Environmental factors influencing DDT-DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques.

The presence and persistence of pesticides in the environment are environmental problems of great concern due to the health implications for humans and wildlife. The persistence of DDT-DDE in a Mediterranean coastal plain where pesticides were widely used and were banned decades ago is the aim of this study. Different sources of analytical information from water and soil analysis and topography and geographical variables were combined with the purpose of analyzing which environmental factors are more likely to condition the spatial distribution of DDT-DDE in the drainage watercourses of the area. An approach combining machine learning techniques, such as Random Forest and Mutual Information (MI), for classifying DDT-DDE concentration levels based on other environmental predictive variables was applied. In addition, classification procedure was iteratively performed with different training/validation partitions in order to extract the most informative parameters denoted by the highest MI scores and larger accuracy assessment metrics. Distance to drain canals, soil electrical conductivity, and soil sand texture fraction were the most informative environmental variables for predicting DDT-DDE water concentration clusters.

Soil Sealing and Hydrological Changes during the Development of the University Campus of Elche (Spain).

The University Miguel Hernández of Elche was created in 1996 and its headquarters is located in the city of Elche. A new campus was developed where new buildings and infrastructures have been established for over 25 years in the north of the city. The university is growing, and the land cover/land use is changing, adapted to the new infrastructures. In fact, the landscape changed from a periurban agricultural area mixed with other activities into an urbanized area integrated into the city. The purpose of this work was to evaluate the progressive sealing of the soil and the consequences on the surface hydrology. The area is close to the Palmeral of Elche, a landscape of date palm groves with an ancient irrigation system, which is a World Heritage Cultural Landscape recognized by UNESCO. The evolution of the land occupation was analyzed based on the Aerial National Orthophotography Plan (PNOA). Soil sealing and the modifications of the hydrological ancient irrigation system were detected. Based on the results, proposals for improvement are made in order to implement green infrastructures and landscape recovery that can alleviate the possible negative effects of the soil sealing in the area occupied by the university.

Wastewater Biosolid Composting Optimization Based on UV-VNIR Spectroscopy Monitoring.

Conventional wastewater treatment generates large amounts of organic matter-rich sludge that requires adequate treatment to avoid public health and environmental problems. The mixture of wastewater sludge and some bulking agents produces a biosolid to be composted at adequate composting facilities. The composting process is chemically and microbiologically complex and requires an adequate aeration of the biosolid (e.g., with a turner machine) for proper maturation of the compost. Adequate (near) real-time monitoring of the compost maturity process is highly difficult and the operation of composting facilities is not as automatized as other industrial processes. Spectroscopic analysis of compost samples has been successfully employed for compost maturity assessment but the preparation of the solid compost samples is difficult and time-consuming. This manuscript presents a methodology based on a combination of a less time-consuming compost sample preparation and ultraviolet, visible and short-wave near-infrared spectroscopy. Spectroscopic measurements were performed with liquid compost extract instead of solid compost samples. Partial least square (PLS) models were developed to quantify chemical fractions commonly employed for compost maturity assessment. Effective regression models were obtained for total organic matter (residual predictive deviation-RPD = 2.68), humification ratio (RPD = 2.23), total exchangeable carbon (RPD = 2.07) and total organic carbon (RPD = 1.66) with a modular and cost-effective visible and near infrared (VNIR) spectroradiometer. This combination of a less time-consuming compost sample preparation with a versatile sensor system provides an easy-to-implement, efficient and cost-effective protocol for compost maturity assessment and near-real-time monitoring.