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Indoor distribution and personal exposure of cooking-generated PM2.5 in rural residences of China: A multizone model study

Shanshan Shi( )Junling YangYushu Liang
School of Architecture and Urban Planning, Nanjing University, Nanjing, Jiangsu Province 210093, China
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Abstract

The fine particulate matter (PM2.5) emitted during cooking is a significant contributor to household air pollution in rural China, resulting in millions of premature deaths annually. Since cooking is an internal pollution source, the indoor concentration of cooking-generated PM2.5 can vary among different rooms in multizone rural residences. This study provides a comprehensive understanding of indoor PM2.5 from cooking in rural residences by utilizing on-site investigations to gather information on cooking behavior and dwelling layout in three Chinese villages, and subsequently simulating indoor spatiotemporal concentrations of cooking-generated PM2.5 using a multizone model. Our findings indicate that the type of zone significantly influences the zonal concentration of PM2.5, with the highest concentrations found in kitchens (i.e., 13.9 to 188.0 μg/m3) and lowest in non-adjacent zones to the kitchen (i.e., 0.01 to 7.5 μg/m3) among all the modeled conditions. More importantly, the study also assesses the resulting personal exposures for occupants with different time-spent patterns, revealing that the main cook at home and preferring to stay in the adjacent rooms to the kitchen are at the highest risk for personal exposure. The highest personal exposure levels of cooking-generated PM2.5 are 28.5 ± 30.1 μg/m3, which is 34 times that of occupants who stay away from the kitchen. The study provides a deeper scientific insight into the indoor spatial distribution and personal exposure to cooking-generated PM2.5 in rural residences, which is crucial for developing effective interventions to mitigate the detrimental health impacts of household air pollution in rural areas.

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References

 
ASHRAE (1994). ASHRAE Duct Fitting Database, Version 1.0. Atlanta, GA, USA: American Society of Heating, Refrigerating And Air-Conditioning Engineers.
 
ASHRAE (2005). ASHRAE Handbook of Fundamentals. Atlanta, GA, USA: American Society of Heating, Refrigerating And Air-Conditioning Engineers.
 

Baumgartner J, Schauer JJ, Ezzati M, et al. (2011). Patterns and predictors of personal exposure to indoor air pollution from biomass combustion among women and children in rural China. Indoor Air, 21: 479–488.

 

Bu X, Xie Z, Liu J, et al. (2021). Global PM2.5-attributable health burden from 1990 to 2017: Estimates from the Global Burden of disease study 2017. Environmental Research, 197: 111123.

 

Buonanno G, Morawska L, Stabile L (2009). Particle emission factors during cooking activities. Atmospheric Environment, 43: 3235–3242.

 

Cao Y, Wang Y, Yu Z, et al. (2022). Spatio-temporal distribution of gaseous pollutants from multiple sources in industrial buildings with different flow patterns. Building Simulation, 15: 1629–1644.

 
Chan WR, Price PN, Sohn MD, et al. (2003). Analysis of US residential air leakage database. Lawrence Berkeley National Lab (LBNL), Berkeley, CA, USA.
 

Chan WR, Nazaroff WW, Price PN, et al. (2005). Analyzing a database of residential air leakage in the United States. Atmospheric Environment, 39: 3445–3455.

 

Chen C, Zhao Y, Zhao B (2018). Emission rates of multiple air pollutants generated from Chinese residential cooking. Environmental Science and Technology, 52: 1081–1087.

 

Connolly CL, Milando CW, Tieskens KF, et al. (2022). Impact of meteorology on indoor air quality, energy use, and health in a typical mid-rise multi-family home in the eastern United States. Indoor Air, 32: e13065.

 

Emmerich SJ, Howard-Reed C, Nabinger SJ (2004). Validation of multizone IAQ model predictions for tracer gas in a townhouse. Building Services Engineering Research and Technology, 25: 305–316.

 

Fabian P, Adamkiewicz G, Levy JI (2012). Simulating indoor concentrations of NO2 and PM2.5 in multifamily housing for use in health-based intervention modeling. Indoor Air, 22: 12–23.

 

Gu Q, Gao X, Chen Y, et al. (2009). The mass concentration characters of indoor PM2.5 in rural areas in Jiangsu Province. Journal of Fudan University (Natural Science), 48: 593–597. (in Chinese)

 

He L, Gao J, Chen J, et al. (2021). Experimental studies of natural make-up air distribution in residential kitchen. Journal of Building Engineering, 44: 102911.

 

Hou J, Zhang Y, Sun Y, et al. (2018). Air change rates at night in northeast Chinese homes. Building and Environment, 132: 273–281.

 

Hu W, Downward GS, Reiss B, et al. (2014). 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. Environmental Science and Technology, 48: 8456–8464.

 

Hu R, Wang S, Aunan K, et al. (2019). Personal exposure to PM2.5 in Chinese rural households in the Yangtze River Delta. Indoor Air, 29: 403–412.

 

Huang Y, Du W, Chen Y, et al. (2017). Household air pollution and personal inhalation exposure to particles (TSP/PM2.5/PM1.0/PM0.25) in rural Shanxi, North China. Environmental Pollution, 231: 635–643.

 

Huang Y, Wang J, Chen Y, et al. (2022). Household PM2.5 pollution in rural Chinese homes: Levels, dynamic characteristics and seasonal variations. The Science of the Total Environment, 817: 153085.

 

Jose RS, Pérez JL, Gonzalez-Barras RM (2021). Multizone airflow and pollution simulations of indoor emission sources. The Science of the Total Environment, 766: 142593.

 

Lai AM, Carter E, Shan M, et al. (2019). Chemical composition and source apportionment of ambient, household, and personal exposures to PM2.5 in communities using biomass stoves in rural China. The Science of the Total Environment, 646: 309–319.

 

Lai AM, Clark S, Carter E, et al. (2020). Impacts of stove/fuel use and outdoor air pollution on chemical composition of household particulate matter. Indoor Air, 30: 294–305.

 

Lee BH, Yee SW, Kang DH, et al. (2017). Multi-zone simulation of outdoor particle penetration and transport in a multi-story building. Building Simulation, 10: 525–534.

 

Li T, Cao S, Fan D, et al. (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.

 

Liu S, Dong J, Cao Q, et al. (2020). Indoor thermal environment and air quality in Chinese-style residential kitchens. Indoor Air, 30: 198–212.

 

Liu Z, Zhu H, Song Y, et al. (2022). Quantitative distribution of human exhaled particles in a ventilation room. Building Simulation, 15: 859–870.

 

Lv L, Wu Y, Cao C, et al. (2022). Impact of different human walking patterns on flow and contaminant dispersion in residential kitchens: Dynamic simulation study.Building Simulation, 15: 1051–1066.

 

Ma L, Dong Z, Wu K, et al. (2015). Indoor air quality and characteristics of fine particle for rural Guizhou. Environmental Monitoring in China, 31(1), 28–34 (in Chinese).

 

Men Y, Li J, Liu X, et al. (2021). Contributions of internal emissions to peaks and incremental indoor PM2.5 in rural coal use households. Environmental Pollution, 288: 117753.

 

Meng C, Song Y, Ji J, et al. (2022). Automatic classification of rural building characteristics using deep learning methods on oblique photography. Building Simulation, 15: 1161–1174.

 
MOHURD (2011). Rural Housing Construction Technology Policy (Trial Implementation). Ministry of Housing and Urban-Rural Development of China. (in Chinese)
 
MOHURD (2013). GB/T 50824-2013. Design Standard for Energy Efficiency of Rural Residential Buildings. Ministry of Housing and Urban-Rural Development of the People of China. (in Chinese)
 

National Bureau of Statitics of China (2021). China Statistical Yearbook 2021. Beijing: China Statistics Press. (in Chinese)

 

Ng LC, Musser A, Persily AK, et al. (2012). Indoor air quality analyses of commercial reference buildings. Building and Environment, 58: 179–187.

 

Nguyen JL, Schwartz J, Dockery DW (2014). The relationship between indoor and outdoor temperature, apparent temperature, relative humidity, and absolute humidity. Indoor Air, 24: 103–112.

 

Norbäck D, Lu C, Zhang Y, et al. (2019a). Onset and remission of childhood wheeze and rhinitis across China—Associations with early life indoor and outdoor air pollution. Environment International, 123: 61–69.

 

Norbäck D, Lu C, Zhang Y, et al. (2019b). Sources of indoor particulate matter (PM) and outdoor air pollution in China in relation to asthma, wheeze, rhinitis and eczema among pre-school children: Synergistic effects between antibiotics use and PM10 and second hand smoke. Environment International, 125: 252–260.

 

Pan M, Li S, Tu R, et al. (2021). Associations of solid fuel use and ambient air pollution with estimated 10-year atherosclerotic cardiovascular disease risk. Environment International, 157: 106865.

 

Patel S, Li J, Pandey A, et al. (2017). Spatio-temporal measurement of indoor particulate matter concentrations using a wireless network of low-cost sensors in households using solid fuels. Environmental Research, 152: 59–65.

 

Qi M, Du W, Zhu X, et al. (2019). Fluctuation in time-resolved PM2.5 from rural households with solid fuel-associated internal emission sources. Environmental Pollution, 244: 304–313.

 

Ruiz-García VM, Edwards RD, Ghasemian M, et al. (2018). Fugitive emissions and health implications of plancha-type stoves. Environmental Science and Technology, 52: 10848–10855.

 

Ruiz-Mercado I, Canuz E, Smith KR (2012). Temperature dataloggers as stove use monitors (SUMs): Field methods and signal analysis. Biomass and Bioenergy, 47: 459–468.

 

Shao Z, Yin X, Bi J, et al. (2019). Spatiotemporal variations of indoor PM2.5 concentrations in Nanjing, China. International Journal of Environmental Research and Public Health, 16: 144.

 

Shen G, Xiong R, Tian Y, et al. (2022). Substantial transition to clean household energy mix in rural China. National Science Review, 9: nwac050.

 

Shi S, Chen C, Zhao B (2015). Air infiltration rate distributions of residences in Beijing. Building and Environment, 92: 528–537.

 

Shi S, Chen C, Zhao B (2017). Modifications of exposure to ambient particulate matter: Tackling bias in using ambient concentration as surrogate with particle infiltration factor and ambient exposure factor. Environmental Pollution, 220: 337–347.

 

Song J, Qian H, Zhao D, et al. (2021). Particulate matter emission by an isolated rotating wheel. Building Simulation, 14: 1163–1173.

 
UN (2022). The Sustainable Development Goals Report 2022. The United Nations.
 

Wang S, Wei W, Li D, et al. (2010). Air pollutants in rural homes in Guizhou, China—Concentrations, speciation, and size distribution. Atmospheric Environment, 44: 4575–4581.

 

Wang Y, Li H, Feng G (2020). Simulating the influence of exhaust hood position on ultrafine particles during a cooking process in the residential kitchen. Building Simulation, 13: 1339–1352.

 
WHO Europe (2013). Health Effects of Particulate Matter. The WHO Regional Office for Europe.
 
WHO (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. The World Health Organization.
 

Wu J, Xiao X, Li Y, et al. (2020). Personal exposure to fine particulate matter (PM2.5) of pregnant women during three trimesters in rural Yunnan of China. Environmental Pollution, 256: 113055.

 

Wu Y, Chen X, Zhou X, et al. (2021). Research on the construction method based on menu-type standard drawing collections in Lin’an District of Hangzhou. World Architecture., 2021(08): 94–101. (in Chinese).

 

Xie W, Gao J, Lv L, et al. (2022). Exhaust rate for range hood at cooking temperature near the smoke point of edible oil in residential kitchen. Journal of Building Engineering, 45: 103545.

 

Yan D, Xia J, Tang W, et al. (2008). DeST—An integrated building simulation toolkit Part I: Fundamentals.Building Simulation, 1: 95–110.

 

Yang L, Fu R, He W, et al. (2020). Adaptive thermal comfort and climate responsive building design strategies in dry-hot and dry-cold areas: Case study in Turpan, China. Energy and Buildings, 209: 109678.

 

Yi KW, Kim YI, Bae G-N (2016). Effect of air flow rates on concurrent supply and exhaust kitchen ventilation system. Indoor and Built Environment, 25: 180–190.

 

Zeng L, Du B, Lv L, et al. (2020). Occupant exposure and ventilation conditions in Chinese residential kitchens: Site survey and measurement for an old residential community in Shanghai. Journal of Building Engineering, 31: 101406.

 

Zhang X, Liu C, Wu Y (2020). Study on standardized self-built houses in rural areas—A case study of Yunzhai ecological house in Kunshan, Jiangsu. Construction Science and Technology, 2020(17): 56–60. (in Chinese).

 

Zhao B, Zheng H, Wang S, et al. (2018). Change in household fuels dominates the decrease in PM2.5 exposure and premature mortality in China in 2005-2015. Proceedings of the National Academy of Sciences of the United States of America, 115: 12401–12406.

 

Zhao D, You X (2021). Cooking grease particles purification review and technology combination strategy evaluation for commercial kitchens. Building Simulation, 14: 1597–1617.

Building Simulation
Pages 1299-1315
Cite this article:
Shi S, Yang J, Liang Y. Indoor distribution and personal exposure of cooking-generated PM2.5 in rural residences of China: A multizone model study. Building Simulation, 2023, 16(8): 1299-1315. https://doi.org/10.1007/s12273-023-0997-1

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Received: 06 November 2022
Revised: 18 January 2023
Accepted: 02 February 2023
Published: 28 February 2023
© Tsinghua University Press 2023
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