Urban Agglomeration Region Research Articles

An accurate and efficient forecast framework for fine PM2.5 maps using spatiotemporal recurrent neural networks

Commonly used numerical prediction models for PM2.5 maps suffer from low accuracy and high computation cost, which cannot meet the requirements for fine-scale air pollution control. In this study, we propose a framework based on the spatiotemporal recurrent neural network (PredRNN) to efficiently generate accurate 3-h and 6-km PM2.5 maps with a lead time of 5 days. In this framework, two PredRNN networks are initially utilized to forecast PM2.5 concentration at ground monitoring sites and the spatial distribution of aerosol optical depth (AOD) by assimilating the output of numerical prediction model. Subsequently, the 3-h and 6-km PM2.5 forecasted maps with a lead time of 5 days can be inferred by establishing the regression links between the forecasted results of PM2.5 concentration at ground sites and AOD maps. We evaluate the proposed framework in the Beijing-Tianjin-Hebei urban agglomeration region during 2017–2020. Compared with the numerical prediction products of the Copernicus Atmosphere Monitoring Service, the proposed framework achieves higher accuracy, with R2 of 0.83 at the forecast base time and 0.70 at the fifth day. The spatial information richness is also enhanced by approximately 15.67% according to the information entropy metrics. Notably, the proposed framework only requires 1 min for forecasting 5-days PM2.5 maps. These results demonstrate that our framework can efficiently generate accurate and fine PM2.5 maps with a lead time of 5 days.

Factors Affecting the Indoor Air Quality and Occupants’ Thermal Comfort in Urban Agglomeration Regions in the Hot and Humid Climate of Pakistan

The World Air Quality Index indicates that Pakistan ranks as the third most polluted country, regarding the average (Particulate Matter) PM2.5 concentration, which is 14.2 times higher than the World Health Organization’s annual air quality guideline. It is crucial to implement a program aimed at reducing PM2.5 levels in Pakistan’s urban areas. This review paper highlights the importance of indoor air pollution in urban regions such as Lahore, Faisalabad, Gujranwala, Rawalpindi, and Karachi, while also considering the effects of outdoor air temperature on occupants’ thermal comfort. The study aims to evaluate past methodological approaches to enhance indoor air quality in buildings. The main research question is to address whether there are statistical correlations between the PM2.5 and the operative air temperature and whether other indoor climatic variables have an impact on the thermal comfort assessment in densely built urban agglomeration regions in Pakistan. A systematic review analysis method was employed to investigate the effects of particulate matter (PM2.5), carbon oxides (COx), nitrogen oxides (NOx), sulfur oxides (SOx), and volatile organic compounds (VOCs) on residents’ health. The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) protocol guided the identification of key terms and the extraction of cited studies. The literature review incorporated a combination of descriptive research methods to inform the research context regarding both ambient and indoor air quality, providing a theoretical and methodological framework for understanding air pollution and its mitigation in various global contexts. The study found a marginally significant relationship between the PM2.5 operative air temperature and occupants’ overall temperature satisfaction, Ordinal Regression (OR) = 0.958 (95%—Confidence Interval (CI) [0.918, 1.000]), p = 0.050, Nagelkerke − Regression (R2) = 0.042. The study contributes to research on the development of an evidence-based thermal comfort assessment benchmark criteria for the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Global Thermal Comfort Database version 2.1.

Coupling synergy level and interaction effect between the ecological environment and low-carbon development in the Yangtze River Delta urban agglomeration: Characteristics of spatial and temporal heterogeneity

Low-carbon development and the ecological environment are often assessed separately, but understanding their interaction effect and the coupled coordination development from a spatially heterogeneous perspective remains limited, particularly in the context of rapidly urbanizing urban agglomeration regions. To address this research gap, this study assessed the levels and variations in coupled coordinated development between low-carbon development and the ecological environment at the urban agglomeration level using a coupled coordination degree model. Key factors of the interaction effect for each system were initially identified by ordinary least squares, then a geographically and temporally weighted regression model was used to reveal the spatial and temporal heterogeneity of these factors. Our study showed that the comprehensive development level of low-carbon development system in all cities exceeded that of the ecological environment system from 2006 to 2021. However, the level of synergy between the two systems showed slight improvement, with most cities exhibiting a moderate balance from 2010 to 2020. Regional heterogeneity was apparent in the coordinated development types, with highly balanced cities located primarily in the west and north of the urban agglomeration. Furthermore, our findings illustrated the positive effect of the air and climate environment on low-carbon development, contrasted with the negative influence of the land environment. These results not only enrich our understanding of low-carbon development and ecological environment interrelations in the Yangtze River Delta urban agglomeration but also provide insights applicable to similar regions worldwide, thereby contributing to the global discourse on synergistic sustainable development of low-carbon and ecological environment.

Spatial source, simulating improvement, and short-term health effect of high PM2.5 exposure during mutation event in the key urban agglomeration regions in China

Air quality in China has significantly improved owing to the effective implementation of pollution control measures. However, mutation events caused by short-term spikes in PM2.5 in urban agglomeration regions continue to occur frequently. Identifying the spatial sources and influencing factors, as well as improving the prediction accuracy of high PM2.5 during mutation events, are crucial for public health. In this study, we firstly introduced discrete wavelet transform (DWT) to identify the mutation events with high PM2.5 concentration in the four key urban agglomerations, and evaluated the spatial sources for the polluted scenario using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Additionally, DWT was combined with a widely used artificial neural network (ANN) to improve the prediction accuracy of PM2.5 concentration seven days in advance (seven-day forecast). Results indicated that mutation events commonly occurred in the northern regions during winter time, which were under the control of both short-range transportation of dirty airmass as well as negative meteorology conditions. Compared with the ANN model alone, the average band errors decreased by 9% when using DWT-ANN model. The average correlation coefficient (R) and root mean square error (RMSE) obtained using the DWT-ANN improved by 10% and 12% compared to those obtained using the ANN, indicating the efficiency and accuracy of simulating PM2.5, by combining the DWT and ANN. The short-term mortality during mutation events was then calculated, with the total averted all-cause, cardiovascular, and respiratory deaths in the four regions, being 4751, 2554, and 582 persons, respectively. A declining trend in prevented deaths from 2018 to 2020 demonstrated that the pollution intensity during mutation events gradually decreased owing to the implementation of the Three-Year Action Plan to Win the Blue Sky Defense War. The method proposed in this study can be used by policymakers to take preventive measures in response to a sudden increase in PM2.5, thereby ensuring public health.

A novel full-resolution convolutional neural network for urban-fringe-rural identification: A case study of urban agglomeration region

Monitoring urbanization processes is important because they are often accompanied by intensive landscape pattern transitions and pluralistic socioeconomic changes. To effectively monitor urban expansion and support regional planning, it is essential to develop a fast, accurate and universal urban–rural classification model, especially identifying the dynamic spatial patterns of urban, urban–rural fringe and rural areas. Although deep learning can effectively detect land cover changes, its applications in urban–rural identification have received little attention due to a lack of high-quality training datasets. In this study, we develop a novel transferable full-resolution convolutional neural network (FR-Net) to identify urban-fringe-rural areas. A large-scale training dataset was constructed using field surveys and aerial photography, and a data cube was stacked by multiple typical socio-natural indicators. We took the Beijing-Tianjin-Hebei (BTH) urban agglomeration region in China as a case study and identified spatiotemporal evolutions of urban-fringe-rural areas from 2000 to 2020. The results indicated that over the past two decades, the urban–rural fringe expanded outward with urban areas, and both areas gradually increased, with an inverted U-shaped growth rate. Accurate identification of these fringes can benefit regional urban–rural planning and social governance. Based on the identification results, complex socio-ecological impacts of urbanization could be further explored. Testing demonstrated that the developed FR-Net model has high accuracy and robustness. Our developed open-source FR-Net model exhibits transferability and can be applied to multi-scale urbanized areas.

Multiscale assessment of the spatiotemporal coupling relationship between urbanization and ecosystem service value along an urban–rural gradient:A case study of the Yangtze River Delta urban agglomeration, China

Evolutions in land use in urban areas exert a growing influence on ecosystem service value (ESV). As a manifestation of spatial arrangement in mature urban growth, the ecological challenges within urban agglomerations and their surroundings deserve attention. Therefore, clarifying the interrelation within the ESV and urbanization is crucial to constructing sustainable progress-oriented cities. Previous researches on the coordination relationship between ESV and urbanization have been limited to the provincial and county levels, and insufficient consideration has been given to the differences across urban and rural areas at different scales. Utilizing the coupling coordination degree (CCD) model, the present investigation assesses the interplay between urbanization and ESV across various scales within the Yangtze River Delta urban agglomeration (YRDUA) region from 2000 to 2020, and explores the differences in their distributions throughout the urban–rural gradient, aiming to help the regional coordinated development. The findings indicate that from 2000 to 2010, with increasing intensity of land use, the ESV in YRDUA area decreased. But between 2010 and 2020, ESV increased. Moreover, the CCD in the YRDUA has a positive relationship across ESV and urbanization, and is in a status of primary coordination from 2000 to 2020, with a slow upward trajectory. Meanwhile, along the rural–urban gradient, ESV and CCD both are declining, and there is a significant plunge from suburban to urban. And the interactions between ESV and urbanization have obvious spatial heterogeneity, which tends to show the coordination at broader scales, such as provincial and county levels, while manifesting conflicts at finer scales, such as the grid scale. Our findings can help city planners and policy makers to minimize the adverse effects of urbanization sprawl on ecosystem services.

Exploring Integrative Development of Urban Agglomeration from the Perspective of Urban Symbiosis and Production–Living–Ecological Function

Integrative development is an effective way to enhance urban potential and implement resource-optimal relocation, especially in urban agglomeration regions. Conventionally, the evaluation of urban integration is usually studied from one aspect of urban interaction intensity or urban functional similarity, but considering both together can better reflect the integrative condition of urban agglomeration. This paper introduces the symbiosis theory into the exploration of urban integration. The production–living–ecological function is taken to analyze urban function, and the improved radiation model is adopted to measure urban interaction. Under the framework of symbiosis theory, we integrate urban function and urban interaction to indicate the integrative condition of urban agglomeration from a production–living–ecological aspect. Urban agglomeration in the Central Yunnan Urban Agglomeration is taken as the study area. The results show that (1) spatial variations occur in high-value areas with distinct functions. The east emphasizes production and living, while the west leans towards ecology. (2) Urban agglomeration is in its early developmental stages without stable symbiosis. Interactions among counties mostly show sporadic point symbiosis, lacking stability. It mainly radiates outward from the central area, with more stable interactions in high-value areas, often causing inter-city competition. (3) Urban agglomeration integration is generally low, with distinct high-value production and ecological areas. The central, eastern, and southern regions exhibit strong production and living interactions, while the west benefits from ecological interactions. These findings can offer some insights for informing relevant policies and fostering the integrated development of urban agglomerations.

Urbanization reshapes extreme precipitation metrics in typical urban agglomerations of Eastern China

A deeper understanding of the impact of urbanization on hourly extreme precipitation is essential for building sustainable and resilient urban environments. In this study, we used a high-resolution land cover dataset and long time series of hourly precipitation observations to assess the impact of urbanization on long-term trends and diurnal patterns of hourly extreme precipitation in typical urban agglomeration regions of eastern China, during the warm season of 1978–2021. The results showed that eastern China experienced an overall increase in hourly precipitation, while the intensity trends were 0.06 mm/h/decade and 0.03 mm/h/decade for urban and rural areas, respectively. Urban areas more likely to experienced strong precipitation with short duration, but urban-rural differences in extreme precipitation showed obvious regional characteristics. The difference in extreme precipitation intensity between urban and rural areas increased in the regions of the lower Yangtze River (LYR) valley, Sichuan Basin and southern China, but decreased in northeast China and Beijing-Tianjin-Hebei (BTH) regions. For the diurnal cycle, extreme precipitation enhancement in urban areas occurred mostly at peak hours. Urban stations had higher hourly extreme precipitation intensity than rural stations throughout the diurnal cycle, with the maximum difference of 1.79 mm/h occurring at 16:00 LST. It found that extreme precipitation amount and intensity in urban areas increased with urban heat island (UHI) effect, and UHI indeed plays an important role in the convective initiation and strengthening of local extreme precipitation.

Identification of Ecological Restoration Priority Areas Integrating Human Activity Intensity and Multi-Criteria Decision Analysis

Restoration action is critical to ensure a safe environment for humans. Reasonable planning is essential to optimize the efficiency of ecological restoration inputs and outputs when implementing restoration measures. In this study, a method that combines human activity intensity assessment and multi-criteria decision analysis to determine ecological restoration priority (ERP) areas was developed to identify priority and feasible areas for ecological restoration in Shaanxi Province in 2020. The results showed that the total area involved in restoration feasibility assessment in Shaanxi is 10.89 × 104 km2. Among them, the percentage of regions with low feasibility (less than 0.2) is 68.86%, mainly located in Qinling area. High feasibility areas (more than 0.6) accounted for 2.47%, mainly located in the Loess Plateau area of northern Shaanxi. The spatial distribution of the human activity intensity is concentrated in urban areas and extended with the distribution of roads. In total, 10.69% of the regions showed high and very high intensity of human activity, including the Guanzhong urban agglomeration region. This study identified 6078 km2 and 671 km2 of medium and high ecological restoration priority areas, which are more concentrated in the north of the study area. The need for ecological restoration work is even more urgent in northern Shaanxi. In general, the framework in this study has spatially located the priority and feasible areas for restoration, and may provide a useful reference for landscape-scale spatial conservation planning.

Understanding recreational ecosystem service supply-demand mismatch and social groups’ preferences: Implications for urban–rural planning

Recreational ecosystem service (RES) supply and demand are fundamentally influenced by urbanization and are closely related to residents’ well-being. Nevertheless, how socio-economic attributes affect spatial RES demand and preferences and can be integrated into urban–rural planning is still unclear. Taking the Beijing-Tianjin-Hebei urban agglomeration region of China as an example, based on survey and spatial data, we applied the Recreation Opportunity Spectrum and a new transferable Quasi-Poisson regression model to examine spatial RES supply and demand. Then, a scaling approach was adopted to interpret the urban–rural spatial patterns of RES. The results showed that the RES supply values exhibited either quadratic relationships or no particular patterns in most cities along urban–rural gradients, while the RES demand values monotonically decreased with an exponential decay or power law. The RES imbalance with higher demand (43%) in urban areas, the supply–demand balance (37%) in urban–rural fringes, and the supply sufficiency (64%) in rural areas were dominant. Divergent RES demand and preferences were identified across genders, ages, income, city scales, and household statuses. Females showed higher preferences for water, grassland, and agricultural landscapes than males; older groups showed higher preferences for natural landscapes but traveled less distance for RES; and high-income groups showed a lower frequency of visits but pursued longer travel distances. We suggest that at both the regional-city and local-community levels, more adaptive and elaborate landscape planning and design should be implemented by integrating urban–rural heterogeneity and the specific population’s preferences into RES management.

Assessment of Land Surface Temperature Variations and Implications of Land Use/Land Cover Changes: A Case of Malappuram Urban Agglomeration Region, Kerala, India

Urbanization is taking place faster, and urban air temperatures are gradually rising in all cities across the world. Uncontrolled and unplanned urbanization leads to constant environmental threats and can alter local and regional climates. According to the survey published by Economist Intelligence Unit, in India, Kerala's Malappuram district ranks first among the fastest-growing urban areas globally, with a 44.05% growth rate. Hence, the present study aims to identify the hotspot regions of extreme heat within the Malappuram urban agglomeration region and suggest strategies for its improvement. The split-window algorithm retrieved land surface temperature (LST) for 1991, 1998, 2014, and 2020 using Landsat 5 ETM and Landsat 8 satellite imageries. A rising trend in LST has been detected in the last 30 years, and the mean value has increased by 1.70°C within the region. Among the selected hotspots, an LST increase of 1.84°C was observed for those areas with the highest increase in urban density with decreased vegetation. The increasing impact of urbanization and the subsequent change in land use patterns at the cost of greenery have caused a substantial effect on the local climate. Accordingly, planning and policy directions are proposed for the local government that can help provide awareness to the people through the effective implementation of mitigation measures.

Spatial heterogeneity and impact scales of driving factors of precipitation changes in the Beijing-Tianjin-Hebei region, China

Changes in land surface properties during urbanization have a significant impact on variations in precipitation. Little research has been carried out on spatial heterogeneity and influence strength of the driving factors of precipitation changes at different urbanization scales. Using a trend analysis and multi-scale geographically weighted regression, this study analysed the spatial heterogeneity and impact scale of driving factors of precipitation changes in 156 urban units in the Beijing-Tianjin-Hebei urban agglomeration region (Jing-Jin-Ji). In summer, RAD (radiation), RHU (relative humidity), WIN (wind speed), and POP (urban population density) were found to act on a small regional scale, AOD (aerosol optical depth) on a medium regional scale, and NDVI (normalized difference vegetation index), NLI (night time light intensity), UHI (urban heat island intensity), and AREA (urban area size) on a global scale. In winter, AREA and WIN acted on a medium regional scale, UHI on a large regional scale, and AOD, NDVI and NLI on a global scale. Across the whole year, NDVI and AREA had a medium regional impact and NLI a large regional one. Variations in natural factors, such as RAD and RHU, had a great influence on the spatial heterogeneity of precipitation changes, whereas human factors, such as NLI and UHI, had a small influence. In summer, AOD mainly affected Tangshan and Qinhuangdao in the northeast and Cangzhou in the southeast of the Jing-Jin-Ji. RHU and AREA primarily affected the cities of Handan and Xingtai. In winter, NLI, AREA, WIN, and UHI had significant effects in the cities of Handan and Xingtai, with AREA being the most important factor. In the Shijiazhuang-Hengshui area, RAD and NLI played a significant role; in the Beijing-Zhangjiakou-Chengde area, the most important factor affecting precipitation changes was the variation in POP. These results provide a scientific basis for flood disaster risk management in the Jing-Jin-Ji and the establishment of differentiated climate policies in different cities.

Drivers of 2015–2021 trends in cold winter surface PM2.5 in the Harbin-Changchun megalopolis in China: Meteorology vs. anthropogenic emission

Harbin-Changchun megalopolis (HCM) is a typical representative of the cold urban agglomeration region in China. Although the average annual PM2.5 concentration has experienced an evolutionary process continuous deterioration to gradual improvement in the past decade, but severe haze episodes still occur during cold winter periods. WRF-CMAQ model and observed dataset of surface PM2.5 concentration were conducted in this study to identify the drivers of 2015–2021 trends in cold winter surface PM2.5 in the HCM by separating contributions from anthropogenic emissions and meteorology changes. Meteorological condition change was the dominant driver of 2015–2021 trends in cold winter surface PM2.5 in the HCM. The years with most unfavorable, typical, and most favorable meteorological conditions for PM2.5 concentration reduction during January from 2015 to 2021 for the whole HCM region were 2020, 2017, and 2018, respectively. The major controlling meteorological factors that affect the surface PM2.5 concentration change were relative humidity, maximum wind direction and speed in cold winter in the HCM. In addition, we found that the emission cut policy began to play a practical role in the air quality improvement in the HCM from year of 2018. The emission reduction during the 2017–2021 resulted in an approximately 19% (15.7 μg/m3) decrease of PM2.5 concentrations in January. Furthermore, sensitivity experiments demonstrated that residential emission cut was the key measures for future air quality improvement in cold winter in the HCM.

Analysis of the coupling coordination degree of the Society-Economy-Resource-Environment system in urban areas: Case study of the Jingjinji urban agglomeration, China

The fast growth of China's economy and society has brought severe consequences on the country’s environmental quality and natural resources. In order to overcome these obstacles, the Chinese government proposed the concept of high-quality development in 2017, aiming to promote the comprehensive, sustainable and balanced development of society, economy, resources and the environment. Exploring the interactions within the Social-Economic-Resource-Environment (SERE) system in urban agglomeration regions can help policy-makers achieve high-quality sustainable urban development. On account of the concept of high-quality development and the theory of complex systems, we constructed the SERE system. Taking the Jingjinji (JJJ) urban agglomeration from 2011 to 2019 as a research sample, we first evaluated the comprehensive quality of the SERE subsystems at two scale levels using the entropy method. Then, the level of coordinated development among the subsystems was studied by building a coupling coordination degree (CCD)model. Finally, the effect of the four subsystems on the CCD was analyzed by using a panel regression model. The results of the study found that first, the SERE system’s comprehensive development quality at the scale of the whole JJJ urban agglomeration showed an upward trend from 2011 to 2019. The resource subsystem always contributed the most, the economic and social subsystem contributed moderately, and the environmental subsystem contributed the least. Secondly, the comprehensive quality of all cities’ SERE subsystems showed different degrees of growth, with notable spatial differences. The comprehensive quality levels of social, economic and resource subsystems in Beijing and Tianjin were significantly higher than those in Hebei. Thirdly, the SERE system’s CCD in each city showed various growth tendencies. The proportion of severely unbalanced cities decreased from 84.62% to 61.54% during the study period, and regional development became more coordinated. However, there were still obvious spatial differences; Beijing and Tianjin consistently performed better, and the scores of cities in Hebei province were generally low. Fourthly, the degree of coupling coordination among the subsystems was positively correlated with their comprehensive quality. The elasticity of the environmental subsystem was the largest, which was essential to the coordinated development of the SERE system. These findings can offer governments valuable references for adopting appropriate policy measures which promote high-quality, sustainable and coordinated urban development.

Multi-step forecast of PM2.5 and PM10 concentrations using convolutional neural network integrated with spatial–temporal attention and residual learning

Accurate and reliable forecasting of PM2.5 and PM10 concentrations is important to the public to reasonably avoid air pollution and for the governmental policy responses. However, the prediction of PM2.5 and PM10 concentrations has great uncertainty and instability because of the dynamics of atmospheric flows, making it difficult for a single model to efficiently extract the spatial–temporal dependences. This paper reports a robust forecasting system to achieve accurate multi-step ahead forecasting of PM2.5 and PM10 concentrations. First, correlation analysis is adopted to screen the spatial information on pollution and meteorology that may facilitate the prediction of concentrations in a target city. Then, a spatial–temporal attention mechanism is used to assign weights to original inputs from both space and time dimensions to enhance the essential information. Subsequently, the residual-based convolutional neural network with feature extraction capabilities is employed to model the refined inputs. Finally, five accuracy metrics and two additional statistical tests are applied to comprehensively assess the performance of the proposed forecasting system. In addition, experimental studies of three major cities in the Yangtze River Delta urban agglomeration region indicate that the forecasting system outperforms various prevalent baseline models in terms of accuracy and stability. Quantitatively, the proposed STA-ResCNN model reduces root mean square error by 5.595 %-15.247 % and 6.827 %-16.906 % for the average of 1–4 h ahead predictions in three major cities of PM2.5 and PM10, respectively, compared to baseline models. The applicability and generalization of the proposed forecasting system are further verified by the extended applications in the other 23 cities in the entire region. The results prove that the forecasting system is promising in the early warning, regional prevention, and control of air pollution.

Network capital and economic growth of major urban agglomeration regions in China

AbstractThis study investigates the effect of regional urban networks on regional economic growth. This study first develops a theoretical framework by combining the concept of network capital with the theory of urban network externality, arguing that regional network capital has two dimensions: the flow volume and the clustering structure. Furthermore, this study empirically examines the role of network capital on the economic growth of major urban agglomeration regions in Chinaand employs historical instruments to address endogeneity issues. The results show that network capital has a significant impact on promoting regional economic growth. These findings imply that the flow volume and the clustering structure in regional urban networks are indispensable factors that enhance regional economic performance.

New insight into the urban PM2.5 pollution island effect enabled by the Gaussian surface fitting model: A case study in a mega urban agglomeration region of China

• Gaussian surface model can well depict the spatial characteristics of UPI effect. • Intensity, footprint, and capacity reveal intensified but heterogeneous UPI effect. • Anthropogenic factors act as the key influencer on the variation of UPI effect. • Panel, cross-section, and time-series analysis show spatiotemporal driven difference. The phenomenon of the urban PM2.5 pollution island (UPI, the PM2.5 concentration difference along the urban–rural gradient) has been a growing concern due to its pernicious impacts on both residential and environmental health. To date, however, studies on the spatialization and indexation of the UPI effect and what is might be attributed to are still lacking. In this study, the Gaussian surface fitting model was innovatively used to depict the spatial morphology of the UPI effect for 13 cities in the Beijing–Tianjin–Hebei urban agglomeration region of China. Multiple indicators were introduced from the fitted Gaussian surface to portray the UPI intensity (magnitude), footprint (impact extent), and capacity (cumulative risk load) characteristics. Then, their potential relationships with several representative natural and anthropogenic factors were examined based on panel, cross-section, and time-series analysis. The main findings can be summarized as follows: 1) The intensity, footprint, and capacity indicators based on the fitted Gaussian model complementarily depict the spatial characteristics of the UPI effect. The spatiotemporal analysis showed that most of the case cities experienced remarkably intensified but heterogeneous UPI effects from 2000 to 2015. 2) The panel, cross-section, and time-series analysis enriched the attribution portrayal of the UPI effect. Anthropogenic factors assigned to urbanization generally had more impact on the UPI effect than natural factors. However, the specific contributors varied across cities and times. Based on the Gaussian surface fitting model, multidimensional indicators, and multi-perspective analysis, the results of this study offer new insight into our understanding of the UPI effect and its potential associations, which is useful to broaden both the research methodology and the perspectives in the current study and thus benefit future environmental regulations by providing a worthwhile scientific reference.

Spatiotemporal Dynamics of Surface Ozone and Its Relationship with Meteorological Factors over the Beijing-Tianjin-Tangshan Region, China, from 2016 to 2019.

In recent years, ozone pollution has been increasing in some parts of the world. In this study, we used the Beijing–Tianjin–Tangshan (BJ-TJ-TS) urban agglomeration region as a case study and used satellite remotely sensed inversion data and hourly ground monitoring observations of surface ozone concentrations, meteorological data, and other factors from 2016 to 2019 to explore the spatiotemporal dynamic characteristics of surface ozone concentration and its pollution levels. We also investigated their coupling relationships with meteorological factors, including temperature, pressure, relative humidity, wind velocity, and sunshine duration, in order to support the development of effective control measures for regional ozone pollution. The results revealed that the surface ozone concentration throughout the BJ-TJ-TS region from 2016 to 2019 exhibited an overall pattern of high values in the northwest and low values in the southeast, as well as an obvious difference between built-up and non-built-up areas (especially in Beijing). Meanwhile, a notable increasing trend of ozone levels was discovered in the BJ and TJ areas from 2016 to 2019, whereas this upward trend was not evident in the TS area. In all three areas, the highest monthly average ozone values occurred in the summer month of June, while the lowest monthly average levels occurred in the winter month of December. Their diurnal variation values reached a maximum value at approximately 3:00–4:00 p.m. and a minimum value at approximately 7:00 a.m. It is clear that high temperature, long sunshine duration, low atmospheric pressure, and weak wind velocity conditions, as well as certain relative humidity levels, readily led to high-concentration ozone pollution. Meanwhile, the daily average values of the five meteorological factors on days with Grade I and Grade II ozone pollution displayed different characteristics.

Assessment of Urban Ecological Resilience and Its Influencing Factors: A Case Study of the Beijing-Tianjin-Hebei Urban Agglomeration of China

Climate change and rapid urbanization bring natural and anthropogenetic disturbance to the urban ecosystem, damaging the sustainability and resilience of cities. Evaluation of urban ecological resilience and an investigation of its impact mechanisms are of great importance to sustainable urban management. Therefore, taking the Beijing-Tianjin-Hebei Urban Agglomeration (BTHUA) region in China as a study area, this study builds an evaluation index to assess urban ecological resilience and its spatial patterns with the resilience surrogate of net primary production during 2000–2020. The evaluation index is constructed from two dimensions, including the sensitivity and adaptability of urban ecosystems, to capture the two key mechanisms of resilience, namely resistance and recovery. Resilience-influencing factors including biophysical and socio-economic variables are analyzed with the multiple linear regression model. The results show that during 2000–2020, the spatial pattern of urban ecological resilience in the BTHUA is characterized by high resilience in the northwest and relatively low resilience in the southeast. High resilience areas account for 40% of the whole region, mainly contributed by Zhangjiakou and Chengde city in Hebei Province, which is consistent with the function orientation of the BTH region in its coordinated development. Along with urbanization in this region, ecological resilience decreases with increased population and increases with GDP growth; this indicates that, although population expansion uses resources, causes pollution and reduces vegetation coverage, with economic growth and technological progress, the negative ecological impact could be mitigated, and the coordinated development of social economy and ecological environment could eventually be reached. Our findings are consistent with mainstream theories examining the ecological impact of socio-economic development such as the Environmental Kuznets Curve, Porter Hypothesis, and Ecological Modernization theories, and provide significant references for future urbanization, carbon neutrality, resilience building, and urban ecological management in China.

Revealing Spatial Spillover Effect in High-Tech Industry Agglomeration from a High-Skilled Labor Flow Network Perspective

Understanding the high-tech industrial agglomeration from a spatial-spillover perspective is essential for cities to gain economic and technological competitive advantages. Along with rapid urbanization and the development of fast transportation networks, socioeconomic interactions between cities have been ever-increasing, traditional spatial metrics are not enough to describe actual inter-city connections. High-skilled labor flow between cities strongly influences the high-tech industrial agglomeration, yet receives less attention. By exploiting unique large-scale datasets and tools from complex network and data mining, the authors construct an inter-city high-skilled labor flow network, which was integrated into spatial econometric models. The regression results indicate that spatial-spillover effects exist in the development of high-tech industries in the Yangtze River Delta Urban Agglomeration region. Moreover, the spatial-spillover effects are stronger among cities with a higher volume of high-skilled labor flows than among cities with just stronger geographic connections. Additionally, the authors investigate the channels for the spillover effects and discover that inadequate local government expenses on science and technology likely hamper the high-tech industrial agglomeration, so does the inadequate local educational provision. The increasing foreign direct investments in one city likely encourages the high-tech industrial agglomeration in other cities because of the policy inertia toward traditional industries.