Using air pollution based community clusters to explore air pollution health effects in children

To study respiratory health effects of long-term exposure to ambient air pollutant mixture, we observed 7058 school children 5-16 years of age living in the four Chinese cities of Lanzhou, Chongqing, Wuhan, and Guangzhou. These children were enrolled from elementary schools located in eight districts, one urban district and one suburban district in each of the above cities. Ambient levels of PM(2.5), PM(10-2.5), total suspended particles (TSP), SO(2), and NO(x) were measured in these districts from 1993 to 1996. Based on a cluster analysis of arithmetic mean concentrations of PM(2.5), PM(10-2.5), (TSP-PM(10)), SO(2), and NO(x), we classified these children into four ordinal categories of exposure to ambient air pollutant mixtures. We tested for exposure-response relationships using logistic regression models, controlling for relevant covariates. We observed monotonic, positive relationships of exposure to the pollutant mixture with prevalence rates of cough with phlegm and wheeze. Other outcomes were not associated with the exposure in a monotonic exposure-response pattern. Even so, odds ratios for cough, phlegm, bronchitis, and asthma in the higher exposure district clusters were all higher than in the lowest exposure district cluster. We found evidence that exposure to the pollutant mixtures had adverse effects on children living in the four Chinese cities.     

Driving violations and health promotion behaviors among undergraduate students: Self-report of on-road behavior

Introduction: The purposes of this study are to characterize Israeli undergraduate students' driving violations in the terms of problem behavior theory and to identify whether there is any relationship between driving violations and health risk behaviors, daring behaviors, excitement seeking, and health promotion behaviors.

Methods: This study is based on a structured self-reported anonymous questionnaire distributed to undergraduate students in an academic institution. The sample included 533 undergraduate students (374 females and 159 males). The mean age was 23.4 (SD = 1.4, range = 5).

Results: A higher prevalence of self-reported driving violations was found among males in comparison to females. All substance use measures were positively related to driving violations; for example, use of cigarettes (OR = 4.287, P <.001) and water pipes (odds ratio [OR] = 3.000, P <.001) as well as binge drinking (OR = 5.707, P <.001) and regular cannabis smoking (OR = 5.667, P <.001) raise the probability of committing rare driving violations. The strongest predictive factors for the frequent driving violations group were alcohol consumption–related variables: binge drinking (OR = 2.560, P <.01) and drunkenness (OR = 2.284, P <.05). Strong odd ratios were also found between the frequent driving violations group and selling or dealing drugs (12.143, P <.001), and stealing something valuable (13.680, P <.001). The strongest predicted variable for the rare driving violations group was physical confrontation due to verbal disagreement (3.439, P <.05) and the concept that selling or dealing drugs is socially acceptable (2.521, P <.05). The probability of executing rare driving violations was higher for subjects who reported intense physical workout regimens (OR = 1.638, P <.05).

Conclusions: Problem behavior theory succeeded in explaining health risk behavior and driving violations. This study shows that bachelors tend to be more involved in risk behaviors, such as substance use, excitement-seeking behaviors, and daring behaviors and are active physically and thus constitute a risk group for driving violations. As such, intervention resources should be directed toward this group.     

A comparison study of aerosol emissions sources in two receptor sites in Salvador (Brazil) city

Total suspended particles (TSP) and metal concentrations were determined in Salvador, Bahia, Brazil at two sites: a residential area and a residential‐commercial‐industrial neighborhood. The determined metals associated with atmospheric aerosols include Al, Ca, Cr, Cd, Fe, Mg, Mn, V, Zn and Na. Factor analysis was employed to identify the major atmospheric deposition sources. At one site the data were best represented by two sources: resuspended soil + marine aerosol and vehicular emissions, which represents 93% of the total system variance. In the second one the data were best represented by three sources: resuspended soil, metallurgy and building construction, which represents 79% of the total system variance.     

Concentrations and source characteristics of airborne carbonyl compounds measured outside urban residences

This paper presents the analysis of ambient air concentrations of 10 carbonyl compounds (aldehydes and ketones) measured in the yards of 87 residences in the city of Elizabeth, NJ, throughout 1999-2001. Most of these residences were measured twice in different seasons; the sampling duration was 48 hr each time. The authors observed higher concentrations for most of the measured carbonyl compounds on warmer days, reflecting larger contributions of photochemical reactions on warmer days. The estimated contributions of photochemical production varied substantially across the measured carbonyl compounds and could be as high as 60%. Photochemical activity, however, resulted in a net loss for formaldehyde. The authors used stepwise multiple linear regression models to evaluate the impact of traffic sources and meteorological conditions on carbonyl concentrations using the data collected on colder days (with lower photochemical activities). They found that the concentrations of formaldehyde, acetaldehyde, acrolein, propionaldehyde, crotonaldehyde, benzaldehyde, glyoxal, and methylglyoxal significantly decreased with increasing distance between a measured residence and one or more major roadways. They also found significant negative associations between concentrations for most of the measured carbonyl compounds and each of the following meteorological parameters: mixing height, wind speed, and precipitation.     

Spatial/temporal variations of elemental carbon, organic carbon, and trace elements in PM10 and the impact of land-use patterns on community air pollution in Paterson, NJ

An urban community PM10 (particulate matter < or = 10 microm in aerodynamic diameter) air pollution study was conducted in Paterson, NJ, a mixed land-use community that is interspersed with industrial, commercial, mobile, and residential land-use types. This paper examines (1) the spatial/temporal variation of PM10, elemental carbon (EC), organic carbon (OC), and nine elements; and (2) the impact of land-use type on those variations. Air samples were collected from three community-oriented locations in Paterson that attempted to capture industrial, commercial, and mobile source-dominated emissions. Sampling was conducted for 24 hr every 6 days from November 2005 through December 2006. Samples were concurrently collected at the New Jersey Department of Environmental Protection-designated air toxics background site in Chester, NJ. PM10 mass, EC, OC, and nine elements (Ca, Cu, Fe, Pb, Mn, Ni, S, Ti, and Zn) that had more than 50% of samples above detection and known sources or are toxic were selected for spatial/temporal analysis in this study. The concentrations of PM10, EC, OC, and eight elements (except S) were significantly higher in Paterson than in Chester (P < 0.05). The concentrations of these elements measured in Paterson were also found to be higher during winter than the other three seasons (except S), and higher on weekdays than on weekends (except Pb). The concentrations of EC, Cu, Fe, and Zn at the commercial site in Paterson were significantly higher than the industrial and mobile sites; however, the other eight species were not significantly different within the city (P > 0.05). These results indicated that anthropogenic sources of air pollution were present in Paterson. The source apportionment confirmed the impact of vehicular and industrial emissions on the PM10 ambient air pollution in Paterson. The multiple linear regression analysis showed that categorical land-use type was a significant predictor for all air pollution levels, explaining up to 42% of the variability in concentration by land-use type only.