Department of Mining and Civil Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.
Department of Information and Communications Technologies, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.
Sensors (Basel). 2020 Oct 1;20(19):5631. doi: 10.3390/s20195631.
Local administrations are increasingly demanding real-time continuous monitoring of pollution in the sanitation system to improve and optimize its operation, to comply with EU environmental policies and to reach European Green Deal targets. The present work shows a full-scale Wastewater Treatment Plant field-sampling campaign to estimate COD, BOD, TSS, P, TN and NON in both influent and effluent, in the absence of pre-treatment or chemicals addition to the samples, resulting in a reduction of the duration and cost of analysis. Different regression models were developed to estimate the pollution load of sewage systems from the spectral response of wastewater samples measured at 380-700 nm through multivariate linear regressions and machine learning genetic algorithms. The tests carried out concluded that the models calculated by means of genetic algorithms can estimate the levels of five of the pollutants under study (COD, BOD5, TSS, TN and NON), including both raw and treated wastewater, with an error rate below 4%. In the case of the multilinear regression models, these are limited to raw water and the estimate is limited to COD and TSS, with less than a 0.5% error rate.
地方当局越来越要求对卫生系统中的污染进行实时连续监测，以改善和优化其运行，遵守欧盟环境政策，并实现欧洲绿色协议的目标。本工作展示了一个全面的污水处理厂现场采样活动，以估算进水和出水的 COD、BOD、TSS、P、TN 和 NON，而不对样品进行预处理或添加化学物质，从而减少了分析的时间和成本。通过多元线性回归和机器学习遗传算法，开发了不同的回归模型，以根据在 380-700nm 波长下测量的废水样品的光谱响应来估算污水系统的污染负荷。所进行的测试得出的结论是，通过遗传算法计算的模型可以估算五种研究污染物（COD、BOD5、TSS、TN 和 NON）的水平，包括原水和处理后的废水，误差率低于 4%。对于多元线性回归模型，这些模型仅限于原水，并且估计仅限于 COD 和 TSS，误差率小于 0.5%。
