Chlorophyll soft-sensor based on machine learning models for algal bloom predictions.

Harmful algal blooms (HABs) are a growing concern to public health and aquatic ecosystems. Long-term water monitoring conducted by hand poses several limitations to the proper implementation of water safety plans. This work combines automatic high-frequency monitoring (AFHM) systems with machine learning (ML) techniques to build a data-driven chlorophyll-a (Chl-a) soft-sensor.

A two-year monitoring of pesticide hazard in-hive: High honey bee mortality rates during insecticide poisoning episodes in apiaries located near agricultural settings.

Pesticide residues in beebread, live and dead honey bees, together with honey bee death rate were monitored from June 2016 to June 2018 in three apiaries, located near agricultural settings and in wildlands. Dead honey bees were only collected and analyzed when significant mortality episodes occurred and pesticide content in beeswax of each experimental apiary was evaluated at the beginning of the study. Samples were extracted by a modified QuEChERS procedure and screened for pesticides residues by liquid chromatography mass spectrometry (LC-MS/MS).

Pesticide occurrence in the waters of Júcar River, Spain from different farming landscapes.

A combined methodology to identify and quantify farming chemicals in the entire Júcar River basin has been developed. The procedure consisted of the application of environmental forensic criteria associating laboratory analytical samples, cartographic analysis using Geographical Information Systems (GIS) and synthetic statistical analysis. Sampling involved the collection of 15 samples in surface waters distributed alongside the Júcar River and its two main tributaries (Cabriel and Magro Rivers).