Source Apportionment of Particulate Matter Exposure in Mail Carriers and the Short-Term Effects on Cardiovascular Function

• Main Authors: Ya-Ru Li, 黎雅如
• Other Authors: Chang-Fu Wu
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/21367501502775756856

碩士 === 國立臺灣大學 === 環境衛生研究所 === 96 === Objectives: This study is intended to provide the scientific information, in aspect of personal exposure, on exposure to airborne particulate matter (PM) and its components, possible sources contributed to them, and their associations with short-term cardiovascular effects. Methods: This panel study was conducted in Sin-Jhuang city, Taipei County, Taiwan. Eighteen mail carriers were recruited from the Sin-Jhuang Post Office. Each subject’s personal PM exposure and ambient PM concentrations were measured during working hours from Monday to Friday (or Saturday). PM samples were collected using a personal sampler, which classifies PM into five size ranges [>2.5 (A), 1.0-2.5 (B), 0.50-1.0 (C), 0.25- 0.50 (D), and < 0.25 (E) µm]. Heart rate variability (HRV) was monitored during working hours from Monday to Friday, and cardio-ankle vascular index (CAVI) was measured before and after working hours on Monday, Thursday, and Friday. Particle filters were digested in a microwave digestion system and the elemental concentrations were determined using the inductively coupled plasma mass spectrometry (ICP-MS) technique. The concentration of 21 elements, the right-side CAVI (r-CAVI), the heart rate (HR), and the 5-min segment of HRV data (SDNN, r-MSSD, HF, LF, and LF/HF) recorded immediately after the PM sampling session were used for data analysis. Absolute principal component analysis (APCA) was applied to PM elemental concentrations to identify sources and then quantify the source contributions. Mixed-effects regression model (MERM) was used to assess potential associations between source-specific PM and cardiovascular end points. Results: Three significant PM1.0-2.5 source factors (urban dust, vehicle exhaust, and brake wear) and two significant PM0.25 source factors (industrial processing and vehicle exhaust) were identified. The urban dust source accounted for the majority of PM1.0-2.5 mass (66.1 %); the largest contributor to PM0.25 mass was industrial processing (43.4 %). We also found that traffic-derived combustion (vehicle exhaust) and noncombustion (brake wear) sources had equal contribution to PM1.0-2.5 in urban areas. In the health analysis, controlling for the covariates, an interquartile range (IQR; 1.9 μg/m3) increase in PM1.0-2.5 from vehicle exhaust accounted for a 2.84 % increase in CAVI [95 % confidence interval (CI), 1.37－4.39 %]; an IQR (1.4 μg/m3) increase in PM1.0-2.5 from brake wear accounted for a 11.60 % decrease in SDNN (CI, -19.68－-2.70 %); and an IQR (10.9 μg/m3) increase in PM0.25 from industrial processing accounted for a 10.36 % decrease in SDNN (CI, -27.97－-1.67 %). We also observed that PM1.0-2.5 attributable to brake wear was positively associated with heart rate, and PM1.0-2.5 attributable to vehicle exhaust had positive association with LF/HF. Conclusions: Our findings indicate that urban dust and industrial sources are particularly important in Sin-Jhuang and suggest that PM derived from traffic (both combustion and noncombustion included) - and industrial-related sources probably trigger adverse cardiovascular effects in healthy subjects. The government should initiate pollution control strategies to reduce these urban PM emissions. We also suggest that mail carriers should stay indoors when air pollution levels are high (e.g., peak hours) or avoid all activity or working near high-traffic areas. In addition, we found that cardiovascular risks may vary among different PM components. Thus, using PM mass as exposure metrics may bias the health estimates of some of its specific components.