Seasonal Control of Water-Soluble Inorganic Ions in PM2.5 from Nanning, a Subtropical Monsoon Climate City in Southwestern China

Guo, Wei; Long, Chenkui; Zhang, Zhongyi; Zheng, Nengjian; Xiao, Huayun; Xiao, Hao

doi:10.3390/atmos11010005

Cited by 12 publications

(7 citation statements)

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“…PM 2.5 has been widely studied in recent years in China due to its potential impacts on air quality and human health. Water-soluble inorganic ions (WSIIs), organic carbon (OC) particles, inorganic carbon (EC) particles, and inorganic elements (IEs), as the main chemical components of PM 2.5 , have been extensively studied in China [7][8][9]. WSIIs are dominated by secondary inorganic aerosols (SIAs), including NH 4 + , NO 3 − , and SO 4 2− .…”

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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“…winter, cluster 3 in spring and cluster 3 in summer). However, the emissions of gaseous precursors (NO2 and SO2, no data for NH3) in these areas are close to Guiyang, such as Kunming and Nanning [21,41]. Thus, lower SNA concentrations were found in some trajectories, which could be ascribed to these trajectories that reflected long-distance air transport and were fast-moving, so the pollutants may not easily be enriched in these trajectories [58,59].…”

Section: The Corresponding Concentrations Of Speciesmentioning

confidence: 95%

Chemical Characteristics of Major Inorganic Ions in PM2.5 Based on Year-Long Observations in Guiyang, Southwest China—Implications for Formation Pathways and the Influences of Regional Transport

Xiao

Zhang

et al. 2020

Atmosphere

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Sulfate, nitrate and ammonium (SNA) are the dominant components of water-soluble ions (WSIs) in PM2.5, which are of great significance for understanding the sources and transformation mechanisms of PM2.5. In this study, daily PM2.5 samples were collected from September 2017 to August 2018 within the Guiyang urban area and the concentrations of the major WSIs in the PM2.5 samples were characterized. The results showed that the average concentration of SNA (SO42−, NO3−, NH4+) was 15.01 ± 9.35 μg m−3, accounting for 81.05% (48.71–93.76%) of the total WSIs and 45.33% (14.25–82.43%) of the PM2.5 and their possible chemical composition in PM2.5 was (NH4)2SO4 and NH4NO3. The highest SOR (sulfur oxidation ratio) was found in summer, which was mainly due to the higher temperature and O3 concentrations, while the lowest NOR (nitrogen oxidation ratio) found in summer may ascribe to the volatilization of nitrates being accelerated at higher temperature. Furthermore, the nitrate formation was more obvious in NH4+-rich environments so reducing NH3 emissions could effectively control the formation of nitrate. The results of the trajectory cluster analysis suggested that air pollutants can be easily enriched over short air mass trajectories from local emission sources, affecting the chemical composition of PM2.5.

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“…Previous studies have shown that the contaminants from fossil fuel combustion, agricultural incineration, and automobile emissions account for more than 60% of the PM 2.5 concentration. (33) Autumn and winter are the periods of heating in northern cities. PM 2.5 pollutants released from fossil fuel combustion into the air increase nearly sevenfold during this period, directly leading to a significant increase in PM 2.5 concentration in autumn and winter.…”

Section: Modeling Accuracymentioning

confidence: 99%

Optimization of Seasonal Geographically and Temporally Weighted Regression Model for Accurate Estimation of Seasonal PM2.5 Concentrations in Beijing–Tianjin–Hebei Region

Zhou¹,

Wang²,

Du³

et al. 2020

Sensors and Materials

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Particulate matter with a diameter of less than 2.5 µm (PM 2.5 ) has a significant impact on air pollution, atmospheric visibility, and human health. The most basic and important step of regional air pollution control is to obtain air pollution data in different seasons from both satellite sensors and ground-level observations. The aim of this paper is to accurately estimate the PM 2.5 concentration in the Beijing-Tianjin-Hebei urban area in different seasons by establishing a seasonal geographically and temporally weighted regression (S-GTWR) model that integrates multiple complex factors. Using a greedy algorithm, the model results were optimized by selecting the characteristic variables that contributed to the accuracy of the model in different seasons. The measured and estimated PM 2.5 concentrations were compared and the cross-validation results were used as a basis for evaluating the accuracy of the model. The results showed that the accuracy of the S-GTWR model that combined the optimal characteristic variables was higher than that of the geographically weighted regression (GWR) model and the kriging method. The mean prediction error (ME), relative prediction error (RPE), and root mean square error (RMSE) of the S-GTWR model were small, and the coefficient of determination (R 2 ) of the model exceeded 0.86 for each season. The accuracy of the S-GTWR model in estimating the PM 2.5 concentration was highest in summer and lowest in winter. In addition, the proposed model can accurately estimate PM 2.5 concentrations in areas without monitoring sites. The results can provide a scientific basis for the study of pollution control and PM 2.5 exposure in large urban agglomerations.

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Seasonal Control of Water-Soluble Inorganic Ions in PM2.5 from Nanning, a Subtropical Monsoon Climate City in Southwestern China

Cited by 12 publications

References 50 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 Characteristics of Major Inorganic Ions in PM2.5 Based on Year-Long Observations in Guiyang, Southwest China—Implications for Formation Pathways and the Influences of Regional Transport

Optimization of Seasonal Geographically and Temporally Weighted Regression Model for Accurate Estimation of Seasonal PM2.5 Concentrations in Beijing–Tianjin–Hebei Region

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