Characterization of ambient PM2.5 concentrations

Statistical spatial and temporal analysis of PM2.5 concentrations in ambient air using principal component analysis may provide health risk information for air-quality management. This investigation simultaneously determines and interprets spatial variations and features of PM2.5 concentrations using ambient air-quality monitoring data during 2006–2008 and emission inventory. Daily mean values of PM2.5 and PM10 and maximum hourly data of SO2, CO, O3 and NO2 were calculated as sampling data for year 2006–2008. Therefore, principal component analysis and descriptive statistics of ambient air pollutants were utilized to assess the spatial features and variations of PM2.5 concentrations. This study also provides PM2.5/PM10 ratios and the rates at which 24-h particulate matter exceed air-quality standards over Taiwan. Analytical results indicate that four rotational components cumulatively explain 87% and 84% of concentration variances for PM10 and PM2.5 and form a delineation of four “influence regimes.” The separated districts of the four “influence regimes” for PM10 and PM2.5 were the same. With the rate at which PM2.5 24-h concentrations were above 65 μg m−3, 36% of air-quality stations, is higher than 10%; 24% of air-quality stations is higher than 15%. Based on analytical results, if the PM2.5 limit would be considered as National Ambient Air-Quality Standard in the future, the priority of reducing PM10 or O3 concentrations in the past decade could be replaced with PM2.5. The novel methodologies presented in this study can spatially assess adequate boundaries of atmospheric carrying capacity for particulate matter. Local governments located at the same influence regime characterized by factor loading isopleths should implement trans-boundary air pollution control programs. Box plots, the rates of particulate matter exceeding air-quality standards and PM2.5/PM10 ratios in distinct “influenced regimes” were also examined. Continued study of spatial and temporal variations in airborne PM2.5 concentrations will provide sufficient information about health risks and for air-quality control programs.