Reducing air pollution does not necessarily reduce related adults' mortality burden: Variations in 177 countries with different economic levels.

Prolonged exposure to PM2.5 is associated with increased mortality. However, reducing air pollution concentrations does not necessarily reduce the related burden of deaths. Here, we aim to estimate the variations in PM2.5-related mortality due to contributions from key factors - PM2.5 concentration, population exposure, and healthcare levels - for 177 countries from 2000 to 2018 at the 1-km grid scale according to the Global Mortality Exposure Model (GEMM) model. We find that global reductions in PM2.5-related deaths mainly come from high and upper-middle income countries, where lowered air pollutant concentration and better healthcare can offset mortality burdens caused by increasing exposed populations. Changes in population exposure to PM2.5 contribute the most (54 %) to change in global related deaths over the examined period, followed by changes in healthcare (-42 %) and pollution concentrations (4 %). The impacts vary across countries and regions within them due to other drivers, which are significantly influenced by development status. Policies aiming at reducing PM2.5 associated health risks need to account for country-specific balances of these key socioeconomic drivers.

Global synergy of carbon and pollution emissions among countries with different income levels and development stages.

The world was drift away on the sustainable development goals (SDGs), whatever global countries claimed fighting for. It's thus essential to illustrate the status of development and environmental quality simultaneously. Resource consumption and energy consumption as the basic needs in supporting human societal development, are commonly used, because they come from the same source and are most directly observed in the open air. We thus examined nexus of carbon and pollution emissions, which also directly indicate residents' livelihood and lifestyle. The possibility of the nexus shifts among income levels with population stack analysis was further investigated. Our findings indicate that the diverse nexus is strongly correlated with development levels, with urban areas being the primary contributor to high carbon and/or pollution emissions despite occupying only 0.5% of global territory. We conclude that expecting leapfrog stages of the nexus is unrealistic, as cross-income-level change requires approximately 80% of the population to significant change its livelihood and lifestyle. Therefore, we recommend setting science-based targets for decoupling carbon and pollution emissions from development are necessary, but should be adapted and tailored to each country's local practice.

Tree species classification improves the estimation of BVOCs from urban greenspace.

Accurate estimation of biogenic volatile organic compounds (BVOCs) emissions from urban plants is important as BVOCs affect the formation of secondary pollutants and human health. However, uncertainties exist for the estimation of BVOCs emissions from urban greenspace due to the lack of tree species classification with high spatial resolution. Here, we generated a tree species classification dataset with 10 m resolution to estimate tree species-level BVOCs emissions and quantify their impact on air quality in Shenzhen in southern China. The results showed that for the entire city, the BVOCs emissions based on traditional plant functional types (PFTs) dataset were substantially underestimated compared with the tree species classification data (6.37 kt versus 8.23 kt, with 22.60 % difference). The underestimation is particularly prominent in urban built-up areas, where our estimation was 1.65 kt, nearly twice of that based on PFTs data (0.86 kt). BVOCs estimation in built-up areas contributed approximately 20.07 % to the total. These BVOCs contributed substantially to the increase of ambient O3, but had limited impacts to ambient fine particulate matter (PM2.5). Our results underscore the importance of high spatial resolution tree species-level classification in more accurate estimation of BVOCs, especially in highly developed urban areas. The enhanced understanding of the patterns of BVOCs emissions by urban trees and the impact on secondary pollutants can better support fine-scale tree planning and management for livable environments in urban areas.

SERS combined with QuEChERS using NBC and Fe3O4 MNPs as cleanup agents to rapidly and reliably detect chlorpyrifos pesticide in citrus.

The surface-enhanced Raman spectroscopy (SERS) technique is being increasingly used for the detection of pesticide residues in agricultural products. However, there are large amounts of fluorescence-producing substances in agricultural products, which seriously affect the Raman signal of the analyte. In this paper, the QuEChERS method was used to remove interfering fluorescent substances in the analyte, and the purification effects of different doses of nano bamboo charcoal (NBC) and Fe3O4 magnetic nanoparticle (Fe3O4 MNP) adsorbents were studied. Meanwhile, the Raman spectral acquisition conditions (AuNPs, test solution, and NaCl) were optimized based on the orthogonal test method. The results showed that 300 µL AuNPs, 40 µL test solution, and 100 µL 1.5% NaCl gave the best SERS response effect. 12.5 mg NBC combined with 10 mg Fe3O4 MNPs could effectively remove the interfering substances from citrus. The Raman spectra of chlorpyrifos molecules were theoretically modeled using density-functional theory (DFT). By comparing the DFT results with the actual tests, five feature peaks, at 338, 522, 558, 672, and 1600 cm-1, were obtained for the detection of chlorpyrifos pesticide residues in citrus. Based on the Raman feature peak intensity at 672 cm-1, the concentration of chlorpyrifos in citrus showed a good linear relationship (R2 = 0.9979) in the concentration range of 3-20 mg kg-1. The recovery rate was 92.12% to 98.38%, and the relative standard deviation (RSD) was 1.77% to 5.29%. The lowest detection concentration was about 3 mg kg-1, and the detection time of a single sample could be completed within 15 min. This study showed that the combination of SERS and QuEChERS preprocessing methods could achieve rapid detection of chlorpyrifos pesticide residues in citrus.

A machine learning strategy-incorporated BiFeO3/Ti3C2 MXene electrochemical platform for simple, rapid detection of Pb2+ with high sensitivity.

The electrochemical technique has been increasingly used for the detection of heavy metal ions in the water system. However, the process for determining the optimum experimental conditions was cumbersome, time-consuming, and unsynchronized, resulting in unsatisfactory detection efficiency. Herein, a new machine learning (ML) strategy combined with BiFeO3/Ti3C2 MXene (BiFeO3/MXene) was used to fabricate a simple but efficient electrochemical Pb2+ sensor. The interconnected BiFeO3/MXene composites prepared by a hydrothermal method possessed an interconnected conductive framework, abundant active sites, and a large surface area, which gave them excellent electronic conductivity and high accumulation of Pb2+. Meanwhile, ML methods such as back-propagation artificial neural network (BPANN) and genetic algorithm (GA) combined with orthogonal experimental design (OED) were used to optimize sensor parameters such as the pH of the supporting electrolyte, the BiFeO3/MXene content, deposition potential, and deposition time. Compared with OED and the one factor at a time (OFAT) methods, the OED-ML method greatly simplified the experimental procedures and improved the electrochemical detection performance. The developed sensor showed superior detection performance for Pb2+ with a detection limit of 0.0001 µg L-1 using the OED-ML method, which was much lower than that of the OED and OFAT methods (0.0003 µg L-1). In addition, the sensor showed good repeatability, reproducibility, stability, and interference capability. The feasibility of the method was verified by detecting Pb2+ in lake samples with recoveries ranging from 98.79% to 101.3%. To our knowledge, the ML strategy was introduced for the first time in an electrochemical sensor for Pb2+ detection, which proved the feasibility and practicality of ML.

Understanding the indirect impacts of urbanization on vegetation growth using the Continuum of Urbanity framework.

Numerous studies investigated the direct impacts of urbanization on the loss and fragmentation of vegetated lands associated with urban expansion. Fewer studies, however, have examined the indirect impacts of urbanization on vegetation related to changes in livelihoods, lifestyles, and connectivity in non-urbanized areas, especially in the context of large-scale urban-rural migration. Here, we employ the Continuum of Urbanity framework to examine how changes in livelihoods, lifestyles, and connectivity in non-urbanized areas associated with urbanization affect vegetation, and thereby to understanding the indirect impacts of urbanization. We found there was a significant trend in human-induced EVI (HEVI) increase in non-urban areas, and such trend was coupled with decreased population density (PD) in forest land and grassland, but increased population density in cropland. The negative correlation between PD and HEVI became increasingly stronger from 2000 to 2011, but weakened since 2011. Livelihood income, lifestyles represented by consumption, and information connectivity to the outside world indirectly impacted HEVI by driving PD changes in non-urban areas. This indirect effect has shifted from positive to negative over the 20 years. These findings suggest that the indirect impacts of urbanization on vegetation growth are complicated and multifaceted, and understanding such impacts would be critically important to help turn urbanization into an opportunity for regional sustainable development.

Distribution and ecological risk of polycyclic aromatic hydrocarbons in wastewater treatment plant sludge and sewer sediment from cities in Middle and Lower Yangtze River.

The objective of this study was to conduct a comparative study of the distribution and ecological risk of polycyclic aromatic hydrocarbons (PAHs) in the sewage collection and treatment system of four cities located in the middle and lower reaches of the Yangtze River. The results revealed that the mean concentration of 16 ΣPAHs was higher in the sewer sediments (1489.45 ng·g-1) than in the sewage sludge (781.78 ng·g-1). PAH monomers were detected in all cases, with higher mean Pyr, Chr, BbF and BaP concentrations. PAHs with 4-6 rings were dominant in both sewage sludge and sewer sediment monomer PAHs. With the isomer ratio method and positive definite matrix factor (PMF) method, the results showed that the major sources of PAHs in sewage sludge were petroleum sources, coal tar, and coking activities, whereas PAHs in sewer sediments were primarily from wood combustion, automobile emissions, and diesel engine emissions. Of all the PAH monomers, BaP and DahA had the highest toxic equivalents, although their levels were not the highest. Based on the assessment of ΣPAHs, it was concluded that both sewage sludge and sewer sediments had medium ecological risk. The results of this study provide reference information for the control of PAHs in the wastewater collection and treatment system of the middle and lower reaches of the Yangtze River.

Depression mediated the relationships between precentral-subcortical causal links and motor recovery in spinal cord injury patients.

Depression after brain damage may impede the motivation and consequently influence the motor recovery after spinal cord injury (SCI); however, the neural mechanism underlying the psychological effects remains unclear. This study aimed to examine the casual connectivity changes of the emotion-motivation-motor circuit and the potential mediating effects of depression on motor recovery after SCI. Using the resting-state functional magnetic resonance imaging data of 35 SCI patients (24 good recoverers, GR and 11 poor recoverers, PR) and 32 healthy controls (HC), the results from the conditional Granger causality (GC) analysis demonstrated that the GR group exhibited sparser emotion-motivation-motor GC network compared with the HC and PR groups, though the in-/out-degrees of the emotion subnetwork and the motor subnetwork were relatively balanced in the HC and GR group. The PR group showed significantly inhibitory causal links from amygdala to supplementary motor area and from precentral gyrus to nucleus accumbens compared with GR group. Further mediation analysis revealed the indirect effect of the 2 causal connections on motor function recovery via depression severity. Our findings provide further evidence of abnormal causal connectivity in emotion-motivation-motor circuit in SCI patients and highlight the importance of emotion intervention for motor function recovery after SCI.

Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods.

A nano-ZnS-decorated hierarchically porous carbon (ZSHPC) was mixed with MWCNTs to obtain ZSHPC/MWCNT nanocomposites. Then, ZSHPC/MWCNTs were used to modify a screen-printed electrode, and a portable electrochemical detection system combined with machine learning methods was used to investigate carbendazim (CBZ) residues in rice and tea. The electrochemical performance of the constructed electrode showed that the electrode had good electrocatalytic ability, large effective surface area, strong stability and anti-interference ability. Support Vector Machine (SVM), Least Square Support Vector Machine (LS-SVM) and Back Propagation-Artificial Neural Network (BP-ANN) were used to establish the prediction model for CBZ residues in rice and tea, and the traditional linear regression was developed. The investigated results showed that the LS-SVM model had the best prediction performance and the lowest prediction error compared with the traditional linear regression, BP-ANN and SVM models. The R2, RMSE, and MAE for the training set samples were 0.9969, 0.3605 and 0.2968, respectively. The R2, RMSE, MAE and RPD for the prediction set samples were 0.9924, 0.6190, 0.5360 and 10.3097, respectively. The average recovery range of CBZ in tea and rice was 98.77-109.32% and that of RSD was 0.47-2.58%, indicating that the rapid analysis of CBZ pesticide residues in agricultural products based on a portable electrochemical detection system combined with machine learning was feasible.

Insights into multisource sludge distributed in the Yangtze River basin, China: Characteristics, correlation, treatment and disposal.

Sludge is the by-product of wastewater treatment process. Multisource sludge can be defined as sludge from different sources. Based on the sludge properties of five typical cities in the Yangtze River basin, including Jiujiang, Wuhu, Lu'an, Zhenjiang and Wuhan, this study investigated and summarized the characteristic variations and distribution differences of multiple indicators and substances from municipal sludge, dredged sludge, and river and lake sediments. The results demonstrated pH of multisource sludge was relatively stable in the neutral range. Organic matter and water content among municipal sludge were high and varied considerably between different wastewater treatment plants. Dredged sludge had an obviously higher sand content and wider particle distribution, which could be considered for graded utilization depending on its size. The nutrients composition of river and lake sediments was usually stable and special, with lower nitrogen and phosphorus content but higher potassium levels. The sources of heavy metals and persistent organic pollutants in multisource sludge were correlated, generally much higher among municipal sludge than dredged sludge and river and lake sediments, which were the most important limitation for final land utilization. Despite various properties of multisource sludge, the final fate and destination have some overall similarities, which need to be supplemented and improved by standards and laws. The study provided a preliminary analysis of suitable technical routes for municipal sludge, dredged sludge, river and lake sediments based on their different characteristics respectively, which was of great significance for multisource sludge co-treatment and disposal in the future of China.

A meta-analysis on plant volatile organic compound emissions of different plant species and responses to environmental stress.

Urban plants are beneficial to residents' physical and mental health, but can also have adverse impacts. One of the remarked examples is the potential contribution of BVOCs released by urban plants to the generation of ground-level ozone and SOA. The choice of urban plant species, therefore, is critical for air quality improvement in cities. Understanding the rates of BVOCs emitted from different urban plants and how they change in response to environmental stressors is a prerequisite to making the right decision on plant species selection. Here, we performed a meta-analysis on the selected 159 studies that include 357 species to address this need. We found: (1) 89% of deciduous trees emit the three major types of BVOCs, isoprene, monoterpene, and sesquiterpene, but only do 53% evergreen ones. (2) The main types of BVOCs emission by broad-leaved and coniferous plants differ. Seventy-eight percent of broad-leaved, but only 48% of coniferous trees emit isoprene, whereas 74% of broad-leaved, but 93% of coniferous plants emit monoterpene. (3) The emission rates of isoprene and monoterpene differ significantly among species. (4) The analysis on the 77 species collected in previous studies indicated that the effect of environmental stressors varies by different compounds, and the combined effect is not precisely the same as that of a single factor. Based on the meta-analysis, we further identified a few key knowledge gaps and research priorities. First, more studies on the BVOCs emission and carbon allocation at the tree species level are needed. Second, the combined effects of multiple environmental stresses, especially long-term ones, on BVOC emissions and the mechanisms warrant further research. Third, it is vital to evaluate BVOC-climate interactions on global change. Furthermore, there is little empirical work on the synergies and tradeoffs between BVOC emissions and ecosystem services provision of urban plants, which warrants future investigation.

Interactions among spatial configuration aspects of urban tree canopy significantly affect its cooling effects.

Increasing urban tree canopy (UTC) has been widely recognized as an effective means for urban heat mitigation and adaptation. While numerous studies have shown that both percent cover of UTC and its spatial configuration can significantly affect urban temperature, the pathways governing these relationships are largely unexplored. Here we present a cross-city comparison aiming to fill this gap by explicitly quantifying the pathways on which percent cover of UTC and its spatial configuration affect land surface temperature (LST) using structural equation modeling (SEM), based on UTC mapped from high resolution imagery and LST derived from Landsat thermal bands. We found: 1) Although both the direct and indirect pathways significantly affected LST regardless of scales and cities, the direct pathway played a more important role in affecting LST in Baltimore, Beijing, and Shenzhen. In contrast, an opposite result was found in Sacramento, likely due to the effects of buildings and their interactions with UTC. 2) Similarly, the direct pathway of mean patch size (MPS) and mean shape index (MSI) played a more important role in affecting LST than their indirect effects via altering edge density (ED). Our results highlighted the necessity for discomposing the effects of different spatial configuration variables on LST. Understanding the pathways through which UTC affects LST can provide insights into urban heat mitigation and adaptation.

Neural efficiency and proficiency adaptation of effective connectivity corresponding to early and advanced skill levels in athletes of racket sports.

This study explored how the neural efficiency and proficiency worked in athletes with different skill levels from the perspective of effective connectivity brain network in resting state. The deconvolved conditioned Granger causality (GC) analysis was applied to functional magnetic resonance imaging (fMRI) data of 35 elite athletes (EAs) and 42 student-athletes (SAs) of racket sports as well as 39 normal controls (NCs), to obtain the voxel-wised hemodynamic response function (HRF) parameters representing the functional segregation and effective connectivity representing the functional integration. The results showed decreased time-to-peak of HRF in the visual attention brain regions in the two athlete groups compared with NC and decreased response height in the advanced motor control brain regions in EA comparing to the nonelite groups, suggesting the neural efficiency represented by the regional HRF was different in early and advanced skill levels. GC analysis demonstrated that the GC values within the middle occipital gyrus had a linear trend from negative to positive, suggesting a stepwise "neural proficiency" of the effective connectivity from NC to SA then to EA. The GC values of the inter-lobe circuits in EA had the trend to regress to NC levels, in agreement with the neural efficiency of these circuits in EA. Further feature selection approach suggested the important role of the cerebral-brainstem GC circuit for discriminating EA. Our findings gave new insight into the complementary neural mechanisms in brain functional segregation and integration, which was associated with early and advanced skill levels in athletes of racket sports.

Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems.

Remote sensing can be used to map tillage practices at large spatial and temporal scales. However, detecting such management practices in smallholder systems is challenging given that the size of fields is smaller than historical readily-available satellite imagery. In this study we used newer, higher-resolution satellite data from Sentinel-1, Sentinel-2, and Planet to map tillage practices in the Eastern Indo-Gangetic Plains in India. We specifically tested the classification performance of single sensor and multiple sensor random forest models, and the impact of spatial, temporal, or spectral resolution on classification accuracy. We found that when considering a single sensor, the model that used Planet imagery (3 m) had the highest classification accuracy (86.55%) while the model that used Sentinel-1 data (10 m) had the lowest classification accuracy (62.28%). When considering sensor combinations, the model that used data from all three sensors achieved the highest classification accuracy (87.71%), though this model was not statistically different from the Planet only model when considering 95% confidence intervals from bootstrap analyses. We also found that high levels of accuracy could be achieved by only using imagery from the sowing period. Considering the impact of spatial, temporal, and spectral resolution on classification accuracy, we found that improved spatial resolution from Planet contributed the most to improved classification accuracy. Overall, it is possible to use readily-available, high spatial resolution satellite data to map tillage practices of smallholder farms, even in heterogeneous systems with small field sizes.

A Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network.

The directed brain functional network construction gives us the new insights into the relationships between brain regions from the causality point of view. The Granger causality analysis is one of the powerful methods to model the directed network. The complex brain network is also hierarchically constructed, which is particularly suited to facilitate segregated functions and the global integration of the segregated functions. Therefore, it is of great interest to explore new approach to model the hierarchical architecture of the directed network. In the present study, we proposed a new approach, namely, stepwise multivariate Granger causality (SMGC), considering both the directed and hierarchical features of brain functional network to explore the stepwise causal relationship in the network. The simulation study demonstrated that the diverse and complex hierarchical organization could be embedded in the apparently simple directed network. The proposed SMGC method could capture the multiple hierarchy of the directed network. When applying to the real functional magnetic resonance imaging (fMRI) datasets, the core triple resting-state networks in human brain showed within-network directed connections in the first-level directed network and rich and diverse between-network pathways in the second-level hierarchical network. The default mode network (DMN) had a prominent role in the resting-state acting as both the causal source and the important relay station. Further exploratory research on the adaption of directed hierarchical network in athletes suggested the enhanced bidirectional communication between the DMN and the central executive network (CEN) and the enhanced directed connections from the salience network (SN) to the CEN in the athlete group. The SMGC approach is capable of capturing the hierarchical architecture of the brain directed functional network, which refreshes the new stepwise causal relationship in the directed network. This might shed light on the potential application for exploring the altered hierarchical organization of brain directed network in neuropsychiatric disorders.

The protective effect of biomineralized BSA-Mn3O4 nanoparticles on HUVECs investigated by atomic force microscopy.

Manganese-based nanozymes have been widely used in the field of cell protection due to their various enzyme-mimicking activities, but their effect on the mechanical properties of cells is not yet known. Here, bovine serum albumin-modified Mn3O4 nanoparticles (BSA-Mn3O4 NPs) with good biocompatibility were synthesized by a one-step biomineralization method using BSA as a template. BSA-Mn3O4 NPs possess scavenging activity against superoxide free radicals (O2˙-), hydroxyl radicals (˙OH) and hydrogen peroxide (H2O2). The excellent reactive oxygen species (ROS) scavenging activity of BSA-Mn3O4 NPs enables them to effectively reduce the intracellular ROS level, thus mitigating the damage of oxidative stress on human umbilical vein endothelial cells (HUVECs). Subsequently, the intracellular antioxidant mechanism of the BSA-Mn3O4 NPs was further investigated. The results show that the BSA-Mn3O4 NPs could inhibit the depolymerization of F-actin, help cells maintain their normal morphology, and reduce the decrease in Young's modulus of cells caused by oxidative stress.

Is Urban Greening an Effective Solution to Enhance Environmental Comfort and Improve Air Quality?

Urban greening has often been proposed as a cost-effective solution to improve environmental comfort, but may also deteriorate air quality. Quantifying these two opposing effects of urban greening is necessary to develop successful environmental policies for specific mega-city clusters. In this study, a high-resolution regional climate and air quality model (WRF-Chem, v4.0.3) was employed to test three scenarios aimed at quantifying the impact of land-use change and biogenic emissions from urban greening on regional climate and air quality. It was found that urban greening could effectively decrease the near-surface temperature by up to 0.45 °C, but the increased biogenic volatile organic compound (BVOC) emissions offset some of this cooling effect (by up to 65%). Land-use change due to urban greening dominated the improvement in human comfort but worsened diffusion conditions to result in the convergence of fine particulate matter in specific areas. The selection of low-emission tree species may be imperative, although increased emissions from urban greening will not change the sensitivity of ozone to precursors under the current scenario of anthropogenic emissions. This is because BVOC emissions due to urban greening will become a more important source of pollution with the development of clean energy and the popularity of low-carbon lifestyles.

Beyond city expansion: multi-scale environmental impacts of urban megaregion formation in China.

Environmental degradation caused by rapid urbanization is a pressing global issue. However, little is known about how urban changes operate and affect environments across multiple scales. Focusing on China, we found urbanization was indeed massive from 2000 to 2015, but it was also very uneven, exhibiting high internal city dynamics. Urban areas in China as a whole became less green, warmer, and had exacerbated PM2.5 pollution. However, environmental impacts differed in newly developed versus older areas of cities. Adverse impacts were prominent in newly urbanized areas, while old urban areas generally showed improved environmental quality. In addition, regional environmental issues are emerging as cities expand, connect and interact to form urban megaregions. To turn urbanization into an opportunity for, rather than an obstacle to, sustainable development, we must move beyond documenting urban expansion to understand the environmental consequences of both internal city dynamics and the formation of urban megaregions.

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

Many cities across the world face the challenge of severe fine particulate matter (PM2.5) pollution. Among the many factors that affect PM2.5 pollution, there is an increasing interest in the impacts of urban structure. However, quantifying these impacts in China has been difficult due to differences of study area and scale in existing research, as well as limited sample sizes. Here, we conducted a continental study focusing on 301 prefectural cities in mainland China to investigate the effects of urban structure, including urban size and urban compactness, on PM2.5 concentrations. Based on PM2.5 raster and land cover data, we used quantile regression and a general multilinear model to estimate the effects and relative contributions of urban size and urban compactness on urban PM2.5 pollution, with explicit consideration for pollution level, urban size and geographical location. We found: (1) nationwide, the larger and more compact that cities were, the heavier the PM2.5 pollution tended to be. Additionally, this relationship became stronger with increasing levels of pollution. (2) In general, urban size played a more important role than urban form, and there were no significant interactive effects between the two metrics on urban PM2.5 concentrations at the national scale. (3) The impacts of urban size and form varied by city size and geographical location. The impacts of urban size were only significant for small or medium-large cities but not for large cities. Among large cities, only urban form had a significantly positive effect on urban PM2.5 concentrations. The further north and west that cities were, the more dependent PM2.5 pollution was on urban form, whereas the further south and east that cities were, the greater the impact of urban size. These results provide insights into how urban design and planning can be used to alleviate air pollution.