Air Pollutants Sources in Winter in Chang-Zhu-Tan Region of China

Zhu, Yingfang; Liao, Juyang; Gong, Wei; Xiang, Wenhua; Huang, Yaqi; Liu, Yan; Zhao, Meifang

doi:10.1155/2022/9717192

Cited by 5 publications

(3 citation statements)

References 19 publications

(33 reference statements)

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“…Te airfow trajectory can correctly refect the characteristics of the nearsurface air mass trajectory at a height of 500 m since the surface has little impact on the airfow trajectory at this altitude. To concentrate on the impact of pollutant transport from neighboring cities to Nanjing, a 48-hour backward trajectory was estimated [13].…”

Section: Methodsmentioning

confidence: 99%

Study on O3 Variations in Nanjing and the Surrounding Source Analysis

Shi,

Wang,

Zhang

et al. 2023

Advances in Meteorology

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To understand the transport patterns and major sources of ozone (O3) in Nanjing, this study carried out the 48-hour backward trajectories of air masses in Nanjing from March 2021 to March 2022, based on the HYSPLIT backward trajectory model driven by GDAS global reanalysis data. The primary transmission routes and putative source locations of O3 pollution in Nanjing were determined through the integration of trajectory clustering analysis, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) analysis with meteorological data and O3 concentration data. The results showed that the high O3 concentrations and exceedance rates in Nanjing were in late spring and early summer, with the highest in June. The diurnal variation of O3 concentrations in all seasons exhibited a single peak with a maximum from 13:00 to 16:00. The southeasterly flow passing through Zhenjiang, Changzhou, Wuxi, Suzhou, and Shanghai dominated the O3 pollution in Nanjing. The PSCF and CWT presented a high consistency of O3 potential sources in Nanjing. Zhenjiang, Ma’anshan, Changzhou, Wuxi, Suzhou, and Huzhou were identified as the main potential source regions of O3 pollution in Nanjing. This study provides accurate theoretical references for regional joint prevention and control of O3 pollution in Nanjing.

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Section: Methodsmentioning

confidence: 99%

Study on O3 Variations in Nanjing and the Surrounding Source Analysis

Shi,

Wang,

Zhang

et al. 2023

Advances in Meteorology

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“…The annual average concentrations of PM2.5, PM10, CO, NO2, and SO2 are higher in the north than in the south, and the rate of decline is also faster in the north than in the south (Ma et al, 2018). The potential sources of air pollutants in the Chang-Zhu-Tan area are the air currents from the northeast and southwest, which carry higher concentrations of pollutants (Zhu et al, 2022). Differences in O3, PM2.5, CO, NO2, and SO2 concentrations between suburban and urban environments in Beijing are decreasing (Li et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants

Shan

Chen

et al. 2023

Environ Monit Assess

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Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO2, and SO2). This study uses the Jinan Central District as the study area, and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) The effect of each spatial distribution index of building on the concentration distribution of the four gaseous pollutants was significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations.(2) The spatial distribution of CO and NO2 is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO2 concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO2 concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.

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“…It has been previously used to distinguish frequency bands where one or two time series are associated. Recently, several examples of wavelet analysis applications on various meteorological data [23][24][25][26][27][28] and aerosol time series [29][30][31] have been studied: in particular, as a method for detecting the short-and long-term periodicity of radiation data [32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Analysis of the Interconnection between Atmospheric Aerosol Types and Direct Irradiation over Cameroon

Douanla

Mamadou

Dembélé

et al. 2022

Advances in Meteorology

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The comparative analysis of the intra- and interannual dynamics between the Direct Normal Irradiation (DNI) under clear sky conditions and five aerosol types (Dust, Sea Salt, Black Carbon, Organic Carbon, and Sulfate) is the purpose of this study. To achieve this aim, we used fifteen-year DNI and aerosols data downloaded at 3-hour time intervals in nine defined zones throughout Cameroon. The wavelet transform is a powerful tool for studying local variability of amplitudes in a temporal dataset and constitutes our principal tool. The results show unequal distribution of aerosol types according to zones, but the Desert Dusts (DU) and Organic Carbon (OM) aerosols have been found as dominant particles in the studied region. The wavelet coherence analysis between DNI and each aerosol type reveals three bands of periodicity: ∼ 4-month band, 8–16-month band, and sometimes after-32-month band, with the most important frequency at 8–16-month band period. However, the intensity of coherence across bands varies with respect to aerosol type as well as each of the nine climate zones. A significant anticorrelation relationship was obtained between DNI and each type of aerosol, emphasizing that the presence of such atmospheric particles could dampen the renewable energy utilized by power systems. Also, the analysis shows that scattering aerosols such as Sulfate and Sea Salt (SU and SS, respectively) lead DNI in phase while absorbing aerosols such as Organic Carbon, Black Carbon, and Dust (OM, BC, and DU, respectively) give phase lag with DNI.

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Air Pollutants Sources in Winter in Chang-Zhu-Tan Region of China

Cited by 5 publications

References 19 publications

Study on O3 Variations in Nanjing and the Surrounding Source Analysis

Study on O3 Variations in Nanjing and the Surrounding Source Analysis

Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants

Wavelet Analysis of the Interconnection between Atmospheric Aerosol Types and Direct Irradiation over Cameroon

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