Mitigation Plan and Water Harvesting of Flashflood in Arid Rural Communities Using Modelling Approach: A Case Study in Afouna Village, Egypt

Assessment of hybrid fixed and moving bed biofilm applications for wastewater treatment capacity increase - In situ tests in El-Gouna WWTP, Egypt

This paper provides a procedure for comparing the performance of different biofilm carrier medias and their surrounding suspended biomass through oxygen uptake rate (OUR) tests. For in situ (oxygen uptake rate (OUR) measurements, three identical lab scale biofilm reactors were set up at the El Gouna wastewater treatment plant (WWTP). In this setup, two options of media for moving-bed biofilm reactors (MBBR) and one media for fixed-bed biofilm reactors (FBBR) were compared. The WWTP also used the same carrier in a real scale hybrid application to analyze how the interactions between the carrier type and the suspended biomass influences the overall performance. The in situ OUR approach is recommended to measure the contribution of the biofilm fixed biomass under site specific conditions. Specifically, settleability and diffusion limitations are the two opposite poles that cannot be predicted adequately for mild climate conditions based on the literature. A biofilm carrier application can add but actually can also reduce the capacity in a hybrid activated sludge system: The added MBBR-media was able to grind down the sludge flocs forming a poorly settleable suspended biomass. The added FBBR-media can lead to extracellular polymeric substances (EPS) rich biofilms that contribute very little as substrate and oxygen are unavailable for the microorganisms present in the biofilm. In this application of the comparison procedure, Kaldnes K1 like MBBR media was compared with a recycling MBBR carrier option (poly propylene bottle caps) and Jäger Envirotech "BioCurlz™" FBBR media. The study showed higher average rates for the MBBR but decreased settleability. The FBBR showed higher peak rates when flushed to break up the biofilm and well settleable sludge. The determination of OUR per g of volatile solids (SOUR) showed comparable results for all the carriers and in warm conditions, only the capacity to accommodate biomass determines the contribution of the carrier.

Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms

In this article, the local spatial correlation of multiple remote sensing datasets, such as those from Sentinel-1, Sentinel-2, and digital surface models (DSMs), are linked to machine learning (ML) regression algorithms for flash floodwater depth retrieval. Edge detection filters are applied to remote sensing images to extract features that are used as independent features by ML algorithms to estimate flood depths. Data of dependent variables were obtained from the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 2D) simulation model, as applied to the New Cairo, Egypt, post-flash flood event from 24–26 April 2018. Gradient boosting regression (GBR), random forest regression (RFR), linear regression (LR), extreme gradient boosting regression (XGBR), multilayer perceptron neural network regression (MLPR), k-nearest neighbors regression (KNR), and support vector regression (SVR) were used to estimate floodwater depths; their outputs were compared and evaluated for accuracy using the root-mean-square error (RMSE). The RMSE accuracy for all ML algorithms was 0.18–0.22 m for depths less than 1 m (96% of all test data), indicating that ML models are relatively portable and capable of computing floodwater depths using remote sensing data as an input.

Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook

A viral outbreak is a global challenge that affects public health and safety. The coronavirus disease 2019 (COVID-19) has been spreading globally, affecting millions of people worldwide, and led to significant loss of lives and deterioration of the global economy. The current adverse effects caused by the COVID-19 pandemic demands finding new detection methods for future viral outbreaks. The environment's transmission pathways include and are not limited to air, surface water, and wastewater environments. The wastewater surveillance, known as wastewater-based epidemiology (WBE), can potentially monitor viral outbreaks and provide a complementary clinical testing method. Another investigated outbreak surveillance technique that has not been yet implemented in a sufficient number of studies is the surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the air. Artificial intelligence (AI) and its related machine learning (ML) and deep learning (DL) technologies are currently emerging techniques for detecting viral outbreaks using global data. To date, there are no reports that illustrate the potential of using WBE with AI to detect viral outbreaks. This study investigates the transmission pathways of SARS-CoV-2 in the environment and provides current updates on the surveillance of viral outbreaks using WBE, viral air sampling, and AI. It also proposes a novel framework based on an ensemble of ML and DL algorithms to provide a beneficial supportive tool for decision-makers. The framework exploits available data from reliable sources to discover meaningful insights and knowledge that allows researchers and practitioners to build efficient methods and protocols that accurately monitor and detect viral outbreaks. The proposed framework could provide early detection of viruses, forecast risk maps and vulnerable areas, and estimate the number of infected citizens.

Spaceborne C-band SAR remote sensing-based flood mapping and runoff estimation for 2019 flood scenario in Rupnagar, Punjab, India

Floods are one of the most disastrous and dangerous catastrophes faced by humanity for ages. Though mostly deemed a natural phenomenon, floods can be anthropogenic and can be equally devastating in modern times. Remote sensing with its non-evasive data availability and high temporal resolution stands unparalleled for flood mapping and modelling. Since floods in India occur mainly in monsoon months, optical remote sensing has limited applications in proper flood mapping owing to lesser number of cloud-free days. Remotely sensed microwave/synthetic aperture radar (SAR) data has penetration ability and has high temporal data availability, making it both weather independent and highly versatile for the study of floods. This study uses space-borne SAR data in C-band with VV (vertically emitted and vertically received) and VH (vertically emitted and horizontally received) polarization channels from Sentinel-1A satellite for SAR interferometry-based flood mapping and runoff modeling for Rupnagar (Punjab) floods of 2019. The flood maps were prepared using coherence-based thresholding, and digital elevation map (DEM) was prepared by correlating the unwrapped phase to elevation. The DEM was further used for Soil Conservation Service-curve number (SCS-CN)-based runoff modelling. The maximum runoff on 18 August 2019 was 350 mm while the average daily rainfall was 120 mm. The estimated runoff significantly correlated with the rainfall with an R² statistics value of 0.93 for 18 August 2019. On 18 August 2019, Rupnagar saw the most devastating floods and waterlogging that submerged acres of land and displaced thousands of people.