Characterization of odour emissions in a wastewater treatment plant using a drone-based chemical sensor system

Burgués, Javier; Doñate, Silvia; Esclapez, María Deseada; Saúco, Lidia; Marco, Santiago

doi:10.1016/j.scitotenv.2022.157290

Cited by 18 publications

(10 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As highlighted by Burgués et al [ 40 ], a common challenge in machine-learning-based prediction lies in the limitations of examples available in the training data.…”

Section: Discussionmentioning

confidence: 99%

Classification in Early Fire Detection Using Multi-Sensor Nodes—A Transfer Learning Approach

Vorwerk,

Kelleter,

Müller

et al. 2024

Sensors

View full text Add to dashboard Cite

Effective early fire detection is crucial for preventing damage to people and buildings, especially in fire-prone historic structures. However, due to the infrequent occurrence of fire events throughout a building’s lifespan, real-world data for training models are often sparse. In this study, we applied feature representation transfer and instance transfer in the context of early fire detection using multi-sensor nodes. The goal was to investigate whether training data from a small-scale setup (source domain) can be used to identify various incipient fire scenarios in their early stages within a full-scale test room (target domain). In a first step, we employed Linear Discriminant Analysis (LDA) to create a new feature space solely based on the source domain data and predicted four different fire types (smoldering wood, smoldering cotton, smoldering cable and candle fire) in the target domain with a classification rate up to 69% and a Cohen’s Kappa of 0.58. Notably, lower classification performance was observed for sensor node positions close to the wall in the full-scale test room. In a second experiment, we applied the TrAdaBoost algorithm as a common instance transfer technique to adapt the model to the target domain, assuming that sparse information from the target domain is available. Boosting the data from 1% to 30% was utilized for individual sensor node positions in the target domain to adapt the model to the target domain. We found that additional boosting improved the classification performance (average classification rate of 73% and an average Cohen’s Kappa of 0.63). However, it was noted that excessively boosting the data could lead to overfitting to a specific sensor node position in the target domain, resulting in a reduction in the overall classification performance.

show abstract

“…As highlighted by Burgués et al [ 40 ], a common challenge in machine-learning-based prediction lies in the limitations of examples available in the training data.…”

Section: Discussionmentioning

confidence: 99%

Classification in Early Fire Detection Using Multi-Sensor Nodes—A Transfer Learning Approach

Vorwerk,

Kelleter,

Müller

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In biological reactors, which qualify as the third zone, there are numerous metabolic processes that take place under various conditions (aerobic and anaerobic) in the presence of microorganisms. These processes are often accompanied by the release of odorous compounds, mainly ammonia and hydrogen sulphide [ 18 ]. Their emissions can be reduced by bacterial strains that have the ability to neutralise selected groups of odorous compounds [ 19 ].…”

Section: Wastewater Treatment Plant As a Source Of Malodorous Substan...mentioning

confidence: 99%

“…One possible solution to this problem is a “sniffing drone”. Burgues [ 18 ] suggests using an “eNose” mounted on a drone for obtaining spatial measurements from wastewater treatment plants. To take a measurement, this device flies to the selected measurement location, where it remains hovering for about 5 min, during which time it takes continuous measurements that allow the variability of emissions from the source under study to be assessed.…”

Section: Odour Measuring Techniquesmentioning

confidence: 99%

“…To take a measurement, this device flies to the selected measurement location, where it remains hovering for about 5 min, during which time it takes continuous measurements that allow the variability of emissions from the source under study to be assessed. This innovative method represents an important step in the aspect of measuring odour emissions from wastewater treatment plants and can also contribute to improving the quality of life for people living near these facilities [ 18 ].…”

Section: Odour Measuring Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Wastewater Treatment Plants as a Source of Malodorous Substances Hazardous to Health, Including a Case Study from Poland

Czarnota

Masłoń

Pajura

2023

IJERPH

View full text Add to dashboard Cite

Using Poland as an example, it was shown that 41.6% of the requests for intervention in 2016–2021 by Environmental Protection Inspections were related to odour nuisance. Further analysis of the statistical data confirmed that approximately 5.4% of wastewater treatment plants in the group of municipal facilities were subject to complaints. Detailed identification of the subject of odour nuisance at wastewater treatment plants identified hydrogen sulphide (H2S), ammonia (NH3) and volatile organic compounds (VOCs) as the most common malodorous substances within these facilities. Moreover, the concentrations of hydrogen sulphide and ammonia exceed the reference values for some substances in the air (0.02 mg/m3 for H2S and 0.4 mg/m3 for NH3). A thorough assessment of the properties of these substances made it clear that even in small concentrations they have a negative impact on the human body and the environment, and their degree of nuisance is described as high. In the two WWTPs analysed in Poland (WWTP 1 and WWTP 2), hydrogen sulphide concentrations were in the range of 0–41.86 mg/m3 (Long-Term Exposure Limit for H2S is 7.0 mg/m3), ammonia 0–1.43 mg/m3 and VOCs 0.60–134.79 ppm. The values recognised for H2S cause lacrimation, coughing, olfactory impairment, psychomotor agitation, and swelling of the cornea with photophobia. Recognition of the methods used in practice at WWTPs to reduce and control malodorous emissions indicates the possibility of protecting the environment and human health, but these solutions are ignored in most facilities due to the lack of requirements specified in legislation.

show abstract

“…In the literature, the majority of the research was focused on determining the source's odor emissions. [4][5][6]9,11,12,14,[17][18][19][20] In the present study, odor pollution was evaluated with a special focus on the behavior and physiological discomfort of individuals residing in the affected area. By drawing attention to odor pollution, which is an important cause of complaints by individuals, the study highlights the importance of supporting the measures to be taken to improve air quality, with social assessments and air quality dispersion modeling.…”

Section: Introductionmentioning

confidence: 99%

Assessment of environmental odor pollution using a dispersion model in an industrialized urban area of Kocaeli, Turkey

Ergün Yüksel,

Çetin Doğruparmak,

Pekey

et al. 2024

CLEAN Soil Air Water

View full text Add to dashboard Cite

The main goal of the present study was to highlight how crucial odor evaluation methods may be for raising the general standard of living in a region. For this, the dispersion of odor concentrations detected by an olfactometric method in an area of Kocaeli, Turkey, where odor problems are frequently experienced, was investigated using the AERMOD atmospheric dispersion modeling system. According to the findings of the olfactometric measurement, the mean odor concentrations in the industrial area was high at 1342 OU m–3, while in the residential areas the values were lower, at 578 OU m–3 within the selected study area. As a result of the modeling study, the maximum odor concentration was 2081 OU m–3 at the 1 h. The model also showed that the coordinate points where the maximum odor concentration was observed changed with time, and that the concentrations recorded at the 1 h may decrease by as much as 88%–91% at the 24 h. In the study, a survey was also conducted to identify possible effects of odor pollution on people. The survey findings showed a statistically significant difference (p < 0.05) between the two areas (industrial and residential) for seven variables of a total 28. We suggest that measuring odor concentrations, modeling their distribution, and conducting surveys are effective methods when managing the urban planning process.

show abstract

Characterization of odour emissions in a wastewater treatment plant using a drone-based chemical sensor system

Cited by 18 publications

References 28 publications

Classification in Early Fire Detection Using Multi-Sensor Nodes—A Transfer Learning Approach

Classification in Early Fire Detection Using Multi-Sensor Nodes—A Transfer Learning Approach

Wastewater Treatment Plants as a Source of Malodorous Substances Hazardous to Health, Including a Case Study from Poland

Assessment of environmental odor pollution using a dispersion model in an industrialized urban area of Kocaeli, Turkey

Contact Info

Product

Resources

About