Assessing the contribution of road transport emission to air pollution and greenhouse gases in Africa: A disaggregate study in Kenya

Air pollution and greenhouse gas emissions affect health, climate and agriculture. In sub-Saharan Africa (SSA) air quality monitoring is underdeveloped which leads to uncertainty in the understanding of air pollution concentrations. However, studies that have been conducted in SSA show that ambient air pollution generally exceeds World Health Organization (WHO) guidelines. These studies show particularly high concentrations in urban areas such as Nairobi, Kenya. One of the key reasons is due to emissions from transport. Therefore, the main objective of this thesis is to quantify transport related emissions, set within the context of emissions from other sectors, using Nairobi, Kenya as a case study. Thus, this thesis has developed a methodology and framework at different scales (individual vehicle, city and national) to improve our understanding of transport-related emissions of air pollutants and greenhouse gas (GHG) to help guide policy making on future mitigation plans. Road transport emissions were investigated at multiple scales; at the finest scale, particulate matter (PM) emissions from the tailpipe were measured for a few vehicles using a novel multiplexed portable measurement system. At the urban scale, a model for fuel economy was constructed for a fleet from data collected in the field. Finally, at the national scale, available data gathered on fuel economy, vehicle activity and emissions were integrated to provide a country-level assessment of air pollution and GHG emissions from road transport, including evaluation of transport policies to reduce air pollution and GHGs

Estimating on-road vehicle fuel economy in Africa : A case study based on an urban transport survey in Nairobi, Kenya

In African cities like Nairobi, policies to improve vehicle fuel economy help to reduce greenhouse gas emissions and improve air quality, but lack of data is amajor challenge. We present a methodology for estimating fuel economy in such cities. Vehicle characteristics and activity data, for both the formal fleet (private cars, motorcycles, light and heavy trucks) and informal fleet-minibuses (matatus), three-wheelers (tuktuks), goods vehicles (AskforTransport) and two-wheelers (bodabodas)-were collected and used to estimate fuel economy. Using two empirical models, general linear modelling (GLM) and artificial neural network (ANN), the relationships between vehicle characteristics for this fleet and fuel economy were analyzed for the first time. Fuel economy for bodabodas (4.6 ± 0.4 L/100 km), tuktuks (8.7 ± 4.6 L/100 km), passenger cars (22.8 ± 3.0 L/100 km), and matatus (33.1 ± 2.5 L/100 km) was found to be 2-3 times worse than in the countries these vehicles are imported from. The GLM provided the better estimate of predicted fuel economy based on vehicle characteristics. The analysis of survey data covering a large informal urban fleet helps meet the challenge of a lack of availability of vehicle data for emissions inventories. This may be useful to policy makers as emissions inventories underpin policy development to reduce emissions

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

Erratum to "The launch of the first-ever Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa" published in Clean Air Journal, 32(2), 2022, https://doi.org/10.17159/caj/2022/32/2.15320 The original article can be found here: https://cleanairjournal.org.za/article/view/1532

Using linear regression and ANN techniques in determining variable importance

Thesis (MTech (Chemical Engineering))--Cape Peninsula University of Technology, 2009. Includes bibliographical references (leaves 95-100).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 nonlinear 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

A street-level assessment of greenhouse gas emissions associated with traffic congestion in the city of Nairobi, Kenya

Traffic congestion significantly contributes to climate change due to the emissions of Greenhouse Gases (GHGs) such as Carbon Dioxide (CO2), Nitrous Oxide (N2O), and Ozone (O3). Rapid urbanization and poor planning coupled with increased motorization and fragmented public transport system in cities such as Nairobi have led to increased vehicular emissions especially during heavy traffic along the various roads and within the Central Business District (CBD). To reduce GHG emissions in the urban transport sector, institutional coordination and relevant policy tools must be considered. This study aimed at estimating CO2 emissions from different vehicles during traffic congestion, using Uhuru Highway as a case study. The relationship between traffic congestion and CO2 emissions was analyzed using qualitative and quantitative methods, through a bottom-up approach. Questionnaires were administered to get individual vehicle characteristics and opinions on the best actions for the reduction of CO2 emissions along Uhuru Highway in Nairobi. The Average Annual Daily Traffic (AADT) for different vehicles from 2014 to 2019 was used to estimate the CO2 emissions. Results showed that private cars predominate over other vehicle types, contributing to 73% of the total CO2 emissions in Nairobi (CBD). Private cars are the highest contributor of CO2 emissions with a total of 25.3 million Carbo dioxide equivalent (gCO2e), between 2014 and 2019. In comparison, Public Service Vehicles, commonly referred to as Matatus emitted 6.89 million gCO2e, Light Commercial Vehicles (1.82 million gCO2e), Heavy Goods Vehicles (251,683 gCO2e), and motorcycles (181,054 gCO2e). To minimize CO2 emissions, the study recommended the enforcement of strong mobility policies to control the high motorization rate. One of these policies is the prioritization of the development of the mass public transport systems to achieve the potential health, economic and environmental gains within the CBD

Decoupling Energy, Water, and Food Resources Production from GHG Emissions: A Footprint Perspective Review of Africa from 1990 to 2017

Decoupling energy, water, and food (EWF) consumption and production from GHG emissions could be an important strategy for achieving the UN Sustainable Development Goals (SDGs), especially SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), and SDG 7 (Clean and Affordable Energy) in Africa. This study applies Tapio’s decoupling method to analyze the relationship between GHG emissions and EWF resources use in 15 African countries over the period 1990–2017. The results show a remarkable relationship, which includes the contamination of EWF by GHG emissions, that mostly exhibits unsatisfactory decoupling state to satisfactory decoupling over a period of several years. The decoupling of water and energy resources from GHG emissions in most countries of Africa has not been able to reach an excellent decoupling state or a strong positive decoupling state. This requires countries in Africa to support environmentally friendly water and energy infrastructures and to promote an integrated, mutually managed, whole resource interaction system. The study also highlights the importance of tracking sources of GHG emissions, whether within individual resource sector activities or across resources to each other

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

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