China Source Profile Shared Service (CSPSS): The Chinese PM2.5 Database for Source Profiles

Abstract

China Source Profile Shared Service (CSPSS, www.speciate.org.cn), a new database of emission source profiles for particulate matter (PM), has been developed by researchers from Chinese Research Academy of Environmental Sciences (CRAES). The first release of CSPSS 1.0 consists of comprehensive data from China that reveals the emission profiles of different sources in selected regions. Related source categories include coal-fired boiler, industrial process, fugitive dust, vehicle exhaust emissions, biomass burning and cooking. Compositing methodology and data quality control were applied to create high quality composite profiles of each source category. Statistical measures of correlation coefficient, t-test and and distribution of weighted differences can be used to compare the similarities and differences among individual and composite profiles. In addition, differences between data of SPECIATE and CSPSS were compared. The chemical composition shows special characteristics in different source categories. For example, SO 4 2-and OC mark coal-fired bolier; Ca and Ca 2+ are the most abundant elements in cement production and construction dust emissions; Cl -, K + and K mark biomass burning; several metals such as V, Zn Sn and Pb could be used as tracers for paved road dust while Sr, Ba and Pb marked industrial emissions. The highest abundances of organic matter are observed in cooking emissions. Toxic species such as Cr and As are enriched in PM 2.5 from coal combustion. Distinguished features of source profiles between SPECIATE and CSPSS indicate that the knowledge of local source profiles are needed for further research. This database should better reflect the emission profiles observed in Chinese environment. Sensitivity tests have been conducted to examine the impact of sub-composite source profiles usually used to establish the composite ones. The result shows that the use of sub-composite source profiles of coal combustion dose not impact the apportionment results for biomass burning, but other sources are varying influenced.

An Amended Chemical Mass Balance Model for Source Apportionment of PM2.5 in Typical Chinese Eastern Coastal Cities

“…Finally, PM 2.5 source apportionment in city Z was obtained by averaging the proportions at locations Z‐1, Z‐2, and Z‐3 via the amended method, as shown in Table . Secondary sulfates made the greatest contribution, of 44.50%, to PM 2.5 pollution, which was in accord with other studies carried out in China . As city Z was a coastal city, secondary sulfates were secondarily converted from SO 2 , which was mainly emitted from coal‐burning boilers and diesel or fuel oil‐burning ships.…”

Chemical Compositions of PM2.5 Emitted from Diesel Trucks and Construction Equipment

“…The measurements were blank corrected using field blanks that were handled and stored in the same manner as the samples. The data quality control methodology applied in this study is same with Liu et al (2017). The composite source profiles were obtained by determining the component mass fractions of PM 2.5 in samples for each vehicle type.…”

Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017

“…For example, levoglucosan is a well-known organic tracer for biomass burning (Lee et al, 2008), azaarenes (nitrogen-heterocyclic polycyclic aromatic compounds) are markers of inefficient CC (Junninen et al, 2009;Bandowe et al, 2016), and sterols, monosaccharide anhydrides, and amides are markers of cooking emissions (Schauer et al, 1999(Schauer et al, , 2002He et al, 2004;Zhao et al, 2007a, b;Cheng et al, 2016). Furthermore, in order to better discriminate sources, Pb stable isotopes, which are not obviously influenced by ordinary chemical, physical, or biological fractionation processes (Gallon et al, 2005;Cheng and Hu, 2010), were determined using an ICP-MS. Additionally, other isotope measurements, including radiocarbon , sulfur (Han et al, 2016), and nitrogen (Pan et al, 2016), as well as natural silicon (Lu et al, 2018), have also been recently used as source indicators.…”