Spatial–Temporal Variations for Pollution Assessment of Heavy Metals in Hengshui Lake of China

Paracoccus benzoatiresistens sp. nov., a benzoate resistance and selenite reduction bacterium isolated from wetland

A Gram-stain-negative, aerobic, non-motile, catalase- and oxidase-positive, pale orange, rod-shaped strain EF6T, was isolated from a natural wetland reserve in Hebei province, China. The strain grew at 25-37 °C (optimum, 30 °C), pH 5-9 (optimum, pH 7), and in the presence of 1.0-4.0% (w/v) NaCl (optimum, 2%). A phylogenetic analysis based on 16S rRNA gene sequence revealed that strain EF6T belongs to the genus Paracoccus, and the closest members were Paracoccus shandongensis wg2T with 98.1% similarity, Paracoccus fontiphilus MVW-1 T (97.9%), Paracoccus everestensis S8-55 T (97.7%), Paracoccus subflavus GY0581T (97.6%), Paracoccus sediminis CMB17T (97.3%), Paracoccus caeni MJ17T (97.0%), and Paracoccus angustae E6T (97.0%). The genome size of strain EF6T was 4.88 Mb, and the DNA G + C content was 65.3%. The digital DNA-DNA hybridization, average nucleotide identity, and average amino acid identity values between strain EF6T and the reference strains were all below the threshold limit for species delineation (< 32.8%, < 88.0%, and < 86.7%, respectively). The major fatty acids (≥ 5.0%) were summed feature 8 (86.3%, C18:1 ω6c and/or C18:1 ω7c) and C18:1 (5.0%) and the only isoprenoid quinone was Q-10. The polar lipids consisted of diphosphatidylglycerol, phosphatidylglycerol, two unidentified glycolipids, five unidentified phospholipids, and an unidentified aminolipid. Strain EF6T displays notable resistance to benzoate and selenite, with higher tolerance levels (25 g/L for benzoate and 150 mM for selenite) compared to the closely related species. Genomic analysis identified six benzoate resistance genes (acdA, pcaF, fadA, pcaC, purB, and catA) and twenty selenite resistance and reduction-related genes (iscR, ssuB, ssuD, selA, selD and so on). Additionally, EF6T possesses unique genes (catA, ssuB, and ssuC) absent in the closely related species for benzoate and selenite resistance. Its robust resistance to benzoate and selenite, coupled with its genomic makeup, make EF6T a promising candidate for the remediation of both organic and inorganic pollutants. It is worth noting that the specific resistance phenotypes described above were not reported in other novel species in Paracoccus. Based on the results of biochemical, physiological, phylogenetic, and chemotaxonomic analyses, combined with comparisons of the 16S rRNA gene sequence and the whole genome sequence, strain EF6T is considered to represent a novel species of the genus Paracoccus within the family Rhodobacteraceae, for which the name Paracoccus benzoatiresistens sp. nov. is proposed. The type strain is EF6T (= GDMCC 1.3400 T = JCM 35642 T = MCCC 1K08702T).

Evaluation of water quality using a Takagi-Sugeno fuzzy neural network and determination of heavy metal pollution index in a typical site upstream of the Yellow River

Assessment of river water quality is very important for understanding the impact of human activities on aquatic ecosystems. As the second-largest river in China, the Yellow River's water environment is closely related to the social development and water security of northern China. The Huangshui River is a major tributary of the upper Yellow River, and it supplies water to cities in the lower reaches. In this study, a Takagi-Sugeno (T-S) fuzzy neural network was used to evaluate water quality of the Huangshui River, and pollutant sources were analyzed. The heavy metal pollution index (HPI) was calculated to assess the heavy metal pollution level, and the health risks posed by heavy metal elements were assessed. The results indicated that the main contaminants in the Huangshui River were ammonia nitrogen (NH₃-N) and total phosphorus (TP), which was affected by various activities of industry, agriculture, and urbanization, and the maximum concentration of NH₃-N and TP was 5.90 mg/L and 0.36 mg/L, respectively. The T-S evaluation results of some points in the middle reaches were 3.317 and 3.197, which belonged to Level Ⅳ and the water quality was poor. The concentrations of Cu, Zn and Cr in the river were 0.57-44.58 μg/L, 10-122.50 μg/L and 2-28.67 μg/L, respectively, and they were relatively large. The T-S fuzzy neural network could evaluate water quality, avoiding extreme evaluation results by using fuzzy rules to reduce the influence of pollutant concentrations that are too high or too low. In addition to qualitative categorization of water quality, this approach can also quantitatively assess water quality within a single category. The results of water quality assessment could provide a scientific data support for river management.

Assessment of toxic metal contamination, distribution and risk in the sediments from lagoons used for fish farming in the central region of Peru

Toxic metal contamination, distribution and risk were evaluated in the sediments of three lagoons used for fish farming in the central region of Peru. The distribution of toxic metals in the sediment was in the following descending order of Zn > V > Ni > Cu > Pb > As > Cr > Co > Cd > Sb. Contamination factor (Cf) and geoaccumulation index (I_geo) values for Co, Cr, Cu, Ni, Pb, Sb, V and Zn indicated low contamination and for Cd moderate contamination. The pollution load index (PLI) ranged from 0.3856 to 0.5622; indicating no appreciable contamination and the modified degree of contamination (mCd) corroborated this result. The potential ecological risk (R_i) in the Tranca Grande and Pomacocha lagoons revealed a low potential ecological risk and in Tipicocha a moderate potential ecological risk. HI values < 1 indicated that non-carcinogenic adverse effects were negligible. In adults, the Total carcinogenic risk (TCR) values for As, Cd, Cr, Ni and Pb were less than 1.00E-04, indicating no significant carcinogenic risk. In children, TCR values showed similar behavior with the exception of As. Therefore, considering that fish production for domestic consumption and export is carried out in these lagoons, it is important to continue monitoring toxic metals to protect the health of these ecosystems and human health.