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CONTENTS
Volume 13, Number 1, March 2024
 

Abstract
In order to understand the pollution characteristics and risk levels of typical antibiotics in the water environment of Chongqing, this paper selected several major water environments that more prominently affected by human activities and poultry breeding pollution as the research objects. Solid phase extraction/high-performance liquid chromatography tandem mass spectrometry was used to determine 20 types of antibiotic residues with high detection rate and high dosage in water, and the concentration and composition characteristics of antibiotics were analyzed, And ecological risk assessment was conducted on antibiotics using the Risk Quotient Method (RQ). The results showed that antibiotics were detected to varying degrees in the water environment of the city, with the detection rates ranging from high to low in order from hospital wastewater>sewage treatment plants> poultry breeding enterprises>surface water, ranging from 24.3% to 87.5%. Compared with its counterparts in other regions, the concentration of antibiotics in the water environment of Chongqing is at a medium level. Except that the RQ value of sulfamethoxazole in each water body is higher than 1.0, indicating a high risk. On the other hand, the RQ value of oxytetracycline, tetracycline, erythromycin and roxithromycin ranges from 0.1 to 1.0, showing a medium risk, and the rest is at a low risk level. Evaluate the health risks of antibiotics in different water sources based on their entropy of risk to human health. The results showed that except for tetracyclines, sulfonamides, and chloramphenicol, which have low human health risks, other antibiotics have no potential health risks to human health.

Key Words
antibiotics; ecological risk assessment; health risk; pollution characteristics; surface water

Address
Zhang Wenbin, Wang Jian, Zhao Jin and Zhang Xiu: Ecological and Environmental Monitoring Center, Chongqing 401147, China

Abstract
This study endeavours to quantify the water footprint of academic and administrative personnel at Konya Technical University. Water footprint assessment, a critical metric for evaluating human impact on water resources, is increasingly recognized as a vital aspect of sustainable resource management. The research involves surveying participants on their water consumption habits, particularly focusing on preferences related to food and sugar intake. Preliminary findings indicate diverse responses in terms of food preference, with a majority favouring low intake. The study aimed to determine the water footprint of the campus in relation to personal consumption behaviours by asking the questions in the "Water Footprint Network (WFN)" to a total of 476 people at the campus, including both the academic and administrative staff. According to the WFN, the average water footprint of the staff was determined as 1694 m3/year.

Key Words
Konya Technical University; sustainability; water footprint; water scarcity

Address
Özgül Çimen Mesutoğlu: Department of Environmental Engineering, Aksaray University, Aksaray, 68100, Turkey

Abstract
Landfills and the associated leachate present significant threats to both biological ecosystems and environmental resources, with uncontrolled leachate pollution posing the potential for severe environmental and human disasters. To address this concern, the use of liners has emerged as a crucial strategy for managing landfill leachate. This study focuses on assessing the effectiveness of integrating nanoclay additives, specifically Montmorillonite, in reducing the permeability of landfill liner layers. A comprehensive set of geotechnical tests, including sieve analysis, hydrometry, Atterberg limits, permeability, and chemical assessments (BOD and COD), was conducted on samples extracted from the core of the Tabriz landfill liner. The samples underwent testing within control groups and varying percentages of additives (3%, 6%, and 9%) over processing durations of 1, 7, and 14 days. A comparative analysis of the results was then performed. The findings indicate that increasing nanoclay percentages led to significant improvements in Atterberg limits (LL, PL, and PI), showing increments of 48%, 33%, and 45%, respectively. Furthermore, the permeability coefficient in the control sample exhibited a substantial decrease of 98% compared to the sample enhanced with 9% nanoclay. Additionally, the addition of nanoclay facilitated the absorption of Pb and As while reducing Bod and Cod in the soil.Collectively, these results underscore the effective role of nanoclay, particularly Montmorillonite, in enhancing the landfill liner system's ability to manage leachate. The implications of this study highlight the considerable potential of nanoclay additives as instrumental agents in addressing leachate-related concerns in landfill environments.

Key Words
geotechnics landfill; leachate; nanotechnology; soil permeability

Address
Mahdi Nikbakht, Fariba Behrooz Sarand and Rouzbeh Dabiri: Department of Civil Engineering, Islamic Azad University, Tabriz Branch, Tabriz 5157944533, Iran
Masoud Hajialilue Bonab: Department of Civil Engineering, University of Tabriz, Tabriz5166616471, Iran

Abstract
This paper addresses the urgent need for sustainable infrastructure by examining the application of artificial intelligence in concrete design, with a specific emphasis on predicting uniaxial compressive strength (UCS) to mitigate environmental impacts. Concrete, a fundamental construction material, is a major contributor to environmental degradation due to its substantial carbon footprint. The study focuses on harnessing the potential of Artificial Neural Networks (ANN) to forecast UCS in conventional construction concrete, which is extensively utilized in global construction practices. This emphasis stems from the recognition of the significant environmental consequences associated with these concrete types, affecting both carbon footprints and ecosystems. The research methodology involved analyzing a dataset comprising 300 cubic concrete specimens with dimensions of 15 cm x 15 cm x 15 cm, split into training and testing sets at a ratio of 70:30. Various machine learning classifiers, including Support Vector Machine and Decision Tree, were employed for comparison alongside the ANN model. Results demonstrated that the ANN-based predictive model outperformed alternative classifiers, achieving high accuracy rates and minimal error values, thereby affirming its reliability in estimating UCS values. These findings highlight the potential of integrating AI technologies to enhance sustainability in construction practices and mitigate environmental impacts associated with concrete usage. By adopting innovative approaches such as ANN prediction models, the construction industry can contribute significantly to environmental preservation and sustainable development efforts.

Key Words
artificial intelligence; concrete; construction management; environmental impact; sustainable structures

Address
Sina Aminbakhsh and Ali Golsoorat Pahlaviani: Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1955847781, Iran
Amin Tohidi: Department of Mining Engineering, Amirkabir University of Technology, Tehran 159163-4311, Iran

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