Urban road greenbelt configuration: The perspective of PM2.5 removal and air quality regulation

Understanding the impacts of street greening patterns and wind directions on the dispersion of fine particles

Inappropriate planting patterns can increase pollutant concentrations and threaten human health. This study examined three greening patterns (trees, trees + hedges, and hedges) using the ENVI-met model to evaluate the different effects of various planting patterns on PM2.5 dispersion within an idealized 3D street canyon under three typical wind directions. Results showed that street greenbelts alter the PM2.5 concentration field within canyons, and the horizontal and vertical distribution characteristics of PM2.5 under different wind directions were significantly different. The arbor-hedge vegetation structure showed the highest total vegetation deposition amount due to larger canopy volumes while hedges have better deposition amounts per unit volume due to their proximity to emission sources. Additionally, this research selected the averaged relative difference in PM2.5 concentration (ARDC) indicator to assess the influence of different green scenarios on the dispersion of PM2.5 concentrations. Wind direction and planting patterns jointly affect the dispersion of PM2.5 in canyons, and the ARDC varied from -4.39 % to 105.36 %. Unilateral-trees on the windward side or two rows of hedges may be the optimal vegetation layout by trade-off with other services. ARDC was significantly correlated (p < 0.01) with most of the 3D green indicators. These results could provide effective suggestions for optimizing the layout of greenbelts in street canyons to improve air quality.

Understanding the drivers of PM2.5 concentrations in Chinese cities: A comprehensive study of anthropogenic and environmental factors

Understanding the factors that drive PM2.5 concentrations in cities with varying population and land areas is crucial for promoting sustainable urban population health. This knowledge is particularly important for countries where air pollution is a significant challenge. Most existing studies have investigated either anthropogenic or environmental factors in isolation, often in limited geographic contexts; however, this study fills this knowledge gap. We employed a multimethodological approach, using both multiple linear regression models and geographically weighted regression (GWR), to assess the combined and individual effects of these factors across different cities in China. The variables considered were urban built-up area, land consumption rate (LCR), population size, population growth rate (PGR), longitude, and latitude. Compared with other studies, this study provides a more comprehensive understanding of PM2.5 drivers. The findings of this study showed that PGR and population size are key factors affecting PM2.5 concentrations in smaller cities. In addition, the extent of urban built-up areas exerts significant influence in medium and large cities. Latitude was found to be a positive predictor for PM2.5 concentrations across all city sizes. Interestingly, the northeast, south, and southwest regions demonstrated lower PM2.5 levels than the central, east, north, and northwest regions. The GWR model underscored the importance of considering spatial heterogeneity in policy interventions. However, this research is not without limitations. For instance, international pollution transfers were not considered. Despite the limitation, this study advances the existing literature by providing an understanding of how both anthropogenic and environmental factors, in conjunction with city scale, shape PM2.5 concentrations. This integrated approach offers invaluable insights for tailoring more effective air pollution management strategies across cities of different sizes and characteristics.

Removal of airborne particulate matter by evergreen tree species in Dhaka, Bangladesh

Urban air quality stands as a pressing concern in cities globally, with airborne particulate matter (PM) emerging as a significant threat to human health. An investigation was carried out to examine the potential of four prevalent evergreen roadside tree species grown at different locations in Dhaka to capture PM using their leaves. The distribution of PM by mass and quantity in Dhaka are presented for the first time for Bangladesh and these results will also be applicable to countries with similar climates and tree species. Separate gravimetric analyses were carried out to quantify PM in three different size ranges (0.2-2.5 μm, 2.5-10 μm, and 10-100 μm) accumulated on surfaces and trapped within waxes by using the rinse and weigh method. The method is validated for the first time through SEM-EDX analysis, which confirmed that the increase in weight from chloroform-rinsed leaves was exclusively attributable to particle deposition on the filter. The chemical composition of the deposited PM2.5 was analyzed quantitatively by determining the concentration of twenty-five trace elements employing ICP-MS. SEM-EDX analysis revealed the significance of leaf microstructural traits in effectively capturing PM. Significant variations in the deposition of PM were found among different species for two PM categories (surface PM and wax-embedded PM) and three size fractions (large, coarse, and fine) (one-way ANOVA; p < 0.05). The quantity of wax retained on the foliage of trees documented in these locations also varied (p < 0.05). Among the species studied, Ficus benghalensis demonstrated a greater ability to retain PM. Mangifera indica was identified to be the most efficient collector of wax-related PM and appears to be the ideal species for traffic-heavy areas distinguished by high concentrations of organic compounds from vehicle emissions.

How does PM2.5 affect forest phenology? Integrating PM2.5 into phenology models for warm-temperate forests in China

Vegetation can regulate particulate matter (PM) through various mechanisms, such as facilitating the deposition of gases and particulates and purifying the air via photosynthesis. Conversely, PM directly damages leaves through dry deposition, while it also indirectly affects plant growth by altering weather conditions. However, the ways in which PM influence vegetation growth patterns, and the driving factors behind these impacts, remain unclear. In this study, we primarily focused on the start of the growing season (SOS) of warm-temperate zone forests in China with severe PM. SOS exhibited a trend of advancing at a rate of 0.15 days/yr during the study period from 2004 to 2022. We assessed the impact of satellite-derived fine PM (PM2.5) and coarser PM (PM10) on forest SOS across warm temperate forest regions in China using partial correlation analysis methods. After removing the effects of PM, we found that the correlation between temperature and SOS weakened. Additionally, PM exhibited a positive correlation with SOS in most pixels. Linear regression analysis revealed a significant negative correlation between relative humidity (RH) and the relationship between PM2.5 and SOS. However, in areas where RH exceeds 60.38%, this effect becomes unstable, presumably due to increased aerosol hygroscopicity or the saturation of aerosol particles. We also found that as road network density increased, the relationship between PM2.5 and SOS strengthened, whereas the impact of nightlight on this relationship was relatively weak. It is important to note that while the observed correlations reveal mechanisms by which PM2.5 affects SOS, they do not directly imply causation, as the complex interactions between environmental factors may influence these relationships. Finally, we incorporated PM2.5 into the phenology model and optimized its parameters using the least squares method, which improved the accuracy of SOS simulations and provided insights for predicting vegetation phenology in areas with severe PM pollution.

Impact of urban tree arrangement on pedestrian exposure to the size-fractional particulate matter in a city boulevard

Trees act as natural filters that mitigate roadside air pollution. However, the filtration impact of different tree arrangements on traffic pollutants with different particle diameters has rarely been analysed in real street canyon environments. To quantify how roadside tree arrangements impact pedestrian exposure to particle number concentrations (PNCs) of different diameters (0.25-32 μm), in situ field measurements were carried out in a boulevard-type street canyon in the city of Xi'an, China. This study analysed the experimental data of PNCs collected along segments of a pedestrian lane under four typical tree arrangements: open space without trees, a sparse-spaced tree arrangement, a medium-spaced tree arrangement, and a dense-spaced tree arrangement in a street canyon. Our results reveal that the effect of tree arrangement on PNCs depended on the particle diameter. In general, trees can significantly reduce coarse PNC (particles with diameters >2.5 μm) but not the fine PNC. Quantitative analysis showed that a medium-spaced tree arrangement, in which tree crowns are adjacent to each other but do not overlap, is the most capable of reducing PNC, followed by a sparse-spaced tree arrangement, while a the dense-spaced tree arrangement has the least impact. The attenuation effect of trees on the PNCs increased with increasing particle diameter. Moreover, the presence of trees altered the local microclimate, which also affected how exposure to PNCs changed. Our empirical findings further highlight the complexity of how trees affect particulate pollutants in street canyons and provide timely insights for enhancing tree-planning management in cities from the perspective of air quality improvement.

Evaluating the impact of evolving green and grey urban infrastructure on local particulate pollution around city square parks

The relationship between green and grey urban infrastructure, local meteorological conditions, and traffic-related air pollution is complex and dynamic. This case study examined the effect of evolving morphologies around a city square park in Dublin and explores the twin impacts of local urban development (grey) and maturing parks (green) on particulate matter (PM) pollution. A fixed air quality monitoring campaign and computational fluid dynamic modelling (ENVI-met) were used to assess current (baseline) and future scenarios. The baseline results presented the distribution of PM in the study area, with bimodal (PM2.5) and unimodal (PM10) diurnal profiles. The optimal vegetation height for air quality within the park also differed by wind direction with 21 m vegetation optimal for parallel winds (10.45% reduction) and 7 m vegetation optimal for perpendicular winds (30.36% reduction). Increased building heights led to higher PM2.5 concentrations on both footpaths ranging from 25.3 to 37.0% under perpendicular winds, whilst increasing the height of leeward buildings increased PM2.5 concentrations by up to 30.9% under parallel winds. The findings from this study provide evidence of the importance of more in-depth analysis of green and grey urban infrastructure in the urban planning decision-making process to avoid deteriorating air quality conditions around city square parks.

Effect of highway greenbelt constrution on groundwater flow in a semi-arid region

The highway greenbelt, vigorously promoted in arid and semi-arid areas, has obvious impacts on beautifying the environment, absorbing dust, reducing noise, and maintaining soil and water. Moreover, it affects the characteristics of how water resources are distributed and the regional groundwater cycle. However, the impact of highway greenbelt construction on groundwater flow in semi-arid areas is unknown. The Hubao Highway greenbelt in the north part of the Tumochuan Plain was studied as an example. The paper combines field investigation, remote sensing and mathematical modeling to quantify the impact of highway green space construction on regional groundwater circulation. The results showed that: Trees, shrubs and grasses were the dominant vegetation types in the landscaped area, accounting for 42.17% of the studied area. The total evapotranspiration water consumption of the green belt during the growing season was 471.35 × 104m3. The groundwater recharge in the study area was mainly derived from the lateral recharge in front of the mountain, and the main discharge was the evapotranspiration water consumption of the green belt. This evapotranspiration accounts for 3.31% of the total groundwater recharge. Under the condition that the recharge in front of the mountain remains constant, the evapotranspiration water consumption of the green belt will still have an increasing trend in the future. Appropriate planting of poplar and other high water-consuming trees may be the best way to mitigate the adverse effects of greenbelt evapotranspiration on groundwater resources. The results of this study provide valuable insights for environmental protection and infrastructure development in similar areas.

The comprehensive impact of thermal-PM2.5 interaction on subjective evaluation of urban outdoor space: A pilot study in a cold region of China

Urban outdoor space has a very important impact on the quality of people's outdoor activities, which has influenced people's health and moods. Its influence is the result of the combined action of various factors. Thermal and air quality environment are important factors affecting the overall comfort of the urban outdoor space. At present, there are few research on interaction with thermal and air quality environment. Therefore, a meteorological measurement and questionnaire survey have been conducted in a representative open space in a campus in Xi'an, China. The following are the research results:(1) Mean physiological equivalent temperature (MPET) is a significant factor affecting thermal sensation vote (TSV) and thermal comfort vote (TCV). PM2.5 has no significant effect on thermal comfort vote (TCV), but it is a considerable factor affecting thermal sensation vote (TSV) when 10.2°C ≤ MPET<21°C (P = 0.023 *). (2) PM2.5 is a significant factor affecting air quality vote (AQV) and breathing comfort vote (BCV).Mean physiological equivalent temperature (MPET) has no significant impact on air quality vote (AQV), but it is a considerable factor affecting breathing comfort vote (BCV) when 10.2°C ≤ MPET<21°C (P = 0.01 **). (3) Mean physiological equivalent temperature (MPET) is a significant factor affecting overall comfort vote (OCV), but PM2.5 is not. In general, When 10.2°C ≤ MPET<21°C (-0.5 < -0.37 ≤ TCV ≤ 0.12 <0.5), the interaction between thermal and PM2.5 environment is significant on thermal sensation vote (TSV) and breathing comfort vote (BCV). This study can provide experimental support for the field of multi-factor interaction, which has shown that improving the thermal environment can better breathing comfort, while reducing PM2.5 concentration can promote thermal comfort. And can also provide reference for the study of human subjective comfort in urban outdoor space in the same latitude of the world.

Uneven PM2.5 dispersion pattern across an open-road vegetation barrier: Effects of planting combination and wind condition

The increased traffic-induced emissions contribute to the exacerbation of airborne particulate matter (PM) pollution. The vegetation barrier (VB) provides a means of reducing the traffic-induced pollutants. However, the effects of VB configuration and local environment on PM dispersion and reduction remain unclear, and thereby needs further advancement on VB design and characteristics. This study constructed a 3D numerical model based on field survey in an open-road VB of Shanghai urban area, and then simulated PM2.5 dispersion under various VB configurations and wind conditions. The results consolidated that the presence of the VB reduced PM2.5 concentration by over 15 % across the VB. A greater bush coverage (2/3 and more) reduces over 14 % more PM2.5 pollution across the VB than that for a greater arbor coverage, and reduces 6 % more PM2.5 pollution in the sidewalk canyon. Given a certain bush planting coverage, planting bushes in the windward area is beneficial to the overall PM2.5 reduction by approximately 4-14 %. The wind directions determine the overall pattern of PM2.5 dispersion across the VB plot, decreasing trends for perpendicular winds but fluctuating curves for parallel winds Wind velocities largely contribute to the changing rates of PM2.5 concentration, the increased wind speed from 1 m/s to 7 m/s accumulated 5-11 % more PM2.5 pollution across the VB plot. This study provides practical insights for effective VB designs in order to mitigate the PM pollution and the human's exposure to PM2.5 in urban open-road 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.

Greenery planning for urban air pollution control based on biomonitoring potential: Explicit emphasis on foliar accumulation of particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs)

In this study, efficiencies of eight indigenous plants of Baishnabghata Patuli Township (BPT), southeast Kolkata, India, were explored as green barrier species and potentials of plant leaves were exploited for biomonitoring of particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs). The present work focused on studying PM capturing abilities (539.32-2766.27 μg cm-2) of plants (T. divaricata, N. oleander and B. acuminata being the most efficient species in retaining PM) along with the estimation of foliar contents of PM adhered to leaf surfaces (total sPM (large + coarse): 526.59-2731.76 μg cm-2) and embedded within waxes (total wPM (large + coarse): 8.73-34.51 μg cm-2). SEM imaging used to analyse leaf surfaces affirmed the presence of innate corrugated microstructures as main drivers for particle capture. Accumulation capacities of PAHs of vehicular origin (total index, TI > 4) were compared among the species based on measured concentrations (159.92-393.01 μg g-1) which indicated T. divaricata, P. alba and N. cadamba as highest PAHs accumulators. Specific leaf area (SLA) of plants (71.01-376.79 cm2 g-1), a measure of canopy-atmosphere interface, had great relevance in PAHs diffusion. Relative contribution (>90%) of 4-6 ring PAHs to total carcinogenic equivalent and potential as well as 5-6 ring PAHs to total mutagenic equivalent and potential had also been viewed with respect to benzo[a]pyrene. In-depth analysis of foliar traits and adoption of plant-based ranking strategies (air pollution tolerance index (APTI) and anticipated performance index (API)) provided a rationale for green belting. Each of the naturally selected plant species showed evidences of adaptations during abiotic stress to maximize survival and filtering effects for reductive elimination of ambient PM and PAHs, allowing holistic management of green spaces.

Comparisons of PM2.5 mitigation with stand characteristics between evergreen Korean pine plantations and deciduous broad-leaved forests in the Republic of Korea

Owing to industrialization and urbanization in recent decades, fine particulate matter (PM2.5) in the atmosphere has become a major environmental problem worldwide. This environmental issue pushed the use of forests as air filtering tools. However, there is a lack of continuous and long-term forest management to efficiently mitigate PM2.5. In this study, we assessed the potential of different forest types to control air pollution by measuring the seasonal PM2.5 concentrations inside and outside the forest for one year. In addition, the PM2.5 reduction efficiencies (PMREs) of two forest types were compared, and their relationship with stand characteristics was analyzed. The results showed that the average PMRE inside the forests was approximately 18.2%; the seasonal PMRE was highest in winter (approximately 28.1%) and lowest in summer (approximately 9.6%). The average PMRE of the Taehwa deciduous broad-leaved forest (TDF) (approximately 18.8%) was significantly higher than that of the Taehwa coniferous forest (TCF) (approximately 17.5%) (P < 0.001); differences were also observed seasonally. The PMRE in the TCF was higher in spring and summer (P < 0.001), while that in the TDF was higher in autumn and winter (P < 0.001). Furthermore, the PMRE in the TDF was negatively correlated with stand density (P = 0.003) and positively correlated with the average diameter at breast height (DBH) (P = 0.028). However, the PMRE in the TCF did not significantly correlate with stand characteristics. As such, the results of this study revealed the differences in PM2.5 mitigation according to stand characteristics, which should be considered in urban forest management.

Foliar particulate matter retention and toxic trace element accumulation of six roadside plant species in a subtropical city

As a major source of air pollution, particulate matter (PM) and associated toxic trace elements pose potentially serious threats to human health and environmental safety. As is known that plants can reduce air PM pollution. However, the relationship between PM of different sizes and toxic trace elements in foliar PM is still unclear. This study was performed to explore the association between PM of different sizes (PM2.5, PM10, PM>10) and toxic trace elements (As, Al, Cu, Zn, Cd, Fe, Pb) as well as the correlation among toxic trace elements of six roadside plant species (Cinnamomum camphora, Osmanthus fragrans, Magnolia grandiflora, Podocarpus macrophyllus, Loropetalum chinense var. rubrum and Pittosporum tobira) in Changsha, Hunan Province, China. Results showed that P. macrophyllus had the highest ability to retain PM, and C. camphora excelled in retaining PM2.5. The combination of P. macrophyllus and C. camphora was highly recommended to be planted in the subtropical city to effectively reduce PM. The toxic trace elements accumulated in foliar PM varied with plant species and PM size. Two-way ANOVA showed that most of the toxic trace elements were significantly influenced by plant species, PM size, and their interactions (P < 0.05). Additionally, linear regression and correlation analyses further demonstrated the homology of most toxic trace elements in foliar PM, i.e., confirming plants as predictors of PM sources as well as environmental monitoring. These findings contribute to urban air pollution control and landscape configuration optimization.

Exploring the effect of ecological land structure on PM2.5: A panel data study based on 277 prefecture-level cities in China

In the context of serious urban air pollution and limited land resources, it is important to understand the environmental value of ecological land. Previous studies focused mostly on the effectiveness of a particular type of green space or the total amount of ecological land on PM_2.5 and have rarely analyzed the association between ecological land structure and PM_2.5 systematically and quantitatively. Therefore, we took 277 cities in China as an example, comprehensively compared the results of different models, and selected a spatial Durbin model using time-fixed effects to dissect the degree of influence of ecological land and different land types within it on PM_2.5. The urban ecological land use structure was closely related to PM_2.5, and the higher the proportion of ecological land use was, the lower the PM_2.5. The degree and direction of influence of different types of land functions within ecological land on PM_2.5 differed, with forests, shrubs, and grasslands causing a weakening impact on PM_2.5, while wetlands and waters did not have a weakening role. The degree of reduction of PM_2.5 by a single type of ecological land was significantly smaller than that by a composite type of ecological land. Green space should be comprehensively considered, designed and adjusted in urban planning to continuously optimize the ecological spatial structure, increase landscape diversity and maximize ecological benefits. The findings of this study help with exploring the effects of land use structure under the goal-oriented control of air pollution and provide theoretical reference and decision-making support for formulating precise air pollution control policies and optimizing the spatial development of national land.

Urban dust pollution tolerance indices of selected plant species for development of urban greenery in Delhi

The rise in urbanization has led to an increase in dust pollution which is hazardous to the health of living beings. The role of roadside plant species in intercepting particulate matter and improving air quality is well reported. Hence, this study was carried out to determine the ability of various plant species to intercept atmospheric dust and withstand the abiotic stress triggered by dust deposition. In the present investigation, three sites (viz., control, commercial, and industrial) differing in anthropogenic activities and vegetation were selected. Sixteen plant species entailing both trees and shrubs that are commonly occurring at all three sites were selected to estimate their dust interception capacity (DIC). The impact of dust pollution on foliage biochemistry and their tolerance in winter and summer seasons were analyzed. Based on biochemical, biological, and socio-economic parameters, air pollution tolerance index (APTI) and anticipated performance index (API) were evaluated. Both dust load and DIC were found to be two times higher in winter than in the summer season. Terminalia arjuna, Ficus benghalensis, and Plumeria alba were the best dust accumulators, while Prosopis juliflora accumulated least. The highest DIC was observed at the industrial site, for Terminalia arjuna (0.025 mg/cm²/d) in winter and Plumeria alba (0.023 mg/cm²/d) in the summer season. Photosynthetic pigments showed a negative correlation with dust load, while pH, ascorbic acid, electrolytic leakage (E.L.), and proline content showed a positive correlation. In the present study, APTI and API values were highest for Ficus religiosa, Ficus benghalensis, Alstonia scholaris, Dalbergia sissoo, and Terminalia arjuna. Such plant species with wide canopy, large and rough leaf surface area with perforated veins are found to be more suitable and, hence, recommended for the development of greenery to improve air quality in urban areas like Delhi.

Analysis of the influencing factors of atmospheric particulate matter accumulation on coniferous species: measurement methods, pollution level, and leaf traits

Urban trees, especially their leaves, have the potential to capture atmospheric particulate matter (PM) and improve air quality. However, the amount of PM deposited on leaf surfaces detected by different methods varies greatly, and quantitative understanding of the relationship between PM retention capacity and various microstructures of leaf surfaces is still limited. In this study, three measurement methods, including the leaf washing (LW) method, aerosol regeneration (AR) method, and scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM-EDX) method, were used to determine the PM retention capacity of leaf surfaces of three coniferous species. Additionally, we analyzed the leaf traits and elemental composition of PM on leaves collected from different sites. The results showed that Pinus tabulaeformis and Abies holophylla were more efficient species in capturing PM than Juniperus chinensis, but different measurement methods could affect the detected results of PM accumulation on leaf surfaces. The concentrations of trace elements accumulated on leaf surfaces differed considerably between different sites. The greatest accumulation of elements that occurred on the leaf surface was at the Shenfu Highway site exposed to high PM pollution levels and the smallest accumulation at the Dongling park site. The stomatal density and contact angle were highly correlated with the PM retention capacity of leaf surfaces of the tested species (Pearson coefficient: r = 0.87, p < 0.01 and r =  - 0.70, p < 0.05), while the roughness and groove width were not significantly correlated (Pearson coefficient: r = 0.16 and r =  - 0.03). This study suggests that a methodological standardization for measuring PM is urgently required and this could contribute to selecting greening tree species with high air purification capacity.

Estimation of On-Road PM2.5 Distributions by Combining Satellite Top-of-Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

How to reduce the health risks for commuters, caused by air pollution such as PM2.5 has always been an urgent issue needing to be solved. Proposed in this study, is a novel framework which enables greater avoidance of pollution and hence assists the provision of healthy travel. This framework is based on the estimation of on-road PM2.5 throughout the whole city. First, the micro-scale PM2.5 is predicted by land use regression (LUR) modeling enhanced by the use of the Landsat-8 top-of-atmosphere (TOA) data and microscale geographic predictors. In particular, the green view index (GVI) factor derived, the sky view factor, and the index-based built-up index, are incorporated within the TOA-LUR modeling. On-road PM2.5 distributions are then mapped in high-spatial-resolution. The maps obtained can be used to find healthy travel routes with less PM2.5. The proposed framework was applied in high-density Hong Kong by Landsat 8 images. External testing was based on mobile measurements. The results showed that the estimation performance of the proposed seasonal TOA-LUR Geographical and Temporal Weighted Regression models is at a high-level with an R 2 of 0.70-0.90. The newly introduced GVI index played an important role in these estimations. The PM2.5 distribution maps at high-spatial-resolution were then used to develop an application providing Hong Kong residents with healthy route planning services. The proposed framework can, likewise, be applied in other cities to better ensure people's health when traveling, especially those in high-density cities.

Successions and interactions of phyllospheric microbiome in response to NH3 exposure

Phyllosphere and numerous phyllospheric microbiomes present a huge potential for air pollution mitigation. Despite research investigating the microbial compositions in the phyllosphere, the successions and interactions of the phyllospheric microbiome under ammonia gas (NH₃) stress remain poorly understood. Herein, we performed 16S rDNA, the internal transcribed spacer (ITS) profiling and a quantitative microbial element cycling (QMEC) method to reveal successions, co-occurrence, and N-cycling functions changes of phyllospheric bacteria and fungi during NH₃ exposure. The NH₃ input mainly elevated ammonium (NH₄⁺-N) and total nitrogen (TN) levels on the leaf surface. The exposure in the phyllosphere decreased fungal concentration with a homogeneity increase while enhanced bacterial concentration with a noticeable richness drop. Both short-term (2-week) and long-term (6-week) exposure induced significant changes in microbial compositions. Bacterial genera (Nocardioides, Pseudonocardia) and fungal genera (Alternaria, Acremonium) dominated throughout the exposure. Intensive microbial interactions compared to that in the natural phyllosphere were observed via network analysis. Our results showed that N-cycling functional genes were largely stimulated by the exposure and might, in turn contribute to NH₃ pollution buffer and alleviation via microbial metabolism. This study extended the knowledge on microbial responses to NH₃ exposure in the phyllosphere and enlightened phylloremediation on NH₃ through the microbial role.

Do We Need More Urban Green Space to Alleviate PM2.5 Pollution? A Case Study in Wuhan, China

Urban green space can help to reduce PM2.5 concentration by absorption and deposition processes. However, few studies have focused on the historical influence of green space on PM2.5 at a fine grid scale. Taking the central city of Wuhan as an example, this study has analyzed the spatiotemporal trend and the relationship between green space and PM2.5 in the last two decades. The results have shown that: (1) PM2.5 concentration reached a maximum value (139 μg/m3) in 2010 and decreased thereafter. Moran’s I index values of PM2.5 were in a downward trend, which indicates a sparser distribution; (2) from 2000 to 2019, the total area of green space decreased by 25.83%. The reduction in larger patches, increment in land cover diversity, and less connectivity led to fragmented spatial patterns of green space; and (3) the regression results showed that large patches of green space significantly correlated with PM2.5 concentration. The land use/cover diversity negatively correlated with the PM2.5 concentration in the ordinary linear regression. In conclusion, preserving large native natural habitats can be a supplemental measure to enlarge the air purification function of the green space. For cities in the process of PM2.5 reduction, enhancing the landscape patterns of green space provides a win-win solution to handle air pollution and raise human well-being.

Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China

PM_2.5 (fine particulate matter with aerodynamics diameter <2.5 μm) is the most important component of air pollutants, and has a significant impact on the atmospheric environment and human health. Using satellite remote sensing aerosol optical depth (AOD) to explore the hourly ground PM_2.5 distribution is very helpful for PM_2.5 pollution control. In this study, Himawari-8 AOD, meteorological factors, geographic information, and a new deep forest model were used to construct an AOD-PM_2.5 estimation model in China. Hourly cross-validation results indicated that estimated PM_2.5 values were consistent with the site observation values, with an R² range of 0.82-0.91 and root mean square error (RMSE) of 8.79-14.72 μg/m³, among which the model performance reached the optimum value between 13:00 and 15:00 Beijing time (R² > 0.9). Analysis of the correlation coefficient between important features and PM_2.5 showed that the model performance was related to AOD and affected by meteorological factors, particularly the boundary layer height. Deep forest can detect diurnal variations in pollutant concentrations, which were higher in the morning, peaked at 10:00-11:00, and then began to decline. High-resolution PM_2.5 concentrations derived from the deep forest model revealed that some cities in China are seriously polluted, such as Xi 'an, Wuhan, and Chengdu. Our model can also capture the direction of PM_2.5, which conforms to the wind field. The results indicated that due to the combined effect of wind and mountains, some areas in China experience PM_2.5 pollution accumulation during spring and winter. We need to be vigilant because these areas with high PM_2.5 concentrations typically occur near cities.

Evaluation of PM2.5 Retention Capacity and Structural Optimization of Urban Park Green Spaces in Beijing

Green space can effectively retain particles and improve air quality. However, most studies have focused on leaf-scale measurements or regional-scale model simulations, and few focus on individual trees. In this study, 176 urban park green spaces were selected within the 5th Ring Road in Beijing, and the i-Tree Eco model was used to estimate the PM2.5 retention levels of individual trees and sample plots. The results show that the retention capacity varied according to tree species. The PM2.5 retention of each sample plot was significantly affected by the tree coverage, species richness, mean tree height, mean crown width, and number of trees. The PM2.5 retention of urban park green spaces in the study area was estimated to be about 6380 t·year−1, and the air quality improvement rate was 1.62%. After structural optimizing, PM2.5 retention of the mixed coniferous and broad-leaved green space was as high as 80,000 g·year−1. This study studied the effects of trees on PM2.5 retention at multiple scales to fill the gaps in existing research at the scales of individual trees and communities, and it can serve as a reliable reference for the design and construction of green spaces aimed at improving air quality.

Pollutant dispersion in a group of courtyard buildings

As stated by the World Health Organization (WHO), the air pollution in the urban environment is the silent cause for around seven million death worldwide. This is due to the indoor and outdoor exposure to various pollutants emitted in the built environment: as the global trend is an increase of the population living in towns, this issue is predicted to become even worser. As a matter of fact, the built environment can cause the trapping of pollutants instead of their dispersion. In this work, the dispersion of a plume of a pollutant (carbon monoxide, CO), emitted from a chimneystack above the roof of courtyard in a group of courtyards, is investigated. This is achieved employing the ENVI-met software, able to model, among the others, the turbulence and pollutant dispersion in the built environment. Results show, among the others, how the pollutant emitted from an upstream building can harm also the downstream buildings.