Artificial Neural Networks for Risk Decision Support in Natural Hazards: A Case Study of Assessing the Probability of House Survival from Bushfires

Risk decision-making in natural hazards encompasses a plethora of environmental, socio-economic and management-related factors, and benefits greatly from exploring possible patterns and relations among these multivariate factors. Artificial neural networks, capable of general pattern classifications, are potentially well suited for risk decision support in natural hazards. This paper reports an example that assesses the risk patterns or probabilities of house survival from bushfires using artificial neural networks, with a simulation data set based on the empirical study by Wilson and Ferguson (Predicting the probability of house survival during bushfires, Journal of Environmental Management 23 (1986) 259–270). The aim of this study was to re-model and predict the relationship between risk patterns of house survival and a series of independent variables. Various configurations for input and output variables were tested using neural networks. An approach for converting linguistic terms into crisp numbers was used to incorporate linguistic variables into the quantitative neural network analysis. After a series of tests, results show that neural networks are capable of predicting risk patterns under all tested configurations of input and output variables, with a great deal of flexibility. Risk-based mathematical functions, be they linear or non-linear, can be re-modelled using neural networks. Finally, the paper concludes that the artificial neural networks serve as a promising risk decision support tool in natural hazards.     

Transforming conservation science and practice for a postnormal world

We examine issues to consider when reframing conservation science and practice in the context of global change. New framings of the links between ecosystems and society are emerging that are changing peoples’ values and expectations of nature, resulting in plural perspectives on conservation. Reframing conservation for global change can thus be regarded as a stage in the evolving relationship between people and nature rather than some recent trend. New models of how conservation links with transformative adaptation include how decision contexts for conservation can be reframed and integrated with an adaptation pathways approach to create new options for global‐change‐ready conservation. New relationships for conservation science and governance include coproduction of knowledge that supports social learning. New processes for implementing adaptation for conservation outcomes include deliberate practices used to develop new strategies, shift world views, work with conflict, address power and intergenerational equity in decisions, and build consciousness and creativity that empower agents to act. We argue that reframing conservation for global change requires scientists and practitioners to implement approaches unconstrained by discipline and sectoral boundaries, geopolitical polarities, or technical problematization. We consider a stronger focus on inclusive creation of knowledge and the interaction of this knowledge with societal values and rules is likely to result in conservation science and practice that meets the challenges of a postnormal world.     

Economic Linkages to Changing Landscapes

Many economic processes are intertwined with landscape change. A large number of individual economic decisions shape the landscape, and in turn the changes in the landscape shape economic decisions. This article describes key research questions about the economics of landscape change and reviews the state of research knowledge. The rich and varied economic–landscape interactions are an active area of research by economists, geographers, and others. Because the interactions are numerous and complex, disentangling the causal relationships in any given landscape system is a formidable research challenge. Limited data with mismatched temporal and spatial scales present further obstacles. Nevertheless, the growing body of economic research on these topics is advancing and shares fundamental challenges, as well as data and methods, with work in other disciplines.     

Organonitrate group concentrations in submicron particles with high nitrate and organic fractions in coastal southern California

During wintertime measurements in coastal southern California, organonitrate groups accounted for up to 10% of organic mass (OM) in submicron particles. In this study, we report the calibrated absorptivity, the uncertainties in the calibrations, the detection limits for 12 and 24 h ambient sampling, and the multipeak retrieval algorithm for the method developed. Organonitrate groups were observed when both submicron particle-phase nitrate and OM concentrations exceeded 1 μg m^?3. These high concentrations were associated with a mixed urban fossil fuel combustion source type that had potential source regions near Riverside and the South Coast Air Basin. The high frequency of these organonitrate observations contrasts with a number of studies of aerosol particles in other regions with more humid conditions, in which organonitrate groups were not detected and submicron sulfate concentrations exceeded those of nitrate. Our results suggest both that organonitrates form and/or exist in significant concentrations during polluted urban conditions and that their lifetime may be limited by hydrolysis in the particle phase.     

Linking 3D spatial models of fuels and fire: Effects of spatial heterogeneity on fire behavior

Crown fire endangers fire fighters and can have severe ecological consequences. Prediction of fire behavior in tree crowns is essential to informed decisions in fire management. Current methods used in fire management do not address variability in crown fuels. New mechanistic physics-based fire models address convective heat transfer with computational fluid dynamics (CFD) and can be used to model fire in heterogeneous crown fuels. However, the potential impacts of variability in crown fuels on fire behavior have not yet been explored. In this study we describe a new model, FUEL3D, which incorporates the pipe model theory (PMT) and a simple 3D recursive branching approach to model the distribution of fuel within individual tree crowns. FUEL3D uses forest inventory data as inputs, and stochastically retains geometric variability observed in field data. We investigate the effects of crown fuel heterogeneity on fire behavior with a CFD fire model by simulating fire under a homogeneous tree crown and a heterogeneous tree crown modeled with FUEL3D, using two different levels of surface fire intensity. Model output is used to estimate the probability of tree mortality, linking fire behavior and fire effects at the scale of an individual tree. We discovered that variability within a tree crown altered the timing, magnitude and dynamics of how fire burned through the crown; effects varied with surface fire intensity. In the lower surface fire intensity case, the heterogeneous tree crown barely ignited and would likely survive, while the homogeneous tree had nearly 80% fuel consumption and an order of magnitude difference in total net radiative heat transfer. In the higher surface fire intensity case, both cases burned readily. Differences for the homogeneous tree between the two surface fire intensity cases were minimal but were dramatic for the heterogeneous tree. These results suggest that heterogeneity within the crown causes more conditional, threshold-like interactions with fire. We conclude with discussion of implications for fire behavior modeling and fire ecology.     

Pollutant Transport During a Regional O3 Episode in the Mid-Atlantic States

ABSTRACT

Ozone (O₃) concentrations in the Baltimore-Washington (B-W) metropolitan area frequently exceed the National Ambient Air Quality Standard (NAAQS) in the summer months. The most extreme O₃ events occur in multi-day high O₃ episodes.¹ These events can be regional in scale, with O₃ concentrations exceeding the NAAQS at numerous locations along the eastern U.S. seaboard, and are typically associated with slow-moving or stagnant high pressure systems.^2-5 In the B-W region, the most extreme events typically occur with surface high pressure overhead or just west of the region and an upper air high-pressure area (ridge) to the west or northwest.¹ Besides providing conditions conducive to local O₃ production (subsidence and strong low-level inversions, weak horizontal winds, little cloud cover), this weather pattern may also result in transport of O₃ and its precursors from heavily industrialized areas west and north of the B-W region. In this paper, observations and back trajectories made during the severe regional O₃ event of July 12-15, 1995, are used to confirm the hypothesis that significant regional-scale transport of O₃ and its precursors occur during extreme O₃ events of the standard type in the B-W area.     

A 10 μm Outpoint Size Selective Inlet for Hi-Vol Samplers

A Two-stage Size Selective Inlet for use with hi-vol samplers was designed and tested. The inlet, which operates at a flow rate of 1.13 m³/min, is shown to have a cutpoint of 9.8 μm and a fractionation curve slope of 1.45. The cutpoint is well within the EPA suggested limits of 10 ± 1 μm. Fractionation is not affected by wind speed over the test range of 2-24 km/h.

Re-entrainment or bounce of solid particles is not of consequence. The difference in penetration of 20 μm aerodynamic diameter glass beads and liquid aerosols is less than 1% at all wind speeds.     

Potential Impact on Standards for Toxic Air Contaminants

Since the Bhopal incident, the public has placed pressure on regulatory agencies to set community exposure limits for the dozens of chemicals that may be released by manufacturing facilities. More or less objective limits can be established for the vast majority of these chemicals through the use of risk assessment. However, each step of the risk assessment process (i.e., hazard identification, dose-response assessment, exposure assessment, and risk characterization) contains a number of pitfalls that scientists need to avoid to ensure that valid limits are established. For example, in the hazard identification step there has been little discrimination among animal carcinogens with respect to mechanism of action or the epidemiology experience. In the dose-response portion, rarely is the range of “plausible” estimated risks presented. Physiologically based pharmacokinetic (PB-PK) models should be used to understand the difference between the tissue doses and the administered dose, as well as the difference in target tissue concentrations of the toxicant between rodents and humans. Biologically-based models like the Moolgavkar-Knudson-Venzon (MKV) should be developed and used, when appropriate. The exposure assessment step can be significantly improved by using more sensitive and specific sampling and analytical methods, more accurate exposure parameters, and computer models that can account for complex environmental factors. Whenever possible, model predictions of exposure and uptake should be validated by biological monitoring of exposed persons (urine, blood, adipose) or by field measurements of plants, soil, fish, air, or water. In each portion of an assessment, the weight of evidence approach should be used to identify the most defensible value. In the risk characterization, the best estimate of the potential risk as well as the highest plausible risk should be presented. Future assessments would be much improved if quantitative uncertainty analyses were conducted. Procedures are currently available for making future assessments. By correcting some of these shortcomings in how health risk assessments have been conducted, scientists and risk managers should be better able to identify scientifically appropriate ambient air standards and emission limits.