Oil spills at sea remain a serious threat to coastal settlements and sensitive ecosystems. Although the impacts of spills are contingent upon a variety of environmental factors and the chemical composition of the oil itself, spill effects can be long lasting in the pelagic zone with broad impacts on sensitive bacterial, microbial, plant, and animal communities. Efforts to contain, deflect, protect, and mitigate the effects of oil are increasingly important, given the massive social, economic, and environmental fallout connected to large spills. The purpose of this paper is to provide geographic perspective for protecting coastal areas with exclusion booms during oil spill events. Specifically, we introduce a generalized, extendable, spatial optimization model that simultaneously minimizes spill effects on vulnerable shorelines and the total costs associated with dispatching booms. The multiobjective model is solved with a weighting method to produce a Pareto optimal curve that reveals how the costs and protection operations change under different priorities. A simulated tanker spill near Mobile Bay, AL, USA, is used as an illustrative example.
States rely upon photochemical models to predict the impacts of air quality attainment strategies, but the performance of those predictions is rarely evaluated retrospectively. State implementation plans (SIPs) developed to attain the 1997 U.S. standard for fine particulate matter (PM2.5; denoting particles smaller than 2.5 microns in diameter) by 2009 provide the first opportunity to assess modeled predictions of PM2.5 reductions at the state level. The SIPs were the first to rely upon a speciated modeled attainment test methodology recommended by the U.S. Environmental Protection Agency to predict PM2.5 concentrations and attainment status. Of the 23 eastern U.S. regions considered here, all but one achieved the 15 μg/m3 standard by 2009, and the other achieved it the following year, with downward trends sustained in subsequent years. The attainment tests predicted 2009 PM2.5 design values at individual monitors with a mean bias of 0.38 μg/m3 and mean error of 0.68 μg/m3, and were 95% accurate in predicting whether a monitor would achieve the standard. All of the errors were false alarms, in which the monitor observed attainment after a modeled prediction of an exceedance; in these cases, the states used weight-of-evidence determinations to argue that attainment was likely. Overall, PM2.5 concentrations at monitors in the SIP regions declined by 2.6 μg/m3 from 2000–2004 to 2007–2009, compared with 1.6 μg/m3 in eastern U.S. regions originally designated as attainment. Air quality improvements tended to be largest at monitors that were initially the most polluted.
ImplicationsAs states prepare to develop plans for attaining a more stringent standard for fine particulate matter, this retrospective analysis documents substantial and sustained air quality improvements achieved under the previous standard. Significantly larger air quality improvements in regions initially designated nonattainment of the 1997 standard indicate that this status prompted heightened control efforts. The speciated modeled attainment test is found to be accurate and slightly conservative in predicting particulate concentrations for the cases considered here, providing confidence for its use in upcoming attainment plans. 相似文献
To verify the applicability of identifying Microcystis aeruginosa by matrix-assisted laser desorption-ionization-time-of-flight mass spectrometry (MALDI-TOF MS), mixed and field samples were employed to study the sensitivity and the analysis power, respectively. Series diluted samples and artificially mixed samples by the M. aeruginosa NIES-843 strain were designed to verify the sensitivity. The lowest detection limit was 1.955?×?106 cells in pure samples, while for mixed samples, the lowest detection limit and ratio of NIES-843 strain were 2.88?×?106 cells and 33.7%, respectively. The results provided a reference for the reasonable volume of the water sample in which the M. aeruginosa could be detected. Ribosomal protein biomarkers for identifying M. aeruginosa which were successfully detected from the field samples in Taihu Lake, indicated that the identification of M. aeruginosa by MALDI-TOF MS could be applied in field samples. Furthermore, different genetic types of M. aeruginosa strains were also detected at different locations in Taihu Lake, which revealed the diversity of M. aeruginosa and the detection power of MALDI-TOF MS at the strain level for the field samples. The sensitivity and detection power in the analysis of M. aeruginosa by the MALDI-TOF MS demonstrated the applicability of this method in routine environmental monitoring. 相似文献