Indoor air pollution from biomass cookstoves in rural Senegal

Energy for Sustainable Development 43 (2018) 224–234

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Energy for Sustainable Development

Indoor air pollution from biomass cookstoves in rural Senegal Candela de la Sota a,⁎, Julio Lumbreras a, Noemí Pérez b, Marina Ealo b, Moustapha Kane c, Issakha Youm c, Mar Viana b a

Technical University of Madrid, José Gutiérrez Abascal, 2, 28006 Madrid, Spain Institute for Environmental Assessment and Water Research, Spanish National Research Council (IDAEA-CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain Centre for Studies and Research on Renewable Energy (CERER) of the Cheikh Anta Diop University of Dakar (UCAD), Route du Service géographique (HB-87) x HB-478, Hann Bel-Air, 476 Dakar, Senegal

b c

a r t i c l e

i n f o

Article history: Received 22 October 2017 Revised 2 February 2018 Accepted 6 February 2018 Available online xxxx Keywords: West Africa Biomass burning Cooking emissions Ventilation Portable monitors Women respiratory health

a b s t r a c t Although indoor air pollution from the use of biomass fuels is a serious health problem in Senegal, little effort has been made in this country to evaluate indoor air quality impacts from biomass combustion with traditional stoves and indoor air quality improvements derived from the use of improved cookstoves. A cross-sectional study was conducted in a rural village of Senegal to determine indoor air pollution during cooking and noncooking periods. PM2.5 and CO concentration levels were determined, along with two far less studied pollutants in cookstove studies, ultrafine particles and black carbon, using portable monitors. A total of 22 households were selected, 12 using the traditional stove and 10 using a locally produced rocket stove. Rocket stoves, the most extended type of improved stove used in sub-Saharan Africa, contributed to a significant reduction of total fine and ultrafine (UFP) particles and carbon monoxide (CO) (75,4%, 30,5% and 54,3%, respectively, p b 0.05) with regard to the traditional stoves, but increased black carbon (BC) concentrations (36,1%, p b 0.05). This proves that the climate and health-relevant properties of stoves do not always scale together and highlights that both dimensions should be always considered. Findings evidence that, in addition to a switch in the emission source (i.e. cookstove and/or fuel), successful strategies focused on the improvement of household air quality in Senegal should contemplate ventilation practices and construction materials. © 2017 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

Introduction More than 80% of the Sub-Saharan Africa population depend on traditional biomass as the primary fuel for cooking (OECD/IEA, 2014). In Senegal, 83% of the demand for domestic fuel use in rural areas and 58% in urban areas is covered by biomass (The World Bank, 2013). In 2009, liquefied petroleum gas (LPG) subsidies were eliminated, and consumers returned in large numbers to wood-based biomass (primarily charcoal) for cooking (Sander, Hyseni, & Haider, 2011). Indoor air pollution caused by traditional biomass burning for cooking purposes poses serious health risks, especially for women and surrounding children during cooking hours (Desai et al., 2004; Smith et al., 2014). In Senegal, indoor air pollution accounts for 4.8% of the national burden of disease (WHO, 2007), and 6300 people are estimated to die prematurely every year because of indoor air pollution (WHO, 2009). Particulate matter (PM) emitted from biomass combustion is mostly in the fine and ultrafine size range (Tiwari, Sahu, Bhangare, Yousaf, &

⁎ Corresponding author. E-mail address: [email protected] (C. de la Sota).

Pandit, 2014). Fine particles have an aerodynamic diameter less than or equal to 2.5 μm and are referred to as PM2.5. Recent studies show that most particles generated from biomass combustion are typically between 0.05 μm and 0.2 μm (Tiwari et al., 2014), and they may be more harmful to human health than coarser particles (Hawley & Volckens, 2013; Just, Rogak, & Kandlikar, 2013; Rapp, Caubel, Wilson, & Gadgil, 2016). A major component of PM2.5 is black carbon (BC) (Petzold et al., 2013), with well-known hazardous health effects (Janssen et al., 2012). Up to 80% of BC emissions in Africa and Asia have been attributed to residential solid fuel use (Bond et al., 2013). Ultrafine particles (UFP) are those with an aerodynamic diameter b0.1 μm (Pope & Dockery, 2006). They are characterized by low mass and large surface area and, therefore, their concentrations are not accurately reflected by particle mass concentrations so they are monitored in terms of particle number concentrations (Sahu, Peipert, Singhal, Yadama, & Biswas, 2011). In addition, the lung-deposited surface area (LDSA) concentration is increasingly emphasized as relevant parameter to estimate health effects of UFPs in urban areas (Ntziachristos, Polidori, Phuleria, Geller, & Sioutas, 2007; Reche et al., 2015). However, to date, research on this parameter with regard to cookstove emissions is still relatively scarce (Hosgood et al., 2012; Leavey et al., 2015; Patel et al.,

https://doi.org/10.1016/j.esd.2018.02.002 0973-0826/© 2017 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

C. de la Sota et al. / Energy for Sustainable Development 43 (2018) 224–234

2016; Sahu et al., 2011), especially when compared to studies focused on CO or PM mass. In Senegal, the international donor community and national governments increased efforts since the 1980’s to disseminate cleaner and more efficient burning stove designs. These devices are known as improved or clean cookstoves (Bensch & Peters, 2011) as they have the potential to substantially reduce emissions and subsequent exposure (Clark et al., 2013). A number of studies have analysed impacts on household air pollution and personal exposure from cooking activities in Asia (Balakrishnan et al., 2015; Bartington et al., 2017; Chengappa, Edwards, Bajpai, Shields, & Smith, 2007; Chowdhury et al., 2013; Dasgupta, Huq, Khaliquzzaman, Pandey, & Wheeler, 2006; Dutta, Shields, Edwards, & Smith, 2007; Gao et al., 2009; Hu et al., 2014; Kar et al., 2012; Siddiqui et al., 2009), Latin America (Albalak, Bruce, McCracken, Smith, & de Gallardo, 2001; Armendáriz et al., 2008; Commodore et al., 2013; Fitzgerald et al., 2012; Naeher, Leaderer, & Smith, 2000; Northcross, Chowdhury, McCracken, Canuz, & Smith, 2010; Park & Lee, 2003; Riojas-Rodriguez et al., 2011; Smith et al., 2010; Zuk et al., 2007) and Africa (Ezzati, Mbinda, & Kammen, 2000; Ochieng, Vardoulakis, & Tonne, 2013, 2017; Pennise et al., 2009). However, in Senegal there are very few field studies evaluating indoor air quality impacts from biomass combustion with traditional stoves and indoor air quality improvements derived from the use of improved cookstoves (de la Sota et al., 2014), evidencing the need for this kind of research in the region. The complexity of these studies is strongly linked with their operational constraints such as the need for portable, battery-operated and lower-cost monitoring instrumentation (de la Sota et al., 2017). This study is, to the best of our knowledge, the first to provide a comprehensive real-world assessment of household pollutant concentrations from the combustion of biomass fuels for cooking in Senegal. It focuses on the quantification and characterization of BC, PM, UFP and CO indoor concentrations from burning wood fuel during cooking activities using traditional and improved cookstoves in a Senegalese rural village, which is considered representative of rural areas in the country. Moreover, the study identifies parameters such as type of wood and household characteristics (i.e. construction materials, ventilation and kitchen size), which influence indoor air pollutant concentrations, in addition to the type of stove. Experimental methods Study area and cookstove usage The study was conducted in March 2016 in Bibane, a rural village with approximately 650 inhabitants (74 families) in the commune of Niakhar, in the region of Fatick (Senegal) (Fig. 1). Typical home structures are made of earth bricks and thatched roof. The kitchen is physically separated from the bedroom area, and very poorly ventilated (Fig. 1). This type of household structure is very common in rural

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areas of Senegal and other Sub-Saharan West African countries (Ochieng et al., 2013). Women are responsible for cooking, as well as for the rest of the household chores. Cooking is among the most time-consuming of women's responsibilities (Wodon & Blackden, 2006). They spend much of their time near the stove and performing other tasks related to food preparation, such as pounding the millet by hand, fetching water, etc. They usually cook inside the kitchen, to protect the food and fire from animals, wind and children, and to be protected from the sun. Fuelwood is scarce in Bibane, so its collection requires long journeys to the forest. As a result, fuel used to cook is heterogeneous, with a high percentage of families frequently using crop residues and dung. All of the families in the village used traditional three-stone fires before improved cook stoves were introduced in 2012, within the framework of a program to optimize the use of forest resources in the region. In total, 2500 improved cookstoves (known as Noflaye Jegg) were distributed in the region, 50 of them in Bibane. These stoves were locally produced adapting the rocket stove type (without chimney) (Bryden, Still, Ogle, & MacCarty, 2005) and have been also implemented in other regions of West Africa (de la Sota et al., 2014). However, the research team discovered that the majority of the improved cookstoves were in disuse, completely or partially damaged. This is not an isolated case. Indeed, a lot of improved cookstoves implementation actions have failed to achieve an adoption and sustained use of the technologies promoted for a wide variety of reasons (Rehfuess, Puzzolo, Stanistreet, Pope, & Bruce, 2013). Finally, only 10 out of 50 were in a good or fair state, and considered adequate to be included in the study. Cookstoves are shown in Fig. S1 in the Supplementary material.

Household selection and study design Before starting measurements, a communal meeting was conducted to explain the study purpose and activities, where women provided oral consent to enrol in the study. A cross-sectional study (Edwards, Hubbard, Khalakdina, Pennise, & Smith, 2007) was conducted to evaluate the comparative impact of improved and traditional stoves on indoor air pollutant concentrations (PM2.5, UFP, BC and CO). Even though the air quality monitors were not worn by the women, it is assumed that indoor air pollutant concentrations may be considered as a proxy for personal exposure during cooking hours due to the small size of the kitchens and their low ventilation rates. The sample size advisable in cross-sectional sample designs depends on the detectable difference in means and the covariance (COV) of measurements. For improved cookstoves without a chimney, it is realistic to expect a difference of 60%, and COV of 0.7. Therefore, the advisable sample size per stove type for our study was estimated to be 22 (Edwards et al., 2007).

Fig. 1. Typical kitchen in Bibane, rural village in the region of Fatick, Senegal.

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As the largest possible sample size of improved cookstoves was 10 (maximum number available), a strategy was designed to reduce the variability (COV) between households, and thus reduce the sample size advisable. It was found that the easiest variable to control was the type of fuelwood and therefore the same wood specie was distributed to all families by the research team. The fuelwood distributed was Cordyla Pinnata (commonly known as dimb), which is one of the dominant species in Senegal and very commonly used as residential fuel for cooking (Ndiaye et al., 2015). A total of 22 households was selected, 12 with the traditional stove and 10 with the improved rocket stove. Information on general kitchen characteristics was annotated, and the daily fuel wood was measured using a digital scale of 0–30 kg range with 1 g resolution. General features of study kitchens are summarized in Table 1 while Tables S1 and S2 in Supplementary material provides individual information of each household. Indoor air pollution monitoring Indoor air pollutant (PM2.5, UFP, BC, CO and LDSA) concentrations were monitored in all kitchens using the same instrumentation. The instrumentation deployed was a function of its portability, autonomy, range of concentration, budget and sensitivity. One household with a traditional stove and one with improved stove were always monitored simultaneously in order to account for meteorological variability. Dusttrak (TSI Inc., Shoreview, MN, model DRX) Aerosol particulate monitor which monitors real time particle concentrations (PM1, PM2.5, PM10) using a laser photometer with a reading range of 0–100 mg/m3 and a resolution of 0.001 mg/m3. It was installed in 21 households during the main cooking periods (lunch and dinner) and during part of non-cooking periods (several minutes before and after cooking). DustTrak was used for studies in similar contexts (Chowdhury et al., 2013; Ochieng et al., 2017) and its performance has been already assessed (Chowdhury et al., 2007; Rivas et al., 2017; Viana et al., 2015). Testo DiSCmini (Diffusion Size Classifier Miniature) Portable monitor (Fierz, Houle, Steigmeier, & Burtscher, 2011) determining particle number concentration and mean diameter in the range 10–700 nm, connected to an impactor with a cutoff at 700 nm to prevent interference with coarse particles. Anti-static tubing was used during all intercomparison exercises (Asbach et al., 2017; Todea et al., 2017; Viana et al., 2015). The DiSCmini compares reasonably well with reference instruments under laboratory and field settings in urban and industrial environments (Koehler & Peters, 2015; Viana et al., 2015), with an uncertainty of 30% in terms of particle number concentration. However, to our knowledge, no field study of indoor air pollution from stoves used this device. It was installed in 6 households, during Table 1 Summary of participant kitchen characteristics. Kitchen characteristics Stove type Wall materials

Roof materials

N Improved Traditional Earth bricks Tin Thatched Missing Thatched Tin Missing data

10 12 16 1 1 4 17 1 4

Kitchen characteristics

Mean ± sd

Kitchen volume (m3) Free area of inlet/outlet opening (windows + doors + holes) (m2) Fuel consumption (kg)

22.0 ± 9.3 2.1 ± 1.6 9.8 ± 5.2

the main cooking periods and part of non-cooking periods. This instrument suffered frequent failures during the study due to the fact that UFP concentrations monitored were frequently outside its monitoring range.

MicroAeth AE-51 (AethLabs) Portable black carbon monitor with no cyclone at the inlet. The flow rate of AE-51 was set at the minimum, i.e. 50 mL/min, but due to very high BC loading in cookstove emissions, the filter strip had to be changed with a high frequency (every 5 min approximately) to prevent saturation (Rehman, Ahmed, Praveen, Kar, & Ramanathan, 2011). This fact complicated the sampling procedure and resulted in data losses, and therefore the use of a diluter is advised for future studies. Previous studies have used this device for indoor BC monitoring studies from cooking activities (Kar et al., 2012; Rehman et al., 2011). Microaeth was installed in 15 households, during the main cooking periods and during part of non-cooking periods. The uncertainty of this instrument was quantified for outdoor air stations as 10% (Viana et al., 2015).

Indoor Air Pollution Meter 5000 Series (IAP meter; Aprovecho Research Center, OR, USA) Red light scattering photometer to measure PM2.5 concentration, with a reading range of 0–60 mg/m3 and a resolution of 0.025 mg/m3. It also includes an electrochemical sensor to measure CO concentration. It was installed in 22 households, during 24-hour periods. This device has already been used in certain indoor air pollution studies from cookstoves, (Grabow, Still, & Bentson, 2013; de la Sota et al., 2014) but its uncertainty has not been reported in the scientific literature. The monitoring devices were deployed according to standard protocols, 1 m away from the emission source and 1.45 m above the ground to represent breathing position of cooks, on the side of the stove opposite to open doors and windows (Chengappa et al., 2007). Inlets were hung approximately 10 cm apart from each other. The sampling interval was set to 1 min for all instruments. The devices were not installed in the same number of households because: i) some of them failed throughout the campaign (e.g. DisCMini), ii) of insufficient filter tickets available, due to the high replacement frequency (Microaeth) and iii) of battery failures (DustTrak). Differences in the sampling period duration are due to the devices' autonomy. While IAP meters have autonomy enough to measure during 24 h periods, DustTrak, DiSCmini and Microaeth have not. Therefore, the latter were deployed during the main cooking periods (lunch and dinner), and during part of the background (non-cooking) period. Tables S3 and S4 describe the devices installed in each household.

Instrument quality control Microaeth, Discmini, and DustTrak monitors were previously intercompared for a 24 h period with their stationary counterparts (Multi-angle absorption photometer (MAAP), condensation particle counter (CPC) and optical particle counter (OPC), respectively) (Viana et al., 2015) in an urban air quality monitoring station in Barcelona (Spain). Detailed results are presented in Supplementary material, Figs. S2 to S12. Comparisons were carried out for quality control purposes, i.e. correction equations were not applied, given the differences between the test (urban) and field (cookstove) in aerosol load and composition. In addition, the first day of the campaign all of the monitors were collocated in a chosen kitchen for field validation, evaluating inter-instrument variability, sensitivity, and consistency (Chowdhury et al., 2013). Detailed results are presented in the Supplementary material (Figs. S13–S16). DustTrak was used for the field-validation of the IAP meter, as explained in Comparison between IAP meter and DustTrak units section.

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Statistical analyses Concentration levels of the different pollutants with the two types of stoves were processed and arithmetic means (AM), standard deviations (SD), medians and range were calculated. The Kruskal–Wallis test, a non-parametric method, was used to test for differences in pollutants concentration between stove types. This test was selected because indoor air pollution data from this study doesn't fit a normal distribution. In addition, a linear mixed-effects model was constructed to assess the effect of factors at household-level on indoor air pollution. This model includes fixed and random effects. The fixed effects were: stove type, kitchen volume, free area of inlet/outlet opening and the kitchen construction materials. In addition, the random effects characterize variation due to individual differences. In this study, households were considered as random effect (Hu et al., 2014). The formula that describes the model is: yit ¼ β0 þ β1x1 þ β2x2 …βnxn þ ui þ εit

ð1Þ

where yit represents the pollutant concentration value modelled for household i at day t; β0 represents the intercept value; β1 through to βn represent the fixed effect variable coefficients for variables x1 through xn; μi are random effects at household level, assumed normally distributed with mean 0 and variance s2u, and εit are the model residuals assumed normally distributed with mean 0 and variance s2u (Rivas et al., 2015). Data analysis was performed using IBM SPSS Statistics Base and Python software. Results and discussion Indoor air pollutant concentrations during cooking activities Fig. 2 shows a representative example of particulate and gaseous indoor concentrations for a household using the rocket stove (BI-02-A,

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Table S1), covering the two main daily cooking activities (lunch and dinner) and a portion of non-cooking period. This example was selected based on data availability for all the parameters under study: particle number concentration, mass (PM10, PM2.5, PM1) and mean diameter, as well as BC and CO concentrations. LDSA concentrations were also available (not shown graphically, for clarity). As shown in Fig. 2, indoor air concentrations were markedly dominated by the cooking periods: particle number concentrations (N) and particle mass (PM2.5) reached up to 3 ∗ 106/cm3 and 2000 μg/m3, respectively during cooking periods, while they were below 80,000/cm3 and 300 μg/m3 during non-cooking hours. LDSA and CO concentrations ranged between 10–4800 μm2/cm3and 0.01–105 ppm, respectively. The pollutant time series shown in Fig. 3, representative for all of the households studied, as well as the absence of other combustion sources in the village (a typical African rural village), point to cooking as the main emission source of indoor air pollution and personal exposure (especially for women and children). CO, BC, PM and N concentrations co-varied in time, as expected (Chengappa et al., 2007). Past studies using solid fuel in biomass burning stoves support the evidence that particulate emissions (PM, N and BC) and CO are co-emitted (Naeher et al., 2000; Northcross et al., 2010). In the example shown in Fig. 2, during non-cooking activities, average indoor CO and BC concentrations were close to zero, 1.28 ppm and 1.33 μg/m3 respectively. Another study conducted in rural villages in Northwest Bangladesh also showed very low indoor CO concentrations during non-cooking activities 0.2 ppm (Chowdhury, 2012), whereas slightly higher BC concentrations (3.7 μg/m3 ± 0.9,) were monitored in villages of northern India surrounded by traffic (Kar et al., 2012). Average indoor background concentrations were PM10 = 159.9 μg/m 3, PM2.5 = 99.1 μg/m3 and PM1 = 88.5 μg/m3, similar to other studies in rural areas in India (PM2.5 from 45 to 207 μg/m3) (Sambandam et al., 2015). High PM10/PM2.5 ratios (1.6 on average) were monitored during non-cooking periods due to indoor and outdoor dust, which was re-suspended by wind, movement of inhabitants, and

Fig. 2. PM1, PM2.5, PM10 (μg/m3), BC (μg/m3), UFP (/m3), mean diameter (nm) and CO (ppm) concentrations measured on March 3, 2016 in a household with rocket cookstove Noflaye Jegg, during lunch and dinner preparation at household BI-02-A (Table S1).

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Fig. 3. PM1, PM2.5, PM10, BC, UFP, size, LDSA and CO concentrations measured during lunch preparation on March 3, 2016 in a household with improved cookstove Noflaye Jegg. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

animals in the kitchen, among other causes. In the present study, during cooking periods PM10/PM2.5 ratios were on average 1.1. This highlights that particles are coarser during non-cooking periods and the important contribution of fine particles to indoor air during cooking periods. Regarding UFPs, mean background N concentrations during noncooking hours were 5568/cm3, 99% lower than during cooking periods. These results are lower than those reported by Chowdhury (2012) in Bangladesh (Chowdhury, 2012), where mean UFP concentrations during non-cooking hours was 15,000 ± 7200/cm3. This may be due to differences in ventilation rates and/or lack of enough time between cooking periods to ventilate the room. As expected, mean particle diameter decreased with increasing particle number concentrations during combustion periods (from 62 nm during non-cooking to 51 during cooking hours). Finally, in this example, LDSA mean concentration during non-cooking periods was 50,5 μm2/cm3, similar to rural background sites measurements found in other studies (Casino, Italy, 69 μm2/cm3) (Buonanno, Marini, Morawska, & Fuoco, 2012). Although ambient air quality data was not monitored simultaneously with indoor air pollution, contributions to household pollution from other combustion sources such as transport or industry were not considered significant in Bibane. Households are sufficiently dispersed so that neighbourhood cooking activities do not affect other households, and no significant additional combustion sources (e.g., forest fires) were recorded during the study period. A study conducted in rural villages of Western Africa showed that 74–87% of total PM2.5 mass concentrations in cooking areas was from biomass burning particles, together with a small percent of crustal material and soil dust, from the Sahara desert (Zhou et al., 2014). Fig. 3 shows, for the same representative household (BI-02-A, Table S1), the detailed time-series of pollutant concentrations during a single combustion episode (lunch preparation). The aim of this analysis was to assess emission patterns for the different pollutants as a function of the cooking cycle.

The fire was lit at 11.31 h am (marked with a red star in Fig. 3) and a peak of all pollutants (particles and CO) was observed immediately after, showing the fast impact of combustion on indoor concentrations (10,000 μg/m3 for PM2.5, 1000 μg/m3 for BC, 2.5 ∗ 106/cm3, 4800 μm2/cm3 for LDSA, 30 ppm for CO). This behaviour was observed in previous studies (Kulshreshtha & Khare, 2011). Particle diameter, conversely, did not show such a fast evolution, which was probably linked to instrumental limitations (particle diameter measurements were close to the instrument's detection limit). In this study, the high emissions during the initial few minutes of the combustion phase were due to the use of straw as accelerant to start the fire. Other authors have reported similar results with the use of ignition products (Arora, Jain, & Sachdeva, 2013; Roden, Bond, Conway, & Pinel, 2006). After the initial increase in concentrations (recorded for all pollutants), PM concentrations decreased (down to 2000 μg/m3), while the impact of emissions on indoor air were more persistent over time with regard to particle number (concentrations remaining at 1.5 ∗ 106/cm3). This has implications with regard to population (mainly women's and children) exposure to UFPs (Ko et al., 2000). Conversely, PM mass concentrations progressively accumulated and increased over time, reaching at the end of the cooking period similar concentrations to those monitored during its initial phase (8000 μg/m3). As shown in Fig. 3, PM concentrations showed a larger variability throughout the combustion period than other parameters such as UFP or CO concentrations. Pollutant peaks during cooking occurred when fuel was added or moved, due to activities such as placing or removing the cooking pot on the fire, stirring the food, or as a result of external factors (e.g., changing wind speed). The high variability in mean particle diameter during the cooking periods is also noteworthy, as it indicates that different types of emissions are generated during the different types of combustion stages (influenced by fuel feeding, strength of the flame, ventilation, etc.). Although the fire was completely extinguished at 13.03 h, PM and BC concentrations began to decrease toward the end of the food preparation period (12.52 h), when the cooker stopped feeding the stove and

−54.3%* 0.01–285.8 0.004–166.3 15.9 34.8 23.8 ± 26.5 41.3 ± 26.8 8 8 36.1%* 0.2–11,806.6 0.02–5778.9

Range Median Am ± sd n

Carbon monoxide (CO) (ppm)

% of change in median Range

30.9–10,886.5 6.5–11,432.3 4014.2 4680.2 4201.3 ± 1962.6 5085.9 ± 2980.1 3 3 Improved cookstove Traditional cookstove

Range Median Am ± sd

1042.8 ± 1213.5 767.9 ± 874.3 7 8 −14.2%*

Median n % of change in median n

Am ± sd

Black carbon (BC) (μg/m3) Lung deposited surface area (LDSA) 26 (μm2/cm3) Type of stove

677.6 497.7

% of change in median

20.7%* 24.4–244.6 10–299 46 38.1 50.4 ± 18.4 41.8 ± 24.5 3 3 −30.01%* 1,713,620 ± 1,079,780 2,576,060 ± 1,913,410 4621.9 ± 8387.1 10,563.9 ± 10,328.5

1780 7240

20–84,200 62–54,600 9 8 Improved cookstove Traditional cookstove

Am ± sd Range Median Am ± sd

1,529,080 2,184,800 3 3 −75.4%*

12,869–6,721,660 372–7,594,540

% of change in median Range Median Am ± sd n

Mean diameter (nm)

% of change in median Range n % of change in median n

Median

Particle number concentration (N) (pt/cm3)

Table 2 presents the descriptive statistics for pollutant concentrations during the main daily cooking periods (lunch and dinner) in the study households, as a function of the type of stove used: traditional or improved. In total, 8 households were available with traditional stoves, and 9 for improved ones (see “n” in Table 2). It means that 16 cooking periods were sampled for the traditional stove and 18 for the improved stove. However, pollutants data are not available for the same number of households, given that instrumental failures were frequent due to the high concentrations monitored. The instrument which was most affected by technical failures was the DiscMini, and thus particle number and LDSA concentrations, as well as mean particle diameter, are only available for 3 improved cookstove and 3 traditional cookstove households (i.e., 6 cooking periods). For each pollutant included in Table 2, the mean, standard deviation, median and range, were determined considering the data sampled during the cooking periods for the set of households with a given type of stove. PM1, PM2.5 and PM10 values during combustion showed b10% of difference for both types of stoves pointing to a predominance of fine particles. For clarity reasons, PM2.5 was selected for presentation in the Table as an indicator for the PM10 and PM1 mass fractions, as it is most commonly used in indoor air pollution studies and international standards. During cooking hours, results show that PM, UFP, LDSA and CO concentrations in homes using improved stoves were statistically lower than in those with traditional stoves. Conversely, BC concentrations were higher in improved cookstove households. All of these differences were statistically significant according to the Kruskal-Wallis test (p b 0.05). Based on this analysis, reductions (or increases) in pollutant concentrations during cooking as a function of the stove were pollutantdependent. The largest reductions due to improved cookstove implementation were observed for PM2.5, with a 75.4% reduction in median concentrations. CO concentrations decreased by 54.3%, followed by particle number concentrations (30.0%) and LDSA (14.2%). As a result, the use of improved cookstoves in our study households (between 3 and 9, depending on the pollutant) showed clear and statistically significant improvements for indoor air quality with regard to particle mass (PM2.5), particle number, LDSA and CO concentrations. Other studies also found CO and PM2.5 reductions with the rocket stove, when compared to a traditional three stone fire. De la Sota et al. found 36% and 33% of 24-h mean percent change for PM2.5 and CO respectively in rural Senegal.(de la Sota et al., 2014) Median percent reductions in 24-h PM2.5 and CO concentrations ranged from 2 to 71% and 10–66% respectively in southern India (Sambandam et al., 2015). Even though this is a positive result in relative terms, it should be noted that, in absolute terms, median concentrations during cooking periods remained high even when improved cookstoves were used: 1780 μg/m3 for PM2.5, 1.5 ∗ 106/cm3 for UFPs, 4014.2 μm2/cm3 for LDSA, and 34.75 ppm for CO.

PM2.5 (μg/m3)

Indoor air pollutant concentrations during cooking periods as a function of the type of stove

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Type of stove

used the residual heat to finish the cooking. PM and BC levels returned to concentrations closer to those prior to the cooking period (117 μg/m3 and 1.2 μg/m3 for BC) around 10 min after the fire was extinguished. CO and N concentrations increased during the final period of cooking, when using residual heat. Once the fire was extinguished, N showed a similar trend to PM and BC, while concentrations of CO showed a gradual decrease over 25 min. This CO behaviour was found in previous studies (Chowdhury, 2012), and it is indicative of the remaining charcoal smouldering once the fire was extinguished. As expected, PM10, PM2.5 and PM1 concentrations were highly similar during the cooking period, indicating that biomass combustion was dominated by fine and ultrafine particles. Indeed, other works analysed particle size distribution during biomass burning showing that PM1 was the main contributor to PM2.5 and PM10 fractions (Arora et al., 2013; Park & Lee, 2003).

Table 2 Descriptive statistics of indoor air pollutants concentrations by stove type. n = number of households sampled. Am = arithmetic mean. Sd = standard deviation. *significant variation (p b 0.05; Kruskal-Wallis tests). % of change in median compared to the traditional stove.

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Following an opposite behaviour, median BC concentrations increased by 36.1% (also statistically significant), from 497.7 μg/m3 with traditional stoves to 677.6 μg/m3 with improved cookstoves. This result highlights the complexity linked to the estimation of indoor air pollution impact of cookstove interventions, as improved stoves do not perform similarly for different pollutants (Arora & Jain, 2015; Jetter & Kariher, 2009; Kar et al., 2012; MacCarty, Ogle, Still, Bond, & Roden, 2008; MacCarty, Still, & Ogle, 2010; Roden et al., 2009). Our findings are consistent with previous studies which have found that some improved stoves models emit more BC than traditional cookstoves. Arora and Jain laboratory controlled cooking test (CCT) reported a BC emission factor (EF) of 0.07 ± 0.007 g/MJ of dry fuel for the traditional stove and BC EF of 0.09 ± 0.009 g/MJ for a natural draft stove (Arora & Jain, 2015). Just et al. (2013) and MacCarty et al. (2008) found 1.27 and 1.11 g BC/kg of dry fuel for a rocket stove and 0.70 and 0.88 g EC/kg of dry fuel for the three stone (Just et al., 2013; MacCarty et al., 2008). This is related to the nature of combustion produced by each type of improved cookstove. The rocket improved stoves have a stronger draft and higher combustion temperature, producing aerosols primarily in the form of black carbon (MacCarty et al., 2008; Rau, 1989). The three-stone fire typically consists of a larger bed of charcoal under the flaming fuel, resulting in both black and organic particles (MacCarty et al., 2008). Higher BC emissions with the improved cookstove may also indicate a low air fuel ratio and poor air circulation in the combustion chamber of the improved cookstove (Arora et al., 2013) due to an excess of the amount of firewood introduced into the combustion chamber. Therefore, efficient stoves with higher combustion temperatures may decrease PM2.5 emissions overall compared with open fires, but may also increase other potentially pollutants such as black carbon (Arora & Jain, 2015; Clark et al., 2013; Just et al., 2013; MacCarty et al., 2008). The consequence of this is that negative health effects from BC exposure can still be expected after installation of improved stoves, in addition to climate warming effects (Baumgartner et al., 2014). Finally, mean particle diameter increased from 42 to 50 nm (20.7%, Table 2) when rocket cookstoves were used. Previous studies showed a shift toward finer particles with some types of improved stoves. (Arora et al., 2013; Just et al., 2013) For example, Just et al. found mean particle diameter of 35 nm with the rocket stove and 61 nm with the three stone fire.(Just et al., 2013) However, such studies have been conducted under controlled conditions and may not represent field conditions. Moreover, particle size distributions are significantly affected by type of cookstoves and fuels (Just et al., 2013; Tiwari et al., 2014). Finally, higher LDSA values for three stone can be attributed to larger emissions of ultrafine particles (Sahu et al., 2011). With regard to previous studies in the literature, although several authors assessed indoor air pollution from the use of cookstoves in the field (Armendáriz et al., 2010, 2008; Chengappa et al., 2007; Chowdhury et al., 2013; de la Sota et al., 2014; Hu et al., 2014; MacCarty et al., 2007; Ochieng et al., 2013; Park & Lee, 2003; Ruth et al., 2014; Sambandam et al., 2015; Siddiqui et al., 2009; Zuk et al., 2007), most of them present average 24 h or 48 h data, which do not reflect acute exposure levels observed during cooking periods. There are few field studies differentiating between cooking and non-cooking periods, whose results are summarized in Table 3, together with those obtained in this work. LDSA values are not included since, to our knowledge, none of the field studies that has evaluated this parameter (Hosgood et al., 2012; Sahu et al., 2011) show indoor concentration values separately for both cooking and non-cooking periods. Table 3 shows that our results are generally higher in comparison with other field results, with the exception of CO and mean particle diameter, which are indeed comparable. This significant variation might be explained by the differences between the studies regarding the

Table 3 Summary of pollutant concentration during cooking activities reported for previous studies and within this study. Pollutant concentration during cooking activities This study CO (ppm) Traditional: Median: 34.8 Mean (SD): 41.3 (26.8) Rocket stove: Median: 15.9 Mean (SD): 23.8 (26.5) PM2.5 (μg/m3) Traditional: Median: 7240 Mean (SD): 10,563.9 (10,328.5) Rocket stove: Median: 1780 Mean (SD): 4621.9 (8387.1) N (pt/cm3) Traditional: Median: 2,184,800 Mean (SD): 2,576,060 (1,913,410) Rocket stove: Median: 1,529,080 Mean (SD): 1,713,620 ± 1,079,780 Mean diameter (nm)

Traditional: Median: 38.1 Mean (SD): 41.8 (24.5) Rocket stove: Median: 46 Mean (SD): 50.4 (18.3)

BC (μg/m3) Traditional: Median: 497.7 Mean (SD): 767.9 (874.3) Rocket stove: Median: 677.6 Mean (SD): 1042.8 (1213.5)

Other studies India (Balakrishnan et al., 2015) Traditional: Median: 57.6 Mean (SD): 56.2 (13.8) Forced-draft advanced cookstove: Median: 20 Mean (SD): 21.5 (4.8) India (Balakrishnan et al., 2015) Traditional: Median: 1451 Mean (SD): 1991 (2060) Forced-draft advanced combustion stove: Median:1046 Mean (SD): 1813 (2686) China (Zhang et al., 2012) Traditional: Mean: 290,000 Bangladesh (Chowdhury, 2012) Chimney biomass improved stove: Mean (SD):75,000 (31,000)

China (Zhang et al., 2012) Traditional: Mean (from start combustion-10 min after): 40–50 Mean (10–15 min after start combustion): 63 India (Kar et al., 2012, India) Traditional: Mean (SD): 127.6 (103.5) Natural draft gasifier: Mean (SD): 75.5 (59.4)

fuels and stoves characteristics, variations in cooking habits, stove location, ventilation, etc. (Just et al., 2013; Siddiqui et al., 2009). Moreover, the range of pollutant concentrations during cooking periods is also highly variable within stove types (Commodore et al., 2013). Fig. 4 presents box-plots of pollutant concentrations for the three-stone fire and the rocket stove. The large variability observed in Fig. 4 within stove type can be attributed to a myriad of factors (e.g., variability in cookstove use and time, activity patterns, weather conditions, household room configuration and ventilation, cooking behaviours, etc.) (Clark et al., 2013). It highlights that averaging concentrations across households or experiments is a major limitation when addressing cookstove studies, as opposed to other types of air quality studies (e.g., ambient air). As shown in Fig. 4, the mean/median values should not be considered fully representative of the datasets, and other metrics of data variability (e.g., standard deviation, interquartile range) are essential to understand the data distribution.

Effect of household-level determinants on indoor air pollution A Linear mixed effect modelling was performed to identify factors at household-level that might have a significant role in determining PM2.5 concentrations during cooking periods (Hu et al., 2014).

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Fig. 4. Box-plots of pollutants concentrations for three-stone fire and the rocket stove. The boxplot presents minimum, first quartile, median, (middle dark line), third quartile and maximum values.

Factors considered for inclusion as fixed effects were: stove type (traditional, improved), walls material (earth bricks, thatched, tin), roof material (thatched, tin), kitchen volume (m3), free area of inlet/ outlet opening (m2), fuel consumption (continuous, kg/day) and elapsed time (minutes). Elapsed time was included as a continuous variable to examine temporal changes in concentrations (Albalak et al., 2001). Fuel characteristics were not considered as a factor, since the same type of wood was distributed between participants during the sampling period. Other factors, such as type of meal prepared, meteorological conditions or cooker behaviours were not measured, so their influences were included in the model as random effect at household level, with a scalar (variance component) covariance structure (Hu et al., 2014). Table 4 provides estimates of individual parameters (β), as well as their standard errors and confidence intervals. The variables selected for inclusion in the final model were those which significantly impacted PM2.5 measurements (p b 0.05) and contributed to the lowest Akaike information criterion (a measure of the relative quality of statistical models for a given set of data) score (Hu et al., 2014). To improve normality and variance homogeneity, log-transformed concentrations were used. The estimate of the “Elapsed time” parameter was not significant (p N 0.05), and therefore it was removed from the model. Coefficients indicate an increase in PM2.5 concentration (in log-μg/m3) for traditional cookstoves with respect to improved cookstoves. For the roof material variable, PM2.5 concentration decreased with thatch roofing with respect to tin, which is the reference format (β = 0). This is due to the fact that thatched roofs enhance ventilation and further decrease indoor air pollutants compared to homes with metal roofs, also observed in previous studies.(Ruth et al., 2014)

Regarding wall materials, thatched walls resulted in the highest predicted PM2.5. However, as only one house had tin walls (see Table 4), this material was not considered representative and therefore more samples should be included to analyse its effect on indoor air pollutant concentrations. Results evidenced that kitchen volume has a significant negative association with PM2.5, with an approximately 1.9% decrease in log-PM2.5 concentrations for every unit increase in volume (m3). This is consistent with other studies (Albalak et al., 2001). Finally, the free area of inlet/outlet opening (sum of areas of windows, doors and other holes in the kitchen) (Iyengar, 2015) was correlated with PM2.5 concentrations, with a 12% decrease in log-PM2.5 Table 4 Linear mixed effect modelling of log-transformed PM2.5. Parameter Type of stove Improved Traditional Roof material Thatched Tin Wall material Earth bricks Thatched Tin Kitchen volume (m3) Free area of inlet opening (m2) Daily fuel consumption (kg/day) Variation explained between households (%) a

Coefficient (β) (log-μg/m3)

Standard error

95% confidence interval

3.61 3.89

0.28 0.27

3.06; 4.16 3.36; 4.42

−0.69 0a

0.16 0

−1.0034; −0.37 –

0.49 1.02 0a −0.019 −0.12 0.040 35

0.047 0.076 0 0.0054 0.015 0.0030

0.40; 0.58 0.87; 1.17 – −0.029; 0.0081 −0.15; −0.092 0.034–0.045

This parameter is set to zero because it is the reference level for the model.

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concentrations for every unit increase in free area of inlet opening. Results are coherent with other studies showing that particulate levels can significantly decrease by the existence of opening structures (Baumgartner et al., 2011; Bruce et al., 2004; Dasgupta et al., 2006; Park & Lee, 2003; Ruth et al., 2014). As shown in Table 4, the linear mixed effect model explained 35% of the variation of indoor PM2.5 concentration. The remaining variation may reasonably be due to imperfect specification of variables, such as cooking time, other household combustion sources, etc. (Pokhrel et al., 2015). It should be noted that our study aimed at evaluating the association of PM2.5 concentrations with factors at household level rather than obtaining a predictive model. PM2.5 was selected to perform the linear mixed effect model because it is the pollutant analysed in a greater number of households (8 traditional and 9 improved). Since the other pollutants are simultaneously produced during biomass combustion (Chengappa et al., 2007), factors at household level could be assumed to affect in a similar way, although additional experiments would be necessary to confirm this hypothesis. Comparison between IAP meter and DustTrak units As previously explained, IAP meter units were co-located with the DustTrak monitors. The aim of this comparison was to assess the performance of this lower cost instrument (IAP meter) (de la Sota et al., 2017). It is relevant to present the correlation between these two methods because although the IAP meter has been used in a number of studies (Gorritty, Torrico, & Montenegro, 2011; Grabow et al., 2013; de la Sota et al., 2014), there are no publications covering field assessments of its performance compared to other commercial light-scattering measuring instruments. Fig. 5 shows the correlation of one DustTrak (code 3, Table S3) with one IAP meter (code 5025, Table S3) during measurements for the same household previously presented in Figs. 2 and 3, including both cooking and non-cooking periods to cover the entire range of typical daily variation of household PM2.5 concentrations. The response of the IAP meter showed a relatively good linearity with the DustTrak on a minute by minute basis (R2 = 0.73). However, the relative differences between the PM2.5 concentration values measured by the IAP meter and the DustTrak ranged between 31% and 166%. This provides a quantitative estimate of the IAP meter uncertainty, when compared with the DustTrak, for the time resolution and the PM2.5 concentration range measured (Viana et al., 2015). Moreover, the correlation equations showed a large inter-variability across the colocation tests, and in certain households there was no such a good fit (see in Supplementary material, Table S5, showing the results for all the co-location tests). R2 values ranged between 0.12 and 0.87 in all of the households where IAP meters and DustTraks were co-located. In

total, a 66.7% of the households (10 households) exhibited R2 values higher than 0.75, while in the remaining 5 cases the dispersion of the data was considered high (R2 b 0.75). This variability in the responses of the IAP meter with regard to the DustTrak (considered here to be the internal reference) may be explained by different reasons: i) the fact that they are different instruments operating on different principles (different laser characteristics); ii) DustTrak and IAP meter were not placed at the exact same point in the kitchen (the inlets of the devices were hung approximately 10–20 cm apart from each other, so it may occur that devices were not equally affected by factors such as ventilation; iii) DustTrak zero was set with an ambient free of pollution (using a zero filter), whereas the IAP meter background was PM2.5 from ambient air. Therefore, results from IAP meter presented in this study should only be interpreted as relative measures, since background concentrations were not considered in the reported PM2.5 values; and to estimate the additional pollution directly caused by cooking, it would be necessary to measure background concentrations in the area; and iv) Two different IAP meters were used during the study. Although both IAP meters belong to the 5000 series, previous studies showed that using inexpensive off-the-shelf smoke detector technology may suffer from substantial intra-unit variability (Chowdhury et al., 2007). This result indicates the need of individual instrument calibrations, which is also necessary when using other more costly air pollution monitors (Chowdhury, 2012; de la Sota et al., 2017; Viana et al., 2015). Conclusions A cross-sectional study was conducted to examine the effectiveness of a stove intervention program to reduce indoor pollutant levels at a rural village in Senegal. Results confirmed that household air pollution in this area is mainly due to cooking activities. The installation of the Noflaye Jegg, a locally produced improved rocket stove, contributed to a significant reduction of total fine and ultrafine particulate number and CO (75,4%, 30,5% and 54,3%, respectively) with respect to traditional stoves, but increased indoor BC concentrations (36,1%). According to these results, improved cookstoves would have a positive effect with regard to health effects, but not for all pollutants monitored. This proves that the climate and health-relevant properties of stoves do not always scale together and highlights that both dimensions should be always considered. Alternative cookstoves focusing on the reduction of BC emissions should be considered to increase co-benefits on climate change mitigation. Findings also evidence that, in addition to a switch in the emission source (i.e. cookstove and/or fuel), successful strategies focused on the improvement of household air quality in Senegal should contemplate ventilation practices and construction materials. The majority of the improved cookstoves (40 out of 50) disseminated in 2012 in the rural village under study were in disuse, completely or partially damaged, showing that adoption and sustained use of stoves by families is plausibly the most important and challenging consideration to implement improved cookstove projects. Thus, successful strategies focused on the improvement of household air quality in Senegal and other populations of sub-Saharan Africa should include information about better ventilation practices and an appropriate education on the cooking environment. Acknowledgements

Fig. 5. Correlation of DustTrak response with IAP meter for PM2.5 concentrations (μg/m3) at 1 min resolution.

We would like to thank the families, especially women, of Bibane for their kindness. We are grateful to the technicians of the University of Dakar, for their assistance with the fieldwork; to Ramón de la Sota for the photos and draws, and Ivan Robla, for his advice with the statistical analyses. This work has been financially supported the Global Alliance for Clean Cookstoves (GACC, Enhancing Capacity of Regional Testing and Knowledge Centers, RTKC2) (UNF-15-674), the Iberdrola Foundation (Grant for Research on Energy and the Environment), and the

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Spanish National Research Council (CSIC, I-COOP programme through project COOPB20122). Support has also been granted by SGR33 AGAUR2014. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.esd.2018.02.002.

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