Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, Imperial College London, London W2 1PG, United Kingdom.
Environ Int. 2013 Jan;51:106-15. doi: 10.1016/j.envint.2012.10.012. Epub 2012 Nov 29.
Affinity zones are defined as areas within which air quality displays consistent behaviour over space and time. Constructed using multivariate statistical techniques and physiographic and landscape variables reflecting underlying sources and spatial patterns of air pollution, affinity zones provide a spatial structure suited to exploring the representativity of monitoring networks and as a basis for air pollution mapping and exposure assessment. The affinity zone method is demonstrated using European air pollution monitoring sites, and environmental data compiled within a 1 km GIS. Organised into three main stages, this method involves: (i) indicator selection, using principal components analysis, (ii) zonation by cluster analysis to classify areas into distinct types, and (iii) site allocation, to confirm similarity within affinity zones in terms of monitored air pollution concentrations. Ten interpretable and coherent air pollution affinity zones were constructed for Europe, including two rural zones and eight related to different types of densely populated and built up environments. Concentrations between affinity zones differed significantly for NO(2) background and traffic sites and for PM(10) traffic sites only. Not all zones, however, were found to be sufficiently represented by monitoring sites, illustrating the importance of affinity zones in identifying deficiencies in monitoring networks. Spatial modelling within affinity zones is also demonstrated, showing that simple kriging of background NO(2) concentrations within zones (compared to kriging ignoring zones) produced a ca. 22% reduction in errors and increased R(2) by 0.25 at reserved validation monitoring sites. The affinity zone method developed here is a robust, statistical approach that can be used for evaluating the representativity of routine monitoring networks often used in continental level environmental and health risk assessments.
亲和区是指在空间和时间上空气质量表现出一致行为的区域。使用多元统计技术以及反映潜在污染源和空气污染空间模式的地形和景观变量构建,亲和区提供了适合探索监测网络代表性的空间结构,并且可以作为空气污染制图和暴露评估的基础。使用欧洲空气污染监测站点和在 1 公里 GIS 内编制的环境数据演示了亲和区方法。该方法分为三个主要阶段:(i)使用主成分分析选择指标,(ii)通过聚类分析进行分区,将区域分类为不同类型,以及(iii)站点分配,以确认在亲和区中监测的空气污染浓度方面的相似性。为欧洲构建了十个可解释和连贯的空气污染亲和区,包括两个农村区和八个与不同类型人口密集和建成环境相关的区。NO2 背景和交通站点以及 PM10 交通站点之间的亲和区之间的浓度差异显著。然而,并非所有区域都被监测站点充分代表,这说明了亲和区在识别监测网络缺陷方面的重要性。还展示了亲和区内的空间建模,表明在区域内进行背景 NO2 浓度的简单克里金(与忽略区域的克里金相比)可使保留验证监测站点的误差减少约 22%,R2 增加 0.25。此处开发的亲和区方法是一种稳健的统计方法,可用于评估通常用于大陆水平环境和健康风险评估的常规监测网络的代表性。