Seasonal Variations and Chemical Characteristics
of PM2.5 Aerosol in the Urban Green Belt
of Beijing, China

Wang, Yu; Zhang, Hui; Zhai, Jiexiu; Wu, Yue; Cong, Ling; Yan, Guoxin; Zhang, Zhenming

doi:10.15244/pjoes/104358

Cited by 11 publications

(8 citation statements)

References 29 publications

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“…Concentrations of water-soluble ions were measured via ion chromatography (Dionex ICS3000; Dionex Ltd., Sunnyvale, CA, USA) based on the reported analysis method (Wang et al, 2019). The relative standard deviation of repetitive measurements was less than 5% for each ion.…”

Section: Chemical and Isotopic Measurementsmentioning

confidence: 99%

“…To further verify the feasibility of using Pas-QW to collect atmospheric SO 4 2− , samples from five sites (BJ, SH, GZ, CD, and XM) were collected in March-June of 2017 and compared with previous studies conducted in the same regions and in the same year (Sun et al, 2019;Tian et al, 2019;Wang et al, 2019;Wu et al, 2019). The mass size distributions of sulfate is mainly in the accumulation mode, and the concentrations of sulfate in PM 2.5 is about 60% of that in TSP (Fang et al, 2013;Lijiang, 2003;Wang et al, 2003).…”

Section: Feasibility Of Using Pas-qw To Determine Sulfate In Airmentioning

confidence: 99%

See 1 more Smart Citation

Regional characteristics of atmospheric δ34S-SO42− over three parts of Asia monitored by quartz wool-based passive samplers

Wang

Sun

et al. 2021

Science of The Total Environment

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Section: Chemical and Isotopic Measurementsmentioning

confidence: 99%

Section: Feasibility Of Using Pas-qw To Determine Sulfate In Airmentioning

confidence: 99%

Regional characteristics of atmospheric δ34S-SO42− over three parts of Asia monitored by quartz wool-based passive samplers

Wang

Sun

et al. 2021

Science of The Total Environment

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“…On the one hand, there is a seasonal difference in PM2.5 concentration variations. It is generally believed that the concentration of PM2.5 is higher in the spring and winter seasons and the seasonal variations in different regions are different, which is reflected in inter-city difference and urban-rural difference (Feng et al, 2013;Ma et al, 2019;Wang et al, 2019;Shen et al, 2020). On the other hand, the diurnal variations of PM2.5 concentration is unimodal or bimodal (Zhao et al, 2009;Carslaw and Beevers, 2013;Elangasinghe et al, 2014;Kim and Kim, 2020).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai

Liu

Wang

et al. 2021

Aerosol Air Qual. Res.

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In recent years, the harmfulness of air pollutants to human health and the environment have received widespread attention. Accurate prediction of air quality is essential to air quality management and policy development, however, the seasonal differences and the diurnal variations of air pollutants have not been fully investigated by traditional forecast models. Furthermore, the available spatio-temporal data for the problem have not been well processed to capture informative predictive patterns from past data. This paper proposes a Staging evolving spiking neural network model (Staging-eSNN), which uses a time series clustering algorithm to distinguish seasonal differences with diurnal variations of PM2.5 concentration. Based on this pretreatment, we use the Staging-eSNN model to predict PM2.5 concentration in the next 1, 3, 6, 12 and 24 hour in Beijing and Shanghai areas. Various evaluation indicators for prediction performance show that the Staging-eSNN model achieves appealing performance than SVR, RF and other eSNN models.

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“…In addition to the smoke and dust from industry, traffic, and construction, the external input of desert dust from the surrounding area are contributing to the increase in particulate pollution in Lanzhou. Due to the special terrain, the PM 2.5 pollution pattern in Lanzhou may be different from that in other regions (Tshehla and Wright, 2019;Sun et al, 2019;Yu et al, 2019;Wang et al, 2020). The results of a study by Yu et al (2012) showed that heavy metals in particles in Lanzhou presented seasonal and regional distribution and exhibited unacceptable carcinogenic risk in winter, where only 6 metals (Zn, Ni, Cu, Pb, Cr, and Cd) were concerned.…”

Section: Introductionmentioning

confidence: 99%

Contamination characteristics, mass concentration, and source analysis of metal elements in PM2.5 in Lanzhou, China

Zhao

Duan

et al. 2021

Elementa: Science of the Anthropocene

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PM2.5 and its bound metals pose a serious threat to human health. Understanding their contamination characteristics and source could provide implication for controlling their spreading and ensuring air quality. In this article, 1,600 of PM2.5 samples were collected from 5 urban sites in Lanzhou, China. The contamination characteristics of PM2.5, its relationship with meteorological factors, and the source of its bound metals were studied based on multiple linear regression analysis, enrichment factor (EF), principal component analysis and correlation analysis. The outcomes show that the PM2.5 concentrations in winter (0.117 mg·m–3) and spring (0.083 mg·m–3) are higher than those in summer (0.043 mg·m–3) and autumn (0.048 mg·m–3). The influence degree of meteorological factors on PM2.5 concentration is in the order of wind speed > atmospheric pressure > temperature > humidity. The major source of Fe and Cu in PM2.5 is construction dust, Pb and As is industrial, and Hg is coal combustion. In addition, Cd, V, Co, and Mn are mainly derived from dust produced by weathering of soil or rock. In general, the spatiotemporal distribution of PM2.5 and its bound metals are different, which is closely related to geographical location, source, and meteorological factors. The results in this article could provide support for the scientific formulation to prevent air pollution in Lanzhou.

show abstract

Seasonal Variations and Chemical Characteristics of PM2.5 Aerosol in the Urban Green Belt of Beijing, China

Cited by 11 publications

References 29 publications

Regional characteristics of atmospheric δ34S-SO42− over three parts of Asia monitored by quartz wool-based passive samplers

Regional characteristics of atmospheric δ34S-SO42− over three parts of Asia monitored by quartz wool-based passive samplers

Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai

Contamination characteristics, mass concentration, and source analysis of metal elements in PM2.5 in Lanzhou, China

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