Application of Multivariate Statistical Analysis in the Development of a Surrogate Water Quality Index (WQI) for South African Watersheds

Balancing aquaculture and estuarine ecosystems: machine learning-based water quality indices for effective management

This study delves into the environmental impact of inland aquaculture on estuarine ecosystems by examining the water quality of four estuarine streams within the key inland aquaculture zone of South India. In this region, extensive and intensive aquaculture practices are common, posing potential challenges to estuarine health. The research explores the predictive capabilities of the Gaussian elimination method (GEM) and machine learning techniques, specifically multi-linear regression (MLR) and support vector regressor (SVR), in forecasting the water quality index of these streams. Through comprehensive evaluation using performance metrics such as coefficient of determination (R2) and average mean absolute percentage error (MAPE), MLR and SVR demonstrate higher prediction efficiency. Notably, employing key water parameters as inputs in machine learning models is also more effective. Biochemical oxygen demand (BOD) emerges as a critical water parameter, identified by both MLR and SVR, exhibiting high specificity in predicting water quality. This suggests that MLR and SVR, incorporating key water parameters, should be prioritized for future water quality monitoring in intensive aquaculture zones, facilitating timely warnings and interventions to safeguard water quality.

Hub Genes in Stable QTLs Orchestrate the Accumulation of Cottonseed Oil in Upland Cotton via Catalyzing Key Steps of Lipid-Related Pathways

Upland cotton is the fifth-largest oil crop in the world, with an average supply of nearly 20% of vegetable oil production. Cottonseed oil is also an ideal alternative raw material to be efficiently converted into biodiesel. However, the improvement in kernel oil content (KOC) of cottonseed has not received sufficient attention from researchers for a long time, due to the fact that the main product of cotton planting is fiber. Previous studies have tagged QTLs and identified individual candidate genes that regulate KOC of cottonseed. The regulatory mechanism of oil metabolism and accumulation of cottonseed are still elusive. In the current study, two high-density genetic maps (HDGMs), which were constructed based on a recombinant inbred line (RIL) population consisting of 231 individuals, were used to identify KOC QTLs. A total of forty-three stable QTLs were detected via these two HDGM strategies. Bioinformatic analysis of all the genes harbored in the marker intervals of the stable QTLs revealed that a total of fifty-one genes were involved in the pathways related to lipid biosynthesis. Functional analysis via coexpression network and RNA-seq revealed that the hub genes in the co-expression network that also catalyze the key steps of fatty acid synthesis, lipid metabolism and oil body formation pathways (ACX4, LACS4, KCR1, and SQD1) could jointly orchestrate oil accumulation in cottonseed. This study will strengthen our understanding of oil metabolism and accumulation in cottonseed and contribute to KOC improvement in cottonseed in the future, enhancing the security and stability of worldwide food supply.

MLR index-based principal component analysis to investigate and monitor probable sources of groundwater pollution and quality in coastal areas: a case study in East India

Identifying groundwater contamination sources and supervising groundwater quality conditions are urgently needed to protect the groundwater resources of coastal areas like Contai of India, as communities here are heavily relying on groundwater which deteriorates progressively. So current research aims to address in detail about origins and influencing factors of groundwater contamination, status, and monitoring water quality by employing extremely useful leading technologies like principal component and factor analyses (PCA/FA), groundwater quality index (GWQI), and multiple linear regression (MLR) that helps to simplify complicated works instead of the conventional methods. Eight groundwater quality parameters were evaluated here, such as pH, TH (total hardness), Tur (turbidity), EC (electrical conductivity), TDS (total dissolved solids), Mn (manganese), Fe (iron), and Cl (chloride) for 38 sites. Three principal components with ~ 81% of the total variance were extracted from the PCA/FA analysis. The origin of maximum loadings of each factor is identified as a result of saline water, disintegration and leaching process, organic or else biogenic activities, and lithogenic or otherwise non-lithogenic links through percolating water. GWQI results show that ~ 87% of the samples fall into the good category and ~ 13% of the samples fall into the poor to very poor category. A model consisting of Tur, Fe, EC, Mn, TH, and Cl as independent parameters is more feasible and is proposed to predict GWQI obtained from MLR analysis. This MLR model also suggests that turbidity with the highest beta coefficient (0.820) is a key contributor relative to the entire groundwater class in this affected area. The findings relating to this research may support the designer and officials in monitoring and protecting coastal groundwater resources like selected areas.

Investigating extreme hydrological risk impact on water quality; evidence from Buffalo catchment headwater, Eastern Cape, South Africa

Evidence from increasing mineralization, micropollutant concentrations, waterborne epidemics, an algal boom, and dissolved organic matter has provided substantial evidence that climate change impacts water quality. While the impact of the extreme hydrological event (EHE) on water quality (WQ) has aroused considerable research interest, research uncertainty has been premised on WQ data scarcity, a short time frame, data non-linearity, data structure, and environmental biases on WQ. This study conceptualized a categorical and periodic correlation using confusion matrices and wavelet coherence for varying standard hydrological drought index (SHDI; 1971-2010) and daily WQ series (1977-2011) of four spatially distinct basins. By condensing the WQ variables using chemometric analyses, confusion matrices were assessed by cascading the SHDI series into 2-, 3-, and 5-phase scenarios. 2-phase revealed an overall accuracy (0.43-0.73), sensitivity analysis (0.52-1.00), and Kappa coefficient (- 0.13 to 0.14), which declines substantially with the phase increase, suggesting the disruptive impact of EHE on WQ. Wavelet coherence depicted the substantial ([Formula: see text]) mid- and long-term (8-32 days; 6-128 days) co-movement of streamflow over WQ, confirming the varying sensitivity of WQ variables. Land use/land cover mapping and the Gibbs diagram corroborate the eventful WQ evolution by EHE and their spatial variability concerning landscape transformation. Overall, the study deduced that hydrologic extreme triggers substantial WQ disruption with dissimilar WQ sensitivity. Consequently, suitable chemometric indicators of EHE impacts such as WQ index, nitrate-nitrogen, and Larson index at designated landscapes were identified for extreme chemodynamics impact assessment. This study proffers a recommendation for monitoring and managing the impact of climate change, floods, and drought on water quality.

Inconsistency of PCA-based water quality index - Does it reflect the quality?

The formalization of a stable water quality index (WQI) from measured hydrogeochemical parameters is essential for the identification and classification of water resources. In the principal component analysis (PCA) based WQI approach, the parameter weight is derived using either PC loading or rotated factor loading from a large number of samples pooled for WQI measurement. The PCA-based approach is paradoxical, as the calculated WQI rating of a sample would rather be dependent on the size, and composition of the population. Though this issue is well anticipated, no attempt has been made to regularize or measure the extent of WQI disagreement. In the present study, the WQI of 106 groundwater samples analyzed for 12 different hydrochemical parameters were modelled using PC loading or rotated factor loading (referred to as PCQ-1, PCQ-2, respectively) approach. Analysis reveals PCQ-1 to be positively biased in 78 % of samples and rating disagreements were evident in 9.43 % of samples. WQI of the data set was estimated using repeated (1000) random non-overlapping 2 to 5-fold data partitioning (containing 21 to 83 samples in each fold) adopting either an in-sample (test set) or out-sample (train set) modelling approach. The mean of WQI deviations in repeated resampling from the reference (i.e., using the entire dataset) has been positive in most of the samples using the PCQ-1 model, irrespective of the fold partition size. The median root mean square deviation values of the data set increased with the number of fold partitioning for in-sample calibration for both PCQ-1 and PCQ-2 approaches. The exclusion of a single water quality parameter from the PCA model can cause up to a 60 % deviation of the WQI score in some water samples. The cross-validation and Monte Carlo resampling approach can serve as a framework to test the stability of PCA-based WQI.

Spatial and temporal analysis and quantification of pollution sources of the surface water quality in a coastal province in Vietnam

The surface water quality in coastal areas may be highly vulnerable to degradation due to various pollution sources such as seawater intrusion and anthropogenic activities. The current study sought to spatially and temporally analyze and quantify pollution sources of the surface water system in the coastal province of Tra Vinh, Vietnam. A total of 600 surface water samples were taken from 30 monitoring sites distributed over 4 spatial zones. Water samples were collected in four campaigns each year during the dry and rainy seasons from 2016 to 2020 and analyzed for 10 physiochemical and biological parameters. The analyzed data were calculated for the water quality index (WQI). Two-way analysis of variance, principal component analysis/factor analysis (PCA/FA), and multivariable regression analysis (MRA) were conducted over the whole dataset. The results showed that the WQI decreased from the inland zone to the coastal area, was greater in the river zone than in the canal zone, and was higher in the dry season than in the rainy season. The PCA/FA revealed that surface water quality was affected by at least 4 main pollution sources, including agricultural production, seawater intrusion, residential activities, and mixed sources. MRA revealed that these pollution sources explained 68.3%, 12.8%, 7.0%, and 2.7% of the total variance of the WQI, respectively. In summary, the surface water quality in the study area significantly changed spatially and temporally, depending on four pollution sources, which need to be managed properly for a better environment and sustainable development.

Groundwater quality assessment using water quality index and multivariate statistical analysis case study: East Matrouh, Northwestern coast, Egypt

Rapid urbanisation has had a significant negative influence on the water bodies that flow through and around urban areas. This study aims to evaluate the water quality and analyse the suitability for drinking and irrigation uses. This study envisaged assessing the water quality status of the groundwater using the pollution index of groundwater (PIG), ecological risk index (ERI) and multivariate statistical techniques, namely cluster analysis (CA) and principal component analysis (PCA), that were applied to differentiate the sources of water quality variation and determine the cause of pollution in the study area. Most groundwater is unsuitable for drinking and irrigation consumption, depending on analyses. PIG values indicated high pollution levels in the studied water body, rendering it unsuitable for any practical purpose. CA results showed the impact of surface water and treatment plant on groundwater. PCA was used to identify four important factors in the groundwater, including mineral and nutrient pollution, heavy metal pollution, organic pollution and faecal contamination. The deteriorating water quality of the groundwater was demonstrated to originate from vast sources of anthropogenic activities, especially municipal sewage discharge. Study wells had greater concentrations of Cl^- and Na⁺ in their water because seawater flows into the aquifer system and mixes with the marine aquifer matrix. Thus, the current work reveals how to employ the PIG and multivariate statistical approaches to obtain more accessible and more meaningful information about the water quality of groundwater and to identify the sources of pollution.

Dynamic impact of financial inclusion and industrialization on environmental sustainability

Purpose

The study aims to investigate the impact of financial inclusion (FI) on environmental quality and the mediating role of industrialization (IZ). In addition, these relationships among the counties with different levels of income and carbon emissions were also analyzed.

Design/methodology/approach

This paper used the International Monetary Fund database for indicators of FI. The environmental indicators were obtained from the World Bank database for a panel of worldwide countries from 2004 to 2019. Separate indices of environmental sustainability (ES) and environmental degradation (ED) were created by using principal component analysis . The generalized method of moments regression was applied to examine the relationship between variables.

Findings

The study found full mediation of IZ between FI and ES, whereas partial mediation between FI and environmental degradation. The results were found robust against alternative measures of carbon emissions. Furthermore, the study also bifurcated the sample according to the level of income and carbon emission. It was found that FI plays a positive role in the betterment of environmental quality for high-income countries, while a negative role in upper-middle-income, lower-middle-income and low-income countries. Besides, FI has a negative role in the ES of the countries having higher or lower carbon emission levels.

Originality/value

Empirically this study contributes by creating two different novel measures of ES and environmental degradation, in contrast to other studies that solely relied on carbon emission. Contrary to previous studies, this study suggests that FI is not solely responsible for environmental damages, and IZ is the key channel by which FI shifts its impact on ES. Moreover, for environmental degradation, there are some other channels involved that need to be investigated further. This study has also noted that the relationship between FI and ES is context-dependent. Theoretically, this paper contributes to the literature by using ecological modernization theory in the nexus of FI, IZ and environmental quality.

Effect of insulin-like growth factor-1 complex of Simmental bull seminal plasma on post-thawed Kacang buck semen fertility

Background and Aim

Kacang buck sperm is cryosensitive due to the seminal plasma of semen itself. Meanwhile, bull seminal plasma contains the insulin-like growth factor-1 (IGF-1) complex, which is cryoprotective. The addition of the crude protein of Simmental bull seminal plasma increased the quality of post-thawed semen of Kacang buck. The study was conducted to determine the effects of Simmental bull seminal plasma with IGF-1 on the fertility of post-thawed Kacang buck semen.

Materials and Methods

Buck semen was diluted in the following skim milk-egg yolk extender preparations: Without the addition of Simmental bull seminal plasma IGF-1 complex protein (T0); with the addition of 12-μg Simmental bull seminal plasma IGF-1 complex protein (T1); and with the addition of 24-μg Simmental bull seminal plasma IGF-1 complex protein (T2). The extended semen was packed in 0.25-mL straws and frozen. Post-thawed semen fertility was evaluated based on the following variables: Sperm motility, viability, intact plasma membrane (IPM), malondialdehyde (MDA) levels, capacitation status, and acrosome reaction. The difference in each variable among the groups was evaluated using analysis of variance, followed by Tukey's honestly significant difference test, at a 95% level of significance. Meanwhile, principal component analysis (PCA) was used to identify the principal component of semen fertility among the seven parameters.

Results

The T1 group showed the highest sperm motility, viability, IPM, and percentage of incapacitated sperm and the lowest MDA levels, percentage of capacitated sperm, and acrosome reaction. PCA revealed that sperm motility had a moderate to very robust correlation with other variables and is the most crucial parameter, accounting for 80.79% of all variables.

Conclusion

The IGF-1 complex in Simmental bull seminal plasma was useful for increasing the fertility of post-thawed Kacang buck semen, and sperm motility was the principal component of semen fertility.

Evaluation of Seasonal and Spatial Variations in Water Quality and Identification of Potential Sources of Pollution Using Multivariate Statistical Techniques for Lake Hawassa Watershed, Ethiopia

The magnitude of pollution in Lake Hawassa has been exacerbated by population growth and economic development in the city of Hawassa, which is hydrologically closed and retains pollutants entering it. This study was therefore aimed at examining seasonal and spatial variations in the water quality of Lake Hawassa Watershed (LHW) and identifying possible sources of pollution using multivariate statistical techniques. Water and effluent samples from LHW were collected monthly for analysis of 19 physicochemical parameters during dry and wet seasons at 19 monitoring stations. Multivariate statistical techniques (MVST) were used to investigate the influences of an anthropogenic intervention on the physicochemical characteristics of water quality at monitoring stations. Through cluster analysis (CA), all 19 monitoring stations were spatially grouped into two statistically significant clusters for the dry and wet seasons based on pollution index, which were designated as moderately polluted (MP) and highly polluted (HP). According to the study results, rivers and Lake Hawassa were moderately polluted (MP), while point sources (industry, hospitals and hotels) were found to be highly polluted (HP). Discriminant analysis (DA) was used to identify the most critical parameters to study the spatial variations, and seven significant parameters were extracted (electrical conductivity (EC), dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), sodium ion (Na+), and potassium ion (K+) with the spatial variance to distinguish the pollution condition of the groups obtained using CA. Principal component analysis (PCA) was used to qualitatively determine the potential sources contributing to LHW pollution. In addition, three factors determining pollution levels during the dry and wet season were identified to explain 70.5% and 72.5% of the total variance, respectively. Various sources of pollution are prevalent in the LHW, including urban runoff, industrial discharges, diffused sources from agricultural land use, and livestock. A correlation matrix with seasonal variations was prepared for both seasons using physicochemical parameters. In conclusion, effective management of point and non-point source pollution is imperative to improve domestic, industrial, livestock, and agricultural runoff to reduce pollutants entering the Lake. In this regard, proper municipal and industrial wastewater treatment should be complemented, especially, by stringent management that requires a comprehensive application of technologies such as fertilizer management, ecological ditches, constructed wetlands, and buffer strips. Furthermore, application of indigenous aeration practices such as the use of drop structures at critical locations would help improve water quality in the lake watershed.

Micro-Clustering and Rank-Learning Profiling of a Small Water-Quality Multi-Index Dataset to Improve a Recycling Process

The efficiency improvement of wastewater recycling has been prioritized by ‘Goal 6’ of the United Nations Sustainable Development initiative. A methodology is developed to synchronously profile multiple water-quality indices of a wastewater electrodialysis (ED) process. The non-linear multifactorial screener is exclusively synthesized by assembling proper R-based statistical freeware routines. In sync with current trends, the new methodology promotes convenient, open and rapid implementation. The new proposal unites the ‘small-and-fast’ data-sampling features of the fractional multifactorial designs to the downsizing, by microclustering, of the multiple water quality indices—using optimized silhouette-based classification. The non-linear multifactorial profiling process is catalyzed by the ‘ordinalization’ of the regular nominal nature of the resulting optimum clusters. A bump chart screening virtually eliminates weak performances. A follow-up application of the ordinal regression succeeds in assigning statistical significance to the resultant factorial potency. The rank-learning aptitude of the new profiler is tested and confirmed on recently published wastewater ED-datasets. The small ED-datasets attest to the usefulness to convert limited data in real world applications, wherever there is a necessity to improve the quality status of water for agricultural irrigation in arid areas. The predictions have been compared with other techniques and found to be agreeable.

Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling Effort

Exposure to contaminated water during aquatic recreational activities can lead to gastrointestinal diseases. In order to decrease the exposure risk, the fecal indicator bacteria Escherichia coli is routinely monitored, which is time-consuming, labor-intensive, and costly. To assist the stakeholders in the daily management of bathing sites, models have been developed to predict the microbiological quality. However, model performances are highly dependent on the quality of the input data which are usually scarce. In our study, we proposed a conceptual framework for optimizing the selection of the most adapted model, and to enrich the training dataset. This frameword was successfully applied to the prediction of Escherichia coli concentrations in the Marne River (Paris Area, France). We compared the performance of six machine learning (ML)-based models: K-nearest neighbors, Decision Tree, Support Vector Machines, Bagging, Random Forest, and Adaptive boosting. Based on several statistical metrics, the Random Forest model presented the best accuracy compared to the other models. However, 53.2 ± 3.5% of the predicted E. coli densities were inaccurately estimated according to the mean absolute percentage error (MAPE). Four parameters (temperature, conductivity, 24 h cumulative rainfall of the previous day the sampling, and the river flow) were identified as key variables to be monitored for optimization of the ML model. The set of values to be optimized will feed an alert system for monitoring the microbiological quality of the water through combined strategy of in situ manual sampling and the deployment of a network of sensors. Based on these results, we propose a guideline for ML model selection and sampling optimization.

Higher Number of Yeast-like Fungi in the Air in 2018 after an Emergency Discharge of Raw Sewage to the Gulf of Gdańsk—Use of Contingency Tables

This study aimed to investigate the differences between the number of yeast-like fungi and molds in the coastal air of five coastal towns of the Gulf of Gdańsk in 2014–2017 vs. 2018, which saw an emergency discharge of sewage. In 2014–2017, a total of 62 duplicate samples were collected in the coastal towns of Hel, Puck, Gdynia, Sopot, and Gdańsk-Brzeźno. In 2018, after the emergency disposal of raw sewage, 26 air samples were collected. A Pearson chi-squared test of independence showed that during 2018 in Hel and Sopot, the mean number of molds and yeast-like fungi was higher than in 2014–2017. The result was significantly positive, p ≤ 2.22 × 10−16. The analysis of the General Asymptotic Symmetry Test showed that in Puck and Gdańsk-Brzeźno, the average number of Aspergillus sp. mold fungi was higher in 2018 after an emergency discharge of sewage into the Gulf of Gdańsk compared to the period 2014–2017. The result was not statistically significant. In addition, the average number of Penicillium sp. molds in 2018 in Gdańsk-Brzeźno was higher than in 2014–2017, but statistically insignificant (p = 0.9593). In 2018, the average number of Cladosporium sp. molds in Sopot was higher, but also statistically insignificant (p = 0.2114) compared to 2014–2017. Our results indicate that the study of the number of yeast-like fungi in the air may indicate coastal areas that may be particularly at risk of bacterial or mycological pathogens, e.g., after an emergency discharge of raw sewage.

Cut Flower Characteristics and Growth Traits under Salt Stress in Lily Cultivars

Salt stress is a major constraint of crop productivity because it reduces yield and limits the expansion of agriculture. This study investigated salt tolerance in 26 cultivars of cut lilies (Lilium hybrids) by examining the effect of salt stress on the growth and morphological characteristics of flowers and leaves and their physiological properties (chlorophyll a fluorescence). Salt stress significantly affected the growth and development of cut lilies. Canonical discriminant analysis indicates that the middle leaf width, number of flowers, first flower diameter, petal width, and chlorophyll a fluorescence were correlated with salt stress, whereas plant height, the middle leaf length, days to flowering, and sepal width were less affected by the stress. The cultivars examined were divided into three groups: Group 1 included the salt-sensitive cultivars, which failed to develop normal flowers; Group 2 included cultivars sensitive to salt stress but tolerant to osmotic stress; and Group 3 was the salt-tolerant group, which developed commercially valuable flowers. In conclusion, the cultivars contained a variable range of cut flower characteristics and growth traits that can be employed for lily breeding programs and as material for molecular mechanisms and signaling networks under salt stress.

Combining Water Quality Indices and Multivariate Modeling to Assess Surface Water Quality in the Northern Nile Delta, Egypt

Assessing surface water quality for drinking use in developing countries is important since water quality is a fundamental aspect of surface water management. This study aims to improve surface water quality assessments and their controlling mechanisms using the drinking water quality index (DWQI) and four pollution indices (PIs), which are supported by multivariate statistical analyses, such as principal component analysis, partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR). Twenty-two physicochemical parameters were analyzed using standard analytical methods for 55 surface water sites in the northern Nile Delta, Egypt. The DWQI results indicated that 33% of the tested samples represented good water, and 67% of samples indicated poor to unsuitable water for drinking use. The PI results revealed that surface water samples were strongly affected by Pb and Mn and were slightly affected by Fe and Cr. The SMLR models of the DWQI and PIs, which were based on all major ions and heavy metals, provided the best estimations with R2 = 1 for the DWQI and PIs. In conclusion, integration between the DWQI and PIs is a valuable and applicable approach for the assessment of surface water quality, and the PLSR and SMLR models can be used through applications of chemometric techniques to evaluate the DWQI and PIs.

Water Quality Index of Suceava River in Suceava City Metropolitan Area

The water quality of rivers worldwide is of persistent interest due to its impact on human life. Five streamwater quality parameters of Suceava River were monitored in 2019 upstream and downstream of Suceava city, Romania: dissolved oxygen, specific conductivity, pH, oxidation-reduction potential, and temperature. Data was recorded at a high temporal frequency, every hour, and produced Water Quality Index (WQI) time series of similar resolution. Our additive WQI has variants with particular advantages. Water quality of Suceava city exhibits a diurnal cycle. Upstream, WQI values indicate a quasi-permanent good water quality; downstream, the water quality oscillates around the average WQI value because of the various sources of water contaminants, especially the wastewaters from the wastewater treatment plant. Parameters from this point source of pollution are taken into account to explain the decaying streamwater quality towards the end of 2019. WQI is useful for detecting time intervals when water self-purification events have a high chance of occurrence.

Towards Monitoring of Nutrient Pollution in Coastal Lake Using Remote Sensing and Regression Analysis

The last few decades have witnessed a tremendous increase in nutrient levels (phosphorus and nitrogen) in coastal water leading to excessive algal growth (Eutrophication). The presence of large amounts of algae turns the water’s color into green or red, in the case of algal blooms. Chlorophyll-a is often used as an indicator of algal biomass. Due to increased human activities surrounding Dubai creek, there have been eutrophication concerns given the levels of nutrients in that creek. This study aims to map chlorophyll-a in Dubai Creek from WorldView-2 imagery and explore the relationship between chlorophyll-a and other eutrophication indicators. A geometrically- and atmospherically-corrected WorldView-2 image and in-situ data have been utilized to map chlorophyll-a in the creek. A spectral model, developed from the WorldView-2 multispectral image to monitor Chlorophyll-a concentration, yielded 0.82 R2 with interpolated in-situ chlorophyll-a data. To address the time lag between the in-situ data and the image, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were used to demonstrate the accuracy of the WorldView-2 model. The images, acquired on 20 May and 23 July 2012, were processed to extract chlorophyll-a band ratios (Band 4/Band 3) following the standard approach. Based on the availability, the 20 May image acquisition date is the closest to the middle of Quarter 2 (Q2) of the in-situ data (15 May). The 23 July 2012 image acquisition date is the closest to the WorldView-2 image date (24 July). Another model developed to highlight the relationship between spectral chlorophyll-a levels, and total nitrogen and orthophosphate levels, yielded 0.97 R2, which indicates high agreement. Furthermore, the generated models were found to be useful in mapping chlorophyll-a, total nitrogen, and orthophosphate, without the need for costly in-situ data acquisition efforts.