Metodología para la caracterización espacio-temporal de PM2.5 en el área urbana de la ciudad de Medellín-Colombia
Metodología para la caracterización espacio-temporal de PM2.5 en el área urbana de la ciudad de Medellín-Colombia
Contenido principal del artículo
Resumen
Descargas
Detalles del artículo
Referencias (VER)
Alam, M., & McNabola, A. (2015). Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis. J. Air Waste Manag. Assoc, 65, 628–640.
Chen, L., Bai, Z., Kong, S., Han, B., You, Y., Ding, X., y otros. (2010). A land use regression for predicting NO2 and PM10 concentrations in different seasons in Tianjin region, China. J. Environ. Sci, 22, 1364–1373.
Deligiorgi, D., & Philippopoulos, K. (2011). Spatial Interpolation Methodologies in Urban Air Pollution Modeling: Application for the Greater Area of Metropolitan Athens, Greece, Advanced Air Pollution. Athens: Dr. Farhad Nejadkoorki.
Dickey, D., & Fuller, W. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74, 427-431.
Diebold, F. (1971). Elements of Forecasting, 2a. ed. South Western.
Dons, E., Van Poppe, M., Panis, L., De Prins, S., Berghmans, P., Koppen, G., y otros. (2014). Land use regression models as a tool for short, medium and long term exposure to traffic related air pollution. Science of The Total Environment, 476–477, 378-386.
Fotheringham, A., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: John Wiley & Sons, Ltd.
Frees E. W. Longitudinal and Panel Data: Analysis and Applications in the Social Sciences, Cambridge University Press, Nueva York, 2004.
Gass, S., & Fu, M. (2013). Saaty, T. Analytic Hierarchy Process Encyclopedia of Operations Research and Management Science. Springer.
Goyal, P., Chan, A., & Jaiswal, N. (2006). Statistical models for the prediction of respirable suspended particulate matter in urban cities. Atmos. Environ, 40, 2068–2077.
Granger, C. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica, 424-438.
Lee, M., Brauer, M., Wong, P., Tang, R., Tsui, T., Choi, C., y otros. (2017). Land use regression modelling of air pollution in high density high rise cities: A case study in Hong Kong. Science of the Total Environment, 592, 306–315.
Li., J., & Heap., D. (2014). Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling & Software, 53, 173 – 189.
Liu, B., Wu, J., Zhang, J., Wang, L., Yang, J., Liang, D., y otros. (2017). Characterization and source apportionment of PM2.5 based on error estimation from EPA PMF 5.0 model at a medium city in China. Environmental Pollution, 222, 10-22.
Londoño, L., & Cañon, J. (2015). Metodología para la aplicación de modelos de regresión de usos del suelo en la estimación local de la concentración mensual de pm10 en Medellín – Colombia. Revista Politécnica, 11(21), 29-40.
Londoño, L., Cañón, J., & Giraldo, J. (2017). Modelo de proximidad espacial para definir sitios de muestreo en redes urbanas de calidad de aire. Revista Facultad Nacional de Salud Pública, 35(1), 111-122.
Londoño, L., Cañón, J., Villada, R., & López, L. (2015). Caracterización espacial de PM10 en la ciudad de Medellín mediante modelos geoestadísticos. Revista Ingenierías USBMED, 6(2), 26-35.
MinAmbiente (2017). Contaminación Atmosférica en Colombia. Recuperado el 23 de agosto de 2017, de Ministerio de Ambiente y Desarrollo Sostenible: http://www.minambiente.gov.co/index.php/component/content/article/1801-plantilla-#1-1-normativa
Paschalidou, A., Karakitsios, S., Kleanthous, S., & Kassomenos, P. (2011). Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: Implications to local environmental management. Environ. Sci. Pollut. Res, 18, 316-327.
Pilsung, K. (2013). Locally linear reconstruction based missing value imputation for supervised learning. Neurocomputing, 118, 65-78.
Ramsay, J., Ramsay, T., & Sangalli, L. (2011). Spatial functional data analysis. Recent Advances in Functional Data Analysis and related topics. (págs. 269-275). Springer.
RedAire. (2015). Laboratorio de Calidad del Aire (CALAIRE). Universidad Nacional de Colombia Sede Medellín, Facultad de Minas. Medellin, Colombia.
Şahin, Ü., Bayat, C., & Uçan, O. (2011). Application of cellular neural network (CNN) to the prediction of missing air pollutant data. Atmospheric Research, 101(1–2), 314-326.
Sangalli, L., Ramsay, J., & Ramsay, T. (2013). Spatial splines regressions models. J. Roy. Statisc. Soc. Ser. B, 75, 681-803.
Sanhueza, P., Torreblanca, M., Diaz-Robles, L., Schiappacasse, L., Silva, M., & Astete, T. (2009). Particulate air pollution and health effects for cardiovascular and respiratory causes in Temuco, Chile: A wood-smoke-polluted urban area. J. Air Wate Manag. Assoc., 59, 1481-1488.
Sayegh, A., Munir, S., & Habeebullah, T. (2014). Comparing the performance of statistical models for predicting PM10 concentrations. Aerosol and Air Quality Research, 14(3), 653 - 665.
Scott, L., Rosenshein, L., & Janikas, M. (2011). Modeling Spatial Relationships using Regression Analysis. ESRI International User Conference. Technical Workshops. San Diego, CA.
Shahraiyni, H., & Sodoudi, S. (2016). Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies. Atmosphere, 7(2)(15).
SIATA. (2016). Sistema de Alerta Temprana de Medellín y el Valle de Aburrá. Sistema de Alerta Temprana de Medellín y el Valle de Aburrá. Medellín, Colombia.
Singh, N., Murari, V., Kumar, M., Barman, S., & Banerjee, T. (2017). Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor model. Environmental Pollution, 223, 121-136.
Stadlober, E., Hörmann, S., & Pfeiler, B. (2008). Quality and performance of a PM10 daily forecasting model. Atmos. Environ, 42, 1098–1109.
Taheri Shahraiyni, H., Sodoudi, S., Cubasch, U., & Kerschbaumer, A. (2015). The influence of the plants on the decrease of air pollutants (Case study: Particulate matter in Berlin). In Presented at the Euro-American Conference for Academic Disciplines. Paris, France.
Taheri, H., & Sodoudi, S. (2016). Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies. Atmosphere, 7(15).
Wang, P., Zhang, H., Qin, Z., & Zhang, G. (2017). A novel hybrid-Garch model based on ARIMA and SVM for PM2.5 concentrations forecasting. Atmospheric Pollution Research, In Press, Corrected Proof.
Wiener, N. (1956). The Theory of Prediction. Modern Mathematics for Engineers. New York: McGraw-Hill.
Woolridge, J. (2002). Econometric Analysis of Cross Section and Panel Data. Massachusetts: MIT Press.
Zhang, G. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175.