Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network

Harsh pollutants that are illegally disposed in the sewer network may spread beyond the sewer network—e.g., through leakages leading to groundwater reservoirs—and may also impair the correct operation of wastewater treatment plants. Consequently, such pollutants pose serious threats to water bodies, to the natural environment and, therefore, to all life. In this article, we focus on the problem of identifying a wastewater pollutant and localizing its source point in the wastewater network, given a time-series of wastewater measurements collected by sensors positioned across the sewer network. We provide a solution to the problem by solving two linked sub-problems. The first sub-problem concerns the detection and identification of the flowing pollutants in wastewater, i.e., assessing whether a given time-series corresponds to a contamination event and determining what the polluting substance caused it. This problem is solved using random forest classifiers. The second sub-problem relates to the estimation of the distance between the point of measurement and the pollutant source, when considering the outcome of substance identification sub-problem. The XGBoost algorithm is used to predict the distance from the source to the sensor. Both of the models are trained using simulated electrical conductivity and pH measurements of wastewater in sewers of a european city sub-catchment area. Our experiments show that: (a) resulting precision and recall values of the solution to the identification sub-problem can be both as high as 96%, and that (b) the median of the error that is obtained for the estimation of the source location sub-problem can be as low as 6.30 m.

Нелинейно-регрессионный алгоритм обработки сигналов полупроводниковых химических сенсоров, обеспечивающий селективное детектирование примесей в искусственном воздухе

New method is developed for proceeding a conductance change response Δσ,μS of a temperature (T) modulated chemical sensor. The method provides reliable substance identification and measurement of its trace concentrations for such impurities in arti-ficial air as ammonia, acetone, hexane, propane, toluene, turpentine etc. Due to this method the response of the ΔσY for a substance Y in actual concentration C range is interpolated with a completely nonlinear regression via discriminant modelling func-tions Fi(z=103/T,Ai, bi,ci,..),i=1-4 or 5. For principal parameters the plots AiY(C) are built which compose the selectivity/gauge portrait of Y in the air. In case when the analogous parameters of unknown substance X fit this portrait the substance is iden-tified as Y. And the common abscissa of corresponding crossing points of AiX with the curves AiY(C) indicates the value of the X concentration in units been used for Y.

Synthesis of Quenchbodies for One-Pot Detection of Stimulant Drug Methamphetamine

The problem of illicit drug use and addiction is an escalating issue worldwide. As such, fast and precise detection methods are needed to help combat the problem. Herein, the synthesis method for an anti-methamphetamine Quenchbody (Q-body), a promising sensor for use in simple and convenient assays, has been described. The fluorescence intensity of the Q-body generated by two-site labeling of Escherichia coli produced anti-methamphetamine antigen-binding fragment (Fab) with TAMRA-C2-maleimide dyes increased 5.1-fold over background in the presence of a hydroxyl methamphetamine derivative, 3-[(2S)-2-(methylamino)propyl]phenol. This derivative has the closest structure to methamphetamine of the chemicals available for use in a laboratory. Our results indicate the potential use of this Q-body as a novel sensor for the on-site detection of methamphetamine, in such occasions as drug screening at workplace, suspicious substance identification, and monitoring patients during drug rehabilitation.