Sand, gravel, and crushed rock—together referred to as construction aggregates—are the world’s most extracted solid materials by mass. China’s annual consumption of construction aggregates reached over 20 billion tons in 2018, accounting for nearly half of global consumption. This article provides an overview of the use of sand and gravel in China, including current supply and demand conflicts and the impacts of mining, transportation, and use. We highlight that: ① the national demand for sand and gravel has continued to grow in the last two decades; crushed rock has become the main source of construction aggregates, whereas the supply of river sand has significantly declined; and ② there are significant environmental, economic, and social challenges associated with sand and gravel mining, transportation, and use, including the emergence of illicit supply networks. We then discuss opportunities to ensure sand and gravel supply, minimize mining impacts, and promote sustainable trajectories for the Chinese aggregates industry. First, the quantification of the material flows and stocks of construction aggregates that includes geological and anthropogenic stocks is crucial to identify supply bottlenecks and ensure more efficient use of resources. This requires establishing a reliable data monitoring system. Second, the government should increase investment and establish relevant institutions to optimize supply systems and minimize their impacts, strengthen the regulatory framework, promote the uptake of alternative materials, and establish standards and implement best practices in the aggregates industry. Finally, interdisciplinary integrated research is needed to analyze the existing challenges associated with the supply of sand and gravel resources as well as the potential and risks of adaptation strategies.
Spatial accessibility is an important reference index to determine whether the layout of various types of facilities is reasonable. Scientific evaluation of the spatial accessibility of service facilities is an important basis for judging regional service differences and optimizing the spatial allocation of resources. Using the basic potential model as a basis for our analysis, we introduced a demand-side threshold, designed a three-level service radius based on the scale requirements for elderly care institutions, and implemented regional priority matching principles. Taking Fengxian District of Shanghai as an example, we analyzed the spatial accessibility of elderly care institutions in the region based on the actual driving time between supply and demand points and ArcGIS spatial analysis technology. The results show that the spatial accessibility of nursing homes in Fengxian District is uneven, and the spatial accessibility in some areas of Nanqiao Town, Zhuanghang Town, and Jinhui Town is significantly higher. A trend of gradually decreasing accessibility can be observed from the city center to the periphery. In some towns and villages of the central region, there are dense elderly care institutions and relatively concentrated elderly resources. The improved potential model considers the influence of factors such as the service capacity of elderly care institutions and the needs of the elderly, which can evaluate the spatial accessibility of institutions more effectively. The research results provide a reference point and offer suggestions for scientific planning and decision-making of elderly care institutions.
In this study, we used the geographic weighted regression (GWR) method to explore the impact of compulsory education quality on housing prices. For the purpose of this analysis, we considered Wuhan in Hubei Province as the study area and collected school and housing price data from websites such as Sofang.com, Jzb.com, Whjyj.gov.cn, etc. We also used the inverse distance weighted (IDW) method for visual analysis. The results showed that: ① The quality of compulsory education has a positive impact on housing prices, and provincial demonstration schools, in particular, create a relatively high price premium on housing prices; ② The capitalization effect of the quality of junior high school is higher than that of elementary school; ③ The quality of primary and junior high school education in new urban areas has the greatest impact on housing prices. The quality of the combination of primary and junior high schools in the central and northern areas of Jiangan District had a significant impact on housing prices, while the quality of schools in the Wuchang, Qiaokou, and Jianghan districts had relatively less impact on housing prices as a whole.
A supply network is an important carrier for the spatial flow of industrial elements, and its structure and evolution can reflect the spatial clustering characteristics of related industries. In this study, we used thermal analysis, network analysis, and other methods to understand and analyze China’s automobile parts supply network at the national and regional levels in 2009, 2014, and 2019; in addition, we explored the evolution of spatiotemporal pattern characteristics of the network’s structure. The study found that: ① Automobile parts companies are primarily distributed across the eastern part of China, followed by the central and western regions. The companies form six clusters centered on the Yangtze River Delta region, Beijing-Tianjin-Hebei region, Northeast region, Pearl River Delta region, Central China region, and Chengdu-Chongqing region. ② The density of the network continues to increase. In 2019, the network density reached 0.5017, showing strong connectivity. Changchun had the highest extroversion in 2009, and Wuhan had the highest in 2014 and 2019. Shanghai has always maintained the highest introversion and continues to increase. More than 50% of the top ten cities are located in the Yangtze River Delta, and the remaining cities are located in the Beijing-Tianjin-Hebei, Northeast, Chengdu-Chongqing, and Central China regions. In addition, the automobile parts supply network has obvious hierarchical characteristics. The first-level links included Shanghai-Changchun in 2009, Shanghai-Changchun and Shiyan-Wuhan in 2014, and Shanghai-Changchun and Shanghai-Wuhan in 2019. ③ If we analyze the spatiotemporal characteristics of the supply network of China’s six major clusters with OEMs and automobile parts factories as nodes, we find that the Northeast region forms a vehicle-parts concentric inward supply network structure, the Yangtze River Delta and Chengdu-Chongqing regions form vehicle-parts concentric outbound supply network structures, the Beijing-Tianjin-Hebei and Pearl River Delta regions form vehicle-parts eccentric outbound supply network structures, and Central China forms a vehicle-parts eccentric inward supply network structure.
Three-dimensional vegetation volume is a comprehensive index that can be used to represent the ecological benefits of urban vegetation. However, the challenge of how to accurately and quickly carry out three-dimensional vegetation volume monitoring in highly heterogeneous urban habitats is an urgent problem that requires attention. In this paper, we used Shanghai Botanical Garden as a case study. We acquired low-altitude, high-resolution images of Shanghai Botanical Garden through a UAV aerial photography system; after extracting the data, we calculated the surface elevation and canopy height models, estimated the three-dimensional vegetation volume, and analyzed the spatial distribution pattern. The results showed that: ① The overall plane and elevation accuracy of UAV images was better than 0.1 m, and the average error and standard deviation of the canopy height model accuracy was 0.27 m and 0.58 m, respectively. ② The vegetation volume of Shanghai Botanical Garden was distributed in a pattern from northeast low to southwest high, with a total vegetation volume of 3538944.50 m3. The average green density of the botanical garden was 6.51 m3/m2. The three gardens with the highest vegetation volume were: Peony Garden (289491.00 m3), Pinetum Garden (338322.10 m3), and the Green Space Attached to The Greenhouse (360587.50 m3). The three gardens with the lowest vegetation volume were: Recreational Green Space (24761.50 m3), Monocotyledon Botanical Garden (31621.40 m3), and Rose Garden (74607.30 m3). The three gardens with the highest vegetation volume density were: Tropical Orchid Room (9.23 m3/m2), Fern Garden (11.30 m3/m2), and Magnolia and Camphor Avenue (13.11 m3/m2). The three gardens with the lowest vegetation volume density were Recreational Green Space (1.57 m3/m2), Scientific Research Center Green Space (1.81 m3/m2), and Rose Garden (2.58 m3/m2). ③ The vegetation volume of each specialized garden was significantly related to the distribution area of the arbor community, the height of the constructive species, and the product thereof. The vegetation volume density of each specialized garden was significantly related to the proportion of the area of the arbor community in the specialized garden, the height of the constructive species, and the product thereof. This research can serve as a methodology reference for the quick estimation of urban vegetation volume, and provide basic data vegetation volume estimates and spatial pattern optimization for Shanghai Botanical Garden.
Estuarine cities are heavily influenced by anthropogenic activities. In turn, their water bodies often face serious eutrophication and pollution problems, thereby exerting significant pressure on the urban production and living environment. This study focuses on the water bodies in the city of Shanghai, an important estuarine megacity in China. Using the Sentinel-2 satellite and in situ measured water spectrum data, we built an inversion model for rapid identification of two critical parameters for eutrophication assessment, namely chlorophyll-a concentration and turbidity. We subsequently analyzed the spatial and temporal variability of these two parameters using time-series satellite data. Our results showed that the correlation coefficient (R2) of turbidity and chlorophyll-a concentration inversion based on remote sensing was 0.95 and 0.87, respectively; the root mean square error (RMSE) was 4.33 μg/L and 8.93 NTU, respectively. Time-series analysis from 2019 showed that both chlorophyll-a concentration and turbidity in different urban water bodies were highest in the summer and lowest in the winter in Shanghai. Specifically, chlorophyll-a concentrations across water bodies decreased in the following sequence: aqua-culture/planting ponds, permanent freshwater lakes, reservoir ponds, permanent rivers, and canals/transportation rivers. In the case of turbidity, the water bodies ordered from highest to the lowest followed the sequence: aqua-culture/planting ponds, permanent rivers, canals/water delivery rivers, permanent freshwater lakes, and reservoir ponds. Time series analysis of chlorophyll-a concentrations and turbidity from 2019 showed that in water bodies with less human disturbance, the correlation between chlorophyll-a concentration and turbidity was stronger than those that were heavily influenced by anthropogenic activities. The use of Sentinel-2 satellite images to retrieve the chlorophyll-a concentration and turbidity in water bodies can generally provide information on the eutrophication status of water bodies in Shanghai; the data, moreover, can serve as a reference for aquatic environmental monitoring of inland water bodies in other cities.
In this paper, the presence of the Tianzhuang fault was confirmed using a combination of petroleum geophysical exploration, geology, remote sensing, and other data. The study concluded that the fault originated from the west of Tiancun, Jinyuan District, Taiyuan City with a total length of about 35 km from Houjiazhai to Tianzhuang. The fault trends from west to east with the pattern EW-NEE-NE, and tends to the SE as a high-angle normal fault. The Tianzhuang fault is a concealed fault associated with the piedmont fault of the East and West Mountains of the Taiyuan Basin. Through the joint drilling exploration across the Tianzhuang fault, near the Ma Lianying Road, there were three distinct sedimentary cycles of river lake swamp facies found in the strata: in the 80 ~ 60 m section, the sedimentary environment tends to frequent gradually, and the sedimentary facies is lake→swamp; in the 60 ~ 30 m section, the sedimentary environment tends to be stable and frequent twice, and the sedimentary facies is river→swamp→river→lake→swamp→river; in the 30 ~ 0 m section, the sedimentary environment tends to be stable gradually, and the sedimentary facies is swamp→lake→swamp. The Quaternary strata in the site gradually thickens from north to south in the horizontal direction, and the coarse-grained deposits become thinner. There is a magnitude change in the borehole, ZK3←→ZK4←→ZK7, and the first layer is thick in the vertical direction. Particle deposition occurs at 20 ~ 30 m, and the floating is not large; the sedimentation cycle number is roughly “M” from deep to shallow, and the sedimentation number reaches a peak at 30 ~ 40 m and 50 ~ 60 m. From the perspective of detecting the strata, all the boreholes in the silty layer of the Holocene boundary were exposed, and the depth was relatively small. It is believed that the sampling rate of the bored sand layer is not the same and hence it is expected that the fault of the Tianzhuang fault is not broken. There are three primary sets of fault-breaking strata in the Tianzhuang fault, all of which are from the Late Pleistocene strata; these did not penetrate the Upper Pleistocene, and thus the target fault was determined to be the late Pleistocene active fault. From top to bottom, the offset of faulted strata increases gradually: 0.4 m, 3.5 m, and 7.2 m, in turn. There are two coseismic displacements of about 3 meters in the exposed depth of the borehole, which can be used to judge the occurrence of two main dislocation events in the identified layer. This provides reliable geological evidence for analyzing the seismic risk of the Tianzhuang fault.
The Beisan River Basin is an important water source for the Jing-Jin-Ji region. It is important to analyze the temporal and spatial changes in basin water yield and the corresponding driving factors to maintain the security and stability of the ecosystem. Based on meteorology, land use, and soil data, the water production module of the InVEST model was used to analyze the temporal and spatial change characteristics of water yield in the Beisan River Basin from 2000 to 2017. The contribution of climate and land use change to the change in water yield was explored through scenario simulation. The results showed that from 2000 to 2017, the average annual water yield of the Beisan River Basin was 17.8 × 108 m3; the annual change showed an increasing trend at a rate of 1.03 × 108 m3/a. The spatial distribution pattern of water yield was high in the south and low in the north. The average depth of water production in the south and north was 70.85 mm and 8.83 mm, respectively. The high value area of water yield was transferred from the southeast Juhe River and Huanxiang River Basin to the southwest Wenhe River and Yongdingbei River Basin. The water supply per unit area, ranked from high to low, across different land use types showed the following order: construction land > cultivated land > water area > unused land > forest land > grassland. From 2000 to 2015, the water yield of cultivated land was the highest, accounting for 51.3% of the total water yield of the basin, while that of construction land increased the most, reaching 144.3%. Scenario simulation results showed that climate and land use change contributed 70.7% and 29.3%, respectively, to the water yield increase, and the surge in precipitation played a leading role.
Using the wave-shaped features of remote sensing images, the wavelength of ocean waves can be determined based on the wavelet method. Shallow water depths can then be estimated from the wavelength because the wavelength becomes shorter as the water depth decreases. In this paper, remote sensing data were replaced by ideal elevation data, and numerical simulation data were used to study the performance of the Complex Morlet Wavelet method in estimating wavelength and water depth. In particular, the effects of data resolution and sub-image size on water depth estimation were explored. The results from the ideal elevation data shows that: when the wavelength has no spatial change and the size of the sub-image is greater than the wavelength, the data resolution has no substantial effect on the wavelength estimation if there are more than nine evenly distributed data grids in one image. This phenomenon can be explained by the wavelength-energy spectrum. When the wavelength changes spatially, accurate estimation of the wavelength requires that the sub-image size is larger than twice the wavelength and there are four data grids in one wavelength. The estimation of wavelength by numerical simulated data requires a similar size for sub-images and the data number. The error of water depth estimation increases slightly if the sub-image size is too large, and also increases slightly as the resolution of the data decreases.
Residual water level is an important factor affecting water depth; the water level depends primarily on river discharge, tidal conditions, and wind stress, and it can change significantly with time and space. Studying the temporal and spatial variations in residual water levels—and the respective influencing factors—is of great scientific significance and can be applied to estuarine water level prediction, water resources utilization, seawall design, flood protection, and navigation. In this paper, we used a validated three-dimensional numerical model of the estuary and coast to: simulate the temporal and spatial variations in the residual water levels of the Changjiang Estuary; analyze the impacts of river discharge, tidal conditions, and wind stress on residual water levels; and determine the dynamic mechanisms for its change. The spatial and temporal variations in residual water levels of the Changjiang Estuary is driven primarily by the fact that upstream residual water levels are higher than downstream levels because of runoff force. The highest residual water level appears in September, reaches 0.861, 0.754, 0.629, 0.554, and 0.298 m at Xuliujing, Chongxi, Nanmen, Baozhen, and the easternmost section of the northern dike of the Deepwater Navigation Channel, respectively. The lowest residual water level appears in: January for Xuliujing (0.420 m) and Chongxi (0.391 m), February for Nanmen (0.313 m) and Baozhen (0.291 m), and April for the easternmost section of the northern dike of the Deepwater Navigation Channel (0.111 m). The residual water level in the North Branch is lower than the level in the South Branch, because a small amount of river water flows into the North Branch. The residual water level is higher in the South Channel than the one in the North Channel. Within the South Channel itself, furthermore, the water level is higher on the south side than the north due to the Coriolis force, which makes the water turn to the right. By using numerical experiments to compare the impact of different factors, we found that runoff has the largest impact on residual water levels, tidal conditions have the second largest impact, and wind has minimal impact. The monthly mean river discharge is largest in July, which should lead to the highest residual water level, but southeasterly winds prevail in the same period leading to small residual water levels. The river discharge in September remains high and northerly winds prevail, driving the Ekman water transport landward and resulting in a residual water level rise in the estuary. The interaction between the river discharge and the northeasterly wind makes the residual water level highest in September rather than in July. In conclusion, this study revealed the dynamic mechanism explaining the highest residual water level observed in September.
In this paper, soil samples were collected from the red soil region of southern China (namely, the Sunjiaba small watershed in Yingtan, Jiangxi) across four different land-use types. Laboratory incubation experiments were subsequently carried out from June 2019 to October 2019. We used a closed chamber to measure soil greenhouse gases (CO2, CH4, N2O) simultaneously with the help of an advanced greenhouse gas analyzer (Picarro-G2508). The aim was to explore the response of soil greenhouse gas emissions across different land-use types to changes in temperature and soil moisture levels under the premise of global climate change. The results showed that the global warming potential (GWP) of the four land-use types increases with paddy, orangery, forest, and upland, respectively. This suggests that greenhouse gas emissions from paddy soils have the greatest relative impact on global warming. In a temperature-controlled experiment, soil CO2 emissions were shown to have a significant positive correlation with soil temperature. The Q10 values of soil respiration coefficients for the four land-use types were: 2.61 (forest), 2.51 (upland), 3.12 (orangery), and 3.17 (paddy). Thus, paddy soil respiration has the highest temperature sensitivity, indicating that paddy soil has a higher CO2 emission potential. Correlations were not significant between CH4 and N2O emissions to soil temperature. In the moisture-controlled experiment, the results indicated that soil CO2 emissions increased at the beginning and then decreased with increasing soil moisture, with the maximum emission rate at 20% GWC (gravity water content). CH4 emissions from paddy soils increased with soil moisture (R2 = 0.8875); CH4 fluxes from the other three land-use types, however, were not significantly related to soil moisture. The soil N2O emissions increased at the beginning and then decreased across the soil moisture range measured; all land-use types had the highest N2O fluxes at 25% GWC.