About

Aaron I. Packman is a Professor of Civil and Environmental Engineering at Northwestern University, with courtesy appointments in Mechanical Engineering and Chemical and Biological Engineering. His research focuses on water, sediments, and microorganisms, particularly at the intersection of physical transport processes with biological and biogeochemical processes. He seeks to understand the role of environmental interfaces in water system dynamics, including the surface-groundwater interface, fluid-particle interactions, and surface-attached microbial communities such as biofilms. His work is highly collaborative and encompasses fluid mechanics, particle transport and morphodynamics, aquatic chemistry, microbiology, and public health. Significant applications of his research include water resources sustainability, urban flooding, nutrient and carbon cycling, contaminant transport, ecosystem degradation and restoration, waterborne disease transmission, and wastewater-based epidemiology. Packman has received numerous recognitions, including the Fulbright Distinguished Chair Award, the Cole-Higgins Award for Excellence in Advising, and the McCormick Faculty Excellence Award. He has served as an associate editor for leading journals in limnology, oceanography, and water resources research, and holds leadership roles in professional organizations such as the International Association for Sediment Water Science. His contributions extend to patents and the development of innovative solutions for water treatment and environmental monitoring.

Research topics

• Computer Science
• Environmental science
• Political Science
• Business
• Sociology
• Environmental planning
• Biology
• Ecology
• Environmental engineering
• Engineering
• Environmental resource management
• Computer Security
• Social Science
• Virology
• Environmental ethics
• Public relations
• Geotechnical engineering
• Waste management
• Geography
• Social psychology
• Applied psychology
• Geology
• Environmental health
• Medicine

Selected publications

• Quality assessment and control of urban environmental sensors using physical thresholding and machine learning-based probabilities

  Big Earth Data · 2026-03-12

  articleOpen access

  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.

  PublisherDOI

• A network of soil moisture, soil temperature, air temperature, net radiation, ground heat flux and ground water for Chicago, Illinois

  DOE Lawrence Berkeley National Laboratory (LBNL) Repository · 2026-01-22

  datasetOpen access

  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.

  PublisherDOI

• Low-Cost Water Quality Buoys: Open-Source Design and AI-Enhanced Monitoring 

  2026-03-14

  articleOpen accessCorresponding

  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.

  PublisherDOI

• Porous Styrene Functionalized β-Cyclodextrin Beads for Organic Contaminant Removal from Wastewater

  ACS ES&T Engineering · 2026-03-06

  articleSenior authorCorresponding

  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 m2 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.

  PublisherDOI

• Subsurface conditions and hydrologic accumulation drive stream connectivity and flow intermittency in urban river networks

  Communications Earth & Environment · 2026-04-10

  articleOpen accessSenior author

  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.

  PublisherOA PDFDOI

• Imputation of urban environmental sensor data using gated attention bidirectional long short-term memory (GA-BiLSTM): methods, performance, and implications

  Environmental Monitoring and Assessment · 2026-02-27

  articleOpen access

  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.

  PublisherOA PDFDOI

• Sap Velocity Data for Urban Trees in Chicago, Illinois (2024-2025)

  DOE Lawrence Berkeley National Laboratory (LBNL) Repository · 2026-01-21

  datasetOpen access

  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.

  PublisherDOI

• CROCUS Weather Data at Northwestern University Rooftop

  DOE Lawrence Berkeley National Laboratory (LBNL) Repository · 2026-03-20

  datasetOpen access

  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.

  PublisherDOI

• Recovering head and flux distributions at the sediment-water interface for arbitrary, transient bedforms by inversion of photographic time series

  Advances in Water Resources · 2025-08-31

  articleOpen access

  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. • Noninvasive method developed to infer head distribution at SWI in flume experiments. • Method allows for analysis of naturally-shaped, non-stationary sand bedforms. • Data sets and inferred head distributions are presented for three dye tracer tests.

  PublisherDOI

• Stormwater storage and retention within an urban prairie wetland complex

  2025-01-17 · 1 citations

  preprintSenior author

  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.

  PublisherDOI

Recent grants

• NIH Grant K25AI062977

  NIH · $719k · 2011

• Collaborative Research: Seston Contributions to metabolism Across Longitudinal Ecosystems (SCALE) -- Dynamics of Organic Particles in River Networks

  NSF · $154k · 2006–2011

• NIH Grant R56AI081983

  NIH · $378k · 2010

• Fine Particle Migration and Solute Fluxes in Sediment Beds: Analysis of Sediment Structural Dynamics Using 3D Micro-Tomography

  NSF · $286k · 2003–2008

• NIH Grant R01AI081983

  NIH · $1.8M · 2016

Frequent coauthors

• Jennifer Drummond

  Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal

  61 shared

• Shai Arnon

  Ben-Gurion University of the Negev

  34 shared

• A. F. Aubeneau

  Purdue University West Lafayette

  33 shared

• Stefan Krause

  École Nationale des Travaux Publics de l'État

  33 shared

• C. B. Phillips

  31 shared

• Kevin Roche

  Boise State University

  28 shared

• Angang Li

  Northwestern University

  25 shared

• R. Schumer

  Desert Research Institute

  24 shared

Awards & honors

• Fulbright Distinguished Chair Award (2013)
• Huber Research Prize, American Society of Civil Engineers (2…
• Career Award, National Institutes of Health (NIAID K25) (200…
• McCormick Faculty Excellence Award, Northwestern University…
• Searle Junior Teaching Fellow, Northwestern University (2001…