A review of strategies for mitigating roadside air pollution in urban street canyons

The street space planning and design of artificial intelligence-assisted deep learning neural network in the Internet of Things

This study begins by discussing Internet of Things (IoT) technology and analyzing the classification of street space into four types, along with the Green Looking Ratio (GLR). Following this, the Fully Convolutional Network (FCN)-8s framework is employed to construct a street view image semantic segmentation model based on FCN principles. Subsequently, IoT technology is utilized to analyze the proportion of GLR and satisfaction in the street space within the historical urban area of T City. The findings reveal a significant positive correlation (significance level p < 0.05, R2 = 0.919) between the GLR satisfaction score of street view images and the average GLR of the area. Among the four types of street space-life leisure, historical streets, traffic areas, and landscape-style streets-the dissatisfaction rates with GLR are 35 %, 33 %, 20 %, and 18 %, respectively, correlating with varying GLR satisfaction levels. To enhance street space greening, planting ponds and boxes are proposed for "blind spots" and "dead corners," thereby completing greenery in these areas. These initiatives aim to improve street greening policies, integrate street function zones, and advance the scientific greening of urban streets. The analysis of GLR and satisfaction in street spaces provides valuable insights for refining urban street space greening efforts.

Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods

In near-road neighborhoods, residents are more frequently exposed to traffic-related air pollution (TRAP), and they are increasingly aware of pollution levels. Given this consideration, this study adopted portable air pollutant sensors to conduct a mobile monitoring campaign in two near-road neighborhoods, one in an urban area and one in a suburban area of Shanghai, China. The campaign characterized spatiotemporal distributions of fine particulate matter (PM2.5) and black carbon (BC) to help identify appropriate mitigation measures in these near-road micro-environments. The study identified higher mean TRAP concentrations (up to 4.7-fold and 1.7-fold higher for PM2.5 and BC, respectively), lower spatial variability, and a stronger inter-pollutant correlation in winter compared to summer. The temporal variations of TRAP between peak hour and off-peak hour were also investigated. It was identified that district-level PM2.5 increments occurred from off-peak to peak hours, with BC concentrations attributed more to traffic emissions. In addition, the spatiotemporal distribution of TRAP inside neighborhoods revealed that PM2.5 concentrations presented great temporal variability but almost remained invariant in space, while the BC concentrations showed notable spatiotemporal variability. These findings provide valuable insights into the unique spatiotemporal distributions of TRAP in different near-road neighborhoods, highlighting the important role of hyperlocal monitoring in urban micro-environments to support tailored designing and implementing appropriate mitigation measures.

Impact of single and combined local air pollution mitigation measures in an urban environment

Urban air pollution is one of the most important environmental problems for human health and several strategies have been developed for its mitigation. The objective of this study is to assess the impact of single and combined mitigation measures on concentrations of air pollutants emitted by traffic at pedestrian level in the same urban environment. The effectiveness of different scenarios of green infrastructure (GI), the implementation of photocatalytic materials and traffic low emission zones (LEZ) are investigated, as well as several combinations of LEZ and GI. A wide set of scenarios is simulated through Computational Fluid Dynamics (CFD) modelling for two different wind directions (perpendicular (0°) and 45° wind directions). Wind flow for the BASE scenario without any measure implemented was previously evaluated using wind-tunnel measurements. Air pollutant concentrations for this scenario are compared with the results obtained from the different mitigation scenarios. Reduction of traffic emissions through LEZ is found to be the most effective single measure to improve local air quality. However, GI enhances the effects of LEZ, which makes the combination of LEZ + GI a very effective measure. The effectiveness of this combination depends on the GI layout, the intensity of emission reduction in the LEZ and the traffic diversion in streets surrounding the LEZ. These findings, in line with previous literature, suggest that the implementation of GI may increase air pollutant concentrations at pedestrian level for some cases. However, this study highlights that this negative effect on air quality can turn into positive when used in combination with reductions of local traffic emissions.

Comprehensive analysis of air pollution and the influence of meteorological factors: a case study of adiyaman province

Adıyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM10 and SO2, indicating moderate positive correlations under pressure conditions (r = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed (r = -0.35, -0.50), and temperature also exhibits a negative correlation (r = -0.39, -0.54). During atmospheric conditions with high pressure, PM10 and SO2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake.

Species-specific influence of powdery mildew mycelium on the efficiency of PM accumulation by urban greenery

Particulate matter (PM) is one of the most important air pollutants, especially in urban areas. The efficiency of PM biofiltration by plants depends on the morphological features of the foliage. More PM is deposited on complex leaves, covered with thick wax layer, trichomes, epidermal glands, and convex venation. Very few literature reports suggest that also the presence of mycelium of nonparasitic and saprophytic fungi positively affects the accumulation of PM on the leaves. In this work, to our best knowledge, for the first time the effect of the mycelium of the parasitic powdery mildew on the efficiency of PM accumulation by urban greenery was studied. Uninfested and fungus-infested leaves of Acer negundo L., Malus domestica Borkh Quercus robur L., and Berberis vulgaris L. were harvested in July in the center of Warsaw city. The effect of powdery mildew infection on PM accumulation was species-specific. A higher amount of PM on leaves not infected with powdery mildew was found in M. domestica and Q. robur, while in A. negundo and B. vulgaris more PM was accumulated on leaves infected with fungus. All species (except A. negundo) accumulated more of the PM of 0.2-2.5-μm and 2.5-10-μm size fractions on leaves not infected with powdery mildew. One of the greatest consequences of the presence of powdery mildew mycelium on the foliage is most probably reduction of the direct involvement of waxes in PM accumulation and retention processes.

Polycyclic aromatic hydrocarbon (PAH) phytoaccumulation in urban areas by Platanus × acerifolia, Celtis australis, and Tilia grandifolia leaves and branches

Polycyclic aromatic hydrocarbon (PAH) concentrations in the leaves and 1-year-old branches of three common tree species growing in a middle-sized city located in a moderate climate zone were estimated. For this purpose, PAH phytoaccumulation in Platanus × acerifolia, Celtis australis, and Tilia grandifolia species from highly urbanized, traffic congested, and highly PAH-contaminated streets was compared with trees from non-contaminated parks in the same urban core. The gathered data was used to define 17 PAH profiles, identify the main PAH pollution emission sources, and determine the organ and species specificity of PAHs accumulation. Due to the direct absorption of polluted air via stomata, the leaves accumulated up to 30% more PAHs compared to the 1-year-old branches. As expected, PAH concentrations were much higher in street trees, while heavy weight PAHs (with five and six rings) were accumulated in the highest concentrations. The highest foliar Σ17 PAH concentrations were detected in street-grown C. australis, followed by P. acerifolia and T. grandifolia (502.68, 488.45, and 339.47 ng g-1 dry weight (DW), respectively). The same pattern was noted for Σ17 PAHs in branches (414.89, 327.58, and 342.99 ng g-1 DW, respectively). Thus, T. grandifolia emerged as the least effective PAH sink as it accumulated up to ~ 40% less PAHs than P. acerifolia and C. australis leaves/branches. Among the 17 tracked PAHs, benzo[a]anthracene, benzo[a]pyrene, dibenzo[a,h]anthracene, and pyrene were found to have accumulated in the highest concentrations in all analyzed species irrespective of the site, and accounted for more than 50% of the total detected PAHs. Finally, a "black box" about species and organ specificity, as well as specific drivers that limit PAHs uptake capacity by trees, was opened, while this work provides insights into further PAH phytoremediation strategies.

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

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

The impact of dynamic traffic and wind conditions on green infrastructure performance to improve local air quality

Road traffic represents the dominant source of air pollution in urban street canyons. Local wind conditions greatly impacts the dispersion of these pollutants, yet street trees complicate ventilation in such settings. This case study adopts a novel modelling framework to account for dynamic traffic and wind conditions to identify the optimal street tree configuration that prevents a deterioration in air quality. Measurement data from a shallow to moderately deep street canyon (average 0.5 H/W aspect ratio and four lanes of 1-way traffic) in Dublin, Ireland was used for model calibration. The computational fluid dynamics (CFD) models were used to examine scenarios of dynamic traffic flows within each traffic lane with respect to its impact on local PM2.5 concentrations on adjacent footpaths, segmenting air quality monitoring results based on different wind conditions for model calibration. The monitoring campaign identified higher PM2.5 concentrations on the leeward (north) footpath, with average differences of 14.1 % (2.15 μg/m3) for early evening peaks. The modelling results demonstrated how street trees negatively impacted air quality on the windward footpath in parallel wind conditions regardless of leaf area density (LAD) or tree spacing, with mixed results observed on the leeward footpath in varying traffic flows and wind speeds. Perpendicular wind direction models and high wind speed exacerbated poor air quality on the windward footpath for all tree spacing models, while improving the air quality on the leeward footpath. The findings advise against planting high-LAD trees in this type of street, with a minimum of 20 m spacing for low-LAD trees to balance reducing local air pollution and ventilation capacity in the street. This study highlights the complexities of those in key decision-marking roles and demonstrates the need to adopt a transparent framework to ensure adequate modelling evidence can inform tree planting in city streets.

Evaluating real-world emissions from in-use buses and taxis using on-road remote sensing

Assessing real-world emissions from buses and taxis is vital to comprehend their impact on urban air quality. Such vehicles differ significantly from the majority of the fleet owing to their higher mileage rates. However, few studies have focused on specifically assessing the emissions from this segment of the vehicle fleet. In this context, this study evaluated the real-world emissions of nitrogen oxides (NOx) from in-use buses and taxis in Dublin, Ireland, using crossroad remote sensing technology. The remote sensing system was deployed at strategic locations throughout the city to capture on-road emissions from passing vehicles. The collected data included vehicle related information such as emission standard, make, and mileage, and pollutants including NOx. Based on this data, analysis was aimed to understand the impact of Euro emission standard, ambient temperature, mileage, and make of the vehicle on NOx emissions. The results reveal that the average emissions from taxis reduce by 37% from Euro 5 to Euro 6b, and average emissions from Euro 6 buses are 87% lower compared to Euro 5. The trends in emission factors (EFs) of buses and taxis were similar during summer and winter sampling. Moreover, on comparing the emissions from the top five taxi manufacturers, different trends in the emission factors were observed. Finally, the study found that the effect of vehicle mileage on emissions was unclear for both buses and taxis. In any case, these findings provide valuable insights into the real-world emission performance of the existing fleet of buses and taxis in Dublin and highlight the need for targeted measures to reduce emissions from these vehicles. The results can assist policymakers and urban planners in formulating evidence-based strategies to improve air quality in Dublin and other cities facing similar challenges.

Reassessing the role of urban green space in air pollution control

The assumption that vegetation improves air quality is prevalent in scientific, popular, and political discourse. However, experimental and modeling studies show the effect of green space on air pollutant concentrations in urban settings is highly variable and context specific. We revisited the link between vegetation and air quality using satellite-derived changes of urban green space and air pollutant concentrations from 2,615 established monitoring stations over Europe and the United States. Between 2010 and 2019, stations recorded declines in ambient NO2, (particulate matter) PM10, and PM2.5 (average of -3.14% y-1), but not O3 (+0.5% y-1), pointing to the general success of recent policy interventions to restrict anthropogenic emissions. The effect size of total green space on air pollution was weak and highly variable, particularly at the street scale (15 to 60 m radius) where vegetation can restrict ventilation. However, when isolating changes in tree cover, we found a negative association with air pollution at borough to city scales (120 to 16,000 m) particularly for O3 and PM. The effect of green space was smaller than the pollutant deposition and dispersion effects of meteorological drivers including precipitation, humidity, and wind speed. When averaged across spatial scales, a one SD increase in green space resulted in a 0.8% (95% CI: -3.5 to 2%) decline in air pollution. Our findings suggest that while urban greening may improve air quality at the borough-to-city scale, the impact is moderate and may have detrimental street-level effects depending on aerodynamic factors like vegetation type and urban form.

Particulate Matter Air Pollution is a Significant Risk Factor for Cardiovascular Disease

Air pollution is responsible worldwide for 9-12 million deaths annually. The major contributor to air pollution is particulate matter ≤2.5 µg per cubic meter of air (PM2.5) from vehicles, industrial emissions, and wildfire smoke. United States ambient air standards recommend annual average PM2.5 concentrations of ≤12 μg/m³ while European standards allow an average annual PM2.5 concentration of ≤20 μg/m3. However, significant PM2.5 cardiovascular and pulmonary health risks exist below these concentrations. Chronic PM2.5 exposure significantly increases major cardiovascular and pulmonary event risks in Americans by 8 to more than 20% for each 10-μg/m3 increase in PM2.5. PM2.5-induced increases in lipid peroxidation, induction of vascular inflammation and endothelial cell injury initiate and propagate respiratory diseases, coronary and carotid atherosclerosis. PM2.5 can cause atherosclerotic vascular plaque rupture and myocardial infarction and stroke by activating metalloproteinases. This article discusses PM2.5 effects on the cardiovascular and pulmonary systems, specific PM2.5 pathophysiologic mechanisms contributing to cardiopulmonary disease, and preventive measures to limit the cardiovascular and pulmonary effects of PM2.5.

Impact of the COVID-19 outbreak on urban air, Land surface temperature and air pollution in cold climate zones

The objective of this study was to analyze air pollution and thermal environment in Turkey's cold region before, during, and after COVID-19 in 2019, 2020 and 2021. The CO, NO2, O3, PM10 and SO2 data from the state air quality stations, as well as ground air temperature data from six weather stations, and land satellite images from the USGS website using ArcGIS 10.4.1 software were collected in January, March, April and August of 2019, 2020 an 2021. In order to evaluate the impact of COVID-19 measures and restrictions on cold region cities, air pollution indicators, land surface temperature and air temperature as well as statistical data were analyzed. The results indicated that the CO, NO2, PM10 and SO2 emissions decreased by 14.9%, 14.3%, 47.1% and 28.5%, but O3 increased by 16.9%, respectively, during the COVID-19 lockdown in 2020 as compared to these of the pre-COVID-19 levels in 2019. A positive correlation between air temperature and O3 in 2019 (r2 = 0.80), and in 2020 and 2021 (r2 = 0.64) was obtained. Air temperature in 2020 and 2021 decreased due to lockdowns and quarantine measures that led to lower O3 emissions as compared to 2019. Negative correlations were also found between air temperature and NO2 (r2 = 0.60) and SO2 (r2 = 0.5). There was no correlation between air temperature and PM10. During the COVID-19 lockdown and intense restrictions in April 2020, the average LST and air temperature values dropped by 14.7 °C and 1.6 °C respectively, compared to April 2019. These findings may be beneficial for future urban planning, particularly in cold regions.

Mitigation strategies for controlling urban particulate pollution from traffic congestion: Road expansion and road public transport

The mismatch between urban transportation demands and local transportation supplies in municipal regions may cause serious traffic congestion and high vehicle exhaust emissions (VEEs). Although it has been found that road expansion and the provision of road public transport can mitigate traffic congestion and VEEs, the effectiveness of these public strategies in alleviating traffic congestion-related VEEs has been empirically compared in few studies. With the use of a panel dataset for 260 prefecture-level cities in China from 2013 to 2018, we fill this gap via empirically examining the effects of several popular strategies that promote the efficiency of urban road transportation, including road expansion and road public transport provision, on urban air pollution of fine particulate matter (PM2.5). Then we further investigate metrics measured by passenger and freight volumes based on local urban road coverage that can suitably reflect the mismatch degree between local transportation demands and supplies. The empirical results indicated a positive and statistically significant correlation between urban PM2.5 levels and road expansion but not between PM2.5 levels and road public transport, suggesting that road expansion may unintentionally increase the transportation volume and thus local PM2.5 levels. Importantly, we found that the passenger and freight volumes per unit road length are negatively and significantly correlated with the local particulate pollution concentration across different model specifications, while the passenger and freight volumes of road transportation do not directly affect local PM2.5 levels. We also employed a series of robustness tests to overcome the concerns associated with measurement errors, omitted variable biases, model misspecifications and endogeneity, and consistent results were obtained. These results indicated that when facing limited public budgets, the passenger and freight volumes per unit road length should be considered to measure the effectiveness of road infrastructure investments in alleviating local particulate pollution caused by traffic congestion.

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

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

Vertical distribution and transport of microplastics in the urban atmosphere: New insights from field observations

The distribution and transport of atmospheric microplastics (AMPs) have raised concerns regarding their potential effects on the environment and human health. Although previous studies have reported the presence of AMPs at ground level, there is a lack of comprehensive understanding of their vertical distribution in urban environments. To gain insight into the vertical profile of AMPs, field observations were conducted at four different heights (ground level, 118 m, 168 m and 488 m) of the Canton Tower in Guangzhou, China. Results showed that the profiles of AMPs and other air pollutants had similar layer distribution patterns, although their concentrations differed. The majority of AMPs were composed of polyethylene terephthalate and rayon fibers ranging from 30 to 50 μm. As a result of atmospheric thermodynamics, AMPs generated at ground level were only partially transported upward, leading to a decrease in their abundance with increasing altitude. The study found that the stable atmospheric stability and lower wind speed between 118 m and 168 m resulted in the formation of a fine layer where AMPs tended to accumulate instead of being transported upward. This study for the first time delineated the vertical profile of AMPs within the atmospheric boundary layer, providing valuable data for understanding the environmental fate of AMPs.

Statistical and spatio-temporal analyses of noise pollution level and its health impact

Due to rapid urbanization and exponential growth in transportation; traffic noise has become a major area of concern. Noise not only disturbs our day-to-day life but also have severe adverse health effects over humans which further may lead to mortality. This paper focuses on the behavior of noise levels of Lucknow city over a decade and establishes its correlation with impact on human health in terms of annoyance and sleep disturbance. Apart from L_eq, different noise parameters like L₁₀, L₅₀, L₉₀, Traffic Noise Index (TNI), Noise Pollution Index (NPI), and Noise Climate (NC) have also been analyzed to understand the variation of noise. At all the locations, the noise level has been found exceeding their prescribed standards during day time and night time except at Amausi. Out of nine locations, TNI was found to be exceeding at three locations during day time and NPI exceeding at one location. However, during night time both values of TNI and NPL were observed within the limit at all the locations. From the noise map of the city during day time and night time, among all sampling locations, Charbagh has been found to be worst affected by noise pollution. A strong positive correlation has been observed among the total population, vehicular count, and day and night time noise data, which directly contribute to a higher percentage of sleep disturbance and annoyance among residents. Due to the increase in noise levels over a period of time, almost four times the population get affected by high annoyance and almost double the population get affected by sleep disturbance.

New insights on precise regulation of pollutant distribution inside a street canyon by different vegetation planting patterns

Mixed-vegetation planting patterns are commonly seen in urban areas for specific reasons like aesthetic, cooling, and particle deposition effects of the vegetation. However, they may have a negative impact on human health by worsening the air quality inside the street canyon due to the decreased air exchange rate. From the view of precise control of pollutant concentration in the sensitive areas of people's concern in the existed street canyons, thirty-four cases with different vegetation planting patterns and pressure loss coefficients (λ) are studied numerically to investigate the effects of vegetation on airflow and pollutant dispersion inside the canyon. The cases of treeless and 2 rows of tree planting patterns in wind-tunnel measurements were selected for the model validation. The results demonstrate that compared to the treeless case, the greenbelts can greatly change the airflow features and reduce the pollutant concentration at the leeward side, while the only-tree planting patterns have little impact on the flow and deteriorate dispersion within the street canyon. Moreover, rows of greenbelts planted under the corresponding trees can reduce the average pollutant concentrations on the leeward wall and the footpath of the street canyon by up to 22.6% and 33.2%, respectively. Besides, the pattern of 1 row of trees with 1 row of greenbelts planted in the street canyon center should be suggested as the optimal mixed vegetation configuration in this study. That is because compared to the treeless case the pollutant concentration on leeward wall, windward wall, leeward footpath, and windward footpath can be reduced by 14.2%, 10.0%, 24.6%, and 37%, respectively. It is helpful to the city planners to consider whether the disadvantages of planting vegetation inside the street canyon would overwhelm the advantages.

Determination of optimal height of barriers to reduce the amount of pollution in the viaduct settings in an idealized urban canyon: a numerical study

In this work, we numerically investigate the process of atmospheric air pollution in idealized urban canyons along the road in the presence of a viaduct, taking into account different height of barriers. To solve this problem, the 3D Reynolds-averaged Navier-Stokes equations (RANS) were used. The closure of this system of equations was achieved by using various turbulent models. The verification of the mathematical model and the numerical algorithm was carried out using a test problem. The obtained results using various turbulent models were compared with experimental data and calculated results of other authors. The main problem considered in this work is characterized as follows: assessment of emissions of pollutants between buildings using barriers of various types in the presence of a viaduct. Computational results have shown that the barrier viaduct plays a large role in improving air quality in urban canyons. So, for example, a barrier erected on a viaduct with a height of 2 m reduces the concentration value to a cross-section x = 84 by more than 2 times in comparison with the case of a complete absence of protective barriers. A similar situation was observed with barriers erected above the earth's surface: located along the road, they also significantly reduce the value of the concentration of pollutants. Thus, the presence of barriers in both cases is necessary to prevent the dispersion and deposition of pollutants.

Effective emissions reduction of high-mileage fleets through a catalytic converter and oxygen sensor replacement program

High-mileage vehicles such as taxis make disproportionately large contributions to urban air pollution due to their accelerated engine deterioration rates and high operation intensities despite their small proportions of the total fleet. Controlling emissions from these high-mileage fleets is thus important for improving urban air quality. This study evaluates the effectiveness of a pilot repair program in reducing emissions from taxis in Hong Kong which account for about 2 % of the total licensed vehicles. The emission factors of a large sample of 684 in-service taxis (including 121 for an emission survey program and 563 for a pilot repair program) were measured on transient chassis dynamometers. The results showed that 63 % of the sampled taxis failed the driving cycle test before the pilot repair program. Most of failed taxis were NO related and 91 % of failed taxis exceeded the emission limits of at least two regulated pollutants simultaneously. After the pilot repair program by replacing catalytic converters and oxygen sensors, the failure rate was significantly reduced to only 7 %. In addition, the fleet average NO, HC and CO emission factors were reduced by 85 %, 82 % and 56 %, respectively. In addition, on-road remote sensing measurements confirmed the real-world emission reductions from the taxis that participated in the pilot repair program. These findings led to the implementation of a large-scale replacement program for all taxis in Hong Kong during 2013-2014, which was estimated to have reduced the total HC, CO and NO emissions by about 420, 2570 and 1000 t per year, respectively (equivalent to 5-8 % emission reductions from the whole road transport sector). Therefore, reducing emissions from the small high-mileage fleets is a highly cost-effective measure to improve urban air quality.

Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

Background

Artificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution.

Methods

All literature on the application of AI to air pollution was searched from the Web of Science database. CiteSpace 5.8.R1 was used to analyze countries/regions, institutions, authors, keywords and references cited, and to reveal hot spots and frontiers of AI in atmospheric pollution.

Results

Beginning in 1994, publications on AI in air pollution have increased in number, with a surge in research since 2017. The leading country and institution were China (N = 524) and the Chinese Academy of Sciences (N = 58), followed by the United States (N = 455) and Tsinghua University (N = 33), respectively. In addition, the United States (0.24) and the England (0.27) showed a high degree of centrality. Most of the identified articles were published in journals related to environmental science; the most cited journal was Atmospheric Environment, which reached nearly 1,000 citations. There were few collaborations among authors, institutions and countries. The hot topics were machine learning, air pollution and deep learning. The majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods, low-cost air quality sensors, indoor air quality, and thermal comfort.

Conclusion

Researches in the field of AI and air pollution are expanding rapidly in recent years. The majority of scholars are from China and the United States, and the Chinese Academy of Sciences is the dominant research institution. The United States and the England contribute greatly to the development of the cooperation network. Cooperation among research institutions appears to be suboptimal, and strengthening cooperation could greatly benefit this field of research. The prediction of air pollutant concentrations, particularly PM_2.5, low-cost air quality sensors, and thermal comfort are the current research hotspot.

Impact of Different Combinations of Green Infrastructure Elements on Traffic-Related Pollutant Concentrations in Urban Areas

Urban air quality is a major problem for human health and green infrastructure (GI) is one of the potential mitigation measures used. However, the optimum GI design is still unclear. The purpose of this study is to provide some recommendation that could help in the design of the GI (mainly, the selection of locations and characteristics of trees and hedgerows). Aerodynamic and deposition effects of each vegetation element of different GI scenarios are investigated. Computational fluid dynamics (CFD) simulations of a wide set of GI scenarios in an idealized three-dimensional urban environment are performed. In conclusion, it was found that trees in the middle of the avenue (median strip) reduce street ventilation, and traffic-related pollutant concentrations increase, in particular for streets parallel to the wind. Trees in the sidewalks act as a barrier for pollutants emitted outside, specifically for a 45° wind direction. Regarding hedgerows, the most important effect on air quality is deposition and the effects of green walls and green roofs are limited to their proximity to the building surfaces.

Effect of sampling duration on the estimate of pollutant concentration behind a heavy-duty vehicle: A large-eddy simulation

Plume chasing is cost-effective, measuring individual, on-road vehicular emissions. Whereas, wake-flow-generated turbulence results in intermittent, rapid pollutant dilution and substantial fluctuating concentrations right behind the vehicle being chased. The sampling duration is therefore one of the important factors for acquiring representative (average) concentrations, which, however, has been seldom addressed. This paper, which is based on the detailed spatio-temporal dispersion data after a heavy-duty truck calculated by large-eddy simulation (LES), examines how sampling duration affects the uncertainty of the measured concentrations in plume chasing. The tailpipe dispersion is largely driven by the jet-like flows through the vehicle underbody with approximate Gaussian concentration distribution for x ≤ 0.6h, where x is the distance after the vehicle and h the characteristic vehicle size. Thereafter for x ≥ 0.6h, the major recirculation plays an important role in near-wake pollutant transport whose concentrations are highly fluctuating and positively shewed. Plume chasing for a longer sampling duration is more favourable but is logistically impractical in busy traffic. Sampling duration, also known as averaging time in the statistical analysis, thus has a crucial role in sampling accuracy. With a longer sampling (averaging) duration, the sample mean concentration converges to the population mean, improving the sample reliability. However, this effect is less pronounced in long sampling duration. The sampling accuracy is also influenced by the locations of sampling points. For the region x > 0.6h, the sampling accuracy is degraded to a large extent. As a result, acceptable sample mean is hardly achievable. Finally, frequency analysis unveils the mechanism leading to the variance in concentration measurements which is attributed to sampling duration. Those data with frequency higher than the sampling frequency are filtered out by moving average in the statistical analyses.

Effects of Creating Street Greenery in Urban Pedestrian Roads on Microclimates and Particulate Matter Concentrations

Urbanization can increase artificial coverage rates and emissions by causing increases in traffic, which represents a serious health risk to pedestrians. To create pedestrian-friendly environments in urban canyons, this study examined their microclimates and air quality; methods to improve the pedestrian environment were also explored, based on the current street green space practices in Seoul, Korea. Under these current standards, the physiologically equivalent temperature (PET) of the pedestrian roads was predicted to decrease by 1.29–1.44 °C, but this was insufficient to adjust the thermal stress level. The air temperature of the pedestrian road declined slightly; fine dust concentrations increased, and ozone concentrations decreased. The effects of increasing the planting occupancy on the pedestrian environment were then simulated, which resulted in decreases in PET of 3.04–3.49 °C, depending on the floor area ratio, thereby effectively lowering the thermal stress stage. The particulate matter concentrations increased, however, and the air quality deteriorated. Therefore, street greenery planning alone cannot sufficiently improve both the microclimate and air quality of urban pedestrian roads. Thus, it is necessary to plan street greenery spaces in parallel with measures to improve air quality.

Large-Eddy Simulation of Airflow and Pollutant Dispersion in a Model Street Canyon Intersection of Dhaka City

The atmospheric flow and dispersion of traffic exhaust were numerically studied in this work while considering a model street canyon intersection of a city. The finite volume method (FVM)-based large-eddy simulation (LES) technique in line with ANSYS Fluent have been used for flow and pollutant dispersion modelling through the consideration of the atmospheric boundary layer (ABL). Hexahedral elements are considered for computational domain discretization in order to numerically solve problems using FVM-LES. The turbulence parameters were superimposed through a spectral synthesizer in the existing LES model through ANSYS Fluent as part of ’damage control’ due to the unsteady k−ϵ simulation. Initially, the code is validated with an experimental study of an urban street canyon where the width and height ratio is in unity. After validation, a model urban street canyon intersection was investigated in this work. The model shows a high pollutant concentration in the intersecting corner areas of the buildings. Additionally, the study of this model intersection shows a high level of pollutant concentration at the leeward wall of downwind building in the case of increased height of an upwind building. Most importantly, it was realized from the street intersection design that three-dimensional interconnection between the dominating canyon vortices and roof level flow plays a pivotal role in pollutant concentration level on the windward walls. The three-dimensional extent of corner eddies and their interconnections with dominating vortices were found to be extremely important as they facilitate enhanced ventilation. Corner eddies only form for the streets towards the freeway and not for the streets towards the intersection. The results and key findings of this work offer qualitative and quantitative data for the estimation, planning, and implementation of exposure mitigation in an urban environment.

Big data-based urban greenness in Chinese megalopolises and possible contribution to air quality control

Urban greenness is essential for people's daily lives, while its contribution to air quality control is unclear. In this study, Streetview big data of urban greenness and air quality data (Air Quality Index, PM_2.5, PM₁₀, SO₂, NO₂, O₃, CO) from 206 monitoring stations from 27 provincial capital cities in China were analyzed. The national averages for the sky, ground and middle-level (shrub and short trees) view greenness were 5.4%, 5.5%, and 15.4%, respectively, and the sky:ground:middle ratio was 2:2:6. Street-view/bird-view greenness ratio averaged at 1.1. Large inter-city variations were observed in all the greenness parameters, and the weak associations between all street-view parameters and bird-eye greenspace percentage (21%-73%) indicate their representatives of different aspects of green infrastructures. All air quality parameters were higher in winter than in summer, except O₃. Over 90% of air quality variation could be explained by socioeconomics and geoclimates, suggesting that air quality control in China should first reduce efflux from social economics, while geoclimatic-oriented ventilation facilitation design is also critical. For different air quality components, greenness had most significant associations with NO₂, O₃ and CO, and street-view/bird-view ratio was the most powerful indicator of all greenness parameters. Pooled-data analysis at national level showed that street-view greenness was responsible for 2.3% of the air quality variations in the summer and 3.6% in the winter; however, when separated into different regions (North-South China; East-West China), the explaining power increased up to 16.2%. Increased NO₂ was accompanied with decreased O₃, indicating NO titration effect. The higher O₃ aligned with the higher street-view greenness, showing the greenness-related precursor risk for O₃ pollution. Our study manifested that big internet data could identify the association of greenness and air pollution from street view scale, which can favor urban greenness management and evaluation in other regions where street-view data are available.

Screening of Plant Species Response and Performance for Green Belt Development: Implications for Semi-Urban Ecosystem Restoration

Screened plant species with potential for green belt development can act as eco-sustainable tools for restoring the polluted ecosystem. Eight plant species from two study locations in Ado-Odo, Ota, Ogun State, Nigeria, were examined to identify their air pollution response and performance by deploying two air pollution indices, namely air pollution tolerance index (APTI) and anticipated performance index (API). APTI results identified all screened plants as sensitive species suitable as bio-indicators of air pollution, with Ficus auriculata (2.42) common to the non-industrial location being the most sensitive. API scores categorized Ficus auriculata (56.25%) as a moderate performer, while Syzygium malaccense (75%) and Mangifera indica (75%) were identified as very good performers, suitable for green belt development. The relationship between each biochemical parameter with APTI was investigated using regression analysis and two-way analysis of variance. The model result showed a significant relationship between each biochemical parameter with APTI, and relative water content had the highest influence on APTI (R2 = 0.99436). Both indices (APTI and API) are suitable for screening and recommending native plant species for cultivation in the polluted environment, thus promoting ecological restoration. Hence, Syzygium malaccense, Mangifera indica and Ficus auriculata, respectively, were recommended for green belts design. Further intensive screening to identify tolerant species and best to excellent performer’s trees suitable for restoring the ecosystem is advised.

Influence of urban form on air quality: The combined effect of block typology and urban planning indices on city breathability

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.

Assessing Ozone Distribution Vertically and Horizontally in Urban Street Canyons Based on Field Investigation and ENVI-met Modelling

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.

Impact of drivers on real-driving fuel consumption and emissions performance

Eco-driving has attracted great attention as a cost-effective and immediate measure to reduce fuel consumption significantly. Understanding the impact of driver behaviour on real driving emissions (RDE) is of great importance for developing effective eco-driving devices and training programs. Therefore, this study was conducted to investigate the performance of different drivers using a portable emission measurement system. In total, 30 drivers, including 15 novice and 15 experienced drivers, were recruited to drive the same diesel vehicle on the same route, to minimise the effect of uncontrollable real-world factors on the performance evaluation. The results show that novice drivers are less skilled or more aggressive than experienced drivers in using the accelerator pedal, leading to higher vehicle and engine speeds. As a result, fuel consumption rates of novice drivers vary in a slightly greater range than those of experienced drivers, with a marginally higher (2%) mean fuel consumption. Regarding pollutant emissions, CO and THC emissions of all drivers are well below the standard limits, while NO_x and PM emissions of some drivers significantly exceed the limits. Compared with experienced drivers, novice drivers produce 17% and 29% higher mean NO_x and PM emissions, respectively. Overall, the experimental results reject the hypothesis that driver experience has significant impacts on fuel consumption performance. The real differences lie in the individual drivers, as the worst performing drivers have significantly higher fuel consumption rates than other drivers, for both novice and experienced drivers. The findings suggest that adopting eco-driving skills could deliver significant reductions in fuel consumption and emissions simultaneously for the worst performing drivers, regardless of driving experience.

Optimization of Ecosystem Services of Shanghai Urban–Suburban Street Trees Based on Low-Carbon Targets

Road traffic carbon emissions are an important cause of global warming, and street trees play an important role in regulating road carbon emissions. During urbanization, major differences in the planting management modes and growth status of the street trees in urban–suburban gradient may exist, leading to significant differences in the low-carbon values of the street trees in urban–suburban gradient. Based on this, this study took two typical urban–suburban gradient zones in Shanghai as an example to analyze the changes in the characteristics of street tree species, planting density, tree sizes, and low-carbon contribution with urban and rural changes, and proposed strategies for optimizing the low-carbon contribution of urban street trees. The results showed that, from the inner ring to the outer ring and the suburban ring, the proportion of London plane tree gradually changed from 82% to 11%, and the proportion of the camphor tree gradually changed from 9% to 70%; the average DBH of the trees gradually decreased from 28.81 to 23.74 cm. The number of plantings per unit road length gradually increased, and the number of plantings per unit area gradually decreased; therefore, the average low-carbon contribution of urban–suburban street trees is not significant, but the low-carbon contribution of upper street trees per unit area is higher, and suburban unit street trees have a higher low-carbon contribution. Finally, this article proposes different optimization strategies for future urban micro-renewal and suburban new-city construction.

Buoyancy effects on the flows around flat and steep street canyons in simplified urban settings subject to a neutral approaching boundary layer: Wind tunnel PIV measurements

The present wind tunnel particle image velocimetry (PIV) measurements document flows around flat and steep street canyons subject to thermal conditions at different levels, ranging from the Richardson number of 0.31 to 2.07. A steepness ratio, that is, the ratio of windward and leeward building heights, is proposed to characterise the geometrical influence of street canyons surrounded by buildings of non-uniform height. To study the thermal effects of building façades and ground on surrounding flow, surfaces of building models and the ground between them are heated up and maintained at three different temperatures to induce buoyant flows of different strength. The transition of the canyon flow from the typical rooftop shear-layer driven vortex to the buoyant plume type of flow is clearly revealed from the measurement results, which enhances the air removal that takes place at the roof-level of the two canyons. However, due to the different steepness of the canyons, the air removal rate from the steep canyon of a steepness ratio 2.52 is approximately 50% of that from the flat canyon with a steepness ratio of 1.53 in the buoyant plume-driven case because the downward flush flow along the windward façade suppresses the ascending plumes in the steep canyon. At the pedestrian level, the wind field is jointly dominated by the interplay between canyon-wide vortical flow and the buoyant plume rising ascending from the ground. The dynamics of non-isothermal flow in flat and steep canyons are revealed in detail, the implication of which is that the steepness of street canyons has to be considered in urban morphology planning, as well as in simplified geometrical representations of street canyons and in simplified urban canopy models.

Do environmental taxes reduce air pollution? Evidence from fossil-fuel power plants in China

Taking environmental protection into consideration, China introduced the new Environmental Protection Tax Law in 2018, which levies taxes for pollutants with higher standards. The vast majority of existing studies have investigated the impacts of environmental taxes (ETs) at the city level or province level. In contrast, we estimate their effects on pollutant reductions for fossil-fuel power plants in 30 provinces in China. Our results show that ETs have positive effects on pollutant emission reductions compared with the pollution discharge fee policy. We provide empirical evidence that sulfur dioxide (SO₂), nitrogen oxide (NOx), and dust from fossil fuel power plants significantly decreased by 2.186 (7.7%), 1.550 (6.84%), and 1.064 (16.1%) tons, respectively, after the implementation of the policy. Moreover, there is an inverted U-shape relationship between pollutant emission reductions and tax rates. We offer further evidence that they play a limited role in high environmental regulation regions. Finally, we compare how different types of firms responded to ETs. The results show that ETs have limited effects on pollutant reductions for large state-owned coal power plants, which may have achieved environmental transformation to a certain extent. Therefore, policymakers could consider promoting tax rates in areas with low tax rates and strengthening tax breaks to stimulate firms to achieve pollutant reduction. In addition, local governments should set tax rates taking regional environmental regulation levels and firm heterogeneity into account.

The removal of PM2.5 by trees in tropical Andean metropolitan areas: an assessment of environmental change scenarios

Metropolitan areas in Andean industrialized valleys with a strong geomorphological structure and a pronounced climate experience an increasing degradation of air quality, which motivates environmental policies that include the expansion of tree coverage in urban areas among the mitigation measures. Using the metropolitan area of the Aburrá Valley, Colombia, as a study case, we evaluate the removal of PM_2.5 by urban trees with the Urban Forest Effects (UFORE) methodology, considering the potential effect of changing tree covers in the valley for several projected meteorological conditions under climate change and different urban management practices. The estimated removals are sensitive to the number and distribution of available ground stations, with a tendency to overestimate with fewer stations. We found that the marginal gains in removal by additional tree plantings are low in the urbanized settings. In the environmental scenarios, the main limiting factor in the removal is precipitation, compared to changes in tree cover and levels of pollution. Spatially, the increase in total removal depends on the increase in tree cover, with more benefits obtained when trees are in areas with high concentrations of PM_2.5. Trees with low values of leaf area index (LAI) seem to optimize the effectiveness of the removal. Seasonally, the greatest removal occurs in rainy months when pollution levels are the highest. Based on our results, the scenarios that meet the plans and programs aimed at improving air quality would achieve removal effectiveness of around 2.5% of the total emissions of PM_2.5 with urban trees. Air quality would achieve removal effectiveness of around 2.5% of the total emissions of PM_2.5 with urban trees.