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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 R Subramanian, Aja Ellis, Elvis Torres-Delgado, Rebecca Tanzer, Carl Malings, Felipe Rivera, Maité Morales, Darrel Baumgardner, Albert A Presto, and Olga L. Mayol-Bracero ACS Earth Space Chem., Just Accepted Manuscript • DOI: 10.1021/ acsearthspacechem.8b00079 • Publication Date (Web): 04 Oct 2018 Downloaded from http://pubs.acs.org on October 6, 2018
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ACS Earth and Space Chemistry
Air quality in Puerto Rico in the aftermath of Hurricane Maria: A case study on the use of lower-cost air quality monitors - R Subramanian1, *, Aja Ellis1, Elvis Torres-Delgado2, Rebecca Tanzer1, Carl Malings1, Felipe Rivera3, Maité Morales2, Darrel Baumgardner4, Albert Presto1, Olga L. Mayol-Bracero2 1
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213
2
Department of Environmental Science, University of Puerto Rico-Rio Piedras, San Juan,
PR 00925 3
Department of Chemistry, University of Puerto Rico-Rio Piedras, San Juan, PR 00925
4
Droplet Measurement Technologies, Longmont, CO 80503
* Corresponding author. Email:
[email protected] Submitted to ACS Earth and Space Chemistry on June 5, 2018
ACS Paragon Plus Environment
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Abstract 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 were 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 MultiPollutant 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-Rio Piedras 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 1hour 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. Keywords: sulfur dioxide, carbon monoxide, Hurricane Maria, low-cost sensors, air quality, backup generators
R Subramanian
[email protected] ACS Paragon Plus Environment
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ACS Earth and Space Chemistry
Introduction Increases in fine particulate matter (PM2.5) and sulfur dioxide (SO2) result in premature mortality (1-3). SO2 has other serious health impacts including severe migraines and reduced heart rate variability (4,5). SO2 is also important as a precursor to PM2.5 downwind of emission sources. Over the past decade, US air quality has significantly improved with the introduction of cleaner fuels, including the replacement of high-sulfur and low-sulfur ( 0.9) as the 15-minute data, which indicates that the strong correlation is not due to an averaging effect. The ambient data for SO2 and CO fall between the EPA’s AP-42 source profiles (20) of emissions from gasoline and diesel stationary combustion units; thus, a combination of emissions from these two types of generators could explain the ambient data (21). There appear to be periods where SO2 concentrations at the low end in Figure 3(a) are relatively higher for a given CO concentration than the rest of the dataset. These “aberrant” data upon closer observation (e.g. Figure 2a) occur usually in the morning periods. However, these data could be an artifact of the MLR calibration model as they are not observed when using the QR calibration model for SO2 (Figure S5 and related discussion there.) This is similar to the findings of Hagan et al. (14), who show that temperature effects on electrochemical sensors are not well-accounted for by a linear model at low SO2 concentrations (
