D-Piper, a modified piper diagram to represent big sets of hydrochemical analyses

Merino, Luis Moreno; Aguilera, Héctor; González-Jiménez, Miguel; Díaz-Losada, Elisabeth

doi:10.1016/j.envsoft.2021.104979

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…The “data” folder contains four example datasets including the one shown in Table 2, which are organized into three different formats (Microsoft Excel, CSV, and tab‐delimited Text) to demonstrate how to organize and process aqueous geochemical data in the WQChartPy‐desired format. The other three datasets organized in CSV formats are taken from previous references (Yang et al 2020; Moreno Merino et al 2021; Liu et al 2021b). The “examples” folder has four Jupyter notebook files that contain Python codes with text and graphical outputs to illustrate how to use WQChartpy for generating the 12 geochemical diagrams for the example datasets.…”

Section: Methodsmentioning

confidence: 99%

“…The "data" folder contains four example datasets including the one shown in Table 2, which are organized into three different formats (Microsoft Excel, CSV, and tab-delimited Text) to demonstrate how to organize and process aqueous geochemical data in the WQChartPydesired format. The other three datasets organized in CSV formats are taken from previous references (Yang et al 2020;Moreno Merino et al 2021;Liu et al 2021b).…”

Section: Wqchartpy Implementationmentioning

confidence: 99%

“…(a) Trilinear Piper plot with background color scheme from Peeters (2014), (b) trilinear Piper plot with updated background color scheme designed to be more perceptually uniform, (c) trilinear Piper representation of 2124 groundwater samples taken from Figure 2b of Moreno Merino et al (2021), and (d) contour‐filled Piper to display the point density by using Gaussian kernel estimation. Black points in the (a) and (b) represent the example water samples shown in Table 2.…”

Section: Geochemical Diagrams and Existing Graphical Toolsmentioning

confidence: 99%

“…It should also be noted that the commercial software only support generating a small number of geochemical diagrams and they do not support the recently proposed diagrams, including the rectangle Piper (Ray and Mukherjee 2008), color‐coded Piper (Peeters 2014), and hydrochemical facies evolution diagram (HFE‐D, Gimenez‐Forcada 2010), which are increasingly popular for hydrogeochemical studies. For open source scripts and Microsoft Excel Macros, their functions are limited to generating specific graphs, that is, the Excel Macros for HFE‐D (Gimenez‐Forcada and Sanchez San Roman 2015) and Trilinear plots (Shikaze and Crowe 2007), the R package for trilinear Piper and Stiff plots (Lorenz and Diekoff 2017), the MATLAB code for PIED Piper (Russoniello and Lautz 2020), and the Python scripts for D‐Piper (Moreno Merino et al 2021) and color‐coded Piper plots (Peeters 2014). To facilitate comprehensive hydrochemical studies that require examining and comparing different types of diagrams, it is necessary but has not been attempted to develop open source codes for plotting multiple types of diagrams for an aqueous geochemical dataset.…”

Section: Introductionmentioning

confidence: 99%

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Visualization of Aqueous Geochemical Data Using Python and WQChartPy

et al. 2022

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Graphical methods have been widely used for visualization, classification, and interpretation of aqueous geochemical data to obtain a better understanding of surface and subsurface hydrologic systems. This method note presents WQChartPy, an open-source Python package developed to plot a total of 12 diagrams for analysis of aqueous geochemical data. WQChartPy can handle various data formats including Microsoft Excel, commaseparated values (CSV), and general delimited text. The 12 diagrams include eight traditional diagrams (trilinear Piper, Durov, Stiff, Chernoff face, Schoeller, Gibbs, Chadha, and Gaillardet) and four recently proposed diagrams (rectangle Piper, color-coded Piper, contour-filled Piper, and HFE-D) that have not been implemented in existing graphing software. The diagrams generated by WQChartPy can be saved as portable network graphics (PNG), scalable vector graphics (SVG), or portable document format (PDF) files for scientific publications. Jupyter and Google Colab notebooks are available online to illustrate how to use WQChartPy with example datasets. The geochemical diagrams can be generated with several lines of Python codes.

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Section: Methodsmentioning

confidence: 99%

Section: Wqchartpy Implementationmentioning

confidence: 99%

Section: Geochemical Diagrams and Existing Graphical Toolsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visualization of Aqueous Geochemical Data Using Python and WQChartPy

et al. 2022

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“…Piper's trilinear water chemistry analysis, on the other hand, is a convenient and visual tool for water quality classification and ion distribution 6 . The modified D-Piper trilinear diagram provides a solution for the challenge of visualizing ion distribution in large data sets 7 , leading to improved visualization and interpretation with an increase in data points. In addition, it is crucial to consider physicochemical information such as isotopes and radioactive elements in water bodies to reflect the essential characteristics and historical evolution of hydrogeology.…”

Section: Introductionmentioning

confidence: 99%

Identification of Mine Water Sources using a Multi-Dimensional Ion-Causative Nonlinear Algorithmic Model

Zheng

Liu

Yang

et al. 2023

Preprint

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This paper investigates the water quality system using the Piper diagram method to qualitatively analyze the distribution, characteristics, and laws of typical ion content. It also compares the differences between ion components of each aquifer with the closeness of the water body to be discriminated by combining statistical characteristic values. The R factor is utilized to simplify the index attributes, reduce input information dimension, and construct the coupled R-SVM discriminative model of Zhaogezhuang Mine. The input index is established as typical ion content, and the output is water source type. The discriminative model automatically establishes the mapping relationship between water quality indexes and discriminative criteria by learning the inherent property law between water quality samples. The independence components after dimensionality reduction are used as new discriminative indexes. The accuracy of the coupled model classification was 90.90% in the verification of the water source discrimination example of Zhaogezhuang mine. The coupled quantitative discriminant model based on R-factor and support vector machine provides an auxiliary verification and scientific decision for qualitative water chemistry analysis and provides a new idea for water source identification. Compared with the traditional qualitative way of water chemistry characteristics, Fisher function discrimination method, and single support vector machine model, this method improved the accuracy by more fully exploiting the internal laws of the data.

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Spatial and physicochemical assessment of groundwater quality in the urban coastal region of Sri Lanka

Perera

Herath

Piyadasa

et al. 2021

Environ Sci Pollut Res

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This study used the groundwater quality index (GWQI) and Geographic Information System (GIS) techniques to examine groundwater quality in the western coastal region of Sri Lanka. The spatial and temporal variation of 18 groundwater samples' physiochemical parameters [pH, electrical conductivity (EC), turbidity, total dissolved solids (TDS), sodium (Na + ), potassium (K + ), calcium (Ca 2+ ), magnesium (Mg 2+ ), chloride (Cl − ), and bicarbonate (HCO 3 − )] were studied. According to the WHO and SLS, 11% of samples had EC levels that were above the acceptable range, and 22% had turbidity levels that were beyond the acceptable range. When considering, pH, TDS, other cations, and anions analyzed in the study were still below the standard permissible levels. The western coastline region, as well as several areas of the central study region, had signi cant concentrations of physicochemical parameters. According to the GWQI, water was consumable in 77.78% of locations in the study region and unsatisfactory in 22.22%. Furthermore, due to severe coastal erosion, the quality of groundwater in the study region is deteriorating, therefore maintaining a comprehensive groundwater management strategy to promote sustainable water consumption is imperative.

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D-Piper, a modified piper diagram to represent big sets of hydrochemical analyses

Cited by 11 publications

References 15 publications

Visualization of Aqueous Geochemical Data Using Python and WQChartPy

Visualization of Aqueous Geochemical Data Using Python and WQChartPy

Identification of Mine Water Sources using a Multi-Dimensional Ion-Causative Nonlinear Algorithmic Model

Spatial and physicochemical assessment of groundwater quality in the urban coastal region of Sri Lanka

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