ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
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.

    FullText(HTML)
  • loading
    • References(77)
  • [1]
    IPCC (2022) Climate Change 2022 Mitigation of Climate Change. Working Group III contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC AR6 WG III, 2022
    [2]
    Ma J, Xu X, Zhao C, Yan P. A review of atmospheric chemistry research in China: Photochemical smog. haze pollution, and gas-aerosol interactions. Adv. Atmos. Sci., 2012, 29 (5): 1006–1026.
    [3]
    Lin, Neng, Huei, Chang, Moo, Been, Hwang J, Kaneyasu N, Zhang R (2018) Overview of the Special Issue "Aerosol Source, Transport, Chemistry, and Emission Control" for the 10th Asian Aerosol Conference 2017. Aerosol Air Qual. Res. 18(7): 1515–1518
    [4]
    IPCC (2013) Climate Change 2013 The Physical Science Basis. Working Group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change, IPCC AR5 WG I. Intergovernmental Panel on Climate Change, New York
    [5]
    IPCC (2007) The physical science basis : summary for policymakers: contribution of Working Group I to the Fourth assessment report. of the Intergovernmental Panel on Climate Change. IPCC, Geneva
    [6]
    WHO (ed) (2013) Ambient air pollution: A global assessment of exposure and burden of diseases
    [7]
    Pope, Francis, D., Gatari M, Ng'anga D, Poynter A, Blake R. Airborne particulate matter monitoring in Kenya using calibrated low-cost sensors. Atmos. Chem. Phys., 2018, 18 (20): 15403–15418. doi: 10.5194/acp-18-15403-2018
    [8]
    UK DFID (2018) East African Regional Analysis of Youth Demographics
    [9]
    Doumbia M, Toure N’D, Silue S, Yoboue V, Diedhiou A, Hauhouot C. Emissions from the Road Traffic of West African Cities: Assessment of Vehicle Fleet and Fuel Consumption. Energies, 2018, 11 (9): 2300. doi: 10.3390/en11092300
    [10]
    Kalisa E, Nagato EG, Bizuru E, Lee KC, Tang N, Pointing SB, Hayakawa K, Archer SDJ, Lacap-Bugler DC (2018) Characterization and Risk Assessment of Atmospheric PM2.5 and PM10 Particulate-Bound PAHs and NPAHs in Rwanda, Central-East Africa. Environmental science & technology 52(21): 12179–12187
    [11]
    Kirenga BJ, Meng Q, van Gemert F, Aanyu-Tukamuhebwa H, Chavannes N, Katamba A, Obai G, van der Molen T, Schwander S, Mohsenin V. The State of Ambient Air Quality in Two Ugandan Cities: A Pilot Cross-Sectional Spatial Assessment. International journal of environmental research and public health, 2015, 12 (7): 8075–8091. doi: 10.3390/ijerph120708075
    [12]
    Yang W, Seager R, Cane MA, Lyon B. The East African Long Rains in Observations and Models. J. Climate, 2014, 27 (19): 7185–7202. doi: 10.1175/JCLI-D-13-00447.1
    [13]
    Camberlin P, Okoola RE. The onset and cessation of the “long rains” in eastern Africa and their interannual variability. Theor. Appl. Climatol., 2003, 75 (1): 43–54. doi: 10.1007/s00704-002-0721-5
    [14]
    García MÁ, Sánchez ML, Los Ríos A de, Pérez IA, Pardo N, Fernández-Duque B (2019) Analysis of PM10 and PM2.5 Concentrations in an Urban Atmosphere in Northern Spain. Archives of environmental contamination and toxicology 76(2): 331–345
    [15]
    Komkoua-Mbienda AJ, Tchawoua C, Vondou DA, Choumbou P, Kenfack Sadem C, Dey S. Impact of anthropogenic aerosols on climate variability over Central Africa by using a regional climate model. Int. J. Climatol., 2017, 37 (1): 249–267. doi: 10.1002/joc.4701
    [16]
    Dezfuli AK, Nicholson SE. The Relationship of Rainfall Variability in Western Equatorial Africa to the Tropical Oceans and Atmospheric Circulation. Part II:The Boreal Autumn. J. Climate, 2013, 26 (1): 66–84.
    [17]
    Hu Y, Li D, Liu J (2007) Abrupt seasonal variation of the ITCZ and the Hadley circulation. Geophys. Res. Lett. 34(18)
    [18]
    Ilunga L, Muhire I, Mbaragijimana C (2004) Pluviometric seasons and rainfall origin in Rwanda. Geo-Eco-Trop: 61–68
    [19]
    Mutemi JN (2003) Climate anomalies over eastern Africa associated with various ENSO evolution phases. PhD. Thesis, , University of Nairobi, Kenya.
    [20]
    Gaita SM, Boman J, Gatari MJ, Wagner A, Jonsson SK. Characterization of Size-Fractionated Particulate Matter and Deposition Fractions in Human Respiratory System in a Typical African City: Nairobi. Kenya. Aerosol Air Qual. Res., 2016, 16 (10): 2378–2385. doi: 10.4209/aaqr.2016.01.0019
    [21]
    Mkoma SL, Chi X, Maenhaut W (2010) Characteristics of carbonaceous aerosols in ambient PM10 and PM2.5 particles in Dar es Salaam, Tanzania. The Science of the total environment 408(6): 1308–1314
    [22]
    Solmon F, Elguindi N, Mallet M (2012) Radiative and climate effects of dust over West Africa, as simulated by a regional climate model: 97–113
    [23]
    Zhao C, Liu X, Ruby Leung L, Hagos S. Radiative impact of mineral dust on monsoon precipitation variability over West Africa. Atmos. Chem. Phys., 2011, 11 (5): 1879–1893. doi: 10.5194/acp-11-1879-2011
    [24]
    Zhao C, Liu X, Leung LR, Johnson B, McFarlane SA, Gustafson WI, Fast JD, Easter R. The spatial distribution of mineral dust and its shortwave radiative forcing over North Africa: modeling sensitivities to dust emissions and aerosol size treatments. Atmos. Chem. Phys., 2010, 10 (18): 8821–8838. doi: 10.5194/acp-10-8821-2010
    [25]
    Molepo KM, Abiodun BJ, Magoba RN. The transport of PM10 over Cape Town during high pollution episodes. Atmospheric Environment, 2019, 213: 116–132. doi: 10.1016/j.atmosenv.2019.05.041
    [26]
    Kuik F, Lauer A, Beukes JP, van Zyl PG, Josipovic M, Vakkari V, Laakso L, Feig GT. The anthropogenic contribution to atmospheric black carbon concentrations in southern Africa: a WRF-Chem modeling study. Atmos. Chem. Phys., 2015, 15 (15): 8809–8830. doi: 10.5194/acp-15-8809-2015
    [27]
    Otieno G, Mutemi JN, Opijah FJ, Ogallo LA, Omondi MH. The Sensitivity of Rainfall Characteristics to Cumulus Parameterization Schemes from a WRF Model. Part I:A Case Study Over East Africa During Wet Years. Pure Appl. Geophys., 2020, 177 (2): 1095–1110.
    [28]
    Kerandi N, Arnault J, Laux P, Wagner S, Kitheka J, Kunstmann H. Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin. Theor Appl Climatol, 2018, 131 (3-4): 1337–1355. doi: 10.1007/s00704-017-2050-8
    [29]
    Komkoua Mbienda AJ, Tchawoua C, Vondou DA, Choumbou P, Kenfack Sadem C, Dey S. Sensitivity experiments of RegCM4 simulations to different convective schemes over Central Africa. Int. J. Climatol., 2017, 37 (1): 328–342. doi: 10.1002/joc.4707
    [30]
    Cook KH, Vizy EK. The Congo Basin Walker circulation: dynamics and connections to precipitation. Clim Dyn, 2016, 47 (3-4): 697–717. doi: 10.1007/s00382-015-2864-y
    [31]
    Pohl B, Crétat J, Camberlin P. Testing WRF capability in simulating the atmospheric water cycle over Equatorial East Africa. Clim Dyn, 2011, 37 (7-8): 1357–1379. doi: 10.1007/s00382-011-1024-2
    [32]
    Beck V, Koch T, Kretschmer R, Marshall J, Ahmadov R, Gerbig C, Pillai D (2011) The WRF Greenhouse Gas Model (WRF-GHG) Technical Report No. 25. Max Planck Institute for Biogeochemistry, Jena
    [33]
    Skamarock W, Klemp, J. , B. , Dudhia J, Gill, D. , O. , Barker DM, Duda, M. , G. , Huang, X. , Y. , Wang W, and Powers, J. , G. (2008) A Description of the Advanced Research WRF Version 3. American Journal of Climate Change
    [34]
    Grell GA, Peckham SE, Schmitz R, McKeen SA, Frost G, Skamarock WC, Eder B. Fully coupled online chemistry within the WRF model. Atmospheric Environment, 2005, 39 (37): 6957–6975. doi: 10.1016/j.atmosenv.2005.04.027
    [35]
    Zhang M, zhao c, Yang Y, Du Q, Shen Y, Lin S, Gu D (2021) Sensitivity of different BVOC emission schemes in WRF-Chem(v3.6) to vegetation distributions and its impacts over East China. Geosci. Model Dev.
    [36]
    Du Q, zhao c, Zhang M, Dong X, Chen Y, Liu Z, Hu Z, Zhang Q, Li Y, Yuan R, Miao S (2020) Modeling diurnal variation of surface PM2.5 concentrations over East China with WRF-Chem: impacts from boundary-layer mixing and anthropogenic emission. Atmos. Chem. Phys. 20(5): 2839–2863
    [37]
    Hu Z, Huang J, zhao c, Bi J, Jin Q, Qian Y, Leung LR, Feng T, Chen S, Ma J. Modeling the contributions of Northern Hemisphere dust sources to dust outflow from East Asia. Atmospheric Environment, 2019, 202: 234–243. doi: 10.1016/j.atmosenv.2019.01.022
    [38]
    Zhao C, Huang M, Fast, Jerome, D., Berg, Larry, K., Qian Y, Guenther A, Gu D, Shrivastava M, Liu Y, Walters S, Pfister G, Jin J, Shilling JE, Warneke C. Sensitivity of biogenic volatile organic compounds to land surface parameterizations and vegetation distributions in California. Geosci. Model Dev., 2016, 9 (5): 1959–1976. doi: 10.5194/gmd-9-1959-2016
    [39]
    Zhao C, Hu Z, Qian Y, Leung LR, Huang J, Huang M, Jin J, Flanner M, Zhang R, Wang H, Yan H, Lu Z, and Streets, D. G. Simulating black carbon and dust and their radiative forcing in seasonal snow: a case study over North China with fieldcampaign measurements. . Atmos. Chem. Phys., 2014, 14 (2): 11475–11491.
    [40]
    Fast JD, Gustafson Jr. , W. I. , Easter RC, Zaveri RA, Barnard JC, Chapman EG (2006) Evolution of ozone, particulates, and aerosol direct forcing in an urban area using a new fully coupled meteorology, chemistry, and aerosol model, . J. Geophys. Res 111
    [41]
    Francis S. Binkowski, Uma Shankar The Regional Particulate Matter Model 1. Model description and preliminary results
    [42]
    Easter R, Liu X, Ghan S, Zaveri R, Gettelman A, Rasch P (2009) Influence of anthropogenic sulfate and black carbon on upper tropospheric clouds in the NCAR CAM3 model coupled to the IMPACT global aerosol model, . J. Geophys. Res. 114(D03204)
    [43]
    Zhao C, Ruby Leung, Easter R, Hand J, Avise J. Characterization of speciated aerosol direct radiative forcing over California. J. Geophys. Res. Atmos., 2013, 118 (5): 2372–2388. doi: 10.1029/2012JD018364
    [44]
    Morrison H, Thompson G, Tatarskii V. Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes. Mon. Wea. Rev., 2009, 137 (3): 991–1007. doi: 10.1175/2008MWR2556.1
    [45]
    Hong Y, Dudhia J. A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 2006, 134: 2318–2341. doi: 10.1175/MWR3199.1
    [46]
    Kain JS. The Kain–Fritsch Convective Parameterization: An Update. J. Appl. Meteor., 2004, 43 (1): 170–181. doi: 10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2
    [47]
    Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA. Radiative transfer for inhomogeneous atmospheres: RRTM. a validated correlated-k model for the longwave. J. Geophys. Res., 1997, 102 (D14): 16663–16682.
    [48]
    Iacono MJ, Mlawer EJ, Clough SA, Morcrette J-J (2000) Impact of an improved longwave radiation model, RRTM, on the energy budget and thermodynamic properties of the NCAR community climate model, CCM3. J. Geophys. Res. 105(D11): 14873–14890
    [49]
    Zhao C, Chen S, Kok J, Leung LR, Qian Y, Huang J, Zaveri R. Uncertainty in modeling dust mass balance and radiative forcing from size parameterization. . Atmos. Chem. Phys., 2013, 13: 10733–10753. doi: 10.5194/acp-13-10733-2013
    [50]
    Seaman NL, Stauffer DR, Lario-Gibbs AM. A Multiscale Four-Dimensional Data Assimilation System Applied in the San Joaquin Valley during SARMAP. Part I:Modeling Design and Basic Performance Characteristics, . J. Appl. Meteor., 1995, 34: 1739–1761.
    [51]
    Liu P, Tsimpidi AP, Hu Y, Stone B, Russell AG, Nenes A. Differences between downscaling with spectral and grid nudging using WRF. Atmos. Chem. Phys., 2012, 12 (8): 3601–3610. doi: 10.5194/acp-12-3601-2012
    [52]
    Stauffer DR, Seaman, Nelson, L. Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model. Part I:Experiments with Synoptic-Scale Data. Mon. Wea. Rev., 1990, 118 (6): 1250–1277.
    [53]
    Janssens-Maenhout G, Crippa M, Guizzardi D, Dentener F, Muntean M, Pouliot G, Keating T, Zhang Q, Kurokawa J, Wankmüller R, Van der Denier Gon, H. , Kuenen J (2015) HTAP_v2.2: a mosaic ofregional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution. Atmos. Chem. Phys. 15: 11411–11432
    [54]
    Wiedinmyer C, Akagi SK, Yokelson RJ, Emmons LK, Al-Saadi JA, Orlando JJ, and Soja AJ. The Fire INventory from NCAR (FINN) – a high resolution global model to estimate the emissions from open burning. . Geosci. Model Dev., 2010, 3: 2439–2476.
    [55]
    Dentener F, Kinne S, Bond T, Boucher O, Cofala J, Generoso S, Ginoux P, Gong S, Hoelzemann JJ, Ito A, Marelli L, Penner JE, Putaud J-P, Textor C, Schulz M, van der Werf GR, Wilson J. Emissions of primary aerosol and precursor gases in the years 2000 and 1750. prescribed data-sets for AeroCom. Atmos. Chem. Phys. Discuss., 2006, 6 (2): 2703–2763.
    [56]
    Gong SL. A parameterization of sea-salt aerosol source function for sub- and supermicron particles. Global Biogeochem. Cycles, 2003, 17 (4): n/a–n/a.
    [57]
    Jaeglé L, Quinn, P., K., Bates, T., S., Alexander B, Lin, J., T. Global distribution of sea salt aerosols: new constraints from in situ and remote sensing observations. . Atmos. Chem. Phys., 2011, 11: 3137–3157. doi: 10.5194/acp-11-3137-2011
    [58]
    Ginoux P, Chin M, Tegen I, Prospero JM, Holben B, Dubovik O, Lin S-J. Sources and distributions of dust aerosols simulated with the GOCART model. J. Geophys. Res., 2001, 106 (D17): 20255–20273. doi: 10.1029/2000JD000053
    [59]
    Kok JF. A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108: 1016–1021. doi: 10.1073/pnas.1014798108
    [60]
    Kaufman YJ, Tanré D, Boucher O. A satellite view of aerosols in the climate system. Nature, 2002, 419 (6903): 215–223. doi: 10.1038/nature01091
    [61]
    Kaufman YJ, Tanré D, Remer LA, Vermote EF, Chu A, Holben BN. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J. Geophys. Res., 1997, 102 (D14): 17051–17067. doi: 10.1029/96JD03988
    [62]
    Sayer AM, Munchak LA, Hsu NC, Levy RC, Bettenhausen C, Jeong M-J. MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue. Dark Target, and “merged” data sets, and usage recommendations. J. Geophys. Res. Atmos., 2014, 119 (24): 13,965–13,989.
    [63]
    Martonchik JV (2004) Comparison of MISR and AERONET aerosol optical depths over desert sites. Geophys. Res. Lett. 31(16)
    [64]
    Holben BN, Eck TF, Slutsker I, Tanré D, Buis JP, Setzer A, Vermote E, Reagan JA, Kaufman YJ, Nakajima T, Lavenu F, Jankowiak I, Smirnov A. AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization. Remote Sensing of Environment, 1998, 66 (1): 1–16. doi: 10.1016/S0034-4257(98)00031-5
    [65]
    Zimmerman N, Presto AA, Kumar SPN, Gu J, Hauryliuk A, Robinson ES, Robinson AL, Subramanian R (2017) Closing the gap on lower cost air quality monitoring: machine learning calibration models to improve low-cost sensor performance
    [66]
    Subramanian R (2017) community air quality monitoring with the ramp sensor package. Accessed 12-May-2020
    [67]
    Ntwali D, Chen H. Diurnal spatial distributions of aerosol optical and cloud micromacrophysics properties in Africa based on MODIS observations. Atmospheric Environment, 2018, 182: 252–262. doi: 10.1016/j.atmosenv.2018.03.054
    [68]
    DeWitt HL, Gasore J, Rupakheti M, Potter KE, Prinn RG, Ndikubwimana JdD, Nkusi J, Safari B. Seasonal and diurnal variability in O 3. black carbon, and CO measured at the Rwanda Climate Observatory. Atmos. Chem. Phys., 2019, 19 (3): 2063–2078.
    [69]
    Boiyo R, Kumar KR, Zhao T, Bao Y. Climatological analysis of aerosol optical properties over East Africa observed from space-borne sensors during 2001–2015. Atmospheric Environment, 2017, 152: 298–313. doi: 10.1016/j.atmosenv.2016.12.050
    [70]
    Boiyo R, Kumar KR, Zhao T. Optical. microphysical and radiative properties of aerosols over a tropical rural site in Kenya, East Africa:Source identification, modification and aerosol type discrimination. Atmospheric Environment, 2018, 177: 234–252.
    [71]
    Kumar KR, Attada R, Dasari HP, Vellore RK, Langodan S, Abualnaja YO, Hoteit I. Aerosol Optical Depth variability over the Arabian Peninsula as inferred from satellite measurements. Atmospheric Environment, 2018, 187: 346–357. doi: 10.1016/j.atmosenv.2018.06.011
    [72]
    Ngarukiyimana JP, Fu Y, Yang Y, Ogwang BA, Ongoma V, Ntwali D. Dominant atmospheric circulation patterns associated with abnormal rainfall events over Rwanda. East Africa. Int. J. Climatol, 2018, 38 (1): 187–202. doi: 10.1002/joc.5169
    [73]
    Kalisa E, Archer S, Nagato E, Bizuru E, Lee K, Tang N, Pointing S, Hayakawa K, Lacap-Bugler D (2019) Chemical and Biological Components of Urban Aerosols in Africa: Current Status and Knowledge Gaps. International journal of environmental research and public health 16(6)
    [74]
    Nahayo L, Nibagwire D, Habiyaremye G, Kalisa E, Udahogora M, Maniragaba A. Awareness on Air Pollution and Risk Preparedness among Residents in Kigali City of Rwanda. International Journal of Sustainable Development & World Policy, 2019, 8 (1): 1–9.
    [75]
    Ayugi BO, Tan G. Recent trends of surface air temperatures over Kenya from 1971 to 2010. Meteorol Atmos Phys, 2019, 131 (5): 1401–1413. doi: 10.1007/s00703-018-0644-z
    [76]
    Anyah RO (2006) Simulated Physical Mechanisms Associated with Climate Variability over Lake Victoria Basin in East Africa
    [77]
    Noll, K., E., Fang, K., Y., P. Development of a dry deposition model for atmospheric coarse particles. Atmospheric Environment, 1989, 23 (3): 589–594.
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    Get Citation
    PDF
    XML

    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.

    [1]
    IPCC (2022) Climate Change 2022 Mitigation of Climate Change. Working Group III contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC AR6 WG III, 2022
    [2]
    Ma J, Xu X, Zhao C, Yan P. A review of atmospheric chemistry research in China: Photochemical smog. haze pollution, and gas-aerosol interactions. Adv. Atmos. Sci., 2012, 29 (5): 1006–1026.
    [3]
    Lin, Neng, Huei, Chang, Moo, Been, Hwang J, Kaneyasu N, Zhang R (2018) Overview of the Special Issue "Aerosol Source, Transport, Chemistry, and Emission Control" for the 10th Asian Aerosol Conference 2017. Aerosol Air Qual. Res. 18(7): 1515–1518
    [4]
    IPCC (2013) Climate Change 2013 The Physical Science Basis. Working Group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change, IPCC AR5 WG I. Intergovernmental Panel on Climate Change, New York
    [5]
    IPCC (2007) The physical science basis : summary for policymakers: contribution of Working Group I to the Fourth assessment report. of the Intergovernmental Panel on Climate Change. IPCC, Geneva
    [6]
    WHO (ed) (2013) Ambient air pollution: A global assessment of exposure and burden of diseases
    [7]
    Pope, Francis, D., Gatari M, Ng'anga D, Poynter A, Blake R. Airborne particulate matter monitoring in Kenya using calibrated low-cost sensors. Atmos. Chem. Phys., 2018, 18 (20): 15403–15418. doi: 10.5194/acp-18-15403-2018
    [8]
    UK DFID (2018) East African Regional Analysis of Youth Demographics
    [9]
    Doumbia M, Toure N’D, Silue S, Yoboue V, Diedhiou A, Hauhouot C. Emissions from the Road Traffic of West African Cities: Assessment of Vehicle Fleet and Fuel Consumption. Energies, 2018, 11 (9): 2300. doi: 10.3390/en11092300
    [10]
    Kalisa E, Nagato EG, Bizuru E, Lee KC, Tang N, Pointing SB, Hayakawa K, Archer SDJ, Lacap-Bugler DC (2018) Characterization and Risk Assessment of Atmospheric PM2.5 and PM10 Particulate-Bound PAHs and NPAHs in Rwanda, Central-East Africa. Environmental science & technology 52(21): 12179–12187
    [11]
    Kirenga BJ, Meng Q, van Gemert F, Aanyu-Tukamuhebwa H, Chavannes N, Katamba A, Obai G, van der Molen T, Schwander S, Mohsenin V. The State of Ambient Air Quality in Two Ugandan Cities: A Pilot Cross-Sectional Spatial Assessment. International journal of environmental research and public health, 2015, 12 (7): 8075–8091. doi: 10.3390/ijerph120708075
    [12]
    Yang W, Seager R, Cane MA, Lyon B. The East African Long Rains in Observations and Models. J. Climate, 2014, 27 (19): 7185–7202. doi: 10.1175/JCLI-D-13-00447.1
    [13]
    Camberlin P, Okoola RE. The onset and cessation of the “long rains” in eastern Africa and their interannual variability. Theor. Appl. Climatol., 2003, 75 (1): 43–54. doi: 10.1007/s00704-002-0721-5
    [14]
    García MÁ, Sánchez ML, Los Ríos A de, Pérez IA, Pardo N, Fernández-Duque B (2019) Analysis of PM10 and PM2.5 Concentrations in an Urban Atmosphere in Northern Spain. Archives of environmental contamination and toxicology 76(2): 331–345
    [15]
    Komkoua-Mbienda AJ, Tchawoua C, Vondou DA, Choumbou P, Kenfack Sadem C, Dey S. Impact of anthropogenic aerosols on climate variability over Central Africa by using a regional climate model. Int. J. Climatol., 2017, 37 (1): 249–267. doi: 10.1002/joc.4701
    [16]
    Dezfuli AK, Nicholson SE. The Relationship of Rainfall Variability in Western Equatorial Africa to the Tropical Oceans and Atmospheric Circulation. Part II:The Boreal Autumn. J. Climate, 2013, 26 (1): 66–84.
    [17]
    Hu Y, Li D, Liu J (2007) Abrupt seasonal variation of the ITCZ and the Hadley circulation. Geophys. Res. Lett. 34(18)
    [18]
    Ilunga L, Muhire I, Mbaragijimana C (2004) Pluviometric seasons and rainfall origin in Rwanda. Geo-Eco-Trop: 61–68
    [19]
    Mutemi JN (2003) Climate anomalies over eastern Africa associated with various ENSO evolution phases. PhD. Thesis, , University of Nairobi, Kenya.
    [20]
    Gaita SM, Boman J, Gatari MJ, Wagner A, Jonsson SK. Characterization of Size-Fractionated Particulate Matter and Deposition Fractions in Human Respiratory System in a Typical African City: Nairobi. Kenya. Aerosol Air Qual. Res., 2016, 16 (10): 2378–2385. doi: 10.4209/aaqr.2016.01.0019
    [21]
    Mkoma SL, Chi X, Maenhaut W (2010) Characteristics of carbonaceous aerosols in ambient PM10 and PM2.5 particles in Dar es Salaam, Tanzania. The Science of the total environment 408(6): 1308–1314
    [22]
    Solmon F, Elguindi N, Mallet M (2012) Radiative and climate effects of dust over West Africa, as simulated by a regional climate model: 97–113
    [23]
    Zhao C, Liu X, Ruby Leung L, Hagos S. Radiative impact of mineral dust on monsoon precipitation variability over West Africa. Atmos. Chem. Phys., 2011, 11 (5): 1879–1893. doi: 10.5194/acp-11-1879-2011
    [24]
    Zhao C, Liu X, Leung LR, Johnson B, McFarlane SA, Gustafson WI, Fast JD, Easter R. The spatial distribution of mineral dust and its shortwave radiative forcing over North Africa: modeling sensitivities to dust emissions and aerosol size treatments. Atmos. Chem. Phys., 2010, 10 (18): 8821–8838. doi: 10.5194/acp-10-8821-2010
    [25]
    Molepo KM, Abiodun BJ, Magoba RN. The transport of PM10 over Cape Town during high pollution episodes. Atmospheric Environment, 2019, 213: 116–132. doi: 10.1016/j.atmosenv.2019.05.041
    [26]
    Kuik F, Lauer A, Beukes JP, van Zyl PG, Josipovic M, Vakkari V, Laakso L, Feig GT. The anthropogenic contribution to atmospheric black carbon concentrations in southern Africa: a WRF-Chem modeling study. Atmos. Chem. Phys., 2015, 15 (15): 8809–8830. doi: 10.5194/acp-15-8809-2015
    [27]
    Otieno G, Mutemi JN, Opijah FJ, Ogallo LA, Omondi MH. The Sensitivity of Rainfall Characteristics to Cumulus Parameterization Schemes from a WRF Model. Part I:A Case Study Over East Africa During Wet Years. Pure Appl. Geophys., 2020, 177 (2): 1095–1110.
    [28]
    Kerandi N, Arnault J, Laux P, Wagner S, Kitheka J, Kunstmann H. Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin. Theor Appl Climatol, 2018, 131 (3-4): 1337–1355. doi: 10.1007/s00704-017-2050-8
    [29]
    Komkoua Mbienda AJ, Tchawoua C, Vondou DA, Choumbou P, Kenfack Sadem C, Dey S. Sensitivity experiments of RegCM4 simulations to different convective schemes over Central Africa. Int. J. Climatol., 2017, 37 (1): 328–342. doi: 10.1002/joc.4707
    [30]
    Cook KH, Vizy EK. The Congo Basin Walker circulation: dynamics and connections to precipitation. Clim Dyn, 2016, 47 (3-4): 697–717. doi: 10.1007/s00382-015-2864-y
    [31]
    Pohl B, Crétat J, Camberlin P. Testing WRF capability in simulating the atmospheric water cycle over Equatorial East Africa. Clim Dyn, 2011, 37 (7-8): 1357–1379. doi: 10.1007/s00382-011-1024-2
    [32]
    Beck V, Koch T, Kretschmer R, Marshall J, Ahmadov R, Gerbig C, Pillai D (2011) The WRF Greenhouse Gas Model (WRF-GHG) Technical Report No. 25. Max Planck Institute for Biogeochemistry, Jena
    [33]
    Skamarock W, Klemp, J. , B. , Dudhia J, Gill, D. , O. , Barker DM, Duda, M. , G. , Huang, X. , Y. , Wang W, and Powers, J. , G. (2008) A Description of the Advanced Research WRF Version 3. American Journal of Climate Change
    [34]
    Grell GA, Peckham SE, Schmitz R, McKeen SA, Frost G, Skamarock WC, Eder B. Fully coupled online chemistry within the WRF model. Atmospheric Environment, 2005, 39 (37): 6957–6975. doi: 10.1016/j.atmosenv.2005.04.027
    [35]
    Zhang M, zhao c, Yang Y, Du Q, Shen Y, Lin S, Gu D (2021) Sensitivity of different BVOC emission schemes in WRF-Chem(v3.6) to vegetation distributions and its impacts over East China. Geosci. Model Dev.
    [36]
    Du Q, zhao c, Zhang M, Dong X, Chen Y, Liu Z, Hu Z, Zhang Q, Li Y, Yuan R, Miao S (2020) Modeling diurnal variation of surface PM2.5 concentrations over East China with WRF-Chem: impacts from boundary-layer mixing and anthropogenic emission. Atmos. Chem. Phys. 20(5): 2839–2863
    [37]
    Hu Z, Huang J, zhao c, Bi J, Jin Q, Qian Y, Leung LR, Feng T, Chen S, Ma J. Modeling the contributions of Northern Hemisphere dust sources to dust outflow from East Asia. Atmospheric Environment, 2019, 202: 234–243. doi: 10.1016/j.atmosenv.2019.01.022
    [38]
    Zhao C, Huang M, Fast, Jerome, D., Berg, Larry, K., Qian Y, Guenther A, Gu D, Shrivastava M, Liu Y, Walters S, Pfister G, Jin J, Shilling JE, Warneke C. Sensitivity of biogenic volatile organic compounds to land surface parameterizations and vegetation distributions in California. Geosci. Model Dev., 2016, 9 (5): 1959–1976. doi: 10.5194/gmd-9-1959-2016
    [39]
    Zhao C, Hu Z, Qian Y, Leung LR, Huang J, Huang M, Jin J, Flanner M, Zhang R, Wang H, Yan H, Lu Z, and Streets, D. G. Simulating black carbon and dust and their radiative forcing in seasonal snow: a case study over North China with fieldcampaign measurements. . Atmos. Chem. Phys., 2014, 14 (2): 11475–11491.
    [40]
    Fast JD, Gustafson Jr. , W. I. , Easter RC, Zaveri RA, Barnard JC, Chapman EG (2006) Evolution of ozone, particulates, and aerosol direct forcing in an urban area using a new fully coupled meteorology, chemistry, and aerosol model, . J. Geophys. Res 111
    [41]
    Francis S. Binkowski, Uma Shankar The Regional Particulate Matter Model 1. Model description and preliminary results
    [42]
    Easter R, Liu X, Ghan S, Zaveri R, Gettelman A, Rasch P (2009) Influence of anthropogenic sulfate and black carbon on upper tropospheric clouds in the NCAR CAM3 model coupled to the IMPACT global aerosol model, . J. Geophys. Res. 114(D03204)
    [43]
    Zhao C, Ruby Leung, Easter R, Hand J, Avise J. Characterization of speciated aerosol direct radiative forcing over California. J. Geophys. Res. Atmos., 2013, 118 (5): 2372–2388. doi: 10.1029/2012JD018364
    [44]
    Morrison H, Thompson G, Tatarskii V. Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes. Mon. Wea. Rev., 2009, 137 (3): 991–1007. doi: 10.1175/2008MWR2556.1
    [45]
    Hong Y, Dudhia J. A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 2006, 134: 2318–2341. doi: 10.1175/MWR3199.1
    [46]
    Kain JS. The Kain–Fritsch Convective Parameterization: An Update. J. Appl. Meteor., 2004, 43 (1): 170–181. doi: 10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2
    [47]
    Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA. Radiative transfer for inhomogeneous atmospheres: RRTM. a validated correlated-k model for the longwave. J. Geophys. Res., 1997, 102 (D14): 16663–16682.
    [48]
    Iacono MJ, Mlawer EJ, Clough SA, Morcrette J-J (2000) Impact of an improved longwave radiation model, RRTM, on the energy budget and thermodynamic properties of the NCAR community climate model, CCM3. J. Geophys. Res. 105(D11): 14873–14890
    [49]
    Zhao C, Chen S, Kok J, Leung LR, Qian Y, Huang J, Zaveri R. Uncertainty in modeling dust mass balance and radiative forcing from size parameterization. . Atmos. Chem. Phys., 2013, 13: 10733–10753. doi: 10.5194/acp-13-10733-2013
    [50]
    Seaman NL, Stauffer DR, Lario-Gibbs AM. A Multiscale Four-Dimensional Data Assimilation System Applied in the San Joaquin Valley during SARMAP. Part I:Modeling Design and Basic Performance Characteristics, . J. Appl. Meteor., 1995, 34: 1739–1761.
    [51]
    Liu P, Tsimpidi AP, Hu Y, Stone B, Russell AG, Nenes A. Differences between downscaling with spectral and grid nudging using WRF. Atmos. Chem. Phys., 2012, 12 (8): 3601–3610. doi: 10.5194/acp-12-3601-2012
    [52]
    Stauffer DR, Seaman, Nelson, L. Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model. Part I:Experiments with Synoptic-Scale Data. Mon. Wea. Rev., 1990, 118 (6): 1250–1277.
    [53]
    Janssens-Maenhout G, Crippa M, Guizzardi D, Dentener F, Muntean M, Pouliot G, Keating T, Zhang Q, Kurokawa J, Wankmüller R, Van der Denier Gon, H. , Kuenen J (2015) HTAP_v2.2: a mosaic ofregional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution. Atmos. Chem. Phys. 15: 11411–11432
    [54]
    Wiedinmyer C, Akagi SK, Yokelson RJ, Emmons LK, Al-Saadi JA, Orlando JJ, and Soja AJ. The Fire INventory from NCAR (FINN) – a high resolution global model to estimate the emissions from open burning. . Geosci. Model Dev., 2010, 3: 2439–2476.
    [55]
    Dentener F, Kinne S, Bond T, Boucher O, Cofala J, Generoso S, Ginoux P, Gong S, Hoelzemann JJ, Ito A, Marelli L, Penner JE, Putaud J-P, Textor C, Schulz M, van der Werf GR, Wilson J. Emissions of primary aerosol and precursor gases in the years 2000 and 1750. prescribed data-sets for AeroCom. Atmos. Chem. Phys. Discuss., 2006, 6 (2): 2703–2763.
    [56]
    Gong SL. A parameterization of sea-salt aerosol source function for sub- and supermicron particles. Global Biogeochem. Cycles, 2003, 17 (4): n/a–n/a.
    [57]
    Jaeglé L, Quinn, P., K., Bates, T., S., Alexander B, Lin, J., T. Global distribution of sea salt aerosols: new constraints from in situ and remote sensing observations. . Atmos. Chem. Phys., 2011, 11: 3137–3157. doi: 10.5194/acp-11-3137-2011
    [58]
    Ginoux P, Chin M, Tegen I, Prospero JM, Holben B, Dubovik O, Lin S-J. Sources and distributions of dust aerosols simulated with the GOCART model. J. Geophys. Res., 2001, 106 (D17): 20255–20273. doi: 10.1029/2000JD000053
    [59]
    Kok JF. A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108: 1016–1021. doi: 10.1073/pnas.1014798108
    [60]
    Kaufman YJ, Tanré D, Boucher O. A satellite view of aerosols in the climate system. Nature, 2002, 419 (6903): 215–223. doi: 10.1038/nature01091
    [61]
    Kaufman YJ, Tanré D, Remer LA, Vermote EF, Chu A, Holben BN. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J. Geophys. Res., 1997, 102 (D14): 17051–17067. doi: 10.1029/96JD03988
    [62]
    Sayer AM, Munchak LA, Hsu NC, Levy RC, Bettenhausen C, Jeong M-J. MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue. Dark Target, and “merged” data sets, and usage recommendations. J. Geophys. Res. Atmos., 2014, 119 (24): 13,965–13,989.
    [63]
    Martonchik JV (2004) Comparison of MISR and AERONET aerosol optical depths over desert sites. Geophys. Res. Lett. 31(16)
    [64]
    Holben BN, Eck TF, Slutsker I, Tanré D, Buis JP, Setzer A, Vermote E, Reagan JA, Kaufman YJ, Nakajima T, Lavenu F, Jankowiak I, Smirnov A. AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization. Remote Sensing of Environment, 1998, 66 (1): 1–16. doi: 10.1016/S0034-4257(98)00031-5
    [65]
    Zimmerman N, Presto AA, Kumar SPN, Gu J, Hauryliuk A, Robinson ES, Robinson AL, Subramanian R (2017) Closing the gap on lower cost air quality monitoring: machine learning calibration models to improve low-cost sensor performance
    [66]
    Subramanian R (2017) community air quality monitoring with the ramp sensor package. Accessed 12-May-2020
    [67]
    Ntwali D, Chen H. Diurnal spatial distributions of aerosol optical and cloud micromacrophysics properties in Africa based on MODIS observations. Atmospheric Environment, 2018, 182: 252–262. doi: 10.1016/j.atmosenv.2018.03.054
    [68]
    DeWitt HL, Gasore J, Rupakheti M, Potter KE, Prinn RG, Ndikubwimana JdD, Nkusi J, Safari B. Seasonal and diurnal variability in O 3. black carbon, and CO measured at the Rwanda Climate Observatory. Atmos. Chem. Phys., 2019, 19 (3): 2063–2078.
    [69]
    Boiyo R, Kumar KR, Zhao T, Bao Y. Climatological analysis of aerosol optical properties over East Africa observed from space-borne sensors during 2001–2015. Atmospheric Environment, 2017, 152: 298–313. doi: 10.1016/j.atmosenv.2016.12.050
    [70]
    Boiyo R, Kumar KR, Zhao T. Optical. microphysical and radiative properties of aerosols over a tropical rural site in Kenya, East Africa:Source identification, modification and aerosol type discrimination. Atmospheric Environment, 2018, 177: 234–252.
    [71]
    Kumar KR, Attada R, Dasari HP, Vellore RK, Langodan S, Abualnaja YO, Hoteit I. Aerosol Optical Depth variability over the Arabian Peninsula as inferred from satellite measurements. Atmospheric Environment, 2018, 187: 346–357. doi: 10.1016/j.atmosenv.2018.06.011
    [72]
    Ngarukiyimana JP, Fu Y, Yang Y, Ogwang BA, Ongoma V, Ntwali D. Dominant atmospheric circulation patterns associated with abnormal rainfall events over Rwanda. East Africa. Int. J. Climatol, 2018, 38 (1): 187–202. doi: 10.1002/joc.5169
    [73]
    Kalisa E, Archer S, Nagato E, Bizuru E, Lee K, Tang N, Pointing S, Hayakawa K, Lacap-Bugler D (2019) Chemical and Biological Components of Urban Aerosols in Africa: Current Status and Knowledge Gaps. International journal of environmental research and public health 16(6)
    [74]
    Nahayo L, Nibagwire D, Habiyaremye G, Kalisa E, Udahogora M, Maniragaba A. Awareness on Air Pollution and Risk Preparedness among Residents in Kigali City of Rwanda. International Journal of Sustainable Development & World Policy, 2019, 8 (1): 1–9.
    [75]
    Ayugi BO, Tan G. Recent trends of surface air temperatures over Kenya from 1971 to 2010. Meteorol Atmos Phys, 2019, 131 (5): 1401–1413. doi: 10.1007/s00703-018-0644-z
    [76]
    Anyah RO (2006) Simulated Physical Mechanisms Associated with Climate Variability over Lake Victoria Basin in East Africa
    [77]
    Noll, K., E., Fang, K., Y., P. Development of a dry deposition model for atmospheric coarse particles. Atmospheric Environment, 1989, 23 (3): 589–594.
    Recommended articles
    TrendMD

    Article Metrics

    Article views (586) PDF downloads(8644)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return