Journal of Exposure Science and Environmental Epidemiology (2014) 24, 269–278 & 2014 Nature America, Inc. All rights reserved 1559-0631/14

www.nature.com/jes

ORIGINAL ARTICLE

Sources of indoor air pollution in New York City residences of asthmatic children Rima Habre1, Brent Coull1,2, Erin Moshier3, James Godbold3, Avi Grunin4, Amit Nath5, William Castro5, Neil Schachter5, Annette Rohr6, Meyer Kattan7, John Spengler1 and Petros Koutrakis1 Individuals spend B90% of their time indoors in proximity to sources of particulate and gaseous air pollutants. The sulfur tracer method was used to separate indoor concentrations of particulate matter (PM) PM2.5 mass, elements and thermally resolved carbon fractions by origin in New York City residences of asthmatic children. Enrichment factors relative to sulfur concentrations were used to rank species according to the importance of their indoor sources. Mixed effects models were used to identify building characteristics and resident activities that contributed to observed concentrations. Significant indoor sources were detected for OC1, Cl, K and most remaining OC fractions. We attributed 46% of indoor PM2.5 mass to indoor sources related to OC generation indoors. These sources include cooking (NO2, Si, Cl, K, OC4 and OP), cleaning (most OC fractions), candle/incense burning (black carbon, BC) and smoking (K, OC1, OC3 and EC1). Outdoor sources accounted for 28% of indoor PM2.5 mass, mainly photochemical reaction products, metals and combustion products (EC, EC2, Br, Mn, Pb, Ni, Ti, V and S). Other indoor sources accounted for 26% and included re-suspension of crustal elements (Al, Zn, Fe, Si and Ca). Indoor sources accounted for B72% of PM2.5 mass and likely contributed to differences in the composition of indoor and outdoor PM2.5 exposures. Journal of Exposure Science and Environmental Epidemiology (2014) 24, 269–278; doi:10.1038/jes.2013.74; published online 30 October 2013 Keywords: air pollution; particulate matter; indoor sources; sulfur tracer method; enrichment factors; carbon fractions

INTRODUCTION Exposure to particulate matter (PM) with aerodynamic diameter o2.5 mm (PM2.5) and the gaseous pollutants ozone (O3), sulfur dioxide (SO2) and nitrogen dioxide (NO2) have been linked to various respiratory and cardiovascular health outcomes, including asthma exacerbation in children.1–4 Personal exposure to these pollutants is a function of outdoor concentrations, indoor concentrations and the time a person spends in different microenvironments (indoor, outdoor or in transit).5 As individuals spend 80 to 90% of their time indoors in close proximity to sources, indoor environments contribute significantly to personal exposures.6–9 Furthermore, PM2.5 generated from indoor sources varies in composition and chemical and physical properties compared with PM2.5 of outdoor origin and could have differential health effects.9,10 Indoor concentrations are a function of emission rates of primary (combustion, mechanical generation and re-suspension) and secondary (gas to particle phase conversions and reactions) sources, air exchange rates that determine the infiltration of outdoor air pollutants indoors and removal rates, either via air exchange, deposition or reactions and phase transformations.5,7,11–13 In order to estimate the concentration of indoor pollutants from indoor sources, the infiltration factor (Finf), or the fraction of outdoor PM2.5 that infiltrates indoors and remains airborne, needs to be determined first. Assuming no indoor particle sources, the infiltration factor (Finf) can be estimated from

the mass balance equation as a function of penetration (P), air exchange rate (a) and deposition (k): Finf ¼P  a=ða þ k Þ

ð1Þ

Penetration (P) is the efficiency of a pollutant in crossing the building envelope from the outdoor environment indoors without getting removed by diffusion, interception or impaction. P depends on particle size. Airborne particles that are too small (o0.1 mm) will tend to be lost by diffusion. Particles in the accumulation mode (0.1–2.5 mm) are the most efficient at penetrating indoors. Larger particles will be lost by impaction, interception or settling.14 Air exchange rate (a) affects the introduction and removal of particles and is determined by natural or artificial ventilation. Deposition rates (k) are also largely determined by particle size, the indoor surface area available for particles to deposit or settle onto and thermodynamic factors. The sulfur tracer method has been widely used to estimate Finf using indoor-to-outdoor (I/O) sulfur concentration ratios as sulfur is mostly of ambient origin and has very few indoor sources.7,8,15–20 I/O sulfur ratios have been shown to be significant predictors of I/O PM2.5 ratios across different housing characteristics, seasons and ranges of air exchange rates, although they are most representative of the infiltration behavior of similarly sized particles (0.2–0.7 mm).19,21,22 Few studies have collected simultaneous indoor and outdoor data to allow estimating the contribution of indoor sources to

1 Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; 2Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA; 3Department of Community Medicine, Mount Sinai School of Medicine, New York, New York, USA; 4Department of Pediatrics, Mount Sinai School of Medicine, New York, New York, USA; 5Department of Medicine, Mount Sinai School of Medicine, New York, New York, USA; 6Electric Power Research Institute, Palo Alto, California, USA and 7College of Physicians and Surgeons, Columbia University, New York, New York, USA. Correspondence to: Rima Habre, Department of Environmental Health, Harvard School of Public Health, Landmark Center West, Room 412-E, 401 Park Drive, Boston, Massachusetts 02215, USA. Tel.: +1 61 7384 8837. Fax: +1 61 7384 8859. E-mail: [email protected] Received 15 February 2013; accepted 24 June 2013; published online 30 October 2013

Indoor sources of air pollution in New York Habre et al

270 indoor concentrations of PM2.5 and its components, especially for thermally resolved carbon fractions. These are defined based on the temperatures at which they evolve in two phases of gradual heating in the thermal optical reflectance (TOR) analysis. The first phase is an oxygen (O2) free, helium (He) environment and the second is a 2% O2/98% He environment.23 Ideally, volatile and semi-volatile organic carbon (OC) will evolve in the first phase by thermal desorption and elemental carbon (EC) will evolve in the second phase by oxidation. Carbon fractions are of interest because they characterize organic particle matter from a wide range of classes, and originating from various sources and processes, into groups based on common physical and/or chemical properties. In this analysis, we compare weekly indoor and outdoor concentrations of PM2.5, its components and gases in 37 residences of asthmatic children living in New York City. We used the sulfur tracer method to separate concentrations of PM2.5 components into fractions of indoor versus outdoor origin. Using enrichment factors (EFs), non-parametric correlations and mixed effects models, we identified and ranked species according to the strength of their indoor sources and apportioned indoor PM2.5 mass into general source categories. Finally, we identified specific occupant activities and residence characteristics that contributed to indoor concentrations of species.

MATERIALS AND METHODS Study Design The Children’s Air Pollution and Asthma Study (CAPAS) recruited asthmatic children living in the South Bronx and East Harlem, NY from the Mount Sinai Hospital pediatric pulmonary clinic, asthma clinic and emergency room. The study spanned 2 years and included four sampling sessions, two in the cold season (February–May 2008 and November 2008–April 2009) and two in the warm season (June–September 2008 and June–October 2009). Outdoor concentrations were measured at a central site throughout the study. Indoor air pollution concentrations in non-smoking residences of participants were measured for an average of 2 weeks in both a cold and warm season. Sampling was originally intended to take place in the winter and summer seasons only; however, some sessions ran into the spring and fall, respectively. Therefore, from this point onwards and in the analysis, we define season by month as fall (September–November), winter (December–February), spring (March–May) and summer (June–August). Thirty-seven subjects had complete environmental monitoring data for at least one sampling week and were included in this analysis, resulting in a sample size of 130 subject-weeks. The ‘‘weekly’’ samples ran for 7.2 days on average (minimum 5 days, maximum 10 days, with the exception of two samples that ran for 14 and 15 days). These were distributed by season as follows: 18 weeks in the fall, 29 weeks in the winter, 30 weeks in the spring and 53 weeks in the summer.

Some of the OC in the TOR analysis pyrolyzes or chars and behaves like EC thermally and optically when O2 is introduced, such that it evolves by oxidation in the second phase of the analysis. When char is forming, the filter darkens and the laser reflectance reading decreases. As the pyrolyzed OC is evolving, the laser reflectance reading increases until it reaches its initial level. This cut-point defines the OP fraction, which can then be calculated using an optical correction method.23 Therefore, the EC1 fraction was corrected by subtracting the OP fraction.25 Total EC was defined as the sum of the corrected EC1, EC2 and EC3, total OC as the sum of OC1, OC2, OC3, OC4 and OP, and total carbon (TC) as the sum of EC and OC. Daily O3 concentrations were obtained from the New York State Department of Environmental Conservation (NYSDEC) CCNY site and NO2 and SO2 concentrations from the MS-302 (previously IS-52) site. Standard quality assurance and quality control procedures were performed. Lab and field blank corrections were performed. Negative values resulting from blank corrections were retained in the analyses to preserve the distribution of the data. Weekly averaged concentrations were calculated from the daily ambient data to match each subject’s home visit start and end dates. Indoor air pollution. An indoor air sampling apparatus was placed in the living room of the residences, at least 2 feet away from walls or other vertical surfaces. It included two collocated multi-pollutant samplers (MPS), modified versions of a previous model designed at Harvard School of Public Health and used in several studies.22,26,27 The MPS contained two personal environmental monitors (PEMs), a mini-PEM and two passive samplers that collected 7-day integrated samples of air pollutants. The two PEMs collected PM10 and PM2.5 by inertial impaction at a flow rate of 1.8 LPM on 37 mm Teflon filters. PM10 and PM2.5 mass concentrations were determined gravimetrically. PM2.5 elemental composition was determined by XRF, and PM2.5-bound BC concentration was determined by optical reflectance. The mini-PEM collected PM2.5 at a flow rate of 0.8 LPM on 15 mm pre-fired quartz filters to determine the concentration of PM2.5-bound elemental and OC fractions by TOR. A nitrite coated glass fiber filter collected O328 and a tri-ethanolamine coated cellulose filter collected SO2 and NO2 by passive diffusion (Ogawa, USA). Filters were extracted and analyzed by ion chromatography. Nicotine in the air was collected by passive diffusion onto a sodium-bisulfite treated filter,29 which was extracted and analyzed by gas chromatography as an indicator of environmental tobacco smoke (ETS). Questionnaires. In the initial home visit, a home environmental evaluation was conducted where participants’ caretakers were asked about building age, floor level and total number of occupants permanently living in the residence. They were also asked to report specific activities that could generate indoor air pollution in an abbreviated, daily time-activity questionnaire. The following four questions were included in this analysis:1 did anyone smoke in your home today?2 Did you vacuum, dust, sweep or use cleaning products?3 Did you fry, bake, toast or saute´e?4 Did you burn candles/incense? Responses to these daily questions were then summed up for each sampling week in each residence.

Data Analysis Data Collection Outdoor air pollution. An ambient monitoring station was installed on the roof of the City College of New York (CCNY) administration building. Modified Harvard Impactors with ChemComb inlets were used to collect daily PM2.5 samples on 37 mm Teflon filters at 16.7 l/min (LPM) and on 37 mm pre-fired quartz filters at 10 LPM starting at 0900 hours local time for the duration of the study.24 Teflon filters were analyzed gravimetrically for PM2.5 mass concentration. Black carbon (BC) concentration was determined using optical reflectance with a smoke stain reflectometer. X-ray fluorescence (XRF) analysis was used to determine daily elemental composition of PM2.5. The following 15 elements with at least 80% of their concentrations above the limit of detection (LOD), defined as three times the analytical uncertainty, were retained in the analysis: aluminum (Al), bromine (Br), calcium (Ca), chlorine (Cl), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), titanium (Ti), vanadium (V) and zinc (Zn). Sodium (Na) and phosphorous (P) were excluded due to known analytical artifacts even though they met the LOD criterion. Pre-fired quartz filters were analyzed using the IMPROVE TOR protocol to determine the concentration of the following temperature-resolved, elemental and OC fractions: EC fractions 1–3 (EC1, EC2 and EC3), OC fractions 1–4 (OC1, OC2, OC3 and OC4) and pyrolyzed OC (OP).23

Summary statistics were calculated for all indoor and outdoor pollutant concentrations and questionnaire variables. The ratios of every EC fraction to total EC and every OC fraction to total OC were calculated and summarized as well. Using the sulfur tracer method, we calculated indoor-to-outdoor weekly sulfur ratios (SIN/SOUT) as estimates of Finf and summarized them over the entire study and by season. In all analyses aside from descriptive summaries listed above, we excluded outliers based on extremely high or low pollutant concentrations and weeks with SIN/SOUT Z1 (where indoor sources of S might have existed) to minimize the influence of irregular weeks with very high indoor sources on source apportionment results. Non-parametric Spearman’s correlation coefficients were also calculated for all indoor pollutant concentrations. Sulfur tracer method. We separated indoor concentrations of PM2.5 components by indoor or outdoor origin as follows: XIN ¼XIS þ XOS

ð2Þ

where XIN is the indoor concentration of species X, XIS is the indoor from indoor sources fraction of XIN and XOS is the indoor from outdoor sources fraction of XIN, or the fraction of ambient origin that has infiltrated indoors.

Journal of Exposure Science and Environmental Epidemiology (2014), 269 – 278

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Indoor sources of air pollution in New York Habre et al

271 SIN/SOUT was used to calculate XOS as follows: XOS ¼SIN =SOUT  XOUT

ð3Þ

where XOUT is the outdoor concentration of species X. XIS was then determined as the difference between XIN and XOS, according to eq. (4) as follows: XIS ¼XIN  SIN =SOUT  XOUT

ð5Þ

The median EF was then calculated for each species and plotted against the R-square (R2) of the linear regression of indoor on outdoor concentrations. This R2 is intended as an approximate indicator of the strength of a species’ indoor-to-outdoor relationship as it does not account for the correlated errors structure due to repeated measurements. Mixed effects models. Furthermore, we apportioned indoor PM2.5 mass into general indoor and outdoor source categories using a sequential model building process. We used mixed effects models with several representative pollutants as predictors of the general form: PMINit ¼ bi þ b0 þ gPollutantðsÞit þ eit

ð6Þ

where PMINit is the indoor concentration of PM2.5 in residence i on sampling week t, bi is a random intercept unique to each residence i, b0 is a fixed intercept and Pollutant(s)it is the concentration of one or more of the following pollutants in residence i on sampling week t: Outdoor PM2.5 (PMOUT), indoor OC of indoor origin (OCIS) and outdoor sulfur (SOUT). OCIS was selected to represent the contribution of indoor-generated pollution as it is known to constitute a large fraction of indoor PM2.5 mass.30 PMOUT and SOUT were selected to represent pollution of outdoor origin.19 The goodness-of-fit Akaike Information Criterion (AIC) was used to select the model with the best fit to the data (model with minimum AIC). The parameter estimates of this final model were then multiplied by their respective pollutants’ average concentrations (only for observations included in the model) to apportion PMIN into general source categories. Finally, we investigated questionnaire variables and residence characteristics as potential indoor sources of pollutants by adopting the form of the best-fitting model identified in equation (6). We used mixed models with a random effect for residence to identify variables that were associated with indoor pollutant concentrations, after accounting for the contribution of outdoor pollution. The general form of these models was as follows: XINit ¼bi þ b0 þ jPotential Indoor Source þ gXOUTit þ eit

ð7Þ

Where XINit and XOUTit are the indoor and outdoor concentrations of species X for residence i and sampling week t, respectively. bi is a random intercept for each residence and b0 is a fixed intercept. ‘‘Potential Indoor Source’’ indicators included: Residence Characteristici (building age, floor level and number of occupants in residence), occupant activityit (questionnaire variables related to smoking, cleaning, cooking and burning candles) and nicotine concentrationit. Only significant predictors with P-value r0.05 were reported. Models were fit using Proc MIXED in SAS 9.231 and graphs were generated in JMP Pro 10.32

RESULTS The distribution of indoor and outdoor pollutant concentrations is presented in Table 1. Weekly indoor PM2.5 concentrations were higher on average than outdoors, with indoor-to-outdoor PM2.5 ratios ranging from 0.47 to 11.61 (mean ¼ 1.98, median ¼ 1.45, SD ¼ 1.61, N ¼ 123). The carbonaceous composition of indoor particles also varied from outdoor particles, suggesting different processes and sources (Table 2). Indoor EC was around 95% EC1 and 5% EC2; outdoor EC was B70% EC1, 29% EC2 and 1% EC3. Approximately half of indoor OC was in the more volatile, & 2014 Nature America, Inc.

Pollutant

Indoor concentration

Outdoor concentration

ð4Þ

We estimated the percentage of the variance in SIN/SOUT due to residence, season and week-to-week random variation by using a mixed effects model of SIN/SOUT with random effects for residence and season nested within residence. Enrichment factors. In order to rank species according to the importance of their indoor sources, we used the property of sulfur as a marker of outdoor pollution and calculated indoor-to-outdoor EFs relative to sulfur as follows: EFX ¼ðXIN =SIN Þ=ðXOUT =SOUT Þ

Table 1. Distribution of weekly indoor and outdoor concentrations of gases (p.p.b.), PM2.5 mass, its carbon fractions (mg/m3) and elemental components (ng/m3).

N

Mean

SD

N

126 126 126

28.5 0.3 2.8

13.7 0.6 3.7

130 130 129

24.6 6.2 19.8

5.6 4.1 8.3

Units: mg/m3 PM2.5 123 OC1 91 OC2 91 OC3 91 OC4 91 OP 91 OC 91 EC1 91 EC2 91 EC3 91 EC 91 TC 91 BC 122

20.9 1.3 2.1 2.2 0.7 0.9 7.2 1.3 0.1 0.0 1.3 8.5 1.1

13.0 1.5 2.6 1.6 0.3 1.0 6.6 1.9 0.0 0.0 1.9 7.0 0.6

130 126 126 126 126 126 126 126 126 126 126 126 130

11.9  0.1 0.7 0.5 0.4 0.4 1.9 0.9 0.4 0.0 1.3 3.2 1.0

4.6 0.2 0.4 0.1 0.1 0.2 0.7 0.4 0.2 0.0 0.4 1.0 0.2

27.9 1.9 88.3 269.8 5.6 93.3 125.6 3.0 7.2 2.4 67.5 765.7 3.3 2.5 29.5

25.6 1.6 60.4 767.3 4.7 46.6 139.4 2.7 10.1 2.6 51.8 368.2 2.4 2.6 25.8

130 130 130 130 130 130 130 130 130 130 130 130 130 130 130

24.9 1.8 72.4 20.9 4.5 108.3 47.1 3.8 8.7 3.1 46.7 1041.6 3.7 4.0 31.7

10.4 0.9 25.8 23.8 1.3 25.7 37.4 1.5 6.0 0.9 20.0 447.2 1.3 1.7 15.2

Units: p.p.b. NO2 SO2 O3

Units: ng/m3 Al Br Ca Cl Cu Fe K Mn Ni Pb Si S Ti V Zn

121 121 121 121 121 121 121 121 121 121 121 121 121 121 121

Mean

SD

Abbreviation: PM, particulate matter.

thermally labile and less polar fractions (OC1 and OC2). In contrast, outdoor OC was mostly composed of OC2 and OC3 (34% and 26%, respectively). The mean SIN/SOUT was 0.74 (SD ¼ 0.15) (Figure 1). Six weeks had SIN/SOUT Z1 (Residences nos 4, 9, 22, 26 and 32) indicating possible indoor sources of S. Excluding those 6 weeks, the distribution of SIN/SOUT by season was as expected, with the lowest estimated infiltration in the winter (0.64) when air exchange rates are lowest, followed by fall and spring (0.74) then summer (0.76) (Figure 2). Based on results of the mixed model of SIN/SOUT with random intercepts for residence and season within residence, season explained 47.4% (P ¼ 0.0087) of the variability in SIN/SOUT, week-to-week random variation explained 46.9% (Po0.0001), and residence explained only 5.7% (P ¼ 0.352) of the variability in SIN/SOUT. These results support our use of weekly measurements of SIN/SOUT to estimate Finf in order to better account for the variability in Finf and reduce exposure error as discussed by Hodas et al.33 Source apportionment results using the sulfur tracer method indicated that around 70% of indoor EC1 was estimated to originate indoors, while all of EC2 and EC3 were from outdoor sources. EC1 is characterized as char from smoldering combustion and EC2 and EC3 as soot from motor vehicle and coal combustion.34 In contrast, OC1 was estimated to be entirely of indoor origin, and large fractions of OC2 (78%), OC3 (85%), OC4

Journal of Exposure Science and Environmental Epidemiology (2014), 269 – 278

Indoor sources of air pollution in New York Habre et al

272 (64%) and OP (72%) were also estimated to originate indoors (Figure 3a). As for the elements, three categories emerged (Figure 3b). Cl and K were estimated to be predominantly of indoor origin. Al, Ca, Cu, Fe, Si and Zn were of mixed origin, and Br, Mn, Ni, Pb, Ti and V were estimated to be exclusively of outdoor origin (S as well by definition). Median EF (dimensionless) for PM2.5 mass, its components and gases are plotted against the R-squares (R2, dimensionless) of the regression of indoor concentrations on outdoor concentrations in Figure 4. Species clustered together in Figure 4 based on the importance of their indoor sources. OC1, Cl and OC3 had the highest median EF, followed by all of the remaining OC fractions and K. The crustal elements Al, Ca and Si which are related to soil and dust had a median EF 41. The metals Ti, Mn, Pb, Ni and V of ambient origin were also grouped together and had a median EF o1 as expected. The results of mixed models apportioning indoor PM2.5 mass concentration into general source categories are presented in Table 3. The decrease in the AIC value from Model 1 to 3 demonstrates the improvement in model goodness-of-fit by accounting for OC of indoor origin (OCIS) and using SOUT as a surrogate of PMOUT. Estimates of PM2.5 from indoor sources related to OC of indoor origin and from outdoor sources were obtained by multiplying the parameter estimates of OCIS and SOUT by their mean concentrations, respectively (using only observations included in Model 3). The intercept estimate represented other indoor sources of PM2.5. Based on Model 3 results, indoor sources of PM2.5 related to the generation of OC indoors constituted on average 46% of PMIN (9.3 mg/m3), whereas, the infiltration of outdoor PM2.5 contributed 28% of PMIN (5.6 mg/m3). Remaining indoor sources accounted for 26% of PMIN (5.3 mg/m3). These could be associated with re-suspension, mechanical generation or other sources (Figure 5).

Residence characteristics, passive nicotine concentrations and questionnaire variables related to occupants’ pollution generating activities indoors are summarized in Table 4. Floor levels varied widely (mean 7, range 1–24), and the average number of occupants was 4.4 and ranged from 2 to 8 individuals. On average, participants reported the highest frequency of cooking (frying, baking or saute´ing, 3.7 times per week) followed by cleaning (vacuuming, dusting, sweeping or using cleaning products 3.1 times per week). Even though a non-smoking home was part of the inclusion criteria in this study, our data indicated the presence of low levels of ETS, with the exception of some outliers. Table 5 presents model results for variables that significantly predicted (P-value r0.05) indoor pollutant concentrations after accounting for outdoor concentrations. Floor level was associated with increased indoor levels of OC1 and OP and inversely associated with Ti, a crustal element related to road dust. One reported instance of burning candles or incense during the sampling week was significantly associated with around 0.1 mg/m3 increase in indoor BC levels, likely due to the charring generated by candles and incense burning.35 Number of occupants permanently residing in the apartment was positively associated with indoor concentrations of OC2, TC, OC, OP, OC1 and OC3, suggesting that the majority of OC fractions were related to human activities indoors, such as cooking, cleaning and use of personal products. Nicotine concentrations were also significantly associated with indoor K levels (1 mg/m3 of nicotine was associated with around 35 ng/m3 increase in K); however, other indoor sources of K (captured by the intercept) were more important. Reported smoking was associated with indoor concentrations of OC1, OP, EC1, OC3 (and both total EC and OC) and marginally, negatively associated with SO2. This last finding seems to be driven by a few outliers reporting high frequency of smoking with low indoor SO2

1.2

Table 2.

Indoors

EC1/EC EC2/EC EC3/EC OC1/OC OC2/OC OC3/OC OC4/OC OP/OC

Indoor to Outdoor Sulfur Ratio

Ratios of elemental and organic carbon fractions to total EC and OC concentrations, respectively, indoors and outdoors. Outdoors

N

Mean

SD

N

Mean

SD

91 91 91 91 91 91 91 91

0.95 0.05 0.00 0.16 0.27 0.32 0.14 0.12

0.14 0.19 0.07 0.12 0.06 0.08 0.06 0.07

126 126 126 126 126 126 126 126

0.70 0.29 0.01  0.05 0.34 0.26 0.21 0.23

0.11 0.10 0.01 0.09 0.10 0.06 0.04 0.07

Abbreviations: BC, black carbon; EC, elemental carbon; OC, organic carbon.

1.0 0.8 0.6 0.4 0.2 0.0 Fall

Spring

Summer

Season

Figure 2. Distribution of weekly indoor-to-outdoor sulfur ratios (SIN/SOUT ) by season.

Cold season Warm season

1.2 Indoor to Outdoor Sulfur Ratio

Winter

1.0 0.8 0.6 0.4 0.2 0.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Residence

Figure 1.

Distribution of weekly SIN/SOUT for each residence in the study, colored by study season (warm or cold).

Journal of Exposure Science and Environmental Epidemiology (2014), 269 – 278

& 2014 Nature America, Inc.

Indoor sources of air pollution in New York Habre et al

273 concentrations. Reported vacuuming, dusting, sweeping or using cleaning products was negatively associated with indoor Fe and Mn concentrations. Finally, the question ‘‘Did you fry, bake, toast or saute´e?’’ was significantly associated with Si and NO2 and inversely associated with Fe. One instance of reported frying, baking, toasting or saute´ing during the sampling week was associated with a 1.44-p.p.b. increase in indoor NO2. DISCUSSION We used PM2.5 sulfur as a tracer of ambient pollution in two different approaches. First, we estimated the weekly Finf using

Concentration (µg/m3)

10

Indoor Concentration from Indoor Sources Indoor Concentration from Outdoor Sources

8

SIN/SOUT in order to estimate the fractions of indoor pollutant concentrations of indoor and outdoor origin. Then we calculated EFs in a novel way relative to sulfur to rank pollutants based on the importance of their indoor sources. Furthermore, we used mixed effects models to first apportion indoor PM2.5 mass into general source categories and second to identify more specific indoor sources of PM2.5 components and gases using questionnaire data on occupant activities and residence characteristics. In our study, weekly averaged indoor PM2.5 concentrations were higher on average than outdoor concentrations (20.9 versus 11.9 mg/m3, respectively), similar to findings of Wallace et al.36 in inner-city residences of asthmatics in several cities, including the Bronx and Manhattan, NY (hourly data). In contrast, 48-h averaged indoor PM2.5 concentrations were almost similar to outdoor levels (17.6 versus 18.1 mg/m3, respectively) in the RIOPA study in three communities in California, New Jersey and Texas.37

Outdoor Concentration

Sulfur Tracer Method Estimates of Finf obtained by our SIN/SOUT ratios were similar to values reported in the literature.19,35,38 Furthermore, the

6

4

2

Table 3. Results of mixed models apportioning indoor PM2.5 mass concentration into general source categories.

0 EC1

EC2

EC3

EC

BC

OC1 OC2 OC3 OC4

OP

OC

TC

Estimate

SE

t-value

P-value

AIC

N

15.55 0.37

3.78 0.28

4.11 1.32

0.000 0.192

730.9 730.9

94 94

(2) PMIN ¼ OCIS þ PMOUT Intercept 7.22 1.48 OCIS 0.32 PMOUT

2.28 0.1 0.17

3.17 14.8 1.89

0.004 o0.0001 0.070

379.7 379.7 379.7

59 59 59

(3) PMIN ¼ OCIS þ SOUT Intercept 5.28 1.49 OCIS 5.61 SOUT

2.22 0.1 1.96

2.37 15.35 2.87

0.025 o0.0001 0.008

370.7 370.7 370.7

59 59 59

Concentration (ng/m3)

Model 1000 600 400 300 200

(1) PMIN ¼ PMOUT Intercept PMOUT

100 60 40 20 10 6 4 3 2 1

Effect

Al

Br

Ca

Cl

Cu

Fe

K

Mn

Ni

Pb

Si

S

Ti

V

Zn

Figure 3. Mean indoor concentrations of carbon fractions (3 A, in mg/m3) and elements (3B, in ng/m3) are indicated by the length of the stacked bar. Red indicates the mean indoor concentration from outdoor sources and blue indicates mean indoor concentration from indoor sources. Mean outdoor concentration is indicated by the black dot. 10 8 7 6 5 4

Abbreviations: OC, organic carbon; PM, particulate matter.

OC1 Cl

OC3 OC

3 OP

TC

OC2

Median EF

OC4 K

PM2.5

2

1 0.8 0.7 0.6 0.5 0.4

EC1 Cu NO2

Si

BC

Br

EC

Al

Ca

Zn Fe Mn Ti Pb

S Ni V

0.3 EC2

0.2

O3

0.1 0.0001

SO2

0.001

0.01

0.1

1

R-Square

Figure 4. Plot of median enrichment factors (EF) against indoor-to-outdoor regression R2 for each indoor pollutant on a logarithmic scale. EC3 was excluded and the median EF of SO2 was doubled for scaling purposes in this graph. & 2014 Nature America, Inc.

Journal of Exposure Science and Environmental Epidemiology (2014), 269 – 278

Indoor sources of air pollution in New York Habre et al

274 assumption that sulfur has negligible indoor sources was supported by our data, as only 6 out of the 121 weeks had SIN/ SOUTZ1. These occurred in residences no. 4, 9, 22, 26 and 32. Upon further investigation, we found evidence of significant cooking activity, high occupancy and smoking in 3 of those weeks (Residences 22 and 32). The remaining 3 weeks had low indoor and outdoor PM2.5 and S concentrations, possibly inflating the ratio (Residences 4, 8 and 26). Gas stoves are known indoor sources of S due to additives such as mercaptans in natural gas. Showering or using ultrasonic humidifiers can also aerosolize dissolved sulfates and sulfides present in tap water.7,39 However, use of humidifiers was not reported in this panel (data not shown). All participants also reported using gas stoves for cooking, window units for air conditioning (ranging from 1 to 4 units, no central AC) and no use ultrasonic humidifiers (data not shown). Our data reflect the importance of indoor sources of PM2.5 pollution in these residences. Indoor PM2.5 of indoor origin contributed around 72% of total indoor PM2.5, while the nonambient contribution to indoor PM was 39.8% on average in the studies by Meng et al.37 analysis of RIOPA data. Zhao et al.40 used an extended receptor model to estimate the contribution outdoor source factors to indoor PM2.5 component concentrations in Raleigh and Chapel Hill, NC residences and a Denver, CO, school for asthmatic children.41 In both analyses, the estimated fractions of outdoor origin for all indoor elemental concentrations were

28%

46%

26% Indoor sources related to OC of indoor origin (9.3 µg/m3) Other indoor sources (5.3 µg/m3) Outdoor sources (5.6 µg/m3)

Figure 5. Pie chart apportioning indoor particulate matter (PM) PM2.5 mass concentration into indoor sources related to organic carbon of indoor origin, outdoor sources and other indoor sources based on results of Model 3 in Table 3.

Table 4.

generally consistent with our findings despite differences in the modeling approach. Our finding that a large portion of indoor PM2.5 is composed of OC agrees with the findings of Polidori et al.30 OC can be a primary (combustion or direct release) or secondary pollutant. Cleaning products and air fresheners used indoors contain volatile and semi-volatile organics such as ethers, alkenes, alcohols, aldehydes, aromatics and terpenes. These can react with O3 to form secondary pollutants indoors.42,43 Therefore, secondary OC can occur indoors through gas to particle phase partitioning of semivolatile organics or secondary organic aerosol (SOA) formation by oxidation reactions (such as O3 reactions with terpenes or nicotine).44–46 We also found that around half of indoor OC is in the more volatile, thermally labile and less polar fractions (OC1 and OC2) compared with outdoor OC that is mostly composed of OC2 and OC3as reported by Naumova et al.47 Outdoors, OC2 is related to diesel combustion in vehicles and engines while OC3 and OC4 are related to gasoline combustion.25 Reff et al.48 found that PM2.5 of indoor origin is largely composed of OC that is enriched in aliphatic carbon-hydrogen functional groups, making it less polar than outdoor OC components. The indoor OC1 fraction could also contain semi-volatiles that adsorb onto the quartz filters.49 Higher molecular weight water-soluble organics present in the OC4 and OP fractions can be generated indoors by cooking50 and outdoors by biomass burning and photochemical reactions.51,52 Polidori et al.53 found that indoor OC is composed of around 35–45% infiltrated outdoor SOA and 36–44% infiltrated outdoor primary OC in a study of Los Angeles retirement homes. In our analysis, infiltrated outdoor OC contributed only B21% of indoor OC; however, we did not model primary and secondary OC separately. Enrichment Factors The use of EFs calculated relative to sulfur is a novel approach that adds greater resolution to the results of the sulfur tracer method. A median EF Z1 indicates species whose indoor concentrations exceed those expected from outdoor infiltration alone, implying indoor sources. We expect the indoor-to-outdoor concentration relationship to be strongest for species of outdoor origin and weakest for species of indoor origin. As discussed earlier, OC fractions had the highest median EF’s, with the exception of Cl, due to their significant indoor sources. Cl can be emitted from bleach-containing cleaning products or the aerosolization of chlorinated municipal water indoors.40,41 Cooking and ETS have also been reported as indoor sources of Cl.6,54 Furthermore, Larson et al.55 reported that personal exposure to chlorine-rich particles was not correlated with outdoor sources in a source apportionment analysis of indoor, outdoor and personal PM2.5 in Seattle, WA, USA. Outdoor sources

Distribution of residence characteristics, nicotine concentrations and questionnaire variables related to indoor sources of air pollution. N

Mean

SD

Min

Median

Max

Building age (years) Floor level Number of occupants in residence Nicotine (mg/m3)

20 37 37 101

6 7 4.4 0.3

2 6 1.4 0.8

1 1 2 0.0

6 5 4 0.1

8 24 8 4.8

Questionnaire variables (Y/N; summed over sampling week)a Did anyone smoke in your home today? Did you vacuum, dust, sweep or use cleaning products? Did you fry, bake, toast or saute´e? Did you burn candles/incense?

110 107 103 110

0.4 3.1 3.7 0.4

1.4 2.7 2.7 1.2

0.0 0.0 0.0 0.0

0.0 2.0 4.0 0.0

7.0 9.0 9.0 7.0

a

Questionnaire variables were collected on a daily basis (Y/N) and summed over the sampling week. Sampling weeks lasted an average of 7.3 days, with a minimum of 5 and maximum of 15 days.

Journal of Exposure Science and Environmental Epidemiology (2014), 269 – 278

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Indoor sources of air pollution in New York Habre et al

275 Mixed effects model results of residence characteristics, nicotine concentrations and occupant activities that significantly (P-value r0.05) predicted indoor pollutant concentrations.

Table 5.

Predictor

Intercept

Predictor

Outdoor concentration

N

Estimate

P-value

Estimate

P-value

Estimate

P-value

59 59 94

0.65 0.59 0.78

0.110 0.177 0.080

0.11 0.07  0.06

0.017 0.022 0.031

1.40  0.14 0.69

0.280 0.876 o0.0001

59 59 59 59 59 59 93

 2.92  2.78  4.23  0.77  1.11 0.42  3.03

0.045 0.500 0.273 0.219 0.148 0.681 0.012

1.14 2.8 2.68 0.43 0.57 0.54 0.45

0.001 0.001 0.001 0.002 0.002 0.006 0.046

0.42  0.13 0.19  0.18 1.23  0.90 0.20

0.532 0.889 0.863 0.828 0.332 0.549 o0.0001

81 81

85.08 14.84

0.001 0.001

34.99 3.72

0.031 0.048

0.33 0.33

1.11 0.65 0.93 6.39 0.74 5.52 2.20  0.24

0.000 0.000 0.026 0.065 o0.0001 0.048 0.011 0.006

0.44 0.1 0.28 1.77 0.09 1.66 0.38  0.07

0.005 0.010 0.014 0.015 0.015 0.020 0.021 0.044

1.15 0.40  0.10 0.48 0.40 0.66  0.19 0.08

0.411 0.002 0.912 0.630 0.006 0.594 0.905 o0.0001

Did you vacuum, dust, sweep or use cleaning products? Fe ng/m3 87 32.16 87 1.13 Mn ng/m3

0.013 0.037

 3.4  0.18

0.007 0.010

0.59 0.56

o0.0001 o0.0001

Did you fry, bake, toast or saute´e? 83 Si ng/m3 p.p.b. 83 NO2 3 83 Fe ng/m

21.25 12.46 28.53

0.060 0.012 0.029

4.53 1.44  2.58

0.007 0.010 0.029

0.43 0.46 0.60

0.022 0.008 o0.0001

0.77

0.000

0.09

0.003

0.22

0.241

Dependent

Units

Residence characteristics Floor level OC1 mg/m3 OP mg/m3 Ti ng/m3 Number of occupants in residence OC2 mg/m3 TC mg/m3 OC mg/m3 OP mg/m3 OC1 mg/m3 OC3 mg/m3 p.p.b. O3 Environmental tobacco smoke Nicotine concentration (mg/m3) K ng/m3 mg/m3 PM2.5

Questionnaire variablesa Did anyone smoke in your home today? 54 OC1 mg/m3 54 EC mg/m3 54 OP mg/m3 3 54 TC mg/m 3 54 EC1 mg/m 54 OC mg/m3 54 OC3 mg/m3 p.p.b. 88 SO2

Did you burn candles/incense? BC mg/m3

88

0.422 0.287

Abbreviations: BC, black carbon; EC, elemental carbon; OC, organic carbon. a Questionnaire variables were collected on a daily basis and summed over the sampling week.

of Cl are sea-salt particles or salt applied on roadways for de-icing purposes in the winter. K had the highest median EF following the OC fractions and Cl. Meng et al.35 found significant indoor sources of K and OC mostly related to sweeping and cleaning. Wallace6 reported K in smoking, wood burning and kerosene heater emissions (OC measurements were not available) and Zhao et al.40,41 reported ETS and personal care activities as indoor sources of OC and K. Biomass burning is also a source of K and OC. The elements Al, Ca and Si related to soil and dust56 typically originate outdoors, infiltrate indoors, deposit and get resuspended by movement of occupants or other activities. Indoor sources of Ca and Si related to cooking (meat especially) have been reported as well.6,35,40 Cu, Fe and Zn are typically associated with motor vehicle and traffic emissions that also originate outdoors and could get re-suspended after depositing indoors along with road dust elements. Outdoors, Cu and Zn are related to brake wear and Zn to tire wear particles.57,58 Cu is also related to & 2014 Nature America, Inc.

emissions from electric motors found in appliances indoors,59 and Fe and Ca are related to cooking.6,54 Finally, Br is minimally related to marine aerosol and possibly other sources outdoors such as traffic.60,61 NO2 is emitted from gas stoves indoors and traffic and combustion sources outdoors.61 As its emission rate indoors exceeds its removal rate by reactions onto surfaces or due to the air exchange rate,62 its median EF was 41. Mn and Ti are crustal elements related to road dust and Pb is related to vehicular and other sources.57 Ni and V are found in residual fuel oil used for space heating or in utilities, and V is also found in shipping emissions, especially in NYC.63,64 Finally, the gases O3 and SO2 had the lowest median EF’s as they have minimal indoor sources (unlike NO2) and their loss rates indoors by reacting onto surfaces is much faster than the rate of deposition of S.16,61,65–68 O3 can also be removed through reactions with NO2 and VOCs such as limonene and other monoterpenes indoors.11,42,44–46,69–71

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Indoor sources of air pollution in New York Habre et al

276 SO2 is typically emitted outdoors from combustion sources such as coal-fired power plants. O3 is photochemically produced outdoors from the reaction of VOCs and NOx in the presence of sunlight. Most of indoor O3 is of outdoor origin, although it is generated by certain types of air purifiers (not present in this study).72 O3 is present in considerable amounts indoors when windows are open and air exchange rates are high, typically on warm summer days.73 Mixed Effects Models After identifying and ranking species according to the strength of their indoor sources, we wanted to apportion total indoor PM2.5 mass into general source categories. We found that indoor sources combined (related to the generation of OC indoors and other sources) were significant and contributed B72% of indoor PM2.5 mass (Figure 5). Consequently, we attempted to identify more specific indoor sources using mixed models with questionnaire variables, residence characteristics and passive nicotine measurements as predictors of indoor concentrations of PM2.5 and components. We found that floor level was associated with lower indoor Ti concentrations. As the elevation of the apartment increases, the road/traffic signal likely decreases. However, EC1 and BC, markers for outdoor traffic and combustion sources, were not associated with floor level (model results not shown).56 This could be due to the evidence of candle and incense burning as indoor sources of BC and EC1 in this study. Baxter et al.74 also found candle burning to be associated with indoor EC concentrations. Number of occupants permanently residing in the apartment was positively associated with indoor concentrations of OC2, TC, OC, OP, OC1 and OC3, suggesting that the majority of OC fractions were related to human activities indoors, such as cooking, cleaning and use of personal products. Nicotine concentrations in our study (median 0.06 mg/m3, maximum 4.82 mg/m3) were lower than those reported in lowincome, multi-unit housing units in Boston, MA, USA (median 0.13 mg/m3, maximum 26.92 mg/m3).75 We found that 1 mg/m3 of nicotine was associated with a 3.72-mg/m3 increase in PM2.5 mass indoors. Exposure to ETS can be due to infiltration from adjacent units in the building75 or from visitors.76 Nicotine concentrations were also significantly associated with indoor K levels (1 mg/m3 of nicotine was associated with around 35 ng/m3 increase in K). Several studies reported K in ETS as well.6,40,41,54 In contrast, the questionnaire variable ‘‘Did anyone smoke in your home today?’’ was not a significant predictor of nicotine concentrations in a mixed effects model (results not shown). Reported vacuuming, dusting, sweeping or using cleaning products was negatively associated with indoor Fe and Mn concentrations. Cleaning activities are typically associated with resuspension of dust and particles indoors and generation of PM2.5. However, it is possible that these short-term influences were not captured by the longer averaging time of a week and cleaning is removing these crustal species. Meng et al.35 found one occurrence of sweeping to be associated with indoor Ca concentrations. Finally, the question ‘‘Did you fry, bake, toast or saute´e?’’ was significantly associated with Si and NO2 and inversely associated with Fe. One instance of reported frying, baking, toasting or saute´ing during the sampling week was associated with a 1.44p.p.b. increase in indoor NO2. All CAPAS residences reported using gas stoves (data not shown). Gas stoves and frying hamburgers have also been linked to Si emissions.77

CONCLUSIONS We found that indoor sources were important in these NYC residences and accounted for around 72% of PM2.5 mass. By using EFs calculated relative to sulfur, we were able to rank species

according to the importance of their indoor sources. We found that OC1, Cl and the remaining OC fractions had the most significant indoor sources. Given the high enrichment of Cl in the indoor environment, further investigation into its indoor sources is warranted. We attributed 46% of indoor PM2.5 mass to indoor sources related to OC of indoor origin, which likely included cooking (NO2, Si, Cl, K, OC4, OP and Fe), cleaning (most OC fractions), some candle/incense burning (BC) and smoking (K, OC1, OC3 and EC1). The number of occupants permanently residing in the residence was also an important predictor of most indoor OC fractions. Further research into how occupant behaviors relate to emissions of specific OC compounds indoors is needed. Outdoor sources accounted for 28% of indoor PM2.5 mass, and these were mostly photochemical reaction products, metals and combustion products that infiltrate indoors (EC, EC2, Br, Mn, Pb, Ni, Ti, V and S). Other indoor sources accounted for 26% and likely included resuspension of crustal elements and metals (Al, Zn, Fe, Si and Ca). Finally, asking subjects about their indoor air pollution generating activities could introduce some recall bias and misreporting; however, important information can be gained by combining measurements and activity questionnaires, as we found for ETS. CONFLICT OF INTEREST Dr. Rohr is employed by the Electric Power Research Institute, which is primarily supported by the electric industry in the United States and abroad. EPRI is an independent non-profit 501(c)3 organization that funds external research at a number of universities and institutes worldwide. Other authors declare no other conflict of interest, personal, financial, or otherwise, with the material presented in the manuscript.

ACKNOWLEDGEMENTS This study was supported by the Electric Power Research Institute (EP-P15909/C7932). Rima Habre was supported by the Harvard School of Public Health Dean’s Scholarship. We thank Tom Gentile, George O’Connor and Lance Wallace, members of the CAPAS study scientific advisory committee, for their guidance in all phases of the study. Steve Ferguson and Mike Wolfson are also acknowledged for designing the air sampling monitors and conducting laboratory analyses. We also thank all the individuals who participated in this study.

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