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Effective degradation of N,N-Dimethylformamide (DMF), an important industrial waste product, is challenging as only few bacterial isolates are known to degrade DMF. Aerobic remediation has typically been used, whereas anoxic remediation attempts are recently made, using nitrate as one electron acceptor, and ideally include methane as a byproduct. Here, we analyzed 20,762 complete genomes and 28 constructed draft genomes for genes associated with DMF degradation. We identified 952 genomes that harbor genes involved in DMF degradation, expanding the known diversity of prokaryotes with these metabolic capabilities. Our findings suggest plasmids play important roles in DMF degradation in the order Rhizobiales and genus Paracoccus, but not in most other lineages. Degradation pathway analysis reveals that most putative DMF degraders using aerobic Pathway I will accumulate methylamine intermediate, while around 6% of the DMF degraders that are primarily members of Paracoccus, Rhodococcus, Achromobacter, and Pseudomonas could potentially mineralize DMF completely. The aerobic DMF degradation via Pathway II is more common than thought and is primarily present in α-, and β-Proteobacteria and Actinobacteria. Around half (446/952) of putative DMF degraders could grow with nitrate anaerobically (Pathway III), however, genes for the use of methyl-CoM to produce methane were not found. These analyses suggest that microbial consortia could be more advantageous in DMF degradation than pure culture, particularly for methane production under the anaerobic condition. The identified genomes and plasmids form an important foundation for optimizing bioremediation of DMF-containing wastewaters.
Junhui Li; Paul Dijkstra; Qihong Lu; Shanquan Wang; Shaohua Chen; Deqiang Li; Zhiheng Wang; Zhenglei Jia; Lu Wang; Hojae Shim. Genomics-informed insights into microbial degradation of N,N-dimethylformamide. International Biodeterioration & Biodegradation 2021, 163, 105283 .
AMA StyleJunhui Li, Paul Dijkstra, Qihong Lu, Shanquan Wang, Shaohua Chen, Deqiang Li, Zhiheng Wang, Zhenglei Jia, Lu Wang, Hojae Shim. Genomics-informed insights into microbial degradation of N,N-dimethylformamide. International Biodeterioration & Biodegradation. 2021; 163 ():105283.
Chicago/Turabian StyleJunhui Li; Paul Dijkstra; Qihong Lu; Shanquan Wang; Shaohua Chen; Deqiang Li; Zhiheng Wang; Zhenglei Jia; Lu Wang; Hojae Shim. 2021. "Genomics-informed insights into microbial degradation of N,N-dimethylformamide." International Biodeterioration & Biodegradation 163, no. : 105283.
Long-term excessive applications of chemical fertilizers may result in adverse impacts on soil functions. This study was to evaluate soil organic carbon (SOC) sequestration efficiency under continuous paddy rice cultivation with an excessive nitrogen (N) fertilization over the period from1980 to 2017 in South China. The SOC and total nitrogen (TN) of total 108 soil samples from continuous paddy soils and new paddy soils collected in 2017 were accordingly compared with those of 54 samples from paddy soils and upland soils obtained in 1980. Results show a total SOC increase of 0.79 g kg−1 from the initial content of 12.82 g kg−1 over a 37-year period despite an increased input of about 550 kg N ha−1yr-1 from fertilizers and 2000 kg C kg−1 yr−1 from all straw incorporation after 1990. This small C sequestration rate (or 0.021 g C kg−1 yr−1) was also observed to couple with a significantly elevated C:N ratio and badly weakened SOC-N correlation as of 2017, which is almost impossible to be revealed by normal fertilization experiments. The SOC sequestration rate of 0.145 g C kg−1 yr−1 of the new paddy soils that were developed from uplands since 1980 implies a declining tendency of SOC sequestration efficiency with rice cultivation time, which could be mainly attributed to both low soil N content and microbial activity rather than to SOC saturation. This case reminds of a need for more dedicated plot studies coupled with field observations on farmers’ routine fertilization practices to elucidate why the low soil N content remains with an excessive N fertilizer input and how N interacts with SOC in the continuous paddy soils.
Yueming Hu; Lu Wang; Feixiang Chen; Xiangning Ren; Zhengxi Tan. Soil carbon sequestration efficiency under continuous paddy rice cultivation and excessive nitrogen fertilization in South China. Soil and Tillage Research 2021, 213, 105108 .
AMA StyleYueming Hu, Lu Wang, Feixiang Chen, Xiangning Ren, Zhengxi Tan. Soil carbon sequestration efficiency under continuous paddy rice cultivation and excessive nitrogen fertilization in South China. Soil and Tillage Research. 2021; 213 ():105108.
Chicago/Turabian StyleYueming Hu; Lu Wang; Feixiang Chen; Xiangning Ren; Zhengxi Tan. 2021. "Soil carbon sequestration efficiency under continuous paddy rice cultivation and excessive nitrogen fertilization in South China." Soil and Tillage Research 213, no. : 105108.
To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.
Shu-Di Fan; Yue-Ming Hu; Lu Wang; Zhen-Hua Liu; Zhou Shi; Wen-Bin Wu; Yu-Chun Pan; Guang-Xing Wang; A-Xing Zhu; Bo Li. Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products. Sustainability 2018, 10, 3459 .
AMA StyleShu-Di Fan, Yue-Ming Hu, Lu Wang, Zhen-Hua Liu, Zhou Shi, Wen-Bin Wu, Yu-Chun Pan, Guang-Xing Wang, A-Xing Zhu, Bo Li. Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products. Sustainability. 2018; 10 (10):3459.
Chicago/Turabian StyleShu-Di Fan; Yue-Ming Hu; Lu Wang; Zhen-Hua Liu; Zhou Shi; Wen-Bin Wu; Yu-Chun Pan; Guang-Xing Wang; A-Xing Zhu; Bo Li. 2018. "Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products." Sustainability 10, no. 10: 3459.
Mercury is one of the five most toxic heavy metals to the human body. In order to select a high-precision method for predicting the mercury content in soil using hyperspectral techniques, 75 soil samples were collected in Guangdong Province to obtain the soil mercury content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A multiple linear regression (MLR), a back-propagation neural network (BPNN), and a genetic algorithm optimization of the BPNN (GA-BPNN) were used to establish a relationship between the hyperspectral data and the soil mercury content and to predict the soil mercury content. In addition, the feasibility and modeling effects of the three modeling methods were compared and discussed. The results show that the GA-BPNN provided the best soil mercury prediction model. The modeling R2 is 0.842, the root mean square error (RMSE) is 0.052, and the mean absolute error (MAE) is 0.037; the testing R2 is 0.923, the RMSE is 0.042, and the MAE is 0.033. Thus, the GA-BPNN method is the optimum method to predict soil mercury content and the results provide a scientific basis and technical support for the hyperspectral inversion of the soil mercury content.
Li Zhao; Yue-Ming Hu; Wu Zhou; Zhen-Hua Liu; Yu-Chun Pan; Zhou Shi; Lu Wang; Guang-Xing Wang. Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing. Sustainability 2018, 10, 2474 .
AMA StyleLi Zhao, Yue-Ming Hu, Wu Zhou, Zhen-Hua Liu, Yu-Chun Pan, Zhou Shi, Lu Wang, Guang-Xing Wang. Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing. Sustainability. 2018; 10 (7):2474.
Chicago/Turabian StyleLi Zhao; Yue-Ming Hu; Wu Zhou; Zhen-Hua Liu; Yu-Chun Pan; Zhou Shi; Lu Wang; Guang-Xing Wang. 2018. "Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing." Sustainability 10, no. 7: 2474.