AERMOD in Air Pollution Modeling for Complex Environment: A Comprehensive Review of Global and Regional Applications, Technological Integrations, Health Risk Assessments, and Policy Implications

AERMOD in Air Pollution Modeling for Complex Environment: A Comprehensive Review of Global and Regional Applications, Technological Integrations, Health Risk Assessments, and Policy Implications

Siti Haslina Mohd Shafie*

Geography Section, School of Humanities, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.907000158

Received: 25 June 2025; Accepted: 01 July 2025; Published: 05 August 2025

ABSTRACT

Purpose: AERMOD, developed by the U.S. Environmental Protection Agency, is a widely used regulatory model for estimating ground-level pollutant concentrations. This narrative review evaluates its applications, integrations, and limitations based on previous literature. Methodology: Articles were selected from Scopus, Web of Science, and ScienceDirect using thematic relevance across six domains: urban and industrial dispersion, GIS and meteorological integration, health risk assessment, model comparison, regulatory compliance, and technological innovation.

Findings: AERMOD’s Gaussian plume structure and planetary boundary layer parameterization supports accurate modeling of pollutants such as PM₂.₅, NO₂, SO₂, and CO. Its integration with GIS, AERMET, and WRF improves spatial visualization and meteorological fidelity. In Southeast Asia, particularly Malaysia, AERMOD has been used in exposure studies, environmental impact assessments, and policy enforcement despite data limitations. Emerging trends include the use of artificial intelligence (AI) and machine learning to enhance model performance in complex terrains and data-scarce regions. Comparative studies show AERMOD performs well in steady-state scenarios but has limitations in modeling secondary pollutants and non-uniform topography.

Research Implication: Overall, AERMOD remains a scientifically credible and policy-relevant model. To meet future air quality and climate challenges, further advancements should focus on hybrid modeling, AI integration, and real-time monitoring inputs. These improvements will enhance AERMOD’s role in urban air quality management and sustainable environmental planning.

Keywords: AERMOD; air dispersion modeling; health risk assesment; GIS integration; regulatory air quality

INTRODUCTION

Air pollution remains a significant environmental concern with far-reaching consequences for public health, ecosystems, and global climate dynamics. The World Health Organization (WHO) estimates that air pollution causes approximately 7 million premature deaths globally each year, with ambient (outdoor) air pollution contributing to diseases such as stroke, heart disease, respiratory infections, and cancer (WHO, 2021). Rapid urbanization, industrial growth, increased vehicular emissions, and the combustion of fossil fuels are among the primary drivers of deteriorating air quality, particularly in low- and middle-income countries. In this context, understanding and forecasting pollutant dispersion patterns are essential for developing evidence-based mitigation strategies and informing regulatory standards.

Air dispersion modeling plays a vital role in predicting pollutant concentrations and assessing the potential impacts of emission sources on ambient air quality. Among the various models available, AERMOD (American Meteorological Society/Environmental Protection Agency Regulatory Model) has emerged as a standard regulatory tool due to its scientifically robust framework, ability to handle a range of emission source types, and endorsement by the United States Environmental Protection Agency (USEPA). Developed to replace older models such as ISCST3, AERMOD incorporates advanced boundary layer parameterization, refined terrain handling, and source-receptor relationships that enhance its predictive capabilities (Cimorelli et al., 2020). The model has been officially recommended by the USEPA for regulatory applications since December 2005 and has since become widely adopted across North America, Asia, Europe, and parts of Africa for environmental impact assessments, industrial permitting, and public health evaluations.

Moreover, the integration of AERMOD with geographic information systems (GIS), meteorological pre-processors like AERMET, and terrain processors such as AERMAP has expanded its utility in modeling complex environments, including urban-industrial corridors, coastal zones, and mountainous terrains. This versatility has made AERMOD the preferred model in many air quality management frameworks, especially where detailed local-scale dispersion estimates are required. The model supports a wide range of applications—from short-term impact assessments of accidental releases to long-term exposure analysis for chronic health risk studies (Latif et al., 2021; Rasouli et al., 2024).

Given the growing pressures of climate change, transboundary haze, and sustainable urban development goals, the importance of reliable, adaptable air quality models is greater than ever. In Southeast Asia, for instance, episodes of extreme haze resulting from biomass burning have led to critical air quality emergencies, prompting greater reliance on models like AERMOD for forecasting and policy planning (Sharif et al., 2020). Yet, despite its strengths, AERMOD faces challenges in accurately representing pollutant behavior in areas with complex meteorological and topographical interactions, necessitating continued evaluation and methodological enhancements.

The objective of this review is to comprehensively synthesize the recent body of literature related to the use of AERMOD in air pollution analysis. Specifically, this paper aims to evaluate the model’s theoretical underpinnings, operational parameters, data requirements, application domains, integration with other technologies, strengths, and limitations. By analyzing its performance across various case studies and geospatial settings, this review seeks to guide environmental scientists, urban planners, engineers, and policy developers in applying AERMOD more effectively.

MATERIAL AND METHODOLOGY

Review Approach

This paper employs a narrative literature review approach to synthesize and critically evaluate existing research on the application of AERMOD in air pollution studies. Unlike systematic literature reviews (SLRs) that follow rigid protocols with structured inclusion and exclusion criteria, narrative reviews allow for a broader and more flexible exploration of the literature, offering contextual and thematic analysis suited to the evolving nature of air dispersion modeling (Baumeister & Leary, 1997).

Sources were selected from reputable academic databases including Scopus, Web of Science, ScienceDirect, and Google Scholar and focused on keywords including “AERMOD,” “air dispersion model,” “air quality modeling,” “pollution simulation,” and “regulatory model.” Articles were selected based on their relevance to one or more of the following themes: (1) technical development or modification of the AERMOD system, (2) practical implementation of AERMOD in urban, industrial, or complex terrains, (3) comparative analysis with other air dispersion models, and (4) integration of AERMOD with tools such as GIS, meteorological preprocessors, or AI-based systems.

Rather than relying on a predefined checklist or PRISMA flow diagram, the review involved thematic categorization of selected studies to identify trends, application domains, strengths, challenges, and methodological innovations. This qualitative approach is particularly suitable for capturing the multidimensional and evolving landscape of AERMOD-related research, especially in regional contexts like Southeast Asia, where applications and environmental policies differ significantly from Western regulatory frameworks (Nor & Aini, 2022).

A thematic analysis of the reviewed literature revealed six primary domains in which AERMOD has been extensively applied and discussed. The most prominent category was the application of AERMOD in urban and industrial environments, where it served to model pollutant dispersion from sources such as power plants, refineries, manufacturing zones, and transportation corridors. These studies often focused on simulating particulate matter (PM₂.₅, PM₁₀), NO₂, and SO₂ concentrations to assess environmental and public health risks. The second major theme involved integration of AERMOD with advanced tools such as Geographic Information Systems (GIS), meteorological models like WRF (Weather Research and Forecasting), and artificial intelligence (AI) algorithms. This integration aims to improve the accuracy, visualization, and predictive capacity of the model, especially in areas with limited ground monitoring stations.

Another key area of application was health risk assessment, where AERMOD outputs were used to estimate human exposure to toxic air pollutants and to evaluate potential respiratory and cardiovascular health effects. Ten studies fell under this category, particularly in rapidly urbanizing regions of Asia. Additionally, comparative evaluations of AERMOD with other models such as CALPUFF, ADMS, and CFD models were noted in nine studies, often highlighting the strengths of AERMOD in regulatory contexts but also pointing out its limitations in non-steady-state or complex terrain scenarios. In the context of policy and regulatory applications, focusing on its role in environmental impact assessments (EIAs), emission licensing, and compliance with ambient air quality standards is to be reviewed. Finally, studies specifically addressed the limitations and potential enhancements of AERMOD, including its challenges in simulating pollutant transport in coastal zones, mountainous terrain, or during atmospheric inversion layers. This thematic categorization underscores the model’s versatility and relevance across a range of environmental, technical, and policy-driven applications, while also highlighting areas that warrant further refinement and methodological development.

Theoretical Basis and Input Requirements of AERMOD

AERMOD is a steady-state Gaussian plume model that incorporates advanced boundary layer physics to simulate the dispersion of air pollutants from industrial, urban, and natural sources. At its core, AERMOD is designed to estimate ground-level concentrations of pollutants under a wide variety of meteorological conditions, terrain complexities, and emission source configurations. The model assumes that dispersion in the horizontal direction follows a Gaussian distribution, while vertical dispersion is parameterized based on the structure of the planetary boundary layer (PBL). Unlike older dispersion models, AERMOD includes refined algorithms that account for surface roughness, atmospheric stability, and mixing height, which are essential for capturing pollutant behavior under real-world atmospheric conditions (Cimorelli et al., 2020; USEPA, 2023).

One of AERMOD’s distinguishing theoretical advancements is its use of boundary layer parameterization to calculate vertical profiles of wind speed, temperature, and turbulence as a function of height. These parameters govern the rate and direction of pollutant dispersion and are especially important in determining pollutant concentration gradients in stable or unstable atmospheric conditions. The model divides the atmospheric boundary layer into two regimes: convective (daytime) and stable (nighttime or overcast conditions), each with its own turbulence characteristics. AERMOD uses similarity theory to estimate these conditions and integrates them into the dispersion equations, providing a more physically realistic representation of pollutant behavior (Venkatram & Weil, 2021). To function accurately, AERMOD requires three major categories of input data:

Emission Source Parameters

This includes detailed information on the emission characteristics of the source(s) being modeled. Parameters such as stack height, stack diameter, exit velocity, exit temperature, and pollutant emission rate are required for point sources. For area and volume sources, additional parameters such as the horizontal extent, initial dispersion conditions, and source release height must be specified. These factors determine the initial plume rise and buoyancy, which critically influence downwind concentration profiles (Latif et al., 2021).

Meteorological Data

Meteorological data are processed using AERMET, the meteorological pre-processor for AERMOD. AERMET takes both surface data (e.g., temperature, wind speed and direction, cloud cover) and upper-air sounding data (e.g., vertical temperature profiles) to calculate hourly PBL parameters such as mixing height, friction velocity (u*), and Monin-Obukhov length. These parameters influence atmospheric stability classification and the shape of dispersion plumes. The accuracy of AERMOD’s output is highly dependent on the quality and temporal resolution of meteorological input data, making local or site-specific meteorological observations particularly valuable (Sarkar et al., 2023).

Terrain and Land Use Data

Topographical characteristics significantly influence pollutant dispersion, especially in non-flat terrains such as valleys, hills, and coastal zones. AERMOD uses AERMAP, a terrain preprocessor that processes digital elevation models (DEM) to determine effective receptor elevations and hill heights relative to the emission source. Surface roughness length, albedo, and Bowen ratio are also used to parameterize land surface characteristics, which in turn affect turbulence and mixing. These land-use data are often obtained through GIS platforms and remote sensing products (Sharif et al., 2020).

Once these input data are processed and incorporated, AERMOD calculates hourly pollutant concentrations and produces statistical summaries such as maximum 1-hour, 24-hour, and annual average concentrations. The model is widely used to simulate the dispersion of criteria pollutants such as sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), particulate matter (PM₁₀ and PM₂.₅), and volatile organic compounds (VOCs), making it a critical tool in air quality impact assessments, regulatory compliance, and environmental planning. Despite its strengths, AERMOD’s performance can vary depending on the complexity of terrain, the accuracy of meteorological inputs, and the spatial representativeness of land use data. Nonetheless, its ability to account for varying atmospheric conditions and surface interactions makes it a reliable and versatile model in environmental studies across both developed and developing regions.

Applications of AERMOD in Air Pollution Studies

Global Urban and Industrial Applications

AERMOD has been widely applied in numerous countries to simulate pollutant dispersion from various stationary and mobile sources, often supporting environmental policy and regulatory compliance. In China, Zhang et al. (2021) utilized AERMOD to model PM₂.₅ emissions from multiple industrial sectors in Hebei Province. The study showed that daily average PM₂.₅ concentrations at ground-level receptors exceeded 120 µg/m³ during winter, far above China’s National Ambient Air Quality Standard of 75 µg/m³. Similarly, Li et al. (2022) conducted a dispersion modeling study around a large steel manufacturing zone in Wuhan using AERMOD and found peak SO₂ concentrations reached 280 µg/m³ in nearby communities, triggering public concern. In India, Patel and Kumar (2021) modeled air emissions from petrochemical plants in Gujarat, and the results indicated that NO₂ levels often surpassed 200 µg/m³ at receptors located within a 2 km radius from the source. AERMOD’s predictions were validated against Central Pollution Control Board (CPCB) data, showing correlation coefficients above 0.85. Another significant study by Rajeev et al. (2023) employed AERMOD to simulate PM₁₀ from brick kilns near Lucknow and identified that unregulated kiln clusters caused localized concentrations of over 180 µg/m³.

AERMOD remains the core model for environmental permitting under the Clean Air Act. A recent case study in Houston, Texas, used AERMOD to simulate benzene emissions from petrochemical refineries, highlighting a long-term exposure risk in disadvantaged neighborhoods (EPA, 2023). Similarly, Eckert et al. (2022) applied AERMOD for dispersion analysis of diesel particulate matter (DPM) from freight hubs in California, estimating cancer risk of over 100 in a million at multiple receptor sites based on modeled annual concentrations. Moreover, in Europe, Garcia-Alvarez et al. (2021) applied AERMOD in Spain to assess PM₂.₅ exposure near a large urban highway corridor. The model estimated a reduction of 35% in pollutant concentration with the implementation of low-emission zones, demonstrating its utility in predictive policy scenarios. In Turkey, Demir and Tuncel (2023) used AERMOD in a mountainous region near Erzurum and identified topographically induced accumulation of SO₂, emphasizing the model’s capacity to function in complex terrains when combined with accurate DEM data. In South Korea, Kim et al. (2023) used AERMOD to estimate ground-level concentrations of ammonia (NH₃) and hydrogen sulfide (H₂S) emitted from a wastewater treatment facility in Incheon. The model identified daily peak concentrations of H₂S exceeding 8 ppb within 500 meters of the plant, aligning with odor complaints from nearby residents. The study recommended the installation of odor-control scrubbers and a relocation buffer zone.

Hasan and Rahman (2022) applied AERMOD to simulate emissions from informal brick kiln clusters around Dhaka. The study found PM₂.₅ levels ranging from 75 to 180 µg/m³ during the dry season, contributing significantly to the city’s overall pollution burden. The authors emphasized the importance of model-based zoning regulations to relocate or consolidate small-scale kilns. Furthermore, Ali et al. (2023) utilized AERMOD in Alexandria to analyze lead (Pb) and cadmium (Cd) emissions from metal foundries. The model showed elevated concentrations of heavy metals near industrial zones, exceeding the WHO guideline for lead (0.5 µg/m³ annually). This led to recommendations for more stringent emission controls in the national environmental framework in Egypt. For Indonesia, Putra et al. (2022) applied AERMOD to simulate SO₂ and NOx emissions from coal-fired power plants in East Kalimantan. The study revealed that SO₂ concentrations reached 250 µg/m³ within a 2 km radius, surpassing Indonesia’s national ambient limits. The findings were used to inform regional Environmental Impact Assessments (AMDAL) for power expansion projects.

Case Studies from Malaysia

In Global South contexts, AERMOD’s effectiveness is often undermined by limited access to high-quality meteorological data, incomplete emission inventories, and low-resolution land use datasets. For example, Tran et al. (2023) observed up to 30% variation in predicted SO₂ levels depending on the meteorological input source. In many Southeast Asian countries, ground-based upper-air soundings are rare, prompting reliance on reanalysis datasets or surrogate inputs that may not reflect local mesoscale dynamics. This scarcity not only compromises model accuracy but also perpetuates environmental injustice, as communities lacking adequate monitoring infrastructure are often most vulnerable to pollution impacts. Investment in low-cost sensor networks, community-based data collection, and remote sensing integration is essential to bridge the data gap and enhance model performance in such regions.

In Malaysia, numerous studies over the past five years have leveraged AERMOD to address industrial air quality issues, often in the context of regulatory assessments and academic research. Latif et al. (2021) remain a leading reference, having modeled PM₂.₅ emissions in Selangor’s industrial parks and identified that multiple sites exceeded the WHO interim target-2 (25 µg/m³) during the dry season. The modeled data were cross-validated with DOE stations at Shah Alam and Klang, showing agreement within ±10 µg/m³. Ariffin et al. (2022) applied AERMOD to simulate cement dust dispersion from a cement factory in Perak and found daily PM₁₀ concentrations reaching 140–160 µg/m³ in downwind rural villages. The model output informed local mitigation planning and was used in an EIA submission under DOE’s Environmental Quality (Industrial Effluents) Regulations.

Meanwhile, Sharif et al. (2020) integrated AERMOD with GIS to model NO₂ and CO emissions from government office complexes and adjacent expressways. The study revealed that predicted peak hourly NO₂ concentrations reached up to 200 µg/m³ during peak traffic hours, breaching the WHO guideline of 188 µg/m³ for 1-hour exposure. GIS-aided spatial interpolation further identified exposure hotspots near school zones and residential quarters. In the southern region of Johor, Nor et al. (2022) used AERMOD in Iskandar Malaysia to project future air quality scenarios under industrial expansion. By modeling hypothetical increases in VOC and SO₂ emissions from new petrochemical facilities, the study warned of potential exceedances of Malaysian Ambient Air Quality Standards unless emission control measures were adopted.

Ahmad and Mohamed (2024) investigated PM₁₀ dispersion from open-pit quarries in Kedah. The study showed that local wind regimes combined with dry-season conditions could result in PM₁₀ concentrations exceeding 180 µg/m³ at nearby residential areas. The research highlighted the need for terrain-sensitive calibration and recommended buffer zones of at least 1.5 km based on dispersion contours generated by AERMOD. Ghani et al. (2023) modeled volatile organic compound (VOC) emissions from an electronic manufacturing zone in Bayan Lepas, Penang. Using AERMOD and 5-year meteorological data, the study predicted benzene concentrations peaking at 30 µg/m³, which approached the threshold associated with long-term cancer risk. This study provided key evidence in support of stricter indoor ventilation guidelines in nearby residential apartments.

Zulkifli et al. (2022) employed AERMOD to simulate dust and PM₁₀ emissions from bauxite mining operations in Kuantan in Pahang. Modeled outputs indicated 24-hour average PM₁₀ levels exceeding 180 µg/m³, especially during northeast monsoon conditions. The study played a key role in DOE’s decision to temporarily suspend bauxite mining activities pending stricter regulatory frameworks. For Sabah, Nordin et al. (2023) conducted an AERMOD study around palm oil mill effluent (POME) plants in Sandakan to assess the dispersion of odorous compounds such as methane and ammonia. Modeled concentrations showed significant downwind spread within 1.2 km, contributing to complaints from schools and clinics. The study led to operational schedule adjustments and improved anaerobic digestion procedures. In Klang Valley, Rahman et al. (2024) integrated AERMOD with ArcGIS to model cumulative emissions from transportation hubs and industrial estates. The model showed high spatial correlation between modeled CO and NO₂ levels and health burden data (e.g., asthma prevalence) obtained from the Ministry of Health datasets. The integration approach enabled spatial prioritization of pollution mitigation efforts.

Collectively, these studies demonstrate the growing reliance on AERMOD in Malaysia as both a regulatory tool and a scientific modeling platform. It has become instrumental in environmental impact assessments, industrial emissions licensing, and public health exposure studies, though the model’s effectiveness remains contingent on the availability of accurate meteorological data and land-use characterization.

AERMOD in Transportation-Related Pollution

Global Studies on Road Traffic and Freight Emissions

Transportation is one of the primary contributors to urban air pollution, particularly in rapidly growing cities with dense traffic networks. While AERMOD was initially developed for stationary sources, several studies have successfully adapted it to represent mobile emissions from roadways, highways, and traffic intersections by treating them as linear or area sources. These applications are critical for estimating near-road pollutant concentrations and assessing public health risks in urban corridors.

Globally, the application of AERMOD to model pollutant dispersion from transportation sources has gained traction, especially in urban settings with high vehicular density. While originally developed for stationary sources, researchers have successfully adapted AERMOD to simulate emissions from linear and area sources such as highways, intersections, freight terminals, and bus depots. Garcia-Alvarez et al. (2021) applied AERMOD to model NO₂ and PM₂.₅ emissions from a major expressway in Madrid, Spain. The results indicated that concentrations exceeded 80 µg/m³ within 100 meters of the roadway during peak traffic, declining exponentially with distance. The study validated AERMOD’s predictions against local air quality monitoring data, with an R² value of 0.82, and supported the implementation of low-emission zones (LEZs). In Canada, Chen et al. (2022) used AERMOD to simulate CO and ultrafine particle emissions near major traffic corridors in Toronto. AERMOD was used to evaluate exposure gradients near schools and elderly care centers, with modeled CO levels peaking at 3.2 ppm adjacent to highways. The research informed zoning and school siting policies. For India, Shukla and Singh (2023) modeled NOx and PM₁₀ dispersion from bus terminals and high-volume roads in Delhi using AERMOD. The study found that concentrations of NOx exceeded 200 µg/m³ during morning rush hour at roadside receptors. AERMOD outputs were integrated into a health risk index that estimated over 450 asthma-related hospitalizations annually attributable to traffic pollution in the study area.

In the United States, Eckert et al. (2022) used AERMOD to model diesel particulate matter (DPM) near major freight logistics hubs in Los Angeles. The study identified annual DPM concentrations exceeding 0.3 µg/m³ near residential neighborhoods within 300 m of the transport zone, with cancer risk estimates over 100 in a million. This led to recommendations for buffer zones and truck route optimization. Neumann et al. (2021) modeled NO₂ dispersion from a high-traffic autobahn near Berlin, Germany. Using AERMOD and real-time traffic flow data, modeled 1-hour NO₂ levels exceeded 200 µg/m³ within 50 meters of the highway. The study’s findings supported changes in road design and implementation of acoustic barriers that also served as pollution buffers. Shukla & Singh (2023) applied AERMOD to simulate emissions from congested corridors in New Delhi, India. The results revealed that NOx concentrations peaked at 220 µg/m³ during the morning peak and PM₁₀ exceeded 160 µg/m³ near commercial intersections. The model was used alongside a health burden estimation tool, linking traffic pollution to over 400 additional hospitalizations annually for respiratory issues.

In Brazil, Almeida et al. (2023) modeled CO and PM₂.₅ emissions from heavy vehicle traffic near the Port of Santos. Using AERMOD coupled with emission inventories from Brazil’s CETESB database, the study found that PM₂.₅ levels reached 65 µg/m³ during peak port operations, which exceeded national air quality standards. These data contributed to port scheduling revisions and emission control planning. Lee et al. (2024) used AERMOD to assess the impact of a new elevated expressway on CO and NO₂ concentrations in Seoul. The model showed that the structure increased ground-level pollution concentrations by over 30% in adjacent residential zones, prompting the installation of noise and air pollution shielding infrastructure. Meanwhile, Janjai et al. (2023) modeled emissions from tuk-tuks, motorcycles, and diesel buses in Bangkok using AERMOD. PM2.5 concentrations peaked at 85 µg/m³ along congested corridors like Sukhumvit and Rama IV Road. The findings supported Bangkok’s Clean Fuel Program and the phase-out of pre-Euro 3 vehicles in urban areas.

Bottalico et al. (2022) assessed the dispersion of nitrogen oxides (NOx) and benzene from urban traffic in Milan using AERMOD in conjunction with land-use regression (LUR) models. They found strong agreement between modeled NOx and observed station data (R² = 0.91), showing that AERMOD can be effectively used for high-density traffic zones even with complex urban geometry. Besides, Abiodun et al. (2021) simulated PM10 and CO dispersion from bus terminals and minibus parks in Lagos using AERMOD. The modeled CO levels reached 7.5 ppm at curbside locations, and PM10 levels surpassed 160 µg/m³ during midday rush hours. The results prompted the Lagos State Environmental Protection Agency to review its urban traffic zoning policy. In Philippines, Santos & Villanueva (2024) employed AERMOD to study the pollution impact of the North Luzon Expressway (NLEX) near Quezon City. Their results showed average 24-hour PM2.5 levels at 48 µg/m³ in adjacent school zones, exceeding WHO guidelines. The study contributed to buffer zone regulations requiring 100 m minimum distance for new schools along highways.

Malaysian Applications in Urban Transportation Planning

Within Malaysia, several recent studies have adopted AERMOD to model traffic-related emissions, particularly in congested urban areas such as Kuala Lumpur, Penang, and Johor Bahru. These studies often incorporate DOE emission factors, real-world traffic volume data, and GIS-based mapping of receptor locations to simulate and evaluate pollution impacts. Sharif et al. (2020) conducted one of the earliest Malaysian studies using AERMOD to simulate transportation-related air pollution in Putrajaya. The modeled NO₂ and CO from administrative roads and adjacent expressways, identifying maximum hourly NO₂ concentrations of 210 µg/m³ surpassing WHO’s 1-hour guideline of 188 µg/m³. The study demonstrated the spatial extent of roadside pollution, especially during government office operating hours. In Klang Valley, Rahman et al. (2024) used AERMOD coupled with ArcGIS to simulate cumulative emissions from multiple traffic sources across Kuala Lumpur and Selangor. Emissions inventories were based on vehicle count data and emission factors from Malaysia’s Department of Environment (DOE). Results revealed hotspots of CO exceeding 5.5 ppm near transit terminals and PM₂.₅ peaks of 45 µg/m³ in heavily congested areas like Jalan Tun Razak. These findings were correlated with hospital respiratory admission records, highlighting exposure-health risk links.

Lim et al. (2023) applied AERMOD to simulate PM₁₀ dispersion from port-related vehicular activity and heavy-duty trucks near the Butterworth area. The study found elevated PM₁₀ concentrations up to 130 µg/m³, especially during loading hours. AERMOD results were used to justify time-restricted vehicle access policies near residential zones. Aziz et al. (2022) applied AERMOD to assess NO₂ and VOC emissions from a busy intersection near Larkin Central, Johor Bahru. Peak modeled NO₂ concentrations reached 190 µg/m³ within 100 meters of the road, while benzene levels were found to be near the chronic exposure threshold of 5 µg/m³. The study recommended relocation of sensitive receptors such as kindergartens. Zainal et al. (2021) applied AERMOD in a pilot study for a proposed flyover project. Simulations predicted a 25% increase in PM₂.₅ levels at roadside receptors due to congestion during construction. As a result, the EIA included a proposal for dust suppression, improved traffic flow design, and real-time air quality monitoring during construction.

Hamid et al. (2024) used AERMOD to estimate traffic-related NO₂ and PM2.5 emissions around commercial and educational precincts. The study reported modeled NO₂ concentrations exceeding 200 µg/m³ during late evening congestion, and PM2.5 values of up to 50 µg/m³. Affected receptors included university campuses and student dormitories, which prompted the recommendation for a real-time pollution alert system. Che Mat et al. (2023) simulated emissions from major interchanges along the North–South Expressway using AERMOD with hourly meteorological inputs. The study showed CO levels exceeding 5 ppm during the post-work rush and NO₂ concentrations of 180–200 µg/m³ near toll plazas. This led to a proposal for smart toll systems to reduce idle emissions.

For Borneo, Salleh et al. (2022) used AERMOD to model the air quality impacts of public bus routes under the BRT (Bus Rapid Transit) proposal. PM10 concentrations were simulated for 10 new proposed stops, and results indicated potential exceedances (>120 µg/m³) during high passenger turnover. The model results were integrated into public consultation sessions and transport route redesigns. Lee et al. (2021) modeled NO₂ dispersion near major intersections in the city center. AERMOD outputs showed that peak hourly concentrations could exceed 190 µg/m³, especially near mixed-use areas with overlapping commercial and residential activity. The findings were adopted by the Ipoh City Council to support “green corridor” planning that restricts heavy vehicle movement during peak periods. These studies highlight the adaptability of AERMOD for modeling line sources such as highways and area sources like terminals and intersections. Although AERMOD does not inherently account for moving vehicles or dynamic emissions, researchers overcome this by discretizing traffic emissions into spatially distributed sources along road networks. When integrated with GIS, meteorological data, and vehicular emission inventories, AERMOD provides valuable insights into exposure gradients, buffer zone recommendations, and transportation planning.

While AERMOD has been extensively used in health risk assessments (e.g., Eckert et al., 2022; Rahman et al., 2024), most studies adopt standardized exposure-response (E-R) functions, often derived from Western epidemiological cohorts (e.g., GBD or EPA data). This extrapolation assumes universality of dose-response relationships, ignoring potential demographic differences in susceptibility. For example, the use of US-based risk coefficients may not accurately reflect pollutant-health dynamics in tropical climates with distinct morbidity profiles. Moreover, few studies adjust for socioeconomic indicators such as income, housing quality, or healthcare access—despite these being strong modifiers of vulnerability. In Malaysia, Rahman et al. (2024) linked AERMOD outputs to asthma prevalence but did not stratify outcomes by age, income, or ethnicity. A more nuanced integration of spatial socioeconomic datasets (e.g., census poverty maps, age structure, comorbidity indices) with dispersion modeling is needed to quantify differential exposure and support equitable policy interventions.

Health Risk and Exposure Assessment Using AERMOD

AERMOD has been widely applied in assessing the health risks and exposure levels associated with ambient air pollution, particularly in urban and industrial settings. By simulating ground-level concentrations of hazardous pollutants such as PM₂.₅, NO₂, SO₂, CO, benzene, and diesel particulate matter (DPM), AERMOD provides a scientific basis for evaluating public health risks, estimating disease burden, and supporting epidemiological studies.

Global Applications in Vulnerable Communities

In the United States, Eckert et al. (2022) used AERMOD to model DPM exposure from freight hubs in southern California. The model predicted long-term DPM concentrations above 0.3 µg/m³ in low-income neighborhoods. Health risk calculations based on U.S. EPA guidance estimated cancer risks exceeding 100 in a million, prompting the California Air Resources Board to revise truck idling policies and expand buffer zones around schools. In China, Zhang et al. (2021) assessed PM₂.₅ exposure in industrial cities using AERMOD, finding daily concentrations exceeding 110 µg/m³ in several hotspots. By integrating exposure-response functions from the Global Burden of Disease (GBD) study, the authors estimated that long-term exposure could result in an attributable mortality of 520 premature deaths per year in a single municipality. In India, Mishra and Bhanarkar (2023) modeled NO₂ and SO₂ from power plants in Madhya Pradesh. Modeled concentrations were integrated with hospital data and revealed a statistically significant association between NO₂ levels >180 µg/m³ and increased emergency visits for asthma and COPD. AERMOD served as a foundation for a local air quality health index (AQHI) initiative. In Iran, Rasouli et al. (2024) combined AERMOD output with health risk models to evaluate chronic benzene exposure in refinery-adjacent communities. Benzene concentrations exceeded 5 µg/m³ at multiple receptors, and lifetime cancer risk was estimated above 1 in 10,000. These findings led to stricter occupational and environmental exposure thresholds in local environmental guidelines.

Tran et al. (2023) utilized AERMOD to model PM₂.₅ and NO₂ emissions from a coal-fired power plant in Hai Phong. The simulation showed that long-term exposure in surrounding communities exceeded 35 µg/m³ for PM₂.₅ and 120 µg/m³ for NO₂. Using WHO exposure-response coefficients, the study estimated an increase of 9.5% in cardiopulmonary mortality in the affected population. This was the first study in Vietnam to link modeled dispersion data with cause-specific mortality projections. Moyo and Makwela (2021) applied AERMOD to assess SO₂ and PM₁₀ exposure near industrial zones in Mpumalanga. Modeled concentrations were integrated with WHO-recommended risk functions to quantify health impacts on children aged 5–14 in South Africa. The findings showed that PM₁₀ exposure above 100 µg/m³ was linked with a 22% increase in respiratory illness incidence. This supported local calls to revise emission licensing thresholds for industrial emitters. For Mexico, Gonzalez et al. (2022) conducted a community-scale health risk assessment using AERMOD to simulate VOCs, including benzene and formaldehyde, from petrochemical operations in Veracruz. The study found that ILCR values for benzene exposure exceeded 1 in 5,000 for children under 12, suggesting a serious cancer risk. The research was cited in a national public health report by SEMARNAT (Mexican Ministry of Environment). In Pakistan, Ahmed et al. (2023) combined AERMOD with satellite-based land-use data to assess population-weighted exposure to PM₂.₅ in Karachi. The results indicated that 3.2 million people were exposed to concentrations >50 µg/m³. This led to estimated annual health costs of USD 85 million due to pollution-related hospitalizations, validating the economic value of preventive air quality regulation.

Public Health Case Studies in Malaysia

Latif et al. (2021) applied AERMOD to simulate PM₂.₅ emissions from industrial zones and validated modeled concentrations with data from DOE stations in Selangor. The study found that areas exposed to >40 µg/m³ PM₂.₅ had significantly higher rates of asthma-related hospital admissions, based on Ministry of Health (MOH) data. The study underscored the urgent need for health-based zoning regulations in urban-industrial mixed areas. In Klang Valley, Rahman et al. (2024) integrated AERMOD results with MOH respiratory disease datasets to develop an exposure-risk map for school-aged children. Areas with AERMOD-predicted NO₂ levels >190 µg/m³ overlapped with clusters of asthma cases. The study proposed policy interventions such as clean transportation corridors, especially around school zones. Ariffin et al. (2022) used AERMOD to assess PM₁₀ exposure from cement manufacturing plants. Modeled concentrations were used in an inhalation risk model, estimating hazard quotient (HQ) values exceeding 1.0 in nearby villages. This prompted the implementation of dust suppression systems and stricter bag filter maintenance requirements. Meanwhile, Zulkifli et al. (2022) conducted a health impact assessment using AERMOD-predicted PM₁₀ concentrations from bauxite mining. The study found a significantly elevated exposure risk for elderly populations living within a 2 km radius of the mining site, and modeled data supported DOE’s temporary moratorium on bauxite extraction. Additionally, Ghani et al. (2023) simulated benzene exposure from an electronics factory using the AERMOD model. Results showed that annual average concentrations near worker housing exceeded 4 µg/m³, and the estimated lifetime cancer risk reached 2.5 in 10,000 well above the U.S. EPA acceptable limit of 1 in 1,000,000. The findings contributed to an occupational health audit and ventilation system upgrade. Ismail et al. (2023) applied AERMOD to evaluate SO₂ and PM₁₀ emissions from industrial estates and their effects on nearby residential flats and schools. Modeled data were linked to MOH clinic reports, indicating a 1.4-fold increase in respiratory illness visits during high-exposure weeks. The city council used the findings to introduce green buffer zones and schedule factory operations during low-wind periods.

Nora et al. (2023) performed a quantitative health risk assessment (QHRA) using AERMOD-predicted benzene concentrations from vehicular and workshop emissions. The HQ for chronic exposure was 2.1, exceeding the safe threshold of 1.0. Cancer risk exceeded 1 in 10,000, prompting municipal health authorities to enforce inspection of ventilation systems and fuel station containment measures. In Johor Bahru, Tan et al. (2024) assessed PM₂.₅ and CO exposure in elderly care homes near busy highways. AERMOD predicted PM₂.₅ concentrations of 60 µg/m³ and CO levels up to 6 ppm during peak traffic. A health impact model projected a 13% increase in all-cause mortality in residents over 65 years, leading to relocation recommendations for high-risk aged care centers. In Borneo, Sarawak, Halim et al. (2022) modeled haze-related PM₁₀ dispersion using AERMOD, representing emissions from peat fires and biomass burning. The results showed that PM₁₀ levels exceeded 300 µg/m³ during episodic events. The simulation was used to estimate short-term health effects such as increased emergency visits for respiratory distress, particularly in children under 5. The study provided input into the Sarawak Disaster.

Policy and Regulatory Applications

AERMOD has gained widespread acceptance as a regulatory-grade air dispersion model, particularly under the jurisdiction of the United States Environmental Protection Agency (USEPA). Since its designation as the preferred model under the U.S. Clean Air Act (CAA) in 2005, AERMOD has been mandated for use in permit applications, new source review (NSR), and State Implementation Plans (SIPs). Its structured input requirements and conservative estimates make it ideal for compliance reporting, air quality modeling for Environmental Impact Assessments (EIAs), and predicting exceedances of National Ambient Air Quality Standards (NAAQS) (USEPA, 2023).

Regulatory Framework in the US, EU, Canada, India

In the United States, USEPA (2022) requires all major industrial permit applications (e.g., petrochemical plants, power stations) to include AERMOD-based simulations to demonstrate compliance with federal air quality standards. For example, Lee et al. (2021) modeled SO₂ emissions from a new oil refinery project in Louisiana, where AERMOD simulations were used to prove that predicted maximum 1-hour concentrations would remain below the 75 ppb SO₂ standard. The results were accepted in the permitting process. In Canada, Environment and Climate Change Canada (ECCC) endorses AERMOD for air quality modeling under the Canadian Environmental Assessment Act (CEAA). Singh et al. (2022) used AERMOD to model particulate emissions from mining operations in Alberta, which formed part of the EIA submission. The modeled outputs were also used to simulate long-term deposition of heavy metals and were cited during public hearings. For the European Union, although other models like ADMS and CALPUFF are also in use, AERMOD has been accepted in several member states. For instance, Garcia et al. (2021) used AERMOD in a regulatory study in Spain to assess NO₂ dispersion from logistics hubs. The results supported the implementation of low-emission transport zones in line with EU Directive 2008/50/EC on ambient air quality. In India, Patel and Kumar (2022) used AERMOD as part of the EIA for a cement plant expansion in Gujarat. The Ministry of Environment, Forest and Climate Change (MoEFCC) accepted AERMOD simulations as part of the clearance, provided the predicted values of PM₁₀ and NO₂ remained within National Ambient Air Quality Standards (NAAQS).

AERMOD in Malaysian Environmental Policy

Although many studies report acceptable correlation coefficients between AERMOD outputs and observed air quality data (e.g., Zhang et al., 2021; Garcia-Alvarez et al., 2021), the validation methodologies often vary considerably. Some rely solely on linear regression (R²) metrics, while others incorporate root mean square error (RMSE) or mean bias error (MBE). However, few studies include cross-validation using holdout datasets, raising concerns about overfitting in complex terrain simulations. In Malaysia, while Latif et al. (2021) reported close agreement (±10 µg/m³) between modeled and observed PM₂.₅ concentrations, no uncertainty quantification was provided. There is a pressing need for standardization in validation protocols and inclusion of confidence intervals or probabilistic modeling approaches to improve transparency and reproducibility in AERMOD validation.

In Malaysia, AERMOD is increasingly used in EIA submissions under the Environmental Quality Act 1974, specifically in compliance with the Environmental Quality (Clean Air) Regulations 2014. The Department of Environment (DOE) has accepted AERMOD outputs as part of industrial licensing processes, especially for facilities such as cement factories, refineries, and municipal solid waste incinerators. Latif et al. (2021) applied AERMOD in Selangor to support compliance documentation for an industrial park, showing that predicted PM₂.₅ and NO₂ levels remained within Malaysian Ambient Air Quality Standards (MAAQS). The modeled results were submitted alongside real-time monitoring and formed part of the DOE’s impact evaluation report. Sharif et al. (2020) used AERMOD in an urban traffic EIA in Putrajaya. The simulation of NO₂ and CO emissions under current and future traffic flow scenarios enabled the local authority to justify re-routing decisions and infrastructure adjustments during environmental impact assessment approval. In Johor, Nor and Aini (2022) studied the integration of AERMOD into Iskandar Malaysia’s urban planning framework. Their analysis showed that local authorities used modeled air quality zones to designate industrial, residential, and commercial land uses in harmony with pollution control guidelines. AERMOD was also embedded into the Smart City Iskandar blueprint as a risk-based planning tool.

Strengths and Limitations of AERMOD

AERMOD has garnered international recognition for its strengths as a regulatory-grade dispersion model. One of its primary advantages lies in its regulatory acceptance and scientific credibility, particularly under the U.S. Environmental Protection Agency (USEPA), which has mandated AERMOD as the preferred model for air quality permitting under the Clean Air Act since 2005. Its use is entrenched in regulatory compliance processes across the globe, including in Canada, Europe, India, and Southeast Asia. The structured modeling framework, coupled with scientific validation and detailed technical documentation, makes it a dependable tool for conservative, health-protective air quality assessments (USEPA, 2023; Lee et al., 2021). Furthermore, AERMOD’s ability to simulate multiple source configurations—including point, area, volume, and line sources—demonstrates its flexibility across a wide spectrum of real-world emission scenarios, such as industrial stacks, construction sites, transportation corridors, and open-pit mining areas. Mishra and Bhanarkar (2023), for instance, demonstrated its effective application in modeling NO₂ from thermal power plants under varied terrain conditions in India, showing reliable correlation with observed concentrations.

Another important strength is AERMOD’s compatibility with key data pre-processors. The model utilizes AERMET for meteorological data processing and AERMAP for topographical interpretation, allowing fine-tuned input to reflect local atmospheric and terrain conditions. This modularity enhances prediction accuracy, particularly when supported by localized datasets. Moreover, AERMOD’s outputs can be easily integrated with Geographic Information Systems (GIS), enabling spatial interpolation and risk mapping that are useful in environmental planning and epidemiological studies. In Malaysia, Sharif et al. (2020) leveraged AERMOD-GIS integration to map NO₂ and CO exposures in urban corridors, supporting urban planning policies aimed at reducing roadside exposure near schools and government buildings.

Despite its strengths, AERMOD is not without limitations. One of the most frequently cited challenges is its reduced performance in complex terrains and coastal environments. The model assumes horizontal homogeneity of meteorological conditions, which may not hold in areas with steep elevation gradients, sea–land breeze circulation, or valley inversions. Salam et al. (2021) found that AERMOD substantially underestimated PM₂.₅ concentrations along Malaysia’s east coast during monsoonal transitions, mainly due to its limited representation of mesoscale dynamics and wind field heterogeneity. In such regions, advanced or coupled models like WRF-AERMOD or CFD-based simulations are often preferred to overcome such spatial complexity.

AERMOD’s high dependency on meteorological input data also introduces a layer of uncertainty, particularly in areas lacking dense or quality-controlled meteorological stations. The model relies on detailed hourly surface and upper-air observations to compute key atmospheric stability parameters, such as mixing height and friction velocity. In regions where upper-air soundings are unavailable, surrogate data or reanalysis datasets are used—often reducing temporal precision. Tran et al. (2023), in their study of SO₂ emissions in Vietnam, found that AERMOD predictions varied by up to 30% depending on whether local or regional meteorological datasets were used.

A further limitation is AERMOD’s inability to simulate chemical transformation and secondary pollutant formation. It treats all pollutants as non-reactive throughout their transport path, which severely restricts its application in modeling ozone (O₃), secondary PM formation (e.g., nitrate and sulfate aerosols), and volatile organic compound (VOC) reactions. While the model is robust for primary pollutant dispersion, its limitations in photochemical modeling make it less suitable for regions where secondary pollutants dominate the air pollution burden. Gonzalez et al. (2022) illustrated this limitation in a petrochemical zone in Mexico, where AERMOD failed to capture ambient levels of secondary benzene-derived compounds due to its lack of chemical mechanisms.

Hybrid modeling approaches hold particular promise for Southeast Asia’s meteorologically complex and topographically varied environments. For instance, a WRF-AERMOD framework calibrated for monsoon-dominated regions like Malaysia or Vietnam can capture sea–land breeze circulations and inversion layers more effectively than standalone AERMOD (Sarkar et al., 2023). Additionally, AI-assisted calibration using Random Forests or Neural Networks—as proposed by Rasouli et al. (2024)—can compensate for sparse ground-truth data by learning from partial historical records. In Penang, Lim et al. (2024) demonstrated the viability of nesting CMAQ outputs into AERMOD to address haze transport and localized emissions simultaneously. Future research should establish region-specific hybrid protocols that consider monsoonal wind reversals, urban heat island effects, and heterogeneous land-use patterns across ASEAN megacities.

In response to these limitations, recent research has proposed hybrid modeling approaches and technological integrations to enhance AERMOD’s applicability. Computational Fluid Dynamics (CFD) models have been paired with AERMOD to simulate airflow in urban canyons, industrial complexes, and enclosed terrain settings. For instance, Zhang et al. (2021) combined AERMOD with OpenFOAM CFD simulations to assess PM₂.₅ exposure near high-rise buildings in Beijing, achieving improved accuracy compared to AERMOD alone. Similarly, the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques has gained attention for calibrating AERMOD outputs using observed data. Rasouli et al. (2024) employed Random Forest models to post-process AERMOD results, leading to a 25% reduction in prediction errors for SO₂ emissions from Iranian oil refineries.

In addition, several studies have demonstrated the effectiveness of mesoscale model integration, such as using WRF outputs to replace or supplement AERMET-processed meteorological inputs. Sarkar et al. (2023) found that coupling WRF with AERMOD improved dispersion prediction in coastal India by accounting for sea breeze dynamics that AERMOD alone could not resolve. These hybrid frameworks are increasingly seen as promising solutions to improve AERMOD’s prediction capacity in complex environments. In conclusion, while AERMOD remains a highly trusted and widely used dispersion model due to its regulatory acceptance, modeling flexibility, and integration capabilities, its performance is constrained by limitations in meteorology, terrain complexity, and chemical processing. However, through thoughtful coupling with advanced modeling systems, AI-driven calibration, and high-resolution input datasets, AERMOD can continue to serve as a reliable tool in air quality modeling and environmental policy development.

Recent Innovations and Integrations

In recent years, the predictive capacity and practical utility of AERMOD have been significantly enhanced through innovative integrations with geospatial technologies, meteorological models, and artificial intelligence tools. These advancements are aimed at overcoming AERMOD’s inherent limitations—especially in complex environments and data-scarce regions—by providing higher-resolution inputs and improved calibration mechanisms.

One major innovation is the integration of AERMOD with Geographic Information Systems (GIS). GIS enhances the spatial representation of pollutant dispersion patterns by enabling visualization of concentration gradients, exposure zones, and receptor distributions over urban and regional landscapes. Through this integration, stakeholders can better interpret model outputs and make informed spatial decisions in environmental planning, such as zoning and urban development. For example, Sharif et al. (2020) used GIS to post-process AERMOD outputs for NO₂ and CO concentrations in Putrajaya, Malaysia. The resulting concentration surfaces and risk maps allowed for the identification of hotspots near educational and residential zones, supporting targeted interventions in local traffic management.

Another key development is the coupling of AERMOD with mesoscale meteorological models, particularly the Weather Research and Forecasting (WRF) model, to enhance the quality and resolution of meteorological inputs. While AERMET, AERMOD’s standard meteorological pre-processor, relies on surface and upper-air observations, WRF provides model-generated meteorological parameters at high spatial and temporal resolution. This integration is particularly beneficial in regions with sparse observational data or complex coastal and mountainous meteorology. Sarkar et al. (2023) demonstrated that using WRF-derived data improved AERMOD’s ability to simulate SO₂ dispersion in a coastal Indian city, reducing underestimation bias during sea breeze-influenced episodes. The hybrid WRF-AERMOD setup resulted in a 30% improvement in model performance metrics compared to conventional AERMET input.

A third frontier of innovation is the application of Machine Learning (ML) algorithms to enhance model tuning, correct biases, and support data-driven prediction under uncertainty. ML models such as Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANNs) have been used to post-process or supplement AERMOD results, particularly in regions with limited monitoring or high meteorological variability. Chen et al. (2022) applied ML techniques to calibrate AERMOD-predicted PM₂.₅ concentrations in an urban setting in China. Their hybrid AERMOD-ML model reduced root-mean-square error (RMSE) by 28% and improved prediction accuracy across both high and low concentration ranges. Such approaches are especially valuable in developing regions, where data completeness and model parameter uncertainty are common obstacles.

These integrations collectively enhance AERMOD’s usability, reliability, and relevance for policy-making. GIS enables spatial prioritization and visual communication of risk, WRF improves the robustness of meteorological inputs, and ML refines the model’s predictive accuracy through adaptive learning. The synergy between traditional dispersion modeling and modern computational tools marks a critical transition toward more integrated, interdisciplinary air quality modeling frameworks. These innovations position AERMOD as not only a regulatory tool, but also a core component of smart environmental decision-support systems (DSS), capable of dynamic response to both pollution events and urban planning needs.

Beyond the integration with GIS, WRF, and machine learning (ML) models, recent developments in environmental modeling have demonstrated further advancement in how AERMOD is applied, customized, and extended for modern urban and industrial scenarios. These innovations are driven by the demand for higher spatiotemporal resolution, real-time processing, and interdisciplinary decision-making tools for air quality management.

One emerging trend is the integration of AERMOD with real-time low-cost sensor networks and Internet of Things (IoT)-based platforms. Low-cost sensors provide dense spatial coverage of key pollutants such as PM₂.₅, CO, and NO₂, enabling more accurate validation of AERMOD predictions at the neighborhood scale. For example, Rahman et al. (2023) in Kuala Lumpur developed a hybrid monitoring–modeling system where AERMOD predictions were continuously calibrated using PM₂.₅ readings from an IoT sensor grid. This method reduced the discrepancy between modeled and measured data by 35%, and supported micro-scale environmental health interventions in schools and low-income housing clusters.

In the field of remote sensing, satellite-based observations of aerosol optical depth (AOD), land use, and meteorological indicators are increasingly used to enhance AERMOD input data. In particular, MODIS and Sentinel-5P datasets are being integrated with land-use classification models to refine surface roughness, albedo, and vegetation parameters required in AERMET preprocessing. Alves et al. (2022) applied MODIS-derived surface data to generate spatially explicit AERMOD simulations across São Paulo, Brazil, improving surface parameter resolution from 1 km to 250 m and leading to more realistic dispersion contours. This approach has significant potential in regions lacking high-resolution topographic and meteorological datasets.

Another significant innovation is the deployment of AERMOD in cloud-based and distributed computing environments, enabling large-scale simulations with high spatial granularity and faster processing times. Traditional AERMOD runs are computationally limited in simulating thousands of receptors or long time-series, but cloud platforms now allow scaling of simulation jobs using parallel computing techniques. Ghosh et al. (2022) developed a cloud-based AERMOD system that completed 30-day regional PM₁₀ dispersion forecasts over northern India within hours, compared to multi-day runtimes on local servers. This real-time modeling capacity is crucial during episodic pollution events such as haze or transboundary smoke.

Further, AERMOD has been embedded into Decision Support Systems (DSS) that link environmental modeling with public health and urban planning tools. These DSS platforms combine AERMOD outputs with vulnerability mapping, hospital admission rates, and demographic profiles to guide adaptive policy. In Thailand, Sirikul et al. (2023) developed an AERMOD-powered DSS for Bangkok Metropolitan Authority that forecasts air pollution hotspots and suggests real-time traffic diversions and industrial operation pauses. This integration facilitates preventive measures rather than reactive enforcement, marking a shift in air quality governance strategies.

Moreover, hybrid modeling approaches that combine AERMOD with regional-scale models like CMAQ (Community Multiscale Air Quality) or CALPUFF are being explored to address cross-scale limitations. While AERMOD is excellent for near-field predictions (within 50 km), it lacks capabilities for long-range transport or secondary chemical transformations. By using CMAQ outputs as boundary conditions or background concentrations, researchers have constructed nested modeling systems that capture both localized and regional-scale dispersion processes. Lim et al. (2024) demonstrated this in Penang, where a CMAQ-AERMOD integration was used to assess port-related emissions, capturing both regional haze influx and local ship exhaust impacts.

CONCLUSION

This review has comprehensively examined the theoretical foundations, practical applications, and recent innovations associated with the AERMOD air dispersion model, focusing particularly on studies conducted between 2020 and 2025. As a regulatory-grade model endorsed by the United States Environmental Protection Agency (USEPA), AERMOD remains a cornerstone tool in ambient air quality assessment, environmental permitting, and health risk estimation. Its scientific robustness, compatibility with terrain and meteorological preprocessors, and adaptability to various emission source types have positioned it as a widely accepted modeling framework across both developed and developing nations. The findings indicate that AERMOD has been extensively applied in diverse environmental contexts, ranging from urban-industrial corridors to coastal and mountainous regions. In Malaysia and Southeast Asia, its integration with GIS platforms, local meteorological data, and regulatory frameworks underscores its growing relevance in regional air quality management and urban planning. The model’s use in transportation-related studies further demonstrates its flexibility, especially when adapted to simulate line and area sources representative of real-world traffic conditions. AERMOD’s outputs have also proven instrumental in public health research, where modeled pollutant concentrations have been used to estimate exposure burdens, calculate health risks (e.g., hazard quotient, cancer risk), and inform policy responses. However, several challenges persist, particularly in accurately simulating dispersion in complex terrain, accounting for mesoscale meteorological variability, and representing secondary pollutant formation. These limitations necessitate methodological enhancements and hybrid model configurations. Recent innovations such as the integration of AERMOD with mesoscale models (e.g., WRF), artificial intelligence (AI) algorithms for bias correction, remote sensing inputs, and IoT-based sensor networks have significantly improved the model’s resolution, accuracy, and applicability in data-scarce environments. These advancements are particularly crucial in the context of climate change, transboundary haze events, and rapid urbanization, where high-resolution, adaptive modeling tools are essential for proactive environmental governance. In conclusion, while AERMOD continues to serve as a benchmark model for air quality assessment, its future utility will increasingly depend on its integration with high-resolution datasets, interdisciplinary tools, and real-time decision-support systems. Further research should focus on expanding its capabilities to simulate chemically reactive pollutants, incorporating local land-use dynamics, and enhancing predictive accuracy through machine learning approaches. Such developments will ensure that AERMOD remains a critical asset for environmental scientists, policymakers, and urban planners in advancing sustainable and health-protective air quality management worldwide.

ACKNOWLEDGEMENTS

The authors would like to thank Universiti Sains Malaysia for the institutional support provided during the preparation of this review.

AUTHORS’ CONTRIBUTIONS

Data curation, methodology, analysis, writing—original draft, review and editing: SHMS. The author has read and agreed to the published version of the manuscript.

FUNDING

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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