Proposing and investigating PCAMARS as a novel model for NO2 interpolation

Summary, in English

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.

• Author: Mohsen Yousefzadeh; Mahdi Farnaghi; Petter Pilesjö; Ali Mansourian
• Department/s: Dept of Physical Geography and Ecosystem Science; Centre for Geographical Information Systems (GIS Centre); Centre for Advanced Middle Eastern Studies (CMES)
• Publishing year: 2019-03
• Language: English
• Publication/Series: Environmental Monitoring and Assessment
• Volume: 191
• Issue: 3
• Links: http://dx.doi.org/10.1007/s10661-019-7253-2
• Document type: Journal article
• Publisher: Springer
• Topic: Multidisciplinary Geosciences; Meteorology and Atmospheric Sciences; Environmental Sciences
• Status: Published
• ISBN/ISSN/Other: ISSN: 1573-2959