The Temporal–Spatial Characteristics of Column NO2 Concentration and Influence Factors in Xinjiang of Northwestern Arid Region in China

Yu, Zhishui; Li, Xia

doi:10.3390/atmos13101533

Cited by 2 publications

(2 citation statements)

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“…Griffin et al (2019) compared TROPOMI NO 2 and surface measurements in the Canadian Oil Sands and found an R 2 of 0.67, demonstrating the improved capability of TROPOMI in capturing fine-scale surface NO 2 variations compared to OMI. Yu and Li (Yu & Li, 2022) explored the agreement of TROPOMI NO 2 with surface monitors in China's Xinjiang Province, finding a province-wide R 2 of 0.78. Their work also explored manuscript submitted to Journal of Geophysical Research: Atmospheres meteorological and economic factors, using annual GDP of industry as a proxy for industrial activity.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Comparison of Regression Methods for Inferring Near-Surface NO2 with Satellite Data

Kim,

Holloway,

Kokandakar

et al. 2024

Preprint

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Nitrogen dioxide (NO2) is emitted during high temperature combustion from anthropogenic and natural sources. Human exposure to high NO2 concentrations causes cardiovascular and respiratory illnesses. The EPA operates ground monitors across the U.S. which take hourly measurements of NO2 concentrations, providing precise measurements for assessing human pollution exposure but with sparse spatial distribution. Satellite-based instruments capture NO2 amounts through the atmospheric column with global coverage at regular spatial resolution, but do not directly measure surface NO2. This study compares regression methods using satellite NO2 data from the TROPospheric Ozone Monitoring Instrument (TROPOMI) to estimate annual surface NO2 concentrations in varying geographic and land use settings across the continental U.S. We then apply the best-performing regression models to estimate surface NO2 at 0.01o by 0.01o resolution, and we term this estimate as quasi-NO2 (qNO2). qNO2 agrees best with measurements at suburban sites (cross-validation (CV) R2 = 0.72) and away from major roads (CV R2 = 0.75). Among U.S. regions, qNO2 agrees best with measurements in the Midwest (CV R2 = 0.89) and agrees least in the Southwest (CV R2 = 0.65). To account for the non-Gaussian distribution of TROPOMI NO2, we apply data transforms, with the Anscombe transform yielding highest agreement across the continental U.S. (CV R2 = 0.78). The interpretability, minimal computational cost, and health relevance of qNO2 facilitates use of satellite data in a wide range of air quality applications.

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Section: Literature Reviewmentioning

confidence: 99%

“…N. Lamsal et al, 2008;Lamsal et al, 2015;H. J. Lee et al, 2023;Penn & Holloway, 2020;Pommier, 2023;Yu & Li, 2022).…”

Section: Introductionunclassified

A Comparison of Regression Methods for Inferring Near-Surface NO2 with Satellite Data

Kim,

Holloway,

Kokandakar

et al. 2024

Preprint

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A Comparison of Regression Methods for Inferring Near‐Surface NO₂ With Satellite Data

Kim,

Holloway,

Kokandakar

et al. 2024

JGR Atmospheres

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Nitrogen dioxide (NO2) is an atmospheric pollutant emitted from anthropogenic and natural sources. Human exposure to high NO2 concentrations causes cardiovascular and respiratory illnesses. The Environmental Protection Agency operates ground monitors across the U.S. which take hourly measurements of NO2 concentrations, providing precise measurements for assessing human pollution exposure but with sparse spatial distribution. Satellite‐based instruments capture NO2 amounts through the atmospheric column with global coverage at regular spatial resolution, but do not directly measure surface NO2. This study compares regression methods using satellite NO2 data from the TROPospheric Ozone Monitoring Instrument (TROPOMI) to estimate annual surface NO2 concentrations in varying geographic and land use settings across the continental U.S. We then apply the best‐performing regression models to estimate surface NO2 at 0.01° by 0.01° resolution, and we term this estimate as quasi‐NO2 (qNO2). qNO2 agrees best with measurements at suburban sites (cross‐validation (CV) R2 = 0.72) and away from major roads (CV R2 = 0.75). Among U.S. regions, qNO2 agrees best with measurements in the Midwest (CV R2 = 0.89) and agrees least in the Southwest (CV R2 = 0.65). To account for the non‐Gaussian distribution of TROPOMI NO2, we apply data transforms, with the Anscombe transform yielding highest agreement across the continental U.S. (CV R2 = 0.77). The interpretability, minimal computational cost, and health relevance of qNO2 facilitates use of satellite data in a wide range of air quality applications.

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The Temporal–Spatial Characteristics of Column NO2 Concentration and Influence Factors in Xinjiang of Northwestern Arid Region in China

Cited by 2 publications

References 52 publications

A Comparison of Regression Methods for Inferring Near-Surface NO2 with Satellite Data

A Comparison of Regression Methods for Inferring Near-Surface NO2 with Satellite Data

A Comparison of Regression Methods for Inferring Near‐Surface NO₂ With Satellite Data

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The Temporal–Spatial Characteristics of Column NO2 Concentration and Influence Factors in Xinjiang of Northwestern Arid Region in China

Cited by 2 publications

References 52 publications

A Comparison of Regression Methods for Inferring Near-Surface NO2 with Satellite Data

A Comparison of Regression Methods for Inferring Near-Surface NO2 with Satellite Data

A Comparison of Regression Methods for Inferring Near‐Surface NO2 With Satellite Data

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A Comparison of Regression Methods for Inferring Near‐Surface NO₂ With Satellite Data