Software
PyGREET - Python Interface for GREET Life Cycle Assessment
Python-based R&D GREET implementation enabling programmatic access to transportation and energy system life cycle assessment.
Platform: PyGREET
Tech Stack: Python, React, Django, SQLite, TypeScript
Methodology: LCA Modeling, Modulith and Layer Architecture
Key Features
- Native Python access to GREET functionality and databases
- Biogenic carbon accounting
- Supply chain dependency tracking
- ACID for ensuring LCI data integrity
Applications
- Transportation and energy system life cycle assessments
- Academic research and policy analysis
- Corporate sustainability reporting
GREET OpenLCA Interface - LCA Data Engineering & AI-Assisted Mapping Platform
AI-assisted data engineering pipeline transforming GREET data into OpenLCA JSON-LD format with intelligent flow mapping to U.S. Federal LCA Commons standards.
Platform: GREET OpenLCA Interface
Tech Stack: Python, Django REST Framework, Streamlit, PostgreSQL
Methodology: RAG, LLM-as-judge, Data Engineering Pipeline
Key Features
- GREET to OpenLCA JSON-LD format conversion
- AI-assisted mapping using RAG and LLM-as-judge workflow
- Automated categorization with 4-digit NAICS codes
- U.S. Federal LCA Commons elementary flow alignment
Applications
- Government sustainability assessments with standardized data
- Academic LCA research with interoperable formats
- Corporate studies with Federal Commons compatibility
TRI4PLADS - Plastic Additives End-of-Life Analysis Platform
Modern data engineering platform for analyzing plastic additives chemical flow patterns using EPA TRI data and statistical modeling.
GitHub: jodhernandezbe/TRI4PLADS
PyPI Package: tri4plads
Tech Stack: Python 3.12+, Poetry, SQLite, Alembic
Methodology: Data engineering pipeline, Statistical modeling
Key Features
- ETL pipeline for EPA TRI data processing
- Plastic additives end-of-life flow analysis
- Discrete distribution generation from regulatory data
- Interactive CLI and database migrations
Applications
- Plastic waste management policy support
- TRI compliance and release pattern analysis
- Academic research with reproducible workflows
Chemical End-of-Life ML Models - Structure-Based Prediction API
Machine learning API for predicting chemical end-of-life scenarios using structure-based classification models.
GitHub: jodhernandezbe/prtr_deployment
Live Demo: Model API
Research Foundation: ACS Sustainable Chemistry & Engineering
Tech Stack: FastAPI, Python, Scikit-learn, AWS EC2, Docker
Methodology: Machine Learning, Structure-based classification
Key Features
- Structure-based chemical classification models
- RESTful API for real-time chemical fate predictions
- Batch processing for large chemical datasets
- Integration with regulatory assessment workflows
Applications
- EPA chemical risk evaluations
- Chemical informatics research
- Environmental fate modeling
PRTR Transfers API - International Pollutant Release Dashboard
FastAPI platform for analyzing and comparing pollutant release data across three international regulatory systems.
GitHub: jodhernandezbe/PRTR_transfers_FastAPI
Live Demo: AWS Deployment
Data Sources: NPRI (Canada), NPI (Australia), TRI (USA)
Tech Stack: FastAPI, SQLite, SQLAlchemy ORM, AWS EC2
Methodology: RESTful API design, Cross-country data harmonization
Key Features
- Cross-country pollutant data comparison API
- Interactive dashboard with visualizations
- Standardized schema across PRTR systems
- AWS deployment with automated documentation
Applications
- International environmental policy analysis
- Comparative regulatory research
- Cross-border pollution monitoring
StEWI - Standardized Emission and Waste Inventories
Official EPA system for standardizing and integrating environmental emissions and waste data across multiple regulatory programs.
GitHub: USEPA/standardizedinventories
Data Products: EPA StEWI Data Catalog
Coverage: 6 major EPA inventories (2011-2023)
Tech Stack: Python, Apache Parquet, Pandas
Methodology: ETL frameworks, Data standardization
Key Features
- Standardized data formats across EPA programs
- Facility and chemical matching across inventories
- Overlap removal and quality assurance
- Multi-year trending capabilities
Applications
- EPA assessment workflows
- National environmental data analysis
- Research data integration
EoL4Chem - Chemical Waste Flow Tracking & Analysis Framework
Python framework for tracking chemical waste flows and identifying recycling, treatment, and disposal facilities using EPA databases.
GitHub: jodhernandezbe/EoL4Chem
Data Sources: EPA CDR, TRI, RCRAInfo, FRS databases
Tech Stack: Python, BeautifulSoup, Pandas, SQLite, Matplotlib
Methodology: Web scraping, ETL pipelines, Circular economy analysis
Key Features
- Chemical waste flow tracking and analysis
- RETDF facility identification and mapping
- Integration with PAU4Chem for pollution abatement analysis
- Circular life cycle analysis capabilities
Applications
- Waste management policy recommendations
- Chemical fate and transport modeling
- Circular economy analysis and optimization
SciFinder Scraper - Chemical Pricing & Supplier Intelligence
Web scraping tool for extracting chemical pricing and supplier information from CAS SciFinder database.
GitHub: jodhernandezbe/SciFinder_Scraper
Data Source: CAS SciFinder Database
Tech Stack: Python, Selenium WebDriver, Pandas
Methodology: Web scraping, Batch processing
Key Features
- Automated SciFinder data extraction
- Chemical pricing and supplier information retrieval
- Geographic supplier mapping
- Batch processing of CAS number lists
Applications
- Chemical cost modeling and optimization
- Supply chain evaluation and risk assessment
- Research budget planning and market intelligence
Properties Scraper - Multi-Source Chemical Property Data Collection
Web scraping framework for gathering chemical properties from multiple authoritative databases.
GitHub: jodhernandezbe/Properties_Scraper
Data Sources: NOAA CAMEO, OSHA, NIST, EPA CompTox, IFA GESTIS
Tech Stack: Python, Selenium WebDriver, Pandas
Methodology: Web scraping, Multi-source data integration
Key Features
- Automated batch processing of CAS number lists
- Multi-source data integration and standardization
- Configurable scraping modules for different databases
- Data validation and CSV export capabilities
Applications
- Chemical property data for environmental modeling
- Regulatory compliance and safety information
- Risk assessment and toxicity analysis
PAU4Chem - Pollution Abatement Unit Chemical Flow Analysis
Python framework for tracking chemical flows in pollution abatement units using EPA TRI data.
GitHub: jodhernandezbe/PAU4Chem
Data Sources: EPA TRI Basic Plus Data Files (1987-2018)
Tech Stack: Python, BeautifulSoup, Pandas, NumPy, SciPy
Methodology: Web scraping, Statistical analysis, Chemical flow modeling
Key Features
- Automated TRI data extraction and processing
- Chemical flow tracking through pollution abatement units
- Multi-year data integration (1987-2018)
- Statistical analysis of waste management processes
Applications
- Environmental policy and pollution control strategies
- Industrial ecology and chemical flow network analysis
- TRI compliance assessment and technology evaluation