Department of Civil and Environmental Engineering, Rice University, Houston, TX, USA.
Department of Civil and Environmental Engineering, Rice University, Houston, TX, USA; School of Engineering, Computing and Construction Management, Roger Williams University, Bristol, RI, USA.
Environ Res. 2022 Sep;212(Pt C):113418. doi: 10.1016/j.envres.2022.113418. Epub 2022 May 4.
Studies increasingly use output from the Environmental Protection Agency's Fused Air Quality Surface Downscaler ("downscaler") model, which provides spatial predictions of daily concentrations of fine particulate matter (PM) and ozone (O) at the census tract level, to study the health and societal impacts of exposure to air pollution. Downscaler outputs have been used to show that lower income and higher minority neighborhoods are exposed to higher levels of PM and lower levels of O. However, the uncertainty of the downscaler estimates remains poorly characterized, and it is not known if all subpopulations are benefiting equally from reliable predictions. We examined how the percent errors (PEs) of daily concentrations of PM and O between 2002 and 2016 at the 2010 census tract centroids across North Carolina were associated with measures of racial and educational isolation, neighborhood disadvantage, and urbanicity. Results suggest that there were socioeconomic and demographic disparities in surface concentrations of PM and O, as well as their prediction uncertainties. Neighborhoods characterized by less reliable downscaler predictions (i.e., higher PE and PE) exhibited greater levels of aerial deprivation as well as educational isolation, and were often non-urban areas (i.e., suburban, or rural). Between 2002 and 2016, predicted PM and O levels decreased and O predictions became more reliable. However, the predictive uncertainty for PM has increased since 2010. Substantial spatial variability was observed in the temporal changes in the predictive uncertainties; educational isolation and neighborhood deprivation levels were associated with smaller increases in predictive uncertainty of PM. In contrast, racial isolation was associated with a greater decline in the reliability of PM predictions between 2002 and 2016; it was associated with a greater improvement in the predictive reliability of O within the same time frame.
越来越多的研究使用环境保护署融合空气质量表面下推模型（“下推模型”）的输出结果，该模型提供了细颗粒物（PM）和臭氧（O）每日浓度在普查区层面的空间预测，以研究暴露于空气污染对健康和社会的影响。下推模型的输出结果表明，低收入和少数民族社区的 PM 暴露水平较高，而臭氧水平较低。然而，下推模型估计的不确定性仍未得到充分描述，也不知道所有亚人群是否都能从可靠的预测中受益。我们研究了 2002 年至 2016 年期间北卡罗来纳州普查区中心 2010 年的每日 PM 和 O 浓度的百分比误差（PE）与种族和教育隔离、邻里劣势和城市化程度的关系。结果表明，PM 和 O 的地表浓度及其预测不确定性存在社会经济和人口统计学上的差异。下推模型预测精度较低（即，PE 和 PE 较高）的社区表现出更高的空气剥夺程度以及教育隔离程度，并且往往是非城市地区（即，郊区或农村）。2002 年至 2016 年间，预测的 PM 和 O 水平下降，O 预测的可靠性提高。然而，自 2010 年以来，PM 的预测不确定性增加了。在预测不确定性的时间变化方面观察到了相当大的空间变异性；教育隔离和邻里剥夺程度与 PM 预测不确定性的较小增加相关。相比之下，种族隔离与 2002 年至 2016 年间 PM 预测可靠性的更大下降有关；它与同一时期内 O 预测可靠性的更大改善有关。
