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Deep Learning for Air Quality Forecasts: a Review

  • Air Pollution (H Zhang and Y Sun, Section Editors)
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Abstract

Air pollution is one of major environmental issues in the twenty-first century due to global industrialization and urbanization. Its mitigation necessitates accurate air quality forecasts. However, current state-of-the-art air quality forecasts are limited from highly uncertain chemistry-transport models (CTMs), shallow statistical methods, and heterogeneous and incomplete observing networks. Recently, deep learning has emerged as a general-purpose technology to extract complex knowledge using massive amount of data and very large networks of neurons and thus has the potential to break the limits of air quality forecasts. Here, we provide a brief review of recent attempts on using deep learning techniques in air quality forecasts. We first introduce architectures of deep networks (e.g., convolutional neural networks, recurrent neural networks, long short-term memory neural networks, and spatiotemporal deep network) and their relevance to explore the nonlinear spatiotemporal features across multiple scales of air pollution. We then examine the potential of deep learning techniques for air quality forecasts in diverse aspects, namely, data gap filling, prediction algorithms, improvements of CTMs, estimations with satellite data, and source estimations for atmospheric dispersion forecasts. Finally, we point out some prospects and challenges for future attempts on improving air quality forecasts using deep learning techniques.

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References

  1. Chen LJ, Ho YH, Lee HC, Wu HC, Liu HM, Hsieh HH, et al. An open framework for participatory pm2.5 monitoring in smart cities. IEEE Access. 2017;5:14441–54. https://doi.org/10.1109/ACCESS.2017.2723919.

    Article  Google Scholar 

  2. Kampa M, Castanas E. Human health effects of air pollution. Environ Pollut. 2008;151(2):362–7. https://doi.org/10.1016/j.envpol.2007.06.012.

    Article  CAS  Google Scholar 

  3. Released, In New Estimates . 7 million premature deaths annually linked to air pollution. Air Quality and Climate Change. 2014. http://www.who.int/mediacentre/news/releases/2014/air-pollution/en/#.UzDo2BhK4xo.facebook.

  4. Geng G, Zhang Q, Martin RV, Donkelaar AV, Huo H, Che H, et al. Estimating long-term PM 2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model. Remote Sens Environ. 2015;166:262–70. https://doi.org/10.1016/j.rse.2015.05.016.

    Article  Google Scholar 

  5. Jeffrey DS, Ashkan A, Emmanuela G, Stephen SL, Degu A, Kalkidan HA, et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2018;392(10159):1923–94. https://doi.org/10.1016/S0140-6736(18)32225-6.

    Article  Google Scholar 

  6. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012;380(9859):2224–60. https://doi.org/10.1016/S0140-6736(12)61766-8.

    Article  Google Scholar 

  7. Rohde RA, Muller RA. Air pollution in China: mapping of concentrations and sources. PLoS One. 2015;10(8):e0135749. https://doi.org/10.1371/journal.Pone.0135749.

    Article  Google Scholar 

  8. Kang GK, Gao JZ, Chiao S, Lu S, Xie G. Air quality prediction: big data and machine learning approaches. Int J Environ Sci Dev. 2018;9(1):8–16. https://doi.org/10.18178/ijesd.2018.9.1.1066.

    Article  Google Scholar 

  9. Zheng Y, Liu F, Hsieh HP. U-Air: when urban air quality inference meets big data. Proceedings of the 19th SIGKDD conference on Knowledge Discovery and Data Mining, 2013;1436–1444. https://www.microsoft.com/en-us/research/publication/u-air-when-urban-air-quality-inference-meets-big-data/.

  10. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44. https://doi.org/10.1038/nature14539.

  11. Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Nuno Carvalhais N, et al. Deep learning and process understanding for data-driven earth system science. Nature. 2019;566(7743):195–204. https://doi.org/10.1038/s41586-019-0912-1.

    Article  CAS  Google Scholar 

  12. Zhu XX, Tuia D, Mou L, Xia GS, Fraundorfer F. Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci Remote Sens Mag. 2017;5(4):8–36. https://doi.org/10.1109/MGRS.2017.2762307.

    Article  Google Scholar 

  13. Zhang Y, Bocquet M, Mallet V, Seigneur C, Baklanov A. Real-time air quality forecasting, part I: history, techniques, and current status. Atmos Environ. 2012;60:632–55. https://doi.org/10.1016/j.atmosenv.2012.06.031.

    Article  CAS  Google Scholar 

  14. Debry E, Mallet V. Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and PM10 on the Prev'Air platform. Atmos Environ. 2014;91:71–84. https://doi.org/10.1016/j.atmosenv.2014.03.049.

    Article  CAS  Google Scholar 

  15. Fan J, Li Q, Hou J, Feng X, Karimian H, Lin S. A spatiotemporal prediction framework for air pollution based on deep RNN. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017;4:15. https://doi.org/10.5194/isprs-annals-IV-4-W2-15-2017.

    Article  Google Scholar 

  16. Byun QW, Ching JK. Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Washington, DC, USA: Environmental Protection Agency; 1999. http://www.epa.gov/asmdnerl/models3/doc /scence/science.html

    Google Scholar 

  17. Chen J, Lu J, Avise JC, DaMassa JA, Kleeman MJ, Kaduwela AP. Seasonal modeling of PM2.5 in California's San Joaquin Valley. Atmos Environ. 2014;92:182–90. https://doi.org/10.1016/j.atmosenv.2014.04.030.

    Article  CAS  Google Scholar 

  18. Grell GA, Peckham SE, Schmitz R, McKeen SA, Frost G, Skamarock WC, et al. Fully coupled “online” chemistry within the WRF model. Atmos Environ. 2005;39(37):6957–75. https://doi.org/10.1016/j.atmosenv.2005.04.027.

    Article  CAS  Google Scholar 

  19. Wang Z, Maeda T, Hayashi M, Hsiao L, Liu K. A nested air quality prediction modeling system for urban and regional scales: application for high-ozone episode in Taiwan. Water Air Soil Pollut. 2001;130(1–4):391–6. https://doi.org/10.1023/a:1013833217916.

    Article  Google Scholar 

  20. Zhang H, Linford JC, Sandu A, Sander R. Chemical mechanism solvers in air quality models. Atmosphere. 2011;2(3):510–32. https://doi.org/10.3390/atmos2030510.

    Article  CAS  Google Scholar 

  21. Kelp MM, Tessum CW, Marshall JD. Orders-of-magnitude speedup in atmospheric chemistry modeling through neural network-based emulation. 2018.

    Google Scholar 

  22. Vautard R, Builtjes P, Thunis P, Cuvelier C, Bedogni M, Bessagnet B, et al. Evaluation and intercomparison of Ozone and PM10 simulations by several chemistry transport models over four European cities within the CityDelta project. Atmos Environ. 2007;41(1):173–88. https://doi.org/10.1016/j.atmosenv.2006.07.039.

    Article  CAS  Google Scholar 

  23. Ziegel ER, Box GEP, Jenkins GM, Reinsel GC. Time series analysis, forecasting, and control. Technometrics. 1995;37(2):238. https://doi.org/10.2307/1269640.

    Article  Google Scholar 

  24. Li C, Hsu NC, Tsay SC. A study on the potential applications of satellite data in air quality monitoring and forecasting. Atmos Environ. 2011;45(22):3663–75. https://doi.org/10.1016/j.atmosenv.2011.04.032.

    Article  CAS  Google Scholar 

  25. Ma Z, Hu X, Huang L, Bi J, Liu Y. Estimating ground-level pm2.5 in China using satellite remote sensing. Environ Sci Technol. 2014;48(13):7436–44. https://doi.org/10.1021/es5009399.

    Article  CAS  Google Scholar 

  26. Carrassi A, Bocquet M, Bertino L, Evensen G. Data assimilation in the geosciences: an overview of methods, issues, and perspectives. Wiley Interdiscip Rev Clim Chang. 2018;9(5):e535. https://doi.org/10.1002/wcc.535.

    Article  Google Scholar 

  27. Wu L, Mallet V, Bocquet M, Sportisse B. A comparison study of data assimilation algorithms for ozone forecasts. J Geophys Res Atmos. 2008;113(D20). https://doi.org/10.1029/2008JD009991.

  28. Elbern H, Schmidt H, Ebel A. Variational data assimilation for tropospheric chemistry modeling. J Geophys Res Atmos. 1997;102:15967–85. https://doi.org/10.1029/97JD01213.

    Article  CAS  Google Scholar 

  29. Eibern H, Schmidt H. A four-dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling. J Geophys Res Atmos. 1999;104(D15):18583–98. https://doi.org/10.1029/1999JD900280.

    Article  Google Scholar 

  30. Donoho DL. High-dimensional data analysis: The curses and blessings of dimensionality. In: AMS Conference on Math Challenges of the 21st Century. 2000. https://doi.org/10.1111/j.1751-0813.1937.tb04127.x.

  31. Zhu Y. Ensemble forecast: a new approach to uncertainty and predictability. Adv Atmos Sci. 2005;22(6):781–8. https://doi.org/10.1007/BF02918678.

    Article  Google Scholar 

  32. Monache LD, Deng X, Zhou Y, Stull R. Ozone ensemble forecasts: 1. A new ensemble design. J Geophys Res Atmos. 2006;111(D5). https://doi.org/10.1029/2005JD006310.

  33. Mckeen SA, Chung SH, Wilczak J, Grell G, Yu S. Evaluation of several PM2.5 forecast models using data collected during the icartt/neaqs 2004 field study. J Geophys Res Atmos. 2007;112(D10). https://doi.org/10.1029/2006JD007608.

  34. Monache DL, Nipen T, Deng X, Zhou Y, Stull R. Ozone ensemble forecasts: 2. A Kalman filter predictor bias correction. J Geophys Res Atmos. 2006;111(D5). https://doi.org/10.1029/2005JD006311.

  35. Garaud D, Mallet V. Automatic calibration of an ensemble for uncertainty estimation and probabilistic forecast: application to air quality. J Geophys Res Atmos. 2011;116(D19). https://doi.org/10.1029/2011JD015780.

  36. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–8. https://doi.org/10.1038/nature21056.

    Article  CAS  Google Scholar 

  37. Donnelly A, Misstear B, Broderick B. Real time air quality forecasting using integrated parametric and non-parametric regression techniques. Atmos Environ. 2015;103(103):53–65. https://doi.org/10.1016/j.atmosenv.2014.12.011.

    Article  CAS  Google Scholar 

  38. Chin C, Brown DE. Learning in science: A comparison of deep and surface approaches. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching. 2000;37(2):109–38. https://doi.org/10.1002/(SICI)1098-2736(200002)37:23.0.CO;2-7.

  39. Gardner M, Dorling S. Artificial neural network (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ. 1998;6(32):2627–36 https://doi-org-443.webvpn.las.ac.cn/10.1016/S1352-2310(97)00447-0.

    Article  Google Scholar 

  40. Prybutok VR, Yi J, Mitchell D. Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentration. Eur J Oper Res. 2000;122(1):31–40. https://doi.org/10.1016/S0377-2217(99)00069-7.

    Article  Google Scholar 

  41. Perez P, Reyes J. An integrated neural network model for PM10 forecasting. Atmos Environ. 2006;40(16):2845–51. https://doi.org/10.1016/j.atmosenv.2006.01.010.

    Article  CAS  Google Scholar 

  42. Aiswarya B, Aneena AA. A review on various techniques used in predicting pollutants. IOP Conf Ser Mater Sci Eng. 2018;396:012016. https://doi.org/10.1088/1757-899X/396/1/012016.

    Article  Google Scholar 

  43. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.

    Article  CAS  Google Scholar 

  44. Kolen JF, Kremer SC. Gradient flow in recurrent nets: the difficulty of learning LongTerm dependencies. A Field Guide to Dynamical Recurrent Networks: IEEE; 2001. p. 237–43. https://doi.org/10.1109/9780470544037.ch14.

  45. Zaytar MA, Amrani CE. 2016. Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. Int J Comput Appl. 2016;143(11):7–11. https://doi.org/10.5120/ijca2016910497.

    Article  Google Scholar 

  46. Cho K, Van Merriënboer B, Bahdanau D, Bengio Y. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259. https://arxiv.org/abs/1409.1259.

  47. Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. Comput Sci. 2014; http://arxiv.org/abs/1412.3555v1.

  48. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324. https://doi.org/10.1109/5.726791.

    Article  Google Scholar 

  49. Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M. Striving for simplicity: the all convolutional net. In International Conference on Learning Representation (ICLR), 2015. https://arxiv.org/abs/1412.6806v3.

  50. Zhang C, Yan Z, Li C, Rui X, Liu L, Bie R. On estimating air pollution from photos using convolutional neural network. Proceedings of the 2016 ACM on Multimedia Conference; 2016. p. 297–301. https://doi.org/10.1145/2964284.2967230.

    Book  Google Scholar 

  51. Klambauer G, Unterthiner T, Mayr A, Hochreiter S. Self-normalizing neural networks. In Proceedings of the Advances in Neural Information Processing Systems 30, 2017. https://arxiv.org/abs/1706.02515v4.

  52. Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst. 2012;25(2):1097–105. https://doi.org/10.1145/3065386.

    Article  Google Scholar 

  53. Oquab M, Bottou L, Laptev I, Sivic J. Learning and transferring mid-level image representations using convolutional neural networks. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR); 2014. p. 1717–24. https://doi.org/10.1109/CVPR.2014.222.

    Book  Google Scholar 

  54. Maharana A, Nsoesie EO. Use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity. JAMA Netw Open. 2018;1(4):e181535. https://doi.org/10.1001/jamanetworkopen.2018.1535.

    Article  Google Scholar 

  55. Lv Y, Duan Y, Kang W, Li Z, Wang FY. Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst. 2015;16(2):865–73. https://doi.org/10.1109/TITS.2014.2345663.

    Article  Google Scholar 

  56. Bengio Y, Lamblin P, Popovici D, Larochelle H. Advances in neural information processing systems, 2007;19:153–160. https://ieeexplore.ieee.org/ document/6287632.

  57. Li X, Peng L, Hu Y, Shao J, Chi T. Deep learning architecture for air quality predictions. Environ Sci Pollut Res. 2016;23(22):22408–17. https://doi.org/10.1007/s11356-016-7812-9.

    Article  Google Scholar 

  58. Wang Q, Lin J, Yuan Y. Salient band selection for hyperspectral image classification via manifold ranking. IEEE transactions on neural networks and learning systems 2016;27(6):1279–89. https://doi.org/10.1109/TNNLS.2015.2477537.

  59. Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–54. https://doi.org/10.1162/neco.2006.18.7.1527.

    Article  Google Scholar 

  60. Hinton GE. Learning multiple layers of representation. Trends Cogn Sci. 2007;11(10):428–34. https://doi.org/10.1016/j.tics.2007.09.004.

    Article  Google Scholar 

  61. Rumelhart DE, McClelland JL. Information processing in dynamical systems: foundations of harmony theory. in parallel distributed processing: explorations in the microstructure of cognition: Foundations, 1987;194–281. https://ieeexplore.ieee.org/document/6302931?T p=&arnumber=6302931.

  62. Mohamed AR, Sainath TN, Dahl GE, Ramabhadran B, Hinton GE, Picheny MA. Deep belief networks using discriminative features for phone recognition. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing; 2011. https://doi.org/10.1109/ICASSP.2011.5947494.

    Book  Google Scholar 

  63. Tolstikov A, Biswas J, Nugent C, Parente G. Comparison of fusion methods based on DST and DBN in human activity recognition. Control Theory Technol. 2011;9(1):18–27. https://doi.org/10.1007/s11768-011-0260-7.

    Article  Google Scholar 

  64. Srivastava N, Salakhutdinov R. Multimodal learning with deep Boltzmann machines. Journal of Machine Learning Research 2012;15(8):1967–2006. https://doi.org/10.1162/NECO_a_00311.

  65. Li T, Shen H, Yuan Q, Zhang X, Zhang L. Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach. Atmos Ocean Phys. 2017; https://arxiv.org/abs/1707.03558v1.

  66. Rodriguez JD, Perez A, Lozano JA. Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans Pattern Anal Mach Intell. 2010;32(3):569–75. https://doi.org/10.1109/TPAMI.2009.187.

    Article  Google Scholar 

  67. Stanley KO, D’Ambrosio DB, Gauci J. A hypercube-based encoding for evolving large-scale neural networks. Artif Life. 2009;15(2):185–212. https://doi.org/10.1162/artl.2009.15.2.15202.

    Article  Google Scholar 

  68. Pan Z, Liang Y, Zhang J, Yi X, Yu Y, Zheng Y. HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting. Mach Learn. 2018; https://arxiv.org/abs/1809.10889.

  69. Li V, Lam J, Chen Y, Gu J. Deep learning model to estimate air pollution using M-BP to fill in missing proxy urban data. GLOBECOM 2017–2017 IEEE Global Communications Conference, 2017;1–6. https://doi.org/10.1109/GLOCOM.2017.8255004.

  70. Qi Z, Wang T, Song G, Hu W, Li X, Zhang Z. Deep air learning: interpolation, prediction, and feature analysis of fine-grained air quality. IEEE Transactions on Knowledge and Data Engineering 2018;30(12):2285–97. https://doi.org/10.1109/TKDE.2018.2823740.

  71. Hong KY, Pinheiro PO, Weichenthal S. Predicting global variations in outdoor PM2:5 concentrations using satellite images and deep convolutional neural networks: Image and Video Processing; 2019. https://arxiv.org/abs/1906.03975

  72. Li T, Shen H, Yuan Q, Zhang L. A novel solution for remote sensing of air quality: from satellite reflectance to ground pm2.5. Atmos Ocean Phys. 2017.

  73. Bui TC, Le VD, Cha SK. A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM. Mach Learn. 2018; https://arxiv.org/abs/1804.07891v3.

  74. Freeman BS, Taylor G, Gharabaghi B, Thé J. Forecasting air quality time series using deep learning. J Air Waste Manage Assoc. 2018;68(8):866–86. https://doi.org/10.1080/10962247.2018.1459956.

    Article  CAS  Google Scholar 

  75. Li X, Peng L, Yao X, Cui S, Hu Y, You C, et al. Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ Pollut. 2017;231:997–1004. https://doi.org/10.1016/j.envpol.2017.08.114.

    Article  CAS  Google Scholar 

  76. Kim S, Lee JM, Lee J, Seo J. Deep-dust: predicting concentrations of fine dust in Seoul using LSTM. Clim Inform. 2019; https://arxiv.org/abs/1901.10106.

  77. Huang CJ, Kuo PH. A deep CNN-LSTM model for particulate matter (PM2.5) forecasting in smart cities. Sensors. 2018;18(7):2220. https://doi.org/10.3390/s18072220.

    Article  Google Scholar 

  78. Soh PW, Chang JW, Huang JW. Adaptive deep learning-based air quality prediction model using the most relevant spatial-temporal relations. IEEE Access, 2018;6:38186–99.

  79. Wang H, Zhuang B, Chen Y, Li N, Wei D. Deep inferential spatial-temporal network for forecasting air pollution concentrations. Mach Learn. 2018; https://arxiv.org/abs/18 09.03964v1.

  80. Keller CA, Evans MJ. Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10. Geosci Model Dev. 2019;12(3):1209–25. https://doi.org/10.5194/gmd-12-1209-2019.

    Article  CAS  Google Scholar 

  81. Kelp MM, Tessum CW, Marshall JD. Orders-of-magnitude speedup in atmospheric chemistry modeling through neural network-based emulation. Atmos Ocean Phys. 2018; https://arxiv.org/abs/1808.03874.

  82. Kelp M, Jacob DJ, Kutz JN, Marshall JD, Tessum C. Toward stable, general machine learned models of the atmospheric chemical system. Submitted to JGR: Atmospheres. 2020.

  83. Ma D, Zhang Z. Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere. J Hazard Mater. 2016;311:237–45. https://doi.org/10.1016/j.jhazmat.2016.03.022.

    Article  CAS  Google Scholar 

  84. Kumar S, Torres C, Ulutan O, Ayasse, A, Roberts D, Manjunath BS. Deep remote sensing methods for methane detection in overhead Hyperspectral imagery. In the IEEE Winter Conference on Applications of Computer Vision, 2020;1776–1785. https://doi.org/10.1109/WACV45572.2020.9093600.

  85. Tobler WR. A computer movie simulating urban growth in the Detroit region. Econ Geogr. 1970;46(2):234–40. https://doi.org/10.2307/143141.

    Article  Google Scholar 

  86. Rao TS. Statistics for spatio-temporal data. J Time. 2012;33(4):699–700. https://doi.org/10.1111/j.1467-9892.2011.00765.x.

    Article  Google Scholar 

  87. Rakthanmanon T, Campana B, Mueen A, Batista G, Westover B, Zhu Q, et al. Searching and mining trillions of time series subsequences under dynamic time warping. Proc ACM SIGKDD Int Conf Knowl Discov Data Min. 2012;2012:262–70. https://doi.org/10.1145/2339530.2339576.

    Article  Google Scholar 

  88. Keogh E, Ratanamahatana CA. Exact indexing of dynamic time warping. Knowl Inf Syst. 2005;7(3):358–86. https://doi.org/10.1007/s10115-004-0154-9.

    Article  Google Scholar 

  89. Soh PW, Chen KH, Huang JW, Chu HJ. Spatial-temporal pattern analysis and prediction of air quality in Taiwan. 10th International Conference on Ubi-media Computing and Workshops (Ubi-Media); 2017. p. 1–6. https://doi.org/10.1109/umedia.2017.8074094.

    Book  Google Scholar 

  90. Yi X, Zhang J, Wang Z, Li T, Zheng Y. Deep distributed fusion network for air quality prediction. the 24th ACM SIGKDD International Conference: ACM; 2018. https://doi.org/10.1145/3219819.3219822.

  91. Lin Y, Mago N, Gao Y, Li Y, Chiang YY, Shahabi C, et al. Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning. In: The 26th ACM SIGSPATIAL International Conference; 2018. p. 359–68. https://doi.org/10.1145/3274895.3274907.

    Chapter  Google Scholar 

  92. Afonin SV. An appraisal of the method of AOD retrieval over land according to MODIS satellite measurements in IR spectral range. Atmos Ocean Optics. 2011;24(6):584–6. https://doi.org/10.1134/S1024856011060029.

    Article  CAS  Google Scholar 

  93. Li T, Shen H, Zeng C, Yuan Q, Zhang L. Point-surface fusion of station measurements and satellite observations for mapping pm2.5 distribution in china: methods and assessment. Atmos Environ. 2016. https://doi.org/10.1016/j.atmosenv.2017.01.004.

  94. Xiao Q, Wang Y, Chang HH, Meng X, Geng G, Lyapustin A, et al. Full-coverage high-resolution daily PM 2.5 estimation using MAIAC AOD in the Yangtze River Delta of China. Remote Sens Environ. 2017;199:437–46. https://doi.org/10.1016/j.rse.2017.07.023.

    Article  Google Scholar 

  95. Moon T, Wang Y, Liu Y, Yu B. Evaluation of a MISR-based high-resolution aerosol retrieval method using AERONET DRAGON campaign data. IEEE Trans Geosci Remote Sens. 2015;53(8):4328–39. https://doi.org/10.1109/tgrs.2015.2395722.

    Article  Google Scholar 

  96. Brauer M, Amann M, Burnett RT, Cohen A, Dentener F, Ezzati M, et al. Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution. Environ Sci Technol. 2012;46(2):652–60. https://doi.org/10.1021/es2025752.

    Article  CAS  Google Scholar 

  97. Di Q, Koutrakis P, Schwartz J. A hybrid prediction model for PM2.5 mass and components using a chemical transport model and land use regression. Atmos Environ. 2016;131:390–9. https://doi.org/10.1016/j.atmosenv.2016.02.002.

    Article  CAS  Google Scholar 

  98. Gupta P, Christopher SA. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: multiple regression approach. J Geophys Res. 2009;114:D14205. https://doi.org/10.1029/2008JD011496.

    Article  CAS  Google Scholar 

  99. Lary DJ, Alavi AH, Gandomi AH, Walker AL. Machine learning in geosciences and remote sensing. Geosci Front. 2015;7(1):3–10. https://doi.org/10.1016/j.gsf.2015.07.003.

    Article  Google Scholar 

  100. Li H, Chang J, Xu F, Liu B, Liu Z, Zhu L, Yang Z. An RBF neural network approach for retrieving atmospheric extinction coefficients based on lidar measurements. Applied Physics B. 2018; 124(9):184. https://doi.org/10.1007/s00340-018-7055-1.

  101. Zhang B, Zhang M, Kang J, Hong D, Jian Xu J, Zhu X. Estimation of PMx concentrations from Landsat 8 OLI images based on a multilayer perceptron neural network. Remote Sens. 2019;11(6):646. https://doi.org/10.3390/rs11060646.

    Article  Google Scholar 

  102. Athira V, Geetha P, Vinayakumar R, Soman KP. DeepAirNet: applying recurrent networks for air quality prediction. Procedia Comput Sci. 2018;132:1394–403. https://doi.org/10.1016/j.procs.2018.05.068.

    Article  Google Scholar 

  103. Xayasouk T, Lee H. Air pollution prediction system using deep learning. WIT Trans Ecol Environ. 2018;230:71–9. https://doi.org/10.2495/AIR180071.

    Article  Google Scholar 

  104. Lodge J. Handbook on atmospheric diffusion. Atmos Environ. 1983;17(3):673–5. https://doi.org/10.1016/0004-6981(83)90164-6.

    Article  Google Scholar 

  105. Seaman NL. Meteorological modeling for air-quality assessments. Atmos Environ. 2000;34(12–14):2231–59. https://doi.org/10.1016/S1352-2310(99)00466-5.

    Article  CAS  Google Scholar 

  106. Flesch TK, Wilson JD, Harper LA, Crenna BP, Sharpe RR. Deducing ground-to-air emissions from observed trace gas concentrations: a field trial. J Appl Meteorol. 2004;43(3):487–502. https://doi.org/10.1175/1520-0450(2004)043<0487:DGEFOT>2.0.CO;2.

    Article  Google Scholar 

  107. Wilson JD, Sawford BL. Review of lagrangian stochastic models for trajectories in the turbulent atmosphere. Bound-Layer Meteorol. 1996;78(1–2):191–210. https://doi.org/10.1007/BF00122492

  108. Pontiggia M, Derudi M, Busini V, Rota R. Hazardous gas dispersion: a CFD model accounting for atmospheric stability classes. J Hazard Mater. 2009;171(1–3):739–47. https://doi.org/10.1016/j.jhazmat.2009.06.064.

    Article  CAS  Google Scholar 

  109. Mazzoldi A, Hill T, Colls JJ. CFD and Gaussian atmospheric dispersion models: a comparison for leak from carbon dioxide transportation and storage facilities. Atmos Environ. 2008;42(34):8046–54. https://doi.org/10.1016/j.atmosenv.2008.06.038.

    Article  CAS  Google Scholar 

  110. So W, Koo J, Shin D, Yoon ES. The estimation of hazardous gas release rate using optical sensor and neural network. Computer Aided Chemical Engineering. Elsevier. 2010;28(10):199–204. https://doi.org/10.1016/S1570-7946(10)28034-3.

  111. Wang B, Chen B, Zhao J. The real-time estimation of hazardous gas dispersion by the integration of gas detectors, neural network and gas dispersion models. J Hazard Mater. 2015;300:433–42. https://doi.org/10.1016/j.jhazmat.2015.07.028.

    Article  CAS  Google Scholar 

  112. Qiu S, Chen B, Wang R, Zhu Z, Wang Y, Qiu X. Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization. Atmos Environ. 2018;178:58–163. https://doi.org/10.1016/j.atmosenv.2018.01.056.

    Article  CAS  Google Scholar 

  113. Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Advances in neural information processing systems. 2015;802–810. https://arxiv.org/abs/1506.04214v1.

  114. Shi X, Gao Z, Lausen L, Wang H, Yeung DY, Wong WK, Woo WC. Deep learning for precipitation nowcasting: A benchmark and a new model. In Advances in neural information processing systems. 2017;5617–27. https://arxiv.org/abs/1706.03458.

  115. Xue T, Zheng Y, Tong D, Zheng B, Li X, Zhu T, et al. Spatiotemporal continuous estimates of PM2. 5 concentrations in China, 2000–2016: a machine learning method with inputs from satellites, chemical transport model, and ground observations. Environ Int. 2019;123:345–57. https://doi.org/10.1016/j.envint.2018.11.075.

    Article  CAS  Google Scholar 

  116. Liu M, Bi J, Ma Z. Visibility-based PM2. 5 concentrations in China: 1957–1964 and 1973–2014. Environ Sci Technol. 2017;51(22):13161–9. https://doi.org/10.1021/acs.est.7b03468.

    Article  CAS  Google Scholar 

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Funding

This work is supported by the Pioneer Hundred Talents Program of Chinese Academy of Sciences, the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDC01040100) and the Key Program of National Natural Science Foundation of China (Grant No. 91644216).

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Authors and Affiliations

  1. State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

    Qi Liao, Mingming Zhu, Lin Wu, Xiaole Pan, Xiao Tang & Zifa Wang

  2. College of Earth Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

    Qi Liao, Mingming Zhu & Zifa Wang

  3. Center for Excellence in Urban Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China

    Zifa Wang

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  1. Qi Liao
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  2. Mingming Zhu
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  3. Lin Wu
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Correspondence to Lin Wu or Xiaole Pan.

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Liao, Q., Zhu, M., Wu, L. et al. Deep Learning for Air Quality Forecasts: a Review. Curr Pollution Rep 6, 399–409 (2020). https://doi.org/10.1007/s40726-020-00159-z

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