This study presents a novel deep-learning framework for predicting the thermal appearance of building envelopes under varying weather conditions based on a new dataset collected using a thermal infrared camera at 10 min intervals over a one-and-a-half-year period. Unlike existing studies that rely on simulated data or physical models that do not always accurately reflect the complex heat transfer processes in real buildings, we have collected a large dataset showing how a building behaves under different climatic conditions. We propose a novel deep-learning approach that integrates weather data and thermal imagery to predict the temperature distribution on the building façade for the next 24 and 48 h. The model uses a state-of-the-art recurrent neural network architecture, PredRNN V2, with an action conditioning mechanism to incorporate weather forecasting data into the prediction process. We evaluate this approach in terms of average accuracy, prediction accuracy in specific regions, and visual-perceptual performance of the images. The proposed framework achieves a prediction accuracy of 1.5 °C (root mean square error—RMSE) for the 24 h prediction and 2.04 °C (RMSE) for the 48 h prediction, outperforming baseline models in terms of temperature prediction accuracy and structural similarity of the predicted images.

Reinforced by this study, New York City has one of the cleanest water systems in the world. Medgar Evers College (MEC) serves 7000 students/1050 faculty/staff. Given that: (1) students/faculty/staff spend 20–30% of their daily time there; (2) potable water sources must abide by the EPA’s maximum contamination levels (MCLs); and (3) a detrimental impact on human health arises from violations to EPA’s water quality mandates, we quantified the abundance of 27 heavy metals (96 samples, N = 3) using MEC as a case study. Water was collected from all potable water sources, following EPA protocols for sample-matrix preparation, collection, and wet-chemical analysis. Linear polyethylene containers/caps were used to prevent sample contamination while the water samples were spiked with HNO₃ (aq) for preservation. Heavy metal concentrations were quantified using New Jersey’s Meadowlands Environmental Research Institute’s Inductively Coupled Plasma-Mass Spectrometer (ICP-MS, Agilent 7700X) in no gas, and He flow modes. Ninety-five percent of sample concentration relative standard deviations (RSDs) reveal four distinct regions: (1) where one mode is more precise than the other, and sample data exhibit very good to excellent precision, RSD ≤ 15%; (2) despite being at low concentrations, measurements exhibit good to excellent precision, RSD ≤ 20%; (3) species concentrations ≥0.1 ppb very good to excellent precision is shown, RSD ≤ 15%; and (4) species at concentrations ≤ 10⁻³ ppb display fair to very poor precision, RSD ≥ 30%. All heavy metals complied with their respective EPA MCLs (except Fe). Over 90% of Fe sample concentrations were enhanced by up to about 30×. Two samples exhibited [Pb] = 13.7 (No gas mode, RSD = 3.32%) and 14.8 ppb (He mode, RSD = 0.75%), which is close to the EPA Primary MCL, 15 ppb. Based on EPA/WHO end-member equations, we estimate a 1/103 to 1/108 chance of cancer attainment from long-term exposure to the range of concentrations of heavy metals measured in this study.

The discourse on vulnerability to COVID-19 or any other pandemic is about the susceptibility to the effects of disease outbreaks. Over time, vulnerability has been assessed through various indices calculated using a confluence of societal factors. However, categorising Arctic communities, without considering their socioeconomic, cultural and demographic uniqueness, into the high and low continuum of vulnerability using universal indicators will undoubtedly result in the underestimation of the communities’ capacity to withstand and recover from pandemic exposure. By recognising vulnerability and resilience as two separate but interrelated dimensions, this study reviews the Arctic communities’ ability to cope with pandemic risks. In particular, we have developed a pandemic vulnerability–resilience framework for Alaska to examine the potential community-level risks of COVID-19 or future pandemics. Based on the combined assessment of the vulnerability and resilience indices, we found that not all highly vulnerable census areas and boroughs had experienced COVID-19 epidemiological outcomes with similar severity. The more resilient a census area or borough is, the lower the cumulative death per 100 000 and case fatality ratio in that area. The insight that pandemic risks are the result of the interaction between vulnerability and resilience could help public officials and concerned parties to accurately identify the populations and communities at most risk or with the greatest need, which, in turn, helps in the efficient allocation of resources and services before, during and after a pandemic. A resilience–vulnerability-focused approach described in this paper can be applied to assess the potential effect of COVID-19 and similar future health crises in remote regions or regions with large Indigenous populations in other parts of the world.

This study quantifies emissivity uncertainty using a new, specifically collected multi-angle thermal hyperspectral dataset, Nittany Radiance. Unlike previous research that primarily relied on model-based simulations, multispectral satellite imagery, or laboratory measurements, we use airborne hyperspectral long-wave infrared (LWIR) data captured from multiple viewing angles. The data was collected using the Blue Heron LWIR hyperspectral imaging sensor, flown on a light aircraft in a circular orbit centered on the Penn State University campus. This sensor, with 256 spectral bands (7.56–13.52 μm), captures multiple overlapping images with varying ranges and angles. We analyzed nine different natural and man-made targets across varying viewing geometries. We present a multi-angle atmospheric correction method, similar to FLAASH-IR, modified for multi-angle scenarios. Our results show that emissivity remains relatively stable at viewing zenith angles between 40 and 50° but decreases as angles exceed 50°. We found that emissivity uncertainty varies across the spectral range, with the 10.14–11.05 μm region showing the greatest stability (standard deviations typically below 0.005), while uncertainty increases significantly in regions with strong atmospheric absorption features, particularly around 12.6 μm. These results show how reliable multi-angle hyperspectral measurements are and why angle-specific atmospheric correction matters for non-nadir imaging applications

Hyperspectral imaging in the long-wave infrared (LWIR) range enables identification of chemical compositions and material properties, but reconstructing 3D models from gimballed pushbroom sensors remains challenging because their unique acquisition geometry is incompatible with conventional photogrammetric software designed for frame cameras. This study presents a workflow for creating explorable 3D models from multi-angle LWIR hyperspectral imagery by co-registering hyperspectral line-scan data with simultaneously acquired RGB frame camera imagery using deep learning-based image matching. The co-registered images are processed in commercial photogrammetric software (Agisoft Metashape), and a texture-to-image mapping algorithm preserves correspondences between 3D model coordinates and original hyperspectral pixels across multiple viewing angles. Quantitative evaluation against reference data demonstrates that co-registration reduces geometric error approaching the accuracy of models built from high-resolution RGB imagery. The resulting models enable the retrieval of 8–50 spectral signatures per surface point, captured from different viewing geometries. This approach facilitates interactive exploration of angular variations in thermal infrared spectra, supporting material identification for non-Lambertian surfaces where single-angle observations may be insufficient for reliable classification.