Properties of concrete paving blocks made with waste marble

Marble industry produces large amounts of waste marble - what causes environmental problems. In paving blocks based on two cement types we have partly replaced aggregate with waste marble. Physical and mechanical tests were performed on blocks so produced. The cement type turns out to be an important factor. Mechanical strength decreases with increasing marble content while freeze-thaw durability and abrasive wear resistance increase. Waste marble is well usable instead of the usual aggregate in the concrete paving block production.     

Sustainable natural resource management and environmental assessment in the Salt Lake (Tuz Golu) Specially Protected Area

The Salt Lake Specially Protected Area is a unique ecosystem for both agricultural activities and natural life in Turkey. In the present study, an attempt was made to develop a conceptual land use strategy and methodology, taking into account ecological factors for regional development in the Salt Lake Specially Protected Area. A detailed Geographic Information System (GIS) analysis was done to create a comprehensive database including land use, land suitability, and environmental factors (soil, climate, water quality, fertilizing status, and heavy metal and pesticide pollution). The results of the land suitability survey for agricultural use showed that, while 62.6% of the study area soils were classified as best and relatively good, about 15% were classified as problematic and restricted lands, only 22.2% of the study area soils were not suitable for agricultural uses. However, this is not enough to derive maximum benefit with minimum degradation. Therefore, environmental factors and ecological conditions were combined to support this aim and to protect the ecosystem. Excessive irrigation practices, fertilizer and pesticide application, and incorrect management practices all accelerate salinization and degradation. In addition to this, it was found that a multi-layer GIS analysis made it easy to develop a framework for optimum land use and could increase the production yield preserving the environmental conditions. Finally, alternative management and crop patterns were undertaken to sustain this unique ecosystem, considering water, soil, climate, land use characteristics, and to provide guidance for planners or decision makers.     

Recycling bottom ash in production of eco-friendly interlocking concrete paving blocks

Journal of Material Cycles and Waste Management - Coal combustion waste reaches huge amount that causes environmental problem. In modern world, wastes generated from an industry can be used by...     

Predicting field capacity,wilting point,and the other physical properties of soils using hyperspectral reflectance spectroscopy: two different statistical approaches

In this study, we examined the ability of reflectance spectroscopy to predict some of the most important soil parameters for irrigation such as field capacity (FC), wilting point (WP), clay, sand, and silt content. FC and WP were determined for 305 soil samples. In addition to these soil analyses, clay, silt, and sand contents of 145 soil samples were detected. Raw spectral reflectance (raw) of these soil samples, between 350 and 2,500-nm wavelengths, was measured. In addition, first order derivatives of the reflectance (first) were calculated. Two different statistical approaches were used in detecting soil properties from hyperspectral data. Models were evaluated using the correlation of coefficient (r), coefficient of determination (R ²), root mean square error (RMSE), and residual prediction deviation (RPD). In the first method, two appropriate wavelengths were selected for raw reflectance and first derivative separately for each soil property. Selection of wavelengths was carried out based on the highest positive and negative correlations between soil property and raw reflectance or first order derivatives. By means of detected wavelengths, new combinations for each soil property were calculated using rationing, differencing, normalized differencing, and multiple regression techniques. Of these techniques, multiple regression provided the best correlation (P?<?0.01) for selected wavelengths and all soil properties. To estimate FC, WP, clay, sand, and silt, multiple regression equations based on first(2,310)-first(2,360), first(2,310)-first(2,360), first(2,240)-first(1,320), first(2,240)-first(1,330), and raw(2,260)-raw(360) were used. Partial least square regression (PLSR) was performed as the second method. Raw reflectance was a better predictor of WP and FC, whereas first order derivative was a better predictor of clay, sand, and silt content. According to RPD values, statistically excellent predictions were obtained for FC (2.18), and estimations for WP (2.0), clay (1.8), and silt (1.63) were acceptable. However, sand values were poorly predicted (RDP?=?0.63). In conclusion, both of the methods examined here offer quick and inexpensive means of predicting soil properties using spectral reflectance data.