Spatiotemporal Distributions and Land-Use Regression Models of Ambient Bacteria and Endotoxins in the Greater Taipei Area

10.4209/aaqr.2015.01.0036

Kraiwuth Kallawicha；Shih-Chun Candice Lung；Ying-Chih Chuang；Chih-Da Wu；Tsun-Hsuan Chen；Ying-Jie Tsai；Hsing Jasmine Chao

Bioaerosols ； Ambient bacteria ； Ambient endotoxins ； Land-use regression (LUR)

Aerosol and Air Quality Research

15卷4期（2015 / 08 / 01）

1448 - 1459+i-ii

英文

Ambient bacteria and endotoxins are components of bioaerosols, which are abundant in ambient air. Exposure to ambient bacteria and endotoxins has been associated with respiratory symptoms. We monitored the spatiotemporal distributions of ambient bioaerosols in the Greater Taipei area by using multisite sampling and developed regression models for ambient bacterial and endotoxin concentrations. Forty-four representative sampling sites were selected from the Greater Taipei area. Samples were collected in 4 seasons throughout a 1-y study period. Ambient bacteria were quantified using acridine orange staining, and endotoxins were analyzed using Limulus amebocyte lysate assays. Land-use types and major determinants of the bioaerosols were used to develop regression models. Ambient bacteria ranged from ＜ limit of detection (LOD) to 1.68×10^5 cells/m^3, whereas endotoxins ranged from ＜ LOD to 20.62EU/m^3. Significant seasonal variations were observed for both bioaerosols, with the highest concentrations observed in spring. Regression analyses revealed temperature, relative humidity, and particulate matter as the major predictors of ambient bacteria and endotoxins. No land-use type was correlated with any of the bioaerosols. The number of schools and gas stations was positively associated with both bioaerosols. The leave-one-out cross-validation R^2 of the final models for ambient bacteria and endotoxins were 0.11 and 0.31, respectively. The results of this study revealed high spatiotemporal variations in the distributions of ambient bacteria and endotoxins in the Greater Taipei area. Additional potential predictors should be included in future studies to develop better predictive models for ambient bacteria and endotoxins.