► The green space coverage in Chinese cities increased steadily from 1991 to 2009. ► Cities in the same region exhibited long-term similar trends of development. ► Population, land area and GDP significantly affected green space coverage. ► Per capita GDP had the highest independent contribution to green space coverage. ► A linear model to predict variance in green space was constructed. a b s t r a c t a r t i c l e i n f o Irrespective of which side is taken in the densification-sprawl debate, insights into the relationship between urban green space coverage and urbanization have been recognized as essential for guiding sustainable urban development. However, knowledge of the relationships between socio-economic variables of urbanization and long-term green space change is still limited. In this paper, using simple regression, hierarchical partitioning and multi-regression, the temporal trend in green space coverage and its relationship with urbanization were investigated using data from 286 cities between 1989 and 2009, covering all provinces in mainland China with the exception of Tibet. We found that: [1] average green space coverage of cities investigated increased steadily from 17.0% in 1989 to 37.3% in 2009; [2] cities with higher recent green space coverage also had relatively higher green space coverage historically; [3] cities in the same region exhibited similar long-term trends in green space coverage; [4] eight of the nine variables characterizing urbanization showed a significant positive linear relationship with green space coverage, with 'per capita GDP' having the highest independent contribution (24.2%);[5] among the climatic and geographic factors investigated, only mean elevation showed a significant effect; and [6] using the seven largest contributing individual factors, a linear model to predict variance in green space coverage was constructed. Here, we demonstrated that green space coverage in built-up areas tended to reflect the effects of urbanization rather than those of climatic or geographic factors. Quantification of the urbanization effects and the characteristics of green space development in China may provide a valuable reference for research into the processes of urban sprawl and its relationship with green space change.

s u m m a r yIn recent decades, the Yongding River in Beijing has ceased to flow due to the impact of climate and anthropogenic factors, which has led to severe environmental degradation. The Beijing government is constructing new freshwater ecosystems on the Yongding River to improve environmental conditions for ecosystem services. Clarification is needed on the influence of precipitation and anthropogenic factors on streamflow decline in Beijing. A hydrological time-series analysis was conducted on recorded streamflow at Guanting Reservoir, Yanchi, and Sanjiadian to estimate the influence of precipitation variability on the drying of the Yongding River in Beijing. From 1980 to 2010, the mean annual rates of streamflow decline were 0.44 m 3 s À1 yr À1 (Guanting), 0.42 m 3 s À1 yr À1 (Yanchi), and 0.03 m 3 s À1 yr À1 (Sanjiadian). The most probable abrupt change-point for annual streamflow was 1999 at Guanting Reservoir and Yanchi, and was 2000 at Sanjiadian. Between the pre-change (1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999) and post-change (2000-2010) periods, mean annual streamflow decreased by 68.56% (Guanting), 66.92% (Yanchi), and 96.78% (Sanjiadian). A multiple regression analysis using annual precipitation and streamflow at Guanting, Yanchi, and Sanjiadian showed an insignificant relationship between local precipitation and streamflow in both periods. Next we assessed the potential impact of upstream human activities on downstream flow using: (1) correlation statistics between upstream flow and downstream flow, (2) water abstracted above Sanjiadian, and (3) upstream socioeconomic data. The results suggest upstream human activities are important drivers on downstream flow decline, which could possibly explain the weak relationship between precipitation and streamflow. Further analysis is needed to clarify the influence of upstream water consumption on Guanting Reservoir to advise management on the new freshwater ecosystems along the Yongding River.

a b s t r a c tSocio-economic factors have significant influences on air quality and are commonly used to guide environmental planning and management. Based on data from 85 long-term daily monitoring cities in China, air quality as evaluated by AOFDAQ-A (Annual Occurrence Frequency of Daily Air Quality above Level III), was correlated to socio-economic variable groups of urbanization, pollution and environmental treatment by variation partitioning and hierarchical partitioning methods. We found: (1) the three groups explained 43.5% of the variance in AOFDAQ-A; (2) the contribution of "environmental investment" to AOFDAQ-A shown a time lag effect; (3) "population in mining sector" and "coverage of green space in built-up area" were respectively the most significant negative and positive explanatory socioeconomic variables; (4) using eight largest contributing individual factors, a linear model to predict variance in AOFDAQ-A was constructed. Results from our study provide a valuable reference for the management and control of air quality in Chinese cities.

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In order to use in situ sensed reflectance to monitor the concentrations of chlorophyll-a (Chl-a) and total suspended particulate (TSP) of waters in the Pearl River Delta, which is featured by the highly developed network of rivers, channels and ponds, 135 sets of simultaneously collected water samples and reflectance were used to test the performance of the traditional empirical models (band ratio, three bands) and the machine learning models of a back-propagation neural network (BPNN). The results of the laboratory analysis with the water samples show that the Chl-a ranges from 3 to 256 µg·L−1 with an average of 39 µg·L−1 while the TSP ranges from 8 to 162 mg·L−1 and averages 42.5 mg·L−1. Ninety sets of 135 samples are used as training data to develop the retrieval models, and the remaining ones are used to validate the models. The results show that the proposed band ratio models, the three-band combination models, and the corresponding BPNN models are generally successful in estimating the Chl-a and the TSP, and the mean relative error (MRE) can be lower than 30% and 25%, respectively. However, the BPNN models have no better performance than the traditional empirical models, e.g., in the estimation of TSP on the basis of the reflectance at 555 and 750 nm (R555 and R750, respectively), the model of BPNN (R555, R750) has an MRE of 23.91%, larger than that of the R750/R555 model. These results suggest that these traditional empirical models are usable in monitoring the optically active water quality parameters of Chl-a and TSP for eutrophic and turbid waters, while the machine learning models have no significant advantages, especially when the cost of training samples is considered. To improve the performance of machine learning models in future applications on the basis of ground sensor networks, large datasets covering various water situations and optimization of input variables of band configuration should be strengthened.

Official data on daily PM 2.5 , pM 10 , So 2 , no 2 , CO, and maximum 8-h average O 3 (o 3 _8h) concentrations from January 2015 to December 2018 were evaluated and air pollution status and dynamics in Shanghai municipality were examined. Factors affecting air quality, including meteorological factors and socioeconomic indicators, were analyzed. The main findings were that: (1) Overall air quality status in Shanghai municipality has improved and number of days meeting 'chinese ambient air quality standards' (CAAQS) Grade II has increased. (2) The most frequent major pollutant in Shanghai municipality is o 3 (which exceeded the standard on 110 days in