Proximity of schools in Detroit, Michigan to automobile and truck traffic

Lipid Dysregulation Induced by Gasoline and Diesel Exhaust Exposure and the Interaction with Age

Limited knowledge exists regarding gasoline and diesel exhaust effects on lipid metabolism. This study collected gasoline and diesel exhaust under actual driving conditions and conducted inhalation exposure on male young and middle-aged C57BL/6J mice for 4 h/day for 5 days to simulate commuting exposure intensity. Additionally, PM2.5 from actual roadways, representing gasoline and diesel vehicles, was generated for exposure to human umbilical vein endothelial cells (HUVECs) and normal liver cells (LO2) for 24, 48, and 72 h to further investigate exhaust particle toxicity. Results showed that diesel exhaust reduced total cholesterol and low-density lipoprotein cholesterol levels in young mice, indicating disrupted lipid metabolism. Aspartate aminotransferase and alanine aminotransferase levels increased by 53.7% and 21.7%, respectively, suggesting potential liver injury. Diesel exhaust exposure decreased superoxide dismutase and increased glutathione peroxidase levels. Cell viability decreased, and reactive oxygen species levels increased in HUVECs and LO2 following exposure to exhaust particles, with dose- and time-dependent effects. Diesel exhaust particles exhibited more severe inhibition of cell proliferation and oxidative damage compared to gasoline exhaust particles. These findings provide novel evidence of the risk of disrupted lipid metabolism due to gasoline and diesel exhaust, emphasizing the toxicity of diesel exhaust.

Exposure to Primary Air Pollutants Generated by Highway Traffic and Daily Mortality Risk in Near-Road Communities: A Case-Crossover Study

Most epidemiologic studies fail to capture the impact of spatiotemporal fluctuations in traffic on exposure to traffic-related air pollutants in the near-road population. Using a case-crossover design and the Research LINE source (R-LINE) dispersion model with spatiotemporally resolved highway traffic data, we quantified associations between primary pollutants generated by highway traffic-particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5), oxides of nitrogen (NOx), and black carbon (BC)-and daily nonaccidental, respiratory, cardiovascular, and cerebrovascular mortality among persons who had resided within 1 km (0.6 mile) of major highways in the Puget Sound area of Washington State between 2009 and 2013. We estimated these associations using conditional logistic regression, adjusting for time-varying covariates. Although highly resolved modeled concentrations of PM2.5, NOx, and BC from highway traffic in the hours before death were used, we found no evidence of an association between mortality and the preceding 24-hour average PM2.5 exposure (odds ratio = 0.99, 95% confidence interval: 0.96, 1.02) or exposure during shorter averaging periods. This work did not support the hypothesis that mortality risk was meaningfully higher with greater exposures to PM2.5, NOx, and BC from highways in near-road populations, though we did incorporate a novel approach to estimate exposure to traffic-generated air pollution based on detailed traffic congestion data.

The impact of Traffic-Related air pollution on child and adolescent academic Performance: A systematic review

BACKGROUND

The negative health impacts of traffic-related air pollution (TRAP) have been investigated for many decades, however, less attention has been paid to the effect of TRAP on children's academic performance. Understanding the TRAP-academic performance relationship will assist in identifying mechanisms for improving students' learning and aid policy makers in developing guidance for protecting children in school environments.

METHODS

This systematic review assessed the relationship between TRAP and academic performance. Web of Science, PubMed, CINAHL, Medline, PsycINFO, SPORTDiscus, Scopus and ERIC databases were searched for relevant, peer reviewed, articles published in English. Articles assessing exposure to TRAP pollutants (through direct measurement, local air quality monitoring, modelling, or road proximity/density proxy measures) and academic performance (using standardised tests) in children and adolescents were included. Risk of bias was assessed within and between studies.

RESULTS

Of 3519 search results, 10 relevant articles were included. Nine studies reported that increased exposure to some TRAP was associated with poorer student academic performance. Study methodologies were highly heterogeneous and no consistent patterns of association between specific pollutants, age groups, learning domains, exposure windows, and exposure locations were established. There was a serious risk of bias within individual studies and confidence in the body of evidence was low.

CONCLUSIONS

This review found evidence suggestive of a negative association between TRAP and academic performance. However, the quality of this evidence was low. The existing body of literature is small, lacks the inclusion of high-quality exposure measures, and presents limitations in reporting. Future research should focus on using valid and reliable exposure measures, individual-level data, consistent controlling for confounders and longitudinal study designs.

Lead Emissions and Population Vulnerability in the Detroit Metropolitan Area, 2006-2013: Impact of Pollution, Housing Age and Neighborhood Racial Isolation and Poverty on Blood Lead in Children

This research investigates the relationships between airborne and depositional industrial lead emission concentrations modeled using Environmental Protection Agency’s (EPA’s) American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and childhood blood lead levels (BLL) in the Detroit Metropolitan Area (DMA) 2006–2013. Linear and mediation interaction regression models estimated the effects of older housing and airborne and depositional lead emission concentrations on black and white childhood BLLs, controlling for neighborhood levels of racial isolation and poverty—important social structures in the DMA. The results showed a direct relationship between airborne and depositional lead emissions and higher childhood BLL, after controlling for median housing age. Lead emissions also exacerbated the effect of older housing on black and white children’s BLLs (indirect relationship), after controlling for social structures. Findings from this research indicate that black and white children exposed to lead-based paint/pipes in older housing are further impacted by industrial lead pollution that may lead to permanent neurological damage.

The association between residential proximity to brownfield sites and high-traffic areas and measures of immunity

The mechanisms by which neighborhood environmental exposures influence health are poorly understood, although immune system dysregulation represents a potential biological pathway. While many neighborhood exposures have been investigated, there is little research on residential proximity to brownfield waste. Using biomarker data from 262 participants in the Detroit Neighborhood Health Study, we estimated the association between proximity to brownfields and heavy traffic and signal joint T-cell receptor excision circles (sjTRECs, a measure of naive T-cell production), C-reactive protein (CRP, a measure of systemic inflammation), and interleukin-6 (IL-6, a pro-inflammatory cytokine). We assessed residential proximity ≤200 m from brownfields and highways on all three biomarkers using multivariate regression. We demonstrated that living ≤200 m from a brownfield site was associated with a 0.30 (95% CI = 0.59, 0.02, p = 0.04) log_e-unit decrease in sjTRECs per million whole blood cells, as well as non-significantly elevated levels of CRP and IL-6. Heavy traffic was not associated with any biomarker. Persons living in close proximity to brownfield sites had significantly lower naive T-cell production, suggesting accelerated immune aging. Decreased T-cell production associated with brownfield proximity may be caused by toxicant exposure in brownfield sites, or may serve as a marker of other neighborhood stressors.

Toxic structures: Speculation and lead exposure in Detroit's single-family rental market

Foreclosure sales permitted investors to purchase large volumes of low-cost residential properties after the last financial crisis, reshaping patterns of property ownership in low-income housing markets across the US. This study links post-foreclosure property acquisitions by investor landlords to subsequent lead poisoning cases among children under age six living in Detroit, Michigan. We find that the odds of exhibiting elevated blood lead levels (≥5 μg/dL) are higher for children living in investor-owned homes purchased through tax foreclosure sale. These findings highlight the potential for property speculation in post-foreclosure housing markets to exacerbate severe and racialized burdens of excess lead toxicity in low-income communities.

The Association Between Obesity, Socio-Economic Status, and Neighborhood Environment: A Multi-Level Analysis of Spokane Public Schools

Socio economic inequities in obesity have been attributed to individuals' psychosocial and behavioral characteristics. School environment, where children spend a large part of their day, may play an important role in shaping their health. This study aims to assess whether prevalence of overweight and obesity among elementary school students was associated with the school's social and built environments. Analyses were based on 28 public elementary schools serving a total of 10,327 children in the city of Spokane, Washington. Schools were classified by percentage of students eligible for free and reduced meals (FRM). Crime rates, density of arterial roads, healthy food access, and walkability were computed in a one-mile walking catchment around schools to characterize their surrounding neighborhood. In the unadjusted multilevel logistic regression analyses, age, sex, percentage of students eligible for FRM, crime, walkability, and arterial road exposure were individually associated with the odds of being overweight or obese. In the adjusted model, the odds of being overweight or obese were higher with age, being male, and percentage of students eligible for FRM. The results call for policies and programs to improve the school environment, students' health, and safety conditions near schools.

Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol

INTRODUCTION

Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks.

METHODS AND ANALYSIS

We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km×5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices' populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case-control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland.Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype.

ETHICS AND DISSEMINATION

The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals.

Lead Emissions and Population Vulnerability in the Detroit (Michigan, USA) Metropolitan Area, 2006-2013: A Spatial and Temporal Analysis

Objective: The purpose of this research is to geographically model airborne lead emission concentrations and total lead deposition in the Detroit Metropolitan Area (DMA) from 2006 to 2013. Further, this study characterizes the racial and socioeconomic composition of recipient neighborhoods and estimates the potential for IQ (Intelligence Quotient) loss of children residing there. Methods: Lead emissions were modeled from emitting facilities in the DMA using AERMOD (American Meteorological Society/Environmental Protection Agency Regulatory Model). Multilevel modeling was used to estimate local racial residential segregation, controlling for poverty. Global Moran's I bivariate spatial autocorrelation statistics were used to assess modeled emissions with increasing segregation. Results: Lead emitting facilities were primarily located in, and moving to, highly black segregated neighborhoods regardless of poverty levels-a phenomenon known as environmental injustice. The findings from this research showed three years of elevated airborne emission concentrations in these neighborhoods to equate to a predicted 1.0 to 3.0 reduction in IQ points for children living there. Across the DMA there are many areas where annual lead deposition was substantially higher than recommended for aquatic (rivers, lakes, etc.) and terrestrial (forests, dunes, etc.) ecosystems. These lead levels result in decreased reproductive and growth rates in plants and animals, and neurological deficits in vertebrates. Conclusions: This lead-hazard and neighborhood context assessment will inform future childhood lead exposure studies and potential health consequences in the DMA.

Effects of particulate matter and antioxidant dietary intake on blood pressure

OBJECTIVES

We assessed 2 pathways through which dietary antioxidants may counter adverse effects of exposure to particulate matter less than 2.5 micrometers in diameter (PM2.5) on blood pressure (BP): main (compensatory) and modifying (protective) models.

METHODS

We used 2002 to 2003 data from the Detroit Healthy Environments Partnership community survey conducted with a multiethnic sample of adults (n = 347) in low- to moderate-income, predominantly Hispanic and non-Hispanic Black neighborhoods in Detroit, Michigan. We used generalized estimating equations to test the effects of ambient exposure to PM2.5 and dietary antioxidant intake on BP, with adjustment for multiple confounders.

RESULTS

Dietary antioxidant intake was inversely associated with systolic BP (b = -0.5; P < .05) and pulse pressure (b  = -0.6; P < .05) in neighborhoods closest to major sources of air pollutants. Adverse effects of PM2.5 remained significant after accounting for antioxidant intakes. Exploratory analyses suggested potential modifying effects of antioxidant intake on associations between ambient PM2.5 exposure and BP.

CONCLUSIONS

Interventions to improve access to antioxidant-rich foods in polluted urban areas may be protective of cardiovascular health. However, efforts to reduce PM2.5 exposure remain critical for cardiovascular health promotion.

High resolution spatial and temporal mapping of traffic-related air pollutants

Vehicle traffic is one of the most significant emission sources of air pollutants in urban areas. While the influence of mobile source emissions is felt throughout an urban area, concentrations from mobile emissions can be highest near major roadways. At present, information regarding the spatial and temporal patterns and the share of pollution attributable to traffic-related air pollutants is limited, in part due to concentrations that fall sharply with distance from roadways, as well as the few monitoring sites available in cities. This study uses a newly developed dispersion model (RLINE) and a spatially and temporally resolved emissions inventory to predict hourly PM_2.5 and NO_x concentrations across Detroit (MI, USA) at very high spatial resolution. Results for annual averages and high pollution days show contrasting patterns, the need for spatially resolved analyses, and the limitations of surrogate metrics like proximity or distance to roads. Data requirements, computational and modeling issues are discussed. High resolution pollutant data enable the identification of pollutant “hotspots”, “project-level” analyses of transportation options, development of exposure measures for epidemiology studies, delineation of vulnerable and susceptible populations, policy analyses examining risks and benefits of mitigation options, and the development of sustainability indicators integrating environmental, social, economic and health information.

Changes in air quality at near-roadway schools after a major freeway expansion in Las Vegas, Nevada

Near-roadway ambient black carbon (BC) and carbon monoxide (CO) concentrations were measured at two schools adjacent to a freeway and at an urban background school 2 km from the freeway to determine the change in concentrations attributable to vehicle emissions after the three-lane expansion of U.S. Highway 95 (US 95) in Las Vegas, Nevada. Between summer 2007 and summer 2008, average weekday small-vehicle volume increased by 40% +/- 2% (standard error). Average weekday large-vehicle volume decreased by 17% +/- 5%, due to a downturn in the economy and an associated decline in goods movement. Average vehicle speed increased from 58 to 69 mph, a 16% +/- 1% increase. The authors compared BC and CO concentrations in summer 2007 with those in summer 2008 to understand what effect the expansion of the freeway may have had on ambient concentrations: BC and CO were measured 17 m north of the freeway sound wall, CO was measured 20 m south of the sound wall, and BC was measured at an urban background site 2 km south of the freeway. Between summer 2007 and summer 2008, median BC decreased at the near-road site by 40% +/- 2% and also decreased at the urban background site by 24% +/- 4%, suggesting that much of the change was due to decreases in emissions throughout Las Vegas, rather than only on US 95. CO concentrations decreased by 14% +/- 2% and 10% +/- 3% at the two near-road sites. The decrease in BC concentrations after the expansion is likely due to the decrease in medium- and heavy-duty-vehicle traffic resulting from the economic recession. The decrease in CO concentrations may be a result of improved traffic flow, despite the increase in light-duty-vehicle traffic. Implications: Monitoring of BC and CO at near-road locations in Las Vegas demonstrated the impacts of changes in traffic volume and vehicle speed on near-road concentrations. However, urban-scale declines in concentrations were larger than near-road changes due to the impacts of the economic recession that occurred contemporaneously with the freeway expansion.

Spatial Resolution Requirements for Traffic-Related Air Pollutant Exposure Evaluations

Vehicle emissions represent one of the most important air pollution sources in most urban areas, and elevated concentrations of pollutants found near major roads have been associated with many adverse health impacts. To understand these impacts, exposure estimates should reflect the spatial and temporal patterns observed for traffic-related air pollutants. This paper evaluates the spatial resolution and zonal systems required to estimate accurately intraurban and near-road exposures of traffic-related air pollutants. The analyses use the detailed information assembled for a large (800 km²) area centered on Detroit, Michigan, USA. Concentrations of nitrogen oxides (NO_x) due to vehicle emissions were estimated using hourly traffic volumes and speeds on 9,700 links representing all but minor roads in the city, the MOVES2010 emission model, the RLINE dispersion model, local meteorological data, a temporal resolution of 1 hr, and spatial resolution as low as 10 m. Model estimates were joined with the corresponding shape files to estimate residential exposures for 700,000 individuals at property parcel, census block, census tract, and ZIP code levels. We evaluate joining methods, the spatial resolution needed to meet specific error criteria, and the extent of exposure misclassification. To portray traffic-related air pollutant exposure, raster or inverse distance-weighted interpolations are superior to nearest neighbor approaches, and interpolations between receptors and points of interest should not exceed about 40 m near major roads, and 100 m at larger distances. For census tracts and ZIP codes, average exposures are overestimated since few individuals live very near major roads, the range of concentrations is compressed, most exposures are misclassified, and high concentrations near roads are entirely omitted. While smaller zones improve performance considerably, even block-level data can misclassify many individuals. To estimate exposures and impacts of traffic-related pollutants accurately, data should be geocoded or estimated at the most-resolved spatial level; census tract and larger zones have little if any ability to represent intraurban variation in traffic-related air pollutant concentrations. These results are based on one of the most comprehensive intraurban modeling studies in the literature and results are robust. Recommendations address the value of dispersion models to portray spatial and temporal variation of air pollutants in epidemiology and other studies; techniques to improve accuracy and reduce the computational burden in urban scale modeling; the necessary spatial resolution for health surveillance, demographic, and pollution data; and the consequences of low resolution data in terms of exposure misclassification.

Relationship of racial composition and cancer risks from air toxics exposure in Memphis, Tennessee, U.S.A

African Americans in the U.S. often live in poverty and segregated urban neighborhoods, many of which have dense industrial facilities resulting in high exposure to harmful air toxics. This study aims to explore the relationship between racial composition and cancer risks from air toxics exposure in Memphis/Shelby County, Tennessee, U.S.A. Air toxics data were obtained from 2005 National Air Toxics Assessment (NATA), and the demographic data, including racial composition, were extracted from the 2000 United States Census. The association was examined using multivariable geographically weighted regression (GWR) analysis. The risk difference between African American and White concentrated areas was defined as the absolute disparity, and the percent difference as the relative disparity. GWR analyses show that cancer risks increase with respect to increasing percent of African Americans at the census tract level. Individuals in African American concentrated tracts bear 6% more cancer risk burden than in White concentrated tracts. The distribution of major roads causes the largest absolute disparity and the distribution of industrial facilities causes the largest relative disparity. Effective strategies for reduction in environmental disparity should especially target sources of large absolute disparities.

Proximity of US schools to major roadways: a nationwide assessment

Long-term exposure to traffic pollution has been associated with adverse health outcomes in children and adolescents. A significant number of schools may be located near major roadways, potentially exposing millions of children to high levels of traffic pollution, but this hypothesis has not been evaluated nationally. We obtained data on the location and characteristics of 114,644 US public and private schools, grades prekindergarten through 12, and calculated their distance to the nearest major roadway. In 2005-2006, 3.2 million students (6.2%) attended 8,424 schools (7.3%) located within 100 m of a major roadway, and an additional 3.2 million (6.3%) students attended 8,555 (7.5%) schools located 100-250 m from a major roadway. Schools serving predominantly Black students were 18% (95% CI, 13-23%) more likely to be located within 250 m of a major roadway. Public schools eligible for Title I programs and those with a majority of students eligible for free/reduced price meals were also more likely to be near major roadways. In conclusion, 6.4 million US children attended schools within 250 m of a major roadway and were likely exposed to high levels of traffic pollution. Minority and underprivileged children were disproportionately affected, although some results varied regionally.

Evaluating socioeconomic and racial differences in traffic-related metrics in the United States using a GIS approach

Previous studies have reported that lower-income and minority populations are more likely to live near major roads. This study quantifies associations between socioeconomic status, racial/ethnic variables, and traffic-related exposure metrics for the United States. Using geographic information systems (GIS), traffic-related exposure metrics were represented by road and traffic densities at the census tract level. Spearman's correlation coefficients estimated relationships between socio-demographic variables and traffic-related exposure metrics, and ANOVA was performed to test for significant differences in socio-demographic variables for census tracts with low and high traffic-related metrics. For all census tracts in the United States, %Whites, %Blacks, and %Hispanics (percent of tract population) had correlation coefficients greater than 0.38 and 0.16 with road density and traffic density, respectively. Regions and states had correlation coefficients as high as 0.78. Compared with tracts with low road and traffic densities (<25th percentile), tracts with high densities (>75th percentile) had values of %Blacks and %Hispanics that were more than twice as high, 20% greater poverty levels, and one-third fewer White residents. Census tracts that had mid-level values for road and traffic densities had the most affluent characteristics. Results suggest that racial/ethnic and socioeconomic disparities exist on national level with respect to lower-income and minority populations living near high traffic and road density areas.

Point source modeling of matched case-control data with multiple disease subtypes

In this paper, we propose nonlinear distance-odds models investigating elevated odds around point sources of exposure, under a matched case-control design where there are subtypes within cases. We consider models analogous to the polychotomous logit models and adjacent-category logit models for categorical outcomes and extend them to the nonlinear distance-odds context. We consider multiple point sources as well as covariate adjustments. We evaluate maximum likelihood, profile likelihood, iteratively reweighted least squares, and a hierarchical Bayesian approach using Markov chain Monte Carlo techniques under these distance-odds models. We compare these methods using an extensive simulation study and show that with multiple parameters and a nonlinear model, Bayesian methods have advantages in terms of estimation stability, precision, and interpretation. We illustrate the methods by analyzing Medicaid claims data corresponding to the pediatric asthma population in Detroit, Michigan, from 2004 to 2006.

Proximity of public elementary schools to major roads in Canadian urban areas

BACKGROUND

Epidemiologic studies have linked exposure to traffic-generated air and noise pollution with a wide range of adverse health effects in children. Children spend a large portion of time at school, and both air pollution and noise are elevated in close proximity to roads, so school location may be an important determinant of exposure. No studies have yet examined the proximity of schools to major roads in Canadian cities.

METHODS

Data on public elementary schools in Canada's 10 most populous cities were obtained from online databases. School addresses were geocoded and proximity to the nearest major road, defined using a standardized national road classification scheme, was calculated for each school. Based on measurements of nitrogen oxide concentrations, ultrafine particle counts, and noise levels in three Canadian cities we conservatively defined distances < 75 m from major roads as the zone of primary interest. Census data at the city and neighborhood levels were used to evaluate relationships between school proximity to major roads, urban density, and indicators of socioeconomic status.

RESULTS

Addresses were obtained for 1,556 public elementary schools, 95% of which were successfully geocoded. Across all 10 cities, 16.3% of schools were located within 75 m of a major road, with wide variability between cities. Schools in neighborhoods with higher median income were less likely to be near major roads (OR per $20,000 increase: 0.81; 95% CI: 0.65, 1.00), while schools in densely populated neighborhoods were more frequently close to major roads (OR per 1,000 dwellings/km²: 1.07; 95% CI: 1.00, 1.16). Over 22% of schools in the lowest neighborhood income quintile were close to major roads, compared to 13% of schools in the highest income quintile.

CONCLUSIONS

A substantial fraction of students at public elementary schools in Canada, particularly students attending schools in low income neighborhoods, may be exposed to elevated levels of air pollution and noise while at school. As a result, the locations of schools may negatively impact the healthy development and academic performance of a large number of Canadian children.

Association of daily asthma emergency department visits and hospital admissions with ambient air pollutants among the pediatric Medicaid population in Detroit: time-series and time-stratified case-crossover analyses with threshold effects

BACKGROUND

Asthma morbidity has been associated with ambient air pollutants in time-series and case-crossover studies. In such study designs, threshold effects of air pollutants on asthma outcomes have been relatively unexplored, which are of potential interest for exploring concentration-response relationships.

METHODS

This study analyzes daily data on the asthma morbidity experienced by the pediatric Medicaid population (ages 2-18 years) of Detroit, Michigan and concentrations of pollutants fine particles (PM2.5), CO, NO2 and SO2 for the 2004-2006 period, using both time-series and case-crossover designs. We use a simple, testable and readily implementable profile likelihood-based approach to estimate threshold parameters in both designs.

RESULTS

Evidence of significant increases in daily acute asthma events was found for SO2 and PM2.5, and a significant threshold effect was estimated for PM2.5 at 13 and 11 μg m(-3) using generalized additive models and conditional logistic regression models, respectively. Stronger effect sizes above the threshold were typically noted compared to standard linear relationship, e.g., in the time series analysis, an interquartile range increase (9.2 μg m(-3)) in PM2.5 (5-day-moving average) had a risk ratio of 1.030 (95% CI: 1.001, 1.061) in the generalized additive models, and 1.066 (95% CI: 1.031, 1.102) in the threshold generalized additive models. The corresponding estimates for the case-crossover design were 1.039 (95% CI: 1.013, 1.066) in the conditional logistic regression, and 1.054 (95% CI: 1.023, 1.086) in the threshold conditional logistic regression.

CONCLUSION

This study indicates that the associations of SO2 and PM2.5 concentrations with asthma emergency department visits and hospitalizations, as well as the estimated PM2.5 threshold were fairly consistent across time-series and case-crossover analyses, and suggests that effect estimates based on linear models (without thresholds) may underestimate the true risk.

Cancer risk from exposure to hazardous air pollutants: spatial and social inequities in Tampa Bay, Florida

Recent environmental justice studies have emphasized the growing need to analyze the health impacts of disproportionate exposure to multiple pollution sources and incorporate geostatistical techniques that are suitable for analyzing spatial data. These objectives are addressed in a case study that evaluates spatial and social inequities in potential cancer risk from inhalation exposure to hazardous air pollutants (HAPs) from four types of emission sources in the Tampa Bay Metropolitan Statistical Area, Florida. This study utilizes modeled estimates of lifetime cancer risk from the 1999 National-Scale Air Toxics Assessment and socio-demographic data from the 2000 US Census. Statistical analyses are based on conventional multiple regression and locally derived spatial regression models that account for residual autocorrelation. Race, ethnicity, and home ownership are found to be significant predictors of cancer risk from ambient exposure to all four HAP source categories, after controlling for other relevant explanatory factors and spatial dependence in the data.

Characterization of coarse particulate matter in school gyms

We investigated the mass concentration, mineral composition and morphology of particles resuspended by children during scheduled physical education in urban, suburban and rural elementary school gyms in Prague (Czech Republic). Cascade impactors were deployed to sample the particulate matter. Two fractions of coarse particulate matter (PM(10-2.5) and PM(2.5-1.0)) were characterized by gravimetry, energy dispersive X-ray spectrometry and scanning electron microscopy. Two indicators of human activity, the number of exercising children and the number of physical education hours, were also recorded. Lower mass concentrations of coarse particulate matter were recorded outdoors (average PM(10-2.5) 4.1-7.4 μg m(-3) and PM(2.5-1.0) 2.0-3.3 μg m(-3)) than indoors (average PM(10-2.5) 13.6-26.7 μg m(-3) and PM(2.5-1.0) 3.7-7.4 μg m(-3)). The indoor concentrations of coarse aerosol were elevated during days with scheduled physical education with an average indoor-outdoor (I/O) ratio of 2.5-16.3 for the PM(10-2.5) and 1.4-4.8 for the PM(2.5-1.0) values. Under extreme conditions, the I/O ratios reached 180 (PM(10-2.5)) and 19.1 (PM(2.5-1.0)). The multiple regression analysis based on the number of students and outdoor coarse PM as independent variables showed that the main predictor of the indoor coarse PM concentrations is the number of students in the gym. The effect of outdoor coarse PM was weak and inconsistent. The regression models for the three schools explained 60-70% of the particular dataset variability. X-ray spectrometry revealed 6 main groups of minerals contributing to resuspended indoor dust. The most abundant particles were those of crustal origin composed of Si, Al, O and Ca. Scanning electron microscopy showed that, in addition to numerous inorganic particles, various types of fibers and particularly skin scales make up the main part of the resuspended dust in the gyms. In conclusion, school gyms were found to be indoor microenvironments with high concentrations of coarse particulate matter, which can contribute to increased short-term inhalation exposure of exercising children.

Indoor and outdoor sources of size-resolved mass concentration of particulate matter in a school gym-implications for exposure of exercising children

INTRODUCTION

It has been noticed many times that schools are buildings with high levels of particulate matter concentrations. Several authors documented that concentrations of particulate matter in indoor school microenvironments exceed limits recommended by WHO namely when school buildings are situated near major roads with high traffic densities. In addition, exercise under conditions of high particulate concentrations may increase the adverse health effects, as the total particle deposition increases in proportion to minute ventilation, and the deposition fraction nearly doubles from rest to intense exercise.

SITE AND METHODS

Mass concentrations of size-segregated aerosol were measured simultaneously in an elementary school gym and an adjacent outdoor site in the central part of Prague by two pairs of collocated aerosol monitors-a fast responding photometer DusTrak and a five stage cascade impactor. To encompass seasonal and annual differences, 89 days of measurements were performed during ten campaigns between 2005 and 2009.

RESULTS AND DISCUSSION

The average (all campaigns) outdoor concentration of PM(2.5) (28.3 μg m(-3)) measured by the cascade impactors was higher than the indoor value (22.3 μg m(-3)) and the corresponding average from the nearest fixed site monitor (23.6 μg m(-3)). Indoor and outdoor PM(2.5) concentrations exceeded the WHO recommended 24-h limit in 42% and 49% of the days measured, respectively. The correlation coefficient (r) between corresponding outdoor and indoor aerosol sizes increased with decreasing aerodynamic diameter of the collected particles (r = 0.32-0.87), suggesting a higher infiltration rate of fine and quasi-ultrafine particles. Principal component analysis revealed five factors explaining more than 82% of the data variability. The first two factors reflected a close association between outdoor and indoor fine and quasi-ultrafine particles confirming the hypothesis of high infiltration rate of particles from outdoors. The third factor indicated that human activity is the main source of indoor emission of coarse particles. The fourth factor involved only outdoor variables showing the resuspension of coarse ambient aerosol on dry and warm days without its seeming effect on the indoor coarse PM levels. Having in mind that high concentrations of both fine and coarse aerosol were frequently observed in the studied space, our results suggest that indoor exercise in polluted urbanized areas may increase the overall exposure and thus represent a potential health risk to young individuals during physical education at schools.

Spatial analysis and land use regression of VOCs and NO(2) from school-based urban air monitoring in Detroit/Dearborn, USA

Passive ambient air sampling for nitrogen dioxide (NO(2)) and volatile organic compounds (VOCs) was conducted at 25 school and two compliance sites in Detroit and Dearborn, Michigan, USA during the summer of 2005. Geographic Information System (GIS) data were calculated at each of 116 schools. The 25 selected schools were monitored to assess and model intra-urban gradients of air pollutants to evaluate impact of traffic and urban emissions on pollutant levels. Schools were chosen to be statistically representative of urban land use variables such as distance to major roadways, traffic intensity around the schools, distance to nearest point sources, population density, and distance to nearest border crossing. Two approaches were used to investigate spatial variability. First, Kruskal-Wallis analyses and pairwise comparisons on data from the schools examined coarse spatial differences based on city section and distance from heavily trafficked roads. Secondly, spatial variation on a finer scale and as a response to multiple factors was evaluated through land use regression (LUR) models via multiple linear regression. For weeklong exposures, VOCs did not exhibit spatial variability by city section or distance from major roads; NO(2) was significantly elevated in a section dominated by traffic and industrial influence versus a residential section. Somewhat in contrast to coarse spatial analyses, LUR results revealed spatial gradients in NO(2) and selected VOCs across the area. The process used to select spatially representative sites for air sampling and the results of coarse and fine spatial variability of air pollutants provide insights that may guide future air quality studies in assessing intra-urban gradients.