Determinants of Indoor PM2.5 Concentrations in Ger, a Traditional Residence, in Mongolia
Abstract
Use of coal by residents of ger, the traditional Mongolian residence, is a major cause of increasing indoor PM2.5 concentrations. While high-level of indoor PM2.5 concentrations of ger have been reported in the previous studies, the contributions of daily activities, such as indoor coal burning, cooking and smoking to the indoor PM2.5 concentrations have not been clearly determined. The aims of this study were to determine the factors of indoor PM2.5 concentration in ger and to quantify the effect of them on both average and real-time indoor PM2.5 concentrations.
PM2.5 concentrations of gers and dwellings were measured in winter over three years. During the measurement, information of residents’ indoor activities were observed. Multiple regression was carried out with daytime average indoor PM2.5 concentration as a dependent variable. In order to determine the effect of indoor activities on real-time indoor PM2.5 concentration, the peak analysis was performed.
Indoor PM2.5 concentration and I/O ratio were significantly higher in gers than dwellings. Outdoor PM2.5 concentration and indoor smoking were significant factors affecting daytime average of indoor PM2.5 concentration in gers. Daily activity factors were associated with real-time PM2.5 concentration - average peak magnitude of 224.3 μg/m3 occurred with fuel addition, 260.1 μg/m3 with cooking, and 407.7 μg/m3 with indoor smoking.
Indoor PM2.5 concentration of ger was extremely high, even more than dwellings in adjacent area. The indoor smoking and outdoor air pollution affected average indoor PM2.5 concentration in ger. Daily activities of residents of ger such as fuel usage, cooking and smoking increased indoor PM2.5 concentration in a short time.
Keywords:
Mongolian residence, indoor PM2.5 concentration, residents, activitiesIntroduction
More than 2.8 billion people in developing countries around the world use solid fuels [1]. Previous studies showed that indoor burning of the solid fuel increased the risk of adult pneumonia and lung cancer [2, 3]. One of the main pollutants of solid fuel, particulate matter less than 2.5 μm (PM2.5), was known to be related to several adverse health effect [4]. Long-term exposure to PM2.5 was associated with cardiovascular and respiratory diseases [5]. High PM2.5 level increased hospital admissions for asthma and respiratory diseases of children [6]. Due to the related health problems listed above, WHO recommended not using a solid fuel for indoor heating or cooking [7, 8].
A ger is a traditional Mongolian house, made of wooden frame and felt. The residents of ger used coal, which was a major source for PM2.5 in residential site of Mongolia [9]. The capital city, Ulaanbaatar, was the most polluted area in country, where 47 % of total population live in [10]. A recent study indicated that exposure of PM2.5 in Ulaanbaatar was responsible for 24% of lung cancer death and 42% of stroke death [11].
Previous studies mainly focused on outdoor air pollution of Ulaanbaatar, and relatively small number of studies were conducted on indoor environment of ger. However, a recent study suggested that most PM2.5 exposures occurred indoors [11]. Considering indoor factors affecting PM2.5 are less known, it is therefore important to conduct a study on the indoor environment of ger. In a previous study, it was observed that PM2.5 concentrations increased due to indoor activities such as coal injection, cooking, and cleaning in ger [12]. However, contribution of these factors to indoor PM2.5 concentration were not determined. According to a subsequent study, frequency of opening stove was associated with average indoor PM2.5 concentration in ger [13]. However, contribution of these factors to indoor PM2.5 concentration were not clearly known due to limited sample size.
Impact of daily activity factors on the real-time PM2.5 concentration were not well known. It was reported that short-term exposure of high level of PM2.5 could have caused detrimental impact on both physical and mental health. For instance, short-term exposure of high PM2.5 level had an immediate effect on cardiovascular outpatient visits [14]. Furthermore, short exposure to high PM2.5 level was associated with increased risk of delirium [15]. It is therefore important to know how indoor activities affect the real-time PM2.5 concentration.
The aim of this study was to determine factors of both average and real-time indoor PM2.5 concentration in ger. We categorized factors as 1) indoor activities – fuel usage, cooking and indoor smoking, 2) characteristics of ger; separation of cooking room and stove type and 3) outdoor air pollution. In order to assess the indoor environment of ger compared to other type of residence, indoor PM2.5 data of adjacent dwellings were collected.
Methods
2.1 Data Collection
This study was conducted on 76 gers and 40 dwellings in ger district, Ulaanbaatar, with 36 gers measured in Jan., 2016, 40 gers measured in Jan, 2018 and 40 dwellings measured in Jan, 2017. In each year, researchers measured temperature, relative humidity, and PM2.5 number concentration of residence for 4 days during daytime (11:00 - 18:00). Temperature and relative humidity were measured by Onset HOBO Datalogger UX100-003 (Onset Computer Corporation, USA), and the number concentration of PM2.5 was measured by utilizing a Dylos DC1799 (Dylos Corporation, USA). The instruments were located at a minimum distance of 0.5 m from the floor. The measurement interval of all instruments was one minute. The obtained number concentrations of PM2.5 were converted to mass concentrations through the following equation (1).
| (1) |
During the measurement, the researchers observed the activities of the inhabitants and made notations in an observation log. The start and end times of activities such as cooking, fuel addition and fuel amount, indoor smoking, candle usage, food and beverage consumption, residents’ exiting their residences, cleaning and ventilation were investigated through observation log. The amount of added fuel was examined quantitatively using a scale.
2.2 Outdoor PM2.5 concentration data
We used public outdoor PM2.5 concentration data of ger district (Figure1). Measuring station was located in Nisekh, sub-district of Khaan-Uul district in Ulaanbaatar (Figure 1). These data were accessed from the OpenAQ Platform (openaq.org) and originated from Mongolia National Agency of Meteorology and Environmental Monitoring (Accessed 3 March, 2018).
Indoor-outdoor PM2.5 distribution of two residential types Error bars indicate one standard deviation from sample average. Regression equation was Cin = 1.07Cout + 32.78 (r = 0.63; 95% C.I 0.47-0.75, p<0.001) for ger and Cin = 0.86Cout + 0.52 (r = 0.72; 95% C.I 0.52-0.84, p<0.001) for dwellings.
2.3 Data analysis
The average indoor PM2.5 concentrations were calculated as the arithmetic mean of the measurement time after excluding adjustment time of the device. The average outdoor PM2.5 concentrations were calculated using the arithmetic mean of PM2.5 from 09:00 to 18:00.
The I/O ratio, representing the strength of indoor PM2.5 concentration in that household, was calculated by dividing average indoor PM2.5 concentration by average outdoor PM2.5 concentration of the day. Based on I/O ratio, we excluded data that were more than three standard deviations from the mean. One dwelling was excluded by the criterion. Data with instrument malfunction during observation was removed. The final number of samples were 76 for the gers and 38 for the dwellings.
Simple linear regression was applied to obtain correlation of indoor-outdoor PM2.5 concentrations for the two residential types. The regression equation was as follows: Cout = aCin+ b, where Cin and Cout represent average indoor and outdoor PM2.5 concentrations, respectively.
Multiple linear regression was used to identify determinants of daytime average indoor PM2.5 concentrations in ger. Gers with missing observation were removed from original data. The final number of samples used in multiple linear regression was 70. The predictors consist of the following three categories outdoor PM2.5 concentrations, occupants’ indoor activities and characteristics of gers. The activity factors include cooking frequency, fuel usage, and smoking (indoor smoking more than once during observation time). And separation of cooking room and stove type (traditional, improved) were classified into the characteristics of gers. To make the PM2.5 data normally distributed, we performed a square root transformation for indoor PM2.5 concentration and outdoor PM2.5 concentration. After transformation, PM2.5 data met normality assumption for the reliability of statistical analysis.
The peak analysis was conducted for residents’ activity factor (fuel usage, cooking and smoking) to determine effect to the real-time indoor PM2.5 concentration in ger. Total number of gers used in peak analysis was 68 (8 gers were excluded for missing observation log and measuring time error).
We defined the peak as the case where the difference between the lowest value and the highest value within 30 minutes of active time was 35 or more. All statistical analysis was performed using R software, version 3.4.1 (R Core Development Team, 2017).
Results
3.1 Description of indoor air quality
Table 1 shows descriptive statistics of temperature, humidity and indoor PM2.5 concentrations by observation dates in gers and dwellings. On average, temperature and indoor PM2.5 concentration were higher in ger, but relative humidity was higher in the dwelling.
(a) Descriptive statistics of indoor air quality in ger
We used the coefficient of variation (cv) as an indicator of between-home variability. In case of temperature, between-home variability was relatively small (cv=0.18). It indicated that there was not much difference in temperature between gers. The relative humidity were moderately variable between gers (cv=0.33). However, the variability of indoor PM2.5 was considerable (cv=0.59). The between-home variability of temperature and indoor PM2.5 concentration in dwelling showed similar to ger (cv=0.16, 0.62, respectively). The variability of relative humidity was slightly smaller than that of ger (cv=0.26), but it was not statistically significant.
3.2 Comparison of Indoor PM2.5 concentration between ger and dwelling
The average indoor PM2.5 concentration in the ger was higher than dwelling (p<0.05). The proportion of households where average indoor PM2.5 concentration is high (> 300 μg/m3) differed between the two residential types. In the case of ger, 15.8 % (n=12) of households had a PM2.5 level above 300 μg/m3, compared to 7.9 % (n=3) for dwellings. 6.6 % (n=5) of gers exhibited extremely high indoor PM2.5 levels over 400 μg/m3, whereas no applicable data were observed in dwellings.
Table 2 shows the I/O ratios of gers and dwellings. The I/O ratio of ger was significantly higher than that of dwellings (p<0.001). It suggested that indoor PM2.5 was still larger than dwellings after accounting for influence of outdoor air. In the case of the I/O ratio over 1 households, ger was 64 % (n=49) and dwelling was 42 % (n=16). For households with I/O ratio over 2, ger was 17 % (n=13) and there was no dwelling.
I/O ratios of two residential types
3.3 Determinants of average indoor PM2.5 concentration
As shown in Figure 1, correlation of indooroutdoor PM2.5 concentration was considerable (r = 0.63, 0.72 for ger and dwelling).
Despite the short observation period, daily variation of average outdoor PM2.5 concentration was large so that data was relevant to infer indoor-outdoor PM2.5 correlation (p<0.001 for both). Regression coefficients were not significantly different between two residential types (i.e. effect modification of outdoor PM2.5 concentration was not confirmed).
In regression models, mean absolute error calculated as average absolute distance from expected value was larger in gers (p<0.05). It resulted in a smaller correlation coefficient of ger than that of dwelling.
Table 3 shows the multiple linear regression result on average indoor PM2.5 concentration in ger. Under this model, outdoor PM2.5 concentration was the most influential factor for average indoor PM2.5 concentration (p<0.001).
Multiple linear regression result on indoor PM2.5 concentrations in ger
The average indoor PM2.5 concentration was higher in the house where smoking was observed more than once during the observation period (p<0.05). The separated cooking room, cooking frequency, stove type, fuel amount and fuel usage frequency were not significantly associated with average indoor PM2.5 concentration.
3.4 Determinants of real-time indoor PM2.5 concentration
Three activity factors - fuel usage, cooking and indoor smoking affected real-time PM2.5 concentration causing peaks of indoor PM2.5 concentration (Table 4). More than half of the fuel usage raised the peaks of more than 35 μg/m3 within 30 minutes before and after fuel usage. In case of the cooking, the average increase was larger than fuel usage, but the peak occurrence rate was smaller than that of fuel usage. As for indoor smoking, it showed that most smoking caused a strong peak within a short time. The average increase of indoor smoking was 407.7 μg/m3, which was significantly the largest factor among the three activity factors.
Activity factors influencing indoor PM2.5 concentrations peaks in a ger
Figure 2 shows examples of real-time changes of indoor PM2.5 concentration in gers according to three activity factors. Each activity frequently caused large increases of PM2.5 concentration in a short time. After peak occurred, PM2.5 concentration gradually decreased and returned to the original level.
Influences of indoor activities to real-time indoor PM2.5 concentrations
Discussion
4.1. Overall indoor air quality in ger
The overall indoor environment of ger was not suitable for occupant’s well-being. The relative humidity was considerably deviated from the indoor air quality standard. Previous studies suggested that atmospheric humidity of at least 30 - 40 % had to be maintained to prevent drying of the nasal mucosa membrane [16]. However, small proportion of households met this standard (22 %). The low indoor humidity was known to affect eyes and skin irritation [17, 18]. According to our survey, substantial proportion of residents had been suffering eye irritation and skin problems (42 %, 17 %, respectively). It is necessary to maintain relative humidity at an adequate level for reducing related adverse health risk of residents.
The indoor PM2.5 concentrations of ger need instant improvement. Indoor PM2.5 concentrations of gers was extremely high on average. It was much worse than the WHO Air Quality Guideline of indoor PM2.5 concentration (25 μg/m3). In some gers, average PM2.5 concentration exceeded the industrial air quality standard. The threshold of eight hour time weighted average exposures to PM2.5 was 300 μg/m3 [19]. This threshold was exceeded in some gers (15.8 %).
In the most gers, the temperature was acceptable. The average temperature in gers met the winter temperature standard of 19.2 - 27.8 ºC of the American Society of Heating, Refrigerating and Air-conditioning Engineers [20].
4.2 Comparison of indoor PM2.5 concentration between ger and dwelling
There was a remarkable difference in PM2.5 level for each residential type: almost as factor of one and half for I/O ratio (Table 2). The I/O ratio was commonly used to indicate the strength of indoor generated pollutants [21]. High indoor PM2.5 level in ger is presumably caused by influence of indoor generated pollutants and infiltration of outdoor pollution.
It could be inferred that the characteristics of house may be related to high indoor PM2.5 level in ger. Because indoor activities of dwelling was not particularly different with ger [12]. The effect of indoor generated pollutants on PM2.5 level according to the characteristics of house remain to be determined.
Controlling indoor particulate matter concentration will be necessary to improve overall health outcomes of Ulaanbaatar. More than half families in Ulaanbaatar live in ger or dwelling other than apartment [11]. Our results suggest a large number of population of Ulaanbaatar live in an indoor environment that is similar to, or even worse than outdoor environment.
4.3. Determinants of average indoor PM2.5 concentration
Determining factors of PM2.5 was a crucial issue for Mongolian government to establish policy for improving indoor PM2.5 level. Our results provided an understanding of factors for indoor PM2.5 concentrations. As expected, the outdoor PM2.5 concentration was a significant factor of indoor PM2.5 concentration of ger. We showed significant association of indoor-outdoor PM2.5 correlation in ger district, Ulaanbaatar (Table 2). It implied that serious air pollution in Ulaanbaatar could directly affect an indoor air of residence in Ulaanbaatar, possibly causing an adverse health effects on residents.
Among indoor activities, smoking was the most influential factor of indoor PM2.5 concentration. Indoor smoking was only significantly associated with average indoor PM2.5 concentrations. Influence of second hand smoke exposure on human health is well documented [22]. It indicated that indoor smoking might have large impact on occupant’s health compared to the other indoor activities.
The impacts of cooking and fuel usage on average PM2.5 concentrations were not determined. However, they frequently increased indoor PM2.5 concentrations after actions (Table 3). It suggested that real-time analysis was more suitable to determine impact of these activities on indoor PM2.5 concentrations.
4.4 Determinants of real-time indoor PM2.5 concentration
All of three indoor activities considerably affected real-time indoor PM2.5 concentrations raising peak after actions. Smoking had the largest peak occurrence rate and magnitude. Peaks of PM2.5 were observed immediately after smoking for most of cases (Table 3).
Cooking also had a large impact on indoor PM2.5 concentration. For cooking, peak occurrence rate was the smallest, but the peak magnitude was larger than fuel usage. It was known that certain type of cooking had a significant effect on indoor PM2.5 concentrations. Frying food affected indoor PM2.5 in the cooking space [23]. According to the observations in this study, only 24.3 % of ger had separate cooking space. Therefore, indoor PM2.5 exposure from cooking might be considerable in ger.
4.5. Limitation of study
Because 24 hours could not be observed, it was difficult to understand how the behavior factors of various residents affected indoor air quality. There were overlapping of various activities at the time when peak concentration occurred. In particular, it was difficult to grasp the influence of certain variables in situations where various activities overlapped in the time of peak occurrence. There might be observational biases from different observers. Outdoor PM2.5 was approximated using publicly available data. Given that the PM2.5 distribution in Ulaanbaatar could be spatially different, it would have been better to collect outdoor PM2.5 concentration data near target households as much as possible to ascertain outdoor influence on indoor PM2.5 concentration. However, we used publicly available data, because device could not directly measure outdoor PM2.5 due to very low temperature of Ulaanbaatar.
Conclusion
This study presented the results of three years of indoor PM2.5 concentration data of ger and dwellings in Ulaanbaatar, Mongolia. Both indoor PM2.5 concentration and I/O ratio of ger were higher than that of dwelling presumably due to high-level of indoor generated pollutants. Three activities of residents such as fuel usage, cooking, and smoking affected real-time PM2.5 concentration in ger by increasing PM2.5 in a short period of time. Among activity factors, only smoking was associated with the average PM2.5 concentration. As expected, outdoor PM2.5 concentration of Ulaanbaatar was considerably correlated with average indoor PM2.5 level in gers.
Acknowledgments
This study was partially supported by the China Medical Board (CMB), the Institute for Global Social Responsibility and Institute of Health and Environment, Seoul National University. This study was conducted as part of Global Environmental Health Practicum coursework in the Graduate School of Public Health, Seoul National University. The authors thank Naae Lee, Yeong Hwa So, Youngji Lee, Se Yeon Kim for their cooperation and work in this field study. The authors would also like to thank students from Mongolian National University of Medical Sciences who participated and assisted during the course of the study.
References
-
Dutta, S., and Banerjee, S., Exposure to Indoor Air Pollution & Women Health: The Situation in Urban India, Environment and Urbanization Asia, (2014), 5(1), p131-145.
[https://doi.org/10.1177/0975425314521545]
-
Shen, M., et al., Coal Use, Stove Improvement, and Adult Pneumonia Mortality in Xuanwei, China: A Retrospective Cohort Study, Environ Health Perspect, (2009), 117(2), p261-266.
[https://doi.org/10.1289/ehp.11521]
- Lan, Q., et al., Indoor Coal Combustion Emissions, GSTM1 and GSTT1 Genotypes, and Lung Cancer Risk: A Case-Contrl Study in Xuan Wei, China, Cancer Epidemiology, Biomarker & Prevention, (2000), 9(6), p605-608.
-
Hu, W., et al., Personal and Indoor PM2.5 Exposure from Burning Solid Fuels in Vented and Unvented Stoves in a Rural Region of China with a High Incidence of Lung Cancer, Environ Sci Technol, (2014), 48(15), p8456-8464.
[https://doi.org/10.1021/es502201s]
- Francesca, D., et al., Fine Particulate Air Pollution and Hospital Admission for Cardiovascular and Respiratory Diseases, Journal of American Medical Association, (2006), 295(10), p1127-1134.
-
Tecer, L. H., et al., Particulate Matter ((PM2.5), PM(10-2.5), and PM(10)) and Children's Hospital Admissions for Asthma and Respiratory Diseases: A Bidirectional Case-Crossover Study, Journal of Toxicol Environmental Health, (2008), 71(8), p512-520.
[https://doi.org/10.1080/15287390801907459]
- World Health Organization (WHO), WHO guidelines: household fuel combustion, Geneva, World Health Organization, (2014), Available at: http://www.who.int/iris/handle/10665/141496 (March, 2017).
- International Agency for Research on Cancer (IARC), Household use of solid fuels and high-temperature frying, International Agency for Research on Cancer, (2010), Available at: http://monographs.iarc.fr/ENG/Monographs/vol95/ (March, 2017).
- Gerelamaa, G., et al., Air Particulate Matter Pollution in Ulaanbaatar City, Mongolia, International Journal of PIXE, (2012), 22(1), p165-171.
- Word Health Organization (WHO), Air pollution in mongolia: policy brief, (2018), Available at http://www.wpro.who.int/mongolia/publications/20180228_policy_brief_on_air_pollution.pdf (March, 2018).
-
Hill, LD., et al., Health assessment of future PM2.5 exposures from indoor, outdoor, and secondhand tobacco smoke concentrations under alternative policy pathways in Ulaanbaatar, Mongolia, PLoS ONE, (2017), 12(10), e0186834.
[https://doi.org/10.1371/journal.pone.0186834]
- Lee, B., et al., Temporal Variation of Winter Indoor PM2.5 Concentrations in Dwellings in ger Town of Ulaanbaatar, Mongolia, Journal of Environmental Health Science, (2018), 44(1), p98-105.
-
Ban, H., et al., Daytime Profile of Residential PM2.5 Concentrations in a ger, a Traditional Residence in Mongolia, The Korean Journal of Public Health, (2017), 54(1), p23-30.
[https://doi.org/10.17262/kjph.2017.03.54.1.23]
- LI, Guangxi, et al., The Association between Short-Term Exposure to Fine Particulate Matter and Outpatient Visit in Beijing, China, Iranian Journal of Public Health, (2016), 46(11), p1486-1494.
-
Che, L., et al., Effect of short-term exposure to ambient air particulate matter on incidence of delirium in a surgical population, Scientific Reports, (2016), 7(1), p15461.
[https://doi.org/10.1038/s41598-017-15280-1]
- Zeterberg, J., A review of respiratory virology and the spread of virulent and possibly antigenic viruses via air conditioning systems, I. Annals of allergy, (1973), 31(5), p228-234.
- McIntyre, D. A., Response to atmospheric humidity at comfortable air temperature: a comparison of three experiments, Ann. Occup. Hyg, (1978), 21(2), p177-190.
-
Goad, N., and D. J. Gawkrodger, Ambient humidity and the skin: the impact of air humidity in healthy and diseased states, Journal of the European Academy of Dermatology and Venereology, (2016), 30(8), p1285-1294.
[https://doi.org/10.1111/jdv.13707]
- American Conference of Governmental Industrial Hygienists (ACGIH), Threshold Limit Values for Chemical Substances and Physical Agents and Biological Exposure Indices, American Conference of Governmental Industrial Hygienists, (2001).
- American Society of Heating Refrigerating and Air-Conditioning Engineers (ASHRAE), Standard 55-2013. Thermal environmental conditions for human occupancy, Atlanta, GA, American Society of Heating Refrigerating and Air-Conditioning Engineers, (2013).
-
Leung, D. Y. C., Outdoor-indoor air pollution in urban environment: challenges and opportunity, Frontiers in Environmental Science, (2015), 2(69).
[https://doi.org/10.3389/fenvs.2014.00069]
-
Barnoya, J., and S. A. Glantz, Secondhand smoke: the evidence of danger keeps growing, The American Journal of Medicine, (2004), 116(3), p201-202.
[https://doi.org/10.1016/j.amjmed.2003.11.005]
- Haryono, S. H., Susumu, T., and Renqiu, C., Indoor PM2.5 Characteristics and CO Concentration Related to Water-Based and Oil-Based Cooking Emissions Using a Gas Stove, Aerosol and Air Quality Research, (2011), 11, p401-411.