Abstract:In this work, we use the gridded precipitation dataset (with a resolution of 0.5 • × 0.5 • ) of the eastern part of inland river basin of Inner Mongolian Plateau from 1961-2015 as the basis and adopt the methods of climatic diagnosis (e.g., the Modified Mann-Kendall method, principal component analysis, and correlation analysis) to analyze the spatial and temporal variations of six extreme precipitation indices. Furthermore, we analyzed the relationship between El Niño-Southern Oscillation (ENSO) events and the observed extreme precipitation. The results indicated that the gridded dataset can be used to describe the precipitation distribution in our study area. In recent 55 years, the inter-annual variation trends of extreme precipitation indices are generally dominated by declination except for the maximum precipitation over five days (RX5DAY) and the heavy precipitation (R95P), in particular, the decreasing regions of consecutive dry days (CDD) accounts for 91% of the entire basin, 17.28% of which is showing the significant downward trend. Contrary to CDD, the spatial distribution of the other five indices is gradually decreasing from northeast to southwest, and the precipitation intensity (SDII) ranges from 3.8-5.3 mm·d −1 , with relatively small spatial differences. To some extent, CDD and R95P can used to characterize the extreme precipitation regimes. Moreover, the number of days with heavy precipitation (RR10), SDII, and R95P are more susceptible to the ENSO events. In addition, the moderate El Niño event may increase the probability of CDD, while the La Niña events may increase the risk of the heavy rainfall regime in the study area.
The state-of-the-art monitoring of drinking water hygiene is based on the cultivation and enumeration of indicator bacteria. Despite its proven reliability, this approach has the disadvantages of being (a) relatively slow and (b) limited to a small number of indicator bacteria. Ideally, alternative methods would be less time-consuming while providing information about a larger set of hygienically relevant microorganisms including viruses. In this paper, we present insights into the design of a modular concentration and detection system for bacteria, bacteriophages and viruses. Following further validation, this or similar techniques have the potential to extend and speed up the monitoring of raw and drinking water hygiene in the future. The system consists of different modules for the concentration of microorganisms, an amplification and detection unit that includes a module for the differentiation between live and dead microorganisms, and an automated system for decision support and self-diagnosis. The ongoing testing under controlled laboratory conditions and real-life conditions in the water supply industry yields further system improvements. Moreover, the increased sensitivity and broader range of microbiological parameters emphasize the need for a reconsideration of the currently used criteria for the assessment of (drinking) water hygiene
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