Land application of olive mill wastewater (OMW) is considered a promising low-cost practice for olive-oil producing countries. The objectives of this work were to investigate: (i) OMW treatment potential of a land treatment system (LTS), planted with a E. camaldulensis species, regarding N, P, C, and phenols; (ii) the effects of OMW on chemical properties of soil and soil solution characteristics; and (iii) the performance of E. camaldulensis in terms of biomass production and N and P recovery. E. camaldulensis received OMW for two growing seasons at rates based on maximum organic loading. These rates were almost equivalent to the reference evapotranspiration of the area. Soil solution and soil samples were collected from three different depths (15, 30 and 60 cm) at specified time intervals. -Also, samples of plant tissues were collected at the end of application periods. OMW land application resulted in significant reduction in inorganic and organic constituents of OMW. At 15 cm of soil profile, the average removal of COD, TKN, NH₄⁺-N, TP, In-P, and total phenols approached 93%, 86%, 70%, 86%, 82%, and 85%, respectively, while an increase in soil depth (30 and 60 cm) did not improve significantly treatment efficiency. Furthermore, OMW increased soil organic matter (SOM), total kjeldahl nitrogen (TKN), and available P, particularly in the upper soil layer. In contrast, low inorganic N content was observed in the soil throughout the study period caused probably by increased competition among soil microorganisms induced by the organic substrate supply and high C/N ratio. Also, electrical conductivity (EC) and SAR increased by OMW addition, but at levels that may do not pose severe risk for soil texture. Enhancement of soil fertility due to OMW application sustained eucalyptus trees and provided remarkable biomass yield. In conclusion, land application of OMW has a great potential for organic matter and phenol assimilation and can be effectively used for OMW detoxification. Full article

This work investigates the effect of plant species (Eucalyptus camaldulensis vs. Arundo donax) on carbon (C) turnover during wastewater application to the land. The study was carried out in 40-liter pots under field conditions and plant species were treated either with pre-treated municipal wastewater or freshwater. Plant species had a strong effect on soil organic matter with pots planted with E. camaldulensis showing greater values than pots planted with A. donax. In accordance, greater respiration rates were measured in E. camaldulensis pots compared to those planted with A. donax. The respiration rate followed a decreasing trend with the progress of the season for both species. These findings suggest differences in soil microbial community composition and/or activity in the rhizosphere of plant species. Minor effects of plant species or effluent were observed in dissolved organic carbon, protein, and hexoses content. In conclusion, the results of the present study reveal an important role of plant species on C cycling in terrestrial environments with potential implications on the sequestration of C and release of nutrients and pollutants. Full article

Partial Least Squares Regression (PLSR) can integrate a great number of variables and overcome collinearity problems, a fact that makes it suitable for intensive agronomical practices such as land application. In the present study a PLSR model was developed to predict important management goals, including biomass production and nutrient recovery (i.e., nitrogen and phosphorus), associated with treatment potential, environmental impacts, and economic benefits. Effluent loading and a considerable number of soil parameters commonly monitored in effluent irrigated lands were considered as potential predictor variables during the model development. All data were derived from a three year field trial including plantations of four different plant species (Acacia cyanophylla, Eucalyptus camaldulensis, Populus nigra, and Arundo donax), irrigated with pre-treated domestic effluent. PLSR method was very effective despite the small sample size and the wide nature of data set (with many highly correlated inputs and several highly correlated responses). Through PLSR method the number of initial predictor variables was reduced and only several variables were remained and included in the final PLSR model. The important input variables maintained were: Effluent loading, electrical conductivity (EC), available phosphorus (Olsen-P), Na⁺, Ca²⁺, Mg²⁺, K²⁺, SAR, and NO₃⁻-N. Among these variables, effluent loading, EC, and nitrates had the greater contribution to the final PLSR model. PLSR is highly compatible with intensive agronomical practices such as land application, in which a large number of highly collinear and noisy input variables is monitored to assess plant species performance and to detect impacts on the environment. Full article