Browsing by Author "Boiyo, Richard"

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Long‑term climatology and spatial trends of absorption, scattering, and total aerosol optical depths over East Africa during 2001–2019
(Environmental Science and Pollution Research, 2021-03-28) Khamala, Geoffrey W.; Makokha, John Wanjala; Boiyo, Richard; Kumar, Kanike Raghavendra; Kumar, Kanike Raghavendra
The unprecedented increase in anthropogenic activities, coupled with the prevailing climatic conditions, has increased the aerosol load over East Africa (EA). Given this, the present study examined the trends in total, absorption, scattering, and total aerosol extinction optical depth (TAOD, AAOD, SAOD, and TAEOD) over EA, alongside trends in single scattering albedo (SSA). For this purpose, the AOD of different optical properties retrieved from multiple sensors and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) model between January 2001 to December 2019 were utilized to estimate trends and assess their statistical significance. The spatial patterns of seasonal mean AOD from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor and MERRA-2 model were generally characterized with high (>0.35) and low (<0.2) AOD centers over EA observed during the local dry and wet seasons, respectively. Also, the spatial trend analysis revealed a general increase in TAOD, being positive and significant over the arid and semi-arid zones of the northeastern part of EA, which is majorly dominated by locally derived dust. The local dry (wet) months generally experienced positive (negative) trends in TAOD, associated with seasonal cycles of rainfall. High and significant positive trends in AAOD were dominated over the study domain, attributed to an increased amount of biomass burning, variations in soil moisture, and changes in the rainfall pattern. The trends in TAEOD showed a distinct pattern, except over some months that depicted significant increasing trends attributed to changes in climatic conditions and anthropogenic activities. At last, the study domain exhibited decreasing trends in SSA, signifying strong absorption of direct solar radiation resulting in a warming effect. The study revealed patterns of trends in aerosol optical properties and forms the basis for further research in aerosols over EA.
Model-Derived Characterization of Particulate Matter (PM2.5) and Its Species over Kenya during 1980-2020
(Open Access Library Journal, 2024-07-30) Abok, Faith A.; Makokha, John Wanjala; Boiyo, Richard
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 in-depth 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 semi-arid 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 inter-annual 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 natural dust emissions over the arid and semi-arid 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 regulations in Kenya, especially on dust reduction mechanisms over the dominant areas.
Spatial-Temporal Variation of Aerosol Optical Depth and Ångström Exponent over Selected Towns in Kenya: Environmental Impact and Climate Change
(Open Access Library Journal, 2024-07-30) Mulago, Stephen K.; Makokha, John Wanjala; Boiyo, Richard
Aerosol optical depth (AOD) and Ångström Exponent (AE) have become the most crucial metrics in assessing climate change. Despite this, studies related to AOD and AE are rare in Kenya. Using Moderate Resolution Imaging Spectroradiometer (MODIS) data, the present study analysed the spatial and temporal variations of aerosol optical depth at 550 nm (AOD550) and examined the impact of these variations on AE over eight selected towns in Kenya during 2001-2021. The findings indicated high (0.22 ± 0.04) AOD during June-July-August-September (JJAS) and low (0.12 ± 0.04) values during March-April-May (MAM), all associated with prevailing local meteorological conditions. The Ångström Exponent in the wavelength (412 μm - 470 μm) was found to be high (1.1 - 1.7) in most towns, attributed to the dominance of fine-mode particles from increased anthropogenic activities. However, AE412-470 exhibited relatively low values in the range of 0.7 to 1.0 in Garissa due to the dominance of coarse mode particles associated with increased dust particles. Also, the coastal regions of Kenya have moderate to high values of AE412-470 associated with industrial emissions from the urbanized coastal regions of Mombasa. The study has contributed to an in-depth understanding of spatial-temporal variations of AOD and AE over the selected towns in Kenya and forms a scientific basis for further research on aerosol science over the region.
Spatiotemporal analysis of absorbing aerosols and radiative forcing over environmentally distinct stations in East Africa during 2001–2018
(Science of The Total Environment, 2023-03-15) Khamala, Geoffrey W.; Makokha, John Wanjala; Boiyo, Richard; Kumar, Kanike Raghavendra
East Africa (EA) suffers from the inadequate characterization of atmospheric aerosols, with far-reaching consequences of its inability to quantify precisely the impacts of these particles on regional climate. The current study aimed at characterizing absorption and radiative properties of aerosols using the long-term (2001–2018) AErosol RObotic NETwork (AERONET) and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data over three environmentally specific sites in EA. The annual mean absorption aerosol optical depth (AAOD ), absorption Angstrom Exponent (AAE ), total effective radius (R ), and total volume concentration (μm/μm) revealed significant spatial heterogeneity over the domain. The study domain exhibited a significant contribution of fine-mode aerosols compared to the coarse-mode particles. The monthly variation in SSA 440 nm over EA explains the strength in absorption aerosols that range from moderate to strong absorbing aerosols. The aerosols exhibited significant variability over the study domain, with the dominance of absorbing fine-mode aerosols over Mbita accounting for ∼40 to ∼50 %, while weakly absorbing coarse-mode particles accounted for ∼8.2 % over Malindi. The study conclusively determined that Mbita was dominated by AAOD mainly from biomass burning in most of the months, whereas Malindi was coated with black carbon. The direct aerosol −2 radiative forcing (DARF) retrieved from both the AERONET and MERRA-2 models showed strong cooling at the top of the atmosphere (TOA; −6 to −27 Wm ) and the bottom of the atmosphere (BOA, −7 to −66 Wm ). However, significant warming was noticed within the atmosphere (ATM; +14 to +76 Wm ), an indication of the role of aerosols in regional −2 −2 PDF climate change. The study contributed to understanding aerosol absorption and radiative characteristics over EA and can form the basis of other related studies over the domain and beyond.
Statistical Analysis of Aerosols Characteristics from Satellite Measurements over East Africa Using Autoregressive Moving Average (ARIMA)
(Open Access Library Journal, 2022-11-04) Khamala, Geoffrey W.; Makokha, John Wanjala; Boiyo, Richard
Aerosols have become a major subject of concern at global, regional and local scales. They influence Earth’s radiation budget by scattering and absorbing solar energy resulting in atmospheric cooling and warming respectively. However, immense efforts have been devoted to monitoring atmospheric aerosols using various techniques ranging from in-situ, ground and satellite-based remote sensing and modeling techniques. Thus, time series analysis and forecasting have gained momentum over recent decades. The current study performed a time series analysis using Box-Jenkins procedure-based ARIMA (Autoregressive Integrated Moving Average) model for aerosol properties (Total Aerosol Optical Depth, TAOD; Absorption Aerosol Optical Depth, AAOD; Scattering Aerosol Optical Depth, SAOD and Direct Aerosol Radiative Forcing, DARF) over EA derived from satellite platforms. The formulation process in MATLAB followed by the current study has been outlined with a view to generating the best fitting seasonal ARIMA (p, q , d ) × (P Q D ) model. The finding for the forementioned characteristics reveals clear seasonal variation, hence, differencing was done. The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) of differenced series are estimated and the significant lags are used to find out the order of the model. The statistical parameters (RMSE, MAE, MAPE, MASE and normalized BIC) were estimated for testing the validity of ARIMA models so formulated. The current study found that ARIMA (1, 0, 0) × (2, 1, 2)12 model is adequate for forecasting and was therefore used to forecast aerosol characteristics for the year 2022-2025 over EA domain. ARIMA model ascertained can be applied to other fields of study such as climatology, and climate change among other areas to predict future values so that timely control measures can effectively be planned.
The spatiotemporal and dependency analysis of selected meteorological parameters and normalized difference vegetation index with aerosol optical depth over east Africa
(Heliyon, 2024-10-29) Khamala, Geoffrey W.; Makokha, John Wanjala; Boiyo, Richard
The unprecedented rise in atmospheric aerosols, coupled with their intricate interactions with the environment through a wide array of physical, chemical, and biological processes, has profoundly impacted global climate. Their presence in the atmosphere scatters and absorbs solar radiation, thus altering the amount of sunlight reaching the Earth’s surface. These direct effects, along with the indirect effects of aerosols, have significantly altered atmospheric temperatures, land surface processes, global surface temperature, hydrological cycle, and ecosystems. Understanding the complex interplay between aerosols and climatic variables necessitates a multidisciplinary approach, such as dependency modeling. Addressing these challenges, the current study conducts a spatiotemporal correlational analysis of selected key meteorological parameters with aerosol optical depth over East Africa (EA) using multisensory data from Moderate-resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Application (MERRA-2) model, and Tropical Rainfall Measurement Mission (TRMM). Employing a weighted least squares regression (WLS) model, the study quantifies trends in the time series of climatic variables and Normalized Difference Vegetation Index (NDVI), further utilizing a statistical dependency modeling technique for correlational analysis. The trend analysis reveals a significant decreasing trend in surface wind speed (SWS) in most months, with sporadic positive trends attributed to anthropogenic activities, notably biomass burning, observed in January. Spatial trend analysis of Precipitation Rate (PR) displays heterogeneity, with significant negative trends in January and March, and positive trends in February, April, November, and December. Negative trends during May to August are attributed to increased anthropogenic activities, while enhanced positive trends in May correlate with low aerosol optical depth (AOD) during this period. Surface air temperature (SAT) exhibits diverse variations across the region, with dry months recording higher averages and trends than wet months. The study notes heterogeneous correlations in NDVI over the study area, with positive and negative correlations observed in different regions. Specifically, positive correlations are noted along the coastal and Lake Victoria regions, attributed to improved PR enhancing vegetation cover in these areas.