Suman Yadav1,2, Taveen Singh Kapoor1, Pradnya Vernekar1, Harish C. Phuleria This email address is being protected from spambots. You need JavaScript enabled to view it.1,2 

1 Interdisciplinary Program in Climate Studies, Indian Institute of Technology, Bombay, Powai, Mumbai-400076, India
2 Environmental Science and Engineering Department, Indian Institute of Technology, Bombay, Powai, Mumbai-400076, India


Received: June 3, 2023
Revised: August 21, 2023
Accepted: August 22, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.230102  


Cite this article:

Yadav, S., Kapoor, T.S., Vernekar, P., Phuleria, H.C. (2023). Examining the Chemical and Optical Properties of Biomass-burning Aerosols and their Impact on Oxidative Potential. Aerosol Air Qual. Res. 23, 230102. https://doi.org/10.4209/aaqr.230102


HIGHLIGHTS

  • Elemental carbon (EC1) showed significant association with oxidative potential
  • Water-soluble absorbing aerosols and BC also associated with oxidative potential.
  • Aerosols emitted from solid biomass fuel cooking are highly oxidising in nature.
 

ABSTRACT


The use of biomass fuels for cooking persists on a large scale in rural areas of many low-and middle-income countries, including India. Exposure to emissions from biomass cooking is linked with adverse respiratory health outcomes - likely mediated through the oxidative potential of particulate matter (PM). This study aims to measure the oxidative potential (OP) of biomass aerosols in rural kitchens and examine its association with aerosol chemical and optical properties. Field measurements were conducted to collect PM2.5 from biomass fuel cooking in rural Maharashtra, India. Chemical and optical methods were employed to understand PM characteristics, while OP was measured using Dithiothreitol (DTT) assay. The average (± SD) indoor PM2.5, OC, EC, BC, and WSOC during cooking using biomass fuels were 1025 ± 1001, 203 ± 196, 140 ± 133, 112 ± 61, and 130 ± 118 µg m–3, respectively, and the corresponding village outdoor levels were ~12.8 (p = 0.04), 4.9 (p = 0.14), 19.8 (p = 0.09), 23.6 (p = 0.01), and 8.1 (p = 0.12) folds (statistical significance of difference) lower, respectively. The volume normalized oxidative potential (DTTv) of PM from biomass cooking was 25.3 nmol DTT min–1 m–3, which was an order of magnitude higher redox active than rural ambient PM. Carbonaceous components of the PM correlated positively with the OP, having a significant association with EC1 (R = 0.83), BC (R = 0.93), and absorption coefficient (WSOC babs, 365) (R = 0.97). Our findings suggest that emissions from biomass cooking may pose a substantial risk to a large population, in particular to women and young children in rural areas and that the toxicity of the emitted PM from biomass cooking is likely due to soot and the absorbing OC in PM.


Keywords: Biomass fuels, Cooking, Oxidative potential, Organic carbon, Rural areas


1 INTRODUCTION


Particulate matter (PM) in the atmosphere is understood to have adverse health effects (Lelieveld et al., 2015; Jin et al., 2016; Lai et al., 2019; Fushimi et al., 2017). It is a complex mixture of several organic and inorganic chemical species that are emitted from a variety of sources. One such source is solid fuel combustion, a source of primary household energy for over 2.8 billion of people globally (Bonjour et al., 2013). Several studies have reported that biomass burning contributes to 9–50% of the carbonaceous aerosols (Salma et al., 2017; Maenhaut et al., 2005; Heo et al., 2013; Morino et al., 2010), and 3–21% of PM2.5in the atmosphere (Fushimi et al., 2017; Villalobos et al., 2015), especially in the south-Asian region (Venkataraman et al., 2005; Pandey et al., 2014).

Carbonaceous aerosols, especially black carbon, are important climate change drivers due to their light-absorbing nature. OC was earlier understood to be only light scattering, but recent reports suggest that OC also absorbs light in the near-UV-visible wavelengths, these are called brown carbon particles (BrC) (Andreae and Gelencsér, 2006). There are several studies on atmospheric BrC but very limited studies on its emission from biomass fuel burning. Xie et al. (2018) measured light absorption from organic carbon particles and their variability from emissions of different fuels (red oak, charcoal, and kerosene) burned in cookstoves (in a lab setting) using methanol extracts of PM. They calculated the mass absorption cross-section (MAC) values for wood to be higher than other fuels. Meanwhile, Pandey et al. (2016) measured the light absorption of aerosol emitted from four different fuel types (firewood, agricultural residue, dung cake, and coal cake) collected from indoor households. These were done using PM collected on Teflon filters in a double integrating sphere UV-Visible spectrophotometer instrument. The estimated MAC values were 0.1 m2 g1 at 550 nm and 3.1 m2 g1 at 350 nm.

The International Agency for Research on Cancer has categorised indoor biomass smoke as a possible (Group 2a) human carcinogen (2010). Cooking with solid fuel is related to various health effects in women and children, including chronic and acute respiratory conditions (Bruce et al., 2015), inflammation of the airways, pulmonary dysfunction, and reduced antioxidant defence (Arbex et al., 2004; Banerjee et al., 2012; Oluwole et al., 2013; Mukherjee et al., 2014; Gupta et al., 2016). These are accountable for approximately 4 million premature deaths annually (Lim et al., 2012). The inhaled PM can potentially produce or catalyse oxidative reactions (i.e., particle oxidative potential or OP) via the generation of reactive oxygen species and free radicals, and deplete the antioxidant enzymes (Kelly, 2003; Borm et al., 2007; Saffari et al., 2014). The measurement of the OP of PM provides insight into the cumulative effect of PM properties like size, surface, and chemical constituents viz., metals and organics, and thus is a metric for studying the integrated effect of PM toxicity – providing a biologically relevant index of activity. Thus, the oxidative potential of PM is proposed to be a better metric for PM toxicity rather than PM mass and has now gained great importance for the assessment of the toxicity of PM sources (Kurmi et al., 2013; Charrier and Anastasio, 2012; Fushimi et al., 2017). However, only a limited number of studies have investigated the oxidative potential of aerosol emissions from biomass combustion (Kurmi et al., 2013; Jin et al., 2016). Jin et al. (2016) reported that the PM from biomass origins including straw and wood burning and cigarette smoking induced stronger oxidative stress than PM from diesel, coal, and ambient air. Mudway et al. (2005) reported highly oxidizing PM emissions from traditional stoves using cow dung, that were larger than diesel, fly ash, and emissions. Similarly, PM emissions from biomass-based cooking in rural Nepal also exhibited large OP (Kurmi et al., 2013). Rehman et al. (2011) conducted field measurements within rural households using solid biomass for cooking, as well as in the ambient air within the Indo-Gangetic-Plains region of India. They reported peak concentrations of BC (up to 1000 µg m−3) during the early morning and early evening hours, aligning with the cooking activity periods. They reported median values were as high as 60 µg m−3 indoors and 30 µg m−3 outdoors. Few other studies have also reported high BC emissions due to biomass cooking events (Dumka et al., 2019; Pandey et al., 2017; Tobler et al., 2020).

There is a lack of studies that have measured the oxidative potential of PM from biomass-based cooking in rural households, and its association with the chemical and optical properties of PM. To our best knowledge, none have measured the same for actual cooking conditions on the field. Moreover, most of the studies in rural areas have focused on indoor kitchen environments only, and outdoor air pollution characterization is largely not undertaken. Thus, the aim of this study is (a) to measure the OP of fine particles collected from emissions of solid biomass fuel combustion for cooking and its comparison with rural outdoor levels; (b) to characterise PM chemical composition and optical properties for different biomass cooking fuels; (c) to investigate the association of different chemical and optical components of PM on free radical generation and its oxidative potential (d) to estimate the effect of biomass smoke exposure on health.

 
2 METHODOLOGY


 
2.1 Study Design

The measurements were conducted in the Dhule district of Maharashtra, India (Bamhane village, 21.34°N, 74.62°E) (Fig. 1). A household survey was also conducted during the same period, before measurements, to understand the primary and secondary cooking fuel use patterns in four of the villages in the district. Surveys were conducted to cover the entire spatial extent of the villages by selecting a variety of roads/localities in each village - towards obtaining a representative sample size. Measurements were conducted in those households that reported cooking with biomass fuels and that had at least one woman who could participate in a health survey. Most of the villagers cooked twice a day so the measurements were planned accordingly, once during lunch and the other during dinner preparation. Ambient measurements were also taken outside the houses using the same sampler. The traditional cookstove (chulha in Hindi) referred to here is a slightly modified version with an inbuilt vent, commonly called in these areas as ‘Madanchulha’.

Fig. 1. (a) Photographs of the stove used for cooking (b) layout of a household type having a kitchen with a partition (c) layout of a household type having a kitchen without partition.Fig. 1. (a) Photographs of the stove used for cooking (b) layout of a household type having a kitchen with a partition (c) layout of a household type having a kitchen without partition.


2.2 Household Survey

As per the Census of India, 2011, 72% population (1,479,826) of the Dhule district lives in rural areas. However, as it is difficult to visit all the villages, a cube root method was applied to narrow down the number of villages to be visited. Accordingly, 5 out of 141 villages were selected of which 4 villages (Bamhane, Chilane, Hatnur, and Salwe) were visited owing to limitations in resources. The selection of villages was done based on household size and fuel type used for cooking. A detailed survey was carried out which included information on age, nature of job, duration and frequency of cooking, type of primary and secondary fuel used for cooking, etc. Additionally, kitchen dimensions, number of doors and windows, and information on whether the doors/windows were closed while cooking were noted (Table S1).

Further, a health survey was carried out for women, as they are the ones who primarily cook in rural India and spend a lot of time indoors near the cookstoves. Women were asked to self-reported health effects associated with specific types of household fuel use, the duration of the symptoms and whether any medical treatment was required. The consent of participants was obtained before conducting the surveys. The selection criteria for the survey were that women cook and are between 18 to 45 years of age. Questions were asked on whether the women suffer from respiratory health issues, eye irritation, nasal allergies, headaches, heart problems, etc., (Table 1). In total 117 women were interviewed.

Table 1. Risk of developing health problems in women exposed to biomass fuel*.

Data were analysed and summarised as frequencies and percentages using Microsoft Excel and SPSS version 16.0. The association between household, health, and fuel characteristics are expressed as odds ratios (OR) with 95% confidence intervals (CI) (Table 1).

 
2.3 PM Sample Collection

Measurements were conducted from 23rd to 27th March 2017. Eight measurements in different kitchens were conducted during the preparation of morning (lunch) and evening (dinner) hours. The cooking period varied between 40 to 90 minutes. The inlet was placed, at heights varying from 1.2 to 1.8 m, depending upon the heights of the kitchen roofs whilst allowing sufficient time for natural dilution (Roden et al., 2006). Cooking was done using crop residue (tur, cotton stalk, and chilli stalk), firewood (neem, sagoon), and mixed biomass (sagoon + cotton stalk and neem+ cotton stalk) (Table 2). The weights of fuels used for cooking varied from 1.5 to 2.5 kg, depending upon the duration and type of cooking process. Some waste paper or kerosene was commonly used as ignitors. More details on the measurement method may be found elsewhere (Yadav et al., 2022).

Table 2. Cooking and fuel use characteristics and measurements settings in 8 households in Bamhane village.

 
2.4 Monitoring Method

Aerosol particles were collected on filter substrates using a multi-stream sampler (flow rate of 20.2 LPM); PM2.5 was collected on two Teflon membrane filters (47 mm, 2 µm pore size, Whatmann Corp., PA, USA) (at flow rates of 6.2 and 4.5 LPM) and two Tissue Quartz (47 mm, 2.2 µm pore size, Whatmann Corp., PA, USA) (at flow rates of 5.0 and 4.5 LPM). The filters were pre-conditioned and stored as per the protocols described in Yadav et al. (2022). The filters were repetitively weighed (Sartorius, Goettingen, Germany; accuracy of 0.001 mg) before and after collection of PM till three reproducible values were attained. The average of triplicate measurements was used for further calculation. The Teflon filters were also used to measure trace metal concentrations using ICP-OES (Analytik Jena, Plasma Quant PQ9000). However, most of the measured concentrations were below the detection limits (0.16 ppm for Fe, 0.08 ppm for Al, and < 0.02 ppm for other elements including Ag, Ba, Be, Bi, Cd, Co, Cu, Cr, Na, Li, Ni, Mn, Pb, Sr, V, and Z), hence, these are not discussed further in the manuscript.

 
2.5 Chemical Analyses


2.5.1 Black carbon

Following the gravimetric analysis, the Teflon filters were used to calculate black carbon (BC) concentrations using optical transmittance across the filter (OT21; Magee Scientific, USA). It measures the intensity of transmitted radiation through a particle-loaded sample filter relative to the same through a reference blank filter at two wavelengths 880 nm (near-infrared) and 370 nm (near-ultraviolet) of the electromagnetic spectrum. The attenuation (ATN) is calculated as ATN = ln(I0/I) × 100, where I0 and I are the intensities of transmitted radiation through the blank and loaded filter substrates (Ahmed et al., 2009; Presler-Jur et al., 2017). These attenuation coefficients were used to calculate the absorption coefficients (at both the measured wavelengths) using filter-matrix scattering correction factor, and further into black carbon concentrations using the manufacturer recommended mass-absorption cross-section (MACBC,880 = 7.77 m2 g1) (Drinovec et al., 2015). The uncertainties in the calculated BC because of assumed MAC value and filter-matrix artefacts cannot be ruled out. We acknowledge these uncertainties but understand that the conclusions made in the manuscript regarding the relative abundances of BC are likely to remain valid. BC values were corrected using corresponding field blanks.

 
2.5.2 Organic and elemental carbon

Carbonaceous aerosol (OC and EC) concentrations were measured using a thermal optical carbon analyser (DRI Model 2015) that uses the IMPROVE-A protocol (Interagency Monitoring of PROtected Visual Environments).The protocol assigns the carbon dioxide emitted at 120°C as OC1, 250°C as OC2, 450°C as OC3, and 550°C as OC4 in a 100% He atmosphere; and the same at 550°C as EC1, 700°C as EC2, 800°C as EC3 in a 2% O2 and 98% He atmosphere (Chow et al., 2007, 2015). The reflectance of data at the 635 nm wavelength was used for the pyrolysis (charring) correction (OCpy) (pyrolitic carbon).

 
2.5.3 Water soluble organic carbon, ions, and trace elements concentrations

For the measurement of water-soluble ions, the filters were halved and extracted in ultrapure water (25 mL) (Milli-Q system, maintained at 18.2 MΩ cm) by ultrasonication for 60 minutes, and filtered through a 0.45 µm polypropylene syringe filter (Whatman, USA). These were subjected to inductively coupled plasma atomic emission spectroscopy (ICP-AES, ARCOS, Spectro, Germany) to estimate elemental concentrations. Acid (HNO3) extraction was not performed for metal detection as we wanted to study the water-soluble metals (bioavailable) which play a role in the oxidative potential of PM. This extract was analysed for both water-soluble organic carbon using a total organic carbon analyser (TOC-V SCH, Shimadzu, Kyoto, Japan) and the major bulk water-soluble ions (Na+, NH4+, K+, Ca2+, SO42, NO3, and Cl) using ion chromatography (Dionex Aquaion, Thermo Fisher Scientific, USA). Calibration for the TOC analyser used standard sucrose solutions prepared by dissolving ultrapure sucrose (> 99.5%; Sigma-Aldrich) in ultrapure Milli-Q water. The same for the ion chromatography was done using mixed ionic standards of different concentrations (Thermo Fisher Scientific, USA). Fisher Scientific, USA).

 
2.6 Light Absorption Properties

The above extracts were used to measure the light absorption by water-soluble PM using a UV-visible spectrophotometer (path length 1 cm) at wavelengths (λ) from 300 to 800 nm (UV-VIS Evolution 220, ThermoFisher). The solvent background absorption was corrected with a reference cuvette containing ultrapure water. Field blank filters were extracted, analysed, and used for blank correction. These were used to calculate light absorption by WSOC (babs,λ,WSOC, Mm–1) using Eq. (1) (Hecobian et al., 2010).

 

here, Aλ is the measured absorbance at a wavelength and the same at 700 nm is represented by A700. A700accounts for baseline drift and is thus subtracted from Aλ. Vext is the volume of extracted solvent used, Vaero is the sampled air volume, and L is the cuvette path length (here 1 cm). The mass absorption cross section (MACλ) of WSOC was calculated as

  

To calculate the MAC-OC and MAC-WSOC, the OC and WSOC concentrations measured using a thermo-optical method and TOC analyser, respectively, were used.

 
2.7 Oxidative Potential of PM

The oxidative potential of particulate matter was calculated using the acellular dithiothreitol (DTT) assay according to previously reported procedures (Cho et al., 2005; Fujitani et al., 2017). This assay can give a measure of the capacity of PM to generate reactive oxygen species (ROS). The reduction of molecular oxygen to superoxide by DTT is catalysed by PM. The superoxide is then converted to its corresponding disulfide (1,2-dithiane-4,5-diol). The rate of consumption of DTT is proportional (linearly) to the redox activity of PM which is measured using a spectrophotometer. The redox activity in this assay is quantified based on the rate of the DTT consumption that may be expressed in either PM mass normalised (nmol min–1 mg–1) or the volume normalised (nmol min–1 m–3) units (Secrest et al., 2016).

For this analysis, half of a PTFE filter was extracted in 5 mL of ultrapure water by ultrasonication for 60 mins and was used for this analysis. 100 µM of DTT was added in 0.10 M of phosphate buffer (pH 7.4), and its depletion over time was measured while maintaining a temperature of 37°C. At time zero 0.5 mL of the sample was added to 3.0 mL of the DTT phosphate solution in an 8.0 mL glass vial that was shaken continuously in an incubator cum shaker. At specific time intervals (5, 10, 15, 25, 40, and 60 min) an aliquot of 0.50 mL of the reaction mixture was taken out and added to 50 µL of 10 mM of Ellman's Reagent (5,5-dithio-bis-(2-nitrobenzoic acid), DTNB) for two hours at room temperature in the final solution. 200 µL of the reaction mixture was then transferred to 96 well plate reader and the absorption was measured using a UV-Vis-spectrophotometer at 412 nm. Duplicates of the analysis were carried out to ensure repeatability. The limit of detection (LOD) of the assay, defined as three times the standard deviation of blanks (N = 10), is 0.22 nmol min–1. This is within the range of previous studies which reported a range of 0.008 to 0.6 nmol min–1 (Fang et al., 2015; Eiguren-Fernandez et al., 2017; Puthussery et al., 2018; Secrest et al., 2016). For all the chemical and optical analyses, a duplicate analysis of a subset of the filter substrates was performed.

 
3 RESULTS AND DISCUSSION


 
3.1 PM Mass Concentration and Its Chemical Composition

The PM2.5 concentrations from the burning of different biomass fuels ranged from 2777 to 4922 µg m–3. The fuel samples were grouped into three categories, i.e., firewood, crop residue, and mixed biomass to study the emitted chemical composition and its effect in terms of oxidative potential in rural households. The average PM2.5 emitted from firewood, crop residues, and mixed fuels for cooking was found to be 340 ± 91 µg m–3, 2878 ± 1853 µg m–3, and 547 ± 139 µg m–3, respectively (Fig. 2(a)). Crop residue burning had significantly higher PM2.5 mass emissions compared to mixed fuel (p < 0.05) and the lowest emissions were for firewood (p < 0.05) which may be due to different burning rates and combustion conditions (Kaskaoutis et al., 2022; de la Sota et al., 2019; MacCarty et al., 2007) - discussed later in the manuscript. Jayarathne et al. (2018) reported 5400 to 25700 µg m–3 of PM2.5 concentrations from the use of various solid fuels for cooking in Nepal. Kurmi et al. (2013) reported the concentration of inhalable PM as 10400 ± 455 µg m–3 for mixed biomass and 8130 ± 409 µg m–3 for firewood burning emissions in Nepal. These are much higher than the present study. This is likely because the present cookstoves, though traditional, had chimneys (or vents). The average rural ambient concentration was 79.2 ± 6.2 µg m–3, which was approximately 12 times lower than the concentration emitted from solid fuels while cooking indoors, 1.3 times the Indian National Ambient Air Quality Standards (NAAQS) (CPCB, 2009) (60 µg m–3), and 3 times higher than the WHO standards (25 µgm–3). These rural ambient concentrations are much higher than those reported in Europe and USA, which are typically 6–15 µg m–3 (Cheng et al., 2000; Schwarz et al., 2016), approximately 1.5–2 times higher than those in south Indian rural regions, which are between 30 to 50 µg m–3 (Kumar et al., 2018; Bisht et al., 2015), but are lower than the north Indian rural areas which are reported to be between 100–150 µg m–3 (Dey et al., 2012; Kulshrestha et al., 2009; Massey et al., 2009).

Fig. 2. Average (a) PM2.5, (b) its carbonaceous composition for different biomass cooking emissions and ambient rural air (n = 2 for firewood, mixed biomass and ambient air, and n = 4 for crop residue).Fig. 2. Average (a) PM2.5, (b) its carbonaceous composition for different biomass cooking emissions and ambient rural air (n = 2 for firewood, mixed biomass and ambient air, and n = 4 for crop residue).

OC concentrations were lower for firewood emissions (45 ± 5 µg m–3) when compared to those from crop residue emissions (952 ± 1062 µg m–3, p = 0.09) and mixed fuel (532 ± 520 µg m–3, p = 0.06) (Fig. 2(b)). Meanwhile, the average EC concentration was almost similar for firewood (62 ± 2 µg m–3) and mixed fuel (63.3 ± 23 µg m–3) (p = 0.48). But concentrations were much higher for crop residue (533 ± 520 µg m–3) burning emissions in comparison to those from firewood (p = 0.08) and mixed fuel (p = 0.08). The outdoor OC and EC levels in the village were 31.5 ± 20.1 µg m–3 and 7.1 ± 4.2 µg m–3, respectively, which were ~6 (p = 0.07) and 19 (p = 0.05) times lower than the observed indoor concentrations.

Various studies have assessed the levels of carbonaceous aerosol in homes that cook with biomass. In Dhaka, Bangladesh, EC was reported to be 244 ± 51 µg m–3 (Begum et al., 2009). In China, OC concentrations ranged from 86 ± 22 µg m–3 for single cooking sessions, to 332 ± 177 µg m–3 for multiple cooking events (Secrest et al., 2016). Another study in Guizhou, China, found that homes burning coal (11.9 ± 1.9 µg m–3) had higher EC than homes burning wood (7.1 ± 3.0 µg m–3), but conversely, the OC from wood burning was observed to be lower than that from burning coal (70.8 ± 3.5 µg m–3) (Wang et al., 2010).

The measured average OC to EC ratio (Fig. 2(b)) was greater than 1 for crop residue (1.7) and mixed fuel (2.3), indicating the dominance of OC, which may be emitted from smoldering combustion (de la Sota et al., 2019; MacCarty et al., 2007); whereas, it was below 1 for firewood (0.7), indicating a dominance of EC which is produced more during flaming conditions (de la Sota et al., 2019; MacCarty et al., 2007). This suggests that the crop residue in the present study which mostly burned with smouldering conditions is likely affected by physical properties such as moisture and packing density (Zhang et al., 2015). The larger OC to EC ratio from crop residue combustion is also observed in the previous studies (Li et al., 2007; Secrest et al., 2016). Li et al. (2007) reported OC to EC ratio of 11.2 for maize residues. Secrest et al. (2016) reported WSOM to BC ratio of 6.1 for crop residues in China. Venkataraman et al. (2005) reported low BC to OC values for crop residues (0.26 ± 0.09) and wood (0.20 ± 0.07; wood with high burn rate) but the ratio was greater than 1 (2.01 ± 1.3) for low burn rate wood. Shen et al. (2012) reported a smaller OC to EC ratio of wood burning in cookstoves (0.58) in lab conditions when compared to the field conditions (2.4) in Shanxi, China. This large difference between the lab and field conditions indicates the need for more field measurements to understand the nature of the aerosol actually emitted.

The OC and EC fractional composition of PM2.5 is shown in Figs. 3(a) and 3(b), respectively. Some studies report EC1 as char-EC and EC2 + EC3 as soot-EC because these EC fractions dominate the total EC for soot samples (Fushimi et al., 2017; Han et al., 2007). Their ratios can be used as an indicator for source identification (Han et al., 2010). In the present study, the char EC to soot EC ratio is highest for mixed biomass (22.4) followed by firewood (12.8) and crop residue (5.5) which is in accordance with the published literature. Han et al. (2010) reported that vehicular emissions have the lowest OC-to-EC and char-EC to soot-EC ratios; generally < 1, in comparison to biomass burning. Another study reported grass combustion produces a lower char-EC to soot-EC ratio in comparison to wood combustion (Chow et al., 2004; Chen et al., 2007). Fushimi et al. (2017) reported the char-EC to soot-EC ratios from crop residue burned in open fields as 0.49 ± 0.03 (rice straws), 0.15 ± 0.13 (wheat straws), 0.12 ± 0.12 (rice husks), and 5.94 ± 0.06 (barley straws). However, Han et al. (2010) have used char-EC as EC1-PyC same as Han et al. (2007) while Fushimi et al. (2017) have used it as EC1 without the pyrolysis correction. The char-EC to soot-EC ratios for crop residue measured in the present study are comparable to ratios reported for barley straws based cooking by Fushimi et al. (2017). This suggests that the total EC emitted in biomass cooking emissions may not always be dominated by char-EC. Combustion conditions and fuel composition (including moisture) may determine this behaviour (Han et al., 2010). The mean (range) WSOC to OC ratios were 0.69 (0.5–0.7) for firewood, 0.55 (0.08–0.85) for crop residue, and 0.45 (0.42–0.46) for mixed fuel which suggests that the water-soluble organic content was highest for firewood followed by crop residue and mixed fuel. Similar ranges of WSOC/OC have been reported by previous studies at ambient locations influenced by biomass burning (Srinivas et al., 2016; Satish et al., 2020; Kaskaoutis et al., 2022). The average ambient WSOC concentration (16.3 ± 10.2 µg m–3) was ~8 times lower than the average indoor concentrations emitted from biomass fuel used for cooking (129.7 ± 118.3 µgm–3).

Fig. 3. Average (a) OC, and (b) EC fractions in PM2.5 emitted from different biomass cooking fuels and ambient rural air.Fig. 3. Average (a) OC, and (b) EC fractions in PM2.5 emitted from different biomass cooking fuels and ambient rural air.

Carbonaceous aerosols dominated the PM in crop residue and mixed fuel combustion, but ionic species comprised relatively large fractions of firewood emissions. The contribution of the sum of ions (F, Br, NO2, NO3, PO43, SO42, NH4+ Na+, Ca2+, Mg2+, K+, and Cl) was 8.9% for crop residue 9.4% for mixed biomass and 21.3% for firewood. Although the total ionic concentration was higher for firewood, the contributions of K+ (often used as a marker for biomass burning) and Cl were highest for crop residue burning emissions, 2.9% and 4% respectively; for mixed fuel 1% and 2.9%, respectively; and for firewood 0.6% and 1.6% respectively (Fig. S1).


3.2 Light Absorption Properties

Fig. 4 shows the spectral absorption coefficient (babs) and MAC of water-soluble aerosols emitted from solid biomass fuel for different fuel categories (firewood, crop residue mixed fuel) along with rural ambient samples. The burning of firewood (MAC365 = 17.4 ± 5.21 m2 g–1) generated the strongest light-absorbing aerosols among the three fuels, which was greater than that of mixed fuel (5.6 ± 3.1 m2 g–1, p = 0.08) and crop residue (4.8 ± 2.2 m2 g–1, p = 0.07). Meanwhile, MAC365 for rural ambient aerosol was 1.5 ± 0.8 m2 g–1. Kim Oanh et al. (1999) reported that the combustion of wood generated relatively more aromatic compounds than charcoal and these aromatic compounds absorb UV-visible radiation more strongly. Xie et al. (2018) also reported higher MAC365 for wood (5.09 ± 3.47 m2 g–1) in comparison to charcoal (2.23 ± 1.17 m2 g–1) and kerosene (2.15 ± 0.93 m2 g–1). They analysed both front and backup filters for light-absorbing carbon and found that the MAC values of back filters were lower than the front filters. This suggests that low volatile components deposited on the front filter absorb more strongly than semi-volatile organics adsorbed on back filters. Pandey et al. (2016) reported MAC350 values for OC for different types of fuels as 4.4 (1.3–12) m2 g–1 for firewood, 3.6 (1.1–9.4) m2 g–1 for crop residue, 3.4 (2–5.4) m2 g1 for cow dung, and 4.1 (2.5–6.4) m2 g–1 for mixed fuel. Fig. S2 presents the spectral dependence of MAC OC for different solid biomass fuels used for cooking in the present study; these are higher than those reported by Pandey et al. (2016). There are limited studies on the optical properties of PM in ambient rural regions of India and those emitted from biomass fuel burning in rural household settings. The rural ambient MAC365 was found comparable with reported urban values at Mumbai, Kanpur, Delhi, and Kolkata in India, ranging from 0.95 to 1.8 m2 g–1 (Sarkar et al., 2019; Choudhary et al., 2017; Kirillova et al., 2014; Rana et al., 2020). Srinivas et al. (2016) reported ambient MACBrC as 1.3 ± 0.7 m2 g–1 from a source region of biomass burning emissions (BBEs) in Patiala, India. However, solid fuel burning aerosols can produce stronger light-absorbing OC, which should be considered in health studies as well as chemical transport models, as residential emissions contribute significantly to global OC (Xie et al., 2018; Bond et al., 2013, 2004; Cao et al., 2006).

Fig. 4. Spectra representing the (a) absorption coefficient, babs, and (b) MAC of the WSOC extracted from the aerosols from solid biomass cooking. Fig. 4. Spectra representing the (a) absorption coefficient, babs, and (b) MAC of the WSOC extracted from the aerosols from solid biomass cooking.


3.3 PM Oxidative Potential

The oxidative potential may be expressed in volume or mass-normalized units. The volume normalized unit is described as nanomole DTT consumed per minute per volume of air (DTTv, OP m–3) and the mass normalized is described as nanomole DTT consumed per minute per µg of PM2.5 (DTTm, OP µg–1). In the present study, volume-normalized acellular oxidative potential of PM2.5 emitted from crop residue was highest (31.6 ± 13.2 nmol min–1 m–3) followed by firewood (26.7 ± 2.9 nmol min–1 m–3) and mixed biomass (20.2 ± 9.5 nmol min–1 m–3). While the DTTv for ambient PM was 2.2 ± 0.6 nmol min–1 m–3 (Fig. 5). This difference in the OP for different fuel categories is likely due to the differences in their chemical composition and combustion conditions. Indoor (household biomass burning) DTTv in the present study is much higher than that for ambient aerosol in previous studies, ranging from 0.2–1.3 nmol min–1 m–3 (Charrier and Anastasio, 2012; Fang et al., 2015; Hu et al., 2008; Ntziachristos et al., 2007; Verma et al., 2009; Cho et al., 2005). However, the DTTv values from the present study are lower than those associated with emissions from trash burning, ranging from 98 to 3510 nmol min–1 m–3 and DTTm ranging from 1–68.2 pmol DTT min–1 µg–1 OC (Vreeland et al., 2016). Secrest et al. (2016) reported DTTv activity as 5.32 nmol min−1 m−3 (range: 0.725–12.5) in inner Mongolia (mixed fuel site) and 2.71 nmol min–1 m–3 (range: 1.95–3.28 nmol min–1 m–3) in Sichuan (biomass burning dominated site). The corresponding DTTm (Fig. S3) from the present study is higher for biomass cooking emissions (2–5 times) than for rural ambient aerosols. The DTTm was lowest for mixed biomass (8.4 ± 2.2 nmol DTT min–1µg–1) samples and there was no significant difference between the DTTm of fuel wood (17.8 ± 8.7 nmol DTT min–1 µg–1) and crop residue (15.8 ± 9.9 nmol DTT min–1 µg–1). Meanwhile, the ambient DTTm was 1.2 ± 0.4 nmol DTT min–1 µg–1. DTTm measured in the present study is higher than those from studies of biomass burning measurements (straw: 0.08 ± 0.002, wood: 0.06 nmol DTT min–1 µg–1) (Jin et al., 2016), open burning of crop residues (0.009–0.030 nmol DTT min–1 µg–1) (Fushimi et al., 2017), wildfire smoke (0.013–0.023 nmol DTT min–1 µg–1) (Verma et al., 2009), and atmospheric fine particles (0.02–0.10 nmol DTT min–1 µg–1) (Saffari et al., 2014; Verma et al., 2009).There are no household biomass fuel cooking studies in India to compare the present measurements with.

Fig. 5. Oxidative potential (measured as average volume normalised DTTv activity) of PM2.5 from firewood (n = 2), crop residue (n = 4), mixed biomass (n = 2) cooking fuels and ambient air (n = 2).Fig. 5. Oxidative potential (measured as average volume normalised DTTv activity) of PM2.5 from firewood (n = 2), crop residue (n = 4), mixed biomass (n = 2) cooking fuels and ambient air (n = 2).


3.4 Association between Oxidative Potential and PM Properties

The DTT activity of PM is associated with most chemical components in various studies, including polycyclic aromatic hydrocarbons (PAHs), humic-like substances (HULIS), quinines, transition metals, and WSOC (Karavalakis et al., 2017; Lin and Yu, 2011; Liu et al., 2014; Saffari et al., 2014); these were also observed in the present study (Fig. 6).The oxidative potential (DTTv) correlates positively with EC1, OC, WSOC, babs,365,WSOC, BC, and babs,370. The correlation is significant with EC1 (R = 0.83), BC (R = 0.84), and WSOC absorption (babs,365,WSOC) (R = 0.79) which suggests that these are the components which are most responsible for the OP of PM emitted from solid biomass cooking. The significant correlation (R = 0.68) between WSOC and babs,365,WSOC in the present study indicates that the biomass burning PM has soluble absorbing chromophores which likely play an important role in oxidative potential of PM. Such high correlations were also observed at ambient locations and periods primarily influenced by biomass burning aerosol particles (Paraskevopoulou et al., 2023; Rana et al., 2020), also indicating that primary biomass burning WSOC emissions are absorbing in nature. Further, EC1 and babs,365,WSOC show a significant positive correlation (R = 0.93) with each other, which suggests that EC1(or char-EC) may be understood as brown carbon close to the soot end of the black-brown carbon absorption continuum (Saleh et al., 2018), influences DTTv. These brown carbon particles, with low volatility, are found to be the most absorbing (Saleh et al., 2018) and may directly contribute to OP or be conducive to the adsorption of OP active species on them. These results suggest that EC, BC, and absorbing species in WSOC have a major impact on oxidative potential which is in accordance with a recent study conducted by Li et al. (2019). They evaluated the OP of BC by adding DTPA to inhibit DTT activity caused by metals and found that BC in PM can lead to high oxidative stresses. They also reported that diesel exhaust BC has the greatest OP followed by biomass and coal burning. Other studies have also reported that model BC has strong DTT activity in cell-free systems (McWhinney et al., 2013; Ntziachristos et al., 2007).

Fig. 6. Correlation matrix of chemical constituents and oxidative potential (DDTv) measured in PM2.5 from various biomass fuels in rural India. Note Dots represent statistically significant correlations p < 0.05.Fig. 6. Correlation matrix of chemical constituents and oxidative potential (DDTv) measured in PM2.5 from various biomass fuels in rural India. Note Dots represent statistically significant correlations p < 0.05.

In contrast, OP showed insignificant or negative correlations with the sum of ions and almost all individual ions except nitrite and calcium. The trace element concentrations were below the detection limit and hence their contribution to oxidative potential could not be determined. Fushimi et al. (2017) reported negative correlations between OP and transition metals (except Mn) and WSOC of emissions from open burning of crop residue. Overall, in the present study we observed some differences in the oxidative potential from PM emitted from various biomass fuels used for cooking. But, considering the relatively small number of measurements, these differences have low confidence. Additional measurements, with some control variables, are needed to make stronger conclusions about the same. Nevertheless, the present study provides important insights into the toxicity of solid cooking fuel emissions and the chemical components therein responsible for the observed toxicity.

 
3.5 Household Characteristics, Fuel Usage, and Respiratory Health of Surveyed Participants

The surveys in the Dhule district showed that two types of stoves were predominantly used in the villages surveyed throughout the year: traditional stoves (63.2%) and liquefied petroleum gas (LPG) stoves (36.7%) (Table S1). Amongst the fuels primarily used for cooking, firewood is the preferred fuel (47.8%) followed by crop residue (39.3%) and mixed fuel (11.9%, crop residue and firewood). 63.9% of households have a secondary cooking stove. However, the surveys (47 households) in Bamhane village, where the measurements were carried out, showed that 51% households used traditional stoves for cooking and the rest used LPG stoves. Among the traditional stove users, 63% of households used firewood, 16% used crop residue, and the remaining used mixed fuels. To light the fire in the traditional stove, igniters such as kerosene, paper, plastic wrappers, and onion peels were used. Lunch was mostly prepared between 8–9:30 AM and dinner between 7–7:30 PM. During lunch, the meals cooked were chappatis, a vegetable curry, kadhi, and papad which were considered baking, stir-frying, boiling, and roasting activities, respectively. During dinner, the common type of meal cooked was khichdi (a mix of rice and lentils), considered a boiling activity (Table 2). Lunch consisted of more items as it was the meal for the day (breakfast and lunch together), whereas dinner consisted of one or two items. Meaning, meal preparation times were also longer for lunch than dinner.

Table 1 summarises the self-reported health conditions faced by women as respiratory issues (wheezing and whistling noise while breathing, rapid breathing, shortness of breath, coughing attack, asthma, nasal allergy), cardiovascular issues (tightness in chest, heart problems), ophthalmic conditions (watery eyes, eye irritation), headache, dizziness, etc. Headache, dizziness, and watery eyes were the most common health problems reported. Headache was significantly associated with exposure to biomass cooking (OR = 3.4; CI: 1.5–7.5; p < 0.01). Wheezing and whistling noises while breathing (OR = 20; CI: 0.9–429; p < 0.03) and shortness of breath (OR = 24; CI: 1.1–505.1; p < 0.02) were also significantly associated with biomass cooking but were highly skewed. Shortness of breath was prevalent in women using solid fuel for cooking. Thus, women with respiratory symptoms had twenty and twenty-four times respectively higher risk of being exposed to biomass fuel emissions compared to those without these symptoms. Similar reports of biomass combustion exposure influencing respiratory symptoms in women are also reported in India and Mexico (James et al., 2020; Sana et al., 2018; Sinha et al., 2015). We found that the ophthalmic conditions reported in women cooking with solid biomass fuel had 1.5 times more chances of these conditions than non-exposed women, but it was not significant. Similar results were reported in Karachi, Pakistan (Kaz, 2018). In addition, other studies have reported ophthalmic conditions to be affected by biomass emissions exposure (James et al., 2020; Ravilla et al., 2016). These studies have reported coughing, tightness in the chest, wheezing and whistling noise, and rapid breathing as the prevalent health issues.

In summary, biomass fuels are used for cooking by more than two-thirds of the households in the district. It was found to be significantly associated with self-reported symptoms of headache and a few respiratory symptoms like wheezing, whistling noise while breathing, and shortness of breath. Therefore, these households in rural India may require sustained public health involvement and regular health education. Limitations of this health survey study include that only a subset of the population was studied due to logistic constraints and that the self-reported symptoms by the women were not verified using clinical diagnosis. Also, a recall bias may be present in the responses of women on questions pertaining the long-term fuel use and time spent cooking.

 
4 CONCLUSIONS


The study presents field measurements of particulate matter and its oxidative potential during actual cooking events in rural households using biomass fuels and provides insight into rural indoor as well as rural ambient PM characteristics which are largely unexplored. The PM emitted from biomass cooking was predominantly of carbonaceous nature, which was 6–19 times higher during cooking periods compared to the ambient concentrations.

Average indoor levels of PM2.5, OC, EC, BC, and WSOC were observed as 1025 ± 1001, 203 ± 196, 140 ± 133, 112 ± 61, and 130 ± 118 µg m–3, respectively, during biomass cooking. Furthermore, the oxidative potential (DTTv) of PM from the biomass cooking, was measured as 25.3 ± 10.2 nmol DTT min–1 m–3. Correspondingly, an order of magnitude higher oxidative potential was observed during cooking periods, which are likely influenced by light-absorbing carbonaceous particles. Together with the environmental health surveys, our findings suggest that exposure to PM from biomass cooking may pose a substantial risk to a large rural population, especially to the women and young children present inside the house while cooking. The study also quantified differences between aerosol emissions from different fuels. These are with the caveat of a limited number of measurements and an absence of constant control variables. Nevertheless, the present study quantifies and compares the toxicity of emissions of various types of biomass cooking fuels in real-world cooking practices/processes, where maintaining control variables is difficult. Nevertheless, since biomass cooking fuels, as well as the type of cooking, can vary across regions and the indoor PM levels can be affected by kitchen ventilation, additional studies are warranted with greater representation of cooking fuel types, cooking activities, kitchen types, kitchen sizes, and ventilation characteristics.

 
ACKNOWLEDGEMENTS


The authors like to acknowledge partial financial support from IRCC, IIT-Bombay (Grant no. RI/0316-10001353-001) and NCAP-COALESCE project (National Carbonaceous Aerosols Programme, Ministry of Environment Forests and Climate Change, India (Grant No. 14/10/2014-CC,Vol.II). The views expressed in this document are solely those of the authors anddo not necessarily reflect those of the Ministry. The Ministry does not endorse any products or commercial services mentioned in this publication. SY also acknowledge the institute postdoctoral fellowship from IIT Bombay. The authorsalso thank Ms D. Sarika,Mr Rohan Rane and Mr Nirav Lekinwala for PM indoor monitoring and conducting household questionnaire surveys in the villages.


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