Does temperature modify short-term effects of ozone on total mortality in 60 large eastern US communities? An assessment using the NMMAPS data

Many studies have indicated that ozone is associated with morbidity and mortality. A few studies have reported that the association is heterogeneous across seasons and geographic regions. However, little information is available on whether both temperature and geographic factors simultaneously modify the ozone effect. This study used a Poisson regression model to explore whether temperature modifies the effect of ozone on mortality in the 60 large eastern US communities during April to October, 1987-2000. Results show that temperature modified ozone-mortality associations and that such modification varied across geographic regions. In the northeast region, a 10-ppb increment in ozone was associated with an increase of 2.22% (95% posterior interval [PI]: 1.19%, 3.13%), 3.06% (95% PI: 2.21%, 3.76%) and 6.22% (95% PI: 4.77%, 7.56%) in mortality at low, moderate and high temperature level, respectively, while in the southeast region a 10-ppb increment in ozone was associated with an increase of 1.13% (95% PI:-1.12%, 3.18%), 1.50% (95% PI: 0.22%, 2.81%) and 1.29% (95% PI:-0.33%, 2.96%) in mortality, respectively. We concluded that temperature synergistically modified the ozone-mortality association in the northeast region, but such a pattern was not apparent in the southeast region. Thus, both temperature and geographic factors should be considered in the assessment of ozone effects.     

On-road ultrafine particle concentration in the M5 East road tunnel,Sydney, Australia

The human health effects following exposure to ultrafine (<100 nm) particles (UFPs) produced by fuel combustion, while not completely understood, are generally regarded as detrimental. Road tunnels have emerged as locations where maximum exposure to these particles may occur for the vehicle occupants using them. This study aimed to quantify and investigate the determinants of UFP concentrations in the 4 km twin-bore (eastbound and westbound) M5 East tunnel in Sydney, Australia. Sampling was undertaken using a condensation particle counter (CPC) mounted in a vehicle traversing both tunnel bores at various times of day from May through July, 2006. Supplementary measurements were conducted in February, 2008. Over three hundred transects of the tunnel were performed, and these were distributed evenly between the bores. Additional comparative measurements were conducted on a mixed route comprising major roads and shorter tunnels, all within Sydney. Individual trip average UFP concentrations in the M5 East tunnel bores ranged from 5.53 × 10⁴ p cm^?3 to 5.95 × 10⁶ p cm^?3. Data were sorted by hour of capture, and hourly median trip average (HMA) UFP concentrations ranged from 7.81 × 10⁴ p cm^?3 to 1.73 × 10⁶ p cm^?3. Hourly median UFP concentrations measured on the mixed route were between 3.71 × 10⁴ p cm^?3 and 1.55 × 10⁵ p cm^?3. Hourly heavy diesel vehicle (HDV) traffic volume was a very good determinant of UFP concentration in the eastbound tunnel bore (R² = 0.87), but much less so in the westbound bore (R² = 0.26). In both bores, the volume of passenger vehicles (i.e. unleaded gasoline-powered vehicles) was a significantly poorer determinant of particle concentration. When compared with similar studies reported previously, the measurements described here were among the highest recorded concentrations, which further highlights the contribution road tunnels may make to the overall UFP exposure of vehicle occupants.     

Spatially stratified sampling using auxiliary information for geostatistical mapping

This paper presents a method of spatial sampling based on stratification by Local Moran’s I _i calculated using auxiliary information. The sampling technique is compared to other design-based approaches including simple random sampling, systematic sampling on a regular grid, conditional Latin Hypercube sampling and stratified sampling based on auxiliary information, and is illustrated using two different spatial data sets. Each of the samples for the two data sets is interpolated using regression kriging to form a geostatistical map for their respective areas. The proposed technique is shown to be competitive in reproducing specific areas of interest with high accuracy.     

The Bayesian conditional independence model for measurement error: applications in ecology

The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.     

Assessing the relationship between global warming and mortality: lag effects of temperature fluctuations by age and mortality categories

Although interests in assessing the relationship between temperature and mortality have arisen due to climate change, relatively few data are available on lag structure of temperature-mortality relationship, particularly in the Southern Hemisphere. This study identified the lag effects of mean temperature on mortality among age groups and death categories using polynomial distributed lag models in Brisbane, Australia, a subtropical city, 1996-2004. For a 1 °C increase above the threshold, the highest percent increase in mortality on the current day occurred among people over 85 years (7.2% (95% CI: 4.3%, 10.2%)). The effect estimates among cardiovascular deaths were higher than those among all-cause mortality. For a 1 °C decrease below the threshold, the percent increases in mortality at 21 lag days were 3.9% (95% CI: 1.9%, 6.0%) and 3.4% (95% CI: 0.9%, 6.0%) for people aged over 85 years and with cardiovascular diseases, respectively. These findings may have implications for developing intervention strategies to reduce and prevent temperature-related mortality.