Seasonal variations and source apportionment of water-soluble inorganic ions in PM2.5 in Nanjing, a megacity in southeastern China

Zhang, Xiaoyu; Zhao, Xin; Ji, Guixiang; Ying, Ruifeng; YanHong, Shan; Lin, Yongcheng

doi:10.1007/s10874-019-09388-z

Cited by 20 publications

(4 citation statements)

References 51 publications

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“…The average nitrate, sulfate and ammonium mass concentrations were 20.4 µg/m 3 , 20.6 µg/m 3 , and 13.6 µg/m 3 , respectively, which were the highest among the seven clusters. The mass concentration of water soluble ions was comparable with those measured in other cities of China during haze pollution periods (An et al., 2019; Wang, Huang et al., 2017; Zhang et al., 2019). Similar to C6, C7 occurred mostly during nighttime.…”

Section: Resultssupporting

confidence: 84%

Cluster Analysis of Submicron Particle Number Size Distributions at the SORPES Station in the Yangtze River Delta of East China

Chen

Nie

et al. 2021

JGR Atmospheres

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Submicron particles in polluted regions have received much attention because of their influences on human health and climate. A k‐means clustering technique was performed on a data set of particle number size distributions (PNSD) that was obtained over more than 3 years in the Yangtze River Delta (YRD) region of East China. With simultaneous measurements of meteorological conditions, trace gases and aerosol compositions, seven clusters were categorized and interpreted. Cluster 1 and cluster 2, which accounted for 9.9% of the total PNSD data, were attributed to new particle formation (NPF) and vehicle exhaust emissions with different intensities; Cluster 3 and Cluster 4, which accounted for 10.5% of the total PNSD data, were related to the growth of nucleation mode particles; Cluster 5, which accounted for 37.9% of the total data, was attributed to the humid YRD background; and Cluster 6 and Cluster 7, which accounted for 41.6% of the total data set, were both pollution‐related clusters with similar mass concentrations but completely different PNSD. Although the PM2.5 mass concentrations were somewhat similar, the particle number concentrations of the accumulation mode particles could vary by more than one order of magnitude from the urban background cluster to the pollution‐related clusters. The cluster proximity diagram and conversion flow chart of clusters clearly show the influence of NPF and growth on haze, as well as the conversion between background and polluted conditions. This study highlights the importance of PNSD for understanding urban air quality and recommends the clustering technique for analyzing complex PNSD datasets.

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Section: Resultssupporting

confidence: 84%

Cluster Analysis of Submicron Particle Number Size Distributions at the SORPES Station in the Yangtze River Delta of East China

Chen

Nie

et al. 2021

JGR Atmospheres

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“…In the present study PCA was carried out utilizing elemental concentration data of 40 samples for the post-monsoon season. Numerous other studies have employed PCA for source apportionment using limited data points viz; Hazarika et al (2015) utilized data from 12 samples for each season (summer, winter and monsoon) to perform PCA analysis, Liu et al (2018) utilized data from 15 day samples, similarly Zhang et al (2019) and Kanellopoulos et al (2020) also utilized limited data set to carry out source apportionment analysis using PCA.…”

Section: Source Apportionment Using Pcamentioning

confidence: 99%

Elemental Characteristics and Source-Apportionment of PM2.5 During the Post-monsoon Season in Delhi, India

Bangar

Mishra

Jangid

et al. 2021

Front. Sustain. Cities

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In this study, we have coupled measurements, modeling, and remote sensing techniques to better delineate the source characteristics and variability of air pollutants in Delhi primarily during the post-monsoon season in 2019. We show a comparison of ambient PM2.5 (particulate matter having aerodynamic diameter ≤2.5 μm) levels and associated elements during the post-monsoon with those during a relatively clean season of monsoon (experiencing frequent wet precipitation). Air-mass back trajectories from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model have been used to infer the possible source pathways of PM2.5 impacting at the receptor site in Delhi. The average concentrations of PM2.5 during monsoon (June–July) and post-monsoon (October–November) were 42.2 ± 15.5 μg m−3 (range: 22–73 μg m−3) and 121.4 ± 53.6 μg m−3 (range: 46–298 μg m−3), respectively. The PM2.5 samples were analyzed for heavy and trace elements (Si, S, Na, Mg, Al, Cl, Ca, K, Ti, V, Cr, Mn, Fe, Ni, Cu, Br, Rb, Zr, and Pb) using an Energy Dispersive X-ray Fluorescence (ED-XRF) technique and their concentrations have been used to carry out the source-apportionment utilizing principal component analysis (PCA) tool. The PCA analysis has identified three major sources of fine aerosols including contributions from the sources viz. vehicular emission, biomass burning, coal combustion, secondary aerosols formation, soil dust, solid-waste burning and industrial emission. The source involving biomass burning contributed largely to the PM2.5 in post-monsoon season through long-range transport of large-scale agriculture-residue burning emissions (occurring in the states of Punjab, Haryana, and western part of Uttar Pradesh). The industrial emissions include primarily, medium- and small-scale metal processing industries (e.g. steel sheet rolling) in Delhi-National Capital Region. Traces of emission from coal based thermal power plants and waste incineration have also been observed in this study.

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“…The average water-soluble inorganic ions concentrations were higher during winter and spring and lower during summer and autumn (Table 2), it is inconsistent with the seasonal variation characteristics in Nanjing (Zhang et al, 2019). The contributions of water-soluble inorganic ions in PM2.5 ranked in an order of summer (52.0%) > spring (49.0%) > autumn (42.1%) = winter (42.1%).…”

Section: Water-soluble Inorganic Ionsmentioning

confidence: 87%

“…Water-soluble inorganic ions are important chemical components of PM2.5, and a large number of studies have been carried out around the world (Deng et al, 2016;Huang et al, 2016;Liang et al, 2018;Luo et al, 2018;Masiol et al, 2015;Meng et al, 2016;Ming et al, 2017;Niu et al, 2016;Saxena et al, 2017;Szigeti et al, 2015;Tolis et al, 2015;Zhang et al, 2019). However, due to differences in geographical location, meteorological conditions, energy structure and industrial layout, results of these studies varied greatly.…”

Section: Introductionmentioning

confidence: 99%

Chemical Composition of Aerosols in the West Coast of Taiwan Strait, China

Zhao¹,

Yang²

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The chemical composition of aerosols was investigated using regular environmental air quality observation, a single particle aerosol mass spectrometer (SPAMS 0515) and an ambient ion monitor (URG 9000D) in Xiamen in 2018. The results showed that the annual average mass concentrations of PM2.5 was 22 μm/m3, and concentrations of water-soluble inorganic ions was 9.94 μm/m3 which accounted for 45.2% of PM2.5. SO42−, NO3− and NH4+ were main components of secondary reactions which contributed more than 77 percent of water-soluble inorganic ion concentration. As a coastal city, Cl− and Na+ contributed 13.9 percent of water-soluble inorganic ion concentration. Based on single particle aerosol mass spectrometer analysing, mobile sources emission was the most important sources of particle matter which contributed over 30%.

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Seasonal variations and source apportionment of water-soluble inorganic ions in PM2.5 in Nanjing, a megacity in southeastern China

Cited by 20 publications

References 51 publications

Cluster Analysis of Submicron Particle Number Size Distributions at the SORPES Station in the Yangtze River Delta of East China

Cluster Analysis of Submicron Particle Number Size Distributions at the SORPES Station in the Yangtze River Delta of East China

Elemental Characteristics and Source-Apportionment of PM2.5 During the Post-monsoon Season in Delhi, India

Chemical Composition of Aerosols in the West Coast of Taiwan Strait, China

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