NA24OARX021G0012
Project Grant
Overview
Grant Description
Purpose: Effective management of water quality requires rigorous scientific understanding of the causal mechanisms driving changes over time, but reliably inferring and modeling these mechanisms is challenging due to the diffuse nature of non-point source pollution and the dynamic complexity of pollution and its downstream effects.
Recently developed empirical methods based on nonlinear dynamics theory offer a reliable means of inferring causality by tracing downstream conditions (e.g., eutrophication) to upstream pollution sources, to guide targeted investments in restoration projects and water infrastructure upgrades.
Studies published by the PI demonstrate the methods scientific validity and practical utility in systems whose flows are controlled by engineered structures, but the method is untested in naturally flowing rivers (unregulated flow).
During Phase I, ECCO Scientific will evaluate the validity of the methodology in this setting, through a case study benchmarked against existing results of a conventional method (watershed modeling or stable isotope tracing).
Further, existing software implementations lack a cohesive, computationally efficient, quality-controlled workflow conducive to performing causal analyses on a large scale.
During Phase I, ECCO Scientific will evaluate the technical benefits of optimizing the code base for parallel execution on a graphics processing unit (GPU), by estimating the associated reduction in execution time.
ECCO Scientific anticipates that the Phase I effort will (1) demonstrate the scientific validity and practical utility of the causal inference methodology in the unregulated-flow setting and (2) estimate execution-time acceleration on the order of 100x with GPU parallelization (compared to execution on a CPU).
The GPU-accelerated software will massively expand the number and scope of feasible causal investigations, in terms of the number of river/hydrological systems that can be analyzed, the number of variables per river system, and the size of the data per variable.
Application of the proposed software will provide scientific insights to (1) inform the development and targeting of interventions for effective management and restoration of water quality, (2) identify patterns of unintended consequences of past and ongoing management actions, and (3) inform refinement of existing process-based models by identifying real-world causal mechanisms that are inadequately represented or entirely absent in model formulations.
Potential clients include public-sector agencies on local, state, and federal levels; universities and non-profit research institutions; and engineering/environmental consulting firms in the private sector.
Commercialization will be pursued through deployment of the proposed software as part of ECCO Scientific's consulting/research practice and potentially through software licensing.
Recently developed empirical methods based on nonlinear dynamics theory offer a reliable means of inferring causality by tracing downstream conditions (e.g., eutrophication) to upstream pollution sources, to guide targeted investments in restoration projects and water infrastructure upgrades.
Studies published by the PI demonstrate the methods scientific validity and practical utility in systems whose flows are controlled by engineered structures, but the method is untested in naturally flowing rivers (unregulated flow).
During Phase I, ECCO Scientific will evaluate the validity of the methodology in this setting, through a case study benchmarked against existing results of a conventional method (watershed modeling or stable isotope tracing).
Further, existing software implementations lack a cohesive, computationally efficient, quality-controlled workflow conducive to performing causal analyses on a large scale.
During Phase I, ECCO Scientific will evaluate the technical benefits of optimizing the code base for parallel execution on a graphics processing unit (GPU), by estimating the associated reduction in execution time.
ECCO Scientific anticipates that the Phase I effort will (1) demonstrate the scientific validity and practical utility of the causal inference methodology in the unregulated-flow setting and (2) estimate execution-time acceleration on the order of 100x with GPU parallelization (compared to execution on a CPU).
The GPU-accelerated software will massively expand the number and scope of feasible causal investigations, in terms of the number of river/hydrological systems that can be analyzed, the number of variables per river system, and the size of the data per variable.
Application of the proposed software will provide scientific insights to (1) inform the development and targeting of interventions for effective management and restoration of water quality, (2) identify patterns of unintended consequences of past and ongoing management actions, and (3) inform refinement of existing process-based models by identifying real-world causal mechanisms that are inadequately represented or entirely absent in model formulations.
Potential clients include public-sector agencies on local, state, and federal levels; universities and non-profit research institutions; and engineering/environmental consulting firms in the private sector.
Commercialization will be pursued through deployment of the proposed software as part of ECCO Scientific's consulting/research practice and potentially through software licensing.
Awardee
Funding Goals
18 CLIMATE ADAPTATION AND MITIGATION 19 WEATHER-READY NATION 20 HEALTHY OCEANS 21 RESILIENT COASTAL COMMUNITIES AND ECONOMIES
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Gainesville,
Florida
326074807
United States
Geographic Scope
Single Zip Code
Related Opportunity
Ecco Scientific was awarded
Project Grant NA24OARX021G0012
worth $174,007
from National Oceanic and Atmospheric Administration in August 2024 with work to be completed primarily in Gainesville Florida United States.
The grant
has a duration of 5 months and
was awarded through assistance program 11.021 NOAA Small Business Innovation Research (SBIR) Program.
The Project Grant was awarded through grant opportunity NOAA SBIR FY 2024 Phase I.
SBIR Details
Research Type
SBIR Phase I
Title
Tracing Non-Point Source Pollution Signals with Accelerated Causal Inference
Abstract
Effective management of water quality requires rigorous scientific understanding of the causal mechanisms driving changes over time, but reliably inferring and modeling these mechanisms is challenging due to the diffuse nature of non-point source pollution and the dynamic complexity of pollution and its downstream effects. Recently developed empirical methods based on nonlinear dynamics theory offer a reliable means of inferring causality by tracing downstream conditions (e.g., eutrophication) to upstream pollution sources, to guide targeted investments in restoration projects and water infrastructure upgrades. Studies published by the PI demonstrate the methods’ scientific validity and practical utility in systems whose flows are controlled by engineered structures, but the method is untested in naturally flowing rivers (unregulated flow). During Phase I, ECCO Scientific will evaluate the validity of the methodology in this setting, through a case study benchmarked against existing results of a conventional method (watershed modeling or stable isotope tracing). Further, existing software implementations lack a cohesive, computationally efficient, quality-controlled workflow conducive to performing causal analyses on a large scale. During Phase I, ECCO Scientific will evaluate the technical benefits of optimizing the code base for parallel execution on a graphics processing unit (GPU), by estimating the associated reduction in execution time.
Topic Code
9.4
Solicitation Number
NOAA-OAR-TPO-2024-2008184
Status
(Complete)
Last Modified 3/5/25
Period of Performance
8/1/24
Start Date
1/31/25
End Date
Funding Split
$174.0K
Federal Obligation
$0.0
Non-Federal Obligation
$174.0K
Total Obligated
Activity Timeline
Transaction History
Modifications to NA24OARX021G0012
Additional Detail
Award ID FAIN
NA24OARX021G0012
SAI Number
NA24OARX021G0012-003
Award ID URI
None
Awardee Classifications
Small Business
Awarding Office
1305N2 DEPT OF COMMERCE NOAA
Funding Office
1333BR OFC OF PROG.PLANNING&INTEGRATION
Awardee UEI
R7XEPBCCJ2X8
Awardee CAGE
None
Performance District
FL-03
Senators
Marco Rubio
Rick Scott
Rick Scott
Modified: 3/5/25