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.

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.

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.

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.

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.

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.

Light-absorbing carbonaceous aerosols that dominate atmospheric aerosol warming over India remain poorly characterized. Here, we delve into UV-visible-IR spectral aerosol absorption properties at nine PAN-India COALESCE network sites (Venkataraman et al., 2020, ). Absorption properties were estimated from aerosol-laden polytetrafluoroethylene filters using a well-constrained technique incorporating filter-to-particle correction factors. The measurements revealed spatiotemporal heterogeneity in spectral intrinsic and extrinsic absorption properties. Absorption analysis at near-UV wavelengths from carbonaceous aerosols at these regional sites revealed large near-ultraviolet brown carbon absorption contributions from 21% to 68%-emphasizing the need to include these particles in climate models. Further, satellite-retrieved column-integrated absorption was dominated by surface absorption, which opens possibilities of using satellite measurements to model surface-layer optical properties (limited to specific sites) at a higher spatial resolution. Both the satellite-modeled and direct in-situ absorption measurements can aid in validating and constraining climate modeling efforts that suffer from absorption underestimations and high uncertainties in radiative forcing estimates. Particulate pollution in the atmosphere scatter and absorb incoming solar energy, thus cooling or warming Earth's atmosphere. In developing countries and especially in India, one of the most polluted regions of the world, the extent to which particles can absorb solar energy and warm the atmosphere is not well understood. Here, for the first time, we measure particle absorption simultaneously at nine ground sites across India, in diverse geographical regions with different levels and types of particulate pollution. We find that organic carbon particles exert large absorption at near-ultraviolet wavelengths, which contain significant solar energy. These light absorbing organic carbon particles, called brown carbon, are emitted in large quantities from biomass burning (e.g., burning crop residue and cooking on wood-fired stoves). Comparing ground measurements of absorption with satellite-retrieved measurements that are representative of the entire atmospheric column, we find that near-surface atmospheric particles can exert significant warming. This study highlights the need to improve climate model simulations of particulate pollution's impact on the climate by incorporating spatiotemporal surface-level absorption measurements, including absorption by brown carbon particles. Measurements at nine regional PAN-India sites reveal several regions with large aerosol absorption strength Brown carbon contributes significantly (21%-68%) to near-ultraviolet absorption, indicating its importance in shortwave light absorption Strong correlations observed between satellite data and surface absorption indicate future potential in modeling surface absorption

A comprehensive global investigation on the impact of reduction (changes) in aerosol emissions due to Coronavirus disease-2019 (COVID-19) lockdowns on aerosol single scattering albedo (SSA) utilizing satellite observations and model simulations is conducted for the first time. The absolute change in Ozone Monitoring Instrument (OMI) retrieved, and two highly-spatially resolved models (Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)) simulated SSA is <4% (<0.04-0.05) globally during COVID (2020) compared to normal (2015-2019) period. Change in SSA during COVID is not significantly different from long-term and year-to-year variability in SSA. A small change in SSA indicates that significant reduction in anthropogenic aerosol emissions during COVID-19 induced lockdowns has a negligible effect in changing the net contribution of aerosol scattering and/or absorption to total aerosol extinction. The changes in species-wise aerosol optical depth (AOD) are examined in detail to explain the observed changes in SSA. Model simulations show that total AOD decreased during COVID-19 lockdowns, consistent with satellite observations. The respective contributions of sulfate and black carbon (BC) to total AOD increased, which resulted in a negligible change in SSA during the spring and summer seasons of COVID over South Asia. Europe and North America experience a small increase in SSA (<2%) during the summer season of COVID due to a decrease in BC contribution. The change in SSA (2%) is the same for a small change in BC AOD contribution (3%), and for a significant change in sulfate AOD contribution (20%) to total AOD. Since, BC SSA is 5-times lower (higher absorption) than that of sulfate SSA, the change in SSA remains the same. For a significant change in SSA to occur, the BC AOD contribution needs to be changed significantly (4-5 times) compared to other aerosol species. A sensitivity analysis reveals that change in aerosol radiative forcing during COVID is primarily dependent on change in AOD rather than SSA. These quantitative findings can be useful to devise more suitable future global and regional mitigation strategies aimed at regulating aerosol emissions to reduce environmental impacts, air pollution, and public health risks.