Application of Adjoint Approach to Oil Spill Problems

Adjoint method is applied to various oil spill problems. A three-dimensional model for describing the dispersion of a quasi-passive substance (a pollutant or a nutrient) and its adjoint model are considered in a limited sea region. Direct and adjoint estimates are used to get dual (equivalent) estimates of the mean concentration of the substance in important zones of the region. The role of dual estimates is illustrated with a few examples. They include such oil spill problems as the search of the most dangerous point of the oil tanker route, the oil dispersion with a climatic velocity, and the dependence of the oil concentration estimates on the oil spill rate. One more example is the application of optimal bioremediation strategy for cleaning a few zones polluted by oil. In this case, instead of oil, the model describes the dispersion of a nutrient released to marine environment. Balanced, unconditionally stable second-order finite-difference schemes based on the splitting method for the solution of the dispersion model and its adjoint are suggested. The main and adjoint difference schemes are compatible in the sense that at every fractional step of the splitting algorithm, the one-dimensional split operators of both schemes satisfy a discrete form of Lagrange identity. In the special unforced and non-dissipative case, each scheme has two conservation laws. Every split one-dimensional problem is solved by Thomas’ factorization method.     

Antibiotic resistance and plasmid profiling of <Emphasis Type="Italic">Vibrio</Emphasis> spp. in tropical waters of Peninsular Malaysia

Vibrio species isolated from four different sampling stations in the west coast of Peninsular Malaysia were screened for their antimicrobial resistance and plasmid profiles. A total of 138 isolates belonging to 15 different species were identified. Vibrio campbellii, V. parahaemolyticus, V. harveyi, and V. tubiashii were found to predominance species at all stations. High incidence of erythromycin, ampicillin, and mecillinam resistance was observed among the Vibrio isolates. In contrast, resistance against aztreonam, cefepime, streptomycin, sulfamethoxazole, and sulfonamides was low. All the Vibrio isolates in this study were found to be susceptible to imipenem, norfloxacin, ofloxacin, chloramphenicol, trimethoprim/sulfamethoxazole, and oxytetracycline. Ninety-five percent of the Vibrio isolates were resistant to one or more different classes of antibiotic, and 20 different resistance antibiograms were identified. Thirty-two distinct plasmid profiles with molecular weight ranging from 2.2 to 24.8 kb were detected among the resistance isolates. This study showed that multidrug-resistant Vibrio spp. were common in the aquatic environments of west coast of Peninsular Malaysia.     

A moving target—incorporating knowledge of the spatial ecology of fish into the assessment and management of freshwater fish populations

Freshwater fish move vertically and horizontally through the aquatic landscape for a variety of reasons, such as to find and exploit patchy resources or to locate essential habitats (e.g., for spawning). Inherent challenges exist with the assessment of fish populations because they are moving targets. We submit that quantifying and describing the spatial ecology of fish and their habitat is an important component of freshwater fishery assessment and management. With a growing number of tools available for studying the spatial ecology of fishes (e.g., telemetry, population genetics, hydroacoustics, otolith microchemistry, stable isotope analysis), new knowledge can now be generated and incorporated into biological assessment and fishery management. For example, knowing when, where, and how to deploy assessment gears is essential to inform, refine, or calibrate assessment protocols. Such information is also useful for quantifying or avoiding bycatch of imperiled species. Knowledge of habitat connectivity and usage can identify critically important migration corridors and habitats and can be used to improve our understanding of variables that influence spatial structuring of fish populations. Similarly, demographic processes are partly driven by the behavior of fish and mediated by environmental drivers. Information on these processes is critical to the development and application of realistic population dynamics models. Collectively, biological assessment, when informed by knowledge of spatial ecology, can provide managers with the ability to understand how and when fish and their habitats may be exposed to different threats. Naturally, this knowledge helps to better evaluate or develop strategies to protect the long-term viability of fishery production. Failure to understand the spatial ecology of fishes and to incorporate spatiotemporal data can bias population assessments and forecasts and potentially lead to ineffective or counterproductive management actions.     

Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF,PCFA and UNMIX receptor models

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM_2.5 and PM_2.5?10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m³ for PM_2.5 and 331.36, 190.01, and 184.60 μg/m³ for PM_2.5?10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM_2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM_2.5?10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM_2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM_2.5?10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.     

Characterizing spatial structure of sediment E. coli populations to inform sampling design

Escherichia coli can persist in streambed sediments and influence water quality monitoring programs through their resuspension into overlying waters. This study examined the spatial patterns in E. coli concentration and population structure within streambed morphological features during baseflow and following stormflow to inform sampling strategies for representative characterization of E. coli populations within a stream reach. E. coli concentrations in bed sediments were significantly different (p?=?0.002) among monitoring sites during baseflow, and significant interactive effects (p?=?0.002) occurred among monitoring sites and morphological features following stormflow. Least absolute shrinkage and selection operator (LASSO) regression revealed that water velocity and effective particle size (D ₁₀) explained E. coli concentration during baseflow, whereas sediment organic carbon, water velocity and median particle diameter (D ₅₀) were important explanatory variables following stormflow. Principle Coordinate Analysis illustrated the site-scale differences in sediment E. coli populations between disconnected stream segments. Also, E. coli populations were similar among depositional features within a reach, but differed in relation to high velocity features (e.g., riffles). Canonical correspondence analysis resolved that E. coli population structure was primarily explained by spatial (26.9–31.7 %) over environmental variables (9.2–13.1 %). Spatial autocorrelation existed among monitoring sites and morphological features for both sampling events, and gradients in mean particle diameter and water velocity influenced E. coli population structure for the baseflow and stormflow sampling events, respectively. Representative characterization of streambed E. coli requires sampling of depositional and high velocity environments to accommodate strain selectivity among these features owing to sediment and water velocity heterogeneity.