ABSTRACT

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 (PM_0.2), fine (PM_2.5) and coarse (PM_2.5–10) size fractions and identify their spatial determinants in southern California (CA). Month-long integrated PM_0.2, PM_2.5 and PM_2.5–10 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 PM_0.2 and PM₁₀ relative to PM_2.5 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 PM_0.2 relative to PM_2.5. Six, seven and five sources (with characteristic species) were resolved in PM_0.2, PM_2.5 and PM_2.5–10, respectively. Combined tailpipe and non-tailpipe traffic sources contributed 66%, 32% and 18% of PM_0.2, PM_2.5 and PM_2.5–10 mass, respectively. Tailpipe traffic emissions (EC, OC, B) were the largest contributor to PM_0.2 mass (58%). Distinct gasoline and diesel tailpipe traffic sources were resolved in PM_2.5. 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 PM_0.2, PM_2.5 and PM_2.5–10, respectively, with important exposure and health implications. Future efforts should consider non-linear relationships amongst predictors when modeling exposures.

Implications: Vehicle emissions result in a complex mix of air pollutants with both tailpipe and non-tailpipe components. As mobile source regulations lead to decreased tailpipe emissions, the relative contribution of non-tailpipe traffic emissions to near-roadway exposures is increasing. This study documents the presence of non-tailpipe abrasive vehicular emissions (AVE) from brake and tire wear, catalyst degradation and resuspended road dust in the quasi-ultrafine (PM_0.2), fine and coarse particulate matter size fractions, with contributions reaching up to 30% in PM_0.2 in some southern California communities. These findings have important exposure and policy implications given the high metal content of AVE and the efficiency of PM_0.2 at reaching the alveolar region of the lungs and other organ systems once inhaled. This work also highlights important considerations for building models that can accurately predict tailpipe and non-tailpipe exposures for population health studies.

摘要

在靠近道路的环境中暴露于交通相关的空气污染 (TRAP) 与多种不利的健康影响有关。表征尾管和非尾管 TRAP 源对准超细 (PM _0.2 )、细 (PM _2.5 ) 和粗 (PM _2.5–10 ) 粒径部分中的颗粒物 (PM) 的相对贡献，并确定它们的空间决定因素南加州 (CA)。在加利福尼亚州南部 8 个社区（2008-9）的凉爽和温暖季节收集了长达一个月的综合 PM _0.2、PM _2.5和 PM _2.5-10样本（n = 461、265 和 298）。获得了 PM 质量、元素、碳和主要离子的浓度。_{PM 0.2}中的富集比 (ER)和 PM ₁₀相对于 PM _2.5计算每个元素。正矩阵分解模型用于解析和估计 TRAP 源对 PM 的三个尺寸分数的相对贡献。具有双变量黄土平滑的广义加性模型 (GAM) 用于了解 TRAP 源的地理变化并确定其空间决定因素。相对于 PM _2.5，EC、OC 和 B 在 PM _0.2中的 ER 中值最高。_{在 PM 0.2}、 PM _2.5和 PM _2.5-10中分别解析了六个、七个和五个来源（具有特征物种）。排气管和非排气管交通来源合计贡献了 PM _{0.2的 66%、32% 和 18%}，PM _2.5和 PM _2.5-10质量，分别。尾气排放（EC、OC、B）是 PM _0.2质量的最大贡献者（58%）。PM _2.5中解决了不同的汽油和柴油尾气流量来源。其他包括燃料油、生物质燃烧、二次无机气溶胶、海盐和地壳/土壤。CALINE4 扩散模型氮氧化物、卡车和十字路口与 TRAP 源的相关性最高。排除长滩后，较小的道路和十字路口的影响变得更加明显。非尾气排放占 PM _0.2、PM _2.5和 PM _{2.5–10的约 8%、11% 和 18%}，分别具有重要的暴露和健康影响。在建模暴露时，未来的努力应该考虑预测变量之间的非线性关系。

影响：车辆排放导致空气污染物与排气管和非排气管成分的复杂混合。由于移动源法规导致尾气排放减少，非尾气交通排放对近道路暴露的相对贡献正在增加。_{本研究记录了由于刹车和轮胎磨损、催化剂降解和重悬浮的道路灰尘在准超细 (PM 0.2} )、细颗粒和粗颗粒物尺寸部分中存在的非排气管磨料车辆排放物 (AVE) ，其贡献高达在南加州的一些社区中，PM _{0.2中的含量为 30%。}鉴于 AVE 的高金属含量和 PM _{0.2的效率，这些发现具有重要的风险和政策意义}吸入后到达肺和其他器官系统的肺泡区域。这项工作还强调了构建模型的重要考虑因素，这些模型可以准确预测人口健康研究中的排气管和非排气管暴露。