Household air pollution in Nairobi's slums: A long-term policy evaluation using participatory system dynamics.

58% of Nairobi's population live in informal settlements in extremely poor conditions. Household air pollution is one of the leading causes of premature death and disease in these settlements. Regulatory frameworks and government budgets for household air pollution do not exist and humanitarian organisations remain largely inattentive and inactive on this issue. The purpose of this paper is to evaluate the effectiveness of potential indoor-air related policies, as identified together with various stakeholders, in lowering household air pollution in Nairobi's slums. Applying a novel approach in this context, we used participatory system dynamics within a series of stakeholder workshops in Nairobi, to map and model the complex dynamics surrounding household air pollution and draw up possible policy options. Workshop participants included community members, local and national policy-makers, representatives from parastatals, NGOs and academics. Simulation modelling demonstrates that under business-as-usual, the current trend of slowly improving indoor air quality will soon come to a halt. If we aim to continue to substantially reduce household PM2.5 levels, a drastic acceleration in the uptake of clean stoves is needed. We identified the potentially high impact of redirecting investment towards household air quality monitoring and health impact assessment studies, therefore raising the public's and the government's awareness and concern about this issue and its health consequences. Such investments, due to their self-reinforcing nature, can entail high returns on investment, but are likely to give 'worse-before-better' results due to the time lags involved. We also discuss the usefulness of the participatory process within similar multi-stakeholder contexts. With important implications for such settings this work advances our understanding of the efficacy of high-level policy options for reducing household air pollution. It makes a case for the usefulness of participatory system dynamics for such complex, multi-stakeholder, environmental issues.

Introduction

Nairobi city, according to the most recent national census, is home to 3.14 million inhabitants (Kenya National Bureau of Statistics, 2010), having grown from just under half a million at the country's independence in 1963. The city's population growth is fuelled both by natural increase and migration from rural and other urban areas. For a long time, Nairobi has been the country's principal city and it remains an attractive destination for people looking for livelihood opportunities that are lacking in the mostly agricultural rural areas.

The rapid growth of the city's population has not been accompanied by sufficient provision of affordable housing and other social amenities, leading to the proliferation of slum settlements. It is estimated that Nairobi has over 150 slum settlements, scattered across the city. These settlements, which occupy <5% of the city's land mass, are home to an estimated 60–70% of the city population (Beguy et al., 2015). Numerous studies have reported the challenges that slum residents face, including the near absence of the public sector and poor access to public goods and services, with negative implications for various health outcomes (Kyobutungi et al., 2008; Mugisha, 2006).

Typical housing units in Nairobi's slums have tin/corrugated iron roofing and mud or tin/corrugated iron sheet walls. Most households rent one room measuring about 10 ft. by 10 ft. and these rooms serve as the kitchen, bedroom and living room (APHRC, n.d.). The rooms usually have one door and one window, although in some cases there are no windows at all. Most households rely on kerosene (paraffin) for cooking and lighting as well as charcoal or wood for cooking. In the poorest of households, the use of plastic waste, cloth rags and other unconventional fuels has been reported (Muindi et al., 2014). These fuels generate high levels of potentially harmful air pollution into the indoor environment. A separate study recorded high levels of particulate matter with aerodynamic diameter of 2.5 μm and less (PM2.5), especially in the evenings and in households burning charcoal/wood and kerosene (Muindi et al., 2016). In addition to housing features and behaviours that impact on the air quality, slums tend to be in areas close to primary sources of air pollutants. For example, many slums are built near busy highways, within industrial zones or near open dumpsites. Outdoor measurements of PM2.5 concentrations in slum areas found that there was spatial and temporal variations with slum villages close to major outdoor sources such as dumpsites having higher concentrations, while mornings and evenings were also noted to have elevated levels (Egondi et al., 2016). In a context of weak or non-existent policies to minimize emissions from various sources, slums experience high exposure to air pollution compared to non-slum areas of the city.

Household air pollution is estimated to result in a global burden on mortality of around 2.6 million premature deaths each year (GBD 2016 Mortality Collaborators, 2017). The most important contributor is biomass burning for cooking or heating, used by roughly half the world's population. The situation is exacerbated by factors including poor housing, inadequate ventilation and overcrowding, with women and children often exposed to particularly high levels of pollution. It has been established that large benefits can be gained from reducing air pollution, e.g. through switching to cleaner fuel sources such as LPG (liquid petroleum gas) (Morgan, 2015). Despite apparent benefits, in Kenya, to the best of the authors' knowledge, there are currently no programmes focusing on air pollution from a health point of view, either at national or at county level. This might be due to lack of specific internal or donor funding but could also be the result of a lack of understanding at governmental level on the magnitude and importance of the issue.

This paper therefore aims to investigate the comparative effects of a series of policy options on household air pollution in two of Nairobi's numerous slums, Korogocho and Viwandani. It analyses prerequisites, implications, and significance of public awareness and concern about household air pollution. We develop a system dynamics model that captures major drivers of household air pollution and potential policies aimed at mitigating them. We estimate potential improvements in the weight of the health burden associated with household air pollution as a result of various combinations of policies in order to identify beneficial policy directions.

Methods

This study combines methods including participatory modelling workshops, system dynamics (SD) modelling, and health impact assessment (HIA). In the following sub-sections, we introduce the study sites as well as each method, review the structure of the SD model, and validate the model to existing data.

The study sites: Korogocho and Viwandani

Korogocho is a slum settlement to the northeast of Nairobi city about 12 km from the city centre. It borders the Dandora dumpsite, Nairobi's official municipal dumpsite which is the final resting place for mixed waste streams from the city. This has been a source of pollution for residents of surrounding communities, with air pollution from burning garbage as well as soil and water pollution being key challenges. Viwandani is a slum settlement in the industrial zone of the city, with industries being visible sources of air and water pollution in the area. It lies about seven kilometres from the city centre and is home to a youthful, more educated and highly mobile population seeking employment in industries. In contrast Korogocho's population is older and less educated, and the majority have lived in the slum longer, compared with Viwandani's residents. Both slums face a shared challenge of poverty and exclusion especially with regards to the provision of government services such as health and education (Emina et al., 2011).

Participatory system dynamics workshops

The issue of household air pollution concerns various stakeholders besides the residents, including local decision makers, government policymakers, parastatals, and non-governmental actors. Therefore, to increase the chances of research resulting in increased awareness and commitment to change among all actors involved, we organised a series of three multi-stakeholder workshops in Nairobi and used participatory system dynamics to frame the discussions. Participants included a diverse group of stakeholders, including individuals with expertise on air quality and its impacts on health, as well as those working on policy development and implementation. Attendants also included community members from both slums, academia, representatives from the local and national governments, national parastatals, national and international NGOs, and United Nations agencies.

In our modelling session, we asked participants to identify the most central variables concerning indoor air quality. These variables were then gradually added to a causal loop diagram on a large whiteboard by asking participants to identify the chains of causality within the system. Following this process, we captured the overall causal structure of the system within the model and identified any emerging feedback loops, i.e. chains of causal relationships involving circular causality.

Each workshop was followed by off-site refinement, formalisation and calibration of the system dynamics model, where the model was further elaborated, adding more variables and closing some of the previously open feedback loops. As part of this refinement, we sent out a questionnaire to ten members of the stakeholder group asking them to rate various policies identified during the workshops based on their relative importance to household air pollution. Subsequently, out of a total of fourteen policies, we picked seven which were considered to be the most important, to be included in the model.

During the second and third workshop rounds, we sought to verify with the participants whether the model's components, inter-linkages, and resulting behaviour resonated with them and reflected their understanding of the many inter-related issues around indoor air pollution in Nairobi's slums.

‘Participatory system dynamics modelling’ and ‘group model building’ (Vennix, 1996) (terms which are sometimes used synonymously) are useful for organizing the collective knowledge of stakeholders in a visual structure that promotes learning and allows for constructive, targeted discussions. When tackling complex problems with multiple stakeholder groups involved, taking a participatory approach is preferred to ‘expert mode’, where the modellers construct a model ‘at their desk’ based on available sources of information (Antunes et al., 2015; Vennix, 1996). This combined approach allowed us to make use of the diverse set of expertise available in our interdisciplinary stakeholder groups, while complementing that with rigorous quantitative modelling to simulate the implications of the group's assumptions about the structure of the system. Allowing policymakers to rely on their own thinking process in collaboratively building a model engenders a sense of ownership and commitment to the outcome of the modelling process and in this way increases the chances of successful implementation of resulting policies (Vennix, 1996).

During our field trips, the project team also held two separate focus group discussions with community members from Korogocho and Viwandani, within the informal settlements where they live. The focus group discussions revolved around indoor air quality, barriers in the community's adoption of clean cook stoves and other issues touching on housing, outdoor air and community/individual agency to agitate for action against known polluters.

Health impact assessment

We used a life table model (Miller and Hurley, 2003) to quantify the impact of changes in exposure to household air pollution. The model was driven by changes in long-term (annual average) exposure to PM2.5, a key constituent of household air pollution and the most consistent and robust predictor of mortality due to air pollution in studies of long-term exposure (Cohen et al., 2017). Based on changes in PM2.5 exposure (generated by the system dynamics model), the life table model calculates changes in the pattern of deaths in the population over time and the corresponding impact of these on the duration of life, expressed as total life years gained or lost in the population. As seen later in the Scenario analysis section, this will enable us to observe how results of our policy scenarios in terms of differences in household air pollution translate to health outcomes in terms of the avoided life years lost. The baseline population and mortality data for the local population, used to set up the model, were obtained from the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) (Emina et al., 2011).

Quantitative system dynamics

The qualitative causal diagram resulting from the participatory workshops was refined, and the health impacts were incorporated into the system dynamics model. We also wanted to understand the results of different policies on this complex model. Since such interactions are too large and complex to simulate mentally, computer simulation is the only practical way to test them (Richardson, 1986; Sterman, 2000). The inherent complexity observed in the structure of the system under investigation in this study makes SD modelling a highly suited methodology to deal with this complexity. It enhances our understanding of complex systems through transparent modelling of the systems' structure. Using computer simulation models, SD helps us pinpoint the sources of policy resistance, and thus, design more effective policies (Sterman, 2000). Therefore, we brought in real world data to develop a quantified and formal system dynamics simulation model from the collaborative maps generated through stakeholder workshops. We quantified and parameterised it before applying it for policy analysis.

Model structure

The participatory process and subsequent off-site refinement resulted in a model with >150 variables. The model structure is identical for both slums, except that it is differently parametrised for each context.1 Due to its complexity, here we describe only a highly simplified causal loop diagram. The full model documentation is detailed in Appendix A. The model in its digital format, along with all scenario and sensitivity runs, can be found as Supplementary material published with this paper.

Fig. 1 portrays the simplified causal loop diagram arrived at by distilling the key feedback structure of the formal system dynamics model. The legend explains the colour coding. Starting with the central variable of household air pollution highlighted in red as our main indicator, we will explain backwards (against the direction of the arrows) along the chains of causality to investigate the key dynamics of the system as modelled here. The key drivers of the average level of household air pollution (proxied in this study by the concentration of PM2.5) in Nairobi's slums are the levels of outdoor air pollution and ventilation, as external factors, and the proportion of households using clean stoves/lighting internally. This study focuses mainly on exploring the internal factors, i.e. the prevalence of clean appliances. In line with the findings of the workshops, we assume in our model that prevalence of clean lighting is mainly driven by the electricity grid coverage and to some extent by the prices of electric lights. The prevalence of clean cook stoves, on the other hand, is driven by their prices, relative prices of clean versus “dirty” fuels, and finally the levels of public expenditure in providing subsidised appliances. The lower the prices of clean cook stoves and/or clean fuels, the higher the take-up and usage of these by residents of the informal settlements.

Fig. 1

Simplified causal loop diagram of the model; hash marks on certain relationships represent delays.

The funds available for subsidising clean cook stoves come from the total funds spent for combatting household air pollution, effective expenditure to reduce household air pollution. This expenditure is modelled to be driven not only by public concern about household air pollution, but also by the extent of enforcement, political will and good governance. To capture this, we have used the World Bank's Worldwide Governance Indicators for Kenya (Kaufmann et al., 2010). This data consists of six separate indicators capturing various aspects of governance: (i) voice and accountability, (ii) political stability and absence of violence, (iii) government effectiveness, (iv) regulatory quality, (v) rule of law, and (vi) control of corruption. These are indicators ranging from −2.5 (weak) to (2.5) strong. We have averaged the six indicators and converted the result to an index between zero and one to come up with an aggregate past governance indicator (0.383 out of 1). The future target is set by the user as a policy/scenario variable (in green). While we acknowledge the fact that the situation in the two slums in this study differs from Kenya as a whole, nonetheless this data was the best proxy available for our purpose.

Public concern about household air pollution comes from two sources in the model: either direct monitoring of indoor air pollution levels or through awareness about the health burden associated with household air pollution, which can be estimated through HIA studies. The extent to which either monitoring or HIA are systematically carried out in the informal settlements depends on the levels of funds available for each, which are in turn determined by multiplying the total effective expenditure to reduce household air pollution by the share of this expenditure going to either of these initiatives. The extent of awareness and concern generated through each channel are also driven by the actual levels of indoor air pollution; directly so in the case of monitoring and in the case of health impact assessment, travelling through exposure to air pollution, health burden due to air pollution and health burden . Exposure itself is a consequence of either household or outdoor air pollution. Outdoor air pollution lies outside the scope of this study (see Section 4.2) and it is fed as exogenous data to the model. Past levels of PM2.5, 166 μg/m3 for Korogocho and 67 μg/m3 for Viwandani, are set according to the limited available real-world data (APHRC, n.d.), and its future levels are incorporated as a scenario variable.

Main feedback loops

The causal structure of the system dynamics model (Fig. 1) shows three noteworthy feedback loops within the system that might drive or counter change in the real world. R1: Monitoring and R2: HIA belong to the class of feedback loops known as ‘reinforcing’, while B1: Clean Stoves is known as a ‘balancing’ loop. The inherently different nature of these feedback loops can have a decisive effect on the ultimate success of policies.

In the B1: Clean Stoves balancing feedback loop, a potential increase in expenditure for clean cook stoves should, ceteris paribus, help bring down household air pollution. A decrease in air pollution is likely to make the public slightly less concerned about this issue, and a less worrisome public (be it the government, the communities, or NGOs) would then perhaps think that the issue has to some extent been contained and no longer warrants the previously increased level of allocated funds and decide to divert those additional funds to other more pressing problems, bringing the level of expenditure back close to the initial lower level; hence the use of the label ‘balancing’.

Yet, if expenditure for monitoring or health impact assessment studies is increased, once the results of such studies are published, this new information could make the public more anxious about indoor air pollution, which in the model leads to a higher budget allocated to this issue for the next year. Therefore, an increase in the share of household air quality for monitoring/health impact assessment has the potential to increase the available resources the next time round. This argument makes a theoretical case for allotting a share of the available budget to monitoring and health impact assessment, a policy that we are going to test in Section 3.2.2.

Model validation

In SD modelling, validation depends on the model purpose, and it consists of an iterative process of building confidence in the usefulness of the model, rather than establishing its objective ‘truth’ (Barlas and Carpenter, 1990). There exist a number of well-established tests to help to verify the usefulness of the model to its purpose, which include both structural and behavioural tests (Barlas, 1996; Senge and Forrester, 1980). Our model has undergone several validity tests, both structurally and behaviourally, including dimensional consistency checks, extreme condition tests, behaviour-reproduction tests and behaviour-sensitivity tests. The structure has also been validated against expert opinion during the multi-stakeholder workshops as well as ongoing collaboration with co-authors from APHRC who have knowledge of the local context. The model has been parametrised using the limited numerical data available from various sources, including the NUHDSS database. Detailed information on model formulations can be found in Appendix A, and a full list of model parameters for the two contexts is reported in Appendix D. Selected sensitivity tests are reported in Appendix B.

The behaviour of the model has also been validated against time-series data from the NUHDSS database. We start the model in 2003, where NUHDSS starts, and validate it using data available until today. For instance, in Fig. 2, the model's Base Run (grey curves) captures the general long-term trend in historical data (black curves) fairly well. Since the focus of this project is long term policymaking, the fact that short-term oscillations are not captured is not considered a limitation of the model for our purpose.

Fig. 2

Proportion of households owning clean stoves (upper plots) and clean lighting (lower plots); black curve: historical data; grey curve: base simulation.

The prevalence of both clean stoves and clean lighting in the two slums under study has been generally increasing since 2003. Note, however, that the scales of the graphs are very different for clean stoves (upper plots) and clean lighting (lower plots), with the take up of clean stoves being far slower than that of clean lighting. The generally upward trend is only broken by the most recent datapoint for access to clean lighting in both communities. This fall could have been due to recent efforts to clamp down on illegal and unsafe connections especially widespread in slum areas. The fall is captured well by model simulation, as prevalence in clean lighting is tightly driven by access to electricity in our model, for which historical data is available. The idiosyncrasies of developments in ownership of clean stoves among households are however less straightforward, and the model only manages to capture the general upwards trend, mainly a result of a slow increase in funds available for the provision of clean cookstoves. In particular, the steep increase in the prevalence of clean stoves in both slums during 2014–2015 is thought to be due to a project called Prima Gas, which made LPG more affordable for low-income groups by allowing customers to partially refill their cylinders from a mobile refill point for the amount of cash they have in hand; starting at a minimum of 50 Kenyan shillings (CapitalFM, 2012). As this external driver is not accounted for in the model, we cannot replicate this recent steep rise. Note however that, as portrayed further ahead in Fig. 4, the scales of the curves for lighting and for stoves are so different that, when shown in the same graph, model simulation seems to overlap completely with historical data. As the same model structure with different parameters is used for both contexts, we maintain that the model has successfully passed the ‘family-member’ test as it can be said to represent a ‘family’ of social systems, i.e. the socio-physical system surrounding household air pollution in low-income slum settings.

Fig. 4

[Korogocho] Coverage of clean lighting ([1] Data, [2] Base Run), and clean stoves ([3] Data, [4] Base Run).

The scarcity of available time-series data for important variables in the model, including our central indicator household air pollution, posed a challenge to the behavioural validation of the model. This is a limitation that entails a degree of caution regarding the use of the model as the only input to policymaking. Nevertheless, while taking such limitations into account, the model still offers valuable insights for policy, as we will further report in the next sections.

Results

In this section, we will start by examining the Base Run, which is the model's projection of current trends under business-as-usual. Next, we will explore three different scenarios and consider potential implications.

Base run

Firstly, let us look at future developments of our main indicators according to the model's projections of current trends under business-as-usual. These projections are not merely extrapolations of current trends. Instead, variables can undergo changes in trend, as the behaviour of the model is driven by its structure, and not by its inputs. For brevity, some of the scenario analysis graphs are presented only for Korogocho. In these instances, the results for Viwandani show similar behaviour with identical implications.

Allowing the model to run up to 2040 (Fig. 3), we see that household air pollution (proxied by PM2.5 concentration) continues to fall slowly (in both slums), before reaching a plateau around 2030. The available data provides reasonable historical evidence on the prevalence of clean appliances since 2003, which enables us to postulate the implied behaviour of household air pollution from our model. This suggests that household air pollution has been slowly falling over recent years, indicating a gradual improvement in household air quality, which is a result of electric lights replacing polluting kerosene lights in most households. An exception to this generally improving trend occurs in the year 2014, when prevalence of electric lighting actually falls (Fig. 4), as mentioned earlier in Model validation, and household air pollution consequently rises. According to the simulation, we will eventually reach a point where almost all households in the informal settlements under study have access to electricity and electric clean lighting.2 Therefore, from that point onwards the only way, within the boundaries of our model, to further reduce the generation of air pollution in households is through more extensive take-up of clean cook stoves. However, as depicted in Fig. 4 (Korogocho only), growth in the prevalence of clean stoves is completely dwarfed by that of clean lighting. In other words, the former is so slow that the resulting improvements from clean stoves are almost imperceptible once the prevalence of clean lighting reaches saturation around 2030. Therefore, under business-as-usual, we will reach a point where even the current slow improvements in household air quality will come to a halt. We will therefore explore some policy scenarios for achieving more substantial reductions in household air pollution.

Fig. 3

Household air pollution: Base run Korogocho [K], Viwandani [V] vs. WHO guideline (flat line at 10 μg/m3).

Scenario analysis

Description of scenarios

Policy and scenario variables used in the model are of three distinct types. The first type consists of what-if scenario variables concerning the prices of different fuel types, prices of stoves, and quality of governance. These are variables determined at a higher, usually national, administrative level. Secondly, there are those decisions that could be made at a local community level concerning the allocation of any available funds to spend towards mitigating household air pollution. It is assumed that these funds could be divided between direct provision of subsidised clean cookstoves to slum residents as well as indoor air monitoring and HIA initiatives. Finally, there are factors related with outdoor air pollution and ventilation, which are only crudely included in the model as exogenous drivers of household air pollution. These variables can be adjusted by the model user to observe effects of changes in outdoor air pollution and ventilation on closing in towards acceptable household air pollution levels.

We envisaged three scenarios corresponding to the three distinct types of policy and scenario variables described above. Our three scenarios are summarised in Table 1. The scenarios are additive. Our first scenario involves manipulating the prices of fuels and appliances. The second scenario adds a modified allocation of resources to that, so that more resources go towards monitoring and HIA. Finally, the third scenario adds an assumption of a substantial improvement in outdoor air quality and ventilation. A detailed characterization of these scenarios with regards to parameter values in the model can be found in Appendix C.

Table 1

Summarised description of scenarios.

Scenario	Summarised description	Notes
Scenario I: fuel and stove prices	• Lower LPG prices • Lower prices of clean stoves • Higher kerosene prices • Better governance	Adjusting prices of fuels can be attained by lowering/increasing subsidies. Lower stove prices could be a result of supporting local manufacturers. Funds for increasing LPG subsidies or supporting stove manufacturers can be sourced from savings on kerosene subsidies.
Scenario II: + monitoring and HIA	• All of the above, • Plus a higher share of available budget spent for monitoring and health impact assessment.
Scenario III: + outdoor and ventilation	• All of the above, • Plus a drastic fall in outdoor air pollution, • Plus an improvement in ventilation (only for Korogocho)	This is the most comprehensive scenario.

Scenario I (fuel and stove prices) involves a redirection of subsidies from kerosene to LPG and to supporting local manufacturers of clean stoves. It also entails drastically improving the enforcement of any existing regulations, as well as reducing existing corruption that could lead to misallocations of available funds for tackling household air pollution. In the model, these assumptions are proxied by a step-wise 50% increase in kerosene prices, 25% decrease in prices of LPG, and 50% decrease in prices of clean cook stoves and clean lighting. These changes are assumed to be implemented in three steps: the first one in 2017, and then every three years in 2020 and 2023. We also assume a gradual 50% improvement in good governance by the end of our simulation period: 2040.

In Scenario II (+ monitoring and HIA), we accompany the above changes in policy with gradually ratcheting up the share of the available budget going towards monitoring and health impact assessment, up to 15% for each by 2023. This will gradually bring down the share of the available budget going to the provision and/or subsidising of clean cook stoves to 70% by 2023. It is worth noting that the size of the available budget is not fixed and is endogenously determined under the influence of public concern about household air pollution. The effective amount of funding is also mediated by good governance.

Finally, in our most comprehensive scenario, Scenario III (+ outdoor and ventilation), we complement the above indoor-air related policies with a drastic (50%) reduction in outdoor air pollution, and, in the case of Korogocho, a drastic (50%) improvement in ventilation, to demonstrate the potential of improving household air via improvements in outdoor air. In Viwandani, our base assumption for the degree of ventilation in households, estimated based on the limited data available, is already quite high (67%). Note that improvement in ventilation in the absence of improvements in outdoor air quality, can lead to a worsening of indoor air quality, as the outdoor is often more polluted than the indoor in Nairobi's slums.

Results of scenarios

We will now compare the results of these scenarios against each other and against the Base Run. In Fig. 5 (Korogocho only), the projected future path of household air pollution under various assumptions is shown. The graph shows how each scenario performs better than the previous one, thanks to a more comprehensive package of policies implemented. However, the scale of improvements resulting from adding each set of policies is different from other ones.

Fig. 5

[Korogocho only] Comparing household air pollution under different scenarios.

Fig. 6 gives a clearer picture of how the three scenarios fare against each other. This bar graph captures the improvement that each portfolio of policies generates above business-as-usual (Base Run), by 2040. This improvement in household air pollution results in a comparable improvement in life years lost to air pollution, as seen in Fig. 7. Results show that manipulating fuel subsidies and appliance prices alone, even combined with drastic improvements in enforcement (Scenario I) does not result in any substantial improvements by the end of our simulation period. If, however, we complement this by investing in monitoring and health impact assessment (Scenario II), we can hope for a much more significant betterment of indoor air quality that is likely to result in roughly proportionate improvements in life years lost to air pollution (Fig. 7). The best results by far, however, are only made possible via combining the above policies with a drastic reduction in outdoor air pollution and an improvement in ventilation (Scenario III).

Fig. 6

Household air pollution under different scenarios by 2040.

Fig. 7

Savings in life years lost to air pollution under different scenarios by 2040. Approx. population size: Korogocho 32,000, Viwandani 57,000.

Therefore, as projected by the model, policies like redirecting fuel subsidies to cleaner fuels, reducing stove prices, and strengthening good governance, even when combined, do relatively little to improve household air pollution. This is because currently the amount of funds allocated to combatting household air pollution is so low that incremental increases or reallocations fall short of achieving any substantial improvements over business-as-usual. This means that to see more visible impact, we require available funds to grow by orders of magnitude, which implies that public concern over household air pollution needs to be raised exponentially. This is precisely what investing in monitoring and health impact assessment can achieve thanks to the self-reinforcing nature of the feedback loops involved, as seen in Fig. 1.

Furthermore, what is striking is that even with investments in monitoring and health impact assessment (Scenario II), we would still end up far above the WHO guideline for acceptable exposure to annual average PM2.5 (10 μg/m3, thin black line in Fig. 5). This points to the fact that without tackling the sources of outdoor air pollution, it will not be possible to get indoor air pollution closer to acceptable levels. However, the alarming result from simulation is that even with Scenario III assumptions, that give the best results among our scenarios, we end up at a level of pollution that is still about five times higher than WHO guideline.

Synergies among policies

We saw earlier that our comprehensive portfolio of exclusively indoor-related policies, Scenario II, yields a 25.5% improvement in household air pollution and prevention of 351 potential life years lost to air pollution per year in Korogocho by 2040. But what is the contribution of each single policy in this total progress? Fig. 8 outlines these contributions for our two indicators of interest, household air pollution and life years lost. These values are obtained by simulating each individual policy separately, in the absence of any other interventions, and then comparing improvements against the baseline scenario.

Fig. 8

[Korogocho only] Individual policy contribution graph.

From Fig. 8, it becomes clear that most improvements stem from investment in HIA and monitoring policies, which then helps bring in further funds for the provision of clean cookstoves. More interestingly, the substantial upper section of the contributions is not brought about by any individual policy, but from the synergy among all those implemented. These synergies are triggered only as a result of the R1 and R2 reinforcing feedback loops described in Section 2.4.2, and therefore depend on the implementation of HIA and monitoring. These reinforcing mechanisms can potentially enlarge the size of the ‘pie’ of available funding and make the resulting improvements larger than the sum of improvements from implementing single policies.

Discussion and conclusion

Findings and implications

In this study we built a quantitative system dynamics model on the problem of household air pollution in Nairobi's slums, using inputs obtained during rounds of multi-stakeholder participatory modelling workshops. We used the formal and tested system dynamics model to compare three hypothetical scenarios involving different portfolios of policies, which helped us in better understanding the dynamics of the socio-physical system.

Although there was not an abundance of numerical data for parameterisation, calibration and validation of the quantitative system dynamics model, which makes the model more suited to exploratory purposes, several well-founded and useful insights still emerged as a result of this study. Our results show that under business-as-usual, the current trend of slowly improving indoor air quality would come to a halt due to the saturation of the take-up of electric lighting and the extremely slow rate of take-up of clean stoves. This should be taken as a warning sign that if we aim to reach WHO's suggested guideline in terms of acceptable PM2.5 levels, a drastic acceleration in the take-up of clean stoves will be needed. According to our model's projections – without investing unfounded faith in their point-accuracy – even with a comprehensive package of indoor-air focused policies, there is little hope of closing the gap between status quo and WHO guidelines for air pollution by 2040.3 Even for the current downward trend to continue, our results, as well as our engagement with the community, have led us to believe that arriving anywhere near the WHO guideline will require addressing sources of outdoor air pollution, such as neighbouring dumpsites, industrial sites, traffic, etc. in parallel to sources of indoor air pollution. This will pose complications in implementation, as these dumpsites are sources of employment and livelihood for many slum residents.

Our simulation results also point to the potentially high impact of working towards raising the public's and the government's awareness and concern about household air pollution and its consequences for residents' health. To achieve this, our study suggests diverting some of the available budget (however big or small it is) to household air quality monitoring and health impact assessment studies, to ‘close the loop’ and bring the issue of household air quality higher up on the list of public/government priorities. Such investments, due to the self-reinforcing nature of the dynamics involved, can entail high return on investment, as the policymaker would be able to leverage the results of such studies to enlarge ‘the size of the pie’ of available money and resources (loops R1 and R2 in Fig. 1). We saw in the previous section how investments in monitoring and HIA have the potential to create synergies among existing policies. However, one must recognise that redirecting investments towards monitoring and health impact assessments may lead to slightly worse results in the short-term due to the time it takes before these policies pay off. In the world of politics, this delay may pose a serious implementation challenge.

The workshops held during this study engaged stakeholders in the gradual but rigorous process of developing a system dynamics model. It also demystified the completed model as stakeholders were involved in the model-building process from identifying simple relationships to complex inter-linkages of sectors. Testimonials from participants led us to believe that they found the process useful, both in terms of discovering aspects and dynamics of the air pollution issue which they were previously unaware of thanks to the expertise brought in by other stakeholders, and in terms of becoming familiar with ‘group model building’ as a powerful problem structuring and policy analysis method.

Previous studies have empirically assessed air pollution (Egondi et al., 2016; Muindi et al., 2016) and its impact on health (GBD 2016 Mortality Collaborators, 2017), or they have focused on the feeling of helplessness of slum residents towards the issue (Muindi et al., 2014). This study offers a framework to try and bring these diverse strands of research together. Through this work, we have contributed to the literature by addressing the issue of the slow take-up of clean cookstoves in low-income slum settings by bringing the physical, social and policy aspects together in an integrated quantitative model with a holistic and dynamic lens. We did this through engaging community members and local policymakers in the process with the aim of raising the issue's priority on their agendas and fostering a shared appreciation of important feedback mechanisms.

Limitations and future work

It is important to note that the model presented in this paper is derived through a participatory process with a particular set of stakeholders and therefore represents one possible model of the system that does not capture every possible mechanism. Therefore, we must stress that the model is not presented here as the definitive model of household air pollution in slums, but only as a highly simplified perspective that we believe is useful for deriving the sort of insights highlighted in the previous section.

One must also recognise the limitations imposed on this study due to a shortage of available time-series data on such key variables such as household and outdoor air pollution (where we had only one data point for each variable), as well as past expenditures on related policies, among other variables. It is not that such data externally drives the model, rather that such data would have been useful for a more in-depth comparison of simulated behaviour against real-world observations.

Importantly, while our replication of historical data for the number of households owning clean lighting was very good, the main driver of this indicator in our model is electricity coverage, which is taken as exogenous real-world data fed into the model, with an assumption of full electricity coverage of slums by 2040. In this respect, all our scenarios are equal. The scenarios differed substantially, however, in the speed of take-up of clean cook stoves, which makes that a key component of the model, far more influential than clean lighting in determining future differences between scenarios. In terms of model validation, however, our simulation only manages to capture the general trend for this variable, and therefore the fit could not be described as an exceptionally good fit (Fig. 2, upper section). Replication of historical data is a key behavioural test for the validation of system dynamics models. Therefore, the model's inability to capture historical developments closely enough entails further caution in using this analysis as the only input to policymaking.

Policies for decreasing household air pollution are certainly not limited to those investigated in this paper. Behavioural change interventions, for instance, could succeed in moving households from cooking inside their rooms to cooking outdoors. Such behavioural policies are not considered within the scope of this study.

Another limitation of this study is the exogenous treatment of the level of outdoor air pollution, which was included as a scenario variable whose future value is set by the model user. For a more realistic treatment of the problem of household air pollution, it would be useful to model outdoor air pollution as an endogenous variable that is a composite of pollution that originates indoors and diffuses locally, as well as industrial, waste and transport pollution. Endogenising outdoor air pollution presents a potentially fruitful opportunity for further research in this line.

When we asked workshop participants to articulate their hopes for Nairobi slums, only one participant mentioned the reduction of household air pollution. Other issues such as land ownership, services and waste management were much higher on people's agendas. Even though the air pollution-related health burden is known to be very large, to our knowledge there are currently no programmes focusing on air pollution from a health point of view, neither at national nor at county level. This lack of attention (Zimmermann et al., 2017) presents an interesting conundrum and another potentially fruitful area for further research.

This project and modelling work was influenced by the limited attention that household air pollution has received so far. Because of this inattention, very little data on household air pollution has been collected to date. Increased investments in researching household air pollution would lead to more abundant scientific evidence, which could be used to produce more useful and reliable policy recommendations.

Table A-1

Model coding explained.

Code	Meaning
Lower-case variable	Endogenous variable (formulated based on other variables within the model). The dynamic behaviour of such variables is given by software simulation.
Upper-case variable	Constant. Such constants are either fixed parameters (black), or policy/scenario variables set by the user (green)
Variable with first word in upper case, rest in lower case	Exogenous (data) variable. Past behaviour of such variables is given by historical data. Variable stays constant for future simulation, unless otherwise specified.
Red variable	Key indicator.
Green variable	Policy/scenario variable, decided upon by the user.
Blue variable, in angle brackets	‘Shadow’ variable, copied from another section of the model.
Blue arrows	Causal relationships, from cause to effect. Each (endogenous) variable is formulated based on variables connected to it via incoming arrows.
Grey arrows	Initial condition setting.

Table A-2

Distribution of households by cooking fuel type and mean PM2.5 levels.

Outcome	Korogocho (24)	Viwandani (48)	Total	Test statistic
Proportion of households using different cooking fuels (%)				χ2 (p-value)24.6 (p < 0.001)
Charcoal or wood	[1] 62.5	[7] 14.6	[13] 30.6
Kerosene	[2] 12.5	[8] 72.9	[14] 52.8
LPG/electricity	[3] 25.0	[9] 12.5	[15] 16.7
PM2.5 mean levels for different cooking fuels (μg/m3)				t-Statistic (p-value)
Charcoal or wood	[4] 126.5	[10] 75.7	[16] 110.0	6.59 (p < 0.001)
Kerosene	[5] 109.0	[11] 58.7	[17] 61.9	7.43(p < 0.001)
LPG/electricity	[6] 72.0	[12] 45.6	[18] 59.1	10.04 (p < 0.001)

Policy/scenario variable	Unit	Base run	Scenario I	Scenario II	Scenario III
Future share of air quality expenditure for appliance subsidies	Dimensionless	98%	=	@2017: 90%@2020: 80%@2023: 70%	@2017: 90%@2020: 80%@2023: 70%
Future share of indoor air quality expenditure for monitoring	Dimensionless	1%	=	@2017: 5%@2020: 10%@2023: 15%	@2017: 5%@2020: 10%@2023: 15%
Future share of air quality expenditure for health impact assessment	Dimensionless	1%	=	@2017: 5%@2020: 10%@2023: 15%	@2017: 5%@2020: 10%@2023: 15%
Future price of kerosene	KSH per litre	63.4	@2017: 70@2020: 80@2023: 90	@2017: 70@2020: 80@2023: 90	@2017: 70@2020: 80@2023: 90
Future price of LPG	KSH per 6 kg cylinder	1246	@2017: 1160@2020: 1080@2023: 1000	@2017: 1160@2020: 1080@2023: 1000	@2017: 1160@2020: 1080@2023: 1000
Target for price of clean stoves 2040	KSH per unit	2000	By 2040 linearly down to 1000	By 2040 linearly down to 1000	By 2040 linearly down to 1000
Target for price of clean lighting 2040	KSH per unit	1000	By 2040 linearly down to 500	By 2040 linearly down to 500	By 2040 linearly down to 500
Target for good governance 2040	Dimensionless (conceptualised as a 0 to 1 index)	0.383	By 2040 linearly up 50% to 0.575	By 2040 linearly up 50% to 0.575	By 2040 linearly up 50% to 0.575
Target for ventilation 2040 (only Korogocho)	Dimensionless	0.4	=	=	By 2040 linearly up to 0.6
Target for outdoor air pollution 2040	Microgram/cubic metre	166	=	=	By 2040 linearly down to 83 (Korogocho) and 33.5 (Viwandani)

Parameter name	Value (Korogocho model)	Value (Viwandani model)	Source	Note
Additional pollution from traditional lighting	67 [μg/m3]	=	Estimated based on Muindi et al. (2016)	See Appendix A.1.1
Additional pollution from traditional stoves	51.7 [μg/m3]	15.9 [μg/m3]	Estimated based on Muindi et al. (2016)	See Appendix A.1.1
Elasticity of clean lighting acquisition to lighting prices	−0.5	=	Obtained through calibration	Calibrated to historical data on prevalence of clean lighting
Elasticity of clean stove acquisition to fuel prices	−0.3	=	Obtained through calibration	Calibrated to historical data on prevalence of clean stoves
Elasticity of clean stove acquisition to public concern	0.2	=	Obtained through calibration	Calibrated to historical data on prevalence of clean stoves
Elasticity of clean stove acquisition to stove prices	−0.4	=	Obtained through calibration	Calibrated to historical data on prevalence of clean stoves
Electricity coverage 2002	0.01	=	Estimated by extrapolating into the past the electricity coverage in 2003 and 2004	NUHDSS data on households supplied with electricity from the national grid
Expenditure growth rate multiplier	0.75	=	Assumption	See Appendix B.1 for an analysis of sensitivity to this assumption
Gap closing time constant	1 [year]	=	Assumption	Standard system dynamics modelling practice
Growth time horizon	0.5 [year]	=	Assumption	Measuring half-yearly growth rate in electricity coverage
Initial expenditure to reduce indoor air pollution	9500 [KSH/year]	12,500 [KSH/year]	Estimation	See Appendix A.1.4
Initial health impact assessment coverage	1	=	Assessment of APHRC co-authors	Virtually non-existent HIA in 2003. See Appendix A.2.2
Initial indoor air monitoring coverage	1	=	Assessment of APHRC co-authors	Virtually non-existent monitoring in 2003. See Appendix A.2.2
Initial households acquiring market price clean stoves	12 [households/year]	21 [households/year]	Obtained through calibration	Calibrated to historical data on prevalence of clean stoves
Initial number of households owning clean lighting	148 [households]	221 [households]	NUHDSS	Nairobi Urban Health and Demographic Surveillance System
Initial number of households owning clean stoves	6 [households]	12 [households]	NUHDSS
Initial public concern growth rate	0	=	Assumption	Representing the initial non-existence of HIA or monitoring initiatives
Investment depreciation time	5 [years]	=	Assumption
Monthly kerosene usage for cooking	24 [litre/month]	=	Price data from the Kenyan National Bureau of Statistics (KNBS). Usage data from residents' estimates	See Appendix A.2.3
Monthly LPG usage for cooking	0.33 [canister/month]	=	Price data, see below. Usage data from residents' estimates.
Outdoor air pollution past	166 [μg/m3]	67 [μg/m3]	Egondi, Muindi, Kyobutungi, Gatari, & Rocklöv (2016)
Price of kerosene past	63.4 [KSH/litre]	=	Prices regulated by the Energy Regulatory Authority	http://www.erc.go.ke/index.php?option=com_content&view=article&id=162&Itemid=666
Price of LPG past	1246 [KSH/canister]	=	Kenya National Bureau of Statistics (KNBS) and local newspapers	https://www.knbs.or.ke/data-releases/
Proportion of appliance prices subsidised	0.4	=	Assumption	This assumption was validated against expert judgment of APHRC co-authors.
Public concern delay	2 [years]	=	Assumption	See Appendix B.2 for an analysis of sensitivity to this assumption
Share of air quality expenditure for appliance subsidies past	0.98	=	Assumption	Representing the allocation of virtually all resources to subsidising appliances in the past
Share of air quality expenditure for health impact assessment past	0.01	=	Assumption	Representing the allocation of virtually no resources to HIA in the past
Share of indoor air quality expenditure for monitoring past	0.01	=	Assumption	Representing the allocation of virtually no resources to monitoring in the past
Target electricity coverage 2040	1	=	See footnote 2	Full coverage assumed by 2040
Unit cost of health impact assessment per household	100 [KSH/household]	=	Appraisal made by co-authors from APHRC
Unit cost of indoor air monitoring per household	100 [KSH/household]	=	Appraisal made by co-authors from APHRC
Ventilation past	0.4	0.68	Estimation based on Muindi et al. (2016)	See Appendix A.1.1
WHO guideline	10 [μg/m3]	=	(World Health Organization, 2006)