Effect on physico-chemical and structural properties of soil amended with distillery effluent and ameliorated by cropping two cereal plant spp.

Molasses-based distilleries are one of the most polluting industries generating large volume of wastewater. Distillery effluent (DE) on its discharge alters the physical and chemical properties of the soil. This study has been conducted to assess the distribution of heavy metals and its effect on physiochemical and structural changes of soil which has been treated with diluted and undiluted effluents. The soil has been assayed both before and after crop plantation. Out of the crop plants studied, Cicer arietinum was least effected and more effective than Pisum sativum in diminishing the heavy metals from soil. However, only at higher concentration DE has some adverse impact on the nutrient contents of the soil. Fourier transform infrared spectrum (FTIR) study of the soil indicated the presence of –OH, –NH, –COOH groups. It was also noted that the soil amended with different concentration of DE and on which Cicer and Pisum were grown has varied IR spectrum. These variations show the presence of different functional groups of soil before and after plants were grown. The presence of inorganic carbonate was found in soil planted with Cicer from 10 to 50% of DE, whereas aliphatic primary amides were observed up to 25 and 100% concentrations of DE in soil planted with Pisum. The appearance of inorganic nitrates, carbonates, aliphatic hydrocarbons and aliphatic primary amides at various concentrations of DE showed a change in functional groups probably because of the differential breakdown of parent component present in the effluent. Besides our main finding that Cicer is a very useful crop species in remediating the soil which has inflicted with even undiluted DE, it is also validated through FTIR spectra that IR technique is an efficient tool in studying the structural changes of soil and with some degree of caution may be summed up that FTIR may be used to correlate the relative efficacy of plants to transform organic contaminants.     

Land transformation assessment using the integration of remote sensing and GIS techniques: a case study of Al-Anbar Province,Iraq

Human activities and climate changes significantly affect our environment, altering hydrologic cycles. Several environmental, social, political, and economical factors contribute to land transformation as well as environmental changes. This study first identified the most critical factors that affect the environment in Al-Anbar city including population growth, urbanization expansion, bare land expansion, and reduction in vegetation cover. The combination of remote sensing data and fuzzy analytic hierarch process (Fuzzy AHP) enabled exploration of land transformations and environmental changes in the study area during 2001 to 2013 in terms of long and short-term changes. Results of land transformation showed that the major changes in water bodies increased radically (94 %) from the long-term change in 2001 to 2013 because of water policies. In addition, the urban class expanded in two short-term periods (2001–2007 and 2007–2013), representing net changes of 46 and 60 %, respectively. Finally, barren land showed 25 % reduction in the first period because of the huge expansion of water in the lake; a small percentage of growth gain was observed in the second period. Based on the land transformation results, the environmental degradation assessment showed that the study area generally had high level of environmental degradation. The degradation was mostly in the center and the north part of the study area. This study suggested for further studies to include other factors that also responsible for environmental degradation such as water quality and desertification threatening.     

Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island,Malaysia

Land surface temperature (LST) plays an important role in local, regional and global climate studies. LST controls the distribution of the budget for radiation heat between the atmosphere and the earth’s surface. Therefore, it is important to evaluate abrupt changes in land use/land cover (LULC). Penang Island, Malaysia has been experiencing a rapid and drastic change in urban expansion over the past two decades due to growth in industrial and residential areas. The aim of this study was to investigate and evaluate the impact of LST with respect to land use changes in Penang Island, Malaysia. Three supervised classification techniques known as maximum likelihood, minimum distance-to-mean and parallelepiped were applied to the images to extract thematic information from the acquired scene by using PCI Geomatica 10.1 image processing software. These remote sensing classification techniques help to examine land-use changes in Penang Island using multi-temporal Landsat data for the period of 1999–2007. Training sites were selected within each scene and seven land cover classes were assigned to each classifier. The relative performance of each technique was evaluated. The accuracy of each classification map was assessed using a reference data set consisting of a large number of samples collected per category. Two Landsat satellite images captured in 1999 and 2007 were chosen to classify the LULC types using the maximum likelihood classification method, determined from visible and near-infrared bands. The study revealed that the maximum likelihood classifier produced superior results and achieved a high degree of accuracy. The LST and normalised difference vegetation index (NDVI) were computed based on changes in LULC. The results showed that the urban (highly built-up) area increased dramatically, and grassland area increased moderately. Inversely, barren land decreased obviously, and forest area decreased moderately. While urban (minimally built-up) area decreased slightly. These changes in LULC caused at significant difference in LST between urban and rural areas. Strong correlation values were observed between LST and NDVI for all LULC classes. The remote sensing technique used in this study was found to be efficient; it reduced the time for the analysis of the urban expansion, and it was found to be a useful tool to evaluate the impact of urbanisation with LST.