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Environ Monit Assess
Environ Monit Assess
Environmental Monitoring and Assessment
0167-6369
1573-2959
Springer International Publishing Cham

39278887
13106
10.1007/s10661-024-13106-y
Research
Assessing personal PM2.5 exposure using a novel neck-mounted monitoring device in rural Rwanda
http://orcid.org/0000-0001-7284-0381
Ishigaki Yo ishigaki@uec.ac.jp

1
Yokogawa Shinji 2
Shimazaki Kan 3
Win-Shwe Tin-Tin 4
Irankunda Elisephane 5
1 https://ror.org/02x73b849 grid.266298.1 0000 0000 9271 9936 Research Center for Realizing Sustainable Societies, University of Electro-Communications, 1-5-1, Chofu, Tokyo, 182-8585 Japan
2 https://ror.org/02x73b849 grid.266298.1 0000 0000 9271 9936 Info-Powered Energy System Research Center (I-PERC), University of Electro-Communications, Chofu, Tokyo, Japan
3 https://ror.org/05kt9ap64 grid.258622.9 0000 0004 1936 9967 Department of Human Factors Engineering and Environmental Design, Kindai University, Wakayama, Japan
4 https://ror.org/02hw5fp67 grid.140139.e 0000 0001 0746 5933 National Institute for Environmental Studies, Tsukuba, Japan
5 https://ror.org/04f9r2a11 grid.449786.0 0000 0004 0570 8286 The East African University, Nairobi, Kenya
16 9 2024
16 9 2024
2024
196 10 93510 4 2024
6 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
There is growing global concern regarding the detrimental health impacts of PM2.5 emissions from traditional stoves that utilize polluting fuels. Conventional methods for estimating daily personal PM2.5 exposure involve personal air samplers and measuring devices placed in a waist pouch, but these instruments are cumbersome and inconvenient. To address this issue, we developed a novel neck-mounted PM2.5 monitoring device (Pocket PM2.5 Logger) that is compact, lightweight, and can operate continuously for 1 week without recharging. Twelve participants who utilized charcoal, firewood, or propane gas for cooking in rural regions of Rwanda wore the Pocket PM2.5 Logger continuously for 1 week, and time-series variations in personal PM2.5 exposure were recorded at 5-min intervals. Individual daily exposure concentrations during cooking differed significantly among users of the different fuel types, and PM2.5 exposure was at least 2.6 and 3.4 times higher for charcoal and firewood users, respectively, than for propane gas users. Therefore, switching from biomass fuels to propane gas would reduce daily individual exposure by at least one-third. An analysis of cooking times showed that the median cooking time per meal was 30 min; however, half the participants cooked for 1.5 h per meal, and one-third cooked for over 4.5 h per meal. Reducing these extremely long cooking times would reduce exposure with all fuel types. The Pocket PM2.5 Logger facilitates the comprehensive assessment of personal PM2.5 exposure dynamics and is beneficial for the development of intervention strategies targeting household air pollution.

Keywords

Biomass fuel
Propane gas
Traditional cookstove
PM2.5 exposure
Household air pollution
Personal monitoring device
http://dx.doi.org/10.13039/501100001691 Japan Society for the Promotion of Science 19H03957 Ishigaki Yo issue-copyright-statement© Springer Nature Switzerland AG 2024
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pmcIntroduction

Globally, 2.4 billion people (approximately one-third of the world’s population) use traditional cooking stoves that burn polluting fuels (such as wood, coal, charcoal, agricultural waste, and kerosene). This occurs primarily in low-income and rural areas, whereas propane gas and electricity are heavily used in developed countries. It is estimated that 3.2 million excess deaths per year are caused by household air pollution (HAP), represented by fine particulate matter less than 2.5 μm in diameter (PM2.5) emitted from polluting stoves (Hall et al., 1982; WHO, 2023). Global 24-h average household PM2.5 concentrations are estimated to be 290 μg/m3 higher from traditional stoves than from electric or propane gas stoves (Shupler et al., 2018).

According to a literature review by Pratiti et al. (2020), the health effects of HAP mainly include increased blood pressure, dyspnea, childhood pneumonia, lung cancer, low birthweight, and cardiovascular diseases. Smith (2000) estimated that in India, HAP resulting from burning biomass causes approximately 400,000 to 550,000 premature deaths annually and is a major risk factor accounting for 4–6% of the national disease burden. Hystad et al. (2019) conducted a prospective cohort study investigating the health effects of HAP in 11 countries and found that the use of solid fuels (coal, wood, dung, or crop residues) for cooking is a risk factor for mortality and cardiorespiratory disease. Makonese et al. (2018) estimated that 66% of households in seven selected Sub-Saharan African countries use biomass for cooking.

To enhance thermal efficiency, mitigate pollutants released from biomass fuel, and alleviate the potential health impacts of traditional polluting stoves, improved cookstoves (ICSs) have been proposed (Memon et al., 2020). However, Urmee and Gyamfi (2014) found that many ICS projects failed soon after donor funding ceased, primarily due to inadequate consideration of maintenance and fuel costs. Khandelwal et al. (2017) identified cost and social factors, including historical background and cultural practices related to traditional stoves, as reasons for the failure of ICS projects in India, despite extensive promotion over decades. Hall et al. (1982) cautioned that ICSs should be crafted using locally available skills and materials and tailored to suit the cooking habits and additional requirements of the local population, such as room heating and pest repellency. Despite efforts to reduce HAP by introducing ICSs, biomass fuel remains widely used in many regions. Stoner et al. (2021) projected that 31% of the population in the Global South will primarily rely on polluting fuels, such as biomass, charcoal, or coal, by 2030, including over 1 billion people in Sub-Saharan Africa by 2025.

Measuring the PM2.5 concentration in a room instantaneously or continuously using a fixed measuring device enables the accurate determination of pollutant concentration and temporal changes emitted from the source. However, estimating individual exposure relies on indirect methods, such as estimating average daily exposure based on the average time spent by an individual in that room. This fixed monitoring method has been widely used to assess the extent of HAP caused by polluting stoves. In Rwanda, a study was conducted to measure PM2.5 and CO concentrations by installing air quality monitors with tabletop displays in cooking areas (Kabera et al., 2020). Champion and Grieshop (2019) applied a real-time, pocket-sized black carbon aerosol monitor in Rwanda to determine HAP reductions from pellet stove use. Giwa et al. (2022) used a handheld instrument to measure PM2.5 and CO concentrations in households in Nigeria with kerosene-fueled stoves, and Kansiime et al. (2022) measured PM2.5 and CO concentrations using handheld sensors in households in informal settlements in Uganda. Furthermore, Naved et al. (2023) measured PM2.5 and CO concentrations in the vicinity of community kitchen tandoors in restaurants in India by fixing a sensor on a tripod.

However, mobile monitoring studies, in which participants are asked to carry their own measuring devices, have also been conducted. Baumgartner et al. (2011) measured PM2.5 personal exposure continuously for 48 h by asking participants in China to wear a waist bag (weighing about 1 kg) containing a personal air sampler. Participants in the study of Chillrud et al. (2021) in Ghana wore a CO and PM2.5 sensors clipped in the vicinity of their breathing zone. Burrowes et al. (2020) measured PM2.5 concentrations continuously using two types of personal air samplers sewn onto the clothing of participants in Peru. In addition, participants in the study of Saleh et al. (2022) in Malawi wore a PM sensor, CO sensor, and a power bank in a waist bag.

To directly measure an individual’s exposure, it is necessary to have the participant carry a measuring device. However, the use of a poorly fitting device, as exemplified in the previous study above, may reduce wearing compliance (Vanker et al., 2023) and cause a behavioral bias due to the restrictions on daily activities. Therefore, personal wearable sensors that are smaller, lighter, and capable of continuous operation for longer periods of time are required for improved participant experience.

The aims of this study were to (1) develop a portable PM2.5 exposure-logging device that is small, lightweight, and capable of operating continuously for 1 week, and (2) demonstrate the effectiveness of this device in a real-world setting by analyzing the microscopic exposure dynamics at 5-min intervals. We selected rural Rwanda as the demonstration site, as 95% of people use solid fuels for cooking (United Nations Framework Convention on Climate Change, 2023). The results of this study support efforts to reduce HAP worldwide.

Materials and methods

Device development

As shown in Fig. 1, we designed an original compact device for logging PM2.5 concentrations named the Pocket PM2.5 Logger. The device is worn suspended from the neck with a string and is equipped with an HPMA115C0-003 sensor module (Honeywell, NC, USA) packaged with a fan, an impactor, and a light-scattering sensor. The device has a PM2.5 concentration measurement range of 0–1000 μg/m3, with a specified measurement accuracy of ± 15 μg/m3. Its operating temperature ranges from − 20 to + 70 °C, and its operating humidity ranges from 0 to 95% relative humidity (non-condensing).Fig. 1 Internal and external components of the Pocket PM2.5 Logger

Rabuan et al. (2023) found that the HPMA115 series sensors had an average coefficient of determination of 0.72, meeting the EPA (Environmental protection Agency) guideline. When deployed near schools in Southampton, the sensors showed a correlation coefficient (R2) of 0.77 compared to the UK Air Quality Urban Network (AURN) stations, and in Baltimore, they demonstrated an R2 of 0.92 against gravimetric methods and Beta Attenuation Monitor (BAM), confirming their high accuracy and reliability (Alfano et al. 2020).

The Pocket PM2.5 Logger has a built-in lithium polymer battery. The main body (including the battery) has a width, height, and depth of 76.5 mm, 30 mm, and 43 mm, respectively, and it weighs 90 g. The device includes a low-power microcontroller supporting a deep-sleep mode (PIC24FJ128GB204, Microchip Technology, AZ, USA), a real-time clock (RX8900CE, Seiko Epson, Tokyo, Japan), and 1 GB of digital memory storage. It has a built-in static memory and can monitor records continuously for up to 1 week without charging. The USB port of the Pocket PM2.5 Logger can be used for both charging and data transfer to and from a PC.

Settings, such as the current time, sleep time (measurement interval), warm-up time before measurement (preliminary operation that involves circulating air in the sensor by turning on the fan), and the number of measurement attempts, can be set from a dedicated PC application and written as a configuration file. The data are saved in CSV format, which includes a timestamp and a column of PM2.5 concentration for each line, and the data can be retrieved as a file from a PC. In this study, the Pocket PM2.5 Logger was set to warm up for 10 s every 5 min, following which three measurements of PM2.5 concentrations were recorded and stored in the internal memory with a timestamp.

The Pocket PM2.5 Logger consists of 37 components including discrete semiconductors, and the total cost of the components is approximately 100 USD. As shown in Table 1, the Pocket PM2.5 Logger appears to be cost-efficient, and it is less than one-third the weight of the devices used in previous studies. Even if we assume that the public price is three times the cost of parts (due to expenses such as calibration, marketing, manufacturing, and profit margins), the Pocket PM2.5 Logger may still be cost-competitive. Also, unlike the pump method, this logger can measure PM2.5 at a high sampling rate of every few minutes rather than the 24-h average, facilitating a detailed evaluation of changes in exposure over the course of a day. Table 1 Comparison of instruments used to measure household air pollution

Citation	Product used	Weight	Price*	Sampling interval	
Baumgartner et al. (2011)	Personal air sampling pump (Apex Pro, Casella CEL, Bedford, UK)	1 kg (stored in waist pack with battery)	835 USD	24 h continuous	
Chillrud et al. (2021)	RTI Enhanced Children’s MicroPEM (ECM, RTI International, Research Triangle Park, NC, USA)	300 g (ECM)	Unknown	24 h continuous	
Burrowes et al. (2020)	ECM and Ultrasonic Personal Air Sampler (UPAS, Access Sensor Technologies, Fort Collins, CO, USA)	300 g (ECM) + 230 g (UPAS)	1212 GBP (UPAS)	24 h continuous	
Saleh et al. (2022)	PA-II-SD (PurpleAir, Draper, UT, USA)	357 g (PA-II-SD) + 475 g (battery)	229 USD	2 min	
Current study	Pocket PM2.5 Logger	90 g (including battery)	100 USD (parts)	5 min	
*Prices are based on data retrieved from the following web pages on March 1, 2024: https://www.esafetyinc.com/product-category/instrumentation/air-monitoring/personal-sampling-pumps/personal-sampling-pumps-casella-apex-2/; https://www.a1-cbiss.com/product/ultrasonic-personal-air-sampler-upas/; and https://www2.purpleair.com/products/list

Study area and participants

The study area (Fig. 2) included Kagarama Sector and Musave Cell, which are located in rural areas of Kigali, Rwanda. After explaining the study content to representatives of each municipality and obtaining permission to conduct the study, we offered to recruit adult male and female participants aged 18 or older. The target population was non-smoking households that cooked with charcoal, firewood, or propane gas. Twelve people (five males and seven females) participated in the study. The study period, including participant recruitment and PM2.5 exposure monitoring, was from October 29, 2021, to March 18, 2022.Fig. 2 Study area (Kagarama Sector and Musave Cell, in rural areas of Kigali, Rwanda)

We provided each participant with a Pocket PM2.5 Logger for 5–10 days and instructed them to wear it around their necks at all times, except when they were exposed to water, such as when taking a bath. They were also instructed to record their daily activities in their logbooks in timeline format.

This study was approved by the Ethics Committee of the University of Electro-Communications, Chofugaoka, Chofu, Tokyo, Japan (approval number: 19012[2]). Written informed consent was obtained from all participants in their local language.

Data collection and analysis

Each participant’s time series data were collected in CSV format every 5 min, and the data characteristics were analyzed by plotting the density contours of PM2.5 concentration using JMP Pro 16.2.0 (SAS Institute, Cary, NC, USA).

Next, for each participant’s measured data, a column for cooking activity (whether or not food was being cooked) and a column for cooking duration were added to each row by performing double-threshold preprocessing using a Python program as described below:Setting the First Threshold: For all participants, the mode and root mean square (RMS) of measured PM2.5 concentrations were calculated for values of 250 μg/m3 or more, a level significantly higher than the ambient average in Rwanda (Subramanian et al., 2020). Excluding outliers, the First Threshold was set as the mode + (RMS × 2).

Setting the Second Threshold: The RMS of the population of measurements after excluding rows larger than the First Threshold was calculated, and this value was set as the Second Threshold.

Determining the activity start row: the participant’s data rows were scanned in chronological order, and if the PM2.5 concentration exceeded the First Threshold, the row was designated as the activity start row. However, when the PM2.5 concentration in the row before the activity start row was greater than the Second Threshold, the concentration was determined to be gradually increasing. In this case, the row was scanned backward, and the row where the concentration first exceeded the Second Threshold was changed to the activity start row.

Determining the activity end row: the data were scanned in chronological order starting from the activity start row and the row before the row where the measured PM2.5 concentration value fell below the Second Threshold value for the first time was set as the activity end row. Steps 3 and 4 were repeated to scan all data lines.

Activity column recording: The activity column was recorded as TRUE for data recordings between the activity start and end rows. For the other recordings, the activity column was recorded as FALSE.

Recording of activity_duration: in the activity_end line, the duration of the activity in question (i.e., the period during which the activity was TRUE consecutively) was recorded in the activity_duration column (in units of seconds). All other activity_duration columns were NULL.

After filtering the data to include only instances where Activity = TRUE, we proceeded to calculate the mean values for each PM2.5_day and conducted the Kruskal–Wallis test to detect variations in mean values across different use types. Subsequently, to examine significant differences, we employed the Kruskal–Wallis rank sum test as a nonparametric multiple comparison method, as the data did not conform to a normal distribution according to the Shapiro–Wilk test.

We then generated plots of activity_duration per participant to assess the duration spent cooking per meal, serving as an indicator of potential exposure time. Upon confirming that activity_duration for each fuel type did not adhere to a normal distribution, we utilized the Kruskal–Wallis test to identify significant variations in activity_duration among different fuel types.

Results and discussion

A total of 12 participants were enrolled in the study, as shown in Table 2. The Pocket PM2.5 Logger device was intended to be used continuously for 1 week without charging; however, the measurement periods ranged from 5 to 10 days due to issues such as equipment repairs (IDs 1 and 10). No participant abandoned the experiment. Table 2 Characteristics of participants and type of fuel used for cooking

ID	Type of fuel	Gender	Age	Measurement period (year/month/day)	Total duration of measurement	
1	Firewood	Woman	70	2022/2/11–2022/2/13

and 2022/2/16–2022/2/19

	7 days	
2	Firewood	Woman	60	2022/2/11–2022/2/17	7 days	
3	Firewood	Man	42	2022/2/11–2022/2/19	9 days	
4	Firewood	Woman	Unanswered	2022/3/7–2022/3/12	6 days	
5	Firewood	Woman	Unanswered	2022/3/7–2022/3/11	5 days	
6	Charcoal	Man	63	2022/2/11–2022/2/16	6 days	
7	Charcoal	Woman	Unanswered	2022/2/20–2022/2/27	8 days	
8	Charcoal	Man	Unanswered	2022/2/20–2022/2/27	8 days	
9	Charcoal	Man	Unanswered	2022/2/26–2022/3/6	7 days	
10	Charcoal	Woman	23	2022/2/28–2022/3/2

and 2022/3/7–2022/3/13

	10 days	
11	Propane gas	Woman	27	2022/2/11–2022/2/17	7 days	
12	Propane gas	Man	32	2022/3/7–2022/3/13	7 days	

The logbooks were excluded from analysis because no meaningful information could be obtained due to variations in literacy and the participants’ concept of time. In the future, it would be desirable to add a function that automatically records activity status using GPS and acceleration sensors. Additionally, the reliance on participant recall for timed activities can introduce significant bias and inaccuracies in the data collection process. Participants may inaccurately recall the duration and timing of their activities, leading to errors in the reported data. Future studies should consider using objective measures, such as activity trackers, to enhance the accuracy of time-activity data.

The results showed that the mean PM2.5 concentration for all participants during the entire measurement period was 32 μg/m3 (Table 2). It is important to note that this mean concentration represents a lower bound estimate, as the sensor saturated at 1000 μg/m3 during high pollution episodes. Therefore, the actual mean concentration could be higher but not lower than the reported value. If the participants continue to experience PM2.5 exposure at this concentration, they will exceed the annual limits set by the ambient air quality standards in Japan (annual mean: 15 μg/m3) (United Nations Environment Programme, 2019), the United States (annual mean: 12 μg/m3) (US Environmental Protection Agency, 2012), and the European Union (annual mean: 25 μg/m3) (European Union, 2008) and the recommended annual and daily limits set by the WHO (annual mean: 5 μg/m3, daily mean: 15 μg/m3) (WHO, 2021). Therefore, measures to reduce HAP are urgently required, and PM2.5 monitoring is an important aspect of such efforts.

Figure 3 shows an example (ID 1) of the determination of cooking activity and duration using the Python program. The colored part of the line graph indicates that Activity = TRUE. The areas that are not colored indicate that the participant was not engaged in activities that exposed them to high concentrations of PM2.5, such as cooking, thus corresponding to background levels of PM2.5 concentration. We performed the same visualization for all data records and confirmed that the peak of activity was detected by the normal operation of preprocessing.Fig. 3 Results of activity judgment on the recorded PM2.5 concentration time series data (ID = 1) (Orange and green indicate that activity = TRUE is determined; for readability, orange and green were alternated)

Figure 4 shows the density contours of PM2.5 concentration for different time periods during the day, where only Activity = TRUE data were considered. Only data with PM2.5 concentrations exceeding 100 μg/m3 were plotted since displaying measurements near background levels made it difficult to visualize the effect of cooking. Notably, data exceeding 100 μg/m3 were measured at time periods corresponding to the breakfast, lunch, and dinner. As previously mentioned, the detection limit of the Pocket PM2.5 Logger is 1000 μg/m3, and the measured values for firewood and charcoal often reached this upper limit. Consequently, the actual concentrations during these events were likely much higher than the recorded values, indicating that the measurements of the sensor were saturated at this threshold.Fig. 4 Density contours of PM2.5 concentration by period of day (only data above 100 μg/m3 are plotted; the detection limit was 1000 μg/m.3)

Although the data suggested that all participants wore the device during the day, true compliance can only be accurately measured by integrating an accelerometer and GPS into the sensor to detect small movements and confirm whether the sensor is being worn or not. It will be necessary in the future to consider how to accommodate a variety of participants, such as children with a smaller body, participants who work outdoors, or those who may be exposed to rainwater while wearing their devices.

When only Activity = TRUE data were considered, the mean PM2.5 concentration was 265 μg/m3 for firewood, 205 μg/m3 for charcoal, and 78.4 μg/m3 for propane gas. The new data set was then averaged by day and the following tests were performed. The Shapiro–Wilk test indicated that only firewood data were likely to follow a normal distribution (p = 0.23), whereas those of charcoal (p < 0.0001) and propane gas (p = 0.0087) data were not considered to be normally distributed. The Kruskal–Wallis test was subsequently performed, and a statistically significant difference in mean PM2.5 values between the different fuel types was identified (p = 0.041). As a nonparametric multiple comparison method, the Kruskal–Wallis rank sum test revealed that the pattern of significant differences was firewood > propane gas (p = 0.018), firewood = charcoal (p = 0.13), and charcoal ≥ propane gas (p = 0.076). In other words, individual daily exposure concentrations during cooking were significantly greater for charcoal (at least 2.6 times) and firewood (at least 3.4 times) users than for propane gas users. This suggests that changing the fuel from highly polluting biomass to less polluting propane gas can reduce daily personal PM2.5 exposure by approximately one-third.

The higher pollution levels derived in this study from biomass fuels, such as charcoal and firewood, compared to that from propane gas are consistent with the results of previous studies in China and Nepal (Johnston et al., 2020; Li et al., 2016). Significantly reducing PM2.5 exposure by switching from biomass fuels to propane gas is supported by the findings of Johnson et al. (2022), who conducted a randomized controlled trial involving 3195 households across four countries using Liquefied Petroleum Gas stoves and found that PM2.5 exposure in the intervention group was reduced by 66% compared to the control group, which used biomass fuels such as wood, charcoal, and dung. However, due to the sensor’s detection limit, the actual concentrations were likely much higher than those recorded. The activity_duration of cooking for each participant is shown in Fig. 5. The Shapiro–Wilk Test showed that the data were not normally distributed: charcoal (p = 1.3 × 10−11), firewood (p = 8.1 × 10−11), and propane gas (p < 0.0001). The Kruskal–Wallis test was subsequently performed, and no significant differences in the mean activity_duration values were identified between the different fuel types (p = 0.45).Fig. 5 Box plot of activity duration by participants

The median activity duration for all participants in this study was 1801s (30 min), which is similar to the standard in developed countries. For example, the average cooking time per meal is reportedly 47 min in Japan (Central Union of Agricultural Cooperatives 2012), 37 min in the USA (US Department of Agriculture and Economic Research Service 2016), and less than 30 min in the UK (Wunsch, 2022). Although there were individual differences in activity duration in the current study, it is noteworthy that the third quartile value of cooking time per meal of six participants (half the participants) was greater than 5400 s (1.5 h), and the value of four participants (one-third of all participants) was over 16,200 s (4.5 h). Stewed beef and bean stews are commonly prepared in Rwanda, and this may have contributed to the occasionally extreme cooking time length per meal. Therefore, in addition to switching from highly polluting biomass fuel to less polluting propane gas, reducing the average cooking time may be an effective measure for alleviating HAP, especially since there was no significant difference between the mean activity_duration among the three fuel types (p = 0.45).

There has been a large public health intervention campaign in Rwanda, represented by the free distribution of ICSs and aimed at reducing PM2.5 emissions (Barstow et al., 2016). However, to sustainably reduce exposure among people with low incomes, it is important to not only temporarily distribute ICSs to reduce PM2.5 concentrations but also to promote a switch from biomass to propane gas fuel. Additionally, programs that educate people about ways to reduce cooking time may be beneficial. For example, the use of pressure cookers, softening of meat and beans with natural enzymes (Anaduaka et al., 2023), or vacuum insulation cooking by retaining heat (Farooqui, 2013) show promise in reducing the overall cooking time. Future studies should examine the practicality of these interventions considering the region’s unique socioeconomic and cultural context. To effectively reduce cooking time, it will be necessary to investigate the relationship between food recipes and cooking time from a domestic science perspective in the future.

A major limitation of this study is that although the sensors demonstrated high accuracy with NaCl aerosols (an R2 of 0.77–0.92, Alfano et al. 2020), their sensitivity to aerosols from different cookstoves may vary. To address this, collocation using the gravimetric method is necessary to accurately determine the sensitivity of the sensor to the specific aerosols produced by the cookstoves used in this study. Additionally, applying machine learning models such as multiple linear regression, random forest, Gaussian mixture regression, and XGBoost, as demonstrated by Raheja et al. (2023) for longer-term outdoor data, could enable high-precision calibration of low-cost optical scattering sensors.

A limitation of the current study is that the restricted sample size precluded analysis based on household age group, gender, cooking availability, or other demographic or occupational characteristics. Additionally, the lack of fail-safe mechanisms for fitting the devices to non-adults (e.g., measures to prevent accidental ingestion or choking by neck straps) prevented obtaining measurement data from children, and evaluating the actual exposure of sensitive groups such as children and pregnant women is a major future challenge (Amegah et al., 2014). Furthermore, as the measurement period was limited to 2 months, it was not possible to analyze seasonal effects.

The double-threshold preprocessing approach helped to isolate relevant data points related to cooking activity from the entire data set. The assumption behind the dual-threshold setting is that cooking will result in the observation of higher PM2.5 concentration peaks that are more than twice the RMS, but this assumption may not hold true depending on the cookware and ventilation conditions. In future studies, the thresholds will need to be optimized by comparing them to direct observations from videotaping in the field.

Another limitation is that our study assumes 100% compliance with wearing the Pocket PM2.5 Logger continuously, which may not be realistic. Periods of non-compliance or device removal by participants could introduce bias into the results. Future studies should incorporate measures such as GPS and accelerometer data to objectively verify compliance and adjust for any non-compliance periods.

The lack of a user feedback section is a further limitation. Understanding user experience and feedback is crucial for assessing the practicality and user-friendliness of the device. Future research should include a user feedback section, possibly under compliance, to provide insights into how users perceive the device and its impact on their daily activities. This is essential for ensuring high compliance rates and improving the design and usability of the device.

Continuous surveying of participants through questionnaires is also needed to assess the short- and long-term effects of PM2.5 exposure. As people exposed to high concentrations of PM2.5 may be aware of their respiratory symptoms, a correlation between survey scores based on the International Study of Asthma and Allergies in Childhood (Asher et al., 1995) or Primary Care Airways Guidelines questionnaire (Sichletidis et al., 2011) and exposure dose can be expected. In addition, since lung function generally declines due to long-term exposure, a correlation with the results of spirometer lung function assessments is also expected. In the future, the scope of this study should be expanded to include health outcomes analysis.

Furthermore, taking advantage of the features of the small wearable device developed in the current study, potential future applications could include providing warnings about PM2.5 concentration in real time using light, sound, vibration, or other means. Although there have been cases of wall-mounted illustrated alarms (Iribagiza et al., 2021), portable intervention devices have not yet been put to practical use.

Conclusions

We successfully developed the Pocket PM2.5 Logger device and assessed PM2.5 personal exposure levels in rural Rwanda. Individual daily exposure concentrations during cooking differed significantly among users of the three different fuel types (p = 0.041), with higher concentrations for charcoal and firewood than for propane gas fuel. Switching from highly polluting biomass fuels to less polluting propane gas could reduce daily personal exposure by approximately one-third.

Our analysis of the duration of cooking activity revealed a median duration of 30 min per meal. However, the third quartile value of the cooking time per meal for one-half of all participants was greater than 1.5 h and that of one-third of all participants was over 4.5 h. Reducing extremely long cooking times is a realistic exposure reduction measure, and it is expected to work across all fuel types, including propane gas. To shorten cooking time, pre-cooking, vacuum cooking, or the use of thermos cooking are promising.

In addition, the installation of vents or ventilation systems, or switching to outdoor cooking may be effective in reducing exposure. However, the implementation of recommendations must include consideration of the lifestyle and customary practices of the local communities in rural Rwanda. The novel device developed in this study supports efforts to monitor, visualize, and reduce HAP worldwide.

Author contributions

Supervision: Yo Ishigaki, Conceptualization: Shinji Yokogawa, Methodology: Kan Shimazaki, Writing: Yo Ishigaki, Project administration: Yo Ishigaki, Formal analysis: Shinji Yokogawa, Visualization: Tin-Tin Win-Shwe, Validation: Kan Shimazaki and Elisephane Irankunda, Resources: Elisephane Irankunda and Yo Ishigaki, Investigation: Tin-Tin Win-Shwe and Elisephane Irankunda.

Funding

This work was supported by a KAKENHI grant from the Japan Society for the Promotion of Science (JSPS) [Grant number 19H03957].

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors.

Ethics approval

This study was approved by the Ethics Committee on Experiments on Human Subjects of the University of Electro-Communications, Chofu, Tokyo, Japan (approval number 19012[2]).

Consent to participate

The authors affirm that the research participants provided informed consent for participation in this study and the publication of results.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

Alfano B Barretta L Del Giudice A De Vito S Di Francia G Esposito E Formisano F Massera E Miglietta ML Polichetti T A Review of Low-Cost Particulate Matter Sensors from the Developers' Perspectives Sensors 2020 20 23 6819 10.3390/s2023681 33260320
Alfano, B., Barretta, L., Del Giudice, A., De Vito, S., Di Francia, G., Esposito, E., Formisano, F., Massera, E., Miglietta, M. L., & Polichetti, T. (2020). A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives. Sensors, 20(23), 6819. 10.3390/s202368133260320 10.3390/s2023681
Amegah AK Quansah R Jaakkola JJK Household air pollution from solid fuel use and risk of adverse pregnancy outcomes: A systematic review and meta-analysis of the empirical evidence PLoS ONE 2014 9 12 e113920 10.1371/journal.pone.0113920 25463771
Amegah, A. K., Quansah, R., & Jaakkola, J. J. K. (2014). Household air pollution from solid fuel use and risk of adverse pregnancy outcomes: A systematic review and meta-analysis of the empirical evidence. PLoS ONE, 9(12), e113920. 10.1371/journal.pone.011392025463771 10.1371/journal.pone.0113920
Anaduaka EG Chibuogwu CC Ezugwu AL Ezeorba TPC Nature-derived ingredients as sustainable alternatives for tenderizing meat and meat products: An updated review Food Biotechnology 2023 37 2 136 165 10.1080/08905436.2023.2201354
Anaduaka, E. G., Chibuogwu, C. C., Ezugwu, A. L., & Ezeorba, T. P. C. (2023). Nature-derived ingredients as sustainable alternatives for tenderizing meat and meat products: An updated review. Food Biotechnology, 37(2), 136–165. 10.1080/08905436.2023.220135410.1080/08905436.2023.2201354
Asher MI Keil U Anderson HR Beasley R Crane J Martinez F Mitchell EA Pearce N Sibbald B Stewart AW International Study of Asthma and Allergies in Childhood (ISAAC): Rationale and methods European Respiratory Journal 1995 8 3 483 491 10.1183/09031936.95.08030483 7789502
Asher, M. I., Keil, U., Anderson, H. R., Beasley, R., Crane, J., Martinez, F., Mitchell, E. A., Pearce, N., Sibbald, B., & Stewart, A. W. (1995). International Study of Asthma and Allergies in Childhood (ISAAC): Rationale and methods. European Respiratory Journal, 8(3), 483–491. 10.1183/09031936.95.080304837789502 10.1183/09031936.95.08030483
Barstow CK Nagel CL Clasen TF Thomas EA Process evaluation and assessment of use of a large scale water filter and cookstove program in Rwanda BMC Public Health 2016 16 584 10.1186/s12889-016-3237-0 27421646
Barstow, C. K., Nagel, C. L., Clasen, T. F., & Thomas, E. A. (2016). Process evaluation and assessment of use of a large scale water filter and cookstove program in Rwanda. BMC Public Health, 16, 584. 10.1186/s12889-016-3237-027421646 10.1186/s12889-016-3237-0
Baumgartner J Schauer JJ Ezzati M Lu L Cheng C Patz JA Bautista LE Indoor air pollution and blood pressure in adult women living in rural China Environmental Health Perspectives 2011 119 10 1390 1395 10.1289/ehp.1003371 21724522
Baumgartner, J., Schauer, J. J., Ezzati, M., Lu, L., Cheng, C., Patz, J. A., & Bautista, L. E. (2011). Indoor air pollution and blood pressure in adult women living in rural China. Environmental Health Perspectives, 119(10), 1390–1395. 10.1289/ehp.100337121724522 10.1289/ehp.1003371
Burrowes VJ Piedrahita R Pillarisetti A Underhill LJ Fandiño-Del-Rio M Johnson M Kephart JL Hartinger SM Steenland K Naeher L Kearns K Peel JL Clark ML Checkley W HAPIN Investigators Comparison of next-generation portable pollution monitors to measure exposure to PM2.5 from household air pollution in Puno Peru. Indoor Air 2020 30 3 445 458 10.1111/ina.12638 31885107
Burrowes, V. J., Piedrahita, R., Pillarisetti, A., Underhill, L. J., Fandiño-Del-Rio, M., Johnson, M., Kephart, J. L., Hartinger, S. M., Steenland, K., Naeher, L., Kearns, K., Peel, J. L., Clark, M. L., Checkley, W., HAPIN Investigators. (2020). Comparison of next-generation portable pollution monitors to measure exposure to PM2.5 from household air pollution in Puno. Peru. Indoor Air, 30(3), 445–458. 10.1111/ina.1263831885107 10.1111/ina.12638
Central Union of Agricultural Cooperatives. Japan (2012). Survey on attitudes toward preparing weekday dinners by the “Everyone’s Good Food Project” (in Japanese) [Press release], Retrieved from https://life.ja-group.jp/pdf/new/Kbg7CBm0Uc.pdf Retrieved March 27, 2024
Champion WM Grieshop AP Pellet-fed gasifier stoves approach gas-stove like performance during in-home use in Rwanda Environmental Science and Technology 2019 53 11 6570 6579 10.1021/acs.est.9b00009 31037940
Champion, W. M., & Grieshop, A. P. (2019). Pellet-fed gasifier stoves approach gas-stove like performance during in-home use in Rwanda. Environmental Science and Technology, 53(11), 6570–6579. 10.1021/acs.est.9b0000931037940 10.1021/acs.est.9b00009
Chillrud SN Ae-Ngibise KA Gould CF Owusu-Agyei S Mujtaba M Manu G Burkart K Kinney PL Quinn A Jack DW Asante KP Kinney PL Quinn A Jack DW Asante KP The effect of clean cooking interventions on mother and child personal exposure to air pollution: Results from the Ghana Randomized Air Pollution and Health Study (GRAPHS) Journal of Exposure Science and Environmental Epidemiology 2021 31 4 683 698 10.1038/s41370-021-00309-5 33654272
Chillrud, S. N., Ae-Ngibise, K. A., Gould, C. F., Owusu-Agyei, S., Mujtaba, M., Manu, G., Burkart, K., Kinney, P. L., Quinn, A., Jack, D. W., Asante, K. P., Kinney, P. L., Quinn, A., Jack, D. W., & Asante, K. P. (2021). The effect of clean cooking interventions on mother and child personal exposure to air pollution: Results from the Ghana Randomized Air Pollution and Health Study (GRAPHS). Journal of Exposure Science and Environmental Epidemiology, 31(4), 683–698. 10.1038/s41370-021-00309-533654272 10.1038/s41370-021-00309-5
European Union (2008). EU air quality standards: Directive 2008/50. EC, Retrieved from https://environment.ec.europa.eu/topics/air/air-quality/eu-air-quality-standards_en
Farooqui SZ A vacuum tube based improved solar cooker Sustainable Energy Technologies and Assessments 2013 3 33 39 10.1016/j.seta.2013.05.004
Farooqui, S. Z. (2013). A vacuum tube based improved solar cooker. Sustainable Energy Technologies and Assessments, 3, 33–39. 10.1016/j.seta.2013.05.00410.1016/j.seta.2013.05.004
Giwa SO Nwaokocha CN Sharifpur M An appraisal of air quality, thermal comfort, acoustic, and health risk of household kitchens in a developing country Environmental Science and Pollution Research International 2022 29 18 26202 26213 10.1007/s11356-021-17788-6 34850347
Giwa, S. O., Nwaokocha, C. N., & Sharifpur, M. (2022). An appraisal of air quality, thermal comfort, acoustic, and health risk of household kitchens in a developing country. Environmental Science and Pollution Research International, 29(18), 26202–26213. 10.1007/s11356-021-17788-634850347 10.1007/s11356-021-17788-6
Hall, D. O., Barnard, G. W., & Moss, P. A. (1982). Biomass for energy in the developing countries: Current role, potential, problems, prospects (first edn.). Elsevier
Hystad, P., Duong, M., Brauer, M., Larkin, A., Arku, R., Kurmi, O. P., ... & Prospective Urban and Rural Epidemiological (PURE) Study investigators]. (2019). Health effects of household solid fuel use: Findings from 11 countries within the prospective urban and rural epidemiology study. Environmental health perspectives, 127(5), 057003
Iribagiza C Sharpe T Coyle J Nkubito P Piedrahita R Johnson M Thomas EA Evaluating the effects of access to air quality data on household air pollution and exposure—An interrupted time series experimental study in Rwanda Sustainability 2021 13 20 11523 10.3390/su132011523
Iribagiza, C., Sharpe, T., Coyle, J., Nkubito, P., Piedrahita, R., Johnson, M., & Thomas, E. A. (2021). Evaluating the effects of access to air quality data on household air pollution and exposure—An interrupted time series experimental study in Rwanda. Sustainability, 13(20), 11523. 10.3390/su13201152310.3390/su132011523
Johnson M Pillarisetti A Piedrahita R Balakrishnan K Peel JL Steenland K Underhill LJ Rosa G Kirby MA Díaz-Artiga A McCracken J Clark ML Waller L Chang HH Wang J Dusabimana E Ndagijimana F Sambandam S Mukhopadhyay K Kearns KA Campbell D Kremer J Rosenthal JP Checkley W Clasen T Naeher L Exposure contrasts of pregnant women during the Household Air Pollution Intervention Network randomized controlled trial Environmental Health Perspectives 2022 130 9 097005 10.1289/EHP10295 36112539
Johnson, M., Pillarisetti, A., Piedrahita, R., Balakrishnan, K., Peel, J. L., Steenland, K., Underhill, L. J., Rosa, G., Kirby, M. A., Díaz-Artiga, A., McCracken, J., Clark, M. L., Waller, L., Chang, H. H., Wang, J., Dusabimana, E., Ndagijimana, F., Sambandam, S., Mukhopadhyay, K., … Naeher, L. (2022). Exposure contrasts of pregnant women during the Household Air Pollution Intervention Network randomized controlled trial. Environmental Health Perspectives, 130(9), 097005. 10.1289/EHP1029536112539 10.1289/EHP10295
Johnston JD Hawks ME Johnston HB Johnson LA Beard JD Comparison of liquefied petroleum gas cookstoves and wood cooking fires on PM2.5 trends in brick workers’ homes in Nepal International Journal of Environmental Research and Public Health 2020 17 16 5681 10.3390/ijerph17165681 32781568
Johnston, J. D., Hawks, M. E., Johnston, H. B., Johnson, L. A., & Beard, J. D. (2020). Comparison of liquefied petroleum gas cookstoves and wood cooking fires on PM2.5 trends in brick workers’ homes in Nepal. International Journal of Environmental Research and Public Health, 17(16), 5681. 10.3390/ijerph1716568132781568 10.3390/ijerph17165681
Kabera T Bartington S Uwanyirigira C Abimana P Pope F Indoor PM 2.5 characteristics and CO concentration in households using biomass fuel in Kigali Rwanda. International Journal of Environmental Studies 2020 77 6 998 1011 10.1080/00207233.2020.1732067
Kabera, T., Bartington, S., Uwanyirigira, C., Abimana, P., & Pope, F. (2020). Indoor PM 2.5 characteristics and CO concentration in households using biomass fuel in Kigali. Rwanda. International Journal of Environmental Studies, 77(6), 998–1011. 10.1080/00207233.2020.173206710.1080/00207233.2020.1732067
Kansiime WK Mugambe RK Atusingwize E Wafula ST Nsereko V Ssekamatte T Nalugya A Coker ES Ssempebwa JC Isunju JB Use of biomass fuels predicts indoor particulate matter and carbon monoxide concentrations; evidence from an informal urban settlement in Fort Portal city Uganda. BMC Public Health 2022 22 1 1723 10.1186/s12889-022-14015-w 36089579
Kansiime, W. K., Mugambe, R. K., Atusingwize, E., Wafula, S. T., Nsereko, V., Ssekamatte, T., Nalugya, A., Coker, E. S., Ssempebwa, J. C., & Isunju, J. B. (2022). Use of biomass fuels predicts indoor particulate matter and carbon monoxide concentrations; evidence from an informal urban settlement in Fort Portal city. Uganda. BMC Public Health, 22(1), 1723. 10.1186/s12889-022-14015-w36089579 10.1186/s12889-022-14015-w
Khandelwal M Hill ME Jr Greenough P Anthony J Quill M Linderman M Udaykumar HS Why have improved cook-stove initiatives in India failed? World Development 2017 92 13 27 10.1016/j.worlddev.2016.11.006
Khandelwal, M., Hill, M. E., Jr., Greenough, P., Anthony, J., Quill, M., Linderman, M., & Udaykumar, H. S. (2017). Why have improved cook-stove initiatives in India failed? World Development, 92, 13–27. 10.1016/j.worlddev.2016.11.00610.1016/j.worlddev.2016.11.006
Li T Cao S Fan D Zhang Y Wang B Zhao X Leaderer BP Shen G Zhang Y Duan X Household concentrations and personal exposure of PM2.5 among urban residents using different cooking fuels Science of the Total Environment 2016 548–549 6 12 10.1016/j.scitotenv.2016.01.038
Li, T., Cao, S., Fan, D., Zhang, Y., Wang, B., Zhao, X., Leaderer, B. P., Shen, G., Zhang, Y., & Duan, X. (2016). Household concentrations and personal exposure of PM2.5 among urban residents using different cooking fuels. Science of the Total Environment, 548–549, 6–12. 10.1016/j.scitotenv.2016.01.03810.1016/j.scitotenv.2016.01.038
Makonese T Ifegbesan AP Rampedi IT Household cooking fuel use patterns and determinants across southern Africa: Evidence from the demographic and health survey data Energy and Environment 2018 29 1 29 48 10.1177/0958305X17739475
Makonese, T., Ifegbesan, A. P., & Rampedi, I. T. (2018). Household cooking fuel use patterns and determinants across southern Africa: Evidence from the demographic and health survey data. Energy and Environment, 29(1), 29–48. 10.1177/0958305X1773947510.1177/0958305X17739475
Memon SA Jaiswal MS Jain Y Acharya V Upadhyay DS A comprehensive review and a systematic approach to enhance the performance of improved cookstove (ICS) Journal of Thermal Analysis and Calorimetry 2020 141 6 2253 2263 10.1007/s10973-020-09736-2
Memon, S. A., Jaiswal, M. S., Jain, Y., Acharya, V., & Upadhyay, D. S. (2020). A comprehensive review and a systematic approach to enhance the performance of improved cookstove (ICS). Journal of Thermal Analysis and Calorimetry, 141(6), 2253–2263. 10.1007/s10973-020-09736-210.1007/s10973-020-09736-2
Naved MM Wathore R Kumbhare H Gupta A Labhasetwar N Community kitchen tandoors (CKT)-A potential candidate for air pollution mitigation strategies? Environmental Science and Pollution Research International 2023 30 19 56317 56329 10.1007/s11356-023-26176-1 36917380
Naved, M. M., Wathore, R., Kumbhare, H., Gupta, A., & Labhasetwar, N. (2023). Community kitchen tandoors (CKT)-A potential candidate for air pollution mitigation strategies? Environmental Science and Pollution Research International, 30(19), 56317–56329. 10.1007/s11356-023-26176-136917380 10.1007/s11356-023-26176-1
Pratiti R Vadala D Kalynych Z Sud P Health effects of household air pollution related to biomass cook stoves in resource limited countries and its mitigation by improved cookstoves Environmental Research 2020 186 109574 10.1016/j.envres.2020.109574 32668541
Pratiti, R., Vadala, D., Kalynych, Z., & Sud, P. (2020). Health effects of household air pollution related to biomass cook stoves in resource limited countries and its mitigation by improved cookstoves. Environmental Research, 186, 109574. 10.1016/j.envres.2020.10957432668541 10.1016/j.envres.2020.109574
Rabuan U Mohd Nadzir MS Abdullah Sham SZ Wan Shaiful Bahri SBI Borah J Majumdar S Lei TMT Md Ali SH A Wahab MI Mohd Yunus NH Evaluations of Low-cost Air Quality Sensors for Particulate Matter (PM2.5) under Indoor and Outdoor Conditions Sensors and Materials 2023 35 8 2881 2895 10.18494/SAM4393
Rabuan, U., Mohd Nadzir, M. S., Abdullah Sham, S. Z., Wan Shaiful Bahri, S. B. I., Borah, J., Majumdar, S., Lei, T. M. T., Md Ali, S. H., A Wahab, M. I., & Mohd Yunus, N. H. (2023). Evaluations of Low-cost Air Quality Sensors for Particulate Matter (PM2.5) under Indoor and Outdoor Conditions. Sensors and Materials, 35(8), 2881–2895. 10.18494/SAM439310.18494/SAM4393
Raheja G Nimo J Appoh EKE Essien B Sunu M Nyante J Amegah M Quansah R Arku RE Penn SL Giordano MR Zheng Z Jack D Chillrud S Amegah K Subramanian R Pinder R Appah-Sampong E Tetteh EN Borketey MA Hughes AF Westervelt DM Low-cost sensor performance intercomparison, correction factor development, and 2+ years of ambient PM 2.5 monitoring in Accra Ghana. Environmental Science and Technology 2023 57 29 10708 10720 10.1021/acs.est.2c09264 37437161
Raheja, G., Nimo, J., Appoh, E. K. E., Essien, B., Sunu, M., Nyante, J., Amegah, M., Quansah, R., Arku, R. E., Penn, S. L., Giordano, M. R., Zheng, Z., Jack, D., Chillrud, S., Amegah, K., Subramanian, R., Pinder, R., Appah-Sampong, E., Tetteh, E. N., … Westervelt, D. M. (2023). Low-cost sensor performance intercomparison, correction factor development, and 2+ years of ambient PM 2.5 monitoring in Accra. Ghana. Environmental Science and Technology, 57(29), 10708–10720. 10.1021/acs.est.2c0926437437161 10.1021/acs.est.2c09264
Saleh S Sambakunsi H Makina D Chinouya M Kumwenda M Chirombo J Semple S Mortimer K Rylance J Mortimer K Rylance J Personal exposures to fine particulate matter and carbon monoxide in relation to cooking activities in rural Malawi Wellcome Open Research 2022 7 251 10.12688/wellcomeopenres.18050.2 36874568
Saleh, S., Sambakunsi, H., Makina, D., Chinouya, M., Kumwenda, M., Chirombo, J., Semple, S., Mortimer, K., Rylance, J., Mortimer, K., & Rylance, J. (2022). Personal exposures to fine particulate matter and carbon monoxide in relation to cooking activities in rural Malawi. Wellcome Open Research, 7, 251. 10.12688/wellcomeopenres.18050.236874568 10.12688/wellcomeopenres.18050.2
Shupler M Godwin W Frostad J Gustafson P Arku RE Brauer M Global estimation of exposure to fine particulate matter (PM2.5) from household air pollution Environment International 2018 120 354 363 10.1016/j.envint.2018.08.026 30119008
Shupler, M., Godwin, W., Frostad, J., Gustafson, P., Arku, R. E., & Brauer, M. (2018). Global estimation of exposure to fine particulate matter (PM2.5) from household air pollution. Environment International, 120, 354–363. 10.1016/j.envint.2018.08.02630119008 10.1016/j.envint.2018.08.026
Sichletidis L Spyratos D Papaioannou M Chloros D Tsiotsios A Tsagaraki V Haidich AB A combination of the IPAG questionnaire and PiKo-6® flow meter is a valuable screening tool for COPD in the primary care setting Primary Care Respiratory Journal: Journal of the General Practice Airways Group 2011 20 2 184 189 10.4104/pcrj.2011.00038
Sichletidis, L., Spyratos, D., Papaioannou, M., Chloros, D., Tsiotsios, A., Tsagaraki, V., & Haidich, A. B. (2011). A combination of the IPAG questionnaire and PiKo-6® flow meter is a valuable screening tool for COPD in the primary care setting. Primary Care Respiratory Journal: Journal of the General Practice Airways Group, 20(2), 184–189. 10.4104/pcrj.2011.0003810.4104/pcrj.2011.00038
Smith KR National burden of disease in India from indoor air pollution Proceedings of the National Academy of Sciences of the United States of America 2000 97 24 13286 13293 10.1073/pnas.97.24.13286 11087870
Smith, K. R. (2000). National burden of disease in India from indoor air pollution. Proceedings of the National Academy of Sciences of the United States of America, 97(24), 13286–13293. 10.1073/pnas.97.24.1328611087870 10.1073/pnas.97.24.13286
Stoner O Lewis J Martínez IL Gumy S Economou T Adair-Rohani H Household cooking fuel estimates at global and country level for 1990 to 2030 Nature Communications 2021 12 1 5795 10.1038/s41467-021-26036-x
Stoner, O., Lewis, J., Martínez, I. L., Gumy, S., Economou, T., & Adair-Rohani, H. (2021). Household cooking fuel estimates at global and country level for 1990 to 2030. Nature Communications, 12(1), 5795.10.1038/s41467-021-26036-x
Subramanian R Kagabo AS Baharane V Guhirwa S Sindayigaya C Malings C Williams NJ Kalisa E Li H Adams P Robinson AL Langley DeWitt H Gasore J Jaramillo P Air pollution in Kigali, Rwanda: Spatial and temporal variability, source contributions, and the impact of car-free Sundays Clean Air Journal 2020 30 2 1 15 10.17159/caj/2020/30/2.8023
Subramanian, R., Kagabo, A. S., Baharane, V., Guhirwa, S., Sindayigaya, C., Malings, C., Williams, N. J., Kalisa, E., Li, H., Adams, P., Robinson, A. L., Langley DeWitt, H., Gasore, J., & Jaramillo, P. (2020). Air pollution in Kigali, Rwanda: Spatial and temporal variability, source contributions, and the impact of car-free Sundays. Clean Air Journal, 30(2), 1–15. 10.17159/caj/2020/30/2.802310.17159/caj/2020/30/2.8023
United Nations Environment Programme (2019). Air pollution in Asia and the Pacific: Science-based solutions, Retrieved from https://www.env.go.jp/content/900514658.pdf
United Nations Framework Convention on Climate Change (2023). Momentum for change. Rwanda: Improved Cook Stoves for East Africa, Retrieved from https://unfccc.int/climate-action/momentum-for-change/activity-database/momentum-for-change-improved-cook-stoves-for-east-africa-rwanda
Urmee T Gyamfi S A review of improved Cookstove technologies and programs Renewable and Sustainable Energy Reviews 2014 33 625 635 10.1016/j.rser.2014.02.019
Urmee, T., & Gyamfi, S. (2014). A review of improved Cookstove technologies and programs. Renewable and Sustainable Energy Reviews, 33, 625–635. 10.1016/j.rser.2014.02.01910.1016/j.rser.2014.02.019
US Department of Agriculture, & Economic Research Service (2016). Americans spend an average of 37 minutes a day preparing and serving food and cleaning up, Retrieved from https://www.ers.usda.gov/amber-waves/2016/november/americans-spend-an-average-of-37-minutes-a-day-preparing-and-serving-food-and-cleaning-up/
US Environmental Protection Agency (2012). The national ambient air quality standards for particle pollution: Revised air quality standards for particle pollution and updates to the air quality index (AQI), Retrieved from https://www.epa.gov/sites/default/files/2016-04/documents/2012_aqi_factsheet.pdf
Vanker A Barnett W Chartier R MacGinty R Zar HJ Personal monitoring of fine particulate matter (PM2.5) exposure in mothers and young children in a South African birth cohort study – A pilot study Atmospheric Environment 2023 294 119513 10.1016/j.atmosenv.2022.119513
Vanker, A., Barnett, W., Chartier, R., MacGinty, R., & Zar, H. J. (2023). Personal monitoring of fine particulate matter (PM2.5) exposure in mothers and young children in a South African birth cohort study – A pilot study. Atmospheric Environment, 294, 119513. 10.1016/j.atmosenv.2022.11951310.1016/j.atmosenv.2022.119513
WHO (2021). WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, Retrieved from https://www.who.int/publications/i/item/9789240034228
WHO (2023). Household air pollution, Retrieved from https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health
Wunsch, N. G. (2022). Time spent cooking a weeknight meal in the UK 2019, by duration. Kitchen stories, Retrieved from https://www.statista.com/statistics/1085302/time-spent-cooking-weeknight-in-the-uk/
