Spatial and temporal variability of fine particle composition and source types in five cities of Connecticut and Massachusetts.
Spatial and temporal variability of fine particle composition and source types in five cities of Connecticut and Massachusetts.
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DOI：
10.1016/j.scitotenv.2011.02.025
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发表时间：
2011-05-01
影响因子：
9.8
通讯作者：
Koutrakis, Petros
中科院分区：
文献类型：
--
作者：
Lee, Hyung Joo;Gent, Janneane F.;Leaderer, Brian P.;Koutrakis, Petros
关键词：
为了保护公众健康免受PM2.5空气污染的影响，确定PM2.5质量的来源类型以及与不良健康结果风险较高相关的化学成分至关重要。采用正矩阵分解（PMF）源解析模型，对美国康涅狄格州和马萨诸塞州的5个城市PM2. 5源类型进行了识别，并对PM2. 5源贡献进行了量化。研究了PM2.5质量、组分和源贡献的时空变化。PMF分析确定了五种来源类型：区域污染，如跟踪硫，机动车，道路灰尘，石油燃烧和海盐。硫相关的区域污染和交通源类型是PM2.5的主要贡献者。由于地面PM2.5监测点稀少，目前的流行病学研究容易受到暴露测量误差的影响。不同地点之间的浓度和源贡献的较高相关性表明空间变异性较小，导致暴露测量误差较小。当浓度和/或贡献相比，区域平均值，相关性一般高于网站之间的相关性。这表明，在分配健康影响研究的暴露量时，使用区域平均浓度或几个PM2.5监测器的贡献比使用最近的中央监测器的数据更可靠。
To protect public health from PM2.5 air pollution, it is critical to identify the source types of PM2.5 mass and chemical components associated with higher risks of adverse health outcomes. Source apportionment modeling using Positive Matrix Factorization (PMF), was used to identify PM2.5 source types and quantify the source contributions to PM2.5 in five cities of Connecticut and Massachusetts. Spatial and temporal variability of PM2.5 mass, components and source contributions were investigated. PMF analysis identified five source types: regional pollution as traced by sulfur, motor vehicle, road dust, oil combustion and sea salt. The sulfur-related regional pollution and traffic source type were major contributors to PM2.5. Due to sparse ground-level PM2.5 monitoring sites, current epidemiological studies are susceptible to exposure measurement errors. The higher correlations in concentrations and source contributions between different locations suggest less spatial variability, resulting in less exposure measurement errors. When concentrations and/or contributions were compared to regional averages, correlations were generally higher than between-site correlations. This suggests that for assigning exposures for health effects studies, using regional average concentrations or contributions from several PM2.5 monitors is more reliable than using data from the nearest central monitor.
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DOI：
10.1097/ede.0b013e3181812bb7
发表时间：
2008-09
期刊：
Epidemiology (Cambridge, Mass.)
影响因子：
--
作者：
Franklin M;Koutrakis P;Schwartz P
通讯作者：
Schwartz P
影响因子：
11.4
作者：
Kim, E;Hopke, PK;Wilson, WE
通讯作者：
Wilson, WE
影响因子：
5
作者：
Kuhn, T;Biswas, S;Sioutas, C
通讯作者：
Sioutas, C
影响因子：
10.4
作者：
Ostro B;Roth L;Malig B;Marty M
通讯作者：
Marty M
影响因子：
10.4
作者：
Mar, TF;Norris, GA;Larson, TV
通讯作者：
Larson, TV