Spatial response of water level and quality shows more significant heterogeneity during dry seasons in large river-connected lakes

The spatial response mechanism of hydrology and water quality of large river-connected lakes is very complicated. In this study, we developed a spatial response analysis method that couples wavelet correlation analysis (WTC) with self-organizing maps (SOM), revealing the spatial response and variation of water level and water quality in Poyang Lake, China's largest river-connected lake, over the past decade. The results show that: (1) there was significant spatial heterogeneity in water level and quality during the dry seasons (2010-2018) compared to other hydrological stages. (2) We identified a more pronounced difference in response of water level and quality between northern and southern parts of Poyang Lake. As the distance increases from the northern lake outlet, the impact of rising water levels on water quality deterioration intensified during the dry seasons. (3) The complex spatial heterogeneity of water level and quality response in the dry seasons is primarily influenced by water level fluctuations from the northern region and the cumulative pollutant entering the lake from the south, which particularly leads to the reversal of the response in the central area of Poyang Lake. The results of this study can contribute to scientific decision-making regarding water environment zoning management in large river-connected lakes amidst complex environment conditions.

A self-organizing map approach to the analysis of lake DOM fluorescence for differentiation of organic matter sources

The sources and properties of dissolved organic matter (DOM) in two lakes with different non-point source inputs were investigated by combining conventional three-dimensional fluorescence spectroscopy methods with a self-organizing map (SOM). To assess the DOM humification level, the representative neurons 1, 11, 25, and 36 were assessed. The SOM model showed that the DOM humification level of the Gaotang Lake (GT) which has a mainly agricultural non-point source input was significantly higher than that of the Yaogao Reservoir (YG) which has a mainly terrestrial source input (P < 0.01). The GT DOM mainly came from factors such as agricultural-related farm compost and decaying plants, while the YG DOM originated from human activities around the lake. The source characteristics of the YG DOM are obvious, with a high level of biological activity. Five representative areas in the fluorescence regional integral (FRI) were compared. The comparison showed that during the flat water period, the GT water column showed more terrestrial characteristics, even though the humus-like fractions in the DOM of both lakes were derived from microbial decomposition. Principal component analysis (PCA) showed that the agricultural lake water DOM (GT) was dominated by humus components, while the urban lake water DOM (YG) was dominated by authigenic sources.

Machine learning can identify the sources of heavy metals in agricultural soil: A case study in northern Guangdong Province, China

Source tracing of heavy metals in agricultural soils is of critical importance for effective pollution control and targeting policies. It is a great challenge to identify and apportion the complex sources of soil heavy metal pollution. In this study, a traditional analysis method, positive matrix fraction (PMF), and three machine learning methodologies, including self-organizing map (SOM), conditional inference tree (CIT) and random forest (RF), were used to identify and apportion the sources of heavy metals in agricultural soils from Lianzhou, Guangdong Province, China. Based on PMF, the contribution of the total loadings of heavy metals in soil were 19.3% for atmospheric deposition, 65.5% for anthropogenic and geogenic sources, and 15.2% for soil parent materials. Based on SOM model, As, Cd, Hg, Pb and Zn were attributed to mining and geogenic sources; Cr, Cu and Ni were derived from geogenic sources. Based on CIT results, the influence of altitude on soil Cr, Cu, Hg, Ni and Zn, as well as soil pH on Cd indicated their primary origin from natural processes. Whereas As and Pb were related to agricultural practices and traffic emissions, respectively. RF model further quantified the importance of variables and identified potential control factors (altitude, soil pH, soil organic carbon) in heavy metal accumulation in soil. This study provides an integrated approach for heavy metals source apportionment with a clear potential for future application in other similar regions, as well as to provide the theoretical basis for undertaking management and assessment of soil heavy metal pollution.

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

One of the purposes of Artificial Intelligence tools is to ease the analysis of large amounts of data. In order to support the strategic decision-making process of the airlines, this paper proposes a Data Mining approach (focused on visualization) with the objective of extracting market knowledge from any database of industry players or competitors. The method combines two clustering techniques (Self-Organizing Maps, SOMs, and K-means) via unsupervised learning with promising dynamic applications in different sectors. As a case study, 30-year data from 18 diverse US passenger airlines is used to showcase the capabilities of this tool including the identification and assessment of market trends, M&A events or the COVID-19 consequences.

Identifying ecosystem service bundles and the spatiotemporal characteristics of trade-offs and synergies in coal mining areas with a high groundwater table

Understanding the spatiotemporal characteristics of the interactions among ecosystem services (ESs) is a crucial but challenging task for maintaining human well-being and achieving sustainable regional development. However, understanding the spatiotemporal interactions of multiple ESs at different grid scales and within different ecosystem services bundles (ESBs) is relatively limited, particularly in coal mining areas with a high groundwater table (CMA-HGT) where the land use has drastically changed as a result of mining subsidence. This study examines CMA-HGT in Huainan, aiming to identify ESBs and explore the spatiotemporal characteristics of trade-offs/synergies among ESs at distinct grid scales and ESBs. Five ESs relating to provisioning, regulation, and maintenance, including food production (FP), water yield (WY), soil conservation (SC), carbon sequestration (CS), and biodiversity maintenance (BM) were quantified using different biological models during the period 1987-2018. Spatiotemporal trade-offs/synergies among ESs were explored using correlation analysis and significance tests at different scales. The spatiotemporal distributions and main characteristics of distinct ESBs were identified using a self-organizing map (SOM) and Calinski criterion. The interactions among ESs in different ESBs were detected. Relationships between ESs and land use or coal production (CP) were explored using redundancy analysis (RDA). The results demonstrate that spatiotemporal trade-offs were generally observed among provisioning services at distinct grid scales and within different ESBs. Meanwhile, spatiotemporal synergies generally appeared between regulation and maintenance services at distinct grid scales. Interactions among ESs presented temporal dynamic, spatial heterogeneity and scales dependence due to the relationships of FP-BM or SC-CS had changed with the increasing of research scales. Three ESBs-ESB1, ESB2, and ESB3-were identified at a grid of scale of 1000 m, and their spatial locations varied across different periods, but the areas of variation covered less than 24% of the study area. BM was synergistic with FP, WY, SC, and CS; while WY had only a trade-off relationship with FP in ESB1. WY had trade-off relationships with FP, SC, CS, and BM in ESB2. In ESB3, BM was synergistic with FP, SC, and CS; while it was in a trade-off relationship with WY. Cultivated land, construction land and CP were the main driving factors in the WSA, ESB1, ESB2 and ESB3. There was a certain degree of change in the relationships between ESs and land use/CP, and the relationships among ESs at different grid scales and ESBs over time and space, which indicates strong regional heterogeneity and scale dependence. These results can provide detailed guidelines for formulating spatially targeted ecosystem management, restoration programs and ES payment policies.

Building 2D classification models and 3D CoMSIA models on small-molecule inhibitors of both wild-type and T790M/L858R double-mutant EGFR

Epidermal growth factor receptor (EGFR) has received widespread attention because it is an important target for anticancer drug design. Mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult. We herein built the structure-activity relationship models of small-molecule inhibitors on wild-type and T790M/L858R double-mutant EGFR with a whole dataset of 379 compounds. For 2D classification models, we used ECFP4 fingerprints to build support vector machine and random forest models and used SMILES to build self-attention recurrent neural network models. Each of all six models resulted in an accuracy of above 0.87 and the Matthews correlation coefficient value of above 0.76 on the test set, respectively. We concluded that inhibitors containing anilinoquinoline and methoxy or fluoro phenyl are highly active against wild EGFR. Substructures such as anilinopyrimidine, acrylamide, amino phenyl, methoxy phenyl, and thienopyrimidinyl amide appeared more in highly active inhibitors against double-mutant EGFR. We also used self-organizing map to cluster the inhibitors into six subsets based on ECFP4 fingerprints and analyzed the activity characteristics of different scaffolds in each subset. Among them, three datasets, which are based on pteridin, anilinopyrimidine, and anilinoquinoline scaffold, were selected to build 3D comparative molecular similarity analysis models individually. Models with the leave-one-out coefficient of determination (q²) above 0.65 were selected, and five descriptor types (steric, electrostatic, hydrophobic, donor, and acceptor) were used to study the effects of side chains of inhibitors on the activity against wild-type and mutant-type EGFR.

Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques

The complex mixtures of local emission sources and regional transportations of air pollutants make accurate PM2.5 prediction a very challenging yet crucial task, especially under high pollution conditions. A symbolic representation of spatio-temporal PM2.5 features is the key to effective air pollution regulatory plans that notify the public to take necessary precautions against air pollution. The self-organizing map (SOM) can cluster high-dimensional datasets to form a meaningful topological map. This study implements the SOM to effectively extract and clearly distinguish the spatio-temporal features of long-term regional PM2.5 concentrations in a visible two-dimensional topological map. The spatial distribution of the configured topological map spans the long-term datasets of 25 monitoring stations in northern Taiwan using the Kriging method, and the temporal behavior of PM2.5 concentrations at various time scales (i.e., yearly, seasonal, and hourly) are explored in detail. Finally, we establish a machine learning model to predict PM2.5 concentrations for high pollution events. The analytical results indicate that: (1) high population density and heavy traffic load correspond to high PM2.5 concentrations; (2) the change of seasons brings obvious effects on PM2.5 concentration variation; and (3) the key input variables of the prediction model identified by the Gamma Test can improve model's reliability and accuracy for multi-step-ahead PM2.5 prediction. The results demonstrated that machine learning techniques can skillfully summarize and visibly present the clusted spatio-temporal PM2.5 features as well as improve air quality prediction accuracy.

Accurate Identification of Human Phosphorylated Proteins by Ensembling Supervised Kernel Self-organizing Maps

Protein phosphorylation is a vital physiological process, which plays a critical role in controlling survival differentiation, cell growth, metabolism and apoptosis. The accurate identification of whether a protein will be phosphorylated solely from protein sequence is especially useful for both basic research and drug development. In this study, a new predictor specifically designed for the prediction of human phosphorylated proteins is proposed. The proposed method first train two supervised kernel self-organizing maps (SKSOMs): one is trained with feature from protein physiochemical composition view, while the other is trained with feature from protein evolutionary information view. Then, the two trained SKSOMs are ensembled to perform the final prediction. Rigorous computational experiments show that the proposed method achieves 78.75 % and 0.561 on ACC and MCC, which are 6.96 % and 12.5 % higher than that of the state-of-the-art predictor. Overall, the study demonstrated a new sensitive avenue to identify human phosphorylated proteins and could be readily extended to recognize phosphorylated proteins for other species.

Combination of self-organizing map and parallel factor analysis to characterize the evolution of fluorescent dissolved organic matter in a full-scale landfill leachate treatment plant

The dissolved organic matter (DOM) characterization in a full-scale landfill leachate treatment plant is of great importance for the design and operation of treatment processes. In this study, the long-term removal behaviors of DOM during landfill leachate treatment were explored using excitation emission matrix fluorescence spectroscopy (EEMs) coupled with parallel factor analysis (PARAFAC) and self-organizing map (SOM). Results indicated that the application of combining PARAFAC and SOM on EEMs analysis effectively characterized long-term removal behaviors of DOM during leachate treatment. The DOM in raw leachate was dominated by humic substances, while its composition exhibited significant seasonal differences. A large proportion of protein-like fluorescent dissolved organic matter (FDOM) and bulk DOM were removed within membrane bioreactor (MBR) system. Meanwhile the humic-like FDOM removal capacity in nanofiltration (NF) process was well comparable with those in the MBR system owing to the bio-recalcitrant nature of humic substances. The protein-like FDOM and bulk DOM were removed synchronously in both the process of MBR and NF. Moreover, samples distribution exhibited obvious differences among NF concentrate samples. In general, the performance of MBR-NF treatment for landfill leachate displayed reasonable stability in DOM removal irrespective of seasonal variations. This study enhanced our understanding of EEMs application in characterizing leachate-derived DOM composition and has potential implications for the associated monitoring investigations in engineered systems.

Artificial neural networks in the selection of shoe lasts for people with mild diabetes

This research addressed the selection of shoe lasts for footwear design to help relieve the pain associated with diabetic neuropathy and foot ulcers. A reverse engineering (RE) technique was used to convert point clouds corresponding to scanned shoe lasts and diabetic foot data into stereo lithograph (STL) meshes. A slicing algorithm was developed and was used to find relevant girth features of diabetic foot and the shoe lasts. An artificial neural network, termed self-organizing map (SOM), classified 60 sets of shoe lasts into similar groups. Foot shapes of three mild diabetic patients were entered into the SOM feature categories to match with suitable shoe lasts. By conducting expert questionnaire analysis of the characteristic girths featured data with analytic hierarchy process (AHP), the weights of the girths were obtained. Grey relational analysis (GRA) was then used to calculate the correlation between foot girth and the corresponding range of shoe lasts. The most suitable shoe last for each patient with a mild diabetic foot can be determined by calculating the relative fitness function for each patient. By correlating diabetic foot with suitable shoe lasts, this study demonstrated an effective strategy for designing shoes for patients with mild diabetes, which can then be manufactured to meet customized requirements.

Mining affective experience for a kansei design study on a recliner

As the technical performance of products progresses, it is becoming more important to design products that satisfy customers' affective experiences. Hence, many studies about Kansei engineering or Kansei design have been conducted to develop products that can satisfy customers' affective experiences. In the Kansei design method, it is important to select affective variables related to the design elements of the product in order to accurately grasp the emotions of customers. Therefore, this study seeks to develop an affective variable extraction methodology that can reflect users' implicit needs effectively and efficiently. In this study, users' affective variables were extracted from online reviews and classified using a self-organizing map (SOM). For verification, the study selected the Amazon e-commerce service and performed a product experiment on recliners. The experimental results show that the most frequently used affective variable in the use of recliners is 'comfort', which is related to various affective variables. In addition, 15 clusters for affective experiences of recliners extracted from Amazon.com were classified through the SOM. The findings suggest that text mining techniques and the SOM can be used to gather and analyze customers' affective experiences effectively and efficiently. The results of this study can also enhance an understanding of customers' emotions regarding recliners.

Organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in Cyprinidae fish: Towards hints of their arrangements using advanced classification methods

To tackle the ever-present global concern regarding human exposure to persistent organic pollutants (POPs) via food products, this study strived to indicate associations between organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in lake-fish tissue depending on the species and sampling season. Apart from the monitoring initiatives recommended in the Global Monitoring Plan for POPs, the study discussed 7 OCPs and 18 PCB congeners determined in three Cyprinidae species (rudd, carp, and Prussian carp) from Vransko Lake (Croatia), which are widely domesticated and reared as food fish across Europe and Asia. We exploit advanced classification algorithms, the Kohonen self-organizing maps (SOM) and Decision Trees (DT), to search for POP patterns typical for the investigated species. As indicated by SOM, some of the dioxin-like and non-dioxin-like PCBs (PCB-28, PCB-74, PCB-52, PCB-101, PCB-105, PCB-114, PCB-118, PCB-156 and PCB-157), α-HCH and β-HCH caused dissimilarities among fish species, but regardless of their weight and length. To support these suggestions, DT analysis sequenced the fish species and seasons based on the concentration of heavier congeners. The presented assumptions indicated that the supplemental application of SOM and DT offers advantageous features over the usually rough interpretation of POPs pattern and over the single use of the methods.

The best data design for applying the taxonomic distinctness index in lotic systems: A case study of the Southern Morava River basin

The taxonomic distinctness (Δ+) index has been recognized as a robust measure to assess human impacts on marine biodiversity. However, its applicability in freshwater ecosystems has still not been confirmed. We aimed to propose the most suitable data design for calculating the Δ+ index for application in assessing anthropogenically caused degradation in lotic environments. We calculated the values of Δ+ based on different taxa groups and taxa resolutions, in order to examine its utility as a potential metric in bioassessment programs. We found that the exclusion of non-insect taxa and selected insect orders significantly increased the index sensitivity. Thus, we believe that an appropriate data design for Δ+ calculation based on macroinvertebrate assemblages is the main prerequisite for the effective estimation of degradation in lotic environments. In addition, we argue that a decrease in taxonomic resolution up to genus level is completely acceptable, as it results in only minor information loss. Bearing this in mind would significantly facilitate its application in rapid bioassessment programs. Despite the observed correlation, the utility of Δ+ as a potential bioassessment metric is rather limited, since its fails to detect fine differences in environmental stress, and instead only roughly distinguishes between two basic classes of degradation level, unimpacted and impacted.

Conservation of groundwater from over-exploitation-Scientific analyses for groundwater resources management

Groundwater over-exploitation has produced many critical problems in the southern Taiwan. The accumulated stresses and demands make groundwater management a complex issue that needs innovative scientific analyses for deriving better water management strategies. In this study, we aimed to provide scientific analyses of the groundwater systems in the Pingtung Plain through soft-computing techniques to explore its spatial-temporal and hydro-geological characteristics for the elaboration of future groundwater management plans and in decision-making process. We conducted a study to assess the essential features of the groundwater systems based on the long-term large datasets of regional groundwater levels by using the principal component analysis (PCA), and the self-organizing map (SOM) with regression analysis. The PCA results demonstrated that two leading components could well present the spatial characteristics of the groundwater systems and classify the region into eastern, western and transition zones. The SOM results could visibly explore the behavior of regional groundwater variations in various aquifers and the multi-relations among climate and hydrogeological variables. Results revealed that the potential of groundwater recharge made by precipitation or river flow was higher in the eastern zone than in the western zone. Analysis results further showed an increase of the groundwater levels in the western zone after year 2006, while there were no obvious increases of the groundwater levels in the eastern or transition zones. Based on the investigated characteristics, we suggest that a sound groundwater management plan should consider zonal difference of the groundwater systems to achieve groundwater conservation.

Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization

The present study devises mapping methodologies and projection techniques that visualize and demonstrate biological sequence data clustering results. The Sequence Data Density Display (SDDD) and Sequence Likelihood Projection (SLP) visualizations represent the input symbolical sequences in a lower-dimensional space in such a way that the clusters and relations of data elements are depicted graphically. Both operate in combination/synergy with the Self-Organizing Hidden Markov Model Map (SOHMMM). The resulting unified framework is in position to analyze automatically and directly raw sequence data. This analysis is carried out with little, or even complete absence of, prior information/domain knowledge.

A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing map

The steep slopes of rivers can easily lead to large variations in river water quality during typhoon seasons in Taiwan, which may poses significant impacts on riverine eco-hydrological environments. This study aims to investigate the relationship between fish communities and water quality by using artificial neural networks (ANNs) for comprehending the upstream eco-hydrological system in northern Taiwan. We collected a total of 276 heterogeneous datasets with 8 water quality parameters and 25 fish species from 10 sampling sites. The self-organizing feature map (SOM) was used to cluster, analyze and visualize the heterogeneous datasets. Furthermore, the structuring index (SI) was adopted to determine the relative importance of each input variable of the SOM and identify the indicator factors. The clustering results showed that the SOM could suitably reflect the spatial characteristics of fishery sampling sites. Besides, the patterns of water quality parameters and fish species could be distinguishably (visually) classified into three eco-water quality groups: 1) typical upstream freshwater fishes that depended the most on dissolved oxygen (DO); 2) typical middle-lower reach riverine freshwater fishes that depended the most on total phosphorus (TP) and ammonia nitrogen; and 3) low lands or pond (reservoirs) freshwater fishes that depended the most on water temperature, suspended solids and chemical oxygen demand. According to the results of the SI, the representative indicators of water quality parameters and fish species consisted of DO, TP and Onychostoma barbatulum. This grouping result suggested that the methodology can be used as a guiding reference to comprehensively relate ecology to water quality. Our methods offer a cost-effective alternative to more traditional methods for identifying key water quality factors relating to fish species. In addition, visualizing the constructed topological maps of the SOM could produce detailed inter-relation between water quality and the fish species of stream habitat units.

Computer modeling in predicting the bioactivity of human 5-lipoxygenase inhibitors

5-Lipoxygenase (5-LOX) is a key enzyme in the inflammatory path. Inhibitors of 5-LOX are useful for the treatment of diseases like arthritis, cancer, and asthma. We have collected a dataset including 220 human 5-LOX inhibitors for classification. A self-organizing map (SOM), a support vector machine (SVM), and a multilayer perceptron (MLP) algorithm were used to build models with selected descriptors for classifying 5-LOX inhibitors into active and weakly active ones. MACCS fingerprints were used in this model building process. The accuracy (Q) and Matthews correlation coefficient (MCC) of the best SOM model (Model 1A) were 86.49% and 0.73 on the test set, respectively. The Q and MCC of the best SVM model (Model 2A) were 82.67% and 0.64 on the test set, respectively. The Q and MCC of the best MLP model (Model 3B) were 84.00% and 0.67 on the test set, respectively. In addition, 180 inhibitors with bioactivities measured by fluorescence method were further used for a quantitative prediction. Multiple linear regression (MLR) and SVM algorithms were used to build models to predict the [Formula: see text] values. The correlation coefficients (R) of the MLR model (Model Q1) and the SVM model (Model Q2) were 0.72 and 0.74 on the test set, respectively.

Investigating the effect of landfill leachates on the characteristics of dissolved organic matter in groundwater using excitation-emission matrix fluorescence spectra coupled with fluorescence regional integration and self-organizing map

For the purpose of investigating the effect of landfill leachate on the characteristics of organic matter in groundwater, groundwater samples were collected near and in a landfill site, and dissolved organic matter (DOM) was extracted from the groundwater samples and characterized by excitation-emission matrix (EEM) fluorescence spectra combined with fluorescence regional integration (FRI) and self-organizing map (SOM). The results showed that the groundwater DOM comprised humic-, fulvic-, and protein-like substances. The concentration of humic-like matter showed no obvious variation for all groundwater except the sample collected in the landfill site. Fulvic-like substance content decreased when the groundwater was polluted by landfill leachates. There were two kinds of protein-like matter in the groundwater. One kind was bound to humic-like substances, and its content did not change along with groundwater pollution. However, the other kind was present as "free" molecules or else bound in proteins, and its concentration increased significantly when the groundwater was polluted by landfill leachates. The FRI and SOM methods both can characterize the composition and evolution of DOM in the groundwater. However, the SOM analysis can identify whether protein-like moieties was bound to humic-like matter.

The utility of two marine community indices to assess the environmental degradation of lotic systems using fish communities

Multimetric approaches are commonly used to evaluate the ecological status of aquatic ecosystems. However, it has been recommended that the sensitivity of existing methods be improved through the investigation of the potential of new metrics to detect environmental disturbances. In this study we tested the effectiveness of two community indices (Taxonomic distinctness index (TDI) and Abundance biomass comparison (ABC) method), primarily proposed for marine ecosystems, to identify sites with different levels of environmental degradation in lotic systems using fish community data. Fish samples were collected over the period 2003-2011 at 131 sampling stations. To generate water and habitat quality classes, a self-organizing map (SOM) based on environmental data was applied. Gradients over the SOM map were investigated for the values of the TDI and ABC indices. The results of this study reveal that the values of both the TDI and ABC indices are highly correlated with water and habitat quality gradients. However, despite the observed correlation, the utility of TDI as a potential metric in bioassessment programs is rather limited, due to its lack of discriminatory power. In contrast, the ABC method could be proposed as a novel metric, but can only be applied in type-specific multimetric approaches.

Advanced Residuals Analysis for Determining the Number of PARAFAC Components in Dissolved Organic Matter

Parallel factor analysis (PARAFAC) has facilitated an explosion in research connecting the fluorescence properties of dissolved organic matter (DOM) to its functions and biogeochemical cycling in natural and engineered systems. However, the validation of robust PARAFAC models using split-half analysis requires an oft unrealistically large number (hundreds to thousands) of excitation-emission matrices (EEMs), and models with too few components may not adequately describe differences between DOM. This study used self-organizing maps (SOM) and comparing changes in residuals with the effects of adding components to estimate the number of PARAFAC components in DOM from two data sets: MS (110 EEMs from nine leaf leachates and headwaters) and LR (64 EEMs from the Lena River). Clustering by SOM demonstrated that peaks clearly persisted in model residuals after validation by split-half analysis. Plotting the changes to residuals was an effective method for visualizing the removal of fluorophore-like fluorescence caused by increasing the number of PARAFAC components. Extracting additional PARAFAC components via residuals analysis increased the proportion of correctly identified size-fractionated leaf leachates from 56.0 ± 0.8 to 75.2 ± 0.9%, and from 51.7 ± 1.4 to 92.9 ± 0.0% for whole leachates. Model overfitting was assessed by considering the correlations between components, and their distributions amongst samples. Advanced residuals analysis improved the ability of PARAFAC to resolve the variation in DOM fluorescence, and presents an enhanced validation approach for assessing the number of components that can be used to supplement the potentially misleading results of split-half analysis.

Influencing factor analysis of phosphorus loads from non-point source: a case study in central China

The influence factor analysis for non-point source (NPS) pollution is very important to taking effective water pollution control measures. In this study, the self-organizing map (SOM) and linear model analysis were used to analyze the relationships between total phosphorus (TP) loads and influencing factors, both qualitatively and quantitatively. The land-use type, topography, and vegetation coverage were the main factors influencing the export of TP loads in Tangxun watershed. Slope and normalized difference vegetation index (NDVI) were chosen as characteristic indices of topography and vegetation coverage, respectively. For the whole watershed, the high TP loads were mainly distributed in areas with high slope and low vegetation coverage for a specific land-use type. For different land types, the slope significantly influenced the export of TP loads in waste/bare land and forest/green land while NDVI influenced the export of TP loads in forest/green land and farmland. In terms of multi-factor analysis, the comprehensive influence of slope and NDVI on TP loads showed as waste/bare land>forest/green land>farmland>rural/urban construction land.

Characterization of heavy-metal-contaminated sediment by using unsupervised multivariate techniques and health risk assessment

This study characterized the sediment quality of the severely contaminated Erjen River in Taiwan by using multivariate analysis methods-including factor analysis (FA), self-organizing maps (SOMs), and positive matrix factorization (PMF)-and health risk assessment. The SOMs classified the dataset with similar heavy-metal-contaminated sediment into five groups. FA extracted three major factors-traditional electroplating and metal-surface processing factor, nontraditional heavy-metal-industry factor, and natural geological factor-which accounted for 80.8% of the variance. The SOMs and FA revealed the heavy-metal-contaminated-sediment hotspots in the middle and upper reaches of the major tributary in the dry season. The hazardous index value for health risk via ingestion was 0.302. PMF further qualified the source apportionment, indicating that traditional electroplating and metal-surface-processing industries comprised 47% of the health risk posed by heavy-metal-contaminated sediment. Contaminants discharged from traditional electroplating and metal-surface-processing industries in the middle and upper reaches of the major tributary must be eliminated first to improve the sediment quality in Erjen River. The proposed assessment framework for heavy-metal-contaminated sediment can be applied to contaminated-sediment river sites in other regions.