The monitoring of emerging pollutants in wastewaters is nowadays an issue of special concern, with the classical quantification methods being time and reagent consuming. In this sense, a FTIR transmission spectroscopy based chemometric methodology was developed for the determination of eight of these pollutants. A total of 456 samples were, therefore, obtained, from an activated sludge wastewater treatment process spiked with the studied pollutants, and analysed in the range of 200 cm⁻¹ to 14,000 cm⁻¹. Then, a k-nearest neighbour (kNN) analysis aiming at identifying each sample pollutant was employed. Next, partial least squares (PLS) and ordinary least squares (OLS) modelling approaches were employed in order to obtain suitable prediction models. This procedure resulted in good prediction abilities regarding the estimation of atrazine, desloratadine, paracetamol, β-estradiol, ibuprofen, carbamazepine, sulfamethoxazole and ethynylestradiol concentrations in wastewaters. These promising results suggest this technology as a fast, eco-friendly and reagent free alternative methodology for the quantification of emerging pollutants in wastewaters.

如今，废水中新兴污染物的监测已成为一个特别令人关注的问题，经典的定量方法既费时又费试剂。从这个意义上讲，开发了一种基于FTIR透射光谱的化学计量学方法，用于测定这些污染物中的八种。因此，从掺有所研究污染物的活性污泥废水处理过程中总共获得456个样品，并在200 cm ^-1至14,000 cm ^-1的范围内进行了分析。。然后，进行了旨在识别每种样品污染物的k最近邻（kNN）分析。接下来，采用偏最小二乘（PLS）和普通最小二乘（OLS）建模方法以获得合适的预测模型。该程序对废水中阿特拉津，去氯雷他定，对乙酰氨基酚，β-雌二醇，布洛芬，卡马西平，磺胺甲恶唑和乙炔雌二醇浓度的估计具有良好的预测能力。这些令人鼓舞的结果表明，该技术是一种快速，环保且无试剂的替代方法，可用于量化废水中的新兴污染物。