Soiling and decay of N.M.E.P. limestone tablets

The British National Materials Exposure Programme (N.M.E.P.) ran from 1987 to 1995 and involved exposure of a range of materials samples (including tablets of Monks Park and Portland Limestones) at over 20 sites around Britain for 1-, 2-, 4- and 8-year periods, under known climate and pollution conditions. Deterioration of the limestone tablets has previously been recorded in terms of weight change, contents of soluble salts, and visual soiling. In the present study samples from exposed and sheltered positions at Wells, Bolsover and Lough Navar have been studied using a spectrophotometer, optical microscopy and scanning electron microscopy (SEM) to investigate the distribution and nature of particulate material and its role in soiling and decay. Clearly, recognisable pollutant particles such as perforated cenospheres, are only rarely present. Organisms and organic remains, including filamentous microorganisms and pollen grains, are widely distributed. At each site, soiling has different characteristics in terms of composition and change over time related in part to differences in climate and pollution histories. There is no general link between degree of soiling and amount of decay (in terms of surface recession) as the nature of decay is a key influence on the relation between soiling and decay.     

Retene emission from residential solid fuels in China and evaluation of retene as a unique marker for soft wood combustion

Retene (1-methyl-7-isopropylphenanthrene) is often used as a marker for softwood combustion and for polycyclic aromatic hydrocarbon (PAH) source apportionment. The emission factors of retene (EF(RET)s) from 11 crop residues, 27 firewood fuels, and 5 coals were measured using traditional rural Chinese stoves. Retene was measured in combustion emissions from all of the residential fuels tested and EF(RET)s varied significantly among the fuels due to the differences in fuel properties and combustion conditions. EF(RET)s for pine (0.34 ± 0.08 mg/kg) and larch (0.29 ± 0.22 mg/kg) were significantly higher than those of other wood types, including fir and cypress (0.081 ± 0.058 mg/kg). However, EF(RET)s for crop residues varied from 0.048 ± 0.008 to 0.37 ± 0.14 mg/kg and were not significantly lower than those for softwood (0.074 ± 0.026 to 0.34 ± 0.08 mg/kg). The EF(RET)s for coal were very high and ranged from 2.2 ± 1.5 (anthracite briquette) to 187 ± 113 mg/kg (raw bituminous chunk). EF(RET) was positively correlated with EFs of coemitted particulate matter (EF(PM)) and phenanthrene (EF(PHE)) for crop residue and coal, but not for wood. In addition, the ratios of EF(PHE)/EF(RET) and EF(PM)/EF(RET) for coals were much lower than those for crop residues and wood. These data suggest that retene is not a unique PAH marker for softwood combustion and that coal combustion, in particular, should be taken into account when retene is used for PAH source apportionment.     

Modeling climate change mitigation from alternative methods of charcoal production in Kenya

Current carbon accounting methodologies do not accommodate activities that involve emissions reductions from both land-use change and energy production. This paper analyzes the climate change mitigation potential of charcoal production in East Africa by examining the impact of changing both land management and technology. Current production in a major charcoal producing region of Kenya where charcoal is made as a by-product of land clearance for commercial grain production is modeled as the “business-as-usual” scenario. Alternative production systems are proposed based on coppice management of native or exotic trees. Improved kilns are also considered. Changes in aboveground, belowground, and soil carbon are modeled and two distinct baseline assessments are analyzed: one is based on a fixed area of land and one is based on the quantity of non-renewable fuel that is displaced by project activities. The magnitude of carbon emissions reductions varies depending on land management as well as the choice of carbonization technology. However, these variations are smaller than the variations arising from the choice of baseline methodology. The fixed-land baseline yields annualized carbon emission reductions equivalent to 0.5–2.8 tons per year (t y^?1) with no change in production technology and 0.7–3.5 t y^?1 with improved kilns. In contrast, the baseline defined by the quantity of displaced non-renewable fuel is 2–6 times larger, yielding carbon emissions reductions of 1.4–12.9 t y^?1 with no change in production technology and 3.2–20.4 t y^?1 with improved kilns. The results demonstrate the choice of baseline, often a political rather than scientific decision, is critical in assessing carbon emissions reductions.     

Queue Lengths and Waiting Times for Multiserver Queues with Abandonment and Retrials

We consider a Markovian multiserver queueing model with time dependent parameters where waiting customers may abandon and subsequently retry. We provide simple fluid and diffusion approximations to estimate the mean, variance, and density for both the queue length and virtual waiting time processes arising in this model. These approximations, which are generated by numerically integrating only 7 ordinary differential equations, are justified by limit theorems where the arrival rate and number of servers grow large. We compare our approximations to simulations, and they perform extremely well.     

A procedure for the determination of lead in green vegetables at concentrations down to 1 microgram/kg

An accurate and highly sensitive dry ashing/graphite furnace atomic absorption spectrometry (GFAAS) procedure has been developed for the measurement of lead in green vegetables. The accuracy of the method was established by analysis of standard reference materials and confirmed by isotope dilution using an inductively coupled plasma-mass spectrometer (ICP-MS). The limit of detection (wet weight) of the technique is 1-3 micrograms/kg with relative standard deviations ranging from 6% for a sample of kale containing 500 micrograms/kg to 20% for cabbage containing 3.0 micrograms/kg.