Abstract
Although considerable success has been achieved in urban air quality prediction (AQP) with machine learning techniques, accurate and long-term prediction is still challenging. One of the key issues for existing AQP approaches is that air quality monitoring stations are sparsely distributed, typically with around ten monitoring stations per city. As air quality may change abruptly in a local area, it is difficult to perform AQP accurately in areas that are far away from observation points. In addition, due to the large distance between every two monitoring stations, we cannot effectively leverage spatial relations among them to improve the AQP accuracy. In this paper, thanks to the development of low-cost air quality sensors, we are now able to collect a large-scale air quality dataset with 393 deployed air quality monitoring stations in a 120 km × 70 km region, which is more than ten times denser than existing AQP datasets. Further, we present a novel method to handle the data effectively. Specifically, we first convert the observed data from irregularly distributed monitoring stations into a regular image-like pollution map, which can then be processed with advanced deep convolutional networks. The experimental results show that the proposed approach can simultaneously model the temporal and spatial relations in our large-scale densely-observed dataset, leading to significantly improved AQP results.
Similar content being viewed by others
References
Stern A C. Air Pollution: the Effects of Air Pollution. Amsterdam: Elsevier, 1977
Brunekreef B, Holgate S T. Air pollution and health. Lancet, 2002, 360: 1233–1242
Chow J C. Health effects of fine particulate air pollution: lines that connect. J Air Waste Manage Assoc, 2006, 56: 707–708
Dominici F, Peng R D, Bell M L, et al. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA, 2006, 295: 1127–1134
Xing Y F, Xu Y H, Shi M H, et al. The impact of PM2.5 on the human respiratory system. J Thoracic Dis, 2016, 8: 69
Brook R D, Rajagopalan S, Pope C A, et al. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the american heart association. Circulation, 2010, 121: 2331–2378
Pope C A, Burnett R T, Thun M J, et al. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA, 2002, 287: 1132–1141
James D E, Chambers J A, Kalma J D, et al. Air quality prediction in urban and semi-urban regions with generalised input-output analysis: the hunter region, australia. Urban Ecol, 1985, 9: 25–44
Bruckman L. Overview of the enhanced geocoded emissions modeling and projection (enhanced gemap) system. In: Proceeding of the Air & Waste Management Association’s Regional Photochemical Measurements and Modeling Studies Conference, San Diego, 1993
Gu K, Qiao J F, Li X L. Highly efficient picture-based prediction of PM2.5 concentration. IEEE Trans Ind Electron, 2019, 66: 3176–3184
Corani G. Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning. Ecol Model, 2005, 185: 513–529
Russo A, Raischel F, Lind P G. Air quality prediction using optimal neural networks with stochastic variables. Atmos Environ, 2013, 79: 822–830
Box G E P, Pierce D A. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J Am Stat Assoc, 1970, 65: 1509–1526
Liu B C, Binaykia A, Chang P C, et al. Urban air quality forecasting based on multi-dimensional collaborative support vector regression (SVR): a case study of Beijing-Tianjin-Shijiazhuang. PLoS ONE, 2017, 12: 0179763
Zheng Y, Yi X W, Li M, et al. Forecasting fine-grained air quality based on big data. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015. 2267–2276
Zheng Y, Liu F R, Hsieh H P. U-air: when urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013. 1436–1444
Kurt A, Oktay A B. Forecasting air pollutant indicator levels with geographic models 3days in advance using neural networks. Expert Syst Appl, 2010, 37: 7986–7992
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Proceedigns of International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015. 234–241
He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016. 770–778
Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017. 4700–4708
Kim Y, Fu J S, Miller T L. Improving ozone modeling in complex terrain at a fine grid resolution: part I — examination of analysis nudging and all PBL schemes associated with LSMs in meteorological model. Atmos Environ, 2010, 44: 523–532
Baklanov A, Mestayer P G, Clappier A, et al. Towards improving the simulation of meteorological fields in urban areas through updated/advanced surface fluxes description. Atmos Chem Phys, 2008, 8: 523–543
Jeong J I, Park R J, Woo J H, et al. Source contributions to carbonaceous aerosol concentrations in Korea. Atmos Environ, 2011, 45: 1116–1125
Stern R, Builtjes P, Schaap M, et al. A model inter-comparison study focussing on episodes with elevated PM10 concentrations. Atmos Environ, 2008, 42: 4567–4588
Li C, Hsu N C, Tsay S C. A study on the potential applications of satellite data in air quality monitoring and forecasting. Atmos Environ, 2011, 45: 3663–3675
Li X, Peng L, Yao X J, et al. Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ Pollution, 2017, 231: 997–1004
Gu K, Qiao J F, Lin W S. Recurrent air quality predictor based on meteorology- and pollution-related factors. IEEE Trans Ind Inf, 2018, 14: 3946–3955
Gu K, Xia Z F, Qiao J F. Stacked selective ensemble for PM2.5 forecast. IEEE Trans Instrum Meas, 2020, 69: 660–671
Yi X W, Zhang J B, Wang Z Y, et al. Deep distributed fusion network for air quality prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018. 965–973
Zheng Y, Capra L, Wolfson O, et al. Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Technol, 2014, 5: 38
Xiong Z, Sheng H, Rong W G, et al. Intelligent transportation systems for smart cities: a progress review. Sci China Inf Sci, 2012, 55: 2908–2914
Deng M, Liu Q L, Wang J Q, et al. A general method of spatio-temporal clustering analysis. Sci China Inf Sci, 2013, 56: 102315
Wang C M, Hu X P, Yao L, et al. Spatio-temporal pattern analysis of single-trial EEG signals recorded during visual object recognition. Sci China Inf Sci, 2011, 54: 2499–2507
Wang W, Hu C B, Chen N C, et al. Spatio-temporal enabled urban decision-making process modeling and visualization under the cyber-physical environment. Sci China Inf Sci, 2015, 58: 100105
Lv Y S, Duan Y J, Kang W W, et al. Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst, 2015, 16: 865–873
Zhang J B, Zheng Y, Qi D K. Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, 2017
Liang Y X, Ke S Y, Zhang J B, et al. Geoman: multi-level attention networks for geo-sensory time series prediction. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018. 3428–3434
Zhang J B, Zheng Y, Qi D K, et al. Dnn-based prediction model for spatio-temporal data. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2016
Parzen E. On estimation of a probability density function and mode. Ann Math Stat, 1962, 33: 1065–1076
Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. 2015. ArXiv:1502.03167
Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010. 807–814
Kingma D P, Ba J. Adam: a method for stochastic optimization. 2014. ArXiv:1412.6980
Broomhead D S, Lowe D. Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks. Royal Signals and Radar Establishment Malvern (United Kingdom) Technical Report, 1988
Friedman J H. Greedy function approximation: a gradient boosting machine. Ann Stat, 2001, 29: 1189–1232
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9: 1735–1780
Acknowledgements
This work was supported in part by National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2018AAA0100701), National Natural Science Foundation of China (Grant Nos. 61906106, 62022048), and Institute for Guo Qiang of Tsinghua University and Beijing Academy of Artificial Intelligence.
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Huang, G., Ge, C., Xiong, T. et al. Large scale air pollution prediction with deep convolutional networks. Sci. China Inf. Sci. 64, 192107 (2021). https://doi.org/10.1007/s11432-020-2951-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-020-2951-1