Proposing and investigating PCAMARS as a novel model for NO2 interpolation

Research output: Contribution to journalArticle


Effective measurement of exposure to air pollution, not least NO2, for epidemiological studies along with the need to better management and control of air pollution in urban areas ask for precise interpolation and determination of the concentration of pollutants in nonmonitored spots. A variety of approaches have been developed and used. This paper aims to propose, develop, and test a spatial predictive model based on multivariate adaptive regression splines (MARS) and principle component analysis (PCA) to determine the concentration of NO2 in Tehran, as a case study. To increase the accuracy of the model, spatial data (population, road network and point of interests such as petroleum stations and green spaces) and meteorological data (including temperature, pressure, wind speed and relative humidity) have also been used as independent variables, alongside air quality measurement data gathered by the monitoring stations. The outputs of the proposed model are evaluated against reference interpolation techniques including inverse distance weighting, thin plate splines, kriging, cokriging, and MARS3. Interpolation for 12 months showed better accuracies of the proposed model in comparison with the reference methods.


External organisations
  • K. N. Toosi University of Technology
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Geosciences, Multidisciplinary
  • Meteorology and Atmospheric Sciences
  • Environmental Sciences
Original languageEnglish
Number of pages12
JournalEnvironmental Monitoring and Assessment
Issue number3
Publication statusPublished - 2019 Mar
Publication categoryResearch