Thai Amy, McKendry Ian, Brauer Michael
Department of Geography, The University of British Columbia, Vancouver, Canada.
Sci Total Environ. 2008 Nov 1;405(1-3):26-35. doi: 10.1016/j.scitotenv.2008.06.035. Epub 2008 Aug 12.
An instrumented bicycle was used to elucidate particulate matter exposures along bicycle routes passing through a variety of land uses over 14 days during summer and fall in a mid-latitude traffic dominated urban setting. Overall, exposures were low or comparable to those found in studies elsewhere (mean PM(2.5) and PM(10) concentrations over each daily bicycle traverse varied between 7-34 microg m(-3) and 26-77 microg m(-3) respectively). Meteorological factors were responsible for significant day-to-day variability with PM(2.5) positively correlated with air temperature, PM(10) negatively correlated with precipitation, and ultrafine particles negatively correlated with both air temperature and wind speed. On individual days, land use and proximity to traffic were factors significantly affecting exposure along designated bicycle routes. While concentrations of PM(2.5) were found to be relatively spatially uniform over the length of the study route, PM(10) showed a more heterogeneous spatial distribution. Specifically, construction sites and areas susceptible to the suspension of road dust have higher concentrations of coarse particles. Ultrafine particles were also heterogeneously distributed in space, with areas with heavy traffic volumes having the highest concentrations. Observations show qualitative agreement in terms of spatial patterns with a land-use regression (LUR) model for annual PM(2.5) concentrations.
在一个以交通为主导的中纬度城市环境中,夏季和秋季期间,使用一辆装有仪器的自行车,在14天内沿着穿过各种土地利用类型的自行车道,对颗粒物暴露情况进行了研究。总体而言,暴露水平较低,或与其他地方研究中发现的水平相当(每次每日自行车行程中,PM(2.5)和PM(10)的平均浓度分别在7 - 34微克/立方米和26 - 77微克/立方米之间变化)。气象因素导致了显著的每日变化,PM(2.5)与气温呈正相关,PM(10)与降水量呈负相关,超细颗粒物与气温和风速均呈负相关。在个别日子里,土地利用和与交通的距离是显著影响指定自行车道沿线暴露情况的因素。虽然发现PM(2.5)的浓度在研究路线长度上相对空间均匀,但PM(10)显示出更不均匀的空间分布。具体而言,建筑工地和易产生道路扬尘的区域粗颗粒浓度较高。超细颗粒物在空间上也分布不均,交通流量大的区域浓度最高。观察结果表明,在空间模式方面,与年度PM(2.5)浓度的土地利用回归(LUR)模型存在定性一致性。