Air pollution significantly threatens human health and natural ecosystems and requires urgent attention from decision makers. The fight against air pollution begins with the rigorous monitoring of its levels, followed by intelligent statistical analysis and the application of advanced machine learning algorithms. To effectively reduce air pollution, decision makers must focus on reducing primary sources such as industrial plants and obsolete vehicles, as well as policies that encourage the adoption of clean energy sources. In this study, data analysis was performed for the first time to evaluate air pollution based on the SPSS program. Correlation coefficients between meteorological parameters and particulate matter concentrations (PM₁, PM_2.5, PM₁₀) were calculated in two urban regions of Romania (Craiova and Drobeta-Turnu Severin) and Turkey (Adana). This study establishes strong relationships between PM concentrations and meteorological parameters with correlation coefficients ranging from −0.617 (between temperature and relative humidity) to 0.998 (between PMs). It shows negative correlations between temperature and particulate matter (−0.241 in Romania and −0.173 in Turkey) and the effects of humidity ranging from moderately positive correlations with PMs (up to 0.360 in Turkey), highlighting the valuable insights offered by independent PM sensor networks in assessing and improving air quality.

This study examines air quality conditions in and around a classroom located in the Sarıçam/Adana region of Türkiye, near the campus of Adana Alparslan Türkeş Science and Technology University and the Sofulu municipal solid waste (MSW) facility. This academic setting was strategically chosen due to its proximity to the waste facility. The study aims to provide a comprehensive view of the environmental and social impacts of solid waste management through a methodological approach that combines quantitative on-site measurements and qualitative survey studies. Findings from measurements and surveys underline the significant and measurable impacts of MSW facilities on the ambient air quality of university residents. The analysis revealed a marked increase in concentrations of key pollutants, including carbon monoxide (CO), hydrogen sulfide (H₂S), dust, and methane (CH₄). At sampling point N1, H₂S levels rose from 0 ppm in July to 13 ppm in November. Methane increased from 0.2% to 2.5% of the Lower Explosive Limit (LEL) at the same point, although it remained within safety limits. Additionally, CO levels showed a 40% increase, and dust concentration levels rose from 0.21 mg/m³ to 2.36 mg/m³ from summer to winter, indicating a seasonal variation likely influenced by the landfill’s operational dynamics, as well as changes in temperature and relative humidity. In particular, the results indicate high concentrations of CO, H₂S and dust, which are directly related to air quality degradation. The study also sheds light on the impacts of these waste disposal facilities on the general well-being and health of the university community, particularly on students and staff. In addition to these findings, the study highlights a general lack of awareness in the university community about the impacts of MSW facilities on air quality. This highlights the need for increased education and information dissemination. The results support the development of comprehensive and effective strategies, including technical solutions and public awareness initiatives, to mitigate the impacts of these facilities on residential areas. In conclusion, the impacts of MSW facilities on air quality should be seen as a multidimensional issue that requires a holistic approach addressing environmental, health, social, and educational dimensions.

Windows are of great importance in improving the energy efficiency of buildings. It is possible to achieve this with the help of the regeneration of window design. The amount of energy used, the expense of heating and cooling, and the emissions of greenhouse gases that contribute to climate change can all be significantly reduced by improving the energy efficiency of windows. For this, computer modeling and BIM-based simulation programs provide significant timesaving in simultaneously evaluating design variations’ visual and thermal results. This study selected a four-story residential building to analyze the energy load and thermal comfort of the windows redesign and examine the energy-saving potential for residential buildings. To analyze the renewed window design strategies, a four-story apartment building is selected as a case study in Izmir/Turkey (38° 4′, 27° 2′). This apartment is built on a 90 m² gross floor area. The existing indoor environmental conditions of the flat are generally observed as cool and low illuminated by the occupants, so the window design options must be compared and renewed. As the first option, current conditions are simulated. The second option is to simulate different patterns for window-to-wall ratio (WWR). Moreover, the third option is to simulate different types of glass in each window. Currently, the WWR of the selected flat in the north, east and south directions is around 10%. But more is needed to provide daylight to the apartment. This article used Autodesk Revit and Green Building Studio simulations to investigate WWR and glass types and evaluate energy use intensity’s (EUI) impact. As a result, this study shows that a 10% WWR on all building facades leads to an EUI of 993.9 MJ/m²/year. In contrast, increasing the WWR to 95% significantly increased EUI, reaching 2121 MJ/m²/year. In addition, it has been shown that the use of low U-value glasses, such as translucent wall panels and super-insulated three-pane clear Low-E, can provide energy savings of up to 5% per year, and especially the super-insulated three-pane Low-E glass type provides the highest efficiency on all facades.

This paper uses hierarchical regression analysis, a statistically robust method, to explore the correlations between two meteorological parameters and three particulate matter concentrations. The dataset is provided by six sensors located in three cities from three countries, and the measurements were taken simultaneously for three months at each minute. Analyses and calculations were performed with the Statistical Package for the Social Sciences (SPSS). The results underscore that the complexity of air pollution dynamics is affected by the location even when the same type of sensors is used, and emphasize that a one-size-fits-all approach cannot effectively address air pollution. The findings are helpful from three perspectives: for education, to show how to handle and communicate a solution for local communities’ issues about air pollution; for research, to understand how easy a university can generate and analyze open-source data; and for policymakers, to design targeted interventions addressing each country’s challenges.

It is known that people generally spend 90% of their time indoors. Therefore, indoor air quality is a major concern for many people. Building materials play an important role in indoor air quality. Therefore, this study evaluates the role of building materials in IAQ by conducting a bibliometric analysis of articles from the Web of Science Core Collection and utilizing VOSviewer software to analyze publications from 2010 to 2023, focusing on the citation, year, country, and keywords co-occurrence. The analysis reveals key trends and gaps in the literature, highlighting the predominance of specific materials and pollutants. It also highlights that variability in building parameters makes attributing pollution sources difficult and underlines the need for context-specific assessments. These findings underscore the critical need to prioritize IAQ in building design and management to ensure safe and healthy indoor environments. This study manifests by methodologically mapping the research landscape on building materials and IAQ, guiding future empirical research.