Impacts of urban form on air quality: Emissions on the road and concentrations in the US metropolitan areas

Suitability of using carbon dioxide as a tracer gas for studying vehicle emission dispersion in a real street canyon

High-rise buildings form deep urban street canyons and restrict the dispersion of vehicle emissions, posing severe health risks to the public by aggravating roadside air quality. Field measurements are important for understanding the dispersion process of tailpipe emissions in street canyons, while a major challenge is the lack of a suitable tracer gas. Carbon dioxide (CO2), which is safe to the public and inexpensive to obtain, can be reliably measured by existing gas analysers. This study investigated the suitability of using CO2 as a tracer gas for characterising vehicle emission dispersion in a real-world street canyon. The tracer gas was released via a line or point source, whose dispersion was characterised by a sensors network comprising low-cost air quality sensors. The results showed that the CO2 contained in the exhaust gas of a test vehicle itself had unmeasurable effect at roadsides. Both the line and point sources produced obvious CO2 level elevations at approximately 30 s after the test vehicle passed by. In addition, for both line and point sources, the CO2 elevations were much more distinct at the roadside next to tailpipe exit than the opposite side, and were higher at 0.8 m than 1.6 m above the ground. The present study demonstrated that using CO2 as a tracer gas is feasible for investigating vehicle emission dispersion in real-world street canyons. Future studies are needed to improve the gas release rate of the developed tracer gas systems for more reliable measurements and larger street canyons.

How does urban morphology impact cities air quality? A modelling study

With the rise of population living in cities an increase in urbanization is expected, with consequent changes to the morphology of urban areas, and thus, impacts in the urban environment. Air pollution is one of these impacts, affecting ecosystem and human health. The objective of this study is to assess the ability of urban morphologies to minimize air quality problems for future multi-core regions. Three urban morphology scenarios were designed, focused on Aveiro, Portugal: two scenarios representing urban compaction (Focused City and Independent City Scenario); and one representing an extreme version of the current urban dispersion (Dispersed City). The impact of urban scenarios on air quality was compared against the current urban morphology (baseline). The modelling system composed by the Weather Research and Forecast meteorological model, coupled with the chemistry model CAMx, adapted to consider a differentiation of urban land use classes (high- and low-density urban areas, and industrial areas), was applied. Annual results show that the compact urban morphology scenarios led to a decrease of air pollutant concentrations (NO2 = -20 %, PM10 = -3 %, PM2.5 = -2 % for IC; and NO2 = -17 %, PM10 = -2 %, PM2.5 = -1 %, for FC), with increases in industrial hotspots, that affect population exposure. The Disperse City scenario showed an increase in NO2 concentrations (+98 %), due to the influence of road transport and lack of active mobility policies, with little change to PM concentrations. Despite population dispersion, the increase in NO2 concentrations increased exposure. This study provides novel insights by applying a comprehensive methodology to assess the urban morphology's impact on air quality, highlighting the importance of the location and distribution of industrial areas, and the role of road transport emissions, and has the potential to provide urban planners and policy makers with the tools to prepare for more sustainable and healthier future urban areas. The applied methodology and modelling tools can be replicated worldwide.

Spatiotemporal analysis of wildfires in Alberta, Canada over the past sixty years: Increased wildfire frequency by human activities

Wildfires are a major socio-ecological issue in Alberta. The region's extensive forests and grasslands provide abundant natural fuel. Since the onset of the 21st century, climate change and human activities have led to an increase in wildfire frequency. While provious studies have focused on specific events and short-term periods, they often lack a comprehensive analysis of long-term trends and fail to integrate environmental and human factors. This study addresses these gaps by providing a detailed analysis of wildfire dynamics in Alberta from 1961 to 2020, utilizing historical datasets to examine trends in frequency and burned areas. Unlike prior research, this study employs advanced spatial analysis methods, including spatial autocorrelation and hotspot analysis, and incorporates 12 variables related to climate, human activities, and topography into a geographically weighted logistic regression (GWLR) model. The results reveal a significant increase in wildfire frequency since the 21st century, with human-caused fires clustering in specific areas, while lightning-caused fires are more dispersed. The GWLR model highlights the spatial variability of influencing factors, with population density emerging as the most significant driver of burned areas, underscoring the critical role of human activities. This research innovates by integrating multiple variables, extending the time frame, and emphasizing the interplay of human, climatic, and topographic factors. The findings provide valuable insights for optimizing wildfire prevention strategies and informing spatial planning policies to mitigate future risks.

Rainwater quality in Special Economic Zone (SEZ) in Poland

In the face of climate change, rainwater harvesting has become a valuable solution providing an alternative water source and helping mitigate the sewage system overload. Using rainwater necessitates ensuring that its quality is appropriate for its intended purpose. In industrial areas, the nature of the activities conducted poses a substantial risk of rainwater quality deterioration, particularly due to heavy metals (HMs) contamination. Within the article scope, the rainwater quality analysis from the Special Economic Zone (SEZ) in Lower Silesia (Poland) was examined. The research included analysis of physicochemical parameters, as well as cations, anions, and HMs concentrations in rainwater samples. The measurements were made in the certified laboratory between April 2022 and June 2023. The samples were taken from 5 representative points of the SEZ area: drains into the ditch and storage tanks. From analyzed anions and cations, Cl- (205 mg/L) and Ca2+ (69.6 mg/L) showed the highest concentration, respectively. Cu was the most abundant HMs (up to 0.049 mg/L). Cd and Cr were not detected at all. It has been shown that the most of the analyzed quality parameters complied with WHO guidelines. The detected contaminants mainly come from industrial activity (like incineration; vehicles, batteries, and electronic manufacturing or concrete production), as well as from materials coating buildings and pavements prone to release undesired components. Vehicle traffic, natural sources (e.g. weathering ions from soils) also was shown to contribute to rainwater quality. Tap water periodically used for washing the roads and pavements unintentionally transported to sampling points may contribute to Cl- and Ca2+ presence.

How urban growth dynamics impact the air quality? A case of eight Indian metropolitan cities

Air pollution is a matter of great significance that confronts the sustainable progress of urban areas. Against India's swift urbanization, several urban areas exhibit the coexistence of escalating populace and expansion in developed regions alongside extensive spatial heterogeneity. The interaction mechanism between the growth of urban areas and the expansion of cities holds immense importance for the remediation of air pollution. Henceforth, the present investigation utilizes geographically weighted regression (GWR) to examine the influence of urban expansion and population growth on air quality. The examination will use a decade of data on the variation in PM2.5 levels from 2010 to 2020 in eight Indian metropolitan cities. The study's findings demonstrate a spatial heterogeneity between urban growth dynamics and air pollution levels. Urban growth and the expansion of cities demonstrate notable positive impacts on air quality, although the growth of infilling within expanding urban areas can significantly affect air quality. Given the unique trajectories of urban development in developing countries, this research provides many suggestions for urban administrators to foster sustainable urban growth.

Exploring the linkages between urban form, mobility and emissions with OLYMPUS: A comparative analysis in two French regions

Nowadays, urban planners and decision-makers are confronted with an increasing number of major urban spaces whose functioning is accompanied by a high density of domestic, private and professional activities, all associated with the consumption of fossil fuels and the emission of atmospheric pollutants and greenhouse gases. As centers of activity and zones of economic and demographic resources, urban centers stand out as privileged areas for the implementation of local strategies aiming at reducing air pollutant emissions, whether through spatial planning, the evolution of services or the transformation of practices. In this work, we propose a diagnosis of the links between population, urban forms, mobility and air pollutant emissions, using the OLYMPUS activity-based emission model. The model is run over two distinct French regions, Pays de la Loire and Île-de-France, characterized by contrasting urban characteristics in terms of structure, density and accessibility. The results highlight the good transposability of the OLYMPUS model over different territories. Then, the interconnections between the specificities of urban systems on one side and the travel demand, modal share, mobility patterns and total road emissions on the other side, are explored. We notably show that the densification of urban centers exerts a pull on peri-urban areas, generating car trips from the suburbs and worsening air quality in the urban cores. The results underline the importance of targeted emission reduction strategies taking into account the unique characteristics and challenges of specific urban landscapes.

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.

Effects of urban form on air quality: A case study from China comparing years with normal and reduced human activity due to the COVID-19 pandemic

This study explored the dynamic and complex relationships between air quality and urban form when considering reduced human activities. Applying the random forest method to data from 62 prefecture-level cities in China, urban form-air quality relationships were compared between 2015 (a normal year) and 2020 (which had significantly reduced air pollution due to COVID-19 lockdowns). Significant differences were found between these two years; urban compactness, shape, and size were of prime importance to air quality in 2020, while fragmentation was the most critical factor in improving air quality in 2015. An important influence of traffic mode was also found when controlling air pollution. In general, in the pursuit of reducing air pollution across society, the best urban forms are continuous and compact with reasonable building layouts, population, and road densities, and high forest area ratios. A polycentric urban form that alleviates the negative impacts of traffic pollution is preferable. Urban development should aim to reduce air pollution, and optimizing the effects of urban form on air quality is a cost-effective way to create better living environments. This study provides a reference for decision-makers evaluating the effects of urban form on air pollution emission, dispersion, and concentration in the post-pandemic era.

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.

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.

Association between Land Use Mix and Respiratory Symptoms and Asthma in Children from the Generation XXI Birth Cohort

Land use mix (LUM) in the neighbourhoods has been associated with healthier lifestyles. However, less is known about the association between LUM and health outcomes, namely during childhood. The objective of this study was to evaluate the association between different LUM indexes for Porto Metropolitan Area and asthma and respiratory symptoms in children. A cross-sectional analysis was performed involving 6260 children enrolled in Generation XXI. Land use around the child's residence was assessed with the Portuguese official map of land cover using a GIS. Generalized linear mixed-effects models were fitted to estimate the association between LUM and respiratory symptoms and asthma at 7 years of age. Adjusted associations were quantified using odds ratio (OR) and 95% confidence interval (95% CI). After adjustment, LUM was associated with a lower odds of wheezing in the last 12 months [OR (95% CI) = 0.37 (0.15; 0.93) using Shannon's Evenness Index within 500 m; and OR = 0.93 (0.89; 0.98) using the number of different land use types within 250 m]. Living in neighbourhoods with high LUM has a protective effect on current wheezing symptoms. Our results highlight the association between LUM and respiratory symptoms among children, suggesting that public health considerations should be incorporated in land use decision-making.

Coupling Cellular Automata and a Genetic Algorithm to Generate a Vibrant Urban Form-A Case Study of Wuhan, China

Enhancing urban vitality is a key goal for both the government and ordinary urban residents, and creating this vitality is emphasized in China's urban development strategy. Enhancing urban vitality through the rational design of urban forms is a leading topic of Western urban research. An urban growth pattern (UGP) reflects the dual characteristics of a static pattern and the dynamic evolution of the external urban form. It affects urban vitality by influencing the spatial allocation of internal structural elements and patterns in the adjacent location. The cellular automata (CA) mode can effectively simulate the aggregation process of urban growth (infilling expansion or edge expansion). However, it does not simulate the diffusion of urban growth, specifically the evolution of outlying expansion. In addition, CA focuses on learning, simulating, and building knowledge about geographic processes, but does not spatially optimize collaborative land use against multiple objectives or model multi-scale land use. As such, this paper applies a coupling model called the "promoting urban vitality model," based on cellular automata (CA) and genetic algorithm (GA) (abbreviated as UV-CAGA). UV-CAGA optimally allocates cells with different UGPs, creating a city form that promotes urban vitality. Wuhan, the largest city in Central China, was selected as a case study to simulate and optimize its urban morphology for 2025. The main findings were as follows. (1) The urban vitality of the optimized urban form scheme was 4.8% higher than the simulated natural expansion scheme. (2) Compared to 2015, after optimization, the simulated sizes of the newly increased outlying, edge, and infilling areas in 2025 were 6.51 km2, 102.69 km2, and 23.48 km2, respectively; these increases accounted for 4.90%, 77.32%, and 17.68%, respectively, of the newly increased construction land area. This indicated that Wuhan is expected to have a very compact urban form. (3) The infilling expansion type resulted in the highest average urban vitality level (0.215); the edge expansion type had the second highest level (0.206); outlying growth achieved the lowest vitality level (0.199). The UV-CAGA model proposed in this paper improves on existing geographical process simulation and spatial optimization models. The study successfully couples the "bottom-up" CA model and "top-down" genetic algorithm to generate dynamic urban form optimization simulations. This significantly improves upon traditional CA models, which do not simulate the "diffusion" process. At the same time, the spatial optimization framework of the genetic algorithm in the model also provides insights related to other effects related to urban form optimization, such as urban environmental security, commuting, and air pollution. The integration of related research is expected to enrich and improve urban planning tools and improve the topic's scientific foundation.

Does urban sprawl exacerbate urban haze pollution?

Is rapid urban sprawl increasing environmental pollution while boosting economic development? This study uses panel data of 30 Chinese provinces during 2002-2018 to investigate the impact of urban sprawl on haze pollution with the use of a spatial lag model. Urban built-up area (ba) and urban public facilities occupation (upfo) are used to measure the differential impact of urban sprawl on haze pollution. The main highlights of the results are as follows: (1) there is a nonlinear relationship between urban sprawl and haze pollution. An inverted U-shaped relationship was found between built-up area and haze pollution, while urban public facility occupation and haze pollution have a U-shaped correlation. (2) The impact of urban sprawl on haze pollution shows a significant time-based difference. The relationship between the two was not significant in 2002-2010, but become significant in 2011-2018. (3) Significant regional differences in the impact of urban sprawl on haze pollution were found. The effects of urban sprawl on urban haze levels are more pronounced in the eastern and western regions than in the central region.

Effects of urban functional fragmentation on nitrogen dioxide (NO2) variation with anthropogenic-emission restriction in China

Urban functional fragmentation plays an important role in assessing Nitrogen Dioxide (NO2) emissions and variations. While the mediated impact of anthropogenic-emission restriction has not been comprehensively discussed, the lockdown response to the novel coronavirus disease 2019 (COVID-19) provides an unprecedented opportunity to meet this goal. This study proposes a new idea to explore the effects of urban functional fragmentation on NO2 variation with anthropogenic-emission restriction in China. First, NO2 variations are quantified by an Autoregressive Integrated Moving Average with external variables-Dynamic Time Warping (SARIMAX-DTW)-based model. Then, urban functional fragmentation indices including industrial/public Edge Density (ED) and Landscape Shape Index (LSI), urban functional Aggregation Index (AI) and Number of Patches (NP) are developed. Finally, the mediated impacts of anthropogenic-emission restriction are assessed by evaluating the fragmentation-NO2 variation association before and during the lockdown during COVID-19. The findings reveal negative effects of industrial ED, public LSI, urban functional AI and NP and positive effects of public ED and industrial LSI on NO2 variation based on the restricted anthropogenic emissions. By comparing the association analysis before and during lockdown, the mediated impact of anthropogenic-emission restriction is revealed to partially increase the effect of industrial ED, industrial LSI, public LSI, urban functional AI and NP and decrease the effect of public ED on NO2 variation. This study provides scientific findings for redesigning the urban environment in related to the urban functional configuration to mitigating the air pollution, ultimately developing sustainable societies.

Modelling Spatial Heterogeneity in the Effects of Natural and Socioeconomic Factors, and Their Interactions, on Atmospheric PM2.5 Concentrations in China from 2000–2015

In recent years, atmospheric PM2.5 pollution in China has become increasingly severe and exploring the relationships among its influencing factors is important in the prevention and control of air pollution. Although previous studies have identified complexity in variations in PM2.5 concentrations and recognized the interaction of multiple factors, little quantitative information is available on the evolution of the relationships among these factors, their spatial heterogeneity, and the multiscale interactions between them. In this study, geographical detector and multiscale geographically weighted regression models have been used to explore the multiscale interactions among natural and socioeconomic factors and PM2.5 concentration in China over the period 2000–2015. The results indicate that the relationship between natural factors and PM2.5 concentration is stronger than that for socioeconomic factors. The type of interaction between each factor is dominated by bivariate and nonlinear enhancement, exhibiting strong interactions between natural factors and anthropogenic factors. Although the effect of each factor on PM2.5 is complex, the relative influence of both human activities and social factors is shown to have gradually increased over time and population, agriculture, urbanization, and socioeconomic activities in general make important contributions to PM2.5. In addition, the scale of effects related to natural factors is smaller and more stable compared to the influence of human activities during the period 2000-2015. There are significant differences in the way natural factors and socioeconomic factors affect PM2.5, and there is strong non-stationarity of spatial relationships. Factors associated with topography, vegetation (NDVI), climate (temperature), natural sources, and agricultural activity are shown to be important determinants of PM2.5 across China and warrant significant attention in terms of managing atmospheric pollution. The study demonstrates that spatial differences in the direction, intensity, and scale of each factor should be accounted for to improve prevention and control measures and alleviate regional PM2.5 pollution.

Stochastic and Self-Organisation Patterns in a 17-Year PM10 Time Series in Athens, Greece

This paper utilises statistical and entropy methods for the investigation of a 17-year PM₁₀ time series recorded from five stations in Athens, Greece, in order to delineate existing stochastic and self-organisation trends. Stochastic patterns are analysed via lumping and sliding, in windows of various lengths. Decreasing trends are found between Windows 1 and 3500-4000, for all stations. Self-organisation is studied through Boltzmann and Tsallis entropy via sliding and symbolic dynamics in selected parts. Several values are below -2 (Boltzmann entropy) and 1.18 (Tsallis entropy) over the Boltzmann constant. A published method is utilised to locate areas for which the PM₁₀ system is out of stochastic behaviour and, simultaneously, exhibits critical self-organised tendencies. Sixty-six two-month windows are found for various dates. From these, nine are common to at least three different stations. Combining previous publications, two areas are non-stochastic and exhibit, simultaneously, fractal, long-memory and self-organisation patterns through a combination of 15 different fractal and SOC analysis techniques. In these areas, block-entropy (range 0.650-2.924) is significantly lower compared to the remaining areas of non-stochastic but self-organisation trends. It is the first time to utilise entropy analysis for PM₁₀ series and, importantly, in combination with results from previously published fractal methods.

Chemistry and Microbiology of Urban Roof Runoff in Kraków, Poland with Ecological and Health Risk Implications

Urban rainwater samples, collected in various districts of Kraków during the period from the spring of 2019 to the winter of 2020, were investigated, and relevant risk implications were assessed. The contents of 31 components were determined, including: bacteria, fungi, pH, EC, Cl-, N-NO3, P-PO4, SO42-, SiO2, K, Mg, Ag, Al, Ba, Ca, Cu, K, Mg, Mo, Na, Pb, Sb, and Zn. The highest contents of the investigated elements were determined in the industrial (eastern) part of Kraków. The values of toxicity potential were <1, indicating a lack of ecological risk. The value of contamination degree indicated moderate rainwater contamination in all analyzed elements (Cdeg = 9.44). The total non-carcinogenic risk values, in both adults (hazard quotient (HQ) = 1.38) and children (HQ = 2.54), exceeded the acceptable level of one. Regarding individual elements in dermal contact, the acceptable level of 1 × 10−6 was exceeded in Pb (adults carcinogenic risk (CR) = 4.87 × 10−5, children CR = 2.23 × 10−5). The contents of the analyzed chemical compounds did not exceed the permissible levels adopted for drinking water; however, rainwater was significantly contaminated with respect to its microbiological parameters. Rainwater was suitable for non-potable use.

Investigating the Impacts of Urbanization on PM2.5 Pollution in the Yangtze River Delta of China: A Spatial Panel Data Approach

Urbanization is a key determinant of fine particulate matter (PM2.5) pollution variability. However, there is a limited understanding of different urbanization factors’ roles in PM2.5 pollution. Using satellite-derived PM2.5 data from 2002 to 2017, we investigated the spatiotemporal evolution and the spatial autocorrelation of PM2.5 pollution in the Yangtze River Delta (YRD) region. Afterwards, the impacts of three urbanization factors (population urbanization, land urbanization and economic urbanization) on PM2.5 pollution were estimated by a spatial Durbin panel data model (SDM). Obtained results showed that: (i) PM2.5 pollution was larger in the north than in the south of YRD; (ii) Lianyungang and Yancheng cities had significant increasing trends in PM2.5 pollution from 2002 to 2017; (iii) the regional median center of PM2.5 pollution was observed in the Nanjing city, with gradual shifting to the northwest during the 16-year period; (iv) PM2.5 pollution showed significant and positive spatial autocorrelation and spillover effect; (v) population urbanization contributed more to the increase in PM2.5 pollution than land urbanization, while economic urbanization had no significant impact. The present study highlights the impacts of three urbanization factors on PM2.5 pollution which represent valuable and relevant information for air pollution control and urban planning.

Assessing the Impact of Road Traffic Reorganization on Air Quality: A Street Canyon Case Study

One of the elements of strategy aimed at minimizing the impact of road transport on air quality is the introduction of its reorganization resulting in decreased pollutant emissions to the air. The aim of the study was to determine the optimal strategy of corrective actions in terms of the air pollutant emissions from road transport. The study presents the assessment results of the emission reduction degree of selected pollutants (PM10, PM2.5, and NOx) as well as the impact evaluation of this reduction on their concentrations in the air for adopted scenarios of the road management changes for one of the street canyons in Krakow (Southern Poland). Three scenarios under consideration of the city authorities were assessed: narrowing the cross-section of the street by eliminating one lane in both directions, limiting the maximum speed from 70 km/h to 50 km/h, and allowing only passenger and light commercial vehicles on the streets that meet the Euro 4 standard or higher. The best effects were obtained for the variant assuming banning of vehicles failing to meet the specified Euro standard. It would result in a decrease of the yearly averaged PM10 and PM2.5 concentrations by about 8–9% and for NOx by almost 30%.

The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm

In the context of rapid urbanization, the spread of cities in the Yangtze River Economic Belt is intensifying, which has an impact on the green and sustainable development of these cities. It is necessary to establish an accurate urban sprawl measurement system. First, the regulation theory of urban sprawl is explained. According to the actual development situation of cities in the Yangtze River Economic Belt, smart growth theory is selected as the basic regulation method of urban sprawl. Second, the back propagation neural network (BPNN) algorithm under deep supervised learning is applied to construct a smart evaluation model of land use growth. Finally, based on the actual development of cities in the Yangtze River Economic Belt, the quantitative growth measurement method is selected to construct a measurement system of urban sprawl in the Yangtze River Economic Belt, and the empirical analysis is carried out. The training results show that the proposed BPNN smart growth evaluation model, based on deep supervised learning, has good evaluation accuracy, and the error is within the preset range. The analysis of the quantitative growth-based measurement system in the increase of urban construction land shows that the increase in urban construction land area of the Yangtze River Economic Belt from 2014 to 2019 was 78.67 km2. Meanwhile, the increases in urban construction land area in different years are different. The empirical results show that the population composition of the Yangtze River Economic Belt and the urban construction area between 2005 and 2019 show a trend of increasing annually; at the same time, urban sprawl development shows a staged characteristic. It is of great significance to apply deep learning fusion neural network algorithm in the construction of the urban sprawl measurement system, which provides a quantitative basis for the in-depth analysis and discussion of urban sprawl.

Exploring the Spatial and Temporal Relationship between Air Quality and Urban Land-Use Patterns Based on an Integrated Method

The deterioration of air quality in urban areas is often closely related to urbanization, as this has led to a significant increase in energy consumption and the massive emission of air pollutants, thereby exacerbating the current state of air pollution. However, the relationship between urban development and air quality is complex, thus making it difficult to be analyzed using traditional methods. In this paper, a framework integrating spatial analysis and statistical methods (based on 170 regression models) is developed to explore the spatial and temporal relationship between urban land use patterns and air quality, aiming to provide solid information for mitigation planning. The thresholds for the influence of urban patterns are examined using different buffer zones. In addition, the differences in the effects of various types of land use pattern on air quality were also explored. The results show that there were significant differences between 1999 and 2013 with regards to the correlations between land use patterns and air pollutant concentrations. Among all land uses, forest, water and built-up areas were proved to influence concentrations the most. It is suggested that the developed framework should be applied further in the real-world mitigation planning decision-making process