Results of Three Years of Ambient Air Monitoring Near a Petroleum Refinery in Richmond, California, USA

• Main Authors: Nancy P. Sanchez, Arian Saffari, Stephanie Barczyk, Beverly K. Coleman, Ziad Naufal, Christopher Rabideau, Adam P. Pacsi
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Atmosphere
• Subjects: air quality; petroleum refineries; ambient air monitoring; PMF; volatile organic compounds; fenceline monitoring; community monitoring
• Online Access: https://www.mdpi.com/2073-4433/10/7/385

Recent regulatory and legislative developments in the state of California (e.g., Assembly Bill (AB) 1647 and AB 617) will increase the extent of air quality monitoring in communities near petroleum refineries and at facility fencelines. This work reports results over a three-year period for currently-installed community and fenceline air quality monitoring for the Chevron refinery in Richmond, California, USA. This paper presents the most comprehensive air quality dataset that has been published to date for a community near a petroleum refinery, including concentration of different air toxics (e.g., benzene, toluene, xylenes, etc.) and criteria air pollutants (e.g., ozone (O₃), sulfur dioxide (SO₂), and particulate matter (PM)). Instrumental techniques such as ultra-violet differential absorption spectroscopy (UV-DOAS), laser-based spectroscopy and real-time gas chromatography (GC) were used in the community and fenceline monitors. From 2015 to 2017, measured concentrations at community monitors near the Richmond refinery were generally below California thresholds for acute and chronic health (only two exceedances were observed for 8-h average benzene concentrations during the three-year monitoring period). Although more detailed speciation for volatile organic compounds (VOCs) and fine particulate matter (PM_2.5) would be needed to confirm certain source profile identities, preliminary application of source apportionment methods indicates the prevalence of typical urban emission profiles, such as from traffic, in the measured community data.