Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data

Prediction model for air particulate matter levels in the households of elderly individuals in Hong Kong

Air pollution has shown to cause adverse health effects on mankind. Aging causes functional decline and leaves elderly people more susceptible to health threats associated with air pollution exposure. Elderly spend approximately 80% of their lifetime at home every day. To understand air pollution exposure, indoor air pollutants are the targets for consideration especially for the elderly population. However, indoor air monitoring for epidemiological studies requires a large population, is labor intensive and time consuming. As a result, a prediction model is necessary. For 3 consecutive days in summer and winter, 24-h average of mass concentrations of fine particulate matter (aerodynamic diameter <2.5 μm: PM_2.5) were measured in indoors for 116 households. A PM_2.5 prediction model for elderly households in Hong Kong has been developed by combining ambient PM_2.5 concentrations obtained from land use regression model and questionnaire-elicited information related to the indoor PM_2.5 sources. The fitted linear mixed-effects model is moderately predictive for the observed indoor PM_2.5, with R² = 0.67 (or R² = 0.61 by cross-validation). The model shows indoor PM_2.5 was positively influenced by outdoor PM_2.5 levels. Meteorological factors (e.g. temperature and relative humidity) were related to the indoor PM_2.5 in a relatively complex manner. Congested living areas, opening windows for extended periods for ventilation and use of liquefied petroleum gas for cooking were the factors determining the ultimate indoor air quality. This study aims to provide information about controlling household air quality and can be used for future epidemiological studies associated with indoor air pollution in large population.

Measuring the Building Envelope Penetration Factor for Ambient Nitrogen Oxides

Much of human exposure to nitrogen oxides (NO_x) of ambient origin occurs indoors. Reactions with materials inside building envelopes are expected to influence the amount of ambient NO_x that infiltrates indoors. However, envelope penetration factors for ambient NO_x constituents have never been measured. Here, we develop and apply methods to measure the penetration factor and indoor loss rates for ambient NO_x constituents using time-resolved measurements in an unoccupied apartment unit. Multiple test methods and parameter estimation approaches were tested, including natural and artificial indoor NO_x elevation with and without accounting for indoor oxidation reactions. Twelve of 16 tests yielded successful estimates of penetration factors and indoor loss rates. The penetration factor for NO was confirmed to be ∼1 and the mean (±s.d.) NO₂ penetration factor was 0.72 ± 0.06 with a mean relative uncertainty of ∼15%. The mean (±s.d.) indoor NO₂ loss rate was 0.27 ± 0.12 h^-1, ranging 0.06-0.47 h^-1, with strong correlations with indoor relative and absolute humidity. Indoor NO loss rates were strongly correlated with the estimated ozone concentration in infiltrating air. Results suggest that envelope penetration factors and loss rates for NO_x constituents can be reasonably estimated across a wide range of conditions using these approaches.

Predicting Indoor Concentrations of Black Carbon in Residential Environments

Black carbon (BC) is a descriptive term that refers to light-absorbing particulate matter (PM) produced by incomplete combustion and is often used as a surrogate for traffic-related air pollution. Exposure to BC has been linked to adverse health effects. Penetration of ambient BC is typically the primary source of indoor BC in the developed world. Other sources of indoor BC include biomass and kerosene stoves, lit candles, and charring food during cooking. Home characteristics can influence the levels of indoor BC. As people spend most of their time indoors, human exposure to BC can be associated to a large extent with indoor environments. At the same time, due to the cost of environmental monitoring, it is often not feasible to directly measure BC inside multiple individual homes in large-scale population-based studies. Thus, a predictive model for indoor BC is needed to support risk assessment in public health. In this study, home characteristics and occupant activities that potentially modify indoor levels of BC were documented in 23 homes, and indoor and outdoor BC concentrations were measured twice. The homes were located in the Cincinnati-Kentucky-Indiana tristate region and measurements occurred from September 2015 through August 2017. A linear mixed-effect model was developed to predict BC concentration in residential environments. The measured outdoor BC concentrations and the documented home characteristics were utilized as predictors of indoor BC concentrations. After the model was developed, a leave-one-out cross-validation algorithm was deployed to assess the predictive accuracy of the output. The following home characteristics and occupant activities significantly modified the concentration of indoor BC: outdoor BC, lit candles and electrostatic or high efficiency particulate air (HEPA) filters in heating, ventilation and air conditioning (HVAC) systems. Predicted indoor BC concentrations explained 78% of the variability in the measured indoor BC concentrations. The data show that outdoor BC combined with home characteristics can be used to predict indoor BC levels with reasonable accuracy.

The Fort Collins commuter study: Variability in personal exposure to air pollutants by microenvironment

This study investigated the role of microenvironment on personal exposures to black carbon (BC), fine particulate mass (PM2.5 ), carbon monoxide (CO), and particle number concentration (PNC) among adult residents of Fort Collins, Colorado, USA. Forty-four participants carried a backpack containing personal monitoring instruments for eight nonconsecutive 24-hour periods. Exposures were apportioned into five microenvironments: Home, Work, Transit, Eateries, and Other. Personal exposures exhibited wide heterogeneity that was dominated by within-person variability (both day-to-day and between microenvironment variability). Linear mixed-effects models were used to compare mean personal exposures in each microenvironment, while accounting for possible within-person correlation. Mean personal exposures during Transit and at Eateries tended to be higher than exposures at Home, where participants spent the majority of their time. Compared to Home, mean exposures to BC in Transit were, on average, 129% [95% confidence interval: 101% 162%] higher and exposures to PNC were 180% [101% 289%] higher in Eateries.

Road proximity influences indoor exposures to ambient fine particle mass and components

Exposure to traffic-related PM_2.5 mass and its components can affect human health. Meanwhile, indoor concentrations are better exposure predictors as compared to outdoor concentrations because individuals spend the majority of their time indoors. We estimated the impact of traffic emissions on indoor PM_2.5 mass and its species as a function of road proximity in Massachusetts. A linear regression model was built using 662 indoor samples and 580 ambient samples. Analysis shows that indoor exposures to traffic-related particles increased dramatically with road proximity. We defined relative concentration decrease, R(α), as the ratio of the indoor concentration at perpendicular distance α in meters from the closest major road to the indoor concentration at 1800 m from the major road. R(13) values for PM_2.5 mass and Black Carbon (BC) were 1.3 (95%CI: 1.4, 1.6) and 2.1 (95%CI: 1.3, 2.8) for A12 roads, and 1.3 (95%CI: 1.2, 1.4) and 1.2 (95%CI: 1.1, 1.3) for A3 roads. R(α) values were also estimated for Fe, Mn, Mo, Sr and Ti for A12 roads, and Ca, Cu, Fe, Mn, Mo, Ni, Si, Sr, V and Zn for A3 roads. R(α) values for species associated mainly with brakes, tires or road dust (e.g., Mn, Mo and Sr) were higher than others. For A12 roads, R(13) values for Mn and Mo were 10.9 (95%CI: 0.9, 20.9) and 6.5 (95%CI: 1.4, 11.5), and ranged from 1.3 to 2.1 for other species; for A3 roads, R(13) values for Mn, Mo and Sr were 1.9 (95%CI: 1.1, 2.9), 1.8 (95%CI: 1.1, 2.4), and 8.5 (95%CI: 5.9, 10.9), and ranged from 1.2 to 1.6 for others. Our results indicate a significant impact of local traffic emissions on indoor air, which depends on road proximity. Thus road proximity which has been used in many epidemiological studies is a reasonable exposure metric.

Modeling of residential indoor PM2.5 exposure in 37 counties in China

It is critical to estimate the exposure to indoor air pollution of residents spending most of their time in such microenvironments. However, the understanding regarding PM_2.5 exposure in residential indoor environments is very limited. In this study, we collected participants' basic information and time-activity patterns, as well as details of other factors related to indoor air pollution exposure, through questionnaires presented to a large population in 37 counties of China. Continuous monitoring of ambient PM_2.5 concentrations was performed using an environmental fixed-site monitoring network. Residential indoor PM_2.5 concentrations were predicted using a mass balance model based on the data obtained. Evaluation of continuous daily average residential indoor PM_2.5 exposure doses for large populations during winter revealed concentrations ranged from 67 to 195 μg/m³. Finally, differences in residential indoor PM_2.5 exposure between northern and southern China were investigated. The results suggested that residential indoor PM_2.5 concentrations in northern China, associated with heating, were higher than in the south. The established model could be important for improved understanding of human exposure to indoor PM_2.5 air pollution.

Source Apportionment and Influencing Factor Analysis of Residential Indoor PM2.5 in Beijing

In order to identify the sources of indoor PM2.5 and to check which factors influence the concentration of indoor PM2.5 and chemical elements, indoor concentrations of PM2.5 and its related elements in residential houses in Beijing were explored. Indoor and outdoor PM2.5 samples that were monitored continuously for one week were collected. Indoor and outdoor concentrations of PM2.5 and 15 elements (Al, As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Pb, Se, Tl, V, Zn) were calculated and compared. The median indoor concentration of PM2.5 was 57.64 μg/m³. For elements in indoor PM2.5, Cd and As may be sensitive to indoor smoking, Zn, Ca and Al may be related to indoor sources other than smoking, Pb, V and Se may mainly come from outdoor. Five factors were extracted for indoor PM2.5 by factor analysis, explained 76.8% of total variance, outdoor sources contributed more than indoor sources. Multiple linear regression analysis for indoor PM2.5, Cd and Pb was performed. Indoor PM2.5 was influenced by factors including outdoor PM2.5, smoking during sampling, outdoor temperature and time of air conditioner use. Indoor Cd was affected by factors including smoking during sampling, outdoor Cd and building age. Indoor Pb concentration was associated with factors including outdoor Pb and time of window open per day, building age and RH. In conclusion, indoor PM2.5 mainly comes from outdoor sources, and the contributions of indoor sources also cannot be ignored. Factors associated indoor and outdoor air exchange can influence the concentrations of indoor PM2.5 and its constituents.

Development of a modeling approach to estimate indoor-to-outdoor sulfur ratios and predict indoor PM2.5 and black carbon concentrations for Eastern Massachusetts households

The effects of indoor air pollution on human health have drawn increasing attention among the scientific community as individuals spend most of their time indoors. However, indoor air sampling is labor-intensive and costly, which limits the ability to study the adverse health effects related to indoor air pollutants. To overcome this challenge, many researchers have attempted to predict indoor exposures based on outdoor pollutant concentrations, home characteristics, and weather parameters. Typically, these models require knowledge of the infiltration factor, which indicates the fraction of ambient particles that penetrates indoors. For estimating indoor fine particulate matter (PM_2.5) exposure, a common approach is to use the indoor-to-outdoor sulfur ratio (S_indoor/S_outdoor) as a proxy of the infiltration factor. The objective of this study was to develop a robust model that estimates S_indoor/S_outdoor for individual households that can be incorporated into models to predict indoor PM_2.5 and black carbon (BC) concentrations. Overall, our model adequately estimated S_indoor/S_outdoor with an out-of-sample by home-season R² of 0.89. Estimated S_indoor/S_outdoor reflected behaviors that influence particle infiltration, including window opening, use of forced air heating, and air purifier. Sulfur ratio-adjusted models predicted indoor PM_2.5 and BC with high precision, with out-of-sample R² values of 0.79 and 0.76, respectively.

24-h Nitrogen dioxide concentration is associated with cooking behaviors and an increase in rescue medication use in children with asthma

Exposure to nitrogen dioxide (NO₂), a byproduct of combustion, is associated with poor asthma control in children. We sought to determine whether gas-fueled kitchen appliance use is associated with 24-h indoor NO₂ concentrations and whether these concentrations are associated with asthma morbidity in children. Children aged 5-12 years old with asthma were eligible. Mean 24-h NO₂ concentration was measured in the kitchen over a four-day sampling period and gas stove use was captured in time activity diaries. The relationship between stove and oven use and daily NO₂ concentration was analyzed. Longitudinal analysis assessed the effect of daily NO₂ exposure on symptoms, inhaler use, and lung function. Multivariate models were adjusted for age, sex, season, and maternal education. Thirty children contributed 126 participant days of sampling. Mean indoor 24-h NO₂ concentration was 58(48)ppb with a median (range) of 45(12-276)ppb. All homes had gas stoves and furnaces. Each hour of kitchen appliance use was associated with an 18ppb increase in 24-h NO₂ concentration. In longitudinal multivariate analysis, each ten-fold increase in previous-day NO₂ was associated with increased nighttime inhaler use (OR = 4.9, p = 0.04). There were no associations between NO₂ and lung function or asthma symptoms. Higher previous-day 24-h concentration of NO₂ is associated with increased nighttime inhaler use in children with asthma.

Perceived haze, stress, and negative emotions: An ecological momentary assessment study of the affective responses to haze

The aim of this study was to examine the mediating role of stress in the association between people's perceived haze and negative emotions in daily life. Using ecological momentary assessment, 95 college students reported their perceived haze, stress, and negative emotions twice a day over the course of 2 weeks. The results showed a positive relationship between perceived haze and negative emotions. More importantly, this association was significantly mediated by levels of stress. Findings suggested that people who perceived more severe haze may report higher stress levels, which in turn may lead to increases in negative emotions.

Indoor air quality of low and middle income urban households in Durban, South Africa

INTRODUCTION

Elevated levels of indoor air pollutants may cause cardiopulmonary disease such as lower respiratory infection, chronic obstructive lung disease and lung cancer, but the association with tuberculosis (TB) is unclear. So far the risk estimates of TB infection or/and disease due to indoor air pollution (IAP) exposure are based on self-reported exposures rather than direct measurements of IAP, and these exposures have not been validated.

OBJECTIVE

The aim of this paper was to characterize and develop predictive models for concentrations of three air pollutants (PM₁₀, NO₂ and SO₂) in homes of children participating in a childhood TB study.

METHODS

Children younger than 15 years living within the eThekwini Municipality in South Africa were recruited for a childhood TB case control study. The homes of these children (n=246) were assessed using a walkthrough checklist, and in 114 of them monitoring of three indoor pollutants was also performed (sampling period: 24h for PM₁₀, and 2-3 weeks for NO₂ and SO₂). Linear regression models were used to predict PM₁₀ and NO₂ concentrations from household characteristics, and these models were validated using leave out one cross validation (LOOCV). SO₂ concentrations were not modeled as concentrations were very low.

RESULTS

Mean indoor concentrations of PM₁₀ (n=105)_, NO₂ (n=82) and SO₂ (n=82) were 64μg/m³ (range 6.6-241); 19μg/m³ (range 4.5-55) and 0.6μg/m³ (range 0.005-3.4) respectively with the distributions for all three pollutants being skewed to the right. Spearman correlations showed weak positive correlations between the three pollutants. The largest contributors to the PM₁₀ predictive model were type of housing structure (formal or informal), number of smokers in the household, and type of primary fuel used in the household. The NO₂ predictive model was influenced mostly by the primary fuel type and by distance from the major roadway. The coefficients of determination (R²) for the models were 0.41 for PM₁₀ and 0.31 for NO₂. Spearman correlations were significant between measured vs. predicted PM₁₀ and NO₂ with coefficients of 0.66 and 0.55 respectively.

CONCLUSION

Indoor PM₁₀ levels were relatively high in these households. Both PM₁₀ and NO₂ can be modeled with a reasonable validity and these predictive models can decrease the necessary number of direct measurements that are expensive and time consuming.

Short-Term Fluctuations in Air Pollution and Asthma in Scania, Sweden. Is the Association Modified by Long-Term Concentrations?

Background and aims

Asthma is one of the most common respiratory diseases in the world. Research has shown that temporal increases in air pollution concentrations can aggravate asthma symptoms. The aim of this study was to assess whether individuals living in areas with higher air pollution concentrations responded differently to short-term temporal exposure to air pollution than those living in lower air pollution areas.

Method

The study was designed as a case-crossover study in Scania, Sweden. Outcome data was visits to primary health care clinics with asthma as the main complaint during the years 2007 to 2010. Nitrogen dioxide levels were obtained from 21 different air pollution monitoring stations. Short-term exposure was defined as the average concentration four days prior to the visit. Data was pooled for areas above and below a two-year average NO2 concentration of 10 μg/m3, dispersion modelled with an emission database.

Results

The short-term association between NO2 and asthma visits seemed stronger in areas with NO2 levels below 10 μg/m3, with an odds ratio (OR) of 1.15 (95% confidence interval (CI): 1.08–1.23) associated with a 10 μg/m3 increase in NO₂ compared to areas above 10 μg/m3 NO2 levels, where corresponding OR of 1.09 (95% CI: 1.02–1.17). However, this difference was not statistically significant. (p = 0.13)

Conclusions

The study provided some evidence, although not statistically significant, that short-term associations between air pollution and asthma may depend on background air pollution levels. However, we cannot rule out that the association is due to other spatially dependent factors in Scania. The study should be reproduced in other study areas.

An evaluation of the impact of flooring types on exposures to fine and coarse particles within the residential micro-environment using CONTAM

Typical resuspension activities within the home, such as walking, have been estimated to contribute up to 25% of personal exposures to PM10. Chamber studies have shown that for moderate walking intensities, flooring type can impact the rate at which particles are re-entrained into the air. For this study, the impact of residential flooring type on incremental average daily (24 h) time-averaged exposure was investigated. Distributions of incremental time-averaged daily exposures to fine and coarse PM while walking within the residential micro-environment were predicted using CONTAM, the multizone airflow and contaminant transport program of the National Institute of Standards and Technology. Knowledge of when and where a person was walking was determined by randomly selecting 490 daily diaries from the EPA's consolidated human activity database (CHAD). On the basis of the results of this study, residential flooring type can significantly impact incremental time-averaged daily exposures to coarse and fine particles (α=0.05, P<0.05, N=490, Kruskal-Wallis test) with high-density cut pile carpeting resulting in the highest exposures. From this study, resuspension from walking within the residential micro-environment contributed 6-72% of time-averaged daily exposures to PM10.

Indoor air sampling for fine particulate matter and black carbon in industrial communities in Pittsburgh

Impacts of industrial emissions on outdoor air pollution in nearby communities are well-documented. Fewer studies, however, have explored impacts on indoor air quality in these communities. Because persons in northern climates spend a majority of their time indoors, understanding indoor exposures, and the role of outdoor air pollution in shaping such exposures, is a priority issue. Braddock and Clairton, Pennsylvania, industrial communities near Pittsburgh, are home to an active steel mill and coke works, respectively, and the population experiences elevated rates of childhood asthma. Twenty-one homes were selected for 1-week indoor sampling for fine particulate matter (PM2.5) and black carbon (BC) during summer 2011 and winter 2012. Multivariate linear regression models were used to examine contributions from both outdoor concentrations and indoor sources. In the models, an outdoor infiltration component explained 10 to 39% of variability in indoor air pollution for PM2.5, and 33 to 42% for BC. For both PM2.5 models and the summer BC model, smoking was a stronger predictor than outdoor pollution, as greater pollutant concentration increases were identified. For winter BC, the model was explained by outdoor pollution and an open windows modifier. In both seasons, indoor concentrations for both PM2.5 and BC were consistently higher than residence-specific outdoor concentration estimates. Mean indoor PM2.5 was higher, on average, during summer (25.8±22.7 μg/m3) than winter (18.9±13.2 μg/m3). Contrary to the study's hypothesis, outdoor concentrations accounted for only little to moderate variability (10 to 42%) in indoor concentrations; a much greater proportion of PM2.5 was explained by cigarette smoking. Outdoor infiltration was a stronger predictor for BC compared to PM2.5, especially in winter. Our results suggest that, even in industrial communities of high outdoor pollution concentrations, indoor activities--particularly cigarette smoking--may play a larger role in shaping indoor exposures.

Modeling indoor air pollution of outdoor origin in homes of SAPALDIA subjects in Switzerland

Given the shrinking spatial contrasts in outdoor air pollution in Switzerland and the trends toward tightly insulated buildings, the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) needs to understand to what extent outdoor air pollution remains a determinant for residential indoor exposure. The objectives of this paper are to identify determining factors for indoor air pollution concentrations of particulate matter (PM), ultrafine particles in the size range from 15 to 300nm, black smoke measured as light absorbance of PM (PMabsorbance) and nitrogen dioxide (NO2) and to develop predictive indoor models for SAPALDIA. Multivariable regression models were developed based on indoor and outdoor measurements among homes of selected SAPALDIA participants in three urban (Basel, Geneva, Lugano) and one rural region (Wald ZH) in Switzerland, various home characteristics and reported indoor sources such as cooking. Outdoor levels of air pollutants were important predictors for indoor air pollutants, except for the coarse particle fraction. The fractions of outdoor concentrations infiltrating indoors were between 30% and 66%, the highest one was observed for PMabsorbance. A modifying effect of open windows was found for NO2 and the ultrafine particle number concentration. Cooking was associated with increased particle and NO2 levels. This study shows that outdoor air pollution remains an important determinant of residential indoor air pollution in Switzerland.

Evaluating the Long-Term Health and Economic Impacts of Central Residential Air Filtration for Reducing Premature Mortality Associated with Indoor Fine Particulate Matter (PM2.5) of Outdoor Origin

Much of human exposure to fine particulate matter (PM2.5) of outdoor origin occurs in residences. High-efficiency particle air filtration in central heating, ventilating, and air-conditioning (HVAC) systems is increasingly being used to reduce concentrations of particulate matter inside homes. However, questions remain about the effectiveness of filtration for reducing exposures to PM2.5 of outdoor origin and adverse health outcomes. Here we integrate epidemiology functions and mass balance modeling to estimate the long-term health and economic impacts of HVAC filtration for reducing premature mortality associated with indoor PM2.5 of outdoor origin in residences. We evaluate 11 classifications of filters (MERV 5 through HEPA) using six case studies of single-family home vintages and ventilation system combinations located in 22 U.S. cities. We estimate that widespread use of higher efficiency filters would reduce premature mortality by 0.002-2.5% and increase life expectancy by 0.02-1.6 months, yielding annual monetary benefits ranging from $1 to $1348 per person in the homes and locations modeled herein. Large differences in the magnitude of health and economic impacts are driven largely by differences in rated filter efficiency and building and ventilation system characteristics that govern particle infiltration and persistence, with smaller influences attributable to geographic location.

Exposure to traffic and lung function in adults: a general population cohort study

OBJECTIVES

To investigate the association between living near dense traffic and lung function in a cohort of adults from a single urban region.

DESIGN

Cross-sectional results from a cohort study.

SETTING

The adult-onset asthma and exhaled nitric oxide (ADONIX) cohort, sampled during 2001-2008 in Gothenburg, Sweden. Exposure was expressed as the distance from participants' residential address to the nearest road with dense traffic (>10,000 vehicles per day) or very dense traffic (>30,000 vehicles per day). The exposure categories were: low (>500 m; reference), medium (75-500 m) or high (<75 m).

PARTICIPANTS

The source population was a population-based cohort of adults (n=6153). The study population included 5441 participants of European descent with good quality spirometry and information about all outcomes and covariates.

OUTCOME MEASURES

Forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) were measured at a clinical examination. The association with exposure was examined using linear regression adjusting for age, gender, body mass index, smoking status and education in all participants and stratified by sex, smoking status and respiratory health status.

RESULTS

We identified a significant dose-response trend between exposure category and FEV1 (p=0.03) and borderline significant trend for FVC (p=0.06) after adjusting for covariates. High exposure was associated with lower FEV1 (-1.0%, 95% CI -2.5% to 0.5%) and lower FVC (-0.9%, 95% CI -2.2% to 0.4%). The effect appeared to be stronger in women. In highly exposed individuals with current asthma or chronic obstructive pulmonary disease, FVC was lower (-4.5%, 95% CI -8.8% to -0.1%).

CONCLUSIONS

High traffic exposure at the residential address was associated with lower than predicted FEV1 and FVC lung function compared with living further away in a large general population cohort. There were particular effects on women and individuals with obstructive disease.

Effect of Traffic Exposure on Sick Building Syndrome Symptoms among Parents/Grandparents of Preschool Children in Beijing, China

INTRODUCTION

Sick building syndrome (SBS) includes general, mucosal and skin symptoms. It is typically associated with an individual's place of work or residence. The aim of this study was to explore the effect of traffic exposure on SBS symptoms in Beijing, China.

METHODS

From January to May, 2011, recruitment occurred at kindergartens in 11 districts in Beijing. Self-administered questionnaires were distributed by teachers to legal guardians of children and then returned to teachers. The questionnaire asked them to recall the presence of 12 SBS symptoms from the previous three months. Living near a highway or main road (within 200 meters) was used as a proxy for traffic exposure. Multivariable logistic regression was used to test the association between traffic exposure and a higher number of SBS symptoms, controlling for key covariates.

RESULTS

There were 5487 valid questionnaires (65.0% response rate). Univariate analysis showed that living near a main road or highway (OR = 1.40), female gender (OR = 1.44), and environmental tobacco smoking (ETS) (OR = 1.13) were significant risk factors for general symptoms. Grandparent's generation (OR = 0.32) and home ownership (owner vs. renter) (OR = 0.89) were significant protective factors. The adjusted odds ratio (aOR) for the association between living close to a highway and general symptoms remained significant in the multivariable model (aOR = 1.39; 95% CI = 1.21: 1.59). ORs and aORs were similar for mucosal and skin symptoms.

CONCLUSIONS

This study found traffic exposure to be significantly associated with SBS symptoms. This finding is consistent with current literature that indicates an association between adverse health effects and living near highway or main road.

Children's health and vulnerability in outdoor microclimates: A comprehensive review

BACKGROUND

Children are routinely identified as a vulnerable population in environmental health risk assessments, experiencing adverse health outcomes due to exposure to a suite of atmospheric constituents.

OBJECTIVE

To provide a substantive overview of the research literature pertaining to biometeorological effects on children. Key information areas within urban environmental health research related to atmospheric variables (heat, air pollution, radiation) are assessed and integrated to better understand health outcomes and vulnerabilities in children. Critical avenues for improvement and understanding of children's health related to such biophysical parameters are also identified.

METHODS

This comprehensive review assesses past and current primary studies, organizational reports, educational books, and review articles. Emphasis is placed on the differential ambient exposures to temperature, air pollution, and radiation within urban microclimates commonly used by children (e.g., schoolyards, urban parks), and the resulting health impacts.

DISCUSSION

Exposure to heat, air pollution, and radiation are often enhanced in urban areas, specifically under the current design of the majority of outdoor child play places. Many heat indices, energy budget models, and health outcome studies fail to adequately parameterize children, yet those that do find enhanced vulnerability to ambient stressors, particularly heat and air pollution. Such environmental exposures relate strongly to behavior, activity, asthma, obesity, and overall child well-being. Current research indicates that a changing climate, growing urban population, and unsustainable design are projected to pose increasing complications.

CONCLUSIONS

Evidence-based research to link children's health, physiology, and behavior to atmospheric extremes is an important future research avenue, underscoring the fact that children are among the population groups disproportionately affected by ambient extremes. However, current methods and population-based models lack child-specific inputs and outputs, as well as designated thresholds for accurate predictions of child health impacts. More substantive evidence is needed for applicable child-specific policies and guidelines.

Sources of indoor air pollution in New York City residences of asthmatic children

Individuals spend ∼90% 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 ∼72% of PM2.5 mass and likely contributed to differences in the composition of indoor and outdoor PM2.5 exposures.

Personal exposure of primary school children to BTEX, NO₂ and ozone in Eskişehir, Turkey: relationship with indoor/outdoor concentrations and risk assessment

Personal exposures of 65 primary school children to benzene, toluene, ethyl benzene, xylenes (BTEX), nitrogen dioxide (NO2) and ozone (O3) were measured during 24h by using organic vapor monitors and tailor-made passive samplers. Two schools were selected to represent students living in more polluted (urban) and less polluted (sub-urban) areas in the city of Eskişehir, Turkey. The pollutant concentrations were also measured in indoor and outdoor environments during the personal sampling to investigate the contribution of each micro-environment on measured personal concentrations. Socio-demographic and personal time-activity data were collected by means of questionnaires and half-hour-time resolution activity diaries. Personal exposure concentrations were found to be correlated with indoor home concentrations. Personal, indoor and outdoor concentrations of all studied pollutants except for ozone were found to be higher for the students living at the urban traffic site. Ozone, on the other hand, had higher concentrations at the sub-urban site for all three types of measurements (personal, indoor and outdoor). Analysis of the questionnaire data pointed out to environmental tobacco smoke, use of solvent based products, and petrol station nearby as factors that affect personal exposure concentrations. Cancer and non-cancer risks were estimated using the personal exposure concentrations. The mean cancer risk for the urban school children (1.7×10(-5)) was found to be higher than the sub-urban school children (0.88×10(-5)). Children living with smoking parents had higher risk levels (1.7×10(-5)) than children living with non-smoking parents (1.08×10(-5)). Overall, the risk levels were <1×10(-4). All hazard quotient values for BTEX for the non-cancer health effects were <1 based on the calculations EPA's Risk Assessment Guidance for Superfund (RAGS) part F.

Source apportionment of indoor residential fine particulate matter using land use regression and constrained factor analysis

Source contributions to urban fine particulate matter (PM(2.5) ) have been modelled using land use regression (LUR) and factor analysis (FA). However, people spend more time indoors, where these methods are less explored. We collected 3-4- day samples of nitrogen dioxide and PM(2.5) inside and outside of 43 homes in summer and winter, 2003-2005, in and around Boston, Massachusetts. Particle filters were analysed for black carbon and trace element concentrations using reflectometry, X-ray fluorescence (XRF), and high-resolution inductively coupled mass spectrometry (ICP-MS). We regressed indoor against outdoor concentrations modified by ventilation, isolating the indoor-attributable fraction, and then applied constrained FA to identify source factors in indoor concentrations and residuals. Finally, we developed LUR predictive models using GIS-based outdoor source indicators and questionnaire data on indoor sources. FA using concentrations and residuals reasonably separated outdoor (long-range transport/meteorology, fuel oil/diesel, road dust) from indoor sources (combustion, smoking, cleaning). Multivariate LUR regression models for factors from concentrations and indoor residuals showed limited predictive power, but corroborated some indoor and outdoor factor interpretations. Our approach to validating source interpretations using LUR methods provides direction for studies characterizing indoor and outdoor source contributions to indoor cocentrations.

PRACTICAL IMPLICATIONS

By merging indoor-outdoor modeling, factor analysis, and LUR-style predictive regression modeling, we have added to previous source apportionment studies by attempting to corroborate factor interpretations. Our methods and results support the possibility that indoor exposures may be modeled for epidemiologic studies, provided adequate sample size and variability to identify indoor and outdoor source contributions. Using these techniques, epidemiologic studies can more clearly examine exposures to indoor sources and indoor penetration of source-specific components, reduce exposure misclassification, and improve the characterization of the relationship between particle constituents and health effects.

Evaluating housing quality, health and safety using an Internet-based data collection and response system: a cross-sectional study

BACKGROUND

Typically housing and health surveys are not integrated together and therefore are not representative of population health or national housing stocks. In addition, the existing channels for distributing information about housing and health issues to the general public are limited. The aim of this study was to develop a data collection and response system that would allow us to assess the Finnish housing stock from the points of view of quality, health and safety, and also to provide a tool to distribute information about important housing health and safety issues.

METHODS

The data collection and response system was tested with a sample of 3000 adults (one per household), who were randomly selected from the Finnish Population Register Centre. Spatial information about the exact location of the residences (i.e. coordinates) was included in the database inquiry. People could participate either by completing and returning a paper questionnaire or by completing the same questionnaire via the Internet. The respondents did not receive any compensation for their time in completing the questionnaire.

RESULTS

This article describes the data collection and response system and presents the main results of the population-based testing of the system. A total of 1312 people (response rate 44%) answered the questionnaire, though only 80 answered via the Internet. A third of the respondents had indicated they wanted feedback. Albeit a majority (>90%) of the respondents reported being satisfied or quite satisfied with their residence, there were a number of prevalent housing issues identified that can be related to health and safety.

CONCLUSIONS

The collected database can be used to evaluate the quality of the housing stock in terms of occupant health and safety, and to model its association with occupant health and well-being. However, it must be noted that all the health outcomes gathered in this study are self-reported. A follow-up study is needed to evaluate whether the occupants acted on the feedback they received. Relying solely on an Internet-based questionnaire for collecting data would not appear to provide an adequate response rate for random population-based surveys at this point in time.

Personal exposures to traffic-related particle pollution among children with asthma in the South Bronx, NY

Personal exposures to fine particulate matter air pollution (PM(2.5)), and to its traffic-related fraction, were investigated in a group of urban children with asthma. The relationships of personal and outdoor school-site measurements of PM(2.5) and elemental carbon (EC) were characterized for a total of 40 fifth-grade children. These students, from four South Bronx, NY schools, each carried air pollution monitoring equipment with them for 24 h per day for approximately 1 month. Daily EC concentrations were estimated using locally calibrated reflectance of the PM(2.5) samples. Personal EC concentration was more closely related to outdoor school-site EC (median subject-specific: r=0.64) than was personal PM(2.5) to school-site PM(2.5) concentration (median subject-specific: r=0.33). Regression models also showed a stronger, more robust association of school site with personal measurements for EC than those for PM(2.5). High traffic pollution exposure was found to coincide with the weekday early morning rush hour, with higher personal exposures for participants living closer to a highway (<500 ft). A significant linear relationship of home distance from a highway with personal EC pollution exposure was also found (up to 1000 ft). This supports the assumptions by previous epidemiological studies using distance from a highway as an index of traffic PM exposure. These results are also consistent with the assumption that traffic, and especially smoke emitted from diesel vehicles, is a significant contributor to personal PM exposure levels in children living in urban areas such as the South Bronx, NY.

Characterizing urban traffic exposures using transportation planning tools: an illustrated methodology for health researchers

Exposure to elevated levels of vehicular traffic has been associated with adverse cardiovascular and respiratory health effects in a range of populations, including children, the elderly, and individuals with pre-existing heart conditions, diabetes, obesity, and genetic susceptibilities. As these relationships become clearer, public health officials will need to have access to methods to identify areas of concern in terms of elevated traffic levels and susceptible populations. This paper briefly reviews current approaches for characterizing traffic exposure and then presents a detailed method that can be employed by public health officials and other researchers in performing screening assessments to define areas of potential concern within a particular locale and, with appropriate caveats, in epidemiologic studies examining traffic-related health impacts at the intra-urban scale. The method is based on two exposure parameters extensively used in numerous epidemiologic studies of traffic and health-proximity to high traffic roadways and overall traffic density. The method is demonstrated with publically available information on susceptible populations, traffic volumes, and Traffic Analysis Zones, a transportation planning tool long used by Metropolitan Planning Agencies and planners across the USA but presented here as a new application which can be used to spatially assess possible traffic-related impacts on susceptible populations. Recommendations are provided for the appropriate use of this methodology, along with its limitations.

Effects of exposure measurement error in the analysis of health effects from traffic-related air pollution

In large epidemiological studies, many researchers use surrogates of air pollution exposure such as geographic information system (GIS)-based characterizations of traffic or simple housing characteristics. It is important to evaluate quantitatively these surrogates against measured pollutant concentrations to determine how their use affects the interpretation of epidemiological study results. In this study, we quantified the implications of using exposure models derived from validation studies, and other alternative surrogate models with varying amounts of measurement error on epidemiological study findings. We compared previously developed multiple regression models characterizing residential indoor nitrogen dioxide (NO(2)), fine particulate matter (PM(2.5)), and elemental carbon (EC) concentrations to models with less explanatory power that may be applied in the absence of validation studies. We constructed a hypothetical epidemiological study, under a range of odds ratios, and determined the bias and uncertainty caused by the use of various exposure models predicting residential indoor exposure levels. Our simulations illustrated that exposure models with fairly modest R(2) (0.3 to 0.4 for the previously developed multiple regression models for PM(2.5) and NO(2)) yielded substantial improvements in epidemiological study performance, relative to the application of regression models created in the absence of validation studies or poorer-performing validation study models (e.g., EC). In many studies, models based on validation data may not be possible, so it may be necessary to use a surrogate model with more measurement error. This analysis provides a technique to quantify the implications of applying various exposure models with different degrees of measurement error in epidemiological research.

Transdisciplinary research strategies for understanding socially patterned disease: the Asthma Coalition on Community, Environment, and Social Stress (ACCESS) project as a case study

As we have seen a global increase in asthma in the past three decades it has also become clear that it is a socially patterned disease, based on demographic and socioeconomic indicators clustered by areas of residence. This trend is not readily explained by traditional genetic paradigms or physical environmental exposures when considered alone. This has led to consideration of the interplay among physical and psychosocial environmental hazards and the molecular and genetic determinants of risk (i.e., biomedical framing) within the broader socioenvironmental context including socioeconomic position as an upstream "cause of the causes" (i.e., ecological framing). Transdisciplinary research strategies or programs that embrace this complexity through a shared conceptual framework that integrates diverse discipline-specific theories, models, measures, and analytical methods into ongoing asthma research may contribute most significantly toward furthering our understanding of socially patterned disease. This paper provides an overview of a multilevel, multimethod longitudinal study, the Asthma Coalition on Community, Environment and Social Stress (ACCESS), as a case study to exemplify both the opportunities and challenges of transdisciplinary research on urban asthma expression in the United States.