Daxing District Research Articles

Carbon metabolism modelling and regulation in Beijing

Land use and cover change leads to significant increases in urban carbon emissions. Considering the insufficiency of spatially fine-grained management in previous carbon emission studies, this study integrated top-down and bottom-up methods to simulate urban carbon metabolism. Metabolic actors were allocated to grids based on land use and POI data and then attributed to districts. Results indicated that from 2000 to 2018, Beijing's Carbon Imbalance Index increased by 55.81% in 2010 and declined by 12.46%. Carbon emission dominated the changes in the Carbon Imbalance Index, with the Production and Supply of Electric Power actors consistently accounting for over 25.96%, while the contribution of Urban and Transportation actors reached 20.11% and 27.74% in 2018, respectively. The metabolic actors exhibited a gradient distribution pattern from southeast to northwest. Artificial actors' carbon emissions decreased from the center to the periphery and spread along the road network, with the High-High cluster in the southeast mainly contributed by Manufacturing (22.33%) and the Production and Supply of Electric Power (20.16%) actors. Spatial distribution changes of Natural actors mainly occurred in Daxing and Chaoyang districts, whose absorption area extended by 45% and 37.26%, respectively, contributing to an increase in carbon absorption by 2.47 and 0.15 times. The Semi-natural actor exhibited net carbon emissions, with the High-High cluster in the southeast zone contributing 60.96% of emissions in 2000. We also categorized Beijing into four Areas based on the relationship between carbon emission and absorption. Targeted policy recommendations were formulated for different districts and metabolic actors.

Distribution and Risk Assessment of VOCs in Road Dust Across Different Urban Areas

ABSTRACT Volatile organic compounds (VOCs) are significant pollutants in urban road dust, but their distribution and associated health risks remain unclear. VOC distribution in road dust from six distinct urban areas in Beijing Daxing District, China was systematically investigated. Eighteen VOCs were detected, with the highest load in commercial areas (7.73 μg/g), followed by industrial parks (6.38 μg/g), residential areas (3.35 μg/g), leisure areas (2.79 μg/g), traffic areas (2.01 μg/g) and urban-rural fringe (1.85 μg/g), much lower than in air and water phases. Typical VOCs in road dust include methyl tertiary butyl ether (MTBE) (0.82–1.24 μg/g), benzene series compounds (0.02–4.19 μg/g) and volatile halogenated alkanes (0.002-0.095 μg/g). Incomplete combustion of road vehicle fuels is the key source of VOCs. Particular attention was given to VOCs in typical sites within the urban-rural fringe, known for their unique pollution characteristics. The VOCs loads were ranked as follows: Gas Stations (2.61–2.62 μg/g) > Intersection (1.41 μg/g) > Main Road (1.34 μg/g) > University Gate (1.27 μg/g). Increased vehicle activity and frequent stalling contribute to higher VOCs accumulation in road dust. Health risk assessment indicates there is no appreciable non-carcinogenic risk associated with exposure to road dust VOCs. Further research should consider VOCs in the broader environmental context, including air and water.

Statistical analysis of the relationships between boundary layer meteorological processes and nocturnal O3 increases in Beijing from 2018 to 2022

In the absence of photochemical reactions, nocturnal ozone (O3) variation was found to be caused by boundary layer meteorological processes. In addition to low-level jet (LLJ) and convective storm (CS) events, cold front and foehn processes also cause nocturnal O3 increases (NOIs). The subsidence warming effect (Subsi.) caused by weak cold front and foehn processes cause the nocturnal surface temperature and O3 to increase. Statistical analysis revealed that NOI days accounted for 39.4 ± 6.4% of the total number of days per year in Beijing, and the average occurrence frequency decreased by 1.7%/year over a recent five-year period (2018–2022). The LLJ process is the primary factor causing NOIs, and the average annual contributions of the LLJ, CS and Subsi. processes to NOIs were approximately 32.2 ± 10.5%, 11.7 ± 2.3% and 8.0 ± 3.0%, respectively. From a seasonal point of view, the most NOI events occurred in summer, and the least occurred in autumn. LLJs contributed the most to the NOIs in each season, and the CS contribution was almost equal to that of LLJs in summer. LLJs that occurred near 00:00 LT and 04:00 LT had a greater probability of causing NOI events. Most of the nocturnal CS events occurred during 22:00 LT-02:00 LT (next day), and the contribution of CSs to NOIs fluctuated around 10.0%. More than half (68.0%) of the Subsi. events occurred during 23:00 LT-03:00 LT (next day). Subsi. events occurred during this period contributed more to NOIs than other periods. Meteorological processes more easily caused NOI events in the urban, Daxing and Tongzhou districts than other areas. The average surface O3 concentration during the early morning hours (04:00–06:00 LT) on NOI days was 8.8 ± 3.2 μg/m3 higher than that on non-NOI days, suggesting that NOIs can elevate the base O3 concentration during the following day.

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

Construction waste is unavoidable in the process of urban development, causing serious environmental pollution. Accurate assessment of municipal construction waste generation requires building construction waste identification models using deep learning technology. However, this process requires high-quality public datasets for model training and validation. This study utilizes Google Earth and GF-2 images as the data source to construct a specific dataset of construction waste landfills in the Changping and Daxing districts of Beijing, China. This dataset contains 3,653 samples of the original image areas and provides mask-labeled images in the semantic segmentation domains. Each pixel within a construction waste landfill is classified into 4 categories of the image areas, including background area, vacant landfillable area, engineering facility area, and waste dumping area. The dataset contains 237,115,531 pixels of construction waste and 49,724,513 pixels of engineering facilities. The pixel-level semantic segmentation labels are provided to quantify the construction waste yield, which can serve as the basic data for construction waste extraction and yield estimation both for academic and industrial research.

A Brief Analysis of Students' Chinese-style Competitive Behavior

This academic paper delves into the intriguing competitive behaviors and phenomena exhibited by Chinese students. It provides a preliminary analysis of the root causes of competitive psychology, considering both the advantages and disadvantages of resulting social phenomena — specifically, over-competition and under-competition — from cultural and sociological perspectives. Utilizing Daxing District in Beijing as a case study, the paper identifies six influential factors and constructs a competitiveness model through expert scoring and a moving weighted average method.

Assessment of firefighter-training effectiveness in China based on human-factor parameters and machine learning.

The emergency rescue ability of firefighters is particularly important in the event of major disasters or accidents. Therefore, an assessment of the firefighter-training effectiveness is necessary. This paper aims to achieve a scientific and effective assessment of the firefighter-training effectiveness in China. An assessment method based on human factor parameters and machine learning was proposed. The model is constructed by collecting the corresponding human factor parameters such as electrocardiographic signals, electroencephalographic signals, surface electromyographic signals, and photoplethysmographic signals through wireless sensors and using them as constraint indicators. For the problems of weak human factor parameters and high noise proportion, an improved flexible analytic wavelet transform algorithm is used to denoise and extract the corresponding feature values. To overcome the limitations of traditional assessment methods, improved machine learning algorithms are used to comprehensively assess the training effectiveness of firefighters and provide targeted training suggestions. The effectiveness of this study's evaluation method is verified by comparing it with the expert scoring method and considering firefighters from a special fire station in Xhongmen, Daxing District, Beijing, as an example. This study can effectively guide the scientific training of firefighters and the method is more objective and accurate than the traditional method.

Coexistence and transformation from urban industrial land to green space in decentralization of megacities: A case study of Daxing District, Beijing, China

Decentralization is essential for the sustainable socio-economic development of megacities. In the context of the decentralization of noncapital functions in Beijing, the renewal of inefficient urban industrial land (UIL) into urban green space (UGS) is an important way to improve the quality of the city. This paper analyzes the urban industrial land use efficiency (UILUE) and its urban green space suitability (UGSS) of 865 study units in Daxing District, Beijing, and combines bivariate spatial autocorrelation with cluster analysis. The results show that (1) the distribution of UIL in Daxing District is “dense in the north and sparse in the south”, and UILUE shows a spatial pattern of “high in the west and low in the east”, with a high-efficiency area of 985.85 ha, accounting for 12.06 % of the study area, and a low-efficiency area of 507.77 ha, accounting for 6.21 % of the study area. (2) Among the existing UIL, the high suitable area is 1106.46 ha, accounting for 13.57 % of the study area, and the low suitable area is 439 ha, accounting for 5.38 % of the study area. (3) Through bivariate spatial autocorrelation analysis, four types of UIL were identified, among which the low–high cluster area had the greatest potential for land conversion, with 175 study units and an area of 1335.22 ha, accounting for 16.28 % of the study area. Finally, this paper proposes recommendations for the renewal and utilization of UIL in Daxing District to provide guidance and suggestions for the decentralization of other megacities in the world.

Analyzing the Impact of Decreasing Out-of-Vehicle Time of Public Transportation Travel on Accessibility to Tertiary Hospitals

Unequal distribution of healthcare resources can lead to many fundamental problems, including the accessibility and equity of care in different regions. Existing studies often focus on administrative divisions, street zones, and conducting analyses of healthcare accessibility, but there is a lack of research on healthcare accessibility analysis specifically targeting apartment complexes. Furthermore, there is insufficient consideration of the impact of out-of-vehicle time on healthcare accessibility through public transportation. Taking Beijing’s 5th Ring Road area as an example, we used multiple data sources to construct a framework for the accessibility of medical care in apartment complexes using public transportation. We assumed two scenarios of 1/2 and 1/3 reduction in out-of-vehicle time. We compared and analyzed the changes in accessibility and equity under the two scenarios to investigate the impact of out-of-vehicle time on accessibility and equity of medical care in apartment complexes. The results show that (1) reducing out-of-vehicle time does not guarantee increased accessibility to all apartment complexes. (2) Under both scenarios, the accessibility of most apartment complexes within the fourth and fifth rings increased, and the accessibility of most apartment complexes within the Daxing District increased; otherwise, the accessibility of most apartment complexes in other areas decreased, and the decrease in accessibility was more significant for the scenario with a 1/2 reduction in out-of-vehicle time than for the scenario with a 1/3 reduction in out-of-vehicle time. (3) In both scenarios, the Gini coefficients of residential accessibility were calculated separately for inter-ring and administrative divisions, and the equity of residential accessibility increased in each division; the equity of accessibility increased more with a 1/2 out-of-vehicle time reduction than with a 1/3 out-of-vehicle time reduction. The framework proposed in this paper allows us to analyze the impact of out-of-vehicle time of public transportation on accessibility to medical care for apartment complexes.

System optimization and mode modification of the solar assisted ground source heat pump system for primary schools in northern rural areas of China

With the increasing application of geothermal and solar energy in northern regions of China, it has become one of the main clean heating technologies for rural primary schools. However, the mismatch in equipment capacity has led to poor operating effect and economic efficiency of such systems. This paper puts forward experimental and analysis on a solar assisted ground source heat pump system in a primary school in Beijing's Daxing District. And then, to address the above issues, a TRNSYS model is established and verified to optimize the system strategy and form during 15 years period. What’ more, the main factors affecting solar collector selection are screened by sensitivity analysis. And through the step-by-step regression analysis, the collector selection correction model based on the coefficient Km is obtained. The results show that the temperature difference of the optimized collector control is 10 °C/1°C, and that of the supplementary heat is 20 °C/1°C. The applicable scenarios of the two system forms are different, and the energy efficiency of the system operation is increased by 23% and 23.4% respectively. In addition, the revised model ensures the balance of soil temperature for 15 years of operation under the condition of considering multiple elements, which provides a reference for the matching and selection of equipment for the same type of project in the future.

Rainstorm and high‐temperature disaster risk assessment of territorial space in Beijing, China

AbstractIt is important for territorial spatial planning to master meteorological disaster risk under the conditions of climate change and then carry out risk adaptation planning according to local conditions. Taking Beijing, a large city in China, as an example, a meteorological disaster risk assessment model was established based on the framework of hazard factors, disaster‐bearing body exposure, and vulnerability of underlying surface. Combined with 11 years of observation data from 293 high‐density weather stations, the rainstorm and high‐temperature risks of urban, agriculture, and ecological spaces were studied. The results show that (1) rainstorms and high‐temperature are mainly distributed in the built‐up areas of plain towns, which are the climate risk factors that need to be considered. (2) The central city of Beijing is at a high risk of rainstorms and high temperature, indicating that the underlying surface and disaster‐bearing body are highly vulnerable to meteorological disasters. (3) In suburbs with agricultural land, there is a certain rainstorm risk in Fangshan and Daxing districts, and a risk of high temperatures in the southern part of Tongzhou and Daxing. (4) The risk of high temperatures in the ecological space (eco‐zone) is relatively low, but the rainstorm risk is relatively high in Pinggu and Miyun. (5) The strategies of coping with rainstorm and high‐temperature disaster risk in Beijing's territorial space planning were discussed.

MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds

Extracting roads from remote sensing images is of significant importance for automatic road network updating, urban planning, and construction. However, various factors in complex scenes (e.g., high vegetation coverage occlusions) may lead to fragmentation in the extracted road networks and also affect the robustness of road extraction methods. This study proposes a multi-scale road extraction method with asymmetric generative adversarial learning (MS-AGAN). First, we design an asymmetric GAN with a multi-scale feature encoder to better utilize the context information in high-resolution remote sensing images (HRSIs). Atrous spatial pyramid pooling (ASPP) and feature fusion are integrated into the asymmetric encoder–decoder structure to avoid feature redundancy caused by multi-level cascading operations and enhance the generator network’s ability to extract fine-grained road information at the pixel level. Second, to maintain road connectivity, topologic features are considered in the pixel segmentation process. A linear structural similarity loss (LSSIM) is introduced into the loss function of MS-AGAN, which guides MS-AGAN to generate more accurate segmentation results. Finally, to fairly evaluate the performance of deep models under complex backgrounds, the Bayesian error rate (BER) is introduced into the field of road extraction for the first time. Experiments are conducted via Gaofen-2 (GF-2) high-resolution remote sensing images with high vegetation coverage in the Daxing District of Beijing, China, and the public DeepGlobe dataset. The performance of MS-AGAN is compared with a list of advanced models, including RCFSNet, CoANet, UNet, DeepLabV3+, and DiResNet. The final results show that (1) with respect to road extraction performance, the Recall, F1, and IoU values of MS-AGAN on the Daxing dataset are 2.17%, 0.04%, and 2.63% higher than the baselines. On DeepGlobe, the Recall, F1, and IoU of MS-AGAN improve by 1.12%, 0.42%, and 0.25%, respectively. (2) On road connectivity, the Conn index of MS-AGAN from the Daxing dataset is 46.39%, with an improvement of 0.62% over the baselines, and the Conn index of MS-AGAN on DeepGlobe is 70.08%, holding an improvement of 1.73% over CoANet. The quantitative and qualitative analyses both demonstrate the superiority of MS-AGAN in preserving road connectivity. (3) In particular, the BER of MS-AGAN is 20.86% over the Daxing dataset with a 0.22% decrease compared to the best baselines and 11.77% on DeepGlobe with a 0.85% decrease compared to the best baselines. The proposed MS-AGAN provides an efficient, cost-effective, and reliable method for the dynamic updating of road networks via HRSIs.

Study on long-term performance sustainability of medium deep borehole heat exchanger based on simplified one-dimensional well model

Heating buildings and residences is a low-cost and high-efficiency renewable energy utilization method through the coupling of medium deep borehole heat exchanger and ground source heat pump systems. Since the performance of the medium deep borehole heat exchanger system will gradually decrease during long-term operation, its sustainability becomes a key issue in current research. In this paper, a comprehensive numerical model with a simplified one-dimensional well is established using COMSOL Multiphysics by considering the influence of factors including layered strata, groundwater flow, thermophysical properties, and geothermal gradient. The rationality of the model is verified by the thermal response test data of the medium deep borehole heat exchanger system located in Daxing District, Beijing, China. The outlet temperature of the medium deep borehole heat exchanger system declined by 0.48 °C (3.1%) next 20 years of continuous operation. The area greatly affected by heat transfer process is basically within 20 m around the borehole. In the fifth, tenth, fifteenth, and twentieth years, the heat transfer impact scope is about 43 m, 56 m, 74 m, and 84 m, respectively. This study can provide a reference for the performance sustainability of long-term operation of medium deep borehole heat exchanger systems.

Chinese academic community speaks out on He Jiankui again: Consensus statement on the challenges and responses of science and technology ethics governance

Chinese academic community speaks out on He Jiankui again: Consensus statement on the challenges and responses of science and technology ethics governance

Surface Subsidence Characteristics and Causes in Beijing (China) before and after COVID-19 by Sentinel-1A TS-InSAR

Surface subsidence is a serious threat to human life, buildings and traffic in Beijing. Surface subsidence is closely related to human activities, and human activities in Beijing area showed a decreasing trend during the Corona Virus Disease 2019 (COVID-19). To study surface subsidence in Beijing before and after the COVID-19 outbreak and its causes, a total of 51 Sentinel-1A SAR images covering Beijing from January 2018 to April 2022 were selected to derive subsidence information by Time Series Interferometry Synthetic Aperture Radar (TS-InSAR). The results of surface subsidence in Beijing demonstrate that Changping, Chaoyang, Tongzhou and Daxing Districts exhibited the most serious subsidence phenomenon before the COVID-19 outbreak. The four main subsidence areas form an anti-Beijing Bay that surrounds other important urban areas. The maximum subsidence rate reached −57.0 mm/year. After the COVID-19 outbreak, the main subsidence area was separated into three giant subsidence funnels and several small subsidence funnels. During this period, the maximum subsidence rate was reduced to −43.0 mm/year. Human activity decrease with the COVID-19 outbreak. This study effectively analysed the influence of natural factors on surface subsidence after excluding most of the human factors. The following conclusions are obtained from the analysis: (1) Groundwater level changes, Beijing’s geological structure and infrastructure construction are the main reasons for surface subsidence in Beijing. (2) Seasonal changes in rainfall and temperature indirectly affect groundwater level changes, thereby affecting surface subsidence in the area. (3) The COVID-19 outbreak in early 2020 reduced the payload of Beijing’s transportation facilities. It also slowed down the progress of various infrastructure construction projects in Beijing. These scenarios affected the pressure on the soft land base in Beijing and reduced the surface subsidence trend to some extent.

Educational Inequity and Skill Formation Differences Experienced by Floating Rural Students in the Process of Urbanization: A Case Study from a School Perspective

In the process of urbanization in China, the migrant worker population entering cities is an important force in building cities. The children of these migrant workers who do not have the qualifications to participate in college entrance examinations in the city generally become floating rural students. The education problem of the children of the migrant worker population entering the city is still insufficiently considered, and the education inequality and skill formation defects faced by floating rural students are worth paying attention to. This study selected P Middle School in Daxing District of Beijing as a case and took “input–process–output” as the thread to investigate and analyze the school’s source of students and enrollment situation, survival strategy and student graduation destination. It tried to present the original ecology of the school’s survival situation from the micro level and further interpret the education inequality and skill formation of floating rural students from the perspective of the school’s survival. Through the case study, we have found that the academic achievement of students in privately run schools for migrant workers’ children is not high. The level of teachers in these schools is low, and teacher turnover is high, resulting in a significant gap in the quality of education compared to public schools. The main source of funding for these schools is donations from members of the community, and government funding is inadequate. Floating rural students in privately run schools for migrant workers’ children have poor graduation destinations, with a low percentage of students going on to key high schools, and some students are forced to become returning children, facing institutional barriers to upward mobility through education. These aspects have led to education inequality and possible defects in the skill formation of floating rural students. We hope to clarify and grasp the actual situation of privately run schools for migrant workers’ children and put forward corresponding policy recommendations to help bridge the educational inequity in China.

Tri-decadal evolution of land subsidence in the Beijing Plain revealed by multi-epoch satellite InSAR observations

The persistent land subsidence in the Beijing Plain induced by the massive exploitation of groundwater caused significant decreases in ground elevation and damages to buildings and civil infrastructures. Although various satellite SAR datasets have been analyzed to monitor subsidence in the Beijing Plain, no multi-decadal scale deformation measurement has been derived from multi-sensor InSAR results, and the recent deceleration of land subsidence or even uplift remains unclear. This study retrieved land subsidence measurements over the past three decades (1992–2022) by integrating multi-sensor C-band InSAR measurements into a logistic model. The cumulative subsidence presented three major subsidence funnels surrounding downtown in the North, Northeast, and East, with the maximum vertical displacement as high as 1.98 m in the Chaoyang District. The land subsidence positively responded to the changes in groundwater depth, showing a clear deceleration since 2016. After five and half years, it became almost stagnated within the context of continuous groundwater recovery, which implied the delayed compaction of lowly draining aquitards. Only slight residual subsidence persisted in Chaoyang, Shunyi, and Daxing Districts. Meanwhile, land uplift happened in Shunyi, Huairou, and Pinggu Districts. Remarkably, a portion of the land subsidence in the Chaobai Plain reversed to uplift due to multiple artificial recharges using diverted water. Although groundwater storage could recover significantly in the next decade, most severely sunken lands and inelastically compacted aquitards are irrecoverable, leading to a permanent loss of storage capacity in the Beijing Plain. This study provides scientific data support for the local government decision-maker to better manage groundwater resources and mitigate land subsidence, which presents a typical example of monitoring long-term land subsidence in similar global megacities.

Soil quality assessment of reclaimed land in the urban–rural fringe

The expansion of the urban–rural fringe (URF) has greatly boosted the economy, but haphazard building and chaotic management have severely harmed the soil, particularly valuable arable land resources. The majority of studies have focused on the regional identification of URF, with little research on the influence of URF on soil quality. The soil quality index (SQI) method was used in this study to compare the soil quality of reclaimed land (FKD) in the URF to nearby grain fields (GD), open-air vegetable plots (CD), and facility vegetable plots (DP) in Daxing District, Beijing, China. The results indicated that the SQI of FKD was much lower than that of other land utilization types. Soil organic carbon (SOC), nitrogen, and phosphorus levels were low in FKD and might be improved with proper fertilization in subsequent management. The contents of Na, Mg, Ca, Fe, and total potassium (TK) in FKD were not statistically different from those in GD and CD, but the urease and sucrase activities in FKD were significantly lower than those in the other three land use patterns. A minimum data set including SOC, available phosphorus, TK, clay, Mn, and Zn was obtained for soil quality assessment using principal component analysis with Norm value and correlation analysis. Soil trace elements were standardized using an “optimum is better” scoring function using the 75th percentile of the element background values as the threshold. To our knowledge, this is the first time in soil quality assessment that the 75th percentile of the element background value for a specific soil type has been chosen as the optimum value.

Research into the Optimal Regulation of the Groundwater Table and Quality in the Southern Plain of Beijing Using Geographic Information Systems Data and Machine Learning Algorithms

The purpose of this paper is to provide new ideas and methods for the sustainable use of groundwater in areas with serious groundwater overexploitation and serious groundwater pollution. Geographic information systems (GIS) were combined with machine learning algorithms, water resources optimization technology, and groundwater numerical simulation to optimize the regulation of the groundwater table and quality beneath the Daxing District in the southern plain of Beijing. By collecting local consumption and supply data and observations of the groundwater table and quality in the connected aquifer beneath Daxing for the years 2006–2020, the corresponding water demands and groundwater impact were extrapolated for the years 2021–2025 based on the basis of the existing development model. Through the combination of GIS and machine learning algorithms, the NO3-N concentration of local groundwater monitoring points in wet years, normal years, and dry years were predicted. With respect to NO3-N pollution, three new groundwater exploitation regimes were devised, which we numbered 1 to 3. The optimal allocation of water resources was then calculated for wet year, typical year, and dry year scenarios for the year 2025. By comparing the water shortage, groundwater utilization rate, and NO3-N pollution under the new groundwater exploitation regimes, the optimal groundwater exploitation mode for the three different types of hydrological year was determined. The results indicate that NO3-N pollution was greatly reduced after the adoption of the optimal regimes and that the groundwater table demonstrated rapid recovery. These results can be of great help in realizing the management, supervision, and regulation of groundwater by combining GIS with machine learning algorithms.

Simulated Validation and Prediction of Land Use under Multiple Scenarios in Daxing District, Beijing, China, Based on GeoSOS-FLUS Model

Land-use changes in urban fringe areas are dramatic, and modelling and predicting land-use changes under different scenarios can provide a basis for urban development regulation and control. As an important part of Beijing’s urban fringe, Daxing District is representative of its land-use changes. Taking the Daxing District of Beijing as an example, this study selected two periods of land-use data in 2008 and 2018 and predicted land-use changes in 2028 and 2038 using the GeoSOS-FLUS model (geographical simulation and optimisation system–future land-use simulation) and Markov chain model, based on the simulation and validation of land use in Daxing District from 2008 to 2018. Meanwhile, three types of scenario simulations were carried out. The results in the future predictions show that: (1) under the natural development scenario, the area of construction land and grassland gradually increased, and the area of cultivated land, woodland and water bodies gradually decreased; (2) under the cultivated land protection scenario, the area of cultivated land remained largely unchanged, the area of grassland decreased before increasing, the expansion of construction land was curbed, and the area of woodland and water bodies increased slowly; and (3) under the ecological control scenario, the area of cultivated land, grassland, woodland and water bodies showed slowly increasing trends, with a small amount of cultivated land being converted to construction land. These results indicate that the setting of cultivated land protection and ecological control can limit the expansion of construction land to a certain extent. This study can provide a basis for the regulation of urban development in the Daxing District in the future.

Effect of Open-Window Gaps on the Thermal Environment inside Vehicles Exposed to Solar Radiation

To avoid a sharp rise in temperature in the cabin of parked vehicles exposed to solar radiation, experienced drivers leave some windows partly open when the vehicle is parked in the sunlight to achieve cooling through natural ventilation. However, the effectiveness of this measure to reduce the temperature under different weather conditions has not been verified. To this end, this study investigates the effect of open windows on the thermal environment of a vehicle under different environmental conditions. A field measurement, in which two identical vehicles with and without window gaps were used, was carried out in Daxing District, Beijing. The measurements were conducted for 15 days under different window gaps and ambient conditions. The results revealed that open windows resulted in a maximum temperature reduction of 6.7 °C in cabin air temperature under high temperature and high solar radiation, while only 0.6 °C can be reduced under low temperature and low solar radiation. The results also showed that when window gaps effectively reduce the air temperature, lower air temperature can be obtained with larger open-window areas.