Comparison of Sources of Variability in School Age Children Exposure

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Comparison of Sources of Variability in School Age Children Exposure to Ambient PM2.5 Wenwei Che, H. Christopher Frey, and Alexis K. H. Lau Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es506275c • Publication Date (Web): 05 Jan 2015 Downloaded from http://pubs.acs.org on January 12, 2015

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Environmental Science & Technology

Comparison of Sources of Variability in School Age Children Exposure to Ambient PM2.5 W. W. Che,1 H. Christopher Frey,1,2,3* Alexis K.H. Lau1,2 1

Division of Environment, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China 2

Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China 3

Department of Civil, Construction and Environmental Engineering, North Carolina State University, Campus Box 7908, Raleigh, North Carolina 27695-7908, United States, [email protected], 1-919-515-1155, fax 1-919-515-7908

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ACS Paragon Plus Environment


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ABSTRACT

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School age children are particularly susceptible to exposure to ambient fine particle (PM2.5). To

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provide insight into factors affecting variability in ambient PM2.5 exposure, distributions of daily

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PM2.5 exposures for school age children are estimated for four seasons in three climatic zones of

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the U.S. using a stochastic microenvironmental exposure model, based on ambient concentration,

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air exchange rate, penetration factor, deposition rate, census data, meteorological data, and time

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pattern data. Estimated daily individual exposure varies largely among seasons, regions and

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individuals. The mean ratio of ambient exposure to ambient concentration (Ea/Ca) ranges from

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0.46 to 0.61 among selected regions and seasons, resulting from differences in air exchange rate.

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The individual Ea/Ca varies by a factor of 2 to 3 over a 95% frequency range among simulated

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children, resulting from variability in children’s time patterns. These patterns are similar among

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age groups, but vary with day of week and outdoor temperature. Variability in exposure is larger

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between individuals than between groups. The high end ratio of the Ea/Ca, at the 95th percentile

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of inter-individual variability, is 30% to 50% higher than the mean Ea/Ca ratio. Results can be

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used to intepret and adjust exposure errors in epidemiology and to assist in development of

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exposure mitigation strategies.

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KEYWORDS

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Children’s exposure; APEX; inter-individual variability; geographic variability; seasonal

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variability

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INTRODUCTION

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Among the general population, children and adults 65 years and older are the two most

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susceptible subpopulations.1, 2 Children of school age (ages 6 to 18 years) account for

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approximately 18% of the U.S. population. Exposure to ambient PM2.5 (particles with

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aerodynamic diameter of 2.5 µm or less) is associated with a variety of adverse effects among

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school age children, including increase in airway inflammation and oxidative stress, exacerbation

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of asthma, induction of DNA damage and long-term deficit in lung function development.3-7

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Furthermore, children suffering from adverse effects such as asthma also experience lower

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school attendance, and lower school performance, and their parents experience challenges in

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work attendance.8-10 Understanding the variations in children’s exposure to ambient PM2.5 and

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contributions to it from various factors may help in evaluating air pollution-induced health risk

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and developing strategies to reduce related risk.11

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Exposure is the time-weighted concentration of various microenvironments in which an

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individual spends time.12 Human exposure to ambient PM2.5 includes exposure to the ambient

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PM2.5 concentration while outdoors, and exposure while indoors or in-vehicles to ambient PM2.5

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that has infiltrated into these microenvironments.13 Since most people, including children, spend

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more than 80% of their daily time indoors, infiltration of ambient PM2.5 to indoor

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microenvironments is an important determinant of personal exposure to PM2.5.14

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The infiltration of ambient PM2.5 depends on the characteristics of indoor ventilation, especially

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the air exchange rate (AER) between outdoors and indoors. Measurements in U.S. residents have

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shown that the average AER ranges from 0.36 h-1 to 1.57 h-1 across regions and seasons.15

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Personal exposure measurements demonstrate substantial variability in the mean personal 3 ACS Paragon Plus Environment


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exposure to ambient PM2.5 between cities and seasons.16, 17 For example, a personal exposure

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study conducted among 68 children aged 8 to 14 years, in three European cities, showed a

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difference of up to 62% (or 11.2 µg/m3) in the mean of 48-hour average personal exposure to

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PM2.5 across cities.18 Heterogeneity in AER is likely to be a key factor in seasonal and regional

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exposure variability. In the RIOPA study, AER was found to explain 24% of the variations in the

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ratio of ambient PM2.5 exposure to ambient concentration (Ea/Ca) among 374 non-smoking

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homes in Houston (TX), Los Angeles County (CA), and Elizabeth (NJ).19

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Recent studies suggest that the pattern of the amount of time spent in each microenvironment in

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each day may be another key contributor to personal PM2.5 exposure. Researchers in Korea

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measured the personal PM2.5 exposure population for multiple groups with different time patterns,

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and found that the group daily average exposure varied by more than a factor of four, from 9.8

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µg/m3 to 43.1 µg/m3.20 The European VESTA study observed an inter-child standard deviation of

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22.7 µg/m3 in the 48-hour average personal exposure to PM2.5 for children living in the same

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city.21 Relatively low correlations were observed in several studies between personal exposure

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and outdoor concentration, which is likely the result of variations in personal time patterns both

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between- and within-subjects.18, 22-25 Understanding the impact of time pattern on person’s

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exposure is an area where research efforts are expanding.26, 27 Ambient PM concentration

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measured at fixed site monitors is usually used as a surrogate for personal exposure to ambient

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PM2.5 in epidemiological health studies. However, the ratio of risk to ambient concentration is

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different from the ratio of risk to exposure.28, 29 The difference between personal exposure and

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ambient concentration contributes to exposure error.30, 31. Inter-individual variability in AER and

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children’s time patterns may contribute to the exposure errors.

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In recent years, stochastic population-based exposure models have been used to evaluate the

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impact of various factors on exposure estimation.32, 33 For example, Jiao et al. evaluated the

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influence of AER on estimated daily PM2.5 exposure for elderly adults over 65 years of age in the

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U.S.33 The Ea/Ca varied by 6% to 36% among selected regions and seasons as a result of

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differences in residential air exchange rates. However, studies on children are scarce.34

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The objectives of this paper are to: (1) evaluate the geographic and seasonal variability in Ea/Ca

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with respect to variations in residential AER; and (2) investigate the inter-individual variability

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in Ea/Ca with respect to variations in time patterns for school-age children. The term “time

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pattern” here refers to the time spent in each microenvironment, but doesn’t account for

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differences in exertion that may affect individual breathing rate.

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METHODOLOGY

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This part includes an overview of the study design and description of model configuration,

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parameterization for estimating microenvironmental exposure concentration, data sources for

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AER in residences and schools and other parameters related to study population and time

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patterns. Methods for statistical analysis of the estimated results are also described.

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Study design

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AER influences the exposure estimates.33, 35 Measurements on residential AERs reveal

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substantial variations across regions and seasons. To evaluate the geographic and seasonal

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variability in Ea/Ca with respect to variations in residential AER, children’s exposure to ambient

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PM2.5 is estimated for three climatic regions in four seasons in 2002.



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Three urban areas are chosen to represent diverse southeast, south central and northeast U.S.

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climate zones. These areas include: (1) a six-county area in North Carolina (NC) along

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Interstate 40, comprised of Wake, Durham, Orange, Alamance, Guilford, and Forsyth Counties,

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that includes the cities of Raleigh, Durham, Burlington, Greensboro, High Point, and Winston-

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Salem; (2) Harris County in Texas (TX), including the city of Houston; and (3) New York City

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(NYC), including Bronx, New York, Kings, Queens, and Richmond Counties. To address

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seasonal differences, one month from each of four seasons is selected, including April for spring,

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July for summer, October for fall, and December for winter.

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A few studies have examined how children spend their time and the sources of variations in time

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pattern.36-42 The sources of variation include age, sex, day type (weekday vs. weekend) and

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outdoor temperature.36, 38, 40-42 To investigate the inter-individual variability in Ea/Ca with respect

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to variations in time patterns, children’s PM2.5 exposure is estimated and compared by age, day

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type and outdoor temperature. Time patterns differentiated by sex were also examined, but they

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are not included in the exposure analysis because the differences in time spent between male and

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female students in microenvironments evaluated here are relatively small (Fig. S2).

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Children of ages 6 to 18 years old are selected to represent school age children. Three factors are

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examined here to evaluate the sensitivity of exposure estimates to children’s time pattern,

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including school age range, day type and outdoor temperature. Children are divided into three

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groups: children of elementary school age (ES), from 6 to 11 years; middle school age (MS),

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from 12 to 14 years; and high school age (HS), from 15 to 18 years. Each age group exposures

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are estimated for three day types: weekdays in school (WDSC); weekdays out of school (WDNS);

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and weekends (WEND). WDNS represents home schooled children or holidays on which 6 ACS Paragon Plus Environment

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schools are closed. Outdoor daily maximum temperature is categorized into three ranges: low

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temperature (LT) 84 ºF. Temperatures below 55 ºF are generally considered cold,43, 44 while 84

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ºF is the average temperature that will trigger a heat wave alert as defined by National Oceanic

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and Atmospheric Administration (NOAA).45

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A random sample of 5,000 individuals was simulated for each of the three age groups (ES, MS,

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HS) for each of three selected regions (NC, TX, NYC) during each of four seasons (spring,

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summer, fall, winter), resulting in 36 scenarios. An example of one scenario is a simulation of

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ambient PM2.5 exposure for 5,000 individuals of the elementary school age group for the North

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Carolina region in spring (ES-NC-spring). Variations in day type (WDSC, WDNS, WEND) and

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outdoor daily maximum temperature (LT, MT, HT) were addressed within each simulation.

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APEX model

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Scenario-based exposure models use stochastic methods to simulate inter-individual variability

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on a population basis. They are based on input data for population distribution by age, sex,

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location, daily time pattern, ambient concentration, and infiltration of ambient pollutant to

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specific microenvironments and other factors.46, 47 Typical microenvironments for school age

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children include home, school, restaurant, hotel, store, in vehicle, outdoor and other indoors.14, 18,

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The Air Pollutants Exposure (APEX) model is one of such models developed by the U.S.

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Environmental Protection Agency (EPA) .47 This model has been refined and improved over the

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last several years as a result of its application to exposure assessments supporting scientific

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review of the U.S. National Ambient Air Quality Standards (NAAQS) for carbon monoxide, 7 ACS Paragon Plus Environment


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nitrogen dioxide, ozone, and sulfur dioxide.48-51 Furthermore, APEX has been developed and

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applied for quantification of human exposure to PM2.5,52 and is expected to be used in the next

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review cycle of the NAAQS for particulate matter.53

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APEX accounts for the most significant factors contributing to inhalation exposure, including the

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temporal and spatial distribution of people and pollutant concentrations throughout the study area

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and among microenvironments, while also allowing flexibility to adjust some of these factors.54

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Although APEX has the capability to account for indoor emission sources, this analysis is

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concerned only with exposures to pollutants of ambient origin.

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Parameterization

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Children’s exposure is simulated for eight microenvironments, including outdoors, home, school,

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in vehicle, store, restaurant, office and other indoors. PM2.5 concentrations in home and school

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are estimated using a mass balance method, which estimates the PM2.5 concentration of ambient

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origin in a specified microenvironment based on physical factors including AER, penetration (p),

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and deposition and decay rate (k).33, 46 The factor method is used to estimate PM2.5 concentration

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in vehicle, store, restaurant, office and other indoor microenvironments, by multiplying the

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ambient outdoor concentration by an indoor/outdoor (I/O) concentration ratio.25, 46

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Under the assumption of steady state and well-mixed microenvironment, the equation of mass

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balance model can be described as:

‫ܥ‬௔,ொ =

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௣×஺ாோ ஺ாோା௞

‫ܥ‬௔

(1)

Where, 8 ACS Paragon Plus Environment

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‫ܥ‬௔,ொ = the PM2.5 concentration of ambient origin in a certain microenvironment (µg/m3);

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a

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= ambient origin;

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= index of microenvironment (ME = o for outdoors, h for home, s for school, v for in-vehicle, t for store, r for restaurant, f for office, and i for other indoors.);

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‫݌‬

= penetration factor (unitless);

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‫ܴܧܣ‬

= air exchange rate (h-1);

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݇

= deposition rate (h-1);

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‫ܥ‬௔

= ambient PM2.5 concentration (µg/m3).

ME

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The user specifies distributions of these parameters. Except in a few cases, primarily involving

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small sample sizes, the measured AER were found to be well fit by lognormal distributions.15, 55

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Here, the fitted lognormal distributions derived in previous studies for four seasons in three

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climatic regions are used for residential AER.15, 55

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School AER has been measured at a few U.S. locations for a variety of classroom types, built

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year, and mechanical system configuration.56-58 Based on these measurements, a log-normal

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distribution of AER was developed here for school microenvironments according to the best fit

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estimates for all reported data (Fig. S1). There is lack of data upon which to assess the effect of

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geographical and seasonal variation in school AER, therefore, the same distribution of AER is

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applied to all seasons and regions.

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Triangular distributions for penetration factor (p) and normal distribution for deposition rate (k)

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are adopted from Jiao et al,33 based on limited studies conducted in the U.S.25, 29 However,

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compared to AER, these two parameters have much less impact on exposure estimates.35 No

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studies were found that report p and k for PM2.5 in U.S. schools. Therefore, as a surrogate, the

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distributions of p and k for home are used here for schools.



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In this study, fixed I/O ratios, either from field measurements or previous studies, are applied for

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selected microenvironments, including 0.71 for vehicle, 0.75 for store, 1.0 for restaurant, 0.18 for

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office , and 0.85 for other indoor.46, 59, 60

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Demographic and time pattern data

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Population distributions by age group and sex were sampled from year 2000 United States

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Census data. Time-pattern diaries for each simulated individual on each simulation day were

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sampled from the Consolidated Human Activity Database (CHAD) based on matching

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demographic characteristics, daily temperature and day type.61 CHAD contains tens of thousands

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of diary records from several national and local studies covering various age, sex, occupation,

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day type, outdoor temperature and locations. There are 12,202 daily diary records for children of

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ages 6 to 18 in CHAD.

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Ambient PM2.5 concentration and outdoor temperature

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Daily average PM2.5 air quality data were obtained from the U.S. EPA based on predictions of

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2002 average concentrations for 12 km by 12 km grid cells from the Community Multiscale Air

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Quality (CMAQ) model, which were updated with available monitoring data using Bayesian

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statistical inference.62 Daily maximum temperature was obtained from State Climate Offices for

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each region and season based on request, in order to investigate the influence of outdoor

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temperature on children’s time patterns.

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Statistical analysis

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APEX output includes the estimated exposure of ambient origin for each simulated individual for

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each microenvironment on each day, and the time spent in each microenvironment. These 10 ACS Paragon Plus Environment

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outputs were processed and analyzed using SAS ver 9.3. The ratio of Ea/Ca and contributions to

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it from each microenvironment were estimated for each individual and simulation day. The

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variability in the Ea/Ca ratio is compared between scenarios in terms of age group, region and

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season, and within a scenario in terms of day type and daily maximum temperature. Two

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statistical summaries are used for comparison, including (1) cumulative distribution functions

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(CDF) and (2) coefficient of variance (CV), which is the standard deviation divided by the mean.

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Exposure at or above the 95th percentile of the population is also reported as it represents a high

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exposure and is of concern in risk management.12

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RESULTS

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The results are presented in four parts, including: (1) key input data; (2) sensitivity tests on

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microenvironmental parameters; (3) total and microenvironmental PM2.5 exposure; (4)

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geographic and seasonal variability in children’s exposure; and (5) factors affecting time patterns.

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Key input data

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Table l shows the 2000 census population distribution by age and gender for each study region,

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and the frequency of days in simulated regions and seasons by daily maximum temperature

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category, which is derived from 2002 meteorological data. In total, there are 1.8 million, 3.4

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million, and 8.0 million people in the NC, TX and NYC domains, of which school age children

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account for 17%, 21% and 17%, respectively. The distribution of days for the temperature

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categories varies with season and region. Over 60% of summer days are high temperature in all

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regions, especially for TX (93.5%); in the winter there are no high temperature days. Low

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temperature days dominate in winter for NC (70.0%) and NYC (96.8%), but are less prevalent

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for TX (10.0%). Medium temperature days dominant in the spring and fall for all three regions. 11 ACS Paragon Plus Environment


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Figure 1 shows the average amount of time spent by children of each age group in selected

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microenvironments by day type. For all age groups and day types, at least 80% of time is spent

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among home, school, outdoor and in vehicle microenvironments. Conversely, less than 3% of

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time is spent among office, store, and restaurants. The balance is spent in other indoor locations.

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Children spend different amounts of time at home, school and outdoors between day types.

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Children spend an average of 14.2 hours, 17.5 hours and 15.6 hours at home on WDSC, WDNS,

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and WEND, respectively. Time spent at school is on average 6.8 hours, 0 hours, and 2.0 hours on

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WDSC, WDNS and WEND, respectively, and the time spent outdoor is 0.8 hours, 2.0 hours and

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1.2 hours for three day types. Time spent at school on WEND is probably due to participation in

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extracurricular enrichment programs.

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ES and MS children have similar daily time patterns for each day type. HS children spend

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slightly less time at home and outdoors, and more time in vehicle and other indoors. Differences

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in time patterns are more pronounced for day types than age groups. For example, the difference

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in average time spent at home is only 1.7 hours among age groups versus 4.2 hours among day

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types.

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Male and female children spend comparable amounts of time at home, at school and in vehicle.

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Male children spend an average of 0.5 hours more time outdoors than females. Major differences

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between outdoor temperatures exist in the time spent outdoor and in school. On average, children

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spent 1.7 hours more outdoor on days with HT than those with LT, and 1.6 hours less time in

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school.


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Children’s daily time patterns are similar across geographic regions but differ among seasons.

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On average, children spend 1 hour more outdoors and 1.5 hours less inside schools in summer

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than in other seasons (Fig. S4). Children have more vacation and less school time in the summer.

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Higher outdoor temperatures in the summer are associated with more time spent outdoors.

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Sensitivity tests on microenvironmental parameters

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For the school microenvironment, fixed I/O ratios were commonly used in previous studies

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which does not adequately account for variability. 26,37 Here, distributions of AER in schools are

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developed and used with the mass balance method to better characterize variability. Sensitivity

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tests are conducted to evaluate the influence of the factor and mass balance methods on exposure

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estimates. The school Ea,s/Ca has more inter-individual variability using the mass balance method

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(Fig. S5).



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To evaluate the impact of uncertainty in the I/O ratios on total exposure estimates, a sensitivity

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test was conducted for vehicles using a fixed I/O ratio of 0.71 and a discrete I/O distribution. The

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distribution ranges from 0.1 to 1.0 based on three measurements conducted in North Carolina,

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China and Lebanon.59, 63, 64 Sensitivity analysis reveals small differences in total exposure (less

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than 1%) when comparing these different input assumptions (Fig. S6). Other microenvironments

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such as office store and restaurant comprise less than 3% of children’s time. Therefore, the

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impact on estimated daily exposures from using fixed I/O ratios rather than distributions of ratios

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in these microenvironments is minor.

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Total PM2.5 exposure and its variations

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Estimated children’s ambient exposure to PM2.5 (Ea) varies substantially across scenarios. The

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average daily ambient exposure varies by a factor of 2.7 across 36 scenarios, ranging from 5.1

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µg/m3 for elementary school children in NYC in fall (ES-NYC-fall) to 13.9 µg/m3 for middle

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school children in NYC in summer (MS-NYC-summer). Variations in the ambient concentration

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are the main cause for these variations in exposure. For example, the average daily ambient

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PM2.5 concentrations are 11.3 µg/m3 for ES-NYC-fall and 22.7 µg/m3 for MS-NYC-summer,

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accounting for about 73% of the differences in the mean exposure. However, there are still

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around 27% of the variations unexplained, which may be caused by the differences in AER and

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time patterns across scenarios.

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Children’s ambient exposure to PM2.5 is significantly lower than the ambient PM2.5 concentration.

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For ES-NC-spring scenario, the average daily ambient exposure is 6.4 µg/m3, while the average

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ambient concentration is 12.9 µg/m3, with the Ea/Ca ratio of 0.5. Within the scenario, the

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estimated children’s PM2.5 exposure varies largely among individuals. The Ea ranges from 2.4 14 ACS Paragon Plus Environment

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µg/m3 to 13.0 µg/m3, a factor of 5 for a 95% frequency range. Children with high daily exposure

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typically have relatively small amounts of time spent at home or schools, with large amounts of

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time spent outdoors or in other indoors. For this scenario, an average of 19.1 hours and 3.2 hours

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is spent in home and school, respectively, for children below the 2.5th percentile of daily

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exposure, compared to12.0 hours and 0.7 hours, respectively, for children above the 97.5th

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percentile. The microenvironmental PM2.5 concentration for home and school is smaller than for

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outdoor and other indoors, therefore, more time spent in home and school leads to smaller

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exposure to ambient PM2.5. The four most important microenvironments of home, outdoor,

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school and vehicle account for 50%, 16%, 11% and 6% of average exposure, respectively.

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Variability in time pattern and parameters affecting microenvironmental concentrations

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contribute substantially to the inter-individual variability in estimated exposure. The individual

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daily Ea/Ca, which excludes the impact from ambient concentration, varies by a factor of 2 to 3

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over a 95% frequency range among simulated children for a given scenario. For

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microenvironments for which PM2.5 concentrations are estimated based on the factor method,

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including outdoor, vehicle, store, office and restaurants, the CV of inter-individual variability in

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Ea/Ca is similar to the CV of time spent in that microenvironment, indicating that the variability

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in time pattern is the dominant factor in determining the variability in Ea/Ca in these

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microenvironments. For home and school, for which microenvironmental concentrations are

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based on the mass balance method, the CV of inter-individual variability in Ea,ME/Ca is higher

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than the CV of time pattern by 67% for home and 8% for school, indicating an influence from

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the distributions of AER, p, k.



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Geographic and seasonal variability

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Estimated inter-individual variability in Ea/Ca differs among climate zones and seasons, as shown

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in Figure 2. Depending on season, the difference in the average daily Ea/Ca ratio ranges from 1%

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to 29% between the selected regions. The regional differences in Ea/Ca are largest in fall and

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smallest in spring. In fall, the largest average Ea/Ca ratio of 0.59 occurs in TX and the lowest

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average Ea/Ca ratio of 0.46 occurs in NYC. The variability in Ea/Ca among regions follows the

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variations in residential AER. For example, the geometric mean of residential AER is highest at

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0.65 h-1 in TX, but lowest at 0.22 h-1 in NYC. The three selected regions are located in different

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climatic zones with different temperature distributions among seasons, which affects the

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frequency of natural ventilation and thus AER and exposure pattern.

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The difference in the average daily Ea/Ca ratio between seasons ranges from 0% to 30%, with the

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largest in NYC and the smallest in TX. The seasonal variability in Ea/Ca is mainly caused by

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seasonal differences in residential AER. Taking NYC as an example, the highest average Ea/Ca

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ratio of 0.59 occurs in summer, for which the residential AER geometric mean of 0.64 h-1 is the

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largest among the seasons, and the lowest average Ea/Ca ratio of 0.46 occurs in fall associated

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with a residential AER geometric mean of 0.22 h-1. Higher residential AER is typically

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associated with more frequent natural ventilation, such as opening windows or doors.

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The trends when comparing CDFs of inter-individual variability for children between seasons

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and regions are similar to those reported by Jiao et al,26 based on an analysis of exposure for

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elderly people of age 65 and older. For example, the sequence of seasons in which the mean

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Ea/Ca ratio varies from high to low in the NC domain is summer, fall, winter and spring, with a

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maximum difference of 16% in the mean Ea/Ca ratio among seasons, for both children and the 16 ACS Paragon Plus Environment

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elderly. The inter-individual variability in the Ea/Ca ratio for children and the elderly is

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comparable for a given season and area, with a slightly wider range for the elderly. The latter

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may be caused by a larger amount of time spent at home at the lower end of the elderly, since the

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microenvironmental PM2.5 concentrations are typically lower than other microenvironments.

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Factors affecting time patterns

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Children’s average exposure differs among WDSC, WDNS and WEND, as indicated in Figure 3.

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For example, for the ES-NC-sping scenario, the average individual daily Ea/Ca varies by 12%

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among three day types, and the 95th percentile varies by 20%. The inter-day type variation in

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Ea/Ca is larger for days with no or little time spent in school. For the ES-NC-spring scenario, the

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ratio of 95th percentile of daily Ea/Ca to the mean Ea/Ca is 1.39, 1.50 and 1.48 for WDSC, WDNS

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and WEND, respectively.

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The differences in daily Ea,ME/Ca among these three day types are particularly evident for the

324

school, home, outdoor and other indoors microenvironments, mainly because of differences in

325

time patterns. The daily Ea,s/Ca for school varies from 0.02 to 0.25 over a 95% frequency range

326

on WDSC versus a value of 0 on WDNS, and values of 0 to 0.04 on WEND. The large variation

327

in Ea,s/Ca on WDSC are related to the variations in the amount of time spent in schools and in

328

AER. The ratios for home (Ea,h/Ca) and outdoors (Ea,o/Ca) are higher on WEND than WDSC,

329

resulting from a larger portion of time spent in these microenvironments. The upper end of

330

Ea,o/Ca approaches 1 for children on WEND, indicating that some of them spent most of their

331

time outdoors, such as camping outside. The values of Ea,h/Ca and Ea,o/Ca on WDNS have a

332

similar distribution as for WEND. No significant differences in distributions of Ea,v/Ca are

333

observed for the vehicle microenvironment among day types. 17 ACS Paragon Plus Environment


334

Similar time patterns among age groups lead to similar patterns in the estimated exposure. For

335

example, for the NC-Spring scenarios, the average Ea/Ca ranges narrowly from 0.50 to 0.52

336

among ES, MS and HS age groups. Similarly, the 95th percentile ranges narrowly from 0.74 to

337

0.76. The 95th percentile of the daily Ea/Ca is 46-50% higher than the mean.

338

The impact of daily maximum temperature on time patterns and exposure depends on age group.

339

For the elementary school students, the average Ea/Ca ratio ranges narrowly from 0.51 to 0.52, a

340

variation of 2%, among three temperature categories in spring in NC. While for the high school

341

students, the average Ea/Ca ratio is 0.54, 0.52 and 0.56 on LT, MT and HT days, respectively, a

342

variation of approximately 8%.

343

The differences in Ea,ME/Ca ratio among temperature categories are mainly associated with

344

variations in the amount of time spent in school, outdoors and “other indoor” locations, for

345

which the microenvironmental PM2.5 is typically higher than other microenvironments except

346

outdoor. The impact of daily maximum temperature on time pattern and exposure is also affected

347

by day type. For the HS-NC-Spring scenario, over 90% of the LT days overlap with WDNS in

348

the simulation, which leads to large Ea/Ca ratios on LT for this scenario.

349

DISCUSSION

350

The estimated children’s ambient exposures to PM2.5 are significantly lower than ambient

351

concentrations and vary substantially among seasons, regions and simulated individuals. The

352

mean exposure varies among regions and seasons by a factor of 2.7, and varies among

353

individuals by a factor of 5 over a 95% frequency range within a region and season. This

354

indicates that the within-group exposure variability is larger than between-group variability.

355

Variations in children’s exposure is affected by ambient concentration, AER, and time pattern. 18 ACS Paragon Plus Environment

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356

The estimated average value of Ea/Ca ratio in this study ranges from 0.46 to 0.61 across regions,

357

seasons. The result is comparable to monthly average value of 0.54 (95% CI: 0.42, 0.65) from a

358

pooled analysis on ambient PM2.5 exposure in field measurements across nine U.S. cities by

359

Kioumourtzoglou et al.17 In Kioumourtzoglou et al., between-city heterogeneity is found among

360

five cities, including Atlanta, Baltimore, Boston, Steubenville, and Seattle, which lends support

361

for the geographic variability observed in the mean Ea/Ca between domains located in different

362

U.S. climatic zones.

363

Seasonal and regional differences in the mean Ea/Ca ratio for children are mainly caused by

364

variations in residential AER and are similar to those estimated for the elderly reported by Jiao et

365

al.26 Time patterns may also affect variations in the mean Ea/Ca between seasons and regions,

366

because of variations in percentage of days with high temperature. Although season is not a

367

criterion in diary selection, ambient temperature is. However, the impact of the temperature on

368

the mean Ea/Ca is much smaller compared to the influence from variations in AER.

369

The CV analysis implies that the majority of the inter-individual variability in Ea/Ca is caused by

370

variation in time pattern. Among factors examined here affecting time patterns, day type has the

371

largest impact on the Ea/Ca ratio, especially for the 95th percentile. Daily maximum temperature

372

also has an impact on Ea/Ca ratio, but the impact depends on age group and is affected by day

373

type. Day type and daily maximum temperature substantially affects children’s time spent in

374

some microenvironments, such as home, school and outdoor, resulting in different exposure

375

patterns in terms of inter-individual distributions of Ea/Ca. ES and MS children have similar time

376

patterns and, thus, similar Ea/Ca distributions.



377

The Ea/Ca ratio may help to interpret exposure errors inherent in epidemiologic studies that use

378

ambient air quality as an exposure surrogate. The mean Ea/Ca indicates the so-called “Classical”

379

component of exposure error, which is the, which is the difference between the aggregated

380

exposure and the ambient PM2.5 concentrations. And the inter-individual variability in Ea/Ca

381

provides insights regarding the so-called “Berkson” component of exposure error, which results

382

from using aggregated instead of individual exposure.17, 30 Factors which cause the variation in

383

exposure errors can be used to interpret and adjust for in health risk estimation observed in

384

epidemiological studies. Daily ambient temperature, which varies with season, has been used as

385

an adjustment factor in epidemiological studies. 28, 65, 66 For example, the effect estimates of

386

PM2.5 on daily mortality for a New York city case study was highest in summer, for which the

387

estimated mean Ea/Ca ratio was also the highest among the seasons.28 The differences in Ea/Ca

388

ratio among temperature categories lend support to the plausibility of temperature adjustment in

389

epidemiological studies. Recent epidemiological studies indicate there may be variance in health

390

effect associated with day type.67 Further investigation is needed to characterize the relationship

391

between differences in time patterns by day type and variations in health effect in

392

epidemiological studies.

393

However, there was seasonal variability in the effect estimate not explained by seasonal

394

variability in the mean Ea/Ca ratio. A possible additional source of variability in effect estimates

395

may be seasonal variation in PM2.5 chemical composition. 68, 69 PM toxicity may vary with

396

chemical composition. Further study is needed to evaluate the variations in Ea/Ca ratio with

397

regard to different PM components. While such work is needed, it is not guaranteed that a better

398

estimate will result.70, 71


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399

For risk assessment, the frequency distribution of exposures within a population, not just the

400

mean exposure, is of concern. Stochastic exposure modeling quantifies the distribution of

401

exposures in a population, which aids in identifying factors that could contribute to elevated

402

exposures.72 The estimated 95th percentile of Ea/Ca is 30% to 50% higher than the mean Ea/Ca

403

among simulated children for all regions, seasons, ages, day type, and outdoor temperature.

404

Exposures at or above the 95th percentile of inter-individual variability represent high end

405

exposures.12 Individuals at the high end of the exposure distribution are often of interest when

406

considering various actions to mitigate exposure. High residential air exchange rates are typically

407

associated with high Ea/Ca ratios. Residential ventilation could be adjusted by home occupants,

408

in combination with filtration options, to reduce infiltration of ambient pollution on days with

409

high concentraion perhaps in response to better advisory messaging.

410

Higher Ea/Ca ratios are observed on WDNS than WDSC due to more time spent outdoors or in

411

“other indoor” locations. In some cities in China, schools are closed on high pollution days.73

412

However, such decision may actually lead to increases in children’s exposures, based on

413

comparison of time pattern data for WDNS versus WDSC, unless outdoor avoidance occurs. For

414

example, some groups, particularly children, the elderly, and those with respiratory problems,

415

modify their behavior on high-O3 days, by reducing time spent outdoors or limiting outdoor activity

416

exertion level.74 In particular, asthmatic children are more likely to avoid high outdoor

417

concentrations. A combination of an air quality warning system and improved communication of

418

hazard and advice could help parents guide their children to avoid high exposure situations

419

through behavior modification.



420

To better characterize children’s exposure in school, the mass balance method for estimating the

421

indoor PM2.5 concentration was used. This method has been shown to appropriately account for

422

variability in residential indoor concentration.24 The school exposure to concentration ratio,

423

Ea,s/Ca, has more variability among simulated individuals based on the mass balance method

424

compared to the factor method. However, due to the limited mesurement data, the distribution of

425

school AERs are not differenciated among seasons and regions. For better characteriztion of

426

children’s exposure, there is a need for further measurments on school AERs by season and

427

region.

428

Another limitation is related to the time pattern database. There are limited data on time patterns

429

when stratifying exposure assessment by exposure factors such as age, sex, day type, and

430

temperature. Moreover, there are insufficient data in CHAD from which to quantify the

431

interaction effects among these factors. For example, there are only 20 records for female

432

elementary school children on school weekday with high ambient temperature. A sample size of

433

only 20 for such a stratified grouping of factors may not adequately quantify inter-individual

434

variability. In some cases if the exposed population is stratified by region and season, there may

435

not be any diary record. One reason for limited diary sample sizes is that traditional survey

436

methods are time consuming and burdensome to participating subjects. To reduce burden on

437

subjects, there is a growing effort to utilize common conveniently available personal electronic

438

devices, such as smart phones, global positioning systems (GPS), and movement sensors

439

(accelerometers), to collect data on personal activities.75 Future work is warranted to integrate

440

this information into CHAD to better identify and quantify various factors associated with

441

children’s time patterns and their interactions.


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442

ACKNOWLEDGEMENTS

443

This work is sponsored by grant GRF 614713 from the Hong Kong Research Grants Council and

444

Oversea Research Awards from the Hong Kong University of Science and Technology. Dr.

445

Frey's participation in this research was supported in part by grant R833863 from the U.S.

446

Environmental Protection Agency's Science to Achieve Results (STAR) program. John Langstaff

447

of U.S. Environmental Protection Agency (EPA) provided guidance on configuring APEX. Dr.

448

Wan Jiao, formerly at North Carolina State University and now at U.S. EPA, provided input data.

449

This research has not been subjected to any EPA review and therefore does not necessarily

450

reflect the views of the Agency, and no official endorsement should be inferred.

451

SUPPORTING INFORMATION

452

Additional information on input parameters for microenvironments is summarized in Tables S1.

453

The average Ea/Ca ratios in selected regions, seasons, and age groups are listed in Table S2. The

454

Coefficient of Variation (CV) of inter-individual variability in time spent and Ea,ME/Ca in eight

455

microenvironments are reported for the ES-NC-SP scenario in Table S3. The inputs for school

456

AERs and a comparison of exposure estimates for schools from using mass balance and factor

457

methods is shown in Figures S1 and S5. A summary of children’s time pattern by age group,

458

gender, day type, daily maximum temperature, season and region is presented in Figures S2, S3,

459

and S4. Sensitivity tests on inputs for vehicles are presented in Figure S6. Inter-individual

460

variability in total and microenvironmental ambient exposure for different school age groups and

461

different daily maximum temperature ranges are presented in Figures S7 and S8, respectively. A

462

comparison of inter-individual variability in exposure estimates for children and the elderly for



463

the NC domain is presented in Figure S9. This information is available free of charge via the

464

Internet at http://pubs.acs.org/


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Table 1. Model input data regarding population distribution and days with different daily maximum temperature Population distribution by age and gender (% of the total population) six-county region, NC Harris County, TX New York City Age Range (years) 6 to 11 12 to 14 15 to 18 6 to 18

Month April July October December

Male Female Male Female Female 4.1 5.0 4.8 4.3 4.1 1.9 2.4 2.2 2.0 1.9 2.5 3.1 2.9 2.6 2.5 8.5 10.5 10.0 8.9 8.5 Distribution of daily maximum temperature (% of days per month) six-county region, NC Harris County, TX New York City a b c LT MT HT LT MT HT LT MT HT 3.3 73.3 23.3 0.0 60.0 40.0 23.3 63.3 13.3 0.0 12.9 87.1 0.0 6.5 93.5 0.0 38.7 61.3 9.7 77.4 12.9 0.0 80.6 19.4 22.6 77.4 0.0 70.0 30.0 0.0 10.0 90.0 0.0 96.8 3.2 0.0 Male 4.3 2.0 2.6 8.8

a

Low temperature range (LT: 84 ºF). b

31



Percentage of Daily Time

Records: 3155 100%

1715 2776 1012

471

936

812

400

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925 Total = 12,202 Restaurant

80%

Office Store

60%

OtherIndoor Vehicle

40%

Outdoor School

20%

Home

ES: Ages 6 to 11

WEND

WDNS

WDSC

WEND

WDNS

WDSC

WEND

WDNS

WDSC

0%

MS: Ages 12 to 14 HS: Ages 15 to 18

Figure 1. Average daily time spent among eight microenvironments on weekday in school (WDSC), weekday out of school (WDNS), and weekend (WEND) for children of elementary school (ES) age (6 to 11), middle school (MS) age (12 to 14) and high school (HS) age (15 to 18) in Consolidated Human Activity Database.

32



Cumulative Frequency

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1.0 0.8 0.6 0.4 0.2 0.0

Spring Summer Fall Winter

0

0.2

0.4

0.6

0.8

1

Ea/Ca


(a)

Six-county area, North Carolina

1.0 0.8 0.6 0.4 0.2 0.0


0

0.2

0.4

0.6

0.8

1

Ea/Ca


(b)

Harris County, Texas

1.0 0.8 0.6 0.4 0.2 0.0


0

0.2

0.4

0.6

0.8

1

Ea/Ca

(c)

New York City

Note: Ea = daily average total ambient exposure; Ca = daily average ambient concentration.

Figure 2. Geographic and seasonal variability in the ratio of estimated daily ambient exposure to ambient concentration for children of elementary school age (6 to 11) in NC domain, Harris County, TX and NYC, 2002. 33


1.0 0.8 0.6 0.4 0.2 0.0




WDSC WDNS WEND

0

0.2

0.4

0.6

0.8

1.0 0.8 0.6 0.4 0.2 0.0

WDSC WDNS WEND

0

1

0.2

1.0 0.8 0.6 0.4 0.2 0.0

WDSC WDNS WEND

0.4

0.6

0.8

1.0 0.8 0.6 0.4 0.2 0.0

1

WEND

0.2

0.6

0.8

1

(d) Ambient outdoor exposure Cumulative Frequency


0.4 Ea, o/Ca

1.0 0.8 0.6 0.4 0.2 0.0

WDSC WDNS WEND

0.4

1

WDNS

0

(c) Ambient exposure at school

0.2

0.8

WDSC

Ea, s/Ca

0

0.6

(b) Ambient exposure at home Cumulative Frequency


(a) Total ambient exposure

0.2

0.4 Ea, h/Ca

Ea/Ca

0

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0.6

0.8

1.0 0.8 0.6 0.4 0.2 0.0

WDSC WDNS WEND

0

1

0.2

0.4

0.6

0.8

Ea, i/Ca

Ea, v/Ca

(e) Ambient exposure in vehicle

(f) Ambient exposure in other indoors

Note: Ea = daily average total ambient exposure; Ca = daily average ambient concentration; Ea,h= daily average ambient exposure at home; Ea,s= daily average ambient exposure at school; Ea,o= daily average ambient exposure outdoor; Ea,v= daily average ambient exposure in vehicle, Ea,i= daily average ambient exposure in other indoors.

Figure 3. Estimated cumulative frequency distributions of individual daily Ea/Ca in selected microenvironments on weekday in school (WDSC), weekday out of school (WDNS), and weekend (WEND) for children of elementary school age (6 to 11) in NC domain, spring 2002. 34 ACS Paragon Plus Environment

1

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Comparison of Sources of Variability in School Age Children Exposure

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