In the Northwest U.S. elevated measurements of PM2.5 from anthropogenic sources occur most often in winter. Major contributors to winter PM2.5 are direct primary emissions of wood smoke from residential wood combustion, primary emissions from motor vehicles, gaseous NOx emissions leading to particulate nitrate, and primary and secondary sources of particulate sulfate. A number of communities in the Northwest U.S. now have long data records of chemically speciated PM2.5 from which receptor-based source apportionment can be performed. This work uses receptor-based source apportionment on data from these monitoring sites to evaluate changes in the major contributors to winter PM2.5 over the available monitoring time span. Data from 9 sites are analyzed in this work using the Positive Matrix Factorization (PMF) source apportionment model. Each site was modeled individually rather than grouping the data from multiple sites. All sites had data through the summer of 2018, with most sites having 11 years of data and one site having 9 years of data. The number of PMF factors identified was between 5 to 10, depending on the site. Associations were made between PMF factors and PM2.5 sources based on comparison of PMF factor chemical profiles with published source test data and source profiles identified in other published studies. The most common factors found were: fresh wood smoke, aged wood smoke, soil dust, gas engines, mixed - gas engines and nitrate, ammonium sulfate, and ammonium nitrate. In this work, total wood smoke was identified as the combined contribution of fresh and aged wood smoke, and winter season data was defined as encompassing the last two months of a year and the first two months of the next year. To evaluate changes over time, average winter season PM2.5 measurements, major PM2.5 chemical components, and PMF factor results for the winter seasons of 2007 - 2009 were compared with the winter seasons of 2015 - 2017. The result for total 3-year average winter season PM2.5 was a decrease between 2% and 29% at the 9 sites, and the decreases were statistically significant at 3 sites. However, total winter season wood smoke contributions to PM2.5 decreased at every site between the two 3-year periods and the decreases were statistically significant at 8 of 9 sites, with decreases from 48% to 74% at those 8 sites. All PMF factors associated with ammonium nitrate (identified at 5 of 9 sites) decreased a statistically significant 11% to 54% between the two 3-year winter season periods. All PMF factors associated with ammonium sulfate (identified at 7 of 9 sites) decreased a statistically significant 27% to 81% between the two 3-year winter season periods. In contrast to the significant reductions in PM2.5 from PMF factors related to wood smoke, ammonium nitrate and ammonium sulfate, PMF factors associated with gas engines increased from 6% to 226% between the two 3-year winter season periods. Increases in PM2.5 contributions from gas engine related factors explain why overall average winter season PM2.5 had more modest percent reductions compared to the percent reductions for wood smoke, ammonium nitrate, and ammonium sulfate factors between the two 3-year winter season periods.

中文翻译：

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美国西北部木材烟雾、机动车和其他来源的冬季 PM2.5 贡献的最新变化

在美国西北部，人为来源的 PM2.5 测量值升高最常发生在冬季。冬季 PM2.5 的主要贡献者是住宅木材燃烧产生的木材烟雾的直接初级排放、机动车辆的初级排放、导致硝酸盐颗粒的气态 NOx 排放以及颗粒硫酸盐的初级和次级来源。美国西北部的许多社区现在拥有长期的化学物质 PM2.5 数据记录，可以从中进行基于受体的源分配。这项工作对来自这些监测站点的数据使用基于受体的源分配来评估可用监测时间跨度内冬季 PM2.5 的主要贡献者的变化。在这项工作中，使用正矩阵分解 (PMF) 源分配模型分析了来自 9 个站点的数据。每个站点都是单独建模的，而不是将来自多个站点的数据分组。所有站点都有截至 2018 年夏季的数据，大多数站点拥有 11 年的数据，一个站点拥有 9 年的数据。确定的 PMF 因素的数量在 5 到 10 之间，具体取决于站点。PMF 因子和 PM2.5 源之间的关联是基于 PMF 因子化学特征与已发表的源测试数据和其他已发表研究中确定的源特征的比较。发现的最常见因素是：新鲜木烟、陈年木烟、土壤灰尘、燃气发动机、混合燃气发动机和硝酸盐、硫酸铵和硝酸铵。在这项工作中，总木烟被确定为新鲜和陈年木烟的综合贡献，冬季数据被定义为包括一年的最后两个月和下一年的前两个月。为了评估随时间的变化，将 2007-2009 年冬季的冬季平均 PM2.5 测量值、主要 PM2.5 化学成分和 PMF 因子结果与 2015-2017 年冬季进行了比较。总共 3- 9个站点的年均冬季PM2.5下降幅度在2%至29%之间，其中3个站点的下降具有统计学意义。然而，在这两个 3 年期间，每个地点的冬季木材烟雾对 PM2.5 的总贡献都有所下降，并且在 9 个地点中的 8 个地点的下降具有统计学意义，这 8 个地点从 48% 下降到 74%。所有与硝酸铵相关的 PMF 因素（在 9 个地点中的 5 个）在两个 3 年冬季期间显着降低了 11% 至 54%。与硫酸铵相关的所有 PMF 因素（在 9 个地点中的 7 个）在两个 3 年冬季期间显着降低了 27% 至 81%。与木材烟雾、硝酸铵和硫酸铵相关的 PMF 因素导致 PM2.5 显着减少相比，与燃气发动机相关的 PMF 因素在两个 3 年冬季期间从 6% 增加到 226%。燃气发动机相关因素对 PM2.5 贡献的增加解释了为什么与木烟、硝酸铵、