MRC-HPA Centre for Environment and Health, Department of Epidemiology & Biostatistics, Imperial College London, London, UK.
Sci Total Environ. 2011 May 15;409(12):2419-29. doi: 10.1016/j.scitotenv.2011.03.004. Epub 2011 Mar 31.
Current methods of air pollution modelling do not readily meet the needs of air pollution mapping for short-term (i.e. daily) exposure studies. The main limiting factor is that for those few models that couple with a GIS there are insufficient tools for directly mapping air pollution both at high spatial resolution and over large areas (e.g. city wide). A simple GIS-based air pollution model (STEMS-Air) has been developed for PM(10) to meet these needs with the option to choose different exposure averaging periods (e.g. daily and annual). STEMS-Air uses the grid-based FOCALSUM function in ArcGIS in conjunction with a fine grid of emission sources and basic information on meteorology to implement a simple Gaussian plume model of air pollution dispersion. STEMS-Air was developed and validated in London, UK, using data on concentrations of PM(10) from routinely available monitoring data. Results from the validation study show that STEMS-Air performs well in predicting both daily (at four sites) and annual (at 30 sites) concentrations of PM(10). For daily modelling, STEMS-Air achieved r(2) values in the range 0.19-0.43 (p<0.001) based solely on traffic-related emissions and r(2) values in the range 0.41-0.63 (p<0.001) when adding information on 'background' levels of PM(10). For annual modelling of PM(10), the model returned r(2) in the range 0.67-0.77 (P<0.001) when compared with monitored concentrations. The model can thus be used for rapid production of daily or annual city-wide air pollution maps either as a screening process in urban air quality planning and management, or as the basis for health risk assessment and epidemiological studies.
当前的空气污染建模方法不能很好地满足短期（即每日）暴露研究中空气污染制图的需求。主要的限制因素是，对于那些与 GIS 耦合的少数模型，缺乏直接在高空间分辨率和大面积（例如全市范围）上绘制空气污染的工具。已经开发了一种基于 GIS 的简单空气污染模型（STEMS-Air），以满足这些需求，并可以选择不同的暴露平均时间段（例如每日和每年）。STEMS-Air 在 ArcGIS 中使用基于网格的 FOCALSUM 函数，结合排放源的精细网格和基本气象信息，实现了空气污染扩散的简单高斯烟羽模型。STEMS-Air 在英国伦敦开发和验证，使用来自常规监测数据的 PM(10)浓度数据。验证研究的结果表明，STEMS-Air 能够很好地预测 PM(10)的日（四个地点）和年（30 个地点）浓度。对于每日建模，仅基于交通相关排放，STEMS-Air 可实现 0.19-0.43（p<0.001）范围内的 r(2)值，当添加 PM(10)“背景”水平的信息时，r(2)值在 0.41-0.63（p<0.001）范围内。对于 PM(10)的年度建模，与监测浓度相比，该模型返回 0.67-0.77（P<0.001）范围内的 r(2)值。因此，该模型可用于快速生成每日或年度全市范围的空气污染图，无论是作为城市空气质量规划和管理中的筛选过程，还是作为健康风险评估和流行病学研究的基础。
