Integrated modelling for mapping spatial sources of dust in central Asia - An important dust source in the global atmospheric system

A - Papers appearing in refereed journals

Gholami, H., Mohammadifar, A., Malakooti, H., Esmaeilpour, Y., Golzari, S., Mohammadi, F., Li, Y., Song, Y., Kaskaoutis, D.G., Fitzsimmons, K.E. and Collins, A. L. 2021. Integrated modelling for mapping spatial sources of dust in central Asia - An important dust source in the global atmospheric system. Atmospheric Pollution Research. 12, p. 101173. https://doi.org/10.1016/j.apr.2021.101173

AuthorsGholami, H., Mohammadifar, A., Malakooti, H., Esmaeilpour, Y., Golzari, S., Mohammadi, F., Li, Y., Song, Y., Kaskaoutis, D.G., Fitzsimmons, K.E. and Collins, A. L.
Abstract

Spatial mapping of dust sources in arid and semi-arid regions is necessary to mitigate on-site and off-site impacts. In this study, we apply a novel integrated modelling approach including leave one feature out (LOFO) – as a technique for feature selection -, deep learning (DL) models (gcForest and bidirectional long short-term memory (Bi-LSTM)), game theory (GT) and a Gaussian copula-based multivariate (GCBM) model for mapping dust sources in Central Asia (CA). Eight factors (precipitation, cation exchange capacity, bulk density, wind speed, slope, silt content, lithology and coarse fragment content) were selected by LOFO as effective for controlling dust emissions, and were used in the novel modelling process. Six statistical indicators were utilized to assess the performance of the two DL models and a hybrid copula-gcForest model, while a sensitivity analysis of the models was also carried out. The hybrid copula-gcForest model was identified as the most accurate, predicting that 16%, 7.1%, 9.5% and 67.4% of the study area is grouped at low, moderate, high and very high susceptibility classes for dust emissions, respectively. Based on permutation feature importance measure (PFIM) and Shapely Additive exPlanations (SHAP), bulk density, precipitation and coarse fragment content were evaluated as the three most important factors with the highest contributions to the predictive model output. The study area suffers from intense wind erosion and the generated spatial maps of dust sources may be helpful for mitigating and controlling dust phenomena in CA.

KeywordsDust spatial mapping; Effective factors; Deep learning; Hybrid copula-gcForest model; Gaussian copula model; Model sensitivity; Central Asia
Year of Publication2021
JournalAtmospheric Pollution Research
Journal citation12, p. 101173
Digital Object Identifier (DOI)https://doi.org/10.1016/j.apr.2021.101173
Web address (URL)https://doi.org/10.1016/j.apr.2021.101173
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeS2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
“Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) Operational Program
PANhellenic infrastructure for Atmospheric Composition and climatE change” PANACEA (MIS 5021516)
Output statusPublished
Publication dates
Online19 Aug 2021
Publication process dates
Accepted17 Aug 2021
PublisherElsevier
ISSN1309-1042

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