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Estimation of PM2.5 infiltration factors and personal exposure factors in two megacities, China
Building and Environment 149 (2019) 297–304
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Estimation of PM2.5 infiltration factors and personal exposure factors in two megacities, China
T
Na Lia, Zhe Liua, Yunpu Lia, Ning Lib, Ryan Chartierc, Andrea McWilliamsc, Junrui Changa, Qin Wanga, Yaxi Wua, Chunyu Xua,∗, Dongqun Xua,∗∗ a
National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China Nanjing Jiangning Center for Disease Control and Prevention, Nanjing, 211100, China c RTI International, Research Triangle Park, NC, 27709, United States b
A R T I C LE I N FO
A B S T R A C T
Keywords: PM2.5 Infiltration Personal exposure Personal-indoor-outdoor relationship Seasonal variability Predictive model
This study estimates infiltration factors (Finf) and ambient personal exposure factors (Fpex) for fine particulate matter (PM2.5) in two Chinese megacities, and constructs predictive models to explore their determinants. Personal-indoor-outdoor PM2.5 filter samples were collected for five consecutive days from 33 residences (of retired adults) in Nanjing (NJ) and Beijing (BJ), China, in both the non-heating season (NHS) and the heating season (HS). Elemental sulfur in filter deposits was determined by energy-dispersive X-ray fluorescence for PM2.5 Finf and Fpex estimations. Season-specific models developed by stepwise multiple linear regression were evaluated using R2 and root mean square error (RMSE). The median [interquartile range (IQR)] of Finf varied from 0.76 (0.15) in the HS to 0.93 (0.11) in the NHS in NJ; and from 0.67 (0.16) to 0.86 (0.12) in BJ. Similarly, Fpex was significantly higher during the NHS [NJ: 0.95 (0.07); BJ: 0.89 (0.14)] than during the HS [NJ: 0.76 (0.17); BJ: 0.67 (0.11); p < 0.0001]. Common predictors of Finf and Fpex included window opening behaviors, meteorological variables, and building age. Moreover, air conditioning and distance to the nearest major road had an influence on Finf, while predictors of Fpex were more related to human behavior and activity (e.g., time spent outdoors and transportation). The models accounted for 35.4%–68.1% (RMSE: 0.065–0.101) and 41.6%–77.0% (RMSE: 0.033–0.103) of the variance in Finf and Fpex, respectively. By indicating the determinants of Finf and Fpex, these models can improve ambient PM2.5 exposure assessment and reduce exposure misclassification.
1. Introduction Accurate measurement and estimation of human exposure to ambient fine particulate matter (PM2.5, aerodynamic diameter ≤ 2.5 μm) have a crucial role in understanding related health impacts and risks. For example, epidemiological studies have demonstrated a significant relationship between exposure to ambient PM2.5 and adverse health effects [1–7]. Nevertheless, because most people spend more than 80% of their time indoors [8–10], and because the chemical properties and physical characteristics of PM2.5 change during outdoor-to-indoor transport [11–13], the validity of using ambient concentrations as measured by stationary monitoring sites as a surrogate for personal exposure has aroused concern [14,15]. For instance, Wilson et al. [14] found a poor correlation between total personal PM2.5 exposure and
outdoor concentrations [determination coefficient (R2) = 0.072]. Consequently, ignoring the differences between outdoor, indoor, and personal PM2.5 exposure may result in exposure misclassification and, therefore, bias the exposure-response relationship in epidemiological studies [16,17]. A vital modifier to minimize this bias is the infiltration factor (Finf), which is defined as the fraction of outdoor particles that penetrate a building and remain suspended, which depends on the particle penetration efficiency, air exchange rate, and deposition rate [18]. Analogous to Finf is the ambient personal exposure factor (Fpex), which quantifies the contribution of outdoor particles to personal exposure [19]. Studies conducted in North America [19–23] and Europe [24–26] have indicated that Finf and Fpex vary substantially between regions and seasons due to the variation of regional factors such as climate, building
Abbreviations: PM2.5, fine particulate matter; Finf, infiltration factor; Fpex, ambient personal exposure factor; HVAC, heating, ventilation, and air-conditioning systems; NHS, non-heating season; HS, heating season; AC, air conditioning; GPS, global position system; AQMS, air quality monitoring sites; TAD, time-activity diary ∗ Corresponding author. No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China. ∗∗ Corresponding author. No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China. E-mail addresses: [email protected] (C. Xu), [email protected] (D. Xu). https://doi.org/10.1016/j.buildenv.2018.12.033 Received 27 October 2018; Received in revised form 11 December 2018; Accepted 14 December 2018 Available online 14 December 2018 0360-1323/ © 2018 Elsevier Ltd. All rights reserved.
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available sealed windows, away from outdoor air-conditioning (AC) units. The MicroPEM and a portable Global Position System (GPS) unit (BT-Q1000XT, QStarz, Taiwan, China) were placed in a mini-messenger bag, which was carried by the participant. The inlet of the MicroPEM was extended to the breathing zone via a 0.3 m length of conductive silicone rubber tube. Participants were instructed to bring the bag with them all the time, but to remove and keep it nearby while sleeping, bathing, dressing, or performing particular activities such as swimming. PM2.5 mass concentrations were determined by gravimetric analysis in accordance with the professional standard (CN HJ 656–2013). The equilibrated Teflon filters were weighed before and after sampling using a microbalance with a readability of 1 μg (XP6; Mettler Toledo International Inc., Switzerland) in a temperature (T, °C) and relative humidity (RH, %) controlled weighing room (25 ± 1 °C; 50 ± 5%, respectively). The filters were passed through a U-Electrode (Mettler Toledo International Inc., Switzerland) to eliminate any electrostatic charge on the filter before weighing. During both pre- and post-sampling, each filter was weighed twice and if the difference between the two weights was more than 4 μg, a third weighing was done, with the final mass being the mean of the two closest weights. Field blanks were collected randomly at a rate of 10%. The method detection limit (MDL) for PM2.5 in the air was 4.3 μg/m3. S content in the collected PM2.5 was quantified by energy-dispersive X-ray fluorescence spectroscopy (EDXRF, Thermo Scientific, USA) following the U.S. EPA Method IO-3.3 (EPA 625/R-96/010a). ED-XRF was optimized for analysis of the MicroPEM filters via beam collimation to accommodate the 10-mm diameter sample deposit area. For precision, at least 10% of the total number of samples were analyzed in duplicate. The MDL for S in the air was 2.9 ng/m3 and the total uncertainty was 5%. Ambient data were retrieved from the China National Environmental Monitoring Center Network, which provides hourly PM2.5 concentrations from the air quality monitoring sites (AQMS). Furthermore, hourly outdoor meteorological data, including T, RH, atmospheric pressure (hpa), and wind speed (m/s), were obtained from the National Meteorological Information Center (http://www.nmic.cn).
characteristics, heating, ventilation and air-conditioning (HVAC) systems, and human behavior [22,27]. Therefore, Finf and Fpex are essential modifiers that should be considered in ambient exposure assessments. Particulate sulfur (S) is a useful ambient PM2.5 tracer that has been commonly measured to evaluate Finf [19–21,24,28] and Fpex [14,19,29]. In particular, the physical behavior of S is roughly similar to ambient PM2.5, and indoor sources of S are limited compared to outdoor sources [28]. This approach requires simultaneous measurements of indoor/ personal and outdoor PM2.5, which makes it difficult to measure Finf/ Fpex among large populations. To overcome this, several studies have developed predictive Finf models based on available data for climate, building characteristics, and human behavior [20–22,30–33]; however, few studies have established predictive models for Fpex [22]. It is not appropriate to apply the Finf and Fpex values obtained from Western countries to assess exposure to ambient PM2.5 in China considering some drastically different regional factors. Very few studies have reported PM2.5 Finf in China [32,33] and even fewer have reported Fpex [34] or explored the potential determinants for use in predictive models. To the best of our knowledge, there have been no investigations into the variability of Finf and Fpex among different cities in China. Therefore, this paper presents the findings from a personal-indooroutdoor PM2.5 exposure study conducted in two megacities with different climates and heating prevalence during winter—Nanjing (NJ, subtropical monsoon climate) and Beijing (BJ, temperate monsoon climate). We focused on retired adults (consisting mostly of the elderly) because they are the most susceptible to the health effects of air pollution, and because their total exposure is dominated by residential indoor exposure. The aims of the study were: (1) to estimate the PM2.5 Finf in typical apartments and Fpex for retired people in NJ and BJ, China; and (2) to investigate the key factors influencing PM2.5 Finf and Fpex. 2. Materials and methods 2.1. Study design The study was conducted in urban districts in NJ and BJ during both the heating season (HS) and the non-heating season (NHS) using stratified sampling (based on building floor arrangement) during 2015–2016. Thirty-three healthy, non-smoking retired adults living in typical apartments participated in the personal-indoor-outdoor PM2.5 monitoring campaign for five consecutive days. In NJ, as two participants withdrew after the NHS, two additional participants were recruited for the HS, with 88% (29/33) taking part in both seasons. Similarly, 31 and 30 participants were monitored in BJ during the HS and the NHS, respectively, with 85% (28/33) of the participants completing the monitoring in both seasons. The study was approved by the Human Investigation Committee of National Institute of Environmental Health, China CDC, and all participants signed informed consent.
2.3. Questionnaire and time-activity diary A standardized and structured questionnaire was used to gather basic information for each residence including age and size, household income, number of inhabitants, pet ownership, cooking fuel, type of ventilation and heating system, and the presence of an AC unit, air purifier, or humidifier. During the sampling periods, participants were also asked to record behaviors that would potentially affect PM2.5 levels using a structured questionnaire. Such activities including cooking, cleaning, window opening, visitor smoking, and the use of any AC, air purifier, or humidifier. The frequency and total time of each participant's activities during the five study days were then calculated, and these data were used for subsequent modeling. In addition, detailed time-activity diaries (TADs) were recorded by participants whenever their locations or activities changed.
2.2. Measurement of PM2.5 and elemental sulfur
2.4. Quality assurance and quality control
The personal-indoor-outdoor exposures to PM2.5 for each participant were monitored simultaneously using MicroPEMs (v3.2, RTI International, NC, USA), which are miniaturized personal PM2.5 environment monitors, including a two-stage impactor with a PM2.5 aerodynamic cut-off point. To prolong battery life and prevent filter overloading, all MicroPEMs were set to sample during a programmed schedule (1 min on and 3 min off for every 4-min cycle). This meant that the monitors effectively collected 30-h samples across the five consecutive study days. Pre-weighed 25 mm Teflon filters (3.0-μm polytetrafluoroethylene with support ring, Pall Corporation, Mexico) were used to collect the PM2.5 samples. Indoor monitors were placed in the room where the participants spent the majority of their waking time, away from ventilation systems and potential pollution sources. Outdoor monitors were extended out of
The baseline of each MicroPEM was set to zero with high-efficiency particulate air filtering before sampling, and was checked after each sampling period. The flow rate was measured and calibrated to 0.5 L/ min ( ± 5%) before monitoring and was measured again at the end of each monitoring session with a TSI model 4199 flowmeter (TSI, Inc., Shoreview, MN). Samples with flow rates that differed by more than 5% from the target rates were considered invalid. In order to be considered a valid five-day mean, 75% (3.75-day) sampling duration completeness was required. Prior to sampling at the beginning of each season, the sintered stainless steel frits of the impactors’ surfaces were lubricated with silicone oil. The impactors were refurbished before and after every sampling period. The microbalance was calibrated with a certified 298
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direction of the effect; 3) there was minimal multicollinearity concern. Multicollinearity within the data was assessed by examining the variance inflation factor (VIF) for each variable. Leave-one-out-cross-validation (LOOCV) was employed to evaluate the model performance [33]. With the variables unaltered, each seasonspecific model was fitted to n-1 participants and the predicted value was estimated using the fitted model for the left-out participant. The fit [R2 and root mean squared error (RMSE)] between the predicted and estimated values were computed to represent the model performance.
stainless steel weight (50 mg) before starting a weighing session, after every 10 samples, and then again after each weighing session to validate the internal calibration. 2.5. Estimates of Finf and Fpex Several previous studies have confirmed that S has few indoor sources [19,28,35]. Finf was therefore calculated as the ratio of the fiveday indoor and outdoor (I/O) S concentrations. Similar to Finf, Fpex was estimated by calculating the personal/outdoor (P/O) S ratio. Previous studies have noted that use of an ultrasonic humidifier is associated with higher I/O S ratios [19,20]. In fact, two participants (20 and 21) who used humidifiers with tap water in their homes for more than five hours per day did have high I/O S ratios (1.41 and 1.13) for the BJ-HS treatment relative to other participants. A recent study in BJ measured sulfates in tap water at a level of 45.17 ± 15.76 mg/L [36]. Consequently, the personal and indoor S values for these two subjects were excluded from the dataset. In addition, while the indoor S data for participant 22 were used in the analysis, the personal S data were invalidated due to use of a humidifier when sleeping with the bedroom door closed. The ratios between 1.00 and 1.03 were included to account for imprecision in the S measurements. Two personal samples (participant 20 and 30 during the NHS and the HS, respectively) were excluded from the BJ datasets for failure to achieve the minimum data completeness as a result of mechanical malfunction.
3. Results and discussion 3.1. Characteristics of households and activity profiles The general household characteristics and a summary of participants’ activities according to the pooled GPS-TADs are presented in Table S1. During the HS, use of central heating (83.9%) systems (hot water radiators) was dominant in BJ while more than half (58.1%) of homes did not use any heating facility in NJ. Only 25.8% (8/31) and 16.1% (5/31) of homes used AC and other forms of temperature regulation (i.e., an electric fan), respectively. Window opening was much more prevalent in the NHS. The median window opening time was 81.5 h and 103.8 h in NJ and BJ, respectively. For comparison, the corresponding HS values were 30 h (NJ) and 4.5 h (BJ). During the NHS, 25.8% (8/31) of families in NJ and 56.7% (17/30) in BJ used AC, with the median total time of use being 11.9 h and 14.7 h, respectively. In addition, only a few (0%–16.1%) homes used air purifiers, for 2.5–44.4 h. Although all households were supposed to be non-smoking, between 12.9% (4/31, BJ-HS) to 29.0% (9/31, NJ-HS) of households reported passive smoking at home by visitors. No candles or incense were burned in any homes. In NJ, the participants spent 93.6% (median) of their time indoors and 89.8% at home during the NHS, and 95.3% indoors and 87.7% at home during the HS, with no significant difference between seasons (p > 0.05). The median time spent outdoors varied from 5.9% to 4.0% in the NHS and the HS, respectively, followed by time in transit (0.3%–0.4%). In BJ, the time spent indoors in the NHS (median = 92.2%) was slightly lower than in the HS (median = 94.2%), and this difference was significant (p = 0.0196). Similarly, the median time spent outdoors in the NHS and the HS were 6.1% and 3.3%, respectively, followed by time spent in transit (1.1% and 0.9%).
2.6. Statistical analyses Descriptive statistical analyses were conducted to describe the levels of PM2.5, S, Finf, and Fpex across both cities and both seasons. Several correlation and regression analyses were used to express associations between, for example, Fpex and Finf. Spearman correlation was applied given that the number of data pairs were relatively low and because most data were not normally distributed (Shapiro-Wilk normality test). The Wilcoxon signed rank test was performed for the paired comparison of PM2.5 concentrations, Finf, and Fpex between seasons, as well as triplet personal-indoor-outdoor PM2.5 data for each home. The Wilcoxon rank sum test was further applied in comparisons of Finf and Fpex between cities. In all cases, a significance level of 0.05 was used, based on a twotailed alternative hypothesis. All analyses were performed using R software (Version 3.5.1) with the “stats” package. 2.7. Predictive models for Finf and Fpex
3.2. Personal-indoor-outdoor PM2.5 Stepwise multiple linear regression was chosen to model the fiveday Finf and Fpex by city and season. The generic equation capturing the model used for these analyses was as follows:
The observed distributions of all triplet personal-indoor-outdoor samples with validated PM2.5 and S measurements are shown in Table 1. The residential outdoor PM2.5 concentrations measured by MicroPEM were highly correlated with the ambient levels recorded at the nearest AQMS (Fig. S1 and Table S2). During the monitoring campaigns, significant seasonal variations were observed (p < 0.0001) for personal-indoor-outdoor PM2.5 concentrations in NJ, with considerably higher levels during the HS. In contrast, the corresponding PM2.5 median concentrations in BJ were somewhat lower in the HS than the NHS, but the difference was not significant (p > 0.3667). The relationships among personal-indoor-outdoor data were also analyzed by season and city. In the HS, personal PM2.5 concentrations were significantly greater than their corresponding indoor levels (NJ: p = 0.0434; BJ: p = 0.0126), but significantly lower than outdoor levels (NJ: p < 0.0001; BJ: p = 0.0002). In the NHS, the same associations between personal and indoor PM2.5 were found as during the HS (NJ: p = 0.0428; BJ: p = 0.0014), but the personal PM2.5 was slightly higher and lower than outdoor level in NJ (p = 0.2727) and BJ (p = 0.1039), respectively. These findings imply that neither the outdoor PM2.5 nor indoor PM2.5 concentrations represent the true personal exposures in NJ and BJ, especially in the HS.
Y = β0 + β1 X1 + β2 X2 + ⋅⋅⋅+βi Xi where Y is the dependent variable Finf or Fpex, Xi is the predictor, βi is the parameter estimate, and β0 is the intercept. The screened potential predictors of Finf and Fpex identified from the questionnaires and pooled GPS-TAD data, and based on preexisting knowledge [20,22] as well as their associated frequency counts/distributions, can be found in Table S1. The focused candidate predictors included building age, floor number, T, RH, heating type, AC use, air purifier use, window opening behavior, and time-activity pattern. A two-stage approach was used to establish the Finf and Fpex models. First, univariate regression analysis was used to determine the relationships between all available potential predictors and Finf or Fpex. The predictor giving the highest adjusted R2 was chosen as the starting model. Second, a manually supervised forward regression analysis was used to assess which of the remaining variables further improved the adjusted R2 of the model [33]. Subsequent variables were retained in the model if: 1) the model adjusted R2 increased more than 0.01; 2) the coefficient of the variables in the model coincided with the right 299
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Table 1 Descriptive statistics for five-day PM2.5 and S mass concentrations (μg/m3), and estimates of Finf and Fpex by city and season. n
NJ-NHS Personal PM2.5 Indoor PM2.5 Outdoor PM2.5 PM2.5 at the AQMS Personal S Indoor S Outdoor S Finf Fpex NJ-HS Personal PM2.5 Indoor PM2.5 Outdoor PM2.5 PM2.5 at the AQMS Personal S Indoor S Outdoor S Finf Fpex BJ-NHS Personal PM2.5 Indoor PM2.5 Outdoor PM2.5 PM2.5 at the AQMS Personal S Indoor S Outdoor S Finf Fpex BJ-HS Personal PM2.5 Indoor PM2.5 Outdoor PM2.5 PM2.5 at the AQMS Personal S Indoor S Outdoor S Finf Fpex
Arithmetic Mean
Standard Deviation
Percentiles Min
P10
P25
P50
P75
P90
Max
31 31 31 31 31 31 31 31 31
47 45 46 41 4.18 4.12 4.53 0.91 0.93
12 12 13 11 1.27 1.37 1.44 0.07 0.06
22 24 29 28 2.55 2.63 2. 92 0.77 0.77
32 30 30 29 2.87 2.90 3.18 0.78 0.83
40 34 34 30 3.34 3.18 3.44 0.85 0.90
48 46 41 44 3.92 3.79 4.79 0.93 0.95
57 51 59 49 4.79 4.67 5.02 0.96 0.97
61 62 64 49 5.46 5.22 5.71 1.00 0.98
78 66 69 63 7.51 7.97 8.13 1.03 1.01
31 31 31 31 31 31 31 31 31
87 83 102 80 4.37 4.26 5.56 0.75 0.77
30 30 20 17 1.92 1.80 1.94 0.10 0.12
41 47 77 61 1.93 1.80 3.17 0.51 0.53
54 51 81 63 2.25 2.27 3.20 0.61 0.66
63 57 83 63 2.48 2.56 3.37 0.67 0.68
84 80 95 76 3.85 3.98 5.46 0.76 0.76
114 99 119 93 6.14 5.87 6.96 0.82 0.85
120 114 131 105 7.04 6.58 8.11 0.85 0.93
174 187 136 107 8.09 7.34 8.60 0.98 0.98
29 30 30 30 29 30 30 30 29
56 52 59 65 4.30 4.16 4.91 0.84 0.87
18 17 15 21 2.05 2.09 2.35 0.10 0.11
28 28 38 38 1.56 1.49 2.08 0.62 0.51
34 31 39 43 1.93 2.01 2.45 0.69 0.75
42 41 43 48 2.45 2.45 2.84 0.79 0.81
52 48 62 68 4.07 3.45 4.81 0.86 0.89
68 63 69 84 5.54 6.07 7.70 0.91 0.95
84 76 75 90 7.61 7.26 8.52 0.95 0.99
97 100 91 114 8.60 8.48 8.77 1.02 1.02
27 29 29 29 27 29 29 29 27
52 48 73 62 1.75 1.65 2.48 0.67 0.68
27 25 44 45 1.36 1.23 1.73 0.11 0.12
23 24 38 26 0.64 0.61 1.13 0.46 0.44
27 28 41 26 0.71 0.74 1.15 0.52 0.55
31 30 47 27 0.83 0.86 1.18 0.58 0.62
41 39 53 47 1.11 1.03 1.72 0.67 0.67
58 53 80 71 2.19 1.81 3.09 0.74 0.73
96 97 171 149 4.37 4.18 5.79 0.82 0.78
118 107 184 173 4.90 4.68 6.56 0.87 0.98
influence the contribution of outdoor PM2.5 to indoor concentrations and personal exposure.
3.3. Estimates of Finf and Fpex All of the results of the regression analyses (Fig. S2) showed that correlations between indoor and corresponding outdoor concentrations were stronger for the S content of PM2.5 (R2 range: 0.910–0.944) relative to the PM2.5 mass concentrations (R2 range: 0.603–0.723). Moreover, the intercepts of indoor-outdoor S regressions were all nonsignificant (−0.66 to −0.05, p > 0.060), confirming the assumption of minimal indoor sources of S, which was the basis of estimating Finf using S as a tracer element.
3.3.2. Variability of Finf and Fpex within cities Within each city, considerable variability of PM2.5 Finf between homes was found, especially in the HS, as shown in Table 1. Finf ranged from 0.51 to 0.98 and from 0.46 to 0.87 in NJ and BJ in the HS, respectively. Similar to Finf, the complete range of Fpex values from 0.53 to 0.98 in NJ and from 0.44 to 0.98 in BJ in the HS. Considerable variations in Finf and Fpex within cities were also found in other studies [14,19,22,32–34,39], emphasizing how ignoring potential variations in outdoor-to-indoor personal PM2.5, and estimating exposure from a central site during epidemiological studies, may increase exposure misclassification even at a city scale.
3.3.1. Variability of Finf and Fpex among cities The five-day Finf and Fpex estimates are shown in Table 1. Between cities, there was significant difference in Finf in both the NHS (BJ median = 0.86, NJ median = 0.93, p = 0.0113) and the HS (BJ median = 0.67, NJ median = 0.76, p = 0.0071). Similar to Finf, Fpex values in NJ were generally higher than corresponding values in BJ in both seasons. According to the definition of Fpex, the population in NJ would be exposed to more PM2.5 of outdoor origin than in BJ under the same ambient pollution conditions. The results were also compared with other studies, as summarized in Table 2. Generally, the PM2.5 Finf and Fpex estimates were among the highest levels for both the NHS and the HS. The variance in Finf and Fpex in the different cities indicates that regional factors such as building type, climate, window opening, and AC use, as well as different populations and their behavior, could
3.3.3. Seasonal variability of Finf and Fpex As illustrated in Fig. 1, the Finf and Fpex of PM2.5 also showed clear seasonal patterns, being significantly higher in the NHS than the HS for both cities (p < 0.0001). For example, the median Fpex value in nonheating season in BJ (0.89) was 32% higher than the responding value in the HS (0.67). This was not unexpected, since inhabitants opened windows for a much longer period of time in the NHS than the HS according to the questionnaires. Aside from the differences in window opening between seasons, a less amount of time spent outdoors might also have contributed to the reduction in Fpex in the HS. In fact, the 300
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Table 2 Summary of Finf and Fpex values for PM2.5 as reported in the literature. City
Subjects
a
n
Sampling duration (d)
Modena, Italy [25] Shanghai, Chinaa [33]
uninhabited apartment non-smoking homes
1 48
15 7
Windsor, Canadab [22] Halifax, Canadab [21] Raleigh, USAa [19] Prince George, Canadab [37] Seattle, USAa [38] Wayne County, USAa [23] Vancouver, Canadab [14] Guangzhou, Chinab [34]
asthmatic children non-smoking homes hypertensive or cardiac patients healthy children elderly adults adults elderly adults healthy adults
47 50 37 15 38 > 140 16 11
5 7 7 1 1 5 7 1
a b c d e
Finf
Fpex
NHS
HS
NHS
HS
0.90 0.79 ± 0.12c 0.92 ± 0.23d 0.34 0.83 0.50 ± 0.06 – – – – –
0.73 0.79 ± 0.14
– –
– –
0.29 0.47 0.62–0.63e 0.49 – – – –
0.27 – – – 0.80 ± 0.17 0.80 0.72 –
0.25 – – 0.54 0.55 ± 0.16 0.55 – 0.70
Values are reported as the mean. Values are reported as the median. Hot season. Transitional season. Other seasons.
(Fig. 2). As can be seen in Table 3, estimates of Fpex were highly correlated with Finf (0.743 ≤ Spearman's r ≤ 0.844), with the exception being for NJ-NHS (Spearman's r = 0.467), and the median estimates of Fpex were identical to Finf values in the HS. This is reasonable considering that the retired adults in this study spent most of their time at home, where Fpex was the same as Finf. The high consistency between Fpex and Finf indicated that Finf measured for a residence can be a good estimator for Fpex, especially in the absence of personal exposure measurements. This result was comparable to the study conducted in Windsor, Canada (Spearman's r = 0.49 in winter; Spearman's r = 0.60 in summer) [22] and Raleigh, USA [19]. In the later study [19], Fpex and Finf had similar ranges and means.
proportion of time spent outdoors in the HS was significantly less than in the NHS both in NJ (HS: 4.0%; NHS: 5.9%; p = 0.0384) and BJ (HS: 3.3%; NHS: 6.1%; p = 0.0301). The seasonal variability of Finf and Fpex found here is consistent with the findings of previous studies [20,21,23,25,30,32,38–42]. However, MacNeill et al. [22] found small seasonal difference of weekly Finf (NHS: median = 0.33; HS: median = 0.24 in 2005) and Fpex (NHS: median = 0.27; HS: median = 0.25 in 2006) in Windsor, Ontario, Canada, due to use of heating in the cold winter and air conditioning in the hot summer. In contrast to our findings, lower Finf and Fpex were reported in the NHS in Raleigh, North Carolina, USA, where AC was mostly used in the summer. Nevertheless, window opening was dominant in the NHS in this study while only 25.8% (NJ) and 56.7% (BJ) of residences used AC for a short time. Generally, the inconsistent seasonal influence on Finf and Fpex between studies might be due to different residential habits associated with modifying home temperatures for comfort (such as window opening and using AC), as would be expected due to local climates.
3.5. Predictive models for Finf and Fpex In the univariate regression analysis, there was a considerable amount of overlap in the following variables between Finf and Fpex: window opening behavior (total window opening time); AC use (frequency); meteorological conditions (outdoor and indoor temperature, absolute I-O temperature difference, outdoor RH, atmospheric pressure, and wind speed); household characteristics (building age and distance to the nearest major road); and heating frequency. In addition to these variables, we also found that human behavior (time spent outdoors, indoors, at home, in other private residences, and transit), use of a range-hood when cooking, and air purifier use were associated with Fpex (p < 0.05).
3.4. Relationship between Finf and Fpex Although Fpex is usually more useful in assessments of personal exposure to ambient PM2.5, it is, in most cases, much more difficult to determine than Finf and then Finf must be used as the indicator of Fpex; it is therefore important to know the relationship between the two. The calculated Fpex values in this study were very similar to the Finf values
Fig. 1. Violin plots of five-day Finf and Fpex by city and season. Width of the violins represent frequency, solid lines in boxes represent median values, dots in boxes represent mean values, boxes represent the 25th and 75th percentiles, and the black lines extended from boxes represent the 95% confidence intervals; outliers are shown as squares. The number of observations for each city and season is listed in Table 1.tbl1
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Fig. 2. Comparison of Fpex and Finf by city, season, and participant.
temperature only influenced Finf in the HS in BJ. These variables, which have also been identified by other studies [20,22,32,33,46], probably influenced window opening and AC use in a way that was not captured by the questionnaires. In addition, building age was shown to have a significant effect on infiltration for BJ-HS, with older residences having higher Finf. Changes in the air-tightness and deterioration state of a building structure over time can lead to increased openings and greater unobstructed infiltration of ambient air. This has also been reported in other studies [21,22,33,42], whereas Allen et al. [40] found the opposite effect. Compared with the Finf model, there were more influencing factors identified in the Fpex predictive model. Considering that the study participants spent most of their time indoors, it was not surprising that the factors identified in the Finf predictive model would also influence Fpex, such as window opening behavior (total window opening time or width), AC use for heating, meteorological variables (maximum wind speed), and building age. Two new household characteristics were identified in the Fpex predictive model—number of adult occupants and room volume. It was interesting that the use of a range-hood when cooking resulted in an increase in Fpex. According to a recent study [47], the air exchange rate for a kitchen was 21.38 h−1 with a corresponding exhaust air volume rate from a range-hood was 3.88 m3/min; this indicated that range-hood operation has a strong influence on air exchange rates. Greater bulk flow of air led to higher Fpex because the ventilated air was replaced via outdoor air being drawn into the house. In addition, two variables recorded in the TADs influenced Fpex. Firstly, for NJ-HS, as expected, time spent outdoors was positively associated with Fpex, which is in line with the study by MacNeill et al. [22]. Secondly, for BJ-NHS, more time spent in transit (via bus, car/taxi, and metro) was associated with a lower Fpex. This can be attributed to the use of advanced HVAC technology in vehicles that incorporate filtration, and their sealed operating conditions during hot weather. Since it was influenced by more factors, these results further confirm that Fpex is a more complicated—but sensitive—indicator of personal exposure to ambient PM2.5, and that it reflects more detailed exposure scenarios.
Table 3 Comparison of Fpex and Finf estimates for PM2.5. City-Season
n
Spearman's rho
median difference
NJ-NHS NJ-HS BJ-NHS BJ-HS *p < 0.01 **p < 0.0001
31 31 29 27
0.467* 0.844** 0.743** 0.770**
0.01 0.01 0.01 0.03
The final season-specific models for Finf and Fpex are shown in Table 4. The VIFs were less than 1.3 for all variables, indicating that there was no serious collinearity. Both the Finf and Fpex models’ R2 fit and RMSE varied between seasons and cities; R2 fit was relatively low for NJ-NHS (Finf: 0.233) and BJ-HS (Finf: 0.180; Fpex: 0.254). In addition to the relatively small variation of the corresponding Finf and Fpex, this also could be attributed to an absence of some potentially influential factors from the questionnaire. For the Finf model, total window opening time was the most consistent predictor related to an increase in Finf across cities and seasons. The same finding has been reported in previous studies [20,32]. Number of windows open was also shown to increase Finf for BJ-NHS, which is consistent with the result from Halifax, Nova Scotia, Canada [21]. This is not unexpected, given that window opening has been found to have the strongest influence on air exchange rates, increasing from an average of 0.65 h−1 with closed windows to 2 h−1 with windows open [43], as well as increasing penetration by permitting ambient air to enter the indoor environment unobstructed. Similar to many studies [20,22,31–33,44], lower Finf was related to more time and frequency of AC use. AC might influence Finf as residents often close windows when AC is operated to save energy, and will increase PM2.5 capture by filters or within air ducts [45]. Aside from householder behaviors, meteorological conditions also effected Finf in both seasons in NJ (NHS: mean wind speed; HS: outdoor RH); whereas outdoor
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Table 4 Final season-specific models for PM2.5 Finf and Fpex. Predictor
Parameter estimate
Standard Error
p-value
Model R
Finf NJ-NHS (n = 31) Intercept Total window opening time (h) Distance to the nearest major road (m) Mean wind speed (m/s) NJ-HS (n = 31) Intercept Outdoor RH (%) Total window opening time (h) Total AC use time (h) BJ-NHS (n = 30) Intercept Total window opening time (h) Number of windows open Frequency of AC used during sampling period BJ-HS (n = 29) Intercept Outdoor temperature (°C) Building age (year) Total window opening time (h) Fpex NJ-NHS (n = 30a) Intercept Total window opening time (h) Volume of the monitored room (m3) Number of adult family member NJ-HS (n = 31) Intercept Time spent outdoors (%) Heating device used during sampling period (yes) Maximum wind speed (m/s) BJ-NHS (n = 29) Intercept Total window opening time (h) Time spent on transportation (%) Number of range hood use when cooking BJ-HS (n = 27) Intercept Width of windows open (%) Building age (year) a
0.755 0.001 −0.0004 0.075
0.080 0.0003 0.0001 0.042
< 0.0001 0.002 0.003 0.088
0.492 0.004 0.002 −0.007
0.059 0.001 0.0005 0.001
< 0.0001 0.001 0.002 0.001
0.552 0.001 0.089 −0.022
0.067 0.0003 0.026 0.010
< 0.0001 0.001 0.002 0.039
0.420 0.009 0.003 0.001
0.075 0.004 0.001 0.001
< 0.0001 0.028 0.030 0.134
0.952 0.001 −0.001 −0.019
0.032 0.0002 0.0002 0.008
< 0.0001 < 0.0001 0.001 0.029
0.861 0.014 −0.094 −0.021
0.040 0.004 0.031 0.007
< 0.0001 0.003 0.006 0.008
0.719 0.002 −0.017 0.007
0.036 0.0003 0.005 0.003
< 0.0001 < 0.0001 0.002 0.040
0.493 0.032 0.004
0.048 0.011 0.001
< 0.0001 0.008 0.015
2
LOOCV Adjusted R
2
R2
RMSE
0.424
0.360
0.233
0.065
0.584
0.537
0.483
0.073
0.681
0.644
0.567
0.065
0.354
0.276
0.180
0.101
0.740
0.710
0.637
0.033
0.571
0.524
0.432
0.089
0.770
0.741
0.678
0.063
0.416
0.368
0.254
0.103
One outlier was discarded (studentized residual = 4.11).
climate, building age, air conditioning, and human activity, influenced the relative contribution of ambient PM2.5 to indoor and personal exposure concentrations. The predictive models developed for PM2.5 Finf and Fpex explained a major portion of their variation, suggesting that a modeling-based approach is feasible for estimation of PM2.5 Finf and Fpex.
The main strength of this study was the simultaneous measurements of PM2.5 Finf and Fpex to elucidate their relationship. Moreover, the repeated measurements in two seasons and in two megacities allowed quantification of the spatiotemporal variability of Finf and Fpex. Finally, the predictive models established using the obtained data could be generalized to retired or elderly subpopulations living in Chinese megacities with similar climates and with similar building characteristics. This could improve personal exposure estimates and offer better correlation with adverse health outcomes. Limitations included a relatively small sample size, and that only retired adults in two cities were investigated. This makes it difficult to extrapolate the results to whole cities or entire countries, especially for other demographics (e.g., children or office workers) and people living in areas with very different climates and household characteristics.
Declarations of interest None. Acknowledgements The authors are grateful to all the participants of this study. We also acknowledge Jiangsu Province and the Nanjing Jiangning Center for Disease Control and Prevention as well as RTI International. The work was supported by the Public Welfare Research Program of the National Health and Family Planning Commission of China (201402022), the National Natural Science Foundation of China (21677136), and the Youth Fund of NIEH (2016).
4. Conclusions This study reports, for the first time, estimated Finf and Fpex for two seasons and two megacities in China. The median Finf and Fpex values in NJ and BJ were generally relatively high compared to other locations. A substantial amount of spatial and temporal variability was found, highlighting the necessity of including Finf or Fpex in assessments of exposure to ambient PM2.5 in epidemiologic studies to reduce exposure misclassification. Several factors, such as window opening behaviors,
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// 303
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Estimation of PM2.5 infiltration factors and personal exposure factors in two megacities, China
T
Na Lia, Zhe Liua, Yunpu Lia, Ning Lib, Ryan Chartierc, Andrea McWilliamsc, Junrui Changa, Qin Wanga, Yaxi Wua, Chunyu Xua,∗, Dongqun Xua,∗∗ a
National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China Nanjing Jiangning Center for Disease Control and Prevention, Nanjing, 211100, China c RTI International, Research Triangle Park, NC, 27709, United States b
A R T I C LE I N FO
A B S T R A C T
Keywords: PM2.5 Infiltration Personal exposure Personal-indoor-outdoor relationship Seasonal variability Predictive model
This study estimates infiltration factors (Finf) and ambient personal exposure factors (Fpex) for fine particulate matter (PM2.5) in two Chinese megacities, and constructs predictive models to explore their determinants. Personal-indoor-outdoor PM2.5 filter samples were collected for five consecutive days from 33 residences (of retired adults) in Nanjing (NJ) and Beijing (BJ), China, in both the non-heating season (NHS) and the heating season (HS). Elemental sulfur in filter deposits was determined by energy-dispersive X-ray fluorescence for PM2.5 Finf and Fpex estimations. Season-specific models developed by stepwise multiple linear regression were evaluated using R2 and root mean square error (RMSE). The median [interquartile range (IQR)] of Finf varied from 0.76 (0.15) in the HS to 0.93 (0.11) in the NHS in NJ; and from 0.67 (0.16) to 0.86 (0.12) in BJ. Similarly, Fpex was significantly higher during the NHS [NJ: 0.95 (0.07); BJ: 0.89 (0.14)] than during the HS [NJ: 0.76 (0.17); BJ: 0.67 (0.11); p < 0.0001]. Common predictors of Finf and Fpex included window opening behaviors, meteorological variables, and building age. Moreover, air conditioning and distance to the nearest major road had an influence on Finf, while predictors of Fpex were more related to human behavior and activity (e.g., time spent outdoors and transportation). The models accounted for 35.4%–68.1% (RMSE: 0.065–0.101) and 41.6%–77.0% (RMSE: 0.033–0.103) of the variance in Finf and Fpex, respectively. By indicating the determinants of Finf and Fpex, these models can improve ambient PM2.5 exposure assessment and reduce exposure misclassification.
1. Introduction Accurate measurement and estimation of human exposure to ambient fine particulate matter (PM2.5, aerodynamic diameter ≤ 2.5 μm) have a crucial role in understanding related health impacts and risks. For example, epidemiological studies have demonstrated a significant relationship between exposure to ambient PM2.5 and adverse health effects [1–7]. Nevertheless, because most people spend more than 80% of their time indoors [8–10], and because the chemical properties and physical characteristics of PM2.5 change during outdoor-to-indoor transport [11–13], the validity of using ambient concentrations as measured by stationary monitoring sites as a surrogate for personal exposure has aroused concern [14,15]. For instance, Wilson et al. [14] found a poor correlation between total personal PM2.5 exposure and
outdoor concentrations [determination coefficient (R2) = 0.072]. Consequently, ignoring the differences between outdoor, indoor, and personal PM2.5 exposure may result in exposure misclassification and, therefore, bias the exposure-response relationship in epidemiological studies [16,17]. A vital modifier to minimize this bias is the infiltration factor (Finf), which is defined as the fraction of outdoor particles that penetrate a building and remain suspended, which depends on the particle penetration efficiency, air exchange rate, and deposition rate [18]. Analogous to Finf is the ambient personal exposure factor (Fpex), which quantifies the contribution of outdoor particles to personal exposure [19]. Studies conducted in North America [19–23] and Europe [24–26] have indicated that Finf and Fpex vary substantially between regions and seasons due to the variation of regional factors such as climate, building
Abbreviations: PM2.5, fine particulate matter; Finf, infiltration factor; Fpex, ambient personal exposure factor; HVAC, heating, ventilation, and air-conditioning systems; NHS, non-heating season; HS, heating season; AC, air conditioning; GPS, global position system; AQMS, air quality monitoring sites; TAD, time-activity diary ∗ Corresponding author. No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China. ∗∗ Corresponding author. No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China. E-mail addresses: [email protected] (C. Xu), [email protected] (D. Xu). https://doi.org/10.1016/j.buildenv.2018.12.033 Received 27 October 2018; Received in revised form 11 December 2018; Accepted 14 December 2018 Available online 14 December 2018 0360-1323/ © 2018 Elsevier Ltd. All rights reserved.
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available sealed windows, away from outdoor air-conditioning (AC) units. The MicroPEM and a portable Global Position System (GPS) unit (BT-Q1000XT, QStarz, Taiwan, China) were placed in a mini-messenger bag, which was carried by the participant. The inlet of the MicroPEM was extended to the breathing zone via a 0.3 m length of conductive silicone rubber tube. Participants were instructed to bring the bag with them all the time, but to remove and keep it nearby while sleeping, bathing, dressing, or performing particular activities such as swimming. PM2.5 mass concentrations were determined by gravimetric analysis in accordance with the professional standard (CN HJ 656–2013). The equilibrated Teflon filters were weighed before and after sampling using a microbalance with a readability of 1 μg (XP6; Mettler Toledo International Inc., Switzerland) in a temperature (T, °C) and relative humidity (RH, %) controlled weighing room (25 ± 1 °C; 50 ± 5%, respectively). The filters were passed through a U-Electrode (Mettler Toledo International Inc., Switzerland) to eliminate any electrostatic charge on the filter before weighing. During both pre- and post-sampling, each filter was weighed twice and if the difference between the two weights was more than 4 μg, a third weighing was done, with the final mass being the mean of the two closest weights. Field blanks were collected randomly at a rate of 10%. The method detection limit (MDL) for PM2.5 in the air was 4.3 μg/m3. S content in the collected PM2.5 was quantified by energy-dispersive X-ray fluorescence spectroscopy (EDXRF, Thermo Scientific, USA) following the U.S. EPA Method IO-3.3 (EPA 625/R-96/010a). ED-XRF was optimized for analysis of the MicroPEM filters via beam collimation to accommodate the 10-mm diameter sample deposit area. For precision, at least 10% of the total number of samples were analyzed in duplicate. The MDL for S in the air was 2.9 ng/m3 and the total uncertainty was 5%. Ambient data were retrieved from the China National Environmental Monitoring Center Network, which provides hourly PM2.5 concentrations from the air quality monitoring sites (AQMS). Furthermore, hourly outdoor meteorological data, including T, RH, atmospheric pressure (hpa), and wind speed (m/s), were obtained from the National Meteorological Information Center (http://www.nmic.cn).
characteristics, heating, ventilation and air-conditioning (HVAC) systems, and human behavior [22,27]. Therefore, Finf and Fpex are essential modifiers that should be considered in ambient exposure assessments. Particulate sulfur (S) is a useful ambient PM2.5 tracer that has been commonly measured to evaluate Finf [19–21,24,28] and Fpex [14,19,29]. In particular, the physical behavior of S is roughly similar to ambient PM2.5, and indoor sources of S are limited compared to outdoor sources [28]. This approach requires simultaneous measurements of indoor/ personal and outdoor PM2.5, which makes it difficult to measure Finf/ Fpex among large populations. To overcome this, several studies have developed predictive Finf models based on available data for climate, building characteristics, and human behavior [20–22,30–33]; however, few studies have established predictive models for Fpex [22]. It is not appropriate to apply the Finf and Fpex values obtained from Western countries to assess exposure to ambient PM2.5 in China considering some drastically different regional factors. Very few studies have reported PM2.5 Finf in China [32,33] and even fewer have reported Fpex [34] or explored the potential determinants for use in predictive models. To the best of our knowledge, there have been no investigations into the variability of Finf and Fpex among different cities in China. Therefore, this paper presents the findings from a personal-indooroutdoor PM2.5 exposure study conducted in two megacities with different climates and heating prevalence during winter—Nanjing (NJ, subtropical monsoon climate) and Beijing (BJ, temperate monsoon climate). We focused on retired adults (consisting mostly of the elderly) because they are the most susceptible to the health effects of air pollution, and because their total exposure is dominated by residential indoor exposure. The aims of the study were: (1) to estimate the PM2.5 Finf in typical apartments and Fpex for retired people in NJ and BJ, China; and (2) to investigate the key factors influencing PM2.5 Finf and Fpex. 2. Materials and methods 2.1. Study design The study was conducted in urban districts in NJ and BJ during both the heating season (HS) and the non-heating season (NHS) using stratified sampling (based on building floor arrangement) during 2015–2016. Thirty-three healthy, non-smoking retired adults living in typical apartments participated in the personal-indoor-outdoor PM2.5 monitoring campaign for five consecutive days. In NJ, as two participants withdrew after the NHS, two additional participants were recruited for the HS, with 88% (29/33) taking part in both seasons. Similarly, 31 and 30 participants were monitored in BJ during the HS and the NHS, respectively, with 85% (28/33) of the participants completing the monitoring in both seasons. The study was approved by the Human Investigation Committee of National Institute of Environmental Health, China CDC, and all participants signed informed consent.
2.3. Questionnaire and time-activity diary A standardized and structured questionnaire was used to gather basic information for each residence including age and size, household income, number of inhabitants, pet ownership, cooking fuel, type of ventilation and heating system, and the presence of an AC unit, air purifier, or humidifier. During the sampling periods, participants were also asked to record behaviors that would potentially affect PM2.5 levels using a structured questionnaire. Such activities including cooking, cleaning, window opening, visitor smoking, and the use of any AC, air purifier, or humidifier. The frequency and total time of each participant's activities during the five study days were then calculated, and these data were used for subsequent modeling. In addition, detailed time-activity diaries (TADs) were recorded by participants whenever their locations or activities changed.
2.2. Measurement of PM2.5 and elemental sulfur
2.4. Quality assurance and quality control
The personal-indoor-outdoor exposures to PM2.5 for each participant were monitored simultaneously using MicroPEMs (v3.2, RTI International, NC, USA), which are miniaturized personal PM2.5 environment monitors, including a two-stage impactor with a PM2.5 aerodynamic cut-off point. To prolong battery life and prevent filter overloading, all MicroPEMs were set to sample during a programmed schedule (1 min on and 3 min off for every 4-min cycle). This meant that the monitors effectively collected 30-h samples across the five consecutive study days. Pre-weighed 25 mm Teflon filters (3.0-μm polytetrafluoroethylene with support ring, Pall Corporation, Mexico) were used to collect the PM2.5 samples. Indoor monitors were placed in the room where the participants spent the majority of their waking time, away from ventilation systems and potential pollution sources. Outdoor monitors were extended out of
The baseline of each MicroPEM was set to zero with high-efficiency particulate air filtering before sampling, and was checked after each sampling period. The flow rate was measured and calibrated to 0.5 L/ min ( ± 5%) before monitoring and was measured again at the end of each monitoring session with a TSI model 4199 flowmeter (TSI, Inc., Shoreview, MN). Samples with flow rates that differed by more than 5% from the target rates were considered invalid. In order to be considered a valid five-day mean, 75% (3.75-day) sampling duration completeness was required. Prior to sampling at the beginning of each season, the sintered stainless steel frits of the impactors’ surfaces were lubricated with silicone oil. The impactors were refurbished before and after every sampling period. The microbalance was calibrated with a certified 298
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direction of the effect; 3) there was minimal multicollinearity concern. Multicollinearity within the data was assessed by examining the variance inflation factor (VIF) for each variable. Leave-one-out-cross-validation (LOOCV) was employed to evaluate the model performance [33]. With the variables unaltered, each seasonspecific model was fitted to n-1 participants and the predicted value was estimated using the fitted model for the left-out participant. The fit [R2 and root mean squared error (RMSE)] between the predicted and estimated values were computed to represent the model performance.
stainless steel weight (50 mg) before starting a weighing session, after every 10 samples, and then again after each weighing session to validate the internal calibration. 2.5. Estimates of Finf and Fpex Several previous studies have confirmed that S has few indoor sources [19,28,35]. Finf was therefore calculated as the ratio of the fiveday indoor and outdoor (I/O) S concentrations. Similar to Finf, Fpex was estimated by calculating the personal/outdoor (P/O) S ratio. Previous studies have noted that use of an ultrasonic humidifier is associated with higher I/O S ratios [19,20]. In fact, two participants (20 and 21) who used humidifiers with tap water in their homes for more than five hours per day did have high I/O S ratios (1.41 and 1.13) for the BJ-HS treatment relative to other participants. A recent study in BJ measured sulfates in tap water at a level of 45.17 ± 15.76 mg/L [36]. Consequently, the personal and indoor S values for these two subjects were excluded from the dataset. In addition, while the indoor S data for participant 22 were used in the analysis, the personal S data were invalidated due to use of a humidifier when sleeping with the bedroom door closed. The ratios between 1.00 and 1.03 were included to account for imprecision in the S measurements. Two personal samples (participant 20 and 30 during the NHS and the HS, respectively) were excluded from the BJ datasets for failure to achieve the minimum data completeness as a result of mechanical malfunction.
3. Results and discussion 3.1. Characteristics of households and activity profiles The general household characteristics and a summary of participants’ activities according to the pooled GPS-TADs are presented in Table S1. During the HS, use of central heating (83.9%) systems (hot water radiators) was dominant in BJ while more than half (58.1%) of homes did not use any heating facility in NJ. Only 25.8% (8/31) and 16.1% (5/31) of homes used AC and other forms of temperature regulation (i.e., an electric fan), respectively. Window opening was much more prevalent in the NHS. The median window opening time was 81.5 h and 103.8 h in NJ and BJ, respectively. For comparison, the corresponding HS values were 30 h (NJ) and 4.5 h (BJ). During the NHS, 25.8% (8/31) of families in NJ and 56.7% (17/30) in BJ used AC, with the median total time of use being 11.9 h and 14.7 h, respectively. In addition, only a few (0%–16.1%) homes used air purifiers, for 2.5–44.4 h. Although all households were supposed to be non-smoking, between 12.9% (4/31, BJ-HS) to 29.0% (9/31, NJ-HS) of households reported passive smoking at home by visitors. No candles or incense were burned in any homes. In NJ, the participants spent 93.6% (median) of their time indoors and 89.8% at home during the NHS, and 95.3% indoors and 87.7% at home during the HS, with no significant difference between seasons (p > 0.05). The median time spent outdoors varied from 5.9% to 4.0% in the NHS and the HS, respectively, followed by time in transit (0.3%–0.4%). In BJ, the time spent indoors in the NHS (median = 92.2%) was slightly lower than in the HS (median = 94.2%), and this difference was significant (p = 0.0196). Similarly, the median time spent outdoors in the NHS and the HS were 6.1% and 3.3%, respectively, followed by time spent in transit (1.1% and 0.9%).
2.6. Statistical analyses Descriptive statistical analyses were conducted to describe the levels of PM2.5, S, Finf, and Fpex across both cities and both seasons. Several correlation and regression analyses were used to express associations between, for example, Fpex and Finf. Spearman correlation was applied given that the number of data pairs were relatively low and because most data were not normally distributed (Shapiro-Wilk normality test). The Wilcoxon signed rank test was performed for the paired comparison of PM2.5 concentrations, Finf, and Fpex between seasons, as well as triplet personal-indoor-outdoor PM2.5 data for each home. The Wilcoxon rank sum test was further applied in comparisons of Finf and Fpex between cities. In all cases, a significance level of 0.05 was used, based on a twotailed alternative hypothesis. All analyses were performed using R software (Version 3.5.1) with the “stats” package. 2.7. Predictive models for Finf and Fpex
3.2. Personal-indoor-outdoor PM2.5 Stepwise multiple linear regression was chosen to model the fiveday Finf and Fpex by city and season. The generic equation capturing the model used for these analyses was as follows:
The observed distributions of all triplet personal-indoor-outdoor samples with validated PM2.5 and S measurements are shown in Table 1. The residential outdoor PM2.5 concentrations measured by MicroPEM were highly correlated with the ambient levels recorded at the nearest AQMS (Fig. S1 and Table S2). During the monitoring campaigns, significant seasonal variations were observed (p < 0.0001) for personal-indoor-outdoor PM2.5 concentrations in NJ, with considerably higher levels during the HS. In contrast, the corresponding PM2.5 median concentrations in BJ were somewhat lower in the HS than the NHS, but the difference was not significant (p > 0.3667). The relationships among personal-indoor-outdoor data were also analyzed by season and city. In the HS, personal PM2.5 concentrations were significantly greater than their corresponding indoor levels (NJ: p = 0.0434; BJ: p = 0.0126), but significantly lower than outdoor levels (NJ: p < 0.0001; BJ: p = 0.0002). In the NHS, the same associations between personal and indoor PM2.5 were found as during the HS (NJ: p = 0.0428; BJ: p = 0.0014), but the personal PM2.5 was slightly higher and lower than outdoor level in NJ (p = 0.2727) and BJ (p = 0.1039), respectively. These findings imply that neither the outdoor PM2.5 nor indoor PM2.5 concentrations represent the true personal exposures in NJ and BJ, especially in the HS.
Y = β0 + β1 X1 + β2 X2 + ⋅⋅⋅+βi Xi where Y is the dependent variable Finf or Fpex, Xi is the predictor, βi is the parameter estimate, and β0 is the intercept. The screened potential predictors of Finf and Fpex identified from the questionnaires and pooled GPS-TAD data, and based on preexisting knowledge [20,22] as well as their associated frequency counts/distributions, can be found in Table S1. The focused candidate predictors included building age, floor number, T, RH, heating type, AC use, air purifier use, window opening behavior, and time-activity pattern. A two-stage approach was used to establish the Finf and Fpex models. First, univariate regression analysis was used to determine the relationships between all available potential predictors and Finf or Fpex. The predictor giving the highest adjusted R2 was chosen as the starting model. Second, a manually supervised forward regression analysis was used to assess which of the remaining variables further improved the adjusted R2 of the model [33]. Subsequent variables were retained in the model if: 1) the model adjusted R2 increased more than 0.01; 2) the coefficient of the variables in the model coincided with the right 299
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Table 1 Descriptive statistics for five-day PM2.5 and S mass concentrations (μg/m3), and estimates of Finf and Fpex by city and season. n
NJ-NHS Personal PM2.5 Indoor PM2.5 Outdoor PM2.5 PM2.5 at the AQMS Personal S Indoor S Outdoor S Finf Fpex NJ-HS Personal PM2.5 Indoor PM2.5 Outdoor PM2.5 PM2.5 at the AQMS Personal S Indoor S Outdoor S Finf Fpex BJ-NHS Personal PM2.5 Indoor PM2.5 Outdoor PM2.5 PM2.5 at the AQMS Personal S Indoor S Outdoor S Finf Fpex BJ-HS Personal PM2.5 Indoor PM2.5 Outdoor PM2.5 PM2.5 at the AQMS Personal S Indoor S Outdoor S Finf Fpex
Arithmetic Mean
Standard Deviation
Percentiles Min
P10
P25
P50
P75
P90
Max
31 31 31 31 31 31 31 31 31
47 45 46 41 4.18 4.12 4.53 0.91 0.93
12 12 13 11 1.27 1.37 1.44 0.07 0.06
22 24 29 28 2.55 2.63 2. 92 0.77 0.77
32 30 30 29 2.87 2.90 3.18 0.78 0.83
40 34 34 30 3.34 3.18 3.44 0.85 0.90
48 46 41 44 3.92 3.79 4.79 0.93 0.95
57 51 59 49 4.79 4.67 5.02 0.96 0.97
61 62 64 49 5.46 5.22 5.71 1.00 0.98
78 66 69 63 7.51 7.97 8.13 1.03 1.01
31 31 31 31 31 31 31 31 31
87 83 102 80 4.37 4.26 5.56 0.75 0.77
30 30 20 17 1.92 1.80 1.94 0.10 0.12
41 47 77 61 1.93 1.80 3.17 0.51 0.53
54 51 81 63 2.25 2.27 3.20 0.61 0.66
63 57 83 63 2.48 2.56 3.37 0.67 0.68
84 80 95 76 3.85 3.98 5.46 0.76 0.76
114 99 119 93 6.14 5.87 6.96 0.82 0.85
120 114 131 105 7.04 6.58 8.11 0.85 0.93
174 187 136 107 8.09 7.34 8.60 0.98 0.98
29 30 30 30 29 30 30 30 29
56 52 59 65 4.30 4.16 4.91 0.84 0.87
18 17 15 21 2.05 2.09 2.35 0.10 0.11
28 28 38 38 1.56 1.49 2.08 0.62 0.51
34 31 39 43 1.93 2.01 2.45 0.69 0.75
42 41 43 48 2.45 2.45 2.84 0.79 0.81
52 48 62 68 4.07 3.45 4.81 0.86 0.89
68 63 69 84 5.54 6.07 7.70 0.91 0.95
84 76 75 90 7.61 7.26 8.52 0.95 0.99
97 100 91 114 8.60 8.48 8.77 1.02 1.02
27 29 29 29 27 29 29 29 27
52 48 73 62 1.75 1.65 2.48 0.67 0.68
27 25 44 45 1.36 1.23 1.73 0.11 0.12
23 24 38 26 0.64 0.61 1.13 0.46 0.44
27 28 41 26 0.71 0.74 1.15 0.52 0.55
31 30 47 27 0.83 0.86 1.18 0.58 0.62
41 39 53 47 1.11 1.03 1.72 0.67 0.67
58 53 80 71 2.19 1.81 3.09 0.74 0.73
96 97 171 149 4.37 4.18 5.79 0.82 0.78
118 107 184 173 4.90 4.68 6.56 0.87 0.98
influence the contribution of outdoor PM2.5 to indoor concentrations and personal exposure.
3.3. Estimates of Finf and Fpex All of the results of the regression analyses (Fig. S2) showed that correlations between indoor and corresponding outdoor concentrations were stronger for the S content of PM2.5 (R2 range: 0.910–0.944) relative to the PM2.5 mass concentrations (R2 range: 0.603–0.723). Moreover, the intercepts of indoor-outdoor S regressions were all nonsignificant (−0.66 to −0.05, p > 0.060), confirming the assumption of minimal indoor sources of S, which was the basis of estimating Finf using S as a tracer element.
3.3.2. Variability of Finf and Fpex within cities Within each city, considerable variability of PM2.5 Finf between homes was found, especially in the HS, as shown in Table 1. Finf ranged from 0.51 to 0.98 and from 0.46 to 0.87 in NJ and BJ in the HS, respectively. Similar to Finf, the complete range of Fpex values from 0.53 to 0.98 in NJ and from 0.44 to 0.98 in BJ in the HS. Considerable variations in Finf and Fpex within cities were also found in other studies [14,19,22,32–34,39], emphasizing how ignoring potential variations in outdoor-to-indoor personal PM2.5, and estimating exposure from a central site during epidemiological studies, may increase exposure misclassification even at a city scale.
3.3.1. Variability of Finf and Fpex among cities The five-day Finf and Fpex estimates are shown in Table 1. Between cities, there was significant difference in Finf in both the NHS (BJ median = 0.86, NJ median = 0.93, p = 0.0113) and the HS (BJ median = 0.67, NJ median = 0.76, p = 0.0071). Similar to Finf, Fpex values in NJ were generally higher than corresponding values in BJ in both seasons. According to the definition of Fpex, the population in NJ would be exposed to more PM2.5 of outdoor origin than in BJ under the same ambient pollution conditions. The results were also compared with other studies, as summarized in Table 2. Generally, the PM2.5 Finf and Fpex estimates were among the highest levels for both the NHS and the HS. The variance in Finf and Fpex in the different cities indicates that regional factors such as building type, climate, window opening, and AC use, as well as different populations and their behavior, could
3.3.3. Seasonal variability of Finf and Fpex As illustrated in Fig. 1, the Finf and Fpex of PM2.5 also showed clear seasonal patterns, being significantly higher in the NHS than the HS for both cities (p < 0.0001). For example, the median Fpex value in nonheating season in BJ (0.89) was 32% higher than the responding value in the HS (0.67). This was not unexpected, since inhabitants opened windows for a much longer period of time in the NHS than the HS according to the questionnaires. Aside from the differences in window opening between seasons, a less amount of time spent outdoors might also have contributed to the reduction in Fpex in the HS. In fact, the 300
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Table 2 Summary of Finf and Fpex values for PM2.5 as reported in the literature. City
Subjects
a
n
Sampling duration (d)
Modena, Italy [25] Shanghai, Chinaa [33]
uninhabited apartment non-smoking homes
1 48
15 7
Windsor, Canadab [22] Halifax, Canadab [21] Raleigh, USAa [19] Prince George, Canadab [37] Seattle, USAa [38] Wayne County, USAa [23] Vancouver, Canadab [14] Guangzhou, Chinab [34]
asthmatic children non-smoking homes hypertensive or cardiac patients healthy children elderly adults adults elderly adults healthy adults
47 50 37 15 38 > 140 16 11
5 7 7 1 1 5 7 1
a b c d e
Finf
Fpex
NHS
HS
NHS
HS
0.90 0.79 ± 0.12c 0.92 ± 0.23d 0.34 0.83 0.50 ± 0.06 – – – – –
0.73 0.79 ± 0.14
– –
– –
0.29 0.47 0.62–0.63e 0.49 – – – –
0.27 – – – 0.80 ± 0.17 0.80 0.72 –
0.25 – – 0.54 0.55 ± 0.16 0.55 – 0.70
Values are reported as the mean. Values are reported as the median. Hot season. Transitional season. Other seasons.
(Fig. 2). As can be seen in Table 3, estimates of Fpex were highly correlated with Finf (0.743 ≤ Spearman's r ≤ 0.844), with the exception being for NJ-NHS (Spearman's r = 0.467), and the median estimates of Fpex were identical to Finf values in the HS. This is reasonable considering that the retired adults in this study spent most of their time at home, where Fpex was the same as Finf. The high consistency between Fpex and Finf indicated that Finf measured for a residence can be a good estimator for Fpex, especially in the absence of personal exposure measurements. This result was comparable to the study conducted in Windsor, Canada (Spearman's r = 0.49 in winter; Spearman's r = 0.60 in summer) [22] and Raleigh, USA [19]. In the later study [19], Fpex and Finf had similar ranges and means.
proportion of time spent outdoors in the HS was significantly less than in the NHS both in NJ (HS: 4.0%; NHS: 5.9%; p = 0.0384) and BJ (HS: 3.3%; NHS: 6.1%; p = 0.0301). The seasonal variability of Finf and Fpex found here is consistent with the findings of previous studies [20,21,23,25,30,32,38–42]. However, MacNeill et al. [22] found small seasonal difference of weekly Finf (NHS: median = 0.33; HS: median = 0.24 in 2005) and Fpex (NHS: median = 0.27; HS: median = 0.25 in 2006) in Windsor, Ontario, Canada, due to use of heating in the cold winter and air conditioning in the hot summer. In contrast to our findings, lower Finf and Fpex were reported in the NHS in Raleigh, North Carolina, USA, where AC was mostly used in the summer. Nevertheless, window opening was dominant in the NHS in this study while only 25.8% (NJ) and 56.7% (BJ) of residences used AC for a short time. Generally, the inconsistent seasonal influence on Finf and Fpex between studies might be due to different residential habits associated with modifying home temperatures for comfort (such as window opening and using AC), as would be expected due to local climates.
3.5. Predictive models for Finf and Fpex In the univariate regression analysis, there was a considerable amount of overlap in the following variables between Finf and Fpex: window opening behavior (total window opening time); AC use (frequency); meteorological conditions (outdoor and indoor temperature, absolute I-O temperature difference, outdoor RH, atmospheric pressure, and wind speed); household characteristics (building age and distance to the nearest major road); and heating frequency. In addition to these variables, we also found that human behavior (time spent outdoors, indoors, at home, in other private residences, and transit), use of a range-hood when cooking, and air purifier use were associated with Fpex (p < 0.05).
3.4. Relationship between Finf and Fpex Although Fpex is usually more useful in assessments of personal exposure to ambient PM2.5, it is, in most cases, much more difficult to determine than Finf and then Finf must be used as the indicator of Fpex; it is therefore important to know the relationship between the two. The calculated Fpex values in this study were very similar to the Finf values
Fig. 1. Violin plots of five-day Finf and Fpex by city and season. Width of the violins represent frequency, solid lines in boxes represent median values, dots in boxes represent mean values, boxes represent the 25th and 75th percentiles, and the black lines extended from boxes represent the 95% confidence intervals; outliers are shown as squares. The number of observations for each city and season is listed in Table 1.tbl1
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Fig. 2. Comparison of Fpex and Finf by city, season, and participant.
temperature only influenced Finf in the HS in BJ. These variables, which have also been identified by other studies [20,22,32,33,46], probably influenced window opening and AC use in a way that was not captured by the questionnaires. In addition, building age was shown to have a significant effect on infiltration for BJ-HS, with older residences having higher Finf. Changes in the air-tightness and deterioration state of a building structure over time can lead to increased openings and greater unobstructed infiltration of ambient air. This has also been reported in other studies [21,22,33,42], whereas Allen et al. [40] found the opposite effect. Compared with the Finf model, there were more influencing factors identified in the Fpex predictive model. Considering that the study participants spent most of their time indoors, it was not surprising that the factors identified in the Finf predictive model would also influence Fpex, such as window opening behavior (total window opening time or width), AC use for heating, meteorological variables (maximum wind speed), and building age. Two new household characteristics were identified in the Fpex predictive model—number of adult occupants and room volume. It was interesting that the use of a range-hood when cooking resulted in an increase in Fpex. According to a recent study [47], the air exchange rate for a kitchen was 21.38 h−1 with a corresponding exhaust air volume rate from a range-hood was 3.88 m3/min; this indicated that range-hood operation has a strong influence on air exchange rates. Greater bulk flow of air led to higher Fpex because the ventilated air was replaced via outdoor air being drawn into the house. In addition, two variables recorded in the TADs influenced Fpex. Firstly, for NJ-HS, as expected, time spent outdoors was positively associated with Fpex, which is in line with the study by MacNeill et al. [22]. Secondly, for BJ-NHS, more time spent in transit (via bus, car/taxi, and metro) was associated with a lower Fpex. This can be attributed to the use of advanced HVAC technology in vehicles that incorporate filtration, and their sealed operating conditions during hot weather. Since it was influenced by more factors, these results further confirm that Fpex is a more complicated—but sensitive—indicator of personal exposure to ambient PM2.5, and that it reflects more detailed exposure scenarios.
Table 3 Comparison of Fpex and Finf estimates for PM2.5. City-Season
n
Spearman's rho
median difference
NJ-NHS NJ-HS BJ-NHS BJ-HS *p < 0.01 **p < 0.0001
31 31 29 27
0.467* 0.844** 0.743** 0.770**
0.01 0.01 0.01 0.03
The final season-specific models for Finf and Fpex are shown in Table 4. The VIFs were less than 1.3 for all variables, indicating that there was no serious collinearity. Both the Finf and Fpex models’ R2 fit and RMSE varied between seasons and cities; R2 fit was relatively low for NJ-NHS (Finf: 0.233) and BJ-HS (Finf: 0.180; Fpex: 0.254). In addition to the relatively small variation of the corresponding Finf and Fpex, this also could be attributed to an absence of some potentially influential factors from the questionnaire. For the Finf model, total window opening time was the most consistent predictor related to an increase in Finf across cities and seasons. The same finding has been reported in previous studies [20,32]. Number of windows open was also shown to increase Finf for BJ-NHS, which is consistent with the result from Halifax, Nova Scotia, Canada [21]. This is not unexpected, given that window opening has been found to have the strongest influence on air exchange rates, increasing from an average of 0.65 h−1 with closed windows to 2 h−1 with windows open [43], as well as increasing penetration by permitting ambient air to enter the indoor environment unobstructed. Similar to many studies [20,22,31–33,44], lower Finf was related to more time and frequency of AC use. AC might influence Finf as residents often close windows when AC is operated to save energy, and will increase PM2.5 capture by filters or within air ducts [45]. Aside from householder behaviors, meteorological conditions also effected Finf in both seasons in NJ (NHS: mean wind speed; HS: outdoor RH); whereas outdoor
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Table 4 Final season-specific models for PM2.5 Finf and Fpex. Predictor
Parameter estimate
Standard Error
p-value
Model R
Finf NJ-NHS (n = 31) Intercept Total window opening time (h) Distance to the nearest major road (m) Mean wind speed (m/s) NJ-HS (n = 31) Intercept Outdoor RH (%) Total window opening time (h) Total AC use time (h) BJ-NHS (n = 30) Intercept Total window opening time (h) Number of windows open Frequency of AC used during sampling period BJ-HS (n = 29) Intercept Outdoor temperature (°C) Building age (year) Total window opening time (h) Fpex NJ-NHS (n = 30a) Intercept Total window opening time (h) Volume of the monitored room (m3) Number of adult family member NJ-HS (n = 31) Intercept Time spent outdoors (%) Heating device used during sampling period (yes) Maximum wind speed (m/s) BJ-NHS (n = 29) Intercept Total window opening time (h) Time spent on transportation (%) Number of range hood use when cooking BJ-HS (n = 27) Intercept Width of windows open (%) Building age (year) a
0.755 0.001 −0.0004 0.075
0.080 0.0003 0.0001 0.042
< 0.0001 0.002 0.003 0.088
0.492 0.004 0.002 −0.007
0.059 0.001 0.0005 0.001
< 0.0001 0.001 0.002 0.001
0.552 0.001 0.089 −0.022
0.067 0.0003 0.026 0.010
< 0.0001 0.001 0.002 0.039
0.420 0.009 0.003 0.001
0.075 0.004 0.001 0.001
< 0.0001 0.028 0.030 0.134
0.952 0.001 −0.001 −0.019
0.032 0.0002 0.0002 0.008
< 0.0001 < 0.0001 0.001 0.029
0.861 0.014 −0.094 −0.021
0.040 0.004 0.031 0.007
< 0.0001 0.003 0.006 0.008
0.719 0.002 −0.017 0.007
0.036 0.0003 0.005 0.003
< 0.0001 < 0.0001 0.002 0.040
0.493 0.032 0.004
0.048 0.011 0.001
< 0.0001 0.008 0.015
2
LOOCV Adjusted R
2
R2
RMSE
0.424
0.360
0.233
0.065
0.584
0.537
0.483
0.073
0.681
0.644
0.567
0.065
0.354
0.276
0.180
0.101
0.740
0.710
0.637
0.033
0.571
0.524
0.432
0.089
0.770
0.741
0.678
0.063
0.416
0.368
0.254
0.103
One outlier was discarded (studentized residual = 4.11).
climate, building age, air conditioning, and human activity, influenced the relative contribution of ambient PM2.5 to indoor and personal exposure concentrations. The predictive models developed for PM2.5 Finf and Fpex explained a major portion of their variation, suggesting that a modeling-based approach is feasible for estimation of PM2.5 Finf and Fpex.
The main strength of this study was the simultaneous measurements of PM2.5 Finf and Fpex to elucidate their relationship. Moreover, the repeated measurements in two seasons and in two megacities allowed quantification of the spatiotemporal variability of Finf and Fpex. Finally, the predictive models established using the obtained data could be generalized to retired or elderly subpopulations living in Chinese megacities with similar climates and with similar building characteristics. This could improve personal exposure estimates and offer better correlation with adverse health outcomes. Limitations included a relatively small sample size, and that only retired adults in two cities were investigated. This makes it difficult to extrapolate the results to whole cities or entire countries, especially for other demographics (e.g., children or office workers) and people living in areas with very different climates and household characteristics.
Declarations of interest None. Acknowledgements The authors are grateful to all the participants of this study. We also acknowledge Jiangsu Province and the Nanjing Jiangning Center for Disease Control and Prevention as well as RTI International. The work was supported by the Public Welfare Research Program of the National Health and Family Planning Commission of China (201402022), the National Natural Science Foundation of China (21677136), and the Youth Fund of NIEH (2016).
4. Conclusions This study reports, for the first time, estimated Finf and Fpex for two seasons and two megacities in China. The median Finf and Fpex values in NJ and BJ were generally relatively high compared to other locations. A substantial amount of spatial and temporal variability was found, highlighting the necessity of including Finf or Fpex in assessments of exposure to ambient PM2.5 in epidemiologic studies to reduce exposure misclassification. Several factors, such as window opening behaviors,
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// 303
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N. Li et al.
doi.org/10.1016/j.buildenv.2018.12.033.
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