Source apportionment resolved by time of day for improved deconvolution of primary source contributions to air pollution
Source apportionment resolved by time of day for improved deconvolution of primary source contributions to air pollution
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DOI：
10.5194/amt-15-6051-2022
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发表时间：
2022-10
影响因子：
3.8
通讯作者：
S. Bhandari;Zainab Arub;G. Habib;J. Apte;Lea Hildebrandt Ruiz
中科院分区：
文献类型：
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作者：
S. Bhandari;Zainab Arub;G. Habib;J. Apte;Lea Hildebrandt Ruiz
抽象的。目前的源解析方法假定源剖面是固定的。由于气象学和人类活动模式的季节性和日变化，源解析技术的应用，而不是较长的时间段产生更有代表性的质谱。在这里，我们提出了一种新的方法来进行源解析解决了一天中的时间使用的基本方法的正矩阵分解（PMF）。我们称这种方法为“一天的时间PMF”，并统计证明了这种方法比传统的PMF的改进。我们报告了在两个季节（2017年冬季和季风季节）的四个示例时间段进行的源解析，使用气溶胶化学形态监测仪（ACSM）的有机气溶胶测量值。我们部署EPA PMF工具与底层的多线性引擎（ME-2）作为PMF求解器。与传统的季节PMF方法相比，我们提取了大量的因素，以及PMF因素，代表了预期的主要有机气溶胶的来源，使用一天中的时间PMF。通过以低计算成本捕获源的昼夜时间序列模式，与不按时间分辨的传统PMF方法相比，日时PMF可以利用使用长期监测收集的大型数据集，并改善有机气溶胶源的表征。
Abstract. Present methodologies for source apportionment assume fixed source profiles. Since meteorology and human activity patterns change seasonally and diurnally, application of source apportionment techniques to shorter rather than longer time periods generates more representative mass spectra. Here, we present a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization (PMF). We call this approach “time-of-day PMF” and statistically demonstrate the improvements in this approach over traditional PMF. We report on source apportionment conducted on four example time periods in two seasons (winter and monsoon seasons of 2017), using organic aerosol measurements from an aerosol chemical speciation monitor (ACSM). We deploy the EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMF solver. Compared to the traditional seasonal PMF approach, we extract a larger number of factors as well as PMF factors that represent the expected sources of primary organic aerosol using time-of-day PMF. By capturing diurnal time series patterns of sources at a low computational cost, time-of-day PMF can utilize large datasets collected using long-term monitoring and improve the characterization of sources of organic aerosol compared to traditional PMF approaches that do not resolve by time of day.