Details

Autor(en) / Beteiligte

• Wang, Wenqiao
• Ying, Yangyang
• Wu, Quanyuan
• Zhang, Haiping
• Ma, Dedong
• Xiao, Wei

Titel

A GIS-based spatial correlation analysis for ambient air pollution and AECOPD hospitalizations in Jinan, China

Ist Teil von

• Respiratory medicine, 2015-03, Vol.109 (3), p.372-378

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2015

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Summary Background Acute exacerbations of COPD (AECOPD) are important events during disease procedure. AECOPD have negative effect on patients' quality of life, symptoms and lung function, and result in high socioeconomic costs. Though previous studies have demonstrated the significant association between outdoor air pollution and AECOPD hospitalizations, little is known about the spatial relationship utilized a spatial analyzing technique- Geographical Information System (GIS). Objective Using GIS to investigate the spatial association between ambient air pollution and AECOPD hospitalizations in Jinan City, 2009. Methods 414 AECOPD hospitalization cases in Jinan, 2009 were enrolled in our analysis. Monthly concentrations of five monitored air pollutants (NO2 , SO2 , PM10, O3 , CO) during January 2009–December 2009 were provided by Environmental Protection Agency of Shandong Province. Each individual was geocoded in ArcGIS10.0 software. The spatial distribution of five pollutants and the temporal-spatial specific air pollutants exposure level for each individual was estimated by ordinary Kriging model. Spatial autocorrelation (Global Moran's I) was employed to explore the spatial association between ambient air pollutants and AECOPD hospitalizations. A generalized linear model (GLM) using a Poisson distribution with log-link function was used to construct a core model. Results At residence, concentrations of SO2 , PM10, NO2 , CO, O3 and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of SO2 , PM10, CO, O3 , NO2 at residence is 15.88, 13.93, 12.60, 4.02, 2.44 respectively, while at workplace, concentrations of PM10, SO2 , O3 , CO and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of PM10, SO2 , O3 , CO at workplace is 11.39, 8.07, 6.10, and 5.08 respectively. After adjusting for potential confounders in the model, only the PM10 concentrations at workplace showed statistical significance, with a 10 μg/m3 increase of PM10 at workplace associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD. Conclusions Ambient air pollution is correlated with AECOPD hospitalizations spatially. A 10 μg/m3 increase of PM10 at workplace was associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD in Jinan, 2009. As a spatial data processing tool, GIS has novel and great potential on air pollutants exposure assessment and spatial analysis in AECOPD research.