Increasing airborne particulate matter (PM) concentration in Kenya is an unfortunate consequence of rapid urbanization, coupled with a lack of strict implementation of air quality regulations. This has led to detrimental effects on human health, environment and local climate. To gain an indepth understanding of these effects, there is a need for a detailed characterization of PM in terms of abundance, sources, and properties, especially over the less characterized areas such as The Republic of Kenya (Kenya). This study presents long-term (1980-2020) spatial-temporal distributions and trends of PM2.5 over Kenya retrieved from the MERRA-2 model. The spatial patterns of annual mean PM2.5 loading were generally characterized by low (<7 μg•m？3), moderate (7 - 9 μg•m？3), and high (>11 μg•m？3) PM2.5 concentrations indicating distinct features of PM2.5 load. High (>11 μg•m？3) PM2.5 concentrations were observed over the arid and semiarid areas of the Northwest part of the country dominated by dust. Whereas, low (<7 μg•m？3) PM concentrations were observed over the Central and South Western parts of the country, with high vegetation and relatively high altitudes and precipitation. The seasonal mean PM2.5 over Kenya was found to be high (low) during the local dry (wet) seasons with mean values of >12 μg•m？3 and <6 μg•m？3, respectively. The magnitude of interannual variability in PM and its components over Kenya was found to be influenced by changes in emissions and local meteorology. The major PM2.5 emissions components were natu-ral dust emissions over the arid and semiarid areas in Northern Kenya with low annual precipitation. Linear trend analysis revealed an increase in PM2.5 over the years. Furthermore, the annual spatial trends revealed a general increase in PM2.5 over Kenya, being positive and significant over the dust-dominated areas of Northern Kenya. Later the spatial correlation between PM2.5 and its components revealed areas of similarities/dissimilarities and the magnitude of a correlation coefficient. PM2.5 correlated positively with dust in most parts of the country, followed by Sulphate (SO4), showing the significant contribution of the two components to PM2.5. On the other hand, a low (<2.5) correlation was observed between PM2.5 and Black Carbon (BC) and Organic Carbon (OC). Further analysis of annual and seasonal spatial variation, linear trends, and correlation of PM2.5 and components revealed dust as the major component of PM2.5 concentrations over the study domain. The study has improved the understanding of PM2.5 concentrations over the domain. It could provide significant information suitable for policy-making on air quality regula-tions in Kenya, especially on dust reduction mechanisms over the dominant areas.
References
[1]
World Health Organization (WHO) (2006) Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide-Global Update 2005-Summary of Risk Assessment. Geneva. https://www.who.int/publications/i/item/WHO-SDE-PHE-OEH-06-02
Jaiprakash, Singhai, A., Habib, G., Raman, R.S. and Gupta, T. (2016) Chemical Characterization of PM1.0 Aerosol in Delhi and Source Apportionment Using Positive Matrix Fac-torization. Environmental Science and Pollution Research, 24, 445-462. https://doi.org/10.1007/s11356-016-7708-8
[4]
Liu, L., Breitner, S., Schneider, A., Cyrys, J., Brüske, I., Franck, U., et al. (2013) Size-Fractioned Particulate Air Pollution and Cardiovascular Emergency Room Visits in Beijing, China. Environ-mental Research, 121, 52-63. https://doi.org/10.1016/j.envres.2012.10.009
[5]
Meng, X., Ma, Y., Chen, R., Zhou, Z., Chen, B. and Kan, H. (2013) Size-Fractionated Particle Number Con-centrations and Daily Mortality in a Chinese City. Environmental Health Perspec-tives, 121, 1174-1178. https://doi.org/10.1289/ehp.1206398
[6]
Mukherjee, A. and Agrawal, M. (2017) A Global Perspective of Fine Particulate Matter Pollution and Its Health Effects. In: de Voogt, P., Ed., Reviews of Environmental Contami-nation and Toxicology, Springer International Publishing, 5-51. https://doi.org/10.1007/398_2017_3
[7]
Kinney, P.L., Gichuru, M.G., Volav-ka-Close, N., Ngo, N., Ndiba, P.K., Law, A., et al. (2011) Traffic Impacts on PM2.5 Air Quality in Nairobi, Kenya. Environmental Science & Policy, 14, 369-378. https://doi.org/10.1016/j.envsci.2011.02.005
[8]
Thurston, G.D., Ahn, J., Cromar, K.R., Shao, Y., Reynolds, H.R., Jerrett, M., et al. (2016) Ambient Partic-ulate Matter Air Pollution Exposure and Mortality in the NIH-AARP Diet and Health Cohort. Environmental Health Perspectives, 124, 484-490. https://doi.org/10.1289/ehp.1509676
[9]
World Health Orgaization (WHO) (2012) Exposure to Particulate Matter with an Aerodynamic Diameter of 10 Mi-crometer or Less (PM10) in 1100 Urban Areas, 2003-2012.
[10]
Egondi, T., Ettarh, R., Kyobutungi, C., Ng, N. and Rocklöv, J. (2018) Exposure to Outdoor Particles (PM2.5) and Associated Child Morbidity and Mortality in Socially Deprived Neighborhoods of Nairobi, Kenya. Atmosphere, 9, Article No. 351. https://doi.org/10.3390/atmos9090351
[11]
Kaufman, Y.J., Tanré, D. and Bou-cher, O. (2002) A Satellite View of Aerosols in the Climate System. Nature, 419, 215-223. https://doi.org/10.1038/nature01091
[12]
Stocker, T., Plattner, G., Tignor, M., Qin, D., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V. and Midgley, P. (2013) The Physical Science Basis: Summary for Policy Makers. Cli-mate Change. https://www.ipcc.ch/report/ar5/wg1/
[13]
Gupta, P., Christopher, S.A., Wang, J., Gehrig, R., Lee, Y. and Kumar, N. (2006) Satellite Remote Sensing of Particulate Matter and Air Quality Assessment over Global Cities. Atmospheric Environment, 40, 5880-5892. https://doi.org/10.1016/j.atmosenv.2006.03.016
[14]
Wang, S., Zhou, C., Wang, Z., Feng, K. and Hubacek, K. (2017) The Characteristics and Drivers of Fine Par-ticulate Matter (PM2.5) Distribution in China. Journal of Cleaner Production, 142, 1800-1809. https://doi.org/10.1016/j.jclepro.2016.11.104
[15]
He, L., Lin, A., Chen, X., Zhou, H., Zhou, Z. and He, P. (2019) Assessment of MERRA-2 Surface PM2.5 over the Yangtze River Basin: Ground-Based Verification, Spatiotemporal Distribution and Meteorological Dependence. Remote Sensing, 11, Article No. 460. https://doi.org/10.3390/rs11040460
[16]
Petkova, E.P., Jack, D.W., Volav-ka-Close, N.H. and Kinney, P.L. (2013) Particulate Matter Pollution in African Cities. Air Quality, Atmosphere & Health, 6, 603-614. https://doi.org/10.1007/s11869-013-0199-6
[17]
Onyango, S., Parks, B., Anguma, S. and Meng, Q. (2019) Spatio-Temporal Variation in the Concentration of Inhalable Particulate Matter (PM10) in Uganda. International Journal of Environmental Re-search and Public Health, 16, Article No. 1752. https://doi.org/10.3390/ijerph16101752
[18]
Singh, A., Ng’ang’a, D., Gatari, M.J., Kidane, A.W., Alemu, Z.A., Derrick, N., et al. (2021) Air Quality Assessment in Three East African Cities Using Calibrated Low-Cost Sensors with a Focus on Road-Based Hotspots. Environmental Research Communications, 3, Article ID: 075007. https://doi.org/10.1088/2515-7620/ac0e0a
[19]
Tesema, G.A., Teshale, A.B. and Tessema, Z.T. (2021) Incidence and Predictors of Under-Five Mortality in East Africa Using Multilevel Weibull Regression Modeling. Archives of Public Health, 79, Article No. 196. https://doi.org/10.1186/s13690-021-00727-9
[20]
van Vliet, E.D.S. and Kinney, P.L. (2007) Impacts of Roadway Emissions on Urban Particulate Matter Concentrations in Sub-Saharan Africa: New Evidence from Nairobi, Kenya. Environmental Research Letters, 2, Article ID: 045028. https://doi.org/10.1088/1748-9326/2/4/045028
[21]
Ngo, N.S., Gatari, M., Yan, B., Chillrud, S.N., Bouhamam, K. and Kinney, P.L. (2015) Occupational Exposure to Roadway Emissions and Inside Informal Settlements in Sub-Saharan Africa: A Pilot Study in Nairobi, Kenya. Atmospheric Environment, 111, 179-184. https://doi.org/10.1016/j.atmosenv.2015.04.008
[22]
Egondi, T., Muindi, K., Kyobutungi, C., Gatari, M. and Rocklöv, J. (2016) Measuring Exposure Levels of Inhalable Airborne Particles (PM2.5) in Two Socially Deprived Areas of Nairobi, Kenya. Environmental Research, 148, 500-506. https://doi.org/10.1016/j.envres.2016.03.018
[23]
Kenya National Bureau of Sta-tistics (KNBS) (2019) Ministry of Planning. Republic of Kenya Economic Survey 2019.
[24]
Makokha, J.W. and Angeyo, H.K. (2013) Investigation of Radiative Characteristics of the Kenyan Atmosphere Due to Aerosols Using Sun Spectro-photometry Measurements and the COART Model. Aerosol and Air Quality Re-search, 13, 201-208. https://doi.org/10.4209/aaqr.2012.06.0146
[25]
Ongoma, V. and Chen, H. (2017) Temporal and Spatial Variability of Temperature and Precip-itation over East Africa from 1951 to 2010. Meteorology and Atmospheric Physics, 129, 131-144.
[26]
Ayugi, B., Tan, G., Ullah, W., Boiyo, R. and Ongoma, V. (2019) Inter-Comparison of Remotely Sensed Precipitation Datasets over Kenya during 1998-2016. Atmospheric Research, 225, 96-109. https://doi.org/10.1016/j.atmosres.2019.03.032
[27]
Yang, W., Seager, R., Cane, M.A. and Lyon, B. (2015) The Annual Cycle of East African Precipitation. Journal of Climate, 28, 2385-2404. https://doi.org/10.1175/jcli-d-14-00484.1
[28]
Boiyo, R., Kumar, K.R. and Zhao, T. (2018) Spatial Variations and Trends in AOD Cli-matology over East Africa during 2002-2016: A Comparative Study Using Three Satellite Data Sets. International Journal of Climatology, 38, e1221-e1240. https://doi.org/10.1002/joc.5446
[29]
Buchard, V., da Silva, A.M., Randles, C.A., Colarco, P., Ferrare, R., Hair, J., et al. (2016) Evaluation of the Surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States. At-mospheric Environment, 125, 100-111. https://doi.org/10.1016/j.atmosenv.2015.11.004
[30]
Zhang, J. and Reid, J.S. (2010) A Decadal Regional and Global Trend Analysis of the Aerosol Optical Depth Using a Data-Assimilation Grade Over-Water MODIS and Level 2 MISR Aerosol Products. Atmospheric Chemistry and Physics, 10, 10949-10963. https://doi.org/10.5194/acp-10-10949-2010
[31]
Kumar, K.R., Sivakumar, V., Yin, Y., Reddy, R.R., Kang, N., Diao, Y., et al. (2014) Long-Term (2003-2013) Climatological Trends and Variations in Aerosol Optical Parameters Retrieved from MODIS over Three Stations in South Africa. Atmospheric Environment, 95, 400-408. https://doi.org/10.1016/j.atmosenv.2014.07.001
[32]
Kumar, K.R., Yin, Y., Sivakumar, V., Kang, N., Yu, X., Diao, Y., et al. (2015) Aerosol Climatology and Discrimination of Aerosol Types Retrieved from MODIS, MISR and OMI over Durban (29.88˚S, 31.02˚E), South Africa. Atmospheric Environment, 117, 9-18. https://doi.org/10.1016/j.atmosenv.2015.06.058
[33]
Hu, K., Kumar, K.R., Kang, N., Boiyo, R. and Wu, J. (2017) Spatiotemporal Characteristics of Aerosols and Their Trends over Mainland China with the Recent Collection 6 MODIS and OMI Satellite Datasets. Environmental Science and Pollution Research, 25, 6909-6927. https://doi.org/10.1007/s11356-017-0715-6
[34]
Boiyo, R., Kumar, K.R., Zhao, T. and Bao, Y. (2017) Climatological Analysis of Aerosol Optical Properties over East Africa Observed from Space-Borne Sensors during 2001-2015. Atmospheric En-vironment, 152, 298-313. https://doi.org/10.1016/j.atmosenv.2016.12.050
[35]
Ngaina, J., Mutai, B., Ininda, J. and Muthama, J. (2014) Monitoring Spatial-Temporal Variability of Aerosol over Kenya. Ethiopian Journal of Environmental Studies and Management, 7, 244-252. https://doi.org/10.4314/ejesm.v7i3.3
[36]
de Graaf, M., Tilstra, L.G., Aben, I. and Stammes, P. (2010) Satellite Observations of the Seasonal Cycles of Absorbing Aerosols in Africa Related to the Monsoon Rainfall, 1995-2008. Atmospheric En-vironment, 44, 1274-1283. https://doi.org/10.1016/j.atmosenv.2009.12.038
[37]
Makokha, J.W., Odhiambo, J.O. and Godfrey, J.S. (2017) Trend Analysis of Aerosol Optical Depth and Ångström Exponent Anomaly over East Africa. Atmospheric and Climate Sciences, 7, 588-603. https://doi.org/10.4236/acs.2017.74043
[38]
Gatebe, C.K., Tyson, P.D., Annegarn, H.J., Helas, G., Kinyua, A.M. and Piketh, S.J. (2001) Characterization and Transport of Aerosols over Equatorial Eastern Africa. Global Biogeochemical Cycles, 15, 663-672. https://doi.org/10.1029/2000gb001340
