The need to continuously monitor the air quality in our environment has continued to grow due to the ever increasing level of atmospheric pollutants. Indoor air pollution and urban air pollution are listed as two of the world’s worst toxic pollution problems in the 2008 Blacksmith institute world’s worst polluted places report. According to the 2014 WHO report, air pollution in 2012 caused the deaths of around 7 million people worldwide. Air pollution can be natural or man-made. The major primary man-made pollutants are Sulphur dioxide (SO2), Nitrogen dioxide, (NO2), Carbon monoxide (CO) and particulate matter (PM). The Air quality Index (AQI) is an index or a number used to characterize the quality of air at a given location. It is used for local and regional air quality reporting and management in many metropolitan or urban cities of the world. Aba is a commercial and industrializing urban city, South East of Nigeria. The multiple linear regression technique is used to estimate the air quality of the city at any given time. The model for the estimation was established as:
The Analysis of Variance (ANOVA) conducted on the model shows that the model is statistically significant and so, good as an estimating technique in that area. The major assumptions of the model were checked and none was found to have been violated.
Climate is a complex and chaotic system having non-linear links between its component variables. Study on temperature trends at macro level may be used to assess the climate variability of a region over the period of time as it is the main component of the climate system. For trend analysis, non-parametric Mann-Kendall test, which is an important tool to find out the existence and magnitude of any statistically significant trend in the climatic data, has been employed in the present study. Another index called Sen Slope has been used to quantify the magnitude of such trend. Temperature data of 39 years (1970-2009) were used for two selected stations over different altitudinal zones of Kashmir valley, viz; Srinagar (1600 metres) and Gulmarg (2644 metres). Both the maximum and minimum temperatures were found to be rising. The overall increase in temperature for Gulmarg is found to be much more than that of Srinagar.
The water footprint refers both direct and indirect water use in production process. Not only the water footprint of products, but also the water footprint of nations can be determined. The main factors which determine the Water Footprint (WF) of a country are gender (since water footprint values for different dietary habits are also different from each other) dietary habits and Gross National Product (GNP). In this study, Germany, France, United Kingdom (UK), Spain, Italy, Turkey, Greece, Bulgaria, Ukraine and Poland were selected considering their development level, their geographical and cultural features. The WF values of these selected countries were calculated based on sex, dietary habits and the annual amount of income via “Your Water Footprint Quick Calculator”. It was found that the country with the highest WF was Spain (3531 m³/year), while the country with the lowest WF was UK (1711 m³/year). It was calculated that Turkey’s WF was 1626 m³/year. In comparison of WF values determined for other countries in the study, it was found that Turkey has a mean WF value. Water footprint was determined 930 m³/year for equal consumption of vegetables, fruits and milk per week. Water footprint values for vegetable-based, fruit-based, milk-based and meat based dietary were respectively 944, 959, 1299 and 993 m3/year. The most important factors that change values of Turkey’s WF were the consumption of meat and dairy products. As a result, every country should be evaluated according to its own characteristics in study related to the determination of the water footprint of the countries.
Monitoring the environment is a key task of remote sensing in particular in areas whose access is difficult or dangerous or where dense cloud cover obscures optical information. This study proposes an assessment of landscape changes related to large refugee camps, where information about environmental conditions is needed by both humanitarian organizations and regional administrations. Our intention is to provide a robust workflow which is applicable for an operational use. The study area is located in Western Kenya hosts a total number of 350.000 people. Images of ERS-2 and Sentinel-1 are used for the assessment of land degradation in a semi-arid savannah between 1997 and 2014.
We expect a relationship between the existence of the refugee camps and the degradation of surrounding landscapes. For this purpose we present an approach which objectively reveals developments in natural resources based on six land-use / land cover classes integrating their relative importance for the ecosystem given by expert-based weights.
An index of Natural Resource Depletion (NRD) is calculated using a Random Forest algorithm in order to classify a time series of SAR images and their textures at different spatial scales (r² = 0.71). Especially large-scale textures turned out to contribute to the classification.
Or results showed a continuous increase of bare soil areas within a radius of five kilometers around the refugee camps and a total decrease of natural resources by 11.8% in the study area. Although the produced NRD maps reveal hot spots of landscape change for selected periods, a clear pattern of land degradation could not be identified and an evident interrelation between the expansion of the camp and the decrease of natural resources has still to be provided.
The proposed approach is applicable to images of other radar sensors as well, such as Sentinel-1 of the European Space Agency which currently collects a multitude of scenes in high spatial resolution. It is therefore suitable for an operational use for the monitoring of land degradation around refugee camps.
Ground geomagnetic survey was conducted around Iseyin Area, part of Ado-Awaiye in order to map and ascertain the nature of variability in the magnetic signature of the subsurface rock, structure of the magnetic basement as well as the depth to basement top. This was aimed at revealing the subsurface basement configuration, lithology and its mineralization.
Total field intensity were measured and recorded at each station with the value of the magnetic anomaly ranging from 30446.47 nT to 33536.10 nT (nanotesla). Regional-residual separation and half - width methods were applied and after necessary data processing, the results were presented in form of magnetic map and profiles.
Qualitative and quantitative interpretations were made based on the maps and profiles. Variation in the magnetic data reveals variation in the basement lithology with the higher value corresponding probably to the amphibolite (basic) component and the lower value corresponding to the granitic or gneissic component of the migmatite-gneiss (the area is underlain by migmatite-gneiss as revealed by surface outcrop). The magnetic character and signature varies being higher at the west and lower at the east. The result also reveals that the magnetic basement is shallow (2.5 m) at some places and relatively deep (22.0 m) at some other places with few basement lineament, suprabasement block and intrabasement block (basement depression). The lineament and intrabasement block can be exploited for groundwater development.