This study presents the first high-resolution Regional Climate Model 5 (RegCM5) analysis of the unprecedented May-June 2024 heatwave in India, evaluating the role of absorbing aerosols-black carbon (BC) and dust-in amplifying extreme heat. Heatwaves have a severe impact on health, mortality, and agriculture, with absorbing aerosols exacerbating warming. MERRA-2 Aerosol Optical Depth (AOD) anomalies show that BC peaked at +0.027 in May, weakening in June, while dust remained higher (up to +0.36), intensifying over the Indo-Gangetic Plain (IGP) and northwestern India. RegCM5 simulations, validated against India Meteorological Department (IMD) observational data, indicate that these aerosols amplified surface temperature anomalies, with BC-induced warming exceeding +4 degrees C in northern India during May, while dust produced stronger anomalies, surpassing +5 degrees C in the IGP and Rajasthan in June. BC-induced warming was vertically distributed and more pronounced under clear skies, whereas dust-induced warming was surface-concentrated and persisted longer in regions with higher dust concentrations. Both aerosols increased net shortwave radiation (SWR; >300 W m(-2) for BC, similar to 270 W m(-2) for dust) and upward longwave radiation (ULR; >130 W m(-2)), inducing surface energy imbalances. This radiative forcing caused lower-tropospheric warming (up to +3 degrees C at 925 hPa for BC and 850 hPa for dust) and humidity deficits (-0.009 kg/kg), which stabilised the atmosphere, suppressed convection, and delayed monsoon onset. These findings highlight aerosol-radiation interactions as critical drivers of heatwave onset and persistence, emphasizing the need for their integration into regional climate models and early warning systems.

Light-absorbing carbonaceous aerosols, comprising black carbon (BC) and brown carbon (BrC), significantly influence air quality and radiative forcing. Unlike traditional approaches that use a fixed value of absorption & Aring;ngstrom exponent (AAE), this study investigated the absorption and optical properties of carbonaceous aerosols in Beijing for both local emission and regional transport events during a wintertime pollution event by using improved AAE results that employs wavelength-dependent AAE (WDA). By calculating the difference of BC AAE at different wavelengths using Mie theory and comparing the calculated results to actual measurements from an Aethalometer (AE31), a more accurate absorption coefficient of BrC can be derived. Through the analysis of air mass sources, local emission was found dominated the pollution events during this study, accounting for 81 % of all cases, while regional transport played a minor role. Carbonaceous aerosols exhibited a continuous increasing trend during midday, which may be attributed to the re-entrainment of nighttime-accumulated carbonaceous aerosols to the surface during the early planetary boundary layer (PBL) development phase, as the mixed layer rises, combined with the variation of PBL and anthropogenic activity. At night, variations in the PBL height, in addition to anthropogenic activities, effectively contributed to surface aerosol concentrations, leading to peak surface aerosol values during local pollution episodes. The diurnal variation of AAE470/880 exhibited a decreasing trend, with a total decrease of approximately 12 %. Furthermore, the BrC fraction showed a constant diurnal variation, suggesting that the declining AAE470/880 was primarily influenced by BC, possibly due to enhanced traffic contributions.

Proper characterization of river flow is essential for the development of structural and non-structural measures to reduce flood damages, restore ecosystem functions, and manage environmental contaminants in riparian zones. The duration of flood events is an important feature that drives riverine processes and functions such as erosion, geomorphic adjustment, habitat suitability, and nutrient and water quality dynamics. Despite this, most flood characterization methods focus on relating the magnitude of annual-maximum discharges to frequency, without addressing the duration of flood events. We investigated event-specific discharge-duration dynamics at 33 USGS stream gages within the US state of Vermont. Building on the method of Feng et al., 2017, , flood events from 15-min discharge timeseries were extracted using an automated threshold method. A statistical model was fit at each gage for both frequency of discharge exceedance and conditional duration of discharge exceedance. This Duration-Over-Threshold model estimates the arrival rate of a discharge threshold, q, being exceeded for a given duration, d. Fitted model parameters were compared to basin and channel physiographical characteristics to develop regional regression equations and examine potential watershed processes underlying the duration dynamics. Model parameters summarizing event duration were best predicted by drainage area, mainstem slope, and soil depth/type. The regional regression equations enable design event estimation in ungaged catchments of the study region, which may be used to improve the predictive capacity of hydraulic and ecosystem models, outline a range of potential geomorphic trajectories, or inform emergency management plans and flood damage rating curves.

Human activities involving combustion and agricultural practices, among others, lead to the release of acidifying compounds such as nitrogen oxides (NOx), sulfur oxides (SOx), and ammonia (NH3). These substances are the main drivers of human-induced terrestrial acidification, a geochemical process resulting mainly in the decline of soil pH, causing ecosystem damage and biodiversity loss. A relevant tool to quantify impacts of human activities is Life Cycle Assessment where characterization factors are used to estimate the potential environmental impacts per unit of emission. These are derived from models of environmental processes occurring along the stressor's impact pathway, connecting an emission to its potential environmental damage. Here, new ecosystem quality characterization factors for terrestrial acidification were developed, assessing the potential global loss of vascular plant species. The final values combine four elements: existing fate factors, updated soil response factors, recently revised effect factors, and the Global Extinction Probability. The latter allows to convert the local decline in species richness into a global species loss. The regionalized marginal characterization factors provided represent the aggregated global biodiversity impact in all the world's ecoregions due to an acidifying emission (of NOx, NHx, or SOx) from a specific country. The values cover five orders of magnitude (from 10- 16 to 10-11 PDFglobal.yr.kgemitted- 1 ), and the comparison to currently implemented values has helped both validate the calculation pathway and confirm the need for updated factors. Following current harmonization recommendations, terrestrial acidification impacts can now be compared to those from other stressors estimated in global Potential Disappeared Fraction of species.

This study analyzed hops from 35 fields located in two states (Washington and Oregon) repeatedly over 2 harvest years (2020 and 2021) to determine the impact that hop variety and regional identity, or terroir, might have on hops' dextrin reducing enzymatic potential. Cascade and Mosaic (R) hops were harvested, kilned, pelletized, and analyzed for dextrin-reducing enzymatic activity using a bench-top dry-hopping assay in a high-dextrin beer. In addition, data for 25 soil, 14 management, 13 climate, and 27 chemistry variables were collected and compared to the enzyme activity results from the bench-top dry-hopping assay. There existed a highly significant difference in enzymatic activity based on hop variety (two sample t-test p-value = 1.18 x 10(-14)) with Cascade hops being approximately 60% higher on average than Mosaic (R) hops regardless of growing region or harvest year. The soil and farm management variables also showed statistically significant interactions with enzymatic activity (p-values of 7.82 x 10(-9) for Cascade and < 2 x 10(-16) for Mosaic (R)), though there was little clarity with respect to the specific terroir variables that might relate to hop creep. Further research is needed to better understand causal interactions between farm, soil, climate, and management practices and dry-hop-induced dextrin-reducing enzymatic activity.

Aviation emissions contribute to climate change and local air pollution, with important contributions from non-CO2 emissions. These exhibit diverse impacts on atmospheric chemistry and radiative forcing (RF), varying with location, altitude, and time. Assessments of local mitigation strategies with global emission metrics may overlook this variability, but detailed studies of aviation emissions in areas smaller than continents are scarce. Integrating the AviTeam emission model and OsloCTM3, we quantify CO2, NOx, BC, OC, and SOx emissions, tropospheric concentration changes, RF, region-specific metrics, and assess alternative fuels for Norwegian domestic aviation. Mitigation potentials fora fuel switch to LH2 differ by up to 3.1 x 108 kgCO2-equivalents (GWP20) when using region-specific compared to global metrics. These differences result from a lower, region- specific contribution of non-CO2 emissions, particularly related to NOx. This study underscores the importance of accounting for non-CO2 variability in regional assessments, whether through region-specific metrics or advanced atmospheric modelling techniques.

Aerosols can alter atmospheric stability through radiative forcing, thereby changing mean and daily extreme precipitation on regional scales. However, it is unclear how extreme sub-daily precipitation responds to aerosol radiative effects. In this study, we use the regional climate model (RCM) Consortium for Small-scale Modeling (COSMO) to perform convection-permitting climate simulations at a kilometer-scale (0.04 degrees/similar to 4.4 km) resolution for the period 2001-2010. By evaluating against the observed hourly precipitation-gauge data, the COSMO model with explicit deep convection can effectively reproduce sub-daily and daily extreme precipitation events, as well as diurnal cycles of summer mean precipitation and wet hour frequency. Moreover, aerosol sensitivity simulations are conducted with sulfate and black carbon aerosol perturbations to assess the direct and semi-direct aerosol effects on extreme sub-daily precipitation in the COSMO model. The destabilizing effects associated with decreased sulfate aerosols intensify extreme sub-daily precipitation, while increased sulfate aerosols tend to induce an opposite change. In contrast, the response of extreme sub-daily precipitation to black carbon aerosol perturbations exhibits a nonlinear behavior and potentially relies on geographical location. Overall, the scaling rates of extreme precipitation intensities decrease and approach the Clausius-Clapeyron rate from hourly to daily time scales, and the responses to sulfate and black carbon aerosols vary with precipitation durations. This study improves the understanding of aerosol radiative effects on sub-daily extreme precipitation events in RCMs.

This study addresses the critical need to understand the seismic behavior of cable-stayed bridges under Multi-Support Excitation (MSE) in order to mitigate earthquake-induced damage to these structures. The primary focus is on the investigation of response amplification phenomena and their seismic implications for cable-stayed bridges. Through a detailed comparative analysis of MSE and Synchronous Excitation (SE) across various structural locations, the study evaluates the impact of site-specific recorded ground motions of different earthquake categories. A pragmatic framework is developed to simulate realistic MSE ground motions for diverse earthquake scenarios, emphasizing the necessity of considering MSE in bridge design. The findings reveal a significant amplification of the design requirements due to antisymmetric mode excitation and increased tower and pier motions. The study also identified the need for in-depth analysis of cable-stayed bridges to address the increased vulnerability of tower-adjacent areas and to devise targeted reinforcement strategies of vulnerable components. These insights are critical for advancing seismic design practices and improving the resilience of cable-stayed bridges, contributing to safer urban infrastructure.

Seismic fragility analysis is a crucial tool for assessing the seismic performance of buildings. In areas with dense clusters of tall buildings, the significant site-city interaction (SCI) effect alters wave propagation mechanisms, influencing the seismic fragility of structures. However, a significant increase in computational workload results from the need for detailed modeling of sites and building clusters for the SCI analysis. To address this challenge, this work first investigates the minimum number of earthquake waves required to characterize SCI-induced response changes. The Central Business District of Shanghai is analyzed. A table for the recommended minimum number for a given accuracy requirement and prediction reliability is provided. Moreover, a seismic fragility analysis method considering the SCI effect is proposed for low-rise buildings. The case study indicates that, buildings with similar height will exhibit various fragility changes after considering SCI. For the complete damage state, the mean intensity value of the fragility curve can be 14.4 % smaller than that without SCI. In addition, this approach provides significant computational workload reduction. For the case study, the computational workload of the proposed method is roughly 1/50 of that using traditional IDA method.

Construction of large underground infrastructure facilities routinely leads to leakage of groundwater and reduction of pore water pressures, causing time-dependent deformation of overburden soft soil. Coupled hydrogeomechanical numerical models can provide estimates of subsidence, caused by the complex time-dependent processes of creep and consolidation, thereby increasing our understanding of when and where deformations will arise and at what magnitude. However, such hydro-mechanical models are computationally expensive and generally not feasible at larger scales, where decisions are made on design and mitigation. Therefore, a computationally efficient Machine Learning-based metamodel is implemented, which emulates 2D finite element scenario-based simulations of ground deformations with the advanced Creep-SCLAY-1S-model. The metamodel employs decision tree-based ensemble learners random forest (RF) and extreme gradient boosting (XGB), with spatially explicit hydrostratigraphic data as features. In a case study in Central Gothenburg, Sweden, the metamodel shows high predictive skill (Pearson's r of 0.9-0.98) on 25% of unseen data and good agreement with the numerical model on unseen cross-sections. Through interpretable Machine Learning, Shapley analysis provides insights into the workings of the metamodel, which alignes with process understanding. The approach provides a novel tool for efficient, scenario-based decision support on large scales based on an advanced soil model emulated by a physically plausible metamodel.