A suspect screening analysis for contaminants of emerging concern in municipal wastewater and surface water using liquid-liquid extraction and stir bar sorptive extraction

An iron-based metal-organic framework as a novel dispersive solid-phase extraction sorbent for the efficient adsorption of tetrabromobisphenol A from environmental water samples

For environmental safety, it is important to establish a simple, rapid, and sensitive method for emerging pollutants. Here, a dispersive solid-phase extraction (d-SPE) method based on an iron-based metal-organic framework (Fe-MIL-88-NH₂) combined with high-performance liquid chromatography (HPLC) was developed for tetrabromobisphenol A (TBBPA) in water samples. Fe-MIL-88-NH₂ was synthesized using a solvothermal method and completely characterized. Fe-MIL-88-NH₂ had good water stability and gave a maximum adsorption capacity of 40.97 mg g^-1 for TBBPA. The adsorption of TBBPA on Fe-MIL-88-NH₂ followed Langmuir adsorption models and a pseudo-second-order kinetic model. The bromine ion and the hydroxyl group of TBBPA could form strong hydrogen bond interactions with the amino protons around the cavity of Fe-MIL-88-NH₂, which was in accord with the molecular simulation calculations. Furthermore, several important d-SPE parameters were optimized, such as the amount of materials, extraction time, pH, ionic strength, elution solvent type, and volume. The established method showed good linearity in the concentration range of 0.005-100 μg g^-1 (r² ≥ 0.9996). This method's limits of detection (LOD) and quantification (LOQ) were 0.001 μg g^-1 and 0.005 μg g^-1, respectively. The recoveries in spiked water samples ranged from 87.5% to 104.9%. The proposed method was applied successfully to detect TBBPA in environmental water samples.

Abundance, fate, and effects of pharmaceuticals and personal care products in aquatic environments

Pharmaceuticals and personal care products (PPCPs) are found in wastewater, and thus, the environment. In this study, current knowledge about the occurrence and fate of PPCPs in aquatic systems-including wastewater treatment plants (WWTPs) and natural waters around the world-is critically reviewed to inform the state of the science and highlight existing knowledge gaps. Excretion by humans is the primary route of PPCPs entry into municipal wastewater systems, but significant contributions also occur through emissions from hospitals, PPCPs manufacturers, and agriculture. Abundance of PPCPs in raw wastewater is influenced by several factors, including the population density and demography served by WWTPs, presence of hospitals and drugs manufacturers in the sewershed, disease burden of the population served, local regulations, and climatic conditions. Based on the data obtained from WWTPs, analgesics, antibiotics, and stimulants (e.g., caffeine) are the most abundant PPCPs in raw wastewater. In conventional WWTPs, most removal of PPCPs occurs during secondary treatment, and overall removal exceeds 90% for treatable PPCPs. Regardless, the total PPCP mass discharged with effluent by an average WWTP into receiving waters (7.35-20,160 g/day) is still considerable, because potential adverse effects of some PPCPs (such as ibuprofen) on aquatic organisms occur within measured concentrations found in surface waters.

Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools

Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis two-dimensional gas chromatography (GCxGC)-mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC-MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects.