Introduction:

Air pollution is an environmental problem caused by anthropogenic activities. One of the pollutants with the greatest impact on health is ozone, derived from nitrogen oxides (NOx) and volatile organic compounds that react with solar radiation to form tropospheric ozone. These oxidation processes in atmospheric chemistry are known as biogenic ozone precursors (biogenic Volatile Organic Compounds, VOCs) affecting the global carbon balance where forest fires are considered emitters of carbon dioxide and NOx (Radke et al. 1991). Plant tissues contain organic compounds that emitted in sufficient quantities can influence atmospheric chemistry (Main, 2003). One of the geospatial tools of geomatics that allows modeling and analysis of the distribution of pollutants in the air is the application of geostatistics through interpolation. The purpose of this analysis is to represent the spatial patterns of O3 concentrations by estimating values in non-sampled areas.

Methods:

Spatial interpolation stands out as a technique for the evaluation of atmospheric pollution, which makes it possible to identify areas exposed to risk levels of some pollutant. Regression and interpolation analysis, such as Kriging, allow the prediction of ozone in non-sampled areas of the city of Durango. It also allows the identification of population strata in urban areas and pollution hotspots, and it is also possible to evaluate the degree of exposure to ozone levels.

Results:

The hourly average per month calculated at the three stations allowed determine the period in which the O3 concentration was at its maximum and minimum. With the interpolations performed with the ordinary Kriging method and by means of map algebra, the zone of influence of the pollutant was determined. There are higher concentrations in the western part of the city; this coincides with other studies where O3 levels were higher in the outskirts than in the central area, due to the vegetation, which provides O3 precursors. The zone of influence is distributed in areas where the average value of 0.032 ppm is exceeded from two to 11 times in 24 hours.

Conclusion:

The analysis of the diurnal ozone cycle showed higher concentrations in May. The ordinary Kriging method performed an acceptable prediction according to the prediction errors presented in other research, considering the number of stations to determine the zone of influence where the average tropospheric ozone concentration was exceeded. Ozone showed a positive correlation with the temperature variable in the central and northwest zone, while it presented a positive and lower correlation in the west zone. This relationship indicates that O3 levels in the city depend significantly on temperature.