Xing et al. (1) report that over the period of 2016 to 2018 an average of 6.2% of the atmospheric moisture in Xi’an, China was combustion-derived water (CDW). They found correlations between CDW and PM2.5 (concentration of particulate matter with an aerodynamic diameter ≤2.5 μm) and with relative humidity during the periods of rising PM2.5. They used the WRF-Chem model to interpret their results and suggest that CDW resulted in an average increase of 4.6 µg/m3 in PM2.5. However, recent findings on the nature of emissions in Xi’an, particularly from residential coal/wood combustion (2–4), were not incorporated into their analyses and interpretation. Differences in emissions from coal combustion depend on whether lump coal and briquettes or powdered coal are being burned. Residential coal combustion (RCC) typically uses lump or briquette coal in stoves that include start-up and burn-out phases in the burn cycle. These burn-cycle phases result in low-temperature combustion that produces SO3 rather than SO2 (2). Dai et al. (3) reported that sulfate accounted for 16.6 ± 7.7% and 29.2 ± 8.7% of the PM2.5 mass emissions from a residential stove burning coal briquettes or chunk coal, respectively, and estimated that 1,215 tons of PM10 sulfate were emitted from RCC in 2014 in Xi’an. Thus, a significant fraction of the observed winter sulfate (4) is primary rather than secondary sulfate if similar coal was being used in 2016 to 2018. This result is consistent with low secondary aerosol formation since cold temperatures result in slower oxidative kinetics in solution along with increased viscosity and slower diffusion in cold droplets. For SO2 oxidization in solution, SO2 and the oxidants must diffuse from the droplet boundary layer into the bulk liquid. As the droplet cools this process will slow because of increasing droplet viscosity. Additionally, organic compounds will not be readily oxidized under winter temperature and oxidant conditions. Li et al. (5) found that 43.0% of the PM2.5 emitted by coal combustion in typical residential stoves was organic carbon with a range of 20.2 to 71.1%. Between 45 and 50% of the organic carbon was humic-like substances (HULIS). This primary HULIS will seem to be secondary organic aerosol and thus, the combination of primary sulfate and primary oxidized organic carbon provides the appearance of much more secondary material in the PM2.5 than was likely to be present. Finally, there were significant NO2 concentrations of 54.3 ± 21.8, 70.8 ± 26.7, 71.1 ± 28.1, 59.4 ± 24.5, and 49.0 ± 23.6 µg/m3 during the winters of 2015 to 2019, respectively. NO2 reacts more rapidly with oxidants than either SO2 or organics, so secondary formation from local emissions except for nitrate will be slow. Therefore, the correlation between PM2.5 and CDW is likely driven by local RCC. Dai et al. (6) reported that during the heating season of 2014 to 2015, 28.1% of the PM2.5 was due to coal combustion and 8.5% from biomass burning at an urban site in Xi’an. Thus, replacement of RCC with natural gas or electricity will lead to substantially improved winter air quality.
Footnotes
The authors declare no competing interest.
References
- 1.Xing M., et al., Vapor isotopic evidence for the worsening of winter air quality by anthropogenic combustion-derived water. Proc. Natl. Acad. Sci. U.S.A. 117, 33005–33010 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Coykendall L., Formation and control of sulfur oxides in boilers. J. Air Pollut. Control Assoc. 12, 567–591 (1962). [Google Scholar]
- 3.Dai Q. L., et al., Residential coal combustion as a source of primary sulfate in Xi’an, China. Atmos. Environ. 196, 66–76 (2019). [Google Scholar]
- 4.Dai Q., et al., Chemical nature of PM2.5 and PM10 in Xi’an, China: Insights into primary emissions and secondary particle formation. Environ. Pollut. 240, 155–166 (2018). [DOI] [PubMed] [Google Scholar]
- 5.Li X., et al., Quantifying primary and secondary humic-like substances in urban aerosol based on emission source characterization and a source-oriented air quality model. Atmos. Chem. Phys. 19, 2327–2341 (2019). [Google Scholar]
- 6.Dai Q., Hopke P. K., Bi X., Feng Y., Improving apportionment of PM2.5 using multisite PMF by constraining G-values with a priori information. Sci. Total Environ. 736, 139657 (2020). [DOI] [PubMed] [Google Scholar]
