Dataset Information

Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM_2.5 from a National Background Site in North China.

ABSTRACT: To explore the utility of combining positive matrix factorization (PMF) with radiocarbon (¹⁴C) measurements for source apportionment, we applied PM_2.5 data collected for 14 months at a national background station in North China to PMF models. The solutions were compared to ¹⁴C results of four seasonally averaged samples and three outlier samples. Comparing the most readily interpretable PMF solutions and ¹⁴C results revealed that PMF modeling was well able to capture the source patterns of PM_2.5 with two and three irrelevant source classifications for the seasonal and outlier samples. The contribution of sources that could not be classified as either fossil or non-fossil sources in the PMF solution, and the errors between the modeled and measured concentrations weakened the effectiveness of the comparison. Based on these two factors, we developed an index for selecting the most suitable ¹⁴C measurement samples for combining with the PMF model. Then we examined the potential for coupling PMF modeling and ¹⁴C data with a constrained PMF run using the ¹⁴C data as a priori information. The restricted run could provide a more reliable solution; however, the PMF model must provide a flexible dialog to input the priori restrictions for executing the constraint simulation.

SUBMITTER: Wang X

PROVIDER: S-EPMC5587569 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM2.5 from a National Background Site in North China.

Wang Xiaoping X Zong Zheng Z Tian Chongguo C Chen Yingjun Y Luo Chunling C Li Jun J Zhang Gan G Luo Yongming Y

Scientific reports 20170906 1

To explore the utility of combining positive matrix factorization (PMF) with radiocarbon (14C) measurements for source apportionment, we applied PM2.5 data collected for 14 months at a national background station in North China to PMF models. The solutions were compared to 14C results of four seasonally averaged samples and three outlier samples. Comparing the most readily interpretable PMF solutions and 14C results revealed that PMF modeling was well ...[more]

PMID: 28878221

Similar Datasets

Project description:Particulate matter less than 2.5 microns in diameter (PM(2.5)) has been linked with a wide range of adverse health effects. Determination of the sources of PM(2.5) most responsible for these health effects could lead to improved understanding of the mechanisms of such effects and more targeted regulation. This has provided the impetus for the Denver Aerosol Sources and Health (DASH) study, a multi-year source apportionment and health effects study relying on detailed inorganic and organic PM(2.5) speciation measurements.In this study, PM(2.5) source apportionment is performed by coupling positive matrix factorization (PMF) with daily speciated PM(2.5) measurements including inorganic ions, elemental carbon (EC) and organic carbon (OC), and organic molecular markers. A qualitative comparison is made between two models, PMF2 and ME2, commonly used for solving the PMF problem. Many previous studies have incorporated chemical mass balance (CMB) for organic molecular marker source apportionment on limited data sets, but the DASH data set is large enough to use multivariate factor analysis techniques such as PMF.Sensitivity of the PMF2 and ME2 models to the selection of speciated PM(2.5) components and model input parameters was investigated in depth. A combination of diagnostics was used to select an optimum, 7-factor model using one complete year of daily data with pointwise measurement uncertainties. The factors included 1) a wintertime/methoxyphenol factor, 2) an EC/sterane factor, 3) a nitrate/polycyclic aromatic hydrocarbon (PAH) factor, 4) a summertime/selective aliphatic factor, 5) an n-alkane factor, 6) a middle oxygenated PAH/alkanoic acid factor and 7) an inorganic ion factor. These seven factors were qualitatively linked with known PM(2.5) emission sources with varying degrees of confidence. Mass apportionment using the 7-factor model revealed the contribution of each factor to the mass of OC, EC, nitrate and sulfate. On an annual basis, the majority of OC and EC mass was associated with the summertime/selective aliphatic factor and the EC/sterane factor, respectively, while nitrate and sulfate mass were both dominated by the inorganic ion factor. This apportionment was found to vary substantially by season. Several of the factors identified in this study agree well with similar assessments conducted in St. Louis, MO and Pittsburgh, PA using PMF and organic molecular markers.

Project description:Gas-phase concentrations of semi-volatile organic compounds (SVOCs) were calculated from gas/particle (G/P) partitioning theory using their measured particle-phase concentrations. The particle-phase data were obtained from an existing filter measurement campaign (27 January 2003-2 October 2005) as a part of the Denver Aerosol Sources and Health (DASH) study, including 970 observations of 71 SVOCs (Xie et al., 2013). In each compound class of SVOCs, the lighter species (e.g. docosane in n alkanes, fluoranthene in PAHs) had higher total concentrations (gas + particle phase) and lower particle-phase fractions. The total SVOC concentrations were analyzed using positive matrix factorization (PMF). Then the results were compared with source apportionment results where only particle-phase SVOC concentrations were used (particle only-based study; Xie et al., 2013). For the particle only-based PMF analysis, the factors primarily associated with primary or secondary sources (n alkane, EC/sterane and inorganic ion factors) exhibit similar contribution time series (r = 0.92-0.98) with their corresponding factors (n alkane, sterane and nitrate+sulfate factors) in the current work. Three other factors (light n alkane/PAH, PAH and summer/odd n alkane factors) are linked with pollution sources influenced by atmospheric processes (e.g. G/P partitioning, photochemical reaction), and were less correlated (r = 0.69-0.84) with their corresponding factors (light SVOC, PAH and bulk carbon factors) in the current work, suggesting that the source apportionment results derived from particle-only SVOC data could be affected by atmospheric processes. PMF analysis was also performed on three temperature-stratified subsets of the total SVOC data, representing ambient sampling during cold (daily average temperature < 10 °C), warm (≥ 10 °C and ≤ 20 °C) and hot (> 20 °C) periods. Unlike the particle only-based study, in this work the factor characterized by the low molecular weight (MW) compounds (light SVOC factor) exhibited strong correlations (r = 0.82-0.98) between the full data set and each sub-data set solution, indicating that the impacts of G/P partitioning on receptor-based source apportionment could be eliminated by using total SVOC concentrations.

Dataset Information

Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM_2.5 from a National Background Site in North China.

Publications

Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM<sub>2.5</sub> from a National Background Site in North China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets