Development of an odor management plan for industrial stationary sources in an urban environment: A case study for Broward County, Florida

Objectionable odors remain at the top of air pollution complaints in urban areas such as Broward County that is subject to increasing residential and industrial developments. The odor complaints in Broward County escalated by 150 percent for the 2001 to 2004 period although the population increased by only 6 percent. It is estimated that in 2010 the population will increase to 2.5 million. Relying solely on enforcing the local odor ordinance is evidently not sufficient to manage the escalating odor complaint trends. An alternate approach similar to odor management plans (OMPs) that are successful in managing major malodor sources such as animal farms is required. ^ This study aims to develop and determine the feasibility of implementing a comprehensive odor management plan (COMP) for the entire Broward County. Unlike existing OMPs for single sources where the receptors (i.e. the complainants) are located beyond the boundary of the source, the COMP addresses a complex model of multiple sources and receptors coexisting within the boundary of the entire county. Each receptor is potentially subjected to malodor emissions from multiple sources within the county. Also, the quantity and quality of the source/receptor variables are continuously changing. ^ The results of this study show that it is feasible to develop a COMP that adopts a systematic procedure to: (1) Generate maps of existing odor complaint areas and malodor sources, (2) Identify potential odor sources (target sources) responsible for existing odor complaints, (3) Identify possible odor control strategies for target sources, (4) Determine the criteria for implementing odor control strategies, (5) Develop an odor complaint response protocol, and (6) Conduct odor impact analyses for new sources to prevent future odor related issues. Geographic Information System (GIS) is used to identify existing complaint areas. A COMP software that incorporates existing United States Environmental Protection Agency (EPA) air dispersion software is developed to determine the target sources, predict the likelihood of new complaints, and conduct odor impact analysis. The odor response protocol requires pre-planning field investigations and conducting surveys to optimize the local agency available resources while protecting the citizen's welfare, as required by the Clean Air Act. ^

Using data from an encounter sampler to model fish dispersal

A method to estimate speed of free-ranging fishes using a passive sampling device is described and illustrated with data from the Everglades, U.S.A. Catch per unit effort (CPUE) from minnow traps embedded in drift fences was treated as an encounter rate and used to estimate speed, when combined with an independent estimate of density obtained by use of throw traps that enclose 1 m2 of marsh habitat. Underwater video was used to evaluate capture efficiency and species-specific bias of minnow traps and two sampling studies were used to estimate trap saturation and diel-movement patterns; these results were used to optimize sampling and derive correction factors to adjust species-specific encounter rates for bias and capture efficiency. Sailfin mollies Poecilia latipinna displayed a high frequency of escape from traps, whereas eastern mosquitofish Gambusia holbrooki were most likely to avoid a trap once they encountered it; dollar sunfish Lepomis marginatus were least likely to avoid the trap once they encountered it or to escape once they were captured. Length of sampling and time of day affected CPUE; fishes generally had a very low retention rate over a 24 h sample time and only the Everglades pygmy sunfish Elassoma evergladei were commonly captured at night. Dispersal speed of fishes in the Florida Everglades, U.S.A., was shown to vary seasonally and among species, ranging from 0· 05 to 0· 15 m s−1 for small poeciliids and fundulids to 0· 1 to 1· 8 m s−1 for L. marginatus. Speed was generally highest late in the wet season and lowest in the dry season, possibly tied to dispersal behaviours linked to finding and remaining in dry-season refuges. These speed estimates can be used to estimate the diffusive movement rate, which is commonly employed in spatial ecological models.