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利用仪器化恶臭监测系统对垃圾处理厂场界恶臭排放进行实时监测:重点关注培训方法。

Real-Time Monitoring of Odour Emissions at the Fenceline of a Waste Treatment Plant by Instrumental Odour Monitoring Systems: Focus on Training Methods.

机构信息

Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy.

出版信息

Sensors (Basel). 2024 May 29;24(11):3506. doi: 10.3390/s24113506.

Abstract

Waste treatment plants (WTPs) often generate odours that may cause nuisance to citizens living nearby. In general, people are becoming more sensitive to environmental issues, and particularly to odour pollution. Instrumental Odour Monitoring Systems (IOMSs) represent an emerging tool for continuous odour measurement and real-time identification of odour peaks, which can provide useful information about the process operation and indicate the occurrence of anomalous conditions likely to cause odour events in the surrounding territories. This paper describes the implementation of two IOMSs at the fenceline of a WTP, focusing on the definition of a specific experimental protocol and data processing procedure for dealing with the interferences of humidity and temperature affecting sensors' responses. Different approaches for data processing were compared and the optimal one was selected based on field performance testing. The humidity compensation model developed proved to be effective, bringing the IOMS classification accuracy above 95%. Also, the adoption of a class-specific regression model compared to a global regression model resulted in an odour quantification capability comparable with those of the reference method (i.e., dynamic olfactometry). Lastly, the validated models were used to process the monitoring data over a period of about one year.

摘要

污水处理厂(WTP)经常会产生气味,可能会对附近的居民造成滋扰。总的来说,人们对环境问题越来越敏感,尤其是对气味污染。仪器化气味监测系统(IOMS)代表了一种新兴的连续气味测量和实时识别气味峰值的工具,它可以提供有关工艺运行的有用信息,并指示可能导致周边地区出现气味事件的异常情况的发生。本文描述了在污水处理厂围栏处实施的两个 IOMS,重点介绍了针对湿度和温度干扰传感器响应的特定实验方案和数据处理程序的定义。比较了不同的数据处理方法,并根据现场性能测试选择了最佳方法。开发的湿度补偿模型证明是有效的,将 IOMS 的分类准确性提高到 95%以上。此外,与全局回归模型相比,采用特定于类别的回归模型可以实现与参考方法(即动态嗅闻法)相当的气味量化能力。最后,验证后的模型用于处理大约一年的监测数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4287/11175214/0a6386d9fea4/sensors-24-03506-g0A1.jpg

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