Source apportionment for fine particulate matter in a Chinese city using an improved gas-constrained method and comparison with multiple receptor models

Abstract

PM is one of the most studied atmospheric pollutants due to its adverse impacts on human health and welfare and the environment. An improved model (the chemical mass balance gas constraint-Iteration: CMBGC-Iteration) is proposed and applied to identify source categories and estimate source contributions of PM The CMBGC-Iteration model uses the ratio of gases to PM as constraints and considers the uncertainties of source profiles and receptor datasets, which is crucial information for source apportionment. To apply this model, samples of PM were collected at Tianjin, a megacity in northern China. The ambient PM dataset, source information, and gas-to-particle ratios (such as SO/PM, CO/PM, and NOx/PM ratios) were introduced into the CMBGC-Iteration to identify the potential sources and their contributions. Six source categories were identified by this model and the order based on their contributions to PM was as follows: secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%), and cement dust (0.40%). In addition, the same dataset was also calculated by other receptor models (CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA), and the results obtained were compared. Ensemble-average source impacts were calculated based on the seven source apportionment results: contributions of secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%), and cement dust (1.3%). The similar results of CMBGC-Iteration and ensemble method indicated that CMBGC-Iteration can produce relatively appropriate results.

A Review of Recent Advances in Research on PM2.5 in China

“…Shi et al [ 70 ] employed a chemical mass balance gas constraint-Iteration (CMBGC-Iteration) method for source appointment analysis in Tianjin. The outcomes from this method were compared with the ensemble-average outcomes of CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF (Weighted Alternating Least Squares Positive Matrix Factorization), and NCAPCA (Non-negative Constrained Absolutely Principle Analysis), and it was found that they were comparable.…”

“…The outdoor sources come from heating fuel, industry, traffic, etc. [ 34 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 ], due to the rapid industrialization, high energy consumption and large proportion of coal (60–70%) in the structure of energy sources in China. It has been acknowledged that there is close correlation between indoor and outdoor PM 2.5 concentration levels, and outdoor PM 2.5 is the main source of indoor PM 2.5 pollution [ 75 , 76 , 77 ].…”

Comparison of five methodologies to apportion organic aerosol sources during a PM pollution event

“…chemical mass balance (CMB) and positive matrix factorization (PMF)) (Watson et al, 1990;Paatero, 1997;Paatero and Tapper, 1994), SOA-tracer method (Kleindienst et al, 2007) and radiocarbon ( 14 C) measurements (Szidat et al, 2009;Gelencsér et al, 2007). These methodologies have been successfully applied worldwide and, in general, good agreements have been reported when compared by twos directly (Song et al, 2006;Kim et al, 2004;Kleindienst et al, 2010;Pachon et al, 2010;Heo et al, 2013;Al-Naiema et al, 2018;Srivastava et al, 2018b;Bove et al, 2018;Bae et al, 2019;Antony Chen and Cao, 2018;Hettiyadura et al, 2018;Jiang et al, 2018;Keerthi et al, 2018;Matawle et al, 2018;Shi et al, 2018;Shirmohammadi et al, 2016;Lanzafame et al, 2020). However, large discrepancies have been also observed when apportioning the SOA fraction, depending on the methods compared, the period of the year considered or atmospheric conditions observed (Srivastava et al, 2018b).…”

Research Progress of HP Characteristics, Hazards, Control Technologies, and Measures in China after 2013

“…Industrial factors, biomass burning, and other sources individually contributed 13%, 4%, and 15%. After 2013, the percentage of dust dropped to 16%, while the contribution of coal burning, traffic, and secondary source separately risen to 19%, 15%, and 24% [61,[70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88], and they were the top four sources as before. This shows that the treatment project of sandstorm sources of Beijing and Tianjin has achieved preliminary success in recent years.…”