Fine-resolution population mapping using OpenStreetMap points-of-interest

Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.     

United Arab Emirates limestones: impact of petrography on thermal behavior

The thermal behavior of selected limestones from representative localities of the United Arab Emirates is investigated for their suitability for soft-burnt lime production. The limestone samples were collected from the Ghalilah, Musandam, Shauiba, Muthaymimah, Dammam and Asmari formations. The samples were characterized for petrography, mineral and chemical composition, together with physico-mechanical characteristics. Investigative methods included transmitted light microscopy (TLM), cathodoluminescence (CLM) and scanning electron microscopy (SEM), as well as X-ray micro-tomography (μ-CT), XRD, XRF and Archimedes method. The limestone samples were fired in an electrical muffle furnace for 0.25, 0.5, 1 and 2 hours at 800, 900, 1,000 and 1,100 °C. After firing the lime grains were tested to determine their hydration rate and microfabric. The Ghalilah and Musandam limes show the lowest and highest maximum hydration rates, respectively, due mainly to the impure nature of the former, and the smaller lime crystallites and dominance of post-calcination micro-cracks of the latter. The Dammam and Asmari limes preserve a “ghost” microfabric of the original limestone. Higher allochem contents impose lower activation energy requirements for calcination, which implies earlier calcination of the allochems. The Musandam, Shauiba and Muthaymimah limestones may be useful for the production of reactive soft-burnt lime under the applied firing conditions, however, the Dammam and Asmari limestones need more advanced calcination conditions than the applied ones. The Ghalilah limestone was found to be unsuitable for the production of lime.     

A modification to dense sand dynamic simulation capability of Pastor–Zienkiewicz–Chan model

A modification to the nonlinear Pastor–Zienkiewicz–Chan (PZC) constitutive model without any change in the number of model parameters is introduced in order to simulate stiffness degradation of dense sands at dynamic loading. The PZC model is based on generalized plasticity and was verified by good prediction of liquefaction and undrained behavior of saturated sand. The PZC is a robust model that can predict drained dynamic behavior of sands, especially stiffness increase in loose sand at reloading of dynamic loading. Yet, this model does not show stiffness degradation of dense sand at reloading. The modification is made through modifying the stress memory factor, H _DM, which is multiplied by the plastic modulus, H _L. This modification does not influence reloading behavior of loose sand. The modified PZC model is verified via results of drained cyclic tests. Two cyclic triaxial tests on loose and dense specimens, along with two cyclic plane strain tests on dense sand are utilized for validation. The model simulation shows that the modified PZC model is able to predict the stiffness degradation of dense sand at reloading well.     

J-Type Shock Chemistry for the W3 IRS5 Region

A chemical evolution of W3 IRS5 have been studied during J-type shock. We modified our code of for one-dimensional J-type shock to satisfy the physical conditions of W3 IRS5. The chemical rate equations and the hydrodynamics of shocks are integrated simultaneously. We find that, a shock of speed V _s = 10 km s^-1 and magnetic induction B=4.1×10^-6 Gauss, propagate into molecular gas density of n=10⁴cm^-3, and with initial temperature of 30 K (pre shock) satisfy the physical conditions of W3 IRS5. The results using the extensive chemical network with the most updating reaction rates show that the calculated fractional abundances are in agreement with observation. This revised version was published online in July 2006 with corrections to the Cover Date.     

Chlorine-bearing molecules in cold and warm interstellar clouds

A new time-dependent model of chlorine chemistry in cold and warm TMC-1 and Orion clouds was constructed using results of recent laboratory and theoretical studies on the reaction of chlorine molecules. The chemistry of chlorine was found to be fairly simple in dense interstellar clouds. Only the species Cl and HCl have a significant fractional abundance. Our results for the HCl molecule are in agreement with those obtained from the observation and from theoretical values of Orion cloud.     

Biodiesel synthesis from non-edible oils by transesterification using the activated carbon as heterogeneous catalyst

The need for renewable environmentally friendly energy resources is growing every day. Biodiesel is one of the most promising alternatives to the conventional non-renewable energy resources. Heterogeneous catalysts proved a high efficiency in the transesterification of oils to produce biodiesel. In this research, activated carbon was tested as a heterogeneous catalyst in the transesterification of two non-edible oils (waste cooking oil and Jatropha oil) with methanol to produce biodiesel. Activated carbon was characterized using X-ray diffraction, scanning electron microscope and Fourier transformed infrared. The effect of different operating parameters, namely operation time (30, 60, 120 and 180 min), alcohol-to-oil molar ratio (4:1, 6:1, 8:1 and 10:1), catalyst loading [0.5, 1, 2, 3 and 5% (w/w)] and rotational speed (100, 200, 300 and 400 rpm), was investigated. Results showed that increasing the operational time, the alcohol-to-oil molar ratio and the catalyst loading increases the conversion to biodiesel but only to some extent; increasing the stirring rate was found to be beneficial to the process. The optimum conditions were found to be 2 h of heating, 6:1 alcohol-to-oil ratio, 1 wt% catalyst loading and 400 rpm stirring. Under optimum conditions, the conversion to biodiesel reached 93.95 and 93.27% for the waste cooking oil and the Jatropha oil, respectively. The properties of the obtained biodiesel (density, viscosity, flash point, pour point and cloud point) were measured giving promising results.     

Forest biomass carbon dynamics (1980–2009) in western Himalaya in the context of REDD+ policy

Carbon emissions from forests have decreased in the past decade due to conservation efforts, however majority of carbon losses suffered in the past went unnoticed until the role of forests in mitigating climate change was realized. Forestry sector in developing countries is recognized as one of the largest and low cost mitigation options to address climate change. The present study was conducted to assess the multi-temporal biomass carbon mitigation in the temperate forests of western Himalaya using satellite (Landsat MSS, TM, ETM+) and forest inventory data. Forest type density mapping was done through on-screen visual interpretation of satellite data. After conducting preliminary survey in 2009, 45 quadrats (0.1 ha) were laid in six forest types for collecting field inventory data viz., diameter at breast height, tree height, slope and aspect. Biomass carbon (t ha^?1) was estimated for different forest types with different crown densities (open with 10–40% crown density and closed with >40%) using recommended regression equations, ratios and factors. A decreasing trend of carbon (145.13–134.87 mt) was observed over the period of time. Temporal biomass carbon dynamics was analyzed for REDD+ opportunities. The temporal variation of carbon observed was found to be more useful for claiming benefits under negative options (deforestation and forest degradation) of REDD+. The study doesn’t take actual conversions to CO₂ into account. However, the findings are useful in establishing baseline emissions through temporal carbon losses. Further, the study helps in identification of location specific socio-economic drivers of losses that can be used for appropriate mitigation interventions.     

Comparative study of the absorptive potential of raw and activated carbon <Emphasis Type="Italic">Acacia nilotica</Emphasis> for Reactive Black 5 dye

Acacia nilotica was used for the adsorption of Reactive Black 5 (RB5) dye from an aqueous solution. Both the raw and activated (with H₃PO₄) carbon forms of Acacia nilotica (RAN and ANAC, respectively) were used for comparison. Various parameters (including dye concentration, contact time, temperature, and pH) were optimized to obtain the maximum adsorption capacity. RAN and ANAC were characterized using Fourier transform infrared spectroscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The maximum experimental adsorption capacities for RAN and ANAC were 34.79 and 41.01 mg g^?1, respectively, which agreed with the maximum adsorption capacities predicted by the Langmuir, Freundlich, and Dubinin–Radushkevich equilibrium isotherm models. The adsorption data of ANAC showed a good fit to the isotherm models based on the coefficient of determination (R ²): Langmuir type II (R ² = 0.99) > Freundlich (R ² = 0.9853) > Dubinin–Radushkevich (R ² = 0.9659). This result suggested monolayer adsorption of RB5 dye. The adsorption of RB5 dye followed pseudo-second-order kinetics. The RAN adsorbent reflected an exothermic reaction (enthalpy change, ΔH = ?0.006 kJ mol^?1) and increased randomness (standard entropy change, ΔS = 0.038 kJ mol^?1) at the solid–solution interface. In contrast, ANAC reflected both exothermic [?0.011 kJ mol^?1 (303–313 K)] and endothermic [0.003 kJ mol^?1 (313–323 K)] reactions. However, the ΔS value of ANAC was lower when the RB5 adsorption increased from 313 to 323 K. The negative values for the Gibbs free energy change at all temperatures indicated that the adsorption of RB5 dye onto RAN and ANAC was spontaneous in the forward direction.     

Comparison of artificial neural networks,geographically weighted regression and Cokriging methods for predicting the spatial distribution of soil macronutrients (N,P, and K)

Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R~2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R~2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R~2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.