Imputation of spatial air quality data using gis-spline and the index of agreement in sparse urban monitoring networks

Keywords: Small area estimation and handling missing spatial data, spatial interpolation spline, index of agreement, spatial models, air pollution

Abstract

This paper presents a procedure to address the lack of spatial air quality data in urban areas, based on the use of Geographic Information Systems (GIS) and spatial interpolation techniques as an alternative to conventional methods of statistical imputation. Two spatial interpolation algorithms are compared: IDW and spline. The procedure considers the spatial interpolation process, the cross validation with the index of agreement (IOA), and the analysis of the effect of sampling density and the coeffi cient of variation (CVOi ), using different error statistics. The interpolation maps are complemented with gradient and directional gradient maps that may serve as complementary aides in the defi nition of critical sampling points. The procedure is applied to data imputation of three pollutants NO2 , PM10 (particulate matter of diameter 10 microns) and TSP (total suspended solids) from observed data samples in the city of Medellín (Colombia).

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Author Biographies

Libardo Antonio Londoño-Ciro, Universidad de Antioquia

Grupo de investigación en Gestión y Modelación Ambiental (GAIA), Facultad de Ingeniería

Julio Eduardo Cañón-Barriga, Universidad de Antioquia

Grupo de investigación en Gestión y Modelación Ambiental (GAIA), Facultad de Ingeniería, docente

References

O. Leal, M. Mendoza, E. Carranza. “Análisis y modelamiento espacial de información climática en la cuenca de Cuitzeo, México”. Invest. Geog. no. 72. 2010. pp. 49-67.

J. Gómez, J. Etchevers, A. Monterroso, C. Gay, J. Campo, M. Martínez. “Spatial estimation of mean temperature and precipitation in areas of scarce meteorological information”. Atmósfera. Vol. 21. 2008. pp. 35-56.

L. Qu, L. Li, Y. Zhang, J. Hu. “PPCA-based missing data imputation for traffic flow volume: A systematical approach”. IEEE Transactions on Intelligent Transportation Systems. Vol. 10. 2009. pp. 512-522.

K. Grønskei, S. Walker, F. Gram. “Evaluation of a model for hourly spatial concentration distributions”. Atmospheric Environment. Part B. Urban Atmosphere. Vol. 27. 1993. pp. 105-120.

M. Rooney, R. Arku, K. Dionisio, C. Paciorek, A. Friedman, H. Carmichael, et al. “Spatial and temporal patterns of particulate matter sources and pollution in four communities in Accra, Ghana”. Science of the Total Environment. Vol. 435-436. 2012. pp. 107-114.

M. Albert, M. Schaap, A. Manders, C. Scannell, C. O’Dowd, G. Leeuw. “Uncertainties in the determination of global sub-micron marine organic matter emissions”. Atmospheric Environment. Vol. 57. 2012. pp. 289-300.

M. Bechle, D. Millet, J. Marshall. “Remote sensing of exposure to NO2 : satellite versus ground based measurement in a large urban area”. Atmospheric Environment. Vol. 69. 2013. pp. 345-353.

M. Žukovič, D. Hristopulos. “Environmental time series interpolation based on Spartan random processes”. Atmospheric Environment. Vol. 42. 2008. pp. 7669-7678.

A. Pollice, G. Jona. “Two Approaches to Imputation and Adjustment of Air Quality Data from a Composite Monitoring Network”. Journal of Data Science. Vol. 7. 2009. pp. 43-59.

C. Willmott, S. Ackleson, R. Davis, J. Feddema, K. Klink, D. Legates, et al. “Statistics for the Evaluation and Comparison of Models”. J. Geophys. Res. Vol. 90. 1985. pp. 8995-9005.

C. Willmott, S. Robeson, K. Matsuura. “A refined index of model performance”. International Journal of Climatology. Vol. 32. 2012. pp. 2088-2094.

J. Urrutia, R. Palomino, H. Salazar. “Metodología para la imputación de datos faltantes en Meteorología”. Scientia et Technica. no. 46. 2010. pp. 44-49.

D. Deligiorgi, K. Philippopoulos. Spatial Interpolation Methodologies in Urban Air Pollution Modeling: Application for the Greater Area of Metropolitan Athens, Greece. 2011. Available on: http://cdn.intechopen.com/pdfs-wm/17390.pdf. Accessed: June 01, 2014.

Ü. Şahin, C. Bayat, O. Uçan. “Application of cellular neural network (CNN) to the prediction of missing air pollutant data”. Atmospheric Research. Vol. 101. 2011. pp. 314-326.

B. Huang, B. Wu, M. Barry. “Geographically and temporally weighted regression for modeling spatiotemporal variation in house prices”. International Journal of Geographical Information Science. Vol. 24. 2010. pp. 383-401.

W. Tobler. “A computer movie simulating urban growth in the Detroit region”. Economic Geography. Vol. 46. 1970. pp. 234-240.

P. Kang. “Locally linear reconstruction based missing value imputation for supervised learning”. Neurocomputing. Vol. 118. 2013. pp. 65-78.

R. Bilonick. “Risk qualified maps of hydrogen ion concentration for the New York state area for 1966- 1978”. Atmos. Environ. Vol. 17. 1983. pp. 2513-2524.

R. Sivacoumar, K. Thanasekaran. “Line source model for vehicular pollution prediction near roadways and model evaluation through statistical analysis”. Environmental Pollution. Vol. 104. 1999. pp. 389-395.

G. Polydoras, J. Anagnostopoulos, G. Bergeles. “Air quality predictions: dispersion model vs. Box–Jenkins stochastic models. An implementation and comparison for Athens, Greece”. Applied Thermal Engineering. Vol. 18. 1998. pp. 1037-1048.

M. Lorber, A. Eschenroeder, R. Robinson. “Testing the USA EPA’s ISCST-Version 3 model on dioxins: a comparison of predicted and observed air and soil concentrations”. Atmospheric Environment. Vol. 34. 2000. pp. 3995-4010.

A. Kousa, J. Kukkonen, A. Karppinen, P. Aarnio, T. Koskentalo. “Statistical and diagnostic evaluation of a newgeneration urban dispersion modeling system against an extensive dataset in the Helsinki area”. Atmospheric Environment. Vol. 35. 2001. pp. 4617-4628.

D. Rojas. “Spatial interpolation techniques for estimating levels of pollutant concentrations in the atmosphere”. Rev. mex. de física. Vol. 53. 2007. pp. 447- 454.

D. Ibarra. “Distribución espacial del pH de los suelos agrícolas de Zapopan, Jalisco, México”. Agric. Téc. Méx. Vol. 35. 2009. pp. 267-276.

Y. Xie, T. Chen, M. Lei, J. Yang, Q. Guo, B. Song, X. Zhou. “Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis”. Chemosphere. Vol. 82. 2011. pp. 468-476.

E. Jabot, I. Zin, T. Lebel, A. Gautheron, C. Obled. “Spatial interpolation of sub-daily air temperatures for snow and hydrologic applications in mesoscale Alpine catchments”. Hydrological Processes. Vol. 26. 2012. pp. 2618-2630.

K. Stahl, R. Moore, J. Floyer, M. Asplin, I. McKendry. “Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density”. Agricultural and Forest Meteorology. Vol. 139. 2006. pp. 224-236.

J. Li, A. Heap. “A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors”. Ecological Informatics. Vol. 6. 2011. pp. 228-241.

J. Li, A. Heap. “Spatial interpolation methods applied in the environmental sciences: A review”. Environmental Modelling & Software. Vol. 53. 2014. pp. 173-189

Published
2015-09-25
How to Cite
Londoño-Ciro L. A., & Cañón-Barriga J. E. (2015). Imputation of spatial air quality data using gis-spline and the index of agreement in sparse urban monitoring networks. Revista Facultad De Ingeniería Universidad De Antioquia, (76), 73-81. https://doi.org/10.17533/udea.redin.n76a09