Airborne particulate matter measurement and prediction with machine learning techniques.

Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated.

Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway.

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.

An analysis of the PM10 chemical composition and its spatial and seasonal variation in Piedmont (Italy) using Raman spectroscopy.

Particulate Matter (PM) dramatically affects the well-being of a growing global population, particularly in urban areas. While air quality control is an important and pressing issue, particulate matter analysis typically focuses on size distribution and concentration, offering limited insights into chemical composition and pollutant sources. This study analyzes PM10 samples collected from five air quality monitoring stations across the Piedmont region. Specifically, the two of the stations are located in the urban environment of Turin, a city known as one of Europe's most polluted cities. The analysis has been carried out using primarily Raman Spectroscopy (RS) to identify the main PM components, investigate the different PM compositions, and evaluate the chemical and seasonal variations. Scanning Electron Microscopy (SEM) equipped with an Energy Dispersion X-ray spectrophotometer (EDX) has also been used to obtain further information about the elemental composition and the size distribution. Amorphous carbon, nitrate salt, sulfate salt, iron oxides, and quartz are the main compounds found. The results of our study highlight significant differences in the chemical composition of PM10, indicating variations in the sources and characteristics of PM. Notably, higher levels of nitrate and sulfate particles are linked respectively to cold and warm seasons. Whereas, amorphous carbon and iron oxides are associated with distinct geographic features at the sampling sites, such as traffic conditions. These findings emphasize the importance of understanding the different sources and characteristics of PM10 to develop effective air pollution mitigation strategies in the Piedmont region.

Interference-resistant gold nanoparticle assay for detecting Enterococcus in fresh and marine waters.

Enterococci are common indicators of fecal contamination and are used to assess the quality of fresh and marine water, sand, soil, and sediment. However, samples collected from these environments contain various cells and other factors that can interfere with the assays used to detect enterococci. We developed a novel assay for the sensitive and specific detection of enterococci that is resistant to interference from other cells and environmental factors. Our interference-resistant assay used 30-nm gold nanoparticles (AuNPs), streptavidin, and a biotinylated Enterococcus antibody. Enterococci inhibited the interaction between streptavidin and biotin and led to the disaggregation of AuNPs. The absence of enterococci led to the aggregation of AuNPs, and this difference was easily detected by spectrophotometry. This interference-resistant AuNP assay was able to detect whole cells of Enterococcus in the range of 10 to 107 CFU/mL within 3 h, had high specificity for enterococci, and was unaffected by the presence of other intestinal bacteria, such as Escherichia coli. Our examination of fresh and marine water samples demonstrated no interference from other cells or environmental factors. The interference-resistant AuNP assay described here has the potential to be used as a rapid, simple, and effective method for monitoring enterococci in diverse environmental samples.

Control charts in healthcare quality monitoring: a systematic review and bibliometric analysis.

Control charts, used in healthcare operations to monitor process stability and quality, are essential for ensuring patient safety and improving clinical outcomes. This comprehensive research study aims to provide a thorough understanding of the role of control charts in healthcare quality monitoring and future perspectives by utilizing a dual methodology approach involving a systematic review and a pioneering bibliometric analysis. A systematic review of 73 out of 223 articles was conducted, synthesizing existing literature (1995-2023) and revealing insights into key trends, methodological approaches, and emerging themes of control charts in healthcare. In parallel, a bibliometric analysis (1990-2023) on 184 articles gathered from Web of Science and Scopus was performed, quantitatively assessing the scholarly landscape encompassing control charts in healthcare. Among 25 countries, the USA is the foremost user of control charts, accounting for 33% of all applications, whereas among 14 health departments, epidemiology leads with 28% of applications. The practice of control charts in health monitoring has increased by more than one-third during the last 3 years. Globally, exponentially weighted moving average charts are the most popular, but interestingly the USA remained the top user of Shewhart charts. The study also uncovers a dynamic landscape in healthcare quality monitoring, with key contributors, research networks, research hotspot tendencies, and leading countries. Influential authors, such as J.C. Benneyan, W.H. Woodall, and M.A. Mohammed played a leading role in this field. In-countries networking, USA-UK leads the largest cluster, while other clusters include Denmark-Norway-Sweden, China-Singapore, and Canada-South Africa. From 1990 to 2023, healthcare monitoring evolved from studying efficiency to focusing on conditional monitoring and flowcharting, with human health, patient safety, and health surveys dominating 2011-2020, and recent years emphasizing epidemic control, COronaVIrus Disease of 2019 (COVID-19) statistical process control, hospitals, and human health monitoring using control charts. It identifies a transition from conventional to artificial intelligence approaches, with increasing contributions from machine learning and deep learning in the context of Industry 4.0. New researchers and journals are emerging, reshaping the academic context of control charts in healthcare. Our research reveals the evolving landscape of healthcare quality monitoring, surpassing traditional reviews. We uncover emerging trends, research gaps, and a transition in leadership from established contributors to newcomers amidst technological advancements. This study deepens the importance of control charts, offering insights for healthcare professionals, researchers, and policymakers to enhance healthcare quality. Future challenges and research directions are also provided.

Evaluation of low-cost sensors to integrate in a water quality monitor for real-time measurements.

Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from - 0.33 to 33.77%, and most of them were ≤ 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors.

A multidirectional pairwise coupling approach with spectral features unmixing to quantify total phosphorus, total nitrogen, and chlorophyll-a in urban rivers.

Comprehensive and effective water quality monitoring is vital to water environment management and prevention of water quality from degradation. Unmanned aerial vehicle (UAV) remote sensing techniques have gradually matured and prevailed in monitoring water quality of urban rivers, posing great opportunity for more effective and flexible quantitative estimation of water quality parameter (WQP) than satellite remote sensing techniques. However, current UAV remote sensing methods often entail large quantities of cost-prohibitive in-situ collected training samples with corresponding chemical analysis in different monitoring watersheds, laying time and fiscal pressure on local environmental protection department. They suffer relatively low calculation accuracy and stability and their applicability in various watersheds is constrained. This study developed a unified two-stage method, multidirectional pairwise coupling (MDPC) with information sharing and delivery of different modeling stages to efficiently predict concentrations of WQPs including total phosphorus (TP), total nitrogen (TN), and chlorophyll-a (Chl-a) from hyperspectral data. MDPC incorporates exterior and interior feature interaction and gravity model variant to improve prediction accuracy and stability with consideration of mutual effect in the proximity. The structure design and workflow of MDPC ensure high robustness and application prospect due to achievement of good performance with less training samples, improving applicability and feasibility. The experiments show that MDPC has achieved good performance on retrieval of WQPs concentrations including TP, TN, and Chl-a, the results mean absolute percent error (MAPE) and coefficient of determination (R2) ranging from 6.34 % to 11.94 % and from 0.74 to 0.93. This study provides a systematic and scientific reference to formulate a feasible and efficient water environment management scheme.

A risk assessment framework utilizing bivariate copula for contaminate monitoring in groundwater.

Regular groundwater quality monitoring in resource-constrained regions present formidable challenges in terms of funding, testing facilities and manpower; necessitating the development of easily implementable monitoring techniques. This study proposes a copula-based risk assessment model utilizing easily measurable indicators (e.g., turbidity, alkalinity, pH, total dissolved solids (TDS), conductivity), to monitor the contaminates in groundwater which are otherwise difficult to measure (i.e., iron, nitrate, sulfate, fluoride, etc.). Preliminary correlation between the indicators and the target contaminates were identified using Pearson coefficient. Best representative univariate distributions for these pairs were selected using the Akaike Information Criterion (AIC), which were used in the formulation of the copula model. Validation against observed data showcased the model's high accuracy, supported by consistent Kendall Tau correlation coefficients. Through this model, conditional probabilities of the contaminants not exceeding the permissible limits set by the Bureau of Indian Standards (BIS) were calculated using indicator concentration. Notably, an inverse correlation between iron concentration and conductivity was noted, with the likelihood of iron exceeding BIS limits decreasing from 90 to 50% as conductivity rose from 500 to 2000 micromhos/cm. TDS emerged as a pivotal indicator for nitrate and sulfate concentrations, with the probability of sulfate surpassing 10 mg/l decreasing from 75 to 25% as TDS increased from 250 to 750 mg/l. Likewise, the probability of nitrate exceeding 1 mg/l decreased from 90 to 60% with TDS levels reaching 1500 mg/l. Furthermore, a 63% probability of fluoride concentrations remaining below 1 mg/l was observed at turbidity levels of 0-10 NTU. These findings hold significant implications for policymakers and researchers since the model can provide crucial insights into the risks associated with the contaminates exceeding the permissible limit, facilitating the development of an efficient monitoring and management strategies to ensure safe drinking water access for vulnerable populations.

Atomically thin-layered WS2based resistive sensors for detection of CO and NO2at room temperature.

The number of layers present in a two-dimensional (2D) nanomaterial plays a critical role in applications that involve surface interaction, for example, gas sensing. This paper reports the synthesis of 2D WS2nanoflakes using the facile liquid exfoliation technique. The nanoflakes were exfoliated using bath sonication (BS-WS2) and probe sonication (PS-WS2). The thickness of the BS-WS2was found to range between 70 and 200 nm, and that of PS-WS2varied from 0.6 to 80 nm, indicating the presence of single to few layers of WS2when characterized using atomic force microscope. All the WS2samples were thoroughly characterized using electron microscopes, x-ray diffractometer, Raman spectroscopy, UV-Visible spectroscopy, Fourier transform infrared spectroscope, and thermogravimetric analyser. Both the nanostructured samples were exposed to 2 ppm of NO2at room temperature. Interestingly, BS-WS2which comprises of a greater number of WS2layers exhibited -14.2% response as against -3.4% response of PS-WS2, the atomically thin sample. The BS-WS2sample was found to be highly selective towards NO2but was slower (with incomplete recovery) as compared to PS-WS2. The PS-WS2sample was observed to exhibit -11.9% to -27.4% response to 2-10 ppm of CO and -3.4%-35.2% response to 2-10 ppm of NO2at room temperature, thereby exhibiting the potential to detect two gases simultaneously. These gases could be accurately predicted and quantified if the response times of the PS-WS2sample were considered. The atomically thin WS2-based sensor exhibited a limit of detection of 131 and 81 ppb for CO and NO2, respectively.

Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes.

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms-the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)-were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρ t), Rayleigh-corrected reflectances (ρ s), and remote sensing reflectances (R rs ). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ t showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales.

Centralized or Onsite Testing? Examining the Costs of Water Quality Monitoring in Rural Africa.

Rural water systems in Africa have room to improve water quality monitoring. However, the most cost-effective approach for microbial water testing remains uncertain. This study compared the cost per E. coli test (membrane filtration) of four approaches representing different levels of centralization: (i) one centralized laboratory serving all water systems, (ii) a mobile laboratory serving all systems, (iii) multiple semi-centralized laboratories serving clusters of systems, and (iv) decentralized analysis at each system. We employed Monte Carlo analyses to model the costs of these approaches in three real-world contexts in Ghana and Uganda and in hypothetical simulations capturing various conditions across rural Africa. Centralized testing was the lowest cost in two real-world settings and the widest variety of simulations, especially those with water systems close to a central laboratory (<36 km). Semi-centralized testing was the lowest cost in one real-world setting and in simulations with clustered water systems and intermediate sampling frequencies (1-2 monthly samples per system). The mobile lab was the lowest cost in the fewest simulations, requiring few systems and infrequent sampling. Decentralized testing was cost-effective for remote systems and frequent sampling, but only if sampling did not require a dedicated vehicle. Alternative low-cost testing methods could make decentralized testing more competitive.

Breaking barriers in passive sampling: The potential of PTFE membranes in the monitoring of hydrophilic micropollutants.

Passive samplers are key tools to sample hydrophilic micropollutants in water. Two main approaches address the influence of hydrodynamics: (1) determining site-specific sampling rate (RS) by characterizing kw, the mass transfer coefficient of the water-boundary layer (WBL), and (2) reducing WBL impact using a diffusive material to control the uptake. The first requires calibration data and the second has only been achieved using fragile diffusive material. This study assesses the transfer of hydrophilic contaminants through polytetrafluoroethylene (PTFE; 30 µm thick), a new membrane material with lower sorption than commonly used polyethersulfone (PES). Combined for the first time in a Chemcatcher-like configuration, we calibrated the modified samplers for 44 micropollutants to provide RS - kw relationships for in-situ RS determination (approach 1). Micropollutants accumulated over 2000 times more on the sorbent than on PTFE. PTFE-based RS (0.027 to 0.300 L day-1) were 2.5 higher than previously reported with PES. Membrane property measurements (porosity, tortuosity) indicated that accumulation is primarily controlled by the membrane. Extrapolation indicated that using thicker PTFE membranes (≥ 100 µm) would shift uptake control entirely to the membrane in river conditions (approach 2). This finding could enable RS prediction based on contaminants properties, thus representing a significant advancement in passive sampling.

Automated identification of toxigenic cyanobacterial genera for water quality control purposes.

Cyanobacteria are the dominating microorganisms in aquatic environments, posing significant risks to public health due to toxin production in drinking water reservoirs. Traditional water quality assessments for abundance of the toxigenic genera in water samples are both time-consuming and error-prone, highlighting the urgent need for a fast and accurate automated approach. This study addresses this gap by introducing a novel public dataset, TCB-DS (Toxigenic Cyanobacteria Dataset), comprising 2593 microscopic images of 10 toxigenic cyanobacterial genera and subsequently, an automated system to identify these genera which can be divided into two parts. Initially, a feature extractor Convolutional Neural Network (CNN) model was employed, with MobileNet emerging as the optimal choice after comparing it with various other popular architectures such as MobileNetV2, VGG, etc. Secondly, to perform classification algorithms on the extracted features of the first section, multiple approaches were tested and the experimental results indicate that a Fully Connected Neural Network (FCNN) had the optimal performance with weighted accuracy and f1-score of 94.79% and 94.91%, respectively. The highest macro accuracy and f1-score were 90.17% and 87.64% which were acquired using MobileNetV2 as the feature extractor and FCNN as the classifier. These results demonstrate that the proposed approach can be employed as an automated screening tool for identifying toxigenic Cyanobacteria with practical implications for water quality control replacing the traditional estimation given by the lab operator following microscopic observations. The dataset and code of this paper are publicly available at https://github.com/iman2693/CTCB.

Optimizing sampling location for water quality degradation monitoring in distribution systems: Assessing global representativeness and potential health risk.

Selecting the optimal monitoring points in a water distribution network is challenging due to the complex spatiotemporal variability of water quality degradation. The lack of a standardized methodology for monitoring point selection forces operators to rely on general recommendations, historical data and professional experience, which can mask water quality problems and increase the risk to consumers. This study proposes a new methodology to optimize the selection of monitoring points in distribution networks. The method considers the spatiotemporal degradation of water quality, the definition of representative zones and two selection criteria: global representativeness and potential health risk. Representative zones were determined for each node of the network based on hydraulic paths and their water quality spatial variability. Part of the distribution network in Quebec City, Canada was used as the case study, in which four water quality parameters were investigated: free chlorine residual (FRC), heterotrophic plate counts (HPC), trihalomethanes (THMs) and haloacetic acids (HAAs). Seasonal variabilities (summer and winter) were also analyzed. The results obtained for the two criteria and for both seasons were compared, and methodological and practical recommendations were established for dynamic monitoring programs that respond to the needs of operators.

Breathing in danger: Understanding the multifaceted impact of air pollution on health impacts.

Air pollution, a pervasive environmental threat that spans urban and rural landscapes alike, poses significant risks to human health, exacerbating respiratory conditions, triggering cardiovascular problems, and contributing to a myriad of other health complications across diverse populations worldwide. This article delves into the multifarious impacts of air pollution, utilizing cutting-edge research methodologies and big data analytics to offer a comprehensive overview. It highlights the emergence of new pollutants, their sources, and characteristics, thereby broadening our understanding of contemporary air quality challenges. The detrimental health effects of air pollution are examined thoroughly, emphasizing both short-term and long-term impacts. Particularly vulnerable populations are identified, underscoring the need for targeted health risk assessments and interventions. The article presents an in-depth analysis of the global disease burden attributable to air pollution, offering a comparative perspective that illuminates the varying impacts across different regions. Furthermore, it addresses the economic ramifications of air pollution, quantifying health and economic losses, and discusses the implications for public policy and health care systems. Innovative air pollution intervention measures are explored, including case studies demonstrating their effectiveness. The paper also brings to light recent discoveries and insights in the field, setting the stage for future research directions. It calls for international cooperation in tackling air pollution and underscores the crucial role of public awareness and education in mitigating its impacts. This comprehensive exploration serves not only as a scientific discourse but also as a clarion call for action against the invisible but insidious threat of air pollution, making it a vital read for researchers, policymakers, and the general public.

How suitable is freshwater sponge Ephydatia fluviatilis (Linnaeus, 1759) for time-integrated biomonitoring of microbial water quality?

Faecal pollution of water by bacteria has a negative effect on water quality and can pose a potential health hazard. Conventional surveillance of microbial water quality relies on the analysis of low-frequency spot samples and is thus likely to miss episodic or periodic pollution. This study aimed to investigate the potential of filter-feeding sponges for time-integrated biomonitoring of microbial water quality. Laboratory trials tested the effects of different ratios of bacterial abundance and the sequence of exposure on bacterial retention by the freshwater sponge Ephydatia fluviatilis (Linnaeus, 1759) to establish its potential to indicate bacterial exposure. Gemmule grown sponges were simultaneously exposed to Escherichia coli and Enterococcus faecalis but at different ratios (Trial 1) or individually exposed to each bacterial species but in different sequential order (Trial 2). The E. coli and E. faecalis retained in each sponge was quantified by culture on selective agars. Data analysis was conducted using the Kruskal-Wallis test and/or the Mann-Whitney U test to compare between the numbers of bacteria retained in each treatment. Additionally, the Wilcoxon matched-paired signed-rank test was used for comparison of the different bacterial abundances retained within each individual sponge. Sponges from all trials retained E. coli and E. faecalis in small numbers relative to the exposure (<0.05 % Trial 1 and <0.07 % Trial 2) but exhibited higher retention of E. coli. Higher abundance of either bacterial species resulted in significantly lower (P<0.005) retention of the same species within sponges (Trial 1). An initial exposure to E. coli resulted in significantly higher (P=0.040) retention of both bacterial species than when sponges were exposed to E. faecalis first (Trial 2).Bacterial retention by sponges was neither quantitatively representative of bacterial abundance in the ambient water nor the sequence of exposure. This implies either selective filtration or an attempt by sponges to prevent infection. However, freshwater sponges may still be useful in biomonitoring as qualitative time-integrated samplers of faecal indicator bacteria as they detect different bacteria present in the water even if their quantities cannot be estimated.

Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors.

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.

Evaluation of the water quality monitoring network layout based on driving-pressure-state-response framework and entropy weight TOPSIS model: A case study of Liao River, China.

The establishment of river water quality monitoring network is crucial for watershed protection. However, the evaluation process of monitoring network layout involves significant subjectivity and has not yet to form a complete indicator system. This study constructed an indicator system based on the DPSR (Driving-Pressure-State-Response) framework in the Liao River Basin, China. SWAT model and ArcGIS were used to quantify the indicators. And the entropy weight-TOPSIS method was employed to rank monitoring points. The results showed that pressure and state indicators had a greater impact on the network layout, with the indicator for proportion of land use in residential areas carrying the largest weight of 0.136. It suggested that the risk of river pollution remained high, and the governance strategies needed to be improved. Priority monitoring points were mainly located in the east and middle of the basin, consistent with the distribution of human activities such as urban areas and farmland. In addition, the redundancy of points should be avoided, and evaluation results should be adjusted based on the actual situation. The study provided an evaluation method for the layout of monitoring points.

A regulatory framework for bottled water quality monitoring: A case of Emfuleni local municipality.

Background: The quality of drinking water has recently become of utmost concern to consumers worldwide, especially in areas where Water Service Authorities (WSAs) failed to provide safe water. To combat this challenge, government entities regulate water to ensure that safe water is provided. The Emfuleni Local Municipality (ELM) has experienced cases of water contamination by human excretion, whereby communities were affected. As a result, there was a sharp increase in bottled water (BW) use, which however gave rise to unregulated and counterfeit versions of popular brands. This situation poses threats to public health. Aim: This study sought to determine the regulation of drinking water and to assess whether environmental health practitioners (EHPs) monitor the quality of water sources (BW and tap water) in ELM as outlined by the National Environmental Health Norms and Standards (NEHNS). Settings: The study was conducted in the Emfuleni Local Municipality in South Africa. Methods: A quantitative cross-sectional study design was employed in this research. Fifteen online questionnaires using a Google Forms survey were distributed amongst all EHPs servicing ELM. Secondary data that included the Integrated Development Plan (IDP) and Service Delivery Budget Implentation Plan (SDBIP) for the 2017-2020 financial years were also evaluated, specifically for water quality monitoring (tap and bottled water). The dataset was analysed using the Statistical Package for the Social Sciences (SPSS) version 29. Results: Due to complexity in the legislation and NEHNS in relation to Municipal Health Services (MHS), bottled water was not sampled at all. A number of EHPs were also not conversant with the regulations governing BW. Moreover, NEHNS consider bottled water as food, which does not fall under the MHS. Conclusion: There should be clarity in the legislation to ensure that bottled water monitoring is intensified to protect public health within the WSAs. Contribution: The findings of this study could assist policy-makers to make informed decisions on water quality monitoring, as well as clarify legislative issues on bottled water.

Predicting leachate impact on groundwater using electrical conductivity and oxidation-reduction potential measurements: An empirical and theoretical approach.

This study developed innovative predictive models of groundwater pollution using in situ electrical conductivity (EC) and oxidation-reduction potential (ORP) measurements at livestock carcass burial sites. Combined electrode analysis (EC and ORP) and machine learning techniques efficiently and accurately distinguished between leachate and background groundwater. Two models-empirical and theoretical-were constructed based on a supervised classification framework. The empirical model constructs a classifier with high accuracy, sensitivity, and specificity, utilizing the comprehensive in situ EC and ORP measurements. The theoretical model with only two end members achieves comparable performance by simulating the leachate-groundwater interactions using a geochemical mixing model. Besides enhancing the early detection capabilities, our approach considerably reduces the reliance on extensive hydrochemical analyses, thus streamlining the monitoring process. Moreover, the use of field parameters was found to proactively identify potential pollution incidents, enhancing the efficiency of groundwater monitoring strategies. Our approach is applicable to various waste disposal sites, indicating its extensive potential for environmental monitoring and management.