Sewage sludge as organic fertilizer in agriculture. A study of physico-chemical properties, agronomic efficiency and environmental safety, with a specific focus on tannery sludge

In recent decades, the use of organic fertilizers has gained increasing interest mainly for two reasons: their ability to improve soil fertility and the need to find a sustainable alternative to mineral and synthetic fertilizers. In this context, sewage sludge is a useful organic matrix that can be successfully used in agriculture, due to its chemical composition rich in organic matter, nitrogen, phosphorus and other micronutrients necessary for plant growth. This work investigated three indispensable aspects (i.e., physico-chemical properties, agronomic efficiency and environmental safety) of sewage sludge application as organic fertilizer, emphasizing the role of tannery sludge. In a comparison study with municipal sewage sludge, results showed that the targeted analyses applied (total carbon and nitrogen content, isotope ratio of carbon and nitrogen, infrared spectroscopy and thermal analysis) were able to discriminate tannery sludge from municipal ones, highlighting differences in composition due to the origin of the wastewater and the treatment processes used in the plants. Regarding agronomic efficiency, N bioavailability was tested in a selection of organic fertilizers, including tannery sludge and tannery sludge-based fertilizers. Specifically, the hot-water extractable N has proven to be a good chemical indicator, providing a rapid and reliable indication of N bioavailability in soil. Finally, the behavior of oxybenzone (an emerging organic contaminant detected in sewage sludge) in soils with different physico-chemical properties was studied. Through adsorption and desorption experiments, it was found that the mobility of oxybenzone is reduced in soils rich in organic matter. Furthermore, through spectroscopic methods (e.g., infrared spectroscopy and surface-enhanced Raman spectroscopy) the mechanisms of oxybenzone-humic acids interaction were studied, finding that H-bonds and π-π stacking were predominantly present.

Advanced statistical models for the segregation, identification and measurement of coexisting sound sources

Long-term monitoring of acoustical environments is gaining popularity thanks to the relevant amount of scientific and engineering insights that it provides. The increasing interest is due to the constant growth of storage capacity and computational power to process large amounts of data. In this perspective, machine learning (ML) provides a broad family of data-driven statistical techniques to deal with large databases. Nowadays, the conventional praxis of sound level meter measurements limits the global description of a sound scene to an energetic point of view. The equivalent continuous level Leq represents the main metric to define an acoustic environment, indeed. Finer analyses involve the use of statistical levels. However, acoustic percentiles are based on temporal assumptions, which are not always reliable. A statistical approach, based on the study of the occurrences of sound pressure levels, would bring a different perspective to the analysis of long-term monitoring. Depicting a sound scene through the most probable sound pressure level, rather than portions of energy, brought more specific information about the activity carried out during the measurements. The statistical mode of the occurrences can capture typical behaviors of specific kinds of sound sources. The present work aims to propose an ML-based method to identify, separate and measure coexisting sound sources in real-world scenarios. It is based on long-term monitoring and is addressed to acousticians focused on the analysis of environmental noise in manifold contexts. The presented method is based on clustering analysis. Two algorithms, Gaussian Mixture Model and K-means clustering, represent the main core of a process to investigate different active spaces monitored through sound level meters. The procedure has been applied in two different contexts: university lecture halls and offices. The proposed method shows robust and reliable results in describing the acoustic scenario and it could represent an important analytical tool for acousticians.