Pengchai Petch, Chantara Somporn, Sopajaree Khajornsak, Wangkarn Sunanta, Tengcharoenkul Urai, Rayanakorn Mongkon
Environmental Engineering, Chiang Mai University, Sutep, Muang, Chiang Mai 50200, Thailand.
Environ Monit Assess. 2009 Jul;154(1-4):197-218. doi: 10.1007/s10661-008-0389-0. Epub 2008 Aug 8.
Daily PM10 concentrations were measured at four sampling stations located in Chiang Mai and Lamphun provinces, Thailand. The sampling scheme was conducted during June 2005 to June 2006; every 3 days for 24 h in each sampling period. The result revealed that all stations shared the same pattern, in which the PM10 (particulate matters with diameter of less than 10 microm) concentration increased at the beginning of dry season (December) and reached its peak in March before decreasing by the end of April. The maximum PM10 concentration for each sampling station was in the range of 140-182 microg/m(3) which was 1.1-1.5 times higher than the Thai ambient air quality standard of 120 microg/m(3). This distinctly high concentration of PM10 in the dry season (Dec. 05-Mar. 06) was recognized as a unique seasonal pattern for the northern part of Thailand. PM10 concentration had a medium level of negative correlation (r = -0.696 to -0.635) with the visibility data. Comparing the maximum PM10 concentration detected at each sampling station to the permitted PM10 level of the national air quality standard, the warning visibility values for the PM10 pollution-watch system were determined as 10 km for Chiang Mai Province and 5 km for Lamphun Province. From the analysis of PM10 constituents, no component exceeded the national air quality standard. The total concentrations of PM10-bond polycyclic aromatic hydrocarbons (PAHs) are calculated in terms of total toxicity equivalent concentrations (TTECs) using the toxicity equivalent factors (TEFs) method. TTECs in Chiang Mai and Lamphun ambient air was found at a level comparable to those observed in Nagasaki, Bangkok and Rome and at a lower level than those reported at Copenhagen. The annual number of lung cancer cases for Chiang Mai and Lamphun Provinces was estimated at two cases/year which was lower than the number of cases in Bangkok (27 cases/year). The principal component analysis/absolute principal component scores (PCA/APCS) model and multiple regression analysis were applied to the PM10 and its constituents data. The results pointed to the vegetative burning as the largest PM10 contributor in Chiang Mai and Lamphun ambient air. Vegetative burning, natural gas burning & coke ovens, and secondary particle accounted for 46-82%, 12-49%, and 3-19% of the PM10 concentrations, respectively. However, natural gas burning & coke ovens as well as vehicle exhaust also deserved careful attention due to their large contributions to PAHs concentration. In the wet season and transition periods, 42-60% of the total PAHs concentrations originated from vehicle exhaust while 16-37% and 14-38% of them were apportioned to natural gas burning & coke ovens and vegetative burning, respectively. In the dry period, natural gas burning & coke ovens, vehicle exhaust, and vegetative burning accounted for 47-59%, 20-25%, and 19-28% of total PAHs concentrations. The close agreement between the measured and predicted concentrations data (R(2) > 0.8) assured enough capability of PCA/APCS receptor model to be used for the PM10 and PAHs source apportionment.
在泰国清迈府和南奔府的四个采样站对每日PM10浓度进行了测量。采样计划于2005年6月至2006年6月实施;每个采样期每3天进行一次24小时采样。结果显示,所有站点呈现相同模式,即PM10(直径小于10微米的颗粒物)浓度在旱季开始时(12月)上升,并在3月达到峰值,随后在4月底下降。每个采样站的PM10最大浓度在140 - 182微克/立方米范围内,比泰国120微克/立方米的环境空气质量标准高出1.1 - 1.5倍。在旱季(2005年12月 - 2006年3月)PM10这种明显较高的浓度被认为是泰国北部的一种独特季节性模式。PM10浓度与能见度数据具有中等程度的负相关(r = -0.696至 -0.635)。将每个采样站检测到的PM10最大浓度与国家空气质量标准允许的PM10水平进行比较,确定清迈府PM10污染监测系统的预警能见度值为10公里,南奔府为5公里。从对PM10成分的分析来看,没有任何成分超过国家空气质量标准。采用毒性当量因子(TEFs)方法,根据总毒性当量浓度(TTECs)计算了与PM10结合的多环芳烃(PAHs)的总浓度。清迈和南奔环境空气中的TTECs水平与长崎、曼谷和罗马观察到的水平相当,低于哥本哈根报告的水平。清迈府和南奔府每年的肺癌病例估计为2例/年,低于曼谷的病例数(27例/年)。将主成分分析/绝对主成分得分(PCA/APCS)模型和多元回归分析应用于PM10及其成分数据。结果表明,植被燃烧是清迈和南奔环境空气中PM10的最大贡献源。植被燃烧、天然气燃烧及焦炉排放和二次颗粒物分别占PM10浓度的46 - 82%、12 - 49%和3 - 19%。然而,天然气燃烧及焦炉排放以及汽车尾气对PAHs浓度的贡献也很大,也应予以密切关注。在雨季和过渡时期,PAHs总浓度的42 - 60%源自汽车尾气,而其中16 - 37%和14 - 38%分别归因于天然气燃烧及焦炉排放和植被燃烧。在旱季,天然气燃烧及焦炉排放、汽车尾气和植被燃烧分别占PAHs总浓度的47 - 59%、20 - 25%和19 - 28%。实测浓度数据与预测浓度数据之间的密切一致性(R² > 0.8)确保了PCA/APCS受体模型有足够能力用于PM10和PAHs源解析。