Abstract
The development of low-cost sensors, the introduction of technical performance specifications, and increasingly effective machine learning algorithms for managing big data have led to a growing interest in the use of instrumental odor monitoring systems (IOMS) for odor measurements from industrial plants. The classification and quantification of odor concentration are the main goals of IOMS installed inside industrial plants in order to identify the most important odor sources and to assess whether the regulatory thresholds have been exceeded. This paper illustrates the use of two machine learning algorithms applied to the concurrent classification and quantification of odors. Random Forest was employed, which is a machine learning algorithm that thus far has not been used in the field of odor quantification and classification for complex industrial situations. Furthermore, the results were compared with commonly used algorithms in this field, such as artificial neural network (ANN), which was here employed in the form of a deep neural network. Both techniques were applied to the data collected from an IOMS installed for fenceline monitoring at a wastewater treatment plant. Cohen’s kappa and Normalized RMSE are used as specifical performance indicators for classification and regression: the indicators were calculated for the test dataset, and the results were compared with data in the literature obtained in contexts of similar complexity. A Cohen’s kappa of 97% was reached for the classification task, while the best Normalized RMSE, namely 4%, for the interval 20–2435 ouE/m3 was obtained with Random Forest.
Highlights
Odors are a relevant component of atmospheric pollution and an important indicator of the health of urban areas close to urban wastewater treatment plants (WWTPs) [1]
It was decided to use features that were between rank 1 and rank 8 of the Recursive Feature Elimination algorithm with cross-validation (RFECV) algorithm to have a balance between the number of selected features, which we set to 12
The results show that it is possible, even for complex situations, to develop field instrumental odor monitoring applications enhanced by machine learning (ML) algorithms, which are capable of simultaneously performing classification and regression, with interesting practical applications
Summary
Odors are a relevant component of atmospheric pollution and an important indicator of the health of urban areas close to urban wastewater treatment plants (WWTPs) [1]. No legislation regulating odor, with specific requirements on odor emissions from sources, is in force in Europe, America, or Australia, various regions within certain member states in the EU have implemented their own regulations in order to define discharge limits from sources and how odor pollution issues for urban WWTPs should be managed [2,3,4] In such a complex and socially sensitive context due to the various reports of bad odors in the proximity of urban WWTPs, the contribution of citizen science for identifying odor emissions [5,6,7] and the electronic nose or Instrumental Odor Monitoring System (IOMS) [8] are powerful tools to help policy makers and environmental protection agencies 4.0/). The rapid development of low-cost sensors, which allows for air pollution monitoring at a lower cost and with a higher spatial density than reference measurement methods, the work undertaken by the Technical Committee CEN/TC 264 “Air quality” and the relative working group (WG41) for defining technical specifications for the performance evaluation of air quality sensors, and the development of the high-speed communications network (5G) are the most significant recent developments for the field of IOMS hardware
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