High density ozone monitoring using gas sensitive semi-conductor sensors in the Lower Fraser Valley, British Columbia.

A cost-efficient technology for accurate surface ozone monitoring using gas-sensitive semiconducting oxide (GSS) technology, solar power, and automated cell-phone communications was deployed and validated in a 50 sensor test-bed in the Lower Fraser Valley of British Columbia, over 3 months from May-September 2012. Before field deployment, the entire set of instruments was colocated with reference instruments for at least 48 h, comparing hourly averaged data. The standard error of estimate over a typical range 0-50 ppb for the set was 3 ± 2 ppb. Long-term accuracy was assessed over several months by colocation of a subset of ten instruments each at a different reference site. The differences (GSS-reference) of hourly average ozone concentration were normally distributed with mean -1 ppb and standard deviation 6 ppb (6000 measurement pairs). Instrument failures in the field were detected using network correlations and consistency checks on the raw sensor resistance data. Comparisons with modeled spatial O3 fields demonstrate the enhanced monitoring capability of a network that was a hybrid of low-cost and reference instruments, in which GSS sensors are used both to increase station density within a network as well as to extend monitoring into remote areas. This ambitious deployment exposed a number of challenges and lessons, including the logistical effort required to deploy and maintain sites over a summer period, and deficiencies in cell phone communications and battery life. Instrument failures at remote sites suggested that redundancy should be built into the network (especially at critical sites) as well as the possible addition of a "sleep-mode" for GSS monitors. At the network design phase, a more objective approach to optimize interstation distances, and the "information" content of the network is recommended. This study has demonstrated the utility and affordability of the GSS technology for a variety of applications, and the effectiveness of this technology as a means substantially and economically to extend the coverage of an air quality monitoring network. Low-cost, neighborhood-scale networks that produce reliable data can be envisaged.

Global deposition of airborne dioxin.

We present a global dioxin model that simulates one year of atmospheric emissions, transport processes, and depositions to the earth's terrestrial and marine habitats. We map starting emission levels for each land area, and we also map the resulting deposits to terrestrial and marine environments. This model confirms that 'hot spots' of deposition are likely to be in northern Europe, eastern North America, and in parts of Asia with the highest marine dioxin depositions being the northeast and northwest Atlantic, western Pacific, northern Indian Ocean and the Mediterranean. It also reveals that approximately 40% of airborne dioxin emissions are deposited to marine environments and that many countries in Africa receive more dioxin than they produce, which results in these countries being disproportionately impacted. Since human exposure to dioxin is largely through diet, this work highlights food producing areas that receive higher atmospheric deposits of dioxin than others.

A FRAMEWORK FOR EVALUATING REGIONAL-SCALE NUMERICAL PHOTOCHEMICAL MODELING SYSTEMS.

This paper discusses the need for critically evaluating regional-scale (~200-2000 km) three-dimensional numerical photochemical air quality modeling systems to establish a model's credibility in simulating the spatio-temporal features embedded in the observations. Because of limitations of currently used approaches for evaluating regional air quality models, a framework for model evaluation is introduced here for determining the suitability of a modeling system for a given application, distinguishing the performance between different models through confidence-testing of model results, guiding model development, and analyzing the impacts of regulatory policy options. The framework identifies operational, diagnostic, dynamic, and probabilistic types of model evaluation. Operational evaluation techniques include statistical and graphical analyses aimed at determining whether model estimates are in agreement with the observations in an overall sense. Diagnostic evaluation focuses on process-oriented analyses to determine whether the individual processes and components of the model system are working correctly, both independently and in combination. Dynamic evaluation assesses the ability of the air quality model to simulate changes in air quality stemming from changes in source emissions and/or meteorology, the principal forces that drive the air quality model. Probabilistic evaluation attempts to assess the confidence that can be placed in model predictions using techniques such as ensemble modeling and Bayesian model averaging. The advantages of these types of model evaluation approaches are discussed in this paper.

Triptorelin 6-month formulation in the management of patients with locally advanced and metastatic prostate cancer: an open-label, non-comparative, multicentre, phase III study.

BACKGROUND AND OBJECTIVES: Triptorelin 6-month formulation was developed to offer greater convenience to both patients and physicians by reducing the injection frequency. The efficacy, pharmacokinetics and safety of a new 6-month formulation of triptorelin were investigated over 12 months (48 weeks). The primary objective was to evaluate the formulation in achieving castrate serum testosterone levels (< or = 1.735 nmol/L or < or = 50 ng/dL) on day 29 and in maintaining castration at months 2-12. Absence of luteinizing hormone (LH) stimulation and change in prostate-specific antigen (PSA) level were also assessed. METHODS: An open-label, non-comparative, phase III study in 120 patients with advanced prostate cancer was conducted from July 2006 to August 2007 in private and public institutions in South Africa. Each patient received two consecutive intramuscular injections of triptorelin embonate (pamoate) 22.5 mg at an interval of 24 weeks. In all patients, testosterone (primary outcome measurement) was measured at baseline and then every 4 weeks; LH was measured before and 2 hours after the two injections. PSA was measured on day 1 and at weeks 12, 24, 36 and 48. Adverse events were recorded at each visit. RESULTS: In the intent-to-treat population, 97.5% (95% CI 92.9, 99.5) of patients achieved castrate serum testosterone levels by day 29, and 93.0% (95% CI 86.8, 97.0) maintained castration at months 2-12. After the second injection, 98.3% of patients showed absence of LH stimulation. The most frequent drug-related adverse events were hot flushes (71.7% of patients). No patient withdrew from the study as a result of an adverse event. CONCLUSIONS: The triptorelin 6-month formulation was well tolerated and was able to achieve and maintain castration for the treatment of locally advanced and metastatic prostate cancer. By reducing the frequency of required injections, this new formulation offers a more convenient treatment regimen. (Clinical Trial Registration,NCT00751790 at www.clinicaltrials.gov).

An innovative land use regression model incorporating meteorology for exposure analysis.

The advent of spatial analysis and geographic information systems (GIS) has led to studies of chronic exposure and health effects based on the rationale that intra-urban variations in ambient air pollution concentrations are as great as inter-urban differences. Such studies typically rely on local spatial covariates (e.g., traffic, land use type) derived from circular areas (buffers) to predict concentrations/exposures at receptor sites, as a means of averaging the annual net effect of meteorological influences (i.e., wind speed, wind direction and insolation). This is the approach taken in the now popular land use regression (LUR) method. However spatial studies of chronic exposures and temporal studies of acute exposures have not been adequately integrated. This paper presents an innovative LUR method implemented in a GIS environment that reflects both temporal and spatial variability and considers the role of meteorology. The new source area LUR integrates wind speed, wind direction and cloud cover/insolation to estimate hourly nitric oxide (NO) and nitrogen dioxide (NO(2)) concentrations from land use types (i.e., road network, commercial land use) and these concentrations are then used as covariates to regress against NO and NO(2) measurements at various receptor sites across the Vancouver region and compared directly with estimates from a regular LUR. The results show that, when variability in seasonal concentration measurements is present, the source area LUR or SA-LUR model is a better option for concentration estimation.