Particulate Matter Air Quality Research Articles

Responses of fine particulate matter (PM2.5) air quality to future climate, land use, and emission changes: Insights from modeling across shared socioeconomic pathways

Air pollution induced by fine particulate matter with diameter ≤ 2.5 μm (PM2.5) poses a significant challenge for global air quality management. Understanding how factors such as climate change, land use and land cover change (LULCC), and changing emissions interact to impact PM2.5 remains limited. To address this gap, we employed the Community Earth System Model and examined both the individual and combined effects of these factors on global surface PM2.5 in 2010 and projected scenarios for 2050 under different Shared Socioeconomic Pathways (SSPs). Our results reveal biomass-burning and anthropogenic emissions as the primary drivers of surface PM2.5 across all SSPs. Less polluted regions like the US and Europe are expected to experience substantial PM2.5 reduction in all future scenarios, reaching up to ~5 μg m−3 (70 %) in SSP1. However, heavily polluted regions like India and China may experience varied outcomes, with a potential decrease in SSP1 and increase under SSP3. Eastern China witness ~20 % rise in PM2.5 under SSP3, while northern India may experience ~70 % increase under same scenario. Depending on the region, climate change alone is expected to change PM2.5 up to ±5 μg m−3, while the influence of LULCC appears even weaker. The modest changes in PM2.5 attributable to LULCC and climate change are associated with aerosol chemistry and meteorological effects, including biogenic volatile organic compound emissions, SO2 oxidation, and NH4NO3 formation. Despite their comparatively minor role, LULCC and climate change can still significantly shape future air quality in specific regions, potentially counteracting the benefits of emission control initiatives. This study underscores the pivotal role of changes in anthropogenic emissions in shaping future PM2.5 across all SSP scenarios. Thus, addressing all contributing factors, with a primary focus on reducing anthropogenic emissions, is crucial for achieving sustainable reduction in surface PM2.5 levels and meeting sustainable pollution mitigation goals.

Responses of fine particulate matter (PM2.5) air quality to future climate, land use, and emission changes: Insights from modeling across shared socioeconomic pathways

Air pollution induced by fine particulate matter with diameter ≤ 2.5 μm (PM2.5) poses a significant challenge for global air quality management. Understanding how factors such as climate change, land use and land cover change (LULCC), and changing emissions interact to impact PM2.5 remains limited. To address this gap, we employed the Community Earth System Model and examined both the individual and combined effects of these factors on global surface PM2.5 in 2010 and projected scenarios for 2050 under different Shared Socioeconomic Pathways (SSPs). Our results reveal biomass-burning and anthropogenic emissions as the primary drivers of surface PM2.5 across all SSPs. Less polluted regions like the US and Europe are expected to experience substantial PM2.5 reduction in all future scenarios, reaching up to ~5 μg m−3 (70 %) in SSP1. However, heavily polluted regions like India and China may experience varied outcomes, with a potential decrease in SSP1 and increase under SSP3. Eastern China witness ~20 % rise in PM2.5 under SSP3, while northern India may experience ~70 % increase under same scenario. Depending on the region, climate change alone is expected to change PM2.5 up to ±5 μg m−3, while the influence of LULCC appears even weaker. The modest changes in PM2.5 attributable to LULCC and climate change are associated with aerosol chemistry and meteorological effects, including biogenic volatile organic compound emissions, SO2 oxidation, and NH4NO3 formation. Despite their comparatively minor role, LULCC and climate change can still significantly shape future air quality in specific regions, potentially counteracting the benefits of emission control initiatives. This study underscores the pivotal role of changes in anthropogenic emissions in shaping future PM2.5 across all SSP scenarios. Thus, addressing all contributing factors, with a primary focus on reducing anthropogenic emissions, is crucial for achieving sustainable reduction in surface PM2.5 levels and meeting sustainable pollution mitigation goals.

Evaluation of Particulate Matter Deposition Efficiency and Air Quality Improvement by Vertical Shrub and Climber Walls

Evaluation of Particulate Matter Deposition Efficiency and Air Quality Improvement by Vertical Shrub and Climber Walls

Health risk assessment of ultrafine particle movement in children's residential spaces based on probabilistic neural networks

The air quality in children's living spaces has a significant impact on their health, and ultrafine particulate matter (PM2.5), as one of the main pollutants in the air, poses a particularly prominent threat to children's respiratory and immune systems. Therefore, it important to conduct health risk assessment on ultrafine particle exercise in children's living spaces. In this article, an intelligent risk assessment model of ultrafine atmospheric particles in children's sports space in residential areas based on probabilistic neural network (PNN) is studied. This model utilizes probabilistic neural networks to monitor and predict ultrafine particles in residential spaces, evaluating their potential risks to children's health. By collecting relevant data such as particulate matter concentration and air quality index in residential spaces, and using probabilistic neural networks for training and prediction, accurate assessment of the health risks of ultrafine particulate matter during exercise can be achieved. This model can be applied to densely populated places such as families and schools, providing parents and relevant institutions with scientific risk warning and prevention measures to ensure the health and safety of children. Probabilistic neural networks can learn and simulate complex nonlinear relationships, making the model more accurate in predicting the concentration of ultrafine particles and health risks. So as to construct a method system of health risk assessment of atmospheric particles, and the model is heavy metal elements in atmospheric fine particles, and corresponding improvement measures are put forward according to the assessment results. For circulatory system diseases, the degree of harm of atmospheric temperature and fine particles to children's health is basically close, and with the continuous decline of temperature, the synergistic effect of the two is more obvious. The concentration of atmospheric ultrafine particles significantly affects the health risk of children's sports activities space. All the experiments in this article are quite good. Among them, the average accuracy of the PNN health risk identification model is 0.840, and the average recall rate is 0.837. The health risk assessment model in this article strengthens the correlation of data information through scientific methods, grasps the basic information of children's sports activity spaces in residential areas, and formulates targeted sports health risk assessments for children's sports activity spaces in residential areas. Compared with the comparison method, the response speed is significantly improved, with an accuracy rate of over 90%.

PM2.5 Is Insufficient to Explain Personal PAH Exposure.

To understand how chemical exposure can impact health, researchers need tools that capture the complexities of personal chemical exposure. In practice, fine particulate matter (PM2.5) air quality index (AQI) data from outdoor stationary monitors and Hazard Mapping System (HMS) smoke density data from satellites are often used as proxies for personal chemical exposure, but do not capture total chemical exposure. Silicone wristbands can quantify more individualized exposure data than stationary air monitors or smoke satellites. However, it is not understood how these proxy measurements compare to chemical data measured from wristbands. In this study, participants wore daily wristbands, carried a phone that recorded locations, and answered daily questionnaires for a 7-day period in multiple seasons. We gathered publicly available daily PM2.5 AQI data and HMS data. We analyzed wristbands for 94 organic chemicals, including 53 polycyclic aromatic hydrocarbons. Wristband chemical detections and concentrations, behavioral variables (e.g., time spent indoors), and environmental conditions (e.g., PM2.5 AQI) significantly differed between seasons. Machine learning models were fit to predict personal chemical exposure using PM2.5 AQI only, HMS only, and a multivariate feature set including PM2.5 AQI, HMS, and other environmental and behavioral information. On average, the multivariate models increased predictive accuracy by approximately 70% compared to either the AQI model or the HMS model for all chemicals modeled. This study provides evidence that PM2.5 AQI data alone or HMS data alone is insufficient to explain personal chemical exposures. Our results identify additional key predictors of personal chemical exposure.

Forecasting PM10 levels in Sri Lanka: A comparative analysis of machine learning models PM10

Forecasting of particulate matter (PM10) which adversely impacts air quality is highly important in ever-urbanizing cities. The relationship between particulate matter and other air quality parameters and climatic parameters is frequently investigated due to their nonlinearity. Machine learning models have been extensively used in these nonlinear predictions and showcased their ability and robustness. However, among the tested machine learning models, a comparative analysis is essential in the localized context to understand the best model that can be used to forecast future scenarios. Therefore, this research investigates the applicability of eight state-of-the-art machine learning models (ANN, Bi-LSTM, Ensemble, XGBoost, CatBoost, LightGBM, LSTM, and GRU) in the prediction of particulate matter in two urbanized areas (Battaramulla and Kandy) Sri Lanka. Regression coefficient, Root Mean Squared Error, Mean Squared Error, Mean Absolute Error, Mean Absolute Relative Error, and Nash-Sutcliffe Efficiency were incorporated to assess the best-suited model for both cities. Results revealed that the Ensemble model has the capability of accurate and precise prediction of PM10 for both cities outperforming all other models (R2≈1). Therefore, the Ensemble model is recommended for future investigation of PM10 for Sri Lanka which has a growing concern due to high air pollution levels.

Predicting real-time within-vehicle air pollution exposure with mass-balance and machine learning approaches using on-road and air quality data

Modelling the air pollutant concentrations within-vehicles is an essential step to estimate our daily exposure to air pollution. This is a challenging issue however, since the processes that affect the exposures within-vehicles change with different driving patterns and ventilation settings. This study introduces an innovative approach that combines mass-balance principles and machine learning techniques, leveraging ambient air quality, on-road and within-vehicle measurements of particulate matter (PM10, PM2.5, PM1), nitrogen dioxide (NO2), nitrogen oxides (NOx), aerosol lung surface deposited area (LSDA) and ultrafine particles (UFP) under different ventilation settings to estimate air pollution exposure levels within vehicles. The first model (MB) includes basic physical and chemical processes and follows a mass-balance approach to estimate the within-vehicle concentrations. The second model (ML) applies data driven machine learning algorithms to a training set of observations to predict unseen within-vehicle concentrations. By using a number generator, the whole observational dataset was divided to 80:20 and 80% was used to build and train the ML model, while 20% was used for validation. Both models demonstrated good predictions of observations apart from an underestimation in UFP and LSDA. The ML model showed better predictive power than the MB model and had skill in predicting the unseen within-vehicle exposures. The ML model predictions were as good as the MB model for most of the species and improved for NO2. The ML model demonstrated good index of agreement (IOA >0.69) and Pearson correlation coefficient (r > 0.80) for all the species. The inclusion of air quality data from nearby monitoring stations instead of on-road (sampled while driving), in the ML model showed promising and new capabilities to within-vehicle exposure predictions. In an era where air pollution is a growing concern, understanding and predicting within-vehicle air pollution exposure is of great importance for public health and environmental research. This research not only advances the field of exposure assessment but (at no extra cost) also demonstrates practical implications for real-time exposure mapping and health impact assessment of vehicle occupants with existing infrastructure.

AirQo sensor kit: A particulate matter air quality sensing kit custom designed for low-resource settings

Air pollution remains a major public health risk. People living in urban spaces are among those most affected by exposure to unhealthy levels of air pollution. However, many urban spaces especially in low- and middle-income countries lack high resolution and long-term data on the state of air quality. Without high resolution air quality data on the different spaces in a city, citizens and authorities are unable to quantify the challenge and act. This is in part attributed to the high cost of air quality monitoring equipment that are expensive to set up, maintain and not designed for local operating conditions that characterise environments in such contexts. In this paper, we describe AirQo sensor kit, a low-cost sensing hardware system designed for and custom made to work in low-resource settings and outdoor urban environments. We describe the design of the air quality sensing device, 3D-printed enclosure, installation-mount, fabrication and deployment configurations. We demonstrate that the low-cost sensing hardware provides a complete solution comparable to the traditional monitoring system and inspires action to tackle air pollution issues. The sensor kit presented in this paper has been widely deployed in cities in Eastern, Western and Central African countries.

Multi-step ahead hourly forecasting of air quality indices in Australia: Application of an optimal time-varying decomposition-based ensemble deep learning algorithm

Recently, researchers have prioritized the accurate forecasting of the particulate matter (PM) air quality indicators PM2.5 and PM10 in urban and industrial locations due to their importance for the human health. However, accurate short-term forecasting via traditional data mining methods is limited due to the complex nature of these indices on hourly scale. To address this issue, a novel deep learning framework composed of classification and regression tree (CART) feature selection, time-varying filter-based empirical mode decomposition (TVF-EMD), and ensemble deep random vector functional link (DRVFL) scheme to forecast 1-h and 3-h ahead PM2.5 and PM10 in two different zones of Australia. In the first pre-processing phase, CART feature selection optimizes the antecedent information (lag) for each target using the computed important factor. Then, the original signal is decomposed into intrinsic mode functions (IMFs) and residual sub-components by the TVF-EMD to overcome the non-stationarity and complexity. The DRVFL approach is then executed by applying the significant lagged-time components as the optimal input feature via each decomposed sub-component. Finally, all the forecast values obtained using the sub-components are summed up to construct the PM values for each horizon. In addition, the classical RVFL and bidirectional gated recurrent unit neural network (Bi-GRU) models are adopted in the form of hybrid and corresponding standalone frameworks to validate the TVD-EMD-DRVFL. The results demonstrate that TVF-EMD-DRVFL model provides best preciseness with R, RMSE, MAPE, NSE, KGE, IA, U95%, and KGE at t+1 and t+3 to forecast PM2.5 and PM10 for Brisbane and North Parramatta station.

Coarse particulate matter air quality in East Asia: implications for fine particulate nitrate

Abstract. Air quality network data in China and South Korea show very high year-round mass concentrations of coarse particulate matter (PM), as inferred by the difference between PM10 and PM2.5. Coarse PM concentrations in 2015 averaged 52 µg m−3 in the North China Plain (NCP) and 23 µg m−3 in the Seoul Metropolitan Area (SMA), contributing nearly half of PM10. Strong daily correlations between coarse PM and carbon monoxide imply a dominant source from anthropogenic fugitive dust. Coarse PM concentrations in the NCP and the SMA decreased by 21 % from 2015 to 2019 and further dropped abruptly in 2020 due to COVID-19 reductions in construction and vehicle traffic. Anthropogenic coarse PM is generally not included in air quality models but scavenges nitric acid to suppress the formation of fine particulate nitrate, a major contributor to PM2.5 pollution. GEOS-Chem model simulation of surface and aircraft observations from the Korea–United States Air Quality (KORUS-AQ) campaign over the SMA in May–June 2016 shows that consideration of anthropogenic coarse PM largely resolves the previous model overestimate of fine particulate nitrate. The effect is smaller in the NCP which has a larger excess of ammonia. Model sensitivity simulations for 2015–2019 show that decreasing anthropogenic coarse PM directly increases PM2.5 nitrate in summer, offsetting 80 % the effect of nitrogen oxide and ammonia emission controls, while in winter the presence of coarse PM increases the sensitivity of PM2.5 nitrate to ammonia and sulfur dioxide emissions. Decreasing coarse PM helps to explain the lack of decrease in wintertime PM2.5 nitrate observed in the NCP and the SMA over the 2015–2021 period despite decreases in nitrogen oxide and ammonia emissions. Continuing decrease of fugitive dust pollution means that more stringent nitrogen oxide and ammonia emission controls will be required to successfully decrease PM2.5 nitrate.

Evaluating aerosols concentration and air quality of Indian urban agglomerations over nationwide and regional lockdown

In the past two years, numerous researches are done on how lockdowns across the world affected air quality; however, less research is done on how geolocation, geo-climate, anthropogenic activities had such impacts. National and regional shutdowns at various lockdown phases are crucial for evaluating such impacts. The goal of the current study was to examine the effects of geographic location, geo-climate, and human activity on Particulate Matter (PM10 and PM2.5) concentration and air quality about COVID-19-induced lockdowns of Mumbai and Delhi, two economically significant megacities in India with contrasting climatic conditions. Based on the incidents where restricted economic activity led to lockdowns, the importance of meteorological and economic activity in controlling air quality was evaluated. To understand spatial distribution, variability, trend, consistency analysis and National Air Quality Index (NAQI) of PM10 and PM2.5 data were collected from the Central Pollution Control Board (CPCB) monitoring system. To account for variation in various urban morphological units and gauge their responses, dispersion analysis was used. Kernel Density Estimation (KDE) plots were used to study how PM affected NAQI. From the analysis, it was clear that (1) nationwide lockdown was more fruitful in Delhi while regional lockdown in Mumbai since air quality improvement rate was substantially high in Mumbai in regional lockdown, 2021, (2) Mumbai city maritime location and long open sea coast help to reduce pollution levels while Delhi's continental location is crucial for being higher pollution levels, (3) control of meteorological factors was not found very significant, however, it was found relatively greater in Delhi, (4) air quality was found to be extremely poor in industrial and commercial areas during unlocking periods, but sensitively greater improvement was also found during lockdown periods, indicating crucial anthropogenic impact on air quality, and (5) rate of air quality improvement was highly correlated with different economic contributors. As a result, this study came to the conclusion that conditions with controlled demand can improve the air quality.

Particulate matter (PM2.5) characterization and air quality level in an urban background in Pretoria

Particulate matter (PM2.5) characterization and air quality level in an urban background in Pretoria

Particulate matter exposure analysis in 12 critical urban zones of Chennai, India.

This research paper examines the exposure to particulate matter (PM) and its deposition on the human respiratory tract (HRT) in 12 critical urban zones - institutional zone, commercial zone, construction zone, hospital zone, landfill zone, industrial zone, residential zone, high-traffic zone, main roads, medium-traffic zone secondary roads, low-traffic zone, coastal zone, and environmentally sensitive zone. This study measured the size-segregated PM concentrations using a Grimm aerosol spectrometer. The multiple-path particle dosimetry model assesses particles' total and regional deposition mass rates for different urban zones. A stochastic model of the 60th percentile is used to illustrate the deposition of PM in the human lung. The deposition rate of PM in the HRT is examined for the different urban zones from different emission sources. The analysis shows that the PM concentration in zone V (dumpsite zone) is at an elevated level (i.e., PM10 = 570.4µg/m3, PM2.5 = 128.3µg/m3, and PM1 = 28.1µg/m3) and lowest at zone XII (eco-sensitive zone) (i.e., PM10 = 25.1µg/m3, PM2.5 = 1 6.9µg/m3, and PM1 = 14.8µg/m3). Further, dumpsite, commercial, and eco-sensitive zones are identified to be critical zones that influence higher deposition in the tracheobronchial and pulmonary regions. The investigation concludes that local turbulence and emission source significantly impacts air quality and deposition of PM at HRT. In addition, as the PM diameter decreases, the acidity of PM increases, and it can penetrate deep into the lower airways. Since this can have profound consequences, it is imperative to better understand the deposition of PM across various urban zones.

And then came this number PM2.5: Atmospheric particulate matter, sociotechnical imaginaries, and the politics of air quality data

This article employs the concept of sociotechnical imaginaries to examine the broader political ecology of particulate air pollution, especially the connections between novel technoscientific practices of making pollution perceptible and data-driven modes of urban governance. Drawing on ethnographic material from Beijing, a city notorious for its pervasive smog, the article highlights the role of air quality data—such as particulate matter measurements (PM2.5) and air quality indexes (AQI)—not only in informing citizens of the health-threatening air pollution but also in influencing new regimes of visibility and shaping practices of urban citizenship. Through the conceptual lens of sociotechnical imaginaries, the article shows that air quality data, increasingly shared digitally in the form of real-time continuous data streams, are not merely technoscientific indexes, nor are they value-neutral. Rather, their conceptualization is informed by technocractic visions of human-air relations and shaped by digital infrastructures. Learning from Beijing, the article argues that in order to generate an expanded understanding of the life-structuring effects of urban air pollution in the era of data-driven urbanism, political ecology of urban air needs to be considerate of how the numbers, which represent pollution, are conceptualized, amassed, and circulated, as well as what and whom they include or exclude.

Laboratory Determination of Gravimetric Correction Factors for Real-time Area Measurements of Electronic Cigarette Aerosols.

Research on secondhand electronic cigarette (ECIG) aerosol exposure using aerosol monitors has demonstrated that ECIG use can generate high concentrations of particulate matter (PM) and impact indoor air quality. However, quantifying indoor air PM concentrations using real-time optical monitors with on-site calibration specifically for different PM exposures has not been established. Therefore, the ECIG aerosol filter correction factors were calculated for different PM sizes (PM1, PM2.5, and PM10) and different aerosol optical monitors, the MiniWRAS, pDR, and SidePak. ECIG aerosol generation was achieved using five ECIGs representing three ECIG types, disposable, pod-mod, and box mod. The aerosol size distribution by mass was measured for the five ECIGs during PM generation. Compared to the discrete filter measurements, the MiniWRAS performed the best when the concentrations were low, followed by the pDR and SidePak. The average PM concentrations and correction factor ranges for the different ECIGs were 323-1,775 μg/m3 and 0.64-6.01 for the MiniWRAS, 1,388-13,365 μg/m3 and 0.41-0.80 for the pDR, and 4,632-55,339 μg/m3 and 0.13-0.20 for the SidePak, respectively. The mass median diameter ranged from 0.41 and 0.62 μm, and most particles generated from the ECIGs were smaller than 1 μm. This study demonstrates that aerosol size distribution varies between ECIGs. Likewise, the correction factors developed for the real-time aerosol monitors are specific to the ECIG used. Thus, these data can help improve ECIG aerosol exposure measurement accuracy.

Wildfire atmospheric chemistry: climate and air quality impacts

Wildfire atmospheric chemistry: climate and air quality impacts

Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations

Abstract. Geostationary satellite measurements of aerosol optical depth (AOD) over East Asia from the Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI) instruments can augment surface monitoring of fine particulate matter (PM2.5) air quality, but this requires better understanding of the AOD–PM2.5 relationship. Here we use the GEOS-Chem chemical transport model to analyze the critical variables determining the AOD–PM2.5 relationship over East Asia by simulation of observations from satellite, aircraft, and ground-based datasets. This includes the detailed vertical aerosol profiling over South Korea from the KORUS-AQ aircraft campaign (May–June 2016) with concurrent ground-based PM2.5 composition, PM10, and AERONET AOD measurements. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate–nitrate–ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and high hygroscopicity of the PBL aerosols. We updated SNA and organic aerosol size distributions in GEOS-Chem to represent aerosol optical properties over East Asia by using in situ measurements of particle size distributions from KORUS-AQ. We find that SNA and organic aerosols over East Asia have larger size (number median radius of 0.11 µm with geometric standard deviation of 1.4) and 20 % larger mass extinction efficiency as compared to aerosols over North America (default setting in GEOS-Chem). Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM2.5 nitrate. The GOCI–AHI AOD data over East Asia in different seasons show agreement with AERONET AODs and a spatial distribution consistent with surface PM2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM2.5. This is due to low PBL depths compounded by high residential coal emissions in winter and high relative humidity (RH) in summer. Seasonality of AOD and PM2.5 over South Korea is much weaker, reflecting weaker variation in PBL depth and lack of residential coal emissions.

The current status and future needs of global bioaerosol research: a bibliometric analysis.

A bibliometric analysis was conducted to reveal the global status and highlight significant or promising areas of bioaerosol research based on the Web of Science database from 1989 to 2019. Yearly publications, main subject categories, journals, the performance of countries, and research hot topics were identified. The network of keywords and collaborations of countries was visualized and cross relationships were determined. Results showed that the annual output in the field increased during the related period. The USA, China, and Germany are the leading countries while the USA, Germany, and the UK are the most collaborative countries in bioaerosol research. “Journal of Aerosol Science” is the most productive journal and “Environmental Sciences & Ecology” is the most popular research area. The research hot spots are health effects, sampling, particulate matter, and indoor air quality in the bioaerosol topic. The findings of this research could provide information to understand the development and trends as well as future needs of bioaerosol research.

A study on students’ perceptions of air pollution, particulate matter, and indoor air quality in classrooms

A study on students’ perceptions of air pollution, particulate matter, and indoor air quality in classrooms

Effect of COVID-19 pandemic on ambient air quality and excess risk of particulate matter in Turkey

The COVID-19 pandemic, which has reached 4 million global cases as of March 10, 2020, has become a worldwide problem. Turkey is one of the most affected (9th in the world) country with 139 771 cases. An intermittent curfew policy that differ for three age groups, and an intercity travel ban varying within the country have been implemented. The effects of changes in social life and industrial activity in terms of environmental pollution are not yet known. The short-term effects on PM2.5, PM10, SO2, NO2, NO, NOx, O3 and CO concentrations measured at 51 air quality measurement stations (AQMS) in 11 cities in March – April period of 2020 were statistically compared with that of the previous year. While PM2.5 (9/14 AQMS) and PM10 (29/35 AQMS) concentrations were not significantly affected, NO (12/24 AQMS), NO2 (20/29 AQMS), NOX (17/25 AQMS) concentrations were decreased, SO2 concentrations at half of the AQMSs (11/25) did not show a significant change. There were stations at which higher pollutant concentrations were measured in the study period in 2020 compared to that of 2019. Excess risks associated with PM2.5 and PM10 were estimated to be variable, albeit with a small difference. In conclusion, the heterogeneous actions taken in response to the COVID-19 pandemic resulted in mixed effects on ambient air quality.