The current study aimed at evaluating an untreated pig manure, firstly for its suitability for soil amendment in combination with an agricultural/bio-solid biochar, and secondly for its potential to be used for adsorption of hazardous species, replacing expensive activated carbons. Column soil leaching experiments were designed to simulate field conditions, and physical, chemical and mineralogical analyses were performed for raw materials and/or leachates. For activated carbon production, the manure was gasified by steam or carbon dioxide at high temperatures. Biochars were analyzed for organic and mineral matter, structural characteristics and organic functional groups. Activation by steam or carbon dioxide greatly enhanced specific surface area, reaching values of 231.4 and 233.3 m2/g, respectively. Application of manure to the soil promoted leaching of nitrates and phosphates and raised COD values of water extracts. Biochar addition retained these ions and reduced COD values up to 10 times at the end of the three-month period. The concentrations of heavy metals in the leachates were low and, in the presence of biochar in soil blends, they were significantly reduced by 50–70%. The manure presents a significant potential for adsorption of various pollutants or improvement of soil amendment if carefully managed.

We focus on the performances of three nature-inspired metaheuristic methods for the optimization of time-domain electromagnetic (TDEM) data: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO) algorithms. While GA and PSO have been used in a plethora of geophysical applications, GWO has received little attention in the literature so far, despite promising outcomes. This study directly and quantitatively compares GA, PSO and GWO applied to TDEM data. To date, these three algorithms have only been compared in pairs. The methods were first applied to a synthetic example of noise-corrupted data and then to two field surveys carried out in Italy. Real data from the first survey refer to a TDEM sounding acquired for groundwater prospection over a known stratigraphy. The data set from the second survey deals with the characterization of a geothermal reservoir. The resulting resistivity models are quantitatively compared to provide a thorough overview of the performances of the algorithms. The comparative analysis reveals that PSO and GWO perform better than GA. GA yields the highest data misfit and an ineffective minimization of the objective function. PSO and GWO provide similar outcomes in terms of both resistivity distribution and data misfits, thus providing compelling evidence that both the emerging GWO and the established PSO are highly valid tools for stochastic inverse modeling in geophysics.