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Preplanned Studies: Baseline Investigation on Residential PM2.5 Pollution of General Living Scenarios — 12 Cities, China, 2018

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  • Summary

    What is already known about this topic?

    Residential air pollution can cause a large disease burden, and residential air quality is directly influenced by residential fine particulate matter (PM2.5). Residential PM2.5 pollution is of critical concern in China given that the characteristics and influencing factors of residential PM2.5 in China are not clear.

    What is added by this report?

    This study focuseed on residential PM2.5 concentration of 12 cities with the on-site investigation in 2018, and provided the latest characteristics and potential influencing factors of residential PM2.5 under general living scenarios in China.

    What are the implications for public health practice?

    This study suggested that the control of residential PM2.5 pollution should be reinforced with revised indoor air quality standards under obvious spatial diversity.

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  • [1] Fang D, Wang QG, Li HM, Yu YY, Lu Y, Qian X. Mortality effects assessment of ambient PM2.5 pollution in the 74 leading cities of China. Sci Total Environ 2016;569-570:1545 − 52. http://dx.doi.org/10.1016/j.scitotenv.2016.06.248CrossRef
    [2] Adgate JL, Ramachandran G, Pratt GC, Waller LA, Sexton K. Spatial and temporal variability in outdoor, indoor, and personal PM2.5 exposure. Atmos Environ 2002;36(20):3255 − 65. http://dx.doi.org/10.1016/S1352-2310(02)00326-6CrossRef
    [3] Liu JJ, Dai XL, Li XD, Jia SS, Pei JJ, Sun YX, et al. Indoor air quality and occupants’ ventilation habits in China: seasonal measurement and long-term monitoring. Build Environ 2018;142:119 − 29. http://dx.doi.org/10.1016/j.buildenv.2018.06.002CrossRef
    [4] Panigrahi A, Padhi BK. Chronic bronchitis and airflow obstruction is associated with household cooking fuel use among never-smoking women: a community-based cross-sectional study in Odisha, India. BMC Public Health 2018;18:924. http://dx.doi.org/10.1186/s12889-018-5846-2CrossRef
    [5] Laden F, Neas LM, Dockery DW, Schwartz J. Association of fine particulate matter from different sources with daily mortality in six U.S. cities. Environ Health Perspect 2000;108(10):941 − 7. http://dx.doi.org/10.1289/ehp.00108941CrossRef
    [6] Polidori A, Arhami M, Sioutas C, Delfino RJ, Allen R, Allen DR. Indoor/Outdoor relationships, trends, and carbonaceous content of fine particulate matter in retirement homes of the Los Angeles Basin. J Air Waste Manag Assoc 2007;57(3):366 − 79. http://dx.doi.org/10.1080/10473289.2007.10465339CrossRef
    [7] Chan LY, Kwok WS. Vertical dispersion of suspended particulates in urban area of Hong Kong. Atmos Environ 2000;34(26):4403 − 12. http://dx.doi.org/10.1016/S1352-2310(00)00181-3CrossRef
    [8] Abdel-Salam MM. Indoor particulate matter in urban residences of Alexandria, Egypt. J Air Waste Manag Assoc 2013;63(8):956 − 62. http://dx.doi.org/10.1080/10962247.2013.801374CrossRef
    [9] Vyas S, Srivastav N, Spears D. An experiment with air purifiers in Delhi during winter 2015−2016. PLoS One 2016;11(12):e0167999. http://dx.doi.org/10.1371/journal.pone.0167999CrossRef
  • FIGURE 1.  Spatial and seasonal distribution of residential PM2.5 of 12 cities in China, 2018 (μg/m3).

    TABLE 1.  Description of residential PM2.5 of 12 cities in China, 2018 (µg/m3).

    CitiesMeanSDMinP25P50P75Max
    Harbin38.0326.370.0021.7531.5047.00193.00
    Panjin83.1050.339.0054.2573.4596.48541.00
    Qingdao49.2448.689.0020.0035.0054.50272.00
    Shijiazhuang66.6047.214.0033.0060.5090.00295.00
    Lanzhou174.30150.410.0089.25144.50232.75968.00
    Luoyang83.6565.728.0043.2562.00109.00380.00
    Xi’an101.88102.8814.0046.7576.50120.25690.00
    Wuxi68.9666.722.0026.5045.0085.50401.00
    Mianyang220.38156.6760.0090.00182.50349.50531.00
    Ningbo33.0921.395.0016.0027.9050.65102.83
    Nanning59.6013.3624.0054.5065.0070.0074.00
    Shenzhen43.8223.5111.0029.2541.0049.00137.00
    Note: To correct the extreme value of PM2.5 in Lanzhou, we used the 95% quantile of the same city in the same season to take place of it.
    Abbreviations: SD=standard deviation, Min=minimum, P25=25th perquartile, P50=median, P75=75th percentile, Max=maximum.
    Download: CSV

    TABLE 2.  Some potential influencing factors and the concentrations of residential PM2.5 of 12 cities in China, 2018.

    VariablesCategoriesOR1 (95% CI)p1OR2 (95% CI)p2
    PM10 (μg/m3)1.0055 (1.0054,1.0056)<0.001
    Temperature (℃)0.9286 (0.9258, 0.9315)<0.0010.9436 (0.9406, 0.9465)<0.001
    Humidity (%)0.9938 (0.9926, 0.9949)<0.0010.9906 (0.9894, 0.9918)<0.001
    Climate zones (vs. Cold)Warm0.9840 (0.9535, 1.0154) 0.3151.4454 (1.3973, 1.4951)<0.001
    Distance from road (vs. <1 km)>1 km0.7739 (0.7328, 0.8167)<0.0010.7511 (0.7103, 0.7937)<0.001
    Window glass types (vs. ≤2 layers)>2 layers0.8546 (0.7792, 0.9349)<0.0011.2841 (1.1694, 1.4066)<0.001
    Living floor (vs. ≤5)5–101.1160 (1.0846, 1.1481)<0.0011.1102 (1.0788, 1.1425)<0.001
    ≥101.1742 (1.1398, 1.2094)<0.0011.1616 (1.1276, 1.1965)<0.001
    Construction house types
    (vs. Bungalow)
    Building1.1953 (1.1245, 1.2719)<0.0011.3860 (1.3028, 1.4760)<0.001
    Villa0.6682 (0.5787, 0.7685)<0.0010.8942 (0.7737, 1.0292) 0.124
    Renovate in the past 5 years (vs. Yes)No1.0519 (1.0259, 1.0787)<0.0010.9912 (0.9663, 1.0168) 0.496
    Income (yearly) (vs. <100,000 RMB)100,000–200,000 RMB0.9030 (0.8803, 0.9263)<0.0010.8209 (0.8004, 0.8419)<0.001
    ≥200,000 RMB0.7842 (0.7555, 0.8139)<0.0010.8074 (0.7778, 0.8379)<0.001
    Average living area (m2)0.9949 (0.9938, 0.9961)<0.0011.0020 (1.0008, 1.0032) 0.001
    House cleaning
    (vs. More than once a month)
    Less than once a month2.8896 (2.5724, 3.2339)<0.0010.9506 (0.8447, 1.0660) 0.393
    Grow plants (vs. Yes)No1.1078 (1.0798, 1.1364)<0.0011.0284 (1.0020, 1.0555) 0.034
    Carpet (vs. Yes)No0.7953 (0.7630, 0.8294)<0.0010.8027 (0.7699, 0.8372)<0.001
    Burn incense (vs. Yes)No1.0935 (1.0538, 1.1350)<0.0010.9965 (0.9599, 1.0347) 0.853
    Air purifier (vs. Yes)No1.0480 (1.0188, 1.0781) 0.0011.0856 (1.0551, 1.1170)<0.001
    Range hook (vs. Never)Occasionally0.6214 (0.5178, 0.7411)<0.0010.5241 (0.4366, 0.6253)<0.001
    Frequently1.0849 (1.0048, 1.1731) 0.0390.8864 (0.8207, 0.9588) 0.002
    Note: The variables after vs. represent the reference variables in statistical analysis. The value of OR1 indicates that while keeping other predictor variables unchanged, the logarithm of residential PM2.5 concentration was OR1 times of the individual reference variable. The value of OR2 indicates that when PM10 was introduced into the model to adjust its impact on PM2.5, while keeping other predictors unchanged, the logarithm of residential PM2.5 concentration was OR2 times of the individual reference variable.
    Abbreviations: CI=confidence interval.
    Download: CSV

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Baseline Investigation on Residential PM2.5 Pollution of General Living Scenarios — 12 Cities, China, 2018

View author affiliation

Summary

What is already known about this topic?

Residential air pollution can cause a large disease burden, and residential air quality is directly influenced by residential fine particulate matter (PM2.5). Residential PM2.5 pollution is of critical concern in China given that the characteristics and influencing factors of residential PM2.5 in China are not clear.

What is added by this report?

This study focuseed on residential PM2.5 concentration of 12 cities with the on-site investigation in 2018, and provided the latest characteristics and potential influencing factors of residential PM2.5 under general living scenarios in China.

What are the implications for public health practice?

This study suggested that the control of residential PM2.5 pollution should be reinforced with revised indoor air quality standards under obvious spatial diversity.

  • 1 Department of Indoor Environment and Health Monitoring, China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
  • Corresponding author:

    Xianliang Wang, wangxianliang@nieh.chinacdc.cn

    Online Date: August 07 2020
    doi: 10.46234/ccdcw2020.165
  • Short-term and long-term exposure to outdoor airborne fine particulate matter (particles with aerodynamic diameter ≤2.5 μm; PM2.5) can increase the morbidity and mortality of cardiovascular and respiratory diseases (1). Considering time-activity patterns, people spent 85%–90% of their daily time in households (2), and for indoor PM2.5 pollution, some studies were reported from many countries with a focus on special conditions such as residential ventilation, biofuel combustion, environmental tobacco smoke, etc (3-4). With rapid urbanization, residential PM2.5 pollution still remains to be studied in many cities of China, especially under daily general living circumstances.

    To explore the representative levels, characteristics, and influencing factors of residential PM2.5 pollution in China, the National Institute of Environmental Health (NIEH) of China CDC initiated a multicenter investigation for indoor air pollution in 2018. The study selected cities and resident families based on two-step random sampling. Twelve representative cities were selected considering various factors such as climatic and geographical locations. These cities were mainly from the Northeast (Harbin and Panjin), Northwest (Lanzhou and Xi’an), Southwest (Mianyang), North (Shijiazhuang), East (Wuxi, Ningbo and Qingdao), Central (Luoyang), and South (Nanning and Shenzhen) of China. The selected cities within the zone of temperate climate were Harbin, Panjin, Qingdao, Shijiazhuang, Lanzhou, Luoyang, and Xi’an, and the selected cities within the zone of subtropical climate included the other five cities. The sampling dates were in the cold season (December) and the warm season (June) in 2018. Samples from families of Xi’an and Mianyang were collected only in the cold season because of unexpected interruptions to the field investigation.

    Resident families in each city were randomly selected from one district downwind of the city center and another district located upwind. At least 25 families in each district were identified as confirmed target households with the following inclusion criteria: 1) families lived in the house for more than 3 years without plans to move away in the next 3 years; 2) families included at least one infant; and 3) families were willing to participate in this investigation. Families including individuals engaged in occupations with high health risks caused by environmental pollution and with any individuals smoking regularly were excluded.

    In order to collect representative data of residential air quality, samples from bedrooms and living rooms of each family were collected by local CDCs. To collect the data of residential pollution with many parameters such as PM2.5 related to the unified general living scenario of each household, the sampling condition of each family was regulated with the following guidelines: 1) the doors and windows were pre-closed for 12 hours for indoor sampling; 2) air conditioners, fans, and other equipment that may interfere with airflow were all turned off during the sampling period; 3) the period between 9 AM to 10 AM was chosen as the preferred sampling time to avoid traffic peaks and indoor cooking; 4) behaviors like indoor smoking were prohibited and temporary visiting guests were avoided for sampling in all families under the general living circumstances; 5) the height of the measuring point was 1 to 1.5 meters above the ground; and 6) the measuring point was not less than 0.5 meters from the wall. Indoor air quality indicators in this study included temperature, humidity, PM2.5, PM10 (particles with aerodynamic diameter ≤10 μm; PM10), etc. PM2.5 and PM10 were monitored by calibrated light-scattering dust meters. The average value of 10 monitoring values recorded on a 5 minute interval was consistently taken as the final representative concentration for a site in living rooms and bedrooms. Approximately 3% of the total rooms were monitored repetitively as the parallel sites, and the sampling method had acceptable repeatability. The family members were interviewed about the lifestyles and living conditions of households with questionnaires. Informed consent forms were signed before the investigation. The sample size of target families was calculated based on a cross-sectional study design. Finally, after accounting for data censoring and the lack of coordination of the families, a total of 642 families were identified to evaluate residential air pollution in 12 major cities in China.

    In 2018, the population in all selected cities was exposed to a residential PM2.5 average concentration of 79.34 μg/m3, and the PM2.5 concentration distribution in each city was also shown (Table 1). A significant difference was found in PM2.5 concentrations among 12 cities (F=72.13, p<0.001). The concentration of residential PM2.5 in two seasons was significantly different in 7 representative cities including Harbin, Panjin, Qingdao, Shijiazhuang, Lanzhou, Luoyang, and Wuxi (p<0.05) (Figure 1).

    CitiesMeanSDMinP25P50P75Max
    Harbin38.0326.370.0021.7531.5047.00193.00
    Panjin83.1050.339.0054.2573.4596.48541.00
    Qingdao49.2448.689.0020.0035.0054.50272.00
    Shijiazhuang66.6047.214.0033.0060.5090.00295.00
    Lanzhou174.30150.410.0089.25144.50232.75968.00
    Luoyang83.6565.728.0043.2562.00109.00380.00
    Xi’an101.88102.8814.0046.7576.50120.25690.00
    Wuxi68.9666.722.0026.5045.0085.50401.00
    Mianyang220.38156.6760.0090.00182.50349.50531.00
    Ningbo33.0921.395.0016.0027.9050.65102.83
    Nanning59.6013.3624.0054.5065.0070.0074.00
    Shenzhen43.8223.5111.0029.2541.0049.00137.00
    Note: To correct the extreme value of PM2.5 in Lanzhou, we used the 95% quantile of the same city in the same season to take place of it.
    Abbreviations: SD=standard deviation, Min=minimum, P25=25th perquartile, P50=median, P75=75th percentile, Max=maximum.

    Table 1.  Description of residential PM2.5 of 12 cities in China, 2018 (µg/m3).

    Figure 1.  Spatial and seasonal distribution of residential PM2.5 of 12 cities in China, 2018 (μg/m3).

    Residential PM2.5 concentration in this study was not normally distributed, so we estimated the odds ratio and corresponding 95% confidence interval (95% CI) for each potential risk factor by using the generalized linear model (GLM) with Poisson connection function (Table 2). After adjusting for the influence of PM10, the concentrations of residential PM2.5 in warm climate zones were found to be likely higher than those in the cold climate zone (OR2=1.4454, 95% CI: 1.3973–1.4951). Statistically significant associations were found with residential PM2.5 and physical indicators including temperature (OR2=0.9436, 95% CI: 0.9406–0.9465) and humidity (OR2=0.9906, 95% CI: 0.9894–0.9918). For the residential environment, households more than 1 kilometer away from the road had a lower residential PM2.5 concentration than those less than 1 kilometer (OR2=0.7511, 95% CI: 0.7103–0.7937).

    VariablesCategoriesOR1 (95% CI)p1OR2 (95% CI)p2
    PM10 (μg/m3)1.0055 (1.0054,1.0056)<0.001
    Temperature (℃)0.9286 (0.9258, 0.9315)<0.0010.9436 (0.9406, 0.9465)<0.001
    Humidity (%)0.9938 (0.9926, 0.9949)<0.0010.9906 (0.9894, 0.9918)<0.001
    Climate zones (vs. Cold)Warm0.9840 (0.9535, 1.0154) 0.3151.4454 (1.3973, 1.4951)<0.001
    Distance from road (vs. <1 km)>1 km0.7739 (0.7328, 0.8167)<0.0010.7511 (0.7103, 0.7937)<0.001
    Window glass types (vs. ≤2 layers)>2 layers0.8546 (0.7792, 0.9349)<0.0011.2841 (1.1694, 1.4066)<0.001
    Living floor (vs. ≤5)5–101.1160 (1.0846, 1.1481)<0.0011.1102 (1.0788, 1.1425)<0.001
    ≥101.1742 (1.1398, 1.2094)<0.0011.1616 (1.1276, 1.1965)<0.001
    Construction house types
    (vs. Bungalow)
    Building1.1953 (1.1245, 1.2719)<0.0011.3860 (1.3028, 1.4760)<0.001
    Villa0.6682 (0.5787, 0.7685)<0.0010.8942 (0.7737, 1.0292) 0.124
    Renovate in the past 5 years (vs. Yes)No1.0519 (1.0259, 1.0787)<0.0010.9912 (0.9663, 1.0168) 0.496
    Income (yearly) (vs. <100,000 RMB)100,000–200,000 RMB0.9030 (0.8803, 0.9263)<0.0010.8209 (0.8004, 0.8419)<0.001
    ≥200,000 RMB0.7842 (0.7555, 0.8139)<0.0010.8074 (0.7778, 0.8379)<0.001
    Average living area (m2)0.9949 (0.9938, 0.9961)<0.0011.0020 (1.0008, 1.0032) 0.001
    House cleaning
    (vs. More than once a month)
    Less than once a month2.8896 (2.5724, 3.2339)<0.0010.9506 (0.8447, 1.0660) 0.393
    Grow plants (vs. Yes)No1.1078 (1.0798, 1.1364)<0.0011.0284 (1.0020, 1.0555) 0.034
    Carpet (vs. Yes)No0.7953 (0.7630, 0.8294)<0.0010.8027 (0.7699, 0.8372)<0.001
    Burn incense (vs. Yes)No1.0935 (1.0538, 1.1350)<0.0010.9965 (0.9599, 1.0347) 0.853
    Air purifier (vs. Yes)No1.0480 (1.0188, 1.0781) 0.0011.0856 (1.0551, 1.1170)<0.001
    Range hook (vs. Never)Occasionally0.6214 (0.5178, 0.7411)<0.0010.5241 (0.4366, 0.6253)<0.001
    Frequently1.0849 (1.0048, 1.1731) 0.0390.8864 (0.8207, 0.9588) 0.002
    Note: The variables after vs. represent the reference variables in statistical analysis. The value of OR1 indicates that while keeping other predictor variables unchanged, the logarithm of residential PM2.5 concentration was OR1 times of the individual reference variable. The value of OR2 indicates that when PM10 was introduced into the model to adjust its impact on PM2.5, while keeping other predictors unchanged, the logarithm of residential PM2.5 concentration was OR2 times of the individual reference variable.
    Abbreviations: CI=confidence interval.

    Table 2.  Some potential influencing factors and the concentrations of residential PM2.5 of 12 cities in China, 2018.

    Architectural characteristics showed potential influences on residential PM2.5. As to the layers of window glass, the PM2.5 concentrations in families with more than two layers were higher than those of less than two layers (OR2=1.2841, 95% CI: 1.1694–1.4066). In addition, the PM2.5 concentrations in households between the 5th and 10th floors (OR2=1.1102, 95% CI: 1.0788–1.1425) and above 10th floor (OR2=1.1616, 95% CI: 1.1276–1.1965) were higher than those in households below the 5th floor. Compared with bungalows, the residential PM2.5 concentrations were higher in buildings (OR2=1.3860, 95% CI: 1.3028–1.4760).

    Some family-related information and lifestyle habits may also influence residential PM2.5. As for the influence of family economic status, compared with families with an annual total income of fewer than 100,000 RMB (roughly 14,300 USD), households with an annual income of 100,000 to 200,000 RMB (OR2=0.8209, 95% CI: 0.8004–0.8419) and an income of more than 200,000 RMB (OR2=0.8074, 95% CI: 0.7778–0.8379) had lower residential PM2.5 concentrations. The study also found that the average living area of family members showed a positive correlation with residential PM2.5 concentration (OR2=1.0020, 95% CI: 1.0008–1.0032). As for lifestyle habits, the PM2.5 concentrations of households that never use air purifiers were significantly higher than those families using air purifiers (OR2=1.0856, 95% CI: 1.0551–1.1170). The frequency of using the range hood in the kitchen was also correlated with residential PM2.5. The PM2.5 concentrations in households using range hoods frequently (OR2=0.8864, 95% CI: 0.8207–0.9588) and occasionally (OR2=0.5241, 95% CI: 0.4366–0.6253) were lower than those that never used the range hoods. Moreover, households without carpets showed lower PM2.5 concentrations than those with carpets (OR2=0.8027, 95% CI: 0.7699–0.8372), and households that never grow plants had a higher PM2.5 concentration than those grow plants (OR2=1.0284, 95% CI: 1.0020–1.0555).

    • This study was one of few studies that uses extensive multi-center data obtained through face-to-face surveys in China. Variability in residential PM2.5 concentrations in 12 cities was possibly related to a combination of differences in the sources of pollution (road dust, automobile exhaust, and coal combustion sources) (5), meteorological factors (wind speed, atmospheric stability), and family living habits. Seasonal variations of PM2.5 in 7 representative cities may be caused partially by outdoor temperature and humidity (3). Moreover, we found significant correlations between residential PM2.5 and physical environmental indicators such as temperature and humidity.

      Outdoor PM2.5 was also a major contributor to residential particle concentrations (6), and the residential PM2.5 concentration showed an upward trend with an increase in altitude. This may be related to the vertical diffusion capacity of the atmosphere and the characteristics of the particulate matter, especially changes in wind speed at varying vertical heights (7). Household economic levels and average per capita area may also affect PM2.5 concentrations to some extent, which may be caused by lifestyle behaviors of family members (8). Some lifestyle habits were associated with the concentration of residential PM2.5. Housekeeping activities, such as sweeping and vacuuming, were associated with increased concentrations of residential PM2.5 because household cleaning could possibly disturb deposited particles from domestic floors and furniture (8). The use of air purifiers and range hoods might also reduce residential PM2.5 concentration to a certain extent (9).

      This study was subject to several limitations. First, in cross-sectional studies, selection bias and information bias could be a problem even though households were selected randomly in each city. Secondly, some potential influencing factors of residential PM2.5 might be missed in this investigation due to the limited two times of sampling.

      The findings highlighted the importance of an improvement plan for residential air quality. Public health supervision of residential PM2.5 pollution should be pushed forward according to the distribution pattern of PM2.5 in different cities. Additional information and incentives to eliminate residential PM2.5 pollution are needed urgently to guide healthier behavior in families.

      Acknowledgments: We sincerely thanked all the households who took part in this study and staff from local CDCs who provided assistance for questionnaires and indoor air sampling.

      Fundings: This study was funded partially by the National Natural Science Foundation of China (No. 21976169); the Natural Science Foundation of Beijing, China (No. 8182055); and the Opening Fund of State Key Laboratory of Building Safety and Built Environment, China (No. BSBE2017-09).

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