Distribution Of Air Pollutants Research Articles

Health and air pollutant emission impacts of net zero CO2 by 2050 scenarios from the energy modeling forum 37 study

Carbon dioxide and non-greenhouse gas air pollutants are emitted from many of the same sources. Decarbonization actions thus typically yield air pollutant emission reductions, resulting in significant air quality benefits. Although several studies have highlighted this connection, including in the context of net zero carbon emission targets, substantial uncertainty remains regarding how alternative technological pathways to this goal will affect the spatial distribution and magnitude of air pollutants. Comprehensive multi-model and multi-scenario analyzes are needed to explore the relative impacts of alternative pathways. Our study begins to address this gap by leveraging the results from the recent Energy Modeling Forum 37 inter-model comparison exercise on U.S. decarbonization pathways. Comparing the results of the six teams who submitted air pollutant emissions suggests that strategies that target net zero U.S. carbon emissions would yield significant reductions in many air pollutants, and that this finding is generally robust across pathways. However, some energy sources, such as biomass and fossil fuels with carbon capture, will emit air pollutants and can potentially influence the magnitude, spatial distribution, and even sign of localized air pollutant emission changes. In the second part of this analysis, a simplified air quality and health impacts screening model is used to evaluate the air quality impacts in 2035 of sectoral emission changes from the three models that provided sectoral detail. Relative to a reference scenario, a net zero pathway is estimated to reduce fine particulate matter concentrations across the contiguous U.S., with health benefits from reduced mortality ranging from $65 billion to $250 billion in 2035 alone (2023$s). These benefits would be expected to grow over time as the net zero trajectory becomes more stringent. Both the magnitude of potential benefits and the substantial variation of the projections across models underscore the need for an EMF-like inter-model comparison exercise focused on air quality.

Comparing Airborne and Along-Sidewall Ground-Based Measurements to Retrieve Thermal and PM10 Concentration Profiles in Two Alpine Valleys

Abstract The paper aims at investigating the effectiveness of estimating vertical profiles of air temperature and PM10 concentrations in Alpine valleys through ground stations positioned at different altitudes on one valley sidewall (i.e., pseudovertical profiles). Two case studies in the Italian Alps are investigated: Chiese Valley in Trentino Province and Camonica Valley in the Lombardy region. Vertical profiles of temperature and PM10 concentrations were derived from airborne measurements at the center of the two valleys by means of low-cost sensors installed on a drone during summer 2019 and a tethered balloon during winter 2020. At the same time, five stations, equipped with the same kind of low-cost sensors, simultaneously monitored the same variables on one mountain slope. Comparisons between pseudoprofiles and airborne soundings revealed that ground stations well approximated temperature and PM10 soundings during the night and early morning, while temperatures along the slopes were higher than in the center of the valley during daytime, due to solar radiative heating, with larger differences in summer than in winter. On the contrary, some episodes with PM10 concentrations slightly higher in the valley center than on the slope were recorded, due to transport events and upslope winds. Nonetheless, the pseudoprofiles based on slope ground measurements faithfully reproduced the vertical gradients of both air temperature and PM10 if compared to those assessed from the soundings performed at the center of the two valleys. Results show that pseudovertical profiles can be a reliable and inexpensive method for continuous monitoring of vertical air temperature and PM10 distribution in mountain valleys. Significance Statement The purpose of this study is to understand whether ground-based measurements of air temperature and particulate matter on mountain slopes can be used as suitable surrogates of vertical atmospheric soundings with tethered balloons or drones in mountain environment. This is important because balloon and drone soundings are expensive and regulative constrained. The knowledge of the vertical distribution of air temperature is crucial to predict the distribution of air pollutants in valleys, namely, the particulate matter emitted by wood burning. Our results showed that ground-based measurements on mountain slopes made with low-cost sensors can satisfactorily reproduce vertical gradients of air temperature and distribution of particulate matter in a reliable and inexpensive way.

Spatiotemporal analysis of air pollution concentration and short-term effects on cardiovascular emergencies in Lombardy

Abstract Introduction Association between both short [1] and long-term exposure to air pollution and cardiovascular (CV) morbidity and mortality is known. Lombardy region, Italy is a hot spot for air pollution spikes in particulate matter (PM) and NO2, with over 60% and 45%, respectively, of days/year exceeding the WHO daily permissible limits. This phenomenon is largely due to the regional land use and its geographical conformation, which hinders pollution dispersion. Our aim was to study the short-term relation between both trends and spatial distribution of air pollution and geo-localized CV events reported to the regional emergency services (AREU) in Lombardy region. Method First, time-series of PM2.5, PM10, NO2 and O3 concentration were obtained using daily average of 10 km x 10 km resolution satellite data from Copernicus Atmosphere Monitoring Service (CAMS) for 2015-2020 period, and superimposed to time-series of CV related emergency calls (CVCALLS) in the same period (total calls 1,094,848). For every year, daily peaks both for pollution concentration and CVCALLS were identified as the top 1st percentile or as exceeding three times the standard deviation. Peaks related to SARS-CoV-2 pandemic in 2020 were excluded. Spatial autocorrelation analyses, using both global (G) and local (L) Moran’s I, were conducted to evaluate the monthly clustering patterns of both pollutants and CVCALLS in uniform districts of 100,000 residents. Results Seasonal peaks were observed for PM2.5, PM10 and NO2 in winter, and for O3 in summer. Identified peak days for NO2 and O3 were coinciding with those from CVCALLS, considering temporal lags up to seven days (see Fig 1-2). Global autocorrelation resulted in a high Moran’s I for concentration levels of all the pollutants (G > 0.6), while comparatively lower clustering emerged for CVCALLS (G <0.65). The monthly L Moran’s I showed high value clustering for PM2.5, PM10 and NO2 in the southern, largely urban, part of the region, and low value clusters in the Alpine northern and north-eastern area. The exact opposite pattern was observed for O3. The spatial clustering of the CVCALLS, coherently with the low global autocorrelation, showed less significant patterns, with a small high value cluster in and around the city of Milan and its southern districts throughout the whole year. Conclusion As Spearman’s regression analysis between pollutants’ concentration and CVCALLS resulted in low correlation coefficient (R2 ≤ 0.16), the use of advanced statistical models, accounting for temporal lags and confounders, is suggested to better understand the underlying phenomena. In conclusion, our study found a strong spatial clustering pattern in all the pollutants throughout the year, but this was not observed for the CV events. However, strong correspondences between peaks of NO2 during winter and O3 during summer and CVCALLS were clearly observed, thus confirming short term effect of such pollutants on emergency CV events.

Vertical distribution of ambient air pollutants (PM2.5, PM10, NOX, and NO2); A systematic review

BackgroundNumerous investigations have explored variations in vertical air pollutant concentrations, yielding diverse findings. So, we have conducted this systematic review to gain a more comprehensive understanding of the vertical distribution of air pollutant concentrations (PM2.5, PM10, NOX, and NO2) and the influencing factors. MethodsRelevant studies were identified by searching the three central electronic databases, PubMed, Web of Science, and Scopus, from the beginning of 2000 to the end of 2023. This study included original articles published in English that examine the pollutant concentration variations below 500 m. FindingOf 3614 articles, 57 studies met our criteria. Our findings showed a decline in PM2.5 and NOX concentrations as altitude increased, while NO2 concentrations exhibited an increase. Conversely, no statistically significant relationship was identified between altitude and PM10 concentrations. The regression analysis yielded coefficients for the relationship between concentration and altitude (0–500 m) as follows: PM2.5 (−0.11), PM2.5/PM10 (0.0008), NOX (−0.11), and NO2 (0.13). Conducting additional research on this topic and investigating the impact of meteorological parameters on pollutant concentrations at different altitudes enhances our understanding of the vertical distribution of pollutant concentrations.

Spatio-Temporal Monitoring and Assessment of Air Quality and its Impact on Public Health from Geospatial perspective over Haryana, India

Abstract. This study aims to evaluate the air quality of the Haryana state, India by utilizing Sentinel-5P satellite data. This study aims to track, quantify, and analyze the level of air pollutants in several regions of Haryana. By integrating and analyzing diverse satellite datasets, the study provides a comprehensive understanding of air quality conditions in the region and supports informed decision-making for pollution mitigation and environmental sustainability. This also examines the public health and environmental impacts of air pollution, identifies pollution sources, reviews existing policies, and also provides recommendations for prevention of air pollution. The outcomes of this study will improve the understanding of air quality dynamics in Haryana and provide preventive measures towards sustainable environmental practices. This paper presents a detailed assessment of air quality in Haryana, India, covering the period from 2019 to 2023 using satellite datasets. This evaluates the concentrations and spatial distributions of air pollutants across Haryana. The experimental results provide significant variations in pollution levels over the four years, with higher concentrations in 2019 and 2021, and lower levels in 2020 and 2023. Moreover, spatial distribution maps illustrate pollutant patterns, emphasizing the temporal and spatial changes in pollution levels. The results also highlight the ongoing challenge of increasing pollution in certain areas of Haryana and the need for continuous monitoring to improve air quality.

Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California

California’s diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989–2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively.Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations.To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)–NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions.Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

Spatial distribution of air pollutants in different urban functional zones based on mobile monitoring and CFD simulation

Spatial distribution of air pollutants in different urban functional zones based on mobile monitoring and CFD simulation

A UAV-based method for efficiently measuring the spatial pattern of multiple air pollutants in street canyons

Street canyons are fundamental units of urban spaces where individuals engage in social activities. The air quality within street canyons directly impacts the health and behavior of residents. Therefore, it is crucial to comprehensively assess and depict the actual distribution of multiple air pollutants (SO2, CO, NO2, O3, PM2.5, and PM10) in street canyons, for both investigation and validation purposes. However, an efficient and accurate method for doing so is rarely available. To address this issue, indoor and field measurements were conducted to determine the response times of these pollutants in various polluted environments, identify the optimal stay duration at individual sampling points, and optimize the sampling layout. A method based on Unmanned Aerial Vehicle (UAV) was developed to efficiently measure the spatial pattern of pollutants in street canyons and verified through field application. The results showed that the actual response times of pollutants in heavily and slightly polluted environments were less than 30 s. The recommended stay duration at an individual sampling point in an outdoor environment was determined to be 2 min, with uncertainty of 2 %. For the sampling layout, it is recommended to take 12 samples to obtain a spatial pattern of a street canyon. These samplings should be arranged at the air inlet, middle, and air outlet along the middle of the street canyon, as well as at their bottom, 20 %–30 %, 50 %–60 %, and top in the vertical direction, respectively. The UAV-based method can efficiently complete the sampling process in just 24 min.

Spatiotemporal Changes in Air Pollution within the Studied Road Segment

Environmental protection is a pivotal element of sustainable development, both essential and indispensable in the construction of smart, green cities. Road transport contributes significantly to atmospheric pollution, accounting for as much as 25% of annual emissions within the European Union (EU). To combat the adverse effects of road transport, the EU has set targets to reduce greenhouse gas emissions from both passenger and commercial vehicles. Consequently, sustainable air pollution management has become a focal point for numerous researchers. This study continues the investigation into the distribution of air pollutants along a specific highway segment in Poland. The article addresses two primary research questions: first, the temporal and spatial variations in air pollution adjacent to a major highway in Poland, and second, whether emission levels exhibit significant annual differences and if there is a correlation between pollutant concentrations and the distance from the roadway. The findings offer valuable data on one of the principal substances polluting the air along EU transportation routes. Moreover, the analysis provides recommendations for future road infrastructure renovation projects and strategies to protect the public from harmful traffic-related pollutants, thereby supporting the development of green cities in accordance with sustainable development principles.

Air Pollution and Mortality in India: Investigating the Nexus of Ambient and Household Pollution Across Life Stages.

Air pollution in India is a foremost environmental risk factor that affects human health. This study first investigates the geographical distribution of ambient and household air pollution (HAP) and then examines the associated mortality risk. Data on fine particulate matter (PM2.5) concentration has been extracted from the Greenhouse Gas Air Pollution Interactions and Synergies (GAINS) model. HAP, mortality and socio-demographic data were extracted from the National Family and Health Survey-5, India, 2019-2021. Regression models were applied to see the difference in age-group mortality by different pollution parameters. The districts with PM2.5 concentration above the National Ambient Air Quality Standard (NAAQS) level of 40μg/m3 show a higher risk of neonatal (OR-1.86, CI 1.418-2.433), postneonatal (OR-2.04, CI 1.399-2.971), child (OR-2.19, CI 0.999-4.803) and adult death (OR-1.13, CI 1.060-1.208). The absence of a separate kitchen shows a higher probability of neonatal (OR: 1.18, CI 1.074-1.306) and adult death (OR-1.06, CI 1.027-1.088). The interaction between PM2.5 levels above NAAQS and HAP leads toa substantial rise in mortality observed for neonatal (OR 1.19 CI 1.051-1.337), child (OR 1.17 CI 1.054-1.289), and adult (OR 1.13 CI 1.096-1.168) age groups. This study advocates that there is a strong positive association between ambient and HAP and mortality risk. PM2.5 pollution significantly contributes to the mortality risk in all age groups. Children are more vulnerable to HAP than adults. In India, policymakers should focus on reducing the anthropogenic PM2.5 emission at least to reach the NAAQS, which can substantially reduce disease burden and, more precisely, mortality.

Simulating air pollution at the province level: A case of Ba Ria – Vung Tau province, Vietnam

Abstract This study aimed to simulate air pollution in Ba Ria - Vung Tau province (BR-VT), a significant transportation hub in Vietnam and Southeast Asia. The FVM-TAPOM model system was employed to create a spatial distribution map of air pollution concentration including PM2.5, PM10, NO2, SO2, and CO. The model had sufficient capability for simulating air pollutant distribution (R2>0.6). For PM2.5, the average simulated 1-hour PM2.5 concentration is highest during the dry and rainy seasons, reaching 68 μg/m3 and 46 μg/m3, respectively. For PM10, the average 1-hour simulated PM10 dust concentration is highest during the dry and rainy seasons, reaching 90 μg/m3 and 62 μg/m3, respectively. For NO2, the average 1-hour simulated NO2 concentration is highest during the dry and rainy seasons, reaching 56 (μg/m3) and 46 μg/m3, respectively, which is lower than the permissible threshold of QCVN 05:2023/BTNMT (1-hour average 200 μg/m3). For SO2, the average 1-hour simulated SO2 concentration is highest during the dry and rainy seasons, reaching 140 μg/m3 and 88 μg/m3, respectively, which is lower than the permissible threshold of QCVN 05:2023/BTNMT (1-hour average 350 μg/m3). For CO, the average 1-hour simulated CO concentration is highest during the dry and rainy seasons, reaching 2.950 μg/m3 and 2.280 μg/m3, respectively, which is lower than the permissible threshold of QCVN 05:2023/BTNMT (1-hour average 30.000 μg/m3).

An investigation of inequalities in exposure to PM2.5 air pollution across small areas in Ireland

The link between exposure to air pollution and adverse effects on human health is well documented. Yet, in a European context, research on the spatial distribution of air pollution and the characteristics of areas is relatively scarce, and there is a need for research using different spatial scales, a wider variety of socioeconomic indicators (such as ethnicity) and new methodologies to assess these relationships. This study uses comprehensive data on a wide range of demographic and socioeconomic indicators, matched to data on PM2.5 concentrations for small areas in Ireland, to assess the relationship between social vulnerability and PM2.5 air pollution. Examining a wide range of socioeconomic indicators revealed some differentials in PM2.5 concentration levels by measure and by rural and urban classification. However, statistical modelling using concentration curves and concentration indices did not present substantial evidence of inequalities in PM2.5 concentrations across small areas. In common with other western European countries, an overall decline in the levels of PM2.5 between 2011 and 2016 was observed in Ireland, though the data indicates that almost all small areas in Ireland were found to have exceeded the World Health Organization (WHO)’s PM2.5 annual guideline (of 5 µg/m3), calling for greater policy efforts to reduce air pollution in Ireland. The recent Clean Air Strategy contains a commitment to achieve the WHO guideline limits for PM2.5 by 2040, with interim targets at various points over the next two decades. Achieving these targets will require policy measures to decarbonise home heating, promote active travel and the transition to electric vehicles, and further regulations on burning fossil fuels and enforcing environmental regulations more tightly. From a research and information-gathering perspective, installing more monitoring stations at key points could improve the quality and spatial dimension of the data collected and facilitate the assessment of the implementation of the measures in the Clean Air Strategy.

Analysis of the Association between Air Pollutant Distribution and Mobile Sources in Busan Using Spatial Analysis

Analysis of the Association between Air Pollutant Distribution and Mobile Sources in Busan Using Spatial Analysis

Statistical Analysis of Spatial Distribution of Ambient Air Pollution in Addis Ababa, Ethiopia

Statistical Analysis of Spatial Distribution of Ambient Air Pollution in Addis Ababa, Ethiopia

Sensitivity Analysis of Modelled Air Pollutant Distribution around Buildings under Different Meteorological Conditions

Sensitivity Analysis of Modelled Air Pollutant Distribution around Buildings under Different Meteorological Conditions

An integrated uncrewed aerial vehicle platform with sensing and sampling systems for the measurement of air pollutant concentrations

Abstract. In this study, an uncrewed aerial vehicle (UAV) platform with sensing and sampling systems was developed for three-dimensional (3D) measurements of air pollutant concentrations. The sensing system of this platform contains multiple microsensors and Internet of Things devices for determining the 3D distributions of four critical air pollutants and two meteorological parameters in real time. Moreover, the sampling system comprises remote-controllable gas sampling kits, each of which contains a 1 L Tedlar bag for the 3D measurement of volatile organic compound (VOC) concentrations according to the Toxic Organics-15 (TO-15) method of the US Environmental Protection Agency. The performance of the developed UAV platform was verified in experiments where it was used to detect air pollutant emissions from a large industrial zone in Taiwan that included a traditional industrial park, a precision machinery park, and a municipal waste incineration plant. Three locations were selected as field measurement sites according to the prevailing local wind direction. The vertical distributions of four critical air pollutants, the ambient temperature, and the relative humidity were determined from data gathered at the aforementioned sites in March and May 2023. A total of 56 and 72 chemical species were qualitatively and quantitatively analyzed in these two periods, respectively. The experimental results verified the feasibility of using the proposed UAV platform for accurately evaluating the air pollutant concentration distribution and transport in an industrial zone. The sampling system can be used as the sampling part of the TO-15 method, thus extending the method to measure the 3D distribution of VOCs in an area. The UAV platform can serve as a useful tool in the management of and decision-making process for air pollution in industrial areas.

Coking exhaust contributes to airborne particulate matter in the Beijing–Tianjin–Hebei region

Coking was regarded as a predominant source of air pollution. Despite the adoption of more environmentally friendly equipment, whether the coking enterprises in the Beijing–Tianjin–Hebei (BTH) region are still causing regional air pollution is worthy of study, which is essential for the control of coking enterprises in this area. To improve the prediction accuracy of large-scale air pollutant distribution, the air particle distribution in the BTH region was simulated via land use regression (LUR) combined with Bayesian maximum entropy (BME); then, the distribution was correlated with the exhaust gas emitted from coking enterprises. Results indicated that the R2 of the “LUR + BME” method reached 0.95, higher than 0.82 using LUR alone. The air quality distribution presented a pattern of “low in the northern mountains and high in the southern plains”, similar to the distribution of coking enterprises in BTH region. A significant correlation was found between exhaust emissions from coking enterprises and air quality in the BTH region, confirming the contribution of coking emissions to air pollution in this region, and the necessity to continue the strict control on coking enterprises in BTH area.

Diurnal variation of air pollutants and their relationship with land surface temperature in Bengaluru and Hyderabad cities of India

Air pollution and Land Surface Temperature (LST) are crucial components of urban environments. As urbanization and industrialization increase, the two components affect the natural environment of the urban areas. The present study investigated the daytime and nighttime spatiotemporal distribution of air pollutants and examined their relationship with LST for Bengaluru and Hyderabad cities of India. Daytime/nighttime/seasonal/spatial variations of air pollutants and LST, sensitivity of air pollutant in affecting LST, relationship between air pollutants and LST, and hotspots/coldspots analysis for LST and air pollutants was carried out. The study utilized, air pollutants data of 5-years from air quality monitoring stations and LST data of overlapping period from MODIS satellite. PM2.5 exceeded the national and international air quality standards while all other parameters were within specified limits/guideline values, at both the cities. The study found that LST had maximum sensitivity towards PM2.5 and NO2 for both the cities indicating that both these parameters are more effective in causing changes in the LST values. High Urban Thermal Field Variance Index (UTFVI) values for urban areas demonstrated strong surface urban heat island (SUHI) during nighttime in both the cities. Pearson's correlation analysis showed that LST, at Hyderabad, was positively correlated with PM2.5 (r = 0.714) during daytime. Relationship of LST with all air pollutants and meteorological parameters was investigated simultaneously and very strong relationship of LST with all other parameters was observed for both daytime and nighttime for both the cities. The R2 values at Bengaluru were 0.98 and 0.92 for nighttime and daytime, respectively, while the corresponding values were 0.87 and 0.93, respectively for Hyderabad. Hotspot analysis showed that total area of hotspots/coldspots undergoes temporal variations and the corresponding area during daytime was higher/lower compared to nighttime for different parameters. The trend of increase/decrease of daytime area of hotspots/coldspots, over nighttime area, was also different from different parameters as well as it was observed different for both the cities. Overall, the present study provides holistic insights into the interaction between air pollutants and LST in urban contexts. By understating these interactions and variations it helps urban planning and policymaking mitigate the challenge of the rise in LST and air pollutants.

Effects of building envelope features on airflow and pollutant dispersion within a symmetric street canyon.

Building envelope features (BEFs) have attracted more and more attention as they have a significant impact on flow structure and pollutant dispersion within street canyons. This paper conducted CFD numerical models validated by wind-tunnel experiments, to explore the effects of the BEFs on characteristics of the airflow and pollutant distribution inside a symmetric street canyon under perpendicular incoming flow. Three different BEFs (balconies, overhangs, and wing walls) and their locations and continuity/discontinuity structures were considered. For each canyon with various BEFs, the air exchange rate (ACH), airflow patterns, and pollutant distributions were evaluated and compared in detail. The results show that compared to the regular canyon, the BEFs will reduce the ACH of the canyon, but increase the disturbances (the proportion of ACH') inside the canyon. The BEFs on the leeward wall have the least influence on the in-canyon airflow and pollutant distributions, followed by that on the windward wall. Then when the BEFs are on both walls, the ventilation capacity of the canyon is weakened greatly, and the pollutant concentration in the ground center is increased significantly, especially near the windward side. Moreover, the discontinuity BEFs will weaken the effect of the continuity BEFs on the in-canyon flow and dispersion, specifically, the discontinuity BEFs reduced the region of high pollutant concentration distributions. These findings can help optimize the BEFs design to enhance ventilation and mitigate traffic pollution.

Impacts of transportation emissions on horizontal and vertical distributions of air pollutants in Shanghai: Insights from emission reduction in COVID-19 lockdown

Transportation is a major sector of anthropogenic emissions in urban areas and deteriorates air quality. The surface and vertical observational data were combined with the model results to reveal its impact on the horizontal and vertical variations of pollutants during the COVID-19 lockdown period. The evident reductions in ambient PM2.5 (∼30%) and NO2 (∼50%) concentrations but a ∼25% increase in O3 concentration were observed at the transportation sites. On the vertical scale, a uniform decrease of ∼28% in PM2.5 concentrations was observed within 600 m. However, the vertical profiles of NO2 and O3 exhibited increasing vertical variation rates with concentrations varying significantly within 400 m. Meanwhile, Ox shared a similar pattern of vertical profile with O3, with a uniform increase (∼5%) within 600 m in the urban area. The WRF-CMAQ model reproduced the variations, and revealed that the reduction of transportation emissions was the key factor contributing to the increase of urban O3 and Ox due to the weakened NO titration effect. The simulated vertical profile of NO2 was featured by a decreasing curve, while that of O3 exhibited the opposite trend. We find that the transportation emissions impact vertical concentrations of NO2 and O3 within at most 400 m. The process analysis revealed that the vertical transport and horizontal transport from bay areas contributed to O3 in the urban area, while chemical processes mainly consumed it. The reduction in transportation emissions weakened the consumption and resulted in O3 accumulation during rush hours and at night. The variation of planetary boundary layer height also favored the rise of urban O3 by promoting vertical transport at daytime and trapping it at night. The reduction in NOx emissions from the transportation enhanced O3 pollution, suggesting that collaborative reductions in VOCs from multiple sectors should be conducted. This study also indicated that regional collaborations in emission reductions were necessary for comprehensive air pollution prevention.