Central Urban Area Research Articles

Dynamics of urban green spaces in a megacity under the green economy framework and their influencing factors: a case study of Chongqing urban area

IntroductionIn the context of escalating public health crises in megacities, promoting green and healthy urban spatial development is crucial. It not only contributes to economic growth and environmental sustainability but also significantly impacts the public health of urban residents.MethodsThis study utilized land use data from 2000 to 2021 in Chongqing, China, to investigate the characteristics and patterns of change in urban green space distribution. Spatial analysis methods were employed, and an econometric model was constructed to analyze the mechanisms of these changes, considering factors such as economic drivers, governmental regulation, and social dynamics.ResultsThe results reveal that urban green spaces in the Chongqing metropolitan area are primarily concentrated in the inner suburbs with limited distribution in central urban areas, exhibiting a distinct spatial gradient. The overall size of urban green spaces has been decreasing, particularly in the inner suburbs. The primary patterns of this reduction include edge erosion in the inner suburbs, inward contraction in central urban areas, and minor perforation in new towns.DiscussionThe analysis indicates that economic drivers, such as industrial development and economic growth, are the dominant factors contributing to the reduction of urban green spaces. The impact of social dynamics, such as public demand, appears to be relatively insignificant. Conversely, government planning policies and public investments play a crucial role in the protection and development of urban green spaces. These findings emphasize the need for effective urban planning strategies that prioritize the conservation and expansion of green spaces to enhance public health and environmental sustainability in megacities.

MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS

The increasing demand for housing in urban agglomerations, particularly in areas like Jakarta, has made homeownership a significant challenge for many, especially first-time buyers and the lower-middle class. Post-pandemic shifts have further influenced housing preferences, driving interest towards suburban areas with green spaces. Despite government efforts through mortgage subsidy programs, affordability remains a concern, particularly in peripheral regions. This study aims to analyze housing prices in various Jakarta regions using machine learning models, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), and Random Forest. A dataset of 554 house prices from West Jakarta, South Jakarta, Central Jakarta, and South Tangerang was used. The analysis focused on key predictors like land area, building area, bedrooms, and carports, with R² and Mean Squared Error (MSE) metrics evaluating model performance. Results showed that LGBM and Random Forest outperformed others with 0.8 R2 and low MSE, with building and land area as the most significant factors influencing prices. The study concludes that property size is a primary determinant of house prices, and there is a need for policy interventions to make housing more affordable. Additionally, apartment rentals offer a viable alternative, especially in central urban areas, where proximity to economic activities and facilities is crucial. The findings suggest that enhancing marketplace features with predictive tools could further assist buyers in making informed decisions.

The Optimization of River Network Water Pollution Control Based on Hydrological Connectivity Measures

Urbanization, driven by socio-economic development, has significantly impacted river ecosystems, particularly in plain city regions, leading to disruptions in river network structure and function. These changes have exacerbated hydrological fluctuations and ecological degradation. This study focuses on the central urban area of Changzhou using a MIKE11 model to assess the effects of four hydrological connectivity strategies—water diversion scheduling, river connectivity, river dredging, and sluice connectivity—across 13 different scenarios. The results show that water diversion, river dredging, and sluice connectivity scenarios provide the greatest improvements in water environmental capacity, with maximum increases of 54.76%, 41.97%, and 25.62%, respectively. The spatial distribution of improvements reveals significant regional variation, with some areas, particularly in Tianning and Zhonglou districts, experiencing declines in environmental capacity under sluice diversion and river-connectivity scenarios. In addition, the Lao Zaogang River is identified as crucial for improving the overall water quality in the network. Based on a multi-objective evaluation, combining environmental and economic factors, the study recommends optimizing water diversion scheduling at sluices (Weicun, Zaogang, and Xiaohe) with flow rates between 20–40 m3/s, enhancing connectivity at key river hubs, and focusing management efforts on the Lao Zaogang and Xinmeng rivers to strengthen hydrological and water quality linkages within the network.

Construction and Application of Urban Green Space Ecosystem Service Assessment Indicator System and Assessment Method: A Case Study of Chifeng Central Urban Area, China

The assessment of urban green space ecosystem services is an important basis for urban ecosystem management and decision-making. To further enhance the accuracy of this evaluation, this paper refers to the main research results of international urban ecosystem service assessment and puts forward the main principles for the construction of an urban green space ecosystem service assessment index system. Based on this principle, the framework of the assessment indicator system was carried out by adopting the expert consultation method and frequency analysis method. This system includes the functions of leisure and recreation and the landscape premium in cultural services, air purification, noise abatement, carbon sequestration, oxygen release, and precipitation storage in regulatory services, and biodiversity conservation in supply services. The assessment methods for each functional category are chosen for their appropriateness, or the inadequacies of the existing methods are refined and improved. Then, we take the urban center of Chifeng City, China, as an example to carry out an empirical study on the urban green space ecosystem service assessment index system and assessment methods proposed in this paper. This study shows that the total value of green space ecosystem services in the downtown area of Chifeng City is CNY 229.91 million (Chinese Yuan). Among them, cultural services, regulating services, and supporting services accounted for 89.62%, 9.06%, and 1.32% of the total value, respectively. This study has important reference significance for the comprehensive quantitative assessment of the ecological service function value of urban green spaces, revealing the socio-economic value of urban green space resources while providing insights and references for the environmental assessment, planning, construction, and management decision-making of urban green spaces.

Examination of Spatial and Temporal Evolution Characteristics of Carbon Emission and Influencing Factors in Territorial Spatial Functional Areas: A Case Study of Mountainous City-Chongqing

Abstract The rapid development of China's economy and the acceleration of the urbanization process have led to a significant increase in carbon emissions, and more effective policies are urgently needed. As the first city in China to be approved for the overall master plan of territorial space, Chongqing is facing new opportunities in urban construction. This research has constructed the classification system of the Territorial Space Functional Areas in Chongqing (CQ-TSFA) and matched the corresponding carbon emission and carbon sequestration projects. Under this classification system, the spatial-temporal evolution characteristics of its carbon emissions have been examined, and at the same time, the main influencing factors have been investigated based on the STIRPAT model. This paper draws the following conclusions: (1) From 2000 to 2020, the carbon emissions of CQ-TSFA increased from 48.87 million tons to 148.05 million tons; (2) The main expansion areas of the urban living sub-areas are the central urban area and the vicinity of the seats of various district and county governments. The main expansion areas of the urban production sub-areas are the edges of the central urban area around the Yangtze River Basin within Chongqing, including many industrial parks; (3) The contributions to carbon emissions in descending order are the economic development effect, the carbon emission intensity of CQ-TSFA, and the population size effect, and the utilization rate of CQ-TSFA plays a strong inhibitory role. Finally, based on the above conclusions, this paper puts forward some policy suggestions to address the challenges of carbon emissions, so as to provide references for promoting the sustainable development of Chongqing and even the whole country.

Human‐Induced Climate Change Intensifies Extreme Precipitation Events in Central China's Urban Areas

AbstractUnderstanding the drivers behind extreme precipitation is crucial for predicting and mitigating the impacts of climate change globally, yet it remains little known how anthropogenic factors contribute to these phenomena. This study investigates the impact of human‐induced climate change on circulation patterns conducive to extreme precipitation over the Central Plains Urban Agglomeration in China, a region frequently experiencing severe flooding and home to a dense population with significant economic and agricultural activities. Using advanced techniques such as deep learning and optimal fingerprinting, this study identifies and analyzes the physical mechanisms behind the extreme precipitation. The findings reveal that greenhouse gas emissions play a pivotal role in altering atmospheric circulation patterns, specifically promoting the westward extension of western North Pacific subtropical high and northwestward shift of South Asian High, along with tropical cyclones. These changes enhance moisture transport and convective activity, leading to more frequent and intense precipitation events.

Integrating ecosystem reference conditions and vulnerability to identify ecological restoration priority areas

ABSTRACT Ecological restoration, as a component of sustainable development, can mitigate ecosystem degradation and improve ecological diversity. Accurate identification of ecological restoration priority areas (ERPAs) is essential for developing restoration practice. However, few studies have considered both ecosystem reference conditions and vulnerability during ecological restoration. In this study, a comprehensive framework of ERPA identification was developed by integrating ecosystem reference conditions and vulnerability. Using the Jialing River Basin (JRB), a representative basin of the Yangtze River, as a case study, our results revealed that under average climate conditions, the areas with high values of ecosystem reference conditions were mostly in the eastern and southeastern mountainous areas of the JRB, whereas the potential areas for ecological restoration were concentrated in the central northern and southern basin. Additionally, regions with high ecosystem vulnerability were found in the northern mountainous areas and southern urban areas and were scattered along major tributaries. Overall, the identified ERPAs were predominantly in the central northern and southern urban areas, with some scattered areas in the central basin, accounting for 9.61% of the JRB. Consequently, the JRB can be divided into four regions with targeted management strategies to address ecosystem degradation and implement restoration activities. Moreover, we suggest that the proposed framework for identifying ERPAs is used for clarifying restoration objectives, assessing the ecological baseline, and offering a scientific reference for large-scale ecological restoration efforts.

Machine learning-based urban noise appropriateness evaluation method and driving factor analysis

The evaluation of urban noise suitability is crucial for urban environmental management. Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study introduces various machine learning methods and applies the Random Forest algorithm, which performed best, to investigate noise suitability in the central urban area of Nanchang City. The findings are as follows: 1.Machine learning algorithms can be effectively used for urban noise evaluation. The optimized model accurately reflects the noise suitability levels in Nanchang City. 2.The feature importance ranking reveals that population spatial distribution has the most significant impact on urban noise, followed by distance to water bodies and road network density. These three features significantly influence the assessment of urban noise suitability and should be prioritized in noise control measures. 3.The weakly suitable noise areas in Nanchang’s central urban region are primarily concentrated on the east bank of the Ganjiang River, making this a key area for noise management. Overall, the Unsuitable, Slightly suitable, Moderately suitable, Relatively suitable, and Height suitable areas constitute 9.38%, 16.03%, 28.02%, 33.31%, and 13.25% of the central urban area, respectively. 4.The SHAP model identifies the top three features in terms of importance, showing that different values of feature variables have varying impacts on noise suitability. This study employs data mining concepts and machine learning techniques to provide an accurate and objective assessment of urban noise levels. The results offer scientific decision-making support for urban spatial planning and noise mitigation measures, ensuring the healthy and sustainable development of the urban environment.

Assessment and Prediction of Groundwater Vulnerability Based on Land Use Change—A Case Study of the Central Urban Area of Zhengzhou

Driven by the rapid advancement of the economy and urbanization, substantial alterations in land use patterns have taken place, exerting certain impacts on groundwater. This study examines the land use changes in Zhengzhou’s central urban area from 2000 to 2020 and projects these changes to 2030 using the PLUS model. It optimizes the groundwater vulnerability assessment methodology from two key aspects, namely the evaluation indicators and the associated weights, to enhance its suitability for the study area. This study employs a multi-indicator and dual-method validation approach to verify the groundwater vulnerability assessment results, ensuring the accuracy and reliability of the findings. Urban, rural, and construction lands increased significantly, while paddy fields, drylands, and forests decreased. The 2030 prediction suggests a continuation of these trends. The groundwater vulnerability in 2020 correlated strongly with the groundwater quality, particularly with chloride ions (AUC = 0.804, Spearman’s rho = 0.83). The 2030 projection indicates a minimal change in the vulnerability distribution but anticipates an increase in high- and very-high-vulnerability areas, particularly in regions with land use changes, potentially increasing the groundwater contamination risk. This suggests the need for targeted groundwater protection policies to mitigate contamination risks.

The Impact of Built Environment on Mixed Land Use: Evidence from Xi’an

Mixed land use is recognized as a crucial strategy for enhancing urban vitality and promoting urban renewal. While existing research has mainly focused on measuring mixed land use along single dimensions, there remains a notable gap in studies that explore multidimensional measurements and their impacts at the block scale. This study introduces a multidimensional mixed-degree index based on diverse data sources such as POI and land use status to comprehensively assess mixed land use levels at the block scale in Xi’an’s central urban area. Additionally, a multiple linear regression model is applied to analyze how the built environment influences mixed land use. Findings reveal that mixed land use at the block scale can be objectively evaluated across three dimensions: quantity, distance, and attribute. In Xi’an, mixed land use demonstrates a spatial distribution characterized by core agglomeration and concentric decline. The study highlights that block area and road network density significantly influence mixed land use, with block area negatively impacting it the most and road network density positively affecting it secondarily. These insights provide valuable guidance for optimizing land use practices and promoting high-quality urban development.

Urban Spatial Pattern Optimization Based on “Double Evaluation”: A Case Study of Urban Agglomeration in Central Yunnan, China

The evaluation of the carrying capacity of urban resources and the environment and the suitability of urban spatial development (referred to as “double evaluation”) is a fundamental prerequisite for territorial spatial planning. A scientifically planned urban spatial layout is key to balancing economic, social, and environmental benefits, which directly impacts urbanization levels and regional high-quality development. In this study, a double evaluation index system was established for the city cluster in central Yunnan based on the theories of “double evaluation” and main functional zones. The PLUS model was used to predict the distribution of future urban spatial patterns, considering factors like main function zones, and to optimize the spatial patterns of cities and towns in the study area. The findings are as follows. (1) The area of land suitable for urban construction in the study area is 25,107.51 km2, mainly distributed in the southern and central urban areas, as well as in the Zhanyi and Qilin districts to the east. (2) More than 76% of the land designated for urban construction is located in suitable areas, with an area of 680.51 km2, which represents only 2.71% of the total suitable land, indicating significant potential for further urban development. The PLUS simulation reveals that the urban area in the study area will reach 235.41 km2 by the future, with 83.67% of urban construction space concentrated in suitable areas. Additionally, 69.11% of urban construction space and 74.53% of suitable land for urban construction are located in the key development area, which covers 618.43 km2 and 18,713.28 km2, respectively. This highlights the key development area’s crucial role in urban expansion and its significant potential for further development. Despite the region’s high level of economic activity and urbanization, there remains considerable potential for the expansion of towns over a wide area. This study provides valuable insights for optimizing urban spatial layouts, the allocation of national land space functions, and promoting orderly development.

Enhancing Detailed Planning from Functional Mix Perspective with Spatial Analysis and Multiscale Geographically Weighted Regression: A Case Study in Shanghai Central Region

Detailed spatial planning serves as statutory guidance for regulating specific spatial functions, including public services, living conditions, and production spaces. It emphasizes meeting the comprehensive needs of the local population, making it crucial to understand the relationship between population distribution and the mix of various city functions, particularly in the era of urban regeneration. Therefore, this study utilized point-of-interest (POI) data representing land functions and population data to investigate these relationships via spatial analysis and Multiscale Geographically Weighted Regression (MGWR). Applied to the central urban area of Shanghai, the study reveals that the level of mixed land use and various functionalities affect population distribution at different adaptive scales. We also found a higher degree of functional mix does not always meet population needs. Although generally there is a positive correlation between functional mix and population distribution, they are not always closely bonded. The proposed method provides an efficient workflow for identifying the applicable scale of various functions to increase functional mix and attract the population, which can provide real-time evidence supporting detailed planning. Test results also reveal the less-considered space along the boundaries of administrative districts. We also found developing tools for detailed planning is an urgent need to facilitate cross-boundary cooperation and development, especially in the context of urban regeneration where they always are overlooked at the detailed planning level. By using open-sourced POI and population data, our proposed workflow can be easily applied to other cities or regions, enhancing their practical value for similar research contexts.

Construction of a composite cooling network for the mitigation of urban heat risk in Fuzhou

Climate change has intensified urban heat risks through extreme heat and heat island effects. Using Fuzhou as a case study, we conducted assessments of heat risk and cool island quality to identify core heat risk sources (CHRSs) and core cold sources (CCSs). Based on the degree of resistance to surface heat transfer, we constructed a comprehensive resistance surface. This was followed by the construction of a composite cooling network using the minimal cumulative resistance and circuit theory models, along with the identification of key nodes to enhance the protection of cool island resources and ensure network stability. Our findings revealed that the central urban area had the highest heat risk, followed by the eastern coastal areas, showing a trend of further expansion towards the southeastern coast. Relatively high-quality cool island resources were distributed in the western mountainous area. We identified 21 CHRSs and 32 CCSs. The composite cooling network included 94 heat transport corridors and 96 cool island synergy corridors, with 148 cooling nodes and 78 barrier nodes. The average land surface temperature of transport and synergy corridors was 27.89°C and 25.34°C, respectively, significantly lower than the high-risk areas (31.14°C). Transport corridors enable heat transfer from CHRSs to CCSs, while synergy corridors can achieve further cooling by enhancing the synergy among cool islands.

Estimation of Vegetation Carbon Sinks and Their Response to Land Use Intensity in the Example of the Beijing–Tianjin–Hebei Region

Accurate regional carbon sequestration estimates are essential for China’s emission reduction and carbon sink enhancement efforts to address climate change. Enhancing the spatial precision of vegetation carbon sink estimates is crucial for a deeper understanding of the underlying response mechanisms, yet this remains a significant challenge. In this study, the Beijing–Tianjin–Hebei (BTH) region was selected as the study area. We employed the GF-SG (Gap filling and Savitzky–Golay filtering) model to fuse Landsat and MODIS data, generating high-resolution imagery to enhance the accuracy of NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) estimates for this region. Subsequently, the Sen+MK model was used to analyze the spatiotemporal variations in carbon sinks. Finally, the land use intensity index, which reflects human activity disturbances, was applied, and the bivariate Moran’s spatial autocorrelation method was used to analyze the response mechanisms of carbon sinks. The results indicate that the fused GF-SG NDVI (Normalized Difference Vegetation Index) data provided highly accurate 30 m resolution imagery for estimating NPP and NEP. The spatial distribution of carbon sinks in the study area showed higher values in the northeastern forest regions, relatively high values in the southeastern plains, and lower values in the northwestern plateau and central urban areas. Additionally, 58.71% of the area exhibited an increasing trend, with 11.73% showing significant or strongly significant growth. A generally negative spatial correlation was observed between land use intensity and carbon sinks, with the impact of land use intensity on carbon sinks exceeding 0.3 in 2010. This study provides methodological insights for obtaining vegetation monitoring data and estimating carbon sinks in large urban agglomerations and offers scientific support for developing ecological and carbon reduction strategies in the BTH region.

Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing

The layout and site selection strategy of commercial facilities are crucial for both enterprise performance and market image, while also significantly impacting the overall planning of urban commercial environments. However, conventional methods of choosing sites sometimes depend on outdated management information systems or static statistical models, which may not take into account all relevant factors and have poor data quality. By utilizing geographical big data and geographical artificial intelligence, this study improves the viability of commercial layout and site selection methods. This study utilizes mobile phone signaling data from Beijing combined with point-of-interest (POI) data from within the Sixth Ring Road of Beijing to identify user behaviors using algorithms. Through a combination of BiLSTM-RF and reinforcement learning algorithms, a population location prediction algorithm is constructed to address the issues of inaccurate and outdated population flow data in commercial site selection. The forecast distribution has a high level of accuracy, with a prediction accuracy rate of 73.2%. Additionally, based on geographical big data, the urban landscape is reconstructed to create a 3D model of Beijing. An immersive interactive commercial site selection system is implemented using the Unreal Engine.

Barriers to Performing Essential Surgery at First-Level Hospitals in Pakistan: A Mixed Methods Study

IntroductionThere are numerous reasons for underutilization of the public health sector for surgery in Pakistan. This results in patients being diverted to private hospitals or tertiary care centers in urban areas. Diversions overburden the hospitals and significantly increase out-of-pocket costs for the patients. This study aims to determine the barriers to surgical care in first-level hospitals in Pakistan's Sindh province. MethodsWe conducted a concurrent nested mixed methods study from May to June 2021 in public sector first-level hospitals in the Sindh province. Fifteen hospitals in six districts were surveyed. A consolidated hospital assessment tool adapted from the World Health Organization's Tool for Situational Analysis to Assess Emergency and Essential Surgical Care was used for quantitative data collection. Interview guides were developed for staff interviews. ResultsAvailability of trained staff was found to be the biggest barrier to the provision of safe surgery. Only eight hospitals had a general surgeon, anesthesiologist, and obstetrician/gynecologist, while the remaining had 1-2 of the three disciplines. Thirteen hospitals had a functioning x-ray machine, while 14 facilities had functioning ultrasound machines with trained personnel to operate them. Only three facilities always had blood available for transfusion. The qualitative component corroborated that the biggest barrier to providing surgical care was the lack of human resources. ConclusionsThe lack of human resources is difficult to overcome. We found evidence of task-shifting to medical officers and trainee anesthesiologists, but this is without discrete regulation and monitoring. Building surgical workforce capacity must be addressed in the interest of quality care.

Deciphering pollution sources and mechanisms controlling groundwater chemistry in a typical dense agricultural plain on Yungui Plateau.

Groundwater is a critical resource for economic growth and livelihoods in the dense agricultural plains of plateaus. However, contaminations from various sources pose significant threats to groundwater quality. Understanding the sources of groundwater contamination and the mechanisms of hydrochemical control is essential for the sustainable development of agriculturally intensive plains. This research utilizes 23 datasets of groundwater chemical measurements to apply hierarchical clustering analysis, positive matrix factorization, and hydrochemical analysis techniques. Through these methods, the study identifies the sources of groundwater contamination and deciphers the hydrochemical control mechanisms within a representative intensive agricultural plain region of Yungui Plateau. The finds indicate that groundwater in the plain primarily derives from the rainfall occurred in the surrounding mountains. During the long underground flow process, groundwater undergoes water-rock interactions and ion exchanges with various lithological strata, resulting in the formation of distinct hydrochemical types. As it traverses regions influenced by human activities, groundwater encounters varying levels and types of contamination. Consequently, there is a notable variation in groundwater quality across different areas of the plain. Groundwater is dominated by the hydrochemical faces of HCO3-Ca type in the southern part of the plain. Groundwater in the piedmont region of this part exhibits the highest quality, acting as the baseline for the overall groundwater quality of the plain. Groundwater in agricultural areas of this part is severely polluted by nitrate-rich agricultural wastewater. In the central urban area, under the control of municipal wastewater discharge and denitrification, groundwater is to some extent polluted by NH4 +. In the northern sector of the plain, groundwater chemistry exhibits greater diversity due to variations in geological strata and exposure to a range of pollution sources. The majority of the regions are contaminated with SO4 2- and Cl- and present a predominance of Cl-Na type for groundwater hydrochemical facies. Groundwater at the northernmost end is polluted by NO2 -, NH4 +, and P. In addition, there is also a small amount of groundwater near the lake that is heavily polluted by fertilizers. This study provides valuable insights for the development of sound groundwater management strategies, applicable not only to the current agricultural plain but also to analogous regions worldwide. PRACTITIONER POINTS: This study probed the impact of agricultural pollution on the groundwater hydrochemistry in a cultivated plain. The research pinpointed the origins and contributions of groundwater chemicals in the cultivated agricultural plain. A conceptual model was established to illustrate groundwater chemistry formation in an intensive agricultural irrigation plain on Yungui Plateau.

Assessing and Predicting Spatiotemporal Alterations in Land-Use Carbon Emission and Its Implications to Carbon-Neutrality Target: A Case Study of Beijing-Tianjin-Hebei Region

Optimizing land use and management are pivotal for mitigating land use-related carbon emissions. Current studies are less focused on the influence of development policies and spatial planning on carbon emissions from land use. This research employs the future land use simulation (FLUS) model to project land-use alterations under the business-as-usual (BAU) and low-carbon ecological security (LCES) scenarios. It assesses and predicts spatiotemporal characteristics of land-use carbon emissions in the Beijing-Tianjin-Hebei (BTH) region across urban agglomerations, cities, counties, and grids from 2000 to 2030. The influence of low-carbon policy is assessed by comparing the land-use carbon emissions between scenarios. The findings demonstrate that: (1) Urban agglomeration-wise, Beijing’s land-use carbon emissions and intensities peaked and declined, while Tianjin and Hebei’s continued to rise. (2) City-wise, central urban areas generally have higher carbon emissions intensities than non-central areas. (3) County-wise, in 2030, high carbon-intensity counties cluster near development axes. Still, the BAU scenario has a larger carbon emission intensity and a greater range of higher intensities. (4) Grid-wise, in 2030, the BAU scenario shows a clear substitution of heavy carbon emission zones for medium ones, and the LCES scenario shows a clear substitution of carbon sequestration zones for light carbon emission zones. Our methodology and findings can optimize spatial planning and carbon reduction policies in the BTH urban agglomeration and similar contexts.

Dynamic Perception of Landscape Along Beijing − Qinhuangdao Railway in the Central Urban Area of Beijing

Dynamic Perception of Landscape Along Beijing − Qinhuangdao Railway in the Central Urban Area of Beijing

Insights into the source characterization, risk assessment and ozone formation sensitivity of ambient VOCs at an urban site in the Fenwei Plain, China

The ground-level O3 concentration has shown a deteriorating trend in the Fenwei Plain of China, which poses a greater challenge for formulating control strategies of O3 precursor (VOCs). To accurately control VOCs sources and effectively reduce O3 concentration from a seasonal perspective, online monitoring of 114 VOCs was conducted at Yuncheng Middle School Station from January 1, 2021 to December 31, 2021. The VOCs concentration showed a seasonal variation with the highest in winter and the lowest in summer. During the four seasons, alkanes (34.5–41.7 %) and OVOCs (36.6–46.9 %) were the most abundant species. The emission ratios of specific VOCs species indicated that vehicular exhaust, industrial source, and combustion were the major VOCs sources. The Positive Matrix Factorization (PMF) model identified that industrial source and secondary conversion were the main contributors in summer, while combustion and LPG/NG contributed more significantly in winter. The 2021-based VOCs emission inventory showed that the total VOCs emissions in the central urban area of Yuncheng was 8128.8 t, in which industrial process was the largest contributor. Alkanes, aromatics, and OVOCs accounted for 31.0 %, 25.8 %, and 25.7 % of the annual VOCs emission, respectively. In addition, the calculated relative incremental reactivity (RIR) values of O3 precursors demonstrated that alkenes and aromatics were the most sensitive groups to O3 formation during the four seasons. The ambient VOCs posed the non-carcinogenic risk across all seasons, which can be attributed to acrolein and three main sources (industrial source, secondary conversion, and combustion). However, ambient VOCs exposed to definite carcinogenic risks due to the appearance of 1,2-dichloroethane, 1,2-dichloropropane, and benzene, and the main risks arose from industrial source, vehicular exhaust, and solvent usage. These findings emphasize the necessity of undertaking scientific and systematic measures for priority species and control sources of VOCs emission.