Water Quality Research Articles

Machine Learning-based Dissolved Oxygen Prediction Modeling and Evaluation in the Yangtze River Estuary

Dissolved oxygen （DO） serves as a pivotal indicator, mirroring the intrinsic self-purification capacity of aquatic ecosystems and the overarching quality of the water environment. In the context of the Yangtze Estuary, a crucial hub for biodiversity and economic activities in China, understanding and forecasting levels of DO is instrumental for effective environmental stewardship and management strategies. Considering this, the introduction of sophisticated machine learning algorithms into the monitoring and predictive analytics of dissolved oxygen levels represents an important stride toward leveraging the power of data-driven insights for environmental sustainability. The Yangtze Estuary, characterized by its dynamic and complex hydrological and ecological systems, demands an insightful and nuanced approach to monitoring water quality parameters. To this end, six key monitoring stations were chosen across the estuary, including Xuliujing, Nantong Port, Qidong Port, Qinglong Port, South Port, and North Port, acting as sentinel sites for gauging the health of the water body. Leveraging three cutting-edge modeling techniques-particle swarm optimization-support vector regression （PSO-SVR）, artificial neural network （ANN）, and random forest （RF）-the research unraveled and forecasted the patterns of dissolved oxygen levels using monthly average water quality data spanning from 2004 to 2020. These models embodied the forefront of machine learning technology, each bringing distinct analytical strengths and perspectives to the table, from the nuanced, non-linear pattern recognition capabilities of ANN to the robustness and interpretability of RF. The meticulous evaluation conducted via the RF model underscored the paramount importance of three water quality variables, namely temperature, five-day biochemical oxygen demand, and ammonia nitrogen, in influencing the spatial-temporal dynamics of dissolved oxygen in the estuary. Comparative analysis of the prediction results yielded by the PSO-SVR, ANN, and RF models illuminated the superior performance of the RF model across the six monitoring stations, with an overall average error margin of 0.19, a testament to its efficacy and reliability. In comparison, the PSO-SVR and ANN models exhibited higher error rates of 0.38 and 0.47, respectively, albeit still contributing valuable insights into the complex dissolved oxygen dynamics in the Yangtze Estuary. The prediction performance of the machine learning models was evaluated, and the overall prediction performance ranking on the training set was RF （R2=0.971； RMSE=0.341 mg·L-1） > PSO-SVR （R2=0.884； RMSE=0.707 mg·L-1） > ANN （R2=0.792； RMSE=0.967 mg·L-1）. The overall prediction performance ranking on the test set was RF （R2 = 0.986； RMSE=0.165 mg·L-1） > PSO-SVR （R2=0.951； RMSE=0.332 mg·L-1） > ANN （R2=0.800； RMSE=0.633 mg·L-1）. Therefore, the RF model exhibited the best predictive ability on all monitoring sections, showing excellent performance and generalization ability both on the training and the test sets. The PSO-SVR model also performed well on most monitored profiles, with slightly lower predictive performance than that of the RF model though with better stability and generalization ability. However, the ANN model did not perform as perfectly as the other two models in some monitoring profiles and its network structure or parameters may need to be further optimized to improve the prediction accuracy and stability.

Effects of Storm Runoff on Bacterial Community Structure and Metabolic Activity and Water Quality in a Drinking Water Reservoir

To explore the influence of storm runoff on bacterial metabolic activity and water quality in Jinpen Reservoir, three-dimensional fluorescence spectroscopy parallel factor analysis, high-throughput DNA sequencing technology, and Biolog technology were used to analyze variations in the components of dissolved organic matter, bacterial community composition, and microbial metabolic activity during three periods （before, during, and one month after runoff）. The results showed that after storm runoff into the reservoir, the water temperature decreased, and dissolved oxygen in the middle of the water column increased significantly. Finally, the aerobic and low-temperature state in the middle water and anaerobic and low-temperature state in the bottom water was formed. Simultaneously, the concentration of total nitrogen, total phosphorus, organic matter, and turbidity increased significantly. After one month of runoff, the concentration of pollutant decreased, however, was still higher than that before runoff. Further, during the runoff period, the proportion of Proteobacteria and Bacteroidetes increased significantly and the sum of their relative abundance accounted for 63.6% of the bacterial community. The bacterial metabolic activity was also higher than that before. One month after runoff, the bacterial population structure did not recover and the bacterial metabolic activity still remained at a high level.

Enrichment Characteristics, Risk Evaluation, and Source Apportionment of Heavy Metals in Wabu Lake of Yangtze River to Huaihe River Water Diversion Project

Wabu Lake serves as a crucial junction within the Yangtze River to Huai River Water Diversion Project （Yangtze-Chao Lake-Wabu Lake-Huai River）, where clarifying the status and risk levels of heavy metal contamination in the lake is crucial for the aquatic ecological safety along the route of the project. Surface water and sediment samples from 24 locations in Wabu Lake were collected and analyzed for the contents of heavy metals （As, Zn, Pb, Ni, Cu, Cr, Fe, and Cd）. Risk evaluation and source apportionment of heavy metals in the lake were conducted using the human risk assessment （HRA） model, sediment quality guidelines （SQGs）, and positive matrix factorization （PMF） method. The results showed that the average concentrations of heavy metals （except for Cd and Pb） in the surface water were lower than the class III limits of the surface water quality standard （GB 3838-2002）. The average concentration of heavy metals （As, Zn, Pb, Ni, Cu, Cr, Fe, and Cd） in interstitial water were lower than the Criteria Continuous Concentration （CCC） of the National Recommended Water Quality Criteria. The average contents of As, Zn, Pb, Ni, Cu, Cr, and Cd in the sediment exceeded the background values by factors of 1.78, 1.85, 1.42, 1.77, 1.41, 1.38, and 3.46, respectively. In addition, the heavy metals Pb, Cd, and Cr in the Wabu Lake water posed severe risks to human health, with the cancer risk （CR） value for Cr reaching 6.14×10-5. The sediment quality guidelines （SQGs） indicated that the probability of adverse effects on lakes by As, Ni, and Cr was relatively high. The spatial distribution of risk areas showed that the risk control zones for As, Cr, and Ni in Wabu Lake covered almost the entire lake area. The PMF analysis indicated that the primary sources of heavy metals in Wabu Lake were exogenous inputs from industrial, agricultural, and residential activities in surrounding areas, followed by endogenous pollution caused by enclosed aquaculture. The subsequent focus should be on the pollution of heavy metals Cr, Cd, and Pb in the overlying water of Wabu Lake as well as the sediment ecological safety of As, Cr, and Ni in the sediment.

Application of multivariate statistical techniques in the assessment of long-term surface water quality in Dong Thap Province, Vietnam.

This study assessed the surface water quality in Dong Thap province during 2013-2023 using multivariate statistical techniques. Water quality index (WQI), Pearson correlation, principal component analysis (PCA), and cluster analysis (CA) were determined based on monitoring data for parameters. The findings showed that only some water samples had pH values below the permissible standard, while 95.5% of samples exceeded the standard of Escherichia coli in surface water. In some years, 100% samples having BOD5, TSS, NH4+, and E. coli exceeded the permissible limits. The PCA identified four, five, and seven major potential sources during 2013-2015, 2016-2020, and 2021-2023, respectively, explaining 75.9%, 63.0%, and 63.0% of variation in the corresponding duration times. For heavy metals, the number of water samples containing As, Cu, and Pb exceeding the water permissible limits were 15.5%, 21.4%, and 2.8% samples in 2021, respectively, but no heavy metal was above standard in 2022 and 2023. The evaluation of average water quality based on WQI showed that 79.31% and 76.19% of the sample sites were classified to be average during 2016-2020 and 2021-2023, respectively, and others were bad water quality. However, the water quality showed an improving trend with 70% of monitoring positions considered as good water quality in 2023. The results showed that anthropogenic activities and hydrological regime were the main source of pollution. This study provides guidance to policymakers in developing water management strategies to have proper actions to prevent water pollution.

Quantitative study on the relationship between land use and land cover and diatom community in urban streams.

The study of the impact of urbanization on river ecosystems is an important part of constructing sustainable cities. How to quantitatively study the impact of urbanization on river ecosystems is the difficulty of urban ecological research. This study quantitatively investigated the effects of LULC on water quality and diatom assemblages in urban streams by correlation analysis, multivariate analysis, and path analysis. The results showed that (1) the percentage of LULC type in buffer 600m of reference stream sites is significantly different from that of urban stream sites. In reference sites, the average percentage of green was 94.2%, barren 3.3%, and impervious surface 2.5%. In urban upstream sites, the average percentage of impervious surface was 63.1%, green 32.8%, water 3.3%, and barren 0.8%. In urban downstream, the average percentage of impervious surface was 59.0%, green 36.5%, water 2.7%, and barren 1.8%. (2) One-way analysis results showed that water quality variables were significantly differences among the sites. The correlation analysis results indicated that LULC had a significant influence on water quality. Green had a significant negative correlation with high concentrations of NO3-N, NH4-N, and Cond. but positively correlated with MSUBST. RDA results showed that the selected water quality variables, MSUBST, and LULC types have a significant impact on the spatial patterns of the diatom assemblages. (3) Path analysis results showed that both LULC types and water quality variables exerted significant effects on diatom assemblages. This study first clarifies the quantitative relationships among LULC types, water quality, and diatom assemblages in the Beijing area. And green land was positively correlated with water quality and river ecosystems. We believed that increasing green space in urban core areas is an effective measure for improving water quality and restoring river ecosystems in the urban area.

Hydrogel-Based Sensor for the Detection of Iron Ions and Excessive Alerting.

The excessive content of heavy metal ions in water has attracted increasing worldwide attention due to the potential harm to human health and ecological systems. Therefore, it is particularly significant to research and develop a sensitive and straightforward water quality monitoring method. Here, a dual-network designed hydrogel poly(acrylic acid-co-N-methylolacrylamide)/poly(3,4-ethylenedioxythiophene): polystyrenesulfonate, P(AA-co-N-MA)/PEDOT: PSS, was constructed and used for Fe3+ detection. Compared with previous reports, the P(AA-co-N-MA)/PEDOT: PSS hydrogel has a much more sensitive and convenient detection of immune iron ions (Fe3+), is applied to monitor a trace of over-the-standard (the lower limit of detection is 0.52 ppm corresponding to 0.06 S/m for human health monitor, and the threshold for water quality monitoring is 0.16 S/m). The relationship between Fe3+ content and the conductivity of the hydrogel allows it to integrate with other electron devices to provide an early warning function for Fe3+ in the human body, which would be of great significance to human health monitoring.

Identifying Organic Chemicals with Acetylcholinesterase Inhibition in Nationwide Estuarine Waters by Machine Learning-Assisted Mass Spectrometric Screening.

Neurotoxicity is frequently observed in the global aquatic environment, threatening aquatic ecosystems and human health. However, a very limited proportion of neurotoxic effects (∼1%) has been explained by known chemicals of concern. Here, we integrated machine learning, nontargeted analysis, and in vitro biotesting to identify neurotoxic drivers of acetylcholinesterase (AChE) inhibition in estuarine waters along the coast of China. Machine learning was used to predict AChE inhibitors in a large chemical space. The prediction output was profiled into a suspect screening list to guide high-resolution mass spectrometry (HRMS) screening of AChE inhibitors in estuarine water samples. Ultimately, 60 chemicals with diverse known and presently unknown structures were identified, explaining 82.1% of the observed AChE inhibition. Polyunsaturated fatty acids were unexpectedly found to be neurotoxic drivers, accounting for 80.5% of the overall effect. This proof-of-concept study demonstrates that machine learning-based toxicological prediction can achieve a virtual fractionation role to pinpoint HRMS features with the bioactivity potential. Our approach is expected to enable rapid and comprehensive screening of organic pollutants associated with various in vitro end points for large-scale monitoring of water quality.

Unveiling the bacterial diversity and potential of the Avicennia marina ecosystem for enhancing plant resilience to saline conditions

BackgroundAvicennia marina ecosystems are critical for coastal protection, water quality enhancement, and biodiversity support. These unique ecosystems thrive in extreme saline conditions and host a diverse microbiome that significantly contributes to plant resilience and growth. Global food security is increasingly threatened by crop yield losses due to abiotic stresses, including saline soils. Traditional plant breeding for salt tolerance is both costly and time-consuming. This study explores the potential of bacteria from A. marina to enhance plant growth under saline conditions, emphasizing their ecological significance.ResultsWe analyzed the microbiome of A. marina from the Red Sea coast using high-throughput Illumina sequencing and culture-dependent methods across various compartments (bulk soil, rhizosphere, rhizoplane, roots, and leaves). Our findings revealed distinct compartment-specific microbial communities, with Proteobacteria being the dominant phylum. Functional predictions indicated diverse microbial roles in metal uptake and plant growth promotion (PGP). Remarkably, our culture-dependent methods allowed us to recover 56% of the bacterial diversity present in the microbiome, resulting in the isolation and characterization of 256 bacterial strains. These isolates were screened for PGP traits, including salt and heat tolerance, siderophore production, and pectinase activity. Out of the 77 bacterial isolates tested, 11 demonstrated a significant ability to enhance Arabidopsis growth under salt stress.ConclusionsOur study highlights the ecological significance of mangrove microbiomes and the potential of culture collections in offering innovative solutions for ecological restoration and crop production in saline conditions. The unique collection of mangrove bacteria, particularly from the rhizosphere and endophytes, showcases significant PGP traits and stress tolerance capabilities. These findings emphasize the importance of functional traits, such as salt tolerance, in the recruitment of endophytic bacteria by plants over taxonomic affiliation. The identified bacterial strains hold potential not only for developing biofertilizers to improve crop productivity but also for ecological restoration projects aimed at rehabilitating saline-degraded lands, thereby contributing to overall ecosystem health and sustainability.

Water Quality Assessment using Selected Macroinvertebrate Based Indices and Water Quality Index of Sungai Air Hitam Selangor

A study was conducted from July to December 2022 at Sungai Air Hitam, a small tributary of the Selangor River located within the Tanjung Karang Sub-basin in Malaysia (coordinates: 3° 24' 27" N, 101° 25' 54" E to 3° 28' 14" N, 101° 26' 59" E). This confluence is situated near three major downstream water treatment plants. The study assessed six water quality parameters—pH, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammonia (NH3), and suspended solids (SS)—to calculate the Water Quality Index (WQI). Macroinvertebrates were sampled simultaneously using the dipping net method to obtain biotic indices for further evaluation of water quality. The results indicated that the WQI classified Sungai Air Hitam as Class III, with scores ranging from 56.9 to 64.6, suggesting the river is suitable for water supply and fisheries. However, the Biological Monitoring Working Party (BMWP) index categorized the water quality as poor, with scores between 30 and 42. Similarly, the Average Score Per Taxon (ASPT) ranged from 3.25 to 5.25, indicating pollution or environmental impact, while the Family Biotic Index (FBI) further classified the river as having poor to very poor water quality, with scores between 6.57 and 8.11. Overall, the study suggests that Sungai Air Hitam has experienced some degree of ecological degradation. These findings emphasize the need for continuous monitoring and remediation efforts to preserve and restore water quality.

Integrating spatial-temporal features into prediction tasks: A novel method for identifying the potential water pollution area in large river basins

Forecasting potential water pollution areas (PWPA) is essential for effective watershed management. However, there remains a limited understanding of the spatial-temporal features that influence water quality (WQ), and advanced technical methods for WQ forecasting. This study developed an integrated framework utilizing spatial-temporal graph convolution networks (STGCN) to enhance comprehension of the spatial-temporal features influencing WQ and to develop a practical module for integrating features into the WQ prediction. The Pearson correlation method and seasonal decomposition analysis described the WQ features. Subsequently, the spatial-temporal distribution of PWPA was assessed using both the comprehensive pollution index method and Cressman space interpolation technique. Data from 403 monitoring stations was collected from the Yangtze River basin (YZR), encompassing pollutants such as COD, TP and NH₄⁺-N. The finding revealed that maximum concentrations of COD (36.4 mg/L), TP (4.078 mg/L) and NH₄⁺-N (13.58 mg/L) exceeded the standard thresholds necessitating early warnings. A significant correlation among pollutants was observed with coefficients ranging from 0.356 to 0.475 (P < 0.001), indicating their potential utility in predicting PWPA. Seasonality components exhibited strong correlations with the original WQ (correlation coefficients ranging from 0.62 to 0.89), followed by residuals (from 0.37 to 0.61) and trend components (from 0.25 to 0.53). The geographic layout of WQ monitoring stations along river lines resembled a graph network structure, suggesting that watershed WQ prediction can be classified as a spatial-temporal prediction task. The STGCN model achieved R2 values ranging from 0.607 to 0.844 for each pollutant on the test datasets, surpassing traditional models such as RNN, LSTM, and GRU in predictive accuracy. PWPA occurrences were predominantly identified in the southwestern regions as well as within the middle and lower reaches of the YZR. These results validated that the developed framework is capable of forecasting PWPA in large-scale watersheds while supporting effective watershed management.

Identification of the Driving Factors to Algal Biomass in Lake Dianchi: Implications for Eutrophication Control

Accurate analysis of spatiotemporal variations in algal biomass and their underlying causes is crucial for controlling algal blooms and enhancing aquatic ecological quality. The present study, spanning 2011 to 2020, was conducted across 10 sites in Lake Dianchi, where peak algal biomass levels occur from May to September, with higher concentrations in the lake’s northern zones compared to other areas. Employing Spearman’s correlation analysis, generalized additive models (GAMs), and random forest (RF) techniques, the relationships between algal biomass and water quality indicators were investigated. Spearman’s correlation analysis revealed a positive relationship between chlorophyll a (Chla) and total phosphorus (TP) across various spatial scales. RF analysis identified TP as the most influential factor on a lake-wide scale; while in localized RF models, organic pollution-related indicators (COD, CODMn, and BOD5) and TP consistently emerged as the primary predictors of Chla at most sites. GAM results indicated spatially variable and nonlinear responses of algal biomass to predictors, reinforcing TP’s significance lake-wide and at many localized scales. This comprehensive analytical approach provides valuable insights into the role of water quality factors and nonlinear dynamics, thereby advancing our understanding of the relationships between algal biomass and environmental conditions. These findings are pivotal for the development of scientifically informed strategies for lake management and conservation.

Water pollution and sanitation in Indonesia: a review on water quality, health and environmental impacts, management, and future challenges.

As an archipelagic tropical developing country, Indonesia is characterized by vast expanses of rural and isolated areas. This review aims to discuss water pollution and sanitation's existing condition, health, and environmental impacts, alongside its management and challenges in Indonesia. The systematic review approach was utilized to ensure transparency and replicability, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Many water sources in Indonesia are contaminated by various pollutants such as heavy metals, microplastics, pesticides, and endocrine-disrupting chemicals (EDCs). Moreover, the existing water and wastewater treatment plants cannot completely remove pollutants, including bacteria that cause waterborne diseases. The lack of proper sanitation facilities, including toilets and latrines, is not only reported in rural areas but also in peri-urban regions of Indonesia. This situation forces many to rely on unimproved water sources, such as river water, for their daily use and engage in open defecation, which may increase the risk of direct exposure to waterborne contaminants. Insufficient sanitation has been linked to significant public health issues in Indonesia, including maternal health complications, childhood stunting, and an increased incidence of waterborne diseases such as diarrhea. Based on the findings of this review, Indonesia still needs to improve its sanitation and water treatment facilities, as well as reduce pollution of the aquatic environment. This condition not only illustrates the condition of water and sanitation in Indonesia but can also be used as an illustration of how developing countries face various pollution and sanitation problems during the massive development and industry.

Recent Developments in Applications of G-CuO Nanocomposites for Photocatalytic Dye Removal

Abstract The extensive application of synthetic dyes across industries poses significant environmental challenges, particularly concerning water quality degradation. Regarding the possible solutions, copper oxide (CuO) stands out as a feasible candidate. Being a p-type Nano-heterogeneous semiconductor with a bandgap of 1.2–2.71 eV, CuO is a reasonable choice and widely studied photocatalyst for addressing such challenges. When the wavelength exceeded the UV-visible region, its functionality showed deterioration. At the same time, there are difficulties associated with reproducibility and reusability, as well as rapid electron-hole recombination, which prevent the widespread application of this technology within this spectral range. In an attempt to eliminate this defect, researchers have been investigating strategies to activate CuO under visible light, with one promising approach being carbon nanomaterials such as graphene to form carbon-CuO composites. The unique properties of graphene, i.e., its large surface area and excellent electron mobility, make it a remarkable candidate for the enhancement of CuO photoactivity. This study highlighted the recent progress in the synthesis of graphene-based CuO photocatalysts, with the main characteristic of extending the light absorption capacity of CuO into the visible spectrum. It reveals achievements in material innovations and applications, with a focus on photocatalytic dye degradation. These breakthroughs are a sign that the field is growing in popularity and is evolving.&amp;#xD;

Protecting the Safe Water Chain in Refugee Camps: An Exploratory Study of Water Handling Practices, Chlorine Decay, and Household Water Safety in South Sudan, Jordan, and Rwanda.

In refugee and internally displaced person settlements, hygienic water handling and free residual chlorine (FRC) are crucial for protecting water against recontamination after distribution up to the household point-of-consumption. We conducted a secondary analysis of water quality and water handling data collected in refugee camps in South Sudan, Jordan, and Rwanda using statistical and process-based modeling to explore how water handling practices affect FRC decay and household FRC outcomes. The two practices that consistently produced a significant effect on FRC decay and household FRC were storing water in direct sunlight and transferring water between containers during household storage. Samples stored in direct sunlight had 0.22-0.31 mg/L lower household FRC and had FRC decay rates between 2 and 3.7 times higher than samples stored in the shade, and samples that were transferred between containers had 0.031-0.51 mg/L lower household FRC and decay rates 1.65-3 times higher than non-transferred samples in sites in which the effect was significant, suggesting that humanitarian responders should aim to provide additional water storage containers to prevent water transferring in households and encourage water-users not to store water in direct sunlight. By contrast, the effect of the three recommended hygienic water handling behaviors (clean, covered containers and drawing by tap or pouring) was mixed or inconclusive. These inconclusive results were likely due to imbalanced or unreliable approaches to gathering the data, and we recommend that hygienic water handling practices that mechanistically provide a physical barrier against recontamination should always be promoted in humanitarian settings.

A Listeria monocytogenes aptasensor on laser inscribed graphene for food safety monitoring in hydroponic water

AbstractConsumption of fresh produce, such as leafy greens, is often encouraged as part of a healthy diet. Hence, indoor facilities for hydroponic production of leafy greens are increasingly being established. However, fresh produce entails a higher risk of microbial foodborne illnesses than processed foods. Listeria monocytogenes is a major source of fresh produce contamination and is among the leading causes of severe foodborne illnesses in the United States, with a 16% mortality rate. Tools for rapid monitoring are needed for pathogens such as L. monocytogenes to prevent outbreaks. In this manuscript, we have demonstrated the feasibility of a multi-aptamer approach for development of label-free aptasensors targeting L. monocytogenes in irrigation water for lettuce hydroponic production. We use screening studies with surface plasmon resonance to rationally develop mixtures of relevant aptamers for targeting L. monocytogenes. Based on this screening, multiple aptamers targeting extracellular structures on intact L. monocytogenes were tethered to platinum-modified laser inscribed graphene electrodes. This is the first report of a L. monocytogenes biosensor based on laser inscribed graphene. We show that mixing multiple aptamers with varying affinity improves the diagnostic performance over one aptamer alone in complex sample matrices (lettuce hydroponic water). Multi-aptamer biosensors showed high accuracy for L. monocytogenes and were at least three times more selective than Escherichia coli (Crooks, K12, O157:H7) with an accuracy of 85%. The limit of detection (10 CFU/10 mL) is based on data which were significantly different after calibration toward L. monocytogenes or E. coli (Crooks) and validated against gold standard molecular analysis (polymerase chain reaction). Rapid screening of pathogens is a global need to meet food safety and water quality regulations. This study shows the importance of sensors targeting more than one bacterial surface structure in complex samples relevant to the food-water nexus.

Sustainable Water Management Practices at ETUT: Innovations and Policies

The paper delves into the water management strategies of Oguz Han Engineering and Technology University of Turkmenistan (ETUT), emphasizing sustainable practices and regulatory compliance. ETUT employs various innovative techniques like rainwater collection through rain gardens, permeable pavement, and cisterns to mitigate runoff and enhance water quality. Rainwater is stored for irrigation, reducing reliance on municipal sources and contributing to groundwater recharge. A water-recycling program repurposes rainwater and fountain water, while innovative structures promote plant growth and water conservation by condensing atmospheric moisture. The university utilizes advanced wastewater treatment methods incorporating plants and microorganisms to purify water efficiently. Complying with national regulations ensures safe drinking water provision and resource protection. Regular monitoring aids in assessing policy effectiveness, fostering continuous improvement. ETUT's commitment to sustainability and environmental stewardship is reflected in its comprehensive water conservation program, fostering a culture of innovation and responsibility within the university community.

Environment that causes diseases: article review

Introduction: Environmentally induced diseases have become a global concern due to their significant impact on public health. The environment plays a significant role in increasing or decreasing the risk of disease through various factors, such as air, water, and soil pollution and exposure to hazardous chemicals. This review aims to analyze the literature on environmental factors that trigger diseases, including infectious diseases, chronic diseases, and mental health disorders, and to understand the mechanisms and impacts caused. Research Methodology: This study used a systematic literature review of scientific articles, research reports, and other reliable sources published in the last two decades. Data were collected, analyzed, and grouped based on the type of environmental factors and their association with various diseases. Result: Studies have shown that air pollution is a major cause of chronic respiratory diseases, such as asthma and chronic obstructive pulmonary disease (COPD), while water pollution and poor sanitation trigger outbreaks of infectious diseases such as diarrhea and cholera. Exposure to hazardous chemicals, such as pesticides and heavy metals, also contributes to increased risks of cancer and neurological disorders. In addition, environmental degradation, such as deforestation and climate change, exacerbate the spread of vector-borne diseases, such as malaria and dengue fever. Conclusion: An unhealthy environment is key in triggering various diseases that endanger public health. Mitigation efforts, such as reducing pollution, improving water quality, and protecting ecosystems, are essential to minimize adverse health impacts. The study recommends increasing cross-sector collaboration and strengthening environmental policies to create an environment that supports public health.

Water quality evolution of karst lakes controlled by regional climate and human-land economic dynamics, southwestern plateau of China

Water quality evolution of karst lakes controlled by regional climate and human-land economic dynamics, southwestern plateau of China

Inhibition of disinfection byproducts formation and microbial stress responses by Cu-Al/coconut shell activated carbon biofiltration: The key role of Al species

The effects of biological activated carbon treatment using CuAl bimetallic modified coconut shell activated carbon (CAAC) were investigated on the formation of disinfection by-products (DBPs) and microbial stress responses with unmodified granular coconut shell activated carbon (GAC) as a reference. In the long-term biological filtration process, the formation potentials of trihalomethanes and haloacetic acids in the effluent of CAAC filter (ACW) was reduced by 63.58 % compared to the effluent of GAC filter (CW). After the 6th day of biofilter operation, the dissolved organic carbon concentration of ACW rapidly decreased to around 1.03 mg/L and was consistently lower than that of CW. Under the enhanced biological flocculation by Al species, the extracellular polymeric substances (EPS) on the CAAC surface exhibited higher content and stronger flocculation efficiency, making the biofilm denser and more difficult to detach into ACW. In addition, the developed biofilms on CAAC surface promoted the biodegradation of organic compounds including DBPs precursors. Interestingly, the coexistence of Cu and Al mitigated the stimulatory effect of Cu, resulting in less intracellular reactive oxygen species and EPS. Our findings revealed that as a biofilter carrier filler, CAAC can effectively improve water quality and continuously control the risk of DBPs in the subsequent transmission and distribution process. More importantly, the introduction of Al species can provide new ideas to promote the realistic application of various modified treatment materials that are stressful to microorganisms.

Photocatalytic degradation evaluation using ternary structures of titanium dioxide, silver and carbon quantum dots

The catalytic photodegradation of organic pollutants offers an eco-friendly alternative to improving water quality by reducing the use of strong oxidants. This method is particularly promising in dealing with emerging pollutants that are not completely removed using only the traditional methodologies. However, limitations of the main photocatalysts used such as high bandgap value and electronic recombination are a challenge to face when trying to upscale applications. In this study, photocatalytic heterostructures were synthesized using TiO2 P25, carbon quantum dots (CQDs), and silver (Ag) to enhance photocatalytic performance and overcome these challenges. The results demonstrated significant improvements in photodegradation efficiency, with an 827% increase for the catalyst containing only CQDs and a 400% increase for the heterostructures containing both CQDs and Ag. These enhancements are attributed to the synergy of the ballistic electronic transferer properties and the diminished availability of recombination sites of the semiconductor which enables the generation of oxidative radicals.