Space-Time Correlation between Air Pollutants and Circulatory and Respiratory Deaths

Keywords: Air Pollutants, Circulatory Deaths, Respiratory Deaths, Space-Time Scan Statistics
Session Type: Virtual Paper Abstract
Day: Monday
Session Start / End Time: 2/28/2022 09:40 AM (Eastern Time (US & Canada)) - 2/28/2022 11:00 AM (Eastern Time (US & Canada))
Room: Virtual 66

Abstract

The need for systematic management and policies regarding diseases caused by air pollution is increasing. However, there is insufficient correlation analysis between pollution and diseases, highlighting the need for prevention plans. This study examined the correlation between air pollution and circulatory and respiratory deaths through comparative analysis of space-time patterns and areas that require preemptive measures.
Study sites included 250 si/gun/gu in Korea. Data on six air pollutants (PM10, PM2.5, CO, NO2, O3, and SO2) in 2015-2019 and circulatory and respiratory deaths were used.
First, Moran’s I, a global spatial autocorrelation measure, was used to determine spatial clusters by air pollutant, and LISA, a local measure, to analyze hotspot patterns. Second, spatial and space-time clusters were comparatively analyzed using space-time scan statistics. Finally, correlations between space-time clusters and air pollutant distribution were evaluated.
Spatial autocorrelation was significant in all variables. PM10, PM2.5, CO, and NO2 formed hotspots in capital areas, and O3 formed hotspots in the southwest. Spatial hotspots of deaths owing to circulatory and respiratory diseases were formed in the southeast and southwest and central areas, respectively. Space-time hotspots from space-time scan statistics were different from those obtained in LISA analysis, indicating that time difference must be considered in analyzing areas with concentrated respiratory and circulatory deaths. O3 showed the highest correlation owing to overlapping space-time clusters of deaths and pollutant concentration distribution.
This study serves as a reference for analyzing space-time clusters and correlations and has important policy implications owing to growing concerns regarding environmental diseases.