Turbidity Level Prediction Based on Suspended Particle Counting Through Image Processing Approach

Abstract

Monitoring of pollutant concentrations in surface water becomes a concern, considering the utilization of surface water as the raw water for drinking water treatment plants (WTP). The fluctuation of pollutant concentrations in surface water can affect the performance of WTP. This research was conducted to assess the potential for turbidity level prediction based on the calculation of the number and surface area of suspended particles through a digital image processing approach. Measurements of the amount and surface area were carried out in the form of laboratory-scale experiments using the open source software ImageJ 1.46r. The algorithm in ImageJ can convert pixels into a number “value” and surface area through a series of digital image processing steps, henceforth compared with the existing measurement method. The results showed that there was a strong correlation between the number of particles and the concentration of formazine suspension (r = 0.9821), but does not apply to the surface area. Referring to the results of laboratory experiments, it can be concluded that the approach to measure the number of suspended particles can be the basis for predicting the turbidity level in the turbidity range 100-800 NTU, but does not apply to the turbidity range 0.02-20 NTU.

Limiting Factors of Simultaneous Measurement Method for Turbidity and Total Suspended Solids Based on Image Processing Approaches

“…Measurement of turbidity level based on image processing is a hot topic of discussion. Measurement of color intensity differences and edge detection techniques (particle counting) are two approaches studied in previous studies [1,2]. A 3D image recognition-based approach has also been carried out to differentiate turbidity caused by abiotic and biotic suspensions [3].…”