Monitoring Pollutants in Wastewater: Traditional Lab Based versus Modern Real-Time Approaches

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These predictive insights play a crucial role in decision-making processes aimed at safeguarding water quality, such as identifying areas in need of immediate attention and implementing preventive measures to avert contamination. This paper aims to provide a comprehensive review of the current state of the art in water quality monitoring, with a specific focus on the employment of IoT wireless technologies and ML techniques. The study examines the utilization of a range of IoT wireless technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, in the context of monitoring water quality. Furthermore, it explores the application of both supervised and unsupervised ML algorithms for analyzing and interpreting the collected data. In addition to discussing the current state of the art, this survey also addresses the challenges and open research questions involved in integrating IoT wireless technologies and ML for water quality monitoring (WQM).

Measuring Environmental Exposure to Enteric Pathogens in Low-Income Settings: Review and Recommendations of an Interdisciplinary Working Group

Infections with enteric pathogens impose a heavy disease burden, especially among young children in low-income countries. Recent findings from randomized controlled trials of water, sanitation, and hygiene interventions have raised questions about current methods for assessing environmental exposure to enteric pathogens. Approaches for estimating sources and doses of exposure suffer from a number of shortcomings, including reliance on imperfect indicators of fecal contamination instead of actual pathogens and estimating exposure indirectly from imprecise measurements of pathogens in the environment and human interaction therewith. These shortcomings limit the potential for effective surveillance of exposures, identification of important sources and modes of transmission, and evaluation of the effectiveness of interventions. In this review, we summarize current and emerging approaches used to characterize enteric pathogen hazards in different environmental media as well as human interaction with those media (external measures of exposure), and review methods that measure human infection with enteric pathogens as a proxy for past exposure (internal measures of exposure). We draw from lessons learned in other areas of environmental health to highlight how external and internal measures of exposure can be used to more comprehensively assess exposure. We conclude by recommending strategies for advancing enteric pathogen exposure assessments.

A Review of State-of-the-Art Microfluidic Technologies for Environmental Applications: Detection and Remediation

Microfluidic systems have advanced beyond natural and life science applications and lab-on-a-chip uses. A growing trend of employing microfluidic technologies for environmental detection has emerged thanks to the precision, time-effectiveness, and cost-effectiveness of advanced microfluidic systems. This paper reviews state-of-the-art microfluidic technologies for environmental applications, such as on-site environmental monitoring and detection. Microdevices are extensively used in collecting environmental samples as a means to facilitate detection and quantification of targeted components with minimal quantities of samples. Likewise, microfluidic-inspired approaches for separation and treatment of contaminated water and air, such as the removal of heavy metals and waterborne pathogens from wastewater and carbon capture are also investigated.

The Next-Generation PCR-Based Quantification Method for Ambient Waters: Digital PCR

Real-time quantitative PCR (qPCR) is increasingly being used for ambient water monitoring, but development of digital polymerase chain reaction (digital PCR) has the potential to further advance the use of molecular techniques in such applications. Digital PCR refines qPCR by partitioning the sample into thousands to millions of miniature reactions that are examined individually for binary endpoint results, with DNA density calculated from the fraction of positives using Poisson statistics. This direct quantification removes the need for standard curves, eliminating the labor and materials associated with creating and running standards with each batch, and removing biases associated with standard variability and mismatching amplification efficiency between standards and samples. Confining reactions and binary endpoint measurements to small partitions also leads to other performance advantages, including reduced susceptibility to inhibition, increased repeatability and reproducibility, and increased capacity to measure multiple targets in one analysis. As such, digital PCR is well suited for ambient water monitoring applications and is particularly advantageous as molecular methods move toward autonomous field application.

Characterization and evaluation of phosphate microsensors to monitor internal phosphorus loading in Lake Erie sediments

Monitoring phosphate concentration is very important to prevent and control eutrophication in natural waters. In this study, cobalt-based microsensors were modified, characterized, and tested to monitor internal soluble phosphorous (SRP) loading in lakes with improved detection limits. The effectiveness of surface modification on the performance of a cobalt-based microelectrode was fully examined by determining detection limit, response time, selectivity, interference with ions (sulfate, nitrate, and nitrite) and dissolved oxygen (DO). To assess their performance, phosphate sensors were applied to sediment samples collected from Lake Erie. SRP loading from sediments was determined under different DO conditions. After increasing the phosphate sensing area and modifying the surface, phosphate microsensors showed an increased detection limit of up to 10(-8) M concentration of phosphate ion. The phosphate microsensor also showed its ability to measure sediment SRP profiling without disturbing sediment structure, and diffusion coefficients of phosphate in sediment could be determined under both oxic and anoxic conditions. Modified phosphate sensors showed improved sensitivity and could be applied to both water and sediment samples with high spatial resolution; however, signal interferences (especially with oxygen) required consideration during sample analysis. Overall, obtained results showed that phosphate microsensors can be an effective tool for measurement of phosphate in lake water and sediment samples for SRP monitoring.