Aaron I. Packman

Hydrological alteration and climate change are making urban rivers drier and more intermittent, but limited understanding of urban stream network dynamics prevents effective, context-specific management. We determined how urban land cover, subsurface characteristics, and weather collectively control stream network connectivity and flow intermittency in the Little Calumet River Watershed, USA. Contrary to prevailing expectations, headwater tributaries with greater impervious cover were more persistent, while tributaries with greater prevalence of permeable soils and paleo-sand deposits were more intermittent, underscoring the importance of subsurface conditions for urban stream connectivity. Active network drainage length was best correlated with antecedent effective precipitation at six days, indicating that these urban headwater systems expand primarily through hydrologic accumulation rather than immediate runoff. These observations challenge prevailing assumptions about fundamental drivers on urban stream connectivity. Our findings show how hydrometeorological, land surface, and subsurface conditions jointly control network dynamics, offering a foundation for building urban watershed resilience. Urban headwaters with less impervious cover are more intermittent due to infiltration in permeable soils, while stream network expansion follows hydrologic accumulation, as revealed by hydrologic observations and modeling in the Little Calumet River Watershed, USA.

This dataset is from the Department of Energy Office of Science funded project, Community Research on Urban and Climate Science (CROCUS) (https://crocus-urban.org/). Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. Datasets are stored in the netCDF data format, and we we encourage users to make use the associated toolkits available from Unidata (https://www.unidata.ucar.edu/software/netcdf/), Project Pythia (https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html), and our “Instrument Cookbooks” (https://crocus-urban.github.io/instrument-cookbooks) for more information on how to process the metadata-rich datasets.

Water quality monitoring networks face an inherent trade-off between measurement precision and spatial-temporal coverage. We present an open-source smart water quality buoy designed to explore the potential of maximising deployment density and sampling frequency through low-cost instrumentation combined with AI-enhanced analytics. The stable buoy enclosure was developed using computational fluid dynamics, water flume validation, and extensive field testing. Initially designed for 3D-printing, it houses three sensors (temperature, turbidity and conductivity) with an ATmega328P microcontroller, real-time clock, flash logging, and/or LoRaWAN connectivity. Laboratory calibration established measurement reliability suitable for network-scale deployment. Field deployments have demonstrated autonomous operation with a relatively light monthly maintenance protocol. This platform enables novel monitoring approaches that leverage density over individual sensor accuracy. Initial Machine Learning models trained on national databases (millions of observations) convert basic sensor measurements into estimates of complex parameters — nutrients, dissolved oxygen, and bacteria — with encouraging accuracy. The high-frequency data from dense sensor networks enables automated pollution detection by analyzing concentration dynamics and comparing them against patterns learned from a large database of water quality measurements.By combining accessible hardware with AI analytics, we investigate whether prioritising spatial-temporal resolution can advance water quality monitoring capabilities, particularly for early pollution detection and regulatory compliance in under-resourced catchments.

Reliable environmental sensor data are fundamental for accurate urban climate modeling and evidence-based planning. Conventional physics-based quality control (QC) methods apply fixed thresholds to flag physically implausible values, but they often fail to detect subtle, context-dependent anomalies. This study introduces a hybrid QC framework that integrates conservative physical constraints with a probabilistic machine-learning approach based on Positive-Unlabeled XGBoost (PU-XGBoost). Using data from the CROCUS Urban Integrated Field Laboratory in Chicago, the framework generates anomaly likelihood probabilities rather than binary flags, allowing confidence-weighted data evaluation. The results demonstrate that the hybrid method effectively captures both gross and latent sensor errors overlooked by rule-based QC, while maintaining interpretability through physically informed features. Feature importance analysis highlights the dominant roles of temporal statistics, sensor type, and environmental context in anomaly detection. Overall, the proposed hybrid framework provides a scalable and interpretable foundation for self-adaptive quality assurance in next-generation urban environmental sensing networks.

Removal of trace organic contaminants (TrOCs) from wastewater is a major challenge, as existing technologies are nonselective and foul quickly in wastewater matrices. We used a rapid synthesis-screening approach to develop and test a range of styrene functionalized β-cyclodextrin (StyDex) bead formulations for the removal of PFAS and pharmaceutical compounds from wastewater. StyDex beads were synthesized using a new suspension polymerization approach. We synthesized 43 individual formulations and screened the resulting beads for size, morphology, and sorption of a mixture of PFOA, PFHxA, PFHxS, PFBA, Bezafibrate, and Diclofenac over periods of 0.5–24 h. The bead formulation that performed best in screening experiments was then used to prepare and sieve beads in two size ranges (125–212 and 425–600 μm) for characterization in more detail. These beads had high BET surface areas in the dry state of 132–167 m 2 g –1, high porosities of 1.72–2.62 mL g –1, and low bulk densities of 0.28–0.38 g mL –1 . The beads showed fast sorption kinetics and high capacities for target contaminants in both DI and wastewater, with complete removal of PFOA, PFHxA, PFHxS, Bezafibrate, and Diclofenac but limited removal of PFBA in wastewater. The larger bead size class had 35.7% higher sorption capacities but 2–5 times lower kinetic rate constants because of slower diffusion to sorption sites in the interior of the beads. Overall, the spherical shape, relatively large size, strong sorption of PFAS and pharmaceutical compounds, and tunability of the StyDex bead formulations show excellent promise for large-scale applications in wastewater treatment systems.

Urban environmental monitoring networks frequently encounter significant data gaps due to sensor malfunctions, environmental disturbances, and communication failures. Reliable approaches to address these gaps are essential for ensuring the continuity and quality of environmental data streams. In this study, we developed a gated attention bidirectional long short-term memory (GA-BiLSTM) model to impute missing data in a dense urban monitoring network. Using observations from the CROCUS network in Chicago, we evaluated GA-BiLSTM against widely used approaches (XGBoost and K-nearest neighbors) under scenarios of both short-term intermittent gaps and prolonged outages. GA-BiLSTM consistently outperformed comparative methods, particularly during extended outages of up to ten days, demonstrating its ability to capture spatiotemporal dependencies across sensor nodes. Beyond performance metrics, feature importance and spatial network analyses highlighted the unexpected but critical predictive role of peripheral rural nodes, underlining their strategic value for maintaining robust urban monitoring systems. These results emphasize that advanced imputation methods can substantially improve the reliability of environmental monitoring networks and support more resilient data infrastructures for urban sustainability.

This dataset contains environmental monitoring data collected using solar-powered Multi-Function Research (MFR) Long Range Wide Area (LoRaWAN)-enabled nodes at 11 sites in Chicago, Illinois, as part of the DOE Urban Integrated Field Lab CROCUS project. The MFR node system consists of an Input/Output Digital Input Module (IB8) interface box (ICT International) providing wired connections for environmental sensors and an MFR-Node-L data logger that manages power, data processing, and LoRaWAN communication. The wireless data are ingested via Sage network (https://sagecontinuum.org/) nodes that contain LoRaWAN antennae. Measurements were collected from 11 MFR nodes deployed across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), West Woodlawn "Blacks in Green" (BIG), and Indian Boundary Prairies (IBP). Each MFR node supports a consistent suite of sensors measuring atmospheric, soil, and hydrological variables. Atmospheric measurements include 2m air temperature (°C), 2m vapor pressure deficit (kPa), and 2m shortwave/longwave radiation (incoming and outgoing, W/m²) measured using ATH-VPD and Apogee SN500 sensors. Soil measurements include volumetric water content (VWC, %) and temperature (°C) at four depths (15, 30, 45, and 60 cm below surface) using Meter Teros54 sensors, and heat flux (W/m²) at 10 cm depth using Huske HFP01-05 sensors. At selected locations, Meter Hydros21 sensors measure groundwater depth (mm), specific conductivity (dS/m), and temperature (°C). The dataset includes timestamps, site identifiers with location names, device IDs, Global Positioning System (GPS) coordinates, variable names with units, measurement depths, values, sensor names, and Sage node identifiers. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 6-bit binary system indicating physical range violations, step spikes, 24-hour flat-line conditions, 6-hour jitter, 7-day ultra-low variance, and persistent high offset. Data is provided in CSV and CF-compliant NetCDF formats. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Sap Flow Meter (SFM1x) sensors.

This dataset contains uncorrected sap velocity measurements using the heat ratio method (HRM) collected using ICT International SFM1x sensors at five urban sites in Chicago, Illinois, as part of the DOE CROCUS project. The data includes continuous monitoring of sap velocity from various tree species, including Maples (Acer spp.): Sugar Maple (Acer saccharum), Silver Maple (Acer saccharinum), and Red Maple (Acer rubrum); Oaks (Quercus spp.): Swamp White Oak (Quercus bicolor); American Elm (Ulmus americana); Honey Locust (Gleditsia triacanthos); Cottonwood (Populus deltoides); and Tree of Heaven (Ailanthus altissima) across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), and West Woodlawn "Blacks in Green" (BIG). These include both street trees and those in urban park locations. Measurements were collected at 15-20 minute intervals, depending on the sensor, and transmitted via Long Range Wide Area Network (LoRaWAN) protocols. The wireless data was collected by Sage Network (https://sagecontinuum.org/) nodes. The dataset includes sensor ID, Global Positioning System (GPS) coordinates, tree species (common and scientific names), tree identification number, diameter at breast height (DBH in cm), uncorrected sap velocity measurements (cm/hr) from both inner and outer probes, and Sage Node identifiers so the data can be mapped to related variables such as air quality and wind speed that were collected on the Sage nodes. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 3-bit binary system indicating physical range violations (< -10 or > 60 cm/hr), step spikes (absolute difference > 36 cm/hr), and stuck sensor conditions (> 10 consecutive identical values). These are raw data, not corrected for wood anatomy or species-specific characteristics. Data is provided in comma separated (CSV) format. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Multi-Function Research LoRaWAN (MFR) Nodes. DOIs for the supporting data are provided as part of this data package.

Abstract. Soil aquifer treatment (SAT) systems are used to remove pollutants from treated wastewater and store freshwater for reclamation and reuse. However, the accumulation of microbial biomass in the soil pore space, bioclogging, reduces water infiltration and hinders SAT efficiency. Since SAT systems play a crucial role in maintaining water resilience by providing an alternative to freshwater supply, optimizing their operation is essential to ensure their effectiveness. However, SAT systems are complex and dynamic systems that involve coupled interactions between microbial activity, water infiltration, and bioclogging in unsaturated media. This work proposes a continuum model that accounts for all these processes while distinguishing between active and inactive biomass, with the latter split into labile and recalcitrant fractions. The model is used to replicate a laboratory column experiment of bioclogging under unsaturated conditions and to explore how to optimize the operation of SAT systems. Specifically, we determined optimal wetting and drying periods that maximize water input to the SAT system while maintaining nutrient transformation rates. Our simulations show that the dry/wet time ratio controls biomass spatial distribution over depth. In contrast, the dry time extent dictates the degree of recovery of the soil relative to its initial (clean) infiltration capacity. We discuss the potential of this model to be extended to larger-scale experiments and to inform daily SAT operations in the field.

Abstract Climate change is expected to increase the frequency and severity of flooding in the Great Lakes region. In many cities, flood‐control infrastructure is insufficient to protect against future climate conditions. Consequently, there is increasing focus on stormwater storage provided by urban greenspace, such as wetlands and prairies, but the ecohydrological behavior of these ecosystems is not well understood when they are embedded within cities. To improve understanding of hydrological connectivity between urban areas and natural greenspaces, we deployed a sensor network in Gensburg Markham Prairie (GMP), a large intact prairie‐wetland complex in south suburban Chicago. We used the resulting high‐frequency time‐series data to assess surface‐subsurface hydrologic dynamics between upland and low‐lying wetland areas, interactions between the prairie and surrounding environment, and stormwater storage provided by the prairie. Rapid infiltration within the prairie during and after storm events provides subsurface flow that stores considerable water, flattens storm hydrographs, and increases the wetland hydroperiod. Much of the stormwater input to GMP derives from the surrounding cityscape. Consequently, storage within the prairie‐wetland system reduces and slows stormwater discharge to downstream urban communities. For a typical 5‐year 24‐hr storm with 10.9 cm of rain, GMP stores 77,100 m 3 , 64% greater than the estimated direct rainfall volume onto the prairie, yielding 30,000 m 3 of offsite stormwater storage. This improved understanding of ecohydrological dynamics in urban prairies and wetlands informs the design and implementation of green infrastructure to meet growing needs for stormwater management.

Per- and polyfluoroalkyl substances (PFASs) are environmentally persistent, bioaccumulative, and toxic chemicals that contaminate global drinking water resources. Their ubiquity and potential impact on human health motivate large-scale remediation. Conventional materials used to remove PFASs during drinking water production are functionally inefficient or energetically expensive, motivating the discovery of new materials and technologies. Here, we introduce cyclodextrin-based monoliths, featuring a highly permeable, mechanically robust porous architecture that enables rapid and continuous PFAS removal from water. The monolith was directly polymerized into flow-through columns and showed water permeability similar to packed sand due to its interconnected macroporous structure. Compared to leading benchmarks under equivalent sorbent mass or contact time, the monolith demonstrated superior PFAS sorption with later breakthroughs for most PFASs. We show the hierarchical morphology of a monolith is more efficient use of sorbent mass than particle-based sorbents, as its macropores enable facile diffusion of PFAS to meso- and microporous sorption sites. The significance of hierarchical pore design was demonstrated by instant PFAS breakthrough passing through the pulverized monolith, where the water primarily flows through interparticle pathways with limited access to sorption sites. After treating 150 L of water spiked with 25 PFAS for 37 days, 47 mg of monolith was regenerated through alcohol wash, with near-complete mass balance of adsorbed PFASs. The regenerated monolithic sorbent treated another 170 L of water for 39 days with comparable removal. The monolith offers a novel morphology with simple and direct synthesis in columns for water treatment and provides superior PFAS removal, regeneration and reuse.

Urban flooding arises from complex mechanisms, making it challenging to capture accurately with a single detection method. This study evaluates three complementary approaches to detect flooding across three Chicago neighborhoods: (i) Sentinel-1 synthetic aperture radar (SAR), offering weather-independent, high-resolution (10 m) imagery of surface inundation; (ii) the storm water management model (SWMM), simulating combined sewer overflow and drainage performance; and (iii) citizen-generated 311 service requests, capturing observed flooding impacts. By analyzing six storms ranging from severe to mild, we examine how each source uniquely contributes to identifying urban flood events. SAR imagery effectively identifies standing water but can miss brief flooding due to satellite revisit constraints. SWMM provides detailed insights into system-wide drainage behavior yet may underestimate localized street-level flooding. Meanwhile, 311 calls reflect real-world flooding impacts but are vulnerable to underreporting. Statistical overlap analysis highlights chronic flood hotspots repeatedly identified across multiple detection methods, indicating persistent infrastructure and topographic vulnerabilities. Temporal analysis further reveals that while SWMM flooding aligns closely with rainfall peaks, 311 calls typically precede or persist beyond these peaks. Our findings emphasize the value of using satellite observations, hydrological modeling, and resident-reported data in a complementary manner to better interpret patterns in flood timing, severity, and spatial distribution—providing insights that can inform targeted infrastructure improvements and contribute to urban flood resilience planning.

We consider streambed head and flux distributions induced by hyporheic exchange flux through irregular and dynamic natural sand bedforms. It has not previously been feasible to study these in the laboratory owing to incompatibility between fixed-location pressure transducers and shifting sand bedforms. We address this problem, presenting a noninvasive technique for regularized inversion of photographic time series of dye front propagation in the hyporheic zone to recover head and flux distributions, compatible with arbitrarily-shaped, generally transient bedforms. We employ the technique to analyze three bench-scale flume experiments performed under different flow regimes, presenting a new data set of digitized bed profiles, corresponding head distributions, and dye fronts. To our knowledge, this is the first such data set collated for naturally-formed sand bedforms.

The Community Research On Climate and Urban Science, CROCUS is a United States Department of Energy Urban Integrated Field Laboratory which brings a Model Driven Experiment (MODEX) approach to elucidating the underlying physics that drive urban climate systems. Chicagoland (the city itself and surrounding counties) is home to over 10 million residents and is highly Heterogeneous. Home to major transport hubs the region represents an urban to rural gradient and is bordered by the 5th largest lake on the planet. MODEX requires a robust observational strategy. CROCUS strategy for this comprises four components: A long-term multi-node observational network, the Micronet, built around AI edge-enabled sensing nodes, fixed instrumentation, and distributed sensing networks enabled by technologies such as LoRaWAN to provide diverse earth science observations in highly heterogeneous urban settings. A series of field campaigns bringing advanced remote sensing and sounding networks to Chicago and the surrounding region, including an advanced weather radar, combined with local ground-truthing. Curation of multi-agency open datasets. Community centered data collection and characterization of affordable sensors. As we round out two and half years in the project, eleven micronet sites have been deployed across the Chicagoland region including sites with advanced in-ground wireless sensors providing a comprehensive view of subsurface to atmosphere. The presentation will highlight several cases and showcase our first field campaign, CROCUS Urban Canyons. The Urban Canyons field campaign involved two 39 hour intensive observational periods (IOPs) over a two week period and launched 42 soundings from four locations across Chicago (a coordinated sounding network). It also brought an advanced air chemistry lab to the city and advanced LIDAR and infrared radiometric profiling. Finally the presentation will provide an overview of the upcoming comprehensive field campaign to be held in the region in 2026/27 and highlight engagement and collaboration opportunities with the project.

The United States Department of Energy-funded Community Research on Climate and Urban Science (CROCUS) Urban Integrated Field Laboratory is a multi-year intensive research program that integrates long-term instrumentation deployments, intensive field observations, and multi-scale modeling efforts across the greater Chicago region to study the community-scale physics and impacts of extreme weather and climate events. Set to occur in the spring of 2025, the CROCUS Urban Flooding and Rainfall Campaign is the second intensive field observation effort. The campaign’s goal is to use novel observational strategies to characterize hydroclimate dynamics from the subsurface through the troposphere before, during, and after flooding events in Chicago to enable physical and agent-based modeling, assess the performance of flood management infrastructure, and improve the resilience of Chicago-area residents to extreme precipitation events. Conducted in partnership with organizations within and around the city of Chicago, this campaign will collect a suite of subsurface, surface, and remote sensing observations at high temporal and spatial scales to benchmark remote sensing, parameterize multi-scale atmospheric and hydrologic models, and provide detailed data mapping for decision-making around the heterogeneity of the region’s flood response. Long-term monitoring of the subsurface and surface conditions across the region will be augmented by targeted soil characterization and soil moisture measurements and the deployment of an X-Band radar, soundings, lidars, and radiometers. Together, these observations will be used to drive coupled models to better understand the drivers for extreme precipitation in an urban setting, as well impacts of heavy precipitation on urban communities. These data collection efforts are embedded within Chicago neighborhoods and neighboring communities, and are thus critically coordinated with education and community engagement efforts to develop the scientific inquiry and build capacity within heavily impacted communities.

Conventional adsorbents applied in wastewater treatment are ineffective at removing trace organic contaminants (TrOCs), including per and poly-fluoroalkyl substances (PFAS) and pharmaceuticals. Cross-linked β-cyclodextrin (β-CD) polymer adsorbents have demonstrated efficient removal of TrOCs and exhibit rapid kinetics and high adsorption capacity in wastewater. We evaluate the removal of a mixture of contaminants from wastewater by a styrene functionalized β-CD adsorbent (StyDex) through rapid small-scale column tests (RSSCTs). We found the kinetics observed in batch adsorption tests are maintained in RSSCTs. However, batch sorption kinetic constants did not match column breakthrough kinetics, due to an inability to describe complex flow-through behaviors. We correlated both batch kinetic constants and treatable bed volumes with hydrophobicity of target compounds, where PFOA and PFHxS had the highest affinity for StyDex in batch tests and the latest breakthroughs in RSSCTs. Breakthrough curves of five of the seven TrOCs were not affected by change in contact time. Conversely, decreasing the contact time led to earlier breakthrough of contaminants with the highest sorption affinity to StyDex: longer chained and sulfonated PFAS compounds. These effects were isolated in two-component competitive sorption experiments between PFOA and PFBA, and we observed the same preferential sorption of hydrophobic compounds identified in the multi-component mixture. Additionally, competitive adsorption-desorption increased with increasing contact times. We discuss how these findings are crucial for scale-up and large-scale testing of novel sorbents.

Abstract The majority of households in high‐income countries have access to safely managed drinking water, but a significant number do not trust or use their tap water. Much remains unknown about the perceptions and behaviors of millions of people who opt to not drink tap water that meets national guidelines. Given that tap water avoidance is associated with myriad adversities and bottled water generates enormous amounts of waste, information about the drivers of trust in tap water is critical. Therefore, we investigated drinking water perceptions and behaviors in 2020–21 in Evanston, Illinois, a mid‐size city on Lake Michigan whose water quality meets or exceeds federal guidelines. In collaboration with a local environmental organization and a university, we conducted community‐based participatory research that included surveys ( n = 756) and in‐depth interviews ( n = 52) with a convenience sample of residents. Most (92.6%) respondents reported primarily consuming tap water. 81.2% of survey respondents ( n = 749) thought their tap water was safer than or as safe as bottled water. Those who drank primarily bottled water (7.4%) were more likely to identify as Black, Indigenous, People of Color (BIPOC) or unhoused. BIPOC individuals had 3.4 times the odds of distrusting tap water than white respondents and men were 44% less likely to distrust tap water than women. Adverse experiences with water and low trust in government were also associated with lower trust in tap water safety. These findings suggest that outreach be targeted toward these groups to ensure widespread access to safe and trusted tap water.

Climate change is expected to increase the frequency and severity of flooding in the Great Lakes region. In many cities, flood-control infrastructure is insufficient to protect against future climate conditions. Consequently, there is increasing focus on stormwater storage provided by urban greenspace, such as wetlands and prairies, but the ecohydrological behavior of these ecosystems is not well understood when they are embedded within cities. To improve understanding of hydrological connectivity between urban areas and natural greenspaces, we deployed a sensor network in Gensburg-Markham Prairie (GMP), a large intact prairie-wetland complex in south suburban Chicago. We used the resulting high-frequency time-series to assess surface-subsurface hydrologic dynamics between upland and low-lying wetland areas, interactions between the prairie and surrounding environment, and stormwater storage provided by the prairie. GMP’s hydrological dynamics are generally controlled by surface-groundwater interactions that vary seasonally. Rapid infiltration during and after storm events provides subsurface flow that stores considerable water, flattens storm hydrographs, and increases the wetland hydroperiod. Much of the stormwater input to GMP derives from the surrounding cityscape. Consequently, storage within the prairie-wetland system reduces and slows stormwater discharge to downstream urban communities. For a typical 5-year 24-hour storm with 10.9 cm of rain, GMP stores 77,100 m3, 64% greater than the estimated direct rainfall volume onto the prairie, yielding 30,000 m3 of off site stormwater storage. This improved understanding of ecohydrological dynamics in urban prairies and wetlands informs the design and implementation of green infrastructure to meet growing needs for stormwater management.

Existing works that predict bedform-induced hyporheic exchange flux (HEF) typically either assume a simplified streambed shape and corresponding sinusoidal head distribution or rely on costly computational fluid dynamics simulations. Experimental data have been lacking for the formulation of a priori prediction rules for hydraulic head and flux distributions induced by spatiotemporally heterogeneous natural bedforms because it has not previously been feasible to determine these in the laboratory. We address this problem, presenting a noninvasive technique for regularized inversion of photographic time series of dye front propagation in the hyporheic zone, compatible with arbitrarily-shaped, generally transient bedforms. We employ the technique to analyze three bench-scale flume experiments performed under different flow regimes, presenting a new data set of digitized bed profiles, corresponding head distributions, and dye fronts. To our knowledge, this is the first such data set collated for naturally-formed sand bedforms.

Abstract. Soil aquifer treatment (SAT) systems are used to remove pollutants from treated wastewater and store freshwater for reclamation and reuse. However, the accumulation of microbial biomass in the soil pore space, bioclogging, reduces water infiltration and hinders SAT efficiency. Since SAT systems play a crucial role in maintaining water resilience by providing an alternative to freshwater supply, optimizing their operation is essential to ensure their effectiveness. However, SAT systems are complex and dynamic systems that involve coupled interactions between microbial activity, water infiltration, and bioclogging in unsaturated media. This work proposes a continuum model that accounts for all these processes while distinguishing between active and inactive biomass, with the latter split into labile and recalcitrant fractions. The model is used to replicate a laboratory column experiment of bioclogging under unsaturated conditions and to explore how to optimize the operation of SAT systems. Specifically, we determined optimal wetting and drying periods that maximize water input to the SAT system while maintaining nutrient transformation rates. Our simulations show that the dry/wet time ratio controls biomass spatial distribution over depth. In contrast, the dry time extent dictates the degree of recovery of the soil relative to its initial (clean) infiltration capacity. We discuss the potential of this model to be extended to larger-scale experiments and to inform daily SAT operations in the field.

Climate change is increasing the frequency and severity of extreme precipitation events, requiring new ways of managing stormwater, particularly in urban areas. Nature-based solutions (NBS) have become increasingly popular to provide distributed stormwater storage while supporting urban biodiversity and access to nature. However, long-term monitoring of the hydrological performance of NBS is limited. To date most literature has focused on monitoring methodologies for specific sites and types of NBS, use of remote sensing and modeling for large-scale assessments, or measuring benefits of NBS for urban heat mitigation. More comprehensive and consistent measurement strategies are needed to understand the effects of distributed NBS on urban hydrology at the regional scale, and improve the design, maintenance, and adoption for community-centered stormwater management. To address these gaps, we review available literature on measurement methods, summarize these methods and provide specific recommendations for instrumentation and in situ monitoring of common types and scales of urban NBS. Based on our findings on performance monitoring for individual NBS sites, we extend recommendations for consistent hydrological assessment of distributed NBS at regional scale and the efficacy of NBS in reducing community flooding impacts. These recommendations are particularly applicable for municipalities, researchers and community-based organizations who are now leading the planning and implementation of community-centered NBS systems in many areas.

Head induced by bedforms at the sediment-water interface (SWI) is typically represented in one of two ways: either by solving the RANS equations for the water column, or by a sinusoidal boundary condition defined by Elliott and Brooks (1997). Both of these methods have been used to model bedform-induced hyporheic exchange flux (HEF) on domains of constant shape. Under sufficiently fast flow conditions, however, bedform shape is irregular and evolves over time. For these conditions, neither method is fully appropriate: RANS is too computationally intensive, while the Elliott and Brooks boundary condition is based on measurements taken using rigid bedforms of an idealized triangular shape (Fehlman, 1985). We present a procedure for estimating head induced by arbitrarily-shaped bedforms using timelapse photos of dye tracer tests. At a given time t, an initial guess of head along the SWI is generated. The predicted evolution of the dye plume observed in the photo at time t is calculated using the model of Teitelbaum et al. (2022). The predicted dye plume location is compared against the observed plume location from the next photo. This comparison is used as the objective criterion in an optimization procedure, which is run until the estimate of head at the SWI converges. Results show agreement with experimental observations from dye tracer tests. The estimated head is used as input data to predict head distribution based solely on SWI shape. This work provides a new way to estimate head under arbitrary SWI shape. Thus, it constitutes an important advance in realistic modeling of bedform-induced hyporheic exchange flux.

In this chapter, the authors provide a comprehensive analysis and synthesis of the interactions between important drivers and controls of hyporheic exchange and biogeochemical cycling and how they vary across scales, integrating results from a wide range of case studies that go beyond current conceptual model frameworks. They discuss the interactions of different local-to-regional controls and drivers of hyporheic zone processes such as hydrodynamic and hydrostatic drivers of hyporheic exchange, sediment hydraulic conductivity, the role of autochthonous organic matter sources, and feedbacks between hydrological exchange and ecological processes in the streambed. The authors explore the implications of these interactions for biogeochemical cycling in the landscape context. They integrate conceptualizations of organizational principles of hyporheic exchange and biogeochemical cycling from reach to catchment scale. The authors expect that increasing awareness and embracing the landscape organizing principles of hyporheic zones will advance the future of research at groundwater–surface water interfaces.

As COVID-19 becomes endemic, public health departments benefit from improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging due to the high noise typical of environmental samples. We have developed a generalized pipeline using in- and out-of-sample model selection to test the ability of different correction models to reduce the variance in wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but the pipeline should be used to reevaluate any new data set as the sources of variance may change across locations, lab methods, and disease states. Using our best-fit model, we investigated the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We did this in a rolling manner for corrected wastewater data and for other prevalence indicators and statistically compared the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We found that wastewater trends often lead other COVID-19 indicators in predicting new surges.

Influenza is a respiratory illness that can result in serious outcomes, particularly among persons who are immunocompromised, aged <5 years or aged >65 years. Traditional influenza surveillance approaches rely upon syndromic surveillance of emergency departments and public health reporting from clinicians and laboratories. Wastewater surveillance infrastructure developed to monitor SARS-CoV-2 is being used for influenza surveillance in the Chicago area. The goal was to evaluate timeliness and correlations between influenza virus detected through wastewater surveillance and traditional influenza surveillance measures to assess utility of wastewater surveillance for influenza at the county level. Specifically, we measured correlations between influenza virus gene copies in wastewater samples and 1) the number of intensive care unit admissions associated with a diagnosis of influenza, 2) the percentage emergency department (ED) visits for influenza-like-illness, and 3) the percentage of ED visits with influenza diagnosis at discharge2 in Cook County. Influenza concentrations in wastewater were strongly correlated with traditional influenza surveillance measures, particularly for catchment areas serving >100,000 residents. Wastewater indicators lagged traditional influenza surveillance measures by approximately one week when analyzed in cross-correlations. Although wastewater data lagged traditional influenza surveillance measures in this analysis, it can serve as a useful surveillance tool as a complement to syndromic surveillance; it is a form of influenza surveillance that does not rely on healthcare-seeking behavior or reporting by healthcare providers.

exposure levels and should be removed from wastewater to enable safe reuse and release of treated effluents. Established adsorbents, such as granular activated carbon (GAC), exhibit variable TrOC removal and fouling by wastewater constituents. These shortcomings motivate the development of selective novel adsorbents that also maintain robust performance in wastewater. Cross-linked β-cyclodextrin (β-CD) polymers are promising adsorbents with demonstrated TrOC removal efficacy. Here, we report a simplified and potentially scalable synthesis of a porous polymer composed of styrene-linked β-CD and cationic ammonium groups. Batch adsorption experiments demonstrate that the polymer is a selective adsorbent exhibiting complete removal for six out of 13 contaminants with less adsorption inhibition than GAC in wastewater. The polymer also exhibits faster adsorption kinetics than GAC and ion exchange (IX) resin, higher adsorption affinity for PFAS than GAC, and is regenerable by solvent wash. Rapid small-scale column tests show that the polymer exhibits later breakthrough times compared to GAC and IX resin. These results demonstrate the potential for β-CD polymers to remediate TrOCs from complex water matrices.

Abstract Hyporheic exchange (HE), fine particle deposition and clogging are tightly coupled processes that control ecosystem services in rivers. While HE is assumed to be induced primarily by riverbed topography, surface flow turbulence also drives significant exchange. We show that turbulence‐driven HE produces large interfacial fluxes and drives long‐term feedback between HE and fine suspended particles via bed clogging. Turbulence significantly increases total HE fluxes as it rapidly delivers suspended particles into porewater over the entire interface, whereas advective pumping exchange only delivers particles into focused downwelling regions on the upstream side of bedforms. While turbulence is associated with rapid fluctuations and shallow HE, it is key on longer‐timescale outcomes, namely bed clogging. However, beyond the general effect of clogging in attenuating HE, turbulence‐driven HE will also be important for other river‐borne materials that are retained and transformed within hyporheic zones, such as nutrients and organic pollutants.

Abstract Clogging of streambeds due to clay deposition influences the stream‐subsurface exchange flux and thus directly modulates hyporheic ecological and biogeochemical processes. Clogging of sandy streambeds has previously been studied under losing and gaining flows and during streambed movement, but not when these two flow conditions coincided. We conducted flume experiments to quantify the combined effect of moving bedforms and losing or gaining flows on kaolinite deposition and streambed clogging. The experiments were conducted by adding pulses of kaolinite in a flume packed with sand under a stream water velocity of 25 cm/s. We measured the deposition rates, dynamics of hyporheic exchange flux (HEF) and vertical hydraulic conductivity ( K v ), and the vertical distribution of kaolinite at the end of the experiments under two losing and two gaining flows (Darcy velocity of 10 and 20 cm/day). Kaolinite deposition led to clogging and reduction in K v and HEF under all flow conditions. Deposition occurred faster under losing flow conditions than under gaining flow conditions. However, the changes in K v and HEF were similar under losing and gaining flow conditions for similar kaolinite concentrations in the bed. Our results indicate that the deposition patterns of kaolinite were more influenced by bedform movement than by losing or gaining flow conditions, which is markedly different from the behavior observed under losing and gaining conditions for stationary bedforms. This implies that bedform morphodynamics control local‐scale clogging of sandy streambeds and should be accounted for when studying the hydrology of catchments at larger scales.

Inland freshwater wetlands throughout the northern U.S. and Canada are experiencing an increase in salinity due to road salt runoff during winter months. Salinization affects soil texture, contaminant transport, microbial activity, and plant growth in wetlands. Therefore, there is a pressing need to understand the dynamics of road salt intrusion in urbanized freshwater ecosystems. We used distributed high-resolution sensors to evaluate the dynamics of road salt intrusion into a wetland complex, Gensburg Markham Prairie (GMP), located in the ex-urban area outside Chicago, Illinois (USA). The in situ sensors measure electrical conductivity (EC), surface and groundwater level, precipitation, water temperature, and air temperature at 30-minute intervals. Water samples were collected monthly from 13 shallow groundwater wells and eight surface water locations and analyzed for Cl - , Mg 2+ , Na + , Ca 2+ , and K + . Two-years of continuous data show periodic spikes in EC during winter months, generally by an order-of-magnitude, due to intrusion of road salt applied on nearby roads. However, this behavior was not evident from monthly water samples, indicating that traditional water quality sampling methods likely miss such abrupt salt intrusion dynamics caused by rapid snowmelt runoff events. Higher levels of EC and Cl - occurred at the periphery of GMP near roadways, as well as in a preferential flow path to the interior of the wetland. Spectral analysis of EC time-series in ditches suggests that there is no correlation between salinity dynamics at super-annual timescales. This indicates that the salinity dynamics at GMP are event-driven, and the introduced solutes are rapidly exported from the site. This research supports development of improved de-icing strategies by local agencies and informs site-specific management of wetland ecosystems under anthropogenic stressors.

Abstract At the inaugural Frontiers in Hydrology Meeting in San Juan, Puerto Rico in the summer of 2022, the Hydrology Section organized a poster session and invited our 2020 and 2021 Classes of AGU Fellows, with the initial goal of both celebrating their careers as well as to provide an opportunity for an informal exchange and connection between the section's early career members and our more senior and established scientists and engineers. Due to the challenges of time zones, virtual poster presentations and other logistics, the formal poster session was adjourned but continued as a hybrid “meet‐up” with six of our Section's Fellows (Suzanne Anderson, Paul Brooks, Aaron Packman, Remko Uijlenhoet, Andrew Western, and Xubin Zeng) from around the world. As you will see, what started as an informal chat quickly took deep dives into pressing issues in our section and science in general, including thoughts on how our community values (or in some cases doesn't value) multi‐ and interdisciplinary accomplishments, critiques of our system of rewards and awards including how we assess publication impacts and finally, a frank and honest discussion of our current efforts to diversify our community and where/why are we still failing. We hope that by sharing this open and impromptu dialogue that these discussions can expand to our entire community, and to encourage future Fellows exchanges such as this to reach our entire community of scientists and engineers.

Urbanization and the increase in urban land cover are growing concerns associated with numerous negative impacts on surface water quality. Currently, many emerging contaminants are difficult to measure with no field deployable sensors currently available. Hence, discrete grab samples are required for subsequent laboratory analysis. To capture the spatiotemporal variability in pollution pulses, autosamplers can be used, but commercial offerings are both expensive and have a large footprint. This can be problematic in urban environments where there is a high density of point source inputs and risk of vandalism or theft. Here, we present a small and robust low-cost autosampler that is ideally suited for deployment in urban environments. The design is based on “off the shelf” open-source hardware components and software and requires no prior engineering, electronics, or computer programming experience to build. The autosampler uses a small peristaltic pump to enable collection of 14 small volume samples (50 mL) and is housed in a small footprint camera case. To illustrate the technology, we present two use cases for rapid sampling of stormwater pulses of: 1) an urban river channel and 2) green roof runoff. When compared with a commercial autosampler, our device showed comparable results and enabled us to capture temporal dynamics in key water quality parameters (e.g., dissolved organic matter) following rain events in an urban stream. Water quality differences associated with differing green roof design/maintenance regimes (managed and unmanaged vegetation) were captured using the autosampler, highlighting how unmanaged vegetation has a greater potential for mitigating the rapid runoff and peaked pollutant inputs associated with impervious surfaces. These two case studies show that our portable autosampler provides capacity to improve understanding of the impact of urban design and infrastructure on water quality and can lead to the development of more effective mitigation solutions. Finally, we discuss opportunities for further technical refinement of our autosampler and applications to improve environmental monitoring. We propose a holistic monitoring approach to address some of the outstanding challenges in urban areas and enable monitoring to shift from discrete point sources towards characterization of catchment or network scale dynamics.

Climate change is leading to more extreme precipitation events, which require new ways of managing stormwater, particularly in urban areas. Nature-based solutions (NBS) have become an increasingly popular way of providing additional stormwater retention and detention, as well as supporting urban biodiversity and access to nature. However, monitoring of the hydrological performance of NBS is often limited. To date most literature has focused on monitoring methodologies for specific sites and types of NBS, using remote sensing and modeling methods, or measuring benefits of NBS for urban heat mitigation. More comprehensive measurement strategies are needed to improve design, inform maintenance, and provide data that can encourage the adoption of NBS. To address this gap, this tutorial review provides specific recommendations for the instrumentation and in situ monitoring of common types and scales of NBS, as well as in-depth discussion of monitoring methods and hydrological performance for two specific NBS installations in the Chicago region. Based on these findings, we make recommendations for consistent hydrological assessment of NBS and development of common metrics that allow for comparison regionally and across different types of NBS.

Abstract The bed of fluvial ecosystems plays a major role in global biogeochemical cycles. All fluvial sediments migrate and although responses of aquatic organisms to such movements have been recorded there is no theoretical framework on how the frequency of sediment movement affects streambed ecology and biogeochemistry. We here developed a theoretical framework describing how the moving‐resting frequencies of fine‐grained sediments constrain streambed communities across spatial scales. Specifically, we suggest that the most drastic impact on benthic and hyporheic communities will exist when ecological and biogeochemical processes are at the same temporal scale as the sediment moving‐resting frequency. Moreover, we propose that the simultaneous occurrence of streambed patches differing in morphodynamics should be considered as an important driver of metacommunity dynamics. We surmise that the frequency of patch transition will add new dimensions to the understanding of biogeochemical cycling and metacommunities from micro‐habitat to segment scales. This theoretical framework is important for fluvial ecosystems with frequent sediment movement, yet it could be applied to any other dynamic habitat. This article is categorized under: Water and Life &gt; Nature of Freshwater Ecosystems