PM_2.5 exposures increased for the majority of Indians and a third of the global population during COVID-19 lockdowns: a residential biomass burning and environmental justice perspective

Following the emergence of COVID-19 in early 2020, many countries ordered nationwide lockdowns and mandatory stay-at-home orders to slow its spread. In India specifically, the COVID-19 lockdown lasted from 23 March to 31 May 2020, halted industrial and commercial activities, restricted private and public transportation, and required all of its 1.33 billion residents to stay-at-home. As a result, PM_2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers) emissions and its precursors from industrial activity, commercial activity, and transportation were largely mitigated, which coupled with favorable meteorological conditions, resulted in historically improved ambient air quality [1, 2].

Exposure to high levels of ambient pollution is one of the leading causes of premature mortality worldwide leading to an estimated 6.6 million mortalities globally each year [3], and the Global Burden of Disease (GBD) estimates that 95% of all premature mortality associated with air pollution exposure is attributed to PM_2.5 [4, 5]. In India, the GBD reports exposure to ambient PM_2.5 is responsible for 0.98 million premature deaths annually, and separately estimates that household PM_2.5 exposure contributes an additional 0.61 million annual premature deaths [6]. Accurately and comprehensively assessing personal exposures has been an ongoing area of research [7], and although novel approaches to directly measure personal exposures that can capture microenvironment PM_2.5 levels have been introduced recently [8], these strategies have not yet been scalable for population-wide assessments. Thus, accurately assessing PM_2.5 exposures on population-wide scales and subsequently implementing strategies to reduce exposures can contribute to improved health outcomes.

Residential biomass burning, a common practice for cooking (and heating, lighting, and waste reduction) globally, has been implicated as the leading source contributor to indoor and ambient PM_2.5 levels in India [9–11]. During lockdowns, while emissions from the industrial, commercial, and transportation sectors were mostly mitigated, household cooking activity and subsequent emissions still occurred and likely at a larger volume as individuals whose countries imposed lockdowns were mandated to stay-at-home. In addition, considering that household air pollution levels in households that use biomass cookstoves are generally higher than ambient concentrations [11], might PM_2.5 exposures have increased for populations that utilize biomass and were mandated to stay-at-home during COVID-19 lockdowns?

Here, we first model pre-COVID-19 lockdown (herewith referred as baseline exposure) and COVID-19 lockdown PM_2.5 exposures specific to each Indian state's urban/rural populations by gender and age. To estimate baseline exposures, we combined fine-scale PM_2.5 concentration estimates in various microenvironments (household [11], ambient [12], work/school [13–15], etc) with the most-detailed time-use survey data in India (n = 445 170), specific to each state's urban/rural populations by gender and age using a robust, Monte-Carlo exposure framework. Exposures during the COVID-19 lockdown were modeled using the same time-use survey and Monte-Carlo framework (where all time was spent in the household microenvironment) while considering an increase in home cooking activity for meals that previously were prepared and consumed outside of the house. We then extend this framework globally by combining primary cooking fuel use data [16] and lockdown status of each country to conservatively estimate the global population that experienced increased PM_2.5 exposures during lockdowns.

2.1. Estimating baseline PM_2.5 exposures for each Indian state's urban/rural populations by gender and age utilizing microenvironment PM_2.5 concentrations and time-use survey data

Household (both in the kitchen and living area) PM_2.5 concentrations were estimated using a semi-empirical modeling framework outlined in detail in Balakrishnan et al [11]. Briefly, their log-linear, India-specific model first predicts 24 h average PM_2.5 concentrations in the kitchen, $K$, as a function of multiple, independent, household-level variables, I, (e.g. fuel type (kerosene, dung, wood, and/or liquefied petroleum gas (LPG)), kitchen type/location (separate outdoor kitchen, indoor with partition kitchen, indoor without partition kitchen, or outdoor kitchen), ventilation status (good, moderate, or poor), hours spent cooking, and geographic region (east, west, south, or north)) as:

$$\begin{align} K\left\{ {{\text{log}}\left( {{\text{P}}{{\text{M}}_{2.5}}} \right)} \right\}& = \;{\beta _0} + {\beta _{f\,1}}{I_{{\text{Kerosene}}}} + {\beta _{f\,2}}{I_{{\text{Dung}}}}\nonumber\\ & \quad + \;{\beta _{f\,3}}{I_{{\text{Wood}}}} + {\beta _{f4}}{I_{{\text{LPG}}}} + {\beta _{k1}}{I_{{\text{SOK}}}}\nonumber\\ & \quad + \;{\beta _{k2}}{I_{{\text{IWPK}}}} + {\beta _{k3}}{I_{{\text{IWOPK}}}}\nonumber\\ & \quad + \;{\beta _{k4}}{I_{{\text{ODK}}}} + {\beta _{V1}}{{\text{I}}_{{\text{Good}}}} \nonumber\\ & \quad+ {\beta _{V2}}{I_{{\text{Moderate}}}} + {\beta _{V3}}{I_{{\text{Poor}}}}\nonumber\\ & \quad + \;{\beta _{ch}}{I_{{\text{Cooking}}{} {\text{hours}}}} + {\beta _{r1}}{I_{{\text{East}}}}\nonumber\\ & \quad + \;{\beta _{r2}}{I_{{\text{West}}}} + {\beta _{r3}}{I_{{\text{South}}{} }} + {\beta _{r4}}{I_{{\text{North}}}} \end{align} \tag{ 1 }$$

where the $\,\beta$ coefficients are fitting parameters derived in their study (listed in SI table 1). The primary fuel type usage rates for each states' urban and rural populations was retrieved from India's Time Use Survey 2019 data (SI table 2). Fuel stacking, using a secondary fuel in addition to a primary fuel, is a common [17, 18], yet mostly unquantified practice nationwide. We utilize microdata from India's Residential Energy Survey (2020) [19] and household expenditure survey conducted by the National Sample Survey Office (2014) [20] to quantify the frequency at which primary LPG-households use biomass as a secondary fuel (SI table 3). The National Sample Survey Office (NSSO) survey was conducted before recent LPG initiatives were introduced in Indian households that resulted in the proportion of Indian households using LPG to increase from 56% to 89% [19]. Despite the improved access to LPG, it has been previously assessed that the vast majority (∼86%) of households under these schemes still use biomass fuels [21]; this penetration rate was applied to the NSSO reported fuel stacking data. Ventilation and kitchen typology data were obtained from Drinking Water, Sanitation, and Hygiene Survey (2012) [22] and Census of India (2011). Hours spent cooking were provided from Time Use Survey Data (2019) [23]. We assume all datasets that did not occur in 2020 scaled linearly from their respective base year to 2020 demographics, i.e. by population.

Balakrishnan et al [11] then modeled living area 24 h average PM_2.5 concentrations, $L$, as:

$$\begin{align}L = 0.147\, \times \,{K^{\,0.32}}.\end{align} \tag{ 2 }$$

Their [11] analysis of 617 households was only conducted in rural homes, but we assume the same regressions apply to urban households for this study, consistent with their nationwide household exposure approach. In addition, their regression equations estimate 24 h average PM_2.5 concentrations; the time spent in the kitchen is likely during more-polluting cooking events, suggesting our household exposure estimate used here is likely conservative.

To represent ambient PM_2.5 concentrations for each Indian state's urban and rural populations, we used spatially-resolved (1 km × 1 km resolution), satellite-derived, surface-level PM_2.5 concentrations that were calibrated with Central Pollution Control Board monitoring sites as described further in Dey et al [12]. Here, we took the five-year average concentration between 2015 and 2019 as a baseline ambient PM_2.5 concentration (the five-year averaging time is consistent with other COVID-19-related air quality studies that assessed baseline, ambient concentrations [24–26]) for each state's urban and rural populations, where the urban/rural grid classification was adopted from Balk et al [27] (SI figure 1 for map of urban/rural grids). The final microenvironment that was characterized was the work/school environment, where PM_2.5 concentrations were estimated using annual-average work/school to ambient PM_2.5 concentration correction factors from previous microenvironment exposure studies specific to India [13–15].

The time-use survey was a survey of 445 170 participants throughout India conducted in 2019 by the Ministry of Statistics & Programme Implementation. Survey participants documented the time they spent conducting various (out of 158) activities (e.g. cooking, eating, work/school, sleeping, commuting, etc) over a 24 h recall period. The survey was conducted in 82 897 rural and 55 902 urban households with respondents from each state's male and female populations across age groups (we separated ages into 17 brackets (0–5, 5–10, ..., 75–80, 80+; SI table 2). The 158 monitored activities were grouped as household (kitchen or living area), outdoor, or at work/school to align with microenvironments where PM_2.5 concentration estimates were available. Recall surveys have well-documented limitations associated with recall bias, particularly when assessing microenvironment exposures from solid-fuel use [18, 28]. Here, we do not directly quantify uncertainties with individual responses, but rather incorporate the distribution of responses from each state's subpopulation groups (e.g. urban/rural, gender, age) when modeling PM_2.5 exposures, as described next.

There is uncertainty in each of the microenvironment PM_2.5 concentrations as well as with the 24 h recall surveys as applied in this exposure framework. We utilize a robust, Monte-Carlo framework (n = 100 000; consistent with the number of simulations in previous PM_2.5 exposure studies [29, 30]) that incorporates uncertainties/distributions from both microenvironment PM_2.5 concentrations and the 24 h recall surveys to estimate average PM_2.5 exposures and 95% distributions for each Indian state's subpopulation groups (e.g. urban/rural, gender, and age; see SI section 1 for a detailed example of this framework for rural Uttar Pradesh women aged 30–35). The PM_2.5 exposure approach outlined here assumes all urban or rural populations in a state have the same ambient exposure distribution for all outdoor activities, respectively, which has inherent misclassification errors, particularly considering the wide range of PM_2.5 concentrations observed in Indian cities at such fine scales [31]. Further, this approach assumes the surveyed population in a state represents the same activities conducted by other individuals within that state and subpopulation (e.g. urban/rural, gender, and age).

2.2. Increased residential biomass burning activity during the COVID-19 lockdown and subsequent PM_2.5 exposure estimates

2.3. Global population estimate of PM_2.5 exposure increases during the COVID-19 lockdown

During India's COVID-19 lockdown, 65% of the population (the percent of the population that uses biomass as either a primary or secondary cooking fuel) experienced higher modeled PM_2.5 concentrations relative to baseline exposures, with a population-wide average increase of 13.1% (95% distribution: 8–26) (from 115.6 (82–157) to 130.8 (104–170) μg m⁻³) (table 1, figure 1 and SI figure 2). The demographic groups that experienced the highest relative modeled PM_2.5 exposure increase during the lockdown were working-age (20–65 years) men and school-age (5–20 years) children, whose average exposure increased by 24.0% (18–48) (from 87.6 (63–117) to 108.3 (94–139) μg m⁻³) and 18.2% (8.1–30.6) (from 97.6 (75–135) to 115.1 (98–145) μg m⁻³), respectively. Although rural women, on average, had the lowest relative modeled PM_2.5 exposure increase among the various demographic groups (e.g. urban/rural, gender, age), they still had the highest exposures during the lockdown at, on average, 163.1 (117–225) μg m⁻³. Geographically, the largest modeled exposure increases occurred throughout South India where baseline exposures were lowest (figure 1 and SI figure 2). Indians in the Indo-Gangetic Basin, which is known for being the most polluted airshed in India [32], had the lowest modeled PM_2.5 exposure increases during the lockdown.

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Table 1. Average Indian PM_2.5 exposures (μg m⁻³) during the baseline and COVID-19 lockdown periods and PM_2.5 exposure percent change between the two periods for each subpopulation group (urban/rural, gender, age). The values in parenthesis represent 95% exposure distributions within the subpopulation group.

During the baseline period, adult, rural females had the highest average modeled PM_2.5 exposures, which was expected considering 86% of rural households use biomass as either a primary or secondary cooking fuel (SI table 3), which results in elevated household PM_2.5 concentrations (SI table 4), and that the average adult, rural female spends 3.5 h in the kitchen environment each day, considerably higher than their male counterparts (SI table 5). By age, children <5 years old had the highest average exposures of all age groups both during the baseline and lockdown periods, which was anticipated as most of their baseline time (17.5 h) is spent at home, presumably in close proximity to their mother (kitchen area exposure). Men of all ages (except <5 years) had nearly uniform modeled average PM_2.5 exposures in both urban and rural India, respectively, during the lockdown (Urban: 93–96 μg m⁻³; Rural: 115–117 μg m⁻³; table 1), suggesting Indian men of all ages spent roughly the same amount of time in the kitchen and the living area during the lockdown for urban and rural populations, respectively. For Indian women, working-age ones experienced the highest modeled PM_2.5 exposures during the baseline and lockdown periods; the time-use survey data showed they are largely responsible for much of the household cooking demand during baseline periods (3.5 h, on average, compared to 0.4 h combined for all other subpopulations) and during the lockdown.

Household PM_2.5 concentrations were substantially higher than ambient concentrations across most states for both urban and rural populations with rural populations having higher levels of household PM_2.5 pollution than urban households, on average (SI figure 3 and SI table 4). Household PM_2.5 concentrations modestly increased during the lockdown from baseline levels (urban kitchen area = 0.84% increase, urban living area = 0.34%, rural kitchen area = 0.73%, rural living area = 0.22%; SI table 6), consistent with the slight 3.9% and 2.0% increase in cooking activity in urban and rural homes, respectively. The cleanest environment is the work/school environment, where school children and working-age men spend 3.9 and 4.0 h per day on average; by comparison, the time-use data found working-age women spend 1.2 h of their average day in those environments. Using time-use survey responses that provide insights on indoor and outdoor exposures, the population-weighted average PM_2.5 exposure found here is 115 (82–157) μg m⁻³, ∼30% higher than the 91.7 μg m⁻³ ambient, home-based estimate over India reported by the GBD [6].

Extending this framework globally, where households that use biomass fuels for cooking will have experienced increased PM_2.5 exposures during lockdowns due to increased cooking activity and more time spent indoors, we conservatively estimate that 34.5% (21–51) of the global population experienced increased PM_2.5 exposures during lockdowns, largely confined to developing regions with high biomass for cooking utilization rates (figure 2).

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The COVID-19 lockdowns, which were observed by most countries around the world, offered a unique natural experiment to assess air quality impacts from a near-entire mitigation of emissions from the industrial, commercial, and transportation sectors. Despite the resulting historic improvements in ambient air quality, PM_2.5 exposures increased for the majority of Indians and a third of the global population, attributed largely to biomass burning as fuel for cooking. Given that residential biomass burning has also been implicated as the leading source contributor to ambient PM_2.5 in India, where air quality standards exist, the Indian government has introduced various initiatives to promote clean cooking (e.g. Pradhan Mantri Ujjwala Yojana (PMUY) and Unnat Chulha Abhiyan (UCA)). These initiatives, which have targeted low-income, rural households, have had tangible success in some areas, but require better strategies to achieve the widespread clean cooking adoption that was desired, including addressing shortcomings associated with inadequate infrastructure, user hesitancy, dropout, unreliable LPG supply/refills, etc [21, 33].

In addition, residential biomass burning for cooking and heating has exacerbated PM_2.5 exposure disparities in non-lockdown times throughout India [31, 34]—with most of the burden on women and rural populations that use biomass as cooking fuel. Surprisingly, during the COVID-19 lockdown when families of similar strata were confined to presumably similar indoor environments, exposure disparities persisted, both in terms of actual PM_2.5 exposures as well as increases from baseline exposures. During India's lockdown, women were still responsible for meal preparation, resulting in 3.0 h, on average, more in the kitchen environment compared to men (SI table 5), which explains their 24.3% (6–35), on average, higher modeled PM_2.5 exposure compared to men (table 1). Working-age men and school-age children experienced the highest exposure increase during the lockdown despite a lower overall exposures compared to other subpopulations. During the baseline period, these groups spent more time in the work/school and ambient environments (SI table 5), which are much less polluted than household concentrations, on average (SI table 4).

Baseline PM_2.5 exposures, as assessed here, provides the first-of-its kind, nationwide exposure estimate for India that incorporates detailed time-use data in various microenvironments among subpopulations. As a result, the exposure estimates found in this study cannot explicitly be evaluated; however, the indoor modeling framework utilized here was built on real-time measurements in Indian homes and the ambient satellite data was rigorously calibrated and fused with India-specific observational data. Recent research in India has determined ambient PM_2.5 concentrations are similar in urban and rural areas [12, 35], but by utilizing time-use data, we find PM_2.5 exposures are higher in rural (127 (91–173) μg m⁻³) compared to urban (93.7 (66–125) μg m⁻³) populations, again driven largely by higher rates of residential biomass burning in rural households (86% of rural households use biomass as either a primary or secondary fuel compared to 23% of urban homes), which cannot be explicitly accounted for in ambient-only exposure studies. In addition, we find that none of any Indian state's subpopulation group's average PM_2.5 exposure meets the 40 μg m⁻³ annual national ambient air quality standard (the lowest exposure group is urban A&N Islands men aged 35–40 at 51.3 (26–69) μg m⁻³; the lowest mainland exposure group is urban Tamil Nadu men aged 25–30 at 59.5 (43–81) μg m⁻³).

Globally, ∼2.7 billion people, mostly concentrated in low-income regions, use various forms of biomass fuels (e.g. wood, charcoal, crop residue, dung, etc) for cooking [16, 36]. Emphasizing the global health burden associated with exposures to these emissions, McDuffie et al [37] found that 20% of the global mortality burden from ambient PM_2.5 exposures is attributed to solid-fuel consumption for residential cooking and heating. There have been a number of interventions, both from public and private entities, to address clean cooking (and heating) around the world, but the findings presented here (and in other regional COVID-19 PM_2.5 exposure assessments [38–42]) that PM_2.5 exposures increased despite historically clean ambient air quality during COVID-19 lockdowns, underscores the urgent need to further address clean cooking (and heating) interventions to achieve improved air quality and subsequent human health benefits.

The findings in this paper should not distract from the millions of COVID-19 induced deaths. We thank Cesunica E Ivey at UC-Berkeley for helpful discussion at the onset of this work. Eric A Kort at the University of Michigan and Kangkang Tong at Shanghai Jiao Tong University provided valuable feedback to the manuscript pre-submission. Ganesh Gupta at IIT-Bombay assisted with GIS plotting. All datasets used in this study are publicly and freely available via email at nagpureajay@gmail.com or by download at vapimodel.blogspot.com. No direct funding sources are responsible for this research. The authors declare no competing interests.

The data that support the findings of this study are openly available at the following URL/DOI: http://vapimodel.blogspot.com/.