Formation of droplet-mode secondary inorganic aerosol dominated the increased PM2.5 during both local and transport haze episodes in Zhengzhou, China

Wang, Shenbo; Wang, Lingling; Wang, Nan; Ma, Shuangliang; Su, Fangcheng; Zhang, Ruiqin

doi:10.1016/j.chemosphere.2020.128744

Cited by 23 publications

(15 citation statements)

References 38 publications

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“…The homogeneous reaction of NO 2 with OH radicals and hydrolysis of N 2 O 5 are the main routes for nitrate formation during haze episodes in this region ( He et al, 2018 ; Liu et al, 2020 ; Wang et al, 2019b , 2020b ). HNO 3 production is generated by NO 2 + OH gas-phase reaction during the day, making the initial nitrate rapidly accumulate.…”

Section: Resultsmentioning

confidence: 99%

“…The oxidation of NO 2 dominates the formation of nitrate in the gas-phase and heterogeneous hydrolysis of N 2 O 5 on the existing wet aerosol surface in Northern China ( Ji et al, 2014 ; Wang et al, 2019a ; Wang et al, 2020b ). Fu et al (2020) reported that abundant O 3 and OH radicals during haze events were beneficial to converting NO 2 to nitrate in winter.…”

Section: Introductionmentioning

confidence: 99%

“…Aerosol acidity (pH) is an important factor for SNA formation through affecting the ionic state of S(IV), the dissolution rate of TMI, and the gas-particle partitioning of semi-volatile and volatile species ( Chu et al, 2016 ; Guo et al, 2017 ; Seinfeld and Pandis, 2016 ; Wang et al, 2019a ). For aqueous-phase sulfate formation, acidity is positively correlated with the oxidation rates of O 3 and NO 2 , negatively correlated with TMI-catalyzed oxidation, and has a negligible effect on H 2 O 2 ( Liu et al, 2020 ; Shi et al, 2017 ; Wang et al, 2020b ). The oxidation of NO 2 and O 3 dominated the sulfate formation under the moderately acidic condition (a particle pH range of 5.4–7.0) in Beijing ( Guo et al, 2017 ; Liu et al, 2017a ).…”

Section: Introductionmentioning

confidence: 99%

“…The oxidation of NO 2 and O 3 dominated the sulfate formation under the moderately acidic condition (a particle pH range of 5.4–7.0) in Beijing ( Guo et al, 2017 ; Liu et al, 2017a ). H 2 O 2 and TMI-catalyzed oxidation exhibited a great contribution to droplet-mode sulfate formation in Zhengzhou, with a particle pH range of 4.0–5.5 ( Wang et al, 2020b ). The particle acidity was determined by chemical composition and meteorological conditions.…”

Section: Introductionmentioning

confidence: 99%

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Elevated particle acidity enhanced the sulfate formation during the COVID-19 pandemic in Zhengzhou, China

Yang

Wang

Zhang

et al. 2022

Environmental Pollution

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The significant reduction in PM 2.5 mass concentration after the outbreak of COVID-19 provided a unique opportunity further to study the formation mechanism of secondary inorganic aerosols. Hourly data of chemical components in PM 2.5 , gaseous pollutants, and meteorological data were obtained from January 1 to 23, 2020 (pre-lockdown) and January 24 to February 17, 2020 (COVID-lockdown) in Zhengzhou, China. Sulfate, nitrate, and ammonium were the main components of PM 2.5 during both the pre-lockdown and COVID-lockdown periods. Compared with the pre-lockdown period, even though the concentration and proportion of nitrate decreased, nitrate was the dominant component in PM 2.5 during the COVID-lockdown period. Moreover, nitrate production was enhanced by the elevated O 3 concentration, which was favorable for the homogeneous and hydrolysis nitrate formation despite the drastic decrease of NO 2 . The proportion of sulfate during the COVID-lockdown period was higher than that before. Aqueous-phase reactions of H 2 O 2 and transition metal (TMI) catalyzed oxidations were the major pathways for sulfate formation. During the COVID-lockdown period, TMI-catalyzed oxidation became the dominant pathway for aqueous-phase sulfate formation because the elevated acidity favored the dissolution of TMI. Therefore, the enhanced TMI-catalyzed oxidation affected by the elevated particle acidity dominated the sulfate formation, resulting in the slight increase of sulfate concentration during the COVID-lockdown period in Zhengzhou.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Elevated particle acidity enhanced the sulfate formation during the COVID-19 pandemic in Zhengzhou, China

Yang

Wang

Zhang

et al. 2022

Environmental Pollution

Self Cite

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“…Investigations have focused on correlations between PM 2.5 components and the formation of secondary particles (Dong et al, 2020;Du et al, 2019;Wang et al, 2020;Yang et al, 2020). The evolutionary processes and formation mechanisms of heavy PM 2.5 pollution have been discussed (Guo et al, 2020a;Hu et al, 2021;Li et al, 2020a;Shi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Chemical Characterization, Source Apportionment, and Health Risk Assessment of PM2.5 in a Typical Industrial Region in North China

Wang¹,

Yan

Zhang

et al. 2021

Preprint

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To clarify the chemical characteristics, source contributions, and health risks of pollution events associated with high PM2.5 in typical industrial areas of North China, manual sampling and analysis of PM2.5 were conducted in the spring, summer, autumn, and winter of 2019 in Pingyin County, Jinan City, Shandong Province. The results showed that the total concentration of 29 components in PM2.5 was 53.4 μg·m-3; the largest contribution was from the NO3- ion, at 14.6 ± 14.2 μg·m-3, followed by organic carbon (OC), SO42-, and NH4+, with concentrations of 9.3 ± 5.5, 9.1 ± 6.4, and 8.1 ± 6.8 μg·m-3, respectively. The concentrations of OC, NO3-, and SO42- were highest in winter and lowest in summer, whereas the NH4+ concentration was highest in winter and lowest in spring. Typical heavy metals had higher concentrations in autumn and winter, and lower concentrations in spring and summer. The annual average sulfur oxidation rate (SOR) and nitrogen oxidation rate (NOR) were 0.30 ± 0.14 and 0.21 ± 0.12, respectively, with the highest SO2 emission and conversion rates in winter, resulting in the SO42- concentration being highest in winter. Although the emission rate of SO2 was low in summer, its conversion rate was high. In winter and autumn, NORs were significantly higher than in spring and summer, and a higher NOR in autumn contributed to significant elevation of the NO3- concentration in autumn relative to spring and summer. The average concentration of secondary organic carbon in 2019 was 2.8±1.9 μg·m-3, and it comprised approximately 30% of total OC. The health risk assessment for typical heavy metals showed that Pb poses a potential carcinogenic risk for adults, whereas As may pose a carcinogenic risk for adults, children, and adolescents. Positive matrix factorization analysis indicated that coal-burning emissions contributed the largest fraction of PM2.5, accounting for 35.9% of the total. The contribution of automotive emissions is similar to that of coal, at 32.1%. The third-largest contributor was industrial sources, which accounted for 17.2%. The contributions of dust and other emissions sources to PM2.5 were 8.4% and 6.4%, respectively.

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Chemical characterization, source apportionment, and health risk assessment of PM2.5 in a typical industrial region in North China

Wang

Yan²,

Zhang

et al. 2022

Environ Sci Pollut Res

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Formation of droplet-mode secondary inorganic aerosol dominated the increased PM2.5 during both local and transport haze episodes in Zhengzhou, China

Cited by 23 publications

References 38 publications

Elevated particle acidity enhanced the sulfate formation during the COVID-19 pandemic in Zhengzhou, China

Elevated particle acidity enhanced the sulfate formation during the COVID-19 pandemic in Zhengzhou, China

Chemical Characterization, Source Apportionment, and Health Risk Assessment of PM2.5 in a Typical Industrial Region in North China

Chemical characterization, source apportionment, and health risk assessment of PM2.5 in a typical industrial region in North China

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