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Open AccessOpen Access JUSTC Earth and Space 14 April 2023

Investigating the mechanisms driving the seasonal variations in surface PM2.5 concentrations over East Africa with the WRF-Chem model

  • 1.

    School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China

  • 2.

    School of Public Health, Environmental Health Science Dept., University of Rwanda (UR-CMHS), Kigali, Rwanda

  • 3.

    Deep Space Exploration Laboratory, University of Science and Technology of China, Hefei 230026, China

  • 4.

    CAS Center for Excellence in Comparative Planetology, University of Science and Technology of China, Hefei 230026, China

  • 5.

    School of Science, Physics Dept., University of Rwanda (UR-CST), Kigali, Rwanda

Cite this:
https://doi.org/10.52396/JUSTC-2022-0142
More Information
  • Corresponding author: Chun Zhao, E-mail: chunzhao@ustc.edu.cn
  • Received Date: 09 October 2022
  • Accepted Date: 29 November 2022
    • Available Online: 14 April 2023
    Abstract

  • Abstract

    Most previous studies on surface PM2.5 concentrations over East Africa focused on short-term in situ observations. In this study, the WRF-Chem model combined with in situ observations is used to investigate the seasonal variation in surface PM2.5 concentrations over East Africa. WRF-Chem simulations are conducted from April to September 2017. Generally, the simulated AOD is consistent with satellite retrieval throughout the period, and the simulations depicted the seasonal variation in PM2.5 concentrations from April to September but underestimated the concentrations throughout the period due to the uncertainties in local and regional emissions over the region. The composition analysis of surface PM2.5 concentrations revealed that the dominant components were OIN and OC, accounting for 80% and 15% of the total concentrations, respectively, and drove the seasonal variation. The analysis of contributions from multiple physical and chemical processes indicated that the seasonal variation in surface PM2.5 concentrations was controlled by the variation in transport processes, PBL mixing, and dry and wet deposition. The variation in PM2.5 concentrations from May to July is due to wind direction changes that control the transported biomass burning aerosols from southern Africa, enhanced turbulent mixing of transported aerosols at the upper level to the surface and decreased wet deposition from decreased rainfall from May to July.

    Graphical abstract

    Spatial distribution surface PM2.5 concentrations and the mechanism driving its seasonal variations.

    Abstract

    Most previous studies on surface PM2.5 concentrations over East Africa focused on short-term in situ observations. In this study, the WRF-Chem model combined with in situ observations is used to investigate the seasonal variation in surface PM2.5 concentrations over East Africa. WRF-Chem simulations are conducted from April to September 2017. Generally, the simulated AOD is consistent with satellite retrieval throughout the period, and the simulations depicted the seasonal variation in PM2.5 concentrations from April to September but underestimated the concentrations throughout the period due to the uncertainties in local and regional emissions over the region. The composition analysis of surface PM2.5 concentrations revealed that the dominant components were OIN and OC, accounting for 80% and 15% of the total concentrations, respectively, and drove the seasonal variation. The analysis of contributions from multiple physical and chemical processes indicated that the seasonal variation in surface PM2.5 concentrations was controlled by the variation in transport processes, PBL mixing, and dry and wet deposition. The variation in PM2.5 concentrations from May to July is due to wind direction changes that control the transported biomass burning aerosols from southern Africa, enhanced turbulent mixing of transported aerosols at the upper level to the surface and decreased wet deposition from decreased rainfall from May to July.

    • Public Summary

    • WRF-Chem simulations and in situ observations of surface PM2.5 concentrations were combined to study the seasonal variation over East Africa.
    • Analysis of contributions from multiple physical and chemical processes found transport, PBL mixing and wet and dry deposition to be driving mechanisms in the variation in surface concentration.
    • Wind direction changes transported aerosols to the region, and turbulent mixing with decreased rainfall increased the surface concentration from May to July.

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    Figure  1.  Domain overview and elevation (left, d01), East Africa (right, d02) with locations of AERONET sites (red stars) and Kigali city (black point).

    Figure  2.  (a) Anthropogenic emissions for the African continent (d01) and East Africa (d02). (b) Dust emissions over Africa (domain d01). (c) Biomass burning emissions (OC) for domain d01 (Africa) and domain d02 (East Africa); the black box represents domain d02.

    Figure  3.  Spatial distribution of the integrated column of PM2.5 mass concentrations averaged for each month over East Africa from the simulation of domain 2 for the April-September period. The black circle shows the region over Rwanda.

    Figure  4.  Spatial distribution of averaged AOD at 550 nm from retrievals of MISR, MODIS Terra, and simulated AOD from WRF-chem over East Africa. Brown dots show the AERONET sites (IC: icipe 34.02°E, 0.43°S; MS: Msamfu, 31.22°E, 10.17°S) for the April-September period. The model results are sampled at the time and locations of the MODIS retrievals. The blank area in the plots means that no data are available.

    Figure  5.  Spatial distribution of surface PM2.5 concentrations from the WRF-Chem simulations collocated with average PM2.5 concentrations from observations over Kigali city for the April-September period. Filled circles represent the observed PM2.5 concentrations.

    Figure  6.  (a) Daily variations in surface PM2.5 concentrations averaged over the stations from WRF-Chem simulations and observations for the April-September period, (b) similar to (a), but the concentrations are normalized by the maximum value during the period for the simulations and observations.

    Figure  7.  (a) Contribution of the individual processes (transport, emission, wet and dry deposition, chemical production/loss, PBL mixing) to PM2.5 concentrations averaged over Kigali. (b) Total budget analysis averaged over Kigali. Red bars represent the sum of individual processes, and the black line represents the tendency of PM2.5 concentrations during May, June and July.

    Figure  11.  (a) Monthly average friction velocity and 10 m wind field pattern for May, June, and July. shading contours represent friction velocity and wind barbs represent wind speed at 2.5 m·s−1 from WRF-Chem (b) Monthly average of spatial distribution of rainfall for May, June and July period from WRF-Chem simulations, the black circle represents Rwanda.

    Figure  8.  Monthly average variation in surface PM2.5 concentration components (dust, EC, OIN, sea salt, OC, sulfate, ammonium, nitrates) averaged over Kigali during May, June and July.

    Figure  9.  Monthly average wind circulation pattern at the 700 hPa level for May, June, and July. The shading contours represent wind speed, and the wind barbs represent wind at 5 m·s−1 from WRF-Chem and ERA-5 reanalysis. The black circle represents Rwanda.

    Figure  10.  Monthly variation in PBLH averaged over the city of Kigali during May, June and July.

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