Exploring deep learning for air pollutant emission estimation

Huang, Lin; Liu, Song; Yang, Zeyuan; Xing, Jia; Zhang, Jia; Bian, Jiang; Li, Siwei; Sahu, Shovan Kumar; Liu, Tie-Yan

doi:10.5194/gmd-14-4641-2021

Cited by 38 publications

(18 citation statements)

References 43 publications

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“…The VAE-encoder employs the same model structure as the VAE-decoder, but the NO x emission replaces the current-day NO 2 as the output of the VAE-encoder, and the latter becomes one of its features. The model structure is nearly identical to that used in our previous study, , with the exception that we exclude VOC emissions from the features, which may have uncertainties but for which there are currently very few observations. Further studies are recommended to constrain both NO x and VOC emissions at the same time using additional observations such as HCHO and O 3 concentrations retrieved from satellites.…”

Section: Methodsmentioning

confidence: 99%

Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder

Xing

Zheng

et al. 2022

Environ. Sci. Technol.

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Accurate timely estimation of emissions of nitrogen oxides (NO x ) is a prerequisite for designing an effective strategy for reducing O3 and PM2.5 pollution. The satellite-based top-down method can provide near-real-time constraints on emissions; however, its efficiency is largely limited by efforts in dealing with the complex emission–concentration response. Here, we propose a novel machine-learning-based method using a physically informed variational autoencoder (VAE) emission predictor to infer NO x emissions from satellite-retrieved surface NO2 concentrations. The computational burden can be significantly reduced with the help of a neural network trained with a chemical transport model, allowing the VAE emission predictor to provide a timely estimation of posterior emissions based on the satellite-retrieved surface NO2 concentration. The VAE emission predictor successfully corrected the underestimation of NO x emissions in rural areas and the overestimation in urban areas, resulting in smaller normalized mean biases (reduced from −0.8 to −0.4) and larger R 2 values (increased from 0.4 to 0.7). The interpretability of the VAE emission predictor was investigated using sensitivity analysis by modulating each feature, indicating that NO2 concentration and planetary boundary layer (PBL) height are important for estimating NO x emissions, which is consistent with our common knowledge. The advantages of the VAE emission predictor in efficiency, flexibility, and accuracy demonstrate its great potential in estimating the latest emissions and evaluating the control effectiveness from observations.

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Section: Methodsmentioning

confidence: 99%

Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder

Xing

Zheng

et al. 2022

Environ. Sci. Technol.

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“…Meteorology and nonlinear atmospheric chemistry complicate the attribution of air quality responses to changes in precursor emissions . For example, despite unprecedented reductions in anthropogenic emissions during the COVID-19 lockdown, increases in O 3 concentrations were widely observed in urban areas worldwide. − On the other hand, PM 2.5 also did not always decline, as it can be enhanced through secondary formation under unfavorable occasions (e.g., higher humidity, enhanced O 3 ). , Chemical transport models (CTMs) are used widely to simulate air pollutant concentrations under different emission and meteorological conditions. ,, However, CTMs rely highly on the accuracy and resolution of uncertain emission inventories and are computationally expensive for instant air quality management . Machine learning (ML)-based approaches have more flexibility and efficiency in leveraging real-world data and are good at capturing complicated nonlinear relationships .…”

Section: Introductionmentioning

confidence: 99%

Urban–Rural Disparities in Air Quality Responses to Traffic Changes in a Megacity of China Revealed Using Machine Learning

Wen

Zhou

Zhang

et al. 2022

Environ. Sci. Technol. Lett.

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Assessing the disparities of urban–rural air quality response to changes in emissions is essential for the development of effective air pollution mitigation strategies in megacities. However, meteorology and nonlinear atmospheric chemistry complicate the determination of emission–air quality responses. Here, we established a machine learning (ML)-based air quality simulator based on hourly air quality, meteorology, traffic activity, and other relevant indicators for Chengdu, a megacity in Southwest China. The ML-based simulator exhibits high fidelity in reproducing hourly pollutant concentrations (with cross validation R2 > 0.6 for NO2, O3, and PM2.5). The results indicated similar trends of meteorological impacts but various effects from traffic activities on air quality between urban and rural areas. Truck restriction policies have significantly reduced the impacts of truck traffic on air quality in the urban area. Repartitioning between NO2 and O3 is observed in both urban and rural areas, indicating a VOC-limited regime in winter across Chengdu. Total gaseous oxidant (i.e., OX = NO2 + O3) and PM2.5 concentrations are more sensitive to changes in nontruck (which emit more VOC) traffic than truck (which emit more NOX) traffic. We suggest that effective mitigation policies of OX should be developed according to local features to improve and alleviate winter haze simultaneously.

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“…The accuracy and generalization ability of the model is further explained through the three largest urban agglomerations in the middle reaches of the Yangtze River [12]. Huang et al (2021) proposed a new method to simulate the dual relationship between emission inventory and pollution concentration for emission inventory estimation [13].…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

Huafang

Cheng

2021

Land

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This study aimed to respond to the national “carbon peak” mid-and long-term policy plan, comprehensively promote energy conservation and emission reduction, and accurately manage and predict carbon emissions. Firstly, the proposed method analyzes the Yangtze River Economic Belt as well as its “carbon peak” and carbon emissions. Secondly, a support vector regression (SVR) machine prediction model is proposed for the carbon emission information prediction of the Yangtze River Economic Zone. This experiment uses a long short-term memory neural network (LSTM) to train the model and realize the experiment’s prediction of carbon emissions. Finally, this study obtained the fitting results of the prediction model and the training model, as well as the prediction results of the prediction model. Information indicators such as the scale of industry investment, labor efficiency output, and carbon emission intensity that affect carbon emissions in the “Yangtze River Economic Belt” basin can be used to accurately predict the carbon emissions information under this model. Therefore, the experiment shows that the SVR model for solving complex nonlinear problems can achieve a relatively excellent prediction effect under the training of LSTM. The deep learning model adopted herein realized the accurate prediction of carbon emission information in the Yangtze River Economic Zone and expanded the application space of deep learning. It provides a reference for the model in related fields of carbon emission information prediction, which has certain reference significance.

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Exploring deep learning for air pollutant emission estimation

Cited by 38 publications

References 43 publications

Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder

Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder

Urban–Rural Disparities in Air Quality Responses to Traffic Changes in a Megacity of China Revealed Using Machine Learning

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

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