The ability of 5 Best Management Practice (BMP) allocation methods that consider 8 pre-selected BMPs, to control 4 Nonpoint Source (NPS) constituents in 4 watersheds with contrasting land covers, is investigated. The methods range from random selection of BMPs on randomly selected sites, to optimized selection of BMPs at optimized locations, and the land covers range from natural to ultra-urban. The optimization methods rely on Genetic Algorithms (GA), and a method that uses expert systems is also applied. Watershed hydrologic and water quality response models are developed, using the Soil Water Assessment Tool (SWAT), to compute baseline outputs from the 4 study watersheds without BMPs, and to obtain predicted reductions in NPS constituent outputs when BMPs are implemented in accordance with the 5 allocation plans. Methods used to represent BMPs in SWAT and to speed up optimization processes are also presented. Results indicate that the most computationally intensive methods produce the best results across landscape types. Results also show that opportunities exist for less intensive methods, particularly in less-built environments. For these, however, siting BMPs to hotspots remains an important requirement. The need to select the most appropriate BMP for each implementation site is observed to increase with the level of urbanization of the landscape. Results indicate that optimized selection of BMPs, sited at optimized locations, results in the highest-performing BMP allocation plans across landscape types. Also, the focus on hotspots has the advantage of resulting in BMP plans requiring involvement of fewer stakeholders than when BMPs are located in non-hotspot zones. This targeted hotspot approach could help reduce cost and increase efficiency of implementation.

Quantification of in-vivo biomolecule mass transport and reaction rate parameters from experimental data obtained by Fluorescence Recovery after Photobleaching (FRAP) is becoming more important. The Osborne-Moré extended version of the Levenberg-Marquardt optimization algorithm was coupled with the experimental data obtained by the Fluorescence Recovery after Photobleaching (FRAP) protocol, and the numerical solution of a set of two partial differential equations governing macromolecule mass transport and reaction in living cells, to inversely estimate optimized values of the molecular diffusion coefficient and binding rate parameters of GFP-tagged glucocorticoid receptor. The results indicate that the FRAP protocol provides enough information to estimate one parameter uniquely using a nonlinear optimization technique. Coupling FRAP experimental data with the inverse modeling strategy, one can also uniquely estimate the individual values of the binding rate coefficients if the molecular diffusion coefficient is known. One can also simultaneously estimate the dissociation rate parameter and molecular diffusion coefficient given the pseudo-association rate parameter is known. However, the protocol provides insufficient information for unique simultaneous estimation of three parameters (diffusion coefficient and binding rate parameters) owing to the high intercorrelation between the molecular diffusion coefficient and pseudo-association rate parameter. Attempts to estimate macromolecule mass transport and binding rate parameters simultaneously from FRAP data result in misleading conclusions regarding concentrations of free macromolecule and bound complex inside the cell, average binding time per vacant site, average time for diffusion of macromolecules from one site to the next, and slow or rapid mobility of biomolecules in cells. To obtain unique values for molecular diffusion coefficient and binding rate parameters from FRAP data, we propose conducting two FRAP experiments on the same class of macromolecule and cell. One experiment should be used to measure the molecular diffusion coefficient independently of binding in an effective diffusion regime and the other should be conducted in a reaction dominant or reaction-diffusion regime to quantify binding rate parameters. The method described in this paper is likely to be widely used to estimate in-vivo biomolecule mass transport and binding rate parameters.

Groundwater is the main source of irrigation and residential use in the Eastern Shore Maryland, which is experiencing challenges regarding overuse, saltwater intrusion, and diminishing productivity. The Chesapeake Bay is also facing the problem of water pollution due to pollutant loading from agricultural fields and wastewater treatment plants (WWTPs). Using recycled water for irrigation has the potential to alleviate the pressure on groundwater and reduce pollutant loading. The objective of this study was to develop a decision tool to explore the use of recycled water for agricultural irrigation in Maryland using Multicriteria Decision Analysis (MCDA) integrated with Geographical Information Systems (GIS). Four main evaluation criteria were included in the GIS-MCDA framework: agricultural land cover, climate, groundwater vulnerability, and characteristics of the WWTPs as sources of recycled water. Groundwater vulnerability zones were developed using the groundwater well density, water extraction data, and the aquifer information. Then, the most suitable areas for irrigation using recycled water were identified. About 13.5% and 32.9% of agricultural land was, respectively, found to be “highly” and “moderately” suitable for irrigation with recycled water when WWTPs were categorized based on their treatment process information. The results provide a useful decision tool to promote the use of recycled water for agricultural irrigation.