Identifying source of dust aerosol using a new framework based on remote sensing and modelling

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dc.contributor.authorRahmati, Omid
dc.contributor.authorMohammadi, Farnoush
dc.contributor.authorGhiasi, Seid Saeid
dc.contributor.authorTiefenbacher, John
dc.contributor.authorMoghaddam, Davoud Davoudi
dc.contributor.authorCoulon, Frederic
dc.contributor.authorNalivan, Omid Asadi
dc.contributor.authorBui, Dieu Tien
dc.date.accessioned2020-06-08T14:39:47Z
dc.date.available2020-06-08T14:39:47Z
dc.date.issued2020-05-19
dc.description.abstractDust particles are transported globally. Dust storms can adversely impact both human health and the environment, but they also impact transportation infrastructure, agriculture, and industry, occasionally severely. The identification of the locations that are the primary sources of dust, especially in arid and semi-arid environments, remains a challenge as these sites are often in remote or data-scarce regions. In this study, a new method using state-of-the-art machine-learning algorithms – random forest (RF), support vector machines (SVM), and multivariate adaptive regression splines (MARS) – was evaluated for its ability to spatially model the distribution of dust-source potential in eastern Iran. To accomplish this, empirically identified dust-source locations were determined with the ozone monitoring instrument aerosol index and the Moderate-Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol optical thickness methods. The identified areas were divided into training (70%) and validation (30%) sets. Measurements of the conditioning factors (lithology, wind speed, maximum air temperature, land use, slope angle, soil, rainfall, and land cover) were compiled for the study area and predictive models were developed. The area-under-the-receiver operating characteristics curve (AUC) and true-skill statistics (TSS) were used to validate the maps of the models' predictions. The results show that the RF algorithm performed best (AUC = 89.4% and TSS = 0.751), followed by the SVM (AUC = 87.5%, TSS = 0.73) and the MARS algorithm (AUC = 81%, TSS = 0.69). The results of the RF indicated that wind speed and land cover are the most important factors affecting dust generation. The region of highest dust-source potential that was identified by the RF is in the eastern parts of the study region. This model can be applied to other arid and semi-arid environments that experience dust storms to promote management that prevents desertification and reduces dust production.en_UK
dc.identifier.citationRahmati O, Mohammadi F, Ghiasi SS, et al., (2020) Identifying source of dust aerosol using a new framework based on remote sensing and modelling. Science of the Total Environment, Volume 737, October 2020, Article number 139508en_UK
dc.identifier.issn0048-9697
dc.identifier.urihttps://doi.org/10.1016/j.scitotenv.2020.139508
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/15481
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDust stormen_UK
dc.subjectModellingen_UK
dc.subjectGISen_UK
dc.subjectRemote sensingen_UK
dc.subjectIranen_UK
dc.titleIdentifying source of dust aerosol using a new framework based on remote sensing and modellingen_UK
dc.typeArticleen_UK

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