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2021 ◽  
Vol 132 ◽  
pp. 102462
Author(s):  
Gustavo A. Ovando-Montejo ◽  
Peter Kedron ◽  
Amy E. Frazier

2020 ◽  
pp. 1-10
Author(s):  
Zhongqi Deng ◽  
Shunfeng Song ◽  
Hongru Tan

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Xue Luo ◽  
Yuqing Zhang ◽  
Dongqi Sun

On the basis of MODIS Enhanced Vegetation Index time series data and multisource data, such as nighttime light data and China City Statistical Yearbook data, we investigated the differences in vegetation phenology along urban-rural gradients in urban areas of different sizes between coastal and inland cities in Liaoning Province, China. The results showed that the following: (1) the iterative extraction of urban built-up areas using the threshold method based on nighttime light data combined with the definition of urban built-up areas had high accuracy. (2) Additionally, we found that the start of the growing season (SOS) in Liaoning Province occurred between day 100 and day 180, while the end of the growing season (EOS) occurred between days 260 and 330. The difference in the SOS between coastal cities (i.e., Dalian, Yingkou, Panjin, Jinzhou, Huludao, and Dandong) and inland cities (i.e., Chaoyang, Fuxin, Tieling, Shenyang, Fushun, Liaoyang, Benxi, and Anshan) was 1.70 days. However, the difference in the EOS was more significant, i.e., the EOS in coastal cities occurred 4.47 days later than that in the inland cities. (3) In urban areas of different sizes, the ∆SOS and ∆EOS of inland cities had negative correlations with urban size. Specifically, when the urban size increased 10-fold, the ∆SOS and ∆EOS advanced by 10.03 and 5.71 days, respectively. In contrast, the ∆SOS and ∆EOS of coastal cities had positive and negative correlations with the urban size, respectively. Specifically, when urban size increased 10-fold, ∆SOS was delayed by 11.29 days while EOS was advanced by 8.83 days.


Cities ◽  
2020 ◽  
Vol 98 ◽  
pp. 102590 ◽  
Author(s):  
Rafał Łopucki ◽  
Daniel Klich ◽  
Ignacy Kitowski ◽  
Adam Kiersztyn

Author(s):  
Qingyu Fan ◽  
Shan Yang ◽  
Shuaibin Liu

Rapid urbanization in China not only promotes the rapid expansion of urban population and economic agglomeration, but also causes the aggravation of haze pollution. In order to better clarify the asymmetric and nonlinear effects of urban scale and agglomeration on haze pollution, this paper quantitatively evaluates the spatial spillover effects of population size and economic agglomeration on haze pollution in 342 Chinese cities from 2001 to 2016 by using exploratory spatial data analysis (ESDA) and spatial econometric model. The results show the following: (1) During the research period, the distribution of urban scale, agglomeration, and haze pollution in China presented complex asymmetrical features, with the former two presenting a “core–periphery” distribution mode, while the latter having a tendency to spread around. In addition, under the influence of urban population size and economic agglomeration, haze pollution in Chinese cities presents significant spatial autocorrelation, with the agglomeration degrees showing a fluctuating upward trend during the study period. (2) Both urban scale and urban agglomeration have positive global spatiotemporal correlation with haze pollution. Local spatial correlation features are more obvious in China’s emerging urban agglomerations like Beijing–Tianjin–Hebei and Yangtze River Delta. (3) The spatial effects of haze pollution are better evaluated by spatial Durbin model (SDM) with spatial fixed effects, obtaining a coefficient of 0.416, indicating haze in neighboring cities affected each other and had significant spillover. By decomposing the effect of urban scale and agglomeration on haze as direct and indirect effects, the direct effect of urban population size and the indirect effect of urban economic agglomeration are found to be more prominent, reflecting that significant asymmetrical characteristics exist in the spatial effects of urban size and agglomeration on urban haze. (4) Among the control variables that affect China’s rapid urbanization, the level of urban economic development has a positive effect on haze pollution, while the high-level industrial structure and improved technical level can effectively reduce haze pollution. Continuous decline of haze concentration of Chinese cities in recent years has been indicating the spatial relationships between haze and urban size and agglomeration have a decoupling trend. The findings contribute to theory by emphasizing the spillover effect and spatial heterogeneities of geographical factors, and have implications for policy makers to deal with haze pollution reasonably and effectively.


2019 ◽  
Vol 8 (2) ◽  
pp. 282-287
Author(s):  
Jiafeng Zong ◽  
Man Guo ◽  
Liang Zhou

Author(s):  
Weicong Fu ◽  
Qunyue Liu ◽  
Cecil Konijnendijk van den Bosch ◽  
Ziru Chen ◽  
Zhipeng Zhu ◽  
...  

Atmospheric visibility (AV), one of the most concerning environmental issues, has shown a continuous decline in China’s urban areas, especially in Southeastern China. Existing studies have shown that AV is affected by air pollutants and climate change, which are always caused by human activities that are linked to socioeconomic factors, such as urban size, residents’ activities, industrial activities, and urban greening. However, the contribution of socioeconomic factors to AV is still not well understood, especially from a long-term perspective, which sometimes leads to ineffective policies. In this study, we used the structural equation model (SEM) in order to quantify the contribution of socioeconomic factors on AV change in Xiamen City, China, between 1987–2016. The results showed that the annual average AV of Xiamen between 1987–2016 was 12.00 km, with a change rate of −0.315 km/year. Urban size, industrial activities, and residents’ activities were found to have a negative impact on AV, while the impact of urban greening on the AV was modest. Among all of the indicators, the number of resident’s vehicles, total retail sales of consumer goods, and household electricity consumption were found to have the highest negative direct impact on the AV. The resident population, urban built-up area, and secondary industry gross domestic product (GDP) were the most important indirect impact factors. Based on our results, we evaluated the existing environmental regulations and policies of Xiamen City.


2018 ◽  
Vol 10 (8) ◽  
pp. 2733 ◽  
Author(s):  
Yang Li ◽  
Hua Shao ◽  
Nan Jiang ◽  
Ge Shi ◽  
Xin Cheng

The development of the Yangtze River Economic Belt (YREB) is an important national regional development strategy and a strategic engineering development system. In this study, the evolution of urban spatial patterns in the YREB from 1990 to 2010 was mapped using the nighttime stable light (NSL) data, multi-temporal urban land products, and multiple sources of geographic data by using the rank-size distribution and the Gini coefficient method. Through statistical results, we found that urban land takes on the feature of “high in the east and low in the west”. The study area included cities of different development stages and sizes. The nighttime light increased in most cities from 1992 to 2010, and the rate assumed an obvious growth tendency in the three urban agglomerations in the YREB. The results revealed that the urban size distribution of the YREB is relatively dispersed, the speed of urban development is unequal, and the trend of urban size structure shows a decentralized distribution pattern that has continuously strengthened from 1990 to 2010. Affected by factors such as geographical conditions, spatial distance, and development stage, the lower reaches of the Yangtze River have developed rapidly, the upper and middle reaches have developed large cities, and a contiguous development trend is not obvious. The evolution of urban agglomerations in the region presents a variety of spatial development characteristics. Jiangsu, Zhejiang, and Shanghai have entered a phase of urban continuation, forming a more mature interregional urban agglomeration, while the YREB inland urban agglomerations are in suburbanization and multi-centered urban areas. At this stage, the conditions for the formation of transregional urban agglomerations do not yet exist, and there are many uncertainties in the boundary and spatial structure of each urban agglomeration.


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