VOC source apportionment: How monitoring characteristics influence positive matrix factorization (PMF) solutions.

Positive matrix factorization (PMF) can be used to develop more targeted air quality mitigation strategies by identifying major sources of a pollutant in an area. This technique is dependent, however, on the ability of PMF to resolve factors that accurately represent all sources of that pollutant in an area. We investigated how the accuracy of PMF solutions might be influenced by monitoring data characteristics, such as temporal resolution, monitoring location, and species composition, to better inform the use of PMF in VOC mitigation strategies. We applied PMF to five VOC monitoring programs collected within a four-year period in Colorado and found generally consistent factors, which we identified as oil extraction, processing, and evaporation; natural gas; vehicle exhaust; and liquid gasoline/short-lived oil and gas. The main determinant influencing whether or not a dataset resolved each of these sources was whether the dataset had a comprehensive list of VOC species covering key species of each source. Pollution spikes were not well-modeled in any of the solutions. Hyperlocal and volatile chemical product factors expected to be resolved in the industrialized, urban location were also missing, highlighting three limitations of PMF analysis. Wind direction dependence and diurnal trends aided in source identification, suggesting that high-time resolution data is important for developing actionable PMF results. Based on these findings, we recommend that air monitoring for PMF-informed VOC mitigation efforts include high temporal resolution and a comprehensive array of VOC species.

WindCline: Sloping wind tunnel for characterizing flame behavior under variable inclines and wind conditions.

Developing accurate computational models of wildfire dynamics is increasingly important due to the substantial and expanding negative impacts of wildfire events on human health, infrastructure, and the environment. Wildfire spread and emissions depend on a number of factors, including fuel type, environmental conditions (moisture, wind speed, etc.), and terrain/location. However, there currently exist only a few experimental facilities that enable testing of the interplay of these factors at length scales <1 m with carefully controlled and characterized boundary conditions and advanced diagnostics. Experiments performed at such facilities are required for informing and validating computational models. Here, we present the design and characterization of a tilting wind tunnel (the "WindCline") for studying wildfire dynamics. The WindCline is unique in that the entire tunnel platform is constructed to pivot around a central axis, which enables the sloping of the entire system without compromising the quality of the flow properties. In addition, this facility has a configurable design for the test section and diffuser to accommodate a suite of advanced diagnostics to aid in the characterization of (1) the parameters needed to establish boundary conditions and (2) flame properties and dynamics. The WindCline thus allows for the measurement and control of several critical wildfire variables and boundary conditions, especially at the small length scales important to the development of high-fidelity computational simulations (10-100 cm). Computational modeling frameworks developed and validated under these controlled conditions can expand understanding of fundamental combustion processes, promoting greater confidence when leveraging these processes in complex combustion environments.

Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring.

The use of low-cost sensors (LCSs) for the mobile monitoring of oil and gas emissions is an understudied application of low-cost air quality monitoring devices. To assess the efficacy of low-cost sensors as a screening tool for the mobile monitoring of fugitive methane emissions stemming from well sites in eastern Colorado, we colocated an array of low-cost sensors (XPOD) with a reference grade methane monitor (Aeris Ultra) on a mobile monitoring vehicle from 15 August through 27 September 2023. Fitting our low-cost sensor data with a bootstrap and aggregated random forest model, we found a high correlation between the reference and XPOD CH4 concentrations (r = 0.719) and a low experimental error (RMSD = 0.3673 ppm). Other calibration models, including multilinear regression and artificial neural networks (ANN), were either unable to distinguish individual methane spikes above baseline or had a significantly elevated error (RMSDANN = 0.4669 ppm) when compared to the random forest model. Using out-of-bag predictor permutations, we found that sensors that showed the highest correlation with methane displayed the greatest significance in our random forest model. As we reduced the percentage of colocation data employed in the random forest model, errors did not significantly increase until a specific threshold (50 percent of total calibration data). Using a peakfinding algorithm, we found that our model was able to predict 80 percent of methane spikes above 2.5 ppm throughout the duration of our field campaign, with a false response rate of 35 percent.

Health impacts of a randomized biomass cookstove intervention in northern Ghana.

BACKGROUND: Household air pollution (HAP) from cooking with solid fuels has adverse health effects. REACCTING (Research on Emissions, Air quality, Climate, and Cooking Technologies in Northern Ghana) was a randomized cookstove intervention study that aimed to determine the effects of two types of "improved" biomass cookstoves on health using self-reported health symptoms and biomarkers of systemic inflammation from dried blood spots for female adult cooks and children, and anthropometric growth measures for children only. METHODS: Two hundred rural households were randomized into four different cookstove groups. Surveys and health measurements were conducted at four time points over a two-year period. Chi-square tests were conducted to determine differences in self-reported health outcomes. Linear mixed models were used to assess the effect of the stoves on inflammation biomarkers in adults and children, and to assess the z-score deviance for the anthropometric data for children. RESULTS: We find some evidence that two biomarkers of oxidative stress and inflammation, serum amyloid A and C-reactive protein, decreased among adult primary cooks in the intervention groups relative to the control group. We do not find detectable impacts for any of the anthropometry variables or self-reported health. CONCLUSIONS: Overall, we conclude that the REACCTING intervention did not substantially improve the health outcomes examined here, likely due to continued use of traditional stoves, lack of evidence of particulate matter emissions reductions from "improved" stoves, and mixed results for HAP exposure reductions. CLINICAL TRIAL REGISTRY: ClinicalTrials.gov (National Institutes of Health); Trial Registration Number: NCT04633135 ; Date of Registration: 11 November 2020 - Retrospectively registered. URL: https://clinicaltrials.gov/ct2/show/NCT04633135?term=NCT04633135&draw=2&rank=1.

Characterizing methane and total non-methane hydrocarbon levels in Los Angeles communities with oil and gas facilities using air quality monitors.

Over the past decade, sensor networks have been proven valuable to assess air quality on highly localized scales. Here we leverage innovative sensors to characterize gaseous pollutants in a complex urban environment and evaluate differences in air quality in three different Los Angeles neighborhoods where oil and gas activity is present. We deployed monitors across urban neighborhoods in South Los Angles adjacent to oil and gas facilities with varying levels of production. Using low-cost sensors built in-house, we measured methane, total non-methane hydrocarbons (TNMHCs), carbon monoxide, and carbon dioxide during three deployment campaigns over four years. The multi-sensor linear regression calibration model developed to quantify methane and TNMHCs offers up to 16% improvement in coefficient of determination and up to a 22% reduction in root mean square error for the most recent dataset as compared to previous models. The deployment results demonstrate that airborne methane concentrations are higher within a 500 m radius of three urban oil and gas facilities, as well as near a natural gas distribution pipeline, likely a result of proximity to sources. While there are numerous additional sources of TNMHCs in complex urban environments, some sites appear to be larger emitters than others. Significant methane emissions were also measured at an idle site, suggesting that fugitive emissions may still occur even if production is ceased. Episodic spikes of both compounds suggested an association with oil and gas activities, demonstrating how sensor networks can be used to elucidate community-scale sources and differences in air quality moving forward.

Photochemical Aging of Atmospheric Particulate Matter in the Aqueous Phase.

This study focused on the photoaging of atmospheric particulate matter smaller than 2.5 µm (PM2.5) in the aqueous phase. PM2.5 was collected during a winter, a spring, and a summer campaign in urban and rural settings in Colorado and extracted into water. The aqueous extracts were photoirradiated using simulated sunlight, and the production rate (r•OH) and the effects of hydroxyl radicals (•OH) were measured as well as the optical properties as a function of the photoaging of the extracts. r•OH was seen to have a strong seasonality with low mean values for the winter and spring extracts (4.8 and 14 fM s-1 mgC-1 L, respectively) and a higher mean value for the summer extracts (65.4 fM s-1 mgC-1 L). For the winter extracts, •OH was seen to mostly originate from nitrate photolysis while for the summer extracts, a correlation was seen between r•OH and iron concentration. The extent of photobleaching of the extracts was correlated with r•OH, and the correlation also indicated that non-•OH processes took place. Using the •OH measurements and singlet oxygen (1O2) measurements, the half-life of a selection of compounds was modeled in the atmospheric aqueous phase to be between 1.9 and 434 h.

Using Gas-Phase Air Quality Sensors to Disentangle Potential Sources in a Los Angeles Neighborhood.

In the late summer of 2016, our team deployed a network of low-cost air quality sensing systems in partnership with community-based organizations in a neighborhood in South Los Angeles, California. Residents of this community were concerned about possible emissions from local oil and gas activity, however in addition to these potential emissions, the neighborhood is also subject to a complex mixture of pollutants from other nearby sources including major highways. For this deployment, metal-oxide VOC sensors were quantified to provide methane (CH4) and total non-methane hydrocarbon (TNMHCs) concentration estimates. This data along with other sensor signals, meteorological data, and community member observations was used to examine the composition and possible origins of observed emissions. The sensor network displayed expected environmental trends and highlighted short-term elevations in CH4 and/or TNMHCs, which we were then able to investigate more closely. The results indicated that sources of both combusted and volatilized hydrocarbons were likely affecting air quality throughout the community, including near the site of the local oil and gas activity. This deployment may serve as a model for how multi-sensor systems deployed in networks can be leveraged to better understand sources in complex areas, potentially supporting future community-based air quality research.

Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources.

An array of low-cost sensors was assembled and tested in a chamber environment wherein several pollutant mixtures were generated. The four classes of sources that were simulated were mobile emissions, biomass burning, natural gas emissions, and gasoline vapors. A two-step regression and classification method was developed and applied to the sensor data from this array. We first applied regression models to estimate the concentrations of several compounds and then classification models trained to use those estimates to identify the presence of each of those sources. The regression models that were used included forms of multiple linear regression, random forests, Gaussian process regression, and neural networks. The regression models with human-interpretable outputs were investigated to understand the utility of each sensor signal. The classification models that were trained included logistic regression, random forests, support vector machines, and neural networks. The best combination of models was determined by maximizing the F1 score on ten-fold cross-validation data. The highest F1 score, as calculated on testing data, was 0.72 and was produced by the combination of a multiple linear regression model utilizing the full array of sensors and a random forest classification model.

Updated Emission Factors from Diffuse Combustion Sources in Sub-Saharan Africa and Their Effect on Regional Emission Estimates.

Diffuse emission sources outside of kitchen areas are poorly understood, and measurements of their emission factors (EFs) are sparse for regions of sub-Saharan Africa. Thirty-one in-field emission measurements were taken in northern Ghana from combustion sources common to rural regions worldwide. Sources sampled included commercial cooking, trash burning, kerosene lanterns, and diesel generators. EFs were calculated for carbon monoxide (CO), carbon dioxide (CO2), as well as carbonaceous particulate matter, specifically elemental carbon (EC) and organic carbon (OC). EC and OC emissions were measured from kerosene lighting events (EFEC = 25.1 g/kg-fuel SD = 25.7, EFOC = 9.5 g/kg-fuel SD = 10.0). OC emissions from trash burning events were large and highly variable (EFOC = 38.9 g/kg-fuel SD = 30.5). Combining our results with other recent in-field emission factors for rural Ghana, we explored updated emission estimates for Ghana using a region specific emissions inventory. Large differences are calculated for all updated source emissions, showing a 96% increase in OC and 78% decrease in EC compared to prior estimates for Ghana's emissions. Differences for carbon monoxide were small when averaged across all updated source types (-1%), though the household wood use and trash burning categories individually show large differences.

Light absorption of organic carbon and its sources at a southeastern U.S. location in summer.

Light-absorbing organic carbon (OC), also referred to as "brown carbon" (BrC), has been intensively investigated in atmospheres impacted by biomass burning. However, other BrC sources (e.g., secondary formation in the atmosphere) are rarely studied in ambient aerosols. In the current work, forty-five PM2.5 filter samples were collected in Research Triangle Park (RTP), NC, USA from June 1st to July 15th, 2013. The bulk carbonaceous components, including OC, elemental carbon (EC), water soluble OC (WSOC), and an array of organic molecular markers were measured; an ultraviolet/visible spectrometer was used to measure the light absorption of methanol extractable OC and WSOC. The average light absorption per OC and WSOC mass of PM2.5 samples in summer RTP are 0.36 ±â€¯0.16 m2 gC-1 and 0.29 ±â€¯0.13 m2 gC-1, respectively, lower than the ambient aerosol samples impacted by biomass burning and/or fossil fuel combustion (0.7-1.6 m2 gC-1) from other places. Less than 1% of the aqueous extracts absorption is attributed to the light-absorbing chromophores (nitroaromatic compounds) identified in this work. To identify the major sources of BrC absorption in RTP in the summer, Positive Matrix Factorization (PMF) was applied to a dataset containing optical properties and chemical compositions of carbonaceous components in PM2.5. The results suggest that the formation of biogenic secondary organic aerosol (SOA) containing organosulfates is an important BrC source, contributing up to half of the BrC absorption in RTP during the summertime.

Prices, peers, and perceptions (P3): study protocol for improved biomass cookstove project in northern Ghana.

BACKGROUND: Despite their potential health and social benefits, adoption and use of improved cookstoves has been low throughout much of the world. Explanations for low adoption rates of these technologies include prices that are not affordable for the target populations, limited opportunities for households to learn about cookstoves through peers, and perceptions that these technologies are not appropriate for local cooking needs. The P3 project employs a novel experimental design to explore each of these factors and their interactive effects on cookstove demand, adoption, use and exposure outcomes. METHODS: The P3 study is being conducted in the Kassena-Nankana Districts of Northern Ghana. Leveraging an earlier improved cookstove study that was conducted in this area, the central design of the P3 biomass stove experiment involves offering stoves at randomly varying prices to peers and non-peers of households that had previously received stoves for free. Using household surveys, electronic stove use monitors, and low-cost, portable monitoring equipment, we measure how prices and peers' experience affect perceptions of stove quality, the decision to purchase a stove, use of improved and traditional stoves over time, and personal exposure to air pollutants from the stoves. DISCUSSION: The challenges that public health and development communities have faced in spreading adoption of potentially welfare-enhancing technologies, like improved cookstoves, have highlighted the need for interdisciplinary, multisectoral approaches. The design of the P3 project draws on economic theory, public health practice, engineering, and environmental sciences, to more fully grasp the drivers and barriers to expanding access to and uptake of cleaner stoves. Our partnership between academic institutions, in the US and Ghana, and a local environmental non-governmental organization creates unique opportunities to disseminate and scale up lessons learned. TRIAL REGISTRATION: ClinicalTrials.gov NCT03617952 7/31/18 (Retrospectively Registered).

Liquified Petroleum Gas (LPG) Supply and Demand for Cooking in Northern Ghana.

Like many other countries, Ghana relies on biomass (mainly wood and charcoal) for most of its cooking needs. A national action plan aims to expand liquefied petroleum gas (LPG) access to 50% of the country's population by 2020. While the country's southern urban areas have made progress toward this goal, LPG use for cooking remains low in the north. The aim of this cross-sectional study was to characterize the current state of the LPG market in this area and examine opportunities and barriers to scale up LPG adoption. We interviewed 16 LPG suppliers (stove, cylinder, and fuel vendors) as well as 592 households in the Kassena-Nankana Districts (KND) of Ghana. We find large rural-urban differences in LPG uptake: less than 10% of rural households own LPG stoves compared with over half of urban households. Awareness of LPG is high across the region, but accessibility of fuel supply is highly limited, with just one refilling station located in the KND. Affordability is perceived as the main barrier to LPG adoption, and acceptability is also limited by widespread concerns about the safety of cooking with LPG. Transitioning to a cylinder recirculation model, and providing more targeted subsidies and credit options, should be explored to expand access to cleaner cooking in this region.

Comparing Building and Neighborhood-Scale Variability of CO2 and O3 to Inform Deployment Considerations for Low-Cost Sensor System Use.

The increased use of low-cost air quality sensor systems, particularly by communities, calls for the further development of best-practices to ensure these systems collect usable data. One area identified as requiring more attention is that of deployment logistics, that is, how to select deployment sites and how to strategically place sensors at these sites. Given that sensors are often placed at homes and businesses, ideal placement is not always possible. Considerations such as convenience, access, aesthetics, and safety are also important. To explore this issue, we placed multiple sensor systems at an existing field site allowing us to examine both neighborhood-level and building-level variability during a concurrent period for CO2 (a primary pollutant) and O3 (a secondary pollutant). In line with previous studies, we found that local and transported emissions as well as thermal differences in sensor systems drive variability, particularly for high-time resolution data. While this level of variability is unlikely to affect data on larger averaging scales, this variability could impact analysis if the user is interested in high-time resolution or examining local sources. However, with thoughtful placement and thorough documentation, high-time resolution data at the neighborhood level has the potential to provide us with entirely new information on local air quality trends and emissions.

Community-Based Health and Exposure Study around Urban Oil Developments in South Los Angeles.

Oilfield-adjacent communities often report symptoms such as headaches and/or asthma. Yet, little data exists on health experiences and exposures in urban environments with oil and gas development. In partnership with Promotoras de Salud (community health workers), we gathered household surveys nearby two oil production sites in Los Angeles. We tested the capacity of low-cost sensors for localized exposure estimates. Bilingual surveys of 205 randomly sampled residences were collected within two 1500 ft. buffer areas (West Adams and University Park) surrounding oil development sites. We used a one-sample proportion test, comparing overall rates from the California Health Interview Survey (CHIS) of Service Planning Area 6 (SPA6) and Los Angeles County for variables of interest such as asthma. Field calibrated low-cost sensors recorded methane emissions. Physician diagnosed asthma rates were reported to be higher within both buffers than in SPA6 or LA County. Asthma prevalence in West Adams but not University Park was significantly higher than in Los Angeles County. Respondents with diagnosed asthma reported rates of emergency room visits in the previous 12 months similar to SPA6. 45% of respondents were unaware of oil development; 63% of residents would not know how to contact local regulatory authorities. Residents often seek information about their health and site-related activities. Low-cost sensors may be useful in highlighting differences between sites or recording larger emission events and can provide localized data alongside resident-reported symptoms. Regulatory officials should help clarify information to the community on methods for reporting health symptoms. Our community-based participatory research (CBPR) partnership supports efforts to answer community questions as residents seek a safety buffer between sensitive land uses and active oil development.

Low-cost measurement techniques to characterize the influence of home heating fuel on carbon monoxide in Navajo homes.

A large fraction of the global population relies on the inefficient combustion of solid fuels for cooking and home heating, resulting in household exposure to combustion byproducts. In the southwestern United States, unhealthy air quality has been observed in some homes that use solid fuels as a primary source of heat on the Navajo Nation. In order to better understand how home heating fuel choice can influence indoor air quality in this region, we used recently developed low-cost electrochemical sensors to measure carbon monoxide (CO) air mole fractions continuously inside and outside 41 homes in two communities on the Navajo Nation. Using low-cost sensors in this study, which don't require extensive training to operate, enabled collaboration with local Diné College students and faculty in the planning and implementation of home deployments. Households used natural gas, propane, pellets, wood, and/or coal for heating. We developed quantification methods that included uncertainty estimation for Alphasense CO-B4 sensors, for measurements both inside and outside homes. CO concentrations elevated above background were observed in homes in each heating fuel group, but the highest hourly concentrations were observed in wood and coal burning homes, some of which exceeded World Health Organization Guidelines on both an hourly and eight-hourly basis. In order to probe the many factors that can influence indoor pollutant concentrations, we developed and implemented methods that employ CO emission and decay time periods observed in homes during everyday activities to estimate air exchange rates as well as CO emission rates on the basis of a given well-mixed volume of air. The air quality measurement tools and methods demonstrated in this study can be readily extended to indoor air quality studies in other communities around the world to inform how home heating and cooking practices are influencing indoor air quality during normal daily activities.

Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments.

Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first was a one-month deployment along the Colorado Front Range and included sites near active oil and gas operations in the Denver-Julesberg basin. The second deployment was in an urban Los Angeles neighborhood, subject to complex mixtures of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models used to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing, and explore cross-sensitivities that impact this sensor type. The results illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area - highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks.

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena.

New Emission Factors and Efficiencies from in-Field Measurements of Traditional and Improved Cookstoves and Their Potential Implications.

Household cooking using solid biomass fuels is a major global health and environmental concern. As part of the Research on Emissions Air quality Climate and Cooking Technologies in Northern Ghana study, we conducted 75 in-field uncontrolled cooking tests designed to assess emissions and efficiency of the Gyapa woodstove, Philips HD4012, threestone fire and coalpot (local charcoal stove). Emission factors (EFs) were calculated for carbon monoxide (CO), carbon dioxide (CO2), and particulate matter (PM). Moreover, modified combustion (MCE), heat transfer (HTE) and overall thermal efficiencies (OTE) were calculated across a variety of fuel, stove and meal type combinations. Mixed effect models suggest that compared to traditional stove/fuel combinations, the Philips burning wood or charcoal showed significant fuel and energy based EF differences for CO, but no significant PM changes with wood fuel. MCEs were significantly higher for Philips wood and charcoal-burning stoves compared to the threestone fire and coalpot. The Gyapa emitted significantly higher ratios of elemental to organic carbon. Fuel moisture, firepower and MCE fluctuation effects on stove performance were investigated with mixed findings. Results show agreement with other in-field findings and discrepancies with some lab-based findings, with important implications for estimated health and air quality impacts.

Quantifying Neighborhood-Scale Spatial Variations of Ozone at Open Space and Urban Sites in Boulder, Colorado Using Low-Cost Sensor Technology.

Recent advances in air pollution sensors have led to a new wave of low-cost measurement systems that can be deployed in dense networks to capture small-scale spatio-temporal variations in ozone, a pollutant known to cause negative human health impacts. This study deployed a network of seven low-cost ozone metal oxide sensor systems (UPods) in both an open space and an urban location in Boulder, Colorado during June and July of 2015, to quantify ozone variations on spatial scales ranging from 12 m between UPods to 6.7 km between open space and urban measurement sites with a measurement uncertainty of ~5 ppb. The results showed spatial variability of ozone at both deployment sites, with the largest differences between UPod measurements occurring during the afternoons. The peak median hourly difference between UPods was 6 ppb at 1:00 p.m. at the open space site, and 11 ppb at 4:00 p.m. at the urban site. Overall, the urban ozone measurements were higher than in the open space measurements. This study evaluates the effectiveness of using low-cost sensors to capture microscale spatial and temporal variation of ozone; additionally, it highlights the importance of field calibrations and measurement uncertainty quantification when deploying low-cost sensors.

Improving present day and future estimates of anthropogenic sectoral emissions and the resulting air quality impacts in Africa.

The African continent is undergoing immense social and economic change, particularly regarding population growth and urbanization, where the urban population in Africa is anticipated to increase by a factor of 3 over the next 40 years. To understand the potential health impacts from this demographical shift and design efficient emission mitigation strategies, we used improved Africa-specific emissions that account for inefficient combustion sources for a number of sectors such as transportation, household energy generation, waste burning, and home heating and cooking. When these underrepresented emissions sources are combined with the current estimates of emissions in Africa, ambient particulate matter concentrations from present-day anthropogenic activity contribute to 13 210 annual premature deaths, with the largest contributions (38%) coming from residential emissions. By scaling both the population and the emissions for projected national-scale levels of growth, the predicted health impact grows to approximately 78 986 annual premature deaths by 2030 with 45% now resulting from emissions related to energy combustion. In order to mitigate this resulting increase in premature deaths, three scenarios have been developed which reduce sector-specific future emissions based on prior targets for technological improvements and emission controls in transportation, energy production and residential activities. These targeted potential mitigation strategies can avoid up to 37% of the estimated annual premature deaths by 2030 with the largest opportunity being a reduction of 10 868 annual deaths from switching half of the energy generation in South Africa to renewable technologies.