http://bura.brunel.ac.uk/handle/2438/25434 Tue, 14 Jul 2026 08:45:59 GMT 2026-07-14T08:45:59Z http://bura.brunel.ac.uk/handle/2438/33562 Title: Antimony: a cryptic metabolism disruptor ubiquitous in food contact materials Authors: Wang, L; Gerassimidou, S; Geueke, B; Groh, KJ; Iacovidou, E; Majid, A; Martin, O; Muncke, J; Parkinson, LV; Weiss, MC; Sargis, RM Abstract: Antimony (Sb) is a group 15 metalloid that is used as a catalyst in the production of polyethylene terephthalate (PET) plastic, a common food contact material (FCM). PET accounts for over 44% of single-use beverage packaging units and is also used in the production of food trays, storage containers, and other items. Due to its frequent co-occurrence with other metals, Sb is also a common contaminant in crystalware, ceramics, and metal FCMs. In light of the increasing use of Sb-containing FCMs in modern society, a thorough evaluation of Sb's potential effect on public health is warranted. Burgeoning evidence suggests Sb is linked to common cardiometabolic conditions, including dyslipidemia, obesity, diabetes, hypertension, heart failure, and atherosclerotic cardiovascular disease. Thus, this review aims to (1) perform a comprehensive systematic assessment of Sb migration from FCMs into foodstuffs and food simulants, (2) obtain an overview of antimony-related health risks, and (3) inform the generation of harm-reduction guidelines at the individual and systems levels. Description: Data availability: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. Thu, 22 Jan 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33562 2026-01-22T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33547 Title: Cross-dwelling validation of indoor environmental monitoring for operational risk screening in social housing portfolios Authors: Thi Nguyen, T-H; Jin, R; Chen, W; Gan, L Abstract: Social housing providers use indoor environmental monitoring within asset management systems. The extent to which these data can differentiate operational risk domains across independent dwellings has not been fully evaluated in operational deployment. Current predictive modelling frequently relies on random data parti tioning, failing to reflect situations in which models are applied to previously unseen properties. This study examines the cross-dwelling explanatory capacity of environmental exposure indicators within a London-based housing portfolio. Five years of monitoring data from 93 UK social housing dwellings were linked with oper ational risk records, yielding 5,748 monthly dwelling-level observations. Indicators derived from temperature, relative humidity, and carbon dioxide were analysed using Ridge regression and Random Forest models under five-fold property-grouped cross-validation. Under grouped validation, indoor air quality and excess heat do mains show positive explanatory power across dwellings. In contrast, envelope-related domains, including heat loss, draught, and cold home risks, produce near-zero or negative R2 values, indicating limited cross-dwelling information in bulk indoor environmental measurements. Random Forest models do not consistently improve over regularised linear models. These findings identify the risk domains that can be informed by environ mental screening at portfolio level and those which require further or direct structural assessment within asset management practice. Mon, 25 May 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33547 2026-05-25T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33541 Title: Quantifying compound hydro-climatological extremes in coastal and deltaic regions Authors: Adnan, MSG; Kebede, AS; Addo, KA; Dewan, A; Chakrabortty, R; White, CJ; Ward, PJ Abstract: Coastal and deltaic regions are increasingly exposed to compound hydro-climatological extremes, particularly the interaction of coastal and riverine flooding with extreme temperature events such as heatwaves and heat stress. These hazards interact across spatial and temporal scales, generating complex multi-hazard events that challenge conventional single-hazard risk-reduction and adaptation strategies. Despite growing attention in recent years, quantifying such multi-hazard interactions remains challenging due to limited long-term extreme-event data and incomplete understanding of the physical processes linking different hazards. This study addresses these gaps by quantifying and characterising compound and consecutive flood–temperature extremes across five coastal or deltaic regions in Bangladesh, India, Ghana, the United Kingdom, and the Netherlands. These case study regions are subject to multiple hydro-climatological extreme events. Long-term observational time series of tidal level, river water level, air temperature, and relative humidity were analysed for each case study. Coastal and riverine flood events were identified using the 90th percentile of tidal and river water levels, respectively, while extreme heat and heat stress events were defined using the 95th percentile of air temperature and wet bulb globe temperature. Interactions among hazards were examined using Kendall’s tau correlation to assess dependency structures, cross-correlation functions to identify precursor relationships and optimal time lags, and a non-parametric copula framework to estimate joint probabilities of hazards occurring in close succession. Results reveal distinct multi-hazard profiles for each region, including characteristic time lags between interacting hazards on an annual timescale. Coastal and riverine flooding exhibited strong multivariate dependence in most of the deltaic regions studied, with optimal time lags generally shorter than three days, indicating a high susceptibility to compound flooding. Similarly, all regions showed strong co-occurrence of extreme heat and heat stress events. Notably, heterogeneous temporally compounding events were observed between Global North and Global South regions. Temporally compounding events involving mixed combinations of flooding and temperature extremes (e.g., river flooding followed by extreme heat or coastal flooding followed by heat stress) were evident in coastal Bangladesh, whereas the United Kingdom and the Netherlands were primarily affected by compound flooding and compound heat events separately. The findings of this study advance the understanding of complex multi-hazard dynamics in vulnerable coastal and deltaic environments and provides evidence to support climate-resilient and adaptive management strategies. Description: Meeting abstract presented at EGU General Assembly 2026 Session NH10.6. Fri, 13 Mar 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33541 2026-03-13T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33534 Title: Web-post buckling resistance prediction models of stainless-steel cellular beams using machine learning algorithms Authors: Bachguar, I; Mouhat, O; Shamass, R; Abarkan, I Abstract: Cellular steel beams are increasingly used in construction projects due to their decorative and economical characteristics, enabling long spans that reduce the number of columns and footings in the structure, thus shortening construction times and reducing infrastructure costs. The objective of this paper is to predict the ultimate strength, and to realize an accurate design method for determining the shear buckling of the web of stainless-steel cellular beams using machine learning. Several machine learning algorithms are trained using dataset generated from validated finite element models (FEM), including artificial neural networks, decision trees and random forests. All these models performed remarkably well, with coefficients of determination R2 greater than 0.9. The artificial neural network stood out for its superior predictive capacity, offering the best results. In particular, the ANN model with 8 neurons produced very accurate predictions for estimating the web-post buckling strength. In conclusion, a formula based on artificial neural networks (ANN) was presented and proved to be highly accurate, with a regression value (R2) equal to 0.99823, and mean absolute error (MAE), root mean square error (RMSE) values equal to 11.19 and 17.29 respectively. The formula based on artificial neural networks can therefore be used as a design tool. Mon, 01 Jun 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33534 2026-06-01T00:00:00Z