[Simultaneous determination of 22 antibiotics in environmental water samples by solid phase extraction-high performance liquid chromatography-tandem mass spectrometry].

The widespread and frequent use of antibiotics to treat diseases or encourage animal growth has resulted in their persistence and accumulation in water, soil, and sediments. As a typical emerging pollutant in the environment, antibiotics have become an important research focus in recent years. Antibiotics are commonly found at trace levels in water environments. Unfortunately, the determination of various types of antibiotics, all of which exhibit different physicochemical properties, remains a challenging endeavor. Thus, developing pretreatment and analytical techniques to achieve the rapid, sensitive, and accurate analysis of these emerging contaminants in various water samples is an essential undertaking.In this paper, a solid phase extraction-high performance liquid chromatography-tandem mass spectrometry (SPE-HPLC-MS/MS) method for the simultaneous determination of 22 antibiotics including 4 penicillins, 12 quinolones and 6 macrolides in environmental water samples was developed. Based on the characteristics of the screened antibiotics and sample matrix, the pretreatment method was optimized, focusing on the SPE column, pH of the water sample, and amount of ethylene diamine tetra-acetic acid disodium (Na2EDTA) added to the water sample. Prior to extraction, a 200 mL water sample was added with 0.5 g of Na2EDTA and pH-adjusted to 3 using sulfuric acid or sodium hydroxide solution. Water sample enrichment and purification were achieved using an HLB column. HPLC separation was carried out on a C18 column (100 mm×2.1 mm, 3.5 µm) via gradient elution with a mobile phase composed of acetonitrile and 0.15% (v/v) formic acid aqueous solution. Qualitative and quantitative analyses were performed on a triple quadrupole mass spectrometer in multiple reaction monitoring mode using an electrospray ionization source. The results showed correlation coefficients greater than 0.995, indicating good linear relationships. The method detection limits (MDLs) and limits of quantification (LOQs) were in the ranges of 2.3-10.7 ng/L and 9.2-42.8 ng/L, respectively. The recoveries of target compounds in surface water at three spiked levels ranged from 61.2% to 157%, with relative standard deviations (RSDs) of 1.0%-21.9%. The recoveries of target compounds in wastewater at three spiked levels were 50.1%-129%, with RSDs of 1.2%-16.9%. The method was successfully applied to the simultaneous determination of antibiotics in reservoir water, surface water, sewage treatment plant outfall, and livestock wastewater. Most of the antibiotics were detected in watershed and livestock wastewater. Lincomycin was detected in 10 surface water samples, with a detection frequency of 90%, and ofloxacin showed the highest contents (127 ng/L) in livestock wastewater. Therefore, the present method exhibits excellent performance in terms of MDLs and recoveries compared with previously reported methods. The developed method presents the advantages of small water sample volumes, wide applicability, and fast analysis times; thus, it can be considered a rapid, efficient, and sensitive analytical method with excellent potential for monitoring emergency environmental pollution. The method could also provide a reliable reference for formulating antibiotic residue standards. The results provide strong support for and an improved understanding of the environmental occurrence, treatment, and control of emerging pollutants.

Exploration of the effect of simultaneous removal of EDCs in the treatment process of different types of wastewater.

The SPE-HPLC-MS/MS method was used to investigate the concentration distribution of nine types of estrogens in 18 locations of pollution source along the Jiuzhou River belonging to river systems in Guangdong province and Guangxi Zhuang autonomous region in China, and the estrogenic activity and potential ecological risks were evaluated by calculating the estradiol equivalency (EEQ). The results showed that the calculated estradiol equivalents (cEEQs) of wastewater treatment plants from 17 locations were all higher than 1 ng/L. To further study the removal effect of the treatment process on the estrogens, the pig breeding wastewater from P4 and the municipal wastewater from P13, as well as the black-odorous water, were sampled and surveyed during the entire process. It turned out that estrogens were effectively removed after nitrification activated sludge treatment. Meanwhile, there was a positive correlation between the removal of NH3-N, total nitrogen (TN) and total phosphorus (TP) and the removal of endocrine disrupting chemicals (EDCs). It is shown in the study the secondary treatment process has achieved a significant effect on the removal of estrogen in both the wet and dry periods and that there has been a positive correlation between the activities of total phosphorus compounds, nitrogen-based compounds and the removal of EDCs.

Robust Feature Matching for 3D Point Clouds with Progressive Consistency Voting.

Feature matching for 3D point clouds is a fundamental yet challenging problem in remote sensing and 3D computer vision. However, due to a number of nuisances, the initial feature correspondences generated by matching local keypoint descriptors may contain many outliers (incorrect correspondences). To remove outliers, this paper presents a robust method called progressive consistency voting (PCV). PCV aims at assigning a reliable confidence score to each correspondence such that reasonable correspondences can be achieved by simply finding top-scored ones. To compute the confidence score, we suggest fully utilizing the geometric consistency cue between correspondences and propose a voting-based scheme. In addition, we progressively mine convincing voters from the initial correspondence set and optimize the scoring result by considering top-scored correspondences at the last iteration. Experiments on several standard datasets verify that PCV outperforms five state-of-the-art methods under almost all tested conditions and is robust to noise, data decimation, clutter, occlusion, and data modality change. We also apply PCV to point cloud registration and show that it can significantly improve the registration performance.