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Variations and characteristics of particulate matter, black carbon and volatile organic compounds in primary school classrooms
Journal of Cleaner Production 252 (2020) 119804
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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Variations and characteristics of particulate matter, black carbon and volatile organic compounds in primary school classrooms Kangwei Li a, b, c, *, Jiandong Shen d, Xin Zhang b, Linghong Chen b, Stephen White e, Mingming Yan b, Lixia Han b, Wen Yang a, Xinhua Wang a, **, Merched Azzi c a
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China CSIRO Energy, PO Box 52, North Ryde, NSW, 1670, Australia d Hangzhou Environmental Monitoring Center Station, Hangzhou, 310007, China e New South Wales Department of Planning, Industry and Environment, PO Box 29, Lidcombe, NSW, 1825, Australia b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 August 2019 Received in revised form 17 December 2019 Accepted 18 December 2019 Available online 20 December 2019
The effect of human activities on the characteristics and variations of indoor air pollutants is poorly understood, in particular for the classroom environment due to the limited reports at present. In this study, an observational campaign (17 days) was carried out in two selected classrooms (one equipped with air purifier) in a primary school of Hangzhou, China. Highly time-resolved particulate matter (PM) and black carbon (BC) were measured using Aerodynamic Particle Sizer (APS) and Aethalometer, and the diurnal variations of PM2.5, PM10, PM20 and BC were characterized in detail. It was found that the student activities occurred throughout the daytime and could result in rapid changes in air pollutants. For example, the daytime student activities (e.g., chasing and running) strongly enhanced the PM level and changed PM diurnal pattern, in particular for coarse particles (2.5e10 mm) with short-term spikes at intervals, which was not observed under vacant conditions. The indoor BC did not show clear diurnal patterns, regardless of working days and weekends. Through absorption exponent analysis, it was inferred that the source of indoor BC was stable at most of the time, and possibly related to traffic emission. Besides, three volatile organic compounds (VOCs) samples were collected from the two classrooms, and the laboratory analysis results showed that oxygenated VOCs was a major contributor to indoor VOCs. Through inter-comparison analysis of indoor and outdoor samples, it was further found that the VOC profile of classrooms has a similar pattern with outdoor environments. Finally, the most abundant VOC species of classroom environment were identified, and their potential sources were estimated. © 2019 Elsevier Ltd. All rights reserved.
^ as de Handling editor: Cecilia Maria Villas Bo Almeida Keywords: Black carbon Classroom air Indoor pollution Particulate matter Volatile organic compounds
1. Introduction Indoor air is an essential contributor to personal pollutant exposure (Morawska et al., 2017; Tong et al., 2016a). Besides formaldehyde, the indoor air pollutants encompass a broad range of pollutants, including particulate matter (PM), black carbon (BC), volatile organic compounds (VOCs), and other species (Liu et al.,
* Corresponding author. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China. ** Corresponding author. E-mail addresses: [email protected] (K. Li), [email protected] (X. Wang). https://doi.org/10.1016/j.jclepro.2019.119804 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
2019). With the increasingly severe haze pollution occurring in China these years (Shi et al., 2016; Tong et al., 2016b; Zhang et al., 2016), indoor air pollution has also received considerable attention. Unlike outdoor air, the indoor air quality is governed by a balance of several factors, such as emissions from indoor sources, intrusion from outdoor air, human activities, and other removal process (e.g., filtration, deposition, and ventilation) (Liu et al., 2018a; Tian et al., 2018). The characteristics of indoor pollutants varied among different indoor environments, such as offices, residences, public facilities, and classrooms, due to different occupancy status and indoor conditions (Morawska et al., 2017). One important knowledge gap of indoor air is to understand how human activities affect the variation of indoor pollutants. Previous studies (Chen and
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Hildemann, 2009; Knibbs et al., 2011) have demonstrated the importance of human occupancy as a source of aerosol. For example, smoking and cooking have been identified as the two major sources for indoor particles (Wallace, 2006; Wang et al., 2018; Waring and Siegel, 2006). The school campus is a place with a highly dense population. The classroom environments are of particular interest because teachers and students spend most of their time in the classroom. Therefore, the air quality of the classroom environment can be directly or indirectly associated with personal exposure and health risk (Morawska et al., 2013), as it has been widely recognized that these pollutants like PM, BC and VOCs are adverse to human health. For example, PM10 is inhalable but sometimes could be blocked by nose hair, while PM2.5 is more harmful as it may enter into the body through respiratory and accumulate in the lungs. Besides, these pollutants may even affect the emotions of students, which could further result in some problems (e.g., lower learning efficiency). From this perspective, a better understanding of the variation and characteristic of indoor pollutants in the classroom environment is urgently needed, especially for PM. Some research on air pollution of the classroom environment has been carried out previously. For example, Qian et al. (2012) investigated size distribution of biological aerosols in a university classroom, and estimated the per-person emission rate of bacterial and fungal genomes. Tang et al. (2015) found that cyclic volatile methylsiloxane was a dominant compound in a classroom. However, there are still very limited studies on indoor air pollution focusing on the classroom environment in the school campus, which should raise attention immediately. At the same time, people have concerned the real performance of the indoor air purifier, which has been widely utilized as a household appliance. Although it can filter a certain amount of PM, most air purifiers are used in indoor environments where intrusion from outdoor air may occur (Zhao et al., 2016), especially for a classroom environment with complex window/door opening behaviors. For this reason, the real performance of the air purifier is explored at the classroom environment from PM measurements. Another focus of classroom environments is that student activities occurred throughout the daytime, which may lead to rapid changes in indoor pollutants. Considering the potential for rapid changes in indoor PM concentrations, sampling methods with high time resolution (in 1 min) can make important contributions to the state of knowledge in this kind of environment. To our best knowledge, there are no reported studies on the characteristic of PM and BC with continuous, high time-resolution measurements in a classroom environment under occupied and vacant conditions. In this study, the classroom air was investigated in a primary school of Hangzhou through an observational campaign with highly time-resolved and continuous measurements of PM and BC. The variations and characteristics of PM and BC were obtained, and the effect of air purifier, student activity, and occupied/vacant conditions was analyzed. Furthermore, the VOC characteristics and composition of the classroom environment were analyzed, and the results were compared with those from the nearby ambient environments. The results are useful for the future work on emission estimation and personal exposure assessment for classroom environments. 2. Methods 2.1. Site description and observational campaign The Qiyuan primary school was selected for this study. It is a public primary school founded by the Administrative Committee of Hangzhou Economic and Technological Development Zone. It was
built in September 2014, with two main buildings of 36 classrooms. As shown in Fig. S1, the school campus is located in the eastern of Hangzhou, surrounded by residential areas and construction sites, and there is an industrial park 7e8 km away. There is an air quality monitoring station (Xiasha) near the Qiyuan primary school, which was about 4 km distance and can be used as a reference for the outdoor air quality. Table 1 lists the detailed information for this campaign. The continuous measurements of PM and BC were started from May 7th to May 24th of 2016 with a totally of 17 days, and the whole campaign consisted of two periods corresponding to the selected two classrooms. The two classrooms are next to each other and functionally similar to the concrete floor, except that classroom #2 equipped with a brand new air purifier, which can filter the PM of air inside the classroom. Therefore, the results of this campaign could provide information for the real performance of the air purifier. Note that the window/door opening behaviors were complex in the classroom environment, and whether the door is under open or closed conditions is random, and this is primarily determined by the teacher’s habit when a class is going on. In most cases, the doors and windows of the classrooms were open under working conditions and closed under vacant conditions. Besides, three samples were collected in two classrooms using Summa canisters for laboratory VOC analysis. Unfortunately, online VOC measurements were not available for this work, though the VOC profiles may change time by time. However, the timeintegrated VOC analysis was still useful in determining the most abundant VOC species. The meteorological parameters were collected from the local department of meteorology, which are shown in Fig. S2. The two periods during the whole campaign showed similar temperature and relative humidity (RH) conditions. The average temperature and RH are ~20 C and 70e80%, with some precipitation processing occurring, which is a typical condition in the spring of Hangzhou. The average wind speed is about 2.1e2.3 m/s, with the prevailing wind direction from north and east.
2.2. Instrumentation and laboratory analysis PM data was measured using an Aerodynamic Particle Sizer (APS; model 3321; TSI Inc, USA), which is one of the most widely used particle analysis instruments. It can measure the aerodynamic size of aerosol particles in real-time with a time resolution down to 1 min, and then the mass concentration of PM2.5, PM10, and PM20 could be derived. The seven-wavelength Aethalometer (model AE33, Magee Company, USA) was used to determine the real-time of BC concentration, which was theoretically based on optical methods. In short, the sample air stream with the aerosol particles are continually sampled on the filter, then the transmission of light through the sample-collected filter is measured, and the optical attenuation is obtained with high time resolution 1 s or 1 min. The attenuation coefficient is converted to the absorption coefficient (babs), and then equivalent BC mass concentration is calculated by dividing the absorption coefficient to the wavelength-dependent mass absorption cross-section (Drinovec et al., 2015), which was described in Eq. (1). The seven wavelengths are 370, 470, 520, 590, 660, 880, and 950 nm, respectively. Previous research has demonstrated that the light absorption of BC at 880 nm accounts for 90e95% of the total aerosol absorption (Moosmüller et al., 2009), thus it is generally considered that the BC mass concentration is calculated from the change in optical attenuation at 880 nm, using the mass absorption cross-section 7.77 m2 g1.
K. Li et al. / Journal of Cleaner Production 252 (2020) 119804
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Table 1 Detailed information of this observational campaign. Sampling sites
Period
Particle measurements
VOC samples
With/without air purifier
Classroom #1 Classroom #2
May 7th ~ May 15th May 17th ~ May 24th
PM, BC PM, BC
#1, #2 #3
without with
Note: VOC sample #1, #2, and #3 were collected from 15:00e17:00 of May 7th, 15:00e17:00 of May 8th, and 12:00e14:00 of May 20th, respectively.
BC ¼
babs ðlÞ MACðlÞ
3. Results
(1)
where BC is the mass equivalent BC concentration; babs (l) is the absorption of aerosol particles at wavelength l; MAC (l) is the mass absorption cross-section at wavelength l. During the campaign, three VOC samples using SUMMA canisters were collected in the classrooms and sent to the laboratory for offline analysis using the technique of pre-concentrator coupled with gas chromatography/mass spectrometry (GC/MS, model 7890B/5977A, Agilent, USA). The pre-concentrator (model 7200, Entech, USA) was used to preprocess the canister samples. The built-in three-stage cold well (supplied by liquid nitrogen) of the pre-concentrator can remove the interference of N2, O2, CO2 and water vapor in the sample, and which largely improved the detection limit of the VOCs species. After pre-concentration, the concentrated samples were sent for qualitative and quantitative analysis of VOCs by GC/MS. An integrated method (TO-15 and PAMS, both are recommended by US EPA for VOCs analysis) was established for the target ~107 species (Fujita and Campbell, 2003; Murphy and Norma, 1989), and the calibration curves for each species were established by internal and external calibration to obtain qualitative and quantitative results of the VOC samples. 2.3. Data analysis APS was used to obtain the particle size distribution within 0.5e20 mm. The previous study (Gao et al., 2007) showed that the particle density in Shanghai was 1.7 g/cm3, and this value was referred in this work to derive PM mass concentration. The mass concentration of particles in different particle size ranges can be obtained according to Eq. (2).
PM ¼
n pX
6
Ni rassumed D3bin;i
(2)
i¼1
where PM is the mass concentration of particulate matter; Dbin,i is the particle size bin i; Ni is the particle number concentration at size bin Dbin,i; rassumed is the assumed particle density. € m absorption exponent (AAE), an aerosol In addition, Ångstro optical property describing the wavelength variation in aerosol absorption (Liu et al., 2018b), was also derived from Aethalometer data. AAE has been widely used for aerosol characterization studies, which can reflect the source, aging degree and chemical information of carbon-containing aerosol (Giles et al., 2012; Russell et al., 2010). In general, the AAE of fresh black carbon is 0.8e1.2, which is usually considered as BC-rich aerosol from fossil fuel burning; while organic aerosols and dust have higher AAE values, and are referred to more complicated sources (e.g., biomass/biofuel burning). The raw data of Aethalometer can be inverted to obtain the light absorption coefficient (babs) at each wavelength, and the et al., 2015). AAE value can be fitted according to Eq. (3) (Massabo
babs ðlÞ∞lAAE
(3)
3.1. Inter-comparison of indoor measurements and ambient data The Xiasha Air Quality Monitoring Station is about 4 km away from Qiyuan primary school, and can be used as a reference for the outdoor air quality. Fig. 1 shows the time series of hourly PM2.5 and PM10 concentrations from indoor (APS measurement) and ambient data (obtained from Xiasha station). By comparing the data from classrooms and nearby air quality monitoring station, a consistent trend (r ¼ 0.48e0.60) was found between indoor and outdoor PM results, indicating that the indoor APS measurement results were reliable and comparable to some extent, and also implying some interactions between indoor and outdoor air. Compared to the classroom #2, the correlation of PM between classroom #1 and outdoor was slightly higher (r ¼ 0.58e0.6). Fig. S3 shows more detailed information by comparing our classroom PM measurement with the ambient monitoring network (10 sites) in Hangzhou. It is clear that the general trend of classroom PM has good consistency with the ambient PM, suggesting that the outdoor PM could be a major source for classroom PM. Table S1 summarized the Person coefficient values between classroom PM and ambient PM for each monitoring site in Hangzhou. The results show that the general variation of classroom PM has a relationship with the ambient environment for most of the time, although it might be affected by student activities for some specific conditions. Also, this quantitative information is useful to improve our data reliability. As shown in Fig. 1(a and b), for classroom #1 (without air purifier), when PM was at a low level, the indoor PM2.5 was usually slightly higher than that of outdoor, while both indoor and outdoor PM10 concentrations were basically within the same level. When severe PM pollution episodes came out (May 9th and May 13th), the indoor PM was higher than that measured from the outdoor station. It can be explained that when the outdoor PM pollution was becoming severe, the outdoor PM could be ventilated into classrooms. Since the indoor air was more likely an enclosed space compared to the outdoor atmosphere, therefore less airflow and dilution of indoor environment could contribute to the rapid accumulation of pollutants. Another reason could be attributed to the indoor activity, since the indoor activities of students (during the working day) could significantly increase the PM level as well (He et al., 2004). As shown in Fig. 1(c and d), for classroom #2 (equipped with an air purifier), although the general trends of PM2.5 and PM10 for both indoor and outdoor were basically consistent, the indoor PM values were obviously lower than that measured at outdoor (e.g., May 17th ~ May 19th). Note that the doors and windows of the classrooms were open at most of the time; therefore it suggested that the air purifier still had positive effects on the indoor PM reduction, especially for PM with larger particle size (PM10). However, when severe PM pollution came out (May 23rd and May 24th), the indoor PM concentrations were slightly higher than that of outdoor, which was more likely due to the rapid accumulation of indoor pollutants and the influence of human activities. Therefore, although the air purifier could alleviate the indoor PM pollution to some extent,
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Fig. 1. Comparisons of time series of PM2.5 and PM10 obtained from two classrooms (APS data) and Xiasha air quality station. Weekend days were marked with light shadow.
there were still some limitations regarding the real performance of air purifier for indoor PM reduction, particularly when outdoor PM pollution was severe. Table 2 lists the averaged PM2.5 and PM10 concentrations for the two classrooms (indoor) and Xiasha station (outdoor) in detail. By comparing the results of two classrooms, the PM2.5 concentration of classroom #2 was 49.2% lower than that of classroom #1 (35.9 vs. 70.6 mg/m3), and PM10 concentration of classroom #2 was 49.1% lower than that of classroom #1 (47.5 vs. 93.3 mg/m3), which could qualitatively indicate that the positive effect of the air purifier on indoor PM reduction. Furthermore, in order to estimate the reduction performance of indoor air purifier, the PM data of Xiasha station (period #1 from May 7th to May 16th) was referred as a baseline, then the PM data of the classroom #2 was normalized. Comparing the normalized PM results from two classrooms, the air purifier was estimated to reduce PM2.5 and PM10 by 33.6% (46.9 vs. 70.6 mg/m3) and 31.4% (64.0 vs. 93.3 mg/m3), respectively. Note that parallel measurements of classroom #1 and #2 at same period were not available during this campaign due to the limited instruments, therefore the above analysis through normalized method could only provide a preliminary assessment for the real
performance of air purifier, especially for a classroom environment with complex window/door open behaviors.
3.2. Diurnal patterns of PM 3.2.1. Diurnal patterns of PM on working days Fig. 2 shows the diurnal patterns of PM, PM2.5/PM10 and PM10/ PM20 during the selected three working days in classroom #1 (without the air purifier). It was clear that the curves of PM10 and PM20 were basically overlapped, indicated that indoor PM mainly consisted of inhalable particulate matter (PM10), which was more harmful to the human respiratory system. The PM10 concentration during the daytime was significantly higher than that under vacant conditions at nighttime. The PM2.5/PM10 ratio at nighttime was almost above 80%, which was much higher than that during the daytime, suggesting the fine particles (<2.5 mm) dominated during nighttime. Three working days of classroom #2 were selected as well, and Fig. 3 showed the diurnal variation of PM2.5, PM10, PM20, PM2.5/ PM10 and PM10/PM20. Similar to the PM variation in the classroom #1, the PM10 and PM20 curves were almost overlapped, and the
Table 2 Averaged PM concentrations of two classrooms and outdoor (Xiasha air quality station) during the two periods of the campaign. Average PM2.5 (mg/m3)
Raw data Normalized data
Period Period Period Period
#1 #2 #1 #2
Average PM10 (mg/m3)
outdoor
indoor
outdoor
indoor
47.9 36.7 47.9 47.9
70.6 35.9 70.6 46.9
83.8 62.2 83.8 83.8
93.3 47.5 93.3 64.0
Note: Period #1 was May 7th ~ May 15th and indoor measurements were conducted at classroom #1; period #2 was May 17th ~ May 24th and indoor measurements were conducted at classroom #2.
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Fig. 2. Diurnal cycles of PM2.5, PM10, PM20, PM2.5/PM10 and PM10/PM20 during three selected working days in classroom #1.
Fig. 3. Diurnal cycles of PM2.5, PM10, PM20, PM2.5/PM10 and PM10/PM20 during three selected working days in classroom #2.
PM10/PM20 values were within 90%~100%, indicating that the PM10 and PM20 mass concentrations were equivalent. For the two classrooms, the time points of inter-class activities are labeled in their PM diurnal patterns. It showed that there was a connection between PM variation and inter-class student activities, as the short-term spikes almost occurred during the intervals between classes. Usually, the PM2.5/PM10 values were above 80% during 16:00e7:40 (þ1) when school was off. Interestingly, after students entering the classroom in the early morning, the PM10 began to increase significantly, and the PM2.5/PM10 ratio dropped from ~80% to ~50%. During the daytime, the PM10 was varied in
cycles, as the time gap among those peaks of PM10 was around 40e50 min, and this phenomenon was also reflected in the variation of PM2.5/PM10 ratio. Although PM2.5 concentrations also increased correspondingly during the daytime, it had a small increase compared to the concentrations of PM10. This indicated that the increased particles due to the inter-class student activities were mainly coarse particles, with size ranging between 2.5 and 10 mm. Apart from the student activities (e.g., chasing and running), the chalk dust from blackboards might also be an important source for coarse particles during the daytime. Compared with the school off period, PM10
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increased approximately 200e500% during the daytime. After school off at 16:00, the students left the classroom, and the doors and windows of the classroom were mostly closed. At this time, the indoor PM began to deposit, especially PM10 and PM20 decreased rapidly. The deposition of large particles generally lasted for 2 h, as the PM2.5/PM10 ratio increased from ~50% to ~80%. Note that though the PM diurnal variations of the two classrooms were generally similar, the daytime PM2.5/PM10 ratio varied more significantly in classroom #1, which indicated that the air purifier may have a more positive effect on reduction of coarse particles. Another interesting thing is that the PM10/PM20 values were close to 90% during the daytime, while the PM10/PM20 values were close to 100% during the nighttime, which further revealed the strong evidence that student activities could increase the indoor PM exposure risk with coarse particles.
3.2.2. Diurnal patterns of PM on weekends Fig. 4 shows the diurnal patterns of PM2.5, PM10, PM20, PM2.5/ PM10 and PM10/PM20 for classroom #1 on weekends. It showed that the PM2.5/PM10 ratio varied largely during the selected two days. The daily variation of PM2.5/PM10 ratio was largely different from that in classroom #2 (Fig. 5), which may be related to the role of the air purifier. In addition, windows of this classroom were not completely closed on May 7th and May 8th. The PM2.5/PM10 obtained from the outdoor environment was also plotted, and the trend of both indoor and outdoor PM2.5/PM10 was generally consistent, which indicated that the PM variation of classroom #1 presented more close relationship with outdoor air. Fig. 5 shows the diurnal patterns of PM2.5, PM10 and PM2.5/PM10 for classroom #2 on weekends. Compared to working days, there were no activities of classroom during the weekend. Due to the effects of large particles deposition and reduction of the air purifier, the particles with larger size (>2.5 mm) were almost removed. The PM2.5/PM10 ratio was above 90% for the whole weekend, which was similar to the situation at night on working days. The results also
supported the conclusion that human activities could promote the mass fraction of coarse particles.
3.3. Diurnal patterns of BC and absorption exponent Fig. 6 shows selected diurnal patterns of BC concentration in classroom #1. Unlike the diurnal variation of PM, there was no discernible difference in BC loadings between daytime and nighttime. However, as shown in Fig. 6(a and b), some short-term spikes of BC were observed on working days (May 10th and May 13th) when the classroom was occupied, which was similar to the daytime variation of PM, and more likely due to the student activities. Fig. 7 presents the selected diurnal patterns of BC concentration in classroom #2. Similar to classroom #1, no apparent diurnal pattern of indoor BC was observed. The above analysis implied that the student activities did not have a significant influence on the indoor BC level, regardless of the classroom was under occupied or vacant conditions. Previous studies demonstrated that the absorption exponent (AAE) of strong light-absorbing particles (e.g., soot produced by combustion) was within 0.8e1.2, while weak light-absorbing particles (e.g., dust) had a higher absorption exponent of 2.9 (Yan et al., 2014). As shown in Figs. 6 and 7, for 8 days selected in both classrooms, the absorption exponent was within 1.1e1.5 at most of the time (except May 7th and May 15th), suggesting the source of indoor BC was reasonably stable during most of the time, and we inferred the main source of indoor BC was more likely related to the traffic emission. Fig. S4 presents the time series of PM2.5 and BC in classroom #1 and #2, respectively. The time series of PM2.5 and BC measured at classroom #2 showed a good correlation (r ¼ 0.82), while the correlation for that at classroom #1 was only 0.27. However, the reason behind it is still not clear. During the whole campaign, the average BC/PM2.5 ratio was ~7.7%. Our previous ambient observation of urban Hangzhou (Li et al., 2018) showed that BC accounted
Fig. 4. Diurnal cycles of PM2.5, PM10, PM20, PM2.5/PM10 and PM10/PM20 during weekends in classroom #1 (The outdoor PM2.5/PM10 was also added in red dots). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
K. Li et al. / Journal of Cleaner Production 252 (2020) 119804
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Fig. 5. Diurnal cycles of PM2.5, PM10, PM20, PM2.5/PM10 and PM10/PM20 during weekends in classroom #2 (The outdoor PM2.5/PM10 was also added in red dots). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6. Diurnal cycles of BC (black line) and absorption exponent (blue line) for selected days in classroom #1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
for about 5.8e7.7% of PM1, which was equivalent to the indoor results to some extent. 3.4. VOCs speciation from indoor and ambient conditions The selected two classrooms air was collected through Summa canisters, and the three VOC samples were sent for offline laboratory analysis using a pre-concentrator coupled with GC/MS. As
mentioned in Sec. 2.2, both methods of TO-15 and PAMS have some identical species, and the final dataset consists of 107 target compounds. In addition to the classroom air, ambient VOC sampling campaign (another study with a purpose for assessing local photochemical reactivity) was conducted during MayeSeptember of 2018, with a totally of 40 ambient samples collected from the nearby site of the primary school campus. Table S2 listed the VOCs species and concentrations of the three classroom samples, and the
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Fig. 7. Diurnal cycles of BC (black line) and absorption exponent (blue line) for selected days in classroom #2. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
averaged ambient VOC dataset was also added for comparison. Classroom VOCs were mainly affected by a few factors, including building materials and furnishings, student activities, as well as airexchange from outdoor (Liu et al., 2019). Although only three indoor samples were collected, the results still could contribute a general knowledge of VOCs speciation for classroom environments. Fig. 8 showed the fraction of major components for different VOC
samples. Among the three indoor samples, oxygenated VOCs (OVOCs) were found as the major contributor (42.5e54.4%) of total VOCs, followed by alkanes with a fraction of 16.3e20%. OVOCs and alkanes were also as the top 2 contributors for ambient VOCs, but the fractions were different compared to the indoor ones. As shown in Table S2, each VOC concentration may vary significantly from different samples, however, the abundance of
Fig. 8. Fraction of major categories for the indoor (aec) and ambient (d) VOCs samples.
K. Li et al. / Journal of Cleaner Production 252 (2020) 119804
each VOC speciation could be determined through intercomparison analysis. As shown in Fig. S5, VOC concentrations were divided into four categories, including <0.01, 0.01e0.1, 0.1e1 and 1e10 ppb. Fig. S5 (a-c) showed that the inter-comparison of the three indoor samples displayed similar and good correlation (R2 ¼ 0.89e0.92), suggesting that the quantification results from the three indoor samples were reliable and comparable. As shown in Fig. S5 (e-f), when each indoor sample and ambient data were compared, the correlation was reasonably good (R2 ¼ 0.80e0.84), which implied that the VOC profile of the classroom has a close relationship with outdoor environments. Fig. S6 showed the top 10 species of different VOC categories for different samples. The top 15 species and concentrations for different samples were also listed in Table 3. Regarding to OVOCs, acetone, isopropanol and ethyl acetate were found as the most abundant species in both indoor and ambient samples. Previously research demonstrated that higher concentrations of aldehydes and ketones were mainly affected by anthropogenic emissions (Liu et al., 2008; Ou et al., 2015), and these species may be related to the emissions from the nearby industrial park or solvent evaporation. Ethylene, ethane, propane and toluene were also abundant species among alkanes, alkenes, and aromatics. The sources of those species are more likely from vehicle exhaust (Guo et al., 2017; Song et al., 2007; Zhang et al., 2013), as there are a few main roads and a highway nearby the school campus. Note that some halocarbons (e.g., methylene chloride and chloromethane) were also identified to be important in both indoor and outdoor samples, and these species were not reactive, and could stay for a long time in the atmosphere. 4. Conclusion An observational campaign (17 days) was conducted in two selected classrooms (one equipped with an air purifier) in a primary school in eastern Hangzhou, and highly time-resolution (1 min) of PM and BC measurements were obtained. Comparing the data of indoor and nearby air quality monitoring station, the trend of indoor and outdoor PM had a good consistency. It was estimated that the air purifier reduced the concentration of indoor PM by nearly 1/3, suggesting that the air purifier still had positive effects on filtering indoor PM, although window/door opening behaviors were complicated in the classroom environment. The diurnal variation of PM indicated that PM10 and PM20 mass concentrations were basically equivalent. Due to the student
9
activities or chalk dust from blackboards during the daytime, the concentration of coarse particles (2.5e10 mm) increased obviously, and the observed short-term spikes of PM also has a close relationship with student activities. The ratio of PM2.5/PM10 was lower (~50%) during the daytime when the classroom was under occupied condition, however, it increased to more than 80% at nighttime, and even up to 95% on weekends under vacant conditions. BC did not present a clear diurnal pattern, and the absorption exponent was within 1.1e1.5 at most of the time, suggesting the source of indoor BC was reasonably stable during most of the time. In addition, three VOC samples obtained from the classrooms were analyzed, and it was found that oxygenated VOCs and alkanes were major components of total VOCs under the classroom environment, accounting for 42.5e54.4% and 16.3e20% of total VOCs, respectively. Through inter-comparison analysis of indoor and outdoor samples, it was further indicated that the VOC profile of the classroom has a close relationship with outdoor environments. The most abundant species were identified, and their sources were estimated. For example, acetone, isopropanol and ethyl acetate are more likely affected by the surrounding industries and solvent evaporation, while ethylene, ethane, propane and toluene are mostly from vehicle emissions of nearby roads. By applying fast and real-time measurement in the classroom environment, we have found that the classroom features and activities are strongly responsible for the classroom PM variation, as evidenced by the working days/weekend difference, day/night difference, and daytime short-term spikes. These results have advanced our scientific understanding of the variation of air pollutants in the classroom environment, and provide implications of air quality in the classroom environment for other schools. However, it should be noted that there are still some limitations of this work, as it is only carried out in a particular city for a short period. For future work, some advanced on-line instruments (e.g., PTR-ToF-MS, SMPS, etc.) are suggested to deploy in the classroom environment, which could provide more insights on unconventional indoor pollutants. Besides, similar classroom air quality studies should be carried out in more cities and more seasons, which could cover a wide range of environmental conditions. This could be implemented by using some new techniques (e.g., sensor network for PM and gases), which could provide more universe and representative understanding of the classroom air, and these data could also be helpful for health risk assessment in the classroom environment.
Table 3 Top 15 VOC species and concentrations of the indoor and ambient samples. rank
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
sample #1
sample #2
sample #3
ambient avg.
species
conc.
species
conc.
species
conc.
species
conc.
acetone ethylene isopropyl alcohol ethane ethyl acetate methylene chloride propane acetylene chloromethane methyl ethyl ketone disulfide carbon toluene 1,2-dichloroethane naphthalene acrolein
8.25 3.29 3.26 3.05 1.75 1.04 0.98 0.91 0.84 0.81 0.69 0.61 0.58 0.50 0.49
acetone isopropyl alcohol ethyl acetate methyl ethyl ketone ethylene ethane acrolein propane toluene disulfide carbon methylene chloride 1,2-dichloroethane chloromethane o-xylene acetylene
5.59 4.60 2.40 2.13 1.89 1.57 1.31 1.23 0.93 0.90 0.76 0.71 0.67 0.59 0.52
isopropyl alcohol acetone ethane acrolein ethyl acetate ethylene propane methyl ethyl ketone toluene naphthalene chloromethane dichlorodifluoromethane acetylene methylene chloride ethylbenzene
5.51 4.51 1.28 0.89 0.82 0.74 0.72 0.64 0.60 0.59 0.57 0.42 0.39 0.36 0.30
acetone ethane propane isopropyl alcohol methylene chloride ethylene toluene ethyl acetate n-butane methyl ethyl ketone chloromethane 1,2-dichloroethane n-Pentane isobutane isopentane
7.83 2.02 2.00 1.98 1.62 1.49 1.39 1.35 1.28 1.05 1.04 0.89 0.77 0.74 0.70
Note: All VOC concentrations are in ppb; those species overlapped among indoor and outdoor samples are in bold.
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K. Li et al. / Journal of Cleaner Production 252 (2020) 119804
Author contributions Conceptualization, Kangwei Li and Linghong Chen; Formal analysis, Kangwei Li, Lixia Han and Mingming Yan; Methodology, Kangwei Li, Mingming Yan and Xin Zhang; Project administration, Kangwei Li; Resources, Kangwei Li and Jiandong Shen; Supervision, Kangwei Li; Writing e original draft, Kangwei Li; Writing e review & editing, Kangwei Li, Wen Yang, Xinhua Wang, Stephen White and Merched Azzi. Declaration of competing interest The authors declared that they have no conflicts of interest to this work. Acknowledgement This work was supported by the Natural Science Foundation of China (No. 51876190), the Environmental Protection Agency of Hangzhou (No. 2017-008), the Innovative Research Groups of the National Natural Science Foundation of China (No. 51621005), and the program of Introducing Talents of Discipline to University (No. B08026). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2019.119804. References Chen, Q., Hildemann, L.M., 2009. The effects of human activities on exposure to particulate matter and bioaerosols in residential homes. Environ. Sci. Technol. 43 (13), 4641e4646. vo ^t, A.S.H., Ruckstuhl, C., Coz, E., Drinovec, L., Mo cnik, G., Zotter, P., Pre Rupakheti, M., Sciare, J., Müller, T., Wiedensohler, A., Hansen, A.D.A., 2015. The“dual-spot”Aethalometer: an improved measurement of aerosol black carbon with real-time loading compensation. Atmos. Meas. Tech. 8 (5), 1965e1979. Fujita, E.M., Campbell, D., 2003. Validation and Applications Protocol for Source Apportionment of Photochemical Assessment Monitoring Stations (PAMS) Ambient Volatile Organic Compound (VOC) Data. Final Report Prepared for United States Environmental Protection Agency. Gao, J., Zhou, Y., Wang, J., Wang, T., Wang, W.X., 2007. Inter-comparison of WPSTMTEOMTM-MOUDITM and investigation on particle density. Environ. Sci. 28 (9), 1929e1934. Giles, D.M., Holben, B.N., Eck, T.F., Sinyuk, A., Smirnov, A., Slutsker, I., Dickerson, R.R., Thompson, A.M., Schafer, J.S., 2012. An analysis of AERONET aerosol absorption properties and classifications representative of aerosol source regions. J. Geophys. Res.: Atmosphere 117 (D17). Guo, H., Ling, Z.H., Cheng, H.R., Simpson, I.J., Lyu, X.P., Wang, X.M., Shao, M., Lu, H.X., Ayoko, G., Zhang, Y.L., 2017. Tropospheric volatile organic compounds in China. Sci. Total Environ. 574, 1021e1043. He, C.R., Morawska, L., Hitchins, J., Gilbert, D., 2004. Contribution from indoor sources to particle number and mass concentrations in residential houses. Atmos. Environ. 38 (21), 3405e3415. Knibbs, L.D., He, C., Duchaine, C., Morawska, L., 2011. Vacuum cleaner emissions as a source of indoor exposure to airborne particles and bacteria. Environ. Sci. Technol. 46 (1), 534e542. Li, K.W., Chen, L.H., White, S.J., Zheng, X.J., Lv, B., Lin, C., Bao, Z., Wu, X.C., Gao, X., Ying, F., 2018. Chemical characteristics and sources of PM1 during the 2016 summer in Hangzhou. Environ. Pollut. 232, 42e54. € m exponent Liu, C., Chung, C.E., Yin, Y., Schnaiter, M., 2018b. The absorption Ångstro of black carbon: from numerical aspects. Atmos. Chem. Phys. 18 (9), 6259e6273. Liu, Y., Misztal, P.K., Xiong, J., Tian, Y., Arata, C., Nazaroff, W.W., Goldstein, A.H.,
2018a. Detailed investigation of ventilation rates and airflow patterns in a northern California residence. Indoor Air 28 (4), 572e584. Liu, Y., Misztal, P.K., Xiong, J., Tian, Y., Arata, C., Weber, R.J., Nazaroff, W.W., Goldstein, A.H., 2019. Characterizing sources and emissions of volatile organic compounds in a northern California residence using space- and time-resolved measurements. Indoor Air 29 (4), 630e644. Liu, Y., Shao, M., Fu, L., Lu, S., Zeng, L., Tang, D., 2008. Source profiles of volatile organic compounds (VOCs) measured in China: Part I. Atmos. Environ. 42 (25), 6247e6260. , D., Caponi, L., Bernardoni, V., Bove, M.C., Brotto, P., Calzolai, G., Cassola, F., Massabo Chiari, M., Fedi, M.E., Fermo, P., 2015. Multi-wavelength optical determination of black and brown carbon in atmospheric aerosols. Atmos. Environ. 108, 1e12. Moosmüller, H., Chakrabarty, R.K., Arnott, W.P., 2009. Aerosol light absorption and its measurement: a review. J. Quant. Spectrosc. Ra. 110 (11), 844e878. €nninen, O., Morawska, L., Afshari, A., Bae, G.N., Buonanno, G., Chao, C.Y.H., Ha Hofmann, W., Isaxon, C., Jayaratne, E.R., Pasanen, P., Salthammer, T., Waring, M., Wierzbicka, A., 2013. Indoor aerosols: from personal exposure to risk assessment. Indoor Air 23 (6), 462e487. Morawska, L., Ayoko, G.A., Bae, G.N., Buonanno, G., Chao, C.Y.H., Clifford, S., Fu, S.C., H€ anninen, O., He, C., Isaxon, C., Mazaheri, M., Salthammer, T., Waring, M.S., Wierzbicka, A., 2017. Airborne particles in indoor environment of homes, schools, offices and aged care facilities: the main routes of exposure. Environ. Int. 108, 75e83. Murphy, Norma, T., 1989. Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air. Atmospheric Research and Exposure Assessment Laboratory. U.S. Environmental Protection Agency. Ou, J., Zheng, J., Li, R., Huang, X., Zhong, Z., Zhong, L., Lin, H., 2015. Speciated OVOC and VOC emission inventories and their implications for reactivity-based ozone control strategy in the Pearl River Delta region, China. Sci. Total Environ. 530, 393e402. Qian, J., Hospodsky, D., Yamamoto, N., Nazaroff, W.W., Peccia, J., 2012. Size-resolved emission rates of airborne bacteria and fungi in an occupied classroom. Indoor Air 22 (4), 339e351. Russell, P.B., Bergstrom, R.W., Shinozuka, Y., Clarke, A.D., DeCarlo, P.F., Jimenez, J.L., Livingston, J.M., Redemann, J., Dubovik, O., Strawa, A., 2010. Absorption Angstrom Exponent in AERONET and related data as an indicator of aerosol composition. Atmos. Chem. Phys. 10 (3), 1155e1169. Shi, H., Wang, Y., Chen, J., Huisingh, D., 2016. Preventing smog crises in China and globally. J. Clean. Prod. 112, 1261e1271. Song, Y., Shao, M., Liu, Y., Lu, S., Kuster, W., Goldan, P., Xie, S., 2007. Source apportionment of ambient volatile organic compounds in Beijing. Environ. Sci. Technol. 41 (12), 4348e4353. Tang, X., Misztal, P.K., Nazaroff, W.W., Goldstein, A.H., 2015. Siloxanes are the most abundant volatile organic compound emitted from engineering students in a classroom. Environ. Sci. Technol. Lett. 2 (11), 303e307. Tian, Y., Liu, Y., Misztal, P.K., Xiong, J., Arata, C.M., Goldstein, A.H., Nazaroff, W.W., 2018. Fluorescent biological aerosol particles: concentrations, emissions, and exposures in a northern California residence. Indoor Air 28 (4), 559e571. Tong, Z., Chen, Y., Malkawi, A., Adamkiewicz, G., Spengler, J.D., 2016a. Quantifying the impact of traffic-related air pollution on the indoor air quality of a naturally ventilated building. Environ. Int. 89e90, 138e146. Tong, Z., Chen, Y., Malkawi, A., Liu, Z., Freeman, R.B., 2016b. Energy saving potential of natural ventilation in China: the impact of ambient air pollution. Appl. Energy 179, 660e668. Wallace, L., 2006. Indoor sources of ultrafine and accumulation mode particles: size distributions, size-resolved concentrations, and source strengths. Aerosol Sci. Technol. 40 (5), 348e360. ~ olo, M., Abbatt, J.P.D., 2018. Wang, C., Collins, D.B., Hems, R.F., Borduas, N., Antin Exploring conditions for ultrafine particle formation from oxidation of cigarette smoke in indoor environments. Environ. Sci. Technol. 52 (8), 4623e4631. Waring, M.S., Siegel, J.A., 2006. An evaluation of the indoor air quality in bars before and after a smoking ban in Austin, Texas. J. Expo. Sci. Environ. Epidemiol. 17, 260. Yan, C.Q., Zheng, M., Zhang, Y.H., 2014. Research progress and direction of atmospheric Brown carbon. Environ. Sci. 11, 4404e4414. Zhang, H., Wang, S., Hao, J., Wang, X., Wang, S., Chai, F., Li, M., 2016. Air pollution and control action in Beijing. J. Clean. Prod. 112, 1519e1527. Zhang, Y., Wang, X., Zhou, Z., Lü, S., Min, S., Lee, F.S.C., Yu, J., 2013. Species profiles and normalized reactivity of volatile organic compounds from gasoline evaporation in China. Atmos. Environ. 79 (7), 110e118. Zhao, Z.H., Cai, Y.F., Cai, J., Chen, Q.C., Gao, S., Shi, S.Z., 2016. Applications of air purifiers in schools in perspectives of administration and policy-making. J. Environ. Occup. Med. 33 (11), 1019e1021.
Contents lists available at ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Variations and characteristics of particulate matter, black carbon and volatile organic compounds in primary school classrooms Kangwei Li a, b, c, *, Jiandong Shen d, Xin Zhang b, Linghong Chen b, Stephen White e, Mingming Yan b, Lixia Han b, Wen Yang a, Xinhua Wang a, **, Merched Azzi c a
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China CSIRO Energy, PO Box 52, North Ryde, NSW, 1670, Australia d Hangzhou Environmental Monitoring Center Station, Hangzhou, 310007, China e New South Wales Department of Planning, Industry and Environment, PO Box 29, Lidcombe, NSW, 1825, Australia b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 August 2019 Received in revised form 17 December 2019 Accepted 18 December 2019 Available online 20 December 2019
The effect of human activities on the characteristics and variations of indoor air pollutants is poorly understood, in particular for the classroom environment due to the limited reports at present. In this study, an observational campaign (17 days) was carried out in two selected classrooms (one equipped with air purifier) in a primary school of Hangzhou, China. Highly time-resolved particulate matter (PM) and black carbon (BC) were measured using Aerodynamic Particle Sizer (APS) and Aethalometer, and the diurnal variations of PM2.5, PM10, PM20 and BC were characterized in detail. It was found that the student activities occurred throughout the daytime and could result in rapid changes in air pollutants. For example, the daytime student activities (e.g., chasing and running) strongly enhanced the PM level and changed PM diurnal pattern, in particular for coarse particles (2.5e10 mm) with short-term spikes at intervals, which was not observed under vacant conditions. The indoor BC did not show clear diurnal patterns, regardless of working days and weekends. Through absorption exponent analysis, it was inferred that the source of indoor BC was stable at most of the time, and possibly related to traffic emission. Besides, three volatile organic compounds (VOCs) samples were collected from the two classrooms, and the laboratory analysis results showed that oxygenated VOCs was a major contributor to indoor VOCs. Through inter-comparison analysis of indoor and outdoor samples, it was further found that the VOC profile of classrooms has a similar pattern with outdoor environments. Finally, the most abundant VOC species of classroom environment were identified, and their potential sources were estimated. © 2019 Elsevier Ltd. All rights reserved.
^ as de Handling editor: Cecilia Maria Villas Bo Almeida Keywords: Black carbon Classroom air Indoor pollution Particulate matter Volatile organic compounds
1. Introduction Indoor air is an essential contributor to personal pollutant exposure (Morawska et al., 2017; Tong et al., 2016a). Besides formaldehyde, the indoor air pollutants encompass a broad range of pollutants, including particulate matter (PM), black carbon (BC), volatile organic compounds (VOCs), and other species (Liu et al.,
* Corresponding author. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China. ** Corresponding author. E-mail addresses: [email protected] (K. Li), [email protected] (X. Wang). https://doi.org/10.1016/j.jclepro.2019.119804 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
2019). With the increasingly severe haze pollution occurring in China these years (Shi et al., 2016; Tong et al., 2016b; Zhang et al., 2016), indoor air pollution has also received considerable attention. Unlike outdoor air, the indoor air quality is governed by a balance of several factors, such as emissions from indoor sources, intrusion from outdoor air, human activities, and other removal process (e.g., filtration, deposition, and ventilation) (Liu et al., 2018a; Tian et al., 2018). The characteristics of indoor pollutants varied among different indoor environments, such as offices, residences, public facilities, and classrooms, due to different occupancy status and indoor conditions (Morawska et al., 2017). One important knowledge gap of indoor air is to understand how human activities affect the variation of indoor pollutants. Previous studies (Chen and
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Hildemann, 2009; Knibbs et al., 2011) have demonstrated the importance of human occupancy as a source of aerosol. For example, smoking and cooking have been identified as the two major sources for indoor particles (Wallace, 2006; Wang et al., 2018; Waring and Siegel, 2006). The school campus is a place with a highly dense population. The classroom environments are of particular interest because teachers and students spend most of their time in the classroom. Therefore, the air quality of the classroom environment can be directly or indirectly associated with personal exposure and health risk (Morawska et al., 2013), as it has been widely recognized that these pollutants like PM, BC and VOCs are adverse to human health. For example, PM10 is inhalable but sometimes could be blocked by nose hair, while PM2.5 is more harmful as it may enter into the body through respiratory and accumulate in the lungs. Besides, these pollutants may even affect the emotions of students, which could further result in some problems (e.g., lower learning efficiency). From this perspective, a better understanding of the variation and characteristic of indoor pollutants in the classroom environment is urgently needed, especially for PM. Some research on air pollution of the classroom environment has been carried out previously. For example, Qian et al. (2012) investigated size distribution of biological aerosols in a university classroom, and estimated the per-person emission rate of bacterial and fungal genomes. Tang et al. (2015) found that cyclic volatile methylsiloxane was a dominant compound in a classroom. However, there are still very limited studies on indoor air pollution focusing on the classroom environment in the school campus, which should raise attention immediately. At the same time, people have concerned the real performance of the indoor air purifier, which has been widely utilized as a household appliance. Although it can filter a certain amount of PM, most air purifiers are used in indoor environments where intrusion from outdoor air may occur (Zhao et al., 2016), especially for a classroom environment with complex window/door opening behaviors. For this reason, the real performance of the air purifier is explored at the classroom environment from PM measurements. Another focus of classroom environments is that student activities occurred throughout the daytime, which may lead to rapid changes in indoor pollutants. Considering the potential for rapid changes in indoor PM concentrations, sampling methods with high time resolution (in 1 min) can make important contributions to the state of knowledge in this kind of environment. To our best knowledge, there are no reported studies on the characteristic of PM and BC with continuous, high time-resolution measurements in a classroom environment under occupied and vacant conditions. In this study, the classroom air was investigated in a primary school of Hangzhou through an observational campaign with highly time-resolved and continuous measurements of PM and BC. The variations and characteristics of PM and BC were obtained, and the effect of air purifier, student activity, and occupied/vacant conditions was analyzed. Furthermore, the VOC characteristics and composition of the classroom environment were analyzed, and the results were compared with those from the nearby ambient environments. The results are useful for the future work on emission estimation and personal exposure assessment for classroom environments. 2. Methods 2.1. Site description and observational campaign The Qiyuan primary school was selected for this study. It is a public primary school founded by the Administrative Committee of Hangzhou Economic and Technological Development Zone. It was
built in September 2014, with two main buildings of 36 classrooms. As shown in Fig. S1, the school campus is located in the eastern of Hangzhou, surrounded by residential areas and construction sites, and there is an industrial park 7e8 km away. There is an air quality monitoring station (Xiasha) near the Qiyuan primary school, which was about 4 km distance and can be used as a reference for the outdoor air quality. Table 1 lists the detailed information for this campaign. The continuous measurements of PM and BC were started from May 7th to May 24th of 2016 with a totally of 17 days, and the whole campaign consisted of two periods corresponding to the selected two classrooms. The two classrooms are next to each other and functionally similar to the concrete floor, except that classroom #2 equipped with a brand new air purifier, which can filter the PM of air inside the classroom. Therefore, the results of this campaign could provide information for the real performance of the air purifier. Note that the window/door opening behaviors were complex in the classroom environment, and whether the door is under open or closed conditions is random, and this is primarily determined by the teacher’s habit when a class is going on. In most cases, the doors and windows of the classrooms were open under working conditions and closed under vacant conditions. Besides, three samples were collected in two classrooms using Summa canisters for laboratory VOC analysis. Unfortunately, online VOC measurements were not available for this work, though the VOC profiles may change time by time. However, the timeintegrated VOC analysis was still useful in determining the most abundant VOC species. The meteorological parameters were collected from the local department of meteorology, which are shown in Fig. S2. The two periods during the whole campaign showed similar temperature and relative humidity (RH) conditions. The average temperature and RH are ~20 C and 70e80%, with some precipitation processing occurring, which is a typical condition in the spring of Hangzhou. The average wind speed is about 2.1e2.3 m/s, with the prevailing wind direction from north and east.
2.2. Instrumentation and laboratory analysis PM data was measured using an Aerodynamic Particle Sizer (APS; model 3321; TSI Inc, USA), which is one of the most widely used particle analysis instruments. It can measure the aerodynamic size of aerosol particles in real-time with a time resolution down to 1 min, and then the mass concentration of PM2.5, PM10, and PM20 could be derived. The seven-wavelength Aethalometer (model AE33, Magee Company, USA) was used to determine the real-time of BC concentration, which was theoretically based on optical methods. In short, the sample air stream with the aerosol particles are continually sampled on the filter, then the transmission of light through the sample-collected filter is measured, and the optical attenuation is obtained with high time resolution 1 s or 1 min. The attenuation coefficient is converted to the absorption coefficient (babs), and then equivalent BC mass concentration is calculated by dividing the absorption coefficient to the wavelength-dependent mass absorption cross-section (Drinovec et al., 2015), which was described in Eq. (1). The seven wavelengths are 370, 470, 520, 590, 660, 880, and 950 nm, respectively. Previous research has demonstrated that the light absorption of BC at 880 nm accounts for 90e95% of the total aerosol absorption (Moosmüller et al., 2009), thus it is generally considered that the BC mass concentration is calculated from the change in optical attenuation at 880 nm, using the mass absorption cross-section 7.77 m2 g1.
K. Li et al. / Journal of Cleaner Production 252 (2020) 119804
3
Table 1 Detailed information of this observational campaign. Sampling sites
Period
Particle measurements
VOC samples
With/without air purifier
Classroom #1 Classroom #2
May 7th ~ May 15th May 17th ~ May 24th
PM, BC PM, BC
#1, #2 #3
without with
Note: VOC sample #1, #2, and #3 were collected from 15:00e17:00 of May 7th, 15:00e17:00 of May 8th, and 12:00e14:00 of May 20th, respectively.
BC ¼
babs ðlÞ MACðlÞ
3. Results
(1)
where BC is the mass equivalent BC concentration; babs (l) is the absorption of aerosol particles at wavelength l; MAC (l) is the mass absorption cross-section at wavelength l. During the campaign, three VOC samples using SUMMA canisters were collected in the classrooms and sent to the laboratory for offline analysis using the technique of pre-concentrator coupled with gas chromatography/mass spectrometry (GC/MS, model 7890B/5977A, Agilent, USA). The pre-concentrator (model 7200, Entech, USA) was used to preprocess the canister samples. The built-in three-stage cold well (supplied by liquid nitrogen) of the pre-concentrator can remove the interference of N2, O2, CO2 and water vapor in the sample, and which largely improved the detection limit of the VOCs species. After pre-concentration, the concentrated samples were sent for qualitative and quantitative analysis of VOCs by GC/MS. An integrated method (TO-15 and PAMS, both are recommended by US EPA for VOCs analysis) was established for the target ~107 species (Fujita and Campbell, 2003; Murphy and Norma, 1989), and the calibration curves for each species were established by internal and external calibration to obtain qualitative and quantitative results of the VOC samples. 2.3. Data analysis APS was used to obtain the particle size distribution within 0.5e20 mm. The previous study (Gao et al., 2007) showed that the particle density in Shanghai was 1.7 g/cm3, and this value was referred in this work to derive PM mass concentration. The mass concentration of particles in different particle size ranges can be obtained according to Eq. (2).
PM ¼
n pX
6
Ni rassumed D3bin;i
(2)
i¼1
where PM is the mass concentration of particulate matter; Dbin,i is the particle size bin i; Ni is the particle number concentration at size bin Dbin,i; rassumed is the assumed particle density. € m absorption exponent (AAE), an aerosol In addition, Ångstro optical property describing the wavelength variation in aerosol absorption (Liu et al., 2018b), was also derived from Aethalometer data. AAE has been widely used for aerosol characterization studies, which can reflect the source, aging degree and chemical information of carbon-containing aerosol (Giles et al., 2012; Russell et al., 2010). In general, the AAE of fresh black carbon is 0.8e1.2, which is usually considered as BC-rich aerosol from fossil fuel burning; while organic aerosols and dust have higher AAE values, and are referred to more complicated sources (e.g., biomass/biofuel burning). The raw data of Aethalometer can be inverted to obtain the light absorption coefficient (babs) at each wavelength, and the et al., 2015). AAE value can be fitted according to Eq. (3) (Massabo
babs ðlÞ∞lAAE
(3)
3.1. Inter-comparison of indoor measurements and ambient data The Xiasha Air Quality Monitoring Station is about 4 km away from Qiyuan primary school, and can be used as a reference for the outdoor air quality. Fig. 1 shows the time series of hourly PM2.5 and PM10 concentrations from indoor (APS measurement) and ambient data (obtained from Xiasha station). By comparing the data from classrooms and nearby air quality monitoring station, a consistent trend (r ¼ 0.48e0.60) was found between indoor and outdoor PM results, indicating that the indoor APS measurement results were reliable and comparable to some extent, and also implying some interactions between indoor and outdoor air. Compared to the classroom #2, the correlation of PM between classroom #1 and outdoor was slightly higher (r ¼ 0.58e0.6). Fig. S3 shows more detailed information by comparing our classroom PM measurement with the ambient monitoring network (10 sites) in Hangzhou. It is clear that the general trend of classroom PM has good consistency with the ambient PM, suggesting that the outdoor PM could be a major source for classroom PM. Table S1 summarized the Person coefficient values between classroom PM and ambient PM for each monitoring site in Hangzhou. The results show that the general variation of classroom PM has a relationship with the ambient environment for most of the time, although it might be affected by student activities for some specific conditions. Also, this quantitative information is useful to improve our data reliability. As shown in Fig. 1(a and b), for classroom #1 (without air purifier), when PM was at a low level, the indoor PM2.5 was usually slightly higher than that of outdoor, while both indoor and outdoor PM10 concentrations were basically within the same level. When severe PM pollution episodes came out (May 9th and May 13th), the indoor PM was higher than that measured from the outdoor station. It can be explained that when the outdoor PM pollution was becoming severe, the outdoor PM could be ventilated into classrooms. Since the indoor air was more likely an enclosed space compared to the outdoor atmosphere, therefore less airflow and dilution of indoor environment could contribute to the rapid accumulation of pollutants. Another reason could be attributed to the indoor activity, since the indoor activities of students (during the working day) could significantly increase the PM level as well (He et al., 2004). As shown in Fig. 1(c and d), for classroom #2 (equipped with an air purifier), although the general trends of PM2.5 and PM10 for both indoor and outdoor were basically consistent, the indoor PM values were obviously lower than that measured at outdoor (e.g., May 17th ~ May 19th). Note that the doors and windows of the classrooms were open at most of the time; therefore it suggested that the air purifier still had positive effects on the indoor PM reduction, especially for PM with larger particle size (PM10). However, when severe PM pollution came out (May 23rd and May 24th), the indoor PM concentrations were slightly higher than that of outdoor, which was more likely due to the rapid accumulation of indoor pollutants and the influence of human activities. Therefore, although the air purifier could alleviate the indoor PM pollution to some extent,
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Fig. 1. Comparisons of time series of PM2.5 and PM10 obtained from two classrooms (APS data) and Xiasha air quality station. Weekend days were marked with light shadow.
there were still some limitations regarding the real performance of air purifier for indoor PM reduction, particularly when outdoor PM pollution was severe. Table 2 lists the averaged PM2.5 and PM10 concentrations for the two classrooms (indoor) and Xiasha station (outdoor) in detail. By comparing the results of two classrooms, the PM2.5 concentration of classroom #2 was 49.2% lower than that of classroom #1 (35.9 vs. 70.6 mg/m3), and PM10 concentration of classroom #2 was 49.1% lower than that of classroom #1 (47.5 vs. 93.3 mg/m3), which could qualitatively indicate that the positive effect of the air purifier on indoor PM reduction. Furthermore, in order to estimate the reduction performance of indoor air purifier, the PM data of Xiasha station (period #1 from May 7th to May 16th) was referred as a baseline, then the PM data of the classroom #2 was normalized. Comparing the normalized PM results from two classrooms, the air purifier was estimated to reduce PM2.5 and PM10 by 33.6% (46.9 vs. 70.6 mg/m3) and 31.4% (64.0 vs. 93.3 mg/m3), respectively. Note that parallel measurements of classroom #1 and #2 at same period were not available during this campaign due to the limited instruments, therefore the above analysis through normalized method could only provide a preliminary assessment for the real
performance of air purifier, especially for a classroom environment with complex window/door open behaviors.
3.2. Diurnal patterns of PM 3.2.1. Diurnal patterns of PM on working days Fig. 2 shows the diurnal patterns of PM, PM2.5/PM10 and PM10/ PM20 during the selected three working days in classroom #1 (without the air purifier). It was clear that the curves of PM10 and PM20 were basically overlapped, indicated that indoor PM mainly consisted of inhalable particulate matter (PM10), which was more harmful to the human respiratory system. The PM10 concentration during the daytime was significantly higher than that under vacant conditions at nighttime. The PM2.5/PM10 ratio at nighttime was almost above 80%, which was much higher than that during the daytime, suggesting the fine particles (<2.5 mm) dominated during nighttime. Three working days of classroom #2 were selected as well, and Fig. 3 showed the diurnal variation of PM2.5, PM10, PM20, PM2.5/ PM10 and PM10/PM20. Similar to the PM variation in the classroom #1, the PM10 and PM20 curves were almost overlapped, and the
Table 2 Averaged PM concentrations of two classrooms and outdoor (Xiasha air quality station) during the two periods of the campaign. Average PM2.5 (mg/m3)
Raw data Normalized data
Period Period Period Period
#1 #2 #1 #2
Average PM10 (mg/m3)
outdoor
indoor
outdoor
indoor
47.9 36.7 47.9 47.9
70.6 35.9 70.6 46.9
83.8 62.2 83.8 83.8
93.3 47.5 93.3 64.0
Note: Period #1 was May 7th ~ May 15th and indoor measurements were conducted at classroom #1; period #2 was May 17th ~ May 24th and indoor measurements were conducted at classroom #2.
K. Li et al. / Journal of Cleaner Production 252 (2020) 119804
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Fig. 2. Diurnal cycles of PM2.5, PM10, PM20, PM2.5/PM10 and PM10/PM20 during three selected working days in classroom #1.
Fig. 3. Diurnal cycles of PM2.5, PM10, PM20, PM2.5/PM10 and PM10/PM20 during three selected working days in classroom #2.
PM10/PM20 values were within 90%~100%, indicating that the PM10 and PM20 mass concentrations were equivalent. For the two classrooms, the time points of inter-class activities are labeled in their PM diurnal patterns. It showed that there was a connection between PM variation and inter-class student activities, as the short-term spikes almost occurred during the intervals between classes. Usually, the PM2.5/PM10 values were above 80% during 16:00e7:40 (þ1) when school was off. Interestingly, after students entering the classroom in the early morning, the PM10 began to increase significantly, and the PM2.5/PM10 ratio dropped from ~80% to ~50%. During the daytime, the PM10 was varied in
cycles, as the time gap among those peaks of PM10 was around 40e50 min, and this phenomenon was also reflected in the variation of PM2.5/PM10 ratio. Although PM2.5 concentrations also increased correspondingly during the daytime, it had a small increase compared to the concentrations of PM10. This indicated that the increased particles due to the inter-class student activities were mainly coarse particles, with size ranging between 2.5 and 10 mm. Apart from the student activities (e.g., chasing and running), the chalk dust from blackboards might also be an important source for coarse particles during the daytime. Compared with the school off period, PM10
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increased approximately 200e500% during the daytime. After school off at 16:00, the students left the classroom, and the doors and windows of the classroom were mostly closed. At this time, the indoor PM began to deposit, especially PM10 and PM20 decreased rapidly. The deposition of large particles generally lasted for 2 h, as the PM2.5/PM10 ratio increased from ~50% to ~80%. Note that though the PM diurnal variations of the two classrooms were generally similar, the daytime PM2.5/PM10 ratio varied more significantly in classroom #1, which indicated that the air purifier may have a more positive effect on reduction of coarse particles. Another interesting thing is that the PM10/PM20 values were close to 90% during the daytime, while the PM10/PM20 values were close to 100% during the nighttime, which further revealed the strong evidence that student activities could increase the indoor PM exposure risk with coarse particles.
3.2.2. Diurnal patterns of PM on weekends Fig. 4 shows the diurnal patterns of PM2.5, PM10, PM20, PM2.5/ PM10 and PM10/PM20 for classroom #1 on weekends. It showed that the PM2.5/PM10 ratio varied largely during the selected two days. The daily variation of PM2.5/PM10 ratio was largely different from that in classroom #2 (Fig. 5), which may be related to the role of the air purifier. In addition, windows of this classroom were not completely closed on May 7th and May 8th. The PM2.5/PM10 obtained from the outdoor environment was also plotted, and the trend of both indoor and outdoor PM2.5/PM10 was generally consistent, which indicated that the PM variation of classroom #1 presented more close relationship with outdoor air. Fig. 5 shows the diurnal patterns of PM2.5, PM10 and PM2.5/PM10 for classroom #2 on weekends. Compared to working days, there were no activities of classroom during the weekend. Due to the effects of large particles deposition and reduction of the air purifier, the particles with larger size (>2.5 mm) were almost removed. The PM2.5/PM10 ratio was above 90% for the whole weekend, which was similar to the situation at night on working days. The results also
supported the conclusion that human activities could promote the mass fraction of coarse particles.
3.3. Diurnal patterns of BC and absorption exponent Fig. 6 shows selected diurnal patterns of BC concentration in classroom #1. Unlike the diurnal variation of PM, there was no discernible difference in BC loadings between daytime and nighttime. However, as shown in Fig. 6(a and b), some short-term spikes of BC were observed on working days (May 10th and May 13th) when the classroom was occupied, which was similar to the daytime variation of PM, and more likely due to the student activities. Fig. 7 presents the selected diurnal patterns of BC concentration in classroom #2. Similar to classroom #1, no apparent diurnal pattern of indoor BC was observed. The above analysis implied that the student activities did not have a significant influence on the indoor BC level, regardless of the classroom was under occupied or vacant conditions. Previous studies demonstrated that the absorption exponent (AAE) of strong light-absorbing particles (e.g., soot produced by combustion) was within 0.8e1.2, while weak light-absorbing particles (e.g., dust) had a higher absorption exponent of 2.9 (Yan et al., 2014). As shown in Figs. 6 and 7, for 8 days selected in both classrooms, the absorption exponent was within 1.1e1.5 at most of the time (except May 7th and May 15th), suggesting the source of indoor BC was reasonably stable during most of the time, and we inferred the main source of indoor BC was more likely related to the traffic emission. Fig. S4 presents the time series of PM2.5 and BC in classroom #1 and #2, respectively. The time series of PM2.5 and BC measured at classroom #2 showed a good correlation (r ¼ 0.82), while the correlation for that at classroom #1 was only 0.27. However, the reason behind it is still not clear. During the whole campaign, the average BC/PM2.5 ratio was ~7.7%. Our previous ambient observation of urban Hangzhou (Li et al., 2018) showed that BC accounted
Fig. 4. Diurnal cycles of PM2.5, PM10, PM20, PM2.5/PM10 and PM10/PM20 during weekends in classroom #1 (The outdoor PM2.5/PM10 was also added in red dots). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
K. Li et al. / Journal of Cleaner Production 252 (2020) 119804
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Fig. 5. Diurnal cycles of PM2.5, PM10, PM20, PM2.5/PM10 and PM10/PM20 during weekends in classroom #2 (The outdoor PM2.5/PM10 was also added in red dots). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6. Diurnal cycles of BC (black line) and absorption exponent (blue line) for selected days in classroom #1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
for about 5.8e7.7% of PM1, which was equivalent to the indoor results to some extent. 3.4. VOCs speciation from indoor and ambient conditions The selected two classrooms air was collected through Summa canisters, and the three VOC samples were sent for offline laboratory analysis using a pre-concentrator coupled with GC/MS. As
mentioned in Sec. 2.2, both methods of TO-15 and PAMS have some identical species, and the final dataset consists of 107 target compounds. In addition to the classroom air, ambient VOC sampling campaign (another study with a purpose for assessing local photochemical reactivity) was conducted during MayeSeptember of 2018, with a totally of 40 ambient samples collected from the nearby site of the primary school campus. Table S2 listed the VOCs species and concentrations of the three classroom samples, and the
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Fig. 7. Diurnal cycles of BC (black line) and absorption exponent (blue line) for selected days in classroom #2. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
averaged ambient VOC dataset was also added for comparison. Classroom VOCs were mainly affected by a few factors, including building materials and furnishings, student activities, as well as airexchange from outdoor (Liu et al., 2019). Although only three indoor samples were collected, the results still could contribute a general knowledge of VOCs speciation for classroom environments. Fig. 8 showed the fraction of major components for different VOC
samples. Among the three indoor samples, oxygenated VOCs (OVOCs) were found as the major contributor (42.5e54.4%) of total VOCs, followed by alkanes with a fraction of 16.3e20%. OVOCs and alkanes were also as the top 2 contributors for ambient VOCs, but the fractions were different compared to the indoor ones. As shown in Table S2, each VOC concentration may vary significantly from different samples, however, the abundance of
Fig. 8. Fraction of major categories for the indoor (aec) and ambient (d) VOCs samples.
K. Li et al. / Journal of Cleaner Production 252 (2020) 119804
each VOC speciation could be determined through intercomparison analysis. As shown in Fig. S5, VOC concentrations were divided into four categories, including <0.01, 0.01e0.1, 0.1e1 and 1e10 ppb. Fig. S5 (a-c) showed that the inter-comparison of the three indoor samples displayed similar and good correlation (R2 ¼ 0.89e0.92), suggesting that the quantification results from the three indoor samples were reliable and comparable. As shown in Fig. S5 (e-f), when each indoor sample and ambient data were compared, the correlation was reasonably good (R2 ¼ 0.80e0.84), which implied that the VOC profile of the classroom has a close relationship with outdoor environments. Fig. S6 showed the top 10 species of different VOC categories for different samples. The top 15 species and concentrations for different samples were also listed in Table 3. Regarding to OVOCs, acetone, isopropanol and ethyl acetate were found as the most abundant species in both indoor and ambient samples. Previously research demonstrated that higher concentrations of aldehydes and ketones were mainly affected by anthropogenic emissions (Liu et al., 2008; Ou et al., 2015), and these species may be related to the emissions from the nearby industrial park or solvent evaporation. Ethylene, ethane, propane and toluene were also abundant species among alkanes, alkenes, and aromatics. The sources of those species are more likely from vehicle exhaust (Guo et al., 2017; Song et al., 2007; Zhang et al., 2013), as there are a few main roads and a highway nearby the school campus. Note that some halocarbons (e.g., methylene chloride and chloromethane) were also identified to be important in both indoor and outdoor samples, and these species were not reactive, and could stay for a long time in the atmosphere. 4. Conclusion An observational campaign (17 days) was conducted in two selected classrooms (one equipped with an air purifier) in a primary school in eastern Hangzhou, and highly time-resolution (1 min) of PM and BC measurements were obtained. Comparing the data of indoor and nearby air quality monitoring station, the trend of indoor and outdoor PM had a good consistency. It was estimated that the air purifier reduced the concentration of indoor PM by nearly 1/3, suggesting that the air purifier still had positive effects on filtering indoor PM, although window/door opening behaviors were complicated in the classroom environment. The diurnal variation of PM indicated that PM10 and PM20 mass concentrations were basically equivalent. Due to the student
9
activities or chalk dust from blackboards during the daytime, the concentration of coarse particles (2.5e10 mm) increased obviously, and the observed short-term spikes of PM also has a close relationship with student activities. The ratio of PM2.5/PM10 was lower (~50%) during the daytime when the classroom was under occupied condition, however, it increased to more than 80% at nighttime, and even up to 95% on weekends under vacant conditions. BC did not present a clear diurnal pattern, and the absorption exponent was within 1.1e1.5 at most of the time, suggesting the source of indoor BC was reasonably stable during most of the time. In addition, three VOC samples obtained from the classrooms were analyzed, and it was found that oxygenated VOCs and alkanes were major components of total VOCs under the classroom environment, accounting for 42.5e54.4% and 16.3e20% of total VOCs, respectively. Through inter-comparison analysis of indoor and outdoor samples, it was further indicated that the VOC profile of the classroom has a close relationship with outdoor environments. The most abundant species were identified, and their sources were estimated. For example, acetone, isopropanol and ethyl acetate are more likely affected by the surrounding industries and solvent evaporation, while ethylene, ethane, propane and toluene are mostly from vehicle emissions of nearby roads. By applying fast and real-time measurement in the classroom environment, we have found that the classroom features and activities are strongly responsible for the classroom PM variation, as evidenced by the working days/weekend difference, day/night difference, and daytime short-term spikes. These results have advanced our scientific understanding of the variation of air pollutants in the classroom environment, and provide implications of air quality in the classroom environment for other schools. However, it should be noted that there are still some limitations of this work, as it is only carried out in a particular city for a short period. For future work, some advanced on-line instruments (e.g., PTR-ToF-MS, SMPS, etc.) are suggested to deploy in the classroom environment, which could provide more insights on unconventional indoor pollutants. Besides, similar classroom air quality studies should be carried out in more cities and more seasons, which could cover a wide range of environmental conditions. This could be implemented by using some new techniques (e.g., sensor network for PM and gases), which could provide more universe and representative understanding of the classroom air, and these data could also be helpful for health risk assessment in the classroom environment.
Table 3 Top 15 VOC species and concentrations of the indoor and ambient samples. rank
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
sample #1
sample #2
sample #3
ambient avg.
species
conc.
species
conc.
species
conc.
species
conc.
acetone ethylene isopropyl alcohol ethane ethyl acetate methylene chloride propane acetylene chloromethane methyl ethyl ketone disulfide carbon toluene 1,2-dichloroethane naphthalene acrolein
8.25 3.29 3.26 3.05 1.75 1.04 0.98 0.91 0.84 0.81 0.69 0.61 0.58 0.50 0.49
acetone isopropyl alcohol ethyl acetate methyl ethyl ketone ethylene ethane acrolein propane toluene disulfide carbon methylene chloride 1,2-dichloroethane chloromethane o-xylene acetylene
5.59 4.60 2.40 2.13 1.89 1.57 1.31 1.23 0.93 0.90 0.76 0.71 0.67 0.59 0.52
isopropyl alcohol acetone ethane acrolein ethyl acetate ethylene propane methyl ethyl ketone toluene naphthalene chloromethane dichlorodifluoromethane acetylene methylene chloride ethylbenzene
5.51 4.51 1.28 0.89 0.82 0.74 0.72 0.64 0.60 0.59 0.57 0.42 0.39 0.36 0.30
acetone ethane propane isopropyl alcohol methylene chloride ethylene toluene ethyl acetate n-butane methyl ethyl ketone chloromethane 1,2-dichloroethane n-Pentane isobutane isopentane
7.83 2.02 2.00 1.98 1.62 1.49 1.39 1.35 1.28 1.05 1.04 0.89 0.77 0.74 0.70
Note: All VOC concentrations are in ppb; those species overlapped among indoor and outdoor samples are in bold.
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Author contributions Conceptualization, Kangwei Li and Linghong Chen; Formal analysis, Kangwei Li, Lixia Han and Mingming Yan; Methodology, Kangwei Li, Mingming Yan and Xin Zhang; Project administration, Kangwei Li; Resources, Kangwei Li and Jiandong Shen; Supervision, Kangwei Li; Writing e original draft, Kangwei Li; Writing e review & editing, Kangwei Li, Wen Yang, Xinhua Wang, Stephen White and Merched Azzi. Declaration of competing interest The authors declared that they have no conflicts of interest to this work. Acknowledgement This work was supported by the Natural Science Foundation of China (No. 51876190), the Environmental Protection Agency of Hangzhou (No. 2017-008), the Innovative Research Groups of the National Natural Science Foundation of China (No. 51621005), and the program of Introducing Talents of Discipline to University (No. B08026). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2019.119804. References Chen, Q., Hildemann, L.M., 2009. The effects of human activities on exposure to particulate matter and bioaerosols in residential homes. Environ. Sci. Technol. 43 (13), 4641e4646. vo ^t, A.S.H., Ruckstuhl, C., Coz, E., Drinovec, L., Mo cnik, G., Zotter, P., Pre Rupakheti, M., Sciare, J., Müller, T., Wiedensohler, A., Hansen, A.D.A., 2015. The“dual-spot”Aethalometer: an improved measurement of aerosol black carbon with real-time loading compensation. Atmos. Meas. Tech. 8 (5), 1965e1979. Fujita, E.M., Campbell, D., 2003. Validation and Applications Protocol for Source Apportionment of Photochemical Assessment Monitoring Stations (PAMS) Ambient Volatile Organic Compound (VOC) Data. Final Report Prepared for United States Environmental Protection Agency. Gao, J., Zhou, Y., Wang, J., Wang, T., Wang, W.X., 2007. Inter-comparison of WPSTMTEOMTM-MOUDITM and investigation on particle density. Environ. Sci. 28 (9), 1929e1934. Giles, D.M., Holben, B.N., Eck, T.F., Sinyuk, A., Smirnov, A., Slutsker, I., Dickerson, R.R., Thompson, A.M., Schafer, J.S., 2012. An analysis of AERONET aerosol absorption properties and classifications representative of aerosol source regions. J. Geophys. Res.: Atmosphere 117 (D17). Guo, H., Ling, Z.H., Cheng, H.R., Simpson, I.J., Lyu, X.P., Wang, X.M., Shao, M., Lu, H.X., Ayoko, G., Zhang, Y.L., 2017. Tropospheric volatile organic compounds in China. Sci. Total Environ. 574, 1021e1043. He, C.R., Morawska, L., Hitchins, J., Gilbert, D., 2004. Contribution from indoor sources to particle number and mass concentrations in residential houses. Atmos. Environ. 38 (21), 3405e3415. Knibbs, L.D., He, C., Duchaine, C., Morawska, L., 2011. Vacuum cleaner emissions as a source of indoor exposure to airborne particles and bacteria. Environ. Sci. Technol. 46 (1), 534e542. Li, K.W., Chen, L.H., White, S.J., Zheng, X.J., Lv, B., Lin, C., Bao, Z., Wu, X.C., Gao, X., Ying, F., 2018. Chemical characteristics and sources of PM1 during the 2016 summer in Hangzhou. Environ. Pollut. 232, 42e54. € m exponent Liu, C., Chung, C.E., Yin, Y., Schnaiter, M., 2018b. The absorption Ångstro of black carbon: from numerical aspects. Atmos. Chem. Phys. 18 (9), 6259e6273. Liu, Y., Misztal, P.K., Xiong, J., Tian, Y., Arata, C., Nazaroff, W.W., Goldstein, A.H.,
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