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：危险环境场监测点位的优先选址可能导致对这些场影响的严重低估。特别是，在未进行适当调整而应用标准统计方法时，人类健康影响可能被严重低估。本报告详细分析了加州南海岸空气盆地（SOCAB）中测量空气污染PM10的网络监测站点选址问题。该分析使用了EPA在1986年至2019年期间收集的数据，并提供了包括美国EPA发布在内的背景描述。分析采用了乔·沃森博士开发的一种通用且快速的优先采样蒙特卡洛检验方法，该方法确认了站点的优先选址性质——这符合预期，因为该网络的预期目的是检测空气质量标准的不合规情况。我们的研究结果既证明了该算法在缺乏此类背景知识的应用场景中的价值，也表明其适用于需要修正标准统计工具的各类情形。