A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.     

New insights into different surfactants’ impacts on sludge fermentation: Focusing on the particular metabolic processes and microbial genetic traits

• The promoting effects for VFA generation follow the order of APG>SDBS>HTAB. • Surfactants improve the WAS solubilization/hydrolysis and acidification processes. • The VFA promotion is associated with surfactants’ distinctive characteristics. • Surfactants induce the enrichment of functional bacteria for VFA biosynthesis. • The vital genes for substrates delivery, metabolism, and VFA yields are upregulated. Surfactants were expected to exhibit positive effects on the waste activated sludge (WAS) disposal. However, the systematic comparison of different categories of surfactants on the WAS fermentation and the functional mechanisms, especially microbial metabolic traits, have not yet been precisely explored. This study revealed the positive effects of different surfactants on the volatile fatty acid (VFA) production, which followed the order of alkyl polysaccharides (APG)>sodium dodecylbenzene sulfonate (SDBS)>hexadecyl trimethyl ammonium bromide (HTAB). Mechanistic exploration found that the presence of different surfactants improved solubilization and hydrolysis steps, and then contributed to the subsequent acidification with different efficiencies. The functional microorganisms associated with VFA generation were enriched in surfactant-conditioned reactors. Metagenomic analysis further indicated that the key genes involved in the particular process of VFA generation were over-expressed. The simultaneous bioavailable substrate improvement, functional bacterial enrichment, and metabolic activity upregulation induced by different surfactants jointly contributed to VFA promotion during WAS fermentation. This study could provide a comprehensive realization of surfactants’ impacts on the WAS fermentation process, and more importantly, it reminded the public to discern the distinct interplaying effects induced by different chemicals in regulating the WAS disposal and resource recovery.     

Coprinus comatus endophytic bacteria characteristics and mechanisms for the cadmium resistance

Environmental Science and Pollution Research - Characteristics and resistant mechanisms of macro-fungus endophytic bacteria to cadmium (Cd) have not been well defined. Strains L1 and L3 with...     

Spatiotemporal dynamics of vegetation in China from 1981 to 2100 from the perspective of hydrothermal factor analysis

Environmental Science and Pollution Research - The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of...     

紫色土旱坡地油菜/玉米轮作下氮流失的年际变化特征

明确三峡库区紫色土旱坡地氮流失的年际变化特征,为库区的施肥管理措施和面源污染治理提供参考.依托中国科学院成都山地灾害与环境研究所三峡库区试验站,于2018～2020年连续3 a设置不施肥(CK)、常规施肥(F)、优化施肥(OF)、化肥减量配施生物炭(BF)和化肥减量配施秸秆覆盖(SF)5种处理,研究紫色土旱坡地在油菜-玉米轮作模式下氮流失的年际变化特征.结果表明：(1)三峡库区紫色土旱坡地地表径流主要发生在5月和8月,壤中流主要发生在6～10月. 2018～2020年降雨量逐年增加,各处理地表径流产流量逐年减少,壤中流产流量大致呈逐年增加的趋势,年尺度下,壤中流产流量和产流次数均高于地表径流.(2)2018～2020年每年各形态氮的最高流失浓度出现时间大概一致,地表径流全氮、铵态氮和硝态氮最高流失浓度分别出现在5、 8～10和7月,壤中流各形态氮最高流失浓度分别出现在10、 10和5～7月.(3)地表径流各形态氮流失通量呈逐年降低趋势,壤中流各形态氮流失通量大致逐年增加.(4)生物炭和秸秆还田第一年降低氮流失通量的效果较好,但后续两年还田反而加剧了氮素流失通量.因此,三峡库区紫色土旱坡...     

基于知识的AVHRR影像的水体自动识别方法与模型研究

根据水体光谱特性，通过分析水体在ＮＯＡＡ气象卫星的ＡＶＨＲＲ影像上的表象特征及与其它相关环境因子在光谱信息上的差异，认为水体在ＡＶＨＲＲ影像上具有光谱可分辨性，从而提出了基于水体光谱知识的水体自动提取识别的水体描述模型，并将该模型应用于太湖、淮河、渤海等地区，取得较好的效果。该模型方法同样可扩展应用于其它传感器获得的影像数据，从而实现遥感信息提取的自动化与智能化     

复合垂直流人工湿地的脱氮机理及影响因素分析

采用沸石和砾石作为填料,研究复合垂直流人工湿地处理农村生活污水的脱氮机理。试验结果表明:复合垂直流人工湿地对污水中氮的去除主要发生在下行流填料柱中,其去除机理主要是微生物对溶解态氮的硝化和反硝化作用;悬浮态氮沉积在填料孔隙中,会出现"氮释放"现象;水温、气水比、碳氮比、氮源形式等因素对湿地系统的脱氮效率有显著影响。     

毛毯印染废水处理技术研究

本文采用投菌水解(酸化)--接触氯化法处理毛毯印染废水.试验表明该工艺流程简单,处理效果好,出水水质稳定.当进水CODc在480～750mg/L范围变化时,出水CODDc稳定在100mg/L以下.其中,水解池对色度有较好的去除效果,同时可提高后续接触氧化池的处理效率;而CODc的去除则主要在接触氧化池进行.     

地下水有氧反硝化的固态有机碳源选择研究

目前生物反硝化多采用液态有机碳源,如甲醇、乙醇等,而对富含有机碳的固态有机碳源研究较少.选取4种农业废弃物:麦杆、稻草、木屑、稻壳作为反硝化细菌的碳源,以菜园土和白蚁侵蚀过的木条为接种物的有氧条件下,研究了含100 mg·L-1 NO3-废水的氮去除情况.研究结果表明充填麦杆和稻草的反应具有较好的反硝化效果,且最终无NO2-的积累;反应过程中无NH4 的产生;pH值随反硝化进行而略有升高,随反硝化结束而趋于定值.因此,麦杆和稻草可作为进一步反应的固态有机碳源.     

聚丙烯酰胺对紫色土中磷素吸附特征的影响

通过等温吸附实验分别研究了特定磷质量浓度下系列PAM用量和系列磷质量浓度下特定PAM用量对磷素在土壤中吸附特征的影响.试验表明,土壤经过PAM处理后其对磷素的吸附性能发生了明显改变.施用PAM使紫色土磷的吸附量降低,PAM用量较低时,吸附量随PAM用量的增加而减少,并且在0.1%～0.5%PAM内两者之间达到极显著负相关.紫色土磷最大吸附量为277.8 靏穏-1 ,0.1%、0.2%、0.4%PAM处理后紫色土对磷的最大吸附量均降低,分别为263.28 靏穏-1、227.38 靏穏-1、212.88 靏穏-1,但吸附强度却没减弱,甚至得到加强.PAM通过改变土壤集结状态和表面性质对土壤磷慢速吸附阶段产生了显著影响,但对快吸附阶段无明显影响.