Investigating green walls for greywater treatment and visualising enzymatic activity in constructed wetlands

L'abstract è presente nell'allegato / the abstract is in the attachmen

Resilience to flow rate variability in a green wall for greywater treatment

Green and blue infrastructures are an innovative solution to contrast climate changes (SDG 13 of UN 2030 Agenda) and increase cities resilience (SDG 11), using a smarter water management that transform wastewater into a new resource for non-potable reuses. Due to the lack of horizontal surfaces in urban areas, green walls are one of the most suitable nature-based solution to treat greywater (i.e. the portion of household wastewater that exclude toilet flush and kitchen sink). Green walls allow for a multidisciplinary approach, providing multiple benefits such as thermal and acoustic regulation, biodiversity preservation, decreasing heat islands effects and removing CO2, improving life quality and buildings value. Green walls have also been proposed for treating the large amount of greywater that is daily produced (e.g. around 100 L/PE/die in Italy), an approach that also provides urban green while reducing the need of irrigation water. Following previous work on a pilot system, this study aims to improve the green walls design and test its resilience to variations in the flow rate of greywater fed to the green wall. Two panels have been built in which synthetic greywater flows by gravity along three levels of pots with different plant species. The 18 pots (arranged in a 3x3 matrix in each panel) have been filled with a mix of coconut fibre and perlite (1:1 in volume) and fed with greywater, and output water samples have been collected almost weekly from June to December 2021. The control panel has been regularly fed with 24 L/die/col (standard flow rate), the other has been fed with different flow rates (standard, underflow, overflow and maintenance) that usually changed after three weeks. Different parameters (e.g. TSS, BOD5, COD, DO, TN, TP, MBAS), have been monitored in the outflow of each pot and average performances of each level has been evaluated. Results indicate a good efficiency of the green wall in removing contaminants even when the provided flow rate is not constant. The treatment performances increase along the columns in both panels and the first two levels guarantee a good compounds removal during standard flow and underflow rates. On the other hand, the overflow rate caused a performances decrease in the variable flow panel for many parameters, followed by a visible plant stress. However, one week of standard flow rate was sufficient to reduce the negative effects of the three- weeks-overflow. This demonstrated the resilience of the green wall facing flow variability, that can be caused by seasonal variation or system failure

Assessment of the Treatment Performance of an Open-Air Green Wall Fed with Graywater under Winter Conditions

Graywater (GW), i.e., the portion of household wastewater that excludes toilet flushes, is an interesting wastewater type because it requires only mild treatment. Green walls have been proposed as example of a nature-based solution for GW treatment due to low energy requirement and high ecological/societal benefits; however, indications about their treatment performances remain limited. This work presents experimental results of a laboratory modular green wall for GW treatment. Experiments have been performed outdoors during the winter season for three months. Each panel included four vertical columns of planted pots, and it was fed with 100 L of synthetic GW per day. Removal efficiencies were as follows (average values): 40% chemical oxygen demand, 97% biochemical oxygen demand, 61% total Kjeldhal nitrogen, 56% NO3–-N, 57% total phosphorus, 99% Escherichia coli, and 63% anionic surfactants. This work proved the potential of an open-air green wall for treating GW, even under challenging conditions for biological treatment processes and with high hydraulic loading rates

Evaluation of the influence of filter medium composition on treatment performances in an open-air green wall fed with greywater.

Abstract According to the European Research and Innovation Policy Agenda, nature-based solutions (NBSs) are key technologies to improve the sustainability of urban areas. Among NBSs, green walls have been recently studied for several applications, among the others the treatment of lowly polluted wastewater flows as greywater (GW, e.g. domestic wastewater excluding toilet flushes). This work is aimed at the evaluation of the influence of four additives (compost, biochar, granular activated carbon, polyacrylate) mixed with a base filter medium made of coconut fibre and perlite, on the performances of a green wall fed in batch mode with synthetic GW. The green wall was operated with a high hydraulic loading rate of GW (740.8 L/m2/day) in open-air winter conditions (3.5–15 °C measured for GW) between January and April. The performances of the green wall have been assessed though the monitoring every 1–2 weeks of physicochemical and biological parameters (pH, electric conductivity, total suspended solids, dissolved oxygen, BOD5 and COD, nitrogen and phosporus compounds, chlorides and sulphates, anionic surfactants and E. coli). Removal performances were excellent for BOD5 (>95%) and E.coli (>98%) for all additives; compared to the base medium, biochar was the best performing additive over the highest number of parameters, achieving removals equal to 51% for COD, 47% for TKN and nitric nitrogen and 71% for anionic surfactants. Compost also achieved high removal performances, but the frequent clogging events occurred during the monitoring period do not make its use recommendable. Granular activated carbon and the combination of biochar and polyacrylate performed better than the base medium, but only about the removal of nitric nitrogen. These results demonstrated that, in the considered experimental boundaries, biochar could improve the overall treatment performances of a green wall fed by GW and operated in challenging conditions

A review of nature-based solutions for greywater treatment: Applications, hydraulic design, and environmental benefits

Abstract Recognizing greywater as a relevant secondary source of water and nutrients represents an important chance for the sustainable management of water resource. In the last two decades, many studies analysed the environmental, economic, and energetic benefits of the reuse of greywater treated by nature-based solutions (NBS). This work reviews existing case studies of traditional constructed wetlands and new integrated technologies (e.g., green roofs and green walls) for greywater treatment and reuse, with a specific focus on their treatment performance as a function of hydraulic operating parameters. The aim of this work is to understand if the application of NBS can represent a valid alternative to conventional treatment technologies, providing quantitative indications for their design. Specifically, indications concerning threshold values of hydraulic design parameters to guarantee high removal performance are suggested. Finally, the existing literature on life cycle analysis of NBS for greywater treatment has been examined, confirming the provided environmental benefits

Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos

Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if the endoscopic camera is moved out of the body of patients and out-of-body scenes are recorded. Therefore, identification of out-of-body scenes in endoscopic videos is of major importance to preserve the privacy of patients and operating room staff. This study developed and validated a deep learning model for the identification of out-of-body images in endoscopic videos. The model was trained and evaluated on an internal dataset of 12 different types of laparoscopic and robotic surgeries and was externally validated on two independent multicentric test datasets of laparoscopic gastric bypass and cholecystectomy surgeries. Model performance was evaluated compared to human ground truth annotations measuring the receiver operating characteristic area under the curve (ROC AUC). The internal dataset consisting of 356,267 images from 48 videos and the two multicentric test datasets consisting of 54,385 and 58,349 images from 10 and 20 videos, respectively, were annotated. The model identified out-of-body images with 99.97% ROC AUC on the internal test dataset. Mean +/- standard deviation ROC AUC on the multicentric gastric bypass dataset was 99.94 +/- 0.07% and 99.71 +/- 0.40% on the multicentric cholecystectomy dataset, respectively. The model can reliably identify out-of-body images in endoscopic videos and is publicly shared. This facilitates privacy preservation in surgical video analysis

Mejora del proceso enseñanza-aprendizaje en laboratorios de ingeniería masificados

Este trabajo describe una metodología de enseñanza de laboratorio para estudiantes universitarios que utiliza tecnologías digitales y aplicaciones de software (Matlab). Los estudiantes tienen acceso en Moodle a un guion de cada práctica, así como a un video explicativo y preguntas insertadas en el mismo con H5P. Luego asisten a una sesión de laboratorio presencial en grupos reducidos, registrando los datos correspondientes. Posteriormente y mediante una aplicación de Matlab desarrollada “ad-hoc” el alumno genera su informe de prácticas y lo entrega vía Moodle. Los resultados son bastante buenos pues los alumnos mejoran sistemáticamente sus notas del laboratorio. Además, sus apreciaciones en base a encuestas son excelentes con notas en torno al 4,5 sobre 5. La metodología busca reducir el trabajo repetitivo y enfocar en el razonamiento y comprensión completa del problema