Stable and transport indices applied to winter air pollution over the Yangtze River Delta, China

2020 ◽  
pp. 115954
Author(s):  
Xiaohui Liu ◽  
Bin Zhu ◽  
Hanqing Kang ◽  
Xuewei Hou ◽  
Jinhui Gao ◽  
...  
Keyword(s):  
Air Pollution ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
The Yangtze River ◽  
The Yangtze River Delta

scholarly journals MAX-DOAS measurements of tropospheric NO<sub>2</sub> and HCHO in Nanjing and the comparison to OMI observations

2019 ◽  
Author(s):  
Ka Lok Chan ◽  
Zhuoru Wang ◽  
Aijun Ding ◽  
Klaus-Peter Heue ◽  
Yicheng Shen ◽  
...  
Keyword(s):  
Air Pollution ◽  
Pollution Control ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
Satellite Observations ◽  
River Delta ◽  
Control Measures ◽  
Air Pollution Control ◽  
The Yangtze River ◽  
The Yangtze River Delta

Abstract. In this paper, we present long term observations of atmospheric nitrogen dioxide (NO2) and formaldehyde (HCHO) in Nanjing using a Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument. Ground based MAX-DOAS measurements were performed from April 2013 to February 2017. The MAX-DOAS measurements of NO2 and HCHO vertical column densities (VCDs) are used to validate OMI satellite observations over Nanjing. The comparison shows that the OMI observations of NO2 correlate well with the MAX-DOAS data with Pearson correlation coefficient (R) of 0.91. However, OMI observations are on average a factor of 3 lower than the MAX-DOAS measurements. Replacing the a priori NO2 profiles by the MAX-DOAS profiles in the OMI NO2 VCD retrieval would increase the OMI NO2 VCDs by ~ 30 % with correlation nearly unchanged. The comparison result of MAX-DOAS and OMI observations of HCHO VCD shows a good agreement with R of 0.75 and the slope of the regression line is 0.99. We developed a new technique to assemble the source contribution map using backward trajectory analysis. The age weighted backward propagation approach is applied to the MAX-DOAS measurements of NO2 and HCHO to reconstruct the spatial distribution of NO2 and HCHO over the Yangtze River Delta during summer and winter time. The reconstructed NO2 fields show a distinct agreement with OMI satellite observations. However, due to the short atmospheric lifetime of HCHO, the backward propagated HCHO data does not show a strong spatial correlation with the OMI HCHO observations. The result shows the MAX-DOAS measurements are sensitive to the air pollution transportation in the Yangtze River Delta, indicating the air quality in Nanjing is significantly influenced by regional transportation of air pollutants. The MAX-DOAS data are also used to evaluate the effectiveness of air pollution control measures implemented during the Youth Olympic Games 2014. The MAX-DOAS data show a significant reduction of ambient aerosol, NO2 and HCHO (30 %–50 %) during the Youth Olympic Games. Our results provide a better understanding of the transportation and sources of pollutants in over the Yangtze River Delta as well as the effect of emission control measures during large international event, which are important for the future design of air pollution control policies.

scholarly journals Analysis of the spatio-temporal network of air pollution in the Yangtze River Delta urban agglomeration, China

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262444
Author(s):  
Chuanming Yang ◽  
Qingqing Zhuo ◽  
Junyu Chen ◽  
Zhou Fang ◽  
Yisong Xu
Keyword(s):  
Air Pollution ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Urban Agglomeration ◽  
Temporal Network ◽  
The Yangtze River ◽  
The Cross ◽  
Spatio Temporal ◽  
The Yangtze River Delta

The complex correlation between regions caused by the externality of air pollution increases the difficulty of its governance. Therefore, analysis of the spatio-temporal network of air pollution (STN-AP) holds great significance for the cross-regional coordinated governance of air pollution. Although the spatio-temporal distribution of air pollution has been analyzed, the structural characteristics of the STN-AP still need to be clarified. The STN-AP in the Yangtze River Delta urban agglomeration (YRDUA) is constructed based on the improved gravity model and is visualized by UCINET with data from 2012 to 2019. Then, its overall-individual-clustering characteristics are analyzed through social network analysis (SNA) method. The results show that the STN-AP in the YRDUA was overall stable, and the correlation level gradually improved. The centrality of every individual city is different in the STN-AP, which reveals the different state of their interactive mechanism. The STN-AP could be subdivided into the receptive block, overflow block, bidirectional block and intermediary block. Shanghai, Suzhou, Hangzhou and Wuxi could be key cities with an all above degree centrality, betweenness centrality and closeness centrality and located in the overflow block of the STN-AP. This showed that these cities had a greater impact on the STN-AP and caused a more pronounced air pollution spillovers. The influencing factors of the spatial correlation of air pollution are further determined through the quadratic assignment procedure (QAP) method. Among all factors, geographical proximity has the strongest impact and deserves to be paid attention in order to prevent the cross-regional overflow of air pollution. Furthermore, several suggestions are proposed to promote coordinated governance of air pollution in the YRDUA.

scholarly journals Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1423
Author(s):  
Debin Lu ◽  
Wanliu Mao ◽  
Lilin Zheng ◽  
Wu Xiao ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Big Data ◽  
Yangtze River ◽  
Regression Models ◽  
Yangtze River Delta ◽  
River Delta ◽  
The Yangtze River ◽  
The Yrd ◽  
The Yangtze River Delta

The lockdown of cities in the Yangtze River Delta (YRD) during COVID-19 has provided many natural and typical test sites for estimating the potential of air pollution control and reduction. To evaluate the reduction of PM2.5 concentration in the YRD region by the epidemic lockdown policy, this study employs big data, including PM2.5 observations and 29 independent variables regarding Aerosol Optical Depth (AOD), climate, terrain, population, road density, and Gaode map Point of interesting (POI) data, to build regression models and retrieve spatially continuous distributions of PM2.5 during COVID-19. Simulation accuracy of multiple machine learning regression models, i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN) were compared. The results showed that the RF model outperformed the SVR and ANN models in the inversion of PM2.5 in the YRD region, with the model-fitting and cross-validation coefficients of determination R2 reached 0.917 and 0.691, mean absolute error (MAE) values were 1.026 μg m−3 and 2.353 μg m−3, and root mean square error (RMSE) values were 1.413 μg m−3, and 3.144 μg m−3, respectively. PM2.5 concentrations during COVID-19 in 2020 have decreased by 3.61 μg m−3 compared to that during the same period of 2019 in the YRD region. The results of this study provide a cost-effective method of air pollution exposure assessment and help provide insight into the atmospheric changes under strong government controlling strategies.

scholarly journals Air quality and emissions in the Yangtze River Delta, China

2010 ◽  
Vol 10 (10) ◽  
pp. 23657-23703
Author(s):  
L. Li ◽  
C. H. Chen ◽  
C. Huang ◽  
Y. J. Wang ◽  
H. Y. Huang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Pollution Monitoring ◽  
The Yangtze River ◽  
Monthly Average ◽  
The Yrd ◽  
The Yangtze River Delta

Abstract. Regional trans-boundary air pollution has become an important issue in the field of air pollution modeling. This paper presents the results of the implementation of the MM5-CMAQ modeling system in the Yangtze River Delta (YRD) for the months of January and July of 2004. The meteorological parameters are obtained by using the MM5 model. A new regional emission inventory with spatial and temporal allocations based on local statistical data has been developed to provide input emissions data to the MM5-CMAQ modeling system. The pollutant concentrations obtained from the MM5-CMAQ modeling system have been compared with observational data from the national air pollution monitoring network. It is found that air quality in winter in the YRD is generally worse than in summer, due mainly to unfavorable meteorological dispersion conditions. In winter the pollution transport from Northern China to the YRD reinforces the pollution caused by large local emissions. The monthly average concentration of SO2 in the YRD is 0.026 ± 0.011 mg m−3 in January and 0.017 ± 0.009 mg m−3 in July. Monthly average concentrations of NO2 in the YRD in January and July are 0.021 ± 0.009 mg m−3, and 0.014 ± 0.008 mg m−3 respectively. Visibility is also a problem, with average deciview values of 26.4 ± 2.95 dcv in winter and 17.6 ± 3.3 dcv in summer. The ozone concentration in the downtown area of a city like Zhoushan can be very high, with the highest simulated value reaching 107 ppb. Our results show that ozone and haze have become extremely important issues in the regional air quality. Thus, regional air pollution control is urgently needed to improve air quality in the YRD.

scholarly journals Impact of biomass burning on haze pollution in the Yangtze River Delta, China: a case study in summer 2011

2013 ◽  
Vol 13 (11) ◽  
pp. 30687-30720 ◽  
Author(s):  
Z. Cheng ◽  
S. Wang ◽  
X. Fu ◽  
J. G. Watson ◽  
J. Jiang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Biomass Burning ◽  
Air Pollutants ◽  
Yangtze River ◽  
Model Simulation ◽  
Yangtze River Delta ◽  
River Delta ◽  
The Yangtze River ◽  
Open Burning ◽  
The Yangtze River Delta

Abstract. Open biomass burning is an important source of air pollution in China and globally. Joint observations of air pollution were conducted in five cities (Shanghai, Hangzhou, Ningbo, Suzhou and Nanjing) of the Yangtze River Delta, and a heavy haze episode with visibility 2.9–9.8 km was observed from 28 May to 6 June 2011. The contribution of biomass burning was quantified using both ambient monitoring data and the WRF/CMAQ model simulation. It was found that the average and maximum daily PM2.5 concentrations during the episode were 82 μg m−3 and 144 μg m−3, respectively. Weather pattern analysis indicated that a stagnant process enhanced the accumulation of air pollutants, while the following precipitation process scavenged the pollution. Daily minimum mixing depth during the stagnant period was below 50 m. Both observation data and CMAQ model simulation indicated that biomass open burning contributed 37% of PM2.5, 70% of organic carbon and 61% of elemental carbon. Satellite-detected fire spots, back-trajectory analysis and air model simulation can be integrated to identify the locations where the biomasses are burned. The results also suggest that the impact of biomass open burning is regional, due to the substantial inter-province transport of air pollutants. These findings would improve the understanding of not only heavy haze and air pollution episodes, but also the emissions of such open fires.

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