PMF Source Contribution Uncertainty Estimation via Effective Variance Least Squares

作者：Jiajia Chen, Qili Dai, Xinyi Zhang, Yingze Tian, Yinchang Feng, Philip K. Hopke · 发表于：ACS ES&T Air · 年份：2025 · DOI：10.1021/acsestair.5c00312 · 被引用次数：3 · 研究领域：Atmospheric chemistry and aerosols、Air Quality and Health Impacts、Air Quality Monitoring and Forecasting

Understanding the sources of atmospheric particulate matter (PM) through reliable source apportionment methods is essential to support effective pollution control policies. Positive matrix factorization (PMF) has become the most widely used source apportionment method worldwide. However, conventional PMF analysis provides source profiles with uncertainties but contributions ( g -value) without uncertainties limiting the utility of the resolved source-specific PM. This study estimates the g -value uncertainties in PMF-resolved PM 2.5 source contributions by combining PMF with an effective variance least-squares (EVLS) analysis. Data from daily PM 2.5 samples collected in Tianjin, China, were analyzed with the EPA PMF to identify the major sources. The EVLS method calculated the uncertainties in the g -value using the displacement intervals as the profile uncertainties. The primary PM 2.5 contributors in Tianjin during 2013 to 2019 included secondary sulfate (25.39 ± 7.51 μg/m 3 ), coal combustion (22.07 ± 9.41 μg/m 3 ), secondary nitrate (19.88 ± 4.74 μg/m 3 ), dust (16.12 ± 7.36 μg/m 3 ), vehicle emissions (12.46 ± 5.04 μg/m 3 ), the steel industry (12.32 ± 8.58 μg/m 3 ), biomass burning (5.93 ± 2.61 μg/m 3 ), and the galvanizing industry (3.37 ± 2.89 μg/m 3 ). PMF-EVLS provided more comprehensive insights by better quantifying the contributions and uncertainties for each source, as shown by the more accurate reproduction of the original concentrations, particularly the highe...