Natural Water Reservoirs Research Articles

The role of hydrological measuring devices in monitoring changes in the flow rate of the Boudraa River during the year 2023 (sub-watershed of Oued M’hasser, Morocco).

Abstract Tracking the water flow and surface runoff is of utmost importance in light of the climate changes experienced globally and specifically in Morocco. In recent years, Morocco has been facing a significant water deficit due to declining rainfall and prolonged drought periods, which affects various water uses. As the Atlas and Rif regions serve as the natural water reservoirs for Morocco, it has become essential to track the water flow and valleys in these areas to study their hydrological behavior. This understanding is crucial for determining their functioning mechanisms and making appropriate decisions to ensure the sustainability of these resources. In this context, we have monitored the changes in the flow of Ain Boudraa in the basin of Wadi Mhasser in the Central High Atlas. We have studied the impact of climatic factors on the water flow and water levels, employing a methodology that involves statistical analysis of precipitation data from the nearby station, along with conducting hydrological measurements of the flow and construction a calibration curve.

Phytoplankton diversity and trophic state of natural water reservoirs in Azagny National Park (Côte d'Ivoire)

Phytoplankton diversity and trophic state of natural water reservoirs in Azagny National Park (Côte d'Ivoire)

Modeling for sustainable groundwater management: Interdependence and potential complementarity of process-based, data-driven and system dynamics approaches

Groundwater systems are vast natural water reservoirs used to support human water demands and ecosystem services. Various modeling approaches have been developed to help manage these complex highly-dynamic systems. This paper discusses the strengths and limitations of three modeling approaches, namely: process-based, data-driven and system dynamics modeling. For demonstration purposes, the three modeling approaches are applied to the Konya Closed Basin, a large agricultural region with semi-dry climate located in central Turkey. Process-based modeling is grounded in the theory-based representation of the governing processes but is somewhat limited by the computational effort and the difficulty of defining the required input parameters that characterize the heterogeneous aquifer system. Process-based models are shown to be powerful tools for resource management purposes provided climatic and water demand scenarios are accurately defined. Data-driven models are efficient tools for the management of groundwater resources but are highly dependent on the availability of large training data sets encompassing the spectrum of possible system responses. The high efficiency of surrogate modeling approaches makes them ideal tools for incorporation into applications such as real-time decision support systems and digital twin platforms. System dynamics modeling examines the groundwater exploitation problem within a socio-economic context that involves multiple stakeholders and their decision making. It combines groundwater flow models with socio-economics and endogenous decision rules to conduct scenario analysis and support policy development. The analyses and model demonstrations presented in this paper underscore the interconnectedness and complementarity of these three modeling approaches and the need for more integrated use of these modeling approaches for enhanced multi-sectoral management of groundwater systems.

Subsidence associated with dewatering and gas extraction from coal seams: Contribution of desorption-induced coal shrinkage

Several coal seams are contained within the formations of Great Artesian Basin (GAB), the largest natural underground water reservoir in Australia and the world. The extraction of coal seam gas (CSG) and its associated water from thousands of wells within the coal measures of the GAB has led to tens of millimetres of subsidence in CSG development areas. Since highly developed farming systems located in these areas rely on very low slope landforms, even this scale of subsidence has caused significant community concern about the potential for CSG extraction to impair farming operations and productivity through changes in land slope and drainage. Coal seam compaction associated with dewatering and gas extraction has two key components: poromechanical compaction and desorption-induced bulk shrinkage. The former results from pore pressure depletion (due to dewatering and gas extraction) and an increase of vertical effective stress, while the latter is induced by gas desorption from the coal matrix, leading to further deformation. While poromechanical compaction of fluid-bearing formations has been extensively addressed in the literature, relatively little research has been conducted on the role of coal shrinkage in CSG-induced subsidence. This paper introduces an innovative practical modelling approach for assessing CSG-induced subsidence at a subregional/regional scale. The approach utilises an analytical model for CSG-induced subsidence derived from constitutive stress–strain relations for poroelastic-sorptive media. This is a novel approach in the context of CSG-induced subsidence which considers both poromechanical compaction and desorption-induced shrinkage. A further distinction to previous work is the integration of the geomechanical-sorptive subsidence model with a numerical groundwater model. Based on this approach, this paper examines the effect of coal shrinkage on subsidence, and its proportions with respect to total compaction for one of the major coal measures in the Surat Basin. Input data are derived from three-dimensional geological, geomechanical, and groundwater models, as well as methane adsorption and desorption reports. Results show that (a) the impact of coal shrinkage on CSG-induced subsidence is likely to be significant in the study area and (b) the contribution of coal shrinkage to CSG-induced subsidence depends on gas content, Langmuir isotherm, shrinkage strain parameters, and the saturation state of coal. This study provides important insights into CSG-induced subsidence and lays the foundation for the development of robust, efficient, and localised predictive models to support environmental impact assessment and management.

Machine Learning Models for Water Quality Prediction: A Comprehensive Analysis and Uncertainty Assessment in Mirpurkhas, Sindh, Pakistan

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water’s appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region’s WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that Random Forest and Gradient Boosting lead with 95% and 96% accuracy, followed closely by SVM at 92%. KNN exhibits an accuracy rate of 84%, and Decision Trees achieve 77%. Traditional water quality assessment methods are time-consuming and error-prone; a transformative approach using artificial intelligence and machine learning addresses these limitations. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization.

Synergistic activation of lamellar bismuth selenide anchored functionalized carbon nanofiber for detecting hazardous carbendazim in environmental water samples

Pesticides pollute natural water reservoirs through persistent accumulation. Therefore, their toxicity and degradability are serious issues. Carbendazim (CBZ) is a pesticide used against fungal infections in agricultural crops, and its overexploitation detrimentally affects aquatic ecosystems and organisms. It is necessary to design a logical, efficient, and field-deployable method for monitoring the amount of CBZ in environmental samples. Herein, a nano-engineered bismuth selenide (Bi2Se3)/functionalized carbon nanofiber (f-CNF) nanocomposite was utilized as an electrocatalyst to fabricate an electrochemical sensing platform for CBZ. Bi2Se3/f-CNF exhibited a substantial electroactive surface area, high electrocatalytic activity, and high conductivity owing to the synergistic interaction of Bi2Se3 with f-CNF. The structural chemical compositions and morphology of the Bi2Se3/f-CNF nanocomposite were confirmed by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and field-emission scanning electron microscopy (FESEM). Electrochemical analysis was carried out using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and differential pulse voltammetry (DPV). The voltammetry and impedance experiments exposed that the Bi2Se3/f–CNF–modified GCE has attained adequate electrocatalytic function with amended features of electron transportation (Rct = 35.93 Ω) and improved reaction sites (0.082 cm2) accessible by CBZ moiety along with exemplary electrochemical stability (98.92%). The Bi2Se3/f-CNF nanocomposite exhibited higher sensitivity of 0.2974 μA μM−1cm−2 and a remarkably low limit of detection (LOD) of 1.04 nM at a broad linera range 0.001–100 μM. The practicability of the nanocomposite was tested in environmental (tap and pond water) samples, which supports excellent signal amplification with satisfactory recoveries. Hence, the Bi2Se3/f-CNF nanocomposite is a promising electrode modifier for detecting CBZ.

Spatio Temporal Analysis of Water Quality at Tasek Bera

Bera Lake is Malaysia’s largest natural fresh water reservoir. It has critical environmental and ecological significance for both humans and wildlife. Nonetheless, the water quality of this lake has deteriorated during the previous few decades as a result of land development projects in the river basin area. This study aims to analyse the spatio temporal pattern of water quality in Tasek Bera and to identify and evaluate the degradation grade for water quality, as well as to identify the environmental crucial factor for Tasek Bera and its surrounding area. This study also implements remote sensing technology to determine the water quality parameters in Tasek Bera. The data for this study was obtained from Landsat 8 OLI and Landsat 5 MSS from 3 different years which are year 2000, 2010 and 2020. The landset image were processed and integrated into the Geographical Information System (GIS) software to undergo classification, principal component analysis and weighted overlay process. Based on the weighted overlay mapping and Malaysia’s water quality index, the result showed that in year 2000, the water quality in Tasek Bera is mostly being in the second class (fair water quality) but in 2010, it shows a prominent increasing performance when the lake is mostly being in the first class (excellent water quality). However, in the year 2020, we had observe a significant decreasing of water quality especially on the upper section and the middle section of Tasek Bera. Through this study, researchers will be able to further analyse Tasek Bera’s current environmental state, and eventually bring evidence in the environmental deterioration that Tasek Bera has endured. This is also to make people realise the ecological importance of wetlands in Malaysia.

Changes in China's Snow Droughts Characteristics From 1993 to 2019

AbstractSnowpacks are natural water reservoirs providing a considerable amount of water for humans and ecosystems. However, current global snow products (e.g., ESA GlobSnow v3.0), lack high spatial resolution and regional calibrations necessary to capture the high heterogeneity of snow water equivalents (SWEs) in complex Asian mountainous terrains. Therefore, our understanding of snow drought characteristics in China remains limited. Herein, we used an improved SWE product calibrated specifically for China to explore the characteristics of snow droughts, delineated by a standardized SWE index (SWEI) between 1993 and 2019. Our analysis was focused over three main snow‐covered regions of China: Qinghai–Tibet Plateau (QTP), northern Xinjiang, Northeast China. Especially during the period from 1993 to 2010, we found that the SWEI increased significantly at rates of 0.022/yr (Northeast China), 0.017/yr (northern Xinjiang), and 0.011/yr (QTP) (p < 0.01, Mann‐Kendall trend test). Increased SWEI contributed to decreasing snow drought events across China, with an obvious short‐term characteristic, whilst area proportion of the identified 1‐month snow droughts was above 46.5% across three regions. Furthermore, we found that the occurrence of snow droughts was likely mediated by large‐scale atmospheric circulation, since increased water vapor transport caused a significant vapor flux convergence in cold seasons over three regions, especially in northern Xinjiang and Northeast China.

Natural Resources and Sustainable Tourism: Opportunities in Kroczyce Commune, Poland

This study aimed to evaluate the tourism potential of Kroczyce municipality, Poland, with a focus on its natural attractions and their role in fostering sustainable tourism. Kroczyce, chosen for its pristine natural features, presents an ideal case for examining the integration of sustainable practices in tourism. The research confirmed that the municipality’s natural landscape, rock formations, water reservoirs, protected areas, and caves are significant tourist attractions and sustainable assets. Additionally, it revealed a strong awareness among residents and visitors of the importance of sustainable tourism. The study’s findings suggest that local authorities can effectively develop Kroczyce as a sustainable tourism destination. This development involves investing in eco-friendly infrastructure, promoting conservation, and engaging the local community, ensuring tourism aligns with environmental conservation. Such an approach not only enhances the tourist experience but also preserves the natural ecosystem for future generations. Furthermore, transitioning Kroczyce into a nature tourism hub could yield substantial socio-economic benefits. The expected increase in tourism can stimulate job creation, boost local trade, and improve amenities, contributing positively to the region’s overall development. This case study provides a roadmap for similar municipalities aiming to develop sustainable, nature-based tourism.

Modelling optimal water retention in hydrogenic habitats using LIDAR laser data

Degradation of hydrogenic habitats in climate change increased rapidly. It is important that we take actions to stop this process. Solution is to increase efficiency of water usage by ecosystems - especially water based ones. Building devices for delaying surface water runoff – like locks and dams – should improve hydrogenic habitats conditions and allow surrounding ecosystems use rainwater more efficient. Modelling of small retention in forests is an important aspect in decision making schema. Aim of this paper is to point optimal solutions for height and placement of devices which delay surface water runoff to set necessary water table level for renaturalization and maintenance of degrading natural habitats. Data used for analyses were acquired in the Polanów Forest Inspectorate in West Pomeranian voivodeship because of the topography diversification and the drainage infrastructure presence. There were three research plots selected based on decreased stability of habitats and historic data stated that there were natural water reservoirs, which were drained in past. Based on 2012 LiDAR (Light Detection and Ranging) point cloud the digital terrain model (DTM) was built. Water outflow points – melioration canals – were identified and analysed for optimal device localization. In following part of research specific data for each hydrogenic habitat were used to model height of devices which delay surface water runoff. Optimal level of device and area covered by water were set for each site separately. The results were handed over to the investor for implementation, then the compliance of the assumptions of the simulation of raising the water table with the as-built field measurements was checked. Study shows that it is possible to use laser technology to optimize location and height of devices which delay surface water runoff what allows to restore degraded hydrogenic habitats. Presented method supports small local retention what increases limited water resources in this region, decreases rapid runoff of surface water which causes frequent floods. Proposed method of modelling the location and height of the dams or locks is universal. Even though results are unique for each object the method is possible to be applied to every other situation.

Antibiotic-Resistant Bacteria in Environmental Water Sources from Southern Chile: A Potential Threat to Human Health

Antimicrobial resistance (AMR) is a critical global issue affecting public and animal health. The overuse of antibiotics in human health, animal production, agriculture, and aquaculture has led to the selection of antibiotic-resistant strains, particularly in Gram-negative bacteria. Mutations and horizontal gene transfer play a significant role in the development of antimicrobial resistance, leading to the reduced efficacy of current antibiotics. Today, AMR in bacteria and antibiotic-resistance genes (ARGs) are increasingly recognized in multiple environmental sources, including recreational and irrigation waters. This study aims to identify Gram-negative bacteria from surface aquatic reservoirs in southern Chile and assess their susceptibility to clinically relevant antibiotics. Water samples were collected from four lakes, five rivers, one waterfall, and one watershed in southern Chile to isolate environmental Gram-negative bacilli (GNB). API-20E and MALDI–TOF were employed for bacterial identification. Kirby–Bauer disc diffusion tests and multiplex PCR were performed to determine their susceptibility profile. A total of 26 GNB strains were isolated from environmental water samples, predominantly belonging to the Pseudomonas (n = 9) and Acinetobacter (n = 7) genera. Among these strains, 96.2% were resistant to ampicillin and cefazoline, while 26.9% and 34.6% showed resistance to ceftazidime and cefepime, respectively. Additionally, 38.5% exhibited resistance to colistin. Two Enterobacter cloacae strains obtained from Cachapoal River (sixth region) and Villarrica Lake (ninth region), respectively, presented a multidrug-resistant (MDR) phenotype and carried at least two extended-spectrum β-lactamase (ESBL) genes. Thus, antibiotic-resistant GNB and ARGs were found in natural water reservoirs, raising concerns about the dissemination of resistance determinants among potentially pathogenic bacteria in environmental microbial communities.

Citizen Science as an Approach for Responding to the Threat of Anopheles stephensi in Africa.

Even as novel technologies emerge and medicines advance, pathogen-transmitting mosquitoes pose a deadly and accelerating public health threat. Detecting and mitigating the spread of Anopheles stephensi in Africa is now critical to the fight against malaria, as this invasive mosquito poses urgent and unprecedented risks to the continent. Unlike typical African vectors of malaria, An. stephensi breeds in both natural and artificial water reservoirs, and flourishes in urban environments. With An. stephensi beginning to take hold in heavily populated settings, citizen science surveillance supported by novel artificial intelligence (AI) technologies may offer impactful opportunities to guide public health decisions and community-based interventions. Coalitions like the Global Mosquito Alert Consortium (GMAC) and our freely available digital products can be incorporated into enhanced surveillance of An. stephensi and other vector-borne public health threats. By connecting local citizen science networks with global databases that are findable, accessible, interoperable, and reusable (FAIR), we are leveraging a powerful suite of tools and infrastructure for the early detection of, and rapid response to, (re)emerging vectors and diseases.

Heavy metal concentrations in drinking water in the region north-east of Jhunjhunu, Rajasthan, India

Groundwater is contaminated by undesirable elements that are introduced directly or indirectly into a natural water reservoir supplied by human activity, thus changing its physico-chemical properties. The north-east of Jhunjhunu in Rajasthan state of India was chosen as the study area due to the lack of research evidence in the past. The heavy metal content of a total of 42 water samples was analysed using an atomic absorption spectrometer. The copper and iron content in the groundwater of the study area is revealing a spatial distribution range of 1.75 to 4.01 mg/L for copper and 0.44 to 1.22 mg/L for iron. The obtained result was compared with Buero of Indian Standard (10500:2012), Indian Council of Medical Research, and World Health Organisation. There are iron and copper mining facilities in the studied area. Therefore, the concentration of both minerals was observed in the groundwater of all sampling stations. The iron and copper content are much higher in groundwater than the permissible limit of Buero of Indian Standard and World Health Organisation. The manganese content was not detected at all sampling sites. Both iron and copper elements are trace elements, and their higher concentration has proven to be a major problem and there are many health risks associated with it. These observed concentrations indicate a hazardous risk to human health. The official authorities should take the necessary measures in this regard.

Do biosurfactants as anti-biofilm agents have a future in industrial water systems?

Biofilms are bacterial communities embedded in exopolymeric substances that form on the surfaces of both man-made and natural structures. Biofilm formation in industrial water systems such as cooling towers results in biofouling and biocorrosion and poses a major health concern as well as an economic burden. Traditionally, biofilms in industrial water systems are treated with alternating doses of oxidizing and non-oxidizing biocides, but as resistance increases, higher biocide concentrations are needed. Using chemically synthesized surfactants in combination with biocides is also not a new idea; however, these surfactants are often not biodegradable and lead to accumulation in natural water reservoirs. Biosurfactants have become an essential bioeconomy product for diverse applications; however, reports of their use in combating biofilm-related problems in water management systems is limited to only a few studies. Biosurfactants are powerful anti-biofilm agents and can act as biocides as well as biodispersants. In laboratory settings, the efficacy of biosurfactants as anti-biofilm agents can range between 26% and 99.8%. For example, long-chain rhamnolipids isolated from Burkholderia thailandensis inhibit biofilm formation between 50% and 90%, while a lipopeptide biosurfactant from Bacillus amyloliquefaciens was able to inhibit biofilms up to 96% and 99%. Additionally, biosurfactants can disperse preformed biofilms up to 95.9%. The efficacy of antibiotics can also be increased by between 25% and 50% when combined with biosurfactants, as seen for the V9T14 biosurfactant co-formulated with ampicillin, cefazolin, and tobramycin. In this review, we discuss how biofilms are formed and if biosurfactants, as anti-biofilm agents, have a future in industrial water systems. We then summarize the reported mode of action for biosurfactant molecules and their functionality as biofilm dispersal agents. Finally, we highlight the application of biosurfactants in industrial water systems as anti-fouling and anti-corrosion agents.

Emergent (branched bur-reed—Sparganium erectum L.) and submergent (river water-crowfoot—Ranunculus fluitans Wimm., 1841) aquatic plants as metal biosorbents under varying water pH conditions in laboratory conditions

A laboratory experiment was performed to find out the potential bioaccumulation of metals (Al, Cu, Cd, Ni, Pb, Fe, Mn) in aquatic plants acting as natural biosorbents in the environment depending on the variation of water pH. Two plants were selected for the study: branched bur-reed—Sparganium erectum L., as a representative of emergent plants; and river water-crowfoot—Ranunculus fluitans Wimm., 1841, as a representative of submergent plants. The bioaccumulation of metals in plants relative to water (BCFW) was determined, and the metal pollution index (MPI) was calculated. The metal content in water and plants before the experiment was arranged in the following series of increasing values: Cd = Ni < Cu < Pb < Mn < Al < Fe (water) and Cd < Cu < Ni < Pb < Al < Fe < Mn (in both plants). The lowest concentration of cadmium was found in water both at the end and beginning of the experiment. In turn, aluminum level increased, and its concentration was the highest in both acidic and alkaline water. This suggests that aluminum is definitely more available to the organisms living in it under such conditions, and in higher concentrations may be a limiting factor for them. Bioaccumulation of metals (BCFW) in neutral and alkaline environments was higher in branched bur-reed, and in acidic conditions in river water-crowfoot. This may provide a basis for using specific plant species to selectively accumulate metals depending on the range of water pH. This is also supported by the MPI values, which indicate that they have a very high and often the highest impact on metal pollution levels. The observed variability of metals bioaccumulation against the condition of hydromacrophytes makes it possible to consider these plants as biosorbents used in constructed biological treatment plants, which, depending on the species placed there, can selectively absorb particular trace elements. The plants used in the experiment are common hydromacrophytes of Europe, so they can be used in the construction of artificial wetlands across the continent. The apparent diversity of metal accumulation in relation to hydromacrophytes indicates that this type of site can be inhabited by specific species, which can significantly improve the purification of natural or anthropogenic water reservoirs, i.e., those that are intended to perform a filtering function.

Flood Prediction using MLP, CATBOOST and Extra-Tree Classifier

Flooding can be one of the many devastating natural catastrophes, resulting in the annihilation of life and damaging property. Additionally, it can harm farmland and kill growing crops and trees. Nowadays, rivers and lakes are being destroyed, and the natural water reservoirs are converted into development sites and buildings. Due to this, even just a bit of rain can cause a flood. To minimize the number of fatalities, property losses, and other flood-related issues, an early flood forecast is necessary. Therefore, machine learning methods can be used for the prediction of floods.To forecast the frequency of floods brought on by rainfall, a forecasting system is built using rainfall data. The dataset is trained using various techniques like the MLP classifier, the CatBoost classifier, and the Extra-Tree classifier to predict the occurrence of floods. Finally, the three models' performances are compared and the best model for flood prediction is presented. The MLP, Extra-Tree, and CatBoost models achieved accuracy of 94.5%, 97.9%, and 98.34%, respectively, and it is observed that CatBoost performed well with high accuracy to predict the occurrence of floods.

Monitoring of Groundwater in a Limestone Island Aquifer Using Ambient Seismic Noise

The limestone islands of Malta face high levels of water stress due to low rainfall over a small land area and a high population density. We investigate an innovative, cost-effective approach to groundwater monitoring in an island environment by computing auto- and cross-correlations of ambient seismic noise recorded on short-period and broadband seismic stations. While borehole readings give accurate site-specific water level data of the groundwater across the islands, this technique provides a more regional approach to quantitative groundwater monitoring. We perform the moving window cross-spectral method to determine temporal changes in seismic velocity (δv/v). Comparison of the δv/v with groundwater levels from boreholes and precipitation shows comparable patterns. We find that the variations of the δv/v from auto-correlations are more pronounced than the cross-correlation, and that short-period seismic stations are also sensitive. The δv/v signal deteriorates at longer interstation distances, presumably because paths traverse complex geology. We conclude that changes in the groundwater level found beneath very small islands, even as small as 3 km2, can be detected seismically. Low-cost, easy-to-deploy seismic stations can thus act as an additional tool for groundwater monitoring, especially in places with limited natural water reservoirs, like rivers and lakes, and infrastructure.

Analysis of urban drainage system to avoid rain water overflow on the access road to Juanda Airport

The access road to Juanda Airport from Jalan Raya Waru (Aloha Resto) Sidoarjo is frequently flooded when it rains with a height of more than 30 cm along 1.6 km. This greatly annoys the flow of traffic to the airport. The airport can only be passed through this street and the Juanda Airport toll road. This research was conducted by observing and recording conditions during the occurrence of floods and analyzing the drainage facilities in the research area based on the 5-years design discharge because this location is a densely populated residential and industrial area. The results showed that the addition of channel capacity using U-ditch was not able to accommodate the 5-year planned discharge of approximately 2.670 m3/sec. Since around the industrial location, there is a natural water reservoir that can keep water up to 3.526 m3/sec, it is necessary to add a water pump to drain water to the temporary water reservoir location so that water does not stagnate on the access road to Juanda Airport.

Microspherical Titanium-Phosphorus Double Oxide: Hierarchical Structure Development for Sensing Applications.

Stable, water-soluble titanium complexed with mandelic acid was used as a precursor for titanium phosphorus double oxide obtained in hydrothermal conditions in the presence of phosphoric acid. Surprisingly, hydrolysis of organic complexes provided a microstructured sphere with narrow size distribution, low aggregation and a small fraction of morphological irregularities. Obtained microspheres had a complex structure comprised of flakes, whose size could be manipulated with temperature conditions. Samples were found to be electrochemically active against sulcotrione, a well-recognized herbicide. Electrochemical sensors based on the synthesized microspheres were successfully adapted for natural water reservoir analysis and exhibited low levels of detection of 0.61 µM, limit of quantification of 1.86 µM, wide dynamic linear range from 2 to 200 µM, good selectivity, excellent reproducibility and in-time stability.

Assessment of the Chemical Reactivity of Brazilian Stone Cutting Plant Waste into Cementitious Matrices

The problems generated by the ornamental stone extraction and processing industry caused by the inadequate disposal of this waste can negatively affect rivers, lakes, streams, and even natural water reservoirs. This study discusses and evaluates the potentiality and challenges of dimension stone waste (DSW) recycling generated from a Brazilian dimension stone processing industry in Portland cement formulations. Cement pastes with different amounts of DSW (10–30 wt.%), quartz (10 wt.%), and calcium carbonate (10 wt.%) were prepared and characterized in the fresh and hardened states. The results showed that DSW can be used in cement formulations, and its reactivity is governed by the size of the particles. With up to 10% DSW in place of cement, the samples had greater workability and compressive strength at 28 days compared with the reference mix. However, the strength was lower at early ages (3 and 7 days). When DSW is milled, the strength of the samples containing the waste matched the reference values at all ages, and the recommended replacement limit rose to 20%. On the other hand, the particle size reduction significantly decreased the workability. The use of DSW in cement-based formulations is encouraged due to the strong presence of stone processing and cement companies in Brazil and worldwide.