Abstract
Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Initially, we built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease—distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2). Subsequently, utilizing this spatial database, a random forest (RF) machine learning model, and a geographic information system, we prepared a map of asthma-prone areas. For modeling and validation, we deployed 70% and 30%, respectively, of the locations of children with asthma. The results of spatial autocorrelation and RF model showed that the criteria of distance to parks and streets as well as PM 2.5 and PM 10 had the greatest impact on asthma occurrence in the study area. Spatial autocorrelation analyses indicated that the distribution of asthma cases was not random. According to receiver operating characteristic results, the RF model had good accuracy (the area under the curve was 0.987 and 0.921, respectively, for training and testing data).
Highlights
Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased
Random forest (RF) is one of the machine learning models that has been considered in environmental modeling in recent years owing to its simplicity, robustness, and capacity to deal with complex data[21]
The results of the research are as follows: 1. The results of spatial autocorrelation showed that the criteria of distance to park, distance to street, particulate matter (PM) 2.5, and PM 10 had a strong spatial correlation with asthma
Summary
Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. We built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease—distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2) Utilizing this spatial database, a random forest (RF) machine learning model, and a geographic information system, we prepared a map of asthma-prone areas. Douglas et al examined environmental factors, asthma reports, diesel particles, and public parks in Los A ngeles[16] For this purpose, the spatial analysis of hot spots, least squares method, and weighted geographical regression were used to map high-risk areas for asthma. According to the authors’ knowledge, the RF model has not been implemented to assess areas susceptible to asthma, its good performance has been proved in other environmental fields, such as groundwater potential[33], groundwater hardness[22], flood risk[23], and PM 10 risk[19]
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