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

Delaney Nelson; Yunsoo Choi; Bavand Sadeghi; Arash Kashfi Yeganeh; Masoud Ghahremanloo; Jincheol Park

doi:10.1016/j.envpol.2023.122223

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

Environ Pollut. 2023 Oct 1:334:122223. doi: 10.1016/j.envpol.2023.122223. Epub 2023 Jul 20.

Authors

Delaney Nelson¹, Yunsoo Choi², Bavand Sadeghi³, Arash Kashfi Yeganeh¹, Masoud Ghahremanloo¹, Jincheol Park¹

Affiliations

¹ Department of Earth and Atmospheric Science, University of Houston, Texas, USA.
² Department of Earth and Atmospheric Science, University of Houston, Texas, USA. Electronic address: ychoi6@uh.edu.
³ Air Resources Laboratory, National Oceanic and Atmospheric Administration, College Park, MD, 20740, USA; Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, MD, 20740, USA.

PMID: 37481031
DOI: 10.1016/j.envpol.2023.122223

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/aromatics. 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). NO_x had a negative impact on ozone production at the urban station due to the NO_x-rich chemical regime, whereas NO_x 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 strategies for Houston and other cities with high levels of ozone.

Keywords: Industrial; Ozone; PMF; SHAP; Source apportionment; Urban.

MeSH terms

Air Pollutants* / analysis
China
Environmental Monitoring / methods
Ethylenes / analysis
Humans
Machine Learning
Meteorology
Ozone* / analysis
Texas
Vehicle Emissions / analysis
Volatile Organic Compounds* / analysis

Substances

Ozone
propylene
decane
Air Pollutants
ethylene
Ethylenes
Volatile Organic Compounds
Vehicle Emissions