Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations

Yu, Manzhu; Liu, Qian

doi:10.1016/j.scitotenv.2021.145145

Cited by 17 publications

(10 citation statements)

References 29 publications

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“…The time when the satellite passes through the study area (Beijing time) is about 13:00-14:00, and the spatial resolution is 7 km × 3.5 km (30 April 2018 to 6 August 2019) and 5.5 km × 3.5 km (7 August 2019 to now), making it the best atmospheric observation spectrometer at present. Under the processing framework of the retrieval-assimilation-modeling system, the TROPOMI NO 2 Level 2 product combines the DOAS algorithm and the TM5 chemical transport model, and converts the measured Level-1B radiance and irradiance spectra into NO 2 vertical column concentrations, in units of molec/cm 2 [24]. For this article, the TROPOMI NO 2 Level 2 product from NASA (https://disc.gsfc.nasa.gov/, accessed on 7 July 2021) was obtained and the tropospheric NO 2 column concentration was taken from it as a modeling factor.…”

Section: Tropomi No 2 Datamentioning

confidence: 99%

“…Earlier statistical models, such as geostatistical models [20,21] and land use regression models [22,23], improved spatial accuracy at the expense of reduced temporal resolution. For the development of models, machine-learning-and deep-learning-based methods have been applied in statistical models for NO 2 estimation, which are able to improve the resolution in both time and space [24,25]. Recent studies have shown that these methods and their variants (e.g., ensemble models) capture the nonlinear variation characteristics of pollutants well [26] and are significantly better than traditional statistical models in terms of modeling accuracy, as they can achieve a certain degree of accuracy in capturing the spatiotemporal heterogeneity of the target gas.…”

Section: Introductionmentioning

confidence: 99%

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An Ensemble Model-Based Estimation of Nitrogen Dioxide in a Southeastern Coastal Region of China

Dong

Zhang

et al. 2022

Remote Sensing

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NO2 (nitrogen dioxide) is a common pollutant in the atmosphere that can have serious adverse effects on the health of residents. However, the existing satellite and ground observation methods are not enough to effectively monitor the spatiotemporal heterogeneity of near-surface NO2 concentrations, which limits the development of pollutant remediation work and medical health research. Based on TROPOMI-NO2 tropospheric column concentration data, supplemented by meteorological data, atmospheric condition reanalysis data and other geographic parameters, combined with classic machine learning models and deep learning networks, we constructed an ensemble model that achieved a daily average near-surface NO2 of 0.03° exposure. In this article, a meteorological hysteretic effects term and a spatiotemporal term were designed, which considerably improved the performance of the model. Overall, our ensemble model performed better, with a 10-fold CV R2 of 0.89, an RMSE of 5.62 µg/m3, and an MAE of 4.04 µg/m3. The model also had good temporal and spatial generalization capability, with a temporal prediction R2 and a spatial prediction R2 of 0.71 and 0.81, respectively, which can be applied to a wider range of time and space. Finally, we used an ensemble model to estimate the spatiotemporal distribution of NO2 in a coastal region of southeastern China from May 2018 to December 2020. Compared with satellite observations, the model output results showed richer details of the spatiotemporal heterogeneity of NO2 concentrations. Due to the advantages of using multi-source data, this model framework has the potential to output products with a higher spatial resolution and can provide a reference for downscaling work on other pollutants.

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Section: Tropomi No 2 Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Ensemble Model-Based Estimation of Nitrogen Dioxide in a Southeastern Coastal Region of China

Dong

Zhang

et al. 2022

Remote Sensing

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“…Beirle et al [10] used TROPOMI to map NOx emissions from power plants near high urban pollution areas in Riyadh, KSA. Ialongo et al [11] and Yu et al [12] compared TROPOMI NO 2 and ground observations and found that TROPOMI underestimated NO 2 . Yang et al [13] constructed a long short-term memory (LSTM) neural network to model the relationship between operational parameters and the NOx emissions of a 660 MW boiler.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants

2022

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Effective and precise monitoring is a prerequisite to control human emissions and slow disruptive climate change. To obtain the near-real-time status of power plant emissions, we built machine learning models and trained them on satellite observations (Sentinel 5), ground observed data (EPA eGRID), and meteorological observations (MERRA) to directly predict the NO2 emission rate of coal-fired power plants. A novel approach to preprocessing multiple data sources, coupled with multiple neural network models (RNN, LSTM), provided an automated way of predicting the number of emissions (NO2, SO2, CO, and others) produced by a single power plant. There are many challenges on overfitting and generalization to achieve a consistently accurate model simply depending on remote sensing data. This paper xaddresses the challenges using a combination of techniques, such as data washing, column shifting, feature sensitivity filtering, etc. It presents a groundbreaking case study on remotely monitoring global power plants from space in a cost-wise and timely manner to assist in tackling the worsening global climate.

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“…Wang et al [26] developed a novel super resolution deep residual network (SRDRN) to downscale daily precipitation and temperature. Yu et al [27] proposed an inverse weighted distance and a feed forward neural network (IDW+DNN) and a deep matrix network (DMN) to downscale tropospheric nitrogen dioxide. To sum up, deep learning methods have shown great potential in environmental parameters downscaling [28].…”

Section: Introductionmentioning

confidence: 99%

Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning

Pang

Sun

et al. 2022

IEEE Access

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Accurately estimating vegetation productivity in the forest areas is important for studying the terrestrial ecosystem and carbon cycles. Global LAnd Surface Satellite (GLASS) vegetation production datasets provide new long-term basic products of gross primary production (GPP) and net primary production (NPP) for monitoring the issues related with carbon exchange and carbon storage. But the coarse spatial resolution of the GLASS GPP/NPP products have limited their application in ecosystem service assessment in regional scales. In this paper, a spatial downscaling method based on GLASS vegetation production datasets and four typical deep learning methods (deep neural network, convolutional neural network, back propagation neural network and recurrent neural network) was proposed to generate high resolution GPP/NPP in the forest areas in the upper Luanhe River basin in the north of Hebei Province in China. Then the downscaled GPP/NPP were validated with ground measurement data and reference high resolution GPP/NPP data, and the accuracy of downscaled GPP/NPP from different deep learning methods were compared. Results of this paper indicated the applicability and feasibility of deep learning methods in downscaling GPP/NPP. Direct validation and cross validation demonstrated that downscaled GPP/NPP using convolutional neural network obtained the highest accuracy.

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Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations

Cited by 17 publications

References 29 publications

An Ensemble Model-Based Estimation of Nitrogen Dioxide in a Southeastern Coastal Region of China

An Ensemble Model-Based Estimation of Nitrogen Dioxide in a Southeastern Coastal Region of China

Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants

Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning

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