Study population
This investigation was conducted in a densely populated (35,200 people/km2, 2011) low-income area of Mirpur, Dhaka, Bangladesh, where the type of cooking fuel used is heterogeneous [3]. Mirpur homes are typically arranged in compounds, with several one-room homes immediately adjacent to each other, a shared latrine, and a small central courtyard or walkway; compounds are often adjacent to other compounds with shared walls (Fig. 1).
Eligibility criteria
We recruited clusters of index and neighbor homes. Index homes exclusively used biomass fuels (dung, crop residue/grass, rice husk, dried leaves, coal, charcoal, wood, and/or bamboo) and a traditional stove for cooking. Only one index home was recruited per compound. Once the index home was identified, we recruited three to four neighbor homes, defined as homes immediately surrounding the index home in any direction that primarily used one of the following cleaner fuels for cooking: natural gas, biogas, electricity, and/or kerosene. Index and neighbor homes located on an upper level (above ground level) of an apartment building were ineligible, because of potential variation in sources, levels, and movement of air pollutants on upper levels of a multi-story building compared to ground level. Eligible respondents intended to reside in their current home for the subsequent one week. If more than four neighbor homes were eligible, we prioritized recruiting those neighboring homes that had main entrances nearest to the main entrance of the index home. It is possible that other nearby homes that we did not recruit may have used biomass-burning stoves.
Once eligible index and neighbor homes were identified, we sought informed consent from the adult who usually cooks in each household. As many participants were unable to read, a consent form was read aloud to them, and they were asked to either sign or provide a thumbprint as a mark of consent. The first ten clusters of households (index and neighbor homes) visited that met all eligibility criteria and consented to participate were enrolled. The study protocol was approved by the institutional review boards at icddr,b and the University at Buffalo. No interventions were performed, therefore this is not registered as a trial.
Data collection
After obtaining written informed consent, study staff administered a questionnaire to each participant to elicit information on demographics, stove type and location, and behaviors that may affect exposure to air pollution, including cigarette smoking, ventilation behavior (opening and closing windows and doors, turning fans on and off), cooking fuel use, and burning substances other than for cooking. The participant was then asked to show his/her home and cooking space. Study staff measured the size of the main sleeping space of the home using a measuring tape. Air quality in the main sleeping space serves as a proxy for individual exposure, as many homes consist of only one room: the sleeping space where family members spend substantial time. Staff recorded the location of the main cooking area, including distance from the home to the cooking area (if external to the home) and the distance from index to neighbor homes in steps (approximately 0.4 m per step). Study staff drew sketches of each cluster, including the index home and all neighboring homes of the index home, including participants and non-participants. Locations of primary stoves, walls, doors, and windows were indicated on the sketch.
After administering the baseline questionnaire and taking measurements of dimensions of the home, study staff installed two sets of PM2.5 and CO monitors for each index and neighbor home: one as close as possible to the primary stove, and one inside the main sleeping space, above the participant’s bed. Additional monitors were placed at one outdoor location per cluster. The outdoor monitors were located between the main entrances of the index home and neighbor home(s), under a roof or eave and out of plain sight, in order to protect instruments from rain and theft. Due to the variety in structure of local homes, we were unable to standardize the locations of these outdoor monitors. PM2.5 and CO monitors recorded measurements once per minute for at least 24 h. We used University of California, Berkeley Particle Monitors (University of California, Berkeley, Berkeley, CA, USA) to measure PM2.5 concentrations and Lascar CO monitors (Lascar Electronics, Salisbury, UK) to measure CO concentrations. The lower limit of detection of the University of California, Berkeley Particle Monitors is 50 μg/m3, which is twice the WHO-recommended 24-h mean PM2.5 concentration of ≤25 μg/m3 [2, 3, 10, 21,22,23]. The lower limit of detection for Lascar CO monitors is 0 ppm, with a resolution of 0.5 ppm. The WHO recommended 24-h mean CO concentration is ≤6.11 ppm [24].
Study staff conducted detailed observations of household activities for approximately 8 h on the working day following the initial interview, concurrent with PM2.5 and CO monitoring. Study staff recorded cooking activities and fuels used, ventilation activities, smoking, and burning substances other than for cooking (e.g. for fragrance, lighting, mosquito control, or garbage disposal). Study staff was stationed near each index home in order to prioritize recording activities occurring at the index home. However, they moved about the cluster as needed in order to record activities occurring at the participating neighbor homes. Residents were asked to continue normal daily activities as much as possible while monitoring was taking place.
Analysis
We calculated geometric mean PM2.5 and CO concentrations at each monitor location for the following times: between 5 and 6 am (baseline), during observed biomass cooking in the index home, the observation period during which no biomass cooking occurred in any home, and 24 h of monitoring time. Previous research has shown that the lowest PM2.5 concentrations are observed in Mirpur from 5 to 6 am [4], so the 5–6 am hour served as a baseline for this study. We used data from observations to identify biomass cooking times in the index home. Although neighbor homes primarily used cleaner fuels, our observations indicated that some biomass fuel use occurred when cleaner fuels were unavailable. For our measure of biomass cooking time in an index home, we excluded any times during which an enrolled neighbor home was also observed to cook with biomass fuel.
We used ANOVA to describe whether geometric mean PM2.5 and CO concentrations varied by monitor location at the pre-specified times. Our independent variable was location of the monitor, categorized as follows: near the index stove, in the index home’s main sleeping space, monitors (near the stove and in the main sleeping space) of neighbor homes that share a wall with the index home, outdoor monitors, and monitors (near the stove and in the main sleeping space) of neighbor homes that do not share a wall with the index home. Although PM2.5 and CO are expected to dissipate differently, we still expected that the order of dissipation would be the same, that is, highest concentrations for both pollutants would be highest in cooking space of biomass homes, then living space, then neighbor homes with a shared wall, then outside, then neighbor homes with no shared wall. Therefore, the ranking of environments is the same for both pollutants. We added a constant of 0.001 for all CO values in order to calculate the geometric mean, which requires nonzero values; the lowest possible value of the geometric mean CO concentration was therefore 0.001 ppm. We substituted a value equal to half the limit of detection for all PM2.5 readings at or below the limit of detection, a common method of analyzing data from instruments with high limits of detection [25]. Therefore, the lowest possible value of the geometric mean PM2.5 concentration was 25 μg/m3. We have provided a supplemental table comparing results using this method to results substituting a value equal to the limit of detection for all PM2.5 readings at or below the limit of detection (Additional file 5).
Using the PM2.5 and CO measurements at the index stove monitor as the referent category, we used linear regression to describe the relationship between the location of a monitor (index home, neighbor home—shared wall, outdoor, neighbor home—no shared wall) and geometric mean PM2.5 and CO concentrations during index biomass cooking and 24 h of monitoring. We examined the following as potential confounders: building material(s) of the home, having a smoker living in the home, distance to the index home (in steps), area of the home, having a secondary biomass stove, ambient temperature, and relative humidity. Covariates that changed the bivariate measure of association by 10% were retained in the final models. We examined ventilation of the home (presence of at least one window in the home) and location of the index stove (indoor or outdoor) as potential effect modifiers by stratifying results by these factors.
We also performed spatial variogram analysis to describe the relationship between geometric mean PM2.5 and CO concentrations in index and neighbor homes, limited to index stove cooking times, and Euclidian distance from a PM2.5 or CO source (the biomass-burning stove). Variograms are used to describe variability in PM2.5 or CO concentrations as a function of the distance between the source of pollution (biomass stove) and the monitor.