Social Contract and Deterring Famine: First Thoughts

The links between certain kinds of political systems and protection against famine are investigated in this paper. The starting-point is a critique of Amartya Sen's observation that famines are unknown in countries with a free press and competitive elections. This holds true only in India because of a unique political history in which freedom from famine became a right, upon which political legitimacy was founded: an anti-famine 'social contract'.
The rise and decline of anti-famine systems in Africa is charted. Major reasons for decay include neo-liberalism and the international humanitarian system, both of which undermine relationships of domestic political accountability that underpin effective famine prevention. A number of politically regressive tendencies in 'actually existing humanitarianism' are identified that work against any nascent anti-famine social contracts in Africa. This is possible because famine prevention has not been established as a right in Africa.     

Adding virtual measuring stations to a network for urban air pollution mapping

Maps of pollutants concentration are usually generated by means of interpolation and extrapolation methods. The quality of the results depends mainly of the number of permanent or temporary measuring stations. This paper deals with a method for the virtual densification of the network of stations. The method creates "virtual measuring stations". It aims at improving the quality of interpolation by increasing the number of data on pollutant concentration. The virtual stations are determined by the means of a classification method applied to each pixel of the area under concern. Discriminating elements are pollutants emission classes, land cover types, urban morphological indicators created to this purpose and distance to major roads. A first implementation was made for particulate matter (PM) for the city of Strasbourg (France) using thin-plates spline interpolation method in Arcview 9 GIS. The relative Root Mean Square Error decreases from 49% for five input stations down to 15% for the virtual stations.     

The role of copper(II) chloride in the formation of organic chlorine in fly ash

In the de-novo synthesis and formation of PCDD/PCDF, the transfer of inorganic chlorine to the carbonaceous material of fly ash plays an important role. Here, copper acts as a catalyst in the chlorination reaction. In experiments in the range of 250-350 degrees C under helium, we determined the stoichiometry of the chlorination reaction with model systems. Therefore, it was necessary to develop a method to quantify the copper(II) and copper(I) ions. In a combination of solid electron paramagnetic (spin) resonance spectroscopy (EPR) for Cu(I), and X-ray fluorescence spectroscopy (XRFA) analysis for Cu (total), we found a way for the quantification of copper(I) and (II). With these experiments, we can show that the chlorination reaction is relatively fast and comes to a stop under helium, after the copper(II) is reduced. The ratio between the organic chlorine formed and copper(II) reduced is, at the end of the reaction, 0.5, which is in agreement with the following reaction: 2CuCl2 + R-H-->2CuCl + R-Cl + HCl.     

Formation rates of polychlorinated dibenzofurans and dibenzo-p-dioxins from polycyclic aromatic hydrocarbons, activated carbon and phenol

The formation rates of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) from polycyclic aromatic hydrocarbons (PAHs), activated carbon, and phenol were compared. The heat treatment at 300°C of all tested PAH on silica in the presence of CUCl₂ Preferred PCDDs over PCDDs. Perylene reproducibly gave a higher yield of PCDDs than activated carbon gave under the same conditions. The formation rate of PCDDs from perylene could be higher than the values in this study because the residence time of perylene in the reactor is shorter than that of activated carbon. PCDDs were mainly formed from phenol.     

Land-Use Change and Carbon Flux Between 1970s and 1990s in Central Highlands of Chiapas, Mexico

6 tons C were released to the atmosphere during the period of time covered by our study, equal to approximately 34% of the 1975 vegetation C pool. The Chiapas highlands, while comprising just 0.3% of Mexico's surface area, contributed 3% of the net national C emissions.     

Comparison of SOC estimates and uncertainties from aerosol chemical composition and gas phase data in Atlanta

In the Southeastern US, organic carbon (OC) comprises about 30% of the PM_2.5 mass. A large fraction of OC is estimated to be of secondary origin. Long-term estimates of SOC and uncertainties are necessary in the evaluation of air quality policy effectiveness and epidemiologic studies. Four methods to estimate secondary organic carbon (SOC) and respective uncertainties are compared utilizing PM_2.5 chemical composition and gas phase data available in Atlanta from 1999 to 2007. The elemental carbon (EC) tracer and the regression methods, which rely on the use of tracer species of primary and secondary OC formation, provided intermediate estimates of SOC as 30% of OC. The other two methods, chemical mass balance (CMB) and positive matrix factorization (PMF) solve mass balance equations to estimate primary and secondary fractions based on source profiles and statistically-derived common factors, respectively. CMB had the highest estimate of SOC (46% of OC) while PMF led to the lowest (26% of OC). The comparison of SOC uncertainties, estimated based on propagation of errors, led to the regression method having the lowest uncertainty among the four methods. We compared the estimates with the water soluble fraction of the OC, which has been suggested as a surrogate of SOC when biomass burning is negligible, and found a similar trend with SOC estimates from the regression method. The regression method also showed the strongest correlation with daily SOC estimates from CMB using molecular markers. The regression method shows advantages over the other methods in the calculation of a long-term series of SOC estimates.     

The measurement of place attachment: Personal,community, and environmental connections

Place attachment has been researched extensively in the social and behavioural sciences over the past two decades. However, it is challenging for researchers to assimilate the mixed messages presented in the place attachment literature and to understand the multiple place attachment terms. In this study, a four-dimensional model of rural landholder attachments to their natural resource management region was conceptually and empirically developed with the aim of developing an integrated approach to the measurement of place attachment that clearly distinguishes between different elements of place scholarship. A 29-item place attachment scale with the dimensions of place identity, place dependence, nature bonding, and social bonding was tested on a random sample of rural landholders in the Adelaide and Mount Lofty Ranges in South Australia (N = 320). The majority of respondents were male (69.3%) and the average age was 59 years. The scale was reduced to 20-items and then administered simultaneously and in the same response format to rural landholders in two other areas of South Australia: the Northern and Yorke region (N = 664) and South Australian Murray-Darling Basin region (N = 659). In both studies, the majority of respondents were male (85%) and the average age was 55 years. Exploratory factor and reliability analyses of Adelaide and Mount Lofty Ranges and Northern and Yorke datasets produced a five-dimensional model of place attachment with high reliabilities. Social bonding divided into the constructs of family bonding and friend bonding. The refined five-dimensional model was then examined for convergent validity, with moderate but significant correlations found between individual attachment constructs and dependent variables expected to be related to the construct, such as place identity and length of residence, and nature bonding and time currently spent in nature. We used confirmatory factor analysis to test the goodness-of-fit of the South Australian Murray-Darling Basin (SAMDB) dataset to the proposed five-dimensional model and then compared its fit to the traditional two-dimensional model of place identity and place dependence. The five-dimensional model provided moderate fit for the SAMDB data. We conclude with a discussion of the validity and reliability of the five-dimensional model and its future role in place attachment research.