A data-driven approach for characterizing community scale air pollution exposure disparities in inland Southern California
A data-driven approach for characterizing community scale air pollution exposure disparities in inland Southern California
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DOI：
10.1016/j.jaerosci.2020.105704
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发表时间：
2021-02-01
影响因子：
4.5
通讯作者：
Ivey, Cesunica E.
中科院分区：
文献类型：
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作者：
Khanh Do;Yu, Haofei;Ivey, Cesunica E.
2017年，加州州批准了617号法案，该法案要求分配资源，以解决全州服务不足社区的空气污染物暴露差异。该法案规定实施社区规模监测并制定当地减排计划。我们的目标是开发一种简化的，强大的，可访问的PM2.5暴露评估方法，以支持环境正义分析。我们试图描述单个PM（2）（.）（5）在内陆南加州地区（包括服务不足的社区圣贝纳迪诺，CA）的多个24小时暴露。在2019年春季进行了为期五周的个人采样，并对18名成年参与者的个人PM2.5暴露进行了多个连续24小时的监测。曝光和位置数据以5秒的分辨率进行分析，参与者数据恢复率平均为50.8%。一个空间聚类算法被用来分类数据点的七个微环境之一。平均值和中位数的个人环境PM2.5的比例聚集沿着SES线合格的数据集。基于GIS的空间聚类有助于对90多万个数据点进行有效的微环境分类。当沿着沿着SES线聚集时，每个微环境的平均（中位数）个人-环境比率范围为0.26（0.14）至2.78（0.65）。综合比率表明，与所有其他社区的参与者相比，来自最低SES社区的参与者在连续24小时监测期间经历了更高的家庭暴露，尽管参与者流动性高，研究期间环境PM2.5的变化相对较低。这里描述的方法强调了个人采样活动的强大和可访问性，这是专门设计的，以减少参与者疲劳和从事内陆南加州社区的成员谁可能会遇到障碍时，与科学界接触。这种方法是有希望的大规模，以社区为重点，个人接触运动的直接和精确的环境正义分析。
In 2017, Assembly Bill 617 was approved in the state of California, which mandated the allocation of resources for addressing air pollutant exposure disparities in underserved communities across the state. The bill stipulated the implementation of community scale monitoring and the development of local emissions reductions plans. We aimed to develop a streamlined, robust, and accessible PM2.5 exposure assessment approach to support environmental justice analyses. We sought to characterize individual PM(2)(.)(5 )exposure over multiple 24-hr periods in the inland Southern California region, which includes the underserved community of San Bernardino, CA. Personal sampling took place over five weeks in the spring of 2019, and personal PM2.5 exposure was monitored for 18 adult participants for multiple, consecutive 24-hr periods. Exposure and location data were analyzed at 5-second resolution, and participant data recovery was 50.8% on average. A spatial clustering algorithm was used to classify data points as one of seven microenvironments. Mean and median personal-ambient PM2.5 ratios were aggregated along SES lines for eligible datasets. GIS-based spatial clustering facilitated efficient microenvironment classification for more than 900,000 data points. Mean (median) personal-ambient ratios ranged from 0.26 (0.14) to 2.78 (0.65) for each microenvironment when aggregated along SES-lines. Aggregated ratios indicated that participants from the lowest SES community experienced higher home exposures compared to participants of all other communities over consecutive 24-hr monitoring periods, despite high participant mobility and relatively low variability in ambient PM2.5 during the study. The methods described here highlight the robust and accessible nature of the personal sampling campaign, which was specifically designed to reduce participant fatigue and engage members of the inland Southern California community who may experience barriers when engaging with the scientific community. This approach is promising for larger-scale, community-focused, personal exposure campaigns for direct and precise environmental justice analyses.