Relationship Between MODIS-based Aerosol Optical Depth and PM10 over Sumatra to Overcome the Limitations of Air Quality Monitoring Data Availability

Syafrijon,; Marzuki, Marzuki; Emriadi,; Pratama, Ridho

doi:10.13005/ojc/3404058

Cited by 4 publications

(1 citation statement)

References 22 publications

(28 reference statements)

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“…In contrast, pre-monsoon and monsoon seasons observe coarse aerosol loading [18]. A forest fire in Sumatra, Indonesia, increased the PM2.5 concentration in the air which is highly correlated with increased AOT concentration [19]. According to [18], dust storms significantly affect aerosols' seasonal and temporal variation over the Indo-Gangetic basin, followed by the enhanced value of AOT immediately after the sand storms from the Thar desert.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Distribution of AOT and Its Relationship with Air Pollutants in Central Bangladesh Using Remote Sensing and Machine Learning Tools

Gomes¹,

Bhuiyan²,

Rahman³

2023

Preprint

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Aerosol Optical Thickness (AOT) is one of the critical factors for global atmospheric conditions, climate change, and air pollution. AOT has been exposed as a major component of air pollution in Bangladesh. This paper aims to map the seasonal distribution of AOT from 2002-2022 and to explore the internal relationship between AOT and ten air pollutants using remote sensing and machine learning tools. These ten air pollutants are Particulate matter (PM2.5), Methane (CH4), Carbon monoxide (CO), Nitrogen dioxide (NO2), Formaldehyde (HCHO), Ozone (O3), Sulfur dioxide (SO2), Aerosol Particulate Radius (APR), Nitrogen oxide (NOx) and Black carbon (BC). The results show that the concentrations of AOT were higher in December-January-February (mean value 0.50) and March-April-May (mean value 0.50) seasons, mostly in the central, western, and southern parts of Dhaka, Narayanganj, and Munshiganj districts. AOT was a bit less in June-July-August (mean value 0.33) and September-October-November (mean value 0.37). This paper also revealed that the AOT was correlated positively with PM2.5 (0.60), CH4 (0.80), NO (0.76), and BC (0.83) while correlated negatively with CO (-0.66), HCHO (-0.16), SO2 (-0.41), APR (-0.48), and NOx (-0.20). From the machine learning, the Rational quadratic GPR (RME-0.0024, MAE-0.0015, R2-0.96), Matern 5/2 GPR (RMSE-0.0023, MAE-0.0015, R2-0.96), and Squared Exponential GPR (RMSE-0.0015, MAE-0.0015, R2-0.96) were found good classifiers to predict AOT. UN agencies, government line departments, and local and regional development councils for air pollution mitigation and long-term protective measures may use the paper's key results.

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Section: Introductionmentioning

confidence: 99%

Seasonal Distribution of AOT and Its Relationship with Air Pollutants in Central Bangladesh Using Remote Sensing and Machine Learning Tools

Gomes¹,

Bhuiyan²,

Rahman³

2023

Preprint

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Ambient air particulate matter (PM10) satellite monitoring and respiratory health effects assessment

Mohebbichamkhorami

Arbabi

Mirzaei

et al. 2020

J Environ Health Sci Engineer

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Purpose Air particulate matter with an aerodynamic diameter of 10 µm or less (PM 10 ) is one of the main causes of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study explored the relationship between PM 10 by remote sensing and AECOPD in Chaharmahal-o-Bakhtiari province from 2014 to2018. Method PM 10 concentrations were predicted and validated based on aerosol optical depth (AOD) from 161 images processed by MODIS sensor and ground air quality monitoring station data. Demographic information and spirometric indices of 2038 patients with AECOPD were collected and analyzed from the hospital during the studied periods. SPSS software was used to analyze the relationships between these two categories of information. Results There was a significant negative relationship between PM10 and FVC, FVC%, FEV1, FEV1%, FEF25-75, FEV1/FVC, PEF, and FEF25FVC indices (p < 0.05). The results showed that over 2014-2018, the annual mean of PM10 concentrations varied from 35 to 52 µg/m3. The result of the regression model showed that the patient's age, body mass index (BMI), and PM 10 concentrations were the most affecting variables on the two important spirometric indices i.e., FVC% and FEV 1 %. The PM 10 concentrations and number of AECOPD patients had a similar pattern during the studied period. The women group, age group above 74 years, normal BMI, and non-smoking patients showed the most sensitivity to the PM 10 concentrations. Conclusions Our findings provide supplementary scientific information on PM 10 concentration related to the incidence of AECOPD and as a variable affecting the most important spirometry indicators by providing local decision-makers information needed to set a priority of air pollution control measures as well as health services.

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Seasonal distribution of AOT and its relationship with air pollutants in central Bangladesh using remote sensing and machine learning tools

Gomes

Bhuiyan

2023

Case Studies in Chemical and Environmental Engineering

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Relationship Between MODIS-based Aerosol Optical Depth and PM10 over Sumatra to Overcome the Limitations of Air Quality Monitoring Data Availability

Cited by 4 publications

References 22 publications

Seasonal Distribution of AOT and Its Relationship with Air Pollutants in Central Bangladesh Using Remote Sensing and Machine Learning Tools

Seasonal Distribution of AOT and Its Relationship with Air Pollutants in Central Bangladesh Using Remote Sensing and Machine Learning Tools

Ambient air particulate matter (PM10) satellite monitoring and respiratory health effects assessment

Seasonal distribution of AOT and its relationship with air pollutants in central Bangladesh using remote sensing and machine learning tools

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