Estimating long-term average particulate air pollution concentrations: Application of traffic indicators and geographic information systems
Estimating long-term average particulate air pollution concentrations: Application of traffic indicators and geographic information systems
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DOI：
10.1097/00001648-200303000-00019
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发表时间：
2003-03-01
期刊：
影响因子：
5.4
通讯作者：
Brunekreef, B
中科院分区：
文献类型：
--
作者：
Brauer, M;Hoek, G;Brunekreef, B
背景资料。作为三项出生队列研究(TRAPCA)中交通相关空气污染与哮喘发病率的多中心研究的一部分，我们使用测量和建模程序来估计荷兰、德国慕尼黑和瑞典斯德哥尔摩县社区长期平均暴露于交通相关颗粒物空气污染的情况。在这三个地点中的每一个都选择了40-42个测量点来代表农村、城市背景和城市交通位置。在1999年2月至2000年7月期间，在大约一年的时间内，测量了每一地点的四个两周期间的细颗粒物和过滤器吸光度(柴油废气颗粒物的标志)。在对时间变化进行调整后，我们使用这些测量结果来计算年平均浓度。利用地理信息系统收集了与交通相关的变量(如人口密度和交通强度)，并将其用于预测年平均浓度的回归模型。根据这些模型，我们估计了队列成员家庭住址的环境空气浓度。使用交通相关变量的回归模型分别解释了荷兰、慕尼黑和斯德哥尔摩县年平均细颗粒物浓度变异性的73%、56%和50%。对于滤器吸光度，回归模型解释了年平均浓度变异性的81%、67%和66%。模型预测误差的交叉验证表明，PM2.5和吸光度的均方根误差分别为1.1-1.6mug/m(3)和0.22-0.31×10(-5)m(-1)。所有地点年平均浓度的变异性中，有很大一部分是由交通相关变量解释的。这种方法可用于估计流行病学研究中的个人接触情况，并比依赖替代变量的替代技术或仅利用环境监测数据的传统方法更具优势。
Background. As part of a multicenter study relating traffic-related air pollution with incidence of asthma in three birth cohort studies (TRAPCA), we used a measurement and modelling procedure to estimate long-term average exposure to traffic-related particulate air pollution in communities throughout the Netherlands; in Munich, Germany; and in Stockholm County, Sweden.Methods. In each of the three locations, 40-42 measurement sites were selected to represent rural, urban background and urban traffic locations. At each site and fine particles and filter absorbance (a marker for diesel exhaust particles) were measured for four 2-week periods distributed over approximately 1-year periods between February 1999 and July 2000. We used these measurements to calculate annual average concentrations after adjustment for temporal variation. Traffic-related variables (eg, population density and traffic intensity) were collected using Geographic Information Systems and used in regression models predicting annual average concentrations. From these models we estimated ambient air concentrations at the home addresses of the cohort members.Results. Regression models using traffic-related variables explained 73%, 56% and 50% of the variability in annual average fine particle concentrations for the Netherlands, Munich arid Stockholm County, respectively. For filter absorbance, the regression models explained 81%, 67% and 66% of the variability in the annual average concentrations. Cross-validation to estimate the model prediction errors indicated root mean squared errors of 1.1-1.6 mug/m(3) for PM2.5 and 0.22-0.31 *10(-5)m(-1) for absorbance.Conclusions. A substantial fraction of the variability in annual average concentrations for all locations was explained by traffic-related variables. This approach can be used to estimate individual exposures for epidemiologic studies and offers advantages over alternative techniques relying on surrogate variables or traditional approaches that utilize ambient monitoring data alone.