Otitis media (OM) is one of the most common early childhood infections, resulting in an enormous economic burden to the health care system through unscheduled doctor visits and antibiotic prescriptions.
The objective of this study was to investigate the potential association between ambient air pollution exposure and emergency department (ED) visits for OM.
Ten years of ED data were obtained from Edmonton, Alberta, Canada, and linked to levels of air pollution: carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide, and particulate matter (PM) of median aerometric diameter
Cites: An Pediatr (Barc). 2004 Feb;60(2):133-814757016
Cites: Vaccine. 2008 Dec 23;26 Suppl 7:G5-1019094935
Air pollution can increase the symptoms of asthma and has an acute effect on the number of emergency room visits and hospital admissions because of asthma, but little is known about the effect of air pollution on the number of primary health care (PHC) visits for asthma.
To investigate the association between air pollution and the number of PHC visits for asthma in Scania, southern Sweden.
Data on daily PHC visits for asthma were obtained from a regional healthcare database in Scania, which covers approximately half a million people. Air pollution data from 2005 to 2010 were obtained from six urban background stations. We used a case-crossover study design and a distributed lag non-linear model in the analysis.
The air pollution levels were generally within the EU air quality guidelines. The mean number of daily PHC visits for asthma was 34. The number of PHC visits increased by 5% (95% confidence interval (CI): 3.91-6.25%) with every 10µg m(-3) increase in daily mean NO2 lag (0-15), suggesting that daily air pollution levels are associated with PHC visits for asthma.
Even though the air quality in Scania between 2005 and 2010 was within EU's guidelines, the number of PHC visits for asthma increased with increasing levels of air pollution. This suggests that as well as increasing hospital and emergency room visits, air pollution increases the burden on PHC due to milder symptoms of asthma.
In many metropolitan areas, traffic is the main source of air pollution. The high concentrations of pollutants in streets have the potential to affect human health. Therefore, estimation of air pollution at the street level is required for health impact assessment. This task has been carried out in many developed countries by a combination of air quality measurements and modeling. This study focuses on how to apply a dispersion model to cities in the developing world, where model input data and data from air quality monitoring stations are limited or of varying quality. This research uses the operational street pollution model (OSPM) developed by the National Environmental Research Institute in Denmark for a case study in Hanoi, the capital of Vietnam. OSPM predictions from five streets were evaluated against air pollution measurements of nitrogen oxides (NO(x)), sulfur dioxide (SO2), carbon monoxide (CO), and benzene (BNZ) that were available from previous studies. Hourly measurements and passive sample measurements collected over 3-week periods were compared with model outputs, applying emission factors from previous studies. In addition, so-called "backward calculations" were performed to adapt the emission factors for Hanoi conditions. The average fleet emission factors estimated can be used for emission calculations at other streets in Hanoi and in other locations in Southeast Asia with similar vehicle types. This study also emphasizes the need to further eliminate uncertainties in input data for the street-scale air pollution modeling in Vietnam, namely by providing reliable emission factors and hourly air pollution measurements of high quality.
Understanding the biogeochemical cycling of mercury is critical for explaining the presence of mercury in remote regions of the world, such as the Arctic and the Himalayas, as well as local concentrations. While we have good knowledge of present-day fluxes of mercury to the atmosphere, we have little knowledge of what emission levels were like in the past. Here we develop a trend of anthropogenic emissions of mercury to the atmosphere from 1850 to 2008-for which relatively complete data are available-and supplement that trend with an estimate of anthropogenic emissions prior to 1850. Global mercury emissions peaked in 1890 at 2600 Mg yr(-1), fell to 700-800 Mg yr(-1) in the interwar years, then rose steadily after 1950 to present-day levels of 2000 Mg yr(-1). Our estimate for total mercury emissions from human activities over all time is 350 Gg, of which 39% was emitted before 1850 and 61% after 1850. Using an eight-compartment global box-model of mercury biogeochemical cycling, we show that these emission trends successfully reproduce present-day atmospheric enrichment in mercury.
Cites: Environ Sci Technol. 2002 Jun 1;36(11):2303-1012075781
Cites: Chemosphere. 2002 Jul;48(1):51-712137057
Cites: Environ Sci Technol. 2003 Jan 1;37(1):22-3112542286
Cites: J Environ Monit. 2003 Dec;5(6):935-4914710936
Cites: Environ Sci Technol. 2006 Sep 1;40(17):5312-816999104
Cites: Environ Sci Technol. 2011 Mar 15;45(6):2042-721355558
Cites: Environ Sci Technol. 2008 Aug 15;42(16):5971-718767653
Cites: Environ Sci Technol. 2009 Apr 15;43(8):2983-819475981
Cites: Proc Natl Acad Sci U S A. 2009 Jun 2;106(22):8830-419451629
Cites: Environ Sci Technol. 2010 Apr 15;44(8):2918-2420345131
Cites: Environ Sci Technol. 2010 Nov 15;44(22):8574-8020973542
Cites: Environ Sci Technol. 2007 Nov 15;41(22):7632-818075067
An investigation of ambient levels and sources of volatile organic compounds (VOCs) and associated public health risks was carried out at two northern Alberta oil sands communities (Fort McKay and Fort McMurray located?30?km from oil sands development, respectively) for the period January 2010-March 2015. Levels of total detected VOCs were comparatively similar at both communities (Fort McKay: geometric mean?=?22.8?µg/m3, interquartile range, IQR?=?13.8-41?µg/m3); (Fort McMurray: geometric mean?=?23.3?µg/m3, IQR?=?12.0-41?µg/m3). In general, methanol (24%-50%), alkanes (26%-32%) and acetaldehyde (23%-30%) were the predominant VOCs followed by acetone (20%-24%) and aromatics (~9%). Mean and maximum ambient concentrations of selected hazardous VOCs were compared to health risk screening criteria used by United States regulatory agencies. The Positive matrix factorization (PMF) model was used to identify and apportion VOC sources at Fort McKay and Fort McMurray. Five sources were identified at Fort McKay, where four sources (oil sands fugitives, liquid/unburned fuel, ethylbenzene/xylene-rich and petroleum processing) were oil sands related emissions and contributed to 70% of total VOCs. At Fort McMurray six sources were identified, where local sources other than oil sands development were also observed. Contribution of aged air mass/regional transport including biomass burning emissions was ~30% of total VOCs at both communities. Source-specific carcinogenic and non-carcinogenic risk values were also calculated and were below acceptable and safe levels of risk, except for aged air mass/regional transport (at both communities), and ethylbenzene/xylene-rich (only at Fort McMurray).
Several studies from various countries within the last decade have shown that ambient air pollutants cause short-term health effects in lower concentrations than believed earlier. Obtaining reliable and comprehensive health and environmental data is difficult but is the basic prerequisite for these studies. Linkage of data on air pollution and on several health effects has been conducted in Helsinki since the late 1980s, using time series analysis. The uniform population, small socioeconomic differences, a practically free national health care system with high coverage and extensive health, pollution and meteorological data render such studies possible. These kinds of local studies are necessary because many confounders or modifiers cause problems for the generalization of results of studies from other locations. Internal standardization, recommendations and guidelines concerning the methodology of linkage studies are needed to save work and money and to ensure the reliability of results.
Land use regression (LUR) models predict spatial variation of ambient concentrations, but little is known about the validity in predicting personal exposures. In this study, the association of LUR modeled concentrations of PM2.5 components with measured personal concentrations was determined. The elements of interest were copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V) and zinc (Zn).
In Helsinki (Finland), Utrecht (the Netherlands) and Barcelona (Spain) five participants from urban background, five from suburban background and five from busy street sites were selected in each city (15 participants per city). Outdoor, indoor and personal 96-hour PM2.5 samples were collected by the participants over periods of two weeks in three different seasons (winter, summer and spring/autumn) and the overall average was calculated. Elemental composition was measured by ED-XRF spectrometry. The LUR models for the average ambient concentrations of each element were developed by the ESCAPE project.
LUR models predicted the within-city variation of average outdoor Cu and Fe concentrations moderately well (range in R(2) 27-67% for Cu and 24-54% for Fe). The outdoor concentrations of the other elements were not well predicted. The LUR modeled concentration only significantly correlated with measured personal Fe exposure in Utrecht and Ni and V in Helsinki. The LUR model predictions did not correlate with measured personal Cu exposure. After excluding observations with an indoor/outdoor ratio of >1.5, modeled Cu outdoor concentrations correlated with indoor concentrations in Helsinki and Utrecht and personal concentrations in Utrecht. The LUR model predictions were associated with measured outdoor, indoor and personal concentrations for all elements when the data for the three cities was pooled.
Within-city modeled variation of elemental composition of PM2.5 did not predict measured variation in personal exposure well.
The association of daily cardiac and respiratory admissions to 168 acute care hospitals in Ontario, Canada, with daily levels of particulate sulfates was examined over the 6-year period 1983-1988. Sulfate levels were recorded at nine monitoring stations in regions of southern and central Ontario spanned by three monitoring networks. A 13-micrograms/m3 increase in sulfates recorded on the day prior to admission (the 95th percentile) was associated with a 3.7% (p
Long-term exposure to ambient fine particles (PM2.)) has been shown to increase mortality. Variables measured on the same spatial scales of air pollution may confound associations, and so the authors' objectives were to evaluate the associations between PM2.5 and individual-level measures of smoking, obesity and sociodemographic status. The authors present an approach to evaluate the impact that uncontrolled confounding from smoking may have on associations between PM2.5 and mortality.
Individual-level behavioural and sociodemographic data were obtained from a 2003 national survey of 122,548 Canadians. Estimates of ground-level PM2.5 at a resolution of 10×10 km between 2001 and 2006 were derived from satellite remote sensing. Exposures were assigned to the residence of the participants at the time of the survey. Differences in the prevalence of smoking across concentrations of PM2.5 and RRs drawn from the literature were used to model the bias on rate ratios.
Participants in areas with higher concentrations of PM2.5 had a higher income and educational attainment, smoked less and were more likely immigrants. Smoking had a negative confounding effect on the associations between PM2.5) and mortality. To compensate for this bias, for a 10 µg/m3 increase in PM2.5, mortality from lung cancer and heart disease in the referent exposure group needed to be increased by 6.9% and 3.2%, respectively.
Associations were found between sociodemographic and lifestyle characteristics and PM2.5 at a resolution of 10×10 km. The authors present a model to adjust for uncontrolled confounding of smoking that can be readily adapted to exposures measured at different spatial resolutions.