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Air Quality in Puerto Rico in the Aftermath of Hurricane Maria: A Case Study on the Use of Lower-Cost Air Quality Monitors

preprint
revised on 25.07.2018 and posted on 25.07.2018 by Subramanian Ramachandran, Aja Ellis, Elvis Torres-Delgado, Rebecca Tanzer, Carl Malings, Felipe Rivera, Maité Morales, Darrel Baumgardner, Albert Presto, Olga L. Mayol-Bracero
In the aftermath of Hurricane Maria, the electricity grid in Puerto Rico was devastated, with over 90% of the island without electricity; as of December 2017, about 50% of the island lacked electricity, and power outages were common elsewhere. Backup generators are widely used, sometimes as the main source of electricity. The hurricane also damaged the island’s existing air monitoring network and the University of Puerto Rico’s observing facilities. We deployed four lower-cost air quality monitors (Real-time Affordable Multi-Pollutant or RAMP monitors) and a black carbon (BC) monitor in the San Juan Metro Area in November 2017. The first month of data collected with the RAMPs showed high sulfur dioxide (SO2) and carbon monoxide (CO) concentrations of varying magnitudes each night. SO2 and CO are strongly correlated (r2 >0.9) at two sites ~5 km apart (University of Puerto Rico and an industrial area, Puerto Nuevo), suggesting a single source type. BC measured at the UPR site is also well correlated with CO and SO2. While the RAMPs are not certified as a federal equivalent method, the RAMP SO2 data suggest that the EPA’s daily 1-hour threshold for SO2 (75 ppb) was exceeded on almost 80% of the first 30 days of deployment (November-December 2017). The widespread reliance on generators for regular electric supply in the aftermath of Hurricane Maria appears to have increased air pollution in San Juan.

Funding

US EPA

History

Email Address of Submitting Author

randomsubu@gmail.com

Institution

Carnegie Mellon University

Country

USA

ORCID For Submitting Author

0000-0002-5553-5913

Declaration of Conflict of Interest

R Subramanian may provide consulting services to SenSevere, and the RAMPs and machine learning algorithms are the subject of a provisional patent. No other conflicts of interest.

Version Notes

Revised in response to reviewer comments.

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