The Campo de Dalias GNSS Network Unveils the Interaction between Roll-Back and Indentation Tectonics in the Gibraltar Arc.

The Gibraltar Arc includes the Betic and Rif Cordilleras surrounding the Alboran Sea; it is formed at the northwest-southeast Eurasia-Nubia convergent plate boundary in the westernmost Mediterranean. Since 2006, the Campo de Dalias GNSS network has monitored active tectonic deformation of the most seismically active area on the north coast of the Alboran Sea. Our results show that the residual deformation rates with respect to Eurasia range from 1.7 to 3.0 mm/year; roughly homogenous west-southwestward displacements of the northern sites occur, while the southern sites evidence irregular displacements towards the west and northwest. This deformation pattern supports simultaneous east-northeast-west-southwest extension, accommodated by normal and oblique faults, and north-northwest-south-southeast shortening that develops east-northeast-west-southwest folds. Moreover, the GNSS results point to dextral creep of the main northwest-southeast Balanegra Fault. These GNNS results thus reveal, for the first time, present-day interaction of the roll-back tectonics of the Rif-Gibraltar-Betic slab in the western part of the Gibraltar Arc with the indentation tectonics affecting the eastern and southern areas, providing new insights for improving tectonic models of arcuate orogens.

An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis.

Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O3) and particulate matter. Due to the high cost of monitoring stations, few examples of this type of infrastructure exist, and the use of low-cost sensors could help in air quality monitoring. The cost of metal-oxide sensors (MOS) is usually below EUR 10 and they maintain small dimensions, but their use in air quality monitoring is only valid through an exhaustive calibration process and subsequent precision analysis. We present an on-field calibration technique, based on the least squares method, to fit regression models for low-cost MOS sensors, one that has two main advantages: it can be easily applied by non-expert operators, and it can be used even with only a small amount of calibration data. In addition, the proposed method is adaptive, and the calibration can be refined as more data becomes available. We apply and evaluate the technique with a real dataset from a particular area in the south of Spain (Granada city). The evaluation results show that, despite the simplicity of the technique and the low quantity of data, the accuracy obtained with the low-cost MOS sensors is high enough to be used for air quality monitoring.