Günümüzde hızlı kentleşme ve beraberindeki nüfus artışları yerleşim alanlarında gürültü artışına neden olmaktadır. Yapılan değerlendirmelerle, kent yaşamında, ulaşım kaynaklı ana çevresel gürültü kaynağının karayolu gürültüsü olduğu ortaya koyulmuştur. Karayolu gürültüsünü azaltmak ve olumsuz etkilerini önlemek amacıyla en etkin ve ekonomik yol kaynakta kontroldür. Ancak kaynakta denetimin yetersiz kaldığı durumlarda, kaynak ile alıcı arasında kullanılan akustik engeller öne çıkmaktadır. Tüm dünyada karayolu kaynaklı gürültü azaltımı için farklı tasarım bileşenlerine sahip gürültü bariyer sistemleri kullanılmaktadır. Karayollarında gürültü kontrolünü sağlamak üzere bariyer sistemlerinin geliştirilmesi öncesinde uygun kesitlerin belirlenmesi gerekmektedir. Stratejik gürültü haritalama çalışması yapılmasıyla, kabul edilebilir gürültü seviyelerinin aşıldığı alanlar ve bu alanlardaki karayolu kesitleri tespit edilebilmektedir. Ancak, söz konusu kesitlere bariyer uygulanması için sınır aşım alanları içerisinde farklı kriterler de değerlendirilmelidir. Bu çalışma kapsamında, Türkiye’de stratejik gürültü haritası hazırlanmış 20 il için karayollarında gürültü bariyer sistemi uygulanabilir potansiyel kesitler belirlenmiştir. Bu kesitler belirlenirken, stratejik gürültü haritalama çalışmaları kapsamında tespit edilen karayolu etki alanlarında sınır aşım değerlendirmelerinden ve gürültü modellemesi girdi verilerinden faydalanılmıştır. Modellenen 620 km ana karayolunun yaklaşık %3,9’una gürültü bariyeri uygulanabileceği tespit edilmiştir.

Mediterranean biogeography is characterized as a global “hotspot” for climate change; understanding the impacts of these changes on local agricultural systems through high-resolution analyses has thus become a critical need. This study addresses this gap by evaluating the holistic effects of climate change on site-specific agriculture systems, focusing on the Eğirdir–Karacaören (EKB) and Beyşehir (BB) lake basins in the Lakes Region of Türkiye. This study employed machine learning modeling techniques to forecast changes in the yields of key crops, such as wheat, maize, apple, alfalfa, and sugar beet. Detailed spatial analyses of changes in agro-climatic conditions (heat stress, chilling requirement, frost days, and growing degree days for key crops) between the reference period (1995–2014) and two decadal periods projected for 2040–2049 and 2070–2079 were conducted under the Shared Socioeconomic Pathways (SSP3-7.0). Daily temperature, precipitation, relative humidity, and solar radiation data, derived from high-resolution climate simulations, were aggregated into annual summaries. These datasets were then spatially matched with district-level yield statistics obtained from the official data providers to construct crop-specific data matrices. For each crop, Random Forest (RF) regression models were fitted, and a Leave-One-Site-Out (LOSOCV) cross-validation method was used to evaluate model performance during the reference period. Yield prediction models were evaluated using the mean absolute error (MAE). The models achieved low MAE values for wheat (33.95 kg da⁻¹ in EKB and 75.04 kg da⁻¹ in BB), whereas the MAE values for maize and alfalfa were considerably higher, ranging from 658 to 986 kg da⁻¹. Projections for future periods indicate declines in relative yield across both basins. For 2070–2079, wheat and maize yields are projected to decrease by 10–20%, accompanied by wide uncertainty intervals. Both basins are expected to experience a substantial increase in heat stress days (>35 °C), a reduction in frost days, and an overall acceleration of plant phenology. Results provided insights to inform region-specific, evidence-based adaptation options, such as selecting heat-tolerant varieties, optimizing planting calendars, and integrating precision agriculture practices to improve resource efficiency under changing climatic conditions. Overall, this study establishes a scientific basis for enhancing the resilience of agricultural systems to climate change in two lake basins within the Mediterranean biogeography.

Particulate Matter (PM) is a type of air pollution that poses risks to human health, the environment, and property. Among the various PM types, PM₁₀ is particularly significant, as it acts as a vector for numerous hazardous trace elements that can negatively impact human health and the ecosystem. Identifying potential sources of PM₁₀ and quantifying their impact on ambient concentrations is crucial for developing efficient control strategies to meet threshold values. Receptor modeling, which identifies sources using chemical species information derived from PM samples, has been widely used for source apportionment. In this study, PM₁₀ samples were collected over three periods (April, May, and June 2021), each lasting 16 days, using semi-automatic dust sampling systems at two sites in Biga, Canakkale, Turkiye. The relative contributions of different source types were quantified using EPA PMF (Positive Matrix Factorization) based on 35 elements comprising PM₁₀. As a result of the analysis, five source types were identified: crustal elements/limestone/calcite quarry (64.9%), coal-fired power plants (11.2%), metal industry (9%), sea salt and ship emissions (8.5%), and road traffic emissions and road dust (6.3%). The distribution of source contributions aligned with the locations of identified sources in the region.