Analysis of PM2.5 and Meteorological Variables Using Enhanced Geospatial Techniques in Developing Countries: A Case Study of Cartagena de Indias City (Colombia)

The dispersion of air pollutants and the spatial representation meteorological variables are subject to complex atmospheric local parameters. To reduce impact particulate matter (PM2.5) on human health, it is great significance know its concentration at high resolution. In order monitor effects an exposed population, geostatistical analysis offers potential obtain high-quality mapping PM2.5 variables. purpose this study was define optimal PM2.5, relative humidity, temperature wind speed in urban district Cartagena, Colombia. lack data due scarcity stations called for ad hoc methodology, which included interpolation implementing ordinary kriging (OK) model, fed by obtained through inverse distance weighting (IDW) model. consider effects, empirical Bayesian regression prediction (EBK) implemented. application these methods clarified areas across city that exceed recommended limits concentrations (Zona Franca, Base Naval Centro district), described a continuous way, surface, three main weather Positive correlations were humidity (R2 0.47), 0.59) 0.64).