A novel method for multi-pollutant monitoring in water supply systems using chemical machine vision.

Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over brief intervals. This paper proposes a direct analysis and rapid detection method of three indicators of arsenic, cadmium, and selenium in complex drinking water systems by combining a novel long-path spectral imager with machine learning models. Our technique can obtain multiple parameters in about 1 s. The experiment involved setting up samples from various drinking water backgrounds and mixed groups, totaling 9360 injections. A raw visible light source ranging from 380 to 780 nm was utilized, uniformly dispersing light into the sample cell through a filter. The residual beam was captured by a high-definition camera, forming a distinctive spectrum. Three deep learning models-ResNet-50, SqueezeNet V1.1, and GoogLeNet Inception V1-were employed. Datasets were divided into training, validation, and test sets in a 6:2:2 ratio, and prediction performance across different datasets was assessed using the coefficient of determination and root mean square error. The experimental results show that a well-trained machine learning model can extract a lot of feature image information and quickly predict multi-dimensional drinking water indicators with almost no preprocessing. The model's prediction performance is stable under different background drinking water systems. The method is accurate, efficient, and real-time and can be widely used in actual water supply systems. This study can improve the efficiency of water quality monitoring and treatment in water supply systems, and the method's potential for environmental monitoring, food safety, industrial testing, and other fields can be further explored in the future.

[Application of the China Infectious Diseases Automated-alert and Response System in Guangxi, 2009-2011].

OBJECTIVE: To analyze and further improvement the application of the China Infectious Diseases Automated-alert and Response System (CIDARS) in Guangxi Zhuang Autonomous Region. METHODS: Results related to the amount of signal, proportion of signal responded, time to signal response, manner of signal verification and on each signal of Guangxi in CIDARS from 2009 to 2011 were described. Performance was compared between the periods of pre/ post the adjustment of parameters in CIDARS on December 10, 2010. RESULTS: A total of 29 788 signals were generated on 16 infectious diseases in the system in Guangxi. 100% signals had been responded with the median time to response as 1.5 hours. The average amount of signal per county per week was 1.7;with 624 signals(2.09%)verified as suspected outbreaks preliminarily and 191 outbreaks of 9 diseases were finally confirmed by further field investigation. The sensitivity of CIDARS was 89.25% , and the timeliness of detection was 2.8 d. After adjusting the parameter of CIDARS, the number of signals reduced, and the sensitivity and timeliness of detection improved for most of the diseases. CONCLUSION: The signals of CIDARS were responded timely, and the performance of CIDARS might be improved by adjusting the parameters of early-warning model, which helped enhance the ability of outbreaks-detection for local public health departments. However the current proportion of false positive signals still seemed to be high, suggesting that both the methods and parameters should be improved, according to the characteristics of different diseases.