Since the industrial revolution, human activities have both expanded and intensified across the globe resulting in accelerated land use change. Land use change driven by China's development has put pressure on the limited arable land resources, which has affected grain production. Competing land use interests are a potential threat to food security in China. Therefore, studying arable land use changes is critical for ensuring future food security and maintaining the sustainable development of arable land. Based on data from several major sources, we analyzed the spatio-temporal differences of arable land among different agricultural regions in China from 2000 to 2010 and identified the drivers of arable land expansion and loss. The results revealed that arable land decreased by 5.92 million ha or 3.31%. Arable land increased in the north and decreased in the south of China. Urbanization and ecological restoration programs were the main drivers of arable land loss, while the reclamation of other land cover types (e.g., forest, grassland, and wetland) was the primary source of the increased arable land. The majority of arable land expansion occurred in the Northwest, but the centroid for grain production moved to northeast, which indicated that new arable land was of poor quality and did not significantly contribute to the grain production capacity. When combined with the current ‘Red Line of Arable Land Policy’ (RAL) and ‘Ecological Redline Policy’ (EPR), this study can provide effective information for arable land policymaking and help guide the sustainable development of arable land.
This study aimed to accurately study the intra-annual spatiotemporal variation in the surface urban heat island intensities (SUHIIs) in 1449 cities in China. First, China was divided into five environmental regions. Then, the SUHIIs were accurately calculated based on the modified definitions of the city extents and their corresponding nearby rural areas. Finally, we explored the spatiotemporal variation of the mean, maximum, and minimum values, and ranges of SUHIIs from several aspects. The results showed that larger annual mean daytime SUHIIs occurred in hot-humid South China and cold-humid northeastern China, and the smallest occurred in arid and semiarid west China. The seasonal order of the SUHIIs was summer > spring > autumn > winter in all the temperate regions except west China. The SUHIIs were obviously larger during the rainy season than the dry season in the tropical region. Nevertheless, significant differences were not observed between the two seasons within the rainy or dry periods. During the daytime, the maximum SUHIIs mostly occurred in summer in each region, while the minimum occurred in winter. A few cold island phenomena existed during the nighttime. The maximum SUHIIs were generally significantly positively correlated with the minimum SUHIIs during the daytime, nighttime and all-day in all environmental regions throughout the year and the four seasons. Moreover, significant correlation scarcely existed between the daytime and nighttime ranges of the SUHIIs. In addition, the daytime SUHIIs were also insignificantly correlated with the nighttime SUHIIs in half of the cases.
Characteristics of air pollution in Northeast China (NEC) received less research attention in the past comparing to other heavily polluted regions in China. Spatiotemporal variations of six criteria air pollutants (PM10, PM2.5, SO2, NO2, O3 and CO) in Central Liaoning Urban Agglomeration (CLUA) and Harbin-Changchun Urban Agglomeration (HCUA) in NEC Plain were analyzed in this study based on three-year hourly observations of air pollutants and meteorological variables from 2015 to 2017. The results indicated that the annual mean concentrations of air pollutants are generally higher in the middle and southern regions in NEC Plain and lower in the northern region. Megacities such as Shenyang, Harbin and Changchun experience severe air pollution, with a three-year averaged air quality index (AQI) larger than 80, far exceeding the daily AQI standard at the first-level of 50 in China. The annual mean PM and SO2 concentrations decrease most significantly in NEC urban agglomerations from 2015 to 2017, followed by CO and NO2, while O3 shows a slight increasing trend. All the six pollutants exhibit obvious seasonal and diurnal variations, and these variations are dictated by local emission and meteorological conditions. PM2.5 and O3 concentrations in NEC urban agglomerations strongly depend on wind conditions. High O3 concentrations at different cities usually occur in presence of strong winds but are independent on wind direction (WD), while high PM2.5 is usually accompanied by weak winds and poor dispersion condition, and sometimes also occur when the northerly or southerly winds are strong. Regional transport of air pollutants between NEC urban agglomerations is common. A severe haze event on November 1-4, 2017 is examined to demonstrate the role of regional transport on pollution.
With the rapid development of population and urbanization and the progress of lighting technology, the influence of artificial light sources has increased. In this context, the problem of light pollution has attracted wide attention. Previous studies have revealed that light pollution can affect biological living environments, human physical and mental health, astronomical observations and many other aspects. Therefore, organizations internationally have begun to advocate for measures to prevent light pollution, many of which are recognized by the International Dark-Sky Association (IDA). In addition to improving public awareness, legal protections, technical treatments and other means, the construction of Dark Sky Reserves (DSR) has proven to be an effective preventive measure. So far, as a pioneer practice in this field, the IDA has identified 11 DSRs worldwide. Based on the DA requirements for DSRs, this paper utilizes NPP-VIIRS nighttime light data and other multi-source spatial data to analyze possible DSR sites in China. The land of China was divided into more than ten thousand 30 km×30 km fishnets, and constraint and suitable conditions were designated, respectively, as light and cloud conditions, and scale, traffic and attractiveness conditions. Using a multiple criteria evaluation, 1443 fishnets were finally selected as most suitable sites for the construction of DSRs. Results found that less than 25% of China is not subject to light pollution, and less than 13% is suitable for DSR construction, primarily in western and northern areas, including Tibet, Xinjiang, Qinghai, Gansu and Inner Mongolia.
Understanding the net primary productivity (NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach (CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia (1737.23×104 km2), while the grassland area in Europe was relatively small (202.83×104 km2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas (560.10 g C/(m2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation (AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.
In the summer of 2012, the US Midwest, the most productive agricultural region in the world, experienced the most intense and widespread drought on record for the past hundred years. The 2012 drought, characterized as ‘flash drought’, developed in May with a rapid intensification afterwards, and peaked in mid-July.~76% of crop region and 60% of grassland and pasture regions have been under moderate to severe dry conditions. This study used multiple lines of evidences, i.e., in-situ AmeriFlux measurements, spatial satellite observations, and scaled ecosystem modeling, to provide independent and complementary analysis on the impact of 2012 flash drought on the US Midwest vegetation greenness and photosynthesis carbon uptake. Three datasets consistently showed that 1) phenological activities of all biomes advanced 1-2 weeks earlier in 2012 compared to the other years of 2010-2014; 2) the drought had a more severe impact on agroecosystems (crop and grassland) than on forests; 3) the growth of crop and grassland was suppressed from June with significant reduction of vegetation index, sun-induced fluorescence (SIF) and gross primary production (GPP), and did not recover until the end of growing season. The modeling results showed that regional total GPP in 2012 was the lowest (1.76 Pg C/yr) during 2010-2014, and decreased by 63 Tg C compared with the other-year mean. Agroecosystems, accounting for 84% of regional GPP assimilation, were the most impacted by 2012 drought with total GPP reduction of 9%, 7%, 6%, and 29% for maize, soybean, cropland, and grassland, respectively. The frequency and severity of droughts have been predicted to increase in future. The results imply the importance to investigate the influences of flash droughts on vegetation productivity and terrestrial carbon cycling.
This study aims to explore the role of spatial heterogeneity in ridership analysis and examine the relationship between built environment, station attributes and urban rapid transit ridership at the station level. Although spatial heterogeneity has been widely acknowledged in spatial data analysis, it has been rarely considered in travel behavior studies. Four models (three global models-ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM) and one local model-geographically weighted regression (GWR) model) are estimated separately to explore the relationship between various independent variables and station ridership, and identify the influence of spatial heterogeneity. Using the data of built environment and station characteristics, the results of diagnostic identify evidence the existence of spatial heterogeneity in station ridership for the metro network in Nanjing, China. Results of comparing the various goodness-of-fit indicators show that, the GWR model yields the best fit of the data, performance followed by the SEM, SLM and OLS model. The results also demonstrate that population, number of lines, number of feeder buses, number of exits, road density and proportion residential area have a significant impact on station ridership. Moreover, the study pays special attention to the spatial variation in the coefficients of the independent variables and their statistical significance. It underlines the importance of taking spatial heterogeneity into account in the station ridership analysis and the decision-making in urban planning.