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Title:   Author  item
                                            ROMERO-FLORES, A. - University Of Marylanditem
                                            MCCONNELL, L.L. - University Of Marylanditem
                                            Hapeman, Cathleenitem
                                            RAMERIZ, M. - Collaboratoritem
                                            TORRENTS, A. - University Of Maryland
  Submitted to: American Chemical Society National Meeting
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 6/15/2016
                                    Publication  Date: 8/21/2016
                                    Citation: Romero-Flores, A., Mcconnell, L., Hapeman, C.J., Rameriz, M., Torrents, A. 2016. Improving continuous monitoring OF VOC’s emissions from alternative fertilizers. American Chemical Society National Meeting. AGRO 134.Interpretive Summary:
                                Technical Abstract: Application of alternative fertilizers, such as biosolids, to agricultural fields is an environmentally-beneficial practice. Concerns regarding nuisance odors caused by specific volatile organic compounds (VOC) have lead to public opposition and may ultimately lead to lack of acceptance of biosolids management programs. Electronic nose sensors are designed to detect differences in complex air sample matrices and have been used in the food industry to monitor process performance and quality control. However, no information is available on the application of sensor arrays to monitor process performance of biosolids treatment processes. The objective of this work, therefore, was to examine the feasibility of an electronic nose to discriminate between treatment conditions of alkaline stabilized biosolids and to explore its performance by comparison with quantitative analysis of key odorants. Seven quick lime treatment rates from 0 to 30% (w/w) were prepared and the off gas was monitored by with an electronic nose with ten metal oxide sensors. Pattern recognition models were created from the electronic nose data using linear discriminant analysis (LDA) and principal component analysis (PCA). LDA performed better than PCA and showed clear discrimination when single tests were evaluated, although when the full data set was included in the pattern creation, the discrimination of classes (lime dosages) was not as clear as with single test data. These limitations are found to be due to the intrinsic variability of the wastewater treatment process and the low specificity of the sensor evaluated. The electronic nose was able to discriminate from 0%, 15% and 30% lime and, to some extent 25% lime. These data suggest that under-dosed and over-dosed classes may be created to support the alkaline stabilization process of biosolids, assuming that the typical 15% lime is the dosage needed to safely achieve a pH of 12 required for 99% pathogen reduction.


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