Ammonium contamination is one of the most nocive forms of groundwater contamination caused due to the extensive use of nitrogen fertilizers. Contaminant transport is governed by the adsorption mechanism, which varies as the contaminant migrates through different types of soils. It is important to understand the adsorption mechanism of ammonium ions and the factors influencing them to predict and mitigate their contamination. This work attempts to thoroughly investigate the effects of clay content present in the soil on the adsorption and eventually on the retardation of ammonium ions movement in the porous media. Ammonium ions transport was examined for three soil types with different clay proportions, by column and batch experiments. The experimental results were verified by simulating ammonium ion migration by numerical modeling using HYDRUS 2D software. It was observed the ammonium ions adsorption increases with the increase in the clay content of the soil. Therefore, greater content of clay in the soil enhances the retardation of ammonium migration in the soil. Further, it can be construed that the soil clay fraction ensures a reduction in deeper penetration of ammonium ions as a contaminant into the subsurface.

The SEIS seismometer deployed at the surface of Mars in the framework of the NASA-InSight mission has been continuously recording the ground motion at Elysium Planitia for more than one martian year. In this work, we investigate the seasonal variation of the near surface properties using both background vibrations and a particular class of high-frequency seismic events. We present measurements of relative velocity changes over one martian year and show that they can be modeled by a thermoelastic response of the Martian regolith. Several families of high-frequency seismic multiplets have been observed at various periods of the martian year. These events exhibit repeatable waveforms with an emergent character and a coda that is likely composed of scattered waves. Taking advantage of these properties, we use coda waves interferometry to measure relative travel-time changes as a function of the date of occurrence of the quakes. While in some families a stretching of the coda waveform is clearly observed, in other families we observe either no variation or a clear contraction of the waveform. Measurements of velocity changes from the analysis of background vibrations above 5Hz are consistent with the results from coda wave interferometry. We identify a frequency band structure in the power spectral density, that can be tracked over hundreds of days. This band structure is the equivalent in the frequency domain of an autocorrelogram and can be efficiently used to measure relative travel-time changes as a function of frequency. The observed velocity changes can be adequately modeled by the thermoelastic response of the regolith to the time-dependent incident solar flux at the seasonal scale. In particular, the model captures the time delay between the surface temperature variations and the velocity changes in the sub-surface. Our observations could serve as a basis for a joint inversion of the seismic and thermal properties in the first meters below InSIght.

The Global Burden of Disease attributes millions of premature deaths to ambient air pollution each year, making it one of the largest environmental health risks faced by society. This mortality is largely due to exposure to fine particulate matter (PM2.5). In the United States, the Environmental Protection Agency estimated that 50.5 million people lived in counties with PM2.5 concentrations above the level of the National Ambient Air Quality Standards in 2020. PM2.5 levels can be derived from satellite aerosol optical depth (AOD) measurements providing comprehensive spatial and temporal coverage. However, the chemical composition of PM2.5 affects the mechanisms by which adverse health effects occur, and thus there is a pressing need for linking satellite data with high-resolution atmospheric modeling of PM2.5 composition. In order to better inform public health policy and decision-making, we aim to estimate near-real-time (NRT) surface PM2.5 composition informed by satellite AOD measurements and chemical transport modeling for the first time. Here, we demonstrate this framework for hindcast estimates in year 2021. NRT AOD is collected from multi-source remote sensing data including Moderate Resolution Imaging Spectroradiometer (MODIS; Aqua and Terra), the Visible Infrared Imaging Radiometer Suite (VIIRS; Dark Target and Deep Blue), and Multi-Angle Imaging SpectroRadiometer (MISR). The data obtained from these products are combined into daily, 10-km AOD estimates and used to scale simulated total PM2.5. GEOS-Chem (v13.1.2) nested regional simulations are run over North America with GEOS-Forward Processing (FP) assimilated meteorology at resolution 0.25° lat. x0.3125° lon. (approximately 20-30km) to simulate daily AOD and get an initial estimate of PM2.5 composition. This estimate is interpolated into the 10-km grid and multiplied with the satellite-adjusted total PM2.5 composition to produce concentrations of each PM2.5 species. This satellite-constrained chemical transport model framework estimates of PM2.5 will ultimately be evaluated against observations and compared to estimates using standard satellite products to inform future use of this framework to predict ambient air pollution health risks in true near-real-time.

Temperature (T) is a key parameter controlling the rheology of lava flows. Since hazardous behavior of eruptions prevents direct measurements of hot magmatic bodies [1], the temperature is mostly retrieved by measuring the infrared (IR) radiance of the lava flow [2, 3]. The determination of T is however subjected to important errors related to the poor knowledge of one of the most critical parameters, namely spectral emissivity (ε). In this study, we explored the temperature–emissivity relationship for basaltic magmas, mostly from the 2014–2015 Holuhraun eruption. We performed in situ spectral emissivity measurements at relevant magmatic temperatures (from room temperature up to 1800 K) over a wide spectral range (400–8000 cm−1) covering TIR, MIR and SWIR regions, using a non-contact IR emissivity apparatus [4]. To unravel the complex radiative behavior of basalts with temperature evolution, structural, chemical and textural analyses (SEM, EMPA, Raman spectroscopy, DSC, XRD, and TEM) were systematically performed. Our results show that spectral emissivity varies accordingly with temperature, wavenumber, and is greatly affected by micro-scale crystallization, emphasizing the effect of small change in silicate structure on magma radiative properties. Because of the multiphase nature of lava, each constitutive phase (glass, melt, crystal, vesicles) contribute differently to the spectral emissivity. The evaluation and quantification of the impact of these phases on effective thermal radiative properties is a key point to improve the accuracy of lava T determination. These new data will ultimately improve our knowledge of the complex lava flow properties that are crucial in thermo-rheological models for hazard assessment [5]. References: [1] Kolzenburg et al. 2017. Bull. Volc. 79:45. [2] Harris, A. 2013: Cambridge University press. 728. [3] Rogic et al. 2019 Remote Sens., 11, 662 [4] De Sousa Meneses et al. 2015. Infrared Physics & Technology 69. [5] Thompson and Ramsey, 2021, Bulletin of Volcanology, 83:41. Keywords: Spectral emissivity, temperature, IR spectroscopy, rheology, basalt

Nutrient enrichment is a major issue to many inland and coastal waterbodies worldwide, including Chesapeake Bay. River water quality integrates the spatial and temporal changes of watersheds and forms the foundation for disentangling the effects of anthropogenic inputs. However, many water-quality studies are focused on limited portions of the watershed or a subset of potential drivers. We demonstrate with the Chesapeake Bay Nontidal Monitoring Network (84 stations) that advanced machine learning approaches – i.e., hierarchical clustering and random forest – can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks. Cluster analysis revealed the regional patterns of short-term TN trends (2007-2018) and categorized the stations to three distinct clusters, namely, V-shape (n = 25), monotonic decline (n = 35), and monotonic increase (n = 26). Random forest models were developed to predict the clusters using watershed characteristics and major N sources, which provided information on regional drivers of TN trends. We show encouraging evidence that improved nutrient management has resulted in declines in agricultural nonpoint sources, which in turn contributed to water quality improvement. Additionally, water-quality improvements are more likely in watersheds underlain by carbonate rocks, reflecting the relatively quick groundwater transport of this terrain. However, TN trends are degrading in forested watersheds, suggesting new sources of N in forests. Finally, TN trends were predicted for the entire Chesapeake Bay watershed at the scale of 979 river segments, providing fine-level information that can facilitate targeted watershed management, especially in unmonitored areas. More generally, this combined use of clustering and classification approaches can be applied to other monitoring networks to address similar questions.

Fire is an essential global phenomenon that existed soon after the appearance of terrestrial plants and is vital for the regeneration of the plant species. Human activities have contributed to a changing climate and impacted fire regimes, resulting in more intense, frequent and severe fires. In particular, the 2019-20 bushfires in south-eastern Australia were unprecedented in their extent and intensity. However, human activities can also play a dominant role in regulating fire behaviour effectively through better fire management practices. In Northern Australia, indigenous fire managers use prescribed burns during the early dry season to prevent large late dry season fires, which shifts the overall temporal distribution of fire activity earlier during the primary biomass burning season. This increasing trend of prescribed burns has helped to significantly reduce the size and extent of the intense late dry season fires, indicating that such fire management practices can be effective at managing wildfires in savannas. Biomass burning can emit many chemical species that have an impact on human health. One of the most abundant and widely measured is carbon monoxide (CO), whose long-term exposure can lead to potential human health risk. CO is also a good proxy for emissions of other shorter-lived and harder-to-measure atmospheric constituents. This study is focussed on understanding how the earlier fire season in Northern Australia impacts the temporal shift in annual cycle of CO. Column CO data from the ground-based Total Carbon Column Observing Network site in Darwin will be used together with surface measurements, complemented by the surface mixing ratio observations from MOPITT, in order to disentangle the CO emitted from the study region from that measured in the column from remote emissions coupled with long-range transport. GEOS-Chem CO tagged tracer modelling capability will be used to better understand the effect of local fire emissions on the surface and column CO.

Urbanization increases runoff, sediment, and nutrient loadings to freshwaters, causing flooding, harmful algal blooms, and increased costs for drinking water filtration. Most watershed models use runoff quality data from specific land uses; coupled with a variety of methods for quantifying loads, the most common being Build-up and Wash-off (BUWO) equations for which loading is a function of antecedent dry period (ADP). However, several studies have shown there is no significant correlation between urban runoff quality and ADP, suggesting predictions based solely on landuse and ADP are questionable. The objective of this study is to discover which parameters, climatological or catchment characteristics, are most significant and should be included in the BUWO relationship. Stormwater quality data was obtained from the National Stormwater Quality Database (NSQD), which is the largest and most recent database of its type in the U.S. Bayesian Network Structure Learner (BNSL) was used to assess the relationships between catchment characteristics, climatological information, and runoff quality for each land use. Given the optimal BN structure, it was determined which parameters affect water quality event mean concentrations the most, and which the least. The results demonstrated that for some constituents (i.e. particulate nitrogen, particulate phosphorous, and sediments), other factors (such as rainfall depth and duration, surface slope) exert a more important influence on urban runoff quality than ADP.

Accelerometer-derived neutral mass density (NMD) is an important measurement of the variability in upper atmosphere and one of the widely used measurements to calibrate and validate models used for satellite orbit determination and prediction. Providing precise estimates of the true uncertainty of these NMD products is a challenging task but essential for the space weather and geodetic communities. Using multiple data assimilation (DA) experiments and robust statistical techniques, we investigate the uncertainty distribution of three different accelerometer-derived NMD products from the CHAMP satellite mission. Here, in three different DA experiments, we use an ensemble Kalman filter to drive a physics-based model with CHAMP in-situ electron density and temperature data as well as neutral wind estimates from an empirical model. Using a multi-model ensemble comprised of both physical and empirical models, we characterize the error variances among the different NMD products. Our results indicate considerable differences among the CHAMP data sets and also show a pronounced latitudinal dependency for the estimated error distributions. On average, the error estimates for NMD vary in the range 6.5–15.6% of the signal. Our experiments demonstrate that DA considerably enhances the capability of the physical model. We note that the generic strategies applied here may be useful and applicable to other space missions spanning over longer time periods.

With population surge, industrialization and urbanization; use of surfactants has increased manifold. After their use in households and industries, surfactants either end up in sewerage systems or are directly discharged into surface waters. They can hence be found dispersed in water phase or adsorbed onto aquatic sediments and sewage sludge. Due to limited metabolic pathways, most of the common surfactants are not degradable in anaerobic conditions that generally prevail in sewage ingressed water bodies so sludge accumulated in these water bodies after the anaerobic digestion process is rich in surfactants (Ying, 2006, Environ. Int.). Sediment is a complex mixture of organic (bacteria, proteins, humic and fulvic acids, humin, etc.) and inorganic (silica, minerals, metal oxides and hydroxides, aluminosilicates) components. Absorptivity of surfactant depends on the sorbent composition and type of surfactant (Ishiguro & Koopal, 2016, Adv. Colloid Interface Sci.). A crucial question related to surfactant absorption is its reversibility- as to whether the surfactant can desorb. The knowledge of this can be beneficial in understanding the environmental fate of surfactants. The presence of surfactants in water bodies can form foams. Generally, foaming in surface water is a result of a mixture of surfactants from various sources (Schilling & Zessner, 2011, Water Res.). However, contribution of sediment as a source of surfactant in a foaming water body has not been studied adequately. Also, existence of surfactants in water, beyond certain concentrations not only induces unpleasant taste and odor, but also causes undesirable changes in the ecosystem. Thus, it becomes imperative to study the ratio of the surfactant associated with water, to the extent of surfactant associated the sorbent such as sediment/sludge to understand the environmental risk associated with surfactant. This study aims to understand a foaming urban lake which foams only after heavy rainfalls. This study tests the hypothesis that surfactants accumulate in the lake sediment in significant proportions and desorbs upon dilution occurring due to addition of rainwater into the lake. This when churned by heavy runoff causes large quantities of stable foam. This study aims at analyzing the role of lake sediment in foaming of a lake.

The scale of wildfires and their associated smoke plumes have increased in the last four decades and have exacerbated the health risks for people living within wildfire-prone areas. While satellite imagery enables us to monitor the location, abundance, and severity of smoke, datasets such as NOAA’s Hazard Mapping System (HMS) smoke product are unvalidated and may not reflect ground-level smoke conditions. We propose that airports monitoring the presence of smoke may offer a localized constraint on satellite smoke products. Our research aims to validate NOAA’s HMS product by comparing how well the HMS smoke data agree with NOAA’s Integrated Surface Database (ISD) of local, hourly airport data from 2010 to 2020 across the US. Our spatial and temporal analysis shows that the trend in the number of smoke days is similar between the two datasets. HMS smoke plumes are qualitatively categorized into 3 densities: light, medium, and heavy. Particularly, HMS smoke plumes with “medium or heavy” densities have the strongest correlation of r = 0.60 and a mean absolute error of 0.73. We observe the greatest difference in the airport and HMS-derived mean smoke days across the west and east coast, especially in California’s Central Valley and eastern Washington. However, there is a very weak correlation of r = 0.17 between surface-level mean smoke days and from HMS “heavy, medium, or light” density smoke days. Further, using all HMS smoke densities tend to overestimate the trends in and magnitude of smoke days. Based on these results, NOAA’s HMS product is somewhat consistent with trends observed at airports, but caution should be used in assuming equivalence with surface conditions in research investigating wildfire smoke and its consequences for public health.

The largest city in New Zealand, Auckland is home to roughly 1.5 million people -- one third of New Zealand's population. Here we assemble a bottom-up inventory of Auckland's fossil fuel carbon dioxide emissions from a variety of data sources. We use these emissions estimates in combination with the UrbanVPRM land surface model to estimate the net carbon balance of the region. This work is part of the larger CarbonWatch NZ project, which aims to produce estimates of New Zealand's net carbon balance quickly enough to assess and refine ongoing national efforts to reach carbon neutrality.

Landslide dams can result in substantial flood hazards caused by dam formation, overtopping, and dam failure. Previous studies have established datasets on a regional or global scale and identified indices to estimate the probability of landslide dam formation. These datasets are collections of landslide dam records from multiple data sources. However, the precision and accuracy of the landslide dam record’s spatial information hinder the completeness of data and prevents the possibility of linking with other relevant datasets, and thus hinders the exploration of factors affecting landslide dam formation. We established a new global-scale landslide dam dataset, named River Augmented Global Landslide Dams (RAGLAD), which geolocates those records whose location was vaguely known or completely unknown and combined this with additional data from global fluvial datasets to make the data record more comprehensive. We use RAGLAD to study the processes of landslide dam formation. The spatial distribution of landslide dam records, data distribution, triggering processes and preconditions, and the relationships between geomorphological parameters directly derived from RAGLAD help understanding areas prone to landslide dam conditions, and delineate potential thresholds for landslide dam formation. The results are compared with relationships achieved from general landslides studies to find the specific conditions of landslide dam formation. These conditions can be further applied for filtering the potential hazard occurrence area and calculating the landslide dam formation susceptibility.

Methane is an important greenhouse gas, and anthropogenic methane emissions from point sources are a frequent target for emissions reductions. Airborne imaging spectrometers measuring shortwave infrared radiance are becoming regular sources of data for methane point source plume detection and flux estimation. Accurate and efficient detection and delineation of methane plumes will play an essential role in quantifying point source fluxes. Methane plumes are highly variable in space and time, whereas surfaces that are typically “false positive” detections in methane enhancement retrievals are more regularly shaped and change on longer time scales. This work aims to take advantage of plume variability by applying a fully convolutional network (FCN) to detection and delineation of methane plumes within imaging spectrometer time series data. Using a time series of matched filter methane retrieval products derived from Airborne Visible and InfraRed Imaging Spectrometer Next Generation (AVIRIS-NG) data, we demonstrate the ability of a FCN to classify methane plumes at each time step. Comparison with plume detection and delineation using conventional statistical methods demonstrates the value of this approach. Automated approaches incorporating deep learning will become increasingly important as future global satellite missions greatly increase the frequency at which methane point sources are imaged.

Researchers dealing with flood hazard and risk assessment typically refer to available in-situ gauging stations for the calibration and validation of hydrological and hydrodynamic (HD) models. However, lack of dense gauging data such as stage-discharge relationship (rating curves) results in higher uncertainty in hydrological studies. The multi-mission satellite altimetry observations are capable since long time to monitor continental water bodies at regular interval. In this study, combined use of hydrodynamic model and satellite altimetry data has been exploited for establishing virtual gauging network in flood-prone sparsely gauged river basin. The virtual gauging stations were established at locations where ground tracks of various altimeters (Jason-2/3, SARAL/AltiKa, and Sentinel-3A/3B) cross the river channel. Rating curves were generated at these virtual stations using calibrated and validated HD model. High agreement between the simulated and altimetry-based water levels at virtual stations showed the potential of satellite altimetry data for the multi-site validation of HD model and constructed rating curves. The availability of water level time series and rating curves at multiple virtual locations in addition to existing in-situ physical gauging stations provides an opportunity to expand gauging network in sparsely gauged basin. Thus, the proposed framework may open new perspectives for enhancing river flow dynamic studies considering upcoming satellite missions like SWOT which will ensure more observations in future.

The main objective of the NASA-NSF SWQU “A New-generation Software to Improve the Accuracy of Space Weather Predictions” effort is to develop a data-driven time-dependent model of the solar corona and heliosphere. This model will provide coronal and solar wind predictions and be made available to the public. One key component of this model is the use of a data-assimilation flux transport model to generate an ensemble of synchronic radial magnetic field maps to use as boundary conditions for the coronal field model. While flux transport models have long been established in the community, they are not open source or available for public use. We therefore are developing a new Open-source Flux Transport (OFT) software suite. The computational core of the OFT is the High-Performance Flux Transport code (HipFT). HipFT implements advection, diffusion, and data assimilation for the solar surface on a logically rectangular non-uniform spherical grid. It is written in Fortran and parallelized for use with multi-core CPUs and GPUs using a combination of OpenACC/MP directives and Fortran’s standard parallel ‘do concurrent’. To alleviate the strict time-step stability criteria for the diffusion equation, we use a Legendre polynomial extended stability Runge-Kutta super time-stepping algorithm (RKL2). The code is designed to be modular, incorporating various differential rotation, meridianal flow, super granular convective flow, and data assimilation models. Multiple realizations of the evolving flux will be computed in parallel using MPI in order to produce an ensemble of model outputs for uncertainty quantification. Here, we describe the initial implementation of the HipFT code and demonstrate its validation and performance. We use an analytic solution of surface diffusion and rigid rotational longitudinal velocity to validate the advection and diffusion implementations. We also compare realistic flux transport test problems against the established AFT flux transport code.

To stabilize global warming within the 1.5-2.0°C Paris Agreement goal, greenhouse gas (GHG) emissions need to reach net-zero. Offsetting is required for net-zero emissions (emissions minus offsets), as achieving zero emissions is unrealistic. Offset schemes differ widely in their capacity to mitigate anthropogenic global warming (AGW). Rayer et al. (2021) provide offsetting guidelines to strengthen their long-term climate benefit, this chapter further develops this topic. We propose a framework for rating different offset types based on their climate risk, moving beyond the usual criteria of verification, audit, and additionality. We apply our offset climate ratings to some offsetting types and case studies including the sorts of schemes proposed by major oil companies, and the PAS 2060 carbon-neutrality standard. Some offsets provide little more than economic nudges towards emissions reduction. Climate offsetting requirements are more exacting. The proposed framework grades offsets from most to least benefit. Grades help classify offsets’ climate risks based on: helping terminate it, slowing warming, or only offering economic incentives with little climate benefit. Ultimately, to stabilize global warming, it will be necessary for policy to encourage the highest rated offsets under the proposed framework. Reference Rayer, Q. G., Jenkins, S., & Walton, P. (2021). Defining net-zero and climate recommendations for carbon offsetting. In Walker, T., Wendt, S., Goubran, S., & Schwartz, T. (Eds.), Beyond the 2ºC: Business and Policy Trajectories to Climate Change Adaptation (chapter 2). Palgrave Macmillan.

One of the most worrisome aspects of anthropogenic climate change is its potential to enhance the frequency and severity of extreme weather events and seasonality (van der Wiel et al., 2021). More accurate reconstructions of short-term climate variability in past warm climates help improve our projections of this type of variability in future climate (IPCC, 2013). Here, we apply our recently developed clumped isotope methodology for absolute seasonal sea surface temperature and salinity reconstructions (de Winter et al., 2021a; b) on fossil mollusk shells from the Pliocene Warm Period (3.0 – 3.3 Ma) of northwestern Europe, an important analogue for equilibrium climate under present-day radiative forcing (pCO2 ≈ 400 ppmV; Haywood et al., 2016). Isotope records from well-preserved shells of four different bivalve species (Arctica islandica, Glycymeris radiolyrata, Angulus benedeni and Ostrea edulis) reveal warm sea surface temperatures and high seasonal variability during the key mid-Pliocene PRISM interval, allowing detailed comparison with long-term geological climate reconstructions and an ensemble of model simulations (Haywood et al., 2016). Moreover, our results shed light on sub-annual variability in water chemistry in the shallow European epicontinental seas during this crucial period. These new findings highlight the effect of a warming climate on shallow marine ecosystems and shed light on the seasonal response to global warming. Haywood, A. M. et al. Integrating geological archives and climate models for the mid-Pliocene warm period. Nat Commun 7, 10646 (2016). IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 1535 pp. (Cambridge Univ. Press, Cambridge, UK, and New York, 2013). van der Wiel, K. & Bintanja, R. Contribution of climatic changes in mean and variability to monthly temperature and precipitation extremes. Commun Earth Environ 2, 1–11 (2021). de Winter, N. J. et al. Optimizing sampling strategies in high-resolution paleoclimate records. Climate of the Past 17, 1315–1340 (2021a). de Winter, N. J. et al. Absolute seasonal temperature estimates from clumped isotopes in bivalve shells suggest warm and variable greenhouse climate. Commun Earth Environ 2, 1–8 (2021b).

The stated mission of the International Arctic Research Center (IARC) at the University of Alaska Fairbanks (UAF) is ”to foster Arctic research to help the nation and the world understand, prepare for, and adapt to the pan-Arctic impacts of climate change.” IARC’s work and collaborations are everywhere in the Arctic and beyond. One of IARC’s core values is actively cultivating an environment of diversity and inclusion where all individuals and groups feel welcomed and are heard. Through the work of the IARC Diversity and Inclusion Working Group, we affirm that our different experiences, expertise, and ways of knowing are our strength, fostering diverse thoughts and ideas. Collaborative science and administrative support by people from diverse backgrounds and knowledge systems helps IARC build a professional culture of inclusion, listening, caring and respect. We face challenges in DEI work and are actively addressing them. For example, in addressing the challenge of time constraints by the all-volunteer membership, a DEI coordinator position to spearhead our efforts is being created and funding sought. We highlight efforts from the IARC Diversity and Inclusivity Working Group to cultivate a culture of inclusion, equity and access: 1) collaboratively developing an institutional diversity and inclusivity statement and land acknowledgement statement, 2) creating safe spaces for conversation and action through open biweekly working group meetings and discussions, 3) elevating awareness through relevant workshops, trainings and discussions, 4) collectively incorporating diversity and inclusion values into the IARC strategic plan, 5) making presentations and documents accessible, and 6) actively collaborating on inclusion efforts within UAF, Alaska and beyond. We welcome new members with their ideas and perspectives to the working group. At IARC, we recognize that inclusion is an ongoing process that has to be woven into all that we do in research, education and outreach.