Nur Hanife Orak

This research investigates the potential of Artificial Intelligence (AI) models to bridge the knowledge gap in environmental education among university students. By focusing on prominent large language models (LLMs) such as GPT-3.5, GPT-4, GPT-4o, Gemini, Claude Sonnet, and Llama 2, the study assesses their effectiveness in conveying environmental concepts and, consequently, facilitating environmental education. The investigation employs a standardized tool, the Environmental Knowledge Test (EKT-19), supplemented by targeted questions, to evaluate the environmental knowledge of university students in comparison to the responses generated by the AI models. The results of this study suggest that while AI models possess a vast, readily accessible, and valid knowledge base with the potential to empower both students and academic staff, a human discipline specialist in environmental sciences may still be necessary to validate the accuracy of the information provided.

A growing interest exists in robust tools to evaluate urban environmental performance. This study introduces a Green City Index framework, designed to assess and compare the sustainability of urban districts. Istanbul, with its significant population density and economic influence, serves as the pilot city in Türkiye. The study included (i) examining existing city indices, (ii) identifying indicators for environmental quality (EQ), community welfare, and DS, (iii) collecting and producing relevant data, and (iv) establishing the Green City Index using the Analytic Hierarchy Process. The highest‐scoring district in Istanbul achieved only 59 out of 100, indicating that no district's environmental performance is sufficiently strong. Even in well‐managed districts, EQ was lacking. A significant relationship between the total index and other subcategories was observed. Recognizing index development as dynamic, this Green City Index provides decision‐makers with a tool for strategic urban management, helping them assess and improve sustainability efforts in line with long‐term goals.

Nature-based solutions are becoming increasingly recognized as effective tools for addressing various environmental problems. This study presents a novel approach to selecting optimal blue–green infrastructure (BGI) solutions tailored to the unique environmental and climatic challenges of Istanbul, Türkiye. The primary objective is to utilize a Bayesian Belief Network (BBN) model for assisting in the identification of the most effective BGI solutions, considering the city’s distinct environmental conditions and vulnerabilities to climate change. Our methodology integrates comprehensive data collection, including meteorological and land use data, and employs a BBN model to analyze and weigh the complex network of factors influencing BGI suitability. Key findings reveal the model’s capacity to effectively predict BGI applicability across diverse climate scenarios, with quantitative results demonstrating a notable enhancement in decision-making processes for urban sustainability. Quantitative results from our model reveal a significant improvement in decision-making accuracy, with a predictive accuracy rate of 82% in identifying suitable BGI solutions for various urban scenarios. This enhancement is particularly notable in densely populated districts, where our model predicted a 25% greater efficiency in stormwater management and urban heat island mitigation compared to traditional planning methods. The study also acknowledges the limitations, such as data scarcity and the need for further model refinement. The results highlight the model’s potential for application in other complex urban areas, making it a valuable tool for improving urban sustainability and climate change adaptation. This study shows the importance of incorporating detailed meteorological and local climate zones data into urban planning processes and suggests that similar methodologies could be beneficial for addressing environmental challenges in diverse urban settings.

The United States has experienced a sharp increase in unconventional natural gas (UNG) development due to the technological development of hydraulic fracturing. The objective of this study is to investigate the emissions at an active Marcellus Shale well pad at the Marcellus Shale Energy and Environment Laboratory (MSEEL) in Morgantown, West Virginia, USA. Using an ambient air monitoring laboratory, continuous sampling started in September 2015 during horizontal drilling and ended in February 2016 when wells were in production. High-resolution data were collected for the following air quality contaminants: volatile organic compounds (VOCs), ozone (O₃), methane (CH₄), nitrogen oxides (NO and NO₂), and carbon dioxide (CO₂), as well as typical meteorological parameters (wind speed and direction, temperature, relative humidity, and barometric pressure). Positive matrix factorization (PMF), a multivariate factor analysis tool, was used to identify possible sources of these pollutants (factor profiles) and determine the contribution of those sources to the air quality at the site. The results of the PMF analysis for well pad development phases indicate that there are three potential factor profiles impacting air quality at the site: natural gas, regional transport/photochemistry, and engine emissions. There is a significant contribution of pollutants during the horizontal drilling stage to the natural gas factor. The model outcomes show that there is an increasing contribution to the engine emission factor over different well pad drilling periods through production phases. Moreover, model results suggest that the regional transport/photochemistry factor is more pronounced during horizontal drilling and drillout due to limited emissions at the site.

This study explores the effect of different phases of unconventional shale gas well-pad development on ambient air quality and the relationship between ambient concentrations of air pollutants and operator activity. The U.S. Department of Energy’s National Energy Technology Laboratory operated a mobile air-monitoring laboratory on two shale well pad sites in Pennsylvania and six shale well pad sites in West Virginia. The purpose of this study is to integrate expert knowledge and collected ambient air monitoring data by developing a Bayesian network (BN) model. The monitoring period included well-pad site development; construction, including vertical and horizontal drilling; hydraulic fracturing; flowback; and production. The observed data includes meteorological data with high time resolution and air quality data (volatile organic compounds (VOCs), ozone, methane and carbon isotopes in methane, carbon dioxide (CO2) and carbon isotopes in CO2, coarse and fine particulate matter (PM10 and PM2.5), and organic and elemental carbon). The results provide useful information for evaluating the influence of on- and off-site pollutant sources and determining future research efforts for building the BN model. The overall results of the developed six scenarios show that the prediction power of the proposed model for the vertical drilling phase is 94%. The high concentration of methane increases the probability of fracturing phase as source; the low concentration of PM10 and O3 occurrence increases the same probability to 82%; the low concentration of ethane and CO2 increases the probability to 98%. This study shows how expert Bayesian models can improve our ability to predict future air pollution risk associated with unconventional shale gas development.