Windsor, Ontario exposure assessment study: design and methods validation of personal, indoor, and outdoor air pollution monitoring

The Windsor, Ontario Exposure Assessment Study evaluated the contribution of ambient air pollutants to personal and indoor exposures of adults and asthmatic children living in Windsor, Ontario, Canada. In addition, the role of personal, indoor, and outdoor air pollution exposures upon asthmatic children's respiratory health was assessed. Several active and passive sampling methods were applied, or adapted, for personal, indoor, and outdoor residential monitoring of nitrogen dioxide, volatile organic compounds, particulate matter (PM; PM-2.5 pm [PM2.5] and < or =10 microm [PM10] in aerodynamic diameter), elemental carbon, ultrafine particles, ozone, air exchange rates, allergens in settled dust, and particulate-associated metals. Participants completed five consecutive days of monitoring during the winter and summer of 2005 and 2006. During 2006, in addition to undertaking the air pollution measurements, asthmatic children completed respiratory health measurements (including peak flow meter tests and exhaled breath condensate) and tracked respiratory symptoms in a diary. Extensive quality assurance and quality control steps were implemented, including the collocation of instruments at the National Air Pollution Surveillance site operated by Environment Canada and at the Michigan Department of Environmental Quality site in Allen Park, Detroit, MI. During field sampling, duplicate and blank samples were also completed and these data are reported. In total, 50 adults and 51 asthmatic children were recruited to participate, resulting in 922 participant days of data. When comparing the methods used in the study with standard reference methods, field blanks were low and bias was acceptable, with most methods being within 20% of reference methods. Duplicates were typically within less than 10% of each other, indicating that study results can be used with confidence. This paper covers study design, recruitment, methodology, time activity diary, surveys, and quality assurance and control results for the different methods used.     

Aquatic Nuisance Species in the Great Lakes and Mississippi River Basin—A Risk Assessment in Support of GLMRIS

Environmental Management - The U.S. Army Corps of Engineers is conducting the Great Lakes and Mississippi River Interbasin Study to identify the highest risk aquatic nuisance species currently...     

Regional Transport and Urban Contributions to Fine Particle Concentrations in Southeastern Canada

Abstract

The impact of various atmospheric transport directions on ambient fine particle (PM_2.5) concentrations at several sites in southeastern Canada was estimated (for May-September) using back-trajectory analysis. Three-day back trajectories (four per day) were paired with 6-hr average PM_2.5 mass concentrations measured using tapered element oscillating microbalances (TEOM). PM_2.5 concentrations at rural locations in the region were affected by nonlocal sources originating in both Canada and the United States. Comparison of sites revealed that, on average, the local contribution to total PM_2.5 in the greater Toronto area (GTA) is approximately 30–35%. At each location, average PM_2.5 concentrations under south/southwesterly flow conditions were 2–4 times higher than under the corresponding northerly flow conditions. The chemical composition of both urban and rural PM_2.5 was determined during two separate 2-week spring/summer measurement campaigns. Components identified included SO₄ ^2?, NO₃ ^?, NH₄+, black carbon and organic carbon (OC), and trace elements. Higher particle mass at the urban Toronto site was composed of a higher proportion of all components. However, black carbon, NO₃ ^?, NaCl, and trace elements were found to be the most enriched over the rural/regional background levels.     

Managing an Endangered Asian Bovid in an Australian National Park: The Role and Limitations of Ecological-Economic Models in Decision-Making

Should north Australia’s extensive populations of feral animals be eradicated for conservation, or exploited as a rare opportunity for Indigenous enterprise in remote regions? We examine options for a herd of banteng, a cattle species endangered in its native Asian range but abundant in Garig Gunak Barlu National Park, an Aboriginal land managed jointly by traditional owners and a conservation agency in the Northern Territory of Australia. We reflect on the paradoxes that arise when trying to deal effectively with such complex and contested issues in natural resource management using decision-support tools (ecological-economic models), by identifying the trade-offs inherent in protecting values whilst also providing incomes for Indigenous landowners.     

A statistical assessment of saturation and mobile sampling strategies to estimate long-term average concentrations across urban areas

The objectives of this study were: (1) to quantify the errors associated with saturation air quality monitoring in estimating the long-term (i.e., annual and 5 yr) mean at a given site from four 2-week measurements, once per season; and (2) to develop a sampling strategy to guide the deployment of mobile air quality facilities for characterizing intraurban gradients of air pollutants, that is, to determine how often a given location should be visited to obtain relatively accurate estimates of the mean air pollutant concentrations. Computer simulations were conducted by randomly sampling ambient monitoring data collected in six Canadian cities at a variety of settings (e.g., population-based sites, near-roadway sites). The 5-yr (1998-2002) dataset consisted of hourly measurements of nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), sulfur dioxide (SO2), coarse particulate matter (PM10), fine particulate matter (PM2.5), and CO. The strategy of randomly selecting one 2-week measurement per season to determine the annual or long-term average concentration yields estimates within 30% of the true value 95% of the time for NO2, PM10 and NOx. Larger errors, up to 50%, are expected for NO, SO2, PM2.5, and CO. Combining concentrations from 85 random 1-hr visits per season provides annual and 5-yr average estimates within 30% of the true value with good confidence. Overall, the magnitude of error in the estimates was strongly correlated with the variability of the pollutant. A better estimation can be expected for pollutants known to be less temporally variable and/or over geographic areas where concentrations are less variable. By using multiple sites located in different settings, the relationships determined for estimation error versus number of measurement periods used to determine long-term average are expected to realistically portray the true distribution. Thus, the results should be a good indication of the potential errors one could expect in a variety of different cities, particularly in more northern latitudes.     

Impacts of hunting on tropical forests in Southeast Asia

Although deforestation and forest degradation have long been considered the most significant threats to tropical biodiversity, across Southeast Asia (Northeast India, Indochina, Sundaland, Philippines) substantial areas of natural habitat have few wild animals (>1 kg), bar a few hunting‐tolerant species. To document hunting impacts on vertebrate populations regionally, we conducted an extensive literature review, including papers in local journals and reports of governmental and nongovernmental agencies. Evidence from multiple sites indicated animal populations declined precipitously across the region since approximately 1980, and many species are now extirpated from substantial portions of their former ranges. Hunting is by far the greatest immediate threat to the survival of most of the region's endangered vertebrates. Causes of recent overhunting include improved access to forests and markets, improved hunting technology, and escalating demand for wild meat, wildlife‐derived medicinal products, and wild animals as pets. Although hunters often take common species, such as pigs or rats, for their own consumption, they take rarer species opportunistically and sell surplus meat and commercially valuable products. There is also widespread targeted hunting of high‐value species. Consequently, as currently practiced, hunting cannot be considered sustainable anywhere in the region, and in most places enforcement of protected‐area and protected‐species legislation is weak. The international community's focus on cross‐border trade fails to address overexploitation of wildlife because hunting and the sale of wild meat is largely a local issue and most of the harvest is consumed in villages, rural towns, and nearby cities. In addition to improved enforcement, efforts to engage hunters and manage wildlife populations through sustainable hunting practices are urgently needed. Unless there is a step change in efforts to reduce wildlife exploitation to sustainable levels, the region will likely lose most of its iconic species, and many others besides, within the next few years.     

Strength of evidence for density dependence in abundance time series of 1198 species

Population limitation is a fundamental tenet of ecology, but the relative roles of exogenous and endogenous mechanisms remain unquantified for most species. Here we used multi-model inference (MMI), a form of model averaging, based on information theory (Akaike's Information Criterion) to evaluate the relative strength of evidence for density-dependent and density-independent population dynamical models in long-term abundance time series of 1198 species. We also compared the MMI results to more classic methods for detecting density dependence: Neyman-Pearson hypothesis-testing and best-model selection using the Bayesian Information Criterion or cross-validation. Using MMI on our large database, we show that density dependence is a pervasive feature of population dynamics (median MMI support for density dependence = 74.7-92.2%), and that this holds across widely different taxa. The weight of evidence for density dependence varied among species but increased consistently, with the number of generations monitored. Best-model selection methods yielded similar results to MMI (a density-dependent model was favored in 66.2-93.9% of species time series), while the hypothesis-testing methods detected density dependence less frequently (32.6-49.8%). There were no obvious differences in the prevalence of density dependence across major taxonomic groups under any of the statistical methods used. These results underscore the value of using multiple modes of analysis to quantify the relative empirical support for a set of working hypotheses that encompass a range of realistic population dynamical behaviors.     

A land use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Ontario, Canada

This paper reports on the development of a land use regression (LUR) model for predicting the intraurban variation of traffic-related air pollution in Hamilton, Ontario, Canada, an industrial city at the western end of Lake Ontario. Although land use regression has been increasingly used to characterize exposure gradients within cities, research to date has yet to test whether this method can produce reliable estimates in an industrialized location. Ambient concentrations of nitrogen dioxide (NO2) were measured for a 2-week period in October 2002 at > 100 locations across the city and subsequently at 30 of these locations in May 2004 to assess seasonal effects. Predictor variables were derived for land use types, transportation, demography, and physical geography using geographic information systems. The LUR model explained 76% of the variation in NO2. Traffic density, proximity to a highway, and industrial land use were all positively correlated with NO2 concentrations, whereas open land use and distance from the lake were negatively correlated with NO2. Locations downwind of a major highway resulted in higher NO2 levels. Cross-validation of the results confirmed model stability over different seasons. Our findings demonstrate that land use regression can effectively predict NO2 variation at the intraurban scale in an industrial setting. Models predicting exposure within smaller areas may lead to improved detection of health effects in epidemiologic studies.     

Regional transport and urban contributions to fine particle concentrations in southeastern Canada

The impact of various atmospheric transport directions on ambient fine particle (PM2.5) concentrations at several sites in southeastern Canada was estimated (for May-September) using back-trajectory analysis. Three-day back trajectories (four per day) were paired with 6-hr average PM2.5 mass concentrations measured using tapered element oscillating microbalances (TEOM). PM2.5 concentrations at rural locations in the region were affected by nonlocal sources originating in both Canada and the United States. Comparison of sites revealed that, on average, the local contribution to total PM2.5 in the greater Toronto area (GTA) is approximately 30-35%. At each location, average PM2.5 concentrations under south/southwesterly flow conditions were 2-4 times higher than under the corresponding northerly flow conditions. The chemical composition of both urban and rural PM2.5 was determined during two separate 2-week spring/summer measurement campaigns. Components identified included SO4(2-) NO3-, NH4+, black carbon and organic carbon (OC), and trace elements. Higher particle mass at the urban Toronto site was composed of a higher proportion of all components. However, black carbon, NO3-, NaCl, and trace elements were found to be the most enriched over the rural/regional background levels.     

Using spatial smoothing to model a functional regression estimator to points on a lattice with application to surface-level ozone in the Eastern United States

Environmental and Ecological Statistics - Spatial functional regression methods allow researchers to model spatially dependent functional random variables, often using a kriging-based interpolation...