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This study examines the evolution of urban heat islands (UHI) in northern Colombia during 2016 and 2021. We employed a multi-approach to analyze the intensity and magnitude of UHI in a coastal zone metropolis. We used Landsat images and WRF model numerical modeling coupling the Urban Canopy Module (WRF/UCM) to estimate the UHI variability. Landsat data show the annual mean LST has increased from 27.78 °C in 2016 to 28.46 °C in 2021 in the coastal cities, especially in central and south zones of built-up areas. Moreover, the differences in mean LST values for the LULC classes showed an increase of 0.48, 1.23, 0.06, and 1.07 °C for vegetation areas, build-up areas, water bodies, and barren land areas, respectively, during the analyzed years. The temperature difference between LSTs of built-up and vegetated areas has increased from 2.14 °C in 2016 to 2.89 °C in 2021. The expansion of UHI intensity (UHII) is directly related to the growth of urban zones and the densification of built-up areas. UHI magnitude is high during the daytime and low during the nighttime. In contrast, the average UHII in the diurnal cycle ranged from 1.46 °C to 3.21 °C In 2021, UHII is higher at night (3.49 °C), while in 2016, it is higher in the daytime (1.60 °C) according to the estimate of WRF/UCM model. The expansion and density of urban areas have led to an increase in surface radiation absorption, which has suppressed evaporation over impervious building materials and resulted in a significant rise in 2-m air temperature. Also, the pronounced disparity in surface temperature and the slight variance in sensible heat flux between impervious and vegetation surfaces give rise to heightened UHII during nighttime and, conversely, during daytime. The results of the study underscore the need to promote sustainable practices in the planning and construction of urban infrastructure to mitigate and adapt to UHI.

This study was designed to assess the concentrations of nine water-soluble ions in PM10 mass at two sites of an open-pit coal mine and to analyze the correlation and variation of the spatial distribution of sulfate ions with the PM10 sulfate aerosol optical depth at 550 nm (suaod550) in two (North and South) stations of the study area. The daily average of PM10 concentrations ranged from 20.48 to 53.10 μg/m3 and thus did not exceed the daily average maximum permissible level of PM10 (100 μg/m3) established in the Colombia standard at any station. The concentrations of nine water-soluble ions in PM10 (Cl−, NO3−, PO43−, SO42– Na+, NH4+, K+, Mg2+, and Ca2+) were determined. The ions under analysis, SO42−, Na+, and NH4+ had the highest concentrations. Combined, they accounted for 75% of the mass of water-soluble ions in a total of 210 samples. The SO42− concentrations in PM10 significantly correlated with suaod550 (r ranging from 0.57 to 0.66), emphasizing the strong effect of suaod550 from Venezuela (Lake Maracaibo) on central and northern Colombia. These results demonstrate that the effects of local sulfate emissions near monitoring sites can be predicted and assessed using satellite data.

Rainfall estimates using the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset constitute a promising complement to rain gauge networks in areas with fewer stations. To determine their usefulness in practical application, these estimates must be validated and the performance of CHIRPS must be accurately determined. In this study, CHIRPS datasets were validated for a coastal region with extensive plains and complex topography in northern Colombia near the Caribbean Sea. This region presents a complex topography, posing significant challenges for retrieval of rainfall data using remote sensing. Daily, monthly, and annual estimates using CHIRPS were compared with records from 37 rain gauges between 1981 and 2020. Continuous and categorical statistical metrics were applied to evaluate performance in estimating and detecting rainfall by analyzing topographic and climatic constraints. CHIRPS performed best at monthly and annual scales (r > 0.79, NRMSE0.87, and FAR5.13, POD>0.39, and FAR3.47, POD>0.45, and FAR

Annual deaths caused by exposure to outdoor-indoor particulate matter air pollution are a problem currently faced by the World Health Organization. This study assessed outdoor and indoor exposure to PM10 particulate matter in the city of Riohacha (Colombia) from March 17 to April 17, 2018, in three representative sites of the urban area. A low-volume gravimetric sampler, more specifically a MiniVol Portable Air Sampler operating at 5 L/min, was used at three sampling stations. Elemental analysis was performed on a microwave plasma atomic emission spectrometer (MP-AES). In total, 14 inorganic elements (Zn, Fe, Cd, V, Ca, Ni, Cu, As, Co, Pb, K, Mn, Mg, and Na) were quantified in the PM10 mass. The indoor and outdoor concentrations of PM10 ranged from 3.81 to 19.07 μg/m3 and from 17.86 to 51.35 μg/m3, respectively. The indoor/outdoor ratio of PM10 concentrations ranged from 0.213 ± 0.01 at the station with the lowest levels of PM10 to 0.360 ± 0.02 at the station with the highest levels of PM10. Chemical elements with the highest levels in PM10 were crustal elements (Ca, Na, K, Fe, Mg, As, and Co), and those with the lowest levels in PM10 were anthropogenic tracers emitted from transportation (Cd, V, Cu, Mn, Ni, Pb, and Zn). Source contributions were estimated by principal component analysis. Three factors were detected inside and three outside the stations. The emissions from motor vehicles, resuspension of road dust and marine aerosol were the major sources contributing to overall PM10 mass in Riohacha City.

Pollutant dispersion models are essential tools to assess the influence of emission sources in the atmosphere. We performed PM10 simulations using the CALMET-CALPUFF system at a mining site in northern. We set the dynamic parameters (TERRAD, RMAX1, RMAX2, R1, R2, and Calmet-Bias) and established different horizontal grid spacing (0.5 km, 1 km, 2 km, 3 km, and 5 km). Also, we adjusted the surface roughness length (0.1 m–0.5 m) values to determine the sensitivity of the dispersion model. Blocking effects due to terrain, represented by TERRAD, and horizontal resolution in the simulations significantly influence PM10 estimates. TERRAD values below 6 km reduced the values of MSE (0.75 μg/m3 - 4 μg/m3), BIAS (0.5 μg/m3–6 μg/m3), and FB (up to 0.32) at all receptors. The modeling's horizontal resolution of 0.5 km allowed us to identify high PM10 concentrations areas. However, it increases the MSE values (6.42 μg/m3–12.29 μg/m3) compared to the EPA-Federal-Land-Manager-recommended parameter configuration settings of the modeling system. Increasing surface roughness length values slightly increased the MSE values (