A psychophysical measurement on subjective well-being and air pollution

Although the physical effects of air pollution on humans are well documented, there may be even greater impacts on the emotional state and health. Surveys have traditionally been used to explore the impact of air pollution on people’s subjective well-being (SWB). However, the survey techniques usually take long periods to properly match the air pollution characteristics from monitoring stations to each respondent’s SWB at both disaggregated spatial and temporal levels. Here, we used air pollution data to simulate fixed-scene images and psychophysical process to examine the impact from only air pollution on SWB. Findings suggest that under the atmospheric conditions in Beijing, negative emotions occur when PM2.5 (particulate matter with a diameter less than 2.5 µm) increases to approximately 150 AQI (air quality index). The British observers have a stronger negative response under severe air pollution compared with Chinese observers. People from different social groups appear to have different sensitivities to SWB when air quality index exceeds approximately 200 AQI

Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations

Daily precipitation statistics as simulated by the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble are evaluated over two distinct regions of the European continent, namely the European Alps and Spain. The potential added value of the high-resolution 12 km experiments with respect to their 50 km resolution counterparts is investigated. The statistics considered consist of wet-day intensity and precipitation frequency as a measure of mean precipitation, and three precipitation-derived indicators (90th percentile on wet days?90pWET, contribution of the very wet days to total precipitation?R95pTOT and number of consecutive dry days?CDD). As reference for model evaluation high resolution gridded observational data over continental Spain (Spain011/044) and the Alpine region (EURO4M-APGD) are used. The assessment and comparison of the two resolutions is accomplished not only on their original horizontal grids (approximately 12 and 50 km), but the high-resolution RCMs are additionally regridded onto the coarse 50 km grid by grid cell aggregation for the direct comparison with the low resolution simulations. The direct application of RCMs e.g. in many impact modelling studies is hampered by model biases. Therefore bias correction (BC) techniques are needed at both resolutions to ensure a better agreement between models and observations. In this work, the added value of the high resolution (before and after the bias correction) is assessed and the suitability of these BC methods is also discussed. Three basic BC methods are applied to isolate the effect of biases in mean precipitation, wet-day intensity and wet-day frequency on the derived indicators. Daily precipitation percentiles are strongly affected by biases in the wet-day intensity, whereas the dry spells are better represented when the simulated precipitation frequency is adjusted to the observed one. This confirms that there is no single optimal way to correct for RCM biases, since correcting some distributional features typically leads to an improvement of some aspects but to a deterioration of others. Regarding mean seasonal biases before the BC, we find only limited evidence for an added value of the higher resolution in the precipitation intensity and frequency or in the derived indicators. Thereby, evaluation results considerably depend on the RCM, season and indicator considered. High resolution simulations better reproduce the indicators? spatial patterns, especially in terms of spatial correlation. However, this improvement is not statistically significant after applying specific BC methods.The authors are grateful to Prof. C. Schär for his helpful comments and E. van Meijgaard for making available the RACMO model data. We acknowledge the observational data providers. Calculations for WRF311F were made using the TGCC super computers under the GENCI time allocation GEN6877. The WRF331A from CRP-GL (now LIST) was funded by the Luxembourg National Research Fund (FNR) through grant FNR C09/SR/16 (CLIMPACT). The KNMI-RACMO2 simulations were supported by the Dutch Ministry of Infrastructure and the Environment. The CCLM and REMO simulations were supported by the Federal Ministry of Education and Research (BMBF) and performed under the Konsortial share at the German Climate Computing Centre (DKRZ). The CCLM simulations were furthermore supported by the Swiss National Supercomputing Centre (CSCS) under project ID s78. Part of the SMHI contribution was carried out in the Swedish Mistra-SWECIA programme founded by Mistra (the Foundation for Strategic Environmental Research). This work is supported by CORWES (CGL2010-22158-C02) and EXTREMBLES (CGL2010-21869) projects funded by the Spanish R&D programme and the European COST ACTION VALUE (ES1102). A. C. thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI programme (BES-2011-047612 and EEBB-I-13-06354). We also thank two anonymous referees for their useful comments that helped to improve the original manuscript

The role of multi-slice computed tomography in stable angina management: a current perspective

Contrast-enhanced CT coronary angiography (CTCA) has evolved as a reliable alternative imaging modality technique and may be the preferred initial diagnostic test in patients with stable angina with intermediate pre-test probability of CAD. However, because CTCA is moderately predictive for indicating the functional significance of a lesion, the combination of anatomic and functional imaging will become increasingly important. The technology will continue to improve with better spatial and temporal resolution at low radiation exposure, and CTCA may eventually replace invasive coronary angiography. The establishment of the precise role of CTCA in the diagnosis and management of patients with stable angina requires high-quality randomised study designs with clinical outcomes as a primary outcome

Wind speed variability over the Canary Islands, 1948-2014: focusing on trend differences at the land-ocean interface and below-above the trade-wind inversion layer

This study simultaneously examines wind speed trends at the land?ocean interface, and below?above the trade-wind inversion layer in the Canary Islands and the surrounding Eastern North Atlantic Ocean: a key region for quantifying the variability of trade-winds and its response to large-scale atmospheric circulation changes. Two homogenized data sources are used: (1) observed wind speed from nine land-based stations (1981?2014), including one mountain weather station (Izaña) located above the trade-wind inversion layer; and (2) simulated wind speed from two atmospheric hindcasts over ocean (i.e., SeaWind I at 30 km for 1948?2014; and SeaWind II at 15 km for 1989?2014). The results revealed a widespread significant negative trend of trade-winds over ocean for 1948?2014, whereas no significant trends were detected for 1989?2014. For this recent period wind speed over land and ocean displayed the same multi-decadal variability and a distinct seasonal trend pattern with a strengthening (late spring and summer; significant in May and August) and weakening (winter?spring?autumn; significant in April and September) of trade-winds. Above the inversion layer at Izaña, we found a predominance of significant positive trends, indicating a decoupled variability and opposite wind speed trends when compared to those reported in boundary layer. The analysis of the Trade Wind Index (TWI), the North Atlantic Oscillation Index (NAOI) and the Eastern Atlantic Index (EAI) demonstrated significant correlations with the wind speed variability, revealing that the correlation patterns of the three indices showed a spatio-temporal complementarity in shaping wind speed trends across the Eastern North Atlantic.C. A. -M. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 703733 (STILLING project). This research was also supported by the Research Projects: Swedish BECC, MERGE, VR (2014–5320), PCIN-2015-220, CGL2014-52135-C03-01 and Red de variabilidad y cambio climático RECLIM (CGL2014-517221-REDT). M.M is indebted to the Spanish Government for funding through the “Ramón y Cajal” program and supported by Grant PORTIO (BIA2015-70644-R