Air pollution in urban centers comes from anthropogenic activities. Tropospheric ozone (O₃) depends on chemical precursors that promote an increase in its production, mainly in wind-dominated and large green areas. It is a gas produced by a series of complex chemical reactions catalyzed by sunlight in the atmosphere. It can be concentrated to a greater or lesser extent depending on factors such as the amount of volatile organic compounds (VOCs), the amount of nitrogen dioxide (NO₂), the intensity of solar radiation, or by climatic conditions such as temperature and other factors. The objective of this study was to predict tropospheric ozone levels from Land Surface Temperature (LST) data of Landsat 8 in the city of Durango, Dgo. Tropospheric O₃ and LST values were obtained from 14 sampling points in the urban area of the city of Durango, of which 11 were obtained by collecting from temperature-monitoring station data and the rest from three fixed monitoring stations established in the city, specifically located in Ministry of Natural Resources and Environment (SRNyMA), Durango Institute of Technology (ITD) and Interdisciplinary Research Center for Regional Integral Development Durango Unit (CIIDIR). A correlation analysis was performed for the 12 months of the year 2023. Subsequently, a linear regression analysis was executed for each month. The results showed a greater positive correlation between O₃ concentration and temperature for January (r = 0.91); additionally, this period showed a greater goodness of fit in the prediction of O₃ (R² = 0.91; RMSE = 0.65 ppm). The LST allows for the spatial prediction of ozone concentrations in terms of covering complete urban areas without measuring air stations.

On this topic focus on determining the volumetric yield of these products, showing a sawmill yield that ranges between 45% and 60% (Luna et al. 2012), so that approximately 40% becomes residues considered of little to no economic value (Fregoso-Madueño et al. 2017).

The sawn timber production process generates up to 63% of residues during primary processing in sawmills. For this industry, the devaluation and disposal of these residues remain significant challenges; proper management requires a more accurate quantification of the volume. This study evaluates and compares two indirect methods for estimating the volume of stacked residues: one based on image processing and the other on terrestrial LiDAR technology. Residues of Pinus spp. from a sawmill were used, with their actual volume determined using a xylometer. The image-based method, which uses threshold-based segmentation, achieved a R² = 0.64 and RMSE = 0.006 m³. In contrast, the LiDAR-based method, which derives measurements directly from 3D reconstruction, obtained an R² = 0.506 and RMSE = 0.009 m³. Despite these differences, ANOVA testing (p > 0.05) indicated no statistically significant differences between the methods. The results suggest that both approaches may serve as preliminary tools for forest residue quantification and provide a solid foundation for future research aimed at developing field-applicable technological solutions.