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Dai Y, Hitchcock P, Mahowald NM, Domeisen DIV, Hamilton DS, Li L, Marticorena B, Kanakidou M, Mihalopoulos N, Aboagye-Okyere A. Stratospheric impacts on dust transport and air pollution in West Africa and the Eastern Mediterranean. Nat Commun 2022; 13:7744. [PMID: 36517478 PMCID: PMC9750965 DOI: 10.1038/s41467-022-35403-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 11/25/2022] [Indexed: 12/15/2022] Open
Abstract
Saharan dust intrusions strongly impact Atlantic and Mediterranean coastal regions. Today, most operational dust forecasts extend only 2-5 days. Here we show that on timescales of weeks to months, North African dust emission and transport are impacted by sudden stratospheric warmings (SSWs), which establish a negative North Atlantic Oscillation-like surface signal. Chemical transport models show a large-scale dipolar dust response to SSWs, with the burden in the Eastern Mediterranean enhanced up to 30% and a corresponding reduction in West Africa. Observations of inhalable particulate (PM10) concentrations and aerosol optical depth confirm this dipole. On average, a single SSW causes 680-2460 additional premature deaths in the Eastern Mediterranean and prevents 1180-2040 premature deaths in West Africa from exposure to dust-source fine particulate (PM2.5). Currently, SSWs are predictable 1-2 weeks in advance. Altogether, the stratosphere represents an important source of subseasonal predictability for air quality over West Africa and the Eastern Mediterranean.
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Affiliation(s)
- Ying Dai
- grid.5386.8000000041936877XDepartment of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853 USA
| | - Peter Hitchcock
- grid.5386.8000000041936877XDepartment of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853 USA
| | - Natalie M. Mahowald
- grid.5386.8000000041936877XDepartment of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853 USA
| | - Daniela I. V. Domeisen
- grid.9851.50000 0001 2165 4204University of Lausanne, Lausanne, Switzerland ,grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland
| | - Douglas S. Hamilton
- grid.5386.8000000041936877XDepartment of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853 USA ,grid.40803.3f0000 0001 2173 6074Present Address: Department of Marine, Earth, and Atmospheric Science, NC State University, Raleigh, NC USA
| | - Longlei Li
- grid.5386.8000000041936877XDepartment of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853 USA
| | - Beatrice Marticorena
- grid.466400.0Laboratoire Interuniversitaire des Systèmes Atmosphériques, Universités Paris Est-Paris Diderot-Paris 7, UMR CNRS 7583, Créteil, France
| | - Maria Kanakidou
- grid.8127.c0000 0004 0576 3437Environmental Chemical Processes Laboratory (ECPL), Department of Chemistry, University of Crete, Heraklion, Greece ,grid.511963.9Center of Studies of Air quality and Climate Change, Institute for Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, Greece ,grid.7704.40000 0001 2297 4381Excellence Chair, Institute of Environmental Physics, University of Bremen, Bremen, Germany
| | - Nikolaos Mihalopoulos
- grid.8127.c0000 0004 0576 3437Environmental Chemical Processes Laboratory (ECPL), Department of Chemistry, University of Crete, Heraklion, Greece ,grid.8663.b0000 0004 0635 693XInstitute for Environmental Research and Sustainable Development, National Observatory of Athens, Pendeli, Greece
| | - Adwoa Aboagye-Okyere
- grid.5386.8000000041936877XDepartment of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853 USA
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Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China. REMOTE SENSING 2019. [DOI: 10.3390/rs11222679] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate surface PM2.5 (dry mass concentration of particles with an in situ aerodynamic diameter smaller than 2.5 µm) concentrations. In this research, a two-stage spatio-temporal statistical model for estimating daily surface PM2.5 concentrations in the Guanzhong Basin of China is proposed, using 6 km × 6 km AOD data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument as the main variable and meteorological factors, land-cover, and population data as auxiliary variables. The model is validated using a cross-validation method. The linear mixed effects (LME) model used in the first stage could be improved by using a geographically weighted regression (GWR) model or the generalized additive model (GAM) in the second stage, and the predictive capability of the GWR model is better than that of GAM. The two-stage spatio-temporal statistical model of LME and GWR successfully captures the temporal and spatial variations. The coefficient of determination (R2), the bias and the root-mean-squared prediction errors (RMSEs) of the model fitting to the two-stage spatio-temporal models of LME and GWR were 0.802, −0.378 µg/m3, and 12.746 µg/m3, respectively, and the model cross-validation results were 0.703, 1.451 µg/m3, and 15.731 µg/m3, respectively. The model prediction maps show that the topography has a strong influence on the spatial distribution of the PM2.5 concentrations in the Guanzhong Basin, and PM2.5 concentrations vary with the seasons. This method can provide reliable PM2.5 predictions to reduce the bias of exposure assessment in air pollution and health research.
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