Speciation and persistence of doxycycline in the aquatic environment: Characterization in terms of steady state kinetics

The aim of the present work was to establish the kinetics for the degradation of doxycycline in the aquatic environment with a view to arriving at a kinetic model that can be used to predict the persistence of antibiotic with confidence. The degradation of doxycycline in both water and sediment phases of aquatic microcosm experiments, as well as in distilled water control experiments, was studied over a period of 90 days. An initial 21% loss due to adsorption by the sediment was observed in the microcosm experiment soon after charging. Biphasic zero-order linear rates of degradation, attributed to microbial degradation of the free and sediment or colloidal particle-adsorbed antibiotic, were observed for both water phase (2.3 × 10^?2 and 4.5 × 10^?3 μgg^?1 day^?1) and sediment phase (7.9 × 10^?3 and 1.5 × 10^?3 μgg^?1 day^?1) of the microcosm experiment. The covered distilled water control experiment exhibited a monophasic zero-order linear rate (1.9 × 10^?3 μgg^?1 day^?1) attributed to hydrolysis, while the distilled water experiment exposed to natural light exhibited biphasic liner rates attributed to a combination of hydrolysis and photolysis (2.9 × 10^?3 μgg^?1 day^?1) and to microbial degradation (9.8 × 10^?3 μgg^?1 day^?1). A kinetic model that takes into account hydrolysis, photolysis, microbial degradation as well as sorption/desorption by colloidal and sediment particles is presented to account for the observed zero-order kinetics. The implications of the observed kinetics on the persistence of doxycycline in the aquatic environment are discussed.     

Waste dumpsites and public health: a case for lead exposure in Zimbabwe and potential global implications

Environmental Geochemistry and Health - Most waste sites in Zimbabwe are not sanitary landfills but open dumps that indiscriminately receive waste from municipalities, industries, commercial...     

Optimum forage allocation through chance-constrained programming

Most resource allocation models developed to aid resource planning have been deterministic; that is, the ecosystem and economic variables are assumed to be known with certainty. It is these elements that present problems concerning risk and uncertainty involved in decision making. The objective of this study is to present a mathematical approach for optimizing resource allocation in management situations in which random events occur. This particular technique of decision analysis is chance-constrained programming. The model makes possible the investigation of risk and uncertainty associated with resource management decision making. Range decision-makers must often stock their range before they are sure of the available forage; thus, the amount of available forage is a random element with which managers must contend. The chance-constrained approach to decision making may be used when such random events occur and when it is not possible to plan exactly for future events. Two parameters are used to adjust the mean value of constraints; these are the standard deviation of the constraint value (S_bi) and the probability term which is specified by the manager (K_αi). The mean values are adjusted by the product K_αiS_bi. In the study reported here, a K_αi value of 0.57 gave results which appeared to have usefulness. The results indicate that the penalties a rancher must assume for over-estimating his carrying capacity are greater than the penalties for underestimating the carrying capacity. With the value of the standard deviation used in the example, numbers of livestock and the corresponding net revenues should be about 22% less than those indicated by average forage production. With greater variation in forage production, the reduction would be greater. By using these values, the chance-constrained approach can meaningfully incorporate random variables into a decision model.