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With a large number of new energy vehicles being put into use, it is the general trend for traditional fuel vehicles to withdraw from the market in an orderly manner. Determining the optimal ratio between them in this process is of great significance to the low-carbon sustainable development of cities. Therefore, considering the constraints of urban automobile development planning and air pollution prevention and control policies, a multi-objective model to minimize pollutants and costs is constructed in this paper. Through model calculation and sensitivity analysis of dynamic impact relationship of different types of vehicles, it is determined that when new energy vehicles account for around 36% in Beijing, 57% in Shanghai and 46% in Guangzhou, the pollutant emissions can be minimized without causing a significant increase in social costs. Additionally, compared with 2030, Beijing, Shanghai and Guangzhou can achieve emission reductions of 320,000 tons, 200,000 tons and 250,000 tons, respectively, in 2050 if they implement the policy of banning the sale and delisting of fuel vehicles, which could provide suggestions for the guidance of the low-carbon development plan of the automobile industry. [ABSTRACT FROM AUTHOR]

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places. [ABSTRACT FROM AUTHOR]

Sky radiative cooling is a kind of passive cooling technology that uses the "atmospheric window" to emit the object's own heat to the low temperature of outer space; this technology has low energy consumption, no pollution, and other useful characteristics, so in recent years it has attracted widespread attention. The cooling effect of the sky radiative cooler is mainly affected by the constantly changing outdoor ambient temperature. In addition, the structure of the radiative cooler itself also means that its radiative cooling power undergoes obvious changes. Here, we utilized COMSOL simulation software to establish a numerical heat transfer model for radiative cooling, aimed at investigating the influencing factors on the sky radiative cooler and methods to enhance the structure of the radiative cooling. This study discusses outdoor ambient wind speed, the inlet flow rate of the cooler, installation angle of the cooler, and different cooler structures. Based on simulation results, it is observed that, for varying wind speeds, when the ambient radiation temperature is higher than the surface temperature of the cooler, a larger ambient wind speed leads to a poorer refrigeration effect. The maximum temperature difference in surface temperature at wind speeds of 0 m/s and 4 m/s is 0.59 °C. When the ambient temperature is lower than the surface temperature of the cooler, a smaller wind speed results in a greater net refrigeration power. The maximum temperature difference in this scenario is 0.32 °C. The net refrigeration power of the radiative cooler increases with an increase in water flow rate. As the water flow rate increases from 0 L/min to 5 L/min, the net refrigeration power increases from 25 W/m2 to 200 W/m2 and gradually stabilizes. Considering the radiative impact of the cooler on the surrounding environment, as the installation angle increases from 0° to 90°, the surface temperature of the cooler first increases and then decreases, reaching its highest temperature of 29.26 °C at 45°. The surface temperature of the cooler varies with the thickness of the air sandwich, increasing from 1 cm to 12 cm, and then decreasing. The lowest temperature of 23.4 °C is achieved at a thickness of 8 cm. The increase in the fin structure on the surface of the radiative cooler leads to a decrease in its refrigeration performance, and the difference between the inlet and outlet temperatures of the radiative cooler with a flat plate structure is always greater than that of the finned plate, and the difference in the average radiance is 23.52 W/m2. Finally, the energy-saving effect of the sky radiative cooling composite system is analyzed. Taking a typical small office building as an example, an energy consumption analysis model is set up, and the energy consumption of the composite system is simulated in four cities with different climates, using EnergyPlus software (version 8.6); the system's power consumption is the largest in hot and humid climates. Compared with the traditional vapor-compression refrigeration system, the composite system reduces air conditioning power consumption by 25.7%, 32.5%, 37.1%, and 44.8% in Guangzhou, Shanghai, Jinan, and Shenyang, respectively. The main innovations of this paper include analyzing and studying the influence of the tilt angle change of the radiative plate on the refrigeration performance of the cooler and the relationship between the surrounding buildings, adding air sandwiches and ribs to the radiative cooler to analyze the influence of convective heat transfer on the refrigeration effect, which plays a guiding role in the design and research of the sky radiative cooler. [ABSTRACT FROM AUTHOR]

High-rise building areas (HRBs) provide significant social and environmental services and play a crucial role in modern urbanization. The large-scale and long-term spatial distribution of HRBs is of great interest to many fields, such as urban planning and local climate analysis. While previous studies have confirmed the value of Sentinel-2 images in extracting HRBs and their changes, current work is limited to relatively local areas and short-term analysis. One reason is due to the fact that the earliest Sentinel-2 image can only date back to 2015. To address this research gap, this paper proposes an efficient procedure to intelligently extract HRBs and their changes from multitemporal Landsat-7 and Sentinel-2 images, using a specifically designed fully convolutional network. To validate the proposed method, we selected four typical cities in China, namely, Beijing, Shanghai, Guangzhou, and Zhengzhou, as study areas. We utilized Landsat-7 images acquired in 2000 and 2010, along with Sentinel-2 images acquired in 2020, as experimental data. We extracted and analyzed three periods of HRBs and their changes in the four cities, along with urban rail terminal data and gross domestic product (GDP) data in the same period. The results show that the proposed method can efficiently extract HRBs and their changes in the four cities over the past 20 years, with an overall accuracy of more than 90%. HRBs changes are primarily driven by urban planning policies and geographical factors. There is a strong positive correlation between the increase in HRBs and the increase in rail terminals, both in terms of quantity and spatial distribution. Additionally, there is a positive correlation between HRBs increase and GDP increase in terms of quantity, but the trend varies in different cities due to their diverse developing modes. Overall, the results indicate that the proposed method can be a potential operational tool to extract large-scale and long-term HRBs and their changes in China. [ABSTRACT FROM AUTHOR]

The deep mining of passengers' travel data can identify competitive segments and gain insights into passengers' characteristics and differentiated demands. This can not only effectively support precise marketing strategy adjustment of railway transport but also improve its competitiveness in the passenger transportation market. In this paper, hidden railway travel behaviour is introduced and integrated with railway travel behaviour to create a complete passenger travel chain, based on existing distance-based competitive segment recognition methods. The loyalty index values of passengers are calculated using this travel chain to identify competitive segments. Furthermore, passenger classification and grouping currently ignore social relationships as well as personal travel characteristics. Therefore, a novel passenger grouping method is proposed; it integrates individuals' travel characteristics and social relations. Individual travel labels are created for travellers based on their travel data. Social relation topologies, such as ticketing relation, the relation of travelling together, and benefit relation via point redemption, can be extracted using these labels. Social relation traits can be retrieved using graph attention networks and multigraph fusion. Finally, travellers are categorised based on their individual travel characteristics. As an example, and a case study, the grouping of Guangzhou–Shanghai passengers in 2020 is taken which shows that the suggested method has the potential to improve both the precision and the feasibility of grouping railway passengers. As a result, new ideas for passenger grouping in railway marketing might be offered. [ABSTRACT FROM AUTHOR]

Super high-rise building is a typical architectural form of urban modernization, and it is also the trend of urban architectural development. This paper reviews the development history of super high-rise buildings in China combined with the engineering practice of representative super high-rise buildings since the 1980s, such as the Shanghai Center, Shanghai Oriental Pearl Tower, Jinmao Tower, Shanghai World Financial Center, Canton Tower, and Shanghai Tower. The development process of the key technologies of super high-rise projects from the aspects of several technologies, such as soft soil pile foundation and foundation pit technology, concrete structure construction technology, integral formwork equipment technology, and steel structure comprehensive installation technology, is also elaborated. At the same time, the Shanghai Tower is selected as a typical case to share the innovative technology of China's tallest building. Finally, prospects for development of super high-rise construction technology are presented. [ABSTRACT FROM AUTHOR]

Online comments have become an important tool for disabled guests because of lower physical movement requirements. In order to illustrate and evaluate disabled guests' decision-making characteristics, this paper has used two steps for studying: (1) Data mining technology to collect e-comments from C-trip (www.ctrip.com) of 97 hotels in the cities of Beijing, Shanghai, Hangzhou and Guangzhou. Over 260,000 words were collected and analyzed by using ROSTcm software for this research. (2) Examined the relationship between the credibility and the perceived risk of disabled guests for their behavioral intentions. The result of this study has showed that: (1) Disabled guests pay more attention to the hotel barrier-free facilities, hotel barrier-free facilities, hotel personalized service, location accessibility, and the attitude and atmosphere of the hotel. (2) Disabled guests show positive attitude towards the hotel, and a neutral attitude accounting for 25.86% which indicates that the hotel industry still has much improving room especially with regards to disabled accessible guestrooms and accessibility. (3) Electronic Word-of-Mouth (eWOM) source credibility has a great influence on the overall perceived risk of the review of disabled guests because they are more likely for seeking internet comments for their lodging decision-making than other common guests. eWOM rational management can reduce the overall potential risk for disabled guests. (4) Hotels can enhance disabled guests' decision-making by advanced eWOM management. The study illustrated that disabled guests who used eWOM could be better managed and reduce potential risks when making decisions. As a result, this study also found that with better management of eWOM, it could help to meet the potential market for the disabled guests and at the meanwhile attract more customers because of higher social reputation. [ABSTRACT FROM AUTHOR]

The quantitative relationship between the spatial variation of building's height and the associated land surface temperature (LST) change in six Chinese megacities is investigated in this paper. The six cities involved are Beijing, Shanghai, Tianjin, Chongqing, Guangzhou, and Shenzhen. Based on both remote sensing and building footprint data, we retrieved the LST using a single-channel (SC) algorithm and evaluate the heating/cooling effect caused by building-height difference via correlation analysis. The results show that the spatial distribution of high-rise buildings is mainly concentrated in the center business districts, riverside zones, and newly built-up areas of the six megacities. In the urban area, the number and the floor-area ratio of high to super high-rise buildings (>24m) account for over 5% and 4.74%, respectively. Being highly urbanized cities, most of urban areas in the six megacities are associated with high LST. Ninety-nine percent of the city areas of Shanghai, Beijing, Chongqing, Guangzhou, Shenzhen, and Tianjin are covered by the LST in the range of 30.2~67.8°C, 34.8~50.4°C, 25.3~48.3°C, 29.9~47.2°C, 27.4~43.4°C, and 33.0~48.0°C, respectively. Building's height and LST have a negative logarithmic correlation with the correlation coefficients ranging from -0.701 to -0.853. In the building's height within range of 0~66m, the LST will decrease significantly with the increase of building's height. This indicates that the increase of building's height will bring a significant cooling effect in this height range. When the building's height exceeds 66m, its effect on LST will be greatly weakened. This is due to the influence of building shadows, local wind disturbances, and the layout of buildings. [ABSTRACT FROM AUTHOR]

With the development of industrialization and urbanization, cities have become the main carriers of economic activities. However, the long-term development of cities has also caused damage to resources and the environment. Hence, objective and scientific evaluation of urban low-carbon sustainable development capacity is very important. An index system of urban low-carbon sustainable development capability is constructed in this paper, and a TOPSIS-BP neural network model is established to evaluate the low-carbon sustainable development capability of Beijing, Shanghai, Shenzhen, and Guangzhou in China. At the same time, the difference degree of low-carbon sustainable development level in these four cities is analyzed by standard deviation and coefficient of variation, and the influencing factors of urban low-carbon sustainable development ability are extracted by grey correlation analysis. The results show that (1) the capability of low-carbon sustainable development in four cities is rising and the difference of low-carbon sustainable development capability is decreasing; (2) the general view that the higher the general investment in low-carbon sustainable development, the higher the level of low-carbon sustainable development in cities has not been verified; (3) with the change of time series, the factors affecting the capability of low-carbon sustainable development in the same city are different and the influence of the same factor on the capability of low-carbon sustainable development in different cities is different. [ABSTRACT FROM AUTHOR]

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems. [ABSTRACT FROM AUTHOR]

With its rapid economic development, China has had to confront the serious issues of high energy consumption and air pollution. Natural ventilation is regarded as an effective method to reduce building energy consumption, but it is largely influenced by indoor and outdoor air pollution. However, most of the previous studies estimating natural ventilation potential (NVP) in China do not consider air pollution. This research estimated the NVP for residential buildings in major cities from four climate regions in China (Guangzhou, Chengdu, Shanghai, Beijing, and Shenyang) while considering the combined effect of indoor and outdoor air pollution. We compared the yearly NVP in three different scenarios, namely without considering air pollution, only considering outdoor air pollution, and considering both outdoor and indoor air pollution. The results show that Guangzhou had the highest yearly NVP, followed by Shanghai, Beijing, Shenyang, and Chengdu. The impact of air pollution could reduce the annual NVP in China by 78–95%. In addition, the main factors causing a low NVP differed between the four cities. The key factors for Chengdu and Guangzhou were natural ventilation flow rate and indoor air pollution, respectively. Beijing and Shenyang were mostly influenced by outdoor air pollution. Shanghai had two main factors with similar influence degrees, namely outdoor air pollution and indoor air pollution. The findings of this study will guide architects and policymakers in better forming natural ventilation strategies. [ABSTRACT FROM AUTHOR]

Spatiotemporal fusion technology effectively improves the spatial and temporal resolution of remote sensing data by fusing data from different sources. Based on the strong time-series correlation of pixels at different scales (average Pearson correlation coefficients > 0.95), a new long time-series spatiotemporal fusion model (LOTSFM) is proposed for land surface temperature data. The model is distinguished by the following attributes: it employs an extended input framework to sidestep selection biases and enhance result stability while also integrating Julian Day for estimating sensor difference term variations at each pixel location. From 2013 to 2022, 79 pairs of Landsat8/9 and MODIS images were collected as extended inputs. Multiple rounds of cross-validation were conducted in Beijing, Shanghai, and Guangzhou with an all-round performance assessment (APA), and the average root-mean-square error (RMSE) was 1.60 °C, 2.16 °C and 1.71 °C, respectively, which proved the regional versatility of LOTSFM. The validity of the sensor difference estimation based on Julian days was verified, and the RMSE accuracy significantly improved (p < 0.05). The accuracy and time consumption of five different fusion models were compared, which proved that LOTSFM has stable accuracy performance and a fast fusion process. Therefore, LOTSFM can provide higher spatiotemporal resolution (30 m) land surface temperature research data for the evolution of urban thermal environments and has great application potential in monitoring anthropogenic heat pollution and extreme thermal phenomena. [ABSTRACT FROM AUTHOR]

The development of the HSR station area is the result of the combined effect of the three elements of transport, place, and industry. This study introduces the industrial dimension and constructs the node-place-industry model to empirically analyze the development potential of station areas along the Hunan section of the Beijing–Guangzhou and the Shanghai–Kunming high-speed railway lines. The results show that (1) the development of the three spatial elements of the station area is mostly out of sync, and the node value has the highest fit with the integrated potential value of the station area; (2) there is a significant correlation between the magnitude of the combined potential of the station area and the site location, station class and time of development; (3) according to the results of the cluster analysis, it was found that most of the stations were in a state of disequilibrium, and the main reason was that the functional value of the place did not match with the value of industrial aggregation. In particular, the introduction of the industry dimension extends the NP model and establishes a tessellated analytical framework for station type classification, providing an interesting assessment tool for the sustainable development of transport hub areas. [ABSTRACT FROM AUTHOR]

In recent years, as China's peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO2, O3, and CO2 column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO2 decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO2, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO2 and O3 concentrations on changes in XCO2. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO2 concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O3 concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO2 concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O3 concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO2 concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O3 concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO2. Moreover, the results show that the effect of NO2 concentration on XCO2 is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO2 and the O3 concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan's carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan's XCO2 fell by about 1.12 ppm in February. At last, the government should consider reducing XCO2 and NO2 concentration at the same time to make a synergistic reduction. [ABSTRACT FROM AUTHOR]

This research investigated the exergy enhancement performance of a hybrid radiant cooling system adapting to a hot and humid summer conditions through comparative case studies and analyses. This study suggested three cooling systems: a general all-air system (AAS), a conventional radiant cooling system (CRCS), and a hybrid radiant cooling system (HRCS). As a case study, an office building with cooling systems was examined in the summer season in four different cities: Beijing, Shanghai, Chengdu, and Guangzhou, China. This study utilized the building energy performance simulation program to analyze the cooling loads of office space in a building with numerical approaches. The comparison analysis using the four different weather datasets showed simple and rational exergy efficiency and the overall impact ratio. According to the results, the ambient conditions, i.e., the surrounding temperature and the humidity ratio, significantly impacted the cooling systems' exergy efficiency ratio. On the basis of the calculated energetic and exergetic performance, the HRCS had a higher exergy efficiency and a higher overall impact ratio. The HRCS system released an additional 20–30% of cooling output, and it could adapt well in extreme hot and humid weather conditions compared to the AAS and the CRCS system. The overall cooling impact ratio of the HRCS with an airbox convector was approximately 185% higher than that of the AAS and 8.5% higher than that of the CRCS. This study can provide the design references for the hybrid radiant cooling system and other cooling systems in hot and humid summer conditions. [ABSTRACT FROM AUTHOR]

Guo, H.W.; Zhou, Y.H., and Cui, W., 2020. An empirical study on the financial development differences of Chinese port cities along the New Maritime Silk Road. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 341-346. Coconut Creek (Florida), ISSN 0749-0208. The financial development of coastal port cities plays a crucial role in their economic development. This paper uses 18 indicators of 16 Chinese port cities along the "New Maritime Silk Road" in the 2018 China Statistical Yearbook to reflect the level of financial development and financial development environment, AHP is used to determine the weight of each indicator, cluster analysis is used to classify the cities according to the financial development level, and then principal component analysis is used to analyze the indicators, so as to get the score ranking of each city's financial development level, find out the reasons for the differences in financial development of port cities. The result shows that the financial industry strength and development speed of Shanghai, Guangzhou and Shenzhen are in the forefront due to historical accumulation factors and long-term national policy support. Cluster analysis further proves that the financial development of each port city has significant differences, and the influencing factors also have regional differences, among which the economic development level, economic growth level and the development of banking and insurance industry have a greater impact on the financial development of the city. [ABSTRACT FROM AUTHOR]

To date, along with the rapid development of urban rail transit (URT) in China, the evaluation of operational efficiency and energy efficiency has become one of the most important topics. However, the extant literature regarding the efficiency of URT at the line level and considering carbon emissions is limited. To fill the gap, an evaluation model based on slacks-based measure (SBM) data envelopment analysis (DEA) is proposed to measure the efficiencies, which is applied to 61 URT lines in China's four megacities. The findings are summarized as follows: (1) The average operational efficiency and energy efficiency of URT lines are low, and both have great room for improvement. (2) There are significant disparities in the efficiency of URT lines in the case cities. For instance, the average operational efficiency of URT lines in Guangzhou is higher than that of other cities, while the average energy efficiency of URT lines in Shanghai is higher than that of other cities. (3) The URT lines operated by state-owned enterprises have higher average operational efficiency, while the lines operated by joint ventures have higher average energy efficiency. Finally, some suggestions are provided to improve the efficiencies. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the industrial production scale and the rapid promotion of urbanization, more and more serious air pollution threatens people's lives and social development. To reduce the losses caused by polluted weather, it is popular to predict the concentration of pollutants timely and accurately, which is also a research hotspot and challenging issue in the field of systems engineering. However, most studies only pursue the improvement of prediction accuracy, ignoring the function of robustness. To make up for this defect, a novel air pollutant concentration prediction (APCP) system is proposed for environmental system management, which is constructed by four modules, including time series reconstruction, submodel simulation, weight search, and integration. It not only realizes the filtering and reconstruction of redundant series based on the decomposition-ensemble mode, but also the weight search mechanism is designed to trade off precision and stability. Taking the hourly concentration of PM2.5 in Guangzhou, Shanghai, and Chengdu, China as an example, the simulation results show that the APCP system has perfect prediction capacity and superior stability performance, which can be used as an effective tool to guide early warning decision-making in the management of environmental engineering. [ABSTRACT FROM AUTHOR]

Abstract: External wall insulation plays an important role in building energy consumption through affecting cooling and heating energy uses. In this paper, a study on the effects of external wall insulation thickness on annual cooling and heating energy uses under different Chinese climates is reported. Firstly, two indexes: Energy Gain Ratio (EGR) and Relative Variation Ratio (RVR) of the sum of annual cooling and heating energy uses, to assess the effects of external wall insulation thickness on energy saving potential are introduced. Secondly, a model space having four different exterior zones facing four different orientations in an office building is presented and the settings of the model space described. Lastly, the simulated annual cooling and heating energy uses for the four different exterior zones at different external wall insulation thicknesses under three different climates in China, Guangzhou, Shanghai and Beijing, are reported. The results suggested that energy saving was significant by increasing the external wall insulation thickness in exterior zones facing all orientations under Beijing’s climate, since the heating energy use was dominant and can be reduced remarkably with the increase in insulation thickness. Under Shanghai’s climate, increasing external wall insulation thickness to over 26mm would not reduce the sum of annual heating and cooling energy uses in the south-facing exterior zone, but would help save energy in exterior zones facing the other three orientations. For all exterior zones under Guangzhou’s climate, it was, however, hardly possible to reduce the sum of annual heating and cooling energy uses by increasing the external wall insulation thickness. [Copyright &y& Elsevier]

Multiyear inventories of vehicular emissions in Beijing, Shanghai and Guangzhou from 1995 through 2005 have been developed in this paper to study the vehicle emissions trends in China''s mega cities during the past decade. The results show that the vehicular emissions of CO, HC, NO x and PM10 have begun to slow their growth rates and perhaps even to decline in recent years due to the implementation of measures to control vehicular emissions in these cities. However, vehicular CO2 emissions have substantially increased and still continue to grow due to little fuel economy improvement. Passenger cars and large vehicles (including heavy duty trucks and buses) are the major sources of vehicular CO2 and CO emissions while large vehicles were responsible for nearly 70% and 80% of the vehicular NO x and PM10 emissions in these mega cities. Motorcycles are also important contributors to vehicular emissions in Guangzhou and Shanghai. [Copyright &y& Elsevier]

With the boom of big data, the Internet contains more and more personal behavior information, but it is difficult to extract effectively. A model involving multivariate processing capability must be constructed to deal with these time series with complex characteristics. In this paper, a novel hybrid model embedding Baidu Search Index is therefore proposed to implement multi-step ahead subway passenger flow forecasting. Firstly, we collect data from informative Baidu Search Index, reduce dimensionality, and screen out the powerful predictors by statistical analysis. Secondly, we extract matching common modes at similar time scales between the subway passenger flow and screened Baidu Search Index via multivariate mode decomposition being optimized by multi-objective algorithm. Furthermore, to eliminate pseudo statistical causality, we select the optimal combination of modal components between subway passenger flow and its corresponding Baidu Search Index at each time scale by an innovative multi–modal analysis strategy. Thirdly, we reconstruct the forecasting values of each selected optimal combination as the final results. The empirical results of Beijing, Shanghai and Guangzhou show that the proposed model can significantly outperform six benchmark models in both the level and directional accuracy. So introducing Baidu Search Index creates a sound opportunity to enhance the subway passenger flow forecasting ability. [ABSTRACT FROM AUTHOR]

• Multifractal method is introduced to determine the interaction between PM 2.5 and O 3. • Multi-temporal scaling of the interaction between PM 2.5 and O 3 is studied. • A mulitfractal index is set up to evaluate PM 2.5 -O 3 coordinated control capability. • Time dependence of the mulitfractal index in different cities verifies its validity. • The mulitfractal index can provide guidance for two-pollutant control strategies. Under combined air pollution conditions, the coordinated control of PM 2.5 and O 3 has significant implications for environmental management. To better understand the coordinated control capability of different metropolises, a prime consideration is how to develop a scientific and objective index for quantifying it. In this paper, based on the multifractal detrended cross-correlation analysis (MF-DCCA) method, a novel dynamical index η is developed to quantitatively determine the capability of metropolis to control PM 2.5 and O 3 pollution cooperatively. Furthermore, combing with the sliding window method, the high temporal scale resolution of η values of four metropolises (Beijing, Shanghai, Guangzhou, and Chengdu) in China is conducted in order to determine the temporal evolution of η. Then, the monthly variation of η values in the four metropolises is investigated. The U-shaped curves in the monthly mean η values indicate that PM 2.5 -O 3 coordinated control capability of the four metropolises is best in summer. The further discussions about the relations between index η and meteorological conditions have been made. Next, long-term trends of η series of four metropolises are studied by Mann–Kendall statistical test. Result shows that the trends of the η series of four metropolises are similar from 2015 to 2017. After 2017, the η series of Beijing, Shanghai and Chengdu show an increasing trend, while that of Guangzhou show a decreasing trend. This suggests that PM 2.5 -O 3 coordinated control capability of Guangzhou is gradually strengthened after 2017. This may be caused by the combined effect of targeted strategies and special climatic conditions in Guangzhou. The η index could directly quantify the two-pollutant coordinated control capability of different metropolises based on field observation, rather than rely on any subjective factor. It can provide guidance for policy-making of pollution emission reduction, so as to control the combined air pollution. [ABSTRACT FROM AUTHOR]

The United Kingdom Chemistry and Aerosols (UKCA) chemistry–climate model is used to quantify the differences in chemical environment for surface O3 for six major industrial regions across China in summer 2016. We first enhance the UKCA gas-phase chemistry scheme by incorporating reactive volatile organic compound (VOC) tracers that are necessary to represent urban and regional-scale O3 photochemistry. We demonstrate that the model with the improved chemistry scheme captures the observed magnitudes and diurnal patterns of surface O3 concentrations across these regions well. Simulated O3 concentrations are highest in Beijing and Shijiazhuang on the North China Plain and in Chongqing, lower in Shanghai and Nanjing in the Yangtze River Delta, and lowest in Guangzhou in the Pearl River Delta despite the highest daytime O3 production rates in Guangzhou. NOx / VOC and H2O2 / HNO3 ratios indicate that O3 production across all regions except Chongqing is VOC limited. We confirm this by constructing O3 response surfaces for each region changing NOx and VOC emissions and further contrast the effectiveness of measures to reduce surface O3 concentrations. In VOC-limited regions, reducing NOx emissions by 20 % leads to a substantial O3 increase (11 %) in Shanghai. We find that reductions in NOx emissions alone of more than 70 % are required to decrease O3 concentrations across all regions. Reductions in VOC emissions alone of 20 % produce the largest decrease (- 11 %) in O3 levels in Shanghai and Guangzhou and the smallest decrease (- 1 %) in Chongqing. These responses are substantially different from those currently found in highly populated regions in other parts of the world, likely due to higher NOx emission levels in these Chinese regions. Our work provides an assessment of the effectiveness of emission control strategies to mitigate surface O3 pollution in these major industrial regions and emphasises that combined NOx and VOC emission controls play a pivotal role in effectively offsetting high O3 levels. It also demonstrates new capabilities in capturing regional air pollution that will permit this model to be used for future studies of regional air-quality–climate interactions. [ABSTRACT FROM AUTHOR]

Air pollution by particulate matter in China affects human health, the ecosystem and the climate. However, the chemical composition of particulate aerosol, especially of the organic fraction, is still not well understood. In this study, particulate aerosol samples with a diameter of ≤2.5 µ m (PM2.5) were collected in January 2014 in three cities located in northeast, east and southeast China, namely Changchun, Shanghai and Guangzhou. Organic aerosol (OA) in the PM2.5 samples was analyzed by an ultrahigh-performance liquid chromatograph (UHPLC) coupled to a high-resolution Orbitrap mass spectrometer in both negative mode (ESI-) and positive mode electrospray ionization (ESI+). After non-target screening including the assignment of molecular formulas, the compounds were classified into five groups based on their elemental composition, i.e., CHO, CHON, CHN, CHOS and CHONS. The CHO, CHON and CHN groups present the dominant signal abundances of 81 %–99.7 % in the mass spectra and the majority of these compounds were assigned to mono- and polyaromatics, suggesting that anthropogenic emissions are a major source of urban OA in all three cities. However, the chemical characteristics of these compounds varied between the different cities. The degree of aromaticity and the number of polyaromatic compounds were substantially higher in samples from Changchun, which could be attributed to the large emissions from residential heating (i.e., coal combustion) during wintertime in northeast China. Moreover, the ESI- analysis showed higher H/C and O/C ratios for organic compounds in Shanghai and Guangzhou compared to samples from Changchun, indicating that OA undergoes more intense photochemical oxidation processes in lower-latitude regions of China and/or is affected to a larger degree by biogenic sources. The majority of sulfur-containing compounds (CHOS and CHONS) in all cities were assigned to aliphatic compounds with low degrees of unsaturation and aromaticity. Here again, samples from Shanghai and Guangzhou show a greater chemical similarity but differ largely from those from Changchun. It should be noted that the conclusions drawn in this study are mainly based on comparison of molecular formulas weighted by peak abundance and thus are associated with inherent uncertainties due to different ionization efficiencies for different organic species. [ABSTRACT FROM AUTHOR]

The Chinese government launched the Air Pollution Prevention and Control Action Plan in 2013, and various stringent measures have since been implemented, which have resulted in significant decreases in emissions and ambient concentrations of primary pollutants such as SO2 , NOx , and particulate matter (PM). However, surface ozone (O3) concentrations have still been increasing in urban areas across the country. In a previous analysis, we examined in detail the roles of meteorological variation during 2013–2017 in the summertime surface O3 trend in various regions of China. In this study, we evaluated the effect of changes in multi-pollutant emissions from anthropogenic activities on O3 levels during the same period by using an up-to-date regional chemical transport model (WRF-CMAQ) driven by an interannual anthropogenic emission inventory. The Community Multiscale Air Quality (CMAQ) model was improved with regard to heterogeneous reactions of reactive gases on aerosol surfaces, which led to better model performance in reproducing the ambient concentrations of those gases. The model simulations showed that the maximum daily 8 h average (MDA8) O3 mixing ratio in urban areas increased by 0.46 ppbv per year (ppbva-1) (p=0.001) from 2013 to 2017. In contrast, a slight decrease in MDA8 O3 by 0.17 ppbva-1 (p=0.005) in rural areas was predicted, mainly attributable to the NOx emission reduction. The effects of changes in individual pollutant emissions on O3 were also simulated. The reduction of NOx emission increased the O3 levels in urban areas due to the nonlinear NOx and volatile organic compound (VOC) chemistry and decreasing aerosol effects; the slight increase in VOC emissions enhanced the O3 levels; the reduction of PM emissions increased the O3 levels by enhancing the photolysis rates and reducing the loss of reactive gases on aerosol surfaces; and the reduction of SO2 emissions resulted in a drastic decrease in sulfate concentrations, which increased O3 through aerosol effects. In contrast to the unfavorable effect of the above changes in pollutant emissions on efforts to reduce surface O3 , the reduction of CO emissions did help to decrease the O3 level in recent years. The dominant cause of increasing O3 due to changes in anthropogenic emissions varied geographically. In Beijing, NOx and PM emission reductions were the two largest causes of the O3 increase; in Shanghai, the reduction of NOx and increase in VOC emissions were the two major causes; in Guangzhou, NOx reduction was the primary cause; in Chengdu, the PM and SO2 emission decreases contributed most to the O3 increase. Regarding the effects of decreasing concentrations of aerosols, the drop in heterogeneous uptake of reactive gases – mainly HO2 and O3 – was found to be more important than the increase in photolysis rates. The adverse effect of the reductions of NOx , SO2 , and PM emissions on O3 abatement in Beijing, Shanghai, Guangzhou, and Chengdu would have been avoided if the anthropogenic VOCs emission had been reduced by 24 %, 23 %, 20 %, and 16 %, respectively, from 2013 to 2017. Our analysis revealed that the NOx reduction in recent years has helped to contain the total O3 production in China. However, to reduce O3 levels in major urban and industrial areas, VOC emission controls should be added to the current NOx - SO2 -PM policy. [ABSTRACT FROM AUTHOR]

Atmospheric oxidation capacity is the basis for converting freshly emitted substances into secondary products and is dominated by reactions involving hydroxyl radicals (OH) during daytime. In this study, we present in situ measurements of ROx radical (hydroxy OH, hydroperoxy HO2 , and organic peroxy RO2) precursors and products; the measurements are carried out in four Chinese megacities (Beijing, Shanghai, Guangzhou, and Chongqing) during photochemically polluted seasons. The atmospheric oxidation capacity is evaluated using an observation-based model and radical chemistry precursor measurements as input. The radical budget analysis illustrates the importance of HONO and HCHO photolysis, which account for ∼50 % of the total primary radical sources. The radical propagation is efficient due to abundant NO in urban environments. Hence, the production rate of secondary pollutants, that is, ozone (and fine-particle precursors (H2SO4 , HNO3 , and extremely low volatility organic compounds, ELVOCs) is rapid, resulting in secondary air pollution. The ozone budget demonstrates its high production in urban areas; also, its rapid transport to downwind areas results in rapid increase in local ozone concentrations. The O3 – NOx –VOC (volatile organic compound) sensitivity tests show that ozone production is VOC-limited and that alkenes and aromatics should be mitigated first for ozone pollution control in the four studied megacities. In contrast, NOx emission control (that is, a decrease in NOx) leads to more severe ozone pollution. With respect to fine-particle pollution, the role of the HNO3 – NO3 partitioning system is investigated using a thermal dynamic model (ISORROPIA 2). Under high relative humidity (RH) and ammonia-rich conditions, nitric acid converts into nitrates. This study highlights the efficient radical chemistry that maintains the atmospheric oxidation capacity in Chinese megacities and results in secondary pollution characterized by ozone and fine particles. [ABSTRACT FROM AUTHOR]

Intra-urban surface water (IUSW) is an indispensable resource for urban living. Accurately acquiring and updating the distributions of IUSW resources is significant for human settlement environments and urban ecosystem services. High-resolution optical remote sensing data are used widely in the detailed monitoring of IUSW because of their characteristics of high resolution, large width, and high frequency. The lack of spectral information in high-resolution remote sensing data, however, has led to the IUSW misclassification problem, which is difficult to fully solve by relying only on spatial features. In addition, with an increasing abundance of water products, it is equally important to explore methods for using water products to further enhance the automatic acquisition of IUSW. In this study, we developed an automated urban surface-water area extraction method (AUSWAEM) to obtain accurate IUSW by fusing GaoFen-1 (GF-1) images, Landsat-8 Operational Land Imager (OLI) images, and GlobeLand30 products. First, we derived morphological large-area/small-area water indices to increase the salience of IUSW features. Then, we applied an adaptive segmentation model based on the GlobeLand30 product to obtain the initial results of IUSW. Finally, we constructed a decision-level fusion model based on expert knowledge to eliminate the problem of misclassification resulting from insufficient information from high-resolution remote sensing spectra and obtained the final IUSW results. We used a three-case study in China (i.e., Tianjin, Shanghai, and Guangzhou) to validate this method based on remotely sensed images, such as those from GF-1 and Landsat-8 OLI. We performed a comparative analysis of the results from the proposed method and the results from the normalized differential water index, with average kappa coefficients of 0.91 and 0.55, respectively, which indicated that the AUSWAEM improved the average kappa coefficient by 0.36 and obtained accurate spatial patterns of IUSW. Furthermore, the AUSWAEM displayed more stable and robust performance under different environmental conditions. Therefore, the AUSWAEM is a promising technique for extracting IUSW with more accurate and automated detection performance. [ABSTRACT FROM AUTHOR]

The limited access to vaccination and vaccine hesitancy are prevalent even among health care providers in less developed countries. This study assessed the relationships between (i) health care providers' knowledge on human papillomavirus (HPV) and vaccination for HPV and (ii) their perceptions, demographic characteristics, and the use of information sources. In this large-scale online survey, participants (n = 1394) were recruited from hospitals of three big cities in China (Shanghai, Guangzhou, and Shenzhen). Descriptive analysis, the chi-square test, and logistic regression analysis were used to answer different research questions. Health care providers' overall knowledge scores are consistent with their perceptions about HPV and HPV vaccine. Occupation, specialty, the type of hospitals, and the frequency of participants' search for information using professional informational sources are the most significant characteristics that are closely associated with their knowledge of HPV and its vaccine. Targeted educational interventions are needed to improve health care providers' engagement in the promotion of the HPV vaccine. Such interventions, besides increasing knowledge, should also emphasize the importance of appropriate information sources to acquire information. [ABSTRACT FROM AUTHOR]

Ozone (O3) pollution has become an increasing concern in China since elevated surface O3 concentrations were observed in recent years. In this study, five provincial cities (Beijing, Shanghai, Guangzhou, Xi'an, and Hefei) located in different regions of China were selected to study the spatiotemporal variations and affecting factors of O3 concentrations during 2014–2015. Beijing, Shanghai, and Guangzhou had suffered more severe O3 pollution, yet Beijing had the highest number of days that exceeded the Chinese MDA8 (maximum daily 8 h average) standard of 160 µg m−3. MDA8 O3 exhibited different seasonal patterns among the five cities. In Beijing and Xi'an, MDA8 O3 showed the highest in summer and lowest in winter. Guangzhou also had the highest O3 concentration in summer, but had similar levels in other three seasons. The O3 levels were similarly high in Shanghai during spring, summer, and autumn, while in Hefei, O3 concentration peaked in autumn. No significant difference between weekend and weekday O3 levels was observed in all the five cities. The diurnal cycle reached a maximum in the afternoon and a minimum in the early morning, which was consistent in the five cities. Correlation analyses showed that the associations between O3 and the other five criteria air pollutants, as well as meteorological parameters, were substantially different among the five cities. Air mass cluster analyses during episodic days revealed that the short-distance transport of O3 and its precursors had a greater impact for high O3 pollution in the five cities. Overall, our results demonstrate that O3 pollution exhibited great divergence among different regions and thus region-oriented control measures are suggested to reduce O3 pollution in China. [ABSTRACT FROM AUTHOR]