Flood management: prediction of microbial contamination in large-scale floods in urban environments

With a changing climate and increased urbanisation, the occurrence and the impact of flooding is expected to increase significantly. Floods can bring pathogens into homes and cause lingering damp and microbial growth in buildings, with the level of growth and persistence dependent on the volume and chemical and biological content of the flood water, the properties of the contaminating microbes, and the surrounding environmental conditions, including the restoration time and methods, the heat and moisture transport properties of the envelope design, and the ability of the construction material to sustain the microbial growth. The public health risk will depend on the interaction of these complex processes and the vulnerability and susceptibility of occupants in the affected areas. After the 2007 floods in the UK, the Pitt review noted that there is lack of relevant scientific evidence and consistency with regard to the management and treatment of flooded homes, which not only put the local population at risk but also caused unnecessary delays in the restoration effort. Understanding the drying behaviour of flooded buildings in the UK building stock under different scenarios, and the ability of microbial contaminants to grow, persist, and produce toxins within these buildings can help inform recovery efforts. To contribute to future flood management, this paper proposes the use of building simulations and biological models to predict the risk of microbial contamination in typical UK buildings. We review the state of the art with regard to biological contamination following flooding, relevant building simulation, simulation-linked microbial modelling, and current practical considerations in flood remediation. Using the city of London as an example, a methodology is proposed that uses GIS as a platform to integrate drying models and microbial risk models with the local building stock and flood models. The integrated tool will help local governments, health authorities, insurance companies and residents to better understand, prepare for and manage a large-scale flood in urban environments.     

Initiation and resolution of jumping spider contests: roles for size, proximity, and early detection of rivals

Animals are commonly expected to assess each other during contests in order to economically identify relative status. Escalated or long contests are expected to arise mainly when rivals have difficulty discriminating small differences. Results of the present study of male-male contests in Plexippus paykulli, a jumping spider (Salticidae) with acute vision, are not in accord with this widely held view. Despite the typical finding that size-advantaged rivals are more likely to win contests and that this tendency increases with size disparity, contest dynamics suggest that these tendencies are achieved in the absence of direct size assessment. In contests between different-sized spiders, maximum escalation and overall duration were predicted by the absolute size of the size-disadvantaged spider (usually the loser) rather than the size difference between the rivals. This result suggests that spiders base decisions of persistence on their own size, such that size-disadvantaged rivals usually reach their limits first, and then retreat. This interpretation is further supported by findings that maximum escalation and total duration were both positively related to size in contests between size-matched spiders. Spiders were more likely to win if they oriented and displayed first, and longer, more escalated, contests ensued if the size-disadvantaged spider was the first to orient and display. Proximity of rivals at contest outset also influenced contest dynamics, but not outcome.     

Mathematical model for photocatalytic destruction of organic contaminants in air

Photocatalytic oxidation (PCO) was investigated in a bench-scale reactor for the abatement of two airborne organic contaminants: toluene and ethanol. A mathematical model that includes the impacts of light intensity, initial contaminant concentration, catalyst thickness, and relative humidity (RH) on the degradation of organic contaminants in a photocatalytic reactor was developed to describe this process. The commercially available catalyst Degussa-PtTiO2 was selected to compare with the MTU-PtTiO2-350 catalyst, which was synthesized by the sol-gel process, platinized, and calcined at 350 degrees C. For toluene removal using the MTU-PtTiO2-350 catalyst, the degradation rate increased with increases in light intensity from 0.2 to 2.2 mW/cm2 and in catalyst thickness from 0.00037 to 0.00361 cm. However, further increases in light intensity and catalyst thickness had only slight effect on the toluene degradation rate. Increasing the initial concentration from 6.29 to 127.9 microg/L and the RH from 10 to 85% resulted in decreases in the toluene degradation rate. For ethanol removal using the MTU-PtTiO2-350 catalyst, the degradation rate increased more rapidly with an increase in RH from 17 to 56%; the RH had little effect on the ethanol degradation rate while it further increased from 56% to 82%. We discuss applicability of the model to estimate the influence of process variables and to evaluate photocatalyst performance.     

Estimating the Impact of Temperature and Rainfall on Malaria Incidence in Ghana from 2012 to 2017

Environmental Modeling & Assessment - Malaria has a significant impact on the lives of many in Ghana. It is one of the key causes of mortality and morbidity, resulting in 32.5% of outpatient...     

Assessing conservation status with extensive but low-resolution data: Application of frequentist and Bayesian models to endangered Athabasca River rainbow trout

Use of extensive but low-resolution abundance data is common in the assessment of species at-risk status based on quantitative decline criteria under International Union for Conservation of Nature (IUCN) and national endangered species legislation. Such data can be problematic for 3 reasons. First, statistical power to reject the null hypothesis of no change is often low because of small sample size and high sampling uncertainty leading to a high frequency of type II errors. Second, range-wide assessments composed of multiple site-specific observations do not effectively weight site-specific trends into global trends. Third, uncertainty in site-specific temporal trends and relative abundance are not propagated at the appropriate spatial scale. A common result is the propensity to underestimate the magnitude of declines and therefore fail to identify the appropriate at-risk status for a species. We used 3 statistical approaches, from simple to more complex, to estimate temporal decline rates for a designatable unit (DU) of rainbow trout in the Athabasca River watershed in western Canada. This DU is considered a native species for purposes of listing because of its genetic composition characterized as >0.95 indigenous origin in the face of continuing introgressive hybridization with introduced populations in the watershed. Analysis of abundance trends from 57 time series with a fixed-effects model identified 33 sites with negative trends, but only 2 were statistically significant. By contrast, a hierarchical linear mixed model weighted by site-specific abundance provided a DU-wide decline estimate of 16.4% per year and a 3-generation decline of 93.2%. A hierarchical Bayesian mixed model yielded a similar 3-generation decline trend of 91.3% and the posterior distribution showed that the estimate had a >99% probability of exceeding thresholds for an endangered listing. We conclude that the Bayesian approach was the most useful because it provided a probabilistic statement of threshold exceedance in support of an at-risk status recommendation.