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Abstract
Air pollution is a significant global environmental issue. Nevertheless, the importance of rational urban planning in mitigating it is frequently disregarded. Conducting air quality optimization simulations that consider UFIs (urban form indicators) is an effective approach for air quality-friendly urban planning. Despite its potential, this technique is still in its infancy. This study aims to identify research gaps and uncertainties by summarizing the current research status of key steps in air quality optimization simulation: selecting UFIs, analyzing impact mechanisms of UFIs on air pollution, building air quality models, and proposing optimization strategies through multiple-scenario predictions. Our findings indicate a lack of consistency in selecting UFIs, and an indicator system considers their impact on air quality and urban planning standards proposed by us might be a viable option. Increases in the proportion of construction land, industrial land, and floor area ratio significantly contribute to air pollution, whereas factors such as forest land, public green space, and sky openness have a noteworthy alleviating effect. Typically, statistical modeling is preferred at the city scale while CFD simulation techniques are used at smaller scales. However, future air quality improving through UFIs optimization still lacks a multi-scale nested tool. Thus, further research is recommended to explore combined impacts of various UFIs on different air pollutants under typical scenarios, and develop intelligent, big data-driven air quality models and tools. This review might contribute to transforming air quality optimization that consider UFIs from fragmented academic research to practical application, thereby assisting in global air pollution mitigation efforts.
The purpose of this research is to examine how data-driven dynamic urban communication affects the effectiveness and optimization of vital municipal services in a variety of contexts. Interestingly, waste management IoT sensors have an efficiency score of 9, which is remarkable and indicates the promise of data-driven approaches in this industry. An impressive 4.3 user satisfaction rating highlights how well these technologies are received. Additionally, data-driven communication techniques provide affordable options, as seen by their $2.5 service request cost, which highlights the possibility of more efficient resource allocation. This study offers strong proof that data-driven communication benefits both municipal service providers and people by increasing service consumption and lowering response times to around 5.2 minutes.
This paper explores the field of data analytics for dynamic urban operations and provides a systematic analysis of the importance and possible implications of this field. Our investigation indicates significant data volumes in an urban setting that is data-rich: 500 GB are generated by traffic sensors, 300 GB by environmental monitors, 150 GB by mobile apps, and 75 GB by emergency calls. A variety of analytics techniques, each with a different processing time, are built upon these data sources. These techniques include descriptive, predictive, prescriptive, and diagnostic analytics. The outcomes, which include 90% accuracy, an average processing time of 40 minutes, 80% resource utilization, and 4.2 user satisfaction ratings, highlight the benefits of data analytics. According to the comparison study, diagnostic analytics has a score of 7.8, indicating room for development, while prescriptive analytics leads with an efficiency score of 8.4. As urban stakeholders and academics work to improve urban systems and solve urban issues, the results give a thorough understanding of the effectiveness and application of data analytics in the context of dynamic urban operations.
Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans’ exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.
Air quality models can support pollution mitigation design by simulating policy scenarios and conducting source contribution analyses. The Intervention Model for Air Pollution (InMAP) is a powerful tool for equitable policy design as its variable resolution grid enables intra‐urban analysis, the scale of which most environmental justice inquiries are levied. However, InMAP underestimates particulate sulfate and overestimates particulate ammonium formation, errors that limit the model's relevance to city‐scale decision‐making. To reduce InMAP's biases and increase its relevancy for urban‐scale analysis, we calculate and apply scaling factors (SFs) based on observational data and advanced models. We consider both satellite‐derived speciated PM2.5 from Washington University and ground‐level monitor measurements from the U.S. Environmental Protection Agency, applied with different scaling methodologies. Relative to ground‐monitor data, the unscaled InMAP model fails to meet a normalized mean bias performance goal of <±10% for most of the PM2.5 components it simulates (pSO4: −48%, pNO3: 8%, pNH4: 69%), but with city‐specific SFs it achieves the goal benchmarks for every particulate species. Similarly, the normalized mean error performance goal of <35% is not met with the unscaled InMAP model (pSO4: 53%, pNO3: 52%, pNH4: 80%) but is met with the city‐scaling approach (15%–27%). The city‐specific scaling method also improves the R² value from 0.11 to 0.59 (ranging across particulate species) to the range of 0.36–0.76. Scaling increases the percent pollution contribution of electric generating units (EGUs) (nationwide 4%) and non‐EGU point sources (nationwide 6%) and decreases the agriculture sector's contribution (nationwide −6%).
Air pollution reduction is a major objective for transport policy makers. This paper considers interventions in the form of clean air zones, and provide a machine learning approach to assess whether the objectives of the policy are achieved under the designed intervention. The dataset from the Newcastle Urban Observatory is used. The paper first tackles the challenge of finding datasets that are relevant to the policy objective. Focusing on the reduction of nitrogen dioxide (NO
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) concentrations, different machine learning algorithms are used to build models. The paper then addresses the challenge of validating the policy objective by comparing the NO
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concentrations of the zone in the two cases of with and without the intervention. A recurrent neural network is developed that can successfully predict the NO
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concentration with root mean square error of 0.95.
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.
A better understanding of urban form metrics and their environmental outcomes can help urban policymakers determine which policies will lead to more sustainable growth. In this study, we have examined five urban form metrics–weighted density, density gradient slope, density gradient intercept, compactness, and street connectivity–for 462 metropolitan areas worldwide. We compared urban form metrics and examined their correlations with each other across geographic regions and socioeconomic characteristics such as income. Using the K-Means clustering algorithm, we then developed a typology of urban forms worldwide. Furthermore, we assessed the associations between urban form metrics and two important environmental outcomes: green space access and air pollution. Our results demonstrate that while higher density is often emphasized as the way to reduce driving and thus PM 2.5 emissions, it comes with a downside–less green space access and more exposure to PM 2.5 . Moreover, street connectivity has a stronger association with reduced PM 2.5 emissions from the transportation sector. We further show that it is not appropriate to generalize urban form characteristics and impacts from one income group or geographical region to another, since the correlations between urban form metrics are context specific. Our conclusions indicate that density is not the only proxy for different aspects of urban form and multiple indicators such as street connectivity are needed. Our findings provide the foundation for future work to understand urban processes and identify effective policy responses.
Fine particulate matter (PM2.5) pollution is a key issue affecting the health of urban residents. To explore the impact of urban building form on PM2.5 pollution, this study focused on Wuhan, a Chinese megacity. Based on the urban building data, various building form indices were first calculated in grids to quantitatively present the spatial distribution of urban buildings. The city-scale PM2.5 distribution was obtained with satellite remote sensing and ground air pollution monitoring data. The impact of urban building forms on PM2.5 pollution distribution was then analyzed. The results show that the changes in PM2.5 concentration in Wuhan in the north–south direction have a relatively obvious correlation with the windward area ratio of buildings. The dense north–south buildings can slow the spread of near-surface particulate pollution. This finding demonstrates that the building blocking effect of PM2.5 diffusion in Wuhan is significant. The results of this study can provide a reference for urban planning, architectural design, and air pollution control strategies.
Fine particulate matter poses a negative effect on air quality and public health. The formation and dispersion of PM2.5 are often affected by the urban built environment. Previous literature has made great efforts to study the relationship between the built environment and air pollution, and propose corresponding policy implications for reducing air pollution. Most proposed policies are instructive but lack spatial heterogeneity, which is essential because the degree of air pollution and measures in each specific place is different. In this study, the built environment was depicted with multisource data and methods such as street view imagery (SVI) and deep learning, and the integration of geographically weighted regression (GWR) and k-means clustering was applied to explore the spatially varying impacts of the built environment factors on air pollution, and classify the study area into different groups with zonal policies and mitigation measures for PM2.5 reduction. Results show that parking lots significantly influenced PM2.5, followed by floor area ratio, road density, street vehicle volume, industrial facility , and street view greenery. Street view greenery was found to be negatively related to PM2.5 concentration in most of Xiamen Island. The study area was categorized into four groups with tailor-made policy implications considering spatial heterogeneity. For example, zone 1 is in the south of Xiamen Island with the highest coefficients of the parking lot and floor area ratio and lowest coefficient of street view greenery. For zone 1, greenery maintenance in the mountains, restricted high-density development, and improvement of greenery around the parking lot may be effective for this zone. These empirical results provide a new perspective for exploring the spatially heterogeneous mechanism of air pollution and a new approach to propose targeted and zonal policy implications for PM2.5 mitigation.
Urban traffic pollution, which is strongly influenced by the complex urban morphology, has posed a great threat to human health. In this study, we performed a high-resolution simulation of traffic pollution in a typical city block in Baoding, China, based on the Parallelized Large-eddy simulation Model (PALM), to examine the distribution patterns of traffic-related pollutants and explore their relationship with urban morphology. Based on the model results, we conducted a multi-linear regression (MLR) analysis and found that the distribution of air pollutants inside the city block was dominated by both traffic emissions and urban morphology, which explained about 70% of the total variance in spatial distribution of air pollutants. Excluding the contribution of emissions, over 50% of the total variance can still be explained by the urban morphology. Among these urban morphological factors, the key factors determining the spatial distribution of air pollution are “Distance from the road” (DR), “Building Coverage Ratio” (BCR) and “Aspect Ratio” (H/W) of the street canyon. Specifically, urban areas with lower Aspect Ratio, lower BCR and larger DR are less affected by traffic pollution. Compiling these individual factors, we developed a complex Urban Morphology Pollution Index (UMPI). Each unit increase in UMPI is associated with a one percent increase of nearby traffic pollution contribution. This index can help urban planners to semi-quantitatively evaluate building groups which tend to trap or ventilate traffic pollution and thus help to reduce human exposure to street canyon level pollution through either traffic emission control or urban morphology amelioration.
Air quality is highly related to the health of a human being. Urban morphology has a significant influence on air quality; however, the specific relationship between urban morphology characteristics and air quality at the neighborhood scale has yet to be investigated, especially the vegetation effect on PM2.5 concentration and diffusion. The relevant morphological parameters based on the affected pathways of urban morphology on air quality were selected, and the sensitivity degree and laws of the selected morphological parameters to PM2.5 were quantified by numerical simulation, bivariate correlation analysis, and regression analysis. The results showed that Building Density (BD), Block Envelope Degree (BED), Average Building Volume (ABV), Average Building Floors (ABF), Standard Deviation of Building Height (SDH) and Greenbelt Coverage Rate (GCR) were Sensitive Morphological Parameters (SMPs). A positive and cosine curve trend of BD and BED with PM2.5 was observed. GCR was significant to dust retention along with vertical canopy height. When ABV = 40,000 m3 and ABF = 20F, the lowest PM2.5 concentration was examined, while increased SDH could promote airflow and enhance the capacity of PM2.5 diffusion. Finally, morphology-optimization strategies were proposed at the neighborhood scale: (1) Decreasing the BED along the street; (2) considering the species of vegetation with the appropriate height and increasing the GCR; (3) increasing the ABF of neighborhoods appropriately while controlling the ABV and distinguishing the internal SDH of neighborhoods. The study could apply the scientific basis for the planning and design of healthy and livable cities.
This work introduces the need to develop competitive, low-cost and applicable technologies to real roads to detect the asphalt condition by means of Machine Learning (ML) algorithms. Specifically, the most recent studies are described according to the data collection methods: images, ground penetrating radar (GPR), laser and optic fiber. The main models that are presented for such state-of-the-art studies are Support Vector Machine, Random Forest, Naïve Bayes, Artificial neural networks or Convolutional Neural Networks. For these analyses, the methodology, type of problem, data source, computational resources, discussion and future research are highlighted. Open data sources, programming frameworks, model comparisons and data collection technologies are illustrated to allow the research community to initiate future investigation. There is indeed research on ML-based pavement evaluation but there is not a widely used applicability by pavement management entities yet, so it is mandatory to work on the refinement of models and data collection methods.
High concentrations of ozone (O3) is a major air problem in urban areas, which creates a serious threat to human health. Urban street canyon morphology plays a key role in air pollutant dispersion and photochemical reaction rate. In this study, a one-year observation at three height levels was performed to investigate the O3 distribution vertically in a street canyon of Shenyang. Then, field investigation and ENVI-met modelling were conducted to quantify the influence of street canyon morphology and microclimatic factors on O3 distribution at the pedestrian level. All O3 concentrations at the three height levels were high from 1:00 p.m. to 4:00 p.m. Both O3 concentrations at pedestrian level and the middle level in the canyon were 40% higher than at roof level. O3 accumulated in the canyons rather than spread out. The in-canyon O3 concentrations had significantly positive correlations with building height, aspect ratio, sky view factor, air temperature, and wind speed. Both field investigation and ENVI-met modelling found high O3 concentrations in medium canyons. Photochemical reaction intensity played a more important role in in-canyon O3 distribution than dispersion. Wide canyons were favorable for removing O3.
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.
As a common air pollutant, particulate matter (PM2.5) can lead to serious health risks when inhaled over a long period of time, and it is therefore especially important for urban residents to understand the distribution characteristics of PM2.5 within the urban space. To predict long-term PM2.5 exposure at high resolution in high-density cities and quantify the influence of urban spatial morphological characteristics on the distribution of PM2.5 concentration produced under long-term conditions. The seasonal mean values of PM2.5 concentration from 23 monitoring stations in Shenzhen, China were used as dependent variables, and 6 categories of 502 potential predictor variables including urban/building spatial morphological indicators were used as independent variables. The seasonal land use regression models were developed by combining stepwise supervision and Akaike information criterion (AIC) selection with a leave-one-out-cross-validation (LOOCV) to test the prediction performance of the model. The adjusted R² values were 0.86, 0.70, 0.70 and LOOCV R² values were 0.83, 0.58, 0.68 in the first three seasons, respectively. In addition to the variables of land use category (i.e., the proportion of land for industrial and manufacturing, the proportion of land for green space and square, and the proportion of land for municipal utilities) and physical geography category (i.e., the count of parks and squares, average elevation), building coverage ratio and the diversity of building volume were also selected into the model as predictor variables. The R² contributions of urban/building spatial morphology factors exceeded 10% for all three seasonal models. This study developed seasonal land use regression (LUR) models with good prediction performance for a high-density city and provided high-resolution spatial mapping of PM2.5 under steady-state conditions. The contribution of urban/building spatial morphological indicators to the interpretation of R² for LUR models confirmed the effect of high-density urban morphology on the distribution of PM2.5 concentration. The quantitative correlation between PM2.5 concentration and spatial morphological factors such as building coverage and building volume diversity can provide a basis for incorporating air pollution dispersion considerations to urban planning at early stage.
Exploring how urban form affects the Particulate Matter 2.5 (PM2.5) concentration could help to find environmentally friendly urbanization. According to the definition of geography, this paper constructs a comprehensive urban form evaluation index system applicable to many aspects. Four urban form metrics, as well as road density and five control variables are selected. Based on 2015 data on China’s 340 prefecture-level cities, the spatial regression model and geographically weighted regression model were used to explore the relationship between the urban form evaluation index system and PM2.5 pollution. The main results show that the spatial distribution of PM2.5 in China follows an increasing trend from northwest to southeast. Urban form indicators such as AI, LPI, PLAND, LSI and road density were all significantly related to PM2.5 concentrations. More compact urban construction, lower fragmentation of urban land, and lower density of the road network are conducive factors for improving air quality conditions. In addition, affected by seasonal changes, the correlation between urban form and PM2.5 concentration in spring and winter is higher than that in summer and winter. This study confirmed that a reasonable urban planning strategies are very important for improving air quality.
Given the serious threat of PM2.5 pollution to the environment and public health in China, reducing PM2.5 concentrations through urban planning practices has become a common interest of urban planners and policymakers. In this regard, identifying the relationship between urban form and PM2.5 concentrations has been a matter of much concern. This paper seeks to investigate how urban form influences PM2.5 concentrations by taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as a case study. First, the urban form was assessed by two indicators, i.e., the patch complexity index (PCI) and urban compactness index (UCI), which were developed based on multiple landscape metrics and the principal component analysis. Then, the relationship between PM2.5 concentrations and the two urban form indicators was revealed by spatial panel models. Our results mainly showed that PCI and UCI were negatively and positively associated with PM2.5 concentrations, respectively, indicating that less compact urban forms with high patch complexity had advantages in reducing the PM2.5 concentrations. According to these empirical results, the mixed-use urban development with high green coverage, which is conducive to constructing a more complex and less compact urban form, would be adoptable in terms of mitigating PM2.5 concentrations in the GBA urban agglomeration.
Increasingly, Chinese cities are proposing city-scale ventilation corridors (VCs) to strengthen wind velocities and decrease pollution concentrations, although their influences are ambiguous. To assess VC impacts, an effort has been made to predict the impact of VC solutions in the high density and diverse land use of the coastal city of Shanghai, China, in this paper. One base scenario and three VC scenarios, with various VC widths, locations, and densities, were first created. Then, the combination of the Weather Research and Forecasting/Single-Layer Urban Canopy Model (WRFv.3.4/UCM) and Community Multiscale Air Quality (CMAQv.5.0.1) numerical simulation models were employed to comprehensively evaluate the impacts of urban spatial form and VC plans on PM2.5 concentrations. The modeling results indicated that concentrations increased within the VCs in both summer and winter, and the upwind concentration decreased in winter. These counter-intuitive results could be explained by decreased planetary boundary layer (PBL), roughness height, deposition rate, and wind speeds induced by land use and urban height modifications. PM2.5 deposition flux decreased by 15–20% in the VCs, which was attributed to the roughness height decrease for it weakens aerodynamic resistance (Ra). PBL heights within the VCs decreased 15–100 m, and the entire Shanghai’s PBL heights also decreased in general. The modeling results suggest that VCs may not be as functional as certain urban planners have presumed.
With rapid economic growth, urbanization and industrialization, fine particulate matter with aerodynamic diameters ≤ 2.5 µm (PM2.5) has become a major pollutant and shows adverse effects on both human health and the atmospheric environment. Many studies on estimating PM2.5 concentrations have been performed using statistical regression models and satellite remote sensing. However, the accuracy of PM2.5 concentration estimates is limited by traditional regression models; machine learning methods have high predictive power, but fewer studies have been performed on the complementary advantages of different approaches. This study estimates PM2.5 concentrations from satellite remote sensing-derived aerosol optical depth (AOD) products, meteorological data, terrain data and other predictors in 2015 in Shaanxi, China, using a combined genetic algorithm-support vector machine (GA-SVM) method, after which the spatial clustering pattern was explored at the season and year levels. The results indicated that temperature (r = -0.684), precipitation (r = -0.602) and normalized difference vegetation index (NDVI) (r = -0.523) were significantly negatively correlated with the PM2.5 concentration, while AOD (r = 0.337) was significantly positively correlated with the PM2.5 concentration. Compared to conventional land use regression (LUR) and SVM models and previous related studies, the GA-SVM method demonstrated a significantly better prediction accuracy of PM2.5 concentration, with a higher 10-fold cross-validation coefficient of determination (R2) of 0.84 and lower root mean square error (RMSE) and mean absolute error (MAE) of 12.1 μg/m3 and 10.07 μg/m3, respectively. Y-scrambling test shows that the models have no chance correlation. The central and southern parts of Shaanxi have high PM2.5 concentrations, which are mainly due to the pollutant emissions and meteorological and topographical conditions in those areas. There was a positive spatial agglomeration characteristic of regional PM2.5 pollution, and the spatial spillover effect of PM2.5 pollution for seasonal and annual variations does exist. In general, the GA-SVM method is robust and accurately estimates PM2.5 concentrations via a novel modeling framework application and high-quality spatiotemporal information. It also has great significance for the exploration of PM2.5 pollution estimation and high-precision mapping methods, especially early warning in high-risk areas. Finally, the prevention and control of atmospheric pollution should take pollution control measures from major cities and surrounding cities, and focus on the joint pollution control measures for plain cities.
Increasingly detrimental effects of fine particulate matter (PM) have been observed in Northeast Asia owing to its rapid economic development. Previous studies have found that dust, combustion, and chemical reactions are the major sources of PM; nevertheless, the spatial configuration of land use and land cover, which is of most interest to planners and landscape architects, also influences the PM levels. Here, we attempted to unveil the relationship between PM and different types of land use cover (i.e., developed, agricultural, woody, grass, and barren lands) in 122 municipalities of Korea. Landscape ecology metrics were applied to measure the spatial configuration of land use pattern and spatial lag models by taking into account the transboundary nature of air pollution, allowing us to conclude the following regarding PM levels: (1) the size of land cover type matters, but their spatial configuration also determines the variations in PM levels; (2) the contiguity and proximity of landcover patches are important; (3) the patterns of grasslands (e.g., simple, compact, and cluster (with large patches) patterns) and woodlands (e.g., complex, contiguous, and cluster (with large patches) patterns) considered desirable for minimizing PM are dissimilar in terms of contiguity.
For a long time, air pollution caused by unreasonable urban spatial structure and excessive urban sprawl has been a prominent environmental problem in China. From the level of all cities, three economic zones and different city scales, panel data of 194 prefecture-level cities in China from 2006 to 2017 were used to construct a dynamic panel model and to analyze the impact of urban spatial structure on SO2, industrial smoke and dust emissions. The results showed that: (1) air pollution had a time cumulative effect year by year, the air pollution of the last year could add air pollution in the script year; (2) urban space expansion could effectively curb air pollution; (3) the urban spatial structure with high population compactness made the air pollution change in an inverted "U" shape; (4) in different economic zone levels and different urban scale levels, the direction of influence and intensity of urban spatial structure on air pollution was different. In the eastern region of China, the residential land, public facilities land and traffic land in the urban structure mainly affected the air pollution. In the central region, the residential land, industrial land, traffic land and municipal land in the urban structure had a significant impact on the air pollution, while the urban scale was the main cause of the air pollution in the western region. Based on this, we recommended the reasonable planning of land use structure, establishment of a population density regulation mechanism, and paying attention to regional differences and urban size differences. This study can help managers of different economic zones and cities of different sizes to improve urban spatial structure and control air pollution in the process of urban development.
The intensification of global urbanization has exacerbated the negative impact of atmospheric environmental factors in urban areas, thus threatening the sustainability of future urban development. In order to ensure the sustainability of urban atmospheric environments, exploring the changing laws of urban air quality, identifying highly polluted areas in cities, and studying the relationship between air quality and land use have become issues of great concern. Based on AQI data from 340 air quality monitoring stations and urban land use data, this paper uses inverse distance weight (IDW), Getis-Ord Gi*, and a negative binomial regression model to discuss the spatiotemporal variation of air quality in the main urban area of Lanzhou and its relationship with urban land use. The results show that urban air quality has characteristics of temporal and spatial differentiation and spatially has characteristics of agglomeration of cold and hot spots. There is a close relationship between urban land use and air quality. Industrial activities, traffic pollution, and urban construction activities are the most important factors affecting urban air quality. Green spaces can reduce urban pollution. The impact of land use on air quality has a seasonal effect.
The surface roughness aerodynamic parameters, z 0 (roughness length) and d (zero-plane displacement height), are vital to the accuracy of the Monin-Obukhov similarity theory (MOST). Deriving improved urban canopy parameterisation (UCP) schemes within the conventional framework remains mathematically challenging. The current study explores the potentiality of a machine learning (ML) algorithm, a random forest (RF), as a complement to the traditional UCP schemes. Using large-eddy simulation (LES) and ensemble sampling, in combination with nonlinear least-squares regression of the logarithmic-layer wind profiles, a dataset of approximately 4.5 × 10 ³ samples is established for the aerodynamic parameters and the morphometric statistics, enabling the training of the ML model. While the prediction for d is not as good as the UCP after Kanda et al. [Bound.-Layer Meteor. 148 (2013), pp. 357-377], the performance for z 0 is notable. The RF algorithm also categorises z 0 and d with an exceptional performance score: the overall bell-shaped distributions are well predicted and the ±0.5 σ category (i.e. the 38% percentile) competently captured (37.8% for z 0 and 36.5% for d ). Amongst the morphometric features, the mean and maximum building heights ( H ave and H max ) are found to be of predominant influence on the prediction of z 0 and d . A perhaps counter-intuitive result is the considerably less striking importance of the building-height variability. Possible reasons are discussed. The feature importance scores could be useful for identifying the contributing factors to the surface aerodynamic characteristics. The results may shed some light on the development of ML-based UCP for mesoscale modelling.
In previous studies, planners have debated extensively whether compact development can improve air quality in urban areas. Most of them estimated pollution exposure with stationary census data that linked exposures solely to residential locations, therefore overlooking residents’ space–time inhalation of air pollutants. In this study, we conducted an air pollution exposure assessment by scrutinizing one-hour resolution population distribution maps derived from hourly smartphone data and air pollutant concentrations derived from inverse distance weighted interpolation. We selected Wuhan as the study area and used Pearson correlation analysis to explore the effect of compactness on population-weighted concentrations. The results showed that even if a compact urban form helps to reduce pollution concentrations by decreasing vehicle traveling miles and tailpipe emissions, higher levels of building density and floor area ratios may increase population-weighted exposure. With regard to downtown areas with high population density, compact development may locate more people in areas with excessive air pollution. In all, reducing density in urban public centers and developing a polycentric urban structure may aid in the improvement of air quality in cities with compact urban forms.
The high-rise and high-density housing development in nearby industry relocations is a general urban sprawl phenomenon in fast-growing cities in Southern China. Aside from the low price, the improved air quality in the suburban area is always a reason for home buyers, but the consistent monitoring of air quality and knowledge about how to plan housing estates are lacking. This paper investigates the relationship between the housing morphology and the air quality in three housing estates in Shenzhen. This research utilizes on-site monitoring equipment to examine negative air ions (NAIs) and fine particulate matter (PM2.5) and the Computational Fluid Dynamics (CFD) simulation to examine the air flow. This study reveals the effect of the urban form on the concentration of NAIs and PM2.5 in spatial variation. A correlation study between the configuration variables of the urban form and the CFD air flow pattern helps to identify the key variables influencing the air quality. This study concludes that in housing estates with good air quality of surroundings, the building density has no remarkable effect. However, the footprint of buildings, the layout of podiums, the roughness length of the building, the distance between buildings, the open space aspect ratio and the mean building height may have a remarkable impact on the air flow and quality. These findings may encourage high-density housing development and provide planning guidance for the configuration of housing forms in Southern China and subtropical climate regions around the world.
The interactions between landscape patterns and ecological processes have become more complex with rapid urbanization. In China, urban haze pollution has become increasingly apparent. Elucidating the relationship between urban landscape patterns and haze effects is essential for understanding the formation and development of haze and implementing effective mitigation measures. In-depth studies of the effects of three-dimensional urban landscape patterns on haze are hindered by data limitations, unreliable methods, and complex processes. This study reviews the use of landscape indices, with the aim of advancing understanding of the impacts of three-dimensional urban landscape patterns on the distribution and diffusion of haze pollution. The literature is examined to assess the status and progress of research on the use of landscape indices, and a summary of case studies in which landscape indices have been used in haze pollution modeling is presented. Furthermore, a feasible perspective based on a landscape pattern index is presented. In light of this review focusing on the relationship between urban landscape patterns and processes, new approaches for determining the spatial and temporal heterogeneity of haze distribution and diffusion and haze-prone areas, and for optimizing landscape patterns, are identified.
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.
There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding the size and representativeness of limited ground reference stations for model development, reconciling multi-source data, and interpretability of deep learning models. This research addresses these challenges by leveraging a strategically deployed, extensive low-cost sensor (LCS) network that was rigorously calibrated through an optimized neural network. A set of raster predictors with varying data quality and spatial scales was retrieved and processed, including gap-filled satellite aerosol optical depth products and airborne LiDAR-derived 3D urban form. We developed a multi-scale, attention-enhanced convolutional neural network model to reconcile the LCS measurements and multi-source predictors for estimating daily PM2.5 concentration at 30-m resolution. This model employs an advanced approach by using the geostatistical Kriging method to generate a baseline pollution pattern and a multi-scale residual method to identify both regional patterns and localized events for high-frequency feature retention. We further used permutation tests to quantify the feature importance, which has rarely been done in DL applications in environmental science. Finally, we demonstrated one application of the model by investigating the air pollution inequality issue across and within various urbanization levels at the block group scale. Overall, this research demonstrates the potential of geospatial AI analysis to provide actionable solutions for addressing critical environmental issues.
conventional Land Use Regression models for air pollution prediction: 1). A combination of urban forest involvement and urban form representation; 2). Scale sensitivity analysis of model variables. Here, we apply lacunarity to investigate the spatial sensitivity of predictors, incorporate 2-D and 3-D urban form to comprehensively characterize the urban environment, and examine the tree diversity impacts on air pollution distribution using unique NO 2 datasets collected through opportunistic mobile monitoring in the Bronx, New York, and Oakland, California. We find that lacunarity-optimized models could reduce the computation burden by extracting the upper limits of the spatial heterogeneity of predictors while keeping the model accuracy simultaneously. Furthermore, there are synthetic effects between the urban form and tree diversity on NO 2 distribution , and such effect directions could be non-monotonic. Finally, although the increase in tree diversity could facilitate the reduction of regional NO 2 concentration, it is essential to seek a balance between tree diversity and tree dominance to effectively improve air quality on the city scale. The findings are useful for environmental scientists striving for better air quality and urban planners caring for the well-being of cities.
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Understanding how urban form (UF) affects CO2 emissions is important to reduce emissions in the practice of urban planning and management. This study investigates the spatiotemporal impacts of UF on CO2 emissions by using Geographically and Temporally Weighted Regression (GTWR) model, in which UF is characterized by metrics across three dimensions underpinning sustainable development, namely environmental-aspect urban form (EnUF), economic-aspect urban form (EcUF) and social-aspect (SUF) urban form. The data are collected from 256 Chinese cities in 2000, 2005, 2010, 2015 and 2018. The results reveal that: (1) On average, EnUF has two-sided effects on CO2 emissions; SUF and EcUF exert negative and positive effects on CO2 emissions respectively. (2) There is significant spatiotemporal heterogeneity in terms of the impacts of UF variables on CO2 emissions. (3) As for EnUF variables, only the eastern coastal Chinese cities effectively show the benefits from urban compactness in terms of CO2 reduction. Increased urban complexity and sprawling lead to more CO2 emissions. The higher the diversity of carbon sink land, the more conducive to CO2 emission reduction. (4) With respect to SUF variables, population density and road density present “U-shaped” and “inverted U-shaped” relationship with CO2 emissions respectively. The accessibility of green space and water bodies contributes significantly to CO2 reduction. (5) As regards EcUF variables, the impact of the degree of economic agglomeration and related industry diversity on CO2 emissions changes from positive to negative in some cities. The impact of unrelated industry diversity on CO2 emissions is spatially characterized as “negative coastal areas surround the positive inland areas”. This study casts a new light on the impacts of UF on CO2 emissions and also proposes differentiated policy implications for carbon emission reduction in different Chinese cities.
Aerodynamic impacts of two tree implant layout with different tree heights on air pollution distribution inside the street canyon, where the walls height and street width are equal, has been investigated using Parallelized Large Eddy simulation model (PALM). A reference tree-free case, six double-row, and center-row planted trees with 5 m, 10 m, and 15 m height cases were studied. The results of the modeling study reveal that the center-row planted trees have direct impacts on air ventilation inside the street canyon. In street canyons with center-row planted trees, taller trees worsen the air quality up to 10%. In street canyons with double-row planted trees, trees higher than roof-top level improve air quality inside the street canyon by about 6%. The highest concentration values were modeled at the pedestrian level about 1.5 m to 2 m near the leeward wall. The center-row tree implant is more favorable for air quality improvement than the double-row tree implant. The trees increase air pollution around the roof level near the leeward wall.
The urban environmental impact of tall buildings has increasingly become an important topic of investigation, with the rapid pace of tall-building constructions around the world. In this study, the effects of a tall building on pedestrian-level flow and pollutant dispersion are investigated using the parallelized large-eddy simulation model (PALM). Numerical simulations are conducted by changing the tall-building height in the configuration of a tall building surrounded by low-rise cubical buildings. As the tall-building height increases, the time- and area-averaged pedestrian-level wind speed and pollutant concentration increases and decreases, respectively. Both the rates of changes in the average wind speed and pollutant concentration decrease with increasing tall-building height. The trend of the average wind speed is attributable to an increasing tendency of oncoming flows to pass by the sides of the tall building rather than going down as the tall-building height increases. The trend of the average pollutant concentration is associated with that of the average wind speed. Instantaneous flow and pollutant dispersion are also analyzed. In the upstream region of the tall building, bulks of polluted air are repeatedly transported from further upstream regions and enter the canyon right in front of the tall building, temporarily increasing the pedestrian-level pollutant concentration near the windward wall of the tall building. In the downstream region of the tall building, at the pedestrian level, two counter-rotating vortices appear in the canyon just behind the tall building. Each of these vortices repeatedly develops at one of the two leeward corners of the tall building, moves downstream while changing its size, and disappears after reaching the windward wall of the neighboring low-rise building. These coherent vortices considerably affect the spatiotemporal variation of pedestrian-level pollutant concentration in the canyon just behind the tall building.
Preventing high concentrations of fine particulate (PM2.5) to realize the goal of sustainable development is becoming a challenge for rapidly urbanized cities. Increasing vehicle emission due to inefficient urban form is thought to be the main cause of traffic congestion and increased PM2.5 concentrations. Previous efforts attributing PM2.5 concentrations to urban forms are yet to reach consistent conclusions on practical environmental protection strategies. In this study, we considered urban compositions and their spatial configuration to propose a new measurement—urban configuration—and document the effects of urban configuration on PM2.5 concentrations. Using 330 Chinese cities as our sample, we found that the areas of two types of urban facilities, namely, residence and industry, are positively related to PM2.5 concentrations, and the area of public service facilities is negatively related to PM2.5 concentrations. Regarding the spatial configuration of different urban compositions, we documented that residence–industry accessibility is a key factor of PM2.5 concentrations and plays a more important role than the residence–commerce accessibility. We also compared the influence of two accessibility indices (distance- and gravity-based accessibilities) and further found that the effect of reducing the residence–industry distance is more remarkable than the effect of increasing residential or industrial area on reducing PM2.5 concentrations. Our results indicate that the key to reaching sustainable urban expansion is to synchronize urban constructions with spatial configuration optimization. For Chinese cities, a 7.52% increase in residence area requires at least 1% decrease in the average residence–industry distance to eliminate the incremental effects of newly constructed residential region on PM2.5 concentrations. This study casts new light on the relationship between urban configuration and PM2.5 concentration and provides decision makers practical and realistic approaches in realizing sustainable development goals.
PM2.5 pollution endangers human health and urban sustainable development. Land use regression (LUR) is one of the most important methods to reveal the temporal and spatial heterogeneity of PM2.5, and the introduction of characteristic variables of geographical factors and the improvement of model construction methods are important research directions for its optimization. However, the complex non-linear correlation between PM2.5 and influencing indicators is always unrecognized by the traditional regression model. The two-dimensional landscape pattern index is difficult to reflect the real information of the surface, and the research accuracy cannot meet the requirements. As such, a novel integrated three-dimensional landscape pattern index (TDLPI) and machine learning extreme gradient boosting (XGBOOST) improved LUR model (LTX) are developed to estimate the spatiotemporal heterogeneity in the fine particle concentration in Shaanxi, China, and health risks of exposure and inhalation of PM2.5 were explored. The LTX model performed well with R² = 0.88, RMSE of 8.73 μg/m³ and MAE of 5.85 μg/m³. Our findings suggest that integrated three-dimensional landscape pattern information and XGBOOST approaches can accurately estimate annual and seasonal variations of PM2.5 pollution The Guanzhong Plain and northern Shaanxi always feature high PM2.5 values, which exhibit similar distribution trends to those of the observed PM2.5 pollution. This study demonstrated the outstanding performance of the LTX model, which outperforms most models in past researches. On the whole, LTX approach is reliable and can improve the accuracy of pollutant concentration prediction. The health risks of human exposure to fine particles are relatively high in winter. Central part is a high health risk area, while northern area is low. Our study provides a new method for atmospheric pollutants assessing, which is important for LUR model optimization, high-precision PM2.5 pollution prediction and landscape pattern planning. These results can also contribute to human health exposure risks and future epidemiological studies of air pollution.
Urban morphology affects airflow, causing pollutant accumulation within the urban canopy. Urban planning can regulate urban form by applying such strategies as defining urban block typology and stipulating urban indices. Consequently, urban planning can contribute to a healthy environment. In this context, modeling pollutant dispersion can assist urban planning decisions. Nonetheless, there is a lack of studies investigating the combined impact of urban block typology and urban indices on air quality. Therefore, this study aims to analyze the impact of these combined strategies on pollutant dispersion. Using computational fluid dynamics techniques, we investigated three combinations of urban indices (floor area ratio, surface coverage, and height) for three urban block typologies (single-block, detached building, and central courtyard). A total of nine urban configurations were distributed into three sets of urban index values for the three block typologies, namely “basic cases,” “1-cases,” and “2-cases.” We used the Unsteady Reynolds-Averaged Navier–Stokes equations and the κ–ω SST turbulence model for the numerical simulations. The validation was conducted using wind tunnel experimental data. To assess city breathability at pedestrian height we used five parameters: pollutant concentration, the mean age of air, net escape velocity, and pollutant mass fluxes. The results showed that both strategies (i.e., block typology and urban indices) affect urban air quality. However, the performance of a block typology depends on the urban index values. For instance, in the “2-cases,” decreasing the surface coverage by increasing the building's height improved ventilation efficiency in all typologies. Nonetheless, this strategy changed the performance ranking of the “basic cases.” In “basic cases” the single-block typology had the best performance; in the “2-cases,” the courtyard typology performed best. Although the courtyard typology improved air quality inside the patio, the outdoor areas displayed more pollutant concentration. Finally, general orientations to developing urban planning strategies were formulated.
Air pollution is a serious global environmental problem, especially in developing countries. PM2.5 is a major air pollutant that poses critical risks in urban areas. In this study, we identified the influence of comprehensive urbanization on regional PM2.5 in the Yangtze River Delta (YRD), the largest metropolitan region in China, from 1998 to 2015. The impacts of four urbanization subsystems (Economic, Spatial, Demographic, and Social) on the spatiotemporal evolution of PM2.5 were investigated at the city level using a linear mixed effect model (LME). The annual average concentration of PM2.5 over the YRD increased during the study period, with rapid growth in the northern plains and slow growth in the mountainous south. The LME model showed good performance between the predicted and observed PM2.5, with an R² value for 10-fold cross-validation reaching 0.87 and a regression coefficient of 0.88. Urban population, GDP ratio of secondary industry, built-up area, total road area, number of students in colleges and universities, and total retail sales of consumer goods all had positive associations with PM2.5 concentration, while the proportion of tertiary industry employment, GDP ratio of primary industry, forest area, and number of hospital beds all had negative associations. Economic had the strongest effect (44%) on PM2.5 early on (1998–2003), but over time the contribution of other subsystems gradually increased. The overall contributions of urbanization type to PM2.5 in the YRD were Spatial (33.10%) > Economic (26.76%) > Demographic (16.85%) > Social (12.72%). Our results help deepen the understanding of regional air pollution in China as well as its correlation with urbanization and its subsystems.
Accurate individual exposure assessment to particulates in complex urban environments requires maps of PM2.5 concentration at high spatiotemporal resolution. Previous empirical researches of PM2.5 mapping usually have ignored the contextual influences of associated factors on pollution variation. This study presents a new thinking about spatial prediction of PM2.5 pollution based on the pollution scene assumption. Methodologically, pollution scenes are areas exert contextual influences on the spatiotemporal variety of air pollution and can be expressed by urban microenvironment dependence and temporal nonstationarity. Taking Changsha, China as a case, a two-stage modelling strategy of geographically weighted regression kriging (GWRK) was developed to validate the assumption based on a high-density sampling campaign and a fine-scale, manually interpreted urban microenvironment map. Our results confirm the potential existence of urban air pollution scene. PM2.5 concentration varies between urban microenvironments; pollution scene based GWRK is effective for high resolution mapping of PM2.5 concentration at the hourly scale and depicts more detailed spatial variations than traditional GWR in this study. This assumption and modelling strategy provide a promising way for mapping urban air pollution at high resolution which will further benefits works on exposure assessment and risk avoidance at fine scales.
With rapid development of urbanization, the atmospheric pollution caused by PM2.5 has attracted widespread concern. Land as the underlying surface of atmosphere, which has a significant effect on PM2.5. This study aimed to explore the spatiotemporal variation of PM2.5 and its relationship to land use in China during 2005–2017, then put forward suggestions for land use optimization to improve air quality. The results suggested that PM2.5 generally showed a downward trend, with obvious spatial pattern. High-pollution areas were formed in the North China Plain, the Middle-Lower Yangtze River Plain, the Sichuan Basin and the Taklimakan Desert. Furthermore, PM2.5 was significantly positive correlated with the area of cultivated land and artificial surfaces, and cultivated land fragmentation was conducive to the decline of PM2.5, while contiguous expansion of artificial surfaces would increase PM2.5. Forest and grassland had an inhibitory effect on PM2.5, and the larger area, the more complex shape, the better PM2.5 retention effect. Moreover, cultivated land and forest were the main land use types that affect PM2.5, and the effect of cultivated land was greater than that of forest. The percentage of cultivated land landscape, the shape index of forest and water bodies were the main landscape metrics that affect PM2.5, and their effect on PM2.5 decreased in turn. Therefore, strengthening the pollution control of cultivated land, promoting the land recuperation, further expanding the scale of forest and grassland, enriching vegetation types, promoting the grain for green, reasonably controlling the scale of urban development, are conducive to reduce PM2.5.
Urban green infrastructure (UGI) is considered to be an effective tool for mitigating PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) pollution in urban areas. However, long and continuous time series analyses of the relationships between the UGI landscape and PM2.5 pollution remain a challenge. In the present study, then we analyzed the PM2.5 variations and their relationships with the UGI landscape patterns at the urban agglomeration (the urban area of CLUA) and neighborhood scales (6 neighborhood zones with radii ranging from 500 m to 3000 m around the sampling point.). The results illustrated PM2.5 concentration increased slightly in the urban expansion areas and decreased in some old urban areas, but the changing trend was not significant. The PM2.5 concentrations were different among the four seasons in this area, and the lowest and highest concentrations occurred in winter (66.79 μg/m³) and summer (24.32 μg/m³), respectively. At the urban agglomeration scale, just a very small proportion of the PM2.5 variations in the CLUA during this study period could be attributed to the UGI landscape pattern, wind speed and relative humidity; PM2.5 concentrations were affected more strongly by wind speed and relative humidity than by UGI landscape patterns; the LSI (landscape shape index) and bridges served as the main landscape indicators affecting the PM2.5 concentrations among the UGI landscape patterns factors. At the neighborhood scale, the LPI (largest patch index), AWMSI (area-weighted mean shape index), AI (aggregation index), Core and Loop served as the main UGI landscape indicators influencing PM2.5 and had different degrees of influence on the PM2.5 concentrations; the seasonal and scale effects of UGI landscape patterns on PM2.5 concentrations at neighborhood scales were observed. Comparatively, the effects of the UGI landscape patterns on PM2.5 concentration seem more significant at the neighborhood scale than at the urban agglomeration scale. The findings of this multiscale analysis can provide new insights into understanding the relationships between UGI landscape patterns and urban air pollution at different scales, and provide a scientific reference for urban planning and air pollution control.
Many cities across the world face the challenge of severe fine particulate matter (PM2.5) pollution. Among the many factors that affect PM2.5 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 PM2.5 concentrations. Based on PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 concentrations. The further north and west that cities were, the more dependent PM2.5 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.
With the prevalence of stroke rising due to both aging societies and more people getting strokes at a younger age, a comprehensive investigation into the relationship between urban characteristics and age-specific stroke mortality for the development of a healthy built environment is necessary. Specifically, assessment of various dimensions of urban characteristics (e.g. short-term environmental change, long-term environmental conditions) is needed for healthy built environment designs and protocols.
A multifactorial assessment was conducted to evaluate associations between environmental and sociodemographic characteristics with age-stroke mortality in Hong Kong. We found that short-term (and temporally varying) daily PM10, older age and being female were more strongly associated with all types of stroke deaths compared to all-cause deaths in general. Colder days, being employed and being married were more strongly associated with hemorrhagic stroke deaths in general. Long-term (and spatially varying) regional-level air pollution were more strongly associated with non-hemorrhagic stroke deaths in general. These associations varied by age. Employment (manual workers) and low education were risk factors for stroke mortality at younger ages (age <65). Greenness and open space did not have a significant association with stroke mortality. Since a significant connection was expected, this leads to questions about the health-inducing efficacy of Hong Kong's compact open spaces (natural greenery being limited to steep slopes, and extensive impervious surfaces on public open spaces). In conclusion, urban plans and designs for stroke mortality prevention should implement age-specific health care to neighborhoods with particular population segments.
Exposure to PM2.5 and CO has been proven to be closely related to physical health. Since 2012, they have been added to the pollutant list for national monitoring in Wuhan. However, the fine-scale variation in pollution, especially at the street level, is complex and requires further exploration. In this study, the influence of urban form on the street-level air pollution distribution was comprehensively assessed according to the urban factors, high-resolution meteorological data, PM2.5 and CO concentration data collected via mobile monitoring along roads in Wuhan. Furthermore, potential urban factors, including the land-use and urban form characteristics, were obtained from geographic information system. Both linear regression and gradient boosting decision tree (GBDT) models were developed to explore the relationship between the observed concentrations and the predictor variables. The modeling results demonstrate that the GBDT model, which captured the non-linear relationship, helps to better explain more of the variations in the pollutant concentrations than the linear model. This study provides insights into machine learning models for pollution prediction and demonstrate the important relationship between urban form and street-level pollutants. The results suggest that the urban form of the podium-level porosity can be set higher than 0.7 to promote the ventilation of street-level PM2.5 and CO, building density can be less than 0.35, and the standard deviation of height can be set to ∼10 m in central Wuhan to mitigate street-level PM2.5. Thus, quantitatively demonstrating the impact of urban form on the PM2.5 and CO concentrations can help decision-makers with urban planning and management.
To improve the atmospheric environment by optimizing urban morphology, this study develops a random forest (RF) model to investigate the influence of urban morphology on PM2.5 variations via the relative importance of urban morphology and the nonlinear response relationship between urban morphology and PM2.5. Two indices—reduction range (C↓) and rate (C˅) of PM2.5 concentrations—are defined to evaluate the temporal variations of PM2.5. Results show that RF models are more accurate and perform better than multiple linear regression models, with R² ranging from 0.861 to 0.936. Five out of nine urban morphological indicators have the most significant contribution to PM2.5 reduction. For each indicator, the nonlinear response relationship shows similar trends in general, despite of the difference at the higher pollution level. Building evenness index and water body area ratio have a similar response such that C↓ and C˅ sharply increase and tend to be stable when they reach at 0.05 and 8 %, respectively. With the increase in vegetated area ratio, the change of C↓ presents an inverted V-shape trend with the turning point of about 20 %; however, the change of C˅ greatly differs from the pollution level. A higher density of the low-rising buildings with one to three floors will lead to a small reduction rate but a greater reduction range of PM2.5. Floor area ratio values generally show a negative and nonlinear influence on C↓ and C˅. This study provides useful implications for planners and managers for PM2.5 reduction through neighborhood morphology optimization.
It is likely that exposure surrogates from monitoring stations with various limitations are not sufficient for epidemiological studies covering large areas. Moreover, the spatiotemporal resolution of air pollution modelling approaches must be improved in order to achieve more accurate estimates. If not, the exposure assessments will not be applicable in future health risk assessments. To deal with this challenge, this study featured Land-Use Regression (LUR) models that use machine learning to assess the spatial-temporal variability of Nitrogen Dioxide (NO 2). Daily average NO 2 data was collected from 70 fixed air quality monitoring stations, belonging to the Taiwanese EPA, on the main island of Taiwan. Around 0.41 million observations from 2000 to 2016 were used for the analysis. Several datasets were employed to determine spatial predictor variables, including the EPA environmental resources dataset, the meteorological dataset, the land-use inventory, the landmark dataset, the digital road network map, the digital terrain model, MODIS Normalized Difference Vegetation Index database, and the power plant distribution dataset. Regarding analyses, conventional LUR and Hybrid Kriging-LUR were performed first to identify important predictor variables. A Deep Neural Network, Random Forest, and XGBoost algorithms were then used to fit the prediction model based on the variables selected by the LUR models. Lastly, data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were applied to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 65% and 78%, respectively, of NO 2 variation. When the XGBoost algorithm was further incorporated in LUR and hybrid-LUR, the explanatory power increased to 84% and 91%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed all other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm to estimate the spatial-temporal variability of NO 2 exposure. For practical application, the associations of specific land-use/land cover types selected in the final model can be applied in land-use management and in planning emission reduction strategies.
The project planning activities of urban air quality and breathability have increasingly become the noticed issues around the world in recent times. In this study, the incorporation of half-open spaces into the ground corners of high-rise buildings is accomplished by slightly modifying the building morphology as a feasible solution. A unified procedure is proposed via a combined framework of parametric CFD study and multivariable regression analysis to optimize the half-open space design for improving ventilation performance and air quality. The influences of four design parameters on wind flow characteristics are investigated, including (i) the building height, (ii) the width of street canyon, (iii) the height of half-open space, and (iv) the width of half-open space. Using the results from this combined framework, CFD simulations are then extended to inspect the effectiveness of merging optimized half-open space layouts into high-rise buildings as the deterministic analysis in a realistic case study. Both CFD simulations are validated with the wind tunnel data for a generic urban array and on-site measurements for a realistic case study. The predictions are discussed to evaluate the outcomes of urban breathability and air pollutant dispersion by the indices of air change per hour (ACH) and purging flow rate (PFR). The incorporation of optimized half-open spaces into constructions can greatly improve urban ventilation and air pollutant dispersion in the pedestrian pathway layer. To complete the combined framework for a realistic high-rise urban area, the optimized half-open space design can increase ACH* and PFR by 75% and 57%, respectively, in the pedestrian pathway layer, as compared to the case of original half-open space design. This strategy relies on the database formulated from the CFD results of varied building morphologies in the generic urban array to realize the optimized design in a more effective and time-saving manner when applying to realistic cases.
Previous findings have indicated better performance attained by modified urban morphologies for wind energy utilization only in single and pair buildings, or medium-dense low-rise building arrays. Hence, the main purpose of this study is to address the research gaps to complete a fundamental understanding of the influences of urban morphology in compact high-rise urban areas on enhancing urban wind energy harvest for sustainable urban development. A comprehensive parametric study is conducted using the computational fluid dynamics tool to analyze the impacts of urban morphologies on the wind energy potential for a 6 × 6 array of generic high-rise buildings, including (i) urban density altered from compact to sparse urban layouts, (ii) building corner shapes of sharp and rounded corners, (iii) urban layouts of in-line and staggered patterns, and (iv) wind directions of 0° and 45°. This investigation implements the three-dimensional steady Reynolds-averaged Navier-Stokes equations with the Reynolds stress model to explore the distributions of wind speed, power density, and turbulence intensity over the building array. The results indicate that decreasing urban plan area density reduces the unacceptable turbulence areas with relatively higher wind power density on the roof. Besides, round corners can produce elevated power densities up to 201% greater than sharp corners beside the building. Even under the oblique wind direction of 45°, the rounded corner still shows better wind energy potentials than the sharp corner. The in-line urban layout demonstrates more significant areas with higher power densities and low turbulence intensities than the staggered urban layout.
Urban air quality has been a long-standing problem in most cities worldwide. Many strategies have been proposed to solve it, including green infrastructures such as green roofs (GRs) and green walls (GWs) that provide multiple environmental benefits. Many studies have focused on GRs and GWs strategies to mitigate urban air pollution. However, to the best of authors’ knowledge, these studies have not dealt with different urban morphologies, specifically the impact of building heights and coverage ratios of GRs and GWs on mitigating air pollution. Therefore, the potential of GRs and GWs to alleviate air pollution has not been fully exploited. This paper aims to investigate different GRs and GWs layouts and evaluate their efficacy for capturing particulate matter (PM2.5) in an urban neighbourhood of Santiago, Chile. We use ENVI-met model to simulate a metropolitan area with buildings, vegetation, paved surfaces, and traffic emissions to estimate air pollution abatement for varying building heights and coverage ratios of GRs and GWs. We simulate these layouts and coverage for a downtown area of Santiago, and results were compared with the base case scenario. Results showed that the air quality improvement by GRs and GWs depends on building height, surrounding urban infrastructure, vegetation cover and proximity to the pollutant source. Specifically, results showed that 50%–75% of GRs coverage on low-rise buildings could improve air quality at the pedestrian/commuter level. However, just a 25% coverage of GWs yields the highest PM2.5 capture. We conclude that to decrease PM2.5 concentrations, priority should be given to instal GRs in buildings lower than 10 m in height. For GWs, the PM2.5 abatement is favorable in all cases. ENVI-met results also show that the combined use of GRs and GWs could reduce PM2.5 up to 7.3% in Santiago compared to the base case scenario.
The urban morphology can significantly change the urban microclimate, which in turn affects the diffusion of air pollutants. Urban planning is the most important means of shaping urban morphology. Therefore, this study takes Wuhan as an example and uses the method of WRF/CMAQ coupled UCM model to analyze the spatial and temporal distribution characteristics of PM2.5 in the Wuhan metropolitan area in winter 2015. The six most important urban morphological indicators in urban planning: the floor area ratio and building height, building density and building width, vegetation coverage ratio, and urban fraction, are selected and classified into three groups. Studying their impact on the spatial and temporal distribution of PM2.5 concentration provides support for urban planners to improve air quality. The results show that the maximum value of PM2.5 concentration in Wuhan urban area occurs in the morning rush hour, and PM2.5 is distributed concentrically in the downtown of the city (within the second ring highway) according to the highways around the city. The PM2.5 concentration in the downtown area with the most extensive urban morphological index is the highest, and it decreases with increasing distance from the downtown. Among the six indicators, building density and urban fraction have the most significant impact on PM2.5 concentration because they have the greatest impact on the wind speed at 10 m. The height of the planetary boundary layer is the key factors affect the vertical and horizontal diffusion of air pollutants. Except for the vegetation coverage ratio, the increase of other urban morphological indicators will lead to a decrease of PM2.5 concentration in Wuhan urban area at night. During the daytime, increasing the floor area ratio and building height will cause an increasing of PM2.5 concentration, but other indicators have the opposite effects.
Particulate matter with an aerodynamic equivalent dimeter less than 2.5 μm (PM2.5) and ozone (O3) are major air pollutants, with coupled and complex relationships. The control of both PM2.5 and O3 pollution requires the identification of their common influencing factors, which has rarely been attempted. In this study, land use regression (LUR) models based on the least absolute shrinkage and selection operator were developed to estimate PM2.5 and O3 concentrations in China’s Pearl River Delta region during 2019. The common factors in the tradeoffs between the two air pollutants and their synergistic effects were analyzed. The model inputs included spatial coordinates, remote sensing observations, meteorological conditions, population density, road density, land cover, and landscape metrics. The LUR models performed well, capturing 54–89% and 42–83% of the variations in annual and seasonal PM2.5 and O3 concentrations, respectively, as shown by the 10-fold cross validation. The overlap of variables between the PM2.5 and O3 models indicated that longitude, aerosol optical depth, O3 column number density, tropospheric NO2 column number density, relative humidity, sunshine duration, population density, the percentage cover of forest, grass, impervious surfaces, and bare land, and perimeter-area fractal dimension had opposing effects on PM2.5 and O3. The tropospheric formaldehyde column number density, wind speed, road density, and area-weighted mean fractal dimension index had complementary effects on PM2.5 and O3 concentrations. This study has improved our understanding of the tradeoff and synergistic factors involved in PM2.5 and O3 pollution, and the results can be used to develop joint control policies for both pollutants.
Numerous statistical models have established the relationship between ambient fine particulate matter (PM2.5, with an aerodynamic diameter of less than 2.5 μm) and satellite aerosol optical depth (AOD) along with other meteorological/land-related covariates. However, all the models assumed that all covariates affect the PM2.5 concentration at the same scale, and none could provide a posterior uncertainty analysis at each regression point. Therefore, a multiscale geographically and temporally weighted regression (MGTWR) model was proposed by specifying a unique bandwidth for each covariate. However, the lack of a method for predicting values at unsampled points in the MGTWR model greatly restricts its corresponding application. Thus, this study developed a method for inferring unsampled points and used the posterior uncertainty assessment value to improve the model accuracy. With the aid of the high-resolution satellite multi-angle implementation of atmospheric correction (MAIAC) AOD product, daily PM2.5 concentrations with a 1 km x 1 km resolution were generated over the Beijing-Tianjin-Hebei region between 2013 and 2019. The coefficient of determination (R²) and root mean square error (RMSE) of the fitted MGTWR results vary from 0.90 to 0.94 and from 10.66 to 25.11 μg/m3, respectively. The sample-based and site-based cross-validation R² and RMSE vary from 0.81 to 0.89 and from 14.40 to 34.43 μg/m3 respectively, demonstrating the effectiveness of the proposed inference method at unsampled points. With the uncertainty constraint, the sample-based and site-based validated MGTWR R² results for all years are further improved by approximately 0.02-0.04, demonstrating the effectiveness of the posterior uncertainty assessment constraint method. These results suggest that the inference method proposed in this study is promising to overcome the defects of the MGTWR model in inferring the prediction values at unsampled points and could consequently enhance the wide applications of MGTWR modeling.