Satellite-based estimation of surface NO2 concentrations over east-central China: A comparison of POMINO and OMNO2d data

Qin, Kai; Han, Xu; Li, Donghui; Xu, Jian; Loyola, Diego; Xue, Yong; Zhou, Xingxing; Li, Ding; Zhang, Kefei; Yuan, Limei

doi:10.1016/j.atmosenv.2020.117322

Cited by 48 publications

(36 citation statements)

References 42 publications

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“…Other approaches to derive pollutant concentrations at surface level include combining vertical column observations from satellite and vertical distribution information from CTM [17][18][19][20], using land use regression approach [21][22][23] as well as the spatio-temporal interpolation method [24]. The recent development of machine leaning approaches offers alternative solutions to simplify complex atmospheric processes by using a simple regression model, providing fast and spatially accurate approximations of pollutant concentrations [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

et al. 2021

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In this paper, we present the estimation of surface NO2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO2 concentrations over Germany from 2018 to 2020. Estimated surface NO2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO2 in Germany. The estimated surface NO2 concentrations provide comprehensive information of NO2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µµg/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µµg/m3. In addition, we used the surface NO2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO2 levels in Germany. In general, 10–30% lower surface NO2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus.

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Section: Introductionmentioning

confidence: 99%

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

et al. 2021

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“…In this research, the AOD product is not adopted for the following reasons. First, in the remote sensing of air quality field, AOD product has been widely considered as a proxy to estimate ground particulate matter [7,44], and the remote sensing retrieval of ground NO 2 usually uses satellite observed NO 2 data [8,9,15]. Hence, the selection of satellite data of this work is in line with previous studies.…”

Section: Impact Of Aerosol On Satellite-based No 2 Modelingmentioning

confidence: 81%

“…As a result, it is an urgent need to monitor NO 2 concentrations. Recently, remote sensing retrieval of ground NO 2 concentrations has aroused wide attention [7][8][9][10][11][12][13], for providing broader spatiotemporal coverages beyond ground monitoring stations, which act as the primary approach for the monitoring of NO 2 all the time.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, machine learning models have achieved widespread applications in remote sensing retrieval of ground NO 2 concentrations, which can be attributed to their advantages in describing the nonlinear relationship between ground NO 2 and influencing factors. These machine learning models, including random forest [9,20], neural network (NN) [21], Bayesian maximum entropy [22], extreme gradient boosting [23], and so on, have generally reported some advantages in estimation accuracy over traditional statistical models and played a critical role in remote sensing retrieval of ground NO 2 concentrations.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Estimation of Regional NO2 via Space-Time Neural Networks

Wang

Yuan

2020

Remote Sensing

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Nitrogen dioxide (NO2) is an essential air pollutant related to adverse health effects. A space-time neural network model is developed for the estimation of ground-level NO2 in this study by integrating ground NO2 station measurements, satellite NO2 products, simulation data, and other auxiliary data. Specifically, a geographically and temporally weighted generalized regression neural network (GTW-GRNN) model is used with the advantage to consider the spatiotemporal variations of the relationship between NO2 and influencing factors in a nonlinear neural network framework. The case study across the Wuhan urban agglomeration (WUA), China, indicates that the GTW-GRNN model outperforms the widely used geographically and temporally weighted regression (GTWR), with the site-based cross-validation R2 value increasing by 0.08 (from 0.61 to 0.69). Besides, the comparison between the GTW-GRNN and original global GRNN models shows that considering the spatiotemporal variations in GRNN modeling can boost estimation accuracy. All these results demonstrate that the GTW-GRNN based NO2 estimation framework will be of great use for remote sensing of ground-level NO2 concentrations.

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“…The retrieval of troposphere NO2 vertical column densities (VCDs) from satellites sensors such as Ozone Monitoring Instrument (OMI) [26], [27] and Global Ozone Monitoring Experiment (GOME) [28]- [30] have been widely used in the mapping of the spatiotemporal distribution and change analysis of tropospheric NO 2 [31]- [34], while higher quality upward looking surface observations such as MAX-DOAS have been used to establish such conditions in specific locations [35], [36]. The relationship between surface and column observations have been constructed to estimate the NS-NO 2 concentration with the support of chemical transport models [37]- [40], geographically and temporally weighted regression [41]- [43], and machine learning methods [13], [20], [32], [35], [44], [45].…”

Section: Introductionmentioning

confidence: 99%

Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO₂ of Mainland China

Li¹,

Qin²,

Cohen³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Near-surface NO2 (NS-NO2) is closely related to human health and the atmospheric environment. While top-down approaches have been widely applied to estimate NS-NO2 using satellite-based NO2 column measurements, there still exist significant defects, resulting in a low overall fit and significant amount of bias. This paper combines GOME-2B and OMI satellite data to estimate daily NS-NO2 with a spatial resolution of 0.1° × 0.1° from 2014 to 2018 over Mainland China, using a machine learning method. The estimated result has four important characteristics. First, the sample-based cross validation with surface observations shows a good result with R 2 = 0.80 and RMSE= 9.0μg/m 3 . Second, the underestimation in high concentration areas and overestimation in low concentration areas are both reduced, compared with the case of using OMI data alone. Third, the estimated NS-NO2 is consistent with surface observations in spatial distribution, and successfully represent both inter-annual changes and seasonal characteristics. Furthermore, the population-weighted NO2 based estimated dataset shows a significant decline of pollution exposure levels from 2014 to 2018.

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Satellite-based estimation of surface NO2 concentrations over east-central China: A comparison of POMINO and OMNO2d data

Cited by 48 publications

References 42 publications

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

Remote Sensing Estimation of Regional NO2 via Space-Time Neural Networks

Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO₂ of Mainland China

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Satellite-based estimation of surface NO2 concentrations over east-central China: A comparison of POMINO and OMNO2d data

Cited by 48 publications

References 42 publications

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

Remote Sensing Estimation of Regional NO2 via Space-Time Neural Networks

Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO2 of Mainland China

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Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO₂ of Mainland China