Introduction
At present, the epidemic caused by the new coronavirus (COVID-19) has
become a global public health emergency. Moreover, 98.54% of the research
participants were extremely nervous or afraid of the epidemic (Qi et al.
2020). Therefore, people suggest higher requirements for indoor environment
quality. The rapid development of various fields increased pollution to the
environment to a greater extent. Air pollution and its health effects have
drawn public attention. Studies have shown that the average air pollution index
in China declined between 2014 and 2017. Particulate matter 2.5 (PM2.5)
was still the main pollutant in urban areas (Zhang and Lin 2019). Several studies have revealed that exposure to higher concentrations
of PM2.5affects the health of residents, which not only endangers
the respiratory system but also increases the risk of cardiovascular and
cerebrovascular diseases (Chen et al. 2017; Yin et al. 2017a, b).
Long-term exposure to high PM concentrations could have significant effects on
health. Monn (2001) introduced the concept of exposure assessment in the 1980s.
Of these, the exposure and potential dose were commonly used parameters in the
evaluation of PM exposure levels. The studies on the assessment of PM exposure
have revealed significant results in theory and practice (Gao 2010; Yan et al. 2018). However, most epidemiological studies typically used
environmental parameters detected by environmental monitoring stations that did
not fit the daily behavior pattern. Studies have shown that Chinese residents
spend approximately 80% of their time indoor (Wang et al. 2014). Outdoor monitoring data alone cannot characterize the actual
exposure (He et al. 2004; Ji and Zhao 2015). Therefore, the effects of indoor air quality on human health are
more crucial.
PM2.5 and PM10
were easy to become carriers of pollutants, such as microorganisms (Ernest 2004; Cao et al. 2018; Li et al. 2019a). PM2.5 can penetrate into the blood circulation and deep
into the respiratory system, thus reaching the lungs. Moreover, PM2.5 has
a strong adsorption capacity (Wei et al. 2001). Studies have shown that
exposure to PM2.5 has significant adverse effects on ventricular
repolarization and may cause vascular insulin resistance (Haberzettl et al. 2016; Wang et al. 2020). In previous studies, more
attention was given to the physical and chemical properties (Li et al. 2019b; Liu et al. 2019b). The biological component is called bioaerosol defined as the
collection of particles released into the atmosphere by the biosphere
(Frhlich-nowoisky et al. 2016). The proportion of bioaerosols in the air was typically about
30%, and the proportion was higher in areas covered by vegetation (Graham et al. 2003; Després et al. 2012; Huffman et al. 2012, 2013). High levels of bacteria are found in vehicle air
conditioning systems (Li et al. 2013). Studies have shown that the
reduction in bioaerosols increases after being transported by air and could
induce respiratory diseases (Estillore et al. 2016; Wang et al. 2018). Moreover, its
negative effects increase in haze weather (Wang et al. 2015). Bioaerosols also increase the risk of spreading resistance
genes (Li et al. 2018; Chen et al. 2020). In the HVAC system, Yu et al. (2019) found that yeast
and Penicillium are the main species on the basement walls of
residential buildings. Liu et al. (2019a) found that live bacteria were
the highest in the fresh air.
Because of the special geographical
location, climatic conditions, and building types in China’s severe cold
regions, the pollution of PM2.5was more serious. To explore PM2.5
and bacteria in the building environment in China’s severe cold regions, this
study selected Daqing, Heilongjiang province. To further analyze the health
effects of PM2.5onpeople of different ages and genders, the study
monitored indoor and outdoor environmental parameters in offices, schools, and
residential buildings. The two indicators of exposure and potential dose were
used in the risk assessment of health exposure.16Sr DNA technology was used to
analyze the composition and diversity of bacteria in indoor PM2.5.
This study aimed to investigate the effects of bioaerosols on the indoor
environment and the health of humans.
Materials and Methods
This study
selected Daqing in the severe cold region of China as the study location.
Daqing (N45°46′~46°55′, E124°19′~125°12′) is in
Heilongjiang province, China. In winter, a large number of fine particles are
emitted because of heating. In addition, Daqing has a leading number of petrochemical
industries; thus, there is serious industrial pollution. The above problems had
exacerbated the pollution of fine particles in the heating season in Daqing.
Considering the frequency of use, daily activities of the population
and length of stay, this study selected three types of buildings for research:
office buildings, teaching buildings, and residential buildings. In this study,
110 sampling points were selected in Daqing, including 30 office buildings, 30
teaching buildings, and 50 residences, to ensure statistically significant
sample sizes. Among them, office buildings and teaching buildings were in the
urban areas, and 38 residential buildings were urban houses, and 12 were rural
houses. The sampling objects of the office building were the conference room.
The indoor objects of the teaching buildings were the classroom, and the
outdoor objects were the corridor. The measurement objects of residential
buildings were living rooms with a balcony. The measurement was taken in the
heating season from November 2016 to March 2017. During the actual measurement
period, there were radiators indoors, and the envelopes such as doors and
windows were generally closed. The arrangement of the measuring points during
the actual measurement is based on the Indoor Air Quality Standards
(GBT18883-2002 2002). The measuring points were categorized into the indoor and
outdoor points of the building. The outdoor points were arranged in the center
of the open external balcony, which was more than 0.5 m away from the wall
structure and higher than the height of the external balcony railing. The
indoor points were arranged uniformly in the living room of the residence, 0.5
m above the wall and 0.5 m above the ground.
The indoor and outdoor environments of each sampling point were
simultaneously monitored. The QT-50 particle online monitor was used to
continuously measure environmental parameters such as temperature, relative
humidity, and PM2.5 mass concentration. The detection interval of
the monitor was set to 15 min and data were collected for seven days at each
monitoring point. The QT-50 particle online monitor was compared with multiple
machines before using. QT-50 and TSI8530 were placed in the same environment
for simultaneous detection. The measurement results of the QT-50 particle
detector were corrected on the basis of the detection results. The biological
components of PM2.5 were collected using the membrane sampling
method. Omni 5000IS mobile air sampling pumps equipped with a PM2.5
cutting head was used for sampling. The flow rate of a single sample was set to
4000 mL min-1, and the time was set to 24 h. The filter membrane
consists of Teflon filters with a diameter of 37 mm and a pore diameter of 2.0
microns (Whattman, Maidstone, Kent, UK). It was checked for pinholes and
wrinkles under the light before using to avoid affecting the filtration
efficiency. The filter membranes before and after use were weighed using an
electronic balance. The filters after sampling were stored in the refrigerator.
The permeability coefficient (Finf) characterizes the
proportion of outdoor PM that enters the room and is suspended in the air at
equilibrium. The generalized permeability coefficient includes the contribution
of all indoor and outdoor ambient air exchange forms to the concentration of
indoor PM, while considering the effects of indoor particulate emission sources
(Cs), which can be expressed using the following formula:
%20521-529_files/image002.png){kind=small}
(1)
Where Cin is indoor particle concentration (μg
hm-³); Cout is outdoor PM concentration (μg hm-³); Cs is indoor particulate emission
source (μg hm-³); and
Finf is permeability coefficient.
For evaluation of PM exposure, this study used four indicators:
comprehensive exposure, average comprehensive exposure, comprehensive potential
dose and average comprehensive potential dose. The amount of exposure reflected
the total amount of particulates that the human body had been exposed to in
different periods. The dose reflected the total exposure time with respiration
inhaled particles (Monn
2001; Yip et al. 2004; Edgar 2013). The formulas were as follows:
%20521-529_files/image004.png){kind=small}
(2)
%20521-529_files/image006.png){kind=small}
(3)
%20521-529_files/image008.png){kind=small}
(4)
%20521-529_files/image010.png){kind=small}
(5)
Where Ez is comprehensive exposure (μg
hm-³); E̅z is average comprehensive exposure (μg hm-³); Ci is
exposure concentration (μg hm-³); Ti is the exposure time (h); Tis the
total exposure time (h); Dz is a comprehensive potential dose (μg); D̅z is average comprehensive potential dose (μg); and IRi is respiratory rate (m³ h-1).
The microbial genes on the filter were extracted using 16S rDNA
technology. DNA was extracted using the cetyltrimethylammonium bromide method,
and the purity and concentration of the DNA were detected using agarose gel
electrophoresis. The library was constructed using the Ion Plus Fragment
Library Kit 48 rxns Library Thermo Kit (Thermo Fisher, Waltham,
Massachusetts, U.S.) and tested. After the library was qualified, it was
sequenced using Life Ion S5TM or Ion S5TMXL (Thermofisher). Using Cutadapt (v. 1.9.1),
the low-quality portion, and barcode primer sequences, the original data were
obtained (Wang et al. 2007; Dons et al. 2011). UCHIME algorithm was used to cross-compare with GOLD database
to remove the chimeric sequence for obtaining valid data. The effective data
were clustered using UPARSE software (Quast et al. 2013), and species annotation was performed using the Mothur method
and SILVA’s SSUrRNA database (Wei and Zhang 2018).
%20521-529_files/image012.png)
Fig. 1: The average PM2.5
concentration of indoor and outdoor measuring points of office buildings in the
heating season of Daqing
%20521-529_files/image014.png)
Fig. 2: The average PM2.5 concentration of indoor and outdoor
measuring points of teaching buildings in the heating season of Daqing
%20521-529_files/image016.png)
Fig. 3: The average PM2.5
concentration of indoor and outdoor measuring points of residential buildings
in the heating season of Daqing
Results
Results of indoor and outdoor fine particulate matter concentration
distribution during heating season
Fig. 1–3 showed the average distribution of indoor and
outdoor fine particle concentrations of three types of buildings in Daqing
during the heating season. The national standard “Ambient Air Quality
Standard” (GB3059-2012 2012) stipulated that the outdoor average daily
PM2.5 concentration in China should be less than 75 μg m-³. The industry
standard “Building Ventilation Effect Test and Evaluation Standard”
(JGJ/T309-2013 2013) limited the average daily PM2.5 concentration
to 75 μg m-³ as a
reference.
Fig. 1 showed that during the heating season, the average PM2.5mass
concentrations of
indoor and outdoor office
buildings in Daqing were 43.7 μg m-³ and 75.5 μg
m-³,
respectively. The average PM2.5 permeability coefficient of indoor and outdoor office buildings was
0.341. Fig. 2 showed that the average PM2.5mass
concentrations of
the indoor and
outdoor teaching buildings in Daqing during the heating season were 23.0 μg m-³ and 30.0 μg
m-³, respectively. The average PM2.5 permeability coefficient inside and outside the teaching buildings was 0.5175. Fig. 3 showed that the average PM2.5mass concentrations of indoor and outdoor residential
buildings in Daqing during the heating season were 28.9 μg m-³ and 36.0 μg
m-³, respectively. The average indoor and
outdoor PM2.5 mass concentrations of the urban and rural residential building were
27.7 μg m-³ and 35 μg m-³ and 21.2 μg m-³ and 27.2 μg
m-³, respectively. The average PM2.5
permeability coefficient of urban residents was 0.6016 and that of rural residents
was 0.5434.
In summary, the average mass concentration of the indoor and outdoor PM2.5of
the three types of buildings in Daqing during the heating season mostly met the
national standards. In addition, because of the low winter temperatures in the
severe cold regions, fossil fuels were required for
heating, and several fine particles were discharged into the air. Because of
adverse meteorological factors, outdoor PM2.5 concentrations can
rise rapidly in a short time. Moreover, as the enclosure structures such as
indoor and outdoor doors and windows were in a closed state during the heating
season, this hinders the transmission process of outdoor PM to the interior, thus increasing the difference between indoor and outdoor PM
concentrations.
Study on exposure assessment methods
Parameters
such as Ci and Ti in the exposure evaluation index formula can be obtained from
the monitored environmental data. However, there were still some parameters
that need to be referenced in related specifications. This study investigated
three types of buildings: office buildings, teaching buildings, and residential
buildings, including people of different ages and genders. The actual
measurement showed that office buildings mainly involved adult groups, whereas
the groups in the teaching buildings were mainly school-aged children. Because
of the different urban and rural environments, the adult population spent a
different amount of time in different types of buildings, leading to
urban-rural differences. In this study, the adult population was mainly divided
into urban and rural populations. The daily activities of urban residents were
mainly concentrated in office buildings and residences. The rural residents
have the agricultural leisure period in the heating season, and most activities
were concentrated in residence. In addition, gender factors were considered.
The teaching building in this study was a primary school, and the group in the
teaching building was mainly children aged from 6 to 12 years. Refer to the
“Chinese Population Exposure Parameter Manual” (Ministry of Environmental Protection 2013a, b) to select the indoor and outdoor activities of urban and
rural populations during the heating season, as shown in Table 1–4. In
addition, the urban population spent13.5 h in indoor activities, 1.5 h in
outdoor activities, and 9 h in office. The indoor activity time of the rural
population was 22.5 h, and the outdoor activity time was 1.5 h.
The actual measurement of this study was performed in Daqing in the
severe cold region. The measured data were collected for seven days at each
measurement point. The measured data were then combined with information such
as the activity status of different groups and used in the formula. The
exposure of the adult population in different regions is shown in Table 5.
Moreover, adult males and females were shown to have the same time activity
pattern. Therefore, the analysis did not distinguish between gender factors in
urban and rural populations.
The
comprehensive exposure of the urban population in the heating season of Daqing
was 6370 μg·hm-³, and
the average comprehensive exposure was 37.92 μg m-³. For the rural population, the comprehensive exposure was 5607 μg hm-³, and the average
comprehensive exposure was 33.38 μg
m-³. In general, the exposure of the urban population was higher than
that of the rural population, with a difference of 12.0%.
In addition, the risk of exposure to fine PM in children of different
ages and genders was analyzed. The exposure of children to fine particles in
three types of buildings during the heating season is shown in Table 6. The combined exposure of males
aged 6 to 9 years was 4626.1 μg
hm-³ and that of females was 4642.5 μg hm-³. Average comprehensive exposure was
approximately 27.5 μg m-³.
Among children aged 9 to 12 years, the combined exposure of males and females
was 4625.9 μg hm-³
and 4625.3 μg hm-³,
respectively. The average comprehensive exposure was approximately 27.5 μg m-³. The comprehensive exposure of children of different ages and genders
was slightly different because of different time-activity patterns, and the
average comprehensive exposure was approximately equal.
The comprehensive exposure and average comprehensive exposure
indicators can only characterize the level of fine particulates in the external
environment. They do not consider factors such as gender, the content of
activities and resulting differences in the breathing rate. Therefore, the
combined potential dose and its average dose were used as two indexes to
characterize the dose of fine particles inhaled into the human body as a
breathing effect.
Table 7 and 8 show the
calculation of the potential doses at different breathing rates for adult males
and females in different states. Table 7 displays that the potential dose of
urban males during the heating season in Daqing was 2769.62 μg, and its average dose was 16.49 μg h-1. The
comprehensive potential dose of rural males was 2162.44 μg, and the average dose was 12.87 μg h−1. The overall potential dose of urban
males was approximately 21.9% higher than that of rural males. Table 8 shows
that the comprehensive potential dose of urban females during the heating season
of Daqing was 2273.04 μg, and
its average dose was 13.53 μg h-1.
The comprehensive potential dose of rural females was 1777.55 μg, and its average dose was 10.58 μg h-1.
Table 1: The activity time of children of different genders in
heating season (Ministry of Environmental Protection 2013b).
|
Age |
Gender |
Indoor time (h) |
School time (h) |
Outdoor time (h) |
|
6 - < 9years old |
Male |
15.57 |
6.00 |
2.43 |
|
Female |
15.65 |
6.00 |
2.35 |
|
|
9 - < 12years old |
Male |
15.60 h |
6.00 |
2.40 |
|
Female |
15.68 h |
6.00 |
2.32 |
Note: The students in the measured area are in school
for 6 h
Table 2: Classification and description of different labor
intensity (Ministry of Environmental Protection 2013a)
|
Description |
|
|
Rest |
sleep, recline, and rest |
|
Extremely light activity |
sitting or standing, typing, sewing, ironing, cooking, etc. |
|
Mild activity |
walking on a flat road at a speed of 4-4.8 km·h−1,
clean room hygiene, childcare, sports, etc. |
|
Moderate activity |
walking at a speed of 5.6-6.4 km·h−1, weeding or
mowing, carrying heavy objects, riding aerobic, etc. |
|
Heavy activity |
load uphill walking, felling trees, vigorous exercise, etc. |
Table 3: Respiratory rate of adults under different activities (Ministry
of Environmental Protection 2013a)
|
Gender |
Respiratory rate (m³·h−1) |
||||
|
Rest |
Extremely light activity |
Mild activity |
Moderate activity |
Heavy activity |
|
|
Male |
0.372 |
0.444 |
0.558 |
1.488 |
2.232 |
|
Female |
0.306 |
0.366 |
0.456 |
1.212 |
1.818 |
Table 4: Respiratory rate of children under different activities (Ministry
of Environmental Protection 2013b).
|
Gender |
Respiratory rate (m³·h−1) |
|||||
|
Rest |
Extremely light activity |
Mild activity |
Moderate activity |
Heavy activity |
||
|
6 to 9 years old |
Male |
0.258 |
0.312 |
0.516 |
1.038 |
2.586 |
|
Female |
0.234 |
0.282 |
0.468 |
0.930 |
2.328 |
|
|
9 to 12 years old |
Male |
0.306 |
0.366 |
0.612 |
1.224 |
3.060 |
|
Female |
0.270 |
0.324 |
0.540 |
1.080 |
2.706 |
|
Table 5: Exposure of fine particulates in different adult populations
|
Types of population |
Location |
Exposure (μg·h·m-³) |
Comprehensive exposure (μg·h·m-³) |
Average comprehensive exposure (μg·m-³) |
|
Urban populations |
Office building |
2772 |
6370 |
37.92 |
|
Residential |
2548 |
|||
|
Outdoor |
1050 |
|||
|
Rural populations |
Residential |
5198 |
5607 |
33.38 |
|
Outdoor |
410 |
Table 6: Exposure of fine particulates in children of different ages
|
Types of population |
Location |
Exposure (μg·h·m−³) |
Comprehensive exposure (μg·h·m−³) |
Average comprehensive exposure (μg·m−³) |
|
|
6 to 9 years old |
Male |
Residential |
3149.8 |
4626.1 |
27.5 |
|
School |
966.0 |
||||
|
Outdoor |
510.3 |
||||
|
Female |
Residential |
3166.0 |
4642.5 |
27.5 |
|
|
School |
966.0 |
||||
|
Outdoor |
493.5 |
||||
|
9 to 12 years old |
Male |
Residential |
3155.9 |
4625.9 |
27.5 |
|
School |
966.0 |
||||
|
Outdoor |
504.0 |
||||
|
Female |
Residential |
3172.1 |
4625.3 |
27.5 |
|
|
School |
966.0 |
||||
|
Outdoor |
487.2 |
||||
Table 9 shows the calculation of the potential dose considering
children’s activity status and respiratory rate in different environments. In male and female children aged
6 to 9 years, the
comprehensive potential dose was 1377.36 μg and
1244.21 μg, respectively. The
average total potential dose for males and females was 8.20 μg h−1 and 7.41 μg h−1,
respectively. In children aged 6 to 9 years, the combined potential dose and
its average dose of males were 9.6% higher than that of females. The combined
potential dose for children aged between 9 and 12 years was 1627.70 μg for males and 1432.53 μg for females, and the average comprehensive potential dose was 9.69
and 8.53 μg h-1, respectively.
The results of bacterial components of fine particles inside and outside
the three types of buildings in Daqing during the heating period. Moreover, the
figures show the top 10 predominant bacteria at the phylum level. The
coordinates of city and farm referring to urban and rural residential
buildings, respectively (Fig. 4 and 5).
Table 7: Potential exposure dose of fine particles for males
|
Types of population |
Location |
Exposure (μg
h m-³) |
Respiratory rate (m³ h-1) |
Comprehensive potential dose (μg) |
Average comprehensive potential dose (μg·h-1) |
|
Urban populations |
Office building |
2772 |
0.444 |
2769.62 |
16.49 |
|
Residential |
2548 |
0.372 |
|||
|
Outdoor |
1050 |
0.558 |
|||
|
Rural populations |
Residential |
5198 |
0.372 |
2162.44 |
12.87 |
|
Outdoor |
410 |
0.558 |
Table 8: Female potential exposure dose of fine particles
|
Types of population |
Location |
Exposure (μg
h m-³) |
Respiratory rate (m³ h-1) |
Comprehensive potential dose (μg) |
Average comprehensive potential dose (μg·h-1) |
|
Urban populations |
Office building |
2772 |
0.366 |
2273.04 |
13.53 |
|
Residential |
2548 |
0.306 |
|||
|
Outdoor |
1050 |
0.456 |
|||
|
Rural populations |
Residential |
5198 |
0.306 |
1777.55 |
10.58 |
|
Outdoor |
410 |
0.456 |
Table 9: Potential exposure dose of fine particles in children
|
Types of population |
Location |
Exposure (μg
h m-³) |
Respiratory rate (m³ h-1) |
Comprehensive potential dose (μg) |
Average comprehensive potential dose (μg h-1) |
|
|
6 to 9 years old |
Male |
Residential |
3149.8 |
0.258 |
1377.36 |
8.20 |
|
School |
966.0 |
0.312 |
||||
|
Outdoor |
510.3 |
0.516 |
||||
|
Female |
Residential |
3166.0 |
0.234 |
1244.21 |
7.41 |
|
|
School |
966.0 |
0.282 |
||||
|
Outdoor |
493.5 |
0.468 |
||||
|
9 to 12 years old |
Male |
Residential |
3155.9 |
0.306 |
1627.70 |
9.69 |
|
School |
966.0 |
0.366 |
||||
|
Outdoor |
504.0 |
0.612 |
||||
|
Female |
Residential |
3172.1 |
0.270 |
1432.53 |
8.53 |
|
|
School |
966.0 |
0.324 |
||||
|
Outdoor |
487.2 |
0.540 |
||||
The dominant bacteria of indoor and outdoor fine particles in office
buildings and urban and rural residential buildings
were Firmicutes, Oxyphotobacteria, Proteobacteria, Bacteroidetes,
Thaumarchaeota, Actinobacteria, Acidobacteria, Chloroflexi,
Verrucomicrobia, and Planctomycetes.
Among these, the main dominant species of rural residential buildings were Firmicutes,
Oxyphotobacteria and Proteobacteria. Fig. 5 shows that the top 10
dominant bacteria of the indoor and outdoor fine particles of Daqing
teaching buildings during the heating season were Proteobacteria, Bacteroidetes,
Firmicutes, Actinobacteria, Thaumarchaeota, Acidobacteria,
Spirochaetes, Verrucomicrobia, Euryarchaeota and Cyanobacteria
(Fig. 4). In summary, Proteobacteria, Bacteroidetes, and Firmicutes
were the main dominant bacteria of fine PM in the indoor and outdoor of
three types of buildings in Daqing during the heating season.
In the three types of buildings in Daqing during the heating season,
the indoor and outdoor flora of residential and office buildings were similar,
and the top 10 dominant bacteria at the phylum level were the same. To further %20521-529_files/image017.png)
compare the
similarities and differences between residential buildings and office
buildings, the bacterial diversity of indoor and outdoor fine particles was
analyzed. Table 8 shows the statistical results of the alpha diversity index of
bacteria of indoor and outdoor fine particles of residential buildings
(including rural and urban residence) and office buildings.
Table 10 shows that the goods
coverage values in the samples collected in the actual measurement exceeded
98%, indicating that most genes could be detected using the library, and the
detection results were more in line with actual conditions.
%20521-529_files/image019.png)
Fig. 4: Histograms of indoor and outdoor top 10 bacterial
components of office and residential buildings in Daqing heating season
The ACE index and the chao1 index
estimate the number of OTUs (Operational Taxonomic Units) in their microbial
communities using different algorithms. The larger the Simpson index value, the
lower the diversity of colonies. The higher the value, the higher the richness.
Shannon and Simpson’ indexes reflect the uniformity of the flora. The higher
the Shannon value, the higher the uniformity. For urban residential buildings,
the number of observed species outdoors was similar to that indoors. The
abundance and uniformity of outdoor bacteria were slightly higher than that of
indoors. However, the number of observed species outdoors in rural residential
buildings was significantly higher than indoors. In addition, the richness of
outdoor bacteria was significantly higher than that of indoor bacteria.
%20521-529_files/image021.png)
Fig. 5: Histograms of indoor and outdoor top 10 bacterial
components of teaching buildings in Daqing heating season
Discussion
The outdoor PM2.5
concentration of office buildings was higher than indoor. The high
concentration of outdoor PM2.5 may contribute to the concentration
of indoor PM2.5. The outdoor PM2.5 concentration at most
measuring points in office buildings met the national standards. Except for
individual measurement points, the indoor PM2.5 concentration of
office buildings met industry standards. The indoor and outdoor PM2.5
mass concentrations at the measuring points of the teaching building were less
than the limit. This may be because teaching buildings were generally far from
industrial areas, and the surrounding air quality was better. In addition,
there was not much difference between the indoor and outdoor PM2.5
concentrations, which may be because of the large number of personnel in the
teaching building and the frequent air circulation. In general, except for
individual measurement points, the indoor and outdoor PM2.5 mass
concentrations of residential buildings were less than the limit. The indoor
and outdoor air quality of rural residential buildings was generally better
than that of urban residential buildings, which may be associated with traffic
pollution caused by dense traffic in cities.
From the perspective of comprehensive exposure indicators, the overall
exposure of the adult population was higher than that of the children. This may
be because of the different living environments of different age groups.
Compared with adults, children spent most of their time in schools and indoors,
and the fine particle concentration was low in the exposure environment. On
comparing people of different genders in the same age group, the average
combined exposure of males and females was not significantly different.
The comprehensive potential dose and its average dose of urban females
were 21.8% higher than that of rural females. Overall, the combined potential
dose and its average dose of the urban population were higher than that of the
rural population. However, regardless of the urban or rural population, the
average potential dose of males was 22.0% higher than that of females because
of the difference in the breathing rate between different genders. Among
children aged 9 to 12 years, the combined potential dose and its average dose
of males were 12% higher than that of females. On comparing children of the
same gender and different ages, the comprehensive potential dose and its
average dose of males aged between 9 and 12 years were 15.4% higher than those
aged between 6 and 9 years. The combined potential dose and its average dose of
female children aged between 9 and 12 years were 13.1% higher than those aged
between 6 and 9 years. In general, the health risk of fine particles in male
children was higher than that in female children.
In summary, the risk of fine particle exposure was higher in urban
areas than in rural areas and in males than females of the same area. Among
children, males of the same age group were at a higher risk of exposure to fine
particles than females. Moreover, the risk of exposure increased with age. Some
studies have revealed that long-term and short-term exposure to PM2.5
was statistically positively correlated with anxiety (Braithwaite et al. 2019). The potential relationship between air pollution and poor mental
health needs further research.
In office and residential buildings, a comparison of indoor and outdoor
bacterial components showed that the indoor Oxyphotobacteria was higher
than outdoor. Oxyphotobacteria is mainly distributed in anoxic areas
where light can transmit in an aquatic environment. The high proportion of Oxyphotobacteria
in rural residential indoors may be because of its low indoor temperature,
which was not conducive to water evaporation and leads to more indoor water
retention. In addition, the proportion of Thaumarchaeota was higher in
indoor air than in other samples. Thaumarchaeota is mainly distributed
in areas without light. Poor lighting conditions in rural homes may result in a
higher proportion of Thaumarchaeota. The dominant species of indoor and
outdoor urban residential buildings were Firmicutes, Proteobacteria,
and Bacteroidetes. A higher proportion of Firmicutes and Proteobacteria
was noted outdoor than indoor, which may be because of the weak indoor
ultraviolet lights. The dominant strains of indoor and outdoor bacteria in
office buildings were Firmicutes, Proteobacteria, and Bacteroidetes.
Moreover, the proportion of bacteria in different categories was similar
indoors and outdoors.
The composition of the dominant species changed in the other two types
of buildings. Proteobacteria accounted for more than 50% in the
classroom. Proteobacteria is the largest group of bacteria, including
several pathogenic bacteria. The high proportion of Proteobacteria in
the classroom building may be because of the pathogenic bacteria carried by
densely packed personnel. In addition, a small amount of Thaumarchaeota was
found outside the school buildings, which may be associated with outdoor
playgrounds and green plants. In addition, Proteobacteria is a spoilage
bacterium, and food is very susceptible to infection. In addition, no obvious
change was observed in the characteristics of contaminated food, which can
easily cause food poisoning. Proteobacteria can also cause chronic
otitis media, traumatic infections, cystitis, infantile diarrhea, etc. (Liu and Zhang 2012).
On comparing residential buildings in urban and rural areas, the
observed species were higher in rural than urban areas. In addition, the
abundance and uniformity of bacteria in rural areas were higher than that in
cities. The indoor and outdoor PM2.5 concentration in rural
residential buildings was higher than that in urban areas, which may provide a
carrier for the spread. Therefore, the bacterial diversity in rural areas was
higher than that in cities. The observed species indoors for urban office
buildings were similar to that of urban residential buildings. The observed
species outside the office buildings was about four times that inside the
buildings. This may be because office buildings were mostly located in areas
with frequent traffic, and the outdoor PM2.5 concentration was high,
which was conducive to the spread of bacteria. The richness and uniformity of
bacteria outside the office building were higher than other types of buildings.
In summary, when the building types were the same, the bacterial
diversity was higher in rural areas with a high concentration
of fine particles than that in urban areas. Compared with urban office
buildings and residential buildings, indoor bacterial diversity was similar to
residential buildings, and the outdoor bacterial diversity of office buildings
was significantly higher than that of urban residential buildings. This may be
associated with the fact that office buildings were mostly located in areas
with frequent traffic and outdoor PM2.5 pollution was serious.
Conclusion
This study investigated the indoor and outdoor fine PM, and
its bacterial contamination in Daqing, a severe cold region in China. The main
conclusions were as follows:
(1) Regarding indoor and outdoor PM2.5
pollution in the heating season, the average indoor and outdoor PM2.5
concentrations in office buildings were higher than in residential buildings,
which was higher than teaching buildings.
(2) The overall exposure of adults was higher than that
of children. The average comprehensive exposure of males and females was not
different. The risk of fine particle exposure in urban areas was higher than
that in rural areas, and the risk of PM2.5 exposure in males was
higher than that in females.
(3) The main bacteria of three types buildings in Daqing
were Proteobacteria, Bacteroidetes, and Firmicutes. The bacterial
diversity was higher in rural residential buildings with a high fine particle
concentration than that in urban. The bacterial diversity of outdoor office
buildings was significantly higher than that of outdoor residential buildings.
Author Contributions
Conceptualization and methology was
proposed by YL; formal analysis and data curation was conducted by HW and XC;
writing—original draft preparation, review and edit and project administration
were done by YL
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