The preferential siting of the locations of monitors of hazardous environmental fields can lead to the serious underestimation of the impacts of those fields. In particular, human health effects can be severely underestimated when standard statistical are applied without appropriate adjustment. This report describes an extensive analysis of the siting of monitors for a network that measures air pollution PM10 in California's South Coast Air Basin SOCAB. That analysis uses EPA data collected during the 1986 to 2019 period. Background descriptions, including those published by the US EPA are provided. The analysis uses a very general and fast Monte Carlo test for preferential sampling developed by Dr Joe Watson, which confirms that the sites were preferentially sited, as would be expected, given the intended purpose of the network to detect noncompliance with air quality standards. Our findings demonstrate both the value of that algorithm for application where where such background knowledge is not available, and hence to situations in which standard statistical tools require modification.
翻译:偏好性环境场地监测站点的位置布置可能导致对这些领域的影响严重低估。特别是当标准统计学方法应用时,如果没有采取适当的调整,很可能会严重低估人类健康影响。本文描述了对加利福尼亚南海岸空气盆地SOCAB中PM10空气污染监测网络的位置进行的广泛分析。该分析使用了美国EPA在1986年至2019年期间收集的数据。提供了背景描述,包括美国EPA发布的描述信息。该分析使用了由Dr Joe Watson开发的非常通用和快速的偏差抽样蒙特卡罗测试,证实站点被偏好布置,这是预期的,因为该网络的目的是检测与空气质量标准不符的情况。我们的研究结果证明了该算法在缺乏此类背景知识的应用中的价值,因此适用于标准统计工具需要修改的情况。