2021 Vol. 31, No. 3

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Spatial Characteristics and Influencing Factors of Urban Resilience from the Perspective of Daily Activity: A Case Study of Nanjing, China
Honghu SUN, Feng ZHEN
2021, 31(3): 387-399. doi: 10.1007/s11769-021-1201-0
Based on the connotation of urban resilience and the main contradictions of China’s urbanization, urban resilience is placed within the main daily activities contradictory scene of the urban man-land system to build a theoretical framework of urban activity resilience. Relying on geographic big data, this study identifies the spatial characteristics of activity resilience, reveals the impact of activity environment on activity resilience in Nanjing, and proposes countermeasures. The main conclusions are as follows. 1) Activity resilience presents a composite spatial structure of circles and clusters, and most areas are resilient but at a low level. 2) There are significantly positive and negative global autocorrelation between activity resilience and activity scale, and activity stability. Simultaneously, there also exists a local spatial autocorrelation with the opposite positive and negative trends. 3) Activity environment has a significant effect on activity resilience, and the degree and direction of influence among different dimensions and regions are heterogeneous. 4) For activity resilience, it is necessary to increase the matching degree between the scale and stability of activities, and reduce the excessive concentration and flow of activities. For the activity environment, it is necessary to improve the accessibility of the ecological environment, strengthen the high-quality supply of the infrastructure environment, optimize the balance of the location environment, and promote the inclusiveness of the social environment.
Does Foreign Direct Investment Affect SO2 Emissions in the Yangtze River Delta? A Spatial Econometric Analysis
Zheng GUO, Sophia Shuang CHEN, Shimou YAO, Charles MKUMBO Anna
2021, 31(3): 400-412. doi: 10.1007/s11769-021-1197-5
As the major source of air pollution, sulfur dioxide (SO2) emissions have become the focus of global attention. However, existing studies rarely consider spatial effects when discussing the relationship between foreign direct investment (FDI) and SO2 emissions. This study took the Yangtze River Delta as the research area and used the spatial panel data of 26 cities in this region for 2004–2017. The study investigated the spatial agglomeration effects and dynamics at work in FDI and SO2 emissions by using global and local measures of spatial autocorrelation. Then, based on regression analysis using a results of traditional ordinary least squares (OLS) model and a spatial econometric model, the spatial Durbin model (SDM) with spatial-time effects was adopted to quantify the impact of FDI on SO2 emissions, so as to avoid the regression results bias caused by ignoring the spatial effects. The results revealed a significant spatial autocorrelation between FDI and SO2 emissions, both of which displayed obvious path dependence characteristics in their geographical distribution. A series of agglomeration regions were observed on the spatial scale. The estimation results of the SDM showed that FDI inflow promoted SO2 emissions, which supports the pollution haven hypothesis. The findings of this study are significant in the prevention and control of air pollution in the Yangtze River Delta.
Impact of the Built Environment on the Spatial Heterogeneity of Regional Innovation Productivity: Evidence from the Pearl River Delta, China
Kangmin WU, Yang WANG, Hong’ou ZHANG, Yi LIU, Yuyao YE
2021, 31(3): 413-428. doi: 10.1007/s11769-021-1198-4
With the global economy increasingly dependent on innovation, urban discourse has shifted to consider what kinds of spatial designs may best nurture innovation. We examined the relationship between the built environment and the spatial heterogeneity of regional innovation productivity (RIP) using the example of China’s Pearl River Delta (PRD). Based on a spatial database of 522 546 patent data from 2017, this study proposed an innovation-based built environment framework with the following five aspects: healthy environment, daily interaction, mixed land use, commuting convenience, and technology atmosphere. Combining negative binomial regression and Geodetector to examine the impact of the built environment on RIP, the results show that the spatial distribution of innovation productivity in the PRD region is extremely uneven. The negative binomial regression results show that the built environment has a significant impact on the spatial differentiation of RIP, and, specifically, that healthy environment, mixed land use, commuting convenience, and technology atmosphere all demonstrate significant positive impacts. Meanwhile, the Geodetector results show that the built environment factor impacts the spatial heterogeneity of RIP to varying degrees, with technology atmosphere demonstrating the greatest impact intensity. We conclude that as regional development discourse shifts focus to the knowledge and innovation economy, the innovation-oriented design and updating of built environments will become extremely important to policymakers.
Spatial-temporal Evolution of the Urban-rural Coordination Relationship in Northeast China in 1990–2018
Ying WANG, Xiaohong CHEN, Pingjun SUN, Hang LIU, Jiaxin HE
2021, 31(3): 429-443. doi: 10.1007/s11769-021-1202-z
To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China, this study uses the coupling coordination degree model and geographically and temporally weighted regression (GTWR) model to analyze the spatial-temporal patterns and the corresponding driving mechanisms of its urban-rural coordination since 1990. The results are as follows. First, the urban-rural coupling coordination degree in Northeast China was very low and improved slowly, but its stages of evolution is a good interpretation of the strategic arrangements of China’s urbanization. Second, the urban-rural coupling coordination degree in Northeast China had spatial differences and was characterized by central polarization, converging on urban agglomeration, which was high in the south and low in the north. Moreover, the gap between the north and south weakened. Third, the spatial-temporal evolution of the urban-rural coordination relationship in Northeast China was influenced by pulling from the central cities, pushing from rural transformation, and government regulations. The influence intensity of the three mechanisms was weak, but the pulling from the central cities was stronger than that of the other two mechanisms. Furthermore, the spatial difference between the three mechanisms determines the spatial pattern and its evolution of the urban-rural coordination relationship in Northeast China. Fourth, to promote the development of urban-rural coordination in Northeast China, it is essential to advance urban-rural economic correlation, enhance the government’s role in regulating and guiding, and adopt different policies for each region in Northeast China.
Spatiotemporal Variations and Controls on Anthropogenic Heat Fluxes in 12 Selected Cities in the Eastern China
Zheng CAO, Ya WEN, Song SONG, Chak Ho HUNG, Hui SUN
2021, 31(3): 444-458. doi: 10.1007/s11769-021-1203-y
Spatiotemporal variations of anthropogenic heat flux (AHF) is reported to be associated with global warming. However, confined to the low spatial resolution of energy consumption statistical data, details of AHF was not well descripted. To obtain high spatial resolution data of AHF, Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light time-series product and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite monthly normalized difference vegetation index (NDVI) product were applied to construct the human settlement index. Based on the spatial regression relationship between human settlement index and energy consumption data. A 1-km resolution dataset of AHF of 12 selected cities in the eastern China was obtained. Ordinary least-squares (OLS) model was applied to detect the mechanism of spatial patterns of AHF. Results showed that industrial emission in selected cities of the eastern China was accountable for 63% of the total emission. AHF emission in megacities, such as Tianjin, Jinan, Qingdao, and Hangzhou, was most significant. AHF increasing speed in most areas in the chosen cities was quite low. High growth or extremely high growth of AHF were located in central downtown areas. In Beijing, Shanghai, Guangzhou, Jinan, Hangzhou, Changzhou, Zhaoqing, and Jiangmen, a single kernel of AHF was observed. Potential influencing factors showed that precipitation, temperature, elevation, normalized different vegetation index, gross domestic product, and urbanization level were positive with AHF. Overall, this investigation implied that urbanization level and economic development level might dominate the increasing of AHF and the spatial heterogeneousness of AHF. Higher urbanization level or economic development level resulted in high increasing speeds of AHF. These findings provide a novel way to reconstruct of AHF and scientific supports for energy management strategy development.
Vegetation Phenology in Permafrost Regions of Northeastern China Based on MODIS and Solar-induced Chlorophyll Fluorescence
Lixiang WEN, Meng GUO, Shuai YIN, Shubo HUANG, Xingli LI, Fangbing YU
2021, 31(3): 459-473. doi: 10.1007/s11769-021-1204-x
Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), extracted from the Moderate Resolution Imaging Spectrometer (MODIS), are widely used to monitor phenology by calculating land surface reflectance. However, the applicability of the vegetation index based on ‘greenness’ to monitor photosynthetic activity is hindered by poor observation conditions (e.g., ground shadows, snow, and clouds). Recently, satellite measurements of solar-induced chlorophyll fluorescence (SIF) from OCO-2 sensors have shown great potential for studying vegetation phenology. Here, we tested the feasibility of SIF in extracting phenological metrics in permafrost regions of the northeastern China, exploring the characteristics of SIF in the study of vegetation phenology and the differences between NDVI and EVI. The results show that NDVI has obvious SOS advance and EOS lag, and EVI is closer to SIF. The growing season length based on SIF is often the shortest, while it can represent the true phenology of vegetation because it is closely related to photosynthesis. SIF is more sensitive than the traditional remote sensing indices in monitoring seasonal changes in vegetation phenology and can compensate for the shortcomings of traditional vegetation indices. We also used the time series data of MODIS NDVI and EVI to extract phenological metrics in different permafrost regions. The results show that the length of growing season of vegetation in predominantly continuous permafrost (zone I) is longer than in permafrost with isolated taliks (zone II). Our results have certain significance for understanding the response of ecosystems in cold regions to global climate change.
Evaluation of Precipitation Datasets from TRMM Satellite and Downscaled Reanalysis Products with Bias-correction in Middle Qilian Mountain, China
Lanhui ZHANG, Chansheng HE, Wei TIAN, Yi ZHU
2021, 31(3): 474-490. doi: 10.1007/s11769-021-1205-9
Accurate estimates of precipitation are fundamental for hydrometeorological and ecohydrological studies, but are more difficult in high mountainous areas because of the high elevation and complex terrain. This study compares and evaluates two kinds of precipitation datasets, the reanalysis product downscaled by the Weather Research and Forecasting (WRF) output, and the satellite product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product, as well as their bias-corrected datasets in the Middle Qilian Mountain in Northwest China. Results show that the WRF output with finer resolution performs well in both estimating precipitation and hydrological simulation, while the TMPA product is unreliable in high mountainous areas. Moreover, bias-corrected WRF output also performs better than bias-corrected TMPA product. Combined with the previous studies, atmospheric reanalysis datasets are more suitable than the satellite products in high mountainous areas. Climate is more important than altitude for the ‘falseAlarms’ events of the TRMM product. Designed to focus on the tropical areas, the TMPA product mistakes certain meteorological situations for precipitation in subhumid and semiarid areas, thus causing significant ‘falseAlarms’ events and leading to significant overestimations and unreliable performance. Simple linear bias correction method, only removing systematical errors, can significantly improves the accuracy of both the WRF output and the TMPA product in arid high mountainous areas with data scarcity. Evaluated by hydrological simulations, the bias-corrected WRF output is more reliable than the gauge dataset. Thus, data merging of the WRF output and gauge observations would provide more reliable precipitation estimations in arid high mountainous areas.
Response of Vegetation Cover Change to Drought at Different Time-scales in the Beijing-Tianjin Sandstorm Source Region, China
Bo CAO, Xiaole KONG, Yixuan WANG, Hang LIU, Hongwei PEI, Yan-Jun SHEN
2021, 31(3): 491-505. doi: 10.1007/s11769-021-1206-8
Dominated by an arid and semiarid continental climate, the Beijing-Tianjin Sandstorm Source Region (BTSSR) is a typical ecologically fragile region with frequently occurring droughts. To provide information for regional vegetation protection and drought prevention, we assessed the relations between vegetation cover change (measured by the Normalized Difference Vegetation Index, NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at different time-scales, in different growth stages, in different subregions and for different vegetation types based on the Pearson’s correlation coefficient in the BTSSR from 2000 to 2017. Results showed that 88.19% of the vegetated areas experienced increased NDVI in the growing season; 48.3% of the vegetated areas experienced significantly increased NDVI (P < 0.05) and were mainly in the south of the BTSSR. During the growing season, a wetter climate contributed to the increased vegetation cover from 2000 to 2017, and NDVI anomalies were closely related to SPEI. The maximum correlation coefficient in the growing season (Rmax) was significantly positive (P < 0.05) in 97.84% of the total vegetated areas. In the vegetated areas with significantly positive Rmax, pixels with short time-scales (1–3 mon) accounted for the largest proportion (33.9%). The sensitivity of vegetation to the impact of drought rose first and then decreased in the growing season, with a peak in July. Compared with two subregions in the south, subregions in the north of the BTSSR were more sensitive to the impacts of drought variations, especially in the Xilingol Plateau and Wuzhumuqin Basin. All four major vegetation types were sensitive to the effects of drought variations, especially grasslands. The time-scales of the most impacting droughts varied with growth stages, regions, and vegetation types. These results can help us understand the relations between vegetation and droughts, which are important for ecological restoration and drought prevention.
Heterogeneity-diversity Relationships in Natural Areas of Yunnan, China
Feng LIU, Jinming HU, Feiling YANG, Xinwang LI
2021, 31(3): 506-521. doi: 10.1007/s11769-021-1207-7
Understanding regional environmental heterogeneity (EH) and biodiversity relationships (heterogeneity-diversity relationships: HDRs) is the first step toward coupling environmental variables with biodiversity surrogates into regional systematic conservation planning. However, there is no universal method for determining regional HDRs that considers various environmental variables and biodiversity in different regions. This study selected 32 nature reserves as natural areas in Yunnan, China, to examine regional HDRs in Yunnan. We calculated 17 EH parameters (of soil, topography, and climate) and three (ecosystem, plant, and animal) biodiversity indices in the nature reserves. By examining the explanatory power of each EH parameter and area of the nature reserve, we identified the primary parameters and constructed an optimal model for each biodiversity index. The explanatory powers of these parameters varied for each biodiversity index, and those of climatic parameters were generally higher than soil and topographic heterogeneity ones. Heterogeneity of the temperature annual range, followed by area and heterogeneity of soil type, were important parameters for ecosystem diversity of Yunnan and the optimal model explained 56.9%. Plant diversity was explained 54.5% by its optimal model, consisting of heterogeneity of precipitation of the coldest quarter and annual precipitation. Heterogeneity of temperature annual range was important for animal diversity in Yunnan and explained 29.6% of its optimal model. This study suggests that EH parameters can be an effective surrogate for biodiversity, therefore, we suggested that the significance and role of climatically heterogeneous regions for the conservation of biodiversity in Yunnan should be further studied in the future.
Spatio-temporal Variations of Sea Surface Wind in Coral Reef Regions over the South China Sea from 1988 to 2017
Xin HE, Zhenghua CHEN, Yongqiang LU, Wei ZHANG, Kefu YU
2021, 31(3): 522-538. doi: 10.1007/s11769-021-1208-6
The seasonal and interannual variabilities of sea surface wind (SSW) in the South China Sea (SCS), especially in coral reef regions such as Nansha Islands, Xisha Islands, Zhongsha Islands and Dongsha Islands were investigated in detail using the Blended Sea Winds dataset (1988–2017). Annual and monthly variations of SSW and sea surface temperature (SST) in the four zones were investigated. Empirical Orthogonal Function (EOF) analysis of wind field was performed to aid in better understanding the different spatial patterns. The results indicate that, as observed in the spatial distribution of the first mode of monthly mean wind speed anomaly, the magnitudes in the four island zones are all negative and are similar to each other, showing that the variations of SSW in the four island zones are consistent. In the second mode, the magnitudes in Nansha Islands are opposite to those in the other three zones. The spatial distribution of the third mode reflects regional differences. The maximum annual SSW appears in Dongsha Islands, and the minimum appears in Nansha Islands. The interannual variations of SSW in all island zones are basically concurrent. The island zones with high SSW mostly have low SST, and vice versa. There may be an inverse relationship between SSW and SST in coral reef regions in the SCS. The multiyear monthly variations of SSW in the island zones present a ‘W’-shaped structural variation. Each island undergoes two months of minimum SSW every year, one during March–May (MAM) and the other during September–November (SON). Both months are in monsoon transition periods. During the months with low SSW, high SST appears. The SST peaks almost correspond to the SSW troughs. This further indicates that SSW and SST may have opposite changes in coral reef regions. Coral bleaching events often correspond to years of high SST and low SSW.
Spatio-temporal Characteristics of Atmospheric Pollution and Cause Analysis of Haze Events in Sichuan Basin, China
Xingjie WANG, Ke GUO, Yuan LIANG, Tingbin ZHANG, Guxi WANG
2021, 31(3): 539-557. doi: 10.1007/s11769-021-1209-5
This study analyzed the spatio-temporal variability of air quality data for six standard air pollutants (Particulate Matter 2.5 (PM2.5), Particulate Matter 10 (PM10), SO2, NO2, CO, and O3) in the Sichuan Basin (SCB), China from 2015 to 2018 in relation to the formation of haze using conventional meteorological data (temperature, wind speed, and relative humidity), satellite data (fire point data, vertical profiles of aerosol subtypes, and aerosol optical depth), planetary boundary layer height, and backward trajectories. The results indicated that the spatio-temporal evolution of the air quality index (AQI) had notable seasonality for the pollution severity in descending order: winter, spring, summer, and autumn. Autumn and winter severe haze events occurred in November and January, respectively, and were caused by higher local pollution emissions under stagnant air conditions. Spring severe haze events occurred in May and were caused by dust from Northwest China and local regions. Severe summer haze events occurred in July and were caused by local burning. Therefore, the analyses showed that local burning, stagnant meteorological conditions, air mass transport and anthropogenic pollution emissions played a key role in haze in the SCB. This study provides scientific insights for fully analyzing heavy air pollution in SCB, China, and also provides a scientific basis for pollution research in regions of complex terrain as basins and mountains.
Characterization of Water Quality in Xiao Xingkai Lake: Implications for Trophic Status and Management
Shuling YU, Xiaoyu LI, Bolong WEN, Guoshuang CHEN, Anne HARTLEYC, Ming JIANG, Xiujun LI
2021, 31(3): 558-570. doi: 10.1007/s11769-021-1199-3
Increasing cases of lake eutrophication globally have raised concerns among stakeholders, and particularly in China. Evaluating the causes of eutrophication in waterways is essential for effective pollution prevention and control. Xiao Xingkai Lake is part of and connected to Xingkai (Khanka) Lake, a boundary lake between China and Russia. In this study, we investigated the spatio-temporal variabilities in water quality (i.e., dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (CODMn) and ammonium-nitrogen (NH4+-N)) in Xiao Xingkai Lake, from 2012 to 2014, after which a Trophic Level Index was used to evaluate trophic status, in addition to the factors influencing water quality variation in the lake. The DO, TN, TP, CODMn and NH4+-N concentrations were 0.44–15.57, 0.16–5.11, 0.01–0.45, 0.16–48.31, and 0.19–0.78 mg/L, respectively. Compared to the Environmental Quality Standards for surface water (GB 3838−2002) in China, the lake transitioned to an oligotrophic status in 2013 and 2014 from a mesotrophic status in 2012, TN and TP concentrations were the key factors influencing water quality of Xiao Xingkai Lake. Non-parametric test results showed that sampling time and sites had significant effects on water quality. Water quality was worse in summer and in tourism and aquaculture areas, followed by agricultural drainage areas. Furthermore, lake water trophic status fluctuated between medium eutrophic and light eutrophic status from September 2012 to September 2014, and was negatively correlated with water level. Water quality in tourism and aquaculture sites were medium eutrophic, while in agricultural areas were light eutrophic. According to the results, high water-level fluctuations and anthropogenic activities were the key factor driving variability in physicochemical parameters associated with water quality in Xiao Xingkai Lake.
Agricultural Non-point Source Pollution in China: Evaluation, Convergence Characteristics and Spatial Effects
Wenwen QIU, Zhangbao ZHONG, Zhaoliang LI
2021, 31(3): 571-584. doi: 10.1007/s11769-021-1200-1
In this study, an inventory analysis approach was used to investigate the intensity of agricultural non-point source pollution (ANSP) and its spatial convergence at national and provincial levels in China from 1999 to 2017. On this basis, spatial factors affecting ANSP were explored by constructing a spatial econometric model. The results indicate that: 1) The intensity of China’s ANSP emission showed an overall upward trend and an obvious spatial difference, with the values being high in the eastern and central regions and relatively low in the western region. 2) Significant spatial agglomeration was shown in China’s ANSP intensity, and the agglomeration effect was increasing gradually. 3) In the convergence analysis, a spatial lag model was found applicable for interpretation of the ANSP intensity, with the convergence rate being accelerated after considering the spatial factors but slower than that of regional economic growth. 4) The spatial factors affecting the ANSP intensity are shown to be reduced by improving agricultural infrastructure investment, labor-force quality, and crop production ratio, while the expansion of agricultural economy scale and precipitation and runoff have positive impact on ANSP in the study region. However, agricultural research and development (R&D) investment showed no direct significant effect on the ANSP intensity. Meanwhile, improving the quality of the labor force would significantly reduce the ANSP intensity in the surrounding areas, while the precipitation and runoff would significantly increase the pollution of neighboring regions. This research has laid a theoretical basis for formulation and optimization of ANSP prevention strategies in China and related regions.