Satellite data from the Climate Change Initiative (CCI) lakes project were used to examine the influence of climate on chlorophyll-a (Chl-a). Nonparametric multiplicative regression and machine learning were used to explain Chl-a concentration trend and dynamics. The main parameters of importance were seasonality, interannual variation, lake level, water temperature, the North Atlantic Oscillation, and antecedent rainfall. No evidence was found for an earlier onset of the summer phytoplankton bloom related to the earlier onset of warmer temperatures. Instead, a curvilinear relationship between Chl-a and the temperature length of season above 20°C (LOS) was found with longer periods of warmer temperature leading to blooms of shorter duration. We suggest that a longer period of warmer temperatures in the summer may result in earlier uptake of nutrients or increased calcite precipitation resulting in a shortening of the duration of phytoplankton blooms. The current scenario of increasing LOS of temperature with climate change may lead to an alteration of phytoplankton phenological cycles resulting in blooms of shorter duration in lakes where nutrients become limiting. Satellite-derived information on lake temperature and Chl-a concentration proved essential in detecting trends at appropriate resolution over time.

Perfluorinated compounds (PFCs) are a wide class of emerging pollutants. In this study, we applied the US EPA method 533 for the determination of 21 PFCs in river water samples. In particular, this method was used to investigate the presence of the target PFCs in six rivers in central Italy during a 4-month-long monitoring campaign. In 73% of the analyzed samples, at least some of the target PFCs were detected at concentrations higher than the limit of detection (LOD). The sum of the 21 target analytes (∑21PFCs) ranged from 4.3 to 68.5 ng L−1, with the highest concentrations measured in the month of June, probably due to a minor river streamflow occurring in the warmer summer months. Considering the individual congeners, PFBA and PFPeA, followed by PFHxA and PFOA, were the predominantly detected compounds. Short- and medium-chain PFCs (C4–C9) prevail over the long-chain PFCs (C10–C18), likely due to the increased industrial use and the higher solubility of short-chain PFCs compared to long-chain PFCs. The ecological risk assessment, conducted by using the risk quotient method, highlighted that the risk for aquatic environments associated with PFBA, PFPeA, PFBS, PFHxA and PFOA was low or negligible. Only for PFOA, there was a medium level of risk in two rivers in the month of June. With regard to PFOS, 54% of the river water samples were classified as “high risk” for the aquatic environment. The remaining 46% of the samples were classified as “medium risk.”

Perfluorinated compounds (PFCs) are a wide class of emerging pollutants still under study. In this work, we developed and validate a sensitive analytical method based on HPLC-MS/MS for the determination of 21 PFCs. This method was then used to investigate the presence of the target PFCs in six rivers in central Italy during a 4-months long monitoring campaign. 73% of the analytical determinations resulted higher than the limit of detection (LOD). The ∑21PFCs ranged from 4.3 to 68.5 ng L− 1 with the highest concentrations measured in June month, due to a minor river streamflow occurring in the warm periods. Between the individual congeners, PFBA and PFPeA, followed by PFHxA and PFOA were the predominant congeners detected. The evidence that short and medium chain PFCs (C4-C9) prevail over the long chain PFCs (C10-C18) could be attributed to the increased use and higher solubility of short chain PFCs compared to long chain PFCs. The ecological risk assessment, conducted by using risk quotient (RQ) method, highlighted that for PFBA, PFPeA, PFBS, PFHxA and PFOA the risk for aquatic environments was low or negligible. Only for PFOA there was a medium risk in 2 rivers in June month. As regard PFOS, 54% of the river water samples were classified as “high risk” for the aquatic environment. The remaining 46% of the samples were classified as “medium risk”.

<p class="PlainText1">In compliance with the European and Italian regulations, the Environmental Protection Agency of Umbria Region (ARPA Umbria) defined specific river monitoring programs and networks based on river type definition, human pressures and risk analysis. The Umbria Region lies in Central Italy and it can be split into three hydro-ecoregions belonging to the Mediterranean area. Data on diatom community composition were collected in five different Mediterranean macrotypes (M1-M5) throughout the diatom-based river monitoring network that is composed by 52 sampling stations in 36 watercourses. The main aim of this study was to characterise and to analyse diatom diversity across the different regional river macrotypes. Specifically, we investigated if: i) there were differences in species diversity (species richness and Shannon Index) among macrotypes; ii) there was difference in three water quality indexes (ICMi, IPS, and TI) among sites; and iii) there was a relationship between the observed ICMi, IPS and TI value and the diatom diversity. Two-hundred diatom species and varieties were identified, and the number of species <em>per</em> sampling station ranged from a minimum of 10 to a maximum of 38 species. The most frequent and abundant species were <em>Amphora pediculus</em>, <em>Achnanthidium minutissimum,</em> <em>Navicula cryptotenella</em>, <em>Nitzschia dissipata</em>, and each macrotype showed some peculiar species. The ecological status evaluation based on Intercalibration Common Metric Index (ICMi) classified 69% of the water bodies in high or good class. Significant differences in diversity and ICMi value among stream macrotypes were found, with M4 (small and medium mountain) and M5 (small, lowland, temporary) typologies showing the lowest species richness, and with M5 showing the lowest Shannon Index. Conversely, M2 (small and medium lowland) and M5 showed the highest ICMi value. Lastly, significant correlations between Shannon Index and the ICMi, IPS and TI indexes were found.</p>