The negative effects of air pollution on our lives have increased significantly. An important environmental risk factor for the development of lung cancer and cardiopulmonary disease is prolonged exposure to fine particle air pollution caused by combustion (Pope et al., 2002). Hospital admission for acute respiratory infections was associated with short-term ambient air pollution exposure, and the main air pollutants compromising respiratory health were PM₁₀, PM_2.5, and NO₂ (Xia et al., 2017). According to the health impact assessment, chronic exposure to PM_2.5 causes over 50,000 fatalities each year in Thailand. Based on the worth of a statistical life, the overall expense of this excess mortality in 2016 amounted to close to 15% of Thailand’s GDP (Mueller et al., 2021). It is primarily brought about by human actions or behaviors, such as population growth, fuel demand, and resource scarcity. Industry, transportation, agriculture, waste management, and transportation are some of the main sources of air pollution. Nonetheless, PM_2.5 may influence weather conditions, such as the frequency and duration of precipitation, as PM_2.5 may serve as sufficient cloud condensation nuclei for precipitation, resulting in lower precipitation on polluted days than on clean days (Zheng et al., 2020), with recent evidence suggesting that if PM_2.5 was controlled, cloud condensation nuclei would be reduced (Wang et al., 2023).

The amount of infectious waste generated by hospitals under the Ministry of Public Health, hospitals under the Department of Academic Affairs within the Ministry of Public Health, sub-district hospitals for health promotion, hospitals associated with other ministries, private hospitals, private clinics, animal hospitals, and a dangerous infection laboratory was 47,962 tons in 2020, down 10% from 2019. Of this amount, 47,440 tons (98.91%) were appropriately managed (Pollution Control Department PCD, 2020). Sulfur dioxide, oxide of nitrogen, and particulate matter were the primary air pollutants produced by infectious waste incineration. These pollutants were tracked to ensure that the emissions requirement was met (Walker and Cooper, 1992).

The National Meteorological Society of the United States, the American Meteorological Society, and the Environmental Protection Agency developed the AERMOD model (American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model) (U.S.EPA, 2021). It was employed to estimate how pollutants will disperse in the atmosphere near the surface. The most popular model for predicting the concentration of pollution spreading from its source over a radius of up to 50 km is AERMOD. There are three primary sections of the information required to prepare AERMOD air models for import: meteorological data obtained from the preparation; data on the area’s height from the preparation’s AERMET subprogram; and information on the AERMEP subprogram and air pollution source. In Thailand, AERMOD was used to model the level of air pollution arising from industrial sources (Jittra et al., 2015; Khamyingkert and Thepanondh, 2015). The level of air pollutants at surrounding receptors and the potential of pollutant reduction resulting from suggested clean technology solutions were evaluated using AERMOD [9 2]. AERMOD was used as part of the environmental impact assessment to investigate the NO₂ emissions from a cement complex (Seangkiatiyuth1 et al., 2011) and assess the impact of SO₂ emissions from the coal-fired power plant’s exhaust stack (Srirattana and Piaowan, 2020). AERMOD was used to determine the ground-level concentrations of air pollutants from the municipal solid-waste incinerators (Srivieng et al., 2021).

However, the ground-based monitoring site did not cover all the areas of air emission sources. The Multi-Angle Implementation of Atmospheric Correction (MAIAC-AOD) technique simultaneously retrieves aerosol optical depth (AOD) at a 1 km spatial resolution with a temporal series of MODIS measurements on a daily basis (Lyapustin and Wang, 2008). Using the retrieved 1-km AOD and Supplementary Data, such as meteorological and land cover, the regional distribution of PM_2.5 was approximated more precisely than with the 3-km AOD (Hu et al., 2014; Chen et al., 2021). In Thailand, it was proposed that MAIAC-AOD would validate the predicted PM_2.5 based on MODIS surface reflectance (Nakapan and Hongthong, 2022). MAIAC-AOD showed high accuracy in East Asia and significant heterogeneity among study sites, and it performed well in areas with high vegetation cover and flat terrain (Wang et al., 2022). Even on high terrain, it produced an excellent representation of ground-level fine particulate matter, which was beneficial for monitoring PM_2.5 and comprehending variation in PM_2.5 pollution. (He et al., 2021). Additionally, it was used to forecast the daily ground PM_2.5 concentration to assess the impacted area for air pollution management mitigation (Xiao et al., 2017; Han et al., 2018).

With the readily available spatial and temporal data of the MAIAC-AOD and the advantage of the AERMOD mathematical model, this study aimed to examine the possibilities of using MAIAC-AOD to determine the concentration and distribution of particulate matter (PM_2.5) from infectious waste incinerators using the linear regression model. However, the distribution of nitrogen oxides (NO_x) and sulfur dioxide (SO₂) was also analyzed using AERMOD as they were the primary pollutants from the incinerator. We obtained the daily MAIAC-AOD data incorporated with the meteorology data for the AERMOD simulation. Then, the validation of air pollutant concentrations between predicted PM_2.5 by MAIAC-AOD and PM_2.5 by AERMOD was performed to investigate the relationship and possibility of using MAIAC-AOD to investigate the environmental and health impact assessment of the sensitive area surrounding an infectious waste incinerator, as normally, air quality monitoring of infectious waste stacks was carried out twice a year in accordance with regulations, which could be too late to detect the risk of air pollution from a waste incinerator. As a result, preliminary PM_2.5 estimation using MAIAC-AOD, which has a high temporal (daily) and spatial distribution (1 km), should be particularly beneficial for assessing the environmental and health impacts of stationary emission sources.

The distribution of air pollutants included SO₂, NO_x, and PM_2.5, which were emitted from the infectious waste incinerator, and were simulated by the AERMOD model for the three average time periods, including at 1 h, 24 h, and annually. The simulated concentration was compared with National Ambient Air Quality Standards (NAAQS); the maximum simulated concentration of air pollutants complied with these standards for 1, 24 h, and the entire year, as shown in Table 2. The result from our study was similar to the health risk assessment for the municipal solid-waste incinerators in the five provinces of Thailand, Lamphun, Khon Kaen, Rayong, Phra Nakhon Si Ayutthaya, and Surat Thani, where the levels of SO₂ and NO_x complied with the National Ambient Air Quality Standards (Srivieng et al., 2021). The AERMOD simulation also identified the location and distance of the highest pollutant concentration from the stack. The highest SO₂ and NO_x concentrations in 1 h occurred in 2020 at 1,000 m in the northwest, with values of 37.68 and 93.33 μg/m³, respectively. The 24-h concentrations of SO₂ and NO_x were highest in 2021, with values of 3.63 and 8.90 μg/m³, respectively, 720 m northeast of the stack. Figures 2, 3 show the annual SO₂ and NO_x concentrations, which were highest in 2019, with respective values of 0.56 and 1.36 μg/m³, 720 m and 730 m to the northeast, respectively. However, the other investigation found that the greatest radii of SO₂ and NO_x distributions from the stack were within 500 m (Koomsang et al., 2015; Afzali et al., 2016), which might have been caused by the wind speed in the area. The maximum PM_2.5 concentration of 0.033 μg/m³ was discovered in 2019, 730 m to the northeast. The distribution of SO₂ over 24 h was much more diffused than the annual one, as shown in Supplementary Figure S2. By contrast, as shown in Supplementary Figure S3, the annual NO_x dispersion and concentration between 2019 and 2021 were distinct from the 24-h and annual NO_x dispersion because the annual NO_x was dispersed to the north, whereas the 24-h NO_x was not.

According to AERMOD simulation results, the wind direction had the largest effect on PM_2.5 dispersion, which gradually decreased with increasing distance downwind (Eibedingil et al., 2022). As illustrated in Figures 4–6, the annual average of the MAIAC-AOD value within a 10 km² radius in 2019 was higher than in 2020 and 2021, with values ranging between 0.59 and 0.88, 0.48–0.65, and 0.49–0.66, respectively. However, the distribution pattern of AOD indicated that the PM_2.5 concentration was correlated with the value of AOD and was higher in the vicinity of the stack than in the outside area. When the predicted PM_2.5 by MAIAC-AOD was overlaid on the AERMOD findings, it was revealed that it had a greater concentration of PM_2.5 than the area closest to it. The findings of numerous studies support the hypothesis that the MAIAC-AOD was related to the PM_2.5 concentration as it demonstrated good agreement between the predicted and measured PM_2.5 concentrations (Arvani et al., 2016). In addition, the value of MAIAC-AOD was high in several sensitive locations, such as in 2019, when AOD at A1 was 0.88 with a predicted PM_2.5 by AOD of 0.014 μg/m³, in 2020, when AOD at A10 was 0.62 with a predicted PM_2.5 by AOD of 0.013 μg/m³, and in 2021, when AOD at A1 was 0.66 with a predicted PM_2.5 by AOD of 0.013 μg/m³; these areas contained temples and therefore the high AODs were influenced by cremations. Over a 24-h period, the SO₂ and NO_x concentrations produced by AERMOD’s simulation of cremation were 3.29 and 17.54 μg/m³, respectively (Couper et al., 2012), which was slightly greater than the study’s finding that cremation was a potential additional source of PM_2.5 in the two areas (A1 and A10). However, most sensitive areas were not impacted by the infectious waste incinerator pollution, but some of the areas still have high levels of MAIAC-AOD, thus it is crucial to investigate the other sources of PM_2.5 emission in the future. Additionally, as the World Health Organization has suggested an annual mean background concentration of PM_2.5 of 10 μg/m³ (Voogt et al., 2009), when the simulated PM_2.5 was combined with the background concentration, it did not exceed the standard air quality of 15 μg/m³. Supplementary Table S2 shows the average annual predicted PM_2.5 by MAIAC-AOD for each 1 km grid cell for each designated sensitive area within the 10 km² area of waste incineration, with the average annual PM_2.5 concentrations that were simulated by the AERMOD. Furthermore, between 2019, 2020, and 2021, the correlation between the predicted PM_2.5 by MAIAC-AOD and the simulated PM_2.5 concentrations by AERMOD was 0.7630, 0.7607, and 0.6504, respectively.

The prediction of the air pollution dispersion from the infectious waste incinerator was performed using the AERMOD model. Although SO₂ and NO_x were spread up to 1 km from the stack, the concentration did not exceed the national permissible limits. The predicted PM_2.5 by MAIAC-AOD was compared with the PM_2.5 simulated by AERMOD. The correlation between them was 0.7630, 0.7607, and 0.6504 for 2019, 2020, and 2021, respectively. According to the results, some areas have high AOD values, which indicates that sources other than infectious waste incinerators may also be causing the presence of PM_2.5. In Jiangsu Province, China, occurrence frequencies of MAIAC-AOD were found to be between 0.3 and 0.5, indicating the possibility of an atmosphere that was turbid as a result of human activities that increased emissions (Wang et al., 2021). Our study indicates that the MAIAC values in the 10 km² area around the infectious waste incinerator ranged from 0.51 to 0.88, which is significantly higher than the values found in previous studies, showing that this area was closer to the emission source. It was found that there was a consistent distribution trend between them, and this trend showed great potential for a thorough human exposure assessment at the community level [30. According to this study, the daily MAIAC-AOD can be employed as a preliminary monitoring tool to determine the risk area of exposure and the level of particulate matter. The MAIAC-AOD was daily and continuously both spatial and temporal, which was contrary to the normal monitoring period for stack emission monitoring of infectious waste that takes place at least twice a year. Therefore, it would be interesting to incorporate the AERMOD model or other air modeling with the daily MAIAC-AOD in the future to validate or cross-check the distribution of air pollution from other stationary emission sources or even the effect of transboundary air pollution.