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Synthetic aperture radar (SAR) records important information about the interaction of electromagnetic waves with the Earth’s surface. However, long-term and high-resolution backscatter coefficient data are still lacking in many urban studies (e.g., building height estimation). Here, we proposed a framework to reconstruct the 1-km backscatter coefficient in 1990–2022 utilizing the Sentinel-1 Ground Range Detected data and Landsat time series data in the Jing–Jin–Ji (JJJ) region. First, we developed a regression model to convert the optical signals from Landsat into backscatter coefficients as the Sentinel-1 data, using observations from 2015 to 2022. Then, we reconstructed backscatter coefficients from 1990 to 2022 using the long-term Landsat data. Using the reconstructed backscatter coefficients, we analyzed the dynamic patterns of building height over the past decades. The proposed approach performs well on estimating the backscatter coefficient and its spatial pattern, with the annual mean absolute error, root mean square error, and R2 of 1.10 dB, 1.50 dB, and 0.64, respectively. The temporal trends revealed from the reconstructed backscatter data are reliable compared with satellite observations at a relatively coarse resolution, with Pearson’s coefficients above 0.92 in 6 sample cities. The derived building height from the reconstructed SAR data indicates that the JJJ region experienced a noticeable upward expansion in 1990–2022, e.g., Beijing has the fastest growth rate of 0.420 km3/decade regarding the total building volumes. The proposed framework of reconstructing SAR data from optical satellite images provides a new insight to complement the long-term and high-resolution backscatter from local to global scales.

Abstract Background Biogenic volatile organic compounds (BVOCs) play an essential role in tropospheric atmospheric chemical reactions. There are few studies conducted on BVOCs emission of dominant forest species in the Jing-Jin-Ji area of China. Based on the field survey, forest resources data and the measured standard emission factors, the Guenther model developed in 1993 (G93) was applied in this paper to estimate the emission of BVOCs from several dominant forest species (Platycladus orientalis, Quercus variabilis, Betula platyphylla, Populus tomentosa, Pinus tabuliformis, Robinia pseudoacacia, Ulmus pumila, Salix babylonica and Larix gmelinii) in the Jing-Jin-Ji area in 2017. Then the spatiotemporal emission characteristics and atmospheric chemical reactivity of these species were extensively evaluated. Results The results showed that the total annual BVOCs emission was estimated to be 70.8 Gg C·year− 1, consisting 40.5 % (28.7 Gg C·year− 1) of isoprene, 36.0 % (25.5 Gg C·year− 1) of monoterpenes and 23.4 % (16.6 Gg C·year− 1) of other VOCs. The emissions from Platycladus orientalis, Quercus variabilis, Populus tomentosa and Pinus tabulaeformis contributed 56.1 %, 41.2 %, 36.0 % and 31.1 %, respectively. The total BVOCs emission from the Jing-Jin-Ji area accounted for 61.9 % and 1.8 % in summer and winter, respectively. Up to 28.8 % of emission was detected from Chengde followed by Beijing with 24.9 %, that mainly distributed in the Taihang Mountains and the Yanshan Mountains. Additionally, the Robinia pseudoacacia, Populus tomentosa, Quercus variabilis, and Pinus tabulaeformis contributed mainly to BVOCs reaction activity. Conclusions The BVOCs emission peaked in summer (June, July, and August) and bottomed out in winter (December, January, and February). Chengde contributed the most, followed by Beijing. Platycladus orientalis, Quercus variabilis, Populus tomentosa, Pinus tabulaeformis and Robinia pseudoacacia represent the primary contributors to BVOCs emission and atmospheric reactivity, hence the planting of these species should be reduced.

This work analyzes and discusses the influence of human activities on the meteorological conditions related to winter haze events in Beijing, Tianjin, and Hebei (i.e., the Jing–Jin–Ji region) during 1961–2016, using the results of two numerical simulation experiments based on the Community Atmosphere Model version 5.1.1 (http://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1_1/book1.html) used in the international Climate Variability and Predictability Programme (CLIVAR) Climate of the 20th Century. Detection and Attribution Project (C20C+ D&A). The results show that, under the influence of human activities, the changes in dynamical and thermal meteorological conditions related to winter haze events in the Jing–Jin–Ji region are conducive to the formation and accumulation of haze, and prevent the diffusion of pollutants. The dynamical conditions mainly include the obvious weakening of the East Asian winter monsoon (EAWM) and the enhancement of the near-surface anomalous southerly wind. The thermal conditions include the obvious increase in surface temperature, and the enhancement of water vapor transport and near-surface inversion. The relative contribution of dynamical and thermal conditions to the variation of haze days in the Jing–Jin–Ji region is analyzed using statistical methods. The results show that the contribution of human activities to the increase of haze days in the Jing–Jin–Ji region is greater than that of natural forcing for the study period. To be specific, the dynamical meteorological factors contribute more to the haze days than the thermal meteorological factors. The contribution of thermal meteorological factors is basically the same in both scenarios.

This study aims to explore the factors affecting variation in ozone concentration, using ozone concentration monitoring data from the Beijing–Tianjin–Hebei (Jing–Jin–Ji) region from 2015 to 2022. The characteristics of the spatial and temporal variation in ozone concentration, as well as the impact of meteorological and land-use types on this variation, were investigated. Seasonally, ozone concentrations in the Jing–Jin–Ji region were highest in summer, followed by spring and autumn, and were lowest in winter. Spatially, a characteristic trend of lower concentrations in urban centers and higher concentrations in suburban areas was found. From the results of correlation analysis and a stepwise regression model, temperature and solar radiation were found to be the primary factors affecting seasonal variations in ozone concentration, explaining 67.9% of the variance. Additionally, the results of geographically weighted regression models confirmed the spatial heterogeneity of the impact of temperature and solar radiation on ozone concentration. In urban centers, such as Beijing and Tianjin, temperature and solar radiation more strongly influenced ozone concentration. To determine the factors driving this spatial heterogeneity, a geodetector was employed to analyze the impact of various geographical factors on the spatial distribution of ozone concentration. In winter, the impact of various factors of urbanization, such as lighting index, population density, and construction land, as well as air quality factors (PM2.5 and NO2), on ozone concentration was stronger, with q values greater than 0.2. However, in autumn, the influence of these factors on ozone concentration weakened. Furthermore, the results of the two-factor interaction detection analysis showed that the q values for the interaction between urban and air quality factors ranged between 0.40 and 0.47, which were substantially higher than those for other interactions. This further confirmed the strong influence of urban activities on ozone concentration. [ABSTRACT FROM AUTHOR]

With the long-term impact of economic globalization, the accelerated cross-border flow of resource elements between cities is increasingly important for the development of urban regions. In this context, the central place theory, which emphasizes the vertical hierarchical relationship between cities, has obvious limitations in interpreting urban interaction. This paper takes the Beijing Tianjin Hebei Urban Agglomeration (including Beijing, Tianjin. and 11 prefecture-level cities in Hebei Province) as the research object, constructs three complex networks of economy, information, and economic information coupling connection, and analyzes the resilience of the network structure. The research results show that: 1) The spatial distribution of the three network structures of Jing-Jin-Ji Urban Agglomeration presents a spatial pattern with Beijing as the core and radiating outward. 2) In terms of weighted degree distribution, Beijing, as an arrow city, has strong competitiveness in the economy and information network, but the hierarchy of cities with balanced information economy development is low. 3) In terms of weighted degree correlation, among the three networks, the cities with a high weighted degree prefer to connect with cities with a low weighted degree, and the mismatch is ranked as information network > information economy coupling network > economic connection network. 4) In terms of network transmission, information network > economy connection network > information economy coupling network, and Beijing and Tianjin have a greater impact. The above results have strong guidance and practical significance for the formulation and adjustment of territorial spatial planning and regional optimization.

This study aims to explore the factors affecting variation in ozone concentration, using ozone concentration monitoring data from the Beijing–Tianjin–Hebei (Jing–Jin–Ji) region from 2015 to 2022. The characteristics of the spatial and temporal variation in ozone concentration, as well as the impact of meteorological and land-use types on this variation, were investigated. Seasonally, ozone concentrations in the Jing–Jin–Ji region were highest in summer, followed by spring and autumn, and were lowest in winter. Spatially, a characteristic trend of lower concentrations in urban centers and higher concentrations in suburban areas was found. From the results of correlation analysis and a stepwise regression model, temperature and solar radiation were found to be the primary factors affecting seasonal variations in ozone concentration, explaining 67.9% of the variance. Additionally, the results of geographically weighted regression models confirmed the spatial heterogeneity of the impact of temperature and solar radiation on ozone concentration. In urban centers, such as Beijing and Tianjin, temperature and solar radiation more strongly influenced ozone concentration. To determine the factors driving this spatial heterogeneity, a geodetector was employed to analyze the impact of various geographical factors on the spatial distribution of ozone concentration. In winter, the impact of various factors of urbanization, such as lighting index, population density, and construction land, as well as air quality factors (PM2.5 and NO2), on ozone concentration was stronger, with q values greater than 0.2. However, in autumn, the influence of these factors on ozone concentration weakened. Furthermore, the results of the two-factor interaction detection analysis showed that the q values for the interaction between urban and air quality factors ranged between 0.40 and 0.47, which were substantially higher than those for other interactions. This further confirmed the strong influence of urban activities on ozone concentration.

Globally all countries encounter air pollution problems along their development path. As a significant indicator of air quality, PM2.5 concentration has long been proven to be affecting the population’s death rate. Machine learning algorithms proven to outperform traditional statistical approaches are widely used in air pollution prediction. However research on the model selection discussion and environmental interpretation of model prediction results is still scarce and urgently needed to lead the policy making on air pollution control. Our research compared four types of machine learning algorisms LinearSVR, K-Nearest Neighbor, Lasso regression, Gradient boosting by looking into their performance in predicting PM2.5 concentrations among different cities and seasons. The results show that the machine learning model is able to forecast the next day PM2.5 concentration based on the previous five days' data with better accuracy. The comparative experiments show that based on city level the Gradient Boosting prediction model has better prediction performance with mean absolute error (MAE) of 9 ug/m3 and root mean square error (RMSE) of 10.25–16.76 ug/m3, lower compared with the other three models, and based on season level four models have the best prediction performances in winter time and the worst in summer time. And more importantly the demonstration of models' different performances in each city and each season is of great significance in environmental policy implications.

National cultural park is an innovative initiative for the protection and development of natural and cultural heritage resources in the new era, reflecting the core strategy of cultural heritage protection and development of representative cultural resources. It is not only an important carrier for the protection of natural and cultural heritage, but also a new model for promoting the development of cultural industry. Based on their own national conditions and experiences, countries have developed distinctive national park management models, including the native recreation value orientation of the United States, the sustainable club economy of Europe, the public supremacy of private ownership of land in the United Kingdom, and the synergistic community-neighborhood development of developing countries in Asia and Africa. In view of the heritage resources brought by the Beijing Winter Olympics, this study explores the idea of ice and snow tourism routes centered on Beijing, Yanqing and Zhangjiakou, and proposes the principles of sustainable development, mutual benefit and symbiosis, and public supremacy, which is committed to constructing a development model integrating culture, tourism and ecology.

The representation of aerosol–cloud interaction (ACI) and its impacts in the current climate or weather model remains a challenge, especially for severely polluted regions with high aerosol concentration, which is even more important and worthy of study. Here, ACI is first implemented in the atmospheric chemistry model GRAPES_Meso5.1/CUACE by allowing for real-time aerosol activation in the Thompson cloud microphysics scheme. Two experiments are conducted focusing on a haze pollution case with coexisting high aerosol and stratus cloud over the Jing–Jin–Ji region in China to investigate the impact of ACI on the mesoscale numerical weather prediction (NWP). Study results show that ACI increases cloud droplet number concentration, water mixing ratio, liquid water path (CLWP), and optical thickness (COT), as a result improving the underestimated CLWP and COT (reducing the mean bias by 21 % and 37 %, respectively) over a certain subarea by the model without ACI. A cooling in temperature in the daytime below 950 hPa occurs due to ACI, which can reduce the mean bias of 2 m temperature in the daytime by up to 14 % (∼ 0.6 ∘C) in the subarea with the greatest change in CLWP and COT. The 24 h cumulative precipitation in this subarea corresponding to moderate-rainfall events increases, which can reduce the mean bias by 18 %, depending on the enhanced melting of the snow by more cloud droplets. In other areas or periods with a slight change in CLWP and COT, the impact of ACI on NWP is not significant, suggesting the inhomogeneity of ACI. This study demonstrates the critical role of ACI in the current NWP model over the severely polluted region and the complexity of the ACI effect.

Identifying the changes and relationships between regional eco-environment quality and landscape pattern in an urban agglomeration have a great significance in realizing sustainable development goal. However, limited research has been performed to understand the spatiotemporal change of eco-environment quality, the variation of landscape pattern, and their relationship in an urban agglomeration. This study selected the Jing-Jin-Ji (JJJ) urban agglomeration as the study area. A comprehensive index, the remote sensing ecological index (RSEI), was utilized to understand the eco-environment spatiotemporal change and landscape pattern variation at class-level and landscape-level of JJJ during 2001~2015, then, their relationship was explored. The major conclusions were as follows: (1) The average RSEI value of JJJ increased from 0.43 to 0.46, which represented that the eco-environment of JJJ had improved in the fourteen years. Among it, the improved region was mainly located in Zhangjiakou city, while the degraded region was mainly distributed in the eastern Hebei plain. (2) The landscape characteristics of entire JJJ eco-environment were becoming more aggregated, connected, diverse, and regular. However, fair, moderate, and good grades were getting more concentrated and continuous; poor grade indicated a more fragmented and disconnected trend; excellent grade displayed an expanded and concentrated situation. (3) Human factors have an increasing influence on regional eco-environment changes. (4) Fair, moderate, and good grades showed a more dominant and stronger influence on the variation of landscape pattern in JJJ. Specifically, the fair grade had a positive correlation with the variation of landscape pattern, while moderate and good grades had a negative one. All of these conclusions could be valuable information for relevant decision-makers in managing or achieving the optimal eco-environment landscape pattern.

Exploring the regional eco-environmental quality (EEQ) and its driving factors is of great significance for regional management. Although existing studies have paid much attention to evaluate EEQ, few studies have been performed to investigate the spatiotemporal variations of EEQ and its driving factors in different ecosystem service regions (ESR) at an urban agglomeration scale. In this study, we selected Jing-Jin-Ji urban agglomeration (JJJ) as the study area to evaluate its EEQ, analyze its spatiotemporal variations, and investigate potential driving factors explanatory power based on the geographical detector methods in different ESR during 2001 ~ 2015. The main conclusions were as follows: (1) The EEQ of JJJ had improved from 2001 to 2015, with the average RSEI increased from 0.43 to 0.46; among them, Bashang Plateau and Western Hebei Ecosystem Service Region (BWHE) had the highest RSEI change rate (+26.19%) and the highest NTEDI value (0.13), while Central Hebei Plain Ecosystem Service Region (CHPE) had the lowest RSEI change rate (-5.41%) and the lowest NTEDI value (-0.02). (2) The EEQ of JJJ had strong spatial agglomeration effects, with the global Moran's I increased from 0.82 to 0.88. Spatially, the LL regions mainly changed into the HH regions in the northwestern part, while in the central and eastern areas, some isolated LL regions displayed an aggregated trend. (3) In terms of the driving factors, soil type and elevation were primary factors in explaining the variations of EEQ. Specifically, natural factors explained the highest variations in BWHE. The interaction of topographical and socio-economic factors had high explanatory power in Yanshan and Taihang Mountain Ecosystem Service Region (YTME) and CHPE; To Bohai and Coastal Ring Ecosystem Service Region (BCRE), the interaction of meteorological and socio-economic factors accounted for the high variations of EEQ. All these findings could provide more valuable advice for relevant policy-makers.