## 2 Materials and Methods  
### System Architecture and Design Methodology  
Figure 1 shows the structure and working principle of a CSO. Under dry weather flow conditions, wastewater flows in the direction of the green arrow direction underneath the screen and downstream of the CSO. In a storm event, the water level will rise within the chamber. If the storm is intense or long enough, then the water level can reach the weir crest to spill through the screen (see the white arrows). The unwanted solids and floatables are trapped on the lower surface of the screen while wastewater carrying a reduced amount of solids is discharged through the outlet pipe into the receiving watercourse (see red arrow). In order to monitor the condition of a CSO screen, an acoustic measurement system is proposed. The general system architecture of this system is described in Figure 2. The system consists of four microphones, speaker, temperature sensor and data acquisition unit. The speaker is connected to a power amplifier and a low-pass filter (LPF) with a cut-off frequency of 1000 Hz. Each of the four microphones is integrated with an amplifier and a band pass filter (BPF). This will ensure that the right spectrum of the excited signal is emitted and the desired frequency band of output signal is recorded in the frequency range between 100 and 1000 Hz. The system is battery operated, but it can be adapted to work with an energy harvesting system (e.g., solar panel or wind turbine). For the safety of operation, as methane gas may be produced or other volatile vapours (from fuel spills) may be present in the CSO chamber, Zener diode barriers were installed to prevent the system from any surge in electrical current incident leading to a spark. This proposed system is based on an acoustic approach [18; 19] developed for the purpose of sewer inspection and adapted in our work for screen condition monitoring. The method is based on measuring the reverberant sound field in the CSO chamber, which is affected by debris accumulating on the CSO screen. Figure 2 illustrates the block diagram of the proposed approach to better explain the signal processing algorithm adopted in this work. As can be seen, an acoustic sensor is installed in a manhole chamber and a 10 s sinusoidal sweep signal (50 Hz to 7500 Hz) is sent out through the speaker. The reflected signal is then captured by four microphones and sent to the data acquisition unit (DAU) to be digitized and recorded. The acquired data are then stored on a data logger, which is regularly collected for off-line data analysis. In the signal analysis process, the recorded four channel microphone acoustic signals are deconvolved to obtain the acoustic impulse response of the chamber, \(s_{j}(t),\ j=1,2,3\), 4 which is filtered through a band pass filter (100 to 1000 Hz) and then converted to the time-dependent sound pressure level (SPL) [18; 19]:  
\[L_{j}(t_{n})=10\log_{10}\left\{\frac{1}{\tau}\int\limits_{t_{n}}^{t_{n}+\tau}s_{ j}^{2}(t)dt\right\} \tag{1}\]  
where the integration constant \(\tau=3\) msec and \(t_{n}=\tau n,\ n=0,\ldots,N\). The choice of this frequency range can be explained by the fact that the selected speaker was only able to produce sounds efficiently at frequencies above 100 Hz. This dictated the low-frequency limit we adopted. At frequencies above 1000 Hz, the attenuation in the chamber was too high because of the relatively high moisture level in the air. As a result, it was difficult to measure the sound pressure level over a large enough dynamic range at frequencies above 1000 Hz. Therefore, 1000 Hz was adopted as the upper frequency limit in the analysis described in the following text.