Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance.
- Klíčová slova
- Artificial intelligence, Contaminants of emerging concern, High-resolution mass spectrometry, PPCPs, Quantitative structure retention relationship, Suspect and non-targeted screening, Wastewater-based epidemiology,
- MeSH
- chemické látky znečišťující vodu * analýza MeSH
- kosmetické přípravky * analýza MeSH
- léčivé přípravky analýza MeSH
- monitorování životního prostředí * metody MeSH
- odpadní voda * chemie analýza MeSH
- umělá inteligence * MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- Názvy látek
- chemické látky znečišťující vodu * MeSH
- kosmetické přípravky * MeSH
- léčivé přípravky MeSH
- odpadní voda * MeSH
Finding a cost-effective, efficient, and environmentally friendly technique for the removal of mercury ion (Hg2+) in water and wastewater can be a challenging task. This paper presents a novel and efficient adsorbent known as the graphene oxide-Cu2SnS3-polyaniline (GO-CTS-PANI) nanocomposite, which was synthesised and utilised to eliminate Hg2+ from water samples. The soft-soft interaction between Hg2+ and sulphur atoms besides chelating interaction between -N and Hg2+ is the main mechanism for Hg2+ adsorption onto the GO-CTS-PANI adsorbent. Various characterisation techniques, including Fourier transform infrared spectrophotometry (FT-IR), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), elemental mapping analysis, and X-ray diffraction analysis (XRD), were employed to analyse the adsorbent. The Box-Behnken method, utilising Design Expert Version 7.0.0, was employed to optimise the crucial factors influencing the adsorption process, such as pH, adsorbent quantity, and contact time. The results indicated that the most efficient adsorption occurred at pH 6.5, with 12 mg of GO-CTS-PANI adsorbent, and 30-min contact time that results in a maximum removal rate of 95% for 50 mg/L Hg2+ ions. The study also investigated the isotherm and kinetics of the adsorption process that the adsorption of Hg2+ onto the adsorbent happened in sequential layers (Freundlich isotherm) and followed by the pseudo-second-order kinetic model. Furthermore, response surface methodology (RSM) analysis indicates that pH is the most influential parameter in enhancing adsorption efficiency. In addition to traditional models, this study employed some artificial intelligence (AI) methods including the Random Forest algorithm to enhance the prediction of adsorption process efficiency. The findings demonstrated that the Random Forest algorithm exhibited high accuracy with a correlation coefficient of 0.98 between actual and predicted adsorption rates. This study highlights the potential of the GO-CTS-PANI nanocomposite for effectively removing of Hg2+ ions from water resources.
- Klíčová slova
- Adsorption, Artificial intelligence, Graphene oxide-Cu2SnS3-PANI, Mercury ion, Response surface methodology,
- MeSH
- adsorpce MeSH
- aniliny * chemie MeSH
- chemické látky znečišťující vodu * chemie MeSH
- čištění vody metody MeSH
- grafit * chemie MeSH
- kinetika MeSH
- měď chemie MeSH
- nanokompozity * chemie MeSH
- rtuť * chemie MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- aniliny * MeSH
- chemické látky znečišťující vodu * MeSH
- grafit * MeSH
- graphene oxide MeSH Prohlížeč
- měď MeSH
- polyaniline MeSH Prohlížeč
- rtuť * MeSH
