Bio

Publications


2015

Juma, GS, Mutai BK, Ngaina JN.  2015.  Socio-Economic Valuation of Information for Climate Change Adaptation and Mitigation: A Case of Farmers’ Responses in Kakamega County . BEST: International Journal of Humanities, Arts, Medicine and Sciences. 3(12):89-104.
Mutai, BK, Muthama NJ, Ng'ang'a JK, Ngaina JN.  2015.  Analysis of the Temporal Evolution of Total Column Nitrogen Dioxide and Ozone over Nairobi, Kenya using OMI Measurements. Ethiopian Journal of Environmental Studies and Management. 8(5):530-540.
Muthama, NJ, Kaume CM, Mutai BK, Ng'ang'a JK.  2015.  Simulation of Potential Impact of Air Pollution from the Proposed Coal Mining Sites in Mui Basin, Kitui County. . Africa Journal of Physical Sciences. 2(1):60-72.

2014

Ngaina, JN, Mutua FM, Muthama NJ, Kirui JWJ, Sabiiti G, Mukhala E, Maingi NW, Mutai BK.  2014.  Drought monitoring in Kenya: A case of Tana River County. International Journal of Agricultural Science Research . 3(7):126-135.
Ngaina, JN, Muthama NJ, Ininda JM, Oprere AO, Mutai BK.  2014.  Towards precipitation enhancement through cloud seeding in Kenya. Global Meteorology. 3(4986)
Mutai, BK, Muthama JN, Ng'ang'a JK, Ngaina JN.  2014.  Confounding Effect of Weather and Outdoor Particulate Matter on Asthma Incidence over Kenya, 26-28, February. The Thirty Sixth Regional Climate Outlook Forum (GHACOF36) and Young Scientists Forum. , Entebbe, Uganda Abstract

Confounding Effect of Weather and Outdoor Particulate Matter on Asthma Incidence over Kenya
*1Mutai, B. K., 1Muthama, J. N., 1Ng’ang’a, J. K. and 1Ngaina, J. N
1 Department of Meteorology, University of Nairobi, P. O. Box 30197 Nairobi, Kenya
*Corresponding Author: E-mail address: berth@uonbi.ac.ke/berth_85@yahoo.com
Phone No: +254729875946/+254720811421; Profile: profiles.uonbi.ac.ke/berth
Abstract
Asthma is a major cause of chronic morbidity and mortality throughout the world. There is evidence that its prevalence has increased considerably over the past 20 years. It is estimated that 300 million individuals are affected worldwide. Outdoor particle pollution and weather are the most commonly and individually linked with triggering symptoms of asthma in patients. However studies have indicated that the levels and distribution of air pollution are highly dependent on the meteorology. This study was conducted based on this confounding-effect theory.
The study sought to examine the combined effect of fine particulate matter (PM2.5) and selected meteorological variables on asthma occurrence over Kenya. Monthly counts of asthma from four provincial and 1 district hospitals in Kenya were obtained from the hospital records during a twelve-year period (2001-2012). Monthly rainfall, temperature and wind speed from synoptic stations and satellite air quality data for the same period were also used. Monthly Global 1o by 1o level-3 Aerosol Optical Depth data was obtained through Giovanni at 550 nm from MODIS-Terra Version. 5.1 and employed as PM2.5 proxy. The confounding effect of PM2.5 pollution and meteorological parameters on asthma incidence was investigated using the Generalized Linear Models with Poisson distribution and logistic analyses executed in an R programming environment.
Asthma incidence had a seasonal pattern. The weather-modified effect of PM2.5 on asthma hospital visits was such that for moderate PM2.5 concentrations, over 5.8% increase in asthma incidence was reported during the hot and wet season (March-April-May) over Nyeri, 3.5% during the generally hot and humid weather over Mombasa, 3.4% during the generally dry and windy weather over Garissa and 4.6% during the cold and dry weather (June-July-August) over Nairobi. There were few statistically significant associations (95% CL) between asthma cases and PM2.5 in any season.
These results suggest that weather variables may be statistically associated more strongly with asthma hospital visits than PM2.5 and may act as confounding factor in epidemiologic studies. Their interaction with air pollution and associated effect on occurrence of respiratory-related diseases should therefore be considered in such studies.
Keywords: Confounding effect, particulate matter, Incidence, Generalized Linear Model, Epidemiology

Ngaina, JN, Mutai BK, Ininda JM, Muthama JN.  2014.  Monitoring spatial-temporal variability of aerosol over Kenya. Ethiopian Journal of Environmental Studies and Management. 7(3):244-252.Weblink
Ngaina, JN, Njoroge JM, Mutua FM, Mutai BK, Opere AO.  2014.  Flood Forecasting over Lower Nzoia Sub-Basin in Kenya. Africa Journal of Physical Sciences. 1(1):25-31.Weblink
Ngaina, JN, Muthama JN, Opere AO, Ininda JM, Ng'etich CK, Ongoma V, Mutai BK.  2014.  Potential of harvesting atmospheric water over urban cities in Kenya. International Journal of Physical Sciences. 2(5):69-75.Weblink

2013

Mutai, BK, Muthama JN, Ng'ang'a JK, Ngaina, J. N.  2013.  Weather-dependence of Fine Particulate Matter Air Quality over Kenya, 12-16 November. The 11th Kenya Meteorological Society International Conference & 2nd save the Earth Expo on Meteorological Research, Application and Services. , KMS Headquarters, Nairobi Abstract

In developing countries, air pollution is increasing especially in urban centers. This has led to enhanced cases of cardio-respiratory diseases. According to World Health Organization, air pollution is estimated to cause about two million premature deaths worldwide annually. Additionally an estimated 800,000 premature deaths are caused each year by urban air pollution, a principle component of which is particulate matter. Studies have indicated that the levels and distribution of air pollution are highly dependent on the meteorology. This study was conducted based on this theory.
The study sought to examine the relationship between fine particulate matter (PM2.5) and selected meteorological variables over Kenya during the period 2001 to 2012. The data used included monthly rainfall, relative humidity, temperature and wind speed from synoptic stations and satellite air quality data from the period 2001 to 2012. Monthly Global 1o by 1o level-3 Aerosol Optical Depth data was obtained through Giovanni at 550 nm from MODIS-Terra Version. 5.1 and employed as PM2.5 proxy. Detailed monitoring of temporal and spatial patterns of fine particulate matter (PM2.5) pollution and meteorological parameters was carried out using time series analysis and surfer software. Correlation, simple regression and multiple regression techniques were used to model PM2.5 concentrations and distribution as a function of meteorological conditions.
The results reveal that a correlation between PM2.5 and rainfall, temperature and wind speed yields reasonable negative relationship with the r-value ranging from -0.429 to -0.785. A positive relationship with RH is realized, r-value ranging between 0.081 and 0.269. Student t-test showed that the results were statistically significant at 95% confidence level. The variation of rainfall, relative humidity, wind speed and temperature on the average explains 47% of PM2.5 concentrations.
Keywords: Fine Particulate Matter, Pollution, Aerosol Optical Depth, Regression.

Ngaina, JN, Mutai BK.  2013.  Observational evidence of climate change on extreme events over East Africa. Global Meteorology. 2(1)Weblink

2012

Muthama, JK, Mutai BK, Ngaina JN.  2012.  Towards Developing an Indicator for Indoor Air Pollution in Nairobi Using Atmospheric Stability Indices. Environmental Public Health - Collaboration for Sustainable Development. :12., Safari Park Hotel & Casino, Nairobi, Kenya. 10th & 11th May 2012: Cardiff Metropolitan University, Cardiff School of Health Sciences Abstract

The quality of indoor air inside offices, schools, other workplaces and homes is important not only for human comfort but also for their health. Poor indoor air quality (IAQ) has been tied to symptoms like headaches, fatigue, trouble concentrating, and irritation of the eyes, nose, throat and lungs. Also, some specific diseases have been linked to specific air contaminants or indoor environments, like asthma with damp indoor environments. Many factors affect IAQ. These factors include poor ventilation (lack of outside air), problems controlling temperatures, high or low humidity, recent remodeling, and other activities in or near a building that can affect the fresh air coming into the building. The quantification of IAQ is therefore necessary.
Hourly data for CO and O3 and daily wind and temperature from Chiromo Environmental Monitoring station was used in this study. Stability Indices were obtained using the Hysplit Model. Time series analysis was used to obtaining the temporal variation of pollutants, meteorological variables and atmospheric stability. The relationship between pollutants, their interaction with meteorological variables and atmospheric stability was determined through correlation analysis.
Minimum concentrations are observed between 0630hrs and 0730hrs and between 2030hrs and 2130hrs when highest concentrations are observed. During the early daylight and evening hours, pollutant concentration rises mainly due to the increase in traffic and acts as catalyst in the breakdown of O3. At midday (between 1300hrs and 1400hrs) maximum ozone concentrations are observed whereas CO depicts low level concentrations .During this period the atmosphere experienced a lot of conversion due to the thermal heating. Changes in day to day weather, is seen as a factor affecting the diurnal variation of indoor CO and O3 as weather determines how quickly pollutants are dispersed away from an area.

2011

Mutai, BK.  2011.  Pollution in environs of the City in the Sun-Nairobi. Weather and Climate Bulletin of the Kenya Meteorological Society. September - December 2011(Fourth Quarter):6-7. Abstract

Study reveals shocking pollutant levels in the city. Excerpts from the GEF project draft report.

Mutai, BK, Muthama JN, Ngaina JN.  2011.  The temporal cycles of carbon monoxide and Ozone and their impact on air quality over Nairobi. College of Health Science 1st International conference tagged “Towards Optimum Health Care” on 15th – 17th June 2011 . , Kenyatta National Hospital Campus, University of Nairobi, Kenya: Colledge of Health Sciences Abstract

This paper seeks to describe the determination of urban air quality of a certain area through monitoring of CO and O3. The diurnal variation of meteorological variables (temperature and wind), their interaction and effects on the diurnal cycles of carbon monoxide and ozone over is discussed.
Hourly data for CO and O3 (surface ozone) the month of December (2011) and daily wind and temperature for the same period from Chiromo Environmental Monitoring station was used in this study. Time series analysis was used in this study to obtain the diurnal cycles of pollutants and the meteorological variables. Graphical method was useful particular in the physical representation of diurnal variation of the variables throughout the entire study period. The relationship between ozone and carbon dioxide and their interaction with wind temperature was determined with the use of correlation analysis method.
Minimum ozone concentrations are observed between 0630h and 0730h and between 2030h and 2130h when highest CO concentrations are observed. During the early daylight and evening hours, pollutant concentration rises mainly due to the increase in traffic and acts as catalyst in the breakdown of O3. At midday (between1300h and 1400h) maximum ozone concentrations are observed whereas CO depicts low-level concentrations. During this period the atmosphere experienced a lot of conversion due to the thermal heating. Changes in day-to-day weather, is seen as a factor affecting the diurnal variation of CO and O3 as weather determines how quickly pollutants are dispersed away from an area. Weather also determines the thickness of the atmospheric layer, where the emissions are diluted in a vertical direction. The average concentrations of ozone were found to be 18.0+8.0ppb.Although O3 concentrations levels within the city of Nairobi are below the threshold levels set by WMO of 51ppb, the health impacts can be aggravated during midday compared to early mornings and evenings.

Mutai, BK.  2011.  Assessing the Impacts of Vegetative Cover Change over Mau Water Towers on the Discharge of River Njoro, Kenya. 10th International Kenya Meteorological Society Workshop on Climate and Socio-Economic Development . , Mombasa, Kenya: Kenya Meteorological Society (KMS) Abstract

Mau water catchment and its environs is a very fragile ecosystem whose dynamics exhibits oscillations in magnitude caused mainly by human impacts and other climatic factors. The most recent oscillation was accompanied by excision of the forested catchment by the communities living around, leading to additional decrease in vegetative cover. The main purpose of this study was to determine the relationship between discharge and normalized difference vegetative index over the catchment of interest. Monthly Normalized Difference Vegetative Index (NDVI), discharge and rainfall datasets for the period 1982 and 2000 were used in this study.
Time series of the NDVI, discharge and rainfall were then obtained. In order to determine the relationship between NDVI and discharge correlation analysis was done between the two variables. The relationship between NDVI and rainfall was also determined through correlation analysis.
From the results obtained it was evident that discharge has been relatively constant over time except for a marked increase between 1996 and 1999. NDVI and rainfall had a constant trend throughout the study period. From correlation analysis it is evident that there is no statistically significant relationship between discharge and rainfall. This is explained by the fact that the clearance of vegetation has been compensated by growth of other vegetation types. NDVI only reflects the vigor of vegetation but does not distinguish between vegetation types e.g. tea from forest. NDVI and rainfall only shows a slight relationship when lagged. This is explained by the fact that the NDVI at a region only affects the rainfall forming processes later in the season, though on a very slight scale.
In conclusion, the variability in discharge is thought to be dependent on other catchment parameters e.g. vegetation type, soil type and slope .Rainfall is completely dependent on other synoptic scale parameters e.g. air masses and other mesoscale forcings e.g. Lake Victoria circulation. It should be noted that a statistically significant relationship could be attained only with the use of very high resolution NDVI.

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