Institute of Health & Environment Seoul National University

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The Korean Journal of Public Health - Vol. 54 , No. 2

[ Article ]
The Korean Journal of Public Health - Vol. 54, No. 1, pp.23-30
Abbreviation: KJPH
ISSN: 1225-6315 (Print)
Print publication date 20 Mar 2017
DOI: https://doi.org/10.17262/KJPH.2017.03.54.1.23

Daytime profile of residential PM2.5 concentrations in a ger, a traditional residence in Mongolia
Hyunkyung Ban1 ; Yunhyung Hwang1 ; Miyoung Lim1 ; Chimedsuren Ochir2 ; Kiyoung Lee1, 3
1Department of Environmental Health, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
2School of Public Health, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia
3Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea

Correspondence to : Kiyoung Lee (cleanair@snu.ac.kr, 02-880-2735) Department of Environmental Health, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea

Funding Information ▼

Abstract

The residents of a ger, a traditional house type in Mongolia, use coal for cooking and heating. The use of unprocessed coal can adversely affect indoor air quality. In this study, the daytime indoor PM2.5 concentration in gers was measured by a real-time PM monitor, while the behavior of the residents in using a stove for heating and cooking was also observed. The indoor PM2.5 concentration was 208±173 μg/m3 (n=28). Although coal was the main fuel, the residents also used other solid fuels such as wood, plastic, rubber, and garbage. High indoor PM2.5 concentrations were observed in the morning, and the daytime temporal profile also suggested a high PM2.5 concentration at night-time. The stove and fuel types were not significantly associated with indoor PM2.5 levels. Further study is needed to determine the effect of stove type through 24 hours of indoor air quality monitoring.


Keywords: Mongolia, PM2.5, indoor air, ger

Introduction

Nearly 50% of the population in developing countries use solid fuels [1]. Solid fuels are often burned in traditional open stoves without a proper ventilation system. Thus, a significant level of indoor air pollution occurs due to incomplete combustion [2, 3]. Among the various solid fuels, the World Health Organization (WHO) recommends against the use of unprocessed coal in the indoor environment [4, 5].

Indoor air pollution related to the burning of solid fuel for heating and cooking in a house affects human health. Coal combustion in a house has been linked to respiratory illnesses, lung cancer, chronic obstructive pulmonary disease (COPD), immune system impairment, and reduction in lung function in China [6]. The PM2.5 related to biomass burning has been associated with hospital admissions for respiratory diseases in children under the age of 5 in Cuiabá city [7]. In Shanghai in China, past use of coal among women was associated with all-causes mortality [8].

About 1.2 million people reside in Ulaanbaatar (UB), the capital city of Mongolia, accounting for more than 40% of the population of Mongolia [9]. The city has a peri-urban ger area where a significant part of the UB population is living in a ger. According to the UB Statistics Department, there are about 164,127 households in the ger areas of six districts surveyed by World Bank (WB) at the end of 2011, an increase of approximately 42,000 since the end of 2007 [10].

A ger is a traditional Mongolian house, which has a one-room tent structure with five or, in some cases, six walls, and typically with an average floor area of 28 m2. The stove for heating and cooking is located in the center of the ger. Households living in a ger in UB are costly to keep warm and comfortable because UB is the coldest capital in the world with an average daily temperature of about - 13ºC and an average night temperature of -40 ºC. The stove is used throughout the winter months from October to March. With the heavy use of coal, indoor and outdoor air pollution in UB can be very high during the winter.

The purpose of this study was to measure indoor PM2.5 levels in gers, and determine the effects of cooking and heating on indoor PM2.5 levels. The indoor PM2.5 levels were measured in 28 gers during the daytime, and cooking and heating activities were observed continuously.


Methods
Study setting

This study was conducted in 28 gers within the 17th sub-district of Bayanzürkh District, UB, Mongolia. The 28 households were recruited by sub-district family clinics. Measurements and observations were conducted during the daytime (11:00 ~ 18:00) in January 2016. The indoor PM2.5 concentration, temperature, and relative humidity were measured simultaneously in all 28 gers for four days. Two researchers in each ger observed the stove use behavior of the residents, including fuel type, the timing of placing fuel in the stove for heating and cooking, amount of fuel used, the time of cooking, and other behavior related to the PM2.5 concentration (opening the stove, smoking, burning herbs, and using a vacuum cleaner).

Indoor air monitoring

The real-time concentration of PM2.5 was monitored using MicroPEM (RTI Inc., USA). MicroPEM uses a nephelometer recording data every 10 seconds and provides gravimetric sampling, while simultaneously monitoring the real-time concentration. Before commencing the monitoring, the zero calibration of the MicroPEM was conducted with a High Efficiency Particulate Air (HEPA) filter and pre-calibrated at 0.50 L/min with a TSI flowmeter 4146 (TSI Inc., USA). The samples were collected gravimetrically on 3 mm polytetrafluoroethylene filters (PTFE, 25 μm pore) (Zefon International, USA). The filters were conditioned in a dry container for 48 h before weighing. The filters were measured in micrograms using a microbalance (METTLER TOLEDO, Switzerland) in a temperature- and humidity-controlled room. Temperature and relative humidity were measured using HOBO UX100-003 (Onset Computer Corp., USA). The PM monitoring instrument and the HOBO were placed on a table near the wall located approximately 1.2 m from the ground and about 5 m away from the entrance of the ger. The PM2.5 concentration values by MicroPEM were adjusted by a filter equipped in MicroPEM filter cassettes. A value below 5 μg/m3 recorded by the MicroPEM was changed to 3 μg/m3 because the lower limit of the MicroPEM was 5 μg/m3. The 1-min averages of the PM2.5 concentration were used for the analysis, taking into account the temperature and relative humidity data intervals from HOBO.

Statistical analysis

The 28 gers were summarized only. Twenty of the 28 gers were included in the statistical analysis. Four gers with smoking and four gers with burning herbs or candles were excluded. The geometric mean of PM2.5 in the ger was used for statistical analysis. The relationship between the PM2.5 concentration and the stove and fuel types was analyzed using the Kruskal-Wallis test. Because the residents in two of the 20 gers did not add additional fuel during the observation period, the analysis between fuel usage and the PM2.5 concentration was based on 18 gers. The correlation between the PM2.5 concentration and the patterns of stove use (the number of times the stove was opened and fuel usage) was analyzed using the Spearman correlation. The SPSS23 software was used in the statistical analysis.


Results

During the winter of 2011-2012, traditional stoves have been replaced by low-emission stoves (called "improved stoves") by WB’s project, as a part of a major effort to reduce outdoor air pollution in Ulaanbaatar [10]. However, there was no significant difference in fuel usage, times of adding fuel, PM2.5 concentration, and indoor temperature.

Eighteen of the 28 gers used a traditional stove, while the others used an improved stove. Up to 16 of the 28 gers used coal, 4 used wood, and 8 used coal with additional materials (wood, plastic bags, nut cover, and paper). The average fuel usage in 26 gers was 8758 ± 4074 g, excluding the two gers where no fuel was added during the observation period. The average number of times fuel was added into the stove was 0.4 ± 0.3 times/h. The average PM2.5 concentration, temperature, and relative humidity in the 28 gers were 208±173 μg/m3 (n=28), 23±4ºC (n=27, one home is missing due to malfunction of the monitor), and 21±7% (n=28), respectively. The descriptive statistics of the PM2.5, temperature, and relative humidity are listed in Table 1. The average PM2.5 concentration in 61% of the gers exceeded 100 μg/m3 (17 of 28 gers) and was under 50 μg/m3 in only 2 of 28 gers. The average temperature was over 21℃ in 70% of the gers (19 of 27 gers) and less than 18℃ in 3 of 27 gers.

Table 1. 
Descriptive statistics of indoor PM2.5, temperature, and relative humidity
AM ± SD 25th percentile Median 75th percentile 95th percentile
PM2.5
concentration (μg/m3)
208 ± 173 79 163 277 511
Temperature (℃) 23 ± 4 20 23 25 30
Relative humidity (%) 21 ± 7 15 17 25 37

Overall, the PM2.5 concentration was higher in the morning and decreased gradually until late afternoon. Five temporal profiles of the indoor PM2.5 concentration in the 28 gers were determined (Figure 1) using the concentration and temporal pattern of the real-time PM2.5 concentration. The average PM2.5 concentrations in the five groups were 384±171 μg/m3 (n=10), 171±71 μg/m3 (n=6), 123±56 μg/m3 (n=4), 74±27 μg/m3 (n=5), and 27±20 μg/m3 (n=3). In the first group, the indoor PM2.5 concentrations were very high, and an extreme peak of about 8,500 μg/m3 was observed. In the second and third groups, the indoor PM2.5 concentrations in the morning were 600 and 400 μg/m3, respectively, and decreased gradually. In the second group, peaks of about 1,100 μg/m3 occurred in the afternoon. In the third group, less peaks were observed. In the fourth group, the indoor PM2.5 concentrations in the morning were 200 μg/m3. There was a peak of about 180 μg/m3 in the afternoon. In the fifth group, the PM2.5 concentration remained below 50 μg/m3 in the afternoon.

The correlation between the PM2.5 concentration and the pattern of stove use is shown in Table 2. The number of times the stove was opened was significantly associated with the PM2.5 concentration. The PM2.5 concentration increased when the amount of fuel usage was higher, but they were not significantly correlated (p=0.078). Fuel usage and the frequency of opening the stove were significantly correlated (R=0.77, p-value<0.01). The peak frequency of PM2.5 was not statistically correlated with the number of times the stove was opened and the fuel usage.

Table 2. 
Correlation between the PM2.5 concentration and pattern of stove use (opening and fuel usage)
Opening stove (#/h) Fuel usage (g)
a PM2.5 concentration (μg/m3) R 0.470 * 0.426
P-value 0.036 0.078
N 20 18
Peak frequency of PM2.5 (#/h) R 0.362 0.244
P-value 0.116 0.330
N 20 18
a. the GM of the PM2.5 concentration of each ger was used in the analysis.
*. p-value < 0.05

The differences in the PM2.5 concentration based on stove type are listed in Table 3. The PM2.5 concentration did not differ based on stove and fuel type. The number of times the stove was opened was significantly higher with the traditional stove. However, 2 of the 5 gers using an improved stove did not add fuel during the observation period. The PM2.5 concentration in these two gers were lower than the other three gers. The differences in the PM2.5 concentration based on fuel type are listed in Table 4. The peak frequency of PM2.5 was higher with the use of mixed fuel. The stove was opened more often when mixed fuel was used.

Table 3. 
Difference in PM2.5 concentration based on stove type (N=20)
Traditional (n=15) Improved (n=5)
AM ± SD AM ± SD bp-value
aPM2.5 concentration (μg/m3) 81.1 ± 49.6 87.6 ± 39.4 0.11
Peak frequency of PM2.5 (#/h) 0.35 ± 0.41 0.44 ± 0.35 0.54
Fuel usage (g) 9610.3 ± 4288.9 7860.0 ± 1551.1 0.59
Frequency of opening stove (#/h) 0.46 ± 0.26 0.19 ± 0.25 0.04*
Temperature (℃) 23.6 ± 4.4 22.0 ± 2.0 0.49
a. the GM of the PM2.5 concentration of each ger was used in the analysis.
b. p-value of Kruskal-Wallis test.
*. p-value <0.05

Table 4. 
Difference in PM2.5 concentration based on fuel type (N=18)
Coal (n=12) Mixed coal (n=6)
AM ± SD AM ± SD bp-value
aPM2.5 concentration (μg/m3) 74.7 ± 48.0 89.5 ± 29.2 0.79
Peak frequency of PM2.5 (#/h) 0.34 ± 0.28 0.79 ± 0.35 0.04*
Fuel usage (g) 8078.3 ± 3625.1 10810.8 ± 3957.6 0.14
Frequency of opening stove (#/h) 0.29 ± 0.17 0.54 ± 0.17 0.04*
Temperature (℃) 24.1 3.6 21.9 4.8 0.41
a. the GM of the PM2.5 concentration of each ger was used in the analysis.
b. p-value of Kruskal-Wallis test.
*. p-value <0.05


Figure 1. 
Five types of temporal profiles of the PM2.5 concentration in the 28 gers.

The boundary of the box closest to zero indicates the 25th percentile, the line within the box marks the median, and the boundary of the box farthest from zero indicates the 75th percentile. Whiskers (error bars) above and below the box indicate the 90th and 10th percentiles, respectively. The dots indicate all data points that are outside the 10th and 90th percentiles.




Discussion

The indoor temperature in the gers was on average 23.0 ± 4ºC, which was on the high end of the acceptable indoor comfort temperature range. The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) recommends an indoor temperature of 19.2 ºC to 27.8 ºC in winter [11]. In 4 of the 27 gers, the indoor temperature was greater than 28ºC. Such high temperatures suggest the overuse of coal. Education to ensure the reasonable use of coal is required.

The relative humidity was very low (21% on average). Low relative humidity adversely affects human health. Eye and skin irritation may be caused under relative humidity of less than 20% [12-14]. Several reports have recommended maintaining indoor relative humidity above 30% to 40% to prevent drying of the mucous membranes and to maintain adequate ciliary activity [15, 16]. The survival of the rhinovirus and the influenza virus also improved in low relative humidity environments [17, 18]. Particles in dry air can transfer to deeper parts of the respiratory system, because particles can become smaller due to the loss of surface water [19].

Coal combustion for heating and cooking in gers caused high concentrations of PM. The indoor PM2.5 concentration was comparable with other studies using solid fuels. In Inner Mongolia, China, the highest respirable particulate matter (RPM) concentration of 719 μg/m3 was observed in a province with biomass burning in the single cooking/living/bedroom [20]. In the primarily coal-burning provinces, RPM concentrations were 202−352 μg/m3 in Guizhou and 187−361 μg/m3 in Shaanxi. In Xuanwei and Fuyuan, China, the geometric means (GM) of indoor PM2.5 air concentrations were GM(GSD):162 (2.1) and 136 (2.0) μg/m3, respectively [21]. In an area of extreme poverty in Metropolitan Santiago, higher pollutant concentrations were observed during hours when heating was used in houses that used coal (mean PM10 250 μg/m3, CO 42 ppm, and SO2 192 ppb). However, the 24-hour average PM10 concentration was 173 ± 14 μg /m3. In four states in India, the 24hr concentration of PM2.5 in households burning solid cook fuel ranged from 163 μg/m3 in the living area to 609 μg/m3 in the kitchen [22].

The temporal profiles of indoor PM2.5 concentration tend to include high levels in the morning. It has been suggested that indoor PM2.5 concentrations may be higher at night-time. We classified the temporal profiles into five groups. Group 1 with the highest concentration may have been affected by smoking and incense burning. Five of the ten gers in this group had either indoor smoking or incense burning. They used more mixed fuel types and opened the stove more frequently. The proportion of traditional improved stoves did not differ between groups. Groups 2 and 4 had slightly more fluctuating PM2.5 concentrations than groups 3 and 5. Only coal was used as fuel in groups 2 and 4. No indoor smoking and incense burning was observed in groups 4 and 5. The stove was opened much less frequently in groups 4 and 5, recorded as 0.2 and 0.3 times/h per house, respectively. Based on the observations, the peaks in the daily profile of the PM2.5 concentration coincided with smoking, incense burning, opening the stove for refueling, cooking fried food, and using the vacuum cleaner.

The residents in the ger used various materials for heating and cooking, although coal was the main fuel type. WHO recommends against the use of unprocessed coal in the indoor environment, because it can release various toxic pollutants. However, other materials, such as plastic and rubber, also release toxic chemicals. Plastic combustion can release hazardous chemicals, including, hydrochloric acid, sulfur dioxide, dioxins, furans, and heavy metals, as well as particulates. These emissions are known to cause respiratory diseases, to stress the human immune system, and are potentially carcinogenic. Therefore, the Mongolian government must educate residents to refrain from burning these materials while limiting the burning of unprocessed coal.

The study could not determine the significant factors causing the indoor PM2.5 concentration. It was not associated with stove or fuel type. In a previous study, the indoor PM2.5 and CO were not significantly decreased by the use of an improved stove [23]. The amount of fuel used was also not associated with the indoor PM2.5 concentration. The peak frequency of the PM2.5 concentration was higher with mixed coal. In previous research, the indoor PM concentration using coal mixed with additional materials was higher than when using coal only [21]. Further study with a larger sample size should be conducted to determine the significant factors. The temporal profile suggests that the number of times the stove door was opened may be associated with the peak frequency. This may be due to the release of PM during the door opening.

This study was limited by sample size. Because the observation of resident behavior was required, the number of gers studied was limited. However, the observations were important to determine the use of other fuels. The effects of stove type and other factors should be further determined with 24hour sampling. Another limitation was that ambient air concentrations were not measured. Ambient monitoring was not possible because of extremely cold temperature. In addition, there might be no significant difference in outdoor concentration for four sampling days. Therefore, the effect of the stove usage pattern on indoor PM2.5 concentration were valuable information.


Conclusion

The indoor PM2.5 concentrations in the gers reached high peak concentrations of up to 8500 μg/m3. However, our measurements may have underestimated the 24-hour exposure due to lack of monitoring during the night and early morning. The indoor PM2.5 concentration was associated with opening the stove. A stove should be developed where the PM emissions are reduced during the opening of the stove. Further study is also necessary to measure the indoor PM concentration for 24 hours and identify the factors associated with daily indoor exposure in a large sample population.


Acknowledgments

This study was partially supported by the China Medical Board (CMB) and the Institute for Global Social Responsibility, Seoul National University (SNU). We thank C. Han of the Department of Preventive Medicine, College of Medicine, SNU, and D.G. Park, S. Lee, S. Xu, and K. Ha of the Graduate School of Public Health, SNU, who have helped and co-operated with us during the field measurements. We also thank T. Monkhnaran, B. Buyuntushig, B. Jeljejtji, D. Otgongerel, Sh. Baynjargal, D. Bayrmaa, Ts. Odontuya, G. Odmaa, Sh. Purevdorj, and B. Yanjinlkham from the School of Public Health, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia for their kind support in translation and helping us observe residents in the gers.


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