Periphyton is ubiquitous in Florida Everglades and has a profound effect on mercury (Hg) cycling. Enhanced methylmercury (MeHg) production in periphyton has been well documented, but the re-distribution of MeHg from periphyton remains unknown. In this study, periphyton, sediments, surface water, periphyton overlying water, and periphyton porewater were collected from Everglades for analyzing the distribution of MeHg and total Hg (THg). Results showed that there were no significant differences in THg and MeHg in different types of periphyton, but they all displayed higher MeHg levels than sediments. MeHg distribution coefficients (logkd) in periphyton were lower than in sediments, suggesting that periphyton MeHg could be more labile entering aquatic cycling and bioaccumulation. In water, the more the distance of water samples taken from periphyton, the lower the MeHg and dissolved organic carbon concentrations were detected. In extracellular polymeric substances of periphyton, MeHg in colloidal fractions was significantly higher than that in capsular fractions. It was estimated that approximately 10% (or 1.35 kg) of periphyton MeHg were passed on to mosquitofish entering the food web during wet season, contributing 73% of total Hg stocked in mosquitofish. These results revealed the importance of periphyton on water MeHg distribution and MeHg bioaccumulation in Everglades.

Raised-bed plasticulture, an intensive production system used around the world for growing high-value crops (e.g., fresh market vegetables), faces a water-food nexus that is actually a food-water-energy-land-economic nexus. Plasticulture represents a multibillion dollar facet of the United States crop production value annually and must become more efficient to be able to produce more on less land, reduce water demands, decrease impacts on surrounding environments, and be economically-competitive. Taller and narrower futuristic beds were designed with the goal of making plasticulture more sustainable by reducing input requirements and associated wastes (e.g., water, nutrients, pesticides, costs, plastics, energy), facilitating usage of modern technologies (e.g., drip-based fumigation), improving adaptability to a changing climate (e.g., flood protection), and increasing yield per unit area.Compact low-input beds were analyzed against conventional beds for the plasticulture production of tomato (Solanum lycopersicum), an economically-important crop, using a systems approach involving field measurements, vadose-zone modeling (HYDRUS), and production analysis. Three compact bed geometries, 61cm (width)× 25cm (height), 45cm× 30cm, 41cm× 30cm, were designed and evaluated against a conventional 76cm× 20cm bed. A two-season field study was conducted for tomato in the ecologically-sensitive and productive Everglades region of Florida. Compact beds did not statistically impact yield and were found to reduce: 1) production costs by $150–$450/ha; 2) leaching losses by up to 5% (1cm/ha water, 0.33kg/ha total nitrogen, 0.05kg/ha total phosphorus); 3) fumigant by up to 47% (48kg/ha); 4) plasticulture's carbon footprint by up to 10% (1711kg CO2-eq/ha) and plastic waste stream by up to 13% (27kg/ha); 5) flood risks and disease pressure by increasing field's soil water storage capacity by up to 33% (≈1cm); and 6) field runoff by 0.48–1.40cm (51–76%) based on HYDRUS model simulations of 10-year, 2-h storm events in other major tomato production regions of California and Virginia.Re-designing the bed geometries in plasticulture production systems to be more compact is an example of win-win production optimization not only for traditional farms in rural areas but also for urban and peri-urban farms which are located closer to city centers. Compact beds could enable more plants per unit area, thus requiring less land area for the same production. Needing less area facilitates urban and peri-urban farming where land values can be high. Urban and peri-urban farming has several benefits, including reductions in transportation energy as production is closer to market and the ability for city wastewater to be reused for irrigation instead of freshwater withdrawals. Compact beds allow plasticulture to have smaller water, chemical, energy, carbon, waste, and economic footprints without impacting production. Improving agricultural systems in this way could enhance economic and environmental viability, which is essential for a sustainable food-water-energy-land-economic nexus.

Urban areas often face versatile stressors (e.g., food security, congestion, energy shortage, water pollution, water scarcity, waste management, and storm and flooding), requiring better resilient and sustainable infrastructure systems. A system dynamics model (SDM), explored for the urban region of Orlando, Florida, acts as a multi-agent model for portraying material and energy flows across the food, energy, water, and waste (FEWW) sectors to account for urban sustainability transitions. The interlinkages between the FEWW sectors in the SDM are formulated with multiple layers of dependencies and interconnections of the available resources and their external climatic, environmental, and socioeconomic drivers through four case studies (scenarios). The vital components in the integrated FEWW infrastructure system include urban agriculture associated with the East End Market Urban Farm; energy from the fuel-diverse Curtis H. Stanton Energy Center; reclaimed wastewater treated by the Eastern Water Reclamation Facility, the Water Conserv II Water Reclamation Facility, and stormwater reuse; and solid waste management and biogas generation from the Orange County Landfill. The four scenarios evaluated climate change impacts, policy instruments, and land use teleconnection for waste management in the FEWW nexus, demonstrating regional synergies among these components. The use of multicriteria decision-making coupled with cost-benefit-risk tradeoff analysis supported the selection of case 4 as the most appropriate option as it provided greater renewable energy production and stormwater reuse. The SDM graphic user interface aids in the visualization of the dynamics of the FEWW nexus framework, demonstrating the specific role of renewable energy harvesting for sustainably transitioning Orlando into a circular economy.

The U.S. Food and Drug Administration (FDA) has defined standards for the microbial quality of agricultural surface water used for irrigation. According to the FDA produce safety rule (PSR), a microbial water quality profile requires analysis of a minimum of 20 samples for Escherichia coli over 2 to 4 years. The geometric mean (GM) level of E. coli should not exceed 126 CFU/100 mL, and the statistical threshold value (STV) should not exceed 410 CFU/100 mL. The water quality profile should be updated by analysis of a minimum of five samples per year. We used an extensive set of data on levels of E. coli and other fecal indicator organisms, the presence or absence of Salmonella, and physicochemical parameters in six agricultural irrigation ponds in West Central Florida to evaluate the empirical and theoretical basis of this PSR. We found highly variable log-transformed E. coli levels, with standard deviations exceeding those assumed in the PSR by up to threefold. Lognormal distributions provided an acceptable fit to the data in most cases but may underestimate extreme levels. Replacing censored data with the detection limit of the microbial tests underestimated the true variability, leading to biased estimates of GM and STV. Maximum likelihood estimation using truncated lognormal distributions is recommended. Twenty samples are not sufficient to characterize the bacteriological quality of irrigation ponds, and a rolling data set of five samples per year used to update GM and STV values results in highly uncertain results and delays in detecting a shift in water quality. In these ponds, E. coli was an adequate predictor of the presence of Salmonella in 150-mL samples, and turbidity was a second significant variable. The variability in levels of E. coli in agricultural water was higher than that anticipated when the PSR was finalized, and more detailed information based on mechanistic modeling is necessary to develop targeted risk management strategies.