The population of Turkey has increased rapidly in parallel with the rise in technology and industrialization. The increasing population of the country has triggered urbanization, and environmental problems have drawn attention. One of the issues resulting from urbanization is municipal solid waste (MSW). Every object we use in our daily life is transformed into solid waste when its economic life has ended. The processes from the storage of these wastes to their disposal, often referred to as MSW management, come under the municipalities' authority and responsibility. MSW must be processed with the most negligible environmental impact. This study aimed to investigate MSW management systems in Turkey, including the quantity, composition, and disposal methods of MSW generated and populations served by MSW, MSW per capita per day. Results from the study showed that the mean MSW generated in Turkey was 26.74 Mton/yr and increased in the following order by region; Eastern Anatolia < South East Anatolia < Black Sea < Mediterranean < Aegean < Central Anatolia < Marmara. This order/ranking was in line with the regions' population. The population explained 84 to 95% of the variation in MSW. Results from the study could be used in MSW management.

Many climate models have been developed because of the importance of climatic factors' effects on the physical and biological environment, e.g., rock weathering, species distribution, and growth patterns of plants. Accurate, reliable climate surfaces are necessary, especially for countries such as Turkey, which has a complex terrain and limited monitoring stations. The accuracy of these models mainly depends on the spatial modeling methods used. In this study, Australian National University spline (ANUSPLIN) model was used to develop climate surfaces and was compared with other methods such as inverse distance weighting, Co-Kriging, lapse rate, and multilinear regression. The results from the developed climate surfaces were validated using three methods: (1) diagnostic statistics from the surface fitting model, such as signal, mean, root mean square predictive error, root mean square error estimate, root mean square residual of the spline, and estimate of the standard deviation of the noise in the spline; (2) a comparison of error statistics between interpolated surfaces with and the withheld climate data from 81 stations; and (3) a comparison with other interpolation methods using model performance metrics, such as mean absolute error, mean error, root mean square error, and R²_adj. The most accurate results were obtained by the ANUSPLIN model. It explained 95%, 88%, 92%, and 71% of the variance in annual mean, minimum and maximum temperature, and total precipitation, respectively. The mean absolute error of these models was 0.63 °C, 1.16 °C, 0.72 °C, and 54.82 mm. The generated climate surfaces, having a spatial resolution of 0.005º x 0.005º could contribute to the fields of forestry, agriculture, and hydrology.

Air pollution due to anthropogenic activities is currently one of the most important problems faced worldwide. This study aimed to determine the associations between air quality and spatial, meteorological, and anthropogenic factors while evaluating air quality using the air stress index (ASI) and the daily air quality (DAQx) scale. The annual mean levels of CO, NOx, O3, PM 2.5, PM10 and SO2 in the region were 718.6, 39.5, 44.4, 25.5, 51.3, and 9.9 μg/m3, respectively. While anthropogenic variables mostly affected NOx (r=0.56 to 0.64) and O3 (r=-0.34 to 0.64), meteorological (r=-0.38 to 0.45) and spatial factors (r=-0.41 to -0.65) mostly affected particulate matter (PM2.5 and PM10). CO and SO2, on the other hand, were affected by all types of variables at varying directions and rates. The mean ASI and DAQx values of 2.1 and 4.3 indicated that the air quality in the region exhibited distinct air stress and sufficient air quality, respectively. The findings and outcomes could contribute to understanding and evaluating the air quality in the region and could be used as a base for further studies.

Bu çalışmada arazi kullanım farklılığının toprakların bazı özellikleri ile azot mineralizasyonu üzerindeki etkilerinin belirlenmesi amaçlanmıştır. Bu çalışma kapsamında Rize, Kalkandere’de orman, çay, fındık ve kivi alanlarının her birinden 5 farklı noktada örnekleme yapılmıştır. Her örnekleme alanından birer toprak çukuru açılarak 0-15 cm ve 15-30 cm derinlik kademelerinden 40’ar adet bozulmuş ve bozulmamış toprak örneği alınmıştır. Toprak örneklerinde tekstür, toprak reaksiyonu (pH), organik karbon, toplam azot, karbon/azot oranı, elektriksel iletkenlik (EC), kireç (CaCO3), hacim ağırlığı ve azot mineralizasyonu gibi özellikler belirlenmiştir. Arazi kullanım durumuna göre toprak özelliklerindeki farklılığı belirlemek için nanparametrik Kruskal Wallis testi uygulanmıştır. Arazi kullanım durumuna ilişkin ikili karşılaştırmalarda ise Dunn’s Bonferroni test uygulanmıştır. Analiz sonuçlarına göre 0-15 cm derinlik kademesinde kil, pH, organik karbon, elektriksel iletkenlik, hacim ağırlığı, T0NO3, T0NH4+NO3, T63NH4 ve T63NH4+NO3 gibi toprak özellikleri anlamlı şekilde farklılık gösterirken, 15-30 cm derinlik kademesinde bu özellikler kum, kil, pH, elektriksel iletkenlik, hacim ağırlığı, T63NH4 ve T63NH4+NO3 şeklinde ortaya çıkmıştır. Bu çalışmaya göre doğal orman alanları topraklarının diğer arazi kullanım durumu topraklarına göre anlamlı bir şekilde daha az kil (p=0,008), EC (p=0,003), hacim ağırlığı (p=0,003), T0NO3 (p=0,006) ve T0NH4+NO3 (p=0,005); daha fazla kum (p=0,006) ve T63NH4+NO3 (p=0,007) içerdiği belirlenmiştir.

The dominant height of forest stands (SDH) is an essential indicator of site productivity in operational forest management. It refers to the capacity of a particular site to support stand growth. Sites with taller dominant trees are typically more productive and may be more suitable for certain management practices. The present study investigated the relationship between the dominant height of oriental beech stands and numerous environmental variables, including physiographic, climatic, and edaphic attributes. We developed models and generated maps of SDH using multilinear regression (MLR) and regression tree (RT) techniques based on environmental variables. With this aim, the total height, diameter at breast height, and age of sample trees were measured on 222 sample plots. Additionally, topsoil samples (0–20 cm) were collected from each plot to analyze the physical and chemical soil properties. The statistical results showed that latitude, elevation, mean annual maximum temperature, and several soil attributes (i.e., bulk density, field capacity, organic carbon, and pH) were significantly correlated with the SDH. The RT model outperformed the MLR model, explaining 57% of the variation in the SDH with an RMSE of 2.37 m. The maps generated by both models clearly indicated an increasing trend in the SDH from north to south, suggesting that elevation above sea level is a driving factor shaping forest canopy height. The assessments, models, and maps provided by this study can be used by forest planners and land managers, as there is no reliable data on site productivity in the studied region.