Literature DB >> 31035082

Spatial variation in the association between NO2 concentrations and shipping emissions in the Red Sea.

Sabah Alahmadi1, Khalid Al-Ahmadi2, Majid Almeshari2.   

Abstract

Air pollution from shipping emissions poses significant health and environmental risks, particularly in the coastal regions. For the first time, this region as one of the busiest seas and most important international shipping lane in the world with significant nitrogen dioxide (NO2) emissions has been analyzed comprehensively. This paper aims to characterize and quantify the contribution of maritime transport sector emissions to NO2 concentrations in the Red Sea using local Geographically Weighted Regression (GWR) model in a geographic information system (GIS) environment. Maritime traffic volume was estimated using SaudiSat satellite-based Automatic Identification System (S-AIS) data, and the remotely measured tropospheric NO2 concentrations data was acquired from the ozone monitoring instrument (OMI) satellite. A significant spatial variation in the NO2 values was detected across the Red Sea, with values ranging from 4.03 × 1014 to 41.39 × 1014 molecules/cm2. Most notably, the NO2 concentrations in international waters were more than double those in the western coastal regions, whereas the concentrations close to seaports were 100% higher than those over international waters. The results indicated that the local GWR model performed significantly better than the global ordinary least squares (OLS) regression model. The GWR model had a strong and significant overall coefficient of determination with an r2 of 0.94 (p < 0.005) in comparison to the OLS model with an r2 of 0.45 (p < 0.005). Maritime traffic volume and proximity to seaports weighted by shipping activities explained about 94% of the variations of NO2 concentrations in the Red Sea. The results of this study suggest that the S-AIS data and environmental satellite measurements can be used to assess the impacts of NO2 concentrations from shipping emissions. These findings should stimulate further research into using additional covariates to explain the NO2 concentrations in areas near seaports where the standardized residuals are high.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Environmental remote sensing; Maritime air pollution; Satellite Automatic Identification System (S-AIS); Spatial local regression

Year:  2019        PMID: 31035082     DOI: 10.1016/j.scitotenv.2019.04.161

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  1 in total

1.  Tracking the global reduction of marine traffic during the COVID-19 pandemic.

Authors:  David March; Kristian Metcalfe; Joaquin Tintoré; Brendan J Godley
Journal:  Nat Commun       Date:  2021-04-27       Impact factor: 14.919

  1 in total

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