Swimming in ocean water, including ocean water at beaches not impacted by known point sources of pollution, is an increasing health concern. This study was an initial evaluation of the presence of indicator microbes and pathogens and the association among the indicator microbes, pathogens, and environmental conditions at a subtropical, recreational marine beach in south Florida impacted by non-point sources of pollution. Twelve water and eight sand samples were collected during four sampling events at high or low tide under elevated or reduced solar insolation conditions. The analyses performed included analyses of fecal indicator bacteria (FIB) (fecal coliforms, Escherichia coli, enterococci, and Clostridium perfringens), human-associated microbial source tracking (MST) markers (human polyomaviruses [HPyVs] and Enterococcus faecium esp gene), and pathogens (Vibrio vulnificus, Staphylococcus aureus, enterovirus, norovirus, hepatitis A virus, Cryptosporidium spp., and Giardia spp.). The enterococcus concentrations in water and sand determined by quantitative PCR were greater than the concentrations determined by membrane filtration measurement. The FIB concentrations in water were below the recreational water quality standards for three of the four sampling events, when pathogens and MST markers were also generally undetectable. The FIB levels exceeded regulatory guidelines during one event, and this was accompanied by detection of HPyVs and pathogens, including detection of the autochthonous bacterium V. vulnificus in sand and water, detection of the allochthonous protozoans Giardia spp. in water, and detection of Cryptosporidium spp. in sand samples. The elevated microbial levels were detected at high tide and under low-solar-insolation conditions. Additional sampling should be conducted to further explore the relationships between tidal and solar insolation conditions and between indicator microbes and pathogens in subtropical recreational marine waters impacted by non-point source pollution.Global estimates indicate that each year more than 120 million cases of gastrointestinal disease and 50 million cases of severe respiratory diseases are caused by swimming and bathing in wastewater-polluted coastal waters (42). Swimming-related illness is attributed predominantly to exposure to microbial pathogens, which enter the water through point sources, such as sewage outfalls. Water quality at beaches may also be impacted by non-point sources, such as storm water runoff, sand resuspension, animal fecal inputs, and human bather shedding (8,12,22,47,59).The concentration of indicator microorganisms in a body of recreational water is used to estimate the health risk to bathers. These microbes serve as surrogates for microbial pathogens. Studies show that the U.S. Environmental Protection Agency (EPA)-recommended indicator microbe for marine beaches, enterococci, shows a significant correlation with illness in marine beaches impacted by point source pollution (38,54). However, a similar correlation has not been identifi...
A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown disturbances. For example, the exhaust gas recirculation (EGR) operation of a spark ignition (SI) engine is modeled by using such a complex nonlinear discrete-time system. A dual-controller approach is undertaken where primary adaptive critic NN controller is designed for the nonstrict feedback nonlinear discrete-time system whereas the secondary one for the affine nonlinear discrete-time system but the controllers together offer the desired performance. The primary adaptive critic NN controller includes an NN observer for estimating the states and output, an NN critic, and two action NNs for generating virtual control and actual control inputs for the nonstrict feedback nonlinear discrete-time system, whereas an additional critic NN and an action NN are included for the affine nonlinear discrete-time system by assuming the state availability. All NN weights adapt online towards minimization of a certain performance index, utilizing gradient-descent-based rule. Using Lyapunov theory, the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight estimates, and observer estimates are shown. The adaptive critic NN controller performance is evaluated on an SI engine operating with high EGR levels where the controller objective is to reduce cyclic dispersion in heat release while minimizing fuel intake. Simulation and experimental results indicate that engine out emissions drop significantly at 20% EGR due to reduction in dispersion in heat release thus verifying the dual-control approach.
A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility function and control inputs. All NN weights adapt online toward minimization of a performance index, utilizing the gradient-descent-based rule, in contrast with iteration-based adaptive-critic schemes. Lyapunov functions are used to show the stability of the closed-loop tracking error, weights, and observer estimates. Separation and certainty equivalence principles, persistency of excitation condition, and linearity in the unknown parameter assumption are not needed. Experimental results on a spark ignition (SI) engine operating lean at an equivalence ratio of 0.75 show a significant (25%) reduction in cyclic dispersion in heat release with control, while the average fuel input changes by less than 1% compared with the uncontrolled case. Consequently, oxides of nitrogen (NO(x)) drop by 30%, and unburned hydrocarbons drop by 16% with control. Overall, NO(x)'s are reduced by over 80% compared with stoichiometric levels.
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