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Riccardo Fornaroli

Researcher at University of Milan

Publications -  34
Citations -  812

Riccardo Fornaroli is an academic researcher from University of Milan. The author has contributed to research in topics: Wastewater & Environmental science. The author has an hindex of 13, co-authored 28 publications receiving 557 citations. Previous affiliations of Riccardo Fornaroli include University of Milano-Bicocca.

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The future of biotic indices in the ecogenomic era: Integrating (e)DNA metabarcoding in biological assessment of aquatic ecosystems

TL;DR: The main advantages and pitfalls of metabarcoding approaches to assess parameters such as richness, abundance, taxonomic composition and species ecological values, to be used for calculation of biotic indices are discussed.
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Modeling global distribution of agricultural insecticides in surface waters

TL;DR: The model predicted the upper limit of observed insecticide exposure measured in water bodies in five different countries reasonably well and provides a global map of hotspots for insecticide contamination guiding future freshwater management and conservation efforts.
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Outdoor pilot-scale raceway as a microalgae-bacteria sidestream treatment in a WWTP.

TL;DR: The proposed process would reduce the aeration demand for nitrification in the water line of the plant, while producing algal biomass to be further valorized for energy or material recovery.
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Outdoor pilot trial integrating a sidestream microalgae process for the treatment of centrate under non optimal climate conditions

TL;DR: In this paper, a bubble-column was used to remove nitrogen in centrate from the biosolid dewatering of a municipal wastewater treatment plant whilst producing biomass for agricultural purposes, and the results showed that the removal rate was positively affected by NH4+-Nin (influent concentration) and by pH, whose increase fosters stripping, and decreased for increasing NH3-N concentrations, responsible for inhibiting nitrifying bacteria.
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Predicting the constraint effect of environmental characteristics on macroinvertebrate density and diversity using quantile regression mixed model

TL;DR: In this article, the authors used quantile regression mixed models and Akaike's information criterion as an indicator of goodness to examine two different datasets, one belonging to Italy and another belonging to Finland, and to detect the limiting action of selected environmental variables.