Odor compounds released from different zones of two adjacent waste treatment facilities: Interactive influence and source identification

A critical review on odor measurement and prediction

Odor pollution has become a global environmental issue of increasing concern in recent years. Odor measurements are the basis of assessing and solving odor problems. Olfactory and chemical analysis can be used for odor and odorant measurements. Olfactory analysis reflects the subjective perception of human, and chemical analysis reveals the chemical composition of odors. As an alternative to olfactory analysis, odor prediction methods have been developed based on chemical and olfactory analysis results. The combination of olfactory and chemical analysis is the best way to control odor pollution, evaluate the performances of the technologies, and predict odor. However, there are still some limitations and obstacles for each method, their combination, and the prediction. Here, we present an overview of odor measurement and prediction. Different olfactory analysis methods (namely, the dynamic olfactometry method and the triangle odor bag method) are compared in detail, the latest revisions of the standard olfactometry methods are summarized, and the uncertainties of olfactory measurement results (i.e., the odor thresholds) are analyzed. The researches, applications, and limitations of chemical analysis and odor prediction are introduced and discussed. Finally, the development and application of odor databases and algorithms for optimizing odor measurement and prediction methods are prospected, and a preliminary framework for an odor database is proposed. This review is expected to provide insights into odor measurement and prediction.

Prediction for odor gas generation from domestic waste based on machine learning

Domestic waste is prone to produce a variety of volatile organic compounds (VOCs), which often has unpleasant odors. A key process in treating odor gases is predicting the production of odors from domestic waste. In this study, four factors of domestic waste (weight, wet composition, temperature, and fermentation time) were adopted to be the prediction indicators in the prediction for domestic waste odor gases. Machine learning models (Random Forest, XGBoost, LightGBM) were established using the odor intensity values of 512 odor gases from domestic waste. Based on these data, the regression prediction with supervised machine learning was achieved, in which three different algorithmic models were evaluated for prediction performance. A Random Forest model with a R² value of 0.8958 demonstrated the most accurate prediction of the production of domestic waste odor gas based on our data. Furthermore, the prediction results in the Random Forest model were further discussed based on the microbial fermentation of domestic waste. In addition to enhancing our knowledge of the production of odor from domestic waste, we also explore the application of machine learning to odor pollution in our study.

Investigation of health risk assessment and odor pollution of volatile organic compounds from industrial activities in the Yangtze River Delta region, China

To investigate composition characteristics and assess occupational health risks and odor pollution of volatile organic compounds (VOCs) from industrial activities in the Yangtze River Delta (YRD) region, China, one-year field measurements of VOCs were conducted simultaneously at an iron and steel industrial park (ISP), one chemical industrial park (CMP) and one petrochemical industrial park (PCP) from September, 2018 to August, 2019. The concentrations of VOCs were 80.2 ± 67.9 ppbv, 28.1 ± 27.2 ppbv and 144 ± 378 ppbv for ISP, CMP and PCP, respectively. Aromatics, alkanes and alkenes were the major components of VOCs at ISP, CMP and PCP, respectively. Moreover, the toluene to benzene ratios were 0.330 ± 0.302, 4.31 ± 6.48 and 1.84 ± 3.34, which generally showed the characteristics of combustion source for ISP, industrial activities for CMP and petrochemical industry for PCP, respectively. The hazard index values were 0.752 ± 0.438, 0.108 ± 0.248 and 0.090 ± 0.260 at ISP, CMP and PCP, which were generally lower than threshold limit value, suggesting a low noncarcinogenic risk for workers. Meanwhile, the 95th percentile LCR values of VOCs were 8.76 × 10^-5, 1.15 × 10^-5 and 1.00 × 10^-5 at ISP, CMP and PCP, respectively, which were also under acceptable risk level, indicating a low carcinogenic risk. Benzene and 1,3-butadiene were main harmful substances for both noncarcinogenic and carcinogenic risks of VOCs. The odor levels of VOCs were 2.12 ± 4.21, 12.5 ± 28.7 and 1.01 ± 7.84 at ISP, CMP and PCP, respectively. Aromatics for ISP and sulfide compounds for CMP and PCP were primary pollutants for odor pollution. This work could improve the understanding of risk levels and odor characteristics of VOCs and benefit policy development on alleviating odor complaints and health risks for workers in YRD region, China.

Real-time measurements of landfill atmospheric ammonia using mobile white cell differential optical absorption spectroscopy system and engineering applications

The real-time measurement of atmospheric ammonia at municipal solid waste (MSW) landfills and adjacent areas is necessary for landfill management and the health of nearby residence. Continuous, fast, and real-time monitoring of landfill odor gases is a challenge, especially for ammonia. To our knowledge, this was the first study for the characteristics and seasonal variabilities of atmospheric ammonia at a whole landfill using a Mobile White cell Differential Optical Absorption Spectroscopy (MW-DOAS) system, which also simultaneously offers high sensitivity and fast response. Results show that atmospheric ammonia levels at various landfill areas were significantly dependent on the characteristics of areas, such as municipal solid waste-related areas, leachate-related areas, sludge-related areas, and fly ash-related area, the atmospheric ammonia peak or average level at the active leachate pool of the active MSW site was the highest among all areas of the whole landfill, and the ammonia concentrations at the closed MSW landfill sites were low and dependent on the ages. Moreover, it was found that the seasonal variabilities of ammonia concentrations at most of those areas were significantly dependent on the ambient temperature, and ambient temperature variation caused the atmospheric ammonia level at the active leachate pool and active MSW landfill site in the summer survey to raise 3.5 times and 5.58 times than in the winter survey, respectively. Implications: Continuous, fast, and real-time monitoring ambient ammonia at or nearby a landfill is critical for landfill operators and local EPAs. This study demonstrates that the mobile White cell Differential Optical Absorption Spectroscopy (MW-DOAS) system is an effective tool for real-time monitoring ambient ammonia of a whole landfill. The results in this article provided a guideline to the characteristics and seasonal changes of ambient ammonia at various types of areas of a whole landfill as well as the impact of age to ambient ammonia at the closed landfill areas.

Trace gas emissions from municipal solid waste landfills: A review

Trace gas emissions from municipal solid waste (MSW) landfills have received increasing attention in recent years. This paper reviews literature published between 1983 and 2019, focusing on (i) the origin and fate of trace gas in MSW landfills, (ii) sampling and analytical techniques, (iii) quantitative emission measurement techniques, (iv) concentration and surface emission rates of common trace compounds at different landfill units and (v) the environmental and health concerns associated with trace gas emissions from MSW landfills. Trace gases can be produced from waste degradation, direct volatilisation of chemicals in waste products or from conversions/reactions between other compounds. Different chemical groups dominate the different waste decomposition stages. In general, organic sulphur compounds and oxygenated compounds are connected with fresh waste, while abundant hydrogen sulphide, aromatics and aliphatic hydrocarbons are usually found during the methane fermentation stage. Selection of different sampling, analytical and emission rate measurement techniques might generate different results when quantifying trace gas emission from landfills, and validation tests are needed to evaluate the reliability of current methods. The concentrations of trace gases and their surface emission rates vary largely from site to site, and fresh waste dumping areas and uncovered waste surfaces are the most important fugitive emission sources. The adverse effects of trace gas emission are not fully understood, and more emission data are required in future studies to assess quantitatively their environmental impacts as well as health risks.

Odor composition analysis and odor indicator selection during sewage sludge composting

On the basis of total temperature increase, normal dehydration, and maturity, the odor compositions of surface and internal piles in a well-run sewage sludge compost plant were analyzed using gas chromatography-mass spectrometry with a liquid nitrogen cooling system and a portable odor detector. Approximately 80 types of substances were detected, including 2 volatile inorganic compounds, 4 sulfur organic compounds, 16 benzenes, 27 alkanes, 15 alkenes, and 19 halogenated compounds. Most pollutants were mainly produced in the mesophilic and pre-thermophilic periods. The sulfur volatile organic compounds contributed significantly to odor and should be controlled primarily. Treatment strategies should be based on the properties of sulfur organic compounds. Hydrogen sulfide, methyl mercaptan, dimethyl disulfide, dimethyl sulfide, ammonia, and carbon disulfide were selected as core indicators. Ammonia, hydrogen sulfide, carbon disulfide, dimethyl disulfide, methyl mercaptan, dimethylbenzene, phenylpropane, and isopentane were designated as concentration indicators. Benzene, m-xylene, p-xylene, dimethylbenzene, dichloromethane, toluene, chlorobenzene, trichloromethane, carbon tetrachloride, and ethylbenzene were selected as health indicators. According to the principle of odor pollution indicator selection, dimethyl disulfide was selected as an odor pollution indicator of sewage sludge composting. Monitoring dimethyl disulfide provides a highly scientific method for modeling and evaluating odor pollution from sewage sludge composting facilities.

IMPLICATIONS

Composting is one of the most important methods for sewage sludge treatment and improving the low organic matter content of many agricultural soils. However, odors are inevitably produced during the composting process. Understanding the production and emission patterns of odors is important for odor control and treatment. Core indicators, concentration indicators, and health indicators provide an index system to odor evaluation. An odor pollution indicator provides theoretical support for further modelling and evaluating odor pollution from sewage sludge composting facilities.