Air Quality Management Strategies Research Articles

Advancing air quality forecasting in Abu Dhabi, UAE using time series models

This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi’s Vision 2030.

Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM2.5) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Assessing Tunisia's urban air quality using combined lichens and Sentinel-5 satellite integration.

In Tunisia, urban air pollution is becoming a bigger problem. This study used a combined strategy of biomonitoring with lichens and satellite mapping with Sentinel-5 satellite data processed in Google Earth Engine (GEE) to assess the air quality over metropolitan Tunis. Lichen diversity was surveyed across the green spaces of the Faculty of Science of Tunisia sites, revealing 15 species with a predominance of pollution-tolerant genera. The Index of Atmospheric Purity (IAP) calculated from the lichen data indicated poor air quality. Spatial patterns of pollutants sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and aerosol index across Greater Tunis were analyzed from Sentinel-5 datasets on the GEE platform. The higher values of these indices in the research area indicate that it may be impacted by industrial activity and highlight the considerable role that vehicle traffic plays in air pollution. The results of the IAP, IBL, and the combined ground-based biomonitoring and satellite mapping techniques confirm poor air quality and an environment affected by atmospheric pollutants which will enable proactive air quality management strategies to be put in place in Tunisia's rapidly expanding cities.

Analysis and Trends Research Publications on Air Quality (PM 2.5) Management Strategies

The study was conducted to analyze the trends in the development of research in the field of air quality management from 1983 to 2022. The study aims to find out the developments of scientific publications from year to year, the authors who dominate, the dominant countries, and the opportunities for innovation in research topics related to air quality strategy. Data collection was done by searching through Scopus with the keywords management OR strategy OR monitoring AND “air pollution” OR “air quality” AND “particulate matter” OR pm OR pm10 OR pm AND 2,5 with the category, article title, abstract, and keywords. The data is analyzed in terms of the number of publications per year, authors, origin of authors, and subjects with Ms. Excel. Whereas trends in publication development are analyzed using software such as biblioshiny and vosviewer. The results of the study show that in the period 1983-2022, research on air quality management strategies has undergone significant progress. The highest publication development began in 2019, with the production of over 100 annual scientific articles. Some of the main authors are Zhang Y, Wang Y, Wang X, Zhang X, Liu Y, and Wang J. The results of this study revealed gaps in spatial distribution where most of the research was conducted in China, the USA, Canada, the United Kingdom, India, and Spain, as well as some European countries. However, it could be an opportunity for researchers from lower income countries like Indonesia to do similar research. Topics that have not been much discussed and studied are topics about mapping, spatial analysis, modelling of air pollution, and dynamic systems for air quality management strategies.

3-D AIR POLLUTION ESTIMATION USING A HYBRID SPATIAL MODEL: A CASE STUDY OF ZHUNAN-MIAOLI AREA, TAIWAN

Abstract. The rapid global urbanization has transformed cityscapes, giving rise to iconic skyscrapers that define modern cities. However, alongside this urban evolution, a pressing concern arises –the air quality within these towering urban environments. Fine particulate matter, known as PM2.5, poses a grave threat to human health and the environment. These tiny particles, measuring 2.5 micrometers or less, can penetrate deep into the human respiratory system, posing severe health risks. Due to the limitations of traditional land-use regression models in estimating the variation of air pollution with altitude, this study employs a novel hybrid spatial model to assess the three-dimensional distribution of PM2.5 in the atmosphere.We employ a comprehensive methodology, integrating diverse datasets and advanced modelling techniques, to uncover significant findings. Our analysis reveals the non-uniform nature of PM2.5 distribution, both horizontally and vertically. Variable selection identifies key factors influencing PM2.5 levels, including Broadleaf Forest, Carbon Monoxide (CO), and Height. Our ensemble model demonstrates robust performance, with Gradient Boosting Regression (GBR) and Random Forest Regression (RFR) exhibiting superior predictive capabilities. This study provides valuable insights into the complex interplay of environmental factors affecting PM2.5 concentrations in high-rise urban environments, emphasizing the need for targeted air quality management strategies considering both horizontal and vertical variations.

Analyzing Air Pollution in Urban Delhi: A Quantitative Assessment of Particulate Matter (PM) Concentrations

Air pollution poses significant challenges to urban environments, with particulate matter (PM) concentrations being a major concern due to their adverse effects on public health and the environment. This research paper aims to quantitatively assess PM concentrations in urban Delhi, focusing on spatial, temporal, and seasonal variations across diverse urban microenvironments. The research design involved mobile air quality monitoring using real-time PM sensors mounted on a vehicle, coupled with Geographic Information System (GIS) analysis to map pollution hotspots and assess correlations with urban features. The key findings of the study reveal significant spatial variability in PM concentrations, with industrial zones and busy intersections emerging as hotspots for pollution. Seasonal variations in PM levels, with elevated concentrations during winter months and lower levels during the monsoon season, underscore the influence of meteorological factors on air quality dynamics. The study also identifies correlations between PM concentrations and respiratory disease cases, highlighting the adverse health effects of air pollution. The implications of these findings extend to air quality management strategies and public health interventions, emphasizing the need for targeted measures to mitigate pollution levels and protect public health in urban environments.

IOT Based Air Quality Monitoring System Using Arduino

In the present time, more and more people are becoming concerned about air quality because of its negative impact on health and the environment. It is important that we have to monitor the levels of pollution in the air, temperature, and humidity to understand how clean. The paper discusses how to design and implement an air monitoring system using Arduino technology. Hence, this system enables users to have knowledge on their air condition. The ability for real time surveillance will enable quick response to changes in levels of pollution thereby helping people living around such areas together with authority take appropriate measures that will reduce health hazards as well as impacts caused by environmental problems. Moreover, it can be used as a tool for collecting data hence acting as a resource for data collection analysis, and interpretation which can assist planners or policy makers come up with informed decisions regarding the air quality management strategies. In general terms therefore, it is economical and easily accessible way for assessing pollution in air temperatures as well as moisture content humidity among others hence contributing towards public safety and preservation of nature from pollution. Key Words: Air Quality, Temperature, Humidity, IOT, Arduino UNO

Air pollution concentration fuzzy evaluation based on evidence theory and the K-nearest neighbor algorithm

Background: Air pollution, characterized by complex spatiotemporal dynamics and inherent uncertainty, poses significant challenges in accurate air quality prediction, and current methodologies often fail to adequately address these complexities.Objective: This study presents a novel fuzzy modeling approach for estimating air pollution concentrations.Methods: This fuzzy evaluation method integrates an improved evidence theory with comprehensive weighting and the K-nearest neighbor (KNN) interval distance within the framework of the matter-element extension model. This involves generating the basic probability assignment (BPA) based on interval similarity, performing sequential fusion using the Dempster–Shafer evidence theory, enhancing the fusion results via comprehensive weighting, and conducting fuzzy evaluation of air pollution concentrations using the matter-element extension KNN interval distance.Results: Our method achieved significant improvements in monitoring air pollution concentrations, incorporating spatiotemporal factors and pollutant concentrations more effectively than existing methods. Implementing sequential fusion and subjective–objective weighting reduced the error rate by 38% relative to alternative methods.Discussion: Fusion of multi-source air pollution data via this method effectively mitigates inherent uncertainty and enhances the accuracy of the KNN method. It produces more comprehensive air pollution concentration fusion results, improving accuracy by considering spatiotemporal correlation, toxicity, and pollution levels. Compared to traditional air-quality indices, our approach achieves greater accuracy and better interpretability, making it possible to develop more effective air quality management strategies. Future research should focus on expanding the dataset to include more diverse geographical and meteorological conditions, further refining the model to integrate external factors like meteorological data and regional industrial activity, and improving computational efficiency for real-time applications.

Estimation of historical daily PM2.5 concentrations for three Chinese megacities: Insight into the socioeconomic factors affecting PM2.5

Studying historical changes in the 2.5 μm particulate matter concentration (PM2.5) can clarify the relationship between air pollution and socioeconomic development. Daily PM2.5 levels and meteorological data (2012–2022) for three large cities (Beijing, Shanghai, and Xi'an) at different development stages in different regions of China were used to construct a random forest (RF) model for estimating historical PM2.5 data for the period 1973–2011, a time period in which few measurements were made. The eigenvalue for visibility was the largest in the RF model; visibility explained 0.76–0.87 of the variance in PM2.5 for the three cities. The daily estimated PM2.5 was validated, with an R2 of 0.654–0.780 and average absolute error of 11.52–31.73 μg m−3 in the model. PM2.5 concentrations predicted by the RF model for 2004–2011 were highly correlated with gravimetric measurements (R = 0.585, p < 0.01). We extensively validated the results of RF using manual weighing PM2.5 data, online monitoring concentration of PM10, and aerosol optical depth (AOD), demonstrating the accuracy of the model. Over the study period, the PM2.5 level first increased and then decreased in the three cities; however, the year at which the trend changed differed. We further explored the effects of urbanization and economic growth on PM2.5 levels by investigating the correlations between socioeconomic indicators and PM2.5. The magnitude of the permanent population of Beijing and gross regional production growth in Shanghai were both significantly positively correlated with the PM2.5 level. Increasing the size of urban green areas can reduce PM2.5; this effect was strongest for the southern city of Shanghai, may due to their different climates and green tree species. Energy consumption and emissions from primary industries were strongly positively correlated with the urban PM2.5 level. An in-depth understanding of the factors affecting PM2.5 concentrations could help policymakers improve air quality management strategies, especially for densely populated megacities.

Air Quality Index Prediction Using Machine Learning

Air pollution poses a significant threat to public health, particularly in rapidly developing countries like India, where urbanization and industrialization contribute to diverse sources of pollutants. This research employs machine learning techniques to comprehensively analyse and predict air quality levels across different regions of India. Extensive air quality data, encompassing various pollutants and temporal variations, is collected and compiled. State-ofthe-art machine learning models are developed to identify key influencing factors, including industrial emissions, vehicular traffic, agricultural practices, and meteorological conditions. The models are evaluated for accuracy and reliability in predicting air quality levels. The study aims to provide valuable insights into the complex dynamics of air pollution in the Indian context. By understanding the contributing factors and predicting future air quality levels, this research contributes to the development of targeted and effective air quality management strategies. Keywords: Machine Learning, Air Pollution, AQI.

Can aerosol optical depth unlock the future of air quality monitoring and lung cancer prevention?

AbstractThis “Policy Brief” explores the potential integration of Aerosol Optical Depth (AOD) into the United Kingdom’s air quality and public health monitoring frameworks, highlighting its potential to enhance existing air pollution control strategies. Amid growing concerns over air pollution’s impact on health, particularly the link between particulate matter and lung cancer, this brief presents a focused investigation into how AOD can be leveraged alongside traditional monitoring methods to provide a more nuanced understanding of air quality trends. By correlating AOD data with lung cancer incidence rates within the UK, the brief aims to uncover potential associations and inform public health decisions. Furthermore, it discusses the advantages and limitations of employing AOD in air pollution and respiratory disease monitoring, advocating for a strategic enhancement of the UK's air pollution monitoring efforts. This approach seeks to complement and refine current monitoring practices with advanced remote sensing techniques, aiming to inform policy innovations that prioritize environmental health and public welfare. Through a comprehensive review of existing data and policies, the brief underscores the urgency of adopting multidimensional air quality management strategies that respond to technological advancements and emerging public health needs.

Integrating satellite and model data to explore spatial-temporal changes in aerosol optical properties and their meteorological relationships in northwest India

This study aims to analyze the temporal and spatial distribution of Aerosol Optical Properties across Northwest India using aerosol data from MODIS (Moderate Resolution Imaging Spectroradiometer) and OMI (Ozone Monitoring Instrument) sensors from 2003 to 2022. Therefore, this study investigated the decadal, interannual, and seasonal changes in aerosol optical properties, vegetation index, and meteorological parameters in the northwest Indian region (8 boxes). Using GIOVANNI (Goddard Earth Sciences Data and Information Services Center (GES DISC) Online Visualization and Analysis Infrastructure), we retrieved daily and monthly Aqua and Terra MODIS products of aerosol optical depth (AOD), Angstrom exponent (AE), normalized difference vegetation index (NDVI), and OMI aerosol index (AI) to examine the spatiotemporal variations by using statistical approaches. The results demonstrated that the decadal averages of aerosol properties showed values of AOD 0.35 (Aqua) and 0.34 (Terra) and AE 1.20 (Aqua) and 1.10 (Terra) with the highest levels during the post-monsoon. Notably, the mean interannual concentrations of AOD and NDVI consistently surpass 0.3, and AE and AI exceed 1 in most locations, underscoring the persistence of high aerosol loading. Also, the study revealed a negative decadal change in AOD of about −8.24 %, while AE, AI, and NDVI showed positive decadal changes of about 9.24 %, 15.09 %, and 12.67 %, respectively. In addition, aerosol optical properties and local meteorology strongly correlated (−0.8 to +0.8). Principal Component Analysis (PCA) identifies meteorological parameters as significant drivers, with the first three components explaining over 70 % of the variation in aerosol optical properties. The NOAA HYSPLIT trajectory model suggests that the long-distance dust transport from the Arabian Peninsula frequently penetrates Gujarat province and then to northwest India. The results contributed to air quality management strategies and provided valuable insights into regional climate and air quality with the influence of meteorology.

An integrated strategy for air quality monitoring and management in industrial port areas

An integrated strategy for air quality monitoring and management in industrial port areas

A Comprehensive Analysis of Risk Assessment of Particulate Matter in Five Health Centers in Akure, Nigeria

This study presents a comprehensive assessment of indoor particulate matter (PM) concentrations, focusing on PM1, PM2.5, and PM10 in five primary health centers (PHCs): Arakale (Urban), the Federal College of Agriculture (FECA) (Urban), Iju (semi-urban), Oba-Ile (semi-urban), and Owode (Rural) within Akure local government areas in Nigeria. The primary novelty of this research lies in its detailed exploration of the toxicity potential of these PM fractions, providing valuable insights into the local air quality and associated health risks. This study assesses the health implications by calculating the toxicity potential (TP), relative risk (RR), excess risk (ER), and attributable fractions (Afs) for cardiopulmonary and lung cancer mortality. The results reveal varying RR values for all-cause mortality, with Arakale showing a slight elevation (RR: 1.061), indicating potential health risks. ER values for cardiopulmonary mortality range from 14.728 ± 7.25 to 19.04 ± 0.38, emphasizing the substantial excess risk associated with long-term PM2.5 exposure. The study also uncovers Afs for cardiopulmonary mortality ranging between 11.03 ± 0.31 and 19.22 ± 0.04, underscoring the significant contribution of PM to cardiovascular and respiratory health risks. Similarly, Afs for lung cancer mortality range from 10.03 ± 0.012 to 17.13 ± 0.10, highlighting the substantial association between PM2.5 exposure and lung cancer risk. These findings underscore the urgent need for targeted air quality management strategies and public health interventions in the studied locations to mitigate the heightened health risks associated with particulate matter pollution. The calculated RR, ER, and AF values offer crucial insights into the complex relationship between PM exposure and adverse health outcomes, providing a foundation for informed decision-making and future research endeavors.

Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model

Ambient particulate matter (PM2.5 and PM10), has been extensively monitored in numerous urban areas across the globe. Over the past decade, there has been a significant improvement in PM2.5 air quality, while improvements in PM10 levels have been comparatively modest, primarily due to the limited reduction in coarse particle (PM2.5–10) pollution. Unlike PM2.5, PM2.5–10 predominantly originates from local emissions and is often characterized by pronounced spatial heterogeneity. In this study, we utilized over one million data points on PM concentrations, collected from >100 monitoring sites within a Chinese megacity, to perform spatial source apportionment of PM2.5–10. Despite the widespread availability of such data, it has rarely been employed for this purpose. We employed an enhanced positive matrix factorization approach, capable of handling large datasets, in conjunction with a Bayesian multivariate receptor model to deduce spatial source impacts. Four primary sources were successfully identified and interpreted, including residential burning, industrial processes, road dust, and meteorology-related sources. This interpretation was supported by a considerable body of prior knowledge concerning emission sources, which is usually unavailable in most cases. The methodology proposed in this study demonstrates significant potential for generalization to other regions, thereby contributing to the development of air quality management strategies.

In the line of fire: Analyzing burning impacts on air pollution and air quality in an Amazonian city, Brazil

Brazil is grappling with escalating wildfire incidents, particularly in the ecotone region of Cerrado-Amazon, attributed to climate change and anthropogenic factors. These wildfires significantly impact air quality, posing health and environmental risks to the population. This study aimed to assess the influence of wildfires on air quality in Sinop, a city located in Northern Mato Grosso, Brazil. The primary focus was on understanding the behavior of air pollutants during and after wildfire events. Air quality data were obtained from modeling dataset, while meteorological data from ground-based monitoring, and wildfire data from the National Institute of Spatial Research of Brazil. Statistical analyses were employed to investigate pollutant variations and their relationships with meteorological parameters and wildfire intensity indicators. The study revealed a significant impact of wildfires on pollutant concentrations, with ozone levels rising immediately after fires and remaining elevated for several days. The particulate matter (PM2.5 and PM10) concentrations increased a few days after fire events. Fire radiative power (FRP) correlated with O3 levels, suggesting it is an intensity indicator. Temperature exhibited a consistent positive correlation with all pollutants, highlighting its role in pollutant dynamics. The findings underscore the multifaceted relationship between wildfires, meteorological conditions, and pollutant concentrations pointing to the importance of holistic air quality management strategies in fire-prone regions. Such studies are crucial for guiding environmental policies, safeguarding public health, and advancing scientific understanding in the face of escalating wildfire challenges.

Identifying urban emission sources and their contribution to the oxidative potential of fine particulate matter (PM2.5) in Kuwait.

In this study, we investigated the seasonal variations, chemical composition, sources, and oxidative potential of ambient PM2.5 (particles with a diameter of less than 2.5 μm) in Kuwait City. The sampling campaign was conducted within the premises of Kuwait Institute for Scientific Research from June 2022 to May 2023, covering different seasons throughout the year. The personal cascade impactor sampler (PCIS) operated at flow rate of 9 L/min was employed to collect weekly PM2.5 samples on PTFE and quarts filters. These collected samples were analyzed for carbonaceous species (i.e., elemental and organic carbon), metals and transition elements, inorganic ions, and DTT (dithiothreitol) redox activity. Furthermore, principal component analysis (PCA) and multi-linear regression (MLR) were used to identify the predominant emission sources and their percentage contribution to the redox activity of PM2.5 in Kuwait. The results of this study highlighted that the annual-averaged ambient PM2.5 mass concentrations in Kuwait (59.9 μg/m3) substantially exceeded the World Health Organization (WHO) guideline of 10 μg/m3. Additionally, the summer season displayed the highest PM2.5 mass concentration (75.2 μg/m3) compared to other seasons, primarily due to frequent dust events exacerbated by high-speed winds. The PCA identified four primary PM2.5 sources: mineral dust, fossil fuel combustion, road traffic, and secondary aerosols. The mineral dust was found to be the predominant source, contributing 36.1% to the PM2.5 mass, followed by fossil fuel combustion and traffic emissions with contributions of 23.7% and 20.3%, respectively. The findings of MLR revealed that road traffic was the most significant contributor to PM2.5 oxidative potential, accounting for 47% of the total DTT activity. In conclusion, this comprehensive investigation provides essential insights into the sources and health implications of PM2.5 in Kuwait, underscoring the critical need for effective air quality management strategies to mitigate the impacts of particulate pollution in the region.

Quantifying the impact of lockdown measures on air pollution levels: A comparative study of Bhopal and Adelaide

This research study presents an in-depth comparison of air quality in Bhopal, India, and Adelaide, Australia, focusing on the impact of COVID-19 restrictions. Utilizing air quality data from 2019 to 2022, the research analyzed the concentrations of pollutants like PM2.5, PM10, NO2, and O3, during pre-lockdown, lockdown, and post-lockdown periods. The findings demonstrate a significant reduction in PM2.5and PM10 levels during lockdown in cities such as Delhi and Haryana in India, and various Chinese cities, while also highlighting complex sources of air pollution like bushfires in regions like Sydney, Australia. In contrast, the study revealed nuanced trends in Bhopal and Adelaide, influenced by local geographical, climatic, and anthropogenic factors. Bhopal exhibited a notable decrease in PM10 and PM2.5levels, but inconsistent patterns in NO2 and CO, while Adelaide experienced marginal changes. The study emphasizes the temporary effectiveness of lockdowns and underscores the need for region-specific, sustainable air quality management strategies. Future implications include considerations for regional specificities, broader atmospheric chemistry, and international collaboration. The research provides valuable insights for urban air quality policy formulation, stressing a data-driven, long-term approach.

AIR QUALITY TESTING- A DESIGN THINKING APPROACH

This paper introduces a groundbreaking and holistic approach to revolutionize air quality testing by integrating design thinking principles, sensor technology, and the Internet of Things (IoT). Focused on addressing the critical challenge of air pollution, this research advocates for the development of user-centric, cost-effective, and scalable solutions for air quality monitoring. The design thinking process is comprehensively examined, covering crucial stages such as problem identification, ideation, prototype development, and user feedback. A central theme of this approach is the active involvement of stakeholders and cross- disciplinary collaboration, which ensures the creation of innovative and practical solutions. Real-world case studies underscore the tangible implications of this approach, emphasizing successful outcomes, including the development of affordable air quality monitoring devices and systems. Sensors and IoT technologies play pivotal roles in enabling real-time data collection, remote monitoring, and seamless integration with urban management systems, thereby contributing to enhanced environmental sustainability and public health. Additionally, this paper discusses the potential for adaptive air quality management strategies, wherein the data generated by these innovative monitoring systems can inform immediate response measures and long-term policy decisions. The adaptability and scalability of the proposed approach make it a valuable tool for addressing not only the challenges of today but also those of the future, such as climate change and evolving pollution sources.

Adverse Health Impacts of Outdoor Air Pollution, Including from Wildland Fires, in the United States: "Health of the Air," 2018-2020.

Rationale: Adverse health impacts from outdoor air pollution occur across the United States, but the magnitude of these impacts varies widely by geographic region. Ambient pollutant concentrations, emission sources, baseline health conditions, and population sizes and distributions are all important factors that need to be taken into account to quantify local health burdens. Objectives: To determine health impacts from ambient air pollution concentrations in the United States that exceed the levels recommended by the American Thoracic Society. Methods: Using a methodology that has been well established in previous "Health of the Air" reports, this study provides policy-relevant estimates for every monitored county and city in the United States for the adverse health impacts of outdoor pollution concentrations using U.S. Environmental Protection Agency design values for years 2018-2020. Additionally, for the first time, the report includes adverse birth outcomes as well as estimates of health impacts specifically attributable to wildland fires using an exposure dataset generated through Community Multiscale Air Quality simulations. Results: The adverse health burdens attributable to air pollution occur across the entire age spectrum, including adverse birth outcomes (10,660 preterm and/or low-weight births; 95% confidence interval [CI], 3,180-18,330), in addition to mortality impacts (21,300 avoidable deaths; 95% CI, 16,180-26,200), lung cancer incidence (3,000 new cases; 95% CI, 1,550-4,390), multiple types of cardiovascular and respiratory morbidity (748,660 events; 95% CI, 326,050-1,057,080), and adversely impacted days (52.4 million days; 95% CI, 7.9-92.4 million days). Two different estimates of mortality impacts from wildland fires were created based on assumptions regarding the underlying toxicity of particles from wildland fires (low estimate of 4,080 deaths, 95% CI, 240-7,890; middle estimate of 28,000 deaths, 95% CI, 27,300-28,700). Conclusions: This year's report identified sizable health benefits that would be expected to occur across the United States with compliance with more health-protective air quality standards such as those recommended by the American Thoracic Society. This study also indicates that a large number of excess deaths are attributable to emissions from wildland fires; air quality management strategies outside what is required by the Clean Air Act will be needed to best address this important source of air pollution and its associated health risks.