Department of Mechanical Engineering, University of British Columbia, Vancouver V6T 1Z4, Canada.
Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver V6T 1Z4, Canada.
Environ Sci Technol. 2022 Mar 1;56(5):2843-2860. doi: 10.1021/acs.est.1c01739. Epub 2022 Feb 8.
Given the serious adverse health effects associated with many pollutants, and the inequitable distribution of these effects between socioeconomic groups, air pollution is often a focus of environmental justice (EJ) research. However, EJ analyses that aim to illuminate whether and how air pollution hazards are inequitably distributed may present a unique set of requirements for estimating pollutant concentrations compared to other air quality applications. Here, we perform a scoping review of the range of data analytic and modeling methods applied in past studies of air pollution and environmental injustice and develop a guidance framework for selecting between them given the purpose of analysis, users, and resources available. We include proxy, monitor-based, statistical, and process-based methods. Upon critically synthesizing the literature, we identify four main dimensions to inform method selection: accuracy, interpretability, spatiotemporal features of the method, and usability of the method. We illustrate the guidance framework with case studies from the literature. Future research in this area includes an exploration of increasing data availability, advanced statistical methods, and the importance of science-based policy.
鉴于许多污染物对健康的严重不良影响，以及这些影响在社会经济群体之间的不公平分布，空气污染往往是环境正义（EJ）研究的重点。然而，旨在阐明空气污染危害是否以及如何不公平地分布的 EJ 分析可能对污染物浓度的估计提出了一套独特的要求，与其他空气质量应用相比。在这里，我们对过去空气污染和环境不公正研究中应用的数据分析和建模方法的范围进行了范围界定审查，并根据分析目的、用户和可用资源制定了在它们之间进行选择的指导框架。我们包括代理、基于监测、统计和基于过程的方法。在批判性地综合文献后，我们确定了四个主要维度来为方法选择提供信息：准确性、可解释性、方法的时空特征和方法的可用性。我们用文献中的案例研究来说明指导框架。该领域的未来研究包括探索增加数据可用性、先进的统计方法以及基于科学的政策的重要性。
