Observation of chemical components of PM2.5 and secondary inorganic aerosol formation during haze and sandy haze days in Zhengzhou, China

Dong, Zhangsen; Su, Fangcheng; Zhang, Zhenya; Wang, Shenbo

doi:10.1016/j.jes.2019.09.016

Cited by 36 publications

(10 citation statements)

References 68 publications

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“…OC is composed of thousands of organic compounds and contains many toxic substances. Heavy metals are an important part of the inorganic elements comprising PM 2.5 , such as Fe, Zn, Cu and Pb [10][11][12]. Whether worldwide or only in China, it is essential to reduce PM 2.5 concentrations to control their sources.…”

Section: Introductionmentioning

confidence: 99%

Mass Concentration, Chemical Composition, and Source Characteristics of PM2.5 in a Plateau Slope City in Southwest China

et al. 2021

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In order to investigate the seasonal variations in the chemical characteristics of PM2.5 at the plateau slope of a mountain city in southwest China, 178 PM2.5 filters (89 quartz and 89 Teflon samples for PM2.5) were collected to sample the urban air of Wenshan in spring and autumn 2016 at three sites. The mass concentrations, water-soluble inorganic ions, organic and inorganic carbon concentrations, and inorganic elements constituting PM2.5 were determined, principal component analysis was used to identify potential sources of PM2.5, and the backward trajectory model was used to calculate the contribution of the long-distance transmission of air particles to the Wenshan area. The average concentration of PM2.5 in spring and autumn was 44.85 ± 10.99 μg/m3. Secondary inorganic aerosols contributed 21.82% and 16.50% of the total PM2.5 in spring and autumn, respectively. The daily mean value of OC/EC indicated that the measured SOC content was generated by the photochemical processes active during the sampling days. However, elements from anthropogenic sources (Ti, Si, Ca, Fe, Al, K, Mg, Na, Sb, Zn, P, Pb, Mn, As and Cu) accounted for 99.38% and 99.24% of the total inorganic elements in spring and autumn, respectively. Finally, source apportionment showed that SIA, dust, industry, biomass burning, motor vehicle emissions and copper smelting emissions constituted the major components in Wenshan. This study is the first to investigate the chemical characterizations and sources of PM2.5 in Wenshan, and it provides effective support for local governments formulating air pollution control policies.

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Section: Introductionmentioning

confidence: 99%

Mass Concentration, Chemical Composition, and Source Characteristics of PM2.5 in a Plateau Slope City in Southwest China

et al. 2021

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“…An important part of the inorganic elements of PM 2.5 are heavy metals, such as Fe, Cu, Zn, and Pb [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Chemical Composition and Source Apportionment of PM2.5 in a Border City in Southwest China

et al. 2021

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This paper studied the chemical characteristics and seasonal changes of PM2.5 in plateau cities on the southwest border of China. Urban air was sampled in Baoshan City during the rainy and dry seasons. Finally, 174 PM2.5 filters were collected (including 87 quartz and 87 Teflon samples for PM2.5). The mass concentrations, water-soluble inorganic ions, organic and inorganic carbon concentrations, and inorganic elements constituting PM2.5 were determined. Positive definite matrix factorization was used to identify potential sources of PM2.5, and the backward trajectory model was used to calculate the contribution of the long-distance transmission of air particles to the Baoshan area. It was found that in the wet season, most of the air masses come from the Indian Ocean and Myanmar. In the dry season, the air mass mainly comes from the China and Myanmar border area. The average concentration of PM2.5 in the wet and dry seasons was 23.17 ± 12.23 μg/m3. The daily mean value of OC/EC indicated that the measured SOC content was generated by the photochemical processes active during the sampling days. However, elements from anthropogenic sources (Na, Mg, Al, Si, P, K, Ca, Ti, Fe, Cu, Zn, Sb, Ba, and Pb) accounted for 99.51% and 99.40% of the total inorganic elements in the wet season and dry season, respectively. Finally, source apportionment showed that SIA, dust, industry, biomass burning, motor vehicle emissions, and copper smelting emissions constituted the major contributions of PM2.5 in Baoshan. Using combined data from three measurement sites provides a focus on the common sources affecting all locations.

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“…During such events, desert winds can transport particles from sand and dust storm sources thousands of kilometers from their source region [2,18,19]. In addition, the intensity of dust events is further enhanced under strong winds and causes more serious soil wind erosion during its transportation [20]. Wind erosion of soil, which is closely related to soil properties, is the primary cause of sandstorm events in arid desert areas having stable sand sources.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Evolution of a Typical Sandstorm Event in an Arid Area of Northwest China in April 2018 Based on Remote Sensing Data

Jiang

et al. 2022

Remote Sensing

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Northwest China is significantly affected by sandstorm disasters. To mitigate the negative impacts of sandstorm events, it is critical to understand the spatio-temporal variations in typical sand and dust storms and their influencing factors. In this work, using ground-based measurements of particulate matter and remote sensing data such as MODIS, OMI, and CALIPSO data, the sources of aerosol pollution and aerosol optical properties of a typical sandstorm event that occurred in Northwest China in 2018 was studied. In addition, the HYSPLIT model was used to explore the air mass trajectories in order to analyze the sand and dust migration process during the sandstorm event. Furthermore, the wind erosion sensitivity of Northwest China was analyzed via single factor analysis and multi-factor superposition of wind field intensity, soil drought index, vegetation coverage, and relief amplitude. Finally, the region of the study area having a high comprehensive wind erosion sensitivity was identified. The results showed that the PM10 concentrations exceeded 400 µg/m3 and the PM2.5/PM10 ratio did not exceeded 0.6 during the sandstorm event, indicating that natural particulate matter was dominant in the ambient air. At the epicenter of pollution, the aerosol optical depth (AOD) at 550 nm was 0.75–1. By combining AOD data with wind speed and direction data from field observation stations, it was found that the sandstorm event in 2018 mainly occurred between 1 April and 3 April, and affected all of Northwest China on 2 April and 3 April. The absorbed aerosol index (AAI) ranged between 2.5 and 4, indicating that the Taklimakan Desert was the main source of sandstorm events in Northwest China. The CALIPSO total attenuated backscatter coefficient at 532 nm indicated that the main component of tropospheric aerosol in this region was distributed in the range of 0–12.5 km. The simulated airflow track showed that it had the same dust source regions as AAI index studies. Moreover, investigation of wind erosion sensitivity in the study areas indicated that the Taklimakan Desert and other desert regions were the main ecologically sensitive areas. These conclusions can provide references and suggestions for the mitigation of damage caused by sandstorm events, in addition to the enhancement of ecological governance.

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Observation of chemical components of PM2.5 and secondary inorganic aerosol formation during haze and sandy haze days in Zhengzhou, China

Cited by 36 publications

References 68 publications

Mass Concentration, Chemical Composition, and Source Characteristics of PM2.5 in a Plateau Slope City in Southwest China

Mass Concentration, Chemical Composition, and Source Characteristics of PM2.5 in a Plateau Slope City in Southwest China

Chemical Composition and Source Apportionment of PM2.5 in a Border City in Southwest China

Spatio-Temporal Evolution of a Typical Sandstorm Event in an Arid Area of Northwest China in April 2018 Based on Remote Sensing Data

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