Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Self-supervised air quality estimation with graph neural network assistance and attention enhancement

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The rapid progress of industrial development, urbanization, and traffic has caused air quality degradation that negatively affects human health and environmental sustainability, especially in developed countries. However, due to the limited number of sensors available, the air quality index at many locations is not monitored. Therefore, many research, including statistical and machine learning approaches, have been proposed to tackle the problem of estimating air quality value at an arbitrary location. Most of the existing research perform interpolation process based on traditional techniques that leverage distance information. In this work, we propose a novel deep-learning-based model for air quality value estimation. This approach follows the encoder–decoder paradigm, with the encoder and decoder trained separately using different training mechanisms. In the encoder component, we proposed a new self-supervised graph representation learning approach for spatio-temporal data. For the decoder component, we designed a deep interpolation layer that employs two attention mechanisms and a fully connected layer using air quality data at known stations, distance information, and meteorology information at the target point to predict air quality at arbitrary locations. The experimental results demonstrate significant improvements in estimation accuracy achieved by our proposed model compared to state-of-the-art approaches. For the MAE indicator, our model enhances the estimation accuracy from 4.93% to 34.88% on the UK dataset, and from 6.89% to 31.94% regarding the Beijing dataset. In terms of the RMSE, the average improvements of our method on the two datasets are 13.33% and 14.37%, respectively. The statistics for MAPE are 36.05% and 13.25%, while for MDAPE, they are 24.48% and 36.33%, respectively. Furthermore, the value of \(R_2\) score attained by our proposed model also shows considerable improvement, with increases of 5.39% and 32.58% compared to that of comparison benchmarks. Our source code and data are available at https://github.com/duclong1009/Unsupervised-Air-Quality-Estimation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1
Algorithm 2
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The code and datasets generated during and/or analyzed during the current study are available in. https://github.com/duclong1009/Unsupervised-Air-Quality-Estimation

References

  1. W. H. O. (WHO): Ambient air pollution: A global assessment of exposure and burden of disease (2016)

  2. Tai AP, Mickley LJ, Jacob DJ (2010) Correlations between fine particulate matter (pm2. 5) and meteorological variables in the united states: implications for the sensitivity of pm2. 5 to climate change. Atmos Environ 44(32):3976–3984. https://doi.org/10.1016/j.atmosenv.2010.06.060

    Article  Google Scholar 

  3. Kulmala M (2018) Build a global Earth observatory. Nature Publishing Group

  4. Rahmati Aidinlou H, Nikbakht AM (2022) Fuzzy-based modeling of thermohydraulic aspect of solar air heater roughened with inclined broken roughness. Neural Comput Appl 34(3):2393–2412. https://doi.org/10.1007/s00521-021-06547-w

    Article  Google Scholar 

  5. Liu X, Jayaratne R, Thai P, Kuhn T, Zing I, Christensen B, Lamont R, Dunbabin M, Zhu S, Gao J, Wainwright D, Neale D, Kan R, Kirkwood J, Morawska L (2020) Low-cost sensors as an alternative for long-term air quality monitoring. Environ Res 185:109438. https://doi.org/10.1016/j.envres.2020.109438

    Article  Google Scholar 

  6. deSouza P, Anjomshoaa A, Duarte F, Kahn R, Kumar P, Ratti C (2020) Air quality monitoring using mobile low-cost sensors mounted on trash-trucks: methods development and lessons learned. Sustain Cities Soc 60:102239. https://doi.org/10.1016/j.scs.2020.102239

    Article  Google Scholar 

  7. Motlagh NH, Lagerspetz E, Nurmi P, Li X, Varjonen S, Mineraud J, Siekkinen M, Rebeiro-Hargrave A, Hussein T, Petaja T, Kulmala M, Tarkoma S (2020) Toward massive scale air quality monitoring. IEEE Commun Mag 58(2):54–59. https://doi.org/10.1109/MCOM.001.1900515

    Article  Google Scholar 

  8. Idrees Z, Zheng L (2020) Low cost air pollution monitoring systems: a review of protocols and enabling technologies. J Ind Inf Integr 17:100123. https://doi.org/10.1016/j.jii.2019.100123

    Article  Google Scholar 

  9. Lin Y-C, Lee S-J, Ouyang C-S, Wu C-H (2020) Air quality prediction by neuro-fuzzy modeling approach. Appl Soft Comput 86:105898. https://doi.org/10.1016/j.asoc.2019.105898

    Article  Google Scholar 

  10. Xiao X, Jin Z, Wang S, Xu J, Peng Z, Wang R, Shao W, Hui Y (2022) A dual-path dynamic directed graph convolutional network for air quality prediction. Sci Total Environ 827:154298. https://doi.org/10.1016/j.scitotenv.2022.154298

    Article  Google Scholar 

  11. Wang J, Li J, Wang X, Wang J, Huang M (2021) Air quality prediction using CT-LSTM. Neural Comput Appl 33(10):4779–4792. https://doi.org/10.1007/s00521-020-05535-w

    Article  Google Scholar 

  12. Wang J, Song G (2018) A deep spatial-temporal ensemble model for air quality prediction. Neurocomputing 314:198–206. https://doi.org/10.1016/j.neucom.2018.06.049

    Article  Google Scholar 

  13. Han J, Liu H, Zhu H, Xiong H, Dou D (2021) Joint air quality and weather prediction based on multi-adversarial spatiotemporal networks. Proceed AAAI Conf Artif Intell 35:4081–4089. https://doi.org/10.1609/aaai.v35i5.16529

    Article  Google Scholar 

  14. Chen P-C, Lin Y-T (2022) Exposure assessment of pm2.5 using smart spatial interpolation on regulatory air quality stations with clustering of densely-deployed microsensors. Environ Pollut 292:118401. https://doi.org/10.1016/j.envpol.2021.118401

    Article  Google Scholar 

  15. Beauchamp M, Malherbe L, de Fouquet C, Létinois L, Tognet F (2018) A polynomial approximation of the traffic contributions for kriging-based interpolation of urban air quality model. Environ Modell Softw 105:132–152. https://doi.org/10.1016/j.envsoft.2018.03.033

    Article  Google Scholar 

  16. Li J, Heap AD (2011) A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Eco Inform 6(3):228–241. https://doi.org/10.1016/j.ecoinf.2010.12.003

    Article  Google Scholar 

  17. Noi E, Murray AT (2022) Interpolation biases in assessing spatial heterogeneity of outdoor air quality in Moscow, Russia. Land Use Policy 112:105783. https://doi.org/10.1016/j.landusepol.2021.105783

    Article  Google Scholar 

  18. Xu C, Wang J, Hu M, Wang W (2022) A new method for interpolation of missing air quality data at monitor stations. Environ Int 169:107538. https://doi.org/10.1016/j.envint.2022.107538

    Article  Google Scholar 

  19. Alimissis A, Philippopoulos K, Tzanis C, Deligiorgi D (2018) Spatial estimation of urban air pollution with the use of artificial neural network models. Atmos Environ 191:205–213. https://doi.org/10.1016/j.atmosenv.2018.07.058

    Article  Google Scholar 

  20. Ma J, Ding Y, Cheng JC, Jiang F, Wan Z (2019) A temporal-spatial interpolation and extrapolation method based on geographic long short-term memory neural network for pm 2.5. J Clean Prod 237:117729. https://doi.org/10.1016/j.jclepro.2019.11772

    Article  Google Scholar 

  21. Qi Z, Wang T, Song G, Hu W, Li X, Zhang Z (2018) Deep air learning: Interpolation, prediction, and feature analysis of fine-grained air quality. IEEE Trans Knowl Data Eng 30(12):2285–2297. https://doi.org/10.1109/TKDE.2018.2823740

    Article  Google Scholar 

  22. Li L, Girguis M, Lurmann F, Pavlovic N, McClure C, Franklin M, Wu J, Oman LD, Breton C, Gilliland F, Habre R (2020) Ensemble-based deep learning for estimating pm2.5 over California with multisource big data including wildfire smoke. Environ Int 145:106143. https://doi.org/10.1016/j.envint.2020.106143

    Article  Google Scholar 

  23. Rijal N, Gutta RT, Cao T, Lin J, Bo Q, Zhang J (2018) Ensemble of deep neural networks for estimating particulate matter from images. In: 2018 IEEE 3rd International conference on image, vision and computing (ICIVC), pp 733–738. https://doi.org/10.1109/ICIVC.2018.8492790

  24. Dixit E, Jindal V (2022) Ieesep: an intelligent energy efficient stable election routing protocol in air pollution monitoring WSNS. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07027-5

    Article  Google Scholar 

  25. Ari D, Alagoz BB (2022) An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07129-0

    Article  Google Scholar 

  26. Al-Janabi S, Alkaim A, Al-Janabi E, Aljeboree A, Mustafa M (2021) Intelligent forecaster of concentrations (pm2. 5, pm10, no2, co, o3, so2) caused air pollution (IFCSAP). Neural Comput Appl 33(21):14199–14229. https://doi.org/10.1007/s00521-021-06067-7

    Article  Google Scholar 

  27. Wardana I, Gardner JW, Fahmy SA (2022) Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07224-2

    Article  Google Scholar 

  28. Liang Y, Ke S, Zhang J, Yi X, Zheng Y (2018) Geoman: Multi-level attention networks for geo-sensory time series prediction. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI-18, pp 3428–3434. https://doi.org/10.24963/ijcai.2018/476

  29. Zhao J, Deng F, Cai Y, Chen J (2018) Long short-term memory–fully connected (LSTM-FC) neural network for pm2.5 concentration prediction. Chemosphere. https://doi.org/10.1016/j.chemosphere.2018.12.128

    Article  Google Scholar 

  30. Qi Y, Li Q, Karimian H, Liu D (2019) A hybrid model for spatiotemporal forecasting of pm2.5 based on graph convolutional neural network and long short-term memory. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2019.01.333

    Article  Google Scholar 

  31. Ma J, Ding Y, Gan VJL, Lin C, Wan Z (2019) Spatiotemporal prediction of pm2.5 concentrations at different time granularities using IDW-BLSTM. IEEE Access 7:107897–107907

    Article  Google Scholar 

  32. Guo C, Liu G, Lyu L, Chen CH (2020) An unsupervised pm2.5 estimation method with different Spatio-temporal resolutions based on KIDW-TCGRU. IEEE Access 8:190263–190276. https://doi.org/10.1109/ACCESS.2020.3032420

    Article  Google Scholar 

  33. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International conference on learning representations. ICLR ’17. https://doi.org/10.48550/ARXIV.1609.02907

  34. Liu Y, Jin M, Pan S, Zhou C, Zheng Y, Xia F, Yu P (2022) Graph self-supervised learning: a survey. IEEE Transactions on knowledge and data engineering abs/2103.00111, 1–1. https://doi.org/10.1109/TKDE.2022.3172903

  35. Kipf TN, Welling M (2016) Variational graph auto-encoders. CoRR abs/1611.07308. 1611.07308. https://doi.org/10.48550/ARXIV.1611.07308

  36. Wang C, Pan S, Long G, Zhu X, Jiang J (2017) Mgae: marginalized graph autoencoder for graph clustering. In: Proceedings of the 2017 ACM on conference on information and knowledge management. CIKM ’17, pp. 889–898. https://doi.org/10.1145/3132847.3132967

  37. Jin W, Derr T, Liu H, Wang Y, Wang S, Liu Z, Tang J (2020) Self-supervised learning on graphs: deep insights and new direction. CoRR abs/2006.10141. https://doi.org/10.48550/ARXIV.2006.10141

  38. Hu Z, Fan C, Chen T, Chang K-W, Sun Y (2019) Pre-training graph neural networks for generic structural feature extraction. In: ICLR 2019 Workshop: representation learning on graphs and manifolds. https://doi.org/10.48550/ARXIV.1905.13728

  39. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International conference on knowledge discovery and data mining. KDD ’14, pp 701–710. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2623330.2623732

  40. Grover A, Leskovec J (2016) Node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International conference on knowledge discovery and data mining. KDD ’16, pp 855–864. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2939672.2939754

  41. Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L (2020) Deep Graph Contrastive Representation Learning. In: ICML Workshop on Graph Representation Learning and Beyond. https://doi.org/10.48550/ARXIV.2006.04131

  42. Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. NIPS’17, pp 1025–1035. Curran Associates Inc., Red Hook, NY, USA. https://doi.org/10.48550/ARXIV.1706.02216

  43. Velickovic P, Fedus W, Hamilton WL, Liò P, Bengio Y, Hjelm RD (2019) Deep graph infomax. In: 7th International conference on learning representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. https://doi.org/10.48550/ARXIV.1809.10341

  44. Opolka FL, Solomon A, Cangea C, Velickovic P, Liò P, Hjelm RD (2019) Spatio-temporal deep graph infomax. ICLR 2019 abs/1904.06316. https://doi.org/10.48550/ARXIV.1904.06316

  45. Winarno E, Hadikurniawati W, Rosso RN (2017) Location based service for presence system using haversine method. In: 2017 International conference on innovative and creative information technology (ICITech), pp 1–4. https://doi.org/10.1109/INNOCIT.2017.8319153. IEEE

  46. copernicus: ERA5 Hourly Data on Single Levels from 1959 to Present. https://doi.org/10.24381/cds.adbb2d47. https://cds.climate.copernicus.eu/cdsapp/#!/dataset/reanalysis-era5-single-levels Accessed 2019-09-30

  47. Li S, Xie G, Ren J, Guo L, Yang Y, Xu X (2020) Urban pm2.5 concentration prediction via attention-based CNN-LSTM. Appl Ci. https://doi.org/10.3390/app10061953

    Article  Google Scholar 

  48. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on deep learning, December. https://doi.org/10.48550/ARXIV.1412.3555

  49. Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46:234–240. https://doi.org/10.2307/143141

    Article  Google Scholar 

  50. Cichowicz R, Wielgosinski G, Fetter W (2020) Effect of wind speed on the level of particulate matter pm10 concentration in atmospheric air during winter season in vicinity of large combustion plant. J Atmos Chem 77:1–14. https://doi.org/10.1007/s10874-020-09401-w

    Article  Google Scholar 

  51. Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2020) T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst 21(9):3848–3858. https://doi.org/10.1109/tits.2019.2935152

    Article  Google Scholar 

  52. Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. https://doi.org/10.48550/ARXIV.1412.6980

  53. Reani M, Lowe D, Gledson A, Topping D, Jay C (2022) UK daily meteorology, air quality, and pollen measurements for 2016–2019, with estimates for missing data. Sci Data 9(1):43. https://doi.org/10.1038/s41597-022-01135-6

    Article  Google Scholar 

  54. Wang H air pollution and meteorological data in Beijing 2017-2018. https://doi.org/10.7910/DVN/USXCAK

  55. Colchado LE, Villanueva E, Ochoa-Luna J (2021) A neural network architecture with an attention-based layer for spatial prediction of fine particulate matter. In: 2021 IEEE 8th International conference on data science and advanced analytics (DSAA), pp 1–10. https://doi.org/10.1109/DSAA53316.2021.9564200

  56. Chen Y, Zang L, Du W, Xu D, Shen G, Zhang Q, Zou Q, Chen J, Zhao M, Yao D (2018) Ambient air pollution of particles and gas pollutants, and the predicted health risks from long-term exposure to pm25 in zhejiang province, china. Environ Sci Pollut Res 25(24):23833–23844. https://doi.org/10.1007/s11356-018-2420-5

    Article  Google Scholar 

  57. Chen Z, Xie X, Cai J, Chen D, Gao B, He B, Cheng N, Xu B (2018) Understanding meteorological influences on pm\(_{2.5}\) concentrations across china: a temporal and spatial perspective. Atmos Chem Phys 18(8):5343–5358

    Article  Google Scholar 

  58. Wang J, Ogawa S (2015) Effects of meteorological conditions on pm2.5 concentrations in Nagasaki, Japan. Int J Environ Res Public Health 12:9089–101. https://doi.org/10.3390/ijerph120809089

    Article  Google Scholar 

  59. Mi K, Zhuang R, Zhang Z, Gao J, Pei Q (2019) Spatiotemporal characteristics of pm2.5 and its associated gas pollutants, a case in china. Sustain Cities Soc 45:287–295. https://doi.org/10.1016/j.scs.2018.11.004

    Article  Google Scholar 

  60. Li K, Bai K (2019) International Journal of Environmental Research and Public Health. Spatiotemporal Assoc Between pm2.5 So2 Well No2 China From 2015 to 2018 16(13):2352. https://doi.org/10.3390/ijerph16132352

    Article  Google Scholar 

  61. Hart S In: Eatwell, J., Milgate, M., Newman, P. (eds.) Shapley Value, pp 210–216. Palgrave Macmillan UK, London (1989). https://doi.org/10.1007/978-1-349-20181-5_25

  62. Jia M, Zhao T, Cheng X, Gong S, Zhang X, Tang L, Liu D, Wu X, Wang L, Chen Y (2017) Inverse relations of pm2.5 and o3 in air compound pollution between cold and hot seasons over an urban area of east china. Atmosphere. https://doi.org/10.3390/atmos8030059

    Article  Google Scholar 

  63. Fu H, Zhang Y, Liao C, Mao L, Wang Z, Hong N (2020) Investigating PM(2.5) responses to other air pollutants and meteorological factors across multiple temporal scales. Sci Rep 10(1):15639. https://doi.org/10.1038/s41598-020-72722-z

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded by Vingroup Joint Stock Company (Vingroup JSC), Vingroup, and supported by the Vingroup Innovation Foundation (VINIF) under project code VINIF.2020.DA09. This research is partially funded by Hanoi University of Science and Technology (HUST) under grant number T2022-PC-049. Viet Hung Vu and Duc Long Nguyen were funded by Vingroup Joint Stock Company and supported by the Domestic Master/PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Vingroup Big Data Institute (VINBIGDATA), under Grant VINIF.2022.Ths.BK.05 and VINIF.2022.Ths.BK.07, respectively.

Author information

Author notes

  1. Viet Hung Vu and Duc Long Nguyen have contributed equally to this work.

Authors and Affiliations

  1. Hanoi University of Science and Technology, Hanoi, Vietnam

    Viet Hung Vu, Duc Long Nguyen, Thanh Hung Nguyen & Phi Le Nguyen

  2. Griffith University, Gold Coast, Queensland, Australia

    Quoc Viet Hung Nguyen

  3. The École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Thanh Trung Huynh

Authors
  1. Viet Hung Vu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Duc Long Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thanh Hung Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Quoc Viet Hung Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Phi Le Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thanh Trung Huynh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thanh Hung Nguyen.

Ethics declarations

Conflict of interest

All authors declare that they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A Details of hyper-parameter settings

Appendix A Details of hyper-parameter settings

All our experiment is conducted on NVIDIA GeForce RTX 2080 Ti graphic card. The Cuda version is 11.4. The deep-learning framework PyTorch version 3.8 is used to implement this approach. In our implementation, we use the default batch size of 32 using the Adam optimizer [52]. The self-supervised training of embedding is carried out for 30 epochs, with the initial learning rate of \(1e^{-3}\). The number of epochs trained for the supervised models is also 30, with the initial learning rate of \(1e^{-3}\). We use early stopping to get the best model weight. The value of patience in early stopping is 10 epochs.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vu, V.H., Nguyen, D.L., Nguyen, T.H. et al. Self-supervised air quality estimation with graph neural network assistance and attention enhancement. Neural Comput & Applic 36, 11171–11193 (2024). https://doi.org/10.1007/s00521-024-09637-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-024-09637-7

Keywords

Search

Navigation