mobile and Fixed IoT Sensing Network. IEEE Access 2020, 8, 89584–89594",2020,South Korea,"PM2.5, PM10","MET, Temporal, Spatial",Minutely,7,No,Hybrid,,RMSE
"Zhai, W.; Cheng, C. A long short-term memory approach to predicting air quality based on social media332data.Atmospheric Environment 2020, p. 117411",2020,China,"PM2.5, PM10, O3, NO2, SO2, CO","MET, Social Media",Daily,731,Yes,NN,24h,"RMSE, MAE"
"Photphanloet,  C.;  Lipikorn,  R.    PM10  concentration  forecast  using  modified  depth-first  search  and supervised learning neural network. Science of The Total Environment 2020, p. 138507",2020,Thailand,PM10,MET,Secondly,59,No,NN,1h,"RMSE, MAE, MAPE, R"
"Liu, H.; Chen, C. Spatial air quality index prediction model based on decomposition, adaptive boosting, and three-stage feature selection: A case study in China. Journal of Cleaner Production 2020, p. 121777",2020,China,AQI,Spatial,Daily,1086,Yes,Hybrid,5day,"RMSE, MAE, MAPE, R"
"Goulier, L.; Paas, B.; Ehrnsperger, L.; Klemm, O.  Modelling of urban air pollutant concentrations with artificial neural networks using novel input variables. International Journal of Environmental Research and Public Health 2020,17, 2025",2020,Germany,"CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10, PN10","MET, Temporal, Traffic, SP",Hourly,62,No,NN,1h,"RMSE, R, NMB, NMSD, RS, SD, SD’"
"Enebish, T.; Chau, K.; Jadamba, B.; Franklin, M. Predicting ambient PM 2.5 concentrations in Ulaanbaatar, Mongolia with machine learning approaches. Journal of Exposure Science & Environmental Epidemiology 2020, pp. 1–10",2020,Mongolia,PM2.5,"MET, Temporal, Land Use, PD",Hourly,2922,No,"Regression, Ensemble",24h,"RMSE, R2"
"Chang, Y.S.; Chiao, H.T.; Abimannan, S.; Huang, Y.P.; Tsai, Y.T.; Lin, K.M.  An LSTM-based aggregated model for air pollution forecasting. Atmospheric Pollution Research 2020",2020,Taiwan,PM2.5,"MET, Temporal, Spatial",Hourly,2192,No,NN,8h,"RMSE, MAE, MAPE"
"Altikat, A.  Modeling air pollution levels in volcanic geological regional properties and microclimatic conditions. International Journal of Environmental Science and Technology 2020, pp. 1–8",2020,Turkey,PM10,MET,Daily,766,No,"Regression, NN",,"RMSE, MAE, R2"
"Hijjawi, M.A.M.S.M. Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan",2020,Jordan,O3,"MET, Temporal",Daily,1496,No,"NN, Regression, Ensemble",24h,"RMSE, MAE, R2"
"Kim, S.H.; Son, D.S.; Park, M.H.; Hwang, H.S. Developing a Big Data Analytic Model and a Platform for Particulate Matter Prediction: A Case Study. International Journal of Fuzzy Logic and Intelligent Systems2019, 19, 242–249",2019,South Korea,"PM10, PM2.5","MET, Spatial, Human Movements",Hourly,115,No,"NN, Regression",1h,"RMSE, R2"
"Chang, S.W.; Chang, C.L.; Li, L.T.; Liao, S.W. Reinforcement Learning for Improving the Accuracy of PM2.5 Pollution Forecast Under the Neural Network Framework. IEEE Access2019, 8, 9864–9874",2019,China/ Taiwan,PM2.5,MET,Hourly,3693,No,"RL, NN",5days,RMSE
"Eslami, E.; Salman, A.K.; Choi, Y.; Sayeed, A.; Lops, Y. A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks. Neural Computing and Applications 2019, pp. 1–17",2019,South Korea,O3,MET,Hourly,1096,No,Ensemble,24h,IA
"Li, L.; Girguis, M.; Lurmann, F.; Wu, J.; Urman, R.; Rappaport, E.; Ritz, B.; Franklin, M.; Breton, C.; Gilliland, F.; others.  Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions. Environment international 2019, 128, 310–323",2019,USA,"NO2, NOx","MET, Spatial, Traffic",biweekly,8023,No,Ensemble,,"RMSE, R2, RMSEIQR"
"Chen, J.; de Hoogh, K.; Gulliver, J.; Hoffmann, B.; Hertel, O.; Ketzel, M.; Bauwelinck, M.; van Donkelaar, A.; Hvidtfeldt, U.A.; Katsouyanni, K.; others.   A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide. Environment international 2019, 130, 104934",2019,Europe,"NO2, PM2.5","AOD, Traffic, Land Use, Altitude",N/S,365,Yes,"Regression, Ensemble, NN",,"RMSE, R2, MSE-R2"
"Li, X.; Zhang, X.  Predicting ground-level PM2.  5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach. Environmental Pollution 2019, 249, 735–749",2019,China,PM2.5,"MET, AOD",Hourly,1096,Yes,Hybrid,24h,"RMSE, R2"
"Li, R.; Cui, L.; Meng, Y.; Zhao, Y.; Fu, H.  Satellite-based prediction of daily SO2 exposure across China using a high-quality random forest-spatiotemporal Kriging (RF-STK) model for health risk assessment. Atmospheric Environment 2019, 208, 10–19",2019,China,SO2,"MET, Temporal, Land Use, OMI-SO2, PPS, TS",Daily,365,Partially,Hybrid,24h,"RMSE, R2, RPE"
"Qin, D.; Yu, J.; Zou, G.; Yong, R.; Zhao, Q.; Zhang, B. A novel combined prediction scheme based on CNN and LSTM for urban PM 2.5 concentration. IEEE Access 2019, 7, 20050–20059",2019,China,PM2.5,MET,Hourly,731,No,NN,3h,RMSE
"Zhang, Y.; Wang, Y.; Gao, M.; Ma, Q.; Zhao, J.; Zhang, R.; Wang, Q.; Huang, L. A predictive data feature exploration-based air quality prediction approach. IEEE Access 2019, 7, 30732–30743",2019,China,PM2.5,"MET, WFD, Spatial",N/S,61,No,Ensemble,24h,"MAE, SMAPE, MSE"
"Tao, Q.; Liu, F.; Li, Y.; Sidorov, D.  Air pollution forecasting using a deep learning model based on 1D convnets and bidirectional GRU. IEEE Access 2019,7, 76690–76698",2019,China,PM2.5,MET,Hourly,1826,Yes,NN,2h,"RMSE, MAE, SMAPE"
"Ameer, S.; Shah, M.A.; Khan, A.; Song, H.; Maple, C.; Islam, S.U.; Asghar, M.N. Comparative analysis of machine learning techniques for predicting air quality in smart cities. IEEE Access 2019, 7, 128325–128338",2019,China,PM2.5,MET,N/S,2191,Yes,Ensemble,1week,"RMSE, MAE"
"Munkhdalai, L.; Munkhdalai, T.; Park, K.H.; Amarbayasgalan, T.; Erdenebaatar, E.; Park, H.W.; Ryu, K.H. An end-to-end adaptive input selection with dynamic weights for forecasting multivariate time series. IEEE Access 2019, 7, 99099–99114",2019,Italy,"CO(GT), NO2(GT)",MET,Hourly,183,Yes,NN,1h,"RMSE, MAE, MAPE"
"Ma, J.; Ding, Y.; Gan, V.J.; Lin, C.; Wan, Z. Spatiotemporal prediction of PM2. 5 concentrations at different time granularities using IDW-BLSTM. IEEE Access 2019, 7, 107897–107907",2019,China,PM2.5,Spatial,Hourly,365,No,NN,1week,"RMSE, MAE, MAPE"
"Chen, L.; Ding, Y.; Lyu, D.; Liu, X.; Long, H.  Deep multi-task learning based urban air quality index modelling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019, 3, 1–17",2019,China,AQI,"MET, WFD, Traffic, POI Distribution, FAPE, RND",Hourly,366,Yes,NN,48h,"MAE, MAP"
"Zhou, Y.; Chang, F.J.; Chang, L.C.; Kao, I.F.; Wang, Y.S.; Kang, C.C. Multi-output support vector machine for regional multi-step-ahead PM2. 5 forecasting. Science of The Total Environment 2019, 651, 230–240",2019,Taiwan,PM2.5,MET,Hourly,2557,No,Hybrid,4h,"RMSE, Gbench"
"Karimian, H.; Li, Q.; Wu, C.; Qi, Y.; Mo, Y.; Chen, G.; Zhang, X.; Sachdeva, S.; others.  Evaluation of different machine learning approaches to forecasting PM2. 5 mass concentrations. Aerosol and Air Quality Research 2019, 19, 1400–1410",2019,Iran,PM2.5,MET,Hourly,1826,No,"Ensemble, NN, Hybrid",48h,"RMSE, MAE, R2"
"Kami  ?nska, J.A. A random forest partition model for predicting NO2 concentrations from traffic flow and meteorological conditions. Science of The Total Environment 2019, 651, 475–483",2019,Poland,NO2,"MET, Temporal, Traffic",Hourly,731,No,Ensemble,,"MAPE, MADE, BIC, R2"
"Krishan, M.; Jha, S.; Das, J.; Singh, A.; Goyal, M.K.; Sekar, C. Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India. Air Quality, Atmosphere & Health 2019, 12, 899–908",2019,India,"O3, PM2.5, NOx, CO","MET, Traffic",Hourly,730,No,NN,,"RMSE, NSE, PBIAS, R"
"Jia, M.; Cheng, X.; Zhao, X.; Yin, C.; Zhang, X.; Wu, X.; Wang, L.; Zhang, R.; others.  Regional Air Quality Forecast Using a Machine Learning Method and the WRF Model over the Yangtze River Delta, East China. Aerosol and Air Quality Research 2019, 19, 1602–1613",2019,China,PM2.5,MET,Hourly,1826,No,NN,72h,"RMSE, IA, MAE, R"
"Xu, X.; Ren, W.  Prediction of Air Pollution Concentration Based on mRMR and Echo State Network. Applied Sciences 2019, 9, 1811",2019,China,PM2.5,MET,Hourly,366,No,NN,10h,"RMSE, NRMSE, MAE, SMAPE, R"
"Xing, Y.; Yue, J.; Chen, C.; Xiang, Y.; Chen, Y.; Shi, M. A Deep Belief Network Combined with Modified Grey Wolf Optimization Algorithm for PM2. 5 Concentration Prediction. Applied Sciences 2019,9, 3765",2019,China,PM2.5,"MET, AOD",N/S,730,Yes,NN,,"RMSE, MAE, MSE, R2"
"Zamani Joharestani, M.; Cao, C.; Ni, X.; Bashir, B.; Talebiesfandarani, S. PM2. 5 Prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data. Atmosphere 2019,10, 373",2019,Iran,PM2.5,"MET, Temporal, Spatial, AOD, Altitude",Daily,1460,Yes,"Ensemble, NN",,"RMSE, MAE, R2"
"Mohan, S.; Saranya, P. A novel bagging ensemble approach for predicting summertime ground-level ozone concentration.Journal of the Air & Waste Management Association 2019,69, 220–233",2019,India,O3,MET,Hourly,92,No,Ensemble,,"IoAd, R2, PEP"
"Feng, R.; Zheng, H.j.; Zhang, A.r.; Huang, C.; Gao, H.; Ma, Y.c.   Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison: A case study in hangzhou, China. Environmental pollution 2019, 252, 366–378",2019,China,O3,MET,Hourly,365,No,"Ensemble, NN",,"RMSE, R, NMB, NME, MFB, MFE"
"Masih, A. Application of ensemble learning techniques to model the atmospheric concentration of SO2. Global Journal of Environmental Science and Management 2019,5, 309–318",2019,UK,SO2,MET,Hourly,120,Yes,Ensemble,,"RMSE, MAE, R2, RAE"
"Shih, D.H.; Wu, T.W.; Liu, W.X.; Shih, P.Y. An Azure ACES Early Warning System for Air Quality Index Deteriorating. International journal of environmental research and public health 2019,16, 4679",2019,Taiwan,AQI,"MET, Temporal",Hourly,851,No,"Regression, NN",6h,"RMSE, MAE, R2"
"Delavar, M.R.; Gholami, A.; Shiran, G.R.; Rashidi, Y.; Nakhaeizadeh, G.R.; Fedra, K.; Hatefi Afshar, S. A novel method for improving air pollution prediction based on machine learning approaches: a case study applied to the capital city of Tehran.ISPRS International Journal of Geo-Information 2019,8, 99",2019,Iran,"PM10, PM2.5","MET, Temporal, Spatial",Daily,3652,Yes,"Regression, NN",1week,"RMSE, R2"
"Chen,  Y.   Prediction algorithm of PM2.  5 mass concentration based on adaptive BP neural network. Computing 2018, 100, 825–838",2018,China,PM2.5,"MET, Temporal, AOD",Hourly,731,Partially,NN,72h,"RMSE, MAE, MSE, IA, TPR, FPR, SI"
"Pucer, J.F.; Pirš, G.; Štrumbelj, E. A Bayesian approach to forecasting daily air-pollutant levels. Knowledge and Information Systems 2018, 57, 635–654",2018,Slovenia,"PM10, O3","MET, Temporal",Hourly,1461,No,,24h,"MAE, RPS"
"Zhan, Y.; Luo, Y.; Deng, X.; Grieneisen, M.L.; Zhang, M.; Di, B. Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environmental Pollution 2018, 233, 464–473",2018,China,O3,"MET, Land Use, Elevation, AEI, NDVI, RND, PD",Hourly,365,Yes,Ensemble,,"RMSE, R2, RPE"
"Huang, K.; Xiao, Q.; Meng, X.; Geng, G.; Wang, Y.; Lyapustin, A.; Gu, D.; Liu, Y.  Predicting monthly high-resolution PM2.5 concentrations with random forest model in the North China Plain. Environmental Pollution 2018, 242, 675–683",2018,China,PM2.5,"MET, AOD, Elevation, PD, RND, NDVI",Daily,1095,Yes,Ensemble,1month,"RMSE, R2, RPE"
"Yang, W.; Deng, M.; Xu, F.; Wang, H.  Prediction of hourly PM2.  5 using a space-time support vector regression model. Atmospheric Environment 2018,181, 12–19",2018,China,PM2.5,"MET, Spatial",Hourly,61,No,Regression,24h,"total accuracy index (pt), a total absolute error index (et)"
"Zhou, Y.; De, S.; Ewa, G.; Perera, C.; Moessner, K.  Data-driven air quality characterization for urban environments: A case study. IEEE Access 2018,6, 77996–78006",2018,UK,AQI,MET,Hourly,605,Yes,NN,,"RMSE, MAPE, band Acc"
"Freeman, B.S.; Taylor, G.; Gharabaghi, B.; Thé, J. Forecasting air quality time series using deep learning. Journal of the Air & Waste Management Association 2018,68, 866–886",2018,Kuwait,O3,MET,Hourly,669,No,NN,72h,"RMSE, MAE"
"Mart?nez-Espana, R.; Bueno-Crespo, A.; Timón, I.; Soto, J.; Munoz, A.; Cecilia, J.M. Air-Pollution Prediction in Smart Cities through Machine Learning Methods: A Case of Study in Murcia, Spain. Journal of Universal Computer Science 2018,24, 261–276",2018,Spain,O3,MET,Hourly,730,Yes,Ensemble,24h,"RMSE, MAE, R2"
"Eldakhly,  N.M.;  Aboul-Ela,  M.;  Abdalla,  A.   A novel approach of weighted support vector machine with applied chance theory for forecasting air pollution phenomenon in Egypt. International Journal of Computational Intelligence and Applications 2018, 17, 1850001",2018,Egypt,PM10,"MET, Temporal",Hourly,276,No,Regression,1h,"RMSE, R, t-Value"
"Huang, C.J.; Kuo, P.H. A deep cnn-lstm model for particulate matter (PM2. 5) forecasting in smart cities. Sensors 2018,18, 2220",2018,China,PM2.5,MET,Hourly,1826,No,NN,1h,"RMSE, MAE, IA, R"
"Zhu, D.;  Cai, C.;  Yang, T.;  Zhou, X.   A machine learning approach for air quality prediction:  Model regularization and optimization. Big data and cognitive computing 2018,2, 5",2018,USA,"O3, PM2.5, SO2",MET,Hourly,3652,Yes,,24h,RMSE
"Awad, Y.A.; Koutrakis, P.; Coull, B.A.; Schwartz, J. A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States. Environmental research 2017,159, 427–434",2017,USA,BC,"MET, Spatial, Temporal",Daily,4383,Yes,Regression,24h,R2
"Peng, H.; Lima, A.R.; Teakles, A.; Jin, J.; Cannon, A.J.; Hsieh, W.W. Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods.Air Quality, Atmosphere & Health2017, 10, 195–211",2017,Canada,"O3, PM2.5, NO2","MET, Temporal",Hourly,1826,No,NN,48h,"MAE, R, ME, SS"
"Ni, X.; Huang, H.; Du, W. Relevance analysis and short-term prediction of PM2. 5 concentrations in Beijing based on multi-source data. Atmospheric environment 2017,150, 146–161",2017,China,PM2.5,"MET, Social Media",Hourly,365,No,NN,24h,RMSE
"Kleine Deters, J.; Zalakeviciute, R.; Gonzalez, M.; Rybarczyk, Y. Modeling PM2. 5 urban pollution using machine learning and selected meteorological parameters. Journal of Electrical and Computer Engineering 2017, 2017",2017,Ecuador,PM2.5,MET,Daily,1827,No,"Ensemble, Regression, NN",,"MSE, MAPE"
"Zhan, Y.; Luo, Y.; Deng, X.; Chen, H.; Grieneisen, M.L.; Shen, X.; Zhu, L.; Zhang, M.  Spatiotemporal prediction of continuous daily PM2.  5 concentrations across China using a spatially explicit machine learning algorithm. Atmospheric environment 2017,155, 129–139",2017,China,PM2.5,"MET, Temporal, Spatial, AOD",Daily,365,Yes,Ensemble,,"RMSE, R2"
"Al-Dabbous, A.N.; Kumar, P.; Khan, A.R. Prediction of airborne nanoparticles at roadside location using a feed–forward artificial neural network. Atmospheric Pollution Research 2017,8, 446–454",2017,Kuwait,PNCs,MET,5min,30,No,NN,,"RMSE, NRMSE, IA, R2"
"Eldakhly, N.M.; Aboul-Ela, M.; Abdalla, A. Air pollution forecasting model based on chance theory and intelligent techniques.International Journal on Artificial Intelligence Tools 2017,26, 1750024",2017,Egypt,PM10,"MET, Temporal",Hourly,368,No,Regression,1h,"RMSE, R, z’, t-value"
"Zhang, J.; Ding, W. Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong.International journal of environmental research and public health 2017,14, 114",2017,China,"NO2, NOx, O3, PM2.5, SO2","MET, Temporal",Daily,2191,No,NN,24h,"RMSE, MAE, IA, R2"
"Liu,   B.C.;   Binaykia,   A.;   Chang,   P.C.;   Tiwari,   M.K.;   Tsao,   C.C.Urban  air  quality  forecasting based   on   multi-dimensional   collaborative   Support   Vector   Regression   (SVR):   A   case   study   of Beijing-Tianjin-Shijiazhuang. PloS one 2017, 12, e0179763",2017,China,AQI,MET,Daily,851,No,Regression,,"RMSE, MAE, MAPE, MSE"
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