2024 Pathfinder Innovation Project Awardees
Congratulations to the 2024 PIPs Winners!
Pathfinder Innovation Projects (PIPs) challenge EPA scientists to answer the question, "Wouldn't it be amazing if we could ... ?" The internal competition provides staff time and seed funding in pursuit of high-risk, high-reward research ideas. The 15 selected 2024 Pathfinder Innovation Projects cover a range or topics including environmental justice, PFAS, 6PPD-Q, and others. Over the past 11 years EPA has funded 153 PIPs, many of which have led the Agency in new, transformational directions. Please find the 2024 awardees and projects listed below.
Air Quality Understanding Through Next-gen Local Observations with Communities (AQ-UNLOC)
Current efforts to monitor volatile organic compounds (VOCs) use either low-cost sensors with little-to-no ability to speciate between chemical classes/compounds (and therefore source), or high-cost, intensive measurements that can identify specific compounds, but do not provide real-time data. This PIP aims to develop a VOC sensing technology that would be an intermediate approach, classifying chemicals and sources while being cheap enough to expand monitoring coverage in communities with environmental justice concerns. The new technology would be especially useful for fenceline communities who could characterize pollution classes and sources from nearby facilities with a suite of sensors at intermediate cost. Upon success, information derived from these sensors would be used to develop communication/educational materials to inform impacted communities.
Bioinformatic Prediction of the Trophic Impacts of Cyanotoxins (BioPTIC)
Toxins produced by cyanobacteria (cyanotoxins) can be harmful to humans and other organisms, yet the effects of most are poorly characterized. Even what we know of the effects of well-studied cyanotoxins are mostly restricted to standard indicator species, which can make extrapolation tenuous when dealing with “real-world” ecological communities. This PIP aims to use EPA’s ECOTOXicology Knowledgebase (ECOTOX) and Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) to create a next-generation bioinformatics pipeline that predicts the susceptibility of individual aquatic species to cyanotoxins and anticipates cyanotoxin effects on whole aquatic ecological communities. This next-generation sequencing and bioinformatics technology might allow for rapid, non-intrusive, cost-effective predictions of sensitivity that would complement and enhance existing indices of biotic integrity as well as inform risk assessment.
Breathe It In – Let It Out: Building a Workflow to Fill Data Gaps in Biotransformation for In Vitro Respiratory Tract Models
As the toxicology community moves away from animal models towards in vitro methods, it is important to determine which new in vitro methods best represent natural systems. This PIP aims to assess how newer sophisticated cell culture approaches at the air-liquid interface represent biotransformation in respiratory tract models, particularly in the tracheobronchial region of the respiratory tract. Upon success, findings will set parameters for model applicability to represent biotransformation potential for the respiratory tract and provide a foundation to explore the interaction of potential toxicants in vitro for better prediction of human health impacts.
Comprehensive Approaches for Calculating Ambient Inhalation Risk
EPA routinely quantifies ambient inhalation risk associated with designated hazardous air pollutants (HAPs). This PIP seeks to accomplish two tasks using the Community Multiscale Air Quality (CMAQ) regional air quality model with the new Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM). One is to compare toxicity and dosimetry of HAPs from this model to other existing new approach methods, and the second is to evaluate the potential impacts of non-HAP pollutants on overall health risks. Results from this work could build confidence in the use of new toxicity estimation approaches and/or identify limitations.
Emissions Measurements Plus Odor Wheel Electronic Reports (EMPOWER)
Fenceline communities can be impacted by local sources of volatile organic compounds (VOCs), hazardous air pollutants (HAPs), and odorous chemicals that enhance exposure risk and exacerbate quality of life issues. Communities that live near these sources have the right to know what is in the air they breathe every day. This PIP aims to develop an air quality dashboard that synthesizes near-source air quality measurements, participatory science odor observations, and online meteorological data into a unique data analysis and visualization framework that can be used by both fenceline communities and air quality researchers.
Enhancing Biothreat Detection in Water Supply Systems Using a Novel High-throughput Identification and Quantification Approach
In the event of a bioterrorism attack that contaminates the US water supply, it is critical to quickly identify the infectious agent that has been released. However, current high-throughput sequencing methods for identifying unknow agents are poorly suited to the organism (single cell) level and lack the capability to quickly and accurately quantify the number of pathogenic cells present. This PIP aims to develop a high-throughput method using droplet digital PCR (ddPCR) to precisely detect and quantify individual bacteria in drinking water systems. These outcomes will provide a solid basis for future studies and potential uses in handling bioterrorism threats.
Event Triggered Passive Aerosol Sampler (ETPAS)
Intermittent hazardous air pollution events are episodes of elevated air pollution characterized by high levels of harmful contaminants. They can be anthropogenic, natural, or sometimes both. These intermittent events present challenges for traditional sampling systems, which are optimized for longer collection periods at lower concentrations. This PIP aims to engineer a passive sampling system that is triggered by intermittent hazardous air pollution events. The system will use a PM10 passive aerosol sampler with a mechanical cover that can be opened upon a triggering threshold event and sealed at the end of this intermittent hazardous air pollution event to capture the event specific contamination. These samples will then be analyzed by computer-controlled electron microscopy (CCSEM) to analyze the size, concentration, and elemental constituents of the aerosol particles transported during intermittent air pollution events. If successful, this PIP could empower impacted communities and decision-makers with actionable information about intermittent pollution events reducing or mitigating their potential harm.
maskynet: A Python Package Leveraging EPA’s Stormwater Management Model for Geoprivacy Tools in Wastewater Surveillance
Wastewater surveillance is an increasingly important public health tool, but the spatial resolution of this method raises questions about data privacy. This PIP aims to develop a Python package to anonymize (or “mask”) the locations of sewer sampling sites. The package will use EPA’s Stormwater Management Model to incorporate sewer network travel times to avoid revealing fine-scale information about individuals near sampling points. Upon success, the final product will modify existing sewer network data to align with desired privacy levels and ensure minimal disruption to disease hotspot identification or targeted intervention models while addressing ethical concerns about privacy, biases, economic, and social harm upon public data release.
Molecular Networking and Modeling for Non-targeted Analysis (MNAMNA)
Non-targeted analysis (NTA) has promise to greatly increase detection of contaminants of emerging concern, but current approaches rely on limited libraries of spectra and known chemical transformations. This PIP aims to use molecular modeling and molecular networking tools linking a priori environmental transformation pathway to a priori mass spectral fragmentation pattern through molecular networking in a bottom-up approach. Successful implementation would provide EPA with another tool to employ during NTA studies increasing the rate of identification for unknown chemicals that lack specific reference information.
Protecting Salmon with Green Infrastructure: An Ecological Approach to Capturing and Attenuating the Tire Additive 6PPD in Stormwater
6PPD—a chemical additive commonly found in tires—and 6PPD-quinone (6PPD-Q)—a degradation byproduct of 6PPD—enters stormwater, threatening salmonoid species, other aquatic wildlife, and potentially human health. This PIP aims to evaluate various green infrastructure medium designs for filtering 6PPD and 6PPD-Q from stormwater runoff. The researchers will test various substrate/biochar matrixes to determine optimal bioretention filtration systems for 6PPD/6PPD-Q, protecting both human health, aquatic resources, and culturally significant salmon populations for Pacific Northwest Tribes.
Quantifying Impacts of Microplastics on Blue Carbon
Mangrove forests are important reservoirs of blue carbon (carbon stored by the ocean and coastal ecosystems), but microplastics are an under-researched threat to the carbon storage potential of these ecosystems. This PIP aims to combine remote sensing with in situ sampling methods to map the distribution of microplastics in Everglades National Park. The researchers will then develop a model to quantify the impact of these microplastics on carbon storage potential. This model will be used to identify “hot spots” that can be targeted for future monitoring and mitigation.
Quantum Enhanced Matrix Factorization Algorithms for Source Apportionment
EPA is currently in the process of developing an open-source replacement for its Positive Matrix Factorization (PMF) tool, which is used for estimating source apportionment of air and water quality data. This PIP aims to explore quantum and quantum hybrid algorithms that could improve this tool. The researchers will evaluate existing quantum-based matrix factorization algorithms and determine which algorithms could be adapted for an environmental science context to optimize PMF workflows. Quantum-based algorithms have the potential to provide improved computational runtimes and/or more optimal solutions with a lower loss value.
Revitalizing Forest Modeling: Unleashing Molecular Biomarkers for Precision in Climate Change Predictions
Forests, vital for ecosystem services and climate change mitigation, face challenges from evolving environmental changes. Existing climate change mathematic models often focus on whole organisms, such as trees, when characterizing and predicting climate change effects on forest development. However, whole organism models do not incorporate that trees may adapt to rising temperatures and increased drought by modifying the quantity and composition of proteins (proteome) in their tissues. This PIP aims to identify predictive region-specific protein biomarkers for temperature and drought adaptation that can be used to assess, predict, and manage forests amid changing climatic conditions. Capturing the interplay between genetic factors, biochemical processes, and environmental stressors enhances predictive accuracy for informed decision-making.
The Role of Viruses in Fish Tumors and Deformities Within the Great Lakes Areas of Concern
EPA is responsible for developing and implementing remediation plans to protect beneficial uses at chemically contaminated areas of concern (AOC) throughout the Great Lakes Region. The prevalence of fish tumors is one of the impairments of beneficial use that determine whether an area is considered an AOC. While high levels of fish tumors have been associated with exposure to environmental contaminants, there is a knowledge gap in the role viruses play in fish tumor development within these AOCs. This PIP aims to examine the connection between viral infection and fish tumors, creating a reference database of fish viruses in the Great Lakes that can be used for future metagenomic analysis and ideally a model addressing the potentially causative relationship between viral infection and fish neoplasia.
Tunable Photonic Activation for Optimized Electrochemical PFAS Mineralization
As PFAS treatment becomes an increasingly important priority, there remains a significant need to investigate technologies for PFAS destruction. This PIP aims to develop stable and robust photoelectrodes, prioritizing abundant materials that can be used to destroy PFAS by mineralization using a photo-enabled electrochemistry approach. This new process could result in rapid, energy-efficient, and cost-effective destruction of PFAS.