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Published March 15, 2021 | Published + Supplemental Material
Journal Article Open

Transboundary transport of ozone pollution to a US border region: A case study of Yuma

Abstract

High concentrations of ground-level ozone affect human health, plants, and animals. Reducing ozone pollution in rural regions, where local emissions are already low, poses challenge. We use meteorological back-trajectories, air quality model sensitivity analysis, and satellite remote sensing data to investigate the ozone sources in Yuma, Arizona and find strong international influences from Northern Mexico on 12 out of 16 ozone exceedance days. We find that such exceedances could not be mitigated by reducing emissions in Arizona; complete removal of state emissions would reduce the maximum daily 8-h average (MDA8) ozone in Yuma by only 0.7% on exceeding days. In contrast, emissions in Mexico are estimated to contribute to 11% of the ozone during these exceedances, and their reduction would reduce MDA8 ozone in Yuma to below the standard. Using satellite-based remote sensing measurements, we find that emissions of nitrogen oxides (NOx, a key photochemical precursor of ozone) increase slightly in Mexico from 2005 to 2016, opposite to decreases shown in the bottom-up inventory. In comparison, a decrease of NOx emissions in the US and meteorological factors lead to an overall of summer mean and annual MDA8 ozone in Yuma (by ∼1–4% and ∼3%, respectively). Analysis of meteorological back-trajectories also shows similar transboundary transport of ozone at the US-Mexico border in California and New Mexico, where strong influences from Northern Mexico coincide with 11 out of 17 and 6 out of 8 ozone exceedances. 2020 is the final year of the U.S.-Mexico Border 2020 Program, which aimed to reduce pollution at border regions of the US and Mexico. Our results indicate the importance of sustaining a substantial cooperative program to improve air quality at the border area.

Additional Information

© 2021 The Authors. Published by Elsevier Under a Creative Commons license - Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Received 18 October 2020, Revised 16 December 2020, Accepted 29 December 2020, Available online 4 January 2021. Z. Qu and D. K. Henze acknowledge funding support from National Aeronautics and Space Administration (NASA) HAQAST NNX16AQ26G. Z. Qu would like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. D. Wu would like to acknowledge the support and resources from the Center for High Performance Computing (CHPC) at the University of Utah. The authors thank the Air Quality Monitoring and Assessments section of Arizona Department of Environmental Quality for assistance with the surface monitoring data. Author statement. Zhen Qu: Conceptualization, Investigation, Software, Validation, Writing – original draft, Supervision. Dien Wu: Methodology, Investigation, Validation, Software. Daven K. Henze: Conceptualization, Writing - original draft. Yi Li: Conceptualization, Investigation, Writing – original draft, Supervision. Mike Sonenberg: Investigation, Data curation, Writing – review & editing. Feng Mao: Data curation, Software, Methodology. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors confirm that there are no conflicts of interest. This paper has been recommended for acceptance by Pavlos Kassomenos.

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Supplemental Material - 1-s2.0-S0269749120371104-mmc1.docx

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Additional details

Created:
August 22, 2023
Modified:
October 23, 2023