Abstract
ABSTRACTDue to the similarity of PM2.5 chemical species profiles of different sources, time synchronization of source contributions and the uncertainties of source-oriented models, it is difficult to get a well-separated and relatively accurate PM2.5 source apportionment result, especially for the secondary components, when only one method was applied. A new PM2.5 source apportionment approach, combining the receptor models, source-oriented models and emission inventory, was developed in this study. The proposed method had following strengths: (1) it could identify the source contributions to secondary components; (2) target (or expected) sources were optional; (3) mixed sources could be avoided. The new approach was then applied in two typical cities in North China – Beijing and Tangshan, based on intensive PM2.5 observation results from 2011 to 2013. The source apportionment result indicated that the annual average contribution to PM2.5 in Tangshan was 7.4%, 21.5%, 7.6%, 18.0%, 14.5%, 10.9% and 20.0% for power, metallurgy, cement, coal combustion, vehicle, dust and other sources, respectively; the annual average contribution ratio for vehicle, industry and industrial coal combustion, residential coal combustion, dust and other sources in Beijing was 31.5%, 22.9%, 10.6%, 14.5% and 20.4%, respectively. Seasonal variation of the source contributions was also analyzed. The demonstration results showed that the combined method was feasible. In addition, the detailed source contribution results could also provide scientific support for making effective PM2.5 mitigation strategy.
Highlights
PM2.5 is the most important atmospheric environment pollution issue in China (Xu et al, 2013)
The source apportionment result indicated that the annual average contribution to PM2.5 in Tangshan was 7.4%, 21.5%, 7.6%, 18.0%, 14.5%, 10.9% and 20.0% for power, metallurgy, cement, coal combustion, vehicle, dust and other sources, respectively; the annual average contribution ratio for vehicle, industry and industrial coal combustion, residential coal combustion, dust and other sources in Beijing was 31.5%, 22.9%, 10.6%, 14.5% and 20.4%, respectively
The sourceoriented model WRF-CAMx-PSAT was used for simulating the source contributions to the secondary components precursors
Summary
PM2.5 (i.e., the fine particles with aerodynamic diameter ≤ 2.5 μm) is the most important atmospheric environment pollution issue in China (Xu et al, 2013). PM2.5 with such high concentration could bring significant impact on human health, atmospheric visibility, climate change and economic development in China (Boldo et al, 2006; Wang et al, 2006; Ramanathan and Feng, 2009; Chen et al, 2013) As a result, it is urgent and of great importance to identify the PM2.5 contribution sources, in order to provide. Receptor models were generally different from dispersion models They apportioned the contributions to sources based on the PM2.5 components in atmosphere (and of sources for CMB), without considering the complex physical diffusion and chemical reactions in the air. Even for the primary PM2.5 components (e.g., elements and EC), it was difficult to get a feasible result by a single model, because of the similarity of chemical species profiles for some emission sources (e.g., soil dust and cement), and the time synchronization of contributions for different sources due to the meteorological conditions
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