Forecasting hourly PM2.5 in Santiago de Chile with emphasis on night episodes

Atmospheric Environment

Volumn 124, Issue , 2016, Pages 22-27

  Perez, Patricio ^a   Gramsch, Ernesto ^a  

^a UNIVERSIDAD DE SANTIAGO DE CHILE   (Chile)

Author keywords

Air quality forecasting; Meteorology forecast; Neural networks; Particulate matter; PM2.5

Indexed keywords

AIR QUALITY; FORECASTING; NEURAL NETWORKS; PARTICLES (PARTICULATE MATTER);

AIR QUALITY FORECASTING; ERROR FORECASTING; FORECASTING MODELING; METEOROLOGICAL VARIABLES; MONITORING STATIONS; PARTICULATE MATTER; PM2.5; PM2.5 CONCENTRATION;

WEATHER FORECASTING;

AIR QUALITY; ARTIFICIAL NEURAL NETWORK; CONCENTRATION (COMPOSITION); DATA ASSIMILATION; ERROR ANALYSIS; FORECASTING METHOD; MODEL TEST; MONITORING SYSTEM; PARTICULATE MATTER; WEATHER FORECASTING;

AIR MONITORING; ARTICLE; CHILE; CONTROLLED STUDY; FORECASTING; HUMIDITY; NIGHT; PARTICULATE MATTER; PRIORITY JOURNAL; SEASON;

CHILE; METROPOLITANA; SANTIAGO [METROPOLITANA];

NAVIA;

EID: 84947280987     PISSN: 13522310     EISSN: 18732844     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2015.11.016     Document Type: Article

References (27)

1. Borrego C., Monteiro A., Pay M.T., Ribeiro I., Miranda A.I., Basart S., Baldasano J.M. How bias-correction can improve air quality forecasts over Portugal. Atmos. Environ. 2011, 37:6629-6641.
2. Chen J., Lu J., Avise J.C., DaMassa J.A., Kleeman M.J., Kadwela A.P. Seasonal modeling of PM2.5 in California's San Joaquin Valley. Atmos. Environ. 2014, 92:182-190.
3. Chu N., Kadane J.B., Davidson C.I. Using statistical regressions to identify factors influencing PM2.5 concentrations: the Pittsburgh supersite as a case study. Aerosol Sci. Technol. 2010, 44:766-774.
4. Cobourn W.G. An enhanced PM2.5 air quality forecast model based on nonlinear régression and back- trajectory concentrations. Atmos. Environ. 2010, 44:3015-3023.
5. Fernando H.J.S., Mammarella M.C., Grandoni C., Fedele P., Di Marco R., Dimitrova R., Hyde P. Forecasting PM10 in metropolitan areas: efficacy of neural networks. Environ. Pollut. 2012, 163:62-67.
6. Gramsch E., Reyes F., Oyola P., Rubio M.A., López G., Pérez P., Martínez R. Particle size distribution and its relationship to black carbon in two urban and one rural site in Santiago de Chile. J. Air Waste Manag. Assoc. 2014, 64(7):785-796.
7. Grivas G., Chaloulakou A. Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmos. Environ. 2006, 40(7):1216-1229.
8. Jorquera H., Barraza F. Source apportionment of PM2.5 in Santiago, Chile: 1999 and 2004 results. Sci. Total Environ. 2012, 435-436:418-429.
9. Kim K.-H., Kabir E., Kabir S. A review on the human health impact of airborne particulate matter. Environ. Int. 2015, 74:136-143.
10. Konovalov I.B., Beekmann M., Meleux F., Dutot A., Foret G. Combining deterministic and statistical approaches for PM10 forecasting in Europe. Atmos. Environ. 2009, 43:6425-6434.
11. Livingstone P.L., Magliano K., Gurer K., Zhang K.M., Ying Q., Jackson B.S., Kaduwela A., Kleeman M., Woodhouse L.F., Turkiewicz K., Horowitz L.W., Scott K., Johnson D., Taylor C., O'Brien G., DalMassa J., Croes B.E., Brinkowski F., Byun D. Simulating PM concentration during a winter episode in a subtropical valley: sensitivity simulations and evaluation methods. Atmos. Environ. 2009, 43:5971-5977.
12. MMA, 2011. http://www.sinia.cl/1292/articles-47699_recurso_2.pdf.
13. MMA, 2014. http://www.sinia.cl/1292/articles-56914_Inf_Inventarios_FINAL.pdf.
14. Neal L.S., Agnew P., Moseley S., Ordoñez C., Savage N.H., Tilbee M. Application of a statistical post- processing technique to a gridded, operational, air quality forecast. Atmos. Environ. 2014, 98:385-393.
15. Ordieres J.B., Vergara E.P., Capuz R.S., Salazar R.E. Neural network prediction model for fine particulate matter (PM2.5) on the US-Mexico border in El Paso (Texas) and Ciudad Juarez (Chihuahua). Environ. Model. Softw. 2005, 20:547-559.
16. Pai T.J., Hanaki K., Chiou R.J. Forecasting hourly roadside particulate matter in Taipei county of Taiwan based on first- order and one variable grey model. Clean Soil Air Water 2013, 41(8):737-742.
17. Perez P., Trier A., Reyes J. Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile. Atmos. Environ. 2000, 34:1189-1196.
18. Perez P., Reyes J. An integrated neural network model for PM10 forecasting. Atmos. Environ. 2006, 40:2845-2851.
19. Perez P., Salini G. PM2.5 forecasting in a large city: comparison of three methods. Atmos. Environ. 2008, 42:8219-8224.
20. Prakash A., Kumar U., Kumar K. A wavelet-based neural network model to predict ambient air pollutants' concentration. Environ. Model Assess. 2011, 16:503-517.
21. Ruttlant J., Garreaud R. Meteorological air pollution potential for Santiago, Chile: towards an objective episode forecasting. Environ. Monit. Assess. 1995, 34:223-244.
22. Russo A., Raischel F., Lind P. Air quality prediction using optimal neural networks with stochastic variables. Atmos. Environ. 2013, 79:822-830.
23. Stern R., Builtjes P., Schaap M., Timmermans R., Vautard R., Hodzic A., Memmeshheimer M., Feldmann H., Renner E., Wolke R., Kerschbaumer A. A model intercomparison study focusing on episodes with elevated PM10 concentrations. Atmos. Environ. 2008, 42:4567-4588.
24. Sun W., Zhang H., Palazoglu A., Singh A., Zhang W., Liu S. Prediction of 24-hour average PM2.5 concentrations using a hidden Markov model with different emission distributions in Northern California. Sci. Total Environ. 2013, 443:93-103.
25. Thomas S., Jacko R.B. Model for forecasting expressway fine particulate matter and carbon monoxide concentrations: application of regression and neural network models. J. Air Waste Manag. Assoc. 2007, 57:480-488.
26. Voukantsis D., Karatzas K., Kukkonen J., Raesaenen T., Karpinen A., Kolehmainen M. Intercomparison of air quality data using principal components analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Sci. Total Environ. 2011, 409:1266-1276.
27. Zhou Q., Jiang H., Wang J., Zhou J. A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Sci. Total Environ. 2014, 496:264-274.