Two statistical models were used to predict the concentration of dimethyl disulfide (DMDS) released from biosolids produced by an advanced wastewater treatment plant (WWTP) located in Washington, DC, USA. The plant concentrates sludge from primary sedimentation basins in gravity thickeners (GT) and sludge from secondary sedimentation basins in dissolved air flotation (DAF) thickeners. The thickened sludge is pumped into blending tanks and then fed into centrifuges for dewatering. The dewatered sludge is then conditioned with lime before trucking out from the plant. DMDS, along with other volatile sulfur and nitrogen-containing chemicals, is known to contribute to biosolids odors. These models identified oxidation/reduction potential (ORP) values of a GT and DAF, the amount of sludge dewatered by centrifuges, and the blend ratio between GT thickened sludge and DAF thickened sludge in blending tanks as control variables. The accuracy of the developed regression models was evaluated by checking the adjusted R2 of the regression as well as the signs of coefficients associated with each variable. In general, both models explained observed DMDS levels in sludge headspace samples. The adjusted R2 value of the regression models 1 and 2 were 0.79 and 0.77, respectively. Coefficients for each regression model also had the correct sign. Using the developed models, plant operators can adjust the controllable variables to proactively decrease this odorant. Therefore, these models are a useful tool in biosolids management at WWTPs.
Wastewater treatment plants perform an important social function in recycling wastewater solids. Beneficial reuse of these wastes through land application occurs on farms, forests, tree farms, and mine reclamation sites. Additionally, these wastes can be disposed of in landfills and incinerators however these options are less directly helpful to society. Despite the fact that land application of biosolids in beneficial reuse projects is an accepted practice and is in fact the backbone of many biosolids programs in North America, this process is still scrutinized when nuisance odors occur. The wastewater industry has usually taken a reactive approach to biosolids odors, reacting to complaints in the field and doing damage control for complaints received. Research in recent years has led to findings showing that several process parameters at the wastewater treatment plant can have a dramatic affect on the odor quality of the biosolids product. In this paper we present several statistical models that predict biosolids odor levels based on processing and management variables as well as ambient conditions. These models offer biosolids managers a tool to act proactively and predict when odorous materials will be produced at the plant. This information, combined with GIS data for receiving areas offers an opportunity to ensure that biosolids of a certain odor profile are matched with a site suitable to accept the odors without adversely impacting the neighboring communities. We illustrate the usefulness of these models via several realistic scenarios that simulate what the odor impact might be. Results of this study include calibrated models using existing process data and field odor data collected by inspectors for one year. Additionally, this study shows how small changes in process parameters can show dramatic changes in odor quality, and that two small changes, while individually may show minor changes in odor, when combined together can have a much larger effect. The model is run for three scenarios that combine different process changes and compares results to the results of the model using average process data.
Biosolids are being beneficially recycled for agricultural purposes. Often, however, biosolids odors diminish marketability of biosolids, bring community opposition or, in the worst case, cause the banning of biosolids land application programs. This study aims to develop practical biosolids odor predicting models that can be applied to biosolids management on a daily basis using existing data available at wastewater treatment plant as explanatory variables. A number of biosolids odor predicting models are presented. Biosolids producers can use the models to early detect and notify hauling contractors when malodorous biosolids are anticipated. As a result, malodorous products can be allocated accordingly to appropriate sites in preventing odor complaints from the communities.KEYWORDS: biosolids odor predicting model, sensory and analytical measurement.
Practical biosolids odor prediction models were developed to be use by managers for predicting the odor emission rates of biosolids generated daily at the District of Columbia's wastewater treatment plant. These models evaluated daily wastewater treatment process data and predicted the expected biosolids odor emissions levels prior to recycling of this material on land. Biosolids are the treated nutrient rich organic solids generated by the wastewater settling and dewatering processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.