Application of Atmospheric Chemical Transport Models to Validation of Pollutant Emissions in Moscow

Пономарев, Н. А.; Elansky, N. F.; Kirsanov, A. A.; Postylyakov, O. V.; Borovski, Alexander; Verevkin, Y. M.

doi:10.1134/s1024856020040090

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…To alleviate this problem, combining deep learning with transfer learning techniques ) may be a possible solution for cross-city air quality prediction. Moreover, it is also interesting to exploit a physics-based method (Ponomarev et al, 2020) that is applicable over different locations or regions in future. Besides, this work focuses on air quality prediction on a single city (Beijing or Taizhou) rather than a special region with multiple cities.…”

Section: Discussionmentioning

confidence: 99%

“…The physical prediction methods are a numerical simulation model on the basis of aerodynamics, atmospheric physics, and chemical reactions for studying pollutant diffusion mechanism (Geng et al, 2015). The wellknown physical prediction models include chemical transport models (CTMs) (Mihailovic et al, 2009;Ponomarev et al, 2020), community multiscale air quality (CMAQ) (Zhang et al, 2014), weather research and forecasting (WRF) (Powers et al, 2017), the GEOS-Chem model (Lee et al, 2017), and so on. Nevertheless, owing to the complicated pollutant diffusion mechanism, leveraging these models leads to several limitations such as expensitive computation, the complexity of processing, uncertainty of parameters, and low prediction accuracy (Wang J. et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Temporal Difference-Based Graph Transformer Networks For Air Quality PM2.5 Prediction: A Case Study in China

Zhang

Zhao

et al. 2022

Front. Environ. Sci.

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Air quality PM2.5 prediction is an effective approach for providing early warning of air pollution. This paper proposes a new deep learning model called temporal difference-based graph transformer networks (TDGTN) to learn long-term temporal dependencies and complex relationships from time series PM2.5 data for air quality PM2.5 prediction. The proposed TDGTN comprises of encoder and decoder layers associated with the developed graph attention mechanism. In particular, considering the similarity of different time moments and the importance of temporal difference between two adjacent moments for air quality PM2.5prediction, we first construct graph-structured data from original time series PM2.5 data at different moments without explicit graph structure. Then we improve the self-attention mechanism with the temporal difference information, and develop a new graph attention mechanism. Finally, the developed graph attention mechanism is embedded into the encoder and decoder layers of the proposed TDGTN to learn long-term temporal dependencies and complex relationships from a graph prospective on air quality PM2.5 prediction tasks. Experiment results on two collected real-world datasets in China, such as Beijing and Taizhou PM2.5 datasets, show that the proposed method outperforms other used methods on both short-term and long-term air quality PM2.5 prediction tasks.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temporal Difference-Based Graph Transformer Networks For Air Quality PM2.5 Prediction: A Case Study in China

Zhang

Zhao

et al. 2022

Front. Environ. Sci.

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“…The CO and NOx emissions within the territory of the Moscow megacity were specified based upon annual emission values provided in [10]. Analysis of data obtained at the MEM network and numerical experiments on the optimization of urban air pollution sources described in [27,28] allowed us to distribute them over time with one-hour time step and across Moscow territory. The CO and NOx emissions outside Moscow were taken from the TNO-2011 Inventory data.…”

Section: Characteristics Of Numerical Experimentsmentioning

confidence: 99%

Air Pollution in Moscow Megacity: Data Fusion of the Chemical Transport Model and Observational Network

2021

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Comparisons of observational data obtained at the Moscow Ecological Monitoring network (MEM) with numerical simulations using a chemical transformation and transport model (SILAM—System for Integrated modeLling of Atmospheric coMposition) showed that the errors in determining the gaseous pollutant concentrations in the urban atmosphere have a more complex structure than those assumed under the conventional algorithms of data assimilation. These errors are statistically nonstationary; they show a pronounced diurnal cycle and a significant lifetime. The statistical features of errors in numerical calculations also depend upon the type of pollutants, i.e., the chemical reactions in which they participate. Our analysis showed that the simulation errors are not small: the ratios of calculated and measured concentrations (even for daily averages at all measuring stations) may vary in a wide range. For the chemically active pollutants, the intradiurnal error variations may reach 100%. The diurnal cycle of such errors was found to vary according to seasons (in our case, summer and winter). The analysis of statistical properties of the errors, including their temporal and spatial variability, allows one to correct and adequately forecast the air pollution in the metropolis area at lead times up to three days in advance.

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“…The physical prediction methods are a numerical simulation model on the basis of aerodynamics, atmospheric physics, and chemical reactions for studying pollutant diffusion mechanism (Geng et al 2015). The well-known physical prediction models include chemical transport models (CTMs) (Mihailovic et al 2009, Ponomarev et al 2020, community multiscale air quality (CMAQ) (Zhang et al 2014), weather research and forecasting (WRF) (Powers et al 2017), the GEOS-Chem model (Lee et al 2017), and so on. Nevertheless, owing to the complicated pollutant diffusion mechanism, leveraging these models leads to several limitations such as expensitive computation, the complexity of processing, uncertainty of parameters, and low prediction accuracy (Wang et al 2019a).…”

Section: Introductionmentioning

confidence: 99%

Temporal Difference-based Graph Transformer Networks for Air Quality PM2.5 Prediction: A Case Study in China

Zhang

Zhao

et al. 2022

Preprint

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The acceleration of industrialization and urbanization has recently brought about serious air pollution problems, which threaten human health and lives, the environmental safety, and sustainable social development. Air quality prediction is an effective approach for providing early warning of air pollution and supporting cleaner industrial production. However, existing approaches have suffered from a weak ability to capture long-term dependencies and complex relationships from time series PM2.5 data. To address this problem, this paper proposes a new deep learning model called temporal difference-based graph transformer networks (TDGTN) to learn long-term temporal dependencies and complex relationships from time series PM2.5 data for air quality PM2.5 prediction. The proposed TDGTN comprises of encoder and decoder layers associated with the developed graph attention mechanism. In particular, considering the similarity of different time moments and the importance of temporal difference between two adjacent moments for air quality prediction, we first construct graph-structured data from original time series PM2.5 data at different moments without explicit graph structure. Then, based on the constructed graph, we improve the self-attention mechanism with the temporal difference information, and develop a new graph attention mechanism. Finally, the developed graph attention mechanism is embedded into the encoder and decoder layers of the proposed TDGTN to learn long-term temporal dependencies and complex relationships from a graph prospective on air quality PM2.5 prediction tasks. To verify the effectiveness of the proposed method, we conduct air quality prediction experiments on two real-world datasets in China, such as Beijing PM2.5 dataset ranging from 01/01/2010 to 12/31/2014 and Taizhou PM2.5 dataset ranging from 01/01/2017 to 12/31/2019. Compared with other air quality forecasting methods, such as autoregressive moving average (ARMA), support vector regression (SVR), convolutional neural network (CNN), long short-term memory (LSTM), the original Transformer, our experiment results indicate that the proposed method achieves more accurate results on both short-term (1 hour) and long-term (6, 12, 24, 48 hours) air quality prediction tasks.

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Application of Atmospheric Chemical Transport Models to Validation of Pollutant Emissions in Moscow

Cited by 15 publications

References 21 publications

Temporal Difference-Based Graph Transformer Networks For Air Quality PM2.5 Prediction: A Case Study in China

Temporal Difference-Based Graph Transformer Networks For Air Quality PM2.5 Prediction: A Case Study in China

Air Pollution in Moscow Megacity: Data Fusion of the Chemical Transport Model and Observational Network

Temporal Difference-based Graph Transformer Networks for Air Quality PM2.5 Prediction: A Case Study in China

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