Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, USA.
Department of Epidemiology, University of Washington, Seattle, WA, USA.
Environ Res. 2023 Jun 1;226:115626. doi: 10.1016/j.envres.2023.115626. Epub 2023 Mar 11.
This study capitalized on coal and oil facility retirements to quantify their potential effects on fine particulate matter (PM) concentrations and cardiorespiratory hospitalizations in affected areas using a generalized synthetic control method.
We identified 11 coal and oil facilities in California that retired between 2006 and 2013. We classified zip code tabulation areas (ZCTA) as exposed or unexposed to a facility retirement using emissions information, distance, and a dispersion model. We calculated weekly ZCTA-specific PM concentrations based on previously estimated daily time-series PM concentrations from an ensemble model, and weekly cardiorespiratory hospitalization rates based on hospitalization data collected by the California Department of Health Care Access and Information. We estimated the average differences in weekly average PM concentrations and cardiorespiratory hospitalization rates in four weeks after each facility retirement between the exposed ZCTAs and the synthetic control using all unexposed ZCTAs (i.e., the average treatment effect among the treated [ATT]) and pooled ATTs using meta-analysis. We conducted sensitivity analyses to consider different classification schemes to distinguish exposed from unexposed ZCTAs, including aggregating outcomes with different time intervals and including a subset of facilities with reported retirement date confirmed via emission record.
The pooled ATTs were 0.02 μg/m (95% confidence interval (CI): -0.25 to 0.29 μg/m) and 0.34 per 10,000 person-weeks (95%CI: -0.08 to 0.75 per 10,000 person-weeks) following the facility closure for weekly PM and cardiorespiratory hospitalization rates, respectively. Our inferences remained the same after conducting sensitivity analyses.
We demonstrated a novel approach to study the potential benefits associated with industrial facility retirements. The declining contribution of industrial emissions to ambient air pollution in California may explain our null findings. We encourage future research to replicate this work in regions with different industrial activities.
本研究利用煤炭和石油设施退役的机会,采用广义综合控制方法,量化了这些设施退役对受影响地区细颗粒物(PM)浓度和心肺住院治疗的潜在影响。
我们确定了加利福尼亚州的 11 家在 2006 年至 2013 年间退役的煤炭和石油设施。我们利用排放信息、距离和扩散模型,将邮政编码区(ZCTA)分类为暴露于设施退役或未暴露于设施退役。我们根据以前从综合模型中估算的每日时间序列 PM 浓度,计算了每周 ZCTA 特定的 PM 浓度,并根据加利福尼亚州医疗保健获取和信息部收集的住院数据,计算了每周心肺住院率。我们使用所有未暴露 ZCTA(即,治疗组的平均治疗效果 [ATT])和荟萃分析中的 pooled ATT,估计了每个设施退役后四周内暴露于设施退役的 ZCTA 与合成控制之间每周平均 PM 浓度和心肺住院率的平均差异。我们进行了敏感性分析,以考虑不同的分类方案,以区分暴露于未暴露 ZCTA,包括汇总不同时间间隔的结果和纳入具有经排放记录确认的报告退役日期的设施子集。
pooled ATT 分别为 0.02μg/m(95%置信区间 [CI]:-0.25 至 0.29μg/m)和 0.34/10,000 人周(95%CI:-0.08 至 0.75/10,000 人周),分别为设施关闭后每周 PM 和心肺住院率的变化。我们在进行敏感性分析后,仍然得到相同的结论。
我们展示了一种研究与工业设施退役相关的潜在效益的新方法。加利福尼亚州大气污染中工业排放的贡献不断下降,可能解释了我们的无效发现。我们鼓励未来的研究在具有不同工业活动的地区复制这项工作。