The relationship between urban form and air pollution depends on seasonality and city size

Dynamics of urban expansion and form changes impacting carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area counties

Cities are the main carriers of social and economic development, and they are also important sources of carbon emissions. Therefore, it is essential to explore the impact of urban expansion and form changes on carbon emissions. Here, we attempted to analyzes the relationship between urban expansion and carbon emissions at the county level in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1997 to 2017. It further decomposes the driving effects of carbon emissions from multiple factors, and considers the spatial heterogeneity between different urban form changes and driving effects. The results show that: The relationship between urban expansion and carbon emissions in the GBA has gone through three stages from 1997 to 2017, with 2012 as a turning point. Optimization of economic development models and strict protection of the ecological environment can effectively control carbon emissions. After 2012, the economic development effect (GE) and population scale effect (PE) are the driving factors of carbon emissions, while the carbon emission intensity effect (CE) and urban land intensity effect (UE) are the inhibitory factors of carbon emissions. The contribution rate of UE to carbon emission reduction can reach 86 %. The impact of urban form changes on carbon emissions has spatial heterogeneity. The changes in urban form have a significant impact on the carbon emissions of counties in Dongguan and Shenzhen. The increase in fragmentation indirectly promotes carbon emissions. In 2007-2012, the increase in centrality significantly weakened the economic development effect, which is conducive to emission reduction. After 2007, the increase in compactness in counties in the eastern part of the GBA, including Zhongshan and Zhuhai, is not conducive to emission reduction.

Mitigating air and noise pollution through highway capping: The Bundang-Suseo Highway Cap Project case study

Highways, while vital for transportation, often lead to heightened air and noise pollution, adversely affecting nearby communities. This study delves into the effectiveness of highway capping, a sustainable urban development strategy, in addressing these environmental challenges, with a specific focus on the Bundang-Suseo Highway in South Korea. This study employed a multifaceted approach, incorporating on-road monitoring, in situ measurements, and vertical assessments using UAVs. Following the cap's installation, the area experienced more stable pollutant levels, marking a notable shift from the previously fluctuating conditions heavily influenced by the highway. In-depth in situ monitoring near the cap revealed significant reductions in noise and pollutants like UFP and BC. Furthermore, UAV monitoring captured these changes in pollutant levels at different altitudes. Notably, the installation of the highway cap led to increased PM2.5, PM10, and NO2 levels at ground level, but a decrease above the cap, emphasizing the critical importance of intentional highway cap design in enhancing urban air quality and reducing exposure to harmful pollutants. This research yields invaluable insights for urban planners, health authorities, and policymakers, aiding the precise identification of pollution-prone areas and advocating for improved highway cap design to enhance urban environments.

Assessing the impact of urban form and urbanization process on tropospheric nitrogen dioxide pollution in the Yangtze River Delta, China

Optimizing urban form through urban planning and management policies can improve air quality and transition to demand-side control. Nitrogen dioxide (NO2) in the urban atmosphere, mainly emitted by anthropogenic sources such as industry and vehicles, is a key precursor of fine particles and ozone pollution. Both NO2 and its secondary pollutants pose health risks for humans. Here we assess the interactions between urban forms and airborne NO2 pollution in different cities with various stages of urbanization in the Yangtze River Delta (YRD) in China, by using the machine learning and geographical regression model. The results reveal a strong correlation between urban fragmentation and tropospheric NO2 vertical column density (TVCD) in YRD cities in 2020, particularly those with lower or higher levels of urbanization. The correlation coefficients (R2) between NO2 TVCD and the largest patch index (a metric of urban fragmentation) in different cities are greater than 0.8. For cities at other urbanization stages, population and road density are strongly correlated with NO2 TVCD, with an R2 larger than 0.61. This study highlights the interdependence among urbanization, urban forms, and air pollution, emphasizing the importance of customized urban landscape management strategies for mitigating urban air pollution.

Urban expansion patterns and their driving forces analysis: a comparison between Chengdu-Chongqing and Middle Reaches of Yangtze River urban agglomerations

Urban agglomerations have emerged as the primary drivers of high-quality economic growth in China. While recent studies have examined the urban expansion patterns of individual cities, a comparative study of the urban expansion patterns of urban agglomerations at two different scales is required for a more comprehensive understanding. Thus, in this study, we conduct a two-scale comparative analysis of urban expansion patterns and their driving factors of the two largest urban agglomerations in western and central China, i.e., Chengdu-Chongqing urban agglomeration (CCUA) and the Middle Reaches of Yangtze River urban agglomerations (MRYRUA) at both the urban agglomeration and city levels. We investigate the urban expansion patterns of CCUA and MRYRUA between 2000 and 2020 using various models, including the urban expansion rate, fractal dimension, modified compactness, and gravity-center method. Then we use multiple linear regression analysis and geographically weighted regression (GWR) to explore the magnitude and geographical differentiation of influences for economic, demographic, industrial structure, environmental conditions, and neighborhood factors on urban expansion patterns. Our findings indicate that CCUA experienced significantly faster urban growth compared to MRYRUA. There is an excessive concentration of resources to megacities within the CCUA, whereas there is a lack of sufficient collaboration among the three provinces within the MRYRUA. Additionally, we identify significant differences in the impacts of driving forces of CCUA and MRYRUA, as well as spatial heterogeneity and regional aggregation in the variation of their strength. Our two-scale comparative study of urban expansion patterns will not only provide essential reference points for CCUA and MRYRUA but also serve as valuable insights for other urban agglomerations in China, enabling them to promote sustainable urban management and foster integrated regional development.

Exposure to ambient air pollution with depressive symptoms and anxiety symptoms among adolescents: A national population-based study in China

BACKGROUND

Air pollution threatens adolescents' physical health and adversely affects adolescents' mental health. Previous studies mostly focused on the effects of air pollution on physical health, but there were few studies on the effects of air pollution on mental health.

METHODS

We collected scores of depressive symptoms and anxiety symptoms from 15,331 adolescents from 43 schools in eleven provinces in September and November 2017. The data on air pollution comes from the China High Air Pollutants dataset, which included concentrations of particulate matter with diameters of ≤1.0 μm (PM₁), diameters of ≤2.5 μm (PM_2.5), and diameters of ≤10 μm (PM₁₀), as well as nitrogen dioxide (NO₂). The associations between air pollution and depressive and anxiety symptoms among adolescents were estimated using generalized linear mixed models.

RESULTS

Depressive and anxiety symptoms among Chinese adolescents were 16% and 32%, respectively. In the adjusted model, an interquartile range (IQR) increase from PM_2.5 was associated with the odds of anxiety symptoms [odds ratio (OR) = 1.01; 95% confidence interval (CI): 1.00, 1.01, P = 0.002]. Also, an IQR increase in PM₁₀ was significantly associated with the odds of anxiety symptoms (OR = 1.01; 95% CI: 1.00, 1.01, P = 0.029). Compared with the lowest quartile, the adjusted OR of anxiety symptoms for the highest quartile of PM_2.5 and PM₁₀ were 1.29 (1.15, 1.44) and 1.23 (1.06, 1.42), respectively. In addition, the association between PM_2.5 and depressive symptoms was significant. The robustness of the results was also confirmed by stratification and sensitivity analyses.

CONCLUSIONS

Exposure values for airborne particulate matter were associated with depressive symptoms and anxiety symptoms in adolescents, particularly for PM_2.5 and PM₁₀ with anxiety symptoms among adolescents.

Reconsidering the effects of urban form on PM2.5 concentrations: an urban shrinkage perspective

The phenomenon of urban shrinkage is currently occurring worldwide; however, the "growth-oriented" planning paradigm is not suitable for these shrinking cities. Reconsidering the relationship between urban form and PM_2.5 concentrations from the perspective of urban shrinkage can help provide a research reference for controlling air pollution and optimizing the spatial layout of shrinking cities. This study takes shrinking areas in China as the research subject, which are divided into four research groups according to their shrinkage degree. The empirical results indicate that the average PM_2.5 concentrations decrease with the aggravation of urban shrinkage. In terms of the effect of urban form on PM_2.5 concentrations, the urban size is always positively related to PM_2.5 concentrations, while the impact of urban fragmentation on PM_2.5 concentrations is negligible. Further, urban shape positively affects PM_2.5 concentrations only in moderately and severely shrinking cities. Cities with sprawling urban forms have higher PM_2.5 concentrations, except for those facing severe shrinking trends. This study suggests that governments in shrinking cities should reasonably adjust both the urban form and land use to improve air quality based on the degree of urban shrinkage.

Effect of urban form on PM2.5 concentrations in urban agglomerations of China: Insights from different urbanization levels and seasons

Planned urban form has become an important strategy to improve air quality in urban agglomerations (UAs), especially pollution due to PM_2.5, but the influencing mechanisms are not yet clear. This study explores the relationship between four metrics of urban form (size, fragmentation, shape, and dispersion) as determined by analysis of remotely sensed images at 30-m resolution and PM_2.5 concentrations in 19 Chinese UAs. The influence of level of urban development and season is examined. Five control variables, including population density, temperature, precipitation, wind speed, and the normalized difference vegetation index (NDVI) are selected for use in multiple linear regression models. Size, fragmentation, and shape of urban form, but not dispersion, were found to have significant effects on PM_2.5 concentrations of different urbanization-level UAs. Urban size and fragmentation have stronger impacts on PM_2.5 concentrations in UAs with lower urbanization levels while urban shape has a greater impact in higher-level UAs. In terms of seasonal variation in all UAs, urban form is more pronouncedly associated with PM_2.5 concentrations during spring and autumn than summer and winter. Urban size and fragmentation are positively associated with PM_2.5 concentrations whereas urban shape and dispersion are on the contrary. The relationships between urban form and PM_2.5 uncovered here underscore the importance of urban planning as a tool to minimize PM_2.5 pollution. Specifically, local government should encourage polycentric urban form with lower fragmentation in urban agglomerations. UAs with lower urbanization levels should control the disordered expansion of construction land and higher-level UAs should promote the mix of green land and construction land. Moreover, measures to control air pollution from anthropogenic activities in spring, autumn and winter are likely to be more effective in decreasing PM_2.5 concentrations in UAs.

Acute and chronic maternal exposure to fine particulate matter and prelabor rupture of the fetal membranes: A nation-wide survey in China

BACKGROUND

Prelabor rupture of the fetal membranes (PROM) is a major contributor to adverse perinatal outcomes. Some epidemiologic studies explored the association between maternal PM_2.5 exposure and PROM but failed to treat the labor induction and prelabor cesarean section as censored observations.

OBJECTIVE

We aimed to evaluated whether acute and chronic maternal ambient PM_2.5 exposure may increase the risk of PROM in China.

METHODS

This study was based on the China Labor and Delivery Survey, a nationwide multicenter investigation. Included in the current analysis were 45,879 singleton spontaneous births in 96 hospitals in mainland China from 2015 to 2017. Outcomes were PROM, preterm PROM (<37 weeks' gestation) and term PROM (≥37 weeks' gestation). Daily concentration of PM_2.5 at 1 km spatial resolution was estimated by gap-filling model. Generalized linear mixed model and mixed effects Cox model were applied to assess the associations of acute (from 0 to 4 days before delivery) and chronic (average gestational and trimester-specific) ambient PM_2.5 exposure with outcomes, respectively.

RESULTS

Significant associations were found between acute PM_2.5 exposures (per interquartile range increase) and the risk of preterm PROM (OR = 1.11; 95 % CI: 1.03, 1.19 for PM_2.5 on delivery day; OR = 1.10; 95 % CI: 1.02, 1.18 for PM_2.5 1 day before delivery) but not for term PROM. An interquartile range increase in PM_2.5 during the second trimester was associated with elevated risks of PROM (HR = 1.14; 95 % CI: 1.07, 1.22), preterm PROM (HR = 1.22; 95 % CI: 1.02, 1.45) and term PROM (HR = 1.13; 95 % CI: 1.06, 1.22), respectively. Women who were less educated, obese, or gave birth in a cold season appeared to be more sensitive to ambient PM_2.5 exposure.

CONCLUSION

Our findings suggest that both acute and chronic maternal exposures to ambient PM_2.5 during pregnancy are risk factors for PROM.

Near-surface wind profile test based on accuracy verification of UAV anemometer lifting height in an urban fringe built-up area

Multirotor UAVs (unmanned aerial vehicles) have been widely used in urban vertical wind environment testing, whereas less attention has been given to the accuracy of wind speed captured by anemometers as drones fly. This paper aims to identify the ideal location of the anemometer on the UAV to obtain more accurate wind speeds and to assess the variation characteristics of wind speed in different spatial types in urban fringe areas. Accuracy verification of the lifting height of the anemometer in the UAV and wind profile test was carried out at three locations (a tennis court, a residential area, and a green park) on the iHarbour campus of Xi'an Jiaotong University. The following results were obtained: (1) the background wind speed was captured more accurately (R = 0.727, P = 0.001) when the lifting height of the anemometer was 0.00 m (as the height of the anemometer was the same as the rotors) and when the multirotor UAV was hovering in the air. However, this optimal lifting height lost 29.6% of the accuracy for capturing the background wind speed. Interestingly, when the lifting height was 0.75 m, the anemometer captured by the anemometer on the drone showed a significant negative correlation (R =  - 0.682, P = 0.005) with the background wind speed. (2) The wind speed at an altitude of 1.5 m in the residential area was significantly lower than that noted at other heights, and the wind speed at 24 m was significantly lower than that at 100 m. (3) In addition, a sudden increase in wind speeds was always observed near the surface of 12 m inside the campus, which may be due to the interaction of hot surface air in this newly built-up area with the cool rural winds around it. The study presents methods and quantitative references for the application of multirotor UAVs in urban vertical wind environment testing and the evaluation of ventilation performance at different heights inside high-rise houses in urban fringe areas.

The Associations Between Urban Form and Major Non-communicable Diseases: a Systematic Review

In the current century, non-communicable diseases (NCDs), particularly cardiovascular diseases, diabetes, cancer, and chronic respiratory diseases, are the most important cause of mortality all over the world. Given the effect of the built environment on people's health, the present study seeks to conduct a systematic review in order to investigate the relationship between urban form and these four major NCDs as well as their main risk factors. Two independent reviewers in November 2020 after an extensive search through PubMed and Scopus identified 77 studies. Studies published in English were included if they addressed one or more attributes of urban form in relation to any major NCDs and their main risk factors. Publication date, country, geographical scale, study design, methods of built environment measurement, and findings of the relationships among variables were extracted from eligible studies. The findings suggest that the elements of urban form (density, transportation and accessibility, characteristics of building and streetscape, land use, spatial layouts and configuration) could increase or inhibit these diseases through their effect on physical activity, diet, air pollution, blood pressure, and obesity. However, there are study shortages, contradictions, and ambiguities in these relationships which are mainly due to methodological and conceptual challenges. As a result, more in-depth research is needed to achieve solid and consistent results that could be made into clear guidelines for planning and designing healthier cities.

Changes in Contemporary Form in Gangneung City through Cadastral Data Analysis and Application of the Spatial Politics Concept

This study aimed to reveal changes in urban forms by analyzing the modern and contemporary development history of Gangneung City, one of the major cities in the Yeongdong county of Gangwon-do, South Korea; it has been a central city in the Yeongdong area since the Japanese colonial period. The study’s scope and analysis focused on a space centered in the old downtown area of Gangneung City. The time span under review was divided into three periods in terms of spatial politics. The original cadastral map, a contemporary cadastral map, and photographic data were analyzed, including the use of Geographic Information System (GIS) Programs, for characteristics corresponding to each period. A field investigation and interviews with residents were also conducted. The results confirmed changes in the spatial environment centered on roads, blocks, plots, and architecture that have historical significance. In particular, the study verified the characteristics of the physical environment of the original downtown area centered on Yonggang-dong, Myeongju-dong, and Seongnae-dong and changes in the form of Lake Gyeongpo. Finally, the study presents the implications of these changes by comprehensively summarizing the history of the modern and contemporary development of Gangneung City through changes in the surrounding area.

Relationships between urban form and air quality: A reconsideration based on evidence from China's five urban agglomerations during the COVID-19 pandemic

The outbreak of Coronavirus disease 2019 (COVID-19) led to the widespread stagnation of urban activities, resulting in a significant reduction in industrial pollution and traffic pollution. This affected how urban form influences air quality. This study reconsiders the influence of urban form on air quality in five urban agglomerations in China during the pandemic period. The random forest algorithm was used to quantitate the urban form-air quality relationship. The urban form was described by urban size, shape, fragmentation, compactness, and sprawl. Air quality was evaluated by the Air Quality Index (AQI) and the concentration of six pollutants (CO, O₃, NO₂, PM_2.5, PM₁₀, SO₂). The results showed that urban fragmentation is the most important factor affecting air quality and the concentration of the six pollutants. Additionally, the relationship between urban form and air quality varies in different urban agglomerations. By analyzing the extremely important indicators affecting air pollution, the urban form-air quality relationship in Beijing-Tianjin-Hebei is rather complex. In the Chengdu-Chongqing and the Pearl River Delta, urban sprawl and urban compactness are extremely important indicators for some air pollutants, respectively. Furthermore, urban shape ranks first for some air pollutants both in the Triangle of Central China and the Yangtze River Delta. Based on the robustness test, the performance of the random forest model is better than that of the multiple linear regression (MLR) model and the extreme gradient boosting (XGBoost) model.

Three-dimensional building morphology impacts on PM2.5 distribution in urban landscape settings in Zhejiang, China

Three-dimensional (3D) urban landscape patterns and building morphology are crucial for urban planning and essential for urban landscape functions. In this study, fixed and mobile monitoring sites were used to determine the spatial distribution of PM_2.5 concentrations in Hangzhou. Six 3D metrics were selected to analyze the response of PM_2.5 pollution to landscape patterns and building morphology, while their two-dimensional (2D) counterparts' metrics were also analyzed to contrast the differences. A variance partitioning analysis (VPA) was performed to measure the combined and relative contribution of 3D and 2D metrics to the changes in PM_2.5 concentrations. The results showed that: (1) on the 3D scale, forming a building pattern with a combination of different building heights can eliminate the accumulation of PM_2.5; (2) on the 2D scale, fragmentation and decentralization of landscapes and building patches alleviate PM_2.5 pollution; and (3) 3D building morphology indicators have the highest explanatory power (40.94%) for the changes of PM_2.5 concentrations. It turns out that the explanatory power of 3D metrics for PM_2.5 concentrations changes is much greater than that of 2D metrics. In addition, when compared to building morphology indicators from a single dimension, the combination of 2D and 3D metrics is better able to reflect urban PM_2.5 pollution. The results of this study expand our understanding of how PM_2.5 pollution responds to 2D and 3D metrics and provide useful information for urban planning.

The Impacts of Urban Form on PM2.5 Concentrations: A Regional Analysis of Cities in China from 2000 to 2015

The urban form (e.g., city size, shape, scale, density, etc.) can impact the air quality and public health. However, few studies have been conducted to assess the relationship between the urban form and PM2.5 concentrations on a regional scale and long-term basis in China. In this study, we explored the impact of the urban form on the PM2.5 concentrations in four different regions (i.e., northeast, central, east, western) across China for the years 2000, 2005, 2010, and 2015. Five landscape metrics were classified into three characteristics of the urban form (compactness, shape complexity, and urban expansion) using high-resolution remote-sensing data. With considerations given to regional differences, panel-data models and city-level panel data were used to calculate the impact of the urban form on the PM2.5 concentrations. The results of the study indicate that urban expansion is positively correlated with the PM2.5 concentrations across China, with the only exception being the country’s western region, which suggests that urban extension is conducive to increasing the PM2.5 levels in relatively developed regions. Meanwhile, the positive relationship between the irregularity of cities and the PM2.5 concentrations indicates that reducing the urban shape complexity will help to mitigate PM2.5 pollution. Moreover, urban compactness, which mainly refers to the landscape-division-index values, proved to have a negative effect on the PM2.5 concentrations, suggesting that the optimization of urban spatial compactness could reduce PM2.5 levels. The findings of this study are beneficial for a better understanding of the intensity and direction of the effect of the urban form on PM2.5 concentrations.

Evaluating the Meteorological Effects on the Urban Form-Air Quality Relationship Using Mobile Monitoring

Predictive models based on mobile measurements have been increasingly used to understand the spatiotemporal variations of intraurban air quality. However, the effects of meteorological factors, which significantly affect the dispersion of air pollution, on the urban-form-air-quality relationship have not been understood on a granular level. We attempt to fill this gap by developing predictive models of particulate matter (PM) in the Bronx (New York City) using meteorological and urban form parameters. The granular PM data was collected by mobile low-cost sensors as the ground truth. To evaluate the effects of meteorological factors, we compared the performance of models using the urban form within fixed and wind-sensitive buffers, respectively. We find better predictive power in the wind-sensitive group (R = 0.85) for NC₁₀ (number concentration for particles with diameters of 1 μm-10 μm) than the control group (R = 0.01), and modest improvements for PM_2.5 (R = 0.84 for the wind sensitive group, R = 0.77 for the control group), indicating that incorporating meteorological factors improved the predictive power of our models. We also found that urban form factors account for 62.95% of feature importance for NC₁₀ and 14.90% for PM_2.5 (9.99% and 4.91% for 3-D and 2-D urban form factors, respectively) in our Random Forest models. It suggests the importance of incorporating urban form factors, especially for the uncommonly used 3-D characteristics, in estimating intraurban PM. Our method can be applied in other cities to better capture the influence of urban context on PM levels.

Spatialization and Prediction of Seasonal NO2 Pollution Due to Climate Change in the Korean Capital Area through Land Use Regression Modeling

Urbanization is causing an increase in air pollution leading to serious health issues. However, even though the necessity of its regulation is acknowledged, there are relatively few monitoring sites in the capital metropolitan city of the Republic of Korea. Furthermore, a significant relationship between air pollution and climate variables is expected, thus the prediction of air pollution under climate change should be carefully attended. This study aims to predict and spatialize present and future NO₂ distribution by using existing monitoring sites to overcome deficiency in monitoring. Prediction was conducted through seasonal Land use regression modeling using variables correlated with NO₂ concentration. Variables were selected through two correlation analyses and future pollution was predicted under HadGEM-AO RCP scenarios 4.5 and 8.5. Our results showed a relatively high NO₂ concentration in winter in both present and future predictions, resulting from elevated use of fossil fuels in boilers, and also showed increments of NO₂ pollution due to climate change. The results of this study could strengthen existing air pollution management strategies and mitigation measures for planning concerning future climate change, supporting proper management and control of air pollution.

How Do China’s Development Zones Affect Environmental Pollution under Government Domination

Environmental pollution has recently become a serious economic issue, and finding ways to reduce pollution in economic development is an urgent task for developing countries, especially China. In this paper, we aim to document the policy role of development zones (DZs) in promoting China’s economic development on environmental pollution and consider the establishment of DZs as a quasi-natural experiment. Specifically, we identify the establishment of DZs on pollution emissions based on the staggered difference-in-difference (DID) approach by setting a dummy variable for DZ policies. Furthermore, we examine the heterogeneity of provincial and national (high-tech and economic development zones) DZs on pollution to detect the government domination effect. Finally, in order to deal with the potential spatial spillover effects of DZs, this paper applies the spatial difference-in-difference (SDID) method to explore the spatial reallocation effects of DZs. The results indicate that the provincial DZs can aggravate China’s pollution intensity, but they will no longer play the same role for national-level policies. Moreover, we find that national high-tech industrial DZs (HTZs) can reduce pollution intensity. In particular, the national DZs can bring about the reallocation of pollution among cities working as a selective place-based policy. That means that the national HTZs will not only reduce the local pollution intensity, but also reduce that of surrounding areas. Our empirical results highlight that cities should be encouraged to set up national HTZs in order to achieve an environmentally friendly high-quality development goal.

Air Pollution within Different Urban Forms in Manchester, UK

Air pollution causes millions of mortalities and morbidities in large cities. Different mitigation strategies are being investigated to alleviate the negative impacts of different pollutants on people. Designing proper urban forms is one of the least studied strategies. In this paper, we modelled air pollution (NO2 concentration) within four hypothetical neighbourhoods with different urban forms: single, courtyard, linear east-west, and linear north-south scenarios. We used weather and air pollution data of Manchester as one of the cities with high NO2 levels in the UK. Results show that the pollution level is highly dependent on the air temperature and wind speed. Annually, air pollution is higher in cold months (45% more) compared to summer. Likewise, the results show that during a winter day, the concentration of air pollution reduces during the warm hours. Within the four modelled scenarios, the air pollution level in the centre of the linear north-south model is the lowest. The linear building blocks in this scenario reduce the concentration of the polluted air and keep a large area within the domain cleaner than the other scenarios. Understanding the location of air pollution (sources) and the direction of prevailing wind is key to design/plan for a neighbourhood with cleaner air for pedestrians.

Can Changes in Urban Form Affect PM2.5 Concentration? A Comparative Analysis from 286 Prefecture-Level Cities in China

It is crucial to the sustainable development of cities that we understand how urban form affects the concentration of fine particulate matter (PM2.5) from a spatial–temporal perspective. This study explored the influence of urban form on PM2.5 concentration in 286 prefecture-level Chinese cities and compared them from national and regional perspectives. The analysis, which explored the influence of urban form on PM2.5 concentration, was based on two types of urban form indicators (socioeconomic urban index and urban landscape index). The results revealed that cities with high PM2.5 concentrations tended to be clustered. From the national perspective, urban built-up area (UA) and road density (RD) have a significant correlation with PM2.5 concentration for all cities. There was a significant negative correlation between the number of patches (NP) and the average concentration of PM2.5 in small and medium-sized cities. Moreover, urban fragmentation had a stronger impact on PM2.5 concentrations in small cities. From a sub-regional perspective, there was no significant correlation between urban form and PM2.5 concentration in the eastern and central regions. On the other hand, the influence of population density on PM2.5 concentration in northeastern China and northwestern China showed a significant positive correlation. In large- and medium-sized cities, the number of patches (NP), the largest patch index (LPI), and the contagion index (CONTAG) were also positively correlated with PM2.5 concentration, while the LPI in small cities was significantly negatively correlated with PM2.5 concentration. This shows that, for more developed areas, planning agencies should encourage moderately decentralized and polycentric urban development. For underdeveloped cities and shrinking cities, the development of a single center should be encouraged.

Too Big or Too Small? The Threshold Effects of City Size on Regional Pollution in China

The relationship between urban agglomeration and environmental pollution was checked using the balanced panel data of 285 cities in China from 2003 to 2016 and applying the fixed-effect model and the threshold effect model. This showed that: (1) the relationship between urban agglomeration (represented by city size) and environmental pollution is not linear but an inverted U-shape. As long as the GDP is less than 800,370 million RMB, the expansion of city size is not conducive to reducing pollutant emissions. When GDP is less than 41,641 million RMB, the influence of city expansion on environmental pollution is relatively less. When GDP is higher than 800,370 million RMB, the city expansion may reduce pollutant emission. (2) The city size is not too big but is in fact too small. Only 18 cities experienced the inverted U-shape with the expansion of their city size, causing the gas and water pollutant emissions to decrease. (3) For cities in an urban agglomeration, environmental pollution can be reduced by expanding the city size through coordinated development of urban agglomeration. In conclusion, for most large cities in urban agglomerations in China, the city size is not too large but too small.

PCA-Based Identification of Built Environment Factors Reducing PM2.5 Pollution in Neighborhoods of Five Chinese Megacities

Air pollution, especially PM2.5 pollution, still seriously endangers the health of urban residents in China. The built environment is an important factor affecting PM2.5; however, the key factors remain unclear. Based on 37 neighborhoods located in five Chinese megacities, three relative indicators (the range, duration, and rate of change in PM2.5 concentration) at four pollution levels were calculated as dependent variables to exclude the background levels of PM2.5 in different cities. Nineteen built environment factors extracted from green space and gray space and three meteorological factors were used as independent variables. Principal component analysis was adopted to reveal the relationship between built environment factors, meteorological factors, and PM2.5. Accordingly, 24 models were built using 32 training neighborhood samples. The results showed that the adj_R2 of most models was between 0.6 and 0.8, and the highest adj_R2 was 0.813. Four principal factors were the most important factors that significantly affected the growth and reduction of PM2.5, reflecting the differences in green and gray spaces, building height and its differences, relative humidity, openness, and other characteristics of the neighborhood. Furthermore, the relative error was used to test the error of the predicted values of five verification neighborhood samples, finding that these models had a high fitting degree and can better predict the growth and reduction of PM2.5 based on these built environment factors.

The impacts of urban structure on PM2.5 pollution depend on city size and location

Many cities across the world face the challenge of severe fine particulate matter (PM_2.5) pollution. Among the many factors that affect PM_2.5 pollution, there is an increasing interest in the impacts of urban structure. However, quantifying these impacts in China has been difficult due to differences of study area and scale in existing research, as well as limited sample sizes. Here, we conducted a continental study focusing on 301 prefectural cities in mainland China to investigate the effects of urban structure, including urban size and urban compactness, on PM_2.5 concentrations. Based on PM_2.5 raster and land cover data, we used quantile regression and a general multilinear model to estimate the effects and relative contributions of urban size and urban compactness on urban PM_2.5 pollution, with explicit consideration for pollution level, urban size and geographical location. We found: (1) nationwide, the larger and more compact that cities were, the heavier the PM_2.5 pollution tended to be. Additionally, this relationship became stronger with increasing levels of pollution. (2) In general, urban size played a more important role than urban form, and there were no significant interactive effects between the two metrics on urban PM_2.5 concentrations at the national scale. (3) The impacts of urban size and form varied by city size and geographical location. The impacts of urban size were only significant for small or medium-large cities but not for large cities. Among large cities, only urban form had a significantly positive effect on urban PM_2.5 concentrations. The further north and west that cities were, the more dependent PM_2.5 pollution was on urban form, whereas the further south and east that cities were, the greater the impact of urban size. These results provide insights into how urban design and planning can be used to alleviate air pollution.

Built environment profiles for Latin American urban settings: The SALURBAL study

The built environment of cities is complex and influences social and environmental determinants of health. In this study we, 1) identified city profiles based on the built landscape and street design characteristics of cities in Latin America and 2) evaluated the associations of city profiles with social determinants of health and air pollution. Landscape and street design profiles of 370 cities were identified using finite mixture modeling. For landscape, we measured fragmentation, isolation, and shape. For street design, we measured street connectivity, street length, and directness. We fitted a two-level linear mixed model to assess the association of social and environmental determinants of health with the profiles. We identified four profiles for landscape and four for the street design domain. The most common landscape profile was the "proximate stones" characterized by moderate fragmentation, isolation and patch size, and irregular shape. The most common street design profile was the "semi-hyperbolic grid" characterized by moderate connectivity, street length, and directness. The "semi-hyperbolic grid", "spiderweb" and "hyperbolic grid" profiles were positively associated with higher access to piped water and less overcrowding. The "semi-hyperbolic grid" and "spiderweb" profiles were associated with higher air pollution. The "proximate stones" and "proximate inkblots" profiles were associated with higher congestion. In conclusion, there is substantial heterogeneity in the urban landscape and street design profiles of Latin American cities. While we did not find a specific built environment profile that was consistently associated with lower air pollution and better social conditions, the different configurations of the built environments of cities should be considered when planning healthy and sustainable cities in Latin America.

Effectiveness of plants and green infrastructure utilization in ambient particulate matter removal

Air pollution is regarded as an increasingly threatening, major environmental risk for human health. Seven million deaths are attributed to air pollution each year, 91% of which is due to particulate matter. Vegetation is a xenobiotic means of removing particulate matter. This review presents the mechanisms of PM capture by plants and factors that influence PM reduction in the atmosphere. Vegetation is ubiquitously approved as a PM removal solution in cities, taking various forms of green infrastructure. This review also refers to the effectiveness of plant exploitation in GI: trees, grasslands, green roofs, living walls, water reservoirs, and urban farming. Finally, methods of increasing the PM removal by plants, such as species selection, biodiversity increase, PAH-degrading phyllospheric endophytes, transgenic plants and microorganisms, are presented.

Ambient fine particulate matter in Latin American cities: Levels, population exposure, and associated urban factors

BACKGROUND

Exposure to particulate matter (PM_2.5) is a major risk factor for morbidity and mortality. Yet few studies have examined patterns of population exposure and investigated the predictors of PM_2.5 across the rapidly growing cities in lower- and middle-income countries.

OBJECTIVES

Characterize PM_2.5 levels, describe patterns of population exposure, and investigate urban factors as predictors of PM_2.5 levels.

METHODS

We used data from the Salud Urbana en America Latina/Urban Health in Latin America (SALURBAL) study, a multi-country assessment of the determinants of urban health in Latin America, to characterize PM_2.5 levels in 366 cities comprising over 100,000 residents using satellite-derived estimates. Factors related to urban form and transportation were explored.

RESULTS

We found that about 172 million or 58% of the population studied lived in areas with air pollution levels above the defined WHO-AQG of 10 μg/m³ annual average. We also found that larger cities, cities with higher GDP, higher motorization rate and higher congestion tended to have higher PM_2.5. In contrast cities with higher population density had lower levels of PM_2.5. In addition, at the sub-city level, higher intersection density was associated with higher PM_2.5 and more green space was associated with lower PM_2.5. When all exposures were examined adjusted for each other, higher city per capita GDP and higher sub-city intersection density remained associated with higher PM_2.5 levels, while higher city population density remained associated with lower levels. The presence of mass transit was also associated with lower PM_2.5 after adjustment. The motorization rate also remained associated with PM_2.5 and its inclusion attenuated the effect of population density.

DISCUSSION

These results show that PM_2.5 exposures remain a major health risk in Latin American cities and suggest that urban planning and transportation policies could have a major impact on ambient levels.

The Effects of Urban Forms on the PM2.5 Concentration in China: A Hierarchical Multiscale Analysis

For a better environment and sustainable development of China, it is indispensable to unravel how urban forms (UF) affect the fine particulate matter (PM2.5) concentration. However, research in this area have not been updated consider multiscale and spatial heterogeneities, thus providing insufficient or incomplete results and analyses. In this study, UF at different scales were extracted and calculated from remote sensing land-use/cover data, and panel data models were then applied to analyze the connections between UF and PM2.5 concentration at the city and provincial scales. Our comparison and evaluation results showed that the PM2.5 concentration could be affected by the UF designations, with the largest patch index (LPI) and landscape shape index (LSI) the most influential at the provincial and city scales, respectively. The number of patches (NP) has a strong negative influence (-0.033) on the PM2.5 concentration at the provincial scale, but it was not statistically significant at the city scale. No significant impact of urban compactness on the PM2.5 concentration was found at the city scale. In terms of the eastern and central provinces, LPI imposed a weighty positive influence on PM2.5 concentration, but it did not exert a significant effect in the western provinces. In the western cities, if the urban layout were either irregular or scattered, exposure to high PM2.5 pollution levels would increase. This study reveals distinct ties of the different UF and PM2.5 concentration at the various scales and helps to determine the reasonable UF in different locations, aimed at reducing the PM2.5 concentration.

Construction and demolition wastes in Beijing: Where they come from and where they go?

Urbanization and related construction activities generate massive construction and demolition (C&D) waste, which poses considerable risks to the environment and human health. However, it is difficult to assess the significance of these issues without a quantitative understanding of spatial patterns of C&D waste generation (source), transportation (flow), and treatment (sink). This paper maps source, sinks, and flows of C&D waste by coupling a geographic information system and material flow analysis. The methodology is new in the field of C&D waste quantification at the city level, especially downscaling to 5 km × 5 km grids. The results showed that a total of 37.72 million metric tonnes (Mt) of C&D wastes were generated in Beijing, 2019, and ~72% of them were reused and recycled. In space, C&D waste generation in suburban and rural districts (28.73 Mt) was over three times more than that in the downtown area (8.99 Mt). However, the downtown area was the net source region and transported massive amounts of C&D waste to suburban and rural districts. In comparison, several suburban and rural districts had self-sufficient treatment capacity. Our study highlights that a series of C&D waste maps on multiple spatial scales are of great help to design effective policies for waste management by providing spatial details of magnitude and components, and explicitly recognizing primary source and sink areas in cities.

Epidemiological Correlation of Pulmonary Aspergillus Infections with Ambient Pollutions and Influenza A (H1N1) in Southern Taiwan

An increase in fungal spores in ambient air is reported during a spike in particulate matter (PM_2.5 and PM₁₀) aerosols generated during dust or smog events. However, little is known about the impact of ambient bioaerosols on fungal infections in humans. To identify the correlation between the incidence of pulmonary aspergillosis and PM-associated bioaerosols (PM_2.5 and PM₁₀), we retrospectively analyzed data between 2015 and 2018 (first stage) and prospectively analyzed data in 2019 (second stage). Patient data were collected from patients in three medical institutions in Tainan, a city with a population of 1.88 million, located in southern Taiwan. PM data were obtained from the Taiwan Air Quality Monitoring Network. Overall, 544 non-repeated aspergillosis patients (first stage, n = 340; second stage, n = 204) were identified and enrolled for analysis. The trend of aspergillosis significantly increased from 2015 to 2019. Influenza A (H1N1) and ambient PMs (PM_2.5 and PM₁₀) levels had significant effects on aspergillosis from 2015 to 2018. However, ambient PMs and influenza A (H1N1) in Tainan were correlated with the occurrence of aspergillosis in 2018 and 2019, respectively. Overall (2015–2019), aspergillosis was significantly correlated with influenza (p = 0.002), influenza A (H1N1) (p < 0.001), and PM_2.5 (p = 0.040) in Tainan City. Using a stepwise regression model, influenza A (H1N1) (p < 0.0001) and Tainan PM₁₀ (p = 0.016) could significantly predict the occurrence of aspergillosis in Tainan. PM-related bioaerosols and influenza A (H1N1) contribute to the incidence of pulmonary aspergillosis.

The Relationship between City Size and Carbon Monoxide (CO) Concentration and Their Effect on Heart Rate Variability (HRV)

Generally, larger cities are characterized by traffic congestion, which is associated with higher concentrations of pollution, including Carbon Monoxide (CO) pollution. However, this convention requires empirical support on the basis of accurate and reliable measurements. In addition, the assessment of the effect of CO on the autonomic nervous system (ANS), as measured by heart rate variability (HRV), has yielded conflicting results. A majority of the (few) studies on the topic have shown that increases in CO concentration of up to about 10 parts per million (ppm) are associated with a decrease in stress and risk to health in subjects. Beyond the hypothesis postulating city size as a determinant of increased CO concentration, the hypothesis proposing a causal link between CO concentration and HRV balance also requires empirical support. This article compares CO concentrations in a large metropolis with those in a small town, analyzing the relationship between CO and the HRV responses of young women in terms of city size. Four different types of environments were compared, taking into account mediating variables. The study participants spent 35 min in selected environments (a city center, a residential environment, a park, and a home) wearing Polar devices to measure HRV, and portable devices to measure noise thermal load and CO. The average concentrations of CO in each environment were calculated, along with the time distribution of the CO concentration, and the regression slopes between the concentrations of CO and the ANS balance, as measured by the low frequency power/high frequency power ratio (LF/HF) expressed as an HRV index. The results show that, regardless of size, the cities measured were all characterized by low levels of CO, far below the maximal accepted threshold standards, and that urban residents were exposed to these concentrations for less than half of the daytime hours. Furthermore, in contrast to the common view, larger cities do not necessarily accumulate higher concentrations of CO compared to small cities, regardless of the level of transport congestion. This study confirms the findings of the majority of the other studies on the subject, which showed a decrease in stress (as measured by HRV) as a result of an increase in CO concentrations below 7 ppm. Finally, following the assessment of the differential contribution attributed to the different environmental factors, it appears that noise, thermal load, and congestion all contribute more to a higher level of HRV balance than CO. This finding highlights the importance of a multivariable approach to the study, and a remediation of the effect of environmental factors on stress in urban environments.

Using a coupled LES aerosol-radiation model to investigate the importance of aerosol-boundary layer feedback in a Beijing haze episode

Haze episodes, characterised by extremely high aerosol concentrations and a reduction in visibility to less than 10 km, are a frequent occurrence in wintertime Beijing, despite policy interventions leading to an overall improvement in average annual air quality. The main drivers in the onset of haze episodes in wintertime Beijing are changing synoptic conditions, however, aerosol-radiation interactions and their feedback on boundary layer meteorology are thought to play an essential role in the intensity and longevity of haze episodes. In this study we use a coupled LES aerosol-radiation model (UCLALES-SALSA), which we have recently configured for the urban environment of Beijing. The model's high resolution and control over meteorological and aerosol conditions as well as atmospheric processes means we can directly elucidate and quantify the importance of specific aspects of the aerosol-radiation-meteorology feedback in the cumulative stage of Beijing haze. The main results presented here show (a) synoptic scale meteorology has a larger impact on boundary layer suppression than high aerosol concentrations and (b) unlike previous results obtained using regional models or observationally driven analyses, there is no threshold value at which the aerosol-radiation-meteorology feedback has a significant effect on PBL height. Rather, our work shows that for the aerosol composition in this case study, the role of the feedback effect in reducing PBL height increases under shallow boundary layer conditions and with increasing pollution loading in an almost linear fashion. This lack of a threshold found for our case study has important policy implications since interventions based on such a value will not result in large reductions associated with turning off the feedback process. Furthermore, this work directly shows that although the right synoptic changes are a prerequisite for pollution episodes in Beijing, local and regional emissions drive increases in aerosol load that are sufficient to initiate the aerosol feedback loop. This further drives suppression of the boundary layer top and promotes stagnation of air and increased stability, which can be self-sustaining. This results in higher surface aerosol concentrations for extended periods of time, with severe consequences for human health [Lv et al., Atmos. Environ., 2016, 124, 98-108; Wang et al., Atmos. Chem. Phys., 2019, 19(10), 6949-6967].

Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends

Most air pollution research has focused on assessing the urban landscape effects of pollutants in megacities, little is known about their associations in small- to mid-sized cities. Considering that the biggest urban growth is projected to occur in these smaller-scale cities, this empirical study identifies the key urban form determinants of decadal-long fine particulate matter (PM2.5) trends in all 626 Chinese cities at the county level and above. As the first study of its kind, this study comprehensively examines the urban form effects on air quality in cities of different population sizes, at different development levels, and in different spatial-autocorrelation positions. Results demonstrate that the urban form evolution has long-term effects on PM2.5 level, but the dominant factors shift over the urbanization stages: area metrics play a role in PM2.5 trends of small-sized cities at the early urban development stage, whereas aggregation metrics determine such trends mostly in mid-sized cities. For large cities exhibiting a higher degree of urbanization, the spatial connectedness of urban patches is positively associated with long-term PM2.5 level increases. We suggest that, depending on the city's developmental stage, different aspects of the urban form should be emphasized to achieve long-term clean air goals.

Measuring the Urban Particulate Matter Island Effect with Rapid Urban Expansion

Rapid urbanization has posed numerous negative impacts on the environment, including fine particulate matter (PM_2.5) pollution. However, quantitative investigations of the PM_2.5 concentration trends over an urban-rural gradient at the local level are still lacking. The urban particulate matter island (UPI) effect, representing the phenomenon that high particle concentrations in urban areas are gradually attenuated to surrounding areas, was adopted and modified in this paper to study the Hangzhou Bay area from 2000 to 2015. We found the following: (1) every urban area in the Hangzhou Bay area experienced rapid expansion, especially during 2000–2005; (2) more than half of the urban areas suffered UPI problems, and these urban areas had relatively high and stable UPI intensity (UPII) values, although the UPI footprint (UPIFP) values decreased with urban expansion; and (3) urban areas could be divided into three categories: plain areas, hilly areas and the junction of plains and hills, and the probability of the UPI effect varied significantly for different categories. This paper can compensate for the lack of research on the UPI effect at the local level and provide scientific evidence for air pollution control during urban agglomeration planning.

The Urban–Rural Heterogeneity of Air Pollution in 35 Metropolitan Regions across China

Urbanization and air pollution are major anthropogenic impacts on Earth’s environment, weather, and climate. Each has been studied extensively, but their interactions have not. Urbanization leads to a dramatic variation in the spatial distribution of air pollution (fine particles) by altering surface properties and boundary-layer micrometeorology, but it remains unclear, especially between the centers and suburbs of metropolitan regions. Here, we investigated the spatial variation, or inhomogeneity, of air quality in urban and rural areas of 35 major metropolitan regions across China using four different long-term observational datasets from both ground-based and space-borne observations during the period 2001–2015. In general, air pollution in summer in urban areas is more serious than in rural areas. However, it is more homogeneously polluted, and also more severely polluted in winter than that in summer. Four factors are found to play roles in the spatial inhomogeneity of air pollution between urban and rural areas and their seasonal differences: (1) the urban–rural difference in emissions in summer is slightly larger than in winter; (2) urban structures have a more obvious association with the spatial distribution of aerosols in summer; (3) the wind speed, topography, and different reductions in the planetary boundary layer height from clean to polluted conditions have different effects on the density of pollutants in different seasons; and (4) relative humidity can play an important role in affecting the spatial inhomogeneity of air pollution despite the large uncertainties.

The Context-Dependent Effect of Urban Form on Air Pollution: A Panel Data Analysis

There have been debates and a lack of understanding about the complex effects of urban-scale urban form on air pollution. Based on the remotely sensed data of 150 cities in the Beijing-Tianjin-Hebei agglomeration in China from 2000 to 2015, we studied the effects of urban form on fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations from multiple perspectives. The panel models show that the elastic coefficients of aggregation index and fractal dimension are the highest among all factors for the whole region. Population density, aggregation index, and fractal dimension have stronger influences on air pollution in small cities, while area size demonstrates the opposite effect. Population density has a stronger impact on medium/high-elevation cities, while night light intensity (NLI), fractal dimension, and area size show the opposite effect. Low road network density can enlarge the influence magnitude of NLI and population density. The results of the linear regression model with multiplicative interactions provide evidence of interactions between population density and NLI or aggregation index. The slope of the line that captures the relationship between NLI on PM2.5 is positive at low levels of population density, flat at medium levels of population density, and negative at high levels of population density. The study results also show that when increasing the population density, the air pollution in a city with low economic and low morphological aggregation degrees will be impacted more greatly.

Land consumption and income in Ecuador: A case of an inverted environmental Kuznets curve

The ratio of building permits to population is a key indicator to evaluate land consumption. However, few researchers focus on land consumption and its environmental spillovers, for developing countries. The aim of our study, using a Bayesian comparison approach applied to a spatial panel, is to analyse the existence of an inverted U-shaped curve relationship between land consumption and economic development, namely the environmental Kuznets curve, with data that ranges from 2007 to 2015 for 221 cantons in Ecuador. The Bayesian comparison approach allows us to identify: i) the spatial weight matrix that best fits the data, and ii) the best spatial model according to the type of spatial spillovers (local or global). These are both of extreme interest because a knowledge of the extent to which the spatial spillovers spread over space, and their functional form, supports the planning of effective land use policies. The results do not support the inverted U-shaped hypothesis of the Kuznets curve. By contrast, the curvature is convex, which means higher levels of land consumption for higher levels of wealth. Spatial spillovers spread to a limited extent, highlighting an imitation game among agents, both institutions and private agents, in the neighbour locations. Policy implications go from the strengthening of the institutional framework and local tax management, to the urban regeneration to limit real estate speculation. All these interventions should be coordinated among neighbours to avoid freeriding behaviours.

Response of dust particle pollution and construction of a leaf dust deposition prediction model based on leaf reflection spectrum characteristics

Urban plants can improve several environmental pollution problems in cities, especially dust prevention, noise reduction, purification of the atmosphere, etc. To explore the influence of dust deposition on the spectral characteristics of the leaf, a foliar dust deposition prediction model based on high-spectrum data was established. Taking Euonymus japonicus L., the common greening tree species in Beijing, as the research object, high (T1), medium (T2), and low (T3) dust pollution gradients were set and hyperspectral data were collected. Results showed that: (1) in the dust-contaminated environment with different concentrations, the trend of the reflectance curve of the leaves of Euonymus japonicus L. was generally consistent. The spectral reflectance of the leaf surface was positively correlated with the amount of leaf dust. (2) There were five obvious reflection peaks and five main absorption valleys with the same positions and ranges in the 350-2500 nm range. (3) The spectral reflectance of leaf flour dust particles of Euonymus japonicus L. was significantly different before and after dusting, and its size was generally clean leaves > dust-depositing leaves. The sensitive range of its spectral response was 695-1400 nm. (4) The overall trend of the first derivative spectrum was basically the same. The red edge slope and the blue edge slope appeared as T3 > T2 > T1, the red edge position and the blue edge position appeared as T1 < T2 < T3. The red edge position of the leaf surface after dust deposition had an obvious "blueshift", and the moving distance increases with the increase of dust retention on leaf surface. (5) The leaf water index (y = - 1.18x² + 0.5424x + 0.9917, R² = 0.8030, RMSE = 0.187) had the highest accuracy in the regression model of leaf surface dust deposition using spectral parameters. The test showed that the R² reached 0.9019, which indicated that the model has a good fitting effect. This prediction model can effectively estimate the dust deposition of the leaf surface of Euonymus japonicus L.

Do Compactness and Poly-Centricity Mitigate PM10 Emissions? Evidence from Yangtze River Delta Area

The Yangtze River Delta (YRD) region is one of the most densely populated and economically developed areas in China, which provides an ideal environment with which to study the various strategies, such as compact and polycentric development advocated by researchers to reduce air pollution. Using the data of YRD cities from 2011-2017, the spatial durbin model (SDM) is presented to investigate how compactness (in terms of urban density, jobs-housing balance, and urban centralization) and poly-centricity (in terms of the number of centers and polycentric cluster) affect PM₁₀ emissions. After controlling some variables, the results suggest that more jobs-housing-balanced and centralized compactness tends to decrease emissions, while poly-centricity by developing too many centers is expected to result in more pollutant emissions. The effect of high-density compactness is more controversial. In addition, for cities with more private car ownerships (>10 million within cities), enhancing the polycentric cluster by achieving a more balanced population distribution between the traditional centers and sub-centers could reduce emissions, whereas this mitigated emissions effect may be limited. The difference between our study and western studies suggests that the correlation between high-density compactness and air pollution vary with the specific characteristics and with spatial planning implications, as this paper concludes.

What Are the Driving Forces of Urban CO2 Emissions in China? A Refined Scale Analysis between National and Urban Agglomeration Levels

With the advancement of society and the economy, environmental problems have increasingly emerged, in particular, problems with urban CO2 emissions. Exploring the driving forces of urban CO2 emissions is necessary to gain a better understanding of the spatial patterns, processes, and mechanisms of environmental problems. Thus, the purpose of this study was to quantify the driving forces of urban CO2 emissions from 2000 to 2015 in China, including explicit consideration of a comparative analysis between national and urban agglomeration levels. Urban CO2 emissions with a 1-km spatial resolution were extracted for built-up areas based on the anthropogenic carbon dioxide (ODIAC) fossil fuel emission dataset. Six factors, namely precipitation, slope, temperature, population density, normalized difference vegetation index (NDVI), and gross domestic product (GDP), were selected to investigate the driving forces of urban CO2 emissions in China. Then, a probit model was applied to examine the effects of potential factors on urban CO2 emissions. The results revealed that the population, GDP, and NDVI were all positive driving forces, but that temperature and precipitation had negative effects on urban CO2 emissions at the national level. In the middle and south Liaoning urban agglomeration (MSL), the slope, population density, NDVI, and GDP were significant influencing factors. In the Pearl River Delta urban agglomeration (PRD), six factors had significant impacts on urban CO2 emissions, all of which were positive except for slope, which was a negative factor. Due to China's hierarchical administrative levels, the model results suggest that regardless of which level is adopted, the impacts of the driving factors on urban CO2 emissions are quite different at the national compared to the urban agglomeration level. The degrees of influence of most factors at the national level were lower than those of factors at the urban agglomeration level. Based on an analysis of the forces driving urban CO2 emissions, we propose that it is necessary that the environment play a guiding role while regions formulate policies which are suitable for emission reductions according to their distinct characteristics.

Urban Parcel Grouping Method Based on Urban Form and Functional Connectivity Characterisation

The grouping of parcel data based on proximity is a pre-processing step of GIS and a key link of urban structure recognition for regional function discovery and urban planning. Currently, most literature abstracts parcels into points and clusters parcels based on their attribute similarity, which produces a large number of coarse granularity functional regions or discrete distribution of parcels that is inconsistent with human cognition. In this paper, we propose a novel parcel grouping method to optimise this issue, which considers both the urban morphology and the urban functional connectivity. Infiltration behaviours of urban components provide a basis for exploring the correlation between morphology mechanism and functional connectivity of urban areas. We measured the infiltration behaviours among adjacent parcels and concluded that the occurrence of infiltration behaviours often appears in the form of groups, which indicated the practical significance of parcel grouping. Our method employed two parcel morphology indicators: the similarity of the line segments and the compactness of the distribution. The line segment similarity was used to establish the adjacent relationship among parcels and the compactness was used to optimise the grouping result in obtain a satisfactory visual expression. In our study, constrained Delaunay triangulation, Hausdorff distance, and graph theory were employed to construct the proximity, delineate the parcel adjacency matrix, and implement the grouping of parcels. We applied this method for grouping urban parcel data of Beijing and verified the rationality of grouping results based on the quantified results of infiltration behaviours. Our method proved to take a good account of infiltration behaviours and satisfied human cognition, compared with a k-means++ method. We also presented a case using Xicheng District in Beijing to demonstrate the practicability of the method. The result showed that our method obtained fine-grained groups while ensuring functional regions-integrity.

Study of the Relationship between Urban Expansion and PM10 Concentration Using Multi-Temporal Spatial Datasets and the Machine Learning Technique: Case Study for Daegu, South Korea

To protect the population from respiratory diseases and to prevent the damages due to air pollution, the main cause of air pollution should be identified. This research assessed the relationship between the airborne particulate concentrations (PM10) and the urban expansion in Daegu City in South Korea from 2007 to 2017 using multi-temporal spatial datasets (Landsat images, measured PM10 data) and the machine learning technique in the following steps. First, the expanded urban areas were detected from the multiple Landsat images using support vector machine (SVM), a widely used machine learning technique. Next, the annual PM10 concentrations were calculated using the long-term measured PM10 data. Finally, the degrees of increase of the expanded urban areas and of the PM10 concentrations in Daegu from 2007 to 2017 were calculated by counting the pixels representing the expanded urban areas and computing variation of the annual PM10 concentrations, respectively. The experiment results showed that there is a minimal or even no relationship at all between the urban expansion and the PM10 concentrations because the urban areas expanded by 55.27 km2 but the annual PM10 concentrations decreased by 17.37 μg/m³ in Daegu from 2007 to 2017.