Environmental Science and Pollution Research

, Volume 26, Issue 2, pp 1902–1910 | Cite as

PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models

  • Hone-Jay Chu
  • Muhammad BilalEmail author
Research Article


An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter (PM2.5) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and PM2.5 data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed PM2.5 and AOD data, were used for mapping of PM2.5 over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution (DT3K) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product (DTB3K) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-PM2.5 with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of PM2.5 from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after PM2.5 mapping. The hotspot and spatial variability of PM2.5 maps can give us a summary of the spatiotemporal patterns of PM2.5 variations.





The authors would like to acknowledge NASA Goddard Space Flight Center for MODIS data. We are thankful to Devin White (Oak Ridge National Laboratory) for MODIS Conversion Tool Kit (MCTK).

Funding information

This research was jointly supported by the National Key Research and Development Program of China (No. 2016YFC1400901), the Startup Foundation for Introduction Talent of NUIST (2017r107), and the MOST and EPA-Taiwan (MOST 105-EPA-F-007-004).


  1. Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27(2):93–115CrossRefGoogle Scholar
  2. Bai Y, Wu L, Qin K, Zhang Y, Shen Y, Zhou Y (2016) A geographically and temporally weighted regression model for ground-level PM2. 5 estimation from satellite-derived 500 m resolution AOD. Remote Sens 8(3):262CrossRefGoogle Scholar
  3. Bilal M, Nichol JE (2015) Evaluation of MODIS aerosol retrieval algorithms over the Beijing–Tianjin–Hebei region during low to very high pollution events. J Geophysic Res-Atmos 120:7941–7957. CrossRefGoogle Scholar
  4. Bilal M, Nichol JE, Nazeer M (2016) Validation of aqua–MODIS C051 and C006 operational aerosol products using AERONET measurements over Pakistan. IEEE JSTARS 9(5):2074–2080. Google Scholar
  5. Bilal M, Nichol JE, Scott NS (2017a) A new approach for estimation of fine particulate concentrations using satellite aerosol optical depth and binning of meteorological variables. Aerosol Air Qual Res 17:356–367. CrossRefGoogle Scholar
  6. Bilal M, Nazeer M, Nichol JE (2017b) Validation of MODIS and VIIRS derived aerosol optical depth over complex coastal waters. Atmos Res 186:43–50. CrossRefGoogle Scholar
  7. Bilal M, Nichol JE, Wang L (2017c) New customized methods for improvement of the MODIS C6 dark target and deep blue merged aerosol product. Remote Sens Environ 197:115–124. CrossRefGoogle Scholar
  8. Bilal, M, Nazeer, M., Qiu, Z., Ding, X., and Wei, J. (2018a) Global validation of MODIS C6 and C6.1 merged aerosol products over diverse vegetated surfaces. Remote Sens, 10 doi:
  9. Bilal, M, Qiu, Z., Campbell, J.R., Scott, S., Shen, J., and Nazeer, M (2018b). A New MODIS C6 Dark Target and Deep Blue Merged Aerosol Product at 3 km Spatial Resolution. Remote Sens 10 doi:
  10. Cheng MT, Chou WC, Chio CP, Hsu SC, Su YR, Kuo PH, Tsuang BJ, Lin SH, Chou CCK (2008) Compositions and source apportionments of atmospheric aerosol during Asian dust storm and local pollution in Central Taiwan. J Atmos Chem 61(2):155–173CrossRefGoogle Scholar
  11. Chu HJ, Yu HL, Kuo YM (2012) Identifying spatial mixture distributions of PM2.5 and PM10 in Taiwan during and after a dust storm. Atmos Environ 54:728–737CrossRefGoogle Scholar
  12. Chu Y, Liu Y, Li X, Liu Z, Lu H, Lu Y, Liu F (2016) A review on predicting ground PM2. 5 concentration using satellite aerosol optical depth. Atmosphere 7(10):129CrossRefGoogle Scholar
  13. Chu HJ, Kong SJ, Chang CH (2018) Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression. Int J Appl Earth Obs Geoinf 65:1–11CrossRefGoogle Scholar
  14. Elahi E, Abid M, Zhang L, Haq SU, Sahito JGM (2018a) Agricultural advisory and financial services; farm level access, outreach and impact in a mixed cropping district of Punjab, Pakistan. Land Use Policy 71:249–260CrossRefGoogle Scholar
  15. Elahi E, Abid M, Zhang H, Cui W, Hasson SU (2018b) Domestic water buffaloes: access to surface water, disease prevalence and associated economic losses. Prev Vet Med.
  16. Fischler, M. A., & Bolles, R. C. (1987). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In Readings in computer vision (pp. 726–740)Google Scholar
  17. Fotheringham AS, Crespo R, Yao J (2015) Geographical and temporal weighted regression (GTWR). Geogr Anal 47(4):431–452CrossRefGoogle Scholar
  18. Guo Y, Tang Q, Gong DY, Zhang Z (2017) Estimating ground-level PM2. 5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model. Remote Sens Environ 198:140–149CrossRefGoogle Scholar
  19. Hannachi A, Jolliffe IT, Stephenson DB (2007) Empirical orthogonal functions and related techniques in atmospheric science: A review. Int J Climatol J R Meteorol Soc 27(9):1119–1152CrossRefGoogle Scholar
  20. He Q, Huang B (2018) Satellite-based mapping of daily high-resolution ground PM 2.5 in China via space-time regression modeling. Remote Sens Environ 206:72–83CrossRefGoogle Scholar
  21. He L, Wang L, Lin A, Zhang M, Bilal M, Wei J (2018) Performance of the NPP-VIIRS and aqua-MODIS aerosol optical depth products over the Yangtze River basin. Remote Sens 10(1):117CrossRefGoogle Scholar
  22. Hu X, Waller LA, Lyapustin A, Wang Y, Al-Hamdan MZ, Crosson WL, Liu Y (2014) Estimating ground-level PM2. 5 concentrations in the southeastern United States using MAIAC AOD retrievals and a two-stage model. Remote Sens Environ 140:220–232CrossRefGoogle Scholar
  23. Hsu NC, Jeong M-J, Bettenhausen C, Sayer AM, Hansell R, Seftor CS et al (2013) Enhanced Deep Blue aerosol retrieval algorithm: The second generation. J Geophys Res Atmos 118:9296–9315CrossRefGoogle Scholar
  24. Huang B, Wu B, Barry M (2010) Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. Int J Geogr Inf Sci 24(3):383–401CrossRefGoogle Scholar
  25. Jiang M, Sun W, Yang G, Zhang D (2017) Modelling seasonal GWR of daily PM2. 5 with proper auxiliary variables for the Yangtze River Delta. Remote Sens 9(4):346CrossRefGoogle Scholar
  26. Kloog I, Sorek-Hamer M, Lyapustin A, Coull B, Wang Y, Just AC, Broday DM (2015) Estimating daily PM2. 5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data. Atmos Environ 122:409–416CrossRefGoogle Scholar
  27. Kumar N (2010) What can affect AOD–PM2.5 association? Environ Health Perspect 118(3):A109CrossRefGoogle Scholar
  28. Levy RC, Mattoo S, Munchak LA, Remer LA, Sayer AM, Patadia F et al (2013) The Collection 6 MODIS aerosol products over land and ocean. Atmos Meas Tech 6:2989–3034CrossRefGoogle Scholar
  29. Li J, Carlson BE, Lacis AA (2015) How well do satellite AOD observations represent the spatial and temporal variability of PM2. 5 concentration for the United States? Atmos Environ 102:260–273CrossRefGoogle Scholar
  30. Lin C, Li Y, Yuan Z, Lau AK, Li C, Fung JC (2015) Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM 2.5. Remote Sens Environ 156:117–128CrossRefGoogle Scholar
  31. Nichol EJ, Bilal M (2016) Validation of MODIS 3 km Resolution Aerosol Optical Depth Retrievals Over Asia. Remote Sens 8:328CrossRefGoogle Scholar
  32. Qin W, Wang L, Lin A, Zhang M, Bilal M (2018) Improving the estimation of daily aerosol optical depth and aerosol Radiative effect using an optimized artificial neural network. Remote Sens 10(7):1022CrossRefGoogle Scholar
  33. Remer LA, Mattoo S, Levy RC, Munchak LA (2013) MODIS 3 km aerosol product: Algorithm and global perspective. Atmos Meas Tech 6:1829–1844CrossRefGoogle Scholar
  34. Sayer AM, Munchak LA, Hsu NC, Levy RC, Bettenhausen C, Jeong MJ (2014) MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations. J Geophys Res Atmos 119:13965–13989CrossRefGoogle Scholar
  35. Toth TD, Zhang J, Campbell JR, Hyer EJ, Reid JS, Shi Y, Westphal DL (2014) Impact of data quality and surface-to-column representativeness on the PM2.5/satellite AOD relationship for the contiguous United States. Atmos Chem Phys 14(12):6049–6062CrossRefGoogle Scholar
  36. Tsai YI, Chen CL (2006) Atmospheric aerosol composition and source apportionments to aerosol in southern Taiwan. Atmos Environ 40:4751–4763CrossRefGoogle Scholar
  37. Xin, J., Gong, C., Liu, Z., Cong, Z., Gao, W., Song, T., ... & Tang, G. (2016). The observation-based relationships between PM2. 5 and AOD over China. J Geophys Res Atmos, 121(18):10,701–10,716Google Scholar
  38. Yang, Q., Yuan, Q., Yue, L., Li, T., Shen, H., & Zhang, L. (2018). The relationships between PM2. 5 and AOD in China: About and behind spatiotemporal variations. arXiv preprint arXiv:1808.05729Google Scholar
  39. Yu HL, Chu HJ (2010) Understanding space–time patterns of groundwater system by empirical orthogonal functions: a case study in the Choshui River alluvial fan, Taiwan. J Hydrol 381(3–4):239–247CrossRefGoogle Scholar
  40. Zhang C, Luo L, Xu W, Ledwith V (2008) Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Sci Total Environ 398(1-3):212–221CrossRefGoogle Scholar
  41. Zhang M, Ma Y, Gong W, Wang L, Xia X, Che H, Hu B, Liu B (2017a) Aerosol radiative effect in UV, VIS, NIR, and SW spectra under haze and high-humidity urban conditions. Atmos Environ 166:9–21CrossRefGoogle Scholar
  42. Zhang T, Zeng C, Gong W, Wang L, Sun K, Shen H, Zhu Z, Zhu Z (2017b) Improving spatial coverage for aqua MODIS AOD using NDVI-based multi-temporal regression analysis. Remote Sens 9(4):340CrossRefGoogle Scholar
  43. Zou B, Qiang P, Bilal M, Qihao W, Liang Z, Nichol J (2016) High–resolution satellite mapping of fine particulates based on geographically weighted regression. IEEE Geosci Remote Sens Lett 13(4):495–499CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of GeomaticsNational Cheng Kung UniversityTainan CityTaiwan
  2. 2.School of Marine SciencesNanjing University of Information Science and TechnologyNanjingChina

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