{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T18:01:20Z","timestamp":1769191280444,"version":"3.49.0"},"reference-count":85,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T00:00:00Z","timestamp":1628812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center)","award":["IITP-2021-2016-0-00312"],"award-info":[{"award-number":["IITP-2021-2016-0-00312"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.<\/jats:p>","DOI":"10.3390\/rs13163222","type":"journal-article","created":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T22:51:27Z","timestamp":1629067887000},"page":"3222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5898-9892","authenticated-orcid":false,"given":"Seyed Vahid","family":"Razavi-Termeh","sequence":"first","affiliation":[{"name":"Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran"}]},{"given":"Abolghasem","family":"Sadeghi-Niaraki","sequence":"additional","affiliation":[{"name":"Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran"},{"name":"Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6710-1434","authenticated-orcid":false,"given":"Soo-Mi","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.5588\/ijtld.15.0472","article-title":"The global economic burden of asthma and chronic obstructive pulmonary disease","volume":"20","author":"FitzGerald","year":"2016","journal-title":"Int. J. Tuberc. Lung Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40733-016-0029-3","article-title":"Asthma costs and social impact","volume":"3","author":"Nunes","year":"2017","journal-title":"Asthma Res. Pract."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1586\/17476348.2016.1114417","article-title":"Is asthma prevalence still increasing?","volume":"10","author":"Backman","year":"2016","journal-title":"Expert Rev. Respir. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109201","DOI":"10.1016\/j.envres.2020.109201","article-title":"Hotspot detection and socio-ecological factor analysis of asthma hospitalization rate in guangxi, china","volume":"183","author":"Ma","year":"2020","journal-title":"Environ. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1097\/ACI.0000000000000346","article-title":"Asthma guidelines: The global initiative for asthma in relation to national guidelines","volume":"17","author":"Becker","year":"2017","journal-title":"Curr. Opin. Allergy Clin. Immunol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.humimm.2016.05.003","article-title":"Polymorphisms and haplotypes of the chromosome locus 17q12-17q21. 1 contribute to adult asthma susceptibility in slovenian patients","volume":"77","author":"Kristan","year":"2016","journal-title":"Hum. Immunol."},{"key":"ref_7","first-page":"1","article-title":"Asthma-prone areas modeling using a machine learning model","volume":"11","author":"Choi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dias, C.S., Dias, M.A.S., Friche, A.A.d.L., Almeida, M.C.d.M., Viana, T.C., Mingoti, S.A., and Caiaffa, W.T. (2016). Temporal and spatial trends in childhood asthma-related hospitalizations in belo horizonte, minas gerais, brazil and their association with social vulnerability. Int. J. Environ. Res. Public Health, 13.","DOI":"10.3390\/ijerph13070704"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.toxlet.2005.10.009","article-title":"Gene by environment interactions and the development of asthma and allergy","volume":"162","author":"Kabesch","year":"2006","journal-title":"Toxicol. Lett."},{"key":"ref_10","first-page":"636","article-title":"Assessment of asthma-prone environment in karachi, pakistan using gis modeling","volume":"70","author":"Khan","year":"2020","journal-title":"JPMA J. Pak. Med Assoc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1080\/09603123.2011.634387","article-title":"High prevalence of childhood asthma in northern israel is linked to air pollution by particulate matter: Evidence from gis analysis and bayesian model averaging","volume":"22","author":"Portnov","year":"2012","journal-title":"Int. J. Environ. health Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"S131","DOI":"10.1093\/aje\/kws274","article-title":"Gis-modeled indicators of traffic-related air pollutants and adverse pulmonary health among children in el paso, texas","volume":"176","author":"Svendsen","year":"2012","journal-title":"Am. J. Epidemiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.sste.2009.07.005","article-title":"A bayesian maximum entropy approach to address the change of support problem in the spatial analysis of childhood asthma prevalence across north carolina","volume":"1","author":"Lee","year":"2009","journal-title":"Spat. Spatio-Temporal Epidemiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s10109-019-00311-4","article-title":"Bayesian spatiotemporal mapping of relative dengue disease risk in bandung, indonesia","volume":"22","author":"Jaya","year":"2020","journal-title":"J. Geogr. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mertikas, S.P., Partsinevelos, P., Mavrocordatos, C., and Maximenko, N.A. (2021). Environmental Applications of Remote Sensing. Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering, Elsevier.","DOI":"10.1016\/B978-0-12-809582-9.00003-7"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dash, J.P., Pearse, G.D., and Watt, M.S. (2018). Uav multispectral imagery can complement satellite data for monitoring forest health. Remote Sens., 10.","DOI":"10.3390\/rs10081216"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105578","DOI":"10.1016\/j.envint.2020.105578","article-title":"Bayesian geostatistical modelling of high-resolution no2 exposure in europe combining data from monitors, satellites and chemical transport models","volume":"138","author":"Beloconi","year":"2020","journal-title":"Environ. Int."},{"key":"ref_18","first-page":"50","article-title":"Mapping the high risk populations against coronavirus disease 2019 in padang west sumatra indonesia","volume":"20","author":"Yuniarti","year":"2020","journal-title":"Int. J. Progress. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Razavi-Termeh, S.V., Sadeghi-Niaraki, A., and Choi, S.-M. (2021). Coronavirus disease vulnerability map using a geographic information system (gis) from 16 april to 16 may 2020. Phys. Chem. Earth Parts A\/B\/C, 103043.","DOI":"10.1016\/j.pce.2021.103043"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.actatropica.2003.09.021","article-title":"Mapping the potential distribution of phlebotomus martini and p. Orientalis (diptera: Psychodidae), vectors of kala-azar in east africa by use of geographic information systems","volume":"90","author":"Malone","year":"2004","journal-title":"Acta Tropica"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/00330124.2020.1844573","article-title":"Combining geospatial analysis with hiv care continuum to identify differential hiv\/aids treatment indicators in uganda","volume":"73","author":"BenBella","year":"2021","journal-title":"Prof. Geogr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"109545","DOI":"10.1016\/j.envres.2020.109545","article-title":"Assessing and modelling vulnerability to dengue in the mekong delta of vietnam by geospatial and time-series approaches","volume":"186","author":"Pham","year":"2020","journal-title":"Environ. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jenila, V.M., Varalakshmi, P., and Rajasekar, S.J.S. (2020, January 3). Geospatial Mapping, Epidemiological Modelling, Statistical Correlation and Analysis of Covid-19 with Forest Cover and Population in the Districts of Tamil Nadu, India. Proceedings of the 2020 IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI), Buldana, India.","DOI":"10.1109\/ICATMRI51801.2020.9398398"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s41182-017-0054-9","article-title":"Environmental factors associated with the distribution of visceral leishmaniasis in endemic areas of bangladesh: Modeling the ecological niche","volume":"45","author":"Abdullah","year":"2017","journal-title":"Trop. Med. Health"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1038\/sj.jea.7500436","article-title":"An investigation of the association between traffic exposure and the diagnosis of asthma in children","volume":"16","author":"Gordian","year":"2006","journal-title":"J. Expo. Sci. Environ. Epidemiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4845","DOI":"10.3390\/ijerph110504845","article-title":"A gis based approach for assessing the association between air pollution and asthma in new york state, USA","volume":"11","author":"Gorai","year":"2014","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.ccm.2018.10.012","article-title":"The use of geographic data to improve asthma care delivery and population health","volume":"40","author":"Camargo","year":"2019","journal-title":"Clin. chest Med."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ou\u00e9draogo, A.M., Crighton, E.J., Sawada, M., To, T., Brand, K., and Lavigne, E. (2018). Exploration of the spatial patterns and determinants of asthma prevalence and health services use in ontario using a bayesian approach. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0208205"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1002\/wmh3.258","article-title":"Integrating spatial analysis into policy formulation: A case study examining traffic exposure and asthma","volume":"10","author":"Zook","year":"2018","journal-title":"World Med Health Policy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.3389\/fmed.2019.00084","article-title":"Spatial enablement to support environmental, demographic, socioeconomics, and health data integration and analysis for big cities: A case study with asthma hospitalizations in new york city","volume":"6","author":"Pala","year":"2019","journal-title":"Front. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.lpm.2019.02.022","article-title":"Environmental risk factors for asthma developement","volume":"48","author":"Leynaert","year":"2019","journal-title":"Presse Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.acap.2019.05.006","article-title":"Socioeconomic and environmental risk factors for pediatric asthma in an american indian community","volume":"19","author":"Kinghorn","year":"2019","journal-title":"Acad. Pediatrics"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1111\/pai.13385","article-title":"Asthma in farm children is more determined by genetic polymorphisms and in non-farm children by environmental factors","volume":"32","author":"Krautenbacher","year":"2021","journal-title":"Pediatric Allergy Immunol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jaci.2019.08.038","article-title":"Proximity to major roadways and asthma symptoms in the school inner-city asthma study","volume":"145","author":"Hauptman","year":"2020","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_35","first-page":"578","article-title":"Spatial analysis of asthma morbidity in the city of morelia, mexico, for the decade 2000\u20132010","volume":"52","year":"2020","journal-title":"Atencion Primaria"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111344","DOI":"10.1016\/j.envres.2021.111344","article-title":"Effects of air pollution in spatio-temporal modeling of asthma-prone areas using a machine learning model","volume":"200","author":"Choi","year":"2021","journal-title":"Environ. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Shinkuma, R., and Nishio, T. (2019, January 7\u20139). Data Assessment and Prioritization in Mobile Networks for Real-Time Prediction of Spatial Information with Machine Learning. Proceedings of the 2019 IEEE First International Workshop on Network Meets Intelligent Computations (NMIC), Dallas, TX, USA.","DOI":"10.1109\/NMIC.2019.00006"},{"key":"ref_38","unstructured":"Shahhosseini, M., Hu, G., and Pham, H. (2019). Optimizing ensemble weights and hyperparameters of machine learning models for regression problems. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105837","DOI":"10.1016\/j.asoc.2019.105837","article-title":"Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series","volume":"86","author":"Ribeiro","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wen, L., and Hughes, M. (2020). Coastal wetland mapping using ensemble learning algorithms: A comparative study of bagging, boosting and stacking techniques. Remote Sens., 12.","DOI":"10.3390\/rs12101683"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.chest.2019.09.018","article-title":"Allergic rhinitis and osa in children residing at a high altitude","volume":"157","author":"Escamilla","year":"2020","journal-title":"Chest"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.scitotenv.2012.02.015","article-title":"An analysis of asthma hospitalizations, air pollution, and weather conditions in Los Angeles County, california","volume":"425","author":"Delamater","year":"2012","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"117859","DOI":"10.1016\/j.envpol.2021.117859","article-title":"Spatio-temporal modeling of pm2. 5 risk mapping using three machine learning algorithms","volume":"289","author":"Shogrkhodaei","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.chest.2018.10.041","article-title":"Air pollution and noncommunicable diseases: A review by the forum of international respiratory societies\u2019 environmental committee, part 2: Air pollution and organ systems","volume":"155","author":"Schraufnagel","year":"2019","journal-title":"Chest"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/j.envint.2019.03.023","article-title":"Exploring the effects of ventilation practices in mitigating in-vehicle exposure to traffic-related air pollutants in china","volume":"127","author":"Tong","year":"2019","journal-title":"Environ. Int."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"9337","DOI":"10.5194\/acp-13-9337-2013","article-title":"Relating aerosol absorption due to soot, organic carbon, and dust to emission sources determined from in-situ chemical measurements","volume":"13","author":"Cazorla","year":"2013","journal-title":"Atmos. Chem. Phys."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.rse.2013.10.019","article-title":"Automated mapping of vegetation trends with polynomials using ndvi imagery over the sahel","volume":"141","author":"Jamali","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhou, M., Lu, X.S., and Yao, C. (2018, January 27\u201329). Weighted Gini Index Feature Selection Method for Imbalanced Data. Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, China.","DOI":"10.1109\/ICNSC.2018.8361371"},{"key":"ref_49","first-page":"612","article-title":"Evaluating the impact of gini index and information gain on classification using decision tree classifier algorithm","volume":"11","author":"Tangirala","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"131","DOI":"10.2134\/agronj2016.04.0196","article-title":"Multicollinearity in path analysis: A simple method to reduce its effects","volume":"109","author":"Olivoto","year":"2017","journal-title":"Agron. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1080\/17435390.2018.1478999","article-title":"Genotoxicity induced by metal oxide nanoparticles: A weight of evidence study and effect of particle surface and electronic properties","volume":"12","author":"Golbamaki","year":"2018","journal-title":"Nanotoxicology"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geomorph.2017.04.002","article-title":"A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the wuyuan area, china","volume":"290","author":"Hong","year":"2017","journal-title":"Geomorphology"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","article-title":"A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches","volume":"42","author":"Galar","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neucom.2020.10.003","article-title":"Multi-stage fault diagnosis framework for rolling bearing based on ohf elman adaboost-bagging algorithm","volume":"433","author":"Xia","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_57","unstructured":"Sultana, N., and Islam, M.M. (2020, January 2\u20134). Meta Classifier-Based Ensemble Learning for Sentiment Classification. Proceedings of the International Joint Conference on Computational Intelligence, Budapest, Hungary."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1016\/B978-0-444-64241-7.50239-1","article-title":"Evaluating the Boosting Approach to Machine Learning for Formation Lithology Classification","volume":"Volume 44","author":"Dev","year":"2018","journal-title":"Computer Aided Chemical Engineering"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1002\/widm.1143","article-title":"Generating ensembles of heterogeneous classifiers using stacked generalization","volume":"5","author":"Sesmero","year":"2015","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"113127","DOI":"10.1016\/j.eswa.2019.113127","article-title":"A hierarchical structure based on stacking approach for skin lesion classification","volume":"145","author":"Ghalejoogh","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Farhangi, F., Sadeghi-Niaraki, A., Nahvi, A., and Razavi-Termeh, S.V. (2020). Spatial modeling of accidents risk caused by driver drowsiness with data mining algorithms. Geocarto Int., 1\u201315.","DOI":"10.1080\/10106049.2020.1831626"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ranjgar, B., Razavi-Termeh, S.V., Foroughnia, F., Sadeghi-Niaraki, A., and Perissin, D. (2021). Land subsidence susceptibility mapping using persistent scatterer sar interferometry technique and optimized hybrid machine learning algorithms. Remote Sens., 13.","DOI":"10.3390\/rs13071326"},{"key":"ref_64","first-page":"627","article-title":"Receiver operating characteristic (roc) curve analysis for medical diagnostic test evaluation","volume":"4","year":"2013","journal-title":"Casp. J. Intern. Med."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2729","DOI":"10.1080\/02626667.2020.1828589","article-title":"Improving groundwater potential mapping using metaheuristic approaches","volume":"65","author":"Khosravi","year":"2020","journal-title":"Hydrol. Sci. J."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Razavi-Termeh, S.V., Sadeghi-Niaraki, A., and Choi, S.-M. (2020). Ubiquitous gis-based forest fire susceptibility mapping using artificial intelligence methods. Remote Sens., 12.","DOI":"10.3390\/rs12101689"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"671","DOI":"10.3109\/10408444.2010.499504","article-title":"Hypothesis-based weight of evidence: A tool for evaluating and communicating uncertainties and inconsistencies in the large body of evidence in proposing a carcinogenic mode of action\u2014naphthalene as an example","volume":"40","author":"Rhomberg","year":"2010","journal-title":"Crit. Rev. Toxicol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1016\/j.envpol.2017.10.051","article-title":"Vertical characteristics of vocs in the lower troposphere over the north china plain during pollution periods","volume":"236","author":"Sun","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1016\/j.mcm.2007.10.016","article-title":"Effect of rain on removal of a gaseous pollutant and two different particulate matters from the atmosphere of a city","volume":"48","author":"Shukla","year":"2008","journal-title":"Math. Comput. Model."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.envres.2007.01.007","article-title":"Air pollution, weather, and associated risk factors related to asthma prevalence and attack rate","volume":"104","author":"Ho","year":"2007","journal-title":"Environ. Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1164\/ajrccm.162.1.9806079","article-title":"Effects of cool, dry air stimulation on peripheral lung mechanics in asthma","volume":"162","author":"Kaminsky","year":"2000","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"114615","DOI":"10.1016\/j.envpol.2020.114615","article-title":"Comparison of the suitability of plant species for greenbelt construction based on particulate matter capture capacity, air pollution tolerance index, and antioxidant system","volume":"263","author":"Zhang","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"118077","DOI":"10.1016\/j.atmosenv.2020.118077","article-title":"Seasonality patterns and distinctive signature of latitude and population on ozone concentrations in southern ontario, canada","volume":"246","author":"Leung","year":"2021","journal-title":"Atmos. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rser.2017.01.143","article-title":"Environmental influence and countermeasures for high humidity flue gas discharging from power plants","volume":"73","author":"Shuangchen","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s00703-005-0168-1","article-title":"Effect of the plume rise and wind speed on extreme value of air pollutant concentration","volume":"93","author":"Essa","year":"2006","journal-title":"Meteorol. Atmos. Phys."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1038\/s41586-019-1554-z","article-title":"Climate and air-quality benefits of a realistic phase-out of fossil fuels","volume":"573","author":"Shindell","year":"2019","journal-title":"Nature"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"7745","DOI":"10.1016\/j.atmosenv.2005.07.070","article-title":"Assessment of contribution of so2 and no2 from different sources in jamshedpur region, india","volume":"39","author":"Bhanarkar","year":"2005","journal-title":"Atmos. Environ."},{"key":"ref_78","first-page":"95","article-title":"Urban air pollution and climate change as environmental risk factors of respiratory allergy: An update","volume":"20","author":"Cecchi","year":"2010","journal-title":"J. Investig. Allergol. Clin. Immunol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1007\/s11869-020-00827-5","article-title":"Monitoring, analysis and spatial and temporal zoning of air pollution (carbon monoxide) using sentinel-5 satellite data for health management in iran, located in the middle east","volume":"13","author":"Safarianzengir","year":"2020","journal-title":"Air Qual. Atmos. Health"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.envsoft.2012.02.009","article-title":"Effects of traffic signal coordination on noise and air pollutant emissions","volume":"35","author":"Can","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"23405","DOI":"10.1007\/s11356-020-12164-2","article-title":"Changes in the concentration of air pollutants before and after the covid-19 blockade period and their correlation with vegetation coverage","volume":"28","author":"Zhou","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_82","first-page":"1","article-title":"Effect of degree of urbanisation on age and sex-specific asthma prevalence in swedish preschool children","volume":"9","author":"Eriksson","year":"2009","journal-title":"BMC Public Health"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.enconman.2018.01.038","article-title":"An improved combination approach based on adaboost algorithm for wind speed time series forecasting","volume":"160","author":"Xiao","year":"2018","journal-title":"Energy Convers. Manag."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"122272","DOI":"10.1016\/j.physa.2019.122272","article-title":"An improved stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms","volume":"541","author":"Jiang","year":"2020","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"4097","DOI":"10.1016\/j.ins.2009.08.025","article-title":"Troika\u2013an improved stacking schema for classification tasks","volume":"179","author":"Menahem","year":"2009","journal-title":"Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3222\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:45:51Z","timestamp":1760165151000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3222"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,13]]},"references-count":85,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163222"],"URL":"https:\/\/doi.org\/10.3390\/rs13163222","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,13]]}}}