A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments
A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments
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DOI：
10.1016/s0048-9697(00)00429-0
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发表时间：
2000-05-15
影响因子：
9.8
通讯作者：
Smallbone, K
中科院分区：
文献类型：
--
作者：
Briggs, DJ;de Hoogh, C;Smallbone, K
需要准确、高分辨率的交通相关空气污染地图，作为流行病学研究中评估暴露的基础，并为城市空气质量政策和交通管理提供信息。本文评估了使用基于GIS的回归映射技术来模拟与交通有关的空气污染的空间格局。该模型使用来自哈德斯菲尔德80个被动采样点的数据开发，作为SAVIAH（空气质量和健康的小区域变化）项目的一部分，使用每个站点周围300米缓冲区的交通流量和土地覆盖数据，以及站点的海拔高度，作为NO浓度的预测因子。它在这里进行了测试，在英国的四个城市地区的应用程序：哈德斯菲尔德（一年后，用于最初的模型开发），谢菲尔德，北安普顿，和伦敦的一部分。在每种情况下，都在ArcInfo中建立了一个地理信息系统，综合了关于道路交通、城市土地使用和地形的相关数据。使用重复被动采样器对NO2进行监测（在伦敦，数据来自作为伦敦网络一部分进行的调查）。在哈德斯菲尔德、谢菲尔德和北安普顿，首先通过将建模结果与随机选择的10个地点监测的NO2浓度进行比较来校准模型;然后根据另外10-28个地点的数据验证校准后的模型。在伦敦，只有11个地点的数据，因此没有进行验证。结果表明，该模型在所有情况下表现良好。在当地校准后，该模型给出了年平均NO2浓度的估计值，其系数为实际平均值的1.5倍（约为1.5倍）。70-90%）的时间，并且在70和100%的时间之间的因子2内。模拟浓度和观测浓度之间的r（2）值在0.58-0.76之间。这些结果与更复杂的色散模型所获得的结果相当。该模型也有几个优势，分散建模。例如，它能够提供整个城市区域的高分辨率地图，而无需在接收点之间进行插值。与正式的色散建模相比，它还大大降低了成本和处理时间。它的结论是，该模型可能因此被用来作为一种手段，映射长期的空气污染浓度，无论是在支持地方当局的空气质量管理战略，或在流行病学研究。(C)2000 Elsevier Science B. V.保留所有权利。
Accurate, high-resolution maps of traffic-related air pollution are needed both as a basis for assessing exposures as part of epidemiological studies, and to inform urban air-quality policy and traffic management. This paper assesses the use of a GIS-based, regression mapping technique to model spatial patterns of traffic-related air pollution. The model - developed using data from 80 passive sampler sites in Huddersfield, as part of the SAVIAH (Small Area Variations in Air Quality and Health) project - uses data on traffic flows and land cover in the 300-m buffer zone around each site, and altitude of the site, as predictors of NO, concentrations. It was tested here by application in four urban areas in the UK: Huddersfield (for the year following that used for initial model development), Sheffield, Northampton, and part of London. In each case, a GIS was built in ArcInfo, integrating relevant data on road traffic, urban land use and topography. Monitoring of NO2 was undertaken using replicate passive samplers (in London, data were obtained from surveys carried out as part of the London network). In Huddersfield, Sheffield and Northampton, the model was first calibrated by comparing modelled results with monitored NO2 concentrations at 10 randomly selected sites; the calibrated model was then validated against data from a further 10-28 sites. In London, where data for only 11 sites were available, validation was not undertaken. Results showed that the model performed well in all cases. After local calibration, the model gave estimates of mean annual NO2 concentrations within a factor of 1.5 of the actual mean (approx. 70-90%) of the time and within a factor of 2 between 70 and 100% of the time. r(2) values between modelled and observed concentrations are in the range of 0.58-0.76. These results are comparable to those achieved by more sophisticated dispersion models. The model also has several advantages over dispersion modelling. It is able, for example, to provide high-resolution maps across a whole urban area without the need to interpolate between receptor points. It also offers substantially reduced costs and processing times compared to formal dispersion modelling. It is concluded that the model might thus be used as a means of mapping long-term air pollution concentrations either in support of local authority air-quality management strategies, or in epidemiological studies. (C) 2000 Elsevier Science B.V. All rights reserved.