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Papers by andriannah mbandi

Assessment of the impact of road transport policies on air pollution and greenhouse gas emissions in Kenya

Energy Strategy Reviews

We compile a detailed road transport inventory for greenhouse gases and air pollutants to explore... more We compile a detailed road transport inventory for greenhouse gases and air pollutants to explore energy emissions from alternative policy scenarios for the Kenya road transport sector. In 2010, road transport emissions accounted for 61% of total nitrogen oxides emissions in Kenya, 39% of fine particulate matter, 20% of carbon dioxide. In the business as usual scenario, road transport emissions increase between 4 and 31-fold from 2010 to 2050, with projected increases of motorcycles accounting for nearly all the increased pollutant emissions. Improved vehicle emission and fuel economy standards, fuel shift and investment in public transport are shown to be effective mitigation options to meet Kenya's climate change goals with the additional benefits of better air quality and improved health.

Energy Strategy Reviews, 2023

Clean air journal, Dec 22, 2022

The launch of the first-ever Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa

CLEAN AIR JOURNAL, 2022

CLEAN AIR JOURNAL Volume 32, No 2, 2022© 2022. The Author(s). Published under aCreative Commons A... more CLEAN AIR JOURNAL Volume 32, No 2, 2022© 2022. The Author(s). Published under aCreative Commons Attribution Licence. 1News The launch of the first-ever Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa November 2022 saw the release of the “Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa - the Summary for Decision Makers Report” (UNEP, 2022), by the African Union Commission (AUC), the Climate and Clean Air Coalition (CCAC), and the UN Environment Programme (UNEP) at Climate COP27 (Figure 1). Developed by African scientists and supported by the Stockholm Environment Institute (SEI), the report unpacks how short-lived climate pollutants (SLCPs), greenhouse gases and other polluting emissions play a role in sustainable development in Africa.

The findings, interpretations and conclusions expressed herein are those of the authors and do no... more The findings, interpretations and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the United Nations or its officials or Member States. The designation employed and the presentation of material on any map in this work do not imply the expression of any opinion whatsoever on the part of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This publication has been externally edited.

Clean Air Journal

Feature: Air quality challenges in low-income settlements A group of youth from the Mukuru settle... more Feature: Air quality challenges in low-income settlements A group of youth from the Mukuru settlements (Community Champions) involved in soil sample collection.

CPUT Theses & Dissertations, Jan 1, 2009

The use of Neural Networks in chemical engineering is well documented. There has also been an inc... more The use of Neural Networks in chemical engineering is well documented. There has also been an increase in research concerned with the explanatory capacity of Neural Networks although this has been hindered by the regard of Artificial Neural Networks (ANN’s) as a black box technology.

Determining variable importance in complex systems that have many variables as found in the fields of ecology, water treatment, petrochemical production, and metallurgy, would reduce the variables to be used in optimisation exercises, easing complexity of the model and ultimately saving money. In the process engineering field, the use of data to optimise processes is limited if some degree of process understanding is not present.

The project objective is to develop a methodology that uses Artificial Neural Network (ANN) technology and Multiple Linear Regression (MLR) to identify explanatory variables in a dataset and their importance on process outputs. The methodology is tested by using data that exhibits defined and well known numeric relationships. The numeric relationships are presented using four equations.

The research project assesses the relative importance of the independent variables by using the “dropping method” on a regression model and ANN’s. Regression used traditionally to determine variable contribution could be unsuccessful if a highly non-linear relationship exists. ANN’s could be the answer for this shortcoming.

For differentiation, the explanatory variables that do not contribute significantly towards the output will be named “suspect variables”. Ultimately the suspect variables identified in the regression model and ANN should be the same, assuming a good regression model and network.

The dummy variables introduced to the four equations are successfully identified as suspect variables. Furthermore, the degree of variable importance was determined using linear regression and ANN models. As the equations complexity increased, the linear regression models accuracy decreased, thus suspect variables are not correctly identified. The complexity of the equations does not affect the accuracy of the ANN model, and the suspect variables are correctly identified.

The use of R2 and average error in establishing a criterion for identifying suspect variables is explored. It is established that the cumulative variable importance percentage (additive percentage), has to be below 5% for the explanatory variable to be considered a suspect variable.

Combining linear regression and ANN provides insight into the importance of explanatory variables and indeed suspect variables and their contribution can be determined. Suspect variables can be eliminated from the model once identified simplifying the model, and increasing accuracy of the model.

Conference Presentations by andriannah mbandi

Particulate Mass (µg/m3) Particulate Number (#/km) Opacity: % Smoke density: m -1 Environment Dep... more Particulate Mass (µg/m3) Particulate Number (#/km) Opacity: % Smoke density: m -1 Environment Department Vehicle emission inventories = Units of emissions: g/year EF: Emission factors (g/km) VKT: Vehicle kilometres travelled Inventories: SO 2 , NO X , CO, CO 2 , NMVOC, NH 3 , PM 10 , PM 2.5 , BC, OC and CH 4 .

Drafts by andriannah mbandi

Strong and sustained economic growth is delivering fresh opportunity to sub-Saharan Africa. Goods... more Strong and sustained economic growth is delivering fresh opportunity to sub-Saharan Africa. Goods, supplies and people are on the move. On the downside, urban air pollution, traffic congestion and road fatalities are increasing. Africa has less than 3% of the world's motor vehicles yet accounts for 16% of road fatalities; hundreds of thousands each year. Vehicle emissions also contribute to urban air pollution; a problem estimated to cause 175,700 deaths in Africa each year. South Africa has the highest motorization rate and one of the highest road fatalities in the sub-Saharan African region.