APPLYING GEOGRAPHIC INFORMATION SYSTEMS (GIS) AND IOT SENSOR DATA TO MODEL THE IMPACT OF AIR POLLUTION ON THE INCIDENCE OF RESPIRATORY DISEASES - Filip Kowal; Kamila Krzyżanowska; Michał Pietrucha; Karol Demel; Justyna Talaska; Adrian Dyląg; Jakub Król; Maciej Łydka; Justyna Lewandowska; Monika Dziedzic (International Journal of Innovative Technologies in Social Science, 2025)
Ambient air pollution is a major global health threat, yet modeling its localized impact on respiratory diseases is hindered by sparse monitoring networks, leading to exposure misclassification. This paper outlines a high-resolution framework integrating Geographic Information Systems (GIS) and Internet of Things (IoT) sensor data to overcome these limitations. The methodology involves using calibrated, dense IoT networks to capture real-time (PM2.5) data, which is then integrated with health records. GIS techniques, including Kriging and Geographically Weighted Regression (GWR), are used to create continuous exposure surfaces and analyze spatially varying health relationships. The anticipated results include the identification of pollution hotspots and disease clusters, with the key finding being the quantification of spatial heterogeneity. This synergistic approach provides a powerful evidence base for targeted public health interventions and addressing environmental inequities. Background: Air pollution, particularly (PM2.5), (NO2), and (O3), is a leading cause of global morbidity and mortality, strongly linked to respiratory diseases like asthma and COPD. Accurately assessing population exposure is a significant challenge. Traditional air quality monitoring relies on sparse, fixed regulatory stations. This method fails to capture the complex, micro-environmental variations in pollutant concentrations, leading to a "spatial data gap." This gap results in exposure misclassification in epidemiological studies, potentially underestimating the true health risks for vulnerable populations living in localized "hotspots." Purpose of Research: The primary aim of this paper is to outline and propose a high-resolution geospatial modeling framework. This framework integrates real-time, high-granularity data from dense, low-cost Internet of Things (IoT) sensor networks with advanced Geographic Information Systems (GIS) analysis. The goal is to more accurately quantify the localized, spatially varying relationship between air pollution exposure and the incidence of respiratory diseases, moving beyond traditional, spatially-uniform risk assessments.