Potential and Shortcomings of Numerical Weather Prediction Models in Providing Meteorological Data for Urban Air Pollution Forecasting

The last decade progress in numericalweather prediction (NWP) modelling and studies of urbanatmospheric processes for providing meteorological data forurban air pollution forecasting is analysed on examples ofseveral European meteorological centres. Modern nested NWP models are utilising land-use databasesdown to 1 km resolution or finer, and are approaching thenecessary horizontal and vertical resolution suitable forcity scale. The recent scientific developments in the fieldof urban atmospheric physics and the growing availabilityof high-resolution urban surface characteristics datapromise further improvements of the capability of NWPmodels for this aim. A strategy to improve NWP data forthe urban air pollution forecasting is suggested.     

Graphical spatial models: a new view on interpreting spatial pattern

Graphical models provide an important tool for facilitating communication between scientists, decision-makers, and statisticians—many complicated ecological processes can be described in terms of “box-and-arrow” conceptual diagrams (e.g., Shipley in Cause and correlation in biology: a user’s guide to path analysis, structural equations and causal inferences, Cambridge Universtiy Press, Cambridge, 2000; Clark and Gelfand TRENDS in Ecology and Evolution 21:375–380, 2006). In particular, problems in landscape ecology often involve modeling relationships among multiple physical and/or biological variables that may operate on differing spatial scales (e.g., Rossi et al. in Ecol Monographs 62:277–314, 1992; Legendre et al. in Ecography 25:601–615, 2002; Overmars et al. in Ecol Model 164:257–270, 2003; Brown and Spector in J Appl Ecol 45:1639–1648, 2008; Koniak and Noy-Meir in Ecol Model 220:1148–1158, 2008). These problems are inherently multivariate, though researchers commonly rely on univariate methods, such as spatial regression models, to address them. In this paper, we introduce a multivariate method—graphical spatial models—that extends path analysis to incorporate spatial autocorrelation in one or more variables in a directed graph. We show how both exogenous and endogenous ecological processes as defined by Legendre et al. (Ecography 25:601–615, 2002) and Lichstein et al. (Ecol Monographs 72:445–463, 2002) can be represented in a graph. Most importantly, we show how to translate graphs representing these ecological processes into statistically estimable models. We motivate our theoretical results using an example of stream health data from the Willamette Valley, Oregon. For these data we are interested in the spatial pattern within both riparian land use and an index of stream health, and whether there is an association between land use and stream health, after accounting for these spatial patterns. We use a graphical spatial model to address these ecological questions simultaneously. We find that the health of a stream decreases as the percent of developed land within a 120-m riparian buffer increases; interestingly, there is only evidence of spatial pattern within land use.     

Isomorphic chain graphs for modeling spatial dependence in ecological data

Graphical models (alternatively, Bayesian belief networks, path analysis models) are increasingly used for modeling complex ecological systems (e.g., Lee, In: Ferson S, Burgman M(eds) Quantative methods for conservation biology. Springer, Berlin Heilin Heideslperk New York, pp.127–147, 2000; Borsuk et al., J Water Res Plann Manage 129:271–282, 2003). Their implementation in this context leverages their utility in modeling interrelationships in multivariate systems, and in a Bayesian implementation, their intuitive appeal of yielding easily interpretable posterior probability estimates. However, methods for incorporating correlational structure to account for observations collected through time and/or space—features of most ecological data—have not been widely studied; Haas et al. (AI Appl 8:15–27, 1994) is one exception. In this paper, an “isomorphic” chain graph (ICG) model is introduced to account for correlation between samples by linking site-specific Bayes network models. Several results show that the ICG preserves many of the Markov properties (conditional and marginal dependencies) of the site-specific models. The ICG model is compared with a model that does not account for spatial correlation. Data from several stream networks in the Willamette River valley, Oregon (USA) are used. Significant correlation between sites within the same stream network is shown with an ICG model.     

The dynamic nature of antipredator behavior: prey fish integrate threat-sensitive antipredator responses within background levels of predation risk

Prey animals often have to face a dynamic tradeoff between the costs of antipredator behavior and the benefits of other fitness-related activities such as foraging and reproduction. According to the threat-sensitive predator avoidance hypothesis, prey animals should match the intensity of their antipredator behavior to the degree of immediate threat posed by the predator. Moreover, longer-term temporal variability in predation risk (over days to weeks) can shape the intensity of antipredator behavior. According to the risk allocation hypothesis, changing the background level of risk for several days is often enough to change the response intensity of the prey to a given stimulus. As the background level of risk increases, the response intensity of the prey decreases. In this study, we tested for possible interactions between immediate threat-sensitive responses to varying levels of current perceived risk and temporal variability in background risk experienced over the past 3 days. Juvenile convict cichlids were preexposed to either low or high frequencies of predation risk (using conspecific chemical alarm cues) for 3 days and were then tested for a response to one of five concentrations (100, 50, 25, 12.5%, or a distilled water control). According to the threat-sensitive predator avoidance hypothesis, we found greater intensity responses to greater concentrations of alarm cues. Moreover, in accordance with the risk allocation hypothesis, we found that cichlids previously exposed to the high background level of risk exhibited a lower overall intensity response to each alarm cue concentration than those exposed to the low background level of risk. It is interesting to note that we found that the background level of risk over the past 3 days influenced the threshold level of response to varying concentrations of alarm cues. Indeed, the minimum stimulus concentration that evoked a behavioral response was lower for fish exposed to high background levels of predation than those exposed to low background levels of predation. These results illustrate a remarkable interplay between immediate (current) risk and background risk in shaping the intensity of antipredator responses.     

Habitat selection models to account for seasonal persistence in radio telemetry data

Models for the analysis of habitat selection data incorporate covariates in an independent multinomial selections model (McCracken et al. 1998) Ramsey and Usner 2003 and an extension of that model to include a persistence parameter (2003). In both cases, all parameters are assumed to be fixed through time. Radio telemetry data collected for habitat selection studies typically consist of animal relocations through time, suggesting the need for an extension to these models. We use a Bayesian approach that allows for the habitat selection probabilities, persistence parameter, or both, to change with season. These extensions are particularly important when movement patterns are expected to differ seasonally and/or when availabilities of habitats change throughout the study period due to weather or migration. We implement and compare the models using radio telemetry data for westslope cutthroat trout in two streams in eastern Oregon.     

Banging heads onboard buses: Assessment scheme to improve injury mitigation for bus passengers

Abstract

Objective: Although bus travel is one of the safest modes of transport, a substantial number of bus passengers in London are still injured in collision and harsh maneuver incidents, in particular emergency braking. It is not well understood how these passengers are injured. The objective was to better understand the injury mechanisms and develop countermeasures with a test and assessment procedure to prevent or mitigate these injuries.

Methods: The UK national STATS19 data were used to determine the size of the problem. Data, including CCTV footage, in combination with inspection of current buses, were used to determine injury mechanisms and identify features and areas in buses associated with more injuries.

An assessment system based on visual inspection was developed to encourage a reduction in the number of features associated with injury.

Results: The STATS19 analysis showed that three quarters of all injured casualties occurred in incidents where there was no impact, with this proportion rising for seriously injured casualties. Overrepresentation of older females was also seen.

The CCTV analysis and bus examinations highlighted issues with poorly positioned handrails, lack of compartmentalization (restraint), and objects with sharp edges and corners. It also showed that a much higher proportion of passengers seated in the area close to the middle doors and wheelchair area were injured compared to other areas of the bus. Factors contributing to this result were that this area contained more features associated with injury and that persons with reduced mobility have greater exposure in this area; that is, more vulnerable passengers currently sit in the less safe areas of the bus.

Conclusions: A novel analysis of CCTV footage has enabled a better understanding of injury mechanisms for bus passengers to be developed. In combination with inspection of current buses, this has been used to develop an assessment system to improve the safety of buses in London.