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This study assesses spatiotemporal and sex‐specific growth of Atlantic cod Gadus morhua in Icelandic waters. We use a Bayesian approach which lends itself to fitting and comparing nested models such as these. We then compare fitted parameters of these models to potential explanatory variables using a redundancy analysis (RDA) to look for drivers of growth in G. morhua. Results indicate that models that incorporate differences in growth among time, space and sex are the best‐fitting models according to deviance information criterion (DIC). Results from RDA indicate that capelin Mallotus villosus recruitment and biomass is highly correlated with deviations in the von Bertalannfy growth parameter k and that L∞ is correlated with G. morhua landings in the model that uses year to account for time‐varying growth and estimated G. morhua recruitment in the model that uses cohort to account for time‐varying growth. [ABSTRACT FROM AUTHOR]

Evidence is mixed on whether society-wide economic conditions affect cardiovascular health and the reasons for the suggested relationship are largely untested. We explore whether a short-term increase in labor supply affects the probability of acute myocardial infarctions, using a natural experiment in Iceland. In 1987 personal income taxes were temporarily reduced to zero, resulting in an overall increase in labor supply. We merge and analyze individual-level, registry-based data on earnings and AMIs including all Icelandic men and women aged 45-74 during the period 1982-1992. The results support the prominent hypothesis of increased work as a mechanism explaining worsening heart health in upswings, for men aged 45-64 who were self-employed. We furthermore find a larger increase in probability of AMIs during the tax-free year in men aged 45-54 than men aged 55-64. [ABSTRACT FROM AUTHOR]

Wind energy harnessing is a new energy production alternative in Iceland. Current installed wind power in Iceland sums to 1.8 MW, which in contrast is 0.1% of the country's total electricity production. This article is dedicated to the exploration of the potential cost of wind energy production at Búrfell in the south of Iceland. A levelized cost of energy (LCOE) approach was applied to the estimation of the potential cost. Weibull simulation is used to simulate wind data for calculations. A confirmation of the power law is done by comparing real data to calculated values. A modified Weibull simulation is verified by comparing results with actual on-site test wind turbines. A wind farm of 99MWis suggested for the site. Key results were the capacity factor (CF) at Búrfell being 38.15% on average and that the LCOE for wind energy was estimated as 0.087-0.088 USD/kWh (assuming 10% weighted average cost of capital (WACC)), which classifies Búrfell among the lowest LCOE sites for wind energy in Europe. [ABSTRACT FROM AUTHOR]

A fundamental modeling assumption in engineering seismology is that seismic ground motion is affected by source, propagation path and site effects. Contrary to the first two, comprehensive studies on site effects, the localized amplification effects on seismic ground motion due to the uppermost geological structure, have been limited in Iceland. As a result, site amplification has been modeled in ground motion models in a simplistic and qualitative way. In this study, we address this limitation by using the new Bayesian hybrid GMM proposed by Kowsari et al. (2020) to model the recorded ground motions at regional strong-motion stations in southwest Iceland, specifically the peak ground acceleration and pseudo-acceleration response spectra as a function of oscillator period (i.e., PGA and PSA at T = 0.01 - 3.0 s), earthquake magnitude, distance and hypocentral depth, and at first, we omit a site-term. Then, we incorporate the GMM in a Bayesian Hierarchical Model (BHM) that allows prior distributions of model parameters to be used and we partition the model residuals into various terms associated with the source, propagation path and station terms. Through Bayesian inference of the model parameters the results show that the station term residuals reveal a frequency-dependent site amplification at the recording sites. By inspecting this systematic behaviour, we propose four groups of station terms by their characteristics. We then carefully inspect the station locations in terms of their associated geological unit and observe that the geological units and station term groups are qualitatively correlated. By combining the two, we propose the first frequency-dependent average site amplification functions associated with key geological units in Iceland based on age and formation. Provisionally we refer to them as hard rock, rock, (Holocene) lava rock, and (unspecified) soil site classes, respectively. We then update the GMM by incorporating these amplification functions so that it now enables the prediction of PGA and PSA at these four site classes. As a result, the new model standard deviations are considerably lower, with those associated with the event term being τ = 0.05, station terms φ S 2 S = 0.10, intra-event φ = 0.17 and total σ = 0.18 in common logarithm units for PGA. The new site class-specific GMM now enables the site-specific ground motion for separate geological units which in turn improves site-specific seismic hazard assessment and informed site-specific engineering decision making in Iceland. • A new empirical ground motion model without a site term is embedded in a Bayesian Hierarchical Modeling (BHM) framework. • The model is calibrated to strong-motion data from mainshocks recorded in Southwest Iceland. • The BHM reveals the frequency-dependent station terms which are a proxy for localized site effects. • The station terms are correlated to key Icelandic surficial geological units, hard rock, rock, lava rock and soil. • The average station terms for each geological unit have been incorporated into the original ground motion model. [ABSTRACT FROM AUTHOR]