Estimating Hourly Nitrogen Oxide Emissions over East Asia from Geostationary Satellite Measurements

Xu, Tianyi; Zhang, Chengxin; Xue, Jingkai; Hu, Qihou; Xing, Chengzhi; Liu, Cheng

doi:10.1021/acs.estlett.3c00467

Cited by 13 publications

(3 citation statements)

References 51 publications

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“…S6 shows the spatial distribution of average (Shen et al, 2023). For instance, Xu et al (2023) observed the minimum NO2 lifetime at noon, which can be attributed to higher photochemical reaction rates resulting from increased temperature and ultraviolet radiation 360 (Gao et al, 2023).…”

Section: Diurnal Variations Of Ground-level No2 Concentrationsmentioning

confidence: 99%

Improving Ground-Level NO₂ Estimation in China Using GEMS Measurements and a Nested Machine Learning Model

Ahmad,

Lin,

Lau

et al. 2024

Preprint

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Abstract. The major bridge linking satellite-derived vertical column densities (VCDs) of nitrogen dioxide (NO2) with ground-level concentration is theoretically the NO2 mixing height (NMH). Various meteorological parameters have been used as a proxy of NMH in existing studies. This study developed a nested machine learning model to convert VCDs of NO2 into ground-level NO2 concentrations across China using Geostationary Environmental Monitoring Spectrometer (GEMS) measurements. This nested model was designed to directly incorporate NMH into the methodological framework and explore its impact on performance. The inner machine learning model predicted the NMH from the meteorological parameters, which were then input into the main machine learning model to predict the ground-level NO2 concentrations from its VCDs. The inclusion of NMH significantly enhanced the accuracy of estimating ground-level NO2 concentration, reducing bias and improving R² values to 0.93 in 10-fold cross-validation and 0.99 in the fully-trained model. Furthermore, NMH was identified as the second most important predictor variable, following the VCDs of NO2. Subsequently, satellite-derived ground-level NO2 data were analyzed across subregions with varying geolocations and urbanization levels. Highly populated areas typically experienced peak NO2 concentrations during early morning rush hours, whereas areas categorized as lightly populated observed a slight increase in NO2 levels one or two hours later, likely due to regional pollutant dispersion from urban sources. This study underscores the importance of incorporating NMH in estimating ground-level NO2 from satellite column measurements and highlights the significant advantages of geostationary satellites in providing detailed air pollution information at an hourly resolution.

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Section: Diurnal Variations Of Ground-level No2 Concentrationsmentioning

confidence: 99%

Improving Ground-Level NO₂ Estimation in China Using GEMS Measurements and a Nested Machine Learning Model

Ahmad,

Lin,

Lau

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The divergence method has mainly been applied to TROPOMI data (primarily NO 2 , but also CO and CH 4 data, e.g., Beirle et al, 2019Beirle et al, , 2021Beirle et al, , 2023de Foy & Schauer, 2022;Dix et al, 2022;Filonchyk & Peterson, 2023;Liu et al, 2021;Lonsdale & Sun, 2023;Rey-Pommier et al, 2022Sun, 2022). Recently, the method has also been applied to other satellites such as GEMS NO 2 data (Xu et al, 2023). A study of its application to synthetic CO 2 data for the upcoming CO2M mission can be found in Hakkarainen et al (2022), which notes that a background and noise removal step must be applied to make the method robust for CO 2 data.…”

Section: Introductionmentioning

confidence: 99%

On the Theory of the Divergence Method for Quantifying Source Emissions From Satellite Observations

Koene,

Brunner,

Kuhlmann

2024

JGR Atmospheres

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The divergence method, a lightweight approach for estimating emission fluxes from satellite images, rests on a few implicit assumptions. This paper explicitly outlines these assumptions by deriving the method from first principles. The assumptions are: the enhanced mass flux is dominated by advection, normal fluxes vanish at the top and bottom of the atmosphere, steady‐state conditions apply, sources are multiplications of temporal and spatial functions, sinks are described as first‐order reactions, and effective wind fields are concentration‐weighted wind fields. No such assumptions have to be made for the background field. A “topography correction term” does not follow from the theory, but is rather shown to be a practical correction for topography‐dependent effective wind speed errors. The cross‐sectional flux method follows naturally from the derived theory, and the methods are compared. Effects of discrete pixels and finite‐difference operations are explored, leading to recommendations, primarily the recommendation to integrate over small regions only to minimize the influence of noise. Numerical examples featuring Gaussian plumes and COSMO‐GHG simulated plumes are provided. The Gaussian plume example suggests that the divergence method might underestimate emissions when assuming only advection in the presence of cross‐wind diffusion. Conversely, the cross‐sectional flux method remains unaffected, provided fluxes are integrated across the entire plume. The COSMO‐GHG example reveals frequent violations of the steady‐state assumption, although the assumption remains valid proximal to the source (<20 km in this example). It is the hope that this paper provides a solid theoretical foundation for the divergence and cross‐sectional flux methods.

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“…In this study, one year of tropospheric NO 2 VCDs of the operational GEMS L2 v2.0 product, the scientific GEMS IUP-UB v1.0 product, the operational TROPOMI product, and 11 ground-based DOAS instruments in South Korea are compared. Evaluating the GEMS NO 2 product is important to ensure the accuracy of the product for use in emission and surface concentration estimates (e.g., Xu et al, 2023;Yang et al, 2023c). The 11 ground-based observation sites are located in different pollution regimes in South Korea which provides the opportunity to observe and analyze different diurnal variations of NO 2 .…”

mentioning

confidence: 99%

Validation of GEMS tropospheric NO₂ columns and their diurnal variation with ground-based DOAS measurements

Lange,

Richter,

Bösch

et al. 2024

Preprint

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Abstract. Instruments for air quality observations on geostationary satellites provide multiple observations per day and allow for the analysis of the diurnal variation of important air pollutants such as nitrogen dioxide (NO2) over large areas. The South Korean instrument GEMS on the GK2B satellite was launched in February 2020 and is the first instrument in geostationary orbit that delivers hourly daytime observations of NO2. The measurements with a spatial resolution of 3.5 km x 8 km cover a large part of Asia. This study compares one year of tropospheric NO2 vertical column density (VCD) observations of the operational GEMS L2 product, the scientific GEMS IUP-UB product, the operational TROPOMI product, and ground-based DOAS measurements in South Korea. The GEMS L2 tropospheric NO2 VCDs overestimate the VCDs retrieved from the ground-based observations with a median relative difference of +64 % and a correlation coefficient of 0.75. The median relative difference is -1 % for the GEMS IUP-UB product and -14 % for the TROPOMI product. The evaluation of the GEMS IUP-UB product and the operational TROPOMI product with ground-based measurements is in good agreement with correlation coefficients of 0.82 and 0.88. The scatter in the GEMS products can be reduced when observations are limited to the TROPOMI overpass time. The observed diurnal variations of the tropospheric NO2 VCDs show a maximum of NO2 during the late morning for urban sites, whereas rural sites show weak or almost no diurnal changes. Investigations of the seasonal diurnal variability show with a minimum in the observed tropospheric NO2 VCDs around noon the importance of chemical loss of NO2 in summer. Most variability is seen in spring and autumn, which dominate the average annual diurnal cycle. Observations under low wind conditions show strong enhancements of NO2 over the day, especially at polluted sites during winter. This indicates that under calm conditions, dilution and the less effective chemical loss in winter do not balance the accumulating emissions. The impact of transport processes is illustrated by the diurnal variability at a rural site following mean wind patterns for specific seasons and observation times. Analyzing the weekday-weekend effect, good agreement was found between the different products. However, the GEMS L2 product while agreeing with the other data sets during weekdays shows significantly less reduction on weekends. Our investigations show that the observed diurnal evolution of NO2 varies significantly at the different measurement sites, with good agreement between the GEMS IUP-UB and ground-based observations. The diurnal variability of tropospheric NO2 VCDs depends on chemistry, emissions, and transport into and out of the measurement region. To interpret the sources and sinks of NO2 requires that all of these factors are considered.

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Estimating Hourly Nitrogen Oxide Emissions over East Asia from Geostationary Satellite Measurements

Cited by 13 publications

References 51 publications

Improving Ground-Level NO₂ Estimation in China Using GEMS Measurements and a Nested Machine Learning Model

Improving Ground-Level NO₂ Estimation in China Using GEMS Measurements and a Nested Machine Learning Model

On the Theory of the Divergence Method for Quantifying Source Emissions From Satellite Observations

Validation of GEMS tropospheric NO₂ columns and their diurnal variation with ground-based DOAS measurements

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Estimating Hourly Nitrogen Oxide Emissions over East Asia from Geostationary Satellite Measurements

Cited by 13 publications

References 51 publications

Improving Ground-Level NO2 Estimation in China Using GEMS Measurements and a Nested Machine Learning Model

Improving Ground-Level NO2 Estimation in China Using GEMS Measurements and a Nested Machine Learning Model

On the Theory of the Divergence Method for Quantifying Source Emissions From Satellite Observations

Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements

Contact Info

Product

Resources

About

Improving Ground-Level NO₂ Estimation in China Using GEMS Measurements and a Nested Machine Learning Model

Improving Ground-Level NO₂ Estimation in China Using GEMS Measurements and a Nested Machine Learning Model

Validation of GEMS tropospheric NO₂ columns and their diurnal variation with ground-based DOAS measurements