Pachaivannan Partheeban

Dr. Pachaivannan Partheeban

orcid

Research Focus & Metrics

Academic Career Length

22Years (2005-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 April 2021 in Sustainable Cities and Society
Verified
Journal Article
Closed access
RJR. Janarthanan
R. Janarthanan
PPPachaivannan Partheeban
Pachaivannan Partheeban
KSK. Somasundaram
K. Somasundaram
PEP. Navin Elamparithi
P. Navin Elamparithi

12 November 2024 in Sustainability
Verified
Journal Article
Open access
PDF file
DBDr. R.M. Bommi
Dr. R.M. Bommi
PPPachaivannan Partheeban
Pachaivannan Partheeban
RHRanganathan Rani Hemamalini
Ranganathan Rani Hemamalini
SBSUNDARAMBAL BALARAMAN
SUNDARAMBAL BALARAMAN
The most prominent source of drinking water is groundwater, followed by lakes and reservoirs. Hydrological parameters like temperature, dissolved oxygen, pH, conductivity, ORP, and turbidity often change due to waste dumping into natural drinking water sources, particularly in densely populated areas. As a result, the water quality must be tested before public consumption to ensure healthy living in society. This research collected water samples from 129 wells in the Kanchipuram district in Tamil Nadu, India. An efficient integrated machine-learning-based prediction model has been proposed and modeled to determine the groundwater quality index (GQI). Several machine learning models were used to predict the water’s quality, including the naïve Bayes model, the KNN classifier, and the XGBoost classifier. Water quality predictions in 2024 were made using a combination of classification algorithms and models based on long short-term memory (LSTM) neural networks. The projected water quality characteristics were analyzed using geographical information system (GIS) technology to better understand and visualize the results. The XGBoost classifier model outperforms prior findings in the literature, with an accuracy of roughly 94.6%. The classification and prediction model was validated using collected and tested current data samples from a selected well. The findings were accurate within the 5% error range, promoting sustainability.

26 March 2025 in Journal of Environmental Management
Verified
Journal Article
Closed access
SMS. Mahalakshmi
S. Mahalakshmi
Jose AnandJose Anand
Jose Anand
PPPachaivannan Partheeban
Pachaivannan Partheeban

29 August 2023 in Sustainability
Verified
Journal Article
Open access
PDF file
HCHariharasudhan Chandarasekar
Hariharasudhan Chandarasekar
SSSuresh Ellappa Subramani
Suresh Ellappa Subramani
PPPachaivannan Partheeban
Pachaivannan Partheeban
MSMADHAVAN SRIDHAR
MADHAVAN SRIDHAR
Globally, construction and demolition wastes (C&DW) are rapidly increasing, occupying 3 billion square yards of land for landfill. In India, C&D debris reaches 150 million tons per year, leading to environmental damage and resource wastage due to poor waste management practices. This study focuses on environmental impact analysis of air and water quality for selected construction and demolition waste dump yards for Chennai metropolitan city in India for two recycling units with 15 legal dumping yards. The Internet of Things (IoT) and Geographic Information System (GIS) is used to monitor and analyze environmental effect due to C&D waste dump yard. The highest concentrations of CO and CO2 in one dump site were observed at one point, with values of 7.49 ppm and 1656.06 ppm, respectively, and at another point with the lowest values of 2.3 ppm and 619.76 ppm. TDS values ranged from 269.2 mg/L to 1347.66 mg/L, and pH values ranged from 6.4 to 11.8, indicating pollution levels in drinking water. The findings suggest that improved waste management practices are urgently needed, including better regulation and enforcement of waste disposal laws, the establishment of recycling facilities with proper air and water pollution control measures, and public education campaigns for responsible waste disposal.

30 September 2021 in Environmental Engineering Research
Verified
Journal Article
Closed access
PDF file
PPPachaivannan Partheeban
Pachaivannan Partheeban
KKKrishnamurthy Karthik
Krishnamurthy Karthik
PEPartheeban Navin Elamparithi
Partheeban Navin Elamparithi
KSKrishnan Somasundaram
Krishnan Somasundaram
BABaskaran Anuradha
Baskaran Anuradha
The sounds produced by humans, industries, transport and animals in the atmosphere that pose a threat to the health of humans or animals can be characterized as noise pollution. Adverse effects due to noise exposure can involve speech communication interference and declining learning skills of children. Highway traffic noise contributes to 80% of all noise. It has grown to a massive scale because of growth in population along the roads leading to a rapid change in land use and has evolved into a common reality in various Indian cities. The main objective of this work is to develop a road traffic noise prediction model using ArcGIS 10.3 for the busy corridors of Chennai. The collected data includes traffic volume, speed, and noise level in lateral and vertical directions. Noise levels were measured in 9 locations using a noise level meter. It is observed that the noise levels vary from 50 dB to 96 dB. It is found that the noise problem is severe in 18% of the area, and 6.3% of people are exposed to the traffic noise problem. The results obtained in this study show that the city is affected by severe noise pollution due to road traffic.

6 September 2023 in Sustainability
Verified
Journal Article
Open access
PDF file
RHRanganathan Rani Hemamalini
Ranganathan Rani Hemamalini
SBShanthini Balusamy
Shanthini Balusamy
PPPachaivannan Partheeban
Pachaivannan Partheeban
CMCharumathy Mani
Charumathy Mani
MSMADHAVAN SRIDHAR
MADHAVAN SRIDHAR
VRVinodhini Rajasekaran
Vinodhini Rajasekaran
Air pollution has become a global health issue, affecting millions of people annually. It is essential to obtain accurate and up-to-date information on air quality levels to reduce the impact of air pollution on human health. Traditional air quality monitoring methods are limited by spatial coverage and the time required for sample collection and analysis. However, the Internet of Things (IoT), drone technology, and Geographic Information System (GIS) technology have enabled the development of real-time air quality monitoring systems that provide accurate and up-to-date information on air quality levels across large areas. The study found that gas concentration varied significantly at different heights, with the highest concentration at 3 m from the ground and the lowest at 9 m. The concentration of gases also varied by month, with the highest concentration of CO recorded in March at 45 ppm at 3 m, and the highest concentration of NH4 recorded in September at 66.7 ppm at 3 m. Kodungaiyur dump yard needs environmental monitoring due to the high levels of environmental pollution and health risks caused by unsegregated waste. In conclusion, developing real-time air quality monitoring systems using GIS technology is necessary to obtain accurate and up-to-date information on air quality levels. It is essential to monitor the Kodungaiyur dump yard and other, similar sites regularly to prevent the health risks associated with environmental pollution.