A simple clogging and backwashing efficiency model for filtration of arsenic-contaminated water

Filtration is a very basic and primitive technique of water treatment. For many remote, under-privileged and poor communities, this is the only pre-treatment of drinking water prior to boiling. With the emergence of arsenic contaminations in many groundwater aquifers, the filtration became imperative for many communities around the world. However, after repetitive/continuous uses, clogging of the filter media is obvious, which eventually causes poor performance of the filtration process. Backwashing is a common technique being used for the recovery of the filtration capacity of clogged filter media. This study presents development of a simple clogging and backwashing efficiency model for a special filter media. '3rd generation IHE family filter' was developed by UNESCO-IHE Institute for Water Education and widely used for treating arsenic-contaminated water in many countries including Bangladesh. Several field tests were conducted in three different sites in Bangladesh having different qualities of influent water. Developed model coefficients were derived using the collected data on flow measurements through the device during successive clogging and backwashing periods up to four months. Developed model with the selected model coefficients can simulate field measurements on flow retardation and recovery with good accuracy. Eventually, selected model coefficients for three sites were correlated with the respective influent water quality. It was found that the coefficients are linearly correlated with the iron and ammonium contents of inflow water

DeepRoadNet: A deep residual based segmentation network for road map detection from remote aerial image

Abstract The extraction of road networks is a critical activity in contemporary transportation networks. Deep neural networks have recently demonstrated excellent performance in the field of road segmentation. However, most of the convolutional neural network (CNN) based architectures could not verify their effectiveness in remote sensing images due to a smaller ratio of the targeted pixels, simple design, and fewer layers. In this study, a practical approach is assessed for road segmentation. The investigation was begun with basic encoder–decoder based segmentation models. Different state‐of‐the‐art segmentation models like U‐Net, V‐Net, ResUNet and SegNet were used for road network detection experiments in this research. A robust model named DeepRoadNet, a more complicated alternative, is proposed by utilizing a pre‐trained EfficientNetB7 architecture in the encoder and residual blocks as the decoder which mostly resembles the U‐Net segmentation process. The proposed model has been trained, validated as well as tested using the high‐resolution aerial image datasets and yielded good segmentation results with a mean intersection over union (mIoU) of 76%, a mean dice coefficient (mDC) of 73.18%, and an accuracy of 97.64% using Massachusetts road dataset. The proposed DeepRoadNet architecture overcomes the issues of lower mIoU, lower mDC, limited flexibility and interpretability already faced by existing models in the road segmentation field. The code is available at https://github.com/Imteaz1998/DeepRoadNet

Evaluation of the impacts of seawater integration to electrocoagulation for the removal of pollutants from textile wastewater

Recent textile industry expansion has a major environmental impact if not addressed. Being a water intensive industry, textile manufacturing is usually associated with wastewater management challenges. Electrocoagulation (EC) is recognized as one of the effective solutions to address these challenges. This study aims to investigate the potential of integrating seawater into the EC process for textile wastewater treatment, targeting optimal pollutant removal efficiencies. A simple electrolytic reactor was designed to investigate the removal efficiency of these treatments for chemical oxygen demand (COD), total suspended solids (TSS), turbidity, and color from textile wastewater at different seawater percentages and retention times. Notably, the addition of seawater not only improves the EC process efficiency but also significantly dilutes pollutants, reducing their concentrations. This dual effect enhances removal efficiency and dilution optimizes the treatment outcome. The highest removal efficiencies were achieved for COD (47.26%), TSS (99.52%), turbidity (99.30%), and color (98.19%). However, pH, dissolved oxygen (DO), and electrical conductivity increased with increasing retention times and seawater percentages in the EC process. Moreover, Seawater − EC integration reduces power usage to 15.769 Am−2 and costs approximately 0.20 USD/m3. To assess the effects of the retention times and seawater percentages on pollutant removal from textile wastewater, an analysis of variance (ANOVA) was conducted utilizing the Design-Expert 11 software. The best model obtained using Central Composite Design (CCD) was quadratic for COD (R2 = 0.9121), color (R2 = 0.9535), turbidity (R2 = 0.9525), and TSS (R2 = 0.9433). This study suggests that higher seawater percentages and longer retention times effectively eliminate contaminants but increase ion concentrations.Funder: IUT Research Seed Grants, Islamic University of Technology (REASP/IUT-RSG/\2022/OL/07/006);Full text license: CC BY</p

Modeling the impacts of best management practices (BMPs) on pollution reduction in the Yarra River catchment, Australia

Pollution of a watershed by different land uses and agricultural practices is becoming a major challenging factor that results in deterioration of water quality affecting human health and ecosystems. Sustainable use of available water resources warrants reduction of Non-Point Source (NPS) pollutants from receiving water bodies through best management practices (BMPs). A hydrologic model such as the Soil and Water Assessment Tool (SWAT) can be used for analyzing the impacts of various BMPs and implementing of different management plans for water quality improvement, which will help decision makers to determine the best combination of BMPs to maximize benefits. The objective of this study is to assess the potential reductions of sediments and nutrient loads by utilizing different BMPs on the Yarra River watershed using the SWAT model. The watershed is subdivided into 51 sub-watersheds where seven different BMPs were implemented. A SWAT model was developed and calibrated against a baseline period of 1998–2008. For calibration and validation of the model simulations for both the monthly and annual nutrients and sediments were assessed by using the Nash–Sutcliffe efficiency (NSE) statistical index. The values of the NSE were found more than 0.50 which indicates satisfactory model predictions. By utilizing different BMPs, the highest pollution reduction with minimal costs can be done by 32% targeted mixed-crop area. Furthermore, the combined effect of five BMPs imparts most sediments and nutrient reductions in the watershed. Overall, the selection of a BMP or combinations of BMPs should be set based on the goals set in a BMP application project. Validerad;2023;Nivå 2;2023-03-21 (johcin)</p

Calibration, validation and uncertainty analysis of a SWAT water quality model

Sediment and nutrient pollution in water bodies is threatening human health and the ecosystem, due to rapid land use changes and improper agricultural practices. The impact of the nonpoint source pollution needs to be evaluated for the sustainable use of water resources. An ideal tool like the soil and water assessment tool (SWAT) can assess the impact of pollutant loads on the drainage area, which could be beneficial for developing a water quality management model. This study aims to evaluate the SWAT model’s multi-objective and multivariable calibration, validation, and uncertainty analysis at three different sites of the Yarra River drainage area in Victoria, Australia. The drainage area is split into 51 subdrainage areas in the SWAT model. The model is calibrated and validated for streamflow from 1990 to 2008 and sediment and nutrients from 1998 to 2008. The results show that most of the monthly and annual calibration and validation for streamflow, nutrients, and sediment at the three selected sites are found with Nash–Sutcliffe efficiency values greater than 0.50. Furthermore, the uncertainty analysis of the model shows satisfactory results where the p-factor value is reliable by considering 95% prediction uncertainty and the d-factor value is close to zero. The model's results indicate that the model performs well in the river's watershed, which helps construct a water quality management model. Finally, the model application in the cost-effective management of water quality might reduce pollution in water bodies due to land use and agricultural activities, which would be beneficial to water management managers. Validerad;2024;Nivå 2;2024-04-02 (joosat);Full text: CC BY License</p