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• Spatiotemporal pattern of habitat quality in Hangzhou was assessed by InVEST model. • Spatial distribution of habitat quality showed positive spatial auto-correlation. • Negative impact of urbanization on habitat quality varied with time and space. • Impacts of landscape pattern on habitat quality differed in magnitude and direction. China has experienced the most serious habitat degradation, especially in fast-growing metropolis cities. Although increasing attentions have been brought to this issue, we still lack the understanding of the quantitative impacts of urbanization and landscape pattern on habitats. In this study, we used the "Integrated Valuation of Environmental Services and Trade-off" (InVEST) model to evaluate the habitat quality in Hangzhou city. We further employed spatial auto-correlation to analyze its spatiotemporal pattern variation characteristics. Finally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to explore the impacts of urbanization and landscape pattern change on habitat quality. The results show that the habitat quality index of Hangzhou decreased from 0.608 to 0.577 during 2004–2015, and these areas mainly located around the suburb decreased significantly. The spatial distribution of habitat quality showed significantly positive spatial auto-correlation, and the overall spatial auto-correlation degree of the habitat quality increased during this time. Rapid urbanization has significant negative effects on habitat quality in various areas, while the magnitude and direction of the impacts of landscape pattern on habitat quality differed in time and space. These results provide decision-making criteria for formulating differential urban development policies and landscape management measures for urban ecological sustainability. [ABSTRACT FROM AUTHOR]

A significant cause of economic recession and environmental change that has attracted the extensive attention of researchers and policy-makers worldwide is the land urbanization bubble (LUB) – a rapid and catastrophic growth and decline in urban development fueled by a mixture of misplaced exuberance and speculation. It is also an important topic for sustainable urban development and urban renewal, and is of particular interest to China, as one of the countries with the largest amount of urban land, most drastic change, and most prominent land urbanization conflicts in the world. This study analyzes the characteristics and presence of dynamic change in LUBs during China's urbanization process through a case study of the changing space-time rules in Hangzhou in Zhejiang province over the years 2004, 2007, 2010, 2015, and 2020. Based on a literature review and analysis of the mechanism driving Hangzhou's LUBs, we design a method for their classification and measurement. This reveals the existence of an overall 18.24 km2 LUB in 2010 and a predicted 40.62 km2 LUB in 2020. Locally, the Jianggan and Yuhang districts are expected to have a higher LUB risk by 2020, the Bingjiang and Xiaoshan districts a medium risk, the Shangcheng and Xiacheng districts a low risk, and the Xihu district having no LUB risk. Finally, three policy recommendations are proposed in consideration of Hangzhou's economic development and land use characteristics, comprising (1) reducing LUBs by promoting intensive urban expansion, (2) reducing the possibility of LUBs by building a market-oriented control system for construction land expansion, and (3) the dynamic control of LUB risks by differential regulation of the expansion of urban construction land. • These identified LUB areas are classified into two categories in this paper, including controllable LUB uncontrollable LUB. • This research found no LUBs in 2007 and 2015, but with an 18.24 km2 LUB in 2010 and an expected 40.62 km2 bubble in 2020. • The concept of LUB risk was introduced and calculated in this paper for decision makers' reference. [ABSTRACT FROM AUTHOR]

• The spatial dynamics of urban construction land were explored by using a hybrid Logistic CA model. • We visualize how predictions on spatial expansion will benefit urban planners and policymakers. • The model could explain the underlying mechanism of urban growth in a spatial-economic and social way. • We indicate the effective quantification of land patches can improve the compact urban growth. • Effective quantification of the construction land patches could reduce the unordered urban growth. Why does some urban area grow faster than others? Although the role of spatial optimization of urban construction land is at the core of regional economic development, the question remains to be answered so far. This paper aims to explore the spatial dynamics of urban construction land in the central urban areas by integrating Logistic regression and cellular automata models. The combination of the two modeling approaches aims to investigate the evolving dynamics of urban land use patterns and further visualize how predictions on spatial expansion will benefit urban planners and policymakers. Simulation analysis emphasized to what extent do the influencing factors promote or inhibit urban growth. Theoretical frameworks tend to explain the underlying mechanism of urban growth in a spatial, economic and social way, taking the city growth as a self-organized organism with complex actors and rules. In this paper, we present a hybrid Logistic cellular automata model to examine the city's self-organizing spatial growth process from a bottom-up perspective and interpret why non-construction land was converted to construction land for urban development purposes at Hangzhou in the past two decades. We argue that although the construction land is dispersed irregularly across the city, the logistic cellular automata model will generate the underlying patterns of urban expansion and offer more facts-based implications. [ABSTRACT FROM AUTHOR]

The daily variations of near-surface ozone (O 3), one of the major air pollutants in China, depend on meteorological conditions. In this study, we analyzed the characteristics of O 3 pollution and the complicated interactions among the controlling meteorological factors. In China, O 3 pollution is becoming severe and high-concentration areas are expanding greatly. We found that O 3 was correlated to numerous meteorological variables including air pressure, temperature, relative humidity, wind speed, wind direction, sunshine duration, evaporation, and precipitation. Using generalized additive models (GAMs), we developed predictive models to forecast the maximum daily 8 h O 3 mass concentration for 334 cities in China. Different cities were influenced by different meteorological variables, with temperature (90.77%), relative humidity (68.15%), and sunshine hours (66.96%) being the top three influencing factors. In addition, we found that the influence of these meteorological factors on O 3 pollution was nonlinear and impacted by the interaction between variables. Considering individual relationships between each meteorological factor and O 3 , the average coefficient of determination (R2) of GAMs for the 334 cities was 0.64 ± 0.16. However, considering the interaction between variables, the average R2 for 334 cities of revised GAMs increased to 0.72 ± 0.15. The R2 was significantly improved in 96.72% of the cities after considering the interactions among the meteorological factors based on a ten-fold cross-validation method. Furthermore, we found that the GAM residuals were unbiased. As an example of using and interpreting the GAM results, we focused on a case study in Hangzhou during the G20 summit in 2016. During this period, nitrogen oxides (NO x) and volatile organic compounds were reduced by 51.67% and 49.00%, respectively, because of local emission controls. Despite these changes in precursor emissions, O 3 changed slightly during this period, suggesting that O 3 in Hangzhou at the time was not controlled by local precursor emissions, but was impacted by meteorological conditions. • GAMs could assess O 3 concentration by considering time variable, non-linear relationship, and meteorological interactions. • T, RH and sunshine hours are top three influence meteorological factors on O 3 concentration for 75.84% cities in China. • GAMs can capture the O 3 concentration accurately even if there were reductions of NOx and VOCs: 51.67% and 49.00%. [ABSTRACT FROM AUTHOR]