Data-Driven Temporal-Spatial Model for the Prediction of AQI in Nanjing

Xuan Zhao 1 , Meichen Song 1 , Anqi Liu 1 , Yiming Wang 1 , Tong Wang 1  and Jinde Cao 1
  • 1 School of Mathematics, Southeast University, Nanjing, China


Air quality data prediction in urban area is of great significance to control air pollution and protect the public health. The prediction of the air quality in the monitoring station is well studied in existing researches. However, air-quality-monitor stations are insufficient in most cities and the air quality varies from one place to another dramatically due to complex factors. A novel model is established in this paper to estimate and predict the Air Quality Index (AQI) of the areas without monitoring stations in Nanjing. The proposed model predicts AQI in a non-monitoring area both in temporal dimension and in spatial dimension respectively. The temporal dimension model is presented at first based on the enhanced k-Nearest Neighbor (KNN) algorithm to predict the AQI values among monitoring stations, the acceptability of the results achieves 92% for one-hour prediction. Meanwhile, in order to forecast the evolution of air quality in the spatial dimension, the method is utilized with the help of Back Propagation neural network (BP), which considers geographical distance. Furthermore, to improve the accuracy and adaptability of the spatial model, the similarity of topological structure is introduced. Especially, the temporal-spatial model is built and its adaptability is tested on a specific non-monitoring site, Jiulonghu Campus of Southeast University. The result demonstrates that the acceptability achieves 73.8% on average. The current paper provides strong evidence suggesting that the proposed non-parametric and data-driven approach for air quality forecasting provides promising results.

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  • [1] W. N Deng, PM10 pollution forecast based on BP Neural Network and MATLAB implementation in Xi’an City, Xi’an university of science and technology, 2008.

  • [2] W. Sun, H. Zhang, P. Ahmet et al., Prediction of 24-hour-average PM2.5 concentrations using a hidden Markov model with different emission distributions in northern California, Science of the Total Environment, 443, 2013, 93-103.

  • [3] J. Kukkonen, M. Pohjola, R. S. Sokhi et al., Analysis and evaluation of selected local-scale PM10 air pollution episodes in four European cities: Helsinki, London, Milan and Oslo, Atmospheric environment, 39, 2004, 2759-2773.

  • [4] Y. Zheng, F. Liu and H. P. Hsieh, U-Air: when urban air quality inference meets big data, ACM SIGKDD international conference on knowledge discovery and data mining, 2013, 1436-1444.

  • [5] Z. Cheng, S. Wang, J. Jiang et al., Long-term trend of haze pollution and impact of particulate matter in the Yangtze River Delta, China, Environmental pollution, 182, 2013, 101-110.

  • [6] N. H. Hanafi, M. H. Hassim, and Z. Z. Noor, Overview of Health Impacts due to Haze Pollution in Johor, Malaysia, Journal of Engineering and Technological Sciences, 50, 2018, 818-831.

  • [7] Y. Bian, Z. Huang, J. Ou et al., Evolution of anthropogenic air pollutant emissions in Guangdong Province, China, from 2006 to 2015, Atmospheric Chemistry and Physics, 19, 2019, 11701-11719.

  • [8] A. R. Deacon, R. G. Derwent, R. M. Harrison et al., Analysis and interpretation of measurements of suspended particulate matter at urban background sites in the United Kingdom, Science of the total environment, 203, 1997, 17–36.

  • [9] R. M. Harrison and A. R. Deacon, Spatial correlation of automatic air quality monitoring at urban background sites: implications for network design, Environmental technology, 19, 1998, 121-132.

  • [10] G. Grivas, A. Chaloulakou, C. Samara et al., Spatial and temporal variation of PM10 mass concentrations within the greater area of Athens, Greece, Water air and soil pollution, 158, 2004, 357-371.

  • [11] J. Kukkonen, M. Pohjola, R. S. Sokhi et al., Analysis and evaluation of selected local-scale PM10 air pollution episodes in four European cities: Helsinki, London, Milan and Oslo, Atmospheric environment, 39, 2004, 2759-2773.

  • [12] X. Querol, A. Alastuey, C. R. Ruiz et al., Speciation and origin of PM10 and PM2.5 in selected European cities, Atmospheric environment, 38, 2004, 6547-6555.

  • [13] M. Statheropoulos, N. Vassiliadis and A. Pappa, Principal component and canonical correlation analysis for examining air pollution and meteorological data, Atmospheric environment, 32, 1998, 1087-1095.

  • [14] M. Viana, X. Querol, A. Alastuey et al., PM levels in the Basque Country (Northern Spain): analysis of a 5-year data record and interpretation of seasonal variations, Atmospheric environment, 37, 2003, 2879-2891.

  • [15] S. W. Jia, X. L. Liu and G. Yan, The dynamic analysis of a vehicle pollutant emission reduction management model under economic means, Clean Technologies and Environmental Policy, 21, 2019, 243-256.

  • [16] X. N. Yue, Z. J. Meng and Z. H. Yuan, Multiple regression analysis on causes of urban fog-haze in China-based on data mining, The 27th Chinese Control and Decision Conference (2015 CCDC), 2015, 4408-4413.

  • [17] W. Q. Huang, H. B. Fan and Y. Qian, Modeling and efficient quantified risk assessment of haze causation system in China related to vehicle emissions with uncertainty consideration, Science of the total environment, 668, 2019, 74-83.

  • [18] Y. Luo, T. Mengfan, Y. Kun et al., Research on PM2.5 estimation and prediction method and changing characteristics analysis under long temporal and large spatial scale-A case study in China typical regions, Science of the Total Environment, 696, 2019, 133983.

  • [19] W. Zhuang, J. Fan, Y. Gao et al., Study on prediction model of space-time distribution of air pollutants based on artificial neural network, Environmental Engineering & Management Journal (EEMJ), 18, 2019.

  • [20] X. T. Li and X. D. Zhang, Predicting ground-level PM2.5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach, Environmental pollution, 249, 2019, 735-749.

  • [21] B. B. Zhou, J. Du, I. Gultepe et al., Forecast of low visibility and fog from NCEP: current status and efforts, Pure and applied geophysics, 169, 2012, 895-909.

  • [22] Y. Miao, R. Potts, X. Huang et al., A fuzzy logic fog forecasting model for Perth Airport, Pure and applied geophysics, 169, 2012, 1107-1119.

  • [23] W. Q. Wang and Y. Guo, Air pollution PM2.5 data analysis in Los Angeles long beach with seasonal ARIMA model, 2009 international conference on energy and environment technology, 3, 2009, 7-10.

  • [24] W. G. Cobourn, An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations, Atmospheric Environment, 44, 2010, 3015-3023.

  • [25] H. L. Yu and C. H. Wang, Retrospective prediction of intraurban spatiotemporal distribution of PM2.5 in Taipei, Atmospheric Environment, 44, 2010, 3053-3065.

  • [26] W. Sun, H. Zhang, P. Ahmet et al., Prediction of 24-hour-average PM2.5 concentrations using a hidden Markov model with different emission distributions in northern California, Science of the Total Environment, 443, 2013, 93-103.

  • [27] L. L. Jiang, Y. H. Zhang, G. X. Song et al., A time series analysis of outdoor air pollution and preterm birth in Shanghai, China, Biomedical and Environmental Sciences, 20, 2007, 426.

  • [28] A. Charbel, C. Carine, B. Agnes et al., SO2 in Beirut: air quality implication and effects of local emissions and long-range transport, Air Quality Atmosphere and Health, 1, 2008, 167-178.

  • [29] D. Kang, R. Mathur and S. T. Rao, Real-time bias-adjusted O3 and PM2.5 air quality index forecasts and their performance evaluations over the continental United States, Atmospheric Environment, 44, 2010, 2203-2212.

  • [30] H. Li, S. You, H. Zhang et al., Modelling of AQI related to building space heating energy demand based on big data analytics, Applied Energy, 203, 2017, 57-71.

  • [31] L. D. Monache, T. Nipen, X. X. Deng et al., Ozone ensemble forecasts: 2. A Kalman filter predictor bias correction, Journal of geophysical research-atmospheres, 111, 2006, D05308.

  • [32] S. McKeen, J. Wilczak, G. Grell et al., Assessment of an ensemble of seven real-time ozone forecasts over eastern north America during the summer of 2004, Journal of Geophysical Research: Atmospheres, 110, 2005, D21307.

  • [33] L. Delle Monache, J. Wilczak, S. Mckeen et al., A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone, Tellus Series b-chemical and physical meteorology, 60, 2008, 238-249.

  • [34] J. Wilczak, S. McKeen, I. Djalalova et al., Bias-corrected ensemble and probabilistic forecasts of surface ozone over eastern North America during the summer of 2004, Journal of geophysical research-atmospheres, 111, 2006, D23S28.

  • [35] D. W. Kang, R. Mathur, S. T. Rao et al., Bias adjustment techniques for improving ozone air quality forecasts, Journal of geophysical research-atmospheres, 113, 2008, D23308.

  • [36] Y. Z. Xu, X. M. Fan, Z. Q. Zhang et al., Trade liberalization and haze pollution: Evidence from china, Ecological Indicators, 109, 2020, 105825.

  • [37] P. Z. Li, Y. Wang and Q. L. Dong, The analysis and application of a new hybrid pollutants forecasting model using modified Kolmogorov–Zurbenko filter, Science of The Total Environment, 583, 2017, 228-240.

  • [38] X. Liu, Q. Liu, Y. Zou et al., A LSTM-Based Approach to Haze Prediction Using a Self-organizing Single Hidden Layer Scheme, International Conference on Security with Intelligent Computing and Big-data Services, 2018, 701-706.

  • [39] K. M. K. K. Yusof, A. Azid, M. S. A. Sani et al., The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study, Malaysian Journal of Fundamental and Applied Sciences, 15, 2019, 164-172.

  • [40] J. Z. Levin, A rational parametric approach to latitude, longitude and altitude, Navigation, 35, 1988, 361–370.

  • [41] H. Mahmoud and N. Akkari, Shortest path calculation: a comparative study for location-based recommender system, 2016 world symposium on computer applications & research (WSCAR), 2016, 1-5.

  • [42]


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