University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, PA 15219, USA.
Drexel University Dornsife School of Public Health, Department of Environmental and Occupational Health, Philadelphia, PA 15219, USA.
Int J Environ Res Public Health. 2018 Oct 5;15(10):2177. doi: 10.3390/ijerph15102177.
Health effects of fine particulate matter (PM) may vary by composition, and the characterization of constituents may help to identify key PM sources, such as diesel, distributed across an urban area. The composition of diesel particulate matter (DPM) is complicated, and elemental and organic carbon are often used as surrogates. Examining multiple elemental and organic constituents across urban sites, however, may better capture variation in diesel-related impacts, and help to more clearly separate diesel from other sources. We designed a "super-saturation" monitoring campaign of 36 sites to capture spatial variance in PM and elemental and organic constituents across the downtown Pittsburgh core (~2.8 km²). Elemental composition was assessed via inductively-coupled plasma mass spectrometry (ICP-MS), organic and elemental carbon via thermal-optical reflectance, and organic compounds via thermal desorption gas-chromatography mass-spectrometry (TD-GCMS). Factor analysis was performed including all constituents-both stratified by, and merged across, seasons. Spatial patterning in the resultant factors was examined using land use regression (LUR) modelling to corroborate factor interpretations. We identified diesel-related factors in both seasons; for winter, we identified a five-factor solution, describing a bus and truck-related factor [black carbon (BC), fluoranthene, nitrogen dioxide (NO₂), pyrene, total carbon] and a fuel oil combustion factor (nickel, vanadium). For summer, we identified a nine-factor solution, which included a bus-related factor (benzo[ghi]fluoranthene, chromium, chrysene, fluoranthene, manganese, pyrene, total carbon, total elemental carbon, zinc) and a truck-related factor (benz[a]anthracene, BC, hopanes, NO₂, total PAHs, total steranes). Geographic information system (GIS)-based emissions source covariates identified via LUR modelling roughly corroborated factor interpretations.
细颗粒物 (PM) 的健康影响可能因成分而异,对成分的特征描述有助于识别关键的 PM 来源,例如在城市范围内分布的柴油。柴油颗粒物 (DPM) 的成分很复杂,通常使用元素碳和有机碳作为替代物。然而,在城市站点中检测多种元素和有机成分可能会更好地捕捉与柴油相关的影响变化,并有助于更清楚地将柴油与其他来源区分开来。我们设计了一项“过饱和”监测活动,在匹兹堡市中心 (~2.8 平方公里) 的 36 个地点监测 PM 以及元素和有机成分的空间变化。通过电感耦合等离子体质谱 (ICP-MS) 评估元素成分,通过热光反射评估有机碳和元素碳,通过热解吸气相色谱-质谱 (TD-GCMS) 评估有机化合物。进行了因子分析,包括所有成分——按季节分层,以及跨季节合并。使用土地利用回归 (LUR) 模型检查所得因子的空间模式,以证实因子解释。我们在两个季节都确定了与柴油相关的因子;对于冬季,我们确定了一个五因子解决方案,描述了一个与公共汽车和卡车相关的因子[黑碳 (BC)、荧蒽、二氧化氮 (NO₂)、苝、总碳]和一个燃料油燃烧因子(镍、钒)。对于夏季,我们确定了一个九因子解决方案,其中包括一个与公共汽车相关的因子(苯并[ghi]芘、铬、䓛、荧蒽、锰、苝、总碳、总元素碳、锌)和一个与卡车相关的因子(苯并[a]芘、BC、藿烷、NO₂、总多环芳烃、总甾烷)。通过 LUR 建模确定的基于地理信息系统 (GIS) 的排放源协变量大致证实了因子解释。