The impact of tourism on marine litter pollution on Santa Marta beaches, Colombian Caribbean.

Tourism is an important socioeconomic activity in coastal communities, which deteriorates marine-coastal ecosystem quality when poorly managed, increasing litter pollution on beaches during the main tourist seasons. This study aims to assess the tourism impact on litter pollution on eleven Santa Marta beaches, Colombian Caribbean. During high and low tourist seasons, people on the beaches were counted, macrolitter and microplastics were sampled, and perception surveys about litter on beaches were conducted. During the high tourist season, the number of people and macrolitter pollution increased, compared to the low tourist season. Plastics accounted for 30%-77% of macrolitter and microplastics ranged from 1 to 355 items/m2. Respondents identified tourism as a main litter source and plastics as the most common litter type. All assessed beaches are impacted by tourism causing litter pollution, therefore, stronger controls, educational, and awareness strategies are needed to reduce litter pollution and prevent ecological and socioeconomic impacts.

Exploring innovative techniques for identifying geochemical elements as fingerprints of sediment sources in an agricultural catchment of Argentina affected by soil erosion.

Identification of hot spots of land degradation is strongly related with the selection of soil tracers for sediment pathways. This research proposes the complementary and integrated application of two analytical techniques to select the most suitable fingerprint tracers for identifying the main sources of sediments in an agricultural catchment located in Central Argentina with erosive loess soils. Diffuse reflectance Fourier transformed in the mid-infrared range (DRIFT-MIR) spectroscopy and energy-dispersive X-ray fluorescence (EDXRF) were used for a suitable fingerprint selection. For using DRIFT-MIR spectroscopy as fingerprinting technique, calibration through quantitative parameters is needed to link and correlate DRIFT-MIR spectra with soil tracers. EDXRF was used in this context for determining the concentrations of geochemical elements in soil samples. The selected tracers were confirmed using two artificial mixtures composed of known proportions of soil collected in different sites with distinctive soil uses. These fingerprint elements were used as parameters to build a predictive model with the whole set of DRIFT-MIR spectra. Fingerprint elements such as phosphorus, iron, calcium, barium, and titanium were identified for obtaining a suitable reconstruction of the source proportions in the artificial mixtures. Mid-infrared spectra produced successful prediction models (R2 = 0.91) for Fe content and moderate useful prediction (R2 = 0.72) for Ti content. For Ca, P, and Ba, the R2 were 0.44, 0.58, and 0.59 respectively.