Odor concentration (OC) prediction based on odor activity values (OAVs) during composting of solid wastes and digestates

• Published in: Atmospheric environment (1994) 2019-03, Vol.201, p.1-12
• Main Authors: Rincón, Carlos Andrés, De Guardia, Amaury, Couvert, Annabelle, Wolbert, Dominique, Le Roux, Sophie, Soutrel, Isabelle, Nunes, Guillaume
• Format: Article
• Language: English
• Subjects: Analytical methods; Chemical Sciences; Dynamic olfactometry; Odor activity values; Odor detection thresholds; Odor monitoring; Partial least squares regression
• ISSN: 1352-2310; 1873-2844
• DOI: 10.1016/j.atmosenv.2018.12.030

Odor monitoring plays a crucial role in implementing suitable odor mitigation strategies at composting plants. Odor activity value (OAV) analysis is an instrumental-based approach that could simplify and reduce costs of odor monitoring by dynamic olfactometry; yet, the relationship between both methodologies has not been fully explored. In this study, the OAV feasibility to predict odor concentration (OC in OUE m−3) was assessed during the active composting phase of six solid wastes and three digestates at pilot scale. To this end, 92 gas samples were analyzed by analytical methods (i.e., GC/MS) and dynamic olfactometry (EN 13725). For 22 quantified odorants, OAV was calculated as the ratio of a compound's chemical concentration to its corresponding odor detection threshold (ODT). OAVs were then correlated to OC by simple linear and partial least squares (PLS) regressions. The sum of all OAVs in the gas samples (OAVsum) and the maximum OAV (OAVmax) yielded moderately strong linear correlations against OC (R2: 0.67–0.73), thus providing overall insight into OC trend along composting. A PLS model consisting of weighted OAVs of 10 odorants enhanced OC predictions. OAVs explained from 76% to 74% of the OC variance throughout the PLS model validation. Furthermore, OC values regressed by the PLS model were less underestimated (7%) than those predicted by OAVmax and OAVsum (11–13%). Based on results from the OAVmax and PLS regression analysis, hydrogen sulfide, methanethiol, dimethyl sulfide, and dimethyl disulfide were highlighted as the major odor contributors during composting of organic substrates. [Display omitted] •Gas emissions from composting were analyzed by sensory and analytical methods.•OAVs provide overall insight into OC trend (R2: 0.67–0.73).•A PLS regression model enhanced OC predictions through OAVs (R2: 0.74–0.76).•Volatile sulfur compounds were the major odor contributors during composting.