Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016

Ye, Lei; Xiang-jun, OU

doi:10.1007/s11769-019-1036-0

Cited by 24 publications

(9 citation statements)

References 24 publications

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“…The Yangtze River Delta urban agglomeration, located at the lower reach of the Yangtze River in the eastern coastal part of China, covers approximately 211,700 km 2 , or 2% of the country's territory. However, the 26 cities in this urban agglomeration, including Shanghai and the majority of the cities in Jiangsu, Zhejiang and Anhui Provinces, account for almost 20% of China's GDP (Ye and Ou, 2019). It is one of the most densely populated areas in China, exhibits the most rapidly-growing urbanization nationwide as well as a robust economy that is unequalled in China.…”

Section: Introductionmentioning

confidence: 99%

How urban agglomeration improve the emission efficiency？A spatial econometric analysis of the Yangtze River Delta urban agglomeration in China

Zheng

et al. 2020

Journal of Environmental Management

195

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Urban areas consume more than 66% of the world's energy and generate more than 70% of global greenhouse gas (GHG) emissions. With the world's population expected to reach 10 billion by 2100, and with nearly 90% of people living in urban areas, a critical question for planetary sustainability is how the size of cities affects energy use and carbon dioxide (CO2) emissions. Are urban agglomerations more energy and emissions efficient than smaller cities? Does urban agglomeration exhibit gains from economies of scale concerning emissions? Here, we examine the relationship between urban agglomeration and CO2 emissions for urban agglomeration in the Yangtze River Delta in China using a STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model considering the spatial effects. Also, it examines the influence of economic development, industrial structure, opening-up level, and technology on carbon emissions by exploring the spatial agglomeration and spillover effects. Our major finding is that urban size has had a negative correlation to carbon emissions, demonstrating that urban agglomeration is more emissions efficient. In addition, our results showed that carbon emission driving factors, such as technology progress, opening-up, population, have spatial dependence and spatial agglomeration effects. Technology progress, opening-up level, and population have a spatial spillover effect on carbon emissions. It means a city's carbon emissions are not only influenced by its own factors but also have an impact on neighboring cities. Therefore, cross-city or urban agglomeration policy, and actions of reducing carbon emissions, are necessary, whilst also developing a low-carbon economy by increasing the proportion of high-tech industry through technological progress and developing vigorous resource-saving and an environmentally friendly tertiary industry.

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Section: Introductionmentioning

confidence: 99%

How urban agglomeration improve the emission efficiency？A spatial econometric analysis of the Yangtze River Delta urban agglomeration in China

Zheng

et al. 2020

Journal of Environmental Management

195

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“…The learning capability of the proposed network structure is a result of the combination of different types of neural networks' capabilities, where the artificial neural networks (ANN) and fuzzy systems are combined to form a firmly structured network-a neuro-fuzzy system. In addition, they allow a really simple transformation of the whole system into if-then rules, which is one of the greatest advantage of these networks [6]. Its framework is related to an arrangement of fuzzy if-then rules which have the learning ability to estimate non-linear functions.…”

Section: Model Developmentmentioning

confidence: 99%

“…Neuro-fuzzy inference systems have been used in many research activities and there is no doubt that neural-fuzzy combined systems are powerful in many fields. As can be concluded from their name, adaptive networks are constructed from nodes and directed paths, and all I/O values can be modified by different sets of parameters which are defined in the architecture of these networks [6]. On the other hand, ANFIS systems can utilize a various range of algorithms to decrease final errors in the training phase.…”

Section: Model Developmentmentioning

confidence: 99%

“…The huge dependency on fossil fuels in the production of energy to support industries, mobility, and urbanization have dramatically increased air pollution worldwide [1][2][3]. Population growth, industrialization, climate change, and the ever-growing fact of urbanization are further accelerating the production of emissions and the severe impacts on air quality [4][5][6]. Air pollution is known as a profound contributor to human mortality and as a potential danger to the environment and ecological systems [2,7,8].…”

Section: Introductionmentioning

confidence: 99%

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Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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“…At present, studies on PM2.5 mainly rely on data acquisition and assessment of the spatiotemporal distribution characteristics and driving factors. Data sources are mainly derived from environmental monitoring sites [ 12 ] and aerosol optical depth (AOD) production from remote sensing [ 13 , 14 , 15 , 16 , 17 ]. However, different data collection methods have also led to obvious discrepancies between the two data sets.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

Yan

Tao

Zhang

et al. 2021

IJERPH

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As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

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Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016

Cited by 24 publications

References 24 publications

How urban agglomeration improve the emission efficiency？A spatial econometric analysis of the Yangtze River Delta urban agglomeration in China

How urban agglomeration improve the emission efficiency？A spatial econometric analysis of the Yangtze River Delta urban agglomeration in China

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

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