Presto Albert A, Dallmann Timothy R, Gu Peishi, Rao Unnati
a Center for Atmospheric Particle Studies, Carnegie Mellon University , Pittsburgh , Pennsylvania , USA.
b International Council on Clean Transit , Washington , DC , USA.
J Air Waste Manag Assoc. 2016 Apr;66(4):387-401. doi: 10.1080/10962247.2016.1139517.
The impacts of emissions plumes from major industrial sources on black carbon (BC) and BTEX (benzene, toluene, ethyl benzene, xylene isomers) exposures in communities located >10 km from the industrial source areas were identified with a combination of stationary measurements, source identification using positive matrix factorization (PMF), and dispersion modeling. The industrial emissions create multihour plume events of BC and BTEX at the measurement sites. PMF source apportionment, along with wind patterns, indicates that the observed pollutant plumes are the result of transport of industrial emissions under conditions of low boundary layer height. PMF indicates that industrial emissions contribute >50% of outdoor exposures of BC and BTEX species at the receptor sites. Dispersion modeling of BTEX emissions from known industrial sources predicts numerous overnight plumes and overall qualitative agreement with PMF analysis, but predicts industrial impacts at the measurement sites a factor of 10 lower than PMF. Nonetheless, exposures associated with pollutant plumes occur mostly at night, when residents are expected to be home but are perhaps unaware of the elevated exposure. Averaging data samples over long times typical of public health interventions (e.g., weekly or biweekly passive sampling) misapportions the exposure, reducing the impact of industrial plumes at the expense of traffic emissions, because the longer samples cannot resolve subdaily plumes. Suggestions are made for ways for future distributed pollutant mapping or intervention studies to incorporate high time resolution tools to better understand the potential impacts of industrial plumes.
Emissions from industrial or other stationary sources can dominate air toxics exposures in communities both near the source and in downwind areas in the form of multihour plume events. Common measurement strategies that use highly aggregated samples, such as weekly or biweekly averages, are insensitive to such plume events and can lead to significant under apportionment of exposures from these sources.
通过固定测量、使用正定矩阵因子分解（PMF）进行源识别以及扩散模型相结合的方法，确定了主要工业源排放羽流对距离工业源区10公里以外社区中黑碳（BC）和BTEX（苯、甲苯、乙苯、二甲苯异构体）暴露的影响。工业排放会在测量站点产生持续数小时的BC和BTEX羽流事件。PMF源分配以及风型表明，观测到的污染物羽流是在低边界层高度条件下工业排放传输的结果。PMF表明，工业排放在受体站点的BC和BTEX物种室外暴露中占比超过50%。对已知工业源的BTEX排放进行扩散模型预测，得出了许多夜间羽流，并且与PMF分析总体上定性一致，但预测测量站点的工业影响比PMF低10倍。尽管如此，与污染物羽流相关的暴露大多发生在夜间，此时居民预计在家中，但可能并未意识到暴露增加。在公共卫生干预中典型的长时间（例如每周或每两周一次的被动采样）对数据样本进行平均，会错误分配暴露情况，以交通排放为代价降低工业羽流的影响，因为较长时间的样本无法分辨每日以下的羽流。针对未来分布式污染物绘图或干预研究如何纳入高时间分辨率工具以更好地理解工业羽流的潜在影响提出了建议。
工业或其他固定源的排放可以通过持续数小时的羽流事件，在源附近和下风向地区的社区中主导空气有毒物质的暴露情况。使用高度汇总样本的常见测量策略，如每周或每两周的平均值，对这种羽流事件不敏感，可能导致这些源的暴露分配严重不足。
