The aim of this study was to evaluate the viability of using Landsat 8 spectral images to estimate water quality parameters and depth in El Guájaro Reservoir. On February and March 2015, two samplings were carried out in the reservoir, coinciding with the Landsat 8 images. Turbidity, dissolved oxygen, electrical conductivity, pH and depth were evaluated. Through multiple regression analysis between measured water quality parameters and the reflectance of the pixels corresponding to the sampling stations, statistical models with determination coefficients between 0.6249 and 0.9300 were generated. Results indicate that from a small number of measured parameters we can generate reliable models to estimate the spatial variation of turbidity, dissolved oxygen, pH and depth, as well the temporal variation of electrical conductivity, so models generated from Landsat 8 can be used as a tool to facilitate the environmental, economic and social management of the reservoir.

In this study, empirical models were generated to estimate water quality parameters, with the objective of showing the benefits of the satellite remote sensing application in the characterization of coastal waters. The study area was Playa Colorada Bay, located in the northwest of Mexico, in the eastern part of the Gulf of California. In two seasons of the year, on-site and laboratory characterizations were carried out to determine the spatial and temporal variation of phosphates (PO4), electrical conductivity (EC), total suspended solids (TSS), turbidity, and pH of water. Samplings were selected to match Landsat 8 satellite overpass in the study area. Radiometric and atmospheric corrections were applied to the images, prior to the generation of the models. The models were generated using the linear regression technique of successive steps; water quality parameters and their logarithms were used as dependent variables, and as independent variables were used corrected reflectance values of Landsat images. The results showed that the concentration of PO4 in the analyzed water samples were higher than those recommended in the Mexican ecological criteria of water quality, to protect the aquatic life of marine water in coastal areas. In autumn, PO4 was correlated with turbidity, T, pH, and TSS. The highest correlation coefficients were presented by TSS with PO4 (r = − 0.979) and pH (r = 0.958). The water quality models that were generated had coefficients of determination (R 2) in the range of 0.637 to 0.955 and show the viability of the application of Landsat 8 images in the characterization of water quality parameters in Playa Colorada Bay. Models allowed the estimation of the distribution of water quality parameters over the whole bay instead of only at the sampling stations, favoring a better understanding of their spatial distribution.

Fragile coastal areas suffer from human activities. Environmental quality is one of the most important aspects in a tourist destination of sun and sand. The quality of bathing water and sand became indicators in the worldwide competition of beach destinations. We studied the water and sand quality along the beach of Puerto Velero, in northern Colombia. Water and sand beach quality were monitored during thirteen months. This allowed identifying the most significant sources of pollution along the beach, and understanding the interrelationship between tourism and the effects on the environment. Linear correlations allow assessing the association between the number of visitors and the physicochemical and microbiological parameters. The number of visitors was directly correlated with the presence of grease and oils, both in water and sand, as well as with fecal coliforms in water. A relation between the suspended solids and the presence of fecal coliforms in water and sand was observed. This statistical approach allows understanding the origin of beach sand and swimming water pollution at tourist beaches.

This study evaluated the concentration and distribution of heavy metals (HM) (Cr, Ni, Pb, Cd, Hg, and Zn) and pesticides (organochlorine and organophosphorus) and the relationship of these pollutants with the physicochemical properties of agricultural soils in an Irrigation District (ID) in Colombia. Soils samples were analyzed for pH, humidity, organic matter, P total, N total, electric conductivity (EC), cation exchange capacity, and texture (% sand, clay and silt). Canonical correlation was used to determined relationship between soil properties and HM. Soil pollution were evaluated with geoaccumulation index (Igeo), contamination factor (CF), degree of contamination (Cdeg) and pollution load index (PLI). The results indicated that, in general, the soils had adequate physicochemical conditions for the establishment and development of crops. The presence of pesticides in the soils was not reported. However, concentrations HM was detected (Zn > Cr > Ni > Pb > Hg > Cd). The soil characteristics (silt, clay, pH and EC) contributed to explain HM concentrations. The Igeo indicated that the soils are heavily contaminated with Hg (3 < Igeo<4). The CF was very high for Hg (>6). The Cdeg presented moderate to considerable variations (>6Cdeg<24). The PLI indicated that the soils are contaminated (1.308). The presence of HM may be associated with the agricultural and quarries activities carried out near the ID. The impact caused by high concentrations of HM can lead environmental, economic and social impacts in the study zone.

Heavy metals have become a subject of special concern worldwide, mainly due to high persistence in the environment, toxicity, biogeochemical recycling and ecological risk. Therefore, the objective of this investigation was to analyze the spatial-temporal distribution of heavy metals in water and sediments to determine the environmental status of El Guájaro Reservoir, where such studies have not been developed. Two measurement campaigns (dry and wet period) were carried out and eight sampling stations were selected. A comparison of water and sediment quality parameters with existing national and international regulations was done. Also, heavy metal distribution maps were generated, and the geoaccumulation index was calculated to identify sources and sediments contamination level. Based on the obtained results, agriculture and mining activities are the main causes of the reservoir contamination. This metals levels could be a potential risk for the aquatic life and the populations that are supplied from this water body.

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This research demonstrated the feasibility of applying Sentinel-2 images to generate empirical models and estimate physicochemical parameters concentration, particularly nutrients in the wetland system called Bajo Sinú wetlands complex, Colombia. Spearman correlations were determined between water quality parameters, which were monitored at 17 points in the wetland on 5 February 2021, with Sentinel-2 images reflectance values from the same monitoring date; the correlations allowed the identification of statistically significant bands in the multiple linear regression algorithm implementation to determine empirical water quality models. The results show significant correlations between the optically active parameters, TSS-Turbidity, which in turn correlated with the optically inactive parameters Turbidity-NO3 and TSS-DO, as well as non-optically active parameters among themselves, TDS-NO3 and TDS-TP; the empirical models presented higher than 74.5% fit (R2), particularly DO (R2 = 0.948), NO3 (R2 = 0.858) and TP (R2 = 0.779) were the models with the highest fits (R2). These models allowed us to properly estimate the spatial distribution of nutrient-forming compounds in the wetlands complex. The determinant role played by turbidity in this type of water body is highlighted; it acts as a connecting constituent that makes the estimation of water quality parameters without spectral response through remote sensing feasible. Sentinel-2 images and multiple linear regression algorithms have been shown to be effective in estimating the concentration of water quality parameters without spectral response, such as NO3 and TP in shallow tropical wetlands, due to the processes of transformation, interaction and dependence between the different environmental variables in aquatic ecosystems.