Evidence map›Paper›PMID 40595184›Full record

ArticleScientific reports2025

Dielectric characterization of sugar mill wastewater and its impact on soil properties in the khadar and bangar regions.

Ankur Kumar, Prachi Palta, Aastha Palta, Sanjay Dhanka, Rohit Bansal, Mochammad Fahlevi, Fazla Rabby, Mohammed Aljuaid

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ankur KumarSchool of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, Mandi, 175005, Himachal Pradesh, India. khatriankur007@yahoo.com.
Prachi PaltaDepartment of Physics, Chandigarh University, Punjab, 140413, India.
Aastha PaltaUCRD Department, Chandigarh University, Punjab, India.
Sanjay DhankaDepartment of Electrical and Instrumentation, Sant Longowal Institute of Engineering and Technology, Sangrur, 148106, Punjab, India.
Rohit BansalAdjunct Faculty, Rockford College, Sydney, Australia.
Mochammad FahleviManagement Department, BINUS Online, Bina Nusantara University, Jakarta, 11480, Indonesia.
Fazla RabbyStanford Institute of Management and Technology, NSW, Australia.
Mohammed AljuaidDepartment of Health Administration, College of Business Administration, King Saud University, Riyadh, Saudi Arabia.

Funding

King Saud University, Riyadh, Saudi Arabia ORF-2025-481
6 · The paper itself

Abstract

This study investigates the dielectric properties of sugar mill wastewater and its impact on surrounding soils and crops, prompted by the environmental pollution resulting from the rapid expansion of sugar mills. By examining the dielectric characteristics of this wastewater and its effects on soil properties, the research emphasizes the urgent need for effective management strategies to mitigate the environmental impact of sugar mill operations. Soil samples were gathered from Uttar Pradesh, India, using a multistage sampling approach. A Dak-12 open-ended coaxial probe paired with a two-port Vector Network Analyzer (model E5071C from Agilent Technologies) was used to assess the dielectric characteristics of these soils. Samples from the Khaddar and Bangar regions were examined to determine how the dielectric properties (both real part ε' and imaginary part ε'') were influenced by contaminants from sugar mills. Predictive models, including artificial neural networks (ANNs) and regression trees, were applied to forecast the dielectric responses. The physicochemical analysis of wastewater revealed significant contamination, with a color range from light brown to black, pH values between 7.63 and 6.26, suspended solids of 40 to 45%, and conductivity of 1222 ± 12.06 to 1313 ± 10.62 µS/cm. Dielectric measurements indicated that the average ε' decreased from 109.04 to 72.61 at 10 MHz and from 38.48 to 25.69 at 3 GHz as temperature rose from 0 to 60°C. In Khaddar soil, ε' decreased from 37.45 to 15.62, while ε'' increased from 149.24 to 232.52. Bangar soil exhibited ε' values decreasing from 60.1 to 49.74 and ε'' rising from 110.55 to 523.88. Machine learning models achieved R² values of 0.9961 for ε' and 0.9594 for ε''. This research highlights the critical role of dielectric properties in assessing sugar mill wastewater quality and its adverse effects on soil and crop health, advocating for sustainable practices to mitigate these impacts.

Indexed as

Artificial neural networks (ANN)Dielectric propertiesEnvironmental affectsOpen ended coaxial probe (DAK-12)Physiochemical propertiesSuger mill wastewater

Identifiers

PMID40595184
PMCPMC12214748

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LicenceCC BY-NC-ND
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