The disposal of excess milk can pose significant challenges for dairy farms during supply chain disruptions, extreme weather events, or plant closures. Improper disposal methods risk causing environmental damage, public health issues, and regulatory violations. This study evaluates three on-farm milk disposal methods, lagoon discharge, composting, and land application, to guide the effective management of large-scale milk disposal events. Laboratory experiments assessed the impact of milk disposal on lagoon water quality, highlighting that lagoon discharge was feasible for large-scale operations but increased total solids and chemical oxygen demand (COD), potentially overloading the system's treatment capacity. Similarly, experiments on composting showed that adding milk enhanced compost quality but required careful monitoring to prevent moisture imbalance and odors. For land application, experiments demonstrated improvements in soil health and plant growth but also revealed risks of nutrient imbalance and gas emissions, particularly at higher application rates. Dividing milk into smaller, multiple applications consistently reduced adverse impacts across all methods. Each method's suitability depends on farm size, infrastructure, and disposal volume. Lagoon discharge is better suited for large farms with sufficient capacity to manage treatment risks. Composting works well for smaller volumes, while land application benefits soil health when carefully managed. The findings have practical applications in helping dairy farms select appropriate disposal strategies, minimizing environmental harm, and complying with regulations during large-scale milk disposal events. Additionally, this study serves as a foundation for creating more comprehensive guidelines and strategies to address milk disposal challenges, fostering sustainable practices across diverse agricultural settings.

Venice, the enchanting Italian city built on a lagoonal environment, faces ongoing geotechnical challenges due to natural processes and anthropogenic influences. Over the past century, extensive geotechnical investigations have been conducted to characterize the unique stratigraphy of Venice's soils. Some key locations, representative of the city's diverse soil profiles, have undergone in-depth analysis, with investigations reaching depths of tens of meters. Three key sites-Malamocco, Treporti, and La Grisa-were strategically selected to study the complex mechanical properties of Venetian soils. In this study, we present a comprehensive synthesis of the most significant findings from the geotechnical investigations conducted throughout the Venetian lagoon over recent decades, focusing on methodologies for the evaluation of stiffness parameters in highly heterogeneous soil layers. These results enhance the understanding of geological and geotechnical behaviour of Venice's subsoil and provide crucial data for developing resilient engineering solutions.

The insufficient taking into account of groundwater as a basis for implementing protection measures for coastal wetlands can be related to the damage they are increasingly exposed to. The aim of this study is to demonstrate the pertinence of combining hydrogeological tools with assessment of pollutant fluxes and stable isotopes of O, H and N, as well as groundwater time-tracers to identify past and present pollution sources resulting from human activities and threatening shallow groundwater-dependent ecosystems. A survey combining physico-chemical parameters, major ions, environmental isotopes (O-18, H-2, N-15 and H-3), with emerging organic contaminants including pesticides and trace elements, associated with a land use analysis, was carried out in southern Italy, including groundwater, surface water and lagoon water samples. Results show pollution of the shallow groundwater and the connected lagoon from both agricultural and domestic sources. The N-isotopes highlight nitrate sources as coming from the soil and associated with the use of manure-type fertilizers related to the historical agricultural context of the area involving high-productivity olive groves. Analysis of EOCs has revealed the presence of 8 pesticides, half of which have been banned for two decades and two considered as pollutant legacies (atrazine and simazine), as well as 15 molecules, including pharmaceuticals and stimulants, identified in areas with human regular presence, including rapidly degradable compounds (caffeine and ibuprofen). Results show that agricultural pollution in the area is associated with the legacy of intensive olive growing in the past, highlighting the storage capacity of the aquifer, while domestic pollution is sporadic and associated with regular human presence without efficient modern sanitation systems. Moreover, results demonstrate the urgent need to consider groundwater as a vector of pollution to coastal ecosystems and the impact of pollutant legacies in planning management measures and policies, with the aim of achieving 'good ecological status' for waterbodies.

Featured Application Python application that uses data science and machine learning to estimate the main properties of acid tars. Its main advantage is that determinations for acid tar properties are no longer necessary, thus saving time and money. However, good machine learning estimations are highly dependent on the number and quality of the training data, meaning that the larger and more consistent the training database, the better the estimations.Abstract Hazardous petroleum wastes are an inevitable source of environmental pollution. Leachates from these wastes could contaminate soil and potable water sources and affect human health. The management of acid tars, as a byproduct of refining and petrochemical processes, represented one of the major hazardous waste problems in Romania. Acid tars are hazardous and toxic waste and have the potential to cause pollution and environmental damage. The need for the identification, study, characterization, and subsequently either the treatment, valorization, or elimination of acid tars is determined by the fact that they also have high concentrations of hydrocarbons and heavy metals, toxic for the storage site and its neighboring residential area. When soil contamination with acid tars occurs, sustainable remediation techniques are needed to restore soil quality to a healthy production state. Therefore, it is necessary to ensure a rapid but robust characterization of the degree of contamination with hydrocarbons and heavy metals in acid tars so that appropriate techniques can then be used for treatment/remediation. The first stage in treating these acid tars is to determine its properties. This article presents a software program that uses machine learning to estimate selected properties of acid tars (pH, Total Petroleum Hydrocarbons-TPH, and heavy metals). The program uses the Automatic Machine Learning technique to determine the Machine Learning algorithm that has the lowest estimation error for the given dataset, with respect to the Mean Average Error and Root Mean Squared Error. The chosen algorithm is used further for properties estimation, using the R2 correlation coefficient as a performance criterion. The dataset used for training has 82 experimental points with continuous, unique values containing the coordinates and depth of acid tar samples and their properties. Based on an exhaustive search performed by the authors, a similar study that considers machine learning applications was not found in the literature. Further research is required because the method presented therein can be improved because it is dataset dependent, as is the case with every ML problem.