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.

译文

亲和区定义为空气质量在空间和时间上表现出一致行为的区域。亲和区使用多元统计技术以及反映空气污染的潜在来源和空间格局的地理和景观变量构建,提供了适合探索监测网络代表性的空间结构,并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点以及在1千米GIS中汇编的环境数据演示了亲和区方法。该方法分为三个主要阶段,包括 :( i) 使用主成分分析进行指标选择,(ii) 通过聚类分析将区域划分为不同类型,以及 (iii) 站点分配,以确认亲和区域内的相似性。监测的空气污染浓度。为欧洲建造了十个可解释且连贯的空气污染亲和力区,包括两个农村地区和八个与不同类型的人口稠密和建筑环境有关的区域。对于NO(2) 背景和交通站点以及仅PM(10) 交通站点,亲和区域之间的浓度差异显着。然而,并非所有区域都被监测点充分代表,这说明了亲和力区域在识别监测网络缺陷方面的重要性。还证明了在亲和区域内的空间建模,表明区域内背景NO(2) 浓度的简单克里金化 (与克里金化忽略区域相比) 产生了约22% 的误差减少,并通过在保留的验证监测站点处的0.25增加了R(2)。此处开发的亲和区方法是一种强大的统计方法,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。

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