Review of ‘Source Apportionment of fine particulate matter in Houston, Texas: Insights to secondary organic aerosols’ by Al-Naiema et al.

2018 ◽  
Author(s):  
Anonymous
Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 267
Author(s):  
Pulong Chen ◽  
Tijian Wang ◽  
Matthew Kasoar ◽  
Min Xie ◽  
Shu Li ◽  
...  

Chemical characteristics of fine particulate matter (PM2.5) in Wuxi at urban, industrial, and clean sites on haze and non-haze days were investigated over four seasons in 2016. In this study, high concentrations of fine particulate matter (107.6 ± 25.3 μg/m3) were measured in haze episodes. The most abundant chemical components were organic matter (OM), SO42−, NO3−, elemental carbon (EC), and NH4+, which varied significantly on haze and non-haze days. The concentrations of OM and EC were 38.5 ± 5.4 μg/m3 and 12.3 ± 2.1 μg/m3 on haze days, which were more than four times greater than those on non-haze days. Source apportionment using a chemical mass balance (CMB) model showed that the dominant sources were secondary sulfate (17.7%), secondary organic aerosols (17.1%), and secondary nitrate (14.2%) during the entire sampling period. The source contribution estimates (SCEs) of most sources at clean sites were lower than at urban and industrial sites. Primary industrial emission sources, such as coal combustion and steel smelting, made larger contributions at industrial sites, while vehicle exhausts and cooking smoke showed higher contributions at urban sites. In addition, the SCEs of secondary sulfate, secondary nitrate, and secondary organic aerosols on haze days were much higher than those on non-haze days, indicating that the secondary particulate matter formations process was the dominating reason for high concentrations of particles on haze days.


2020 ◽  
Vol 223 ◽  
pp. 117227 ◽  
Author(s):  
Ibrahim M. Al-Naiema ◽  
John H. Offenberg ◽  
Carter J. Madler ◽  
Michael Lewandowski ◽  
Josh Kettler ◽  
...  

2013 ◽  
Vol 13 (10) ◽  
pp. 26657-26698
Author(s):  
Y. Hu ◽  
S. Balachandran ◽  
J. E. Pachon ◽  
J. Baek ◽  
C. Ivey ◽  
...  

Abstract. A hybrid fine particulate matter (PM2.5) source apportionment approach based on a receptor-model (RM) species balance and species specific source impacts from a chemical transport model (CTM) equipped with a sensitivity analysis tool is developed to provide physically- and chemically-consistent relationships between source emissions and receptor impacts. This hybrid approach enhances RM results by providing initial estimates of source impacts from a much larger number of sources than are typically used in RMs, and provides source-receptor relationships for secondary species. Further, the method addresses issues of source collinearities, and accounts for emissions uncertainties. Hybrid method results also provide information on the resulting source impact uncertainties. We apply this hybrid approach to conduct PM2.5 source apportionment at Chemical Speciation Network (CSN) sites across the US. Ambient PM2.5 concentrations at these receptor sites were apportioned to 33 separate sources. Hybrid method results led to large changes of impacts from CTM estimates for sources such as dust, woodstove, and other biomass burning sources, but limited changes to others. The refinements reduced the differences between CTM-simulated and observed concentrations of individual PM2.5 species by over 98% when using a weighted least squared error minimization. The rankings of source impacts changed from the initial estimates, revealing that CTM-only results should be evaluated with observations. Assessment with RM results at six US locations showed that the hybrid results differ somewhat from commonly resolved sources. The hybrid method also resolved sources that typical RM methods do not capture without extra measurement information on unique tracers. The method can be readily applied to large domains and long (such as multi-annual) time periods to provide source impact estimates for management- and health-related studies.


2003 ◽  
Vol 53 (4) ◽  
pp. 386-395 ◽  
Author(s):  
Jacob D. McDonald ◽  
Barbara Zielinska ◽  
John C. Sagebiel ◽  
Mark R. McDaniel ◽  
Pierre Mousset-Jones

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