Household concentrations and female and child exposures to air pollution in peri-urban sub-Saharan Africa: measurements from the CLEAN-Air(Africa) study

BACKGROUND

Relatively clean cooking fuels such as liquefied petroleum gas (LPG) emit less fine particulate matter (PM2·5) and carbon monoxide (CO) than polluting fuels (eg, wood, charcoal). Yet, some clean cooking interventions have not achieved substantial exposure reductions. This study evaluates determinants of between-community variability in exposures to household air pollution (HAP) across sub-Saharan Africa.

METHODS

In this measurement study, we recruited households cooking primarily with LPG or exclusively with wood or charcoal in peri-urban Cameroon, Ghana, and Kenya from previously surveyed households. In 2019-20, we conducted monitoring of 24 h PM2·5 and CO kitchen concentrations (n=256) and female cook (n=248) and child (n=124) exposures. PM2·5 measurements used gravimetric and light scattering methods. Stove use monitoring and surveys on cooking characteristics and ambient air pollution exposure (eg, walking time to main road) were also administered.

FINDINGS

The mean PM2·5 kitchen concentration was five times higher among households cooking with charcoal than those using LPG in the Kenyan community (297 μg/m3, 95% CI 216-406, vs 61 μg/m3, 49-76), but only 4 μg/m3 higher in the Ghanaian community (56 μg/m3, 45-70, vs 52 μg/m3, 40-68). The mean CO kitchen concentration in charcoal-using households was double the WHO guideline (6·11 parts per million [ppm]) in the Kenyan community (15·81 ppm, 95% CI 8·71-28·72), but below the guideline in the Ghanaian setting (1·77 ppm, 1·04-2·99). In all communities, mean PM2·5 cook exposures only met the WHO interim-1 target (35 μg/m3) among LPG users staying indoors and living more than 10 min walk from a road.

INTERPRETATION

Community-level variation in the relative difference in HAP exposures between LPG and polluting cooking fuel users in peri-urban sub-Saharan Africa might be attributed to differences in ambient air pollution levels. Thus, mitigation of indoor and outdoor PM2·5 sources will probably be critical for obtaining significant exposure reductions in rapidly urbanising settings of sub-Saharan Africa.

FUNDING

UK National Institute for Health and Care Research.

Short-Term Increases in NO2 and O3 Concentrations during Pregnancy and Stillbirth Risk in the U.S.: A Time-Stratified Case-Crossover Study

Associations between gaseous pollutant exposure and stillbirth have focused on exposures averaged over trimesters or gestation. We investigated the association between short-term increases in nitrogen dioxide (NO2) and ozone (O3) concentrations and stillbirth risk among a national sample of 116 788 Medicaid enrollees from 2000 to 2014. A time-stratified case-crossover design was used to estimate distributed (lag 0-lag 6) and cumulative lag effects, which were adjusted for PM2.5 concentration and temperature. Effect modification by race/ethnicity and proximity to hydraulic fracturing (fracking) wells was assessed. Short-term increases in the NO2 and O3 concentrations were not associated with stillbirth in the overall sample. Among American Indian individuals (n = 1694), a 10 ppb increase in NO2 concentrations was associated with increased stillbirth odds at lag 0 (5.66%, 95%CI: [0.57%, 11.01%], p = 0.03) and lag 1 (4.08%, 95%CI: [0.22%, 8.09%], p = 0.04) but not lag 0-6 (7.12%, 95%CI: [-9.83%, 27.27%], p = 0.43). Among participants living in zip codes within 15 km of active fracking wells (n = 9486), a 10 ppb increase in NO2 concentration was associated with increased stillbirth odds in single-day lags (2.42%, 95%CI: [0.37%, 4.52%], p = 0.02 for lag 0 and 1.83%, 95%CI: [0.25%, 3.43%], p = 0.03 for lag 1) but not the cumulative lag (lag 0-6) (4.62%, 95%CI: [-2.75%, 12.55%], p = 0.22). Odds ratios were close to the null in zip codes distant from fracking wells. Future studies should investigate the role of air pollutants emitted from fracking and potential racial disparities in the relationship between short-term increases in NO2 concentrations and stillbirth.

Deepened socioeconomic inequality in clean cooking fuel use in India from 2005-2006 to 2015-2016

Uptake of clean cooking fuels (CCF), such as liquefied petroleum gas (LPG), in place of traditional cooking fuels such as wood, charcoal, and kerosene can improve public health by reducing household air pollution exposures. Though studies have cross-sectionally examined socioeconomic determinants of cooking fuel adoption, little is known about socioeconomic disparities in CCF use over time. Data from the third (2005-06) and fourth (2015-16) rounds of the National Family Health Survey covering 109,041 and 601,509 households, respectively, were used to examine inequities in CCF use in India. While CCF use in India increased nationally from 25% in 2005-06 to 44% in 2015-16, the adoption of CCF varied widely across states and socio-economic groups. Approximately 2% of households in the poorest wealth quintile gained access to LPG during the study period, compared with an increase of 10% or more among households in the middle or richer wealth quintiles; the LPG access gap between the low (0.2%) and middle class (19.2%) was 19% in 2005-06 and nearly doubled to 35% (2.5% vs. 37.4%, respectively) in 2015-16. At the state level, there was a four-fold difference in the uptake of CCF over the two survey periods. The use of CCF increased by less than 10% in Himachal Pradesh, Bihar, Assam, Manipur, Mizoram, and Meghalaya as compared to the increases of at least 30% in Tamil Nadu (42%), undivided Andhra Pradesh (34%), and Kerala (30%). Further, in wealthier states (Delhi, Goa, Punjab, Haryana, Tamil Nadu, Kerala, and undivided Andhra Pradesh), CCF use increased by more than 20% among the poorest individuals compared with less than 1% among the poorest families in lower income states (Tripura, Meghalaya, Madhya Pradesh, Jharkhand, Chhattisgarh, Bihar). To promote a more equitable clean energy transition, poorer and rural Indian households should be prioritized for CCF promotion programs.

Complex dynamics in sustaining clean cooking and food access through a pandemic: A COVID-19 impact study in peri-urban Cameroon

Access to clean energy for cooking is central to achieving Sustainable Development Goal 7. Latest predictions suggest that this goal will not be met by 2030, with further setbacks due to the COVID-19 pandemic. We investigated the impacts of COVID-19 restrictions on household cooking fuel, practices and dietary behaviours in a peri-urban community in Central Cameroon. Using surveys (n = 333) and qualitative semi-structured interviews (n = 12), we found negative financial impacts and high levels of food insecurity, with 83 % and 56 % of households reporting reduced income and insufficient food, respectively. Households reduced food intake and cooking frequency and relied more heavily on local sources (e.g., farmland) to feed their families. Changes in primary cooking fuel were less pronounced and fuel choice was inherently linked to cooking behaviours, with some households utilising LPG more often for simple tasks, such as reheating food. Local systems were key in sustaining food and fuel access and households demonstrated resilience by employing numerous mechanisms to overcome challenges. Our findings underline the vulnerability of households in maintaining sufficient food intake and sustaining clean cooking, highlighting how policy needs to take a nuanced approach considering food-energy dynamics and strengthening local systems to ensure access to clean energy is resistant to system shocks.

Personal and household PM2.5 and black carbon exposure measures and respiratory symptoms in 8 low- and middle-income countries

BACKGROUND

Household air pollution (HAP) from cooking with solid fuels has been associated with adverse respiratory effects, but most studies use surveys of fuel use to define HAP exposure, rather than on actual air pollution exposure measurements.

OBJECTIVE

To examine associations between household and personal fine particulate matter (PM_2.5) and black carbon (BC) measures and respiratory symptoms.

METHODS

As part of the Prospective Urban and Rural Epidemiology Air Pollution study, we analyzed 48-h household and personal PM_2.5 and BC measurements for 870 individuals using different cooking fuels from 62 communities in 8 countries (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Self-reported respiratory symptoms were collected after monitoring. Associations between PM_2.5 and BC exposures and respiratory symptoms were examined using logistic regression models, controlling for individual, household, and community covariates.

RESULTS

The median (interquartile range) of household and personal PM_2.5 was 73.5 (119.1) and 65.3 (91.5) μg/m³, and for household and personal BC was 3.4 (8.3) and 2.5 (4.9) x10^-5 m^-1, respectively. We observed associations between household PM_2.5 and wheeze (OR: 1.25; 95%CI: 1.07, 1.46), cough (OR: 1.22; 95%CI: 1.06, 1.39), and sputum (OR: 1.26; 95%CI: 1.10, 1.44), as well as exposure to household BC and wheeze (OR: 1.20; 95%CI: 1.03, 1.39) and sputum (OR: 1.20; 95%CI: 1.05, 1.36), per IQR increase. We observed associations between personal PM_2.5 and wheeze (OR: 1.23; 95%CI: 1.00, 1.50) and sputum (OR: 1.19; 95%CI: 1.00, 1.41). For household PM_2.5 and BC, associations were generally stronger for females compared to males. Models using an indicator variable of solid versus clean fuels resulted in larger OR estimates with less precision.

CONCLUSIONS

We used measurements of household and personal air pollution for individuals using different cooking fuels and documented strong associations with respiratory symptoms.

Measuring and predicting personal and household Black Carbon levels from 88 communities in eight countries

Black Carbon (BC) is an important component of household air pollution (HAP) in low- and middle- income countries (LMICs), but levels and drivers of exposure are poorly understood. As part of the Prospective Urban and Rural Epidemiological (PURE) study, we analyzed 48-hour BC measurements for 1187 individual and 2242 household samples from 88 communities in 8 LMICs (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Light absorbance (10^-5 m^-1) of collected PM_2.5 filters, a proxy for BC concentrations, was calculated via an image-based reflectance method. Surveys of household/personal characteristics and behaviors were collected after monitoring. The geometric mean (GM) of personal and household BC measures was 2.4 (3.3) and 3.5 (3.9)·10^-5 m^-1, respectively. The correlation between BC and PM_2.5 was r = 0.76 for personal and r = 0.82 for household measures. A gradient of increasing BC concentrations was observed for cooking fuels: BC increased 53% (95%CI: 30, 79) for coal, 142% (95%CI: 117, 169) for wood, and 190% (95%CI: 149, 238) for other biomass, compared to gas. Each hour of cooking was associated with an increase in household (5%, 95%CI: 3, 7) and personal (5%, 95%CI: 2, 8) BC; having a window in the kitchen was associated with a decrease in household (-38%, 95%CI: -45, -30) and personal (-31%, 95%CI: -44, -15) BC; and cooking on a mud stove, compared to a clean stove, was associated with an increase in household (125%, 95%CI: 96, 160) and personal (117%, 95%CI: 71, 117) BC. Male participants only had slightly lower personal BC (-0.6%, 95%CI: -1, 0.0) compared to females. In multivariate models, we were able to explain 46-60% of household BC variation and 33-54% of personal BC variation. These data and models provide new information on exposure to BC in LMICs, which can be incorporated into future exposure assessments, health research, and policy surrounding HAP and BC.

Multinational prediction of household and personal exposure to fine particulate matter (PM2.5) in the PURE cohort study

INTRODUCTION

Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM_2.5). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM_2.5 levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM_2.5 exposure models.

METHODS

The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM_2.5 kitchen concentrations (n = 2,365) and male and/or female PM_2.5 exposure monitoring (n = 910) in a subset of households in Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania and Zimbabwe. PURE-AIR measurements were combined with survey data on cooking environment characteristics in hierarchical Bayesian log-linear regression models. Model performance was evaluated using leave-one-out cross validation. Predictive models were applied to survey data from the larger PURE cohort (22,480 households; 33,554 individuals) to quantitatively estimate PM_2.5 exposures.

RESULTS

The final models explained half (R² = 54%) of the variation in kitchen PM_2.5 measurements (root mean square error (RMSE) (log scale):2.22) and personal measurements (R² = 48%; RMSE (log scale):2.08). Primary cooking fuel type, heating fuel type, country and season were highly predictive of PM_2.5 kitchen concentrations. Average national PM_2.5 kitchen concentrations varied nearly 3-fold among households primarily cooking with gas (20 μg/m³ (Chile); 55 μg/m³ (China)) and 12-fold among households primarily cooking with wood (36 μg/m³ (Chile)); 427 μg/m³ (Pakistan)). Average PM_2.5 kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM_2.5 female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile).

CONCLUSION

Using survey data to estimate PM_2.5 exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM_2.5 exposures can be used in future epidemiological studies and inform policies targeting HAP reduction.

Energy poverty influences urban outdoor air pollution levels during COVID-19 lockdown in south-central Chile

The effect of COVID-19 lockdowns on ambient air pollution levels in urban south-central Chile, where outdoor air pollution primarily originates indoors from wood burning for heating, may differ from trends in cities where transportation and industrial emission sources dominate. This quasi-experimental study compared hourly fine (PM_2.5) and coarse (PM₁₀) particulate matter measurements from six air monitors (three beta attenuation monitors; three low-cost sensors) in commercial and low/middle-income residential areas of Temuco, Chile between 2019 and 2020. The potential impact of varying annual meterological conditions on air quality was also assessed. During COVID-19 lockdown, average monthly ambient PM_2.5 concentrations in a commercial and middle-income residential neighborhood of Temuco were up to 50% higher (from 12 to 18 μg/m³) and 59% higher (from 22 to 35 μg/m³) than 2019 levels, respectively. Conversely, PM_2.5 levels decreased by up to 52% (from 43 to 21 μg/m³) in low-income areas. The fine fraction of PM₁₀ in April 2020 was 48% higher than in April 2017-2019 (from 50% to 74%) in a commercial area. These changes did not appear to result from meterological differences between years. During COVID-19 lockdown, higher outdoor PM_2.5 pollution from wood heating existed in more affluent areas of Temuco, while PM_2.5 concentrations declined among poorer households refraining from wood heating. To reduce air pollution and energy poverty in south-central Chile, affordability of clean heating fuels (e.g. electricity) should be a policy priority.

Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya

This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM_2.5 , black carbon, CO); household air pollution (PM_2.5 , CO); personal exposure (PM_2.5 , CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single-zone model; indirect exposure models, which combine person-location and area-level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single-zone model was reasonably well-correlated with measured kitchen concentrations of PM_2.5 (R²  = 0.45) and CO (R²  = 0.45), but lacked precision. The best performing regression model used a combination of survey-based data and physical measurements (R²  = 0.76) and a root mean-squared error of 85 µg/m³ , and the survey-only-based regression model was able to predict PM2.5 exposures with an R² of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R² of 0.57 and RMSE of 98 µg/m³ .

COVID-19 impacts on household energy & food security in a Kenyan informal settlement: The need for integrated approaches to the SDGs

This longitudinal study presents the joint effects of a COVID-19 community lockdown on household energy and food security in an informal settlement in Nairobi, Kenya. Randomly administered surveys were completed from December 2019-March 2020 before community lockdown (n = 474) and repeated in April 2020 during lockdown (n = 194). Nearly universal (95%) income decline occurred during the lockdown and led to 88% of households reporting food insecurity. During lockdown, a quarter of households (n = 17) using liquefied petroleum gas (LPG), a cleaner cooking fuel typically available in pre-set quantities (e.g. 6 kg cylinders), switched to polluting cooking fuels (kerosene, wood), which could be purchased in smaller amounts or gathered for free. Household size increases during lockdown also led to participants' altering their cooking fuel, and changing their cooking behaviors and foods consumed. Further, households more likely to switch away from LPG had lower consumption prior to lockdown and had suffered greater income loss, compared with households that continued to use LPG. Thus, inequities in clean cooking fuel access may have been exacerbated by COVID-19 lockdown. These findings demonstrate the complex relationship between household demographics, financial strain, diet and cooking patterns, and present the opportunity for a food-energy nexus approach to address multiple Sustainable Development Goals (SDGs): achieving zero hunger (SDG 2) and universal affordable, modern and clean energy access (SDG 7) by 2030. Ensuring that LPG is affordable, accessible and meets the dietary and cooking needs of families should be a policy priority for helping improve food and energy security among the urban poor.

COVID-19 impacts on household energy & food security in a Kenyan informal settlement: The need for integrated approaches to the SDGs

This longitudinal study presents the joint effects of a COVID-19 community lockdown on household energy and food security in an informal settlement in Nairobi, Kenya. Randomly administered surveys were completed from December 2019-March 2020 before community lockdown (n = 474) and repeated in April 2020 during lockdown (n = 194). Nearly universal (95%) income decline occurred during the lockdown and led to 88% of households reporting food insecurity. During lockdown, a quarter of households (n = 17) using liquefied petroleum gas (LPG), a cleaner cooking fuel typically available in pre-set quantities (e.g. 6 kg cylinders), switched to polluting cooking fuels (kerosene, wood), which could be purchased in smaller amounts or gathered for free. Household size increases during lockdown also led to participants' altering their cooking fuel, and changing their cooking behaviors and foods consumed. Further, households more likely to switch away from LPG had lower consumption prior to lockdown and had suffered greater income loss, compared with households that continued to use LPG. Thus, inequities in clean cooking fuel access may have been exacerbated by COVID-19 lockdown. These findings demonstrate the complex relationship between household demographics, financial strain, diet and cooking patterns, and present the opportunity for a food-energy nexus approach to address multiple Sustainable Development Goals (SDGs): achieving zero hunger (SDG 2) and universal affordable, modern and clean energy access (SDG 7) by 2030. Ensuring that LPG is affordable, accessible and meets the dietary and cooking needs of families should be a policy priority for helping improve food and energy security among the urban poor.

Pay-as-you-go liquefied petroleum gas supports sustainable clean cooking in Kenyan informal urban settlement during COVID-19 lockdown

Approximately 2.8 billion people rely on polluting fuels (e.g. wood, kerosene) for cooking. With affordability being a key access barrier to clean cooking fuels, such as liquefied petroleum gas (LPG), pay-as-you-go (PAYG) LPG smart meter technology may help resource-poor households adopt LPG by allowing incremental fuel payments. To understand the potential for PAYG LPG to facilitate clean cooking, objective evaluations of customers' cooking and spending patterns are needed. This study uses novel smart meter data collected between January 2018-June 2020, spanning COVID-19 lockdown, from 426 PAYG LPG customers living in an informal settlement in Nairobi, Kenya to evaluate stove usage (e.g. cooking events/day, cooking event length). Seven semi-structured interviews were conducted in August 2020 to provide context for potential changes in cooking behaviours during lockdown. Using stove monitoring data, objective comparisons of cooking patterns are made with households using purchased 6 kg cylinder LPG in peri-urban Eldoret, Kenya. In Nairobi, 95% of study households continued using PAYG LPG during COVID-19 lockdown, with consumption increasing from 0.97 to 1.22 kg/capita/month. Daily cooking event frequency also increased by 60% (1.07 to 1.72 events/day). In contrast, average days/month using LPG declined by 75% during lockdown (17 to four days) among seven households purchasing 6 kg cylinder LPG in Eldoret. Interviewed customers reported benefits of PAYG LPG beyond fuel affordability, including safety, time savings and cylinder delivery. In the first study assessing PAYG LPG cooking patterns, LPG use was sustained despite a COVID-19 lockdown, illustrating how PAYG smart meter technology may help foster clean cooking access.

Household and personal air pollution exposure measurements from 120 communities in eight countries: results from the PURE-AIR study

BACKGROUND

Approximately 2·8 billion people are exposed to household air pollution from cooking with polluting fuels. Few monitoring studies have systematically measured health-damaging air pollutant (ie, fine particulate matter [PM2·5] and black carbon) concentrations from a wide range of cooking fuels across diverse populations. This multinational study aimed to assess the magnitude of kitchen concentrations and personal exposures to PM2·5 and black carbon in rural communities with a wide range of cooking environments.

METHODS

As part of the Prospective Urban and Rural Epidemiological (PURE) cohort, the PURE-AIR study was done in 120 rural communities in eight countries (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Data were collected from 2541 households and from 998 individuals (442 men and 556 women). Gravimetric (or filter-based) 48 h kitchen and personal PM2·5 measurements were collected. Light absorbance (10-5m-1) of the PM2·5 filters, a proxy for black carbon concentrations, was calculated via an image-based reflectance method. Surveys of household characteristics and cooking patterns were collected before and after the 48 h monitoring period.

FINDINGS

Monitoring of household air pollution for the PURE-AIR study was done from June, 2017, to September, 2019. A mean PM2·5 kitchen concentration gradient emerged across primary cooking fuels: gas (45 μg/m3 [95% CI 43-48]), electricity (53 μg/m3 [47-60]), coal (68 μg/m3 [61-77]), charcoal (92 μg/m3 [58-146]), agricultural or crop waste (106 μg/m3 [91-125]), wood (109 μg/m3 [102-118]), animal dung (224 μg/m3 [197-254]), and shrubs or grass (276 μg/m3 [223-342]). Among households cooking primarily with wood, average PM2·5 concentrations varied ten-fold (range: 40-380 μg/m3). Fuel stacking was prevalent (981 [39%] of 2541 households); using wood as a primary cooking fuel with clean secondary cooking fuels (eg, gas) was associated with 50% lower PM2·5 and black carbon concentrations than using only wood as a primary cooking fuel. Similar average PM2·5 personal exposures between women (67 μg/m3 [95% CI 62-72]) and men (62 [58-67]) were observed. Nearly equivalent average personal exposure to kitchen exposure ratios were observed for PM2·5 (0·79 [95% 0·71-0·88] for men and 0·82 [0·74-0·91] for women) and black carbon (0·64 [0·45-0·92] for men and 0·68 [0·46-1·02] for women).

INTERPRETATION

Using clean primary fuels substantially lowers kitchen PM2·5 concentrations. Importantly, average kitchen and personal PM2·5 measurements for all primary fuel types exceeded WHO's Interim Target-1 (35 μg/m3 annual average), highlighting the need for comprehensive pollution mitigation strategies.

FUNDING

Canadian Institutes for Health Research, National Institutes of Health.

Health and Climate Impacts of Scaling Adoption of Liquefied Petroleum Gas (LPG) for Clean Household Cooking in Cameroon: A Modeling Study

BACKGROUND

The Cameroon government has set a target that, by 2030, 58% of the population will be using Liquefied Petroleum Gas (LPG) as a cooking fuel, in comparison with less than 20% in 2014. The National LPG Master Plan (Master Plan) was developed for scaling up the LPG sector to achieve this target.

OBJECTIVES

This study aimed to estimate the potential impacts of this planned LPG expansion (the Master Plan) on population health and climate change mitigation, assuming primary, sustained use of LPG for daily cooking.

METHODS

We applied existing and developed new mathematical models to calculate the health and climate impacts of expanding LPG primary adoption for household cooking in Cameroon over two periods: a) short-term (2017-2030): Comparing the Master Plan 58% target with a counterfactual LPG adoption of 32% in 2030, in line with current trends; and b) long-term (2031-2100, climate modeling only), assuming Cameroon will become a mature and saturated LPG market by 2100 (73% adoption, based on Latin American countries). We compared this with a counterfactual adoption of 41% by 2100, in line with current trends.

RESULTS

By 2030, successful implementation of the Master Plan was estimated to avert about 28,000 (minimum = 22,000 , maximum = 35,000 ) deaths and 770,000 (minimum = 580,000 maximum = 1   million ) disability-adjusted life years. For the same period, we estimated reductions in pollutant emissions of more than a third in comparison with the counterfactual, leading to a global cooling of - 0.1   milli   ° C in 2030. For 2100, a cooling impact from the Master Plan leading to market saturation (73%) was estimated to be - 0.70   milli   ° C in comparison with to the counterfactual, with a range of - 0.64 to - 0.93   milli   ° C based on different fractions of nonrenewable biomass.

DISCUSSION

Successful implementation of the Master Plan could have significant positive impacts on population health in Cameroon with no adverse impacts on climate. https://doi.org/10.1289/EHP4899.

Analysis of black carbon on filters by image-based reflectance

Black carbon (BC) is an important contributor to global particulate matter emissions. BC is associated with adverse health effects, and an important short-lived climate pollutant. Here, we describe a low cost method of analysis that utilizes images of PTFE filters taken with a digital camera to estimate BC content on filters. This method is compared with two existing optical methods for analyzing BC (Smokestain Reflectance and Hybrid Integrating Plate and Sphere System) as well as the standard chemical analysis method for determining elemental carbon (Thermal-Optical Reflectance). In comparisons of aerosol generated under controlled conditions (using an inverted diffusion flame burner to cover a range of mass loading and reflectance levels) (N=12) and in field samples collected from residential solid fuel combustion in China and India (N=50), the image-based method was found to correlate well (normalized RMSE <10% for all comparisons) with existing methods. A correlational analysis of field samples between the optical methods and Fourier-transform infrared spectroscopy indicated that the same functional groups were predominantly responsible for light attenuation in each optical method. This method offers reduced equipment cost, rapid analysis time, and is available at no cost, which may facilitate more measurement of BC where PM2.5 mass concentrations are already measured, especially in low income countries or other sampling efforts with limited resources.

Household, Community, Sub-National and Country-level Predictors of Primary Cooking Fuel Switching in Nine Countries from the PURE Study

INTRODUCTION

Switching from polluting (e.g. wood, crop waste, coal) to clean cooking fuels (e.g. gas, electricity) can reduce household air pollution (HAP) exposures and climate-forcing emissions. While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood.

METHODS

We examined longitudinal survey data from 24,172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology (PURE) study. We assessed household-level primary cooking fuel switching during a median of 10 years of follow up (~2005-2015). We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching.

RESULTS

One-half of study households (12,369) reported changing their primary cooking fuels between baseline and follow up surveys. Of these, 61% (7,582) switched from polluting (wood, dung, agricultural waste, charcoal, coal, kerosene) to clean (gas, electricity) fuels, 26% (3,109) switched between different polluting fuels, 10% (1,164) switched from clean to polluting fuels and 3% (522) switched between different clean fuels. Among the 17,830 households using polluting cooking fuels at baseline, household-level factors (e.g. larger household size, higher wealth, higher education level) were most strongly associated with switching from polluting to clean fuels in India; in all other countries, community-level factors (e.g. larger population density in 2010, larger increase in population density between 2005-2015) were the strongest predictors of polluting-to-clean fuel switching.

CONCLUSIONS

The importance of community and sub-national factors relative to household characteristics in determining polluting-to-clean fuel switching varied dramatically across the nine countries examined. This highlights the potential importance of national and other contextual factors in shaping large-scale clean cooking transitions among rural communities in low- and middle-income countries.

Global estimation of exposure to fine particulate matter (PM2.5) from household air pollution

BACKGROUND

Exposure to household air pollution (HAP) from cooking with dirty fuels is a leading health risk factor within Asia, Africa and Central/South America. The concentration of particulate matter of diameter ≤ 2.5 μm (PM_2.5) is an important metric to evaluate HAP risk, however epidemiological studies have demonstrated significant variation in HAP-PM_2.5 concentrations at household, community and country levels. To quantify the global risk due to HAP exposure, novel estimation methods are needed, as financial and resource constraints render it difficult to monitor exposures in all relevant areas.

METHODS

A Bayesian, hierarchical HAP-PM_2.5 global exposure model was developed using kitchen and female HAP-PM_2.5 exposure data available in peer-reviewed studies from an updated World Health Organization Global HAP database. Cooking environment characteristics were selected using leave-one-out cross validation to predict quantitative HAP-PM_2.5 measurements from 44 studies. Twenty-four hour HAP-PM_2.5 kitchen concentrations and male, female and child exposures were estimated for 106 countries in Asia, Africa and Latin America.

RESULTS

A model incorporating fuel/stove type (traditional wood, improved biomass, coal, dung and gas/electric), urban/rural location, wet/dry season and socio-demographic index resulted in a Bayesian R² of 0.57. Relative to rural kitchens using gas or electricity, the mean global 24-hour HAP-PM_2.5 concentrations were 290 μg/m³ higher (range of regional averages: 110, 880) for traditional stoves, 150 μg/m³ higher (range of regional averages: 50, 290) for improved biomass stoves, 850 μg/m³ higher (range of regional averages: 310, 2600) for animal dung stoves, and 220 μg/m³ higher (range of regional averages: 80, 650) for coal stoves. The modeled global average female/kitchen exposure ratio was 0.40. Average modeled female exposures from cooking with traditional wood stoves were 160 μg/m³ in rural households and 170 μg/m³ in urban households. Average male and child rural area exposures from traditional wood stoves were 120 μg/m³ and 140 μg/m³, respectively; average urban area exposures were identical to average rural exposures among both sub-groups.

CONCLUSIONS

A Bayesian modeling approach was used to generate unique HAP-PM_2.5 kitchen concentrations and personal exposure estimates for all countries, including those with little to no available quantitative HAP-PM_2.5 exposure data. The global exposure model incorporating type of fuel-stove combinations can add specificity and reduce exposure misclassification to enable an improved global HAP risk assessment.

Global household air pollution database: Kitchen concentrations and personal exposures of particulate matter and carbon monoxide

The Global Household Air Pollution (HAP) Measurements database, commissioned by the World Health Organization, provides an organized summary of data reported in the literature describing HAP microenvironments, methods and measurements. As of June 2018, the database contains measurements from 43 countries obtained from 196 studies published through 2016. The database includes information useful for understanding the range of household and personal air pollution measurements that have been collected in a country, as well as characteristics of the cooking environment, including primary cooking fuel type, stove type, heating fuel type and kitchen location. Quantitative particulate matter (PM) of various size fractions and/or carbon monoxide (CO) exposure measurements included in the database can be aggregated and analyzed to generate summary statistics (e.g. average sub-national, national, regional and global HAP exposures) to assess temporal and spatial relationships. The quantitative PM exposure measurements in the database have been used in global predictive modeling of HAP-PM_2.5 exposures (“Global Estimation of Exposure to Fine Particulate Matter (PM2.5) from Household Air Pollution” (Shupler et al., 2018) [1])

Characterizing exposure to household air pollution within the Prospective Urban Rural Epidemiology (PURE) study

BACKGROUND

Household air pollution (HAP) from combustion of solid fuels is an important contributor to disease burden in low- and middle-income countries (LIC, and MIC). However, current HAP disease burden estimates are based on integrated exposure response curves that are not currently informed by quantitative HAP studies in LIC and MIC. While there is adequate evidence supporting causal relationships between HAP and respiratory disease, large cohort studies specifically examining relationships between quantitative measures of HAP exposure with cardiovascular disease are lacking.

OBJECTIVE

We aim to improve upon exposure proxies based on fuel type, and to reduce exposure misclassification by quantitatively measuring exposure across varying cooking fuel types and conditions in diverse geographies and socioeconomic settings. We leverage technology advancements to estimate household and personal PM2.5 (particles below 2.5 μm in aerodynamic diameter) exposure within the large (N~250,000) multi-country (N~26) Prospective Urban and Rural Epidemiological (PURE) cohort study. Here, we detail the study protocol and the innovative methodologies being used to characterize HAP exposures, and their application in epidemiologic analyses.

METHODS/DESIGN

This study characterizes HAP PM2.5 exposures for participants in rural communities in ten PURE countries with >10% solid fuel use at baseline (Bangladesh, Brazil, Chile, China, Colombia, India, Pakistan, South Africa, Tanzania, and Zimbabwe). PM2.5 monitoring includes 48-h cooking area measurements in 4500 households and simultaneous personal monitoring of male and female pairs from 20% of the selected households. Repeat measurements occur in 20% of households to assess impacts of seasonality. Monitoring began in 2017, and will continue through 2019. The Ultrasonic Personal Aerosol Sampler (UPAS), a novel, robust, and inexpensive filter based monitor that is programmable through a dedicated mobile phone application is used for sampling. Pilot study field evaluation of cooking area measurements indicated high correlation between the UPAS and reference Harvard Impactors (r = 0.91; 95% CI: 0.84, 0.95; slope = 0.95). To facilitate tracking and to minimize contamination and analytical error, the samplers utilize barcoded filters and filter cartridges that are weighed pre- and post-sampling using a fully automated weighing system. Pump flow and pressure measurements, temperature and RH, GPS coordinates and semi-quantitative continuous particle mass concentrations based on filter differential pressure are uploaded to a central server automatically whenever the mobile phone is connected to the internet, with sampled data automatically screened for quality control parameters. A short survey is administered during the 48-h monitoring period. Post-weighed filters are further analyzed to estimate black carbon concentrations through a semi-automated, rapid, cost-effective image analysis approach. The measured PM2.5 data will then be combined with PURE survey information on household characteristics and behaviours collected at baseline and during follow-up to develop quantitative HAP models for PM2.5 exposures for all rural PURE participants (~50,000) and across different cooking fuel types within the 10 index countries. Both the measured (in the subset) and the modelled exposures will be used in separate longitudinal epidemiologic analyses to assess associations with cardiopulmonary mortality, and disease incidence.

DISCUSSION

The collected data and resulting characterization of cooking area and personal PM2.5 exposures in multiple rural communities from 10 countries will better inform exposure assessment as well as future epidemiologic analyses assessing the relationships between quantitative estimates of chronic HAP exposure with adult mortality and incident cardiovascular and respiratory disease. This will provide refined and more accurate exposure estimates in global CVD related exposure-response analyses.

Socio-economic status and fruit juice consumption in Canada

OBJECTIVES

The role of socio-economic status (SES) in fruit juice and fruit drink consumption is not well understood in a Canadian context. This study examines the relationship between SES and Canadian fruit juice and fruit drink consumption.

METHODS

The Canadian Community Health Survey (2011-2012), a cross-sectional survey that employs multistage cluster sampling, provided relevant data for a sample of 103 125 Canadians, aged 12 and older, living in the 10 provinces. Household income level decile, ranked at the health region level, was used as a surrogate measure of SES. Fruit juice and fruit drink consumption data were collected via self-report in telephone/in-person interviews. Multivariable gamma regression was used to model the relationship between SES and frequency of fruit juice and fruit drink consumption, adjusting for age, sex, diabetes status, daily fruit and vegetable consumption, education level, racial identity and physical activity.

RESULTS

A negative relationship was found, with a decreasing daily rate of fruit juice and fruit drink consumption associated with increasing SES. In the adjusted model, Canadians in the lowest SES category consumed fruit juice and fruit drinks at an average daily rate 1.18 times (95% CI: 1.14-1.23) that of Canadians in the highest SES category.

CONCLUSION

The negative association between health region-adjusted SES and fruit juice and fruit drink consumption highlights the potentially important role of socio-economic factors at a local level. Canadian policy that aims to lower fruit juice and fruit drink consumption, and thus sugar intake, should target financial avenues (such as making fruit juice less financially attractive by lowering the cost of whole fruit and vegetables) in addition to communicating health benefits.