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Auckland is the largest city in New Zealand with 1.6 million residents. This major metropolitan region is situated upon the Auckland Volcanic Field (AVF), posing a considerable risk to the city’s population and essential services. On 17 December 2018, a workshop jointly run by DEVORA (Determining Volcanic Risk in Auckland) and Auckland Emergency Management (AEM) was held to discuss evacuation decision-support options in the event of a volcanic crisis in Auckland. The workshop brought together emergency management officials, members of the emergency services, transportation managers, academics and researchers. Participants discussed existing decision-making processes and communication pathways before being presented with how ongoing research could support decision-makers with volcanic crisis planning and management. An exercise was conducted to learn the types of decisions and related information requirements of different groups at different stages of a crisis. The exercise focused on three early stages of an existing AVF eruption crisis scenario centred on Birkenhead: Initial unrest; heightened unrest; and initial eruption phase. Each stage of the scenario was presented to groups along with a series of questions relating to their likely actions based on the information presented. The purpose of the exercise was to learn what type of information would be required at each step, and to identify how ongoing research into a quantitative decision-support framework using cost-benefit analysis could support decision-makers in a future AVF eruption. The exercise revealed that risk to life is a primary consideration for making an evacuation decision; the declaration of a state of emergency is critical before making an evacuation call; key locations and services critical for crisis management exist and can be identified in advance of a crisis; and a staged evacuation would likely be recommended. The findings from this workshop will support ongoing research exploring how hazard and risk information can best be presented to support decision-makers in the event of an evacuation during an AVF event. (auth)

Background: Research suggests that independent and joint effects of genetic variability in the dopamine transporter (DAT) locus and pesticides may influence Parkinson's disease (PD) risk. Materials and Methods: In 324 incident PD patients and 334 population controls from our rural California case-control study, we genotyped rs2652510, rs2550956 (for the DAT 5′ clades), and the 3′ variable number of tandem repeats (VNTR). Using geographic information system methods, we determined residential exposure to agricultural maneb and paraquat applications. We also collected occupational pesticide use data. Employing logistic regression, we calculated odds ratios (ORs) for clade diplotypes, VNTR genotype, and number of susceptibility (A clade and 9-repeat) alleles and assessed susceptibility allele-pesticide interactions. Results: PD risk was increased separately in DAT A clade diplotype carriers [AA vs. BB: OR = 1.66; 95% confidence interval (CI), 1.08-2.57] and 3′ VNTR 9/9 carriers (9/9 vs. 10/10: OR = 1.8; 95% CI, 0.96-3.57), and our data suggest a gene dosing effect. Importantly, high exposure to paraquat and maneb in carriers of one susceptibility allele increased PD risk 3-fold (OR = 2.99; 95% CI, 0.88-10.2), and in carriers of two or more alleles more than 4-fold (OR = 4.53; 95% CI, 1.70-12.1). We obtained similar results for occupational pesticide measures. Discussion: Using two independent pesticide measures, we a) replicated previously reported gene-environment interactions between DAT genetic variants and occupational pesticide exposure in men and b) overcame previous limitations of nonspecific pesticide measures and potential recall bias by employing state records and computer models to estimate residential pesticide exposure. Conclusion: Our results suggest that DAT genetic variability and pesticide exposure interact to increase PD risk.

As a real-time optimization technique, modifier adaptation (MA) has gained much significance in recent years. This is mainly due to the fact that MA can deal explicitly with structural plant-model mismatch and unknown disturbances. MA is an iterative technique that is ideally suited to real-life applications. Its two main features are the way measurements are used to correct the model and the role played by the model in actually computing the next inputs. This paper analyzes these two features and shows that, although MA computes the next inputs via numerical optimization, it can be viewed as a feedback control scheme, that is, optimization implements tracking of the plant Karush-Kuhn-Tucker (KKT) conditions. As a result, the role of the model is downplayed to the point that model accuracy is not an important issue. The key issues are gradient estimation and model adequacy, the latter requiring that the model possesses the correct curvature of the cost function at the plant optimum. The main role of optimization is to identify the proper set of controlled variables (the active constraints and reduced gradients) as these might change with the operating point and disturbances. Thanks to this reduced requirement on model accuracy, MA is ideally suited to drive real-life processes to optimality. This is illustrated through two experimental systems with very different optimization features, namely, a commercial fuel-cell system and an experimental kite setup for harnessing wind energy.