Hourly Photochemical Assessment Monitoring Station (PAMS) Monitoring of NMHCs by AutoGC: Building Networks of AutoGCís and Monitoring Performance

Poster Presentation

Prepared by A. Plummer, C. Meyer
Orsat, LLC, 1416 E Southmore, Pasadena, TX, 77502, United States


Contact Information: amy@conscicorp.com; 713-920-1696


ABSTRACT

The analysis of ozone precursors has been a feature of the EPA air quality surveillance regulations since 1992 with the establishment of Photochemical Assessment Monitoring Stations (PAMS) as part of State Implementation Plans (SIP) for ozone nonattainment areas classified as serious, severe or extreme. At that time, guidance documentation allowed for the measurements of VOC precursors either by canister sampling or by continuous measurement using a GC/FID with a Thermal desorber collecting hourly samples. Only a few agencies chose to do continuous sampling, and since that time, a lot has been learned about the issues associated with the continuous field measurement of VOCs.

In 2011, the EPA initiated an effort to re-evaluate the PAMS requirements and the technology being used for continuous field measurements in conjunction with upcoming changes to the National Ambient Air Quality Standards (NAAQS) for ozone. With guidance from Clean Air Science Advisory Committee Air Monitoring Methods Subcommittee (CASAC AMMS) and National Association of Clean Air Agencies (NACAA) Monitoring Steering Committee (MSC), the EPA has promulgated revisions to the network design and is evaluating newer technology for continuous measurements. Since the primary use of this data is for photochemical modeling, the new EPA ruling has recommended a redistribution of PAMS sites in an effort to increase the spatial coverage of this data for modeling performance evaluations. More agencies may find themselves responsible for implementing continuous hourly Volatile Organic Carbon (VOC) monitoring in conjunction with the existing NCORE network. While this type of hourly AutoGC monitoring represents a significant increase in complexity in both implementation and data management, systems have been developed and deployed to fully automate and streamline data collection and management.

In conjunction with the implementation of this type of monitoring, agencies will have to develop the necessary Quality Assurance Project Plan (QAPP) as well as the necessary Standard Operating Procedures to accomplish this more enhanced monitoring. Simplification of the quality control strategies as well as calibration requirements will play a key role in the success of any monitoring plan. The identification and quantitation of up to 56 non-methane hydrocarbon (NMHC) species hourly require a quality control strategy which is easy to implement and maintain. . While a number of commercially available systems are currently being evaluated for use in PAMS monitoring activities, the PerkinElmer Ozone Precursor system has been used in Texas since 1992, and there are now currently 35 of these AutoGCís collecting data hourly across the state. The PerkinElmer Ozone Precursor system comprised of the Turbomatrix Thermal Desorber in conjunction with a Clarus Dual FID Gas Chromatograph equipped with a deanís switch has been used extensively in operations in Texas for over 20 years. This system has been completely automated using the Totalchrom Data System in conjunction with automation software supplied by Orsat, LLC.

While currently available equipment has proven to be robust and capable of fully unattended operation, the operational aspects of quality assurance must be considered as an integral part of the successful collection of large amounts of VOC data. In order to ensure that data produced by each system is comparable, it becomes important that the analytical system be operated with minimum input from users to reduce errors in data collection and potential equipment failures related to instrument manipulation. To facilitate this, it is necessary to implement quality control checks which allow operators to access the performance of instrumentation regularly without having to interfere with its ongoing operation. Strategies for implementing quality controls as well as the most common failures will be discussed. Calibration strategies and their impact on the data will be presented and reviewed with emphasis on ease of maintenance and data review. A review of the most common biasí seen in quality control data and their sources will be discussed and data presented on the effects of sample conditioning and sampling hardware. Performance evaluations and operational criteria used to ensure all systems are operating at the same sensitivity for all targets will be presented and a review of the network performance criteria used to evaluate inter-site variance will be presented.