Application of remote sensing technology in water quality monitoring: From traditional approaches to artificial intelligence.

Quantitative estimation is a key and challenging issue in water quality monitoring. Remote sensing technology has increasingly demonstrated its potential to address these challenges. Remote sensing imagery, combined with retrieval algorithms such as empirical band ratio methods, analytical bio-optical models, and semi-empirical three-band models, enables efficient, large-scale, real-time acquisition of water quality distribution characteristics, overcoming the limitations of traditional monitoring methods. Furthermore, artificial intelligence (AI), with its powerful autonomous learning capabilities and ability to solve complex problems, can deal with the nonlinear relationships between different spectral bands' apparent optical properties and various water quality parameter concentrations. This review provides a comprehensive overview of remote sensing applications in retrieving concentrations of nine water quality parameters, ranging from traditional methods to AI-based approaches. These parameters include chlorophyll-a (Chl-a), phycocyanin (PC), total suspended matter (TSM), colored dissolved organic matter (CDOM) and five non-optically active constituents (NOACs). Finally, it discusses five major issues that need further research in the application of remote sensing technology and AI in water quality monitoring. This review aims to provide researchers and relevant management departments with a potential roadmap and information support for innovative exploration in automated and intelligent water quality remote sensing monitoring.

Passive sampling of microbes in various water sources: A systematic review.

Traditional methods for monitoring pathogens in environmental waters have numerous drawbacks. Sampling approaches that are low-cost and time efficient that can capture temporal variation in microbial contamination are needed. Passive sampling of aquatic environments has shown promise as an alternative water monitoring technique for waterborne pathogens and microbial contaminants. The present systematic review aimed to compile and synthesize existing literature on the use of passive samplers for the monitoring of microbes in different water sources and identify research gaps. The review summarizes current knowledge on materials used for detection, deployment durations, analytical methods, quantification as well as benefits and limitations of passive sampling. This review found that electronegative nitrocellulose membrane filters are effective for both detection and quantification of viruses in wastewater, while gauze passive samplers have been effective for detecting bacterial targets in wastewater. There is a large knowledge gap in the use of passive samplers in a quantitative manner, especially for the back-calculation of water-column microbial concentrations or for correlation to outcomes of interest (e.g. prevalence rates). Further, there is very limited attention paid to the use of membrane filters for the monitoring of bacteria in any water source as well as a lack of studies utilizing passive sampling approaches for protozoa.

Review and Analysis of Modern Laser Beam Welding Processes.

Laser beam welding is the most modern and promising process for the automatic or robotized welding of structures of the highest Execution Class, EXC3-4, which are made of a variety of weldable structural materials, mainly steel, titanium, and nickel alloys, but also a limited range of aluminum, magnesium, and copper alloys, reactive materials, and even thermoplastics. This paper presents a systematic review and analysis of the author's research results, research articles, industrial catalogs, technical notes, etc., regarding laser beam welding (LBW) and laser hybrid welding (LHW) processes. Examples of industrial applications of the melt-in-mode and keyhole-mode laser welding techniques for low-alloy and high-alloy steel joints are analyzed. The influence of basic LBW and LHW parameters on the quality of welded joints proves that the laser beam power, welding speed, and Gas Metal Arc (GMA) welding current firmly decide the quality of welded joints. A brief review of the artificial intelligence (AI)-supported online quality-monitoring systems for LBW and LHW processes indicates the decisive influence on the quality control of welded joints.

Monitoring Escherichia coli in Water through Real-Time Loop-Mediated Isothermal Amplification on Biochips.

Access to clean water is fundamental to public health and safety, serving as the cornerstone of well-being in communities. Despite the significant investments of millions of dollars in water testing and treatment processes, the United States continues to grapple with over 7 million waterborne-related cases annually. This persistent challenge underscores the pressing need for the development of a new, efficient, rapid, low-cost, and reliable method for ensuring water quality. The urgency of this endeavor cannot be overstated, as it holds the potential to safeguard countless lives and mitigate the pervasive risks associated with contaminated water sources. In this study, we introduce a biochip LAMP assay tailored for water source monitoring. Our method swiftly detects even extremely low concentrations of Escherichia coli (E. coli) in water, and 10 copies/µL of E. coli aqueous solution could yield positive results within 15 min on a PC-MEDA biochip. This innovation marks a significant departure from the current reliance on lab-dependent methods, which typically necessitate several days for bacterial culture and colony counting. Our multifunctional biochip system not only enables the real-time LAMP testing of crude E. coli samples but also holds promise for future modifications to facilitate on-site usage, thereby revolutionizing water quality assessment and ensuring rapid responses to potential contamination events.

Development of new PDMS in tube extraction microdevice for enhanced monitoring of polycyclic aromatic hydrocarbons and their derivatives in water.

Contamination by polycyclic aromatic hydrocarbons (PAHs) is an urgent environmental concern, given its atmospheric dispersion and deposition in water bodies and soils. These compounds and their nitrated and oxygenated derivatives, which can exhibit high toxicities, are prioritized in environmental analysis contexts. Amid the demand for precise analytical techniques, comprehensive two-dimensional chromatography coupled with mass spectrometry (GCxGC/Q-TOFMS) has emerged as a promising tool, especially in the face of challenges like co-elution. This study introduces an innovation in the pre-concentration and detection of PAHs using an extraction fiber based on polydimethylsiloxane (PDMS), offering greater robustness and versatility. The proposed technique, termed in-tube extraction, was developed and optimized to effectively retain PAHs and their derivatives in aqueous media, followed by GCxGC/Q-TOFMS determination. Fiber characterization, using techniques such as TG, DTG, FTIR, and SEM, confirmed the hydrophobic compounds retention properties of the PDMS. The determination method was validated, pointing to a significant advancement in the detection and analysis of PAHs in the environment, and proved effective even for traces of these compounds. The results showed that the detection limits (LOD) and quantification limits (LOQ) ranged from 0.07 ng L-1 to 1.50 ng L-1 and 0.33 ng L-1 to 6.65 ng L-1, respectively; recovery ranged between 72 % and 117 %; and the precision intraday and interday ranged from 1 % to 20 %. The fibers were calibrated in the laboratory, with exposure times for analysis in the equilibrium region ranging from 3 to 10 days. The partition coefficients between PDMS and water were also evaluated, showing logarithm values ranging from 2.78 to 5.98. The fibers were applied to the analysis of real water samples, demonstrating high capacity. Additionally, given the growing demand for sustainable methods, the approach presented here incorporates green chemistry principles, providing an efficient and eco-friendly solution to the current chemical analysis scenario.

Integrating deep learning techniques for effective river water quality monitoring and management.

Effective river water quality monitoring is essential for sustainable water resource management. In this study, we established a comprehensive monitoring system along the Kaveri River, capturing real-time data on multiple critical water quality parameters. The parameters collected encompassed water contamination levels, turbidity, pH measurements, temperature, and total dissolved solids (TDS), providing a holistic view of river water quality. The monitoring system was meticulously set up with strategically positioned sensors at various river locations, ensuring data collection at regular 5-min intervals. This data was then transmitted to a cloud-based web portal, facilitating storage and analysis. To assess water quality, we introduced a novel hybrid approach, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The proposed CNN-LSTM model achieved a validation accuracy of 98.40%, surpassing the performance of other state-of-the-art methods. Notably, the practical application of this system includes real-time alerts, promptly notifying stakeholders when water quality parameters exceed predefined thresholds. This feature aids in making informed decisions in water resource management. The study's contributions lie in its effective river water quality monitoring system, which encompassing various parameters, and its potential to positively impact environmental conservation efforts by providing a valuable tool for informed decision-making and timely interventions.

Evaluation of distribution system water quality during a free chlorine conversion.

Chloraminated drinking water systems commonly use free chlorine conversions (FCCs) to prevent or control nitrification, but unintended water quality changes may occur, including increased disinfection by-product and metal concentrations. This study evaluated water quality in a chloraminated drinking water system and residential locations before, during, and after their annual, planned FCC. Water quality alternated between relatively consistent and variable periods when switching disinfectants. During the FCC, regulated four trihalomethane and five haloacetic acid concentrations increased by four and seven times, respectively, and exceeded corresponding maximum contaminant levels. Implications of disinfection by-product sampling during an FCC were assessed, and an approach to account for increased FCC disinfection by-product concentrations was proposed. For metals, the FCC had minor impacts on distribution system concentrations and did not appear to impact residential concentrations. Overall, observed variable water quality appeared primarily associated with switching disinfectants and depended on distribution system hydraulics.

Assessment of the quality of oral squamous cell carcinoma clinical records in oral surgery with Surgical Tool for Auditing Records (STAR) scoring.

BACKGROUND: The Surgical Tool for Auditing Records scoring system [STAR] focuses on surgical record auditing with promising outcomes. It offers a structured approach to evaluating the quality of surgical notes. AIMS AND OBJECTIVES: This study aimed to assess the effectiveness of the STAR in evaluating oral surgical records and identifying areas for improvement in documentation practices. MATERIALS AND METHODS: The data was obtained from the Dental Information Archival Software (DIAS) of our institution. The sample size was determined using G*Power 3.1.9.4 software. Fifty consecutive oral surgery clinical records of oral squamous cell carcinoma patients were evaluated using STAR. Each record was reviewed for adherence to documentation standards including Initial Assessment (10 points), Follow-up Entries (8 points), Consent Documentation (7 points), Anesthesia Report (7 points), Surgical Log (9 points), and Discharge Synopsis (9 points). compiling a total STAR score (50 points). The data was tabulated in Google Sheets. The descriptive statistics with inter-observer agreement and the mean score were recorded. RESULTS: We observed that each of the 50 records received a score of 49/50 points on the STAR. Deductions were necessary in the Operative record section due to the lack of information regarding the sutures used. CONCLUSION: To summarize, this study emphasizes the effectiveness of the STAR scoring system in evaluating the quality of oral surgical records. Identifying deficiencies, particularly in documenting operative details, can improve the completeness and accuracy of patient records. It can ultimately enhance patient care and facilitate better communication among healthcare professionals.

IoT-enabled effective real-time water quality monitoring method for aquaculture.

Aquaculture is growing industry from the perspective of sustainable food fulfillment and county's economic development. Technology oriented aquafarming is the solution for effective water quality monitoring and high yield production. Internet of Things (IoT) integrated aquaculture can cater to such requirements. This research article introduces a comprehensive method aimed at seamlessly incorporate IoT sensors into aquafarming environments, utilizing Arduino boards and communication modules. The proposed method measures accurate water quality parameters, such as temperature, pH levels, and Dissolved Oxygen (DO), which are essential for maintaining optimal conditions for suitable aquaculture environment. This method enables the real-time collection of critical data points that are essential prevent fish diseases and mortality with low human intervention and maintenance cost. The key contributions of the methodology are mentioned below.•Design and development of a compact and efficient Printed Circuit Board (PCB) to achieve accurate sensor data readings and reliable communication in an aqua environment.•Prevent fish disease and mortality rate through data-driven decision incorporating correlation of DO, pH, and temperature sensor data.•Conducted instrument calibration checks and cross-validated automated system data with manual observations through repeatability tests to ensure precise measurements of sensor parameters.

Multi-channel surface-enhanced Raman spectroscopy (SERS) platform for pollutant detection in water fabricated on polydimethylsiloxane.

A miniature multi-channel surface-enhanced Raman scattering (SERS) sensor based on polydimethylsiloxane (PDMS) is constructed to achieve rapid delivery of polluted water and specific identification of multiple components. Hg2+, organic pollutants, and sodium nitrite are successfully identified by the multi-channel SERS sensor using Cy5, cyclodextrin, and urea in the corresponding detection area. This multi-channel sensor exhibits excellent sensitivity and specificity, with detection limits of 3.2 × 10-10 M for Hg2+, 1.0 × 10-8 M for aniline, 6.9 × 10-9 M for diphenylamine, 9.1 × 10-8 M for PCB-77, and 7.5 × 10-9 M for pyrene, and 5.0 × 10-7 M for sodium nitrite. Compared with traditional analysis techniques, this method exhibited excellent recovery for the water pollutants ranging from 82.1 to 115.8%. The PDMS-based microchannel allows for simultaneous and rapid identification of multiple environmental pollutants, offering a portable detection method for emergency testing of environmental pollutants and routine determination of water pollutants.

Widespread occurrence of fecal indicator bacteria in oligotrophic tropical streams. Are common culture-based coliform tests appropriate?

Monitoring of stream water quality is a key element of water resource management worldwide, but methods that are commonly used in temperate habitats may not be appropriate in humid tropical systems. We assessed the influence of four land uses on microbial water quality in 21 streams in the Panama Canal Watershed over a one-year period, using a common culture-based fecal indicator test and 16S rDNA metabarcoding. Each stream was located within one of four land uses: mature forest, secondary forest, silvopasture, and traditional cattle pasture. Culturing detected total coliforms and Escherichia coli across all sites but found no significant differences in concentrations between land uses. However, 16S rDNA metabarcoding revealed variability in the abundance of coliforms across land uses and several genera that can cause false positives in culture-based tests. Our results indicate that culture-based fecal indicator bacteria tests targeting coliforms may be poor indicators of fecal contamination in Neotropical oligotrophic streams and suggest that tests targeting members of the Bacteroidales would provide a more reliable indication of fecal contamination.

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.

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.

Construction of "ant-like tentacle" structure for ultra-sensitive detection of low-concentration ammonia through colorimetric fluorescent dual-signal gas-sensitive cotton fabric.

Detection and monitoring of ammonia (NH3) are crucial in various industries, including plant safety management, food freshness testing, and water pollution control. Nevertheless, creating portable, low-cost, highly sensitive, and easily regenerated ppm-level NH3 sensors poses a significant challenge. In this investigation, an innovative "ant-like tentacle" fabrication strategy was proposed, and a colorimetric fluorescent dual-signal gas-sensitive cotton fabric (PAH-fabric) for NH3 detection was successfully prepared by conventional dyeing using suitable molecular-level photoacid (PAH) sensitive units. The visual recognition lower detection limit of the ultra-low is 1.09 ppm-level. PAH-fabric is not only straightforward, convenient, and cost-effective to prepare, but it can also be efficiently regenerated and recycled multiple times (maintaining excellent gas-sensitive performance even after 100 cycles) by strategically leveraging volatile acid fumigation. Detailed molecular reaction mechanisms involved in the NH3 response and PAH-fabric regeneration are elucidated. PAH-fabric, available either as a portable kit or an alarm system, offers a promising approach for ultra-low NH3 detection. The demonstrated "ant-like tentacle" fabrication strategy introduces numerous possibilities for designing and developing sensors with adjustable response thresholds, particularly those requiring high sensitivity.

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.

Phytoplankton in Deep Lakes of the Dinaric Karst: Functional Biodiversity and Main Ecological Features.

Phytoplankton is a polyphyletic group of organisms that responds rapidly to environmental conditions and provides a reliable response to changes, making it a good ecological indicator for water quality monitoring. However, a gradient is almost essential for a reliable relationship between pressure and impact. In a low-gradient environment, ingenuity is required to outsmart the limitations of the commonly used linear relationship. Here, we examine changes in biomass and functional biodiversity by analysing larger data sets (2013-2022) in six ecologically diverse, natural, deep Croatian karst lakes with low nutrient gradients using nonlinear correlation coefficients and multivariate analyses in 209 samples. We found that phytoplankton biomass was most strongly influenced by nutrients, salinity and alkalinity, while light availability and total nitrogen strongly influenced phytoplankton functional biodiversity. An additional analysis of the TN:TP ratio revealed that the oligotrophic Lake Vransko is nitrogen-limited, and lakes Kozjak and Prosce are phosphorus-limited. This further clarified the relationship of phytoplankton to nutrients despite the low gradient. The complex analysis in this study provides a new perspective for predicting changes in the structure and succession of phytoplankton in deep karst lakes for successful management under apparent anthropogenic pressure and climate change.

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.

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.

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.