Geographical variation in total and inorganic arsenic content of polished (white) rice

An extensive data set of total arsenic analysis for 901 polished (white) grain samples, originating from 10 countries from 4 continents, was compiled. The samples represented the baseline (i.e., not specifically collected from arsenic contaminated areas), and all were for market sale in major conurbations. Median total arsenic contents of rice varied 7-fold, with Egypt (0.04 mg/kg) and India (0.07 mg/kg) having the lowest arsenic content while the U.S. (0.25 mg/kg) and France (0.28 mg/kg) had the highest content. Global distribution of total arsenic in rice was modeled by weighting each country’s arsenic distribution by that country’s contribution to global production. A subset of 63 samples from Bangladesh, China, India, Italy, and the U.S. was analyzed for arsenic species. The relationship between inorganic arsenic content versus total arsenic content significantly differed among countries, with Bangladesh and India having the steepest slope in linear regression, and the U.S. having the shallowest slope. Using country-specific rice consumption data, daily intake of inorganic arsenic was estimated and the associated internal cancer risk was calculated using the U.S. Environmental Protection Agency (EPA) cancer slope. Median excess internal cancer risks posed by inorganic arsenic ranged 30-fold for the 5 countries examined, being 0.7 per 10,000 for Italians to 22 per 10,000 for Bangladeshis, when a 60 kg person was considered.

Sparse model construction using coordinate descent optimization

We propose a new sparse model construction method aimed at maximizing a model’s generalisation capability for a large class of linear-in-the-parameters models. The coordinate descent optimization algorithm is employed with a modified l1- penalized least squares cost function in order to estimate a single parameter and its regularization parameter simultaneously based on the leave one out mean square error (LOOMSE). Our original contribution is to derive a closed form of optimal LOOMSE regularization parameter for a single term model, for which we show that the LOOMSE can be analytically computed without actually splitting the data set leading to a very simple parameter estimation method. We then integrate the new results within the coordinate descent optimization algorithm to update model parameters one at the time for linear-in-the-parameters models. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Towards radiocarbon calibration beyond 28ka using speleothems from the Bahamas

We present a new speleothem record of atmospheric Δ14C between 28 and 44 ka that offers considerable promise for resolving some of the uncertainty associated with existing radiocarbon calibration curves for this time period. The record is based on a comprehensive suite of AMS 14C ages, using new low-blank protocols, and U–Th ages using high precision MC-ICPMS procedures. Atmospheric Δ14C was calculated by correcting 14C ages with a constant dead carbon fraction (DCF) of 22.7 ± 5.9%, based on a comparison of stalagmite 14C ages with the IntCal04 (Reimer et al., 2004) calibration curve between 15 and 11 ka. The new Δ14C speleothem record shows similar structure and amplitude to that derived from Cariaco Basin foraminifera (Hughen et al., 2004, 2006), and the match is further improved if the latter is tied to the most recent Greenland ice core chronology (Svensson et al., 2008). These data are however in conflict with a previously published 14C data set for a stalagmite record from the Bahamas — GB-89-24-1 (Beck et al., 2001), which likely suffered from 14C analytical blank subtraction issues in the older part of the record. The new Bahamas speleothem ∆14C data do not show the extreme shifts between 44 and 40 ka reported in the previous study (Beck et al., 2001). Causes for the observed structure in derived atmospheric Δ14C variation based on the new speleothem data are investigated with a suite of simulations using an earth system model of intermediate complexity. Data-model comparison indicates that major fluctuations in atmospheric ∆14C during marine isotope stage 3 is primarily a function of changes in geomagnetic field intensity, although ocean–atmosphere system reorganisation also played a supporting role.

Satellite-based estimate of the direct and indirect aerosol climate forcing

The main uncertainty in anthropogenic forcing of the Earth’s climate stems from pollution aerosols, particularly their ‘‘indirect effect’’ whereby aerosols modify cloud properties. We develop a new methodology to derive a measurement-based estimate using almost exclusively information from an Earth radiation budget instrument (CERES) and a radiometer (MODIS). We derive a statistical relationship between planetary albedo and cloud properties, and, further, between the cloud properties and column aerosol concentration. Combining these relationships with a data set of satellite-derived anthropogenic aerosol fraction, we estimate an anthropogenic radiative forcing of �-0.9 ± 0.4 Wm�-2 for the aerosol direct effect and of �-0.2 ± 0.1 Wm�-2 for the cloud albedo effect. Because of uncertainties in both satellite data and the method, the uncertainty of this result is likely larger than the values given here which correspond only to the quantifiable error estimates. The results nevertheless indicate that current global climate models may overestimate the cloud albedo effect.

Terrorism and cabinet duration

Terrorism can strengthen or weaken electoral support for ruling governments. We show in a simple model of coalition formation that, regardless of the direction of a public opinion shock, the impact of terrorism on cabinet duration is ambiguous. However, in an analysis of a data set including 2,400 cabinets in over 150 countries in the period 1970–2002, we find that terrorism, on average, shortens cabinet duration. This result is robust for a range of alternative terror measures and is present in both democratic as well as autocratic political regimes.

Islamic mutual funds' financial performance and international investment style: evidence from 20 countries

We pursue the first large-scale investigation of a strongly growing mutual fund type: Islamic funds. Based on an unexplored, survivorship bias-adjusted data set, we analyse the financial performance and investment style of 265 Islamic equity funds from 20 countries. As Islamic funds often have diverse investment regions, we develop a (conditional) three-level Carhart model to simultaneously control for exposure to different national, regional and global equity markets and investment styles. Consistent with recent evidence for conventional funds, we find Islamic funds to display superior learning in more developed Islamic financial markets. While Islamic funds from these markets are competitive to international equity benchmarks, funds from especially Western nations with less Islamic assets tend to significantly underperform. Islamic funds’ investment style is somewhat tilted towards growth stocks. Funds from predominantly Muslim economies also show a clear small cap preference. These results are consistent over time and robust to time varying market exposures and capital market restrictions.

Effects of temporal resolution of input precipitation on the performance of hydrological forecasting

Flood prediction systems rely on good quality precipitation input data and forecasts to drive hydrological models. Most precipitation data comes from daily stations with a good spatial coverage. However, some flood events occur on sub-daily time scales and flood prediction systems could benefit from using models calibrated on the same time scale. This study compares precipitation data aggregated from hourly stations (HP) and data disaggregated from daily stations (DP) with 6-hourly forecasts from ECMWF over the time period 1 October 2006–31 December 2009. The HP and DP data sets were then used to calibrate two hydrological models, LISFLOOD-RR and HBV, and the latter was used in a flood case study. The HP scored better than the DP when evaluated against the forecast for lead times up to 4 days. However, this was not translated in the same way to the hydrological modelling, where the models gave similar scores for simulated runoff with the two datasets. The flood forecasting study showed that both datasets gave similar hit rates whereas the HP data set gave much smaller false alarm rates (FAR). This indicates that using sub-daily precipitation in the calibration and initiation of hydrological models can improve flood forecasting.

Updated radiative forcing estimates of 65 halocarbons and nonmethane hydrocarbons

The direct radiative forcing of 65 chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons, hydrofluoroethers, halons, iodoalkanes, chloroalkanes, bromoalkanes, perfluorocarbons and nonmethane hydrocarbons has been evaluated using a consistent set of infrared absorption cross sections. For the radiative transfer models, both line-by-line and random band model approaches were employed for each gas. The line-by-line model was first validated against measurements taken by the Airborne Research Interferometer Evaluation System (ARIES) of the U.K. Meteorological Office; the computed spectrally integrated radiance of agreed to within 2% with experimental measurements. Three model atmospheres, derived from a three-dimensional climatology, were used in the radiative forcing calculations to more accurately represent hemispheric differences in water vapor, ozone concentrations, and cloud cover. Instantaneous, clear-sky radiative forcing values calculated by the line-by-line and band models were in close agreement. The band model values were subsequently modified to ensure exact agreement with the line-by-line model values. Calibrated band model radiative forcing values, for atmospheric profiles with clouds and using stratospheric adjustment, are reported and compared with previous literature values. Fourteen of the 65 molecules have forcings that differ by more than 15% from those in the World Meteorological Organization [1999] compilation. Eleven of the molecules have not been reported previously. The 65-molecule data set reported here is the most comprehensive and consistent database yet available to evaluate the relative impact of halocarbons and hydrocarbons on climate change.

On the environmental valuation of catastrophe: revisiting mining disasters in the United Kingdom, 1851-1962

In the context of environmental valuation of natural disasters, an important component of the evaluation procedure lies in determining the periodicity of events. This paper explores alternative methodologies for determining such periodicity, illustrating the advantages and the disadvantages of the separate methods and their comparative predictions. The procedures employ Bayesian inference and explore recent advances in computational aspects of mixtures methodology. The procedures are applied to the classic data set of Maguire et al (Biometrika, 1952) which was subsequently updated by Jarrett (Biometrika, 1979) and which comprise the seminal investigations examining the periodicity of mining disasters within the United Kingdom, 1851-1962.

The effects of corporate social performance on the cost of corporate debt and credit ratings

This study investigates the differential impact that various dimensions of corporate social performance have on the pricing of corporate debt as well as the assessment of the credit quality of specific bond issues. The empirical analysis, based on an extensive longitudinal data set, suggests that overall, good performance is rewarded and corporate social transgressions are penalized through lower and higher corporate bond yield spreads, respectively. Similar conclusions can be drawn when focusing on either the bond rating assigned to a specific debt issue or the probability of it being considered to be an asset of speculative grade.

On the radius of centrality in evolving communication networks

In this article, we investigate how the choice of the attenuation factor in an extended version of Katz centrality influences the centrality of the nodes in evolving communication networks. For given snapshots of a network, observed over a period of time, recently developed communicability indices aim to identify the best broadcasters and listeners (receivers) in the network. Here we explore the attenuation factor constraint, in relation to the spectral radius (the largest eigenvalue) of the network at any point in time and its computation in the case of large networks. We compare three different communicability measures: standard, exponential, and relaxed (where the spectral radius bound on the attenuation factor is relaxed and the adjacency matrix is normalised, in order to maintain the convergence of the measure). Furthermore, using a vitality-based measure of both standard and relaxed communicability indices, we look at the ways of establishing the most important individuals for broadcasting and receiving of messages related to community bridging roles. We compare those measures with the scores produced by an iterative version of the PageRank algorithm and illustrate our findings with two examples of real-life evolving networks: the MIT reality mining data set, consisting of daily communications between 106 individuals over the period of one year, a UK Twitter mentions network, constructed from the direct \emph{tweets} between 12.4k individuals during one week, and a subset the Enron email data set.

DIRTMAP: the geological record of dust.

Atmospheric dust is an important feedback in the climate system, potentially affecting the radiative balance and chemical composition of the atmosphere and providing nutrients to terrestrial and marine ecosystems. Yet the potential impact of dust on the climate system, both in the anthropogenically disturbed future and the naturally varying past, remains to be quantified. The geologic record of dust provides the opportunity to test earth system models designed to simulate dust. Records of dust can be obtained from ice cores, marine sediments, and terrestrial (loess) deposits. Although rarely unequivocal, these records document a variety of processes (source, transport and deposition) in the dust cycle, stored in each archive as changes in clay mineralogy, isotopes, grain size, and concentration of terrigenous materials. Although the extraction of information from each type of archive is slightly different, the basic controls on these dust indicators are the same. Changes in the dust flux and particle size might be controlled by a combination of (a) source area extent, (b) dust emission efficiency (wind speed) and atmospheric transport, (c) atmospheric residence time of dust, and/or (d) relative contributions of dry settling and rainout of dust. Similarly, changes in mineralogy reflect (a) source area mineralogy and weathering and (b) shifts in atmospheric transport. The combination of these geological data with process-based, forward-modelling schemes in global earth system models provides an excellent means of achieving a comprehensive picture of the global pattern of dust accumulation rates, their controlling mechanisms, and how those mechanisms may vary regionally. The Dust Indicators and Records of Terrestrial and MArine Palaeoenvironments (DIRTMAP) data base has been established to provide a global palaeoenvironmental data set that can be used to validate earth system model simulations of the dust cycle over the past 150,000 years.

Tropical palaeoclimates at the Last Glacial Maximum: comparison of Palaeoclimate Modeling Intercomparison Project (PMIP)simulations and paleodata.

Seventeen simulations of the Last Glacial Maximum (LGM) climate have been performed using atmospheric general circulation models (AGCM) in the framework of the Paleoclimate Modeling Intercomparison Project (PMIP). These simulations use the boundary conditions for CO2, insolation and ice-sheets; surface temperatures (SSTs) are either (a) prescribed using CLIMAP data set (eight models) or (b) computed by coupling the AGCM with a slab ocean (nine models). The present-day (PD) tropical climate is correctly depicted by all the models, except the coarser resolution models, and the simulated geographical distribution of annual mean temperature is in good agreement with climatology. Tropical cooling at the LGM is less than at middle and high latitudes, but greatly exceeds the PD temperature variability. The LGM simulations with prescribed SSTs underestimate the observed temperature changes except over equatorial Africa where the models produce a temperature decrease consistent with the data. Our results confirm previous analyses showing that CLIMAP (1981) SSTs only produce a weak terrestrial cooling. When SSTs are computed, the models depict a cooling over the Pacific and Indian oceans in contrast with CLIMAP and most models produce cooler temperatures over land. Moreover four of the nine simulations, produce a cooling in good agreement with terrestrial data. Two of these model results over ocean are consistent with new SST reconstructions whereas two models simulate a homogeneous cooling. Finally, the LGM aridity inferred for most of the tropics from the data, is globally reproduced by the models with a strong underestimation for models using computed SSTs.

PDFOS: PDF estimation based over-sampling for imbalanced two-class problems

This contribution proposes a novel probability density function (PDF) estimation based over-sampling (PDFOS) approach for two-class imbalanced classification problems. The classical Parzen-window kernel function is adopted to estimate the PDF of the positive class. Then according to the estimated PDF, synthetic instances are generated as the additional training data. The essential concept is to re-balance the class distribution of the original imbalanced data set under the principle that synthetic data sample follows the same statistical properties. Based on the over-sampled training data, the radial basis function (RBF) classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier’s structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed PDFOS approach is demonstrated by the empirical study on several imbalanced data sets.

Transaction costs, information technologies and the choice of marketplace among farmers in Northern Ghana

Using a transactions costs framework, we examine the impact of information and communication technologies (mobile phones and radios) use on market participation in developing country agricultural markets using a novel transaction-level data set of Ghanaian farmers. Our analysis of the choice of markets by farmers suggests that market information from a broader range of markets may not always induce farmers to sell in more distant markets; instead farmers may use broader market information to enhance their bargaining power in closer markets. Finally, we find weak evidence on the impact of using mobile phones in attracting farm gate buyers.