Modeling the benefits of power plant emission controls in Massachusetts
Modeling the benefits of power plant emission controls in Massachusetts
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DOI：
10.1080/10473289.2002.10470753
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发表时间：
2002-01-01
影响因子：
2.7
通讯作者：
Spengler, JD
中科院分区：
文献类型：
--
作者：
Levy, JI;Spengler, JD
在美国，较老的化石燃料发电厂提供了标准空气污染物排放的很大一部分，部分原因是这些设施不需要满足与清洁空气法规定的新来源相同的排放标准。对老发电厂的未决法规需要有关减排的任何潜在公共健康效益的信息，这些信息可以通过结合排放信息、扩散模型和流行病学证据来估计。在这篇文章中，我们开发了一个分析模型框架，可以评估排放控制的健康效益，我们将我们的模型应用于马萨诸塞州的两个发电厂。使用CALPUFF大气扩散模型，我们估计，使用最佳可用控制技术（BACT）的氮氧化物和二氧化硫将导致最大的年平均二次颗粒物（PM）浓度降低0.2 mug/m（3）。当我们将联合收割机浓度降低与当前的健康证据相结合时，我们的中心估计是，这两个发电厂的二次PM减少将在3300万人口中每年避免70人死亡。虽然受益估计值可能会因对健康文献的不同解释而大不相同，但从总体风险角度来看，CALPUFF内的参数扰动和其他简单模型变化的影响相对较小。虽然需要进一步的分析来减少不确定性并扩展我们的分析模型，但我们的框架可以帮助决策者评估不同控制情景下的收益大小和分布。
Older fossil-fueled power plants provide a significant portion of emissions of criteria air pollutants in the United States, in part because these facilities are not required to meet the same emission standards as new sources under the Clean Air Act. Pending regulations for older power plants need information about any potential public health benefits of emission reductions, which can be estimated by combining emissions information, dispersion modeling, and epidemiologic evidence. In this article, we develop an analytical modeling framework that can evaluate health benefits of emission controls, and we apply our model to two power plants in Massachusetts. Using the CALPUFF atmospheric dispersion model, we estimate that use of Best Available Control Technology (BACT) for NOx and SO2 would lead to maximum annual average secondary particulate matter (PM) concentration reductions of 0.2 mug/m(3). When we combine concentration reductions with current health evidence, our central estimate is that the secondary PM reductions from these two power plants would avert 70 deaths per year in a population of 33 million individuals. Although benefit estimates could differ substantially with different interpretations of the health literature, parametric perturbations within CALPUFF and other simple model changes have relatively small impacts from an aggregate risk perspective. While further analysis would be required to reduce uncertainties and expand on our analytical model, our framework can help decision-makers evaluate the magnitude and distribution of benefits under different control scenarios.