The Impact of Domestic Energy Efficiency Retrofit Schemes on Householder Attitudes and Behaviours

Retrofitting existing housing stock to improve energy efficiency is often required to meet climate mitigation, public health and fuel poverty targets. Increasing uptake and effectiveness of retrofit schemes requires understanding of their impacts on householder attitudes and behaviours. This paper reports results of a survey of 500 Kirklees householders in the UK, where the Kirklees Warm Zone scheme took place. This was a local government led city-scale domestic retrofit programme that installed energy efficiency measures at no charge in over 50,000 houses. The results highlight key design features of the scheme, socio-economic and attitudinal factors that affected take-up of energy efficiency measures and impacts on behaviour and energy use after adoption. The results emphasise the role that positive feedback plays in reinforcing pro-environmental attitudes and behaviours of participants and in addressing concerns of non-participants. Our findings have implications for the design and operation of future domestic energy efficiency retrofit schemes

Heparin requirements for full anticoagulation are higher for patients on dabigatran than for those on warfarin – a model-based study

Thomas Edrich,1,2 Gyorgy Frendl,2 Gregory Michaud,3 Ioannis Ch Paschalidis4 1Department of Anesthesiology, Perioperative Medicine and General Intensive Care Medicine, Salzburg General Hospital and Paracelsus Private Medical University, Salzburg, Austria, 2Department of Anesthesiology, Perioperative and Pain Medicine, 3Department of Medicine, Division of Cardiology, Brigham and Women's Hospital, Harvard Medical School, 4Department of Electrical and Computer Engineering, Division of Systems Engineering, Boston University, Boston, MA, USA Purpose: Dabigatran (D) is increasingly used for chronic anticoagulation in place of warfarin (W). These patients may present for catheter-based procedures requiring full anticoagulation with heparin. This study compares the heparin sensitivity of patients previously on dabigatran, on warfarin, or on no chronic anticoagulant during ablation of atrial fibrillation. Patients and methods: In a retrospective study of patients treated with D, W, or neither drug (N) undergoing atrial ablation, the timing of heparin doses and resulting activated clotting times were collected. First, the initial activated clotting time response to the first heparin bolus was compared. Then, a non-linear mixed effects modelling (NONMEM) analysis was performed, fitting a pharmacokinetic and -dynamic model to the entire anticoagulation course of each patient. Resulting model coefficients were used to compare the different patient groups. Results: Data for 66 patients on dabigatran, 95 patients on warfarin, and 27 patients on no anticoagulation were retrieved. The last dose of dabigatran or warfarin had occurred 27 hours and 15 hours before the procedure. Groups D and N both responded significantly less (P<0.05) to the initial heparin bolus than Group W (approximately 50%). Likewise, the model coefficients resulting from the fit to each group reflected a significantly lower heparin sensitivity in groups D and N compared to W. Clearances of the heparin effect in the model did not differ significantly among groups. Conclusion: Patients on warfarin with an average INR of 1.5 or higher are more sensitive to heparin than patients not previously anticoagulated or patients who discontinued dabigatran 27 hours earlier (approximately two half-lives) warfarin. Keywords: atrial fibrillation, electrophysiology, NONMEM, PKPD mode

Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties

Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1∘ × 0.1∘. The machine learning model is trained on AQ-Bench (“air quality benchmark dataset”), a pre-compiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data.Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods, we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the input features, we ensure that the model is only applied in regions where it is reliable.We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced

Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties

Abstract. Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here we present a data-driven ozone mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1° × 0.1°. The machine learning model is trained on AQ-Bench, a precompiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data. Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the feature space, we ensure that the model is only applied in regions where it is reliable. We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced