Wuxi Environmental Monitoring Center

Wuxi, China

Wuxi Environmental Monitoring Center

Wuxi, China

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Zhang J.,Nanjing Southeast University | Zhang J.,Wuxi Environmental Monitoring Center | Zhu C.,Tsinghua University | Guan R.,Nanjing Southeast University | And 9 more authors.
Environmental Science and Pollution Research | Year: 2017

Understanding of the bacterial community structure in drinking water resources helps to enhance the security of municipal water supplies. In this study, bacterial communities were surveyed in water and sediment during a heavy cyanobacterial bloom in a drinking water resource of Lake Taihu, China. A total of 325,317 high-quality sequences were obtained from different 16S ribosomal RNA (rRNA) regions (V3, V4, and V6) using the Miseq sequencing platform. A notable difference was shown between the water and sediment samples, as predominated by Cyanobacteria, Proteobacteria, and Actinobacteria in the water and Proteobacteria, Chloroflexi, and Verrucomicrobia in the sediment, respectively. The LD12 family dominated the water surface and was tightly associated with related indicators of cyanobacterial propagation, indicating involvement in the massive proliferation of cyanobacterial blooms. Alternatively, the genus Nitrospira dominated the sediment samples, which indicates that nitrite oxidation was very active in the sediment. Although pathogenic bacteria were not detected in a large amount, some genera such as Mycobacterium, Acinetobacter, and Legionella were still identified but in very low abundance. In addition, the effects of different V regions on bacterial diversity survey were evaluated. Overall, V4 and V3 were proven to be more promising V regions for bacterial diversity survey in water and sediment samples during heavy water blooms in Lake Taihu, respectively. As longer, cheaper, and faster DNA sequencing technologies become more accessible, we expect that bacterial community structures based on 16S rRNA amplicons as an indicator could be used alongside with physical and chemical indicators, to conduct comprehensive assessments for drinking water resource management. © 2017 The Author(s)


Zhang J.-Y.,Wuxi Environmental Monitoring Center | Zhang J.-Y.,Nanjing Southeast University | Zhu B.-C.,Wuxi Environmental Monitoring Center | Xu C.,Wuxi Environmental Monitoring Center | And 5 more authors.
Chinese Journal of Applied Ecology | Year: 2015

The advent of next generation sequencing technology enables parallel analysis of the whole microbial community from multiple samples. Particularly, sequencing 16S rRNA hypervariable tags has become the most efficient and cost-effective method for assessing microbial diversity. Due to its short read length of the 2nd-generation sequencing methods that cannot cover the full 16S rRNA genomic region, specific hypervariable regions or V-regions must be selected to act as the proxy. Over the past decade, selection of V-regions has not been consistent in assessing microbial diversity. Here we evaluated the current strategies of selecting 16S rRNA hypervariable regions for surveying microbial diversity. The environmental condition was considered as one of the important factors for selection of 16S rRNA hypervariable regions. We suggested that a pilot study to test different V-regions is required in bacterial diversity studies based on 16S rRNA genes. © 2015, Editorial Board of Chinese Journal of Applied Ecology. All right reserved.


Zhang J.-Y.,Nanjing Southeast University | Guan R.,Nanjing Southeast University | Zhang H.-J.,Wuxi Environmental Monitoring Center | Li H.,CAS Wuhan Institute of Hydrobiology | And 7 more authors.
Standards in Genomic Sciences | Year: 2016

The cyanobacterial genus Microcystis is well known as the main group that forms harmful blooms in water. A strain of Microcystis, M. panniformis FACHB1757, was isolated from Meiliang Bay of Lake Taihu in August 2011. The whole genome was sequenced using PacBio RS II sequencer with 48-fold coverage. The complete genome sequence with no gaps contained a 5,686,839 bp chromosome and a 38,683 bp plasmid, which coded for 6,519 and 49 proteins, respectively. Comparison with strains of M. aeruginosa and some other water bloom-forming cyanobacterial species revealed large-scale structure rearrangement and length variation at the genome level along with 36 genomic islands annotated genome-wide, which demonstrates high plasticity of the M. panniformis FACHB1757 genome and reveals that Microcystis has a flexible genome evolution. © 2016 Zhang et al.


PubMed | Shenzhen University, Nanjing Southeast University, Wuxi Environmental Monitoring Center, Nextomics Biosciences Co. and 2 more.
Type: | Journal: Standards in genomic sciences | Year: 2016

The cyanobacterial genus Microcystis is well known as the main group that forms harmful blooms in water. A strain of Microcystis, M. panniformis FACHB1757, was isolated from Meiliang Bay of Lake Taihu in August 2011. The whole genome was sequenced using PacBio RS II sequencer with 48-fold coverage. The complete genome sequence with no gaps contained a 5,686,839 bp chromosome and a 38,683 bp plasmid, which coded for 6,519 and 49 proteins, respectively. Comparison with strains of M. aeruginosa and some other water bloom-forming cyanobacterial species revealed large-scale structure rearrangement and length variation at the genome level along with 36 genomic islands annotated genome-wide, which demonstrates high plasticity of the M. panniformis FACHB1757 genome and reveals that Microcystis has a flexible genome evolution.


Han B.,Nankai University | Bi X.,Nankai University | Xue Y.,Nankai University | Wu J.,Nankai University | And 4 more authors.
Frontiers of Environmental Science and Engineering in China | Year: 2011

A total of 168 PM10 samples were collected during the year of 2005 at eight sites in the city of Wuxi in China. Fifteen chemical elements, three water-soluble ions, total carbon and organic carbon were analyzed. Six source categories were identified and their contributions to ambient PM10 in Wuxi were estimated using a nested chemical mass balance method that reduces the effects of colinearity on the chemical mass balance model. In addition, the concentrations of secondary aerosols, such as secondary organic carbon, sulfate and nitrate, were quantified. The spatially averaged PM10 was high in the spring and winter (123 μg·m-3 and low in the summer-fall (90 μg·m-3). According to the result of source apportionment, resuspended dust was the largest contributor to ambient PM10, accounting for more than 50% of the PM10 mass. Coal combustion (14. 6%) and vehicle exhaust (9. 4%) were also significant source categories of ambient PM10. Construction and cement dust, sulfates, secondary organic carbon, and nitrates made contributions ranging between 4. 1% and 4. 9%. Other source categories such as steel manufacturing dust and soil dust made low contributions to ambient PM10. © 2011 Higher Education Press and Springer-Verlag Berlin Heidelberg.


Song T.,Wuxi Environmental Monitoring Center | Duan Z.,Technical University of Delft | Liu J.,Nanjing Normal University | Shi J.,Wuxi Environmental Monitoring Center | And 4 more authors.
Journal of Remote Sensing | Year: 2015

Land Surface Temperature (LST) plays an important role in energy exchange between the land surface and the atmosphere. LST is a key variable in many applications, such as land surface modeling. Many satellite-based algorithms have been proposed to retrieve LST, such as Split-Window (SW), dual-angle, and single-channel algorithms. In this study, four satellite-based LST retrieval algorithms, including two SW algorithms (Juan C. Jiménez-Muñoz and Offer Rozenstein SW algorithms) and two mono-window algorithms (Juan C. Jiménez-Muñoz and Qin Zhihao mono-window algorithms), were compared with Landsat-8 satellite data over the region around Wuxi City. The accuracy of the four algorithms was evaluated against the ground measurements from 16 floating stations over Lake Tai. The results showed that the performance of the two SW algorithms, which have an average error of 0.7 K, was better than that of the SW algorithms, which have an average error of 1.3-1.4 K, when compared with ground measurements. The sensitivity analysis of these algorithms showed that the Juan C. Jiménez-Muñoz SW algorithm was the least sensitive to key input parameters (emissivity and water vapor), whereas the Offer Rozenstein SW algorithm and the Qin Zhihao mono-window algorithm showed high sensitivity to input parameters. The limitations of these four LST retrieving algorithms were also discussed. ©, 2015, Science Press. All right reserved.

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