区域级交通相关空气污染措施汇总的种族和社会经济公平性。

Andrew Vancil, Stephen Colegate, Erika Rasnick Manning, Anushka Palipana, Rhonda Szczesniak, Cole Brokamp
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引用次数: 0

摘要

背景:环境保护局的环境正义屏幕交通接近度(EJ屏幕)和交通部的平均每日交通量(AADT)通常作为交通相关污染暴露的代理。然而,用于汇总区域水平测量的方法尚未经过偏差测试。方法:采用为俄亥俄州汉密尔顿县开发的包裹级测量方法,在三个地理水平上确定上述两种测量方法的一致性:人口普查区组、人口普查区和邮政编码制表区(ZCTA)。采用线性回归评估公平性。结果:一般情况下,AADT值与包裹接近测量值显著一致,而EJ筛选值则不一致。EJ筛查普遍存在种族和社区剥夺偏见。讨论:虽然检测到的偏见并不直接针对大多数黑人和物质贫困社区,但这些偏见可能表现为负面的下游影响。这些表现包括在随后的研究中抑制已知的与交通有关的污染效应。影响声明:环境保护署的环境正义屏幕交通接近度(EJ Screen)和交通部的年平均日交通流量(AADT)是广泛使用的交通相关污染代理,然而,由于普通的聚合技术在很大程度上未经公平性测试,本研究发现了EJ屏幕产品中的潜在偏差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Racial and Socioeconomic Fairness of Area-Level Traffic-Related Air Pollution Measure Aggregation.

Background: The Environmental Protection Agency's Environmental Justice Screen traffic proximity (EJ Screen) and the Department of Transportation's Average Annual Daily Traffic (AADT) commonly serve as proxies of traffic-related pollution exposure. However, the methods used to aggregate to area-level measures have been untested for bias.

Methods: Using a parcel-level measured developed for Hamilton County, Ohio, agreement was determined with both above measures at three geographic levels: census block group, census tract and zip code tabulation area (ZCTA). Fairness was assessed using linear regression.

Results: Generally, the values of AADT were in significant agreement with the parcel proximity measure while the EJ Screen was not. Racial and community deprivation bias was widely detected for EJ Screen.

Discussion: While the biases detected were not directly against majority black and materially deprived neighborhoods, the biases could manifest in negative downstream effects. These manifestations include suppression of known traffic-related pollution effects in subsequent research.

Impact statement: The Environmental Protection Agency's Environmental Justice Screen traffic proximity (EJ Screen) and the Department of Transportation's Average Annual Daily Traffic (AADT) are widely used traffic related pollution proxies however, with common aggregation techniques largely untested for fairness, this research has detected potential biases in the EJ Screen product.

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