Search Results (35)

Water resources, energy, and food are important resources in China, which play an important role in the process of urban development and are important basic resources for sustainable urban development. This study applied water footprint theory to water–energy–food relations. The regional integration of the Jing-Jin-Ji region faced new challenges during urbanization, and unified measures were applied to quantify the urban water demands and energy and food competition in the Jing-Jin-Ji region from 2003 to 2017. The index was used to evaluate the intensity of the competition for water for food and energy. The results indicated that from 2003 to 2017, the water footprint of grain production in the Jing-Jin-Ji region decreased from 30.984 billion m³ to 21.36 billion m³, of which the blue water footprint decreased from 13.032 billion m³ to 9.854 billion m³. The water footprint of energy production increased from 578 million m³ to 1.175 billion m³. The competition relation between cities in the Jing-Jin-Ji region was obtained according to the competition index, and corresponding measures were identified according to different competition levels. This study provides valuable insights for policymakers in designing sustainable urban development strategies for cities facing similar challenges of water resource, energy, and food competition during rapid urbanization. Full article

O₃ poses a significant threat to human health and the ecological environment. In recent years, O₃ pollution has become increasingly serious, making it difficult to accurately control O₃ precursor emissions. Satellite indicator methods, such as the FNR (formaldehyde-to-nitrogen dioxide ratio (HCHO/NO₂ ratio)), provide an effective way to identify ozone pollution control areas on a large geographical scale due to their simple acquisition of datasets. This can help determine the primary factors contributing to O₃ pollution and assist in managing it. Based on TROPOMI data from May 2018 to December 2022, combined with ground-based monitoring data from the China National Environmental Monitoring Centre, we explored the uncertainty associated with using the HCHO/NO₂ ratio (FNR) as an indicator in ozone control area determination. We focused on the four representative regions in China: Jing-Jin-Ji-Lu-Yu (JJJLY), Jiang-Zhe-Hu-Wan (JZHW), Chuan-Yu (CY), and South China. By using the statistical curve-fitting method, we found that the FNR thresholds were 3.5–5.1, 2.0–4.0, 2.5–4.2, and 1.7–3.5, respectively. Meanwhile, we analyzed the spatial and temporal characteristics of the HCHO, NO₂, and O₃ control areas. The HCHO concentrations and NO₂ concentrations had obvious cyclical patterns, with higher HCHO column densities occurring in summer and higher NO₂ concentrations in winter. These high values always appeared in areas with dense population activities and well-developed economies. The distribution characteristics of the ozone control areas indicated that during O₃ pollution periods, the urban areas with industrial activities and high population densities were primarily controlled by VOCs, and the suburban areas gradually shifted from VOC-limited regimes to transitional regimes and eventually reverted back to VOC-limited regimes. In contrast, the rural and other remote areas with relatively less development were mainly controlled by NOx. The FNR also exhibited periodic variations, with higher values mostly appearing in summer and lower values appearing in winter. This study identifies the main factors contributing to O₃ pollution in different regions of China and can serve as a valuable reference for O₃ pollution control. Full article

The advantages of performing aerosol retrieval with multi-angle, multi-spectral photopolarimetric measurements over intensity-only measurements come from this technique’s sensitivity to aerosols’ microphysical properties, such as their particle size, shape, and complex refraction index. In this study, an extended LUT (Look Up Table) algorithm inherited from a previous work based on the assumption of surface reflectance spectral shape invariance is proposed and applied to PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) measurements to retrieve aerosols’ optical properties including aerosol optical depth (AOD) and aerosol fine-mode fraction (FMF). Case studies conducted over East China for different aerosol scenes are investigated. A comparison between the retrieved AOD regional distribution and the corresponding MODIS (Moderate-resolution Imaging Spectroradiometer) C6 AOD products shows similar spatial distributions in the Jing-Jin-Ji (Beijing–Tianjin–Hebei, China’s mega city cluster) region. The PARASOL AOD retrievals were compared against the AOD measurements of seven AERONET (Aerosol Robotic Network) stations in China to evaluate the performance of the retrieval algorithm. In the fine-particle-dominated regions, lower RMSEs were found at Beijing and Hefei urban stations (0.16 and 0.18, respectively) compared to those at other fine-particle-dominated AERONET stations, which can be attributed to the assumption of surface reflectance spectral shape invariance that has significant advantages in separating the contribution of surface and aerosol scattering in urban areas. For the FMF validation, an RMSE of 0.23, a correlation of 0.57, and a bias of −0.01 were found. These results show that the algorithm performs reasonably in distinguishing the contribution of fine and coarse particles. Full article

Urban forests provide considerable ecosystem services for city dwellers, yet the function of forest species is increasingly challenged by urban drought. Understanding drought tolerance of urban forest species would facilitate vegetation conservation and establishment within urban ecosystems. Here, we report on the drought resistance of leaves for two exotic and three indigenous tree species common to the Jing-Jin-Ji metropolitan region (covering Beijing, Tianjin, and Hebei province) of north China. Xylem vulnerability to drought-induced embolism and leaf gas exchange, together with various morphological and anatomical traits that potentially relate to plant water use, were measured for pot-grown seedlings. In addition, seedlings were subjected to dry-down at two different drought intensities until death, and the tree mortality rate was recorded. We found that species differ markedly in xylem embolism resistance, with indigenous species showing more negative P₅₀ (the water potential triggering 50% loss of xylem hydraulic conductivity), but less canopy leaf area at a given branch basal diameter, compared with exotic species. Furthermore, P₅₀ well predicted tree mortality rate under protracted drought stress. Species characterized by more negative P₅₀ also exhibited higher maximum leaf photosynthetic rates. In addition, leaf P₅₀ was found to correlate with specific leaf area, while the hydraulic safety margin was related to sapwood density and the thickness of the leaf upper epidermis. Collectively, these results highlight the role of embolism resistance in dictating drought response and the promise of morphological traits as proxies of plant physiological drought resistance. Our findings contribute to understanding drought response for urban tree species and will guide the establishment and management of urban forests. Full article

Among the critical global crises curbing world development and sustainability, air quality degradation has been a long-lasting and increasingly urgent one and it has been sufficiently proven to pose severe threats to human health and social welfare. A higher level of model prediction accuracy can play a fundamental role in air quality assessment and enhancing human well-being. In this paper, four types of machine learning models—random forest model, ridge regression model, support vector machine model, extremely randomized trees model—were adopted to predict PM_2.5 concentration in ten cities in the Jing-Jin-Ji region of north China based on multi-sources spatiotemporal data including air quality and meteorological data in time series. Data were fed into the model by using the rolling prediction method which is proven to improve prediction accuracy in our experiments. Lastly, the comparative experiments show that at the city level, RF and ExtraTrees models have better predictive results with lower mean absolute error (MAE), root mean square error (RMSE), and higher index of agreement (IA) compared to other selected models. For seasonality, level four models all have the best prediction performances in winter time and the worst in summer time, and RF models have the best prediction performance with the IA ranging from 0.93 to 0.98 with an MAE of 5.91 to 11.68 μg/m³. Consequently, the demonstration of how each model performs differently in each city and each season is expected to shed light on environmental policy implications. Full article

As a high-quality and sustainable growth model, green development has different economic, ecological, and social dimensions and is strategically important for the realization of modern city construction and the sustainable development of human society. The low-carbon city pilot policy (LCCP) is an innovative initiative for promoting green urban development and building a harmonious society in China. Based on balanced panel data from 277 prefecture-level cities from 2007 to 2020, this paper measures the level of urban green development in terms of three dimensions: green economic growth, ecological welfare enhancement, and social welfare increase. This paper also adopts a multi-period difference-in-differences (DID) method for investigating the impact of LCCP on green development with the panel dataset. The results of the study show that: (1) LCCP is generally beneficial to urban green development, and the results still hold after a series of robustness check analyses. (2) The results of the mechanism analysis show that the construction of low-carbon cities has improved the level of green technology innovation, thereby promoting the level of regional green development. Environmental regulation has a masking effect between low-carbon city construction and green development in this study. When environmental regulation is controlled for, the coefficient of the effect of LCCP on green development increases, reflecting that environmental regulation also plays an important role between the two. (3) According to the geographical location, whether it is a resource-based city, and the city cluster, we found that the low-carbon city pilot policy has a significant positive role in promoting green development in the central region, non-resource-based cities, and the Jing-Jin-Ji, but not in the eastern region, the western region, the Yangtze River Delta and Pearl River Delta. We also found that in resource-based cities, this effect presents a significant negative relationship. The above findings enrich the literature on low-carbon city pilot policies and green development and provide Empirical evidence for relevant countries and regions to carry out low-carbon city pilots. Full article

With the development of industrialization and the increase in the number of motor vehicles in megacities in China, ozone pollution has become a prominent problem. Although different models have been used on ozone concentration simulation, the accuracy of different models still varies. In this study, the performance of two models including a linear stepwise regression (SR) model and a non-linear artificial neural network (ANN) model on the simulation of ozone concentration were analyzed in the Jing-Jin-Ji region, which is one of the most polluted areas in China. Results showed that the performance of the ANN model (adjusted R² = 0.8299, RMSE = 22.87, MAE = 16.92) was better than the SR model (adjusted R² = 0.7324, RMSE = 28.61, MAE = 22.30). The performance of the ANN on simulating an ozone pollution event was better than the SR model since a higher probability of detection (POD) and threat score (TS) values were obtained by the ANN model. The model performance for spring, autumn and winter was generally higher than that for summer, which may because the weights of factors on simulating high and low ozone concentrations were different. The method proposed by this study can be used in ozone concentration estimation. Full article

Due to the fast growth of China’s economy, urban atmospheric pollution has become a serious problem affecting the public’s physical and mental health. The ‘2 + 26’ cities, as the Jing-Jin-Ji atmospheric pollution transmission channel, has attracted widespread concern. There were several previous studies on the economic loss of public health caused by PM_2.5 pollution in ‘2 + 26’ cities. To assess the economic loss caused by PM_2.5 on human health in ‘2 + 26’ cities, this paper used the exposure-response model, the health effect loss model and willingness to pay method to obtain the economic loss from PM_2.5 pollution with the latest available data in 2020. It was concluded that, in 2020, the economic loss of ‘2 + 26’ cities from PM_2.5 was spatially distributed low in the east and high in the west. In addition, it was larger in the southern and northern part, which was smaller in the middle of the region. Based on the conclusions, policy recommendations were put forward. Full article

Studies on human activities and the natural water cycle as a coupled system are essential for effective water resource management in river basins. However, existing calculation methods based solely on the natural water cycle do not meet the accuracy requirements of natural society dualistic water cycle simulations. Therefore, it is necessary to establish a more scientific and reasonable calculation unit division method and river confluence relationship determination method. This paper presents a socio-hydrological unit with natural society dual characteristics based on both the hydrological characteristics and the social administrative characteristics of the river basin. According to the elevation of the river buffer zone, river confluence relationships among socio-hydrological units are determined, and upstream and downstream confluence of the human–water system is obtained. Finally, a case study of the Jing-Jin-Ji region in China, an area of intensive human activities, was performed. A reliability of 94.3% was reached using the proposed socio-hydrological unit division and river confluence calculation method, suggesting that the approach is highly applicable. Thus, the proposed method for generating socio-hydrological units and determining river confluence relationships can be applied to study the mutual influence and spatial distribution characteristics of natural society dualistic water cycles. The data requirement is minimal, and the approach can provide benefits in research on human water systems. Full article

The development of metropolitan cities inevitably relies on natural resources beyond their boundary through trade of materials and products, particularly within the same urban agglomeration. Meanwhile trade facilitates the optimization of resource allocations under scarcity, among cities and sectors, and therefore generates economic gains. This study constructs an economic evaluation model combining a Multi-Regional Input-Output model and a Data Envelopment Analysis (DEA) to quantify the economic impacts of virtual water trades among the 13 cities in the JingJinJi region (China national capital area), one of the most water-scarce regions in China. We found that the total virtual water trade among the 13 cities amounted to 927 million m³ in 2012, among which agricultural sectors contributed 90% while the industrial sector and service sector together made up the remaining 10%. While Beijing and Tianjin are the main virtual water importers, importing respectively 300.48 and 226.92 million m³ in 2012, Shijiazhuang was the largest virtual water exporter, exporting 173.29 million m³ virtual water in the same year. Due to their more advanced economic conditions, Beijing and Tianjin also have the highest shadow prices of water, at respectively 912.21 and 831.86 CNY per m³, compared to a range of 79.31 to 263.03 CNY per m³ in cities in Hebei. Virtual water flows from cities in Hebei to Beijing and Tianjin thus generate economic gains. It is estimated that virtual water trades in the JingJinJi region have generated a net economic gain of 403.62 billion CNY in 2012, particularly owing to trades of agricultural products from Shijiazhuang to Beijing and Tianjin. Full article

Understanding the urban thermal environment is vital for improving urban planning and strategy development when mitigating urban heat islands. However, urban thermal characteristics of local climate zones (LCZ) are different within cities and most studies lack regional perspective. This study explored surface thermal performances of cities in three urban agglomerations (Jing-Jin-Ji, Yangtze River Delta and Pearl River Delta) in China using MODIS land surface temperature (LST). Besides that, the diurnal and seasonal LST variations of LCZs are also studied. Moreover, the optimal LCZs for better urban cooling are also investigated in this study. Although the thermal distributions of LCZs are different in China, there are still some similar features. Our four key findings were as follows. (1) LCZs in China are well classified, with average overall accuracy of 82% being higher than that in some previous studies. (2) The LST of mid-rise (LCZ 2, 5) is higher than that of high- and low-rise buildings (LCZ 1, 3, 4, 6); and compact buildings are warmer than open buildings (LCZ 1–3 > LST 4–6) in summer of China. That shows both mid-rise and compact buildings are not beneficial to cool urban. In addition, LST variations at daytime and in summer are more significant than nighttime and other seasons. (3) LST differences within LCZs are significant at p < 0.05, and are most significant in Jing-Jin-Ji (JJJ). The LST difference within built types (LCZ 1–10) is more significant than within natural types (LCZ A–G), showing that built types alteration will be more effective for thermal environmental improvement. (4) Under the current population and urban area, increasing greenness and water area in compact high-rise buildings are the most effective strategies for urban cooling in all three urban agglomerations, with the largest reduction in LST of 4.11 K in JJJ. These findings will provide support for thermal environment mitigation, urban planning and sustainable urban development. Full article

Land use/cover and vegetation patterns are influenced by many ecological factors. However, the effect of various factors on different classification systems and different levels of the same system is unclear. We conducted a redundancy analysis with 10 landscape metrics and ecological factors in four periods (1986–2005/2007, 1991–2005/2007, 1996–2005/2007, 2001–2005/2007) to explore their effects on the land use/cover system, vegetation group and vegetation type, and formation and subformation levels of the vegetation classification system in the Jing-Jin-Ji region. Soil, temperature and precipitation from 1986–2005, 1991–2005, and 2001–2005 were the important causal factors, and anthropogenic disturbance and atmospheric factors in 1996–2005 were causal factors at the land use/cover level. The total explained variance from 1996–2005 and 2001–2005 was higher than that from 1986–2005 and 1991–2005 at the land use/cover level. Causal factors and the variance explained by causal factors at the vegetation group, vegetation type, and formation and subformation levels were similar but different in the land use/cover system. Geography, soil and anthropogenic disturbance were the most important causal factors at the three vegetation levels, and the total explained variance from 2001–2007 was higher than that from 1986–2007, 1991–2007, and 1996–2007 at the three vegetation levels. In environmental research, natural resource management and urban or rural planning, geographic factors should be considered at the vegetation group, vegetation type and formation and subformation levels while atmospheric and temperature factors should be considered at the land use/cover level. Full article

At the city scale, the diurnal and seasonal variations in the relationship between urban form and the urban heat island effect remains poorly understood. To address this deficiency, we conducted an empirical study based on data from 150 cities in the Jing-Jin-Ji region of China from 2000 to 2015. The results derived from multiple regression models show that the effects of urban geometric complexity, elongation, and vegetation on urban heat island effect differ among different seasons and between day and night. The impacts of urban geometric factors and population density in summer, particularly those during the daytime, are significantly larger than those in winter. The influence of urban area and night light intensity is greater in winter than in summer and is greater during the day than at night. The effect of NDVI is greater in summer during the daytime. Urban vegetation is the factor with the greatest relative contribution during the daytime, and urban size is the dominant factor at night. Urban geometry is the secondary dominant factor in summer, although its contribution in winter is small. The relative contribution of urban geometry shows an upward trend at a decadal time scale, while that of vegetation decreases correspondingly. The results provide a valuable reference for top-level sustainable urban planning. Full article

The timely and accurate mapping and monitoring of mine tailings dams is crucial to the improvement of management practices by decision makers and to the prevention of disasters caused by failures of these dams. Due to the complex topography, varying geomorphological characteristics, and the diversity of ore types and mining activities, as well as the range of scales and production processes involved, as they appear in remote sensing imagery, tailings dams vary in terms of their scale, color, shape, and surrounding background. The application of high-resolution satellite imagery for automatic detection of tailings dams at large spatial scales has been barely reported. In this study, a target detection method based on deep learning was developed for identifying the locations of tailings ponds and obtaining their geographical distribution from high-resolution satellite imagery automatically. Training samples were produced based on the characteristics of tailings ponds in satellite images. According to the sample characteristics, the Single Shot Multibox Detector (SSD) model was fine-tuned during model training. The results showed that a detection accuracy of 90.2% and a recall rate of 88.7% could be obtained. Based on the optimized SSD model, 2221 tailing ponds were extracted from Gaofen-1 high resolution imagery in the Jing–Jin–Ji region in northern China. In this region, the majority of tailings ponds are located at high altitudes in remote mountainous areas. At the city level, the tailings ponds were found to be located mainly in Chengde, Tangshan, and Zhangjiakou. The results prove that the deep learning method is very effective at detecting complex land-cover features from remote sensing images. Full article

The new-generation sensor TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel 5 precursor (S5P) satellite is promising for monitoring air pollutants with greater spatial resolution, especially for China, which suffers from severe pollution. As tropospheric NO₂ vertical column densities (VCDs) from TROPOMI have become available since February 2018, this study presents the comparisons of NO₂ data measured by TROPOMI and its predecessor Ozone Monitoring Instrument (OMI) over China, together with validation against ground Multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements. At the nationwide scale, we used two different filters performed for the TROPOMI data (named TROPOMI50 and TROPOMI75), and the TROPOMI50 yielded larger values than TROPOMI75. The TROPOMI NO₂ datasets from different filters show consistent spatial patterns with OMI, and the correlation coefficient values were both above 0.93. However, linear regression indicates that NO₂ loadings in TROPOMI is about 2/3 to 4/5 of those in OMI, which is presumably due to a different cloud mask and uncertainties of air mass factors. The absolute difference is prominent over the high pollution areas such as Jing-Jin-Ji region and during winter and autumn, exceeding 0.6 × 10¹⁶ molecules cm⁻² (molec cm⁻²). However, the NO₂ concentrations retrieved from TROPOMI50 in the southern China may be somewhat higher than OMI. When it comes to the local-scale Jing-Jin-Ji hotspot, the analysis focuses on a comparison to TROPOMI75. TROPOMI manifests high quality and exhibits a significantly better performance of representing spatial variability. In contrast, OMI shows fewer effective pixels and does a poor job of capturing local details due to its row anomaly and low resolution. The absolute difference between two datasets shows the same seasonal behavior with NO₂ variation, which is most striking in the winter (0.31 × 10¹⁶ molec cm⁻²) and is lowest in the summer (0.05 × 10¹⁶ molec cm⁻²). Furthermore, the ground MAX-DOAS instrument in Xianghe station, the representative site in Jing-Jin-Ji, is used to assess the skill of satellite retrievals. It turns out that both OMI and TROPOMI underestimate the observations, ranging from 30% to 50%, with OMI being less biased. In spite of the negative drift, the temporal structures of changes derived from OMI and TROPOMI closely match the ground-based records, since the correlation coefficients are above 0.8 and 0.95 for daily and monthly scales, respectively. Overall, TROPOMI NO₂ retrievals are better suited for applications in China as well as the Jing-Jin-Ji hotspot due to its higher spatial resolution, although some improvements are also needed in the near future. Full article