This study evaluated the field-scale performance of an amorphous iron hydroxide (Fe(OH)3)-based desulfurizing agent for the removal of sulfur-based odorous compounds emitted from wastewater treatment facilities, including equalization tanks and sludge dewatering unit facilities. Hydrogen sulfide (H2S), methyl mercaptan (MM), dimethyl sulfide (DMS), and dimethyl disulfide (DMDS), which account for over 60~80% of total odor impact in such facilities, were targeted in this research. A drytype adsorption system packed with porous amorphous Fe(OH)3 was installed at a wastewater treatment plant and operated continuously for 45 days. Odorous gas concentrations were measured before and after treatment using portable analyzers and gas chromatography-pulsed flame photometric detector (GC-PFPD). The desulfurizing agent demonstrated a high H2S removal efficiency of over 99.9%, even under high inlet concentrations exceeding 500 ppm. Physicochemical analyses including XRD, XRF, EDS and BET confirmed that the material was amorphous, possessed a high surface area (243.4 m2/g), and exhibited a mesoporous structure favorable for gas adsorption. Hysteresis observed in nitrogen adsorption isotherms indicated a bottleneck-shaped pore structure, which enhances adsorption of odorous gases and removal efficiency. Notably, the system maintained stable performance under varying humidity without significant degradation.

Measuring service quality and related key dimensions has been an important problem in Marketing. In this research, we would introduce a smart methodological framework to efficiently identify refined, key sentiment dimensions for measuring the service quality using both traditional survey and unstructured online reviews (natural survey). The proposed framework consists of three parts: (1) steps for preprocessing the unstructured reviews to generate attribute-level sentiments for independent variables (2) Bayesian regression to efficiently identify key groups of correlated attributes and (3) post-hoc analysis for identifying dimensions from the selected groups of correlated attributes and predicting dimension-level effects. Note, the first part of the framework (i.e., preprocessing) is not required for analyzing traditional surveys. Our framework provides two sets of complementing results such as attribute-level effects under the identified dimensions and aggregate dimension-level effects. In the first application study to traditional SERVQUAL data, we successfully validated the proposed framework by comparing the results between our framework and three commonly used existing methods of regression, lasso regression, and factor analysis. In the second empirical application study with the online reviews from a major game review website, STEAM platform, we found that our framework provided a significantly reduced number of key dimensions which were surprisingly efficient for predicting and explaining the service quality ratings, compared with the same set of compared methods in the first study plus the topic model. In particular, with reviews of 2,825 games, three key dimensions of Mechanical playability, Fun in fantasy and Money for value were identified, and we also found that the Mechanical playability could be an important driver of game popularity.