Odorous compound emissions and odor complaints from the public are rising concerns for agricultural, industrial, and water resource recovery facilities (WRRFs) near urban areas. Many facilities are deploying sensors that measure malodorous compounds and other factors related to odor creation and dispersion. Focusing on the Metropolitan Water Reclamation District of Greater Chicago's (MWRDGCs) Thornton Composite Reservoir (7.9 billion gallon capacity), we used meteorological, operational, and H2S sensor data to train a 3-day advance-warning predictor of local odor complaints, so as to implement targeted odor prevention measures. Using a machine learning approach, we bypassed difficulties in modeling both physical dispersion and human perception of odors. Utilizing random forest algorithms with varied settings and input attributes, we find that a small network of H2S sensors, meteorological data, and operational data are able to predict odor complaints three days in advance with greater than 60% accuracy and less than 25% false-positive rates, exceeding MWRDGC's standards required for full-scale deployment. PRACTITIONER POINTS: A random forest algorithm trained on H S, weather, and operations data successfully predicted odor complaints surrounding a large composite reservoir. Thirty-two data attribute combinations were tested. It was found that H S sensor data alone are insufficient for predicting odor complaints. The best predictor was a Random Forest Classifier trained on weather, operational, and H S readings from the reservoir corner locations. This study demonstrates odor complaint prediction capability utilizing a limited set of data sources and open-source machine learning techniques. Given a small network of H S sensors and organized data management, WRRFs and similar facilities can conduct advance-warning odor complaint prediction.
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http://dx.doi.org/10.1002/wer.1191 | DOI Listing |
Medicine (Baltimore)
December 2024
Department of Periodontology, Necmettin Erbakan University Faculty of Dentistry, Konya, Turkey.
Halitosis is defined as an unpleasant odor emanating from the oral cavity and has social and economic effects. Halitosis is a common complaint in individuals with periodontal disease, but limited data are available. The aim of this study is to evaluate self-reported halitosis and related conditions.
View Article and Find Full Text PDFWiad Lek
December 2024
UNIVERSITY OF PRESOV, PRESOV, SLOVAK REPUBLIC.
Objective: Aim: Investigation of hyperproliferative diseases of the female genital organs as a consequence of mixed urogenital infections.
Patients And Methods: Materials and Methods: The study included 56 women of reproductive age who experienced discomfort in the external genital area in the form of excessive vaginal discharge and/or unpleasant odour of the discharge, itching in the external genital area (main group). The control group consisted of 30 somatically and gynaecologically healthy patients.
Oral Health Prev Dent
December 2024
Purpose: To evaluate the effect of a fresh-breath mild effervescent tablet on halitosis as an alternative to mouthwash.
Materials And Methods: Halitosis is the unpleasant and offensive odour emanating from the oral cavity (bad breath), which is linked to the presence of volatile sulphur compounds (VSCs). A randomised, single-blind, controlled clinical trial was conducted with 102 volunteers who had oral complaints (range 18-60 years).
BMC Pediatr
November 2024
Department of Clinical Biochemistry, Institute of Medicine, Maharajgunj Medical Campus, Tribhuvan University, Maharajgunj, Kathmandu, Nepal.
Background: Maple Syrup Urine Disease (MSUD) is a rare inherited disorder of metabolism, which manifests early in life in classical forms. Recurrent illness and exertion aggravate neurotoxicity. This case highlights MSUD diagnosed in association with COVID-19 complications from Nepal.
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