Towards stormwater reuse risk management plans: Methodology and catchment scale evaluation of QMRA.

The reuse of stormwater represents a potential option for meeting water demands in water stressed regions as well as preventing and mitigating diffuse pollution of receiving water bodies. Particularly, the elaboration of a risk management plan for stormwater reuse may help to understand associated environmental and public health risks and design fit-for-purpose water treatment processes. In this work, it is presented an innovative methodology to perform quantitative microbial risk assessment (QMRA) for stormwater reuse by using data simulated by SWMM software.

Machine learning analysis of PM1 impact on visibility with comprehensive sensitivity evaluation of concentration, composition, and meteorological factors.

This study introduces a novel approach to visibility modelling, focusing on PM1 concentration, its chemical composition, and meteorological conditions in two distinct Polish cities, Zabrze and Warsaw. The analysis incorporates PM1 concentration measurements as well as its chemical composition and meteorological parameters, including visibility data collected during summer and winter measurement campaigns (120 samples in each city). The developed calculation procedure encompasses several key steps: formulating a visibility prediction model through machine learning, identifying data in clusters using unsupervised learning methods, and conducting global sensitivity analysis for each cluster.

Novel extended hybrid tool for real time control and practically support decisions to reduce GHG emissions in full scale wastewater treatment plants.

In this paper, a novel methodology and extended hybrid model for the real time control, prediction and reduction of direct emissions of greenhouse gases (GHGs) from wastewater treatment plants (WWTPs) is proposed to overcome the lack of long-term data availability in several full-scale case studies. A mechanistic model (MCM) and a machine learning (ML) model are combined to real time control, predict the emissions of nitrous oxide (NO) and carbon dioxide (CO) as well as effluent quality (COD - chemical oxygen demand, NH-N - ammonia, NO-N - nitrate) in activated sludge method. For methane (CH), using the MCM model, predictions are performed on the input data (VFA, CODs for aerobic and anaerobic compartments) to the MLM model.

Application of a generalized hybrid machine learning model for the prediction of HS and VOCs removal in a compact trickle bed bioreactor (CTBB).

This study presents a generalized hybrid model for predicting HS and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odor removal efficiency (K) using a classification model, and (ii) prediction of K <100%, based on inlet concentration, time of day, pH and retention time. Global sensitivity analysis (GSA) was used to test the relationships between the inputs and outputs of the K-NN model.

Tool for fast assessment of stormwater flood volumes for urban catchment: A machine learning approach.

Specific flood volume is an important criterion for evaluating the performance of sewer networks. Currently, mechanistic models - MCMs (e.g.

Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge.

Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a "gas fingerprint", which enables it to differentiate between the analyzed samples of gas mixtures.

A simplified method for determining potential heavy metal leached from sediments of stormwater and combined sewer systems - importance for public health.

Introduction And Objective: The identification and understanding of interactions between contaminants present in sediments from stormwater and combined sewer systems is a prerequisite for their proper management, and provides a basis for developing effective strategies to minimize their negative impact on humans and the environment. The studypresents the method described in PN-EN 12457-2:2006 as a possible technique for studying the mobility of heavy metals in sediments from stormwater and combined sewer systems.

Material And Methods: The presented PN-EN 12457-2:2006 method is a relatively simple technique for preparing extracts for the determination of heavy metals in sediments from stormwater and combined sewer systems, consisting of one-step leaching, which is quick to perform.

Polyethylene pipes exposed to chlorine dioxide in drinking water supply system: A critical review of degradation mechanisms and accelerated aging methods.

Polyethylene (PE) pipes have been widely used in drinking water distribution systems across the world. In many cases, chlorine dioxide (ClO) is used to maintain a residual disinfectant concentration in potable water. Practical experiences have shown that the lifetime of PE pipes is significantly reduced due to exposure to drinking water with ClO.

Application of Machine Learning Methods for an Analysis of E-Nose Multidimensional Signals in Wastewater Treatment.

The work represents a successful attempt to combine a gas sensors array with instrumentation (hardware), and machine learning methods as the basis for creating numerical codes (software), together constituting an electronic nose, to correct the classification of the various stages of the wastewater treatment process. To evaluate the multidimensional measurement derived from the gas sensors array, dimensionality reduction was performed using the t-SNE method, which (unlike the commonly used PCA method) preserves the local structure of the data by minimizing the Kullback-Leibler divergence between the two distributions with respect to the location of points on the map. The k-median method was used to evaluate the discretization potential of the collected multidimensional data.

Flood occurrence analysis in small urban catchments in the context of regional variability.

An original method for analyzing the influence of the meteorological, as well as physical-geographical conditions on the flooding of stormwater in small urban catchment areas is proposed. A logistical regression model is employed for the identification of the flooding events. The elaborated model enables to simulate the stormwater flooding in a single rainfall event, on the basis of the rainfall depth, duration, imperviousness of the catchment and its spatial distribution within the analyzed area, as well as the density of the stormwater network.

Wafer-level calibration of large-scale integrated optical phased arrays.

We present the wafer-level characterization of a 256-channel optical phased array operating at 1550 nm, allowing the sequential testing of different OPA circuits without any packaging steps. Using this, we establish that due to random fabrication variations, nominally identical circuits must be individually calibrated. With this constraint in mind, we present methods that significantly reduce the time needed to calibrate each OPA circuit.

Modeling nutrient removal and energy consumption in an advanced activated sludge system under uncertainty.

Activated sludge models are widely used to simulate, optimize and control performance of wastewater treatment plants (WWTP). For simulation of nutrient removal and energy consumption, kinetic parameters would need to be estimated, which requires an extensive measurement campaign. In this study, a novel methodology is proposed for modeling the performance and energy consumption of a biological nutrient removal activated sludge system under sensitivity and uncertainty.

New insights into submicron particles impact on visibility.

The aim of the study was to analyze the impact of very fine atmospheric particles (submicron particulate matter; PM) on visibility deterioration. Taking into consideration not only their entirely different physio-chemical properties in comparison to a well-recognized PM but also the origin and a growing environmental awareness of PM, the main research problem has been solved in few steps. At first, the chemical composition of PM was determined in two selected urban areas in Poland.

Metamaterial-Engineered Silicon Beam Splitter Fabricated with Deep UV Immersion Lithography.

Subwavelength grating (SWG) metamaterials have garnered a great interest for their singular capability to shape the material properties and the propagation of light, allowing the realization of devices with unprecedented performance. However, practical SWG implementations are limited by fabrication constraints, such as minimum feature size, that restrict the available design space or compromise compatibility with high-volume fabrication technologies. Indeed, most successful SWG realizations so far relied on electron-beam lithographic techniques, compromising the scalability of the approach.

In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method Results.

Soil moisture content simulation models have continuously been an important research objective. In particular, the comparisons of the performance of different model types deserve proper attention. Therefore, the quality of selected physically-based and statistical models was analyzed utilizing the data from the Time Domain Reflectometry technique.

Silicon photonic on-chip spatial heterodyne Fourier transform spectrometer exploiting the Jacquinot's advantage.

Silicon photonics on-chip spectrometers are finding important applications in medical diagnostics, pollution monitoring, and astrophysics. Spatial heterodyne Fourier transform spectrometers (SHFTSs) provide a particularly interesting architecture with a powerful passive error correction capability and high spectral resolution. Despite having an intrinsically large optical throughput (étendue, also referred to as Jacquinot's advantage), state-of-the-art silicon SHFTSs have not exploited this advantage yet.

Modeling and Optimization of Pollutants Removal during Simultaneous Adsorption onto Activated Carbon with Advanced Oxidation in Aqueous Environment.

The paper presents the results of studies on the modeling and optimization of organic pollutant removal from an aqueous solution in the course of simultaneous adsorption onto activated carbons with varied physical characteristics and oxidation using HO. The methodology for determining the models used for predicting the sorption and catalytic parameters in the process was presented. The analysis of the influence of the sorption and catalytic parameters of activated carbons as well as the oxidizer dose on the removal dynamics of organic dyes-phenol red and crystal violet-was carried out based on the designated empirical models.

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station.

Optical phased arrays (OPAs) can produce low-divergence laser beams and can be used to control the emission angle electronically without the need for moving mechanical parts. This technology is particularly useful for beam steering applications. Here, we focus on OPAs integrated into SiN photonic circuits for a wavelength in the near infrared.

Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning.

The paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including: artificial neural networks, support vector machines, random forests, boosted trees, and logistic regression.

The Influence of COD Fraction Forms and Molecules Size on Hydrolysis Process Developed by Comparative OUR Studies in Activated Sludge Modelling.

The activated sludge models (ASMs) commonly used by the International Water Association (IWA) task group are based on chemical oxygen demand (COD) fractionations. However, the proper evaluation of COD fractions, which is crucial for modelling and especially oxygen uptake rate (OUR) predictions, is still under debate. The biodegradation of particulate COD is initiated by the hydrolysis process, which is an integral part of an ASM.

Sub-decibel silicon grating couplers based on L-shaped waveguides and engineered subwavelength metamaterials.

The availability of low-loss optical interfaces to couple light between standard optical fibers and high-index-contrast silicon waveguides is essential for the development of chip-integrated nanophotonics. Input and output couplers based on diffraction gratings are attractive coupling solutions. Advanced grating coupler designs, with Bragg or metal mirror underneath, low- and high-index overlays, and multi-level or multi-layer layouts, have proven less useful due to customized or complex fabrication, however.

SiN integrated optical phased arrays for two-dimensional beam steering at a single near-infrared wavelength.

In this work, we present two-dimensional beam steering in the near-infrared using a SiN integrated circuit, containing optical phased arrays. Beam steering was achieved over a range of 17.6° × 3°, at a fixed wavelength of 905 nm.

30 GHz heterogeneously integrated capacitive InP-on-Si Mach-Zehnder modulators.

We demonstrate high-bandwidth O-band Mach-Zehnder modulators with indium phosphide-on-silicon (InP-on-Si) capacitive phase shifters that are compatible with heterogeneous laser fabrication processes. An electro-optic conversion efficiency of 1.3 V⋅cm and a 3 dB bandwidth of up to 30 GHz was observed for a phase modulator length of 250 μm at a 0 V bias.

Statistical models to predict discharge overflow.

In the paper, a comparison of prediction results concerning the annual number of discharges of stormwater from the drainage system due to stormwater overflows is depicted. The prediction has been computed by means of storm water management model (SWMM) and probabilistic models. Regarding the probabilistic modelling some simple statistical models such as logit, probit, Gompertz and linear discriminant analysis model have been applied, and as for the hydrodynamic modelling a generator of synthetic rainfall based on the Monte Carlo method has been used.