Modelling Clean Cooking and Climate Policy for Future Heart Disease Reduction

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Downward, Gabriela Matias de Pinho, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5796902/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Nature Sustainability → Version 1 posted You are reading this latest preprint version Abstract Long-term PM 2.5 exposure is a risk factor for cardiovascular mortality. Fossil fuel burning is a large source of PM 2.5 . Here, a global health impact assessment was conducted utilizing 7 future scenarios evaluating strategies to reduce PM 2.5 exposure, including reducing fossil fuel use, air pollution control, adopting cleaner cooking methods and combinations thereof. Under current trends, air quality is projected to improve by 2050, but the absolute attributable burden of ischemic heart disease remains high in many regions. Promoting cleaner cooking is effective in the short term (by 2030) in South and Central America, Asia, and Africa in reducing the health burden. In the long term (by 2050), for most regions, only strategies that simultaneously target ambient and cooking related PM 2.5 resulted in sustained improvements for reducing ischemic heart disease burden. For, North Africa and the Middle East region the population attributable fraction remains high across all scenarios. PM 2.5 exposure remains above the WHO Air Quality Guidelines across all scenarios therefore additional strategies are required to improve air quality. Health sciences/Risk factors Health sciences/Diseases/Cardiovascular diseases Earth and environmental sciences/Climate sciences/Climate change/Climate and Earth system modelling Figures Figure 1 Figure 2 Figure 3 Main Air pollution (both indoor and ambient) contributes to an estimated 8.1 million deaths per year globally, making it a leading modifiable environmental risk factor 1 . Air pollution consists of a heterogenous mixture of particles and gases, with particulate matter with a diameter of 2.5 micrometers or smaller (PM 2.5 ) being the most studied in relation to health. Due to its small size PM 2.5 can penetrate deeply into the respiratory system and enter systemic circulation resulting in a variety of pro-inflammatory and detrimental health effects 2 . There is strong evidence that suggests that it causes heart disease, and nearly 2.5 million ischemic heart disease deaths can be attributed to excess PM 2.5 in 2021 1 . Sources of ambient PM 2.5 include fuel combustion, and agricultural waste burning. A major source of indoor PM 2.5 is residential energy production 3 as several billion people worldwide have unreliable or unaffordable energy access, leading to a reliance on solid fuels (firewood, dung, coal) for residential heating and cooking 4–6 7 . At the same time, fossil fuel combustion is also a major driver of climate change, providing synergistic opportunities (i.e. health-related co-benefits) when addressed simultaneously. 8 Various strategies exist to reduce PM 2.5 exposure. For ambient air quality, strategies include end-of pipe control, climate mitigation policies, and reduction of waste burning. The main strategy for improving indoor air quality has been to adopt ‘clean’ or ‘cleaner’ cooking stoves or fuels namely, liquid petroleum gas, natural gas or electricity. Previous studies have traditionally focused on the health impacts of either ambient control measures or cookstove interventions separately, whereas studies combining the two are limited. As a result, a notable research gap exists, as many regions with high reliance on solid fuels for household energy also lack regulations and technologies for reducing ambient PM, leading to populations being doubly burdened by high exposures to both ambient and indoor air pollution 9,10 . An additional gap, is that few studies incorporate future trends, limiting the scientific community’s ability to respond to the question of what long term benefit(s) can be expected if these strategies are adopted either in isolation from each other or collectively. In line with the Sustainable Development Goals (SDG’s) for which the global community has agreed to combat climate change (SDG13), increase energy access (SDG7) and improve health (SDG3) our study aims to use multiple future scenarios, utilizing different energy use, agricultural waste management and climate policies to assess the plausible co-benefits of such policies on both indoor and ambient air quality and on the plausible future global burden of ischemic disease. Results PM 2.5 Exposure Trends Across Scenarios to 2050 Seven scenarios (Methods, Table 1 .) were used to evaluate the impact of ambient and indoor cooking PM 2.5 exposure in relation to ischemic heart disease burden. This is done by coupling the IMAGE model which projects future energy and land use to the TM5-FASST model for ambient pollution for 26 world regions. The WHO ‘Household Multiple Emission Source’ (HOMES) model was used to estimate indoor concentrations of PM 2.5 related to cooking. The 7 scenarios can be grouped into 3 categories: a) a baseline scenario depicting current trends, b) four scenarios looking into individual measures (tighter air pollution control, climate policy, agricultural waste burning reduction and clean cooking) and c) two scenarios where strategies are combined. In the 'Baseline’ scenario, (following socio-demographic assumptions underlying the SSP2 scenario) 13 , moderate economic growth and population growth are projected. Here, the SSP2 scenario shows an increase in global energy use –with a slow trend towards cleaner fuels. For air pollution control, current policies are projected to lead to tighter standards based on observed trends and announced measures 11,13 These trends together imply that PM 2.5 exposure is estimated to decrease in the 2030–2050 period for almost all regions. However, absolute PM 2.5 remains at a high level in most regions. The ambient PM 2.5 exposure for the regions Mexico, Rest of South America, Korea and Indonesia are projected in the15-20µg/m 3 range. For Northern, Western and Eastern Africa, Central America, the Middle East and China the ambient exposures are in the 20–35µg/m 3 range and for the regions Rest of South Asia and India ambient exposures are above 40µg/m 3 (See Fig. 1 . b ‘ Baseline 2050 ambient’). The reduction in total PM 2.5 exposure is largely driven by reductions in indoor PM 2.5 exposure due to use of cleaner cooking fuels driven by increasing income levels (Supplementary Table 1. & Fig. 1 g relative to f ). In contrast, trends in ambient PM 2.5 exposure diverge across regions. In most regions, exposure is projected to decrease as a result of tighter air pollution control. In other regions, including Western and Eastern Africa, India and Indonesia, increased energy consumption is expected to outpace tighter standards leading to higher exposure levels (Fig. 1 a relative to b ). For example, in East Africa ambient PM 2.5 is estimated to from 12.9 µg/m3 to 33.2 µg/m3 by 2050 under the ‘Baseline' scenario. These findings indicate an need for additional measures beyond the ‘Baseline’ to decrease PM 2.5 exposure. The additional individual intervention scenarios do lead to varying magnitudes of improvement in PM 2.5 exposure. In particular the clean cooking and climate policy scenario shows considerable improvement. The climate policy scenario is projected to improve ambient air quality around 5µg/m 3 in many regions. The impact of the scenarios involving tighter air pollution control and waste burning reduction is much smaller. This is partly a result of already tightening of air pollution control standards embedded in the ‘Baseline’ (based on the projections under current legislation). For scenario ‘Waste’ (involving waste burning reduction) the most noticeable PM 2.5 reductions are specifically in 'Southeast Asia', 'India' and the 'Rest of South Asia'. Under the 'CCooking' scenario, 11 sub-regions (Rest of South America, North Africa, West Africa, East Africa Central Asia, Middle East, India, Korea, China, South East Asia, Indonesia, Rest of South Asia) were unable to achieve total average PM 2.5 exposures (indoor and outdoor) under the interim target 1 of 35µg/m 3 , despite large reductions in PM 2.5 (Fig. 1 c and h ).The scenarios combining strategies led to the most improvements in PM 2.5 with the ‘ClimPol&CC’ having almost as much improvement as the ‘All’ scenario which maximally improves PM 2.5 exposure. Air Pollution Related Ischemic Heart Disease As PM 2.5 exposure is expected to reduce under the 'Baseline' scenario, the cardiovascular disease burden attributable to air pollution will be expected to improve already given current policies (i.e., the burden of mortality will decrease). However, the degree of improvement varies between regions. In the North America and Russia, Oceania, and North Africa and Middle East regions the bulk of the improvement in PAF will be achieved by 2030, after which no further improvement in current policies is expected. For example, in Canada the PAF (95% UR) under ' Baseline' reduces from 7.8% (UR 6.7%,9.1%) to 2.5% (UR1.5%,3.6%) in 2030 where it remains relatively stable by 2050 ( PAF: 2.3%, UR 1.1%,3.4%) In contrast the Europe, Asia, Central and South America, and African regions will continue to show improvements through to 2050. Despite consistent improvements in PAF, large absolute burdens of disease persist for most Low-and-Middle-Income countries where a PAF exceeding 15% is consistently observed. Individual Measures As expected based on the exposure, the ‘AirPol’ and ‘Waste’ scenarios have – on their own- little additional impact on ischemic heart disease by 2050 at the global scale (See Fig. 2 .). The 'ClimPol' scenario beneficially impacts PAF's for South and Central America (10% by 2050), Asia (18%), and Africa (23%) via the co-benefits of climate policy in reducing air pollutant emissions (See Fig. 2 ) . A milder improvement is observed in the Europe and North Africa and Middle East regions. More discrete changes in PAF are observed at the sub-regional level. In the ‘CCooking’ scenario, major reductions in PAF (relative to ‘Baseline’) are most evident in the African region, with a reduction to 13% (compared to 33% under ‘Baseline’ ) by 2030 being observed (albeit slightly worsening to 17% by 2050, compared to 26% under ‘Baseline’ ). Combined Measures Combining all interventions maximally improves the PAF for much of the global population - specifically the Asian, African and South and Central American regions where improvements beyond any of the other individual scenarios are observed. The 'ClimPol&CC’ scenario generally results in similar PAF reductions to what was observed in the “All” scenario by 2050 (Fig. 2 .) In the 'All' scenario, the sub-regions Brazil and India show particularly noteworthy benefits in this scenario by 2050 with Brazil's PAF reaching 3.9% (UR 2.8%,5.2%), and India's 9.7% (UR 8.1%,11.5%) which, compared to ‘Baseline’, would be 9.7% (UR 8.4%, 11.3%) for Brazil and 32.3% (UR 30.7%-33.9%) for India. This is also reflected in the regions Asia and South America. Discussion This study is the first to concurrently analyze scenarios related to household cooking and ambient PM 2.5 exposure on future ischemic heart disease at a global scale. By examining various scenarios, we assessed how various strategies affecting PM 2.5 exposure impacts ischemic heart disease. While the current study examines the scenario outcomes of widely used and validated models, limitations exist 14,15 . First, the demand for energy use, and corresponding projected PM 2.5 depends on several underlying assumptions about underlying drivers (such as economic activity and population) 16 . Other examples, include the assumed air pollution policy embedded in the ‘Baseline’ , and the effectiveness of carbon pricing when it relates to achieving climate targets. Generally, the socio-economic assumptions embedded in the 'Baseline' about drivers medium economic growth, technical development and population growth are uncertain. In spite of this, the focus of this paper is on the potential to reduce exposure to PM 2.5 and improve ischemic heart disease burden, through various measures rather than diverging socio-demographic possibilities. Some of the most important uncertainties are examined in other studies 11 . It is also important to note that the trends observed in the ‘Baseline’ findings are dependent on continued implementation of legislation and pollution control technologies (e.g. 'AirPol’) 11 . Earlier it was shown that air pollution control (‘AirPol’) is not much different from the ‘ Baseline’ , however, that is assuming a continuation of previous trends of air pollution control, so this interpretation could differ if less improvement was assumed in the ‘ Baseline’ . Future major changes in legislation or socio-political circumstances may cause deviation from these ‘ Baseline’ findings. Still, our results are consistent with earlier finding that most regions do not achieve the air quality guideline of 5 µg/m3 set by the WHO, nor under current trends are expected to 17 . One additional assumption, is the distribution of average cooking times in order to account for cultural differences in cooking length. Utilizing these longer cooking times results in a higher estimated PM 2.5 exposure and therefore a higher PAF in regions and sub-regions with higher amounts of biomass cooking. The sensitivity analysis containing these results for all regions can be found in the supplementary materials (pages 26 − 37). Finally, broad regional trends are shown which may mask significant variations at more granular levels, which is mildly reflected by the findings of sub-regional variations. Several key findings emerge from this work. First, under ‘Baseline’ exposure to PM 2.5 is expected to decrease assuming that current rates of air pollution control, and cleaner fuel use are continued. Corresponding with this decrease is an overall reduction in ischemic heart disease related PAF’s in all regions by 2030. Despite this improvement the absolute PM exposures and corresponding PAFs remain high, and all regions (at the aggregate) do not achieve the WHO guideline of 5 µg/m 3 . Second, implementing more rapid usage of cleaner cooking fuels particularly benefits Africa and Asia, in terms of both PM 2.5 exposure and disease burden reduction yet can only be sustained to 2050 with co-implementation of climate policy. Third, maximal long-term improvements are observed if all measures are combined, especially for the African, South and Central American, and Asian regions (see ‘All’ scenario). Fourth, combining ‘All’ strategies provides rapid and sustained long term reductions in ischemic heart disease burden, with ‘ClimPol&CC’ achieving almost the same reduction across most continents (except Asia) while simultaneously achieving the 1.5-degree Paris Target. Fifth, despite the above, regional variation exists, in particular the North Africa and Middle East Region where PAF is high regardless of scenario. Thus, more tailored approaches in other systems (e.g. transport) may need to be developed to improve air quality for North Africa and the Middle East. To conclude, this study demonstrated that mitigation policies combined with waste burning reduction, end of pipe control, and clean household cooking energy usage can reduce ischemic heart disease burden for a majority of the world’s population (especially the Asian, African, and South and Central American regions) to a greater extent especially over the long term than to if and when policies are only adopted in isolation. Methods Here we conduct a model-based scenario analysis (see Table 1 . for scenario descriptions) to investigate possible strategies and their impact on PM 2.5 concentrations and its subsequent impact on the Joint Population Attributable Fraction (PAF) for ischemic heart disease in the years 2015, 2030 and 2050 18 . IMAGE Model to Project Future Energy and Land Use The IMAGE integrated assessment model represents the energy and land use sectors in an integrated manner was used to project future greenhouse gas emissions (GHG) for 26 world regions 19 . Greenhouse gas and air pollutant emissions factors are obtained from the Emissions Database for Global Atmospheric Research (EDGAR) database 20 . The energy model was used to determine household fuel choices in scenarios that did not have clean cooking (See Below). For more details refer to PBL (Netherlands Environmental Assessment Agencies) or Appendix C 21 . Ambient PM Concentrations Projected changes in ambient air quality are based on emissions, calculated by multiplying energy and land use activities with corresponding emission factors. Subsequently, the emission scenarios can be used to calculate ambient PM 2.5 concentrations using the IMAGE TM5-FASST sub-model. The TM5-FAAST model is a simplified atmospheric chemistry model, which is less computationally intensive than the TM5 model but is validated and described in detail elsewhere 22 . Briefly, it assumes a linear relationship between emission changes in one region to estimate pollution concentrations in another region, through source receptor coefficients stored in matrix form. These calculations first take precursor emissions such as sulfur dioxide (SO 2 ), nitrogen Oxide (NOx), volatile organic compounds (VOCs) and ammonia (NH 3 ) which undergo chemical reactions in the atmosphere to contribute to PM 2.5. The TM5-FAAST model then estimates ambient population weighted yearly mean PM 2.5 concentrations for 56 regions which is subsequently adapted to IMAGE’s 26 world regions 13,23 . Cooking PM Concentrations Household cooking PM 2.5 exposure was determined in two steps. First the residential energy demand sub-component of the IMAGE model was used to determine the distribution of future fuel choices within scenarios where full clean cooking usage is not yet attained 24,25 . All stoves using a particular fuel were assumed to have an emission rate corresponding to a specific ISO VPT tier (see Appendix A). Second, the World Health Organizations Household Multiple Emissions Sources (WHO, HOMES) single zone model was used to estimate average daily indoor kitchen PM 2.5 concentrations for three cooking events 26 . The HOMES model simulates kitchen concentrations arising in this case from a single cooking source via the following equations: 26 Equation 1. \(\:C\left(t\right)=\frac{{q}_{1}{f}_{1}{q}_{2}{f}_{2}{q}_{3}{f}_{3}\dots\:{q}_{n}{f}_{n}}{\alpha\:V}\left(1-{e}^{-\alpha\:t}\right)+{C}_{0}\left({e}^{-\alpha\:t}\right)+{C}_{b}\) Equation 2. \(\:{C}_{k}={\sum\:}_{1}^{1440}\frac{{C}_{i}}{1440}\) Equation 3. \(\:{E}_{r}={C}_{k}R\:where\:{E}_{r}\ge\:{C}_{b}\) where C(t) = Concentration for a given time point qx = the emission rate for source x (mass/min) fx = fraction of emissions from source x that enters the kitchen environment α = air change rate (changes/min) V = kitchen volume (m3 ) t = time interval (1 min) Co = concentration from preceding time interval (unit mass/m3 ) Cb = Background concentration (assumed to be the ambient concentration estimated by TM5-FAAST[mass/m3 ]). 26 Since cooking time and household characteristics can vary, a distribution of different variables was used as HOMES input parameters (see Supplementary Fig. 1). Household concentrations were then calculated via a Monte Carlo Simulation, which leveraged values across the distributions to produce 5000 different results across the input parameters 27 . The mean daily PM 2.5 exposure of these outputs were calculated for each major fuel type (Traditional fuelwood/coal, Improved Cookstove, LPG/Electricity, Kerosene/Biogas) 28 . For scenarios where cleaner cooking was not incorporated the proportion of the population currently using each fuel type was used to weight the mean PM 2.5 concentrations. Finally, as the personal exposure ratio is not 100%, due to movement in and out of the kitchen space, and differs by sex, the weighted PM 2.5 was multiplied by 0.6 29 , 28 . For cooking time, the IPUMS Multinational Time Use Study database was used to calculate a distribution of the ‘short’ cooking times (Mean:49.95, SD:56.36). IPUMS collects harmonized time use data under the variable entitled ‘unpaid domestic work'’ 27 . The remaining assumptions about input distributions were based on the WHO database of input variables (See Appendix for rest of assumptions) 30 . Two separate mean daily PM 2.5 calculations, one for short and another for long cooking times were made. The longer analysis was treated as a sensitivity analysis since transitioning to cleaner cooking is expected to also reduce cooking time. All estimates in the figures shown in our main analysis were for shorter cooking times, ultimately resulting in an underestimation of the disease burden. Ischemic Heart Disease Burden Estimation The burden of ischemic heart disease was calculated for all scenarios for the population of adults over 35 years of age 1 . This was done through calculating the joint population attributable fraction (PAF) of indoor and ambient air pollution for 26 world regions with the Meta-Regression-Bayesian Regularized Trimmed (MRBRT) curve obtained from GBD 2019 1 . The MRBRT curve is a summary risk measure that pools all available studies collected from the GBD across a range of PM 2.5 exposures 31 . The specific equations used are described below (Equations 4 to 10). Eq. 4 represents the relative risk (RR) of those not exposed to household air pollution (HAP) from solid cooking fuels. Equations 5 and 6 were used to represent populations exposed to both ambient and cooking with solid fuels. Equations 7 through 10 are the population attributable fractions for overall PM exposure, which was determined for each IMAGE region estimating the number of excess cases of ischemic heart disease attributable to particulate matter exposure relative to a plausible theoretical minimum exposure range of PM 2.5 exposure. Equation 4. \(\:{RR}_{OAP}=MRBRT\left(z={Exp}_{OAP}\:)/MRBRT(z=TMREL)\right)\) Equation 5 \(\:{RR}_{HAP}=MRBRT\left(z={Exp}_{OAP}+{Exp}_{HAP}\:)/MRBRT(z=TMREL)\right)\) Equation 6. \(\:{RR}_{PM}={RR}_{OAP}\left(1-{P}_{HAP}\right)+{RR}_{HAP}{P}_{HAP}\) Equation 7. \(\:{PAF}_{PM}=\frac{{RR}_{PM}-1}{{RR}_{PM}}\) Equation 8. \(\:{PAF}_{OAP}=\frac{{Exp}_{OAP}}{{Exp}_{OAP}+{P}_{HAP}\times\:{Exp}_{HAP}}\:{PAF}_{PM}\) Equation 9. \(\:{PAF}_{HAP}=\frac{{P}_{HAP}\times\:{Exp}_{HAP}}{{Exp}_{OAP}+{P}_{HAP}\times\:{Exp}_{HAP}}\:{PAF}_{PM}\) Equation 10. \(\:{PAF}_{PM}={PAF}_{HAP}+{PAF}_{OAP}\) A total of 1000 PAFs were predicted and the lowest 2.5 and highest 97.5 percentiles were used to derive 95% uncertainty intervals 32 . Scenarios In this analysis, estimates on the current trends are described by a baseline scenario ( ‘Baseline’ ) that describes the world under moderate assumptions for all socio-economic and technological drivers. All policy scenarios represent interventions which would improve off of this baseline scenario to improve PM 2.5 exposures. The policy scenarios explored are: 1) Tighter Air Pollution Control, 2) Clean Cooking, 3) Climate Policy (limiting global mean temperature increase to1.5°C) 4) a reduction of agricultural waste burning (‘ Waste’ ) 5) (‘ All’ ) of the aforementioned strategies combined. The sixth was a scenario combining mitigation with clean cooking (‘ ClimPol&CC’ ) (see Table 1 ). Table 1 Description of Scenarios Used Scenario Name Description Baseline ('Baseline') The Shared Socioeconomic Pathway SSP2 scenario was used to represent the 'Baseline' 21 . This scenario assumes a continuation of current trends concerning population, economic growth, and technological development. Some end or pipe air pollution controls are assumed to be within this scenario, but not to the same extent as the end of pipe scenario 22 . Tighter Air Pollution Control (‘AirPol’) The tighter air pollution control scenario extends the 'SSP2 Baseline' to incorporate the effects of implementing strong pollution control alongside an assumption of rapid technology development and stringent leglislation 33 . Climate Policy (‘ClimPol’) The Climate Policy scenario represents the air pollution reduction which arises due to efforts to control greenhouse gas emissions under radiative forcing target 1.9 (RCP1.9) in the energy and land use sectors, in line with the 1.5°C target set out in the Paris Agreement This scenario projects major transitions in the energy sector, through higher penetration of renewable energy sources, increased electrification of energy services (i.e. space heating, transport, etc.) and phasing out fossil fuels. Waste Burning Reduction (‘Waste’) In the Waste Reduction scenario extends the baseline by incorporating the reduction of agricultural waste burning is by 40% by 2030, then by 80% by 2050. Clean Cooking (‘CCooking’) The Clean Cooking scenario includes the effect of the universal usage of cleaner cooking fuels. LPG, natural gas, and electricity were considered clean cooking fuels. While fuels such as LPG can generate small amounts of PM, they are several orders of magnitude less than those generated by solid fuels and thus are considered “null” in the PAF analysis (not in the PM 2.5 exposure estimation) Climate Policy + CC (‘ClimPol&CC’) The Climate Policy + CC is the Climate Policy Scenario where Clean Cooking is co-implemented. All Strategies Combined (‘All’) All the above individual interventions combined. Declarations Data and Code Availability Most data relevant to the results are listed in the supplementary information. If additional data is needed it is available upon request of the corresponding author. All code used for analysis will be posted on a public git repository. Funding This project received funding from the European Research Council (ERC) under the Horizon Europe program (PICASSO project; grant agreement ID 819566). The funder had no role in the study design, data collection, data analysis, writing of the report, or data interpretation. V.D received funding from the European Union's Horizon Europe programme under grant agreement No 101081604. Acknowledgements E.W conducted the health impact analyses, made all figures, conceptualized the use of HOMEs model in this type of analysis and wrote the first draft. V.D. ran the energy modules of the IMAGE model for all scenarios and provided proportions of fuel usage for the SSP2 Baseline scenario. L.V ran the TM5-FAAST analyses. Jonathan Doelman ran the land model and provided results for the waste burning scenario. Mathijs Harmsen provided insight into precursor emissions. Martijn van der Marel helped with the HOMES model code validation. 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Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryNovFinalized1.docx Supplementary Information Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Nature Sustainability → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5796902","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":404771681,"identity":"40d3cfb9-a1d5-4aea-ad4a-5a9aaf24acf2","order_by":0,"name":"Eartha Weber","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYHACAxjJ+ADE4CNFCzOYxUaKFjYJorTwMzBvYPjBcFjOnL33WTVvG0MeQS2SDWwFjD0Mh40te46b3QZqKSaoxeAAjwEzA8PtxA030thAWhLbCGmxh2qp33D/GVsxUVoMGCBaEgxusLExE6VF4jBbwcEeg/+GO3vSmCXnnJMgrIW/vXnjgx8VafLm7McYP7wps0nsJ6SFAeiqA9CoAdtKUMMoGAWjYBSMAiIAALlzMsD5vBstAAAAAElFTkSuQmCC","orcid":"","institution":"Utrecht University","correspondingAuthor":true,"prefix":"","firstName":"Eartha","middleName":"","lastName":"Weber","suffix":""},{"id":404771682,"identity":"5e84ae27-21bb-4cf2-bfad-5aa9f041a233","order_by":1,"name":"Detlef van Vuuren","email":"","orcid":"https://orcid.org/0000-0003-0398-2831","institution":"PBL Netherlands Environmental Assessment Agency","correspondingAuthor":false,"prefix":"","firstName":"Detlef","middleName":"van","lastName":"Vuuren","suffix":""},{"id":404771683,"identity":"f5700635-f565-4265-b9e0-98c0a286801d","order_by":2,"name":"G.S. Downward","email":"","orcid":"","institution":"Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"G.S.","middleName":"","lastName":"Downward","suffix":""},{"id":404771684,"identity":"f38ed86a-36f6-462d-a8b5-b77691f34fd6","order_by":3,"name":"Gabriela Matias de Pinho","email":"","orcid":"","institution":"Copernicus Institute of Sustainable Deve","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"Matias","lastName":"de Pinho","suffix":""},{"id":404771685,"identity":"00b52f54-a7af-4cb6-8fa8-ca624f675fbe","order_by":4,"name":"Vassilis Daioglou","email":"","orcid":"https://orcid.org/0000-0002-6028-352X","institution":"PBL Netherlands Environmental Assessment Agency","correspondingAuthor":false,"prefix":"","firstName":"Vassilis","middleName":"","lastName":"Daioglou","suffix":""},{"id":404771686,"identity":"756e9971-f43f-46d4-8cd2-92b7caaa3906","order_by":5,"name":"Lazlo Vreedenburgh","email":"","orcid":"","institution":"Delft University","correspondingAuthor":false,"prefix":"","firstName":"Lazlo","middleName":"","lastName":"Vreedenburgh","suffix":""},{"id":404771687,"identity":"69ae46cb-6933-40e7-b244-d375380b9d10","order_by":6,"name":"Jonathan Doelman","email":"","orcid":"https://orcid.org/0000-0002-6842-573X","institution":"PBL","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Doelman","suffix":""}],"badges":[],"createdAt":"2025-01-09 13:30:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5796902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5796902/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41893-025-01676-9","type":"published","date":"2025-11-14T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74440422,"identity":"cef90544-730c-4a8f-8573-c4b3b7eeb197","added_by":"auto","created_at":"2025-01-22 10:08:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":566928,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual Average Ambient and Cooking PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e Concentration in (µg/m3) for Baseline Year 2015 and Selected 2050 Scenarios\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a-e) \u003c/strong\u003eAnnual population weighted average ambient PM\u003csub\u003e2.5\u003c/sub\u003e\u003csub\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e\u0026nbsp;(f-j)\u003c/strong\u003e population weighted average cooking PM\u003csub\u003e2.5\u003c/sub\u003e\u0026nbsp;, \u003cstrong\u003e(e,j) \u003c/strong\u003e\u003cem\u003e‘All’\u003c/em\u003e Scenario incorporates: \u003cem\u003e‘Clean Cooking’\u003c/em\u003e— clean cooking stoves usage for all, air pollution control, 80% agricultural waste burning reduction, \u003cem\u003e‘Climate Policy’\u003c/em\u003e—entails achieving the 1.5°C target )\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5796902/v1/712d8ca701e167a634f5d67f.png"},{"id":74440201,"identity":"713e3be3-ed80-4e1e-915c-2476c86f6674","added_by":"auto","created_at":"2025-01-22 10:00:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103866,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Estimated PAF of Ischemic Heart Disease Attributable to Total PM\u003c/strong\u003e\u003csub\u003e2.5\u003c/sub\u003e \u003cstrong\u003e\u0026nbsp;Air Pollution from Both Ambient and Household Cooking by Scenario\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5796902/v1/e730b6fc0226fcfd255381df.png"},{"id":74440202,"identity":"820a0b31-7c19-447f-8b2d-81b54729ed6b","added_by":"auto","created_at":"2025-01-22 10:00:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72046,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology Flowchart\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5796902/v1/d7cdff98d8e0948312bb534c.png"},{"id":95972968,"identity":"562090e3-84e4-4293-9471-cb42fa18800e","added_by":"auto","created_at":"2025-11-15 08:06:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1601393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5796902/v1/25ad5901-96dd-453a-aa6a-11544e18324b.pdf"},{"id":74440204,"identity":"6ea065be-a9df-4a01-9cb0-ac40f2064384","added_by":"auto","created_at":"2025-01-22 10:00:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":832519,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryNovFinalized1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5796902/v1/9e451c56750cb1c9d54a8ac9.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Modelling Clean Cooking and Climate Policy for Future Heart Disease Reduction","fulltext":[{"header":"Main","content":"\u003cp\u003eAir pollution (both indoor and ambient) contributes to an estimated 8.1\u0026nbsp;million deaths per year globally, making it a leading modifiable environmental risk factor\u003csup\u003e1\u003c/sup\u003e. Air pollution consists of a heterogenous mixture of particles and gases, with particulate matter with a diameter of 2.5 micrometers or smaller (PM\u003csub\u003e2.5\u003c/sub\u003e ) being the most studied in relation to health. Due to its small size PM\u003csub\u003e2.5\u003c/sub\u003e can penetrate deeply into the respiratory system and enter systemic circulation resulting in a variety of pro-inflammatory and detrimental health effects\u003csup\u003e2\u003c/sup\u003e. There is strong evidence that suggests that it causes heart disease, and nearly 2.5\u0026nbsp;million ischemic heart disease deaths can be attributed to excess PM\u003csub\u003e2.5\u003c/sub\u003e in 2021\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSources of ambient PM\u003csub\u003e2.5\u003c/sub\u003e include fuel combustion, and agricultural waste burning. A major source of indoor PM\u003csub\u003e2.5\u003c/sub\u003e is residential energy production \u003csup\u003e3\u003c/sup\u003e as several billion people worldwide have unreliable or unaffordable energy access, leading to a reliance on solid fuels (firewood, dung, coal) for residential heating and cooking\u003csup\u003e4\u0026ndash;6 7\u003c/sup\u003e. At the same time, fossil fuel combustion is also a major driver of climate change, providing synergistic opportunities (i.e. health-related co-benefits) when addressed simultaneously.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eVarious strategies exist to reduce PM\u003csub\u003e2.5\u003c/sub\u003e exposure. For ambient air quality, strategies include end-of pipe control, climate mitigation policies, and reduction of waste burning. The main strategy for improving indoor air quality has been to adopt \u0026lsquo;clean\u0026rsquo; or \u0026lsquo;cleaner\u0026rsquo; cooking stoves or fuels namely, liquid petroleum gas, natural gas or electricity. Previous studies have traditionally focused on the health impacts of either ambient control measures or cookstove interventions separately, whereas studies combining the two are limited. As a result, a notable research gap exists, as many regions with high reliance on solid fuels for household energy also lack regulations and technologies for reducing ambient PM, leading to populations being doubly burdened by high exposures to both ambient and indoor air pollution\u003csup\u003e9,10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn additional gap, is that few studies incorporate future trends, limiting the scientific community\u0026rsquo;s ability to respond to the question of what long term benefit(s) can be expected if these strategies are adopted either in isolation from each other or collectively. In line with the Sustainable Development Goals (SDG\u0026rsquo;s) for which the global community has agreed to combat climate change (SDG13), increase energy access (SDG7) and improve health (SDG3) our study aims to use multiple future scenarios, utilizing different energy use, agricultural waste management and climate policies to assess the plausible co-benefits of such policies on both indoor and ambient air quality and on the plausible future global burden of ischemic disease.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e Exposure Trends Across Scenarios to 2050\u003c/p\u003e \u003cp\u003eSeven scenarios (Methods, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.) were used to evaluate the impact of ambient and indoor cooking PM\u003csub\u003e2.5\u003c/sub\u003e exposure in relation to ischemic heart disease burden. This is done by coupling the IMAGE model which projects future energy and land use to the TM5-FASST model for ambient pollution for 26 world regions. The WHO \u0026lsquo;Household Multiple Emission Source\u0026rsquo; (HOMES) model was used to estimate indoor concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e related to cooking. The 7 scenarios can be grouped into 3 categories: a) a baseline scenario depicting current trends, b) four scenarios looking into individual measures (tighter air pollution control, climate policy, agricultural waste burning reduction and clean cooking) and c) two scenarios where strategies are combined.\u003c/p\u003e \u003cp\u003eIn the \u003cem\u003e'Baseline\u0026rsquo;\u003c/em\u003e scenario, (following socio-demographic assumptions underlying the SSP2 scenario)\u003csup\u003e13\u003c/sup\u003e, moderate economic growth and population growth are projected. Here, the SSP2 scenario shows an increase in global energy use \u0026ndash;with a slow trend towards cleaner fuels. For air pollution control, current policies are projected to lead to tighter standards based on observed trends and announced measures\u003csup\u003e11,13\u003c/sup\u003e These trends together imply that PM \u003csub\u003e2.5\u003c/sub\u003e exposure is estimated to decrease in the 2030\u0026ndash;2050 period for almost all regions. However, absolute PM\u003csub\u003e2.5\u003c/sub\u003e remains at a high level in most regions. The ambient PM\u003csub\u003e2.5\u003c/sub\u003e exposure for the regions Mexico, Rest of South America, Korea and Indonesia are projected in the15-20\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e range. For Northern, Western and Eastern Africa, Central America, the Middle East and China the ambient exposures are in the 20\u0026ndash;35\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e range and for the regions Rest of South Asia and India ambient exposures are above 40\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003eb\u003c/b\u003e \u0026lsquo; Baseline 2050 ambient\u0026rsquo;). The reduction in total PM\u003csub\u003e2.5\u003c/sub\u003e exposure is largely driven by reductions in indoor PM\u003csub\u003e2.5\u003c/sub\u003e exposure due to use of cleaner cooking fuels driven by increasing income levels (Supplementary Table\u0026nbsp;1. \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg \u003cb\u003erelative to f\u003c/b\u003e). In contrast, trends in ambient PM \u003csub\u003e2.5\u003c/sub\u003e exposure diverge across regions. In most regions, exposure is projected to decrease as a result of tighter air pollution control. In other regions, including Western and Eastern Africa, India and Indonesia, increased energy consumption is expected to outpace tighter standards leading to higher exposure levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea \u003cb\u003erelative to b\u003c/b\u003e). For example, in East Africa ambient PM\u003csub\u003e2.5\u003c/sub\u003e is estimated to from 12.9 \u0026micro;g/m3 to 33.2 \u0026micro;g/m3 by 2050 under the \u003cem\u003e\u0026lsquo;Baseline'\u003c/em\u003e scenario.\u003c/p\u003e \u003cp\u003eThese findings indicate an need for additional measures beyond the \u003cem\u003e\u0026lsquo;Baseline\u0026rsquo;\u003c/em\u003e to decrease PM\u003csub\u003e2.5\u003c/sub\u003e exposure. The additional individual intervention scenarios do lead to varying magnitudes of improvement in PM\u003csub\u003e2.5\u003c/sub\u003e exposure. In particular the clean cooking and climate policy scenario shows considerable improvement. The climate policy scenario is projected to improve ambient air quality around 5\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e in many regions. The impact of the scenarios involving tighter air pollution control and waste burning reduction is much smaller. This is partly a result of already tightening of air pollution control standards embedded in the \u003cem\u003e\u0026lsquo;Baseline\u0026rsquo;\u003c/em\u003e (based on the projections under current legislation). For scenario \u003cem\u003e\u0026lsquo;Waste\u0026rsquo;\u003c/em\u003e (involving waste burning reduction) the most noticeable PM\u003csub\u003e2.5\u003c/sub\u003e reductions are specifically in 'Southeast Asia', 'India' and the 'Rest of South Asia'. Under the \u003cem\u003e'CCooking'\u003c/em\u003e scenario, 11 sub-regions (Rest of South America, North Africa, West Africa, East Africa Central Asia, Middle East, India, Korea, China, South East Asia, Indonesia, Rest of South Asia) were unable to achieve total average PM\u003csub\u003e2.5\u003c/sub\u003e exposures (indoor and outdoor) under the interim target 1 of 35\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, despite large reductions in PM\u003csub\u003e2.5\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec \u003cb\u003eand h\u003c/b\u003e).The scenarios combining strategies led to the most improvements in PM\u003csub\u003e2.5\u003c/sub\u003e with the \u003cem\u003e\u0026lsquo;ClimPol\u0026amp;CC\u0026rsquo;\u003c/em\u003e having almost as much improvement as the \u003cem\u003e\u0026lsquo;All\u0026rsquo;\u003c/em\u003e scenario which maximally improves PM\u003csub\u003e2.5\u003c/sub\u003e exposure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAir Pollution Related Ischemic Heart Disease\u003c/h2\u003e \u003cp\u003eAs PM\u003csub\u003e2.5\u003c/sub\u003e exposure is expected to reduce under \u003cem\u003ethe 'Baseline'\u003c/em\u003e scenario, the cardiovascular disease burden attributable to air pollution will be expected to improve already given current policies (i.e., the burden of mortality will decrease). However, the degree of improvement varies between regions. In the North America and Russia, Oceania, and North Africa and Middle East regions the bulk of the improvement in PAF will be achieved by 2030, after which no further improvement in current policies is expected. For example, in Canada the PAF (95% UR) under '\u003cem\u003eBaseline'\u003c/em\u003e reduces from 7.8% (UR 6.7%,9.1%) to 2.5% (UR1.5%,3.6%) in 2030 where it remains relatively stable by 2050 ( PAF: 2.3%, UR 1.1%,3.4%) In contrast the Europe, Asia, Central and South America, and African regions will continue to show improvements through to 2050. Despite consistent improvements in PAF, large absolute burdens of disease persist for most Low-and-Middle-Income countries where a PAF exceeding 15% is consistently observed.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndividual Measures\u003c/h3\u003e\n\u003cp\u003eAs expected based on the exposure, the \u003cem\u003e\u0026lsquo;AirPol\u0026rsquo;\u003c/em\u003e and \u003cem\u003e\u0026lsquo;Waste\u0026rsquo;\u003c/em\u003e scenarios have \u0026ndash; on their own- little additional impact on ischemic heart disease by 2050 at the global scale (See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.). The 'ClimPol' scenario beneficially impacts PAF's for South and Central America (10% by 2050), Asia (18%), and Africa (23%) via the co-benefits of climate policy in reducing air pollutant emissions \u003cem\u003e(See\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e. A milder improvement is observed in the Europe and North Africa and Middle East regions. More discrete changes in PAF are observed at the sub-regional level.\u003c/p\u003e \u003cp\u003eIn the \u003cem\u003e\u0026lsquo;CCooking\u0026rsquo;\u003c/em\u003e scenario, major reductions in PAF (relative to \u003cem\u003e\u0026lsquo;Baseline\u0026rsquo;)\u003c/em\u003e are most evident in the African region, with a reduction to 13% (compared to 33% under \u003cem\u003e\u0026lsquo;Baseline\u0026rsquo;\u003c/em\u003e) by 2030 being observed (albeit slightly worsening to 17% by 2050, compared to 26% under \u003cem\u003e\u0026lsquo;Baseline\u0026rsquo;\u003c/em\u003e).\u003c/p\u003e\n\u003ch3\u003eCombined Measures\u003c/h3\u003e\n\u003cp\u003eCombining all interventions maximally improves the PAF for much of the global population - specifically the Asian, African and South and Central American regions where improvements beyond any of the other individual scenarios are observed. The \u003cem\u003e'ClimPol\u0026amp;CC\u0026rsquo;\u003c/em\u003e scenario generally results in similar PAF reductions to what was observed in the \u003cem\u003e\u0026ldquo;All\u0026rdquo;\u003c/em\u003e scenario by 2050 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.) In the \u003cem\u003e'All'\u003c/em\u003e scenario, the sub-regions Brazil and India show particularly noteworthy benefits in this scenario by 2050 with Brazil's PAF reaching 3.9% (UR 2.8%,5.2%), and India's 9.7% (UR 8.1%,11.5%) which, compared to \u003cem\u003e\u0026lsquo;Baseline\u0026rsquo;, would be\u003c/em\u003e 9.7% (UR 8.4%, 11.3%) for Brazil and 32.3% (UR 30.7%-33.9%) for India. This is also reflected in the regions Asia and South America.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to concurrently analyze scenarios related to household cooking and ambient PM\u003csub\u003e2.5\u003c/sub\u003e exposure on future ischemic heart disease at a global scale. By examining various scenarios, we assessed how various strategies affecting PM\u003csub\u003e2.5\u003c/sub\u003e exposure impacts ischemic heart disease.\u003c/p\u003e \u003cp\u003eWhile the current study examines the scenario outcomes of widely used and validated models, limitations exist\u003csup\u003e14,15\u003c/sup\u003e. First, the demand for energy use, and corresponding projected PM\u003csub\u003e2.5\u003c/sub\u003e depends on several underlying assumptions about underlying drivers (such as economic activity and population)\u003csup\u003e16\u003c/sup\u003e. Other examples, include the assumed air pollution policy embedded in the \u003cem\u003e\u0026lsquo;Baseline\u0026rsquo;\u003c/em\u003e, and the effectiveness of carbon pricing when it relates to achieving climate targets. Generally, the socio-economic assumptions embedded in the \u003cem\u003e'Baseline'\u003c/em\u003e about drivers medium economic growth, technical development and population growth are uncertain. In spite of this, the focus of this paper is on the potential to reduce exposure to PM\u003csub\u003e2.5\u003c/sub\u003e and improve ischemic heart disease burden, through various measures rather than diverging socio-demographic possibilities. Some of the most important uncertainties are examined in other studies\u003csup\u003e11\u003c/sup\u003e. It is also important to note that the trends observed in the \u003cem\u003e\u0026lsquo;Baseline\u0026rsquo;\u003c/em\u003e findings are dependent on continued implementation of legislation and pollution control technologies (e.g. \u003cem\u003e'AirPol\u0026rsquo;)\u003c/em\u003e\u003csup\u003e11\u003c/sup\u003e. Earlier it was shown that air pollution control (\u0026lsquo;AirPol\u0026rsquo;) is not much different from the \u0026lsquo;\u003cem\u003eBaseline\u0026rsquo;\u003c/em\u003e, however, that is assuming a continuation of previous trends of air pollution control, so this interpretation could differ if less improvement was assumed in the \u0026lsquo;\u003cem\u003eBaseline\u0026rsquo;\u003c/em\u003e. Future major changes in legislation or socio-political circumstances may cause deviation from these \u0026lsquo;\u003cem\u003eBaseline\u0026rsquo;\u003c/em\u003e findings. Still, our results are consistent with earlier finding that most regions do not achieve the air quality guideline of 5 \u0026micro;g/m3 set by the WHO, nor under current trends are expected to\u003csup\u003e17\u003c/sup\u003e. One additional assumption, is the distribution of average cooking times in order to account for cultural differences in cooking length. Utilizing these longer cooking times results in a higher estimated PM\u003csub\u003e2.5\u003c/sub\u003e exposure and therefore a higher PAF in regions and sub-regions with higher amounts of biomass cooking. The sensitivity analysis containing these results for all regions can be found in the supplementary materials (pages 26 \u0026minus;\u0026thinsp;37). Finally, broad regional trends are shown which may mask significant variations at more granular levels, which is mildly reflected by the findings of sub-regional variations.\u003c/p\u003e \u003cp\u003eSeveral key findings emerge from this work. First, under \u003cem\u003e\u0026lsquo;Baseline\u0026rsquo;\u003c/em\u003e exposure to PM\u003csub\u003e2.5\u003c/sub\u003e is expected to decrease assuming that current rates of air pollution control, and cleaner fuel use are continued. Corresponding with this decrease is an overall reduction in ischemic heart disease related PAF\u0026rsquo;s in all regions by 2030. Despite this improvement the absolute PM exposures and corresponding PAFs remain high, and all regions (at the aggregate) do not achieve the WHO guideline of 5 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. Second, implementing more rapid usage of cleaner cooking fuels particularly benefits Africa and Asia, in terms of both PM\u003csub\u003e2.5\u003c/sub\u003e exposure and disease burden reduction yet can only be sustained to 2050 with co-implementation of climate policy. Third, maximal long-term improvements are observed if all measures are combined, especially for the African, South and Central American, and Asian regions (see \u003cem\u003e\u0026lsquo;All\u0026rsquo;\u003c/em\u003e scenario). Fourth, combining \u003cem\u003e\u0026lsquo;All\u0026rsquo;\u003c/em\u003e strategies provides rapid and sustained long term reductions in ischemic heart disease burden, with \u003cem\u003e\u0026lsquo;ClimPol\u0026amp;CC\u0026rsquo;\u003c/em\u003e achieving almost the same reduction across most continents (except Asia) while simultaneously achieving the 1.5-degree Paris Target. Fifth, despite the above, regional variation exists, in particular the North Africa and Middle East Region where PAF is high regardless of scenario. Thus, more tailored approaches in other systems (e.g. transport) may need to be developed to improve air quality for North Africa and the Middle East.\u003c/p\u003e \u003cp\u003eTo conclude, this study demonstrated that mitigation policies combined with waste burning reduction, end of pipe control, and clean household cooking energy usage can reduce ischemic heart disease burden for a majority of the world\u0026rsquo;s population (especially the Asian, African, and South and Central American regions) to a greater extent especially over the long term than to if and when policies are only adopted in isolation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eHere we conduct a model-based scenario analysis (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. for scenario descriptions) to investigate possible strategies and their impact on PM\u003csub\u003e2.5\u003c/sub\u003e concentrations and its subsequent impact on the Joint Population Attributable Fraction (PAF) for ischemic heart disease in the years 2015, 2030 and 2050\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIMAGE Model to Project Future Energy and Land Use\u003c/h2\u003e \u003cp\u003eThe IMAGE integrated assessment model represents the energy and land use sectors in an integrated manner was used to project future greenhouse gas emissions (GHG) for 26 world regions\u003csup\u003e19\u003c/sup\u003e. Greenhouse gas and air pollutant emissions factors are obtained from the Emissions Database for Global Atmospheric Research (EDGAR) database\u003csup\u003e20\u003c/sup\u003e. The energy model was used to determine household fuel choices in scenarios that did not have clean cooking (See Below). For more details refer to PBL (Netherlands Environmental Assessment Agencies) or Appendix C\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAmbient PM Concentrations\u003c/h3\u003e\n\u003cp\u003eProjected changes in ambient air quality are based on emissions, calculated by multiplying energy and land use activities with corresponding emission factors. Subsequently, the emission scenarios can be used to calculate ambient \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e concentrations using the IMAGE TM5-FASST sub-model. The TM5-FAAST model is a simplified atmospheric chemistry model, which is less computationally intensive than the TM5 model but is validated and described in detail elsewhere\u003csup\u003e22\u003c/sup\u003e. Briefly, it assumes a linear relationship between emission changes in one region to estimate pollution concentrations in another region, through source receptor coefficients stored in matrix form. These calculations first take precursor emissions such as sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), nitrogen Oxide (NOx), volatile organic compounds (VOCs) and ammonia (NH\u003csub\u003e3\u003c/sub\u003e) which undergo chemical reactions in the atmosphere to contribute to \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5.\u003c/em\u003e\u003c/sub\u003e The TM5-FAAST model then estimates ambient population weighted yearly mean \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e concentrations for 56 regions which is subsequently adapted to IMAGE\u0026rsquo;s 26 world regions\u003csup\u003e13,23\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eCooking PM Concentrations\u003c/h3\u003e\n\u003cp\u003eHousehold cooking \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e exposure was determined in two steps. First the residential energy demand sub-component of the IMAGE model was used to determine the distribution of future fuel choices within scenarios where full clean cooking usage is not yet attained \u003csup\u003e24,25\u003c/sup\u003e. All stoves using a particular fuel were assumed to have an emission rate corresponding to a specific ISO VPT tier (see Appendix A). Second, the World Health Organizations Household Multiple Emissions Sources (WHO, HOMES) single zone model was used to estimate average daily indoor kitchen \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e concentrations for three cooking events\u003csup\u003e26\u003c/sup\u003e. The HOMES model simulates kitchen concentrations arising in this case from a single cooking source via the following equations:\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEquation 1. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:C\\left(t\\right)=\\frac{{q}_{1}{f}_{1}{q}_{2}{f}_{2}{q}_{3}{f}_{3}\\dots\\:{q}_{n}{f}_{n}}{\\alpha\\:V}\\left(1-{e}^{-\\alpha\\:t}\\right)+{C}_{0}\\left({e}^{-\\alpha\\:t}\\right)+{C}_{b}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eEquation 2. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{k}={\\sum\\:}_{1}^{1440}\\frac{{C}_{i}}{1440}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section4\"\u003e \u003ch2\u003eEquation 3. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{r}={C}_{k}R\\:where\\:{E}_{r}\\ge\\:{C}_{b}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cp\u003ewhere C(t)\u0026thinsp;=\u0026thinsp;Concentration for a given time point qx\u0026thinsp;=\u0026thinsp;the emission rate for source x (mass/min) fx\u0026thinsp;=\u0026thinsp;fraction of emissions from source x that enters the kitchen environment α\u0026thinsp;=\u0026thinsp;air change rate (changes/min) V\u0026thinsp;=\u0026thinsp;kitchen volume (m3 ) t\u0026thinsp;=\u0026thinsp;time interval (1 min) Co\u0026thinsp;=\u0026thinsp;concentration from preceding time interval (unit mass/m3 ) Cb\u0026thinsp;=\u0026thinsp;Background concentration (assumed to be the ambient concentration estimated by TM5-FAAST[mass/m3 ]).\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSince cooking time and household characteristics can vary, a distribution of different variables was used as HOMES input parameters (see Supplementary Fig.\u0026nbsp;1). Household concentrations were then calculated via a Monte Carlo Simulation, which leveraged values across the distributions to produce 5000 different results across the input parameters\u003csup\u003e27\u003c/sup\u003e. The mean daily \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e exposure of these outputs were calculated for each major fuel type (Traditional fuelwood/coal, Improved Cookstove, LPG/Electricity, Kerosene/Biogas)\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor scenarios where cleaner cooking was not incorporated the proportion of the population currently using each fuel type was used to weight the mean \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e concentrations. Finally, as the personal exposure ratio is not 100%, due to movement in and out of the kitchen space, and differs by sex, the weighted \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e was multiplied by 0.6 \u003csup\u003e29\u003c/sup\u003e,\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor cooking time, the IPUMS Multinational Time Use Study database was used to calculate a distribution of the \u0026lsquo;short\u0026rsquo; cooking times (Mean:49.95, SD:56.36). IPUMS collects harmonized time use data under the variable entitled \u003cem\u003e\u0026lsquo;unpaid domestic work'\u0026rsquo;\u003c/em\u003e\u003csup\u003e27\u003c/sup\u003e. The remaining assumptions about input distributions were based on the WHO database of input variables (See Appendix for rest of assumptions)\u003csup\u003e30\u003c/sup\u003e. Two separate mean daily \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e calculations, one for short and another for long cooking times were made. The longer analysis was treated as a sensitivity analysis since transitioning to cleaner cooking is expected to also reduce cooking time. All estimates in the figures shown in our main analysis were for shorter cooking times, ultimately resulting in an underestimation of the disease burden.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIschemic Heart Disease Burden Estimation\u003c/h2\u003e \u003cp\u003eThe burden of ischemic heart disease was calculated for all scenarios for the population of adults over 35 years of age\u003csup\u003e1\u003c/sup\u003e. This was done through calculating the joint population attributable fraction (PAF) of indoor and ambient air pollution for 26 world regions with the Meta-Regression-Bayesian Regularized Trimmed (MRBRT) curve obtained from GBD 2019\u003csup\u003e1\u003c/sup\u003e. The MRBRT curve is a summary risk measure that pools all available studies collected from the GBD across a range of \u003cem\u003ePM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e exposures \u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe specific equations used are described below (Equations 4 to 10). Eq.\u0026nbsp;4 represents the relative risk (RR) of those not exposed to household air pollution (HAP) from solid cooking fuels. Equations\u0026nbsp;5 and 6 were used to represent populations exposed to both ambient and cooking with solid fuels. Equations\u0026nbsp;7 through 10 are the population attributable fractions for overall PM exposure, which was determined for each IMAGE region estimating the number of excess cases of ischemic heart disease attributable to particulate matter exposure relative to a plausible theoretical minimum exposure range of PM\u003csub\u003e2.5\u003c/sub\u003e exposure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEquation 4. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{RR}_{OAP}=MRBRT\\left(z={Exp}_{OAP}\\:)/MRBRT(z=TMREL)\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eEquation 5 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{RR}_{HAP}=MRBRT\\left(z={Exp}_{OAP}+{Exp}_{HAP}\\:)/MRBRT(z=TMREL)\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section4\"\u003e \u003ch2\u003eEquation 6. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{RR}_{PM}={RR}_{OAP}\\left(1-{P}_{HAP}\\right)+{RR}_{HAP}{P}_{HAP}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e \u003cb\u003eEquation 7.\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{PAF}_{PM}=\\frac{{RR}_{PM}-1}{{RR}_{PM}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEquation 8. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{PAF}_{OAP}=\\frac{{Exp}_{OAP}}{{Exp}_{OAP}+{P}_{HAP}\\times\\:{Exp}_{HAP}}\\:{PAF}_{PM}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eEquation 9. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{PAF}_{HAP}=\\frac{{P}_{HAP}\\times\\:{Exp}_{HAP}}{{Exp}_{OAP}+{P}_{HAP}\\times\\:{Exp}_{HAP}}\\:{PAF}_{PM}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section4\"\u003e \u003ch2\u003eEquation 10. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{PAF}_{PM}={PAF}_{HAP}+{PAF}_{OAP}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eA total of 1000 PAFs were predicted and the lowest 2.5 and highest 97.5 percentiles were used to derive 95% uncertainty intervals\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eScenarios\u003c/h2\u003e \u003cp\u003eIn this analysis, estimates on the current trends are described by a baseline scenario (\u003cem\u003e\u0026lsquo;Baseline\u0026rsquo;\u003c/em\u003e) that describes the world under moderate assumptions for all socio-economic and technological drivers. All policy scenarios represent interventions which would improve off of this baseline scenario to improve PM \u003csub\u003e2.5\u003c/sub\u003e exposures. The policy scenarios explored are: 1) Tighter Air Pollution Control, 2) Clean Cooking, 3) Climate Policy (limiting global mean temperature increase to1.5\u0026deg;C) 4) a reduction of agricultural waste burning (\u0026lsquo;\u003cem\u003eWaste\u0026rsquo;\u003c/em\u003e) 5) (\u0026lsquo;\u003cem\u003eAll\u0026rsquo;\u003c/em\u003e) of the aforementioned strategies combined. The sixth was a scenario combining mitigation with clean cooking (\u0026lsquo;\u003cem\u003eClimPol\u0026amp;CC\u0026rsquo;\u003c/em\u003e) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of Scenarios Used\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline ('Baseline')\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Shared Socioeconomic Pathway SSP2 scenario was used to represent the 'Baseline'\u003csup\u003e21\u003c/sup\u003e. This scenario assumes a continuation of current trends concerning population, economic growth, and technological development. Some end or pipe air pollution controls are assumed to be within this scenario, but not to the same extent as the end of pipe scenario\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTighter Air Pollution Control\u003c/p\u003e \u003cp\u003e(\u0026lsquo;AirPol\u0026rsquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe tighter air pollution control scenario extends the 'SSP2 Baseline' to incorporate the effects of implementing strong pollution control alongside an assumption of rapid technology development and stringent leglislation\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate Policy\u003c/p\u003e \u003cp\u003e(\u0026lsquo;ClimPol\u0026rsquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Climate Policy scenario represents the air pollution reduction which arises due to efforts to control greenhouse gas emissions under radiative forcing target 1.9 (RCP1.9) in the energy and land use sectors, in line with the 1.5\u0026deg;C target set out in the Paris Agreement This scenario projects major transitions in the energy sector, through higher penetration of renewable energy sources, increased electrification of energy services (i.e. space heating, transport, etc.) and phasing out fossil fuels.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaste Burning Reduction\u003c/p\u003e \u003cp\u003e(\u0026lsquo;Waste\u0026rsquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn the Waste Reduction scenario extends the baseline by incorporating the reduction of \u003cb\u003eagricultural\u003c/b\u003e waste burning is by 40% by 2030, then by 80% by 2050.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean Cooking\u003c/p\u003e \u003cp\u003e(\u0026lsquo;CCooking\u0026rsquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Clean Cooking scenario includes the effect of the universal usage of cleaner cooking fuels. LPG, natural gas, and electricity were considered clean cooking fuels. While fuels such as LPG can generate small amounts of PM, they are several orders of magnitude less than those generated by solid fuels and thus are considered \u0026ldquo;null\u0026rdquo; in the PAF analysis (not in the PM\u003csub\u003e2.5\u003c/sub\u003e exposure estimation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate Policy\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e \u003cp\u003e(\u0026lsquo;ClimPol\u0026amp;CC\u0026rsquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Climate Policy\u0026thinsp;+\u0026thinsp;CC is the Climate Policy Scenario where Clean Cooking is co-implemented.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll Strategies Combined\u003c/p\u003e \u003cp\u003e(\u0026lsquo;All\u0026rsquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll the above individual interventions combined.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eData and Code Availability\u003c/h2\u003e \u003cp\u003eMost data relevant to the results are listed in the supplementary information. If additional data is needed it is available upon request of the corresponding author. All code used for analysis will be posted on a public git repository.\u003c/p\u003e \u003c/div\u003e \n\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis project received funding from the European Research Council (ERC) under the Horizon Europe program (PICASSO project; grant agreement ID 819566). The funder had no role in the study design, data collection, data analysis, writing of the report, or data interpretation. V.D received funding from the European Union's Horizon Europe programme under grant agreement No 101081604.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eE.W conducted the health impact analyses, made all figures, conceptualized the use of HOMEs model in this type of analysis and wrote the first draft. V.D. ran the energy modules of the IMAGE model for all scenarios and provided proportions of fuel usage for the SSP2 Baseline scenario. L.V ran the TM5-FAAST analyses. Jonathan Doelman ran the land model and provided results for the waste burning scenario. Mathijs Harmsen provided insight into precursor emissions. Martijn van der Marel helped with the HOMES model code validation. Maarten Van Den Berg created IMAGE\u0026rsquo;s version of the TM5-FAAST sub-model. Rakesh Ghosh advised EW on early stages of the work about MR-BRT curves.. D.vV. conceptualized the scenarios, scenario orders, supervised and was the source of funding. G.D., and M.G provided input about health analyses and were the main supervisors and leaders of the project. All listed co-authors edited and revised the final drafts.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMurray, C. J. 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Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 106 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5796902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5796902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLong-term PM\u003csub\u003e2.5\u003c/sub\u003e exposure is a risk factor for cardiovascular mortality. Fossil fuel burning is a large source of PM\u003csub\u003e2.5\u003c/sub\u003e. Here, a global health impact assessment was conducted utilizing 7 future scenarios evaluating strategies to reduce PM\u003csub\u003e2.5\u003c/sub\u003e exposure, including reducing fossil fuel use, air pollution control, adopting cleaner cooking methods and combinations thereof. Under current trends, air quality is projected to improve by 2050, but the absolute attributable burden of ischemic heart disease remains high in many regions. Promoting cleaner cooking is effective in the short term (by 2030) in South and Central America, Asia, and Africa in reducing the health burden. In the long term (by 2050), for most regions, only strategies that simultaneously target ambient and cooking related PM\u003csub\u003e2.5\u003c/sub\u003e resulted in sustained improvements for reducing ischemic heart disease burden. For, North Africa and the Middle East region the population attributable fraction remains high across all scenarios. PM\u003csub\u003e2.5\u003c/sub\u003e exposure remains above the WHO Air Quality Guidelines across all scenarios therefore additional strategies are required to improve air quality.\u003c/p\u003e","manuscriptTitle":"Modelling Clean Cooking and Climate Policy for Future Heart Disease Reduction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-22 10:00:07","doi":"10.21203/rs.3.rs-5796902/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-sustainability","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natsustain","sideBox":"Learn more about [Nature Sustainability](http://www.nature.com/natsustain/)","snPcode":"","submissionUrl":"","title":"Nature Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d8a47ea1-499e-49f8-a75c-09b9de71ec30","owner":[],"postedDate":"January 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43152698,"name":"Health sciences/Risk factors"},{"id":43152699,"name":"Health sciences/Diseases/Cardiovascular diseases"},{"id":43152700,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate and Earth system modelling"}],"tags":[],"updatedAt":"2025-11-15T08:06:37+00:00","versionOfRecord":{"articleIdentity":"rs-5796902","link":"https://doi.org/10.1038/s41893-025-01676-9","journal":{"identity":"nature-sustainability","isVorOnly":false,"title":"Nature Sustainability"},"publishedOn":"2025-11-14 05:00:00","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-01-22 10:00:07","video":"","vorDoi":"10.1038/s41893-025-01676-9","vorDoiUrl":"https://doi.org/10.1038/s41893-025-01676-9","workflowStages":[]},"version":"v1","identity":"rs-5796902","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5796902","identity":"rs-5796902","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}