Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing

Li, Jing; Garshick, Eric; Hart, Jaime E.; Li, Longxiang; Shi, Liuhua; Al-Hemoud, Ali; Huang, Su; Koutrakis, Petros

doi:10.1016/j.envint.2021.106445

Cited by 42 publications

(36 citation statements)

References 46 publications

(64 reference statements)

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“…Fine particulate matter (PM 2.5 ) is an important air pollutant worldwide and consists of a complex mixture of carbonaceous fractions, water-soluble ions, metals, crustal elements, and other constituents. 1 , 2 , 3 Exposure to PM 2.5 has been associated with adverse health effects, including cardiovascular disease, respiratory disease, lung cancer, and premature mortality. 4 , 5 , 6 , 7 , 8 Several studies also suggest that long-term PM 2.5 exposure is a risk factor for neurodegenerative diseases.…”

Section: Introductionmentioning

confidence: 99%

Long-term effects of PM2.5 components on incident dementia in the northeastern United States

Wang

Steenland

et al. 2022

The Innovation

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Section: Introductionmentioning

confidence: 99%

Long-term effects of PM2.5 components on incident dementia in the northeastern United States

Wang

Steenland

et al. 2022

The Innovation

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“…PM10 and PM2.5 also originate from industrial and intensive commercial activities (Fava and Letizia Ruello, 2008). Li et al (2020Li et al ( , 2021 suggested that airborne dust events contributed to high PM2.5 concentrations in Middle Eastern countries such as Kuwait, Iraq, Iran and Saudi Arabia. High concentrations of PM2.5 in Malaysia have been linked to Southeast Asian haze incidents, which are the consequence of the uncontrolled burning of forests in Indonesia (Rahman et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Assessment of PM2.5 Patterns in Malaysia Using the Clustering Method

Rahman

Hamzah

Latif

et al. 2022

Aerosol Air Qual. Res.

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Particulate matter is the parameter of most concern in air quality monitoring in Malaysia. This study discusses the variations and clustering of PM2.5 recorded from 2018 to 2019 at 65 stations of the Continuous Air Quality Monitoring Network of the Malaysian Department of Environment. PM2.5 concentrations were recorded continuously using a tapered element oscillating microbalance. The cluster analysis was conducted using the Agglomerative Hierarchical Cluster (AHC) method. The results show that the daily average of PM2.5 concentrations ranged between 8 and 31 µg m -3 . The cluster regions were classified into High Pollution Regions (HPR), Medium Pollution Regions (MPR) and Low Pollution Regions (LPR) based on the AHC analysis. The mean concentration of PM2.5 recorded in HPR was significantly higher with 23.04 µg m -3 followed by MPR and LPR. The results also showed that the highest concentration of PM2.5 was recorded during the 2019 haze episode for all three regions, with the air pollutant index indicating very unhealthy and dangerous levels.

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“…Finally, a test of model transferability demonstrated the difficulty of using models trained with data from one region to predict PM 2.5 in another region. This approach was taken by Li et al (2021) where their final model trained only on PM 2.5 data in Kuwait was used to predict PM 2.5 in neighboring Iraq [36]. In our study, the trained and tested Kuwait-based model was transferred 850 km southeast to similarly coastal U.A.E.…”

Section: Discussionmentioning

confidence: 99%

“…We also incorporated assimilated aerosol optical depth data from the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), which is the latest atmospheric reanalysis of the modern satellite era produced by NASA's Global Modeling and Assimilation Office (GMAO) [33,34]. Although MERRA-2 data are provided at relatively low resolution (∼50 km), their source-specific AODs have proven to be useful for estimating PM 2.5 in other studies [5,35,36]. While there have been some efforts to downscale MERRA-2 to a finer spatial resolution [35], in this study we used MERRA-2 AOD variables (dust, black carbon (BC), organic carbon (OC), and sulfate (SO 4 )) at their native resolution.…”

Section: Assimilated Aerosol Datamentioning

confidence: 99%

Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East

et al. 2021

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Exposure to fine particulate matter (PM2.5) air pollution has been shown in numerous studies to be associated with detrimental health effects. However, the ability to conduct epidemiological assessments can be limited due to challenges in generating reliable PM2.5 estimates, particularly in parts of the world such as the Middle East where measurements are scarce and extreme meteorological events such as sandstorms are frequent. In order to supplement exposure modeling efforts under such conditions, satellite-retrieved aerosol optical depth (AOD) has proven to be useful due to its global coverage. By using AODs from the Multiangle Implementation of Atmospheric Correction (MAIAC) of the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) combined with meteorological and assimilated aerosol information from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), we constructed machine learning models to predict PM2.5 in the area surrounding the Persian Gulf, including Kuwait, Bahrain, and the United Arab Emirates (U.A.E). Our models showed regional differences in predictive performance, with better results in the U.A.E. (median test R2 = 0.66) than Kuwait (median test R2 = 0.51). Variable importance also differed by region, where satellite-retrieved AOD variables were more important for predicting PM2.5 in Kuwait than in the U.A.E. Divergent trends in the temporal and spatial autocorrelations of PM2.5 and AOD in the two regions offered possible explanations for differences in predictive performance and variable importance. In a test of model transferability, we found that models trained in one region and applied to another did not predict PM2.5 well, even if the transferred model had better performance. Overall the results of our study suggest that models developed over large geographic areas could generate PM2.5 estimates with greater uncertainty than could be obtained by taking a regional modeling approach. Furthermore, development of methods to better incorporate spatial and temporal autocorrelations in machine learning models warrants further examination.

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Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing

Cited by 42 publications

References 46 publications

Long-term effects of PM2.5 components on incident dementia in the northeastern United States

Long-term effects of PM2.5 components on incident dementia in the northeastern United States

Assessment of PM2.5 Patterns in Malaysia Using the Clustering Method

Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East

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