Effects of regional air pollutants on respiratory diseases in the basin metropolitan area of central Taiwan

Abstract

AbstractThis study divided a basin metropolitan area with high air pollution into three subareas, namely urban, suburban, and rural, on the basis of population density for a systematic analysis of the effects of local air pollutants on respiratory diseases. A panel data regression model was used to estimate the annual incidence growth rates (AIGRs) of the four respiratory diseases, namely lung cancer, chronic obstructive pulmonary disease, asthma, and pneumonia, resulting from exposure to fine particulate matter (PM2.5, diameter of 2.5 μm or less), odd oxygen (ODO), or nonmethane hydrocarbon (NMHC). The results indicate that the prevailing wind direction is not a major factor determining the distribution of air pollutants. The spatial distributions of ODO and NMHC differed from that of PM2.5. Three air pollutants contributed to positive AIGRs of the four diseases in the study area, but PM2.5 which had a negative AIGR for asthma in the rural subarea. The pollutants with the strongest effects on AIGR, in descending order, were NMHC, PM2.5, and ODO. The effect of ambient NMHC was significant and nonnegligible, especially in the urban subarea. A dimensionless potential AIGR (PAIGR) formula was established to quantitatively compare the effects of different air pollutants on the four respiratory diseases. The results indicate that ambient NMHC had the strongest effect on the incidences of the respiratory diseases, followed by that of ambient PM2.5. The effect of ambient NMHC was significant and nonnegligible, especially in the urban subarea. The PAIGR ratio ranges of PM2.5 to ODO and NMHC to ODO for the four diseases in urban subsarea were from 3 to 19 and from 289 to 920, respectively. This study also applied multivariate regression to assess the association among 5 aspects, namely air quality, point source, line source, area source, and socioeconomic status, and the incidences of the four respiratory diseases. The results indicate that the model has favorable fit and can thus reflect the associations of the 15 factors of 5 aspects with the four respiratory diseases in each subarea.