2019 by Argos Scientific, Inc.

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Recent Events

November 12, 2019








Hosted by the Bay Area Air Quality Management District, California Air Resources Board, and Argos Scientific, Inc.

Don Gamiles, Argos Scientific, Inc. - High Level Overview of Open-path Technologies

Mark Wicking-Baird, Argos Scientific Africa, Inc. - Auditing Operational Performance

Quentin Hurt, Skyside Africa - Auditing Management System

Mark Wicking-Baird, Argos Scientific Africa, Inc. - Case Study: Implementing an ISO 17025 at an Oil Refinery

Don Gamiles, Argos Scientific, Inc. - Logistics (Costs & Timelines)

November 6-8, 2019

Use of Open Path UV-DOAS as an Alternative Method to Meet Fence-line Monitoring Provisions for Federal Benzene Monitoring Rule - A Case Study

Mark Wicking-Baird, Argos Scientific Africa, Inc.

On December 1, 2015, the EPA finalized the Risk and Technology Review for petroleum refineries.  Among other things, the finalized rule requires petroleum refineries to conduct fence-line monitoring on a continuous basis.  Benzene was defined as the target compound, and an annual average, action level of 9 µg/m3 was established, triggering a refinery lead root cause analysis and corrective action. The fence-line monitoring provisions found in 40 CFR 63.658 describe the use of a network of passive diffusive tube samplers placed along the refinery's boundary as the primary method for detecting fugitive emissions of benzene.

The fence-line monitoring provisions allow a refinery owner or operator to submit a request for an alternative test method, such as the use of open-path instrumentation. The use of this type of technology presents the opportunity to meet the requirements of the rule in a more simplified and cost-effective way, while offering advantages in terms of time resolution and potentially identifying and eliminating data points that correspond to non-facility emission sources.  A field validation study has been conducted using latest generation, open-path UV-DOAS technology to detect benzene at a refinery fence line on a continuous basis. This study includes the development of a quality assurance program that is compliant with the ISO-17025 standard for the operation of a gas analyzers as a field analytic laboratory. The analysis includes a case study on the lessons learned in developing this program, and presents a path forward in utilizing the open-path fence-line monitoring systems installed at refineries in California to meet the federal fence-line rule for benzene monitoring.

Employing Machine Learning Techniques to Determine Emission Sources at Industrial Facilities Use of Open Path Air Monitoring Systems

Don Gamiles, Argos Scientific, Inc.

The next step to using the data from fence-line air monitoring programs is using the information generated by the systems to identify emission sources and predict downwind community exposure.  This can be achieved by using the data sets from open-path systems and data sets generated from point monitors in the same area.  This data can be used to assess the overall health impact on those communities.  A novel approach is to analyze the information from multiple data sets using  machine learning systems to evaluate relationships between long-path and point monitoring systems.  This artificial intelligence accession utilizes statistical and data processing algorithms to identify emission sources in near real-time.  Data generated by the program can then be used to determine downwind health impacts associated with the release of fugitive emissions.

Data will be presented showing how open-path UV air monitoring data that is generated by a long-path, fence-line air monitoring system is currently being used, along with point sampling data collected by an automated field gas chromatography point analyzer to identify fugitive emission sources.  Data from both systems are combined in a manner to estimate the plume diameter of fugitive emissions crossing the fence line boundary.  This information is then combined with meteoritical data to determine fugitive emission sources.  Once identified, activities can occur to mitigate those sources.  In addition, modelling is then employed to determine the overall health impact to the downwind community.