EnviroByte leverages advanced data analytics, environmental science, engineering, and machine learning to revolutionize sustainability reporting and management. Our innovative solutions enhance the efficiency and accuracy of tracking greenhouse gas emissions, criteria air contaminants, and more.
One Byte at a Time: Data-Driven Sustainability.
Almost all Enviromental sofware on the market has common problems: Black Box, Vendor Lock-in, Generic solution.
Our software has been developed from the regulator's perspective, and will allow for a seamless verification process. This approach not only guarantees precision but also saves reporters and operators valuable time and effort. Trust EnviroByte for efficient, reliable, and cutting-edge sustainability reporting and management.
Every quantification step is meticulously documented for full transparency and traceability.
Our source code is freely accessible for use, modification, and repurposing to suit your needs.
Tailor-made solutions for your specific facility and reporting requirements to enhance compliance and accuracy.
Utilizes advanced AI and machine learning to ensure superior data quality and accuracy.
Applications are fully containerized, allowing companies to run them independently without our intervention.
Engineered with a strong emphasis on verification and assurance to meet rigorous standards.
Many existing GHG reporting solutions are generic by design. This is primarily because maintaining facility-specific applications at scale is not feasible. EnviroByte offers a customizable platform that adapts to each facility’s unique needs and aligns with specific reporting programs.
This article introduces key concepts in fuel allocation and estimation methods, with a focus on identifying and correcting notable errors found in Alberta’s Quantification Methodologies (AQM) version 2.3. Several inaccuracies related to fuel proration are addressed, and improved, accurate methodologies are proposed to support better emissions reporting and compliance.
Since publishing my work on Predictive Emission Monitoring Systems (PEMS) in the IEEE journal in 2019, several companies have adopted the approach outlined in that paper, with some even attempting to commercialize it. However, as clearly stated in the publication, the goal of the research was to develop a free and open-source PEMS solution to support industry in real-time emissions monitoring.
We share tutorials on applying data science to various environmental reporting
