Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms.

Remote sensing has long been an effective method for water quality monitoring because of its advantages such as high coverage and low consumption. For non-optically active parameters, traditional empirical and analytical methods cannot achieve quantitative retrieval. Machine learning has been gradually used for water quality retrieval due to its ability to capture the potential relationship between water quality parameters and satellite images. This study is based on Sentinel-2 images and compared the ability of four machine learning algorithms (eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Network (ANN)) to retrieve chlorophyll-a (Chl-a), dissolved oxygen (DO), and ammonia-nitrogen (NH3-N) for inland reservoirs. The results indicated that XGBoost outperformed the other three algorithms. We used XGBoost to reconstruct the spatial-temporal patterns of Chl-a, DO, and NH3-N for the period of 2018-2020 and further analyzed the interannual, seasonal, and spatial variation characteristics. This study provides an efficient and practical way for optically and non-optically active parameters monitoring and management at the regional scale.

Discoxenus Wasmann new to China with description of a new species (Coleoptera: Staphylinidae: Aleocharinae).

The termitophilous genus Discoxenus Wasmann, 1904 (Aleocharini: Compactopediina) is recorded from China for the first time and a new species from Guangdong is described under the name: Discoxenus shenzhenensis sp. nov.

Quantitative, morphological, and structural analysis of Ni incorporated with struvite during precipitation.

Struvite precipitation is a promising strategy for the simultaneous recovery of nitrogen and phosphorus from waste streams. However, waste streams typically contain high amounts of metal contaminants, including Ni, which can be easily sequestered by struvite, but the behavior of Ni during struvite precipitation remains unclear. Thus, this study investigates the influence of Ni concentrations on struvite precipitation. The quantitative X-ray diffraction (QXRD) results revealed that the purity of struvite decreased from 96.6 to 41.1% with the Ni concentrations increased from 0.1-100 mg·L-1. At lower Ni concentrations of 0.1-1 mg·L-1, scanning electron microscopy (SEM) showed a roughened surface of struvite crystal, and this was combined with X-ray absorption near edge structure (XANES) data that indicated a stack of Ni-OH and Ni-PO4 on struvite surface. At Ni concentrations of 10-25 mg·L-1, Ni primarily crystalized as Ni-struvite (NiNH4PO4·6H2O), as detected by QXRD. At higher Ni concentrations of 25-100 mg·L-1, the co-precipitation of amorphous Ni phosphate(s) (e.g., Ni3(PO4)2) and Ni hydroxide (e.g., Ni(OH)2) was identified by XANES. Specifically, the X-ray photoelectron spectroscopy (XPS) analysis detected the formation of amorphous Mg hydroxide(s) and phosphate(s) at Ni of 25-100 mg·L-1. The overall results revealed that Ni formed Ni-OH and Ni-PO4 on struvite surface at 0.1-1 mg·L-1, whereas Ni precipitated as separated phases (e.g. Ni-struvite, Ni hydroxide and phosphate) at 10-100 mg·L-1. The existence of Ni disturbed the crystal growth of struvite and promoted the formation of Ni-struvite, amorphous products during struvite formation.

Exposure to perfluoroalkyl substances in waste recycling workers: Distributions in paired human serum and urine.

Occupational exposure to per- and polyfluoroalkyl substances (PFASs) is an emerging public health concern, because of their potential adverse health effects. In this study, concentrations of 21 legacy and alternative PFASs in paired serum and urine samples collected from 163 workers (from five waste recycling plants) were analyzed. The results showed that the average concentration of 21 PFASs in urine samples (66.6 ng mL-1) were higher than in serum (31.3 ng mL-1). Concentrations of perfluorocarboxylates (PFCAs) in urine were also considerably higher than perfluorosulfonates (PFSAs), especially for short-chain PFCAs. Demographic factors (such as sex, age, working age, and job assignment) on PFAS exposure were also assessed based on the obtained results. PFAS concentrations in serum samples from males were significantly higher than in females, and working age was positively (p < 0.05) associated with most PFAS serum levels. Higher levels of PFAS were found in sorters than in workers with other job assignments, such as managers, suggesting that sorters may be directly exposed to PFASs. Interestingly, perfluorophosphonates (PFPAs) were first to be detected in human urine with >80% detection frequency. The average level of three PFPAs in the serum (7.58 ng mL-1) and urine (1.45 ng mL-1) samples appeared to be higher in comparison with most PFCAs and PFSAs. Thus, the toxicity of PFPAs in human beings needs to be further studied.

Effects of temperature and relative humidity on soil-air partition coefficients of organophosphate flame retardants and polybrominated diphenyl ethers.

The soil-air partition coefficients (KSA) of polybrominated diphenyl ethers (PBDEs) and organophosphate flame retardants (OPFRs) is important for determining their fate in soil and air media. However, KSA values of OPFRs and PBDEs are not available from the current literature, and the effects of environmental factors such as temperature and relative humidity (RH) on KSA values are not clear. In this study, a solid-phase fugacity meter was used to measure the KSA values of PBDEs and OPFRs at different temperatures (25, 30, 35, 40, and 45 °C) and relative humidity (RH) conditions (<3 and 100% RH), the relationships between KSA and octanol-air partition coefficients (KOA) for OPFRs and PBDEs were analyzed. The results showed that an increase in temperature and RH resulted in a decrease of all KSA values for PBDEs and OPFRs. Furthermore, the effects of RH on the soil-air partitioning behavior of PBDEs were larger than that of OPFRs. In addition, a significant correlation (p < 0.0001) was observed between log KSA and log KOA. The experimental KSA values of OPFRs and PBDEs were quite different from the predicted KSA, when calculated with their KOA values. Overall, this study provides a better understanding for predicting the behavior and fate of OPFRs and PBDEs in soil-air systems.

Distribution characteristics of per- and polyfluoroalkyl substances (PFASs) in human urines of acrylic fiber plant and chemical plant.

Per- and polyfluoroalkyl substances (PFASs) are persistent and bioaccumulative substances that have many adverse effects on human bodies. This study investigated the PFASs distribution characteristics in urine samples of workers from an acrylic fiber plant and a chemical plant. It was found that perfluorobutanoic acid (PFBA) was the predominant PFASs both in urine samples from the chemical plant (detection frequency: 86.52%; median value: 39.01 ng/mL) and the acrylic fiber plant (detection frequency: 88.16%; median value: 44.36 ng/mL). Meanwhile, perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) were detected with very low frequencies and low concentrations. Furthermore, the results showed that PFASs levels in urine samples of workers from different units of the plants were quite different. PFASs concentrations of urine samples in males were higher than those in females, especially for PFBA, PFHxA, and PFDoA. The age had limited effects on the PFASs distribution in urine samples in this study, as short-chain PFASs were the dominant compounds. The correlations between PFASs concentrations in urine and gender/ages of workers were finally analyzed by Pearson correlation. The overall results may indicate that short-chain PFASs (such as: PFBA and PFBS) were becoming dominant for human exposure, especially occupational workers.

Characteristics of spatial and seasonal bacterial community structures in a river under anthropogenic disturbances.

In this study, the seasonal characteristics of microbial community compositions at different sites in a river under anthropogenic disturbances (Maozhou River) were analyzed using Illumina HiSeq sequencing. Taxonomic analysis revealed that Proteobacteria was the most abundant phylum in all sites, followed by Actinobacteria, Bacteroidetes, Chloroflexi, Acidobacteria and Firmicutes. The variations of the community diversities and compositions between the seasons were not significant. However, significant differences between sites as well as water and sediment samples were observed. These results indicated that sites under different levels of anthropogenic disturbances have selected distinct bacterial communities. pH, dissolved oxygen (DO), concentrations of total nitrogen (TN) and heavy metals were the main factors that influence the diversity and the composition of bacterial community. Specifically, the relative abundance of Proteobacteria was negatively correlated with pH and DO and positively correlated with TN, while Actinobacteria and Verrucomicrobia showed the opposite pattern. Moreover, positive correlations between the relative abundances of Firmicutes and Bacteroidetes and the concentration of heavy metals were also found. Results of functional prediction analysis showed no significant differences of the carbon, nitrogen and phosphorus metabolism across the sites and seasons. Potential pathogens such as Vibrio, Arcobacter, Acinetobacter and Pseudomonas were found in these samples, which may pose potential risks for environment and human health. This study reveals the effect of anthropogenic activities on the riverine bacterial community compositions and provides new insights into the relationships between the environmental factors and the bacterial community distributions in a freshwater ecosystem under anthropogenic disturbances.