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Subsurface 3D analysis, modeling, and presentation of pollutant nitrate in semi-arid region

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Abstract

The work incorporates the field data collection, assessment, and use of interpolation and extrapolation methods in modeling the existence of pollutant in groundwater, namely nitrate, and future prediction. To view the variation of concentration of pollutants in any form, 3D representation is required. However, not many techniques are available to achieve the objective accurately. Many modeling studies are done but very little is known about the variation of a group of chemical parameters at vertical scale in any of the climatic zone or habitable region. This kind of study is needed to help stakeholders for better planning. Earlier studies do not show the variation of chemical parameters (contaminants) at vertical scale in the climatic zone, and modellings are very objective specific. Present work presents 3D models using Inverse Distance Weighting technique in Matlab. The concentration pattern of nitrate is studied in 3D and presented in lucid manner at regional scale. The 3D block presentation demonstrates its affiliation and dispersion. The relationship from these models between parameter fixation and profundity shows the presence of distinct layers up to desired depth. The relationship plots are developed to extract the information how the groundwater quality is being transmitted beneath the surface. The projection is verified with the real field data, which will help in future resource management actions and minimize the pollution risks to mankind and the environment. The modeling helps in selecting the danger zones for ground water recharge and discharge for natural cause of elevated concentration of nitrate in groundwater. This study opens up the methodology for finding the variation of other contaminants against depth and that of total water quality.

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Correspondence to Rajiv Gupta.

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Gupta, R., Singhal, A. & Singh, A.N. Subsurface 3D analysis, modeling, and presentation of pollutant nitrate in semi-arid region. Environ Monit Assess 191, 271 (2019). https://doi.org/10.1007/s10661-019-7433-0

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