Henneman Lucas Rf, Mickley Loretta J, Zigler Corwin M
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA.
Environ Res Lett. 2019 Nov;14(11). doi: 10.1088/1748-9326/ab4861. Epub 2019 Oct 29.
Recent studies have sought epidemiological evidence of the effectiveness of energy transitions. Such evidence often relies on so-called "natural experiments," wherein environmental and/or health outcomes are assessed before, during, and after the transition of interest. Often, these studies attribute air pollution exposure changes-either modeled or measured-directly to the transition. We formalize a framework for separating the fractions of a given exposure change attributable to meteorological variability and emissions changes. Using this framework, we quantify relative impacts of wind variability and emissions changes from coal-fired power plants on exposure to SO emissions across the United States under three unique combinations of spatial-temporal and source scales. We find that the large emissions reductions achieved by United States coal-fired power plants after 2005 dominated population exposure changes. In each of the three case studies, however, we identified periods and regions in which meteorology dampened or accentuated differences in total exposure relative to exposure change expected from emissions reductions alone. The results evidence a need for separating meteorology-induced variability in exposure when attributing health impacts to specific energy transitions.
近期的研究一直在寻找能源转型有效性的流行病学证据。此类证据通常依赖于所谓的“自然实验”，即在感兴趣的转型之前、期间和之后评估环境和/或健康结果。通常，这些研究将空气污染暴露的变化——无论是模拟的还是测量的——直接归因于转型。我们构建了一个框架，用于区分给定暴露变化中可归因于气象变异性和排放变化的部分。利用这个框架，我们量化了在时空和源尺度的三种独特组合下，风力变异性和燃煤电厂排放变化对美国二氧化硫排放暴露的相对影响。我们发现，2005年后美国燃煤电厂实现的大幅减排主导了人口暴露变化。然而，在每一个案例研究中，我们都确定了一些时期和地区，在这些时期和地区，气象因素减弱或加剧了总暴露相对于仅由减排预期的暴露变化的差异。结果表明，在将健康影响归因于特定能源转型时，有必要区分气象引起的暴露变异性。
