A comprehensive approach combining positive matrix factorization modeling, meteorology, and machine learning for source apportionment of surface ozone precursors: Underlying factors contributing to ozone formation in Houston, Texas

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Abstract

Ozone concentrations in Houston, Texas, are among the highest in the United States, posing significant risks to human health. This study aimed to evaluate the impact of various emissions sources and meteorological factors on ozone formation in Houston from 2017 to 2021 using a comprehensive PMF-SHAP approach. First, we distinguished the unique sources of VOCs in each area and identified differences in the local chemistry that affect ozone production. At the urban station, the primary sources were n_decane, biogenic/industrial/fuel evaporation, oil and gas flaring/production, industrial emissions/evaporation, and ethylene/propylene/aro- matics. At the industrial site, the main sources were industrial emissions/evaporation, fuel evaporation, vehicle- related sources, oil and gas flaring/production, biogenic, aromatic, and ethylene and propylene. And then, we performed SHAP analysis to determine the importance and impact of each emissions factor and meteorological variables. Shortwave radiation (SHAP values are ~5.74 and ~6.3 for Milby Park and Lynchburg, respectively) and humidity (~4.87 and ~4.71, respectively) were the most important variables for both sites. For the urban station, the most important emissions sources were n_decane (~2.96), industrial emissions/evaporation (~1.89), and ethylene/propylene/aromatics (~1.57), while for the industrial site, they were oil and gas flaring/ production (~1.38), ethylene/propylene (~1.26), and industrial emissions/evaporation (~0.95). NOx had a negative impact on ozone production at the urban station due to the NOx-rich chemical regime, whereas NOx had positive impacts at the industrial site. The study’s findings suggest that the PMF-SHAP approach is efficient, inexpensive, and can be applied to other similar applications to identify factors contributing to ozone- exceedance events. The study’s results can be used to develop more effective air quality management strate- gies for Houston and other cities with high levels of ozone.

Type
Publication
Environmental Pollution, Volume 334, 122223
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Supplementary notes can be accessed here.

Delaney Nelson
Delaney Nelson
Ph.D. candidate of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Climate Sciences, Air-Quality, Remote Sensing and AI (Deep Learning/ Machine Learning).

Prof. Yunsoo Choi
Prof. Yunsoo Choi
Professor of Atmospheric Chemistry, Air-Quality Modeling, AI (Deep Learning/Machine Learning), Satellite Remote Sensing

My research interests include Atmospheric Chemistry, Air-Quality Modeling and AI (Deep Learning/ Machine Learning).

Arash Kashfi Yeganeh
Arash Kashfi Yeganeh
Ph.D. candidate of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Air-Quality, Sensitivity Analysis and Numerical Modeling.

Masoud Ghahremanloo
Masoud Ghahremanloo
Postdoctoral scholar specializing in satellite remote sensing, artificial intelligence (deep/machine learning), and atmospheric sciences.

My research interests include Air-Quality, Remote Sensing and AI (Deep Learning/ Machine Learning).

Jincheol Park
Jincheol Park
Ph.D. candidate of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Air-Quality, Sensitivity Analysis and Numerical Modeling.