Articles | Volume 9, issue 2
https://doi.org/10.5194/ascmo-9-121-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-9-121-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
22 Dec 2023
| 22 Dec 2023
Forecasting 24 h averaged PM2.5 concentration in the Aburrá Valley using tree-based machine learning models, global forecasts, and satellite information
Jhayron S. Pérez-Carrasquilla
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, USA
Área Metropolitana del Valle de Aburrá, Proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá (SIATA), Medellín, Colombia
Paola A. Montoya
CORRESPONDING AUTHOR
Área Metropolitana del Valle de Aburrá, Proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá (SIATA), Medellín, Colombia
Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia
Juan Manuel Sánchez
Área Metropolitana del Valle de Aburrá, Proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá (SIATA), Medellín, Colombia
Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia
K. Santiago Hernández
Área Metropolitana del Valle de Aburrá, Proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá (SIATA), Medellín, Colombia
Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia
Mauricio Ramírez
Área Metropolitana del Valle de Aburrá, Proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá (SIATA), Medellín, Colombia
Related authors
No articles found.
Maria P. Velásquez-García, K. Santiago Hernández, James A. Vergara-Correa, Richard J. Pope, Miriam Gómez-Marín, and Angela M. Rendón
EGUsphere, https://doi.org/10.5194/egusphere-2024-695, https://doi.org/10.5194/egusphere-2024-695, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
For the Aburrá Valley, Colombia, local emissions dominate aerosol concentrations, which degrade air quality (AQ) and impact human health. However, this can be exacerbated by the influx of external emissions from sources such as regional fires, Saharan dust, and volcanic degassing. While substantially increasing city-wide aerosols, these external sources can also degrade the aerosol chemical composition (i.e. their toxicity) and impact AQ, which we investigate in this study.
Related subject area
Atmospheric science
A generalized Spatio-Temporal Threshold Clustering method for identification of extreme event patterns
Nonlinear time series models for the North Atlantic Oscillation
Comparing forecast systems with multiple correlation decomposition based on partial correlation
Postprocessing ensemble forecasts of vertical temperature profiles
Using wavelets to verify the scale structure of precipitation forecasts
Automated detection of weather fronts using a deep learning neural network
Low-visibility forecasts for different flight planning horizons using tree-based boosting models
Skewed logistic distribution for statistical temperature post-processing in mountainous areas
Hourly probabilistic snow forecasts over complex terrain: a hybrid ensemble postprocessing approach
A statistical framework for conditional extreme event attribution
Mixture model-based atmospheric air mass classification: a probabilistic view of thermodynamic profiles
A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors
Characterization of extreme precipitation within atmospheric river events over California
Vitaly Kholodovsky and Xin-Zhong Liang
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 35–52, https://doi.org/10.5194/ascmo-7-35-2021, https://doi.org/10.5194/ascmo-7-35-2021, 2021
Short summary
Short summary
Consistent definition and verification of extreme events are still lacking. We propose a new generalized spatio-temporal threshold clustering method to identify extreme event episodes. We observe changes in the distribution of extreme precipitation frequency from large-scale well-connected spatial patterns to smaller-scale, more isolated rainfall clusters, possibly leading to more localized droughts and heat waves.
Thomas Önskog, Christian L. E. Franzke, and Abdel Hannachi
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 141–157, https://doi.org/10.5194/ascmo-6-141-2020, https://doi.org/10.5194/ascmo-6-141-2020, 2020
Short summary
Short summary
The North Atlantic Oscillation (NAO) has a significant impact on seasonal climate and surface weather conditions throughout Europe, North America and the North Atlantic. In this paper, we study a number of linear and nonlinear models for a station-based time series of the daily winter NAO. We find that a class of nonlinear models, including both short and long lags, excellently reproduce the characteristic statistical properties of the NAO. These models can hence be used to simulate the NAO.
Rita Glowienka-Hense, Andreas Hense, Sebastian Brune, and Johanna Baehr
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 103–113, https://doi.org/10.5194/ascmo-6-103-2020, https://doi.org/10.5194/ascmo-6-103-2020, 2020
Short summary
Short summary
A new method for weather and climate forecast model evaluation with respect to observations is proposed. Individually added values are estimated for each model, together with shared information both models provide equally on the observations. Finally, shared model information, which is not present in the observations, is calculated. The method is applied to two examples from climate and weather forecasting, showing new perspectives for model evaluation.
David Schoenach, Thorsten Simon, and Georg Johann Mayr
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 45–60, https://doi.org/10.5194/ascmo-6-45-2020, https://doi.org/10.5194/ascmo-6-45-2020, 2020
Short summary
Short summary
State-of-the-art statistical methods are applied to postprocess an ensemble of numerical forecasts for vertical profiles of air temperature. These profiles are important tools in weather forecasting as they show the stratification and the static stability of the atmosphere. Flexible regression models combined with the multi-dimensionality of the data lead to better calibration and representation of uncertainty of the vertical profiles.
Sebastian Buschow and Petra Friederichs
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 13–30, https://doi.org/10.5194/ascmo-6-13-2020, https://doi.org/10.5194/ascmo-6-13-2020, 2020
Short summary
Short summary
Two-dimensional wavelet transformations can be used to analyse the local structure of predicted and observed precipitation fields and allow for a forecast verification which focuses on the spatial correlation structure alone. This paper applies the novel concept to real numerical weather predictions and radar observations. Systematic similarities and differences between nature and simulation can be detected, localized in space and attributed to particular weather situations.
James C. Biard and Kenneth E. Kunkel
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 147–160, https://doi.org/10.5194/ascmo-5-147-2019, https://doi.org/10.5194/ascmo-5-147-2019, 2019
Short summary
Short summary
A deep learning convolutional neural network (DL-FRONT) was around 90 % successful in determining the locations of weather fronts over North America when compared against front locations determined manually by forecasters. DL-FRONT detects fronts using maps of air pressure, temperature, humidity, and wind from historical observations or climate models. DL-FRONT makes it possible to do science that was previously impractical because manual front identification would take too much time.
Sebastian J. Dietz, Philipp Kneringer, Georg J. Mayr, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 101–114, https://doi.org/10.5194/ascmo-5-101-2019, https://doi.org/10.5194/ascmo-5-101-2019, 2019
Short summary
Short summary
Low-visibility conditions reduce the flight capacity of airports and can lead to delays and supplemental costs for airlines and airports. In this study, the forecasting skill and most important model predictors of airport-relevant low visibility are investigated for multiple flight planning horizons with different statistical models.
Manuel Gebetsberger, Reto Stauffer, Georg J. Mayr, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 87–100, https://doi.org/10.5194/ascmo-5-87-2019, https://doi.org/10.5194/ascmo-5-87-2019, 2019
Short summary
Short summary
This article presents a method for improving probabilistic air temperature forecasts, particularly at Alpine sites. Using a nonsymmetric forecast distribution, the probabilistic forecast quality can be improved with respect to the common symmetric Gaussian distribution used. Furthermore, a long-term training approach of 3 years is presented to ensure the stability of the regression coefficients. The research was based on a PhD project on building an automated forecast system for northern Italy.
Reto Stauffer, Georg J. Mayr, Jakob W. Messner, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 65–86, https://doi.org/10.5194/ascmo-4-65-2018, https://doi.org/10.5194/ascmo-4-65-2018, 2018
Short summary
Short summary
Snowfall forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. This work presents a novel statistical approach to provide accurate forecasts for fresh snow amounts and the probability of snowfall combining data from various sources. The results demonstrate that the new approach is able to provide reliable high-resolution hourly snowfall forecasts for the eastern European Alps up to 3 days ahead.
Pascal Yiou, Aglaé Jézéquel, Philippe Naveau, Frederike E. L. Otto, Robert Vautard, and Mathieu Vrac
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 17–31, https://doi.org/10.5194/ascmo-3-17-2017, https://doi.org/10.5194/ascmo-3-17-2017, 2017
Short summary
Short summary
The attribution of classes of extreme events, such as heavy precipitation or heatwaves, relies on the estimate of small probabilities (with and without climate change). Such events are connected to the large-scale atmospheric circulation. This paper links such probabilities with properties of the atmospheric circulation by using a Bayesian decomposition. We test this decomposition on a case of extreme precipitation in the UK, in January 2014.
Jérôme Pernin, Mathieu Vrac, Cyril Crevoisier, and Alain Chédin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 115–136, https://doi.org/10.5194/ascmo-2-115-2016, https://doi.org/10.5194/ascmo-2-115-2016, 2016
Short summary
Short summary
Here, we propose a classification methodology of various space-time atmospheric datasets into discrete air mass groups homogeneous in temperature and humidity through a probabilistic point of view: both the classification process and the data are probabilistic. Unlike conventional classification algorithms, this methodology provides the probability of belonging to each class as well as the corresponding uncertainty, which can be used in various applications.
Laura D. Riihimaki, Jennifer M. Comstock, Kevin K. Anderson, Aimee Holmes, and Edward Luke
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 49–62, https://doi.org/10.5194/ascmo-2-49-2016, https://doi.org/10.5194/ascmo-2-49-2016, 2016
Short summary
Short summary
Between atmospheric temperatures of 0 and −38 °C, clouds contain ice crystals, super-cooled liquid droplets, or a mixture of both, impacting how they influence the atmospheric energy budget and challenging our ability to simulate climate change. Better cloud-phase measurements are needed to improve simulations. We demonstrate how a Bayesian method to identify cloud phase can improve on currently used methods by including information from multiple measurements and probability estimates.
S. Jeon, Prabhat, S. Byna, J. Gu, W. D. Collins, and M. F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 45–57, https://doi.org/10.5194/ascmo-1-45-2015, https://doi.org/10.5194/ascmo-1-45-2015, 2015
Short summary
Short summary
This paper investigates the influence of atmospheric rivers on spatial coherence of extreme precipitation under a changing climate. We use our TECA software developed for detecting atmospheric river events and apply statistical techniques based on extreme value theory to characterize the spatial dependence structure between precipitation extremes within the events. The results show that extreme rainfall caused by atmospheric river events is less spatially correlated under the warming scenario.
Cited articles
Ballesteros-González, K., Sullivan, A. P., and Morales-Betancourt, R.: Estimating the air quality and health impacts of biomass burning in northern South America using a chemical transport model, Sci. Total Environ., 739, 139755, https://doi.org/10.1016/j.scitotenv.2020.139755, 2020. a
Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R., Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J., Razinger, M., Schulz, M., Serrar, S., Simmons, A. J., Sofiev, M., Suttie, M., Tompkins, A. M., and Untch, A.: Aerosol analysis and forecast in the European centre for medium-range weather forecasts integrated forecast system: 2. Data assimilation, J. Geophys. Res.-Atmos., 114, D06206, https://doi.org/10.1029/2008JD011235, 2009. a
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson, M. Z., Kaiser,J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. S.: Bounding the role of black carbon in the climate system: A scientific assessment, J. Geophys. Res.-Atmos., 118, 5380–5552, 2013. a
Breiman, L.: Random forests, Machine Learning, 45, 5–32, 2001. a
Chellali, M., Abderrahim, H., Hamou, A., Nebatti, A., and Janovec, J.: Artificial neural network models for prediction of daily fine particulate matter concentrations in Algiers, Environ. Sci. Pollut. R., 23, 14008–14017, 2016. a
Cutler, A., Cutler, D. R., and Stevens, J. R.: Random Forests, 157–175, Springer US, Boston, MA, ISBN 978-1-4419-9326-7, https://doi.org/10.1007/978-1-4419-9326-7_5, 2012. a
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M. D., Tanré, D., and Slutsker, I.: Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59, 590–608, 2002. a
Friedman, J. H.: Greedy function approximation: a gradient boosting machine, Ann. Stat., 29, 1189–1232, 2001. a
Giglio, L., Descloitres, J., Justice, C. O., and Kaufman, Y. J.: An enhanced contextual fire detection algorithm for MODIS, Remote Sens. Environ., 87, 273–282, 2003. a
Giglio, L., Schroeder, W., and Justice, C. O.: The collection 6 MODIS active fire detection algorithm and fire products, Remote Sens. Environ., 178, 31–41, https://doi.org/10.1016/j.rse.2016.02.054, 2016. a
Gregorich, M., Strohmaier, S., Dunkler, D., and Heinze, G.: Regression with highly correlated predictors: variable omission is not the solution, Int. J. Env. Res. Pub. He., 18, 4259, https://doi.org/10.3390/ijerph18084259, 2021. a
Guo, L.-C., Bao, L.-J., She, J.-W., and Zeng, E. Y.: Significance of wet deposition to removal of atmospheric particulate matter and polycyclic aromatic hydrocarbons: A case study in Guangzhou, China, Atmos. Environ., 83, 136–144, 2014. a
Guo, W., Zhang, B., Wei, Q., Guo, Y., Yin, X., Li, F., Wang, L., and Wang, W.: Estimating ground-level PM2.5 concentrations using two-stage model in Beijing-Tianjin-Hebei, China, Atmos. Pollut. Res., 12, 101154, https://doi.org/10.1016/j.apr.2021.101154, 2021. a
Gutowski, W. J., Ullrich, P. A., Hall, A., Leung, L. R., O’Brien, T. A., Patricola, C. M., Arritt, R., Bukovsky, M., Calvin, K. V., Feng, Z., Jones, A. D., Kooperman, G. J., Monier, E., Pritchard, M. S., Pryor, S. C., Qian, Y., Rhoades, A. M., Roberts, A. F., Sakaguchi, K., Urban, N., and Zarzycki, C.: The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information, B. Am. Meteorol. Soc., 101, E664–E683, 2020. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with NumPy, Nature, 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Henao, J. J., Mejía, J. F., Rendón, A. M., and Salazar, J. F.: Sub-kilometer dispersion simulation of a CO tracer for an inter-Andean urban valley, Atmos. Pollut. Res., 11, 928–945, 2020. a
Hernandez, A. J., Morales-Rincon, L. A., Wu, D., Mallia, D., Lin, J. C., and Jimenez, R.: Transboundary transport of biomass burning aerosols and photochemical pollution in the Orinoco River Basin, Atmos. Environ., 205, 1–8, https://doi.org/10.1016/j.atmosenv.2019.01.051, 2019. a
Hernández, K. S., Henao, J. J., and Rendón, A. M.: Dispersion simulations in an Andean city: Role of continuous traffic data in the spatio-temporal distribution of traffic emissions, Atmos. Pollut. Res., 13, 101361, https://doi.org/10.1016/j.apr.2022.101361, 2022. a
Herrera-Mejía, L. and Hoyos, C. D.: Characterization of the atmospheric boundary layer in a narrow tropical valley using remote-sensing and radiosonde observations and the WRF model: the Aburrá Valley case-study, Q. J. Roy. Meteor. Soc., 145, 2641–2665, https://doi.org/10.1002/qj.3583, 2019. a, b, c, d, e, f
Hoyos, C. D., Herrera-Mejía, L., Roldán-Henao, N., and Isaza, A.: Effects of fireworks on particulate matter concentration in a narrow valley: the case of the Medellín metropolitan area, Environ. Monit. Assess., 192, 6, https://doi.org/10.1007/s10661-019-7838-9, 2020. a, b
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019. a
Isaza Uribe, A.: Evaluación de la variabilidad temporal de la estructura termodinámica de la atmósfera y su influencia en las concentraciones de material particulado dentro del Valle de Aburrá, Escuela de Geociencias y Medio Ambiente, Master's thesis, Collections: Maestría en Ingeniería – Recursos Hidráulicos [171], Universidad Nacional de Colombia, Medellín, https://repositorio.unal.edu.co/handle/unal/69429 (last access: 19 December 2023), 2020. a
Pérez-Carrasquilla, J. S.: jhayron-perez/ForecastPM2.5-SIATA: ForecastPM2.5-SIATA (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.10383573, 2023. a
Justice, C., Giglio, L., Korontzi, S., Owens, J., Morisette, J., Roy, D., Descloitres, J., Alleaume, S., Petitcolin, F., and Kaufman, Y.: The MODIS fire products, Remote Sens. Environ., 83, 244–262, 2002. a
Ke, H., Gong, S., He, J., Zhang, L., Cui, B., Wang, Y., Mo, J., Zhou, Y., and Zhang, H.: Development and application of an automated air quality forecasting system based on machine learning, Sci. Total Environ., 806, 151204, https://doi.org/10.1016/j.scitotenv.2021.151204, 2022. a, b
Lee, M., Lin, L., Chen, C.-Y., Tsao, Y., Yao, T.-H., Fei, M.-H., and Fang, S.-H.: Forecasting air quality in Taiwan by using machine learning, Scientific Reports, 10, 1–13, https://doi.org/10.1038/s41598-020-61151-7, 2020. a
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The contribution of outdoor air pollution sources to premature mortality on a global scale, Nature, 525, 367–371, 2015. a
Lepeule, J., Laden, F., Dockery, D., and Schwartz, J.: Chronic exposure to fine particles and mortality: an extended follow-up of the Harvard Six Cities study from 1974 to 2009, Environ. Health Persp., 120, 965–970, 2012. a
Lewis, T. C., Robins, T. G., Dvonch, J. T., Keeler, G. J., Yip, F. Y., Mentz, G. B., Lin, X., Parker, E. A., Israel, B. A., Gonzalez, L., and Hill, Y.: Air pollution–associated changes in lung function among asthmatic children in Detroit, Environ. Health Persp., 113, 1068–1075, 2005. a
Lin, C.-Y., Chang, Y.-S., and Abimannan, S.: Ensemble multifeatured deep learning models for air quality forecasting, Atmos. Pollut. Res., 12, 101045, https://doi.org/10.1016/j.apr.2021.03.008, 2021. a
Loecher, M.: Unbiased variable importance for random forests, Communications in Statistics – Theory and Methods, 51, 1413–1425, 2022. a
Lorenz, E. N.: Three approaches to atmospheric predictability, B. Am. Meteorol. Soc, 50, 345–349, 1969. a
Lundberg, S. M., Erion, G. G., and Lee, S.-I.: Consistent individualized feature attribution for tree ensembles, arXiv [preprint], https://doi.org/10.48550/arXiv.1802.03888, 2018. a
Lv, L., Wei, P., Li, J., and Hu, J.: Application of machine learning algorithms to improve numerical simulation prediction of PM2.5 and chemical components, Atmos. Pollut. Res., 12, 101211, https://doi.org/10.1016/j.apr.2021.101211, 2021. a, b
Mabahwi, N. A. B., Leh, O. L. H., and Omar, D.: Human health and wellbeing: Human health effect of air pollution, Procedia – Social and Behavioral Sciences, 153, 221–229, 2014. a
McDonald, G. C.: Ridge regression, Wiley Interdisciplinary Reviews: Computational Statistics, 1, 93–100, 2009. a
Mendez-Espinosa, J., Belalcazar, L., and Betancourt, R. M.: Regional air quality impact of northern South America biomass burning emissions, Atmos. Environ., 203, 131–140, 2019. a
Meyer, H. and Pebesma, E.: Predicting into unknown space? Estimating the area of applicability of spatial prediction models, Methods Ecol. Evol., 12, 1620–1633, 2021. a
National Centers for Environmental Prediction, National Weather Service, NOAA, U.S. Department of Commerce: NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive, NCAR [data set], https://doi.org/10.5065/D65D8PWK, 2015. a
Orru, H., Maasikmets, M., Lai, T., Tamm, T., Kaasik, M., Kimmel, V., Orru, K., Merisalu, E., and Forsberg, B.: Health impacts of particulate matter in five major Estonian towns: main sources of exposure and local differences, Air Quality, Atmosphere & Health, 4, 247–258, 2011. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a, b
Perez, P. and Gramsch, E.: Forecasting hourly PM2.5 in Santiago de Chile with emphasis on night episodes, Atmos. Environ., 124, 22–27, 2016. a
Pérez-Carrasquilla, J. S.: Forecasting 24-hour-averaged PM2.5 concentration in the Aburrá Valley using tree-based ML models, global forecasts, and satellite information: Dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.7091239, 2022. a
Perišić, M., Maletić, D., Stojić, S. S., Rajšić, S., and Stojić, A.: Forecasting hourly particulate matter concentrations based on the advanced multivariate methods, Int. J. Environ. Sci. Te., 14, 1047–1054, 2017. a
Posada-Marín, J. A., Rendón, A. M., Salazar, J. F., Mejía, J. F., and Villegas, J. C.: WRF downscaling improves ERA-Interim representation of precipitation around a tropical Andean valley during El Niño: implications for GCM-scale simulation of precipitation over complex terrain, Clim. Dynam., 52, 3609–3629, 2019. a
Quinlan, J. R.: Induction of decision trees, Machine Learning, 1, 81–106, 1986. a
Rincón-Riveros, J. M., Rincón-Caro, M. A., Sullivan, A. P., Mendez-Espinosa, J. F., Belalcazar, L. C., Quirama Aguilar, M., and Morales Betancourt, R.: Long-term brown carbon and smoke tracer observations in Bogotá, Colombia: association with medium-range transport of biomass burning plumes, Atmos. Chem. Phys., 20, 7459–7472, https://doi.org/10.5194/acp-20-7459-2020, 2020. a
Rodriguez-Gomez, C., Echeverry, G., Jaramillo, A., and Ladino, L. A.: The negative impact of biomass burning and the Orinoco low-level jet on the air quality of the Orinoco River basin, edited by: Grutter, M., Atmósfera, 35, 497–520, https://doi.org/10.20937/atm.52979, 2022. a, b
Samet, J. M., Dominici, F., Curriero, F. C., Coursac, I., and Zeger, S. L.: Fine particulate air pollution and mortality in 20 US cities, 1987–1994, New Engl. J. Med., 343, 1742–1749, 2000. a
Spyromitros-Xioufis, E., Tsoumakas, G., Groves, W., and Vlahavas, I.: Multi-target regression via input space expansion: treating targets as inputs, Machine Learning, 104, 55–98, 2016. a
Steininger, M., Kobs, K., Davidson, P., Krause, A., and Hotho, A.: Density-based weighting for imbalanced regression, Machine Learning, 110, 2187–2211, 2021. a
Tao, Q., Li, Z., Xu, J., Xie, N., Wang, S., and Suykens, J. A.: Learning with continuous piecewise linear decision trees, Expert Syst. Appl., 168, 114214, https://doi.org/10.1016/j.eswa.2020.114214, 2021. a
Tian, J. and Chen, D.: A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements, Remote Sens. Environ., 114, 221–229, 2010. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Xing, Y.-F., Xu, Y.-H., Shi, M.-H., and Lian, Y.-X.: The impact of PM2.5 on the human respiratory system, J. Thorac. Dis., 8, E69–E74, https://doi.org/10.3978/j.issn.2072-1439.2016.01.19, 2016. a
Xu, X., Tong, T., Zhang, W., and Meng, L.: Fine-grained prediction of PM2.5 concentration based on multisource data and deep learning, Atmos. Pollut. Res., 11, 1728–1737, 2020. a
Yang, G., Lee, H., and Lee, G.: A hybrid deep learning model to forecast particulate matter concentration levels in Seoul, South Korea, Atmosphere, 11, 348, https://doi.org/10.3390/atmos11040348, 2020. a
Yang, J., Yan, R., Nong, M., Liao, J., Li, F., and Sun, W.: PM2.5 concentrations forecasting in Beijing through deep learning with different inputs, model structures and forecast time, Atmos. Pollut. Res., 12, 101168, https://doi.org/10.1016/j.apr.2021.101168, 2021. a
Zhang, T., He, W., Zheng, H., Cui, Y., Song, H., and Fu, S.: Satellite-based ground PM2.5 estimation using a gradient boosting decision tree, Chemosphere, 268, 128801, https://doi.org/10.1016/j.chemosphere.2020.128801, 2021. a
Zhang, X., Sun, J., Wang, Y., Li, W., Zhang, Q., Wang, W., Quan, J., Cao, G., Wang, J., Yang, Y., and Zhang, Y.: Factors contributing to haze and fog in China, Chinese Sci. Bull., 58, 1178–1187, 2013. a
Short summary
This study uses tree-based machine learning (ML) to forecast PM2.5 in a complex terrain region. The models show the potential to predict pollution events with several hours of anticipation, and they integrate multiple sources of information, including in situ stations, satellite data, and deterministic model outputs. The importance analysis helps understand the processes affecting air quality in the region and highlights the relevance of external sources of pollution in PM2.5 predictability.
This study uses tree-based machine learning (ML) to forecast PM2.5 in a complex terrain region....
