Spatial-temporal prediction of air quality by deep learning and kriging interpolation approach


  • K.Krishna Rani Samal Samal Vellore Institute of Technology University image/svg+xml
  • Korra Sathya Babu Indian Institute of Information Technology Design and Manufacturing image/svg+xml
  • Santos Kumar Das National Institute of Technology Rourkela image/svg+xml



Deep learning, Transfer learning, Ordinary kriging, PM10


Air quality level is closely associated with our day-to-day life due to its serious negative impact on human health. Air pollution monitoring is one of the major steps of air pollution control and prevention. However, limited air pollution monitoring sites make it difficult to measure each corner of a region's pollution level. This research work proposes a methodology framework incorporating a deep learning network, namely CNN-BIGRU-ANN and geostatistical Ordinary Kriging Interpolation model, to address this research gap. The proposed CNN-BIGRU-ANN time series prediction model predicts the $P{M_{10}}$ pollutant level for existing monitoring sites. Each monitoring site's predicted output is transferred as input to the geostatistical Ordinary Kriging interpolation layer to generate the entire region's spatial-temporal interpolation prediction map. The experimental results show the effectiveness of the proposed method in regional control of air pollution.


Shahbazi, H., Taghvaee, S., Hosseini, V. and Afshin, H. (2016) A gis based emission inventory development for tehran. Urban Climate 17: 216–229.

Saunders, B.M., Smith, J.D., Smith, T.E., Green, D.C. and Barratt, B. (2019) Spatial variability of fine particulate matter pollution (pm2. 5) on the london underground network. Urban Climate 30: 100535.

Ma, J., Ding, Y., Cheng, J.C., Jiang, F., Gan, V.J. and Xu, Z. (2020) A lag-flstm deep learning network based on bayesian optimization for multi-sequential-variant pm2. 5 prediction. Sustainable Cities and Society 60: 102237.

Gogikar, P., Tyagi, B. and Gorai, A. (2019) Seasonal prediction of particulate matter over the steel city of india using neural network models. Modeling Earth Systems and Environment 5(1): 227–243.

Soni, K., Parmar, K.S. and Agrawal, S. (2017) Modeling of air pollution in residential and industrial sites by integrating statistical and daubechies wavelet (level 5) analysis. Modeling Earth Systems and Environment 3(3): 1187–1198.

Nwosisi, M.C., Oguntoke, O. and Taiwo, A.M. (2020) Dispersion and emission patterns of no 2 from gas flaring stations in the niger delta, nigeria. Modeling Earth Systems and Environment 6(1): 73–84.

Shams, S.R., Jahani, A., Moeinaddini, M. and Khorasani, N. (2020) Air carbon monoxide forecasting using an artificial neural network in comparison with multiple regression. Modeling Earth Systems and Environment 6(3): 1467–1475.

Chiang, Y.C., Li, X., Lee, C.Y., Rui, J., Hu, C.W., Yang, H.J., Chen, S.C. et al. (2020) Protective equipment and health education program could benefit students from dust pollution. Air Quality, Atmosphere & Health : 1–10.

Khosravi, T., Hadei, M., Hopke, P.K., Namvar, Z., Shahsavani, A., Nazari, S.S.H., Querol, X. et al. (2020) Association of short-term exposure to air pollution with mortality in a middle eastern tourist city. Air Quality, Atmosphere & Health : 1–12.

Javan, S., Rahdar, S., Miri, M., Djahed, B., Kazemian, H., Fakhri, Y., Eslami, H. et al. (2020) Modeling of the pm 10 pollutant health effects in a semi-arid area: a case study in zabol, iran. Modeling Earth Systems and Environment : 1–9.

Devi, N.L., Kumar, A. and Yadav, I.C. (2020) Pm10 and pm2. 5 in indo-gangetic plain (igp) of india: Chemical characterization, source analysis, and transport pathways. Urban Climate 33: 100663.

Sundar, S. and Naresh, R. (2017) Modeling the effect of dust pollutants on plant biomass and their abatement from the near earth atmosphere. Modeling Earth Systems and Environment 3(1): 42.

Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C. and Baklanov, A. (2012) Real-time air quality forecasting, part i: History, techniques, and current status. Atmospheric Environment 60: 632–655.

Ma, J., Cheng, J.C., Lin, C., Tan, Y. and Zhang, J. (2019) Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques. Atmospheric Environment 214: 116885.

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

Xu, X. and Yoneda, M. (2019) Multitask air-quality prediction based on lstm-autoencoder model. IEEE transactions on cybernetics .

Mao, W., Wang, W., Jiao, L., Zhao, S. and Liu, A. (2020) Modeling air quality prediction using a deep learning approach: Method optimization and evaluation. Sustainable Cities and Society : 102567.

Bai, Y., Li, Y., Zeng, B., Li, C. and Zhang, J. (2019) Hourly pm2. 5 concentration forecast using stacked autoencoder model with emphasis on seasonality. Journal of Cleaner Production 224: 739–750.

Landolsi, J., Rehimi, F. and Kalboussi, A. (2017) Urban traffic and induced air quality modeling and simulation: Methodology and illustrative example. Urban Climate 21: 154–172.

Zhang, B., Zhang, H., Zhao, G. and Lian, J. (2020) Constructing a pm2. 5 concentration prediction model by combining auto-encoder with bi-lstm neural networks. Environmental Modelling & Software 124: 104600.

Daneshvar, M.R.M. and Abadi, N.H. (2017) Spatial and temporal variation of nitrogen dioxide measurement in the middle east within 2005–2014. Modeling Earth Systems and Environment 3(1): 20.

Gholizadeh, A., Neshat, A.A., Conti, G.O., Ghaffari, H.R., Aval, H.E., Almodarresi, S.A., Aval, M.Y. et al. (2019) Pm 2.5 concentration modeling and mapping in the urban areas. Modeling Earth Systems and Environment 5(3): 897–906.

Wang, W. and Guo, Y. (2009) Air pollution pm2. 5 data analysis in los angeles long beach with seasonal arima model. In 2009 International Conference on Energy and Environment Technology (IEEE), 3: 7–10.

Lee, N.U., Shim, J.S., Ju, Y.W. and Park, S.C. (2018) Design and implementation of the sarima–svm time series analysis algorithm for the improvement of atmospheric environment forecast accuracy. Soft Computing 22(13): 4275–4281.

Shahri, A.A., Larsson, S. and Renkel, C. (2020) Artificial intelligence models to generate visualized bedrock level: a case study in sweden. Model Earth Syst Environ .

Isiyaka, H.A., Mustapha, A., Juahir, H. and Phil-Eze, P. (2019) Water quality modelling using artificial neural network and multivariate statistical techniques. Modeling Earth Systems and Environment 5(2): 583–593.

Ibrir, A., Kerchich, Y., Hadidi, N., Merabet, H. and Hentabli, M. (2020) Prediction of the concentrations of pm1, pm2. 5, pm4, and pm10 by using the hybrid dragonfly-svm algorithm. Air Quality, Atmosphere & Health : 1–11.

Yeganeh, B.,Motlagh, M.S.P., Rashidi, Y. and Kamalan, H. (2012) Prediction of co concentrations based on a hybrid partial least square and support vector machine model. Atmospheric Environment 55: 357–365.

Rajendra, P., Murthy, K., Subbarao, A. and Boadh, R. (2019) Use of ann models in the prediction of meteorological data. Modeling Earth Systems and Environment 5(3): 1051–1058.

Wang, Z. and Long, Z. (2018) Pm2. 5 prediction based on neural network. In 2018 11th International Conference on Intelligent Computation Technology and Automation (ICICTA) (IEEE): 44–47.

Athira, V., Geetha, P., Vinayakumar, R. and Soman, K. (2018) Deepairnet: Applying recurrent networks for air quality prediction. Procedia computer science 132: 1394– 1403.

Cabaneros, S.M., Calautit, J.K. and Hughes, B. (2020) Spatial estimation of outdoor no2 levels in central london using deep neural networks and a wavelet decomposition technique. Ecological Modelling 424: 109017.

Fu, R., Zhang, Z. and Li, L. (2016) Using lstm and gru neural network methods for traffic flow prediction. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC) (IEEE): 324–328.

Maggiolo, M. and Spanakis, G. (2019) Autoregressive convolutional recurrent neural network for univariate and multivariate time series prediction. arXiv preprint arXiv:1903.02540 .

Rao, K.S., Devi, G.L. and Ramesh, N. (2019) Air quality prediction in visakhapatnam with lstm based recurrent neural networks. International Journal of Intelligent Systems and Applications 11(2): 18.

Wang, B., Kong, W. and Guan, H. (2019) Air quality forcasting based on gated recurrent long short-term memory model. In Proceedings of the ACM Turing Celebration Conference-China (ACM): 128.

Kim, J. and Moon, N. (2019) Bilstm model based on multivariate time series data in multiple field for forecasting trading area. Journal of Ambient Intelligence and Humanized Computing : 1–10.

Wang, Z., Sugaya, S. and Nguyen, D.P. (2019) Salary prediction using bidirectional-gru-cnn model .

Ge, L., Zhou, A., Li, H. and Liu, J. (2019) Spatially finegrained air quality prediction based on dbu-lstm. In Proceedings of the 16th ACM International Conference on Computing Frontiers (ACM): 202–205.

Huang, C.J. and Kuo, P.H. (2018) A deep cnn-lstm model for particulate matter (pm2. 5) forecasting in smart cities. Sensors 18(7): 2220.

Shi, X. and Yeung, D.Y. (2018) Machine learning for spatiotemporal sequence forecasting: A survey. arXiv preprint arXiv:1808.06865 .

Sun, X., Xu, W. and Jiang, H. (2019) Spatial-temporal prediction of air quality based on recurrent neural networks. In Proceedings of the 52nd Hawaii International Conference on System Sciences.

Xie, H., Ji, L., Wang, Q. and Jia, Z. (2019) Research of pm2. 5 prediction system based on cnns-gru in wuxi urban area. In IOP Conference Series: Earth and Environmental Science (IOP Publishing), 300: 032073.

Lin, Y., Mago, N., Gao, Y., Li, Y., Chiang, Y.Y., Shahabi, C. and Ambite, J.L. (2018) Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning. In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM): 359–368.

Guttikunda, S.K., Nishadh, K. and Jawahar, P. (2019) Air pollution knowledge assessments (apna) for 20 indian cities. Urban Climate 27: 124–141.

Samal, K., Babu, K.S., Das, S.K. and Acharaya, A. (2019) Time series based air pollution forecasting using sarima and prophet model. In Proceedings of the 2019 International Conference on Information Technology and Computer Communications (ACM): 80–85.

GovernmentofIndia (2017), Ambient air quality data of odisha. URL ambient-air-quality-data-odisha/.

Mosaffaei, Z. and Jahani, A. (2020) Modeling of ash (fraxinus excelsior) bark thickness in urban forests using artificial neural network (ann) and regression models. Modeling Earth Systems and Environment : 1–10.

Singh, D. and Singh, B. (2020) Investigating the impact of data normalization on classification performance. Applied Soft Computing 97: 105524.

Tao, Q., Liu, F., Li, Y. and Sidorov, D. (2019) Air pollution forecasting using a deep learning model based on 1d convnets and bidirectional gru. IEEE Access 7: 76690–76698.

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. and Bengio, Y. (2014) Learning phrase representations using rnn encoderdecoder for statistical machine translation. arXiv preprint arXiv:1406.1078 .

Zhou, X., Xu, J., Zeng, P. and Meng, X. (2019) Air pollutant concentration prediction based on gru method. In Journal of Physics: Conference Series (IOP Publishing), 1168: 032058.

Wang, B., Kong, W., Guan, H. and Xiong, N.N. (2019) Air quality forcasting based on gated recurrent long short term memory model in internet of things. IEEE Access .

Ma, J., Ding, Y., Cheng, J.C., Jiang, F. andWan, Z. (2019) A temporal-spatial interpolation and extrapolation method based on geographic long short-term memory neural network for pm2. 5. Journal of Cleaner Production 237: 117729.

Feng, X., Li, Q., Zhu, Y., Hou, J. and Wang, J. (2015) An estimate of population exposure to automobile source pm 2.5 in beijing using spatiotemporal analysis. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (IEEE): 3029–3032.

Kostopoulou, E. (2020) Applicability of ordinary kriging modeling techniques for filling satellite data gaps in support of coastal management. Modeling Earth Systems and Environment : 1–14.

Mokarram, M., Najafi-Ghiri, M., Negahban, S. and Roshan, G. (2016) Relationship between landform and soil salinity in the surface and subsurface soils (case study: Southeast of fars province, iran). Modeling Earth Systems and Environment 2(1): 16.

Tamburi, V., Shetty, A. and Shrihari, S. (2020) Characterization of spatial variability of vertisol micronutrients by geostatistical techniques in deccan plateau of india. Modeling Earth Systems and Environment 6(1): 173–182.

Calderón, G.F.A. (2009) Spatial regression analysis vs. kriging methods for spatial estimation. International Advances in Economic Research 15(1): 44–58.

MinistryofRoadTransportandHighways (2018), Category-wise number of newly registered and total registered motor vehicles in odisha during 2015-16. URL

Shahriar, S.A., Kayes, I., Hasan, K., Salam, M.A. and Chowdhury, S. (2020) Applicability of machine learning in modeling of atmospheric particle pollution in bangladesh. Air Quality, Atmosphere & Health : 1–10.

Pessanha, M.S., dos Santos, L.M., Lyra, G.B., Lima, A.O., Lyra, G.B. and de Souza, J.L. (2020) Interpolation methods applied to the spatialisation of monthly solar irradiation in a region of complex terrain in the state of rio de janeiro in the southeast of brazil. Modeling Earth Systems and Environment : 1–14.

Seyedmohammadi, J., Esmaeelnejad, L. and Shabanpour, M. (2016) Spatial variation modelling of groundwater electrical conductivity using geostatistics and gis. Modeling earth systems and environment 2(4): 1–10.

Biswas, R.N., Islam, M.N. and Islam, M.N. (2018) Modeling on management strategies for spatial assessment of earthquake disaster vulnerability in bangladesh. Modeling Earth Systems and Environment 4(4): 1377–1401.

Pathak, A.A. and Dodamani, B. (2020) Trend analysis of rainfall, rainy days and drought: a case study of ghataprabha river basin, india. Modeling Earth Systems and Environment 6(3): 1357–1372.




How to Cite

Samal KRS, Babu KS, Das SK. Spatial-temporal prediction of air quality by deep learning and kriging interpolation approach. EAI Endorsed Scal Inf Syst [Internet]. 2023 Aug. 7 [cited 2024 Jul. 22];10(5). Available from: