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Geographical location of the Tiaozini Land Reclamation (32°42′28.61′′ E to 32°52′56.25′′ E and 120°53′35.10′′ N to 121°04′51.13′′ N).
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Identification of the key environmental indicators (KEIs) from a large number of environmental variables is important for environmental management in tidal flat reclamation areas. In this study, a modified principal component analysis approach (MPCA) has been developed for determining the KEIs. The MPCA accounts for the two important attributes of...
Citations
... Engaging local communities in water conservation efforts and educating them about the impacts of pollution and the importance of maintaining water quality will further support these initiatives. By explicitly connecting PCA results to specific water quality management actions, these findings can inform more effective and targeted mitigation strategies, ultimately contributing to the sustainable management of the Sutlej River (Benkov et al., 2023;Chu et al., 2018;Liu et al., 2021). ...
In this investigation, the spatiotemporal distribution of cyanobacteria and their relationships with variations in water chemistry (physico-chemical parameters and heavy metal) of Sutlej River, Punjab (India) has been analyzed by employing multivariate statistical methods. Sutlej River exhibits a rich array of cyanobacterial diversity, comprising 28 species across 15 genera, distributed among 11 families and spanning 5 orders within the class Cyanophyceae. In terms of relative abundance, Microcystis aeruginosa (17.47%) was documented as the most abundant taxa followed by Microcystis robusta (16.55%), Merismopedia punctata (11.03%), Arthrospira fusiformis (6.67%) and Pseudanabaena galeata (3.68%). Significant variations were observed among sampling sites in most of the physico-chemical parameters. Principal Component Analysis delineated sampling sites into two discernible groups according to variations in water chemistry. River Pollution Index (RPI) showed that river water is under the unpolluted (RPI 1.5) to negligibly polluted category in the upstream sites, while moderately polluted (RPI 5.5) in the downstream sites. Heavy metal Pollution Index (HPI) revealed consistent heavy metal contamination at sites RWS7 and RWS8 across all seasons. Conversely, site RWS1 consistently exhibited lower HPI values throughout the three studied seasons. Further, Canonical Correspondence Analysis identified that pH, TDS, TA, NO3, Na, and NH4 are the key physicochemical parameters which affect the spatiotemporal distribution of cyanobacteria in the studied river system. Overall, this study will offer significant information for hydrologists, ecologists, and taxonomists to develop future holistic strategies for further monitoring of the Sutlej River and other similar habitats.
... A principal component analysis (PCA) applied on a large data set reduces dimensionality and increases interpretability as well as maintains its original structure to a maximum extent (Chu et al., 2018). The PCA of the parameters was applied in order to assess the variance posed by different explanatory variables. ...
The invasion of species in new regions depends on multiple factors, especially, the prevailing environmental factors. The environmental conditions are essential to understand for planning effective management strategies related to invasive species. Little is known about Cannabis as an established invasive weed. We hypothesized that the successful establishment of this invading species is influenced by the environmental variables; however, some of them have a much stronger influence than the others. Quantitative ecological methods were adopted for sampling the habitats invaded by Cannabis sativa, in a total of 165 quadrats. Soil samples were collected for soil analyses from each of those quadrat. Ecological and statistical approaches including Structure Equation Modeling (SEM) procedures were applied to evaluate the impact of environmental factors, ecological interrelationships, and the resultant invasiveness of the C. sativa. Our findings indicate that elevation, temperature, humidity, anthropogenic pressure, physio-chemical prperties of soil and habitat degradation play significant roles in determining the distribution and abundance of C. sativa. Principal Component Analysis (PCA) of the parameters further clarifies that elevation is the most important driver in explaining the successful establishment of the invader species with a 30.1% variance. Structural equation modeling further confirms the significant role played by elevation, which not only directly affects the abundance of Cannabis but also indirectly influences other variables such as anthropogenic pressure, temperature, and humidity etc. However, the invasion of C. sativa is less affected by soil saturation pH, electrical conductivity, phosphorous, potassium, and CaCO3. Our study provides valuable scientific information that could be used for the early detection of invasive species at the early stage of invasion and in devising policies for their management and control.
... PCA is an exploratory method for reducing data dimensionality, it is applied to evaluate the association between indirect measurements of metal mixture exposure patterns or latent variables in childhood neurodevelopment. This expands its conventional use by providing insights into the relationships between these variables (Chu et al. 2018;Pozo et al. 2012). PCA projects each data point on a smaller number of variables, preserving as much as possible the data variability. ...
Neurodevelopmental disorders are increasing globally, and metal exposure may play a significant role as an environmental factor. This cross-sectional study aimed to identify metal mixture patterns and assess their impact on children’s neurodevelopment. Data from 962 children (aged 4–5 years) participating in the Spanish INMA cohort study were analysed. Urinary metal concentrations (cobalt (Co), copper (Cu), molybdenum (Mo), selenium (Se), lead (Pb), zinc (Zn), and arsenic speciation) were used as exposure biomarkers. Principal component analysis (PCA) revealed four latent exposure variables representing uncorrelated metal mixture patterns. Linear regression analyses examined the associations between these variables and children’s neuropsychological functions assessed through the McCarthy Scales of Children’s Abilities. The first latent exposure variable (Cu, Se, Pb, Zn) and the second (inorganic arsenic, monomethylarsonic acid) showed negative associations with verbal executive function (ß = − 1.88, 95% confidence interval (CI) = − 3.17 to − 0.59) and gross motor function (ß = − 1.41, 95% CI = − 2.36 to − 0.46), respectively. Conversely, the third variable (Mo, Co) and the fourth (arsenobetaine) exhibited positive associations with visual and verbal span functions (ß = 1.14, 95% CI = 0.16 to 2.12) and fine motor function (ß = 1.01, 95% CI = 0.11 to 1.92), respectively. This study suggests that even relatively low levels of metal latent exposures, notably inorganic arsenic and a mixture of metals including Pb, adversely affect children’s neuropsychological development function scores, while exposure to arsenobetaine and a mixture of Co and Mo has a positive impact.
... Aside from its ability to reduce the dimensionality of a given dataset, it is also useful when the variables being considered in a multivariate analysis are strongly correlated whether negatively or positively (Chanai et al. 2022). It assists greatly in the reduction of the number of correlated variables which ultimately lead to better interpretation and improvement of results (Chanai et al. 2022;Chu et al. 2018). ...
This study assesses the degradation of Ndemunde River caused by the uncontrollable disposal of industrial waste close to the river. Overall, 24 water samples were collected in the study area using the grab method. Afterwards, various physicochemical tests were conducted both in the field and laboratory. An ultraviolet–visible spectrophotometer apparatus was employed to determine the heavy metals present in the samples. The concentration of these physicochemical variables and heavy metals in the water was compared with various standards such as those from the Nigeria General Specification (NIS), World Health Organization (WHO) and Federal Environmental Protection Agency (FEPA). Hence, cadmium (0.042–0.209 mg/L), chromium (0.130–0.754 mg/L), lead (0.221–0.627 mg/L), arsenic (0.018–0.297 mg/L), nickel (0.012–0.160 mg/L), and mercury (0.008–0.024 mg/L) were above the WHO permissible limits while copper (0.129–1.045 mg/L) was below the permissible limits. In addition, the samples were analysed using multivariate statistical analysis, water quality index (WQI), and five pollution measures such as pollution index, modified degree of contamination, geo-accumulation index, and Nemerow pollution index. The pollution index exhibited very high pollution (PI > 5) for chromium, cadmium, arsenic, and lead. Nickel in all the sampling points had a slight level of pollution index (1 < PI < 2) and mercury had a moderate pollution index (2 < PI < 3) while copper exhibited no pollution in all the sampling points with PI < 1. The average Nemerow pollution index (NPI) indicated that most of the sampling points were seriously polluted (> 3.0) with significant presence of chromium, cadmium, arsenic, and lead. Mercury exhibited a medium pollution index (2 < NPI < 3) and nickel had a slight pollution index (1 < NPI < 2) while copper showed a clean pollution level (< 1). The geo-accumulation indices ranged from − 4.5 (uncontaminated) to 4.3 (heavy contamination). The modified degree of contamination (mCd) indicated high degree of contamination for the samples (4 < mCd < 8). In addition, an extreme value of 1998.677 was determined from the water quality index analysis which denotes the high contamination of the river with timber industry waste. The adverse impact on the water quality is associated with the kind of chemical preservatives, metal corrosion of industrial tools, and atmospheric emission during the industrial processes. Based on these outcomes, it is essential that a highly advanced and technological treatment is used in the river if the water is to be used for domestic or industrial purposes. Also, environmental policy should be implemented to check the uncontrollable disposal of sawdust close to the river.
... Though a careful examination of distinct WQIs would reveal different intermediary processes. The following steps are taken for WQI development (a) Principal Component Analysis (PCA) for parameter selection and parameter reduction (Chu et al. 2018) and (b) WQI estimation using the Weighted Average WQI. Estimations of the sub-index, assigning weights factor and aggregation of weighted sub-index values of the quality parameter were done. ...
This study is conducted to investigate the physico-chemical parameters of the Kangsabati River during the pre- and post-COVID scenarios. The values of these parameters for the Gandhighat monitoring station were collected from the webportal of 1West Bengal Pollution Control Board for sampling point ID CPCB-2507. The aim of the study is to calculate the Water Quality Index (WQI) by analyzing a total of 19 physico-chemical parameters, one biological parameter and the rest are chemical parameters namely bio-chemical oxygen demand (BOD),dissolved oxygen, faecal coliform (FC), 2nitrate (No3), pH, temperature, chemical oxygen demand (COD), total alkalinity (TA), fluoride, total hardness, calcium, magnesium, chloride, phosphate (PO4), total dissolved solids, total 4 hardness, total suspended solids, turbidity (TU), and electrical conductivity. The weighted arithmetic WQI method is used to assess and compare the suitability of water for drinking, irrigation and other human uses. Most of the parameters included here are chemical parameters, as the monitored station is situated immediately behind an industrial area that contributes industrial effluents near the Kharagpur Industrial belt. The analysis shows that only a few matrices exceed the permissible b limits recommended by bIS 10500: 2012, IS11624: 2019 such as TU, TA, PO4, COD, FC and 4BOD. According to the quality indices, these are also the primary pollutants detected in this study in the pre- and post-COVID scenarios.
... Pearson correlation among the predictor variables was checked to avoid unusual spatial collinearity. In case of a high correlation value of coefficient r =|0.7|, variables were processed for principal component analysis; otherwise, provided variables were retained (Chu et al. 2018). ...
Background
Large-scale hunting and various anthropogenic pressures in the recent past have pushed the Asiatic caracal ( Caracal caracal schmitzi ), an elusive medium-sized and locally threatened felid species towards local extinction in India. Though widely distributed historically, it has been sparsely reported from several regions of central and northern states in India till twentieth century. Later, the species distribution became confined only to the states of Rajasthan, Gujarat and Madhya Pradesh, which have had reported sightings in the twenty-first century. In order to highlight the potentially suitable habitats for Asiatic caracals in India, we targeted forth-filtering of the spatial model ensemble by creating and utilizing the validated and spatially thinned species presence information ( n = 69) and related ecological variables (aridity, NDVI, precipitation seasonality, temperature seasonality, terrain ruggedness), filtered with anthropological variable (nightlight).
Results
Out of eight spatial prediction models, the two most parsimonious models, Random Forest (AUC 0.91) and MaxEnt (AUC 0.89) were weighted and ensembled. The ensemble model indicated several clustered habitats, covering 1207.83 km ² areas in Kachchh (Gujarat), Aravalli mountains (Rajasthan), Malwa plateau (Rajasthan and Madhya Pradesh), and Bundelkhand region (Madhya Pradesh) as potentially suitable habitats for caracals. Output probabilities of pixels were further regressed with converted vegetation height data within selected highly potential habitats, i.e., Ranthambore Kuno Landscape (RKL) (suitability ~ 0.44 + 0.03(vegetation height) **, R ² = 0.27). The regression model inferred a significant positive relation between vegetation height and habitat suitability, hence the lowest ordinal class out of three classes of converted vegetation height was masked out from the RKL, which yielded in an area of 567 km ² as potentially highly suitable habitats for caracals, which can be further proposed as survey areas and conservation priority areas for caracals.
Conclusion
The study charts out the small pockets of landscape in and around dryland protected areas, suitable for caracal in the Indian context, which need attention for landscape conservation.
... Pearson correlation among the predictor variables was checked to avoid unusual spatial collinearity. In case of a high correlation value of coefficient r =|0.7|, variables were processed for principal component analysis; otherwise, provided variables were retained (Chu et al. 2018). ...
Background:
Large-scale hunting and various anthropogenic pressures in the recent past have pushed the Asiatic caracal (Caracal caracal schmitzi), an elusive medium-sized and locally threatened felid species towards local extinction in India. Though widely distributed historically, it has been sparsely reported from several regions of central and northern states in India till twentieth century. Later, the species distribution became confined only to the states of Rajasthan, Gujarat and Madhya Pradesh, which have had reported sightings in the twenty-first century. In order to highlight the potentially suitable habitats for Asiatic caracals in India, we targeted forth-filtering of the spatial model ensemble by creating and utilizing the validated and spatially thinned species presence information (n = 69) and related ecological variables (aridity, NDVI, precipitation seasonality, temperature seasonality, terrain ruggedness), filtered with anthropological variable (nightlight).
Results:
Out of eight spatial prediction models, the two most parsimonious models, Random Forest (AUC 0.91) and MaxEnt (AUC 0.89) were weighted and ensembled. The ensemble model indicated several clustered habitats, covering 1207.83 km2 areas in Kachchh (Gujarat), Aravalli mountains (Rajasthan), Malwa plateau (Rajasthan and Madhya Pradesh), and Bundelkhand region (Madhya Pradesh) as potentially suitable habitats for caracals. Output probabilities of pixels were further regressed with converted vegetation height data within selected highly potential habitats, i.e., Ranthambore Kuno Landscape (RKL) (suitability ~ 0.44 + 0.03(vegetation height) **, R2 = 0.27). The regression model inferred a significant positive relation between vegetation height and habitat suitability, hence the lowest ordinal class out of three classes of converted vegetation height was masked out from the RKL, which yielded in an area of 567 km2 as potentially highly suitable habitats for caracals, which can be further proposed as survey areas and conservation priority areas for caracals.
Conclusion:
The study charts out the small pockets of landscape in and around dryland protected areas, suitable for caracal in the Indian context, which need attention for landscape conservation.
... Thus, the identification of parameters that allow a WQI to be determined for High Andean basins can be established through the application of this methodology. In comparison to multivariate methods, which allow the identification of water-quality parameters, which result just from the statistical decision [44][45][46]. However, the Delphi method collects the expert experience in water quality, for specific uses, who include within the selection criteria, the perception of the water body and its surroundings [38,43,[47][48][49]. ...
The water from the high Andean rivers is peculiar due to its composition and the geomorphology of its sources, and naturally or anthropogenically contamination is not discarded along its course. This water is used for agriculture and human consumption, therefore knowing its quality is important. This research aimed to proposing and formulate a water-quality index for high Andean basins through the Delphi method, and its application in the Chumbao River located in Andahuaylas-Peru. Forty-three water-quality parameters were evaluated through the Delphi method, and the water-quality index (WQIHA) was formulated with a weighted average of the weights of the selected parameters, it was compared with the WQI Dinius. For this purpose, ten sampling points were considered along the Chumbao River located between 4274 and 2572 m of altitude and the WQIHA was applied. In addition, field and laboratory analyses were carried out in 2018, 2019, and 2021, in dry and rainy seasons. Twenty parameters were grouped in the physicochemical sub-index (SIPC), heavy metals sub-index (SIHM), and organic matter sub-index (SIOM). Each group contributed with weights of 0.30, 0.30, and 0.40, respectively, for the WQIHA formulation. The SIPC and SIOM showed that the areas near the head of the basin presented excellent and good quality, while the urbanized areas were qualified as marginal to poor; SIHM reported good quality in all points and seasons. Regarding the WQIHA, the index shows good quality in the zones above 3184 m of altitude, contrasting with poor quality downstream, decreasing notably in both seasons, suggesting continuous degradation of the water body.
... In Wu et al. (2010) applied modular ANN technique to forecast the rainfall time series. In Chu et al. (2018) proposed a modified principal component analysis (MPCA) method for assessing environmental variables to track ecological changes in coastal recovery areas. In Ghadim et al. (2018) discuss using the Holt-Winters time series model's additive and multiplicative types to forecast environmental variables one year in advance. ...
Flood is a recurrent and crucial natural phenomenon affecting almost the entire planet. It is a critical problem that causes crop destruction, destruction to the population, loss of infrastructure, and demolition of several public utilities. An effective way to deal with this is to alert the community from incoming inundation and provide ample time to evacuate and protect property. In this article, we suggest an IoT-based energy-efficient flood prediction and forecasting system. IoT sensor nodes are constrained in battery and memory, so the fog layer uses an energy-saving approach based on data heterogeneity to preserve the system’s power consumption. Cloud storage is used for efficient storage. The environmental conditions such as temperature, humidity, rainfall, and water body parameters, i.e., water flow and water level, are being investigated for India’s Kerala region to calibrate the flood phases. PCA (Principal Component Analysis) approach is used at the fog layer for attribute dimensionality reduction. ANN (Artificial Neural Network) algorithm is used to predict the flood, and the simulation technique of Holt Winter is used to forecast the future flood. Data are obtained from the Indian government meteorological database, and experimental assessment is carried out. The findings showed the feasibility of the proposed architecture.
... Some cut-offs of monetary indicators may not make sense when currency or minimum wage change over time (Thomas & Jesse, 2012). Besides, not all-important indicators for representing the multidimensional phenomenon are relative in nature, as the average income for social exclusion , the concentration of substances in water for risk of environmental contamination (Chu et al., 2018), or gross domestic product for sustainable development (Davidescu et al., 2020). ...
Composite Indicators are one-dimensional measurements that simplify the interpretation of multidimensional phenomena that facilitate public policies' elaboration. The literature on composite indicators is abundant, diversified, and inserted in practically all knowledge areas. Part of this literature aims to reduce uncertainties that propagate through the structure of the composite indicator during the process of normalization, weighting, and aggregation of indicators. Even if no composite indicator is exempt from criticism, the current literature is already sufficiently large and deep to guide researchers in constructing reliable composite indicators. However, most related works are concerned with representing multidimensional phenomena in time or space. Although some studies are interested in representing multidimensional phenomena that co-occur in time–space, the portion of the literature that addresses composite indicators is still not comprehensive, therefore leaving several open questions: What are the additional challenges in representing multidimensional phenomena in time–space? What methods can be used? Which method is most appropriate for this type of representation? What are the shortcomings of this method? How to reduce these shortcomings? This research aims at answering these questions in order to advance the time–space analysis of multidimensional phenomena. As a general contribution, the work presents a scheme of procedures that reduce subjectivities and uncertainties in the representations of multidimensional phenomena in time–space. As a specific contribution, it provides accurate and reliable information on the trajectory of social exclusion in the analyzed region.
