Contribution of tailpipe and non-tailpipe traffic sources to quasi-ultrafine, fine and coarse particulate matter in southern California.
Contribution of tailpipe and non-tailpipe traffic sources to quasi-ultrafine, fine and coarse particulate matter in southern California.
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在南加州排气管和非排气管交通源对准超细、细和粗颗粒物的贡献。
DOI：
10.1080/10962247.2020.1826366
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Gilliland F
中科院分区：
文献类型：
--
作者：
Habre R;Girguis M;Urman R;Fruin S;Lurmann F;Shafer M;Gorski P;Franklin M;McConnell R;Avol E;Gilliland F
暴露于近道路环境中的交通相关空气污染（TRAP）与多种不良健康影响有关。为了表征排气管和非排气管TRAP源对准超细（PM0.2）、细（PM2.5）和粗（PM2.5-10）粒径级颗粒物（PM）的相对贡献，并确定其在南加州（CA）的空间决定因素。为期一个月的综合PM0.2，PM2.5和PM2.5-10样本（n= 461，265和298，分别）收集了在8个南方CA社区（2008-9）的冷，暖季节。获得了颗粒物质量、元素、碳和主要离子的浓度。计算了每种元素在PM0.2和PM10中相对于PM2.5的富集比（ER）。正矩阵分解模型被用来解决和估计的相对贡献的TRAP源PM在三个尺寸级。广义加性模型（GAMs）与双变量黄土平滑被用来了解TRAP源的地理变化，并确定其空间决定因素。相对于PM2.5，EC、OC和B在PM0.2中具有最高的ER中值。在PM0.2、PM2.5和PM2.5-10中分别分辨出6个、7个和5个来源（具有特征物种）。尾气和非尾气混合排放源对PM0.2、PM2.5和PM2.5-10质量的贡献率分别为66%、32%和18%。尾气排放（EC，OC，B）是最大的贡献PM0.2质量（58%）。在PM2.5中解决了不同的汽油和柴油尾气排放源。其他包括燃油、生物质燃烧、二次无机气溶胶、海盐和地壳/土壤。CALINE 4扩散模型氮氧化物、卡车和交叉口与TRAP源的相关性最大。一旦长滩被排除在外，较小的道路和交叉口的影响就变得更加明显。非尾气排放分别占PM0.2、PM2.5和PM2.5-10的约8%、11%和18%，具有重要的暴露和健康影响。未来的工作应考虑非线性关系的预测模型时，曝光。
Exposure to traffic-related air pollution (TRAP) in the near-roadway environment is associated with multiple adverse health effects. To characterize the relative contribution of tailpipe and non-tailpipe TRAP sources to particulate matter (PM) in the quasi-ultrafine (PM0.2), fine (PM2.5) and coarse (PM2.5–10) size fractions and identify their spatial determinants in southern California (CA). Month-long integrated PM0.2, PM2.5 and PM2.5–10 samples (n= 461, 265 and 298, respectively) were collected across cool and warm seasons in 8 southern CA communities (2008–9). Concentrations of PM mass, elements, carbons and major ions were obtained. Enrichment ratios (ER) in PM0.2 and PM10 relative to PM2.5 were calculated for each element. The Positive Matrix Factorization model was used to resolve and estimate the relative contribution of TRAP sources to PM in three size fractions. Generalized additive models (GAMs) with bivariate loess smooths were used to understand the geographic variation of TRAP sources and identify their spatial determinants. EC, OC, and B had the highest median ER in PM0.2 relative to PM2.5. Six, seven and five sources (with characteristic species) were resolved in PM0.2, PM2.5 and PM2.5–10, respectively. Combined tailpipe and non-tailpipe traffic sources contributed 66%, 32% and 18% of PM0.2, PM2.5 and PM2.5–10 mass, respectively. Tailpipe traffic emissions (EC, OC, B) were the largest contributor to PM0.2 mass (58%). Distinct gasoline and diesel tailpipe traffic sources were resolved in PM2.5. Others included fuel oil, biomass burning, secondary inorganic aerosol, sea salt, and crustal/soil. CALINE4 dispersion model nitrogen oxides, trucks and intersections were most correlated with TRAP sources. The influence of smaller roadways and intersections became more apparent once Long Beach was excluded. Non-tailpipe emissions constituted ~8%, 11% and 18% of PM0.2, PM2.5 and PM2.5–10, respectively, with important exposure and health implications. Future efforts should consider non-linear relationships amongst predictors when modeling exposures.
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影响因子：
10.4
作者：
Delfino RJ;Staimer N;Tjoa T;Arhami M;Polidori A;Gillen DL;Kleinman MT;Schauer JJ;Sioutas C
通讯作者：
Sioutas C
影响因子：
3.6
作者：
Ferreira, Antonio J. D.;Soares, Daniel;Ferreira, Carla S. S.
通讯作者：
Ferreira, Carla S. S.
影响因子：
11.4
作者：
Fruin, Scott A.;Hudda, Neelakshi;Sioutas, Constantinos;Defino, Ralph J.
通讯作者：
Defino, Ralph J.
DOI：
10.1016/j.atmosenv.2013.10.063
发表时间：
2014-02-01
期刊：
Atmospheric environment (Oxford, England : 1994)
影响因子：
--
作者：
Fruin S;Urman R;Lurmann F;McConnell R;Gauderman J;Rappaport E;Franklin M;Gilliland FD;Shafer M;Gorski P;Avol E
通讯作者：
Avol E
DOI：
10.1016/0957-1272(92)90013-i
发表时间：
1992-09-01
期刊：
ATMOSPHERIC ENVIRONMENT PART B-URBAN ATMOSPHERE
影响因子：
--
作者：
BENSON, PE
通讯作者：
BENSON, PE