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

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

doi:10.1016/j.envpol.2023.122223

Cited by 13 publications

(2 citation statements)

References 58 publications

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“…Process-based modeling studies to identify inconsistencies in NEI emission estimates (and trends) relative to observed pollutant levels must be pursued. The use of CTMs for multi-pollutant emission estimation and data assimilation studies, e.g., refs − , together with the application of source apportionment and attribution strategies, e.g., refs − , including machine-learning approaches, , provide opportunities to reduce uncertainties in these emission estimates. This should, however, be conducted in association with improvements in input and ancillary datasets that account for local heterogeneity especially land cover classification and urban development, e.g., refs and , as well as emission trends and scenarios like changes in mobile and stationary sources, e.g., ref , and even the shift to renewable energy, , increasing contribution of noncombustion sources, , all of which can drive potential shifts in chemical regimes.…”

Section: Steps Forwardmentioning

confidence: 99%

Ozone in the Desert Southwest of the United States: A Synthesis of Past Work and Steps Ahead

Sorooshian,

Arellano,

Fraser

et al. 2024

ACS EST Air

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A region often neglected in the grander scale of general atmospheric chemistry studies and model evaluation for gasphase chemistry is the desert southwest of the U.S. Despite regulatory progress, challenges in meeting the National Ambient Air Quality Standard for ozone motivate a re-examination of the unique meteorological conditions, interactions between the desert, agricultural, and built environmental landscapes, emissions across natural and anthropogenic sources, and regional transport of precursors that govern ozone formation in the desert Southwest. Arizona includes multiple nonattainment counties with a unique situation in terms of its environment (e.g., vegetation, meteorology, fire prone areas), complex terrain, urban growth, transport vulnerability, and limited knowledge base. Here we summarize past works investigating the ozone over Arizona, including 61 peerreviewed publications found since the first one in 1996, and determine significant knowledge gaps to guide future research with the aim of improving regulatory policy. A more in-depth focus is placed here on Maricopa County, which includes the Phoenix Metropolitan area, where significant population growth in recent decades coupled with the extreme high temperatures and surrounding complex terrain creates a poorly understood airshed in terms of ozone chemistry, thereby complicating regulatory decisions. We suggest paths forward, including improved monitoring, assessment, and modeling tools for the region, better leveraging of archived data, and engagement with the public, government, and policy. This Review is highly relevant as well to other semiarid and arid regions, which represent the most common land type globally, warranting more attention.

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Section: Steps Forwardmentioning

confidence: 99%

Ozone in the Desert Southwest of the United States: A Synthesis of Past Work and Steps Ahead

Sorooshian,

Arellano,

Fraser

et al. 2024

ACS EST Air

View full text Add to dashboard Cite

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“…Moreover, the SHAP model, serving as an explanatory tool for machine learning models, has found extensive application in various studies involving PM 2.5 (Hou et al, 2022), O 3 (Ahmad et al, 2022;Cheng et al, 2023;Ghahremanloo et al, 2023;Nelson et al, 2023), precipitation (He et al, 2023;Li et al, 2023;Lin et al, 2023), and wind speed (Santos et al, 2023) investigations.…”

Section: Shapley Additive Explanation (Shap) Approachmentioning

confidence: 99%

Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models

Luo,

Lu,

Chen

et al. 2024

Earth and Space Science

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Accurate estimation of ozone (O3) concentrations and quantitative meteorological contribution are crucial for effective control of O3 pollution. In recent years, there has been a growing interest in leveraging machine learning for O3 pollution research due to its advantages, such as high accuracy, strong generalization, and ease of use. In this study, we utilized meteorological parameters obtained from european center for medium—range weather forecasts (EMCWF) Reanalysis v5 data as input and employed five distinct machine learning methods to estimate values of maximum daily 8‐hr average (MDA8) O3 concentrations and analyze meteorological contributions. To improve the accuracy and generalization capabilities of the estimation, we employed Grid SearchCV techniques to select optimal parameters and mitigate the risk of overfitting. Additionally, we incorporated meteorological normalization and the SHAP model to quantify the influence of various parameters. Among the models evaluated, the Extreme Gradient Boost model exhibited exceptional performance from 2015 to 2022, yielding determination coefficients of 0.85 and 0.80 for the training and test data sets, respectively. The outcomes of meteorological normalization revealed that meteorological parameters accounted for 87.7% of the impacts in 2018, while emission‐related factors constituted 80.8% of the impacts in 2021. Over the period spanning 2015–2022, 2 m temperature emerged as the most influential parameter affecting daily MDA8 O3 concentration, with an average contribution of 9.4 μg m−3.

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Sustainable organic–inorganic hybrid coating system with multiple cure capabilities

Hayeri,

Mannari

2024

J Coat Technol Res

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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

Cited by 13 publications

References 58 publications

Ozone in the Desert Southwest of the United States: A Synthesis of Past Work and Steps Ahead

Ozone in the Desert Southwest of the United States: A Synthesis of Past Work and Steps Ahead

Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models

Sustainable organic–inorganic hybrid coating system with multiple cure capabilities

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