Wavelength-Dependent Seeing Systematically Changes the Normalized Slope of Telescopic Reflectance Spectra of Mercury

Telescopic observations of Mercury consistently report systematic variations of the normalized spectral slope of visible-to-near-infrared reflectance spectra. This effect was previously assumed to be a photometric property of the regolith, but it is not yet fully understood. After the MESSENGER mission, detailed global spectral maps of Mercury are available that better constrain Mercury’s photometry. So far, wavelength-dependent seeing has not been considered in the context of telescopic observations of Mercury. This study investigates the effect of wavelength-dependent seeing on systematic variations of Mercury’s normalized spectral reflectance slope. Therefore, we simulate the disk of Mercury for an idealized scenario, as seen by four different telescopic campaigns using the Hapke and the Kaasalainen–Shkuratov photometric model, the MDIS global mosaic, and a simple wavelength-dependent seeing model. The simulation results are compared with the observations of previous telescopic studies. We find that wavelength-dependent seeing affects the normalized spectral slope in several ways. The normalized slopes are enhanced near the limb, decrease toward the rim of the seeing disk, and even become negative. The decrease of the normalized spectral slope is consistent with previous observations. However, previous studies have associated the spectral slope variations with photometric effects that correlate with the emission angle. Our study suggests that wavelength-dependent seeing may cause these systematic variations. The combined reflectance and seeing model can also account for slope variations between different measurement campaigns. We report no qualitative differences between results based on the Hapke model or the Kaasalainen–Shkuratov model.

Determination of PM1 Sources at a Prague Background Site during the 2012–2013 Period Using PMF Analysis of Combined Aerosol Mass Spectra

Two intensive measurement campaigns using a compact time-of-flight aerosol mass spectrometer were carried out at the suburban site in Prague (Czech Republic) in summer (2012) and winter (2013). The aim was to determine the aerosol sources of the NR-PM1 fraction by PMF analysis of organic (OA) and inorganic aerosol mass spectra. Firstly, an analysis of the OA mass spectra was performed. Hydrocarbon-like OA (HOA), biomass burning OA (BBOA), and two types of oxygenated OA (OOA1) and (OOA2) were identified in summer. In winter, HOA, BBOA, long-range oxygenated OA (LROOA), and local oxygenated OA (LOOA) were determined. The identified HOA and BBOA factors were then used as additional input for the subsequent ME-2 analysis of the combined organic and inorganic spectra. This analysis resulted in six factors in both seasons. All of the previously reported organic factors were reidentified and expanded with the inorganic part of the spectra in both seasons. Two predominantly inorganic factors ammonium sulphate (AMOS) and ammonium nitrate (AMON) were newly identified in both seasons. Despite very similar organic parts of the mass profiles, the daily cycles of HOA and LOOA differed significantly in winter. It appears that the addition of the inorganic part of the mass profile, in some cases, reduces the ability of the model to identify physically meaningful factors.

Monitoring of wastewater quality in Lodz sewage system (Poland)—do the current solutions enable the protection of WWTP and receiving water?

Solving urban wastewater management problems requires knowledge of wastewater composition and variability. In the case of combined sewerage, this applies to both dry and wet weather. Wastewater composition is changing as a result of the appearance of new substances on the market, the changes in inhabitant lifestyle and the catchment characteristic; therefore, it must be constantly monitored. At the same time, due to the time-consuming and high costs of measurement campaigns, solutions that could limit their scope and facilitate the interpretation of the results are sought. This paper presents the results of the measurement campaign conducted in 2018–2021. The aim of the monitoring was, inter alia, assessment of wastewater composition in terms of threats to wastewater treatment plant and urban rivers, which are receivers of discharge from combined sewer overflows. The obtained results were analyzed using the multivariate statistical methods: Principal Component Analysis and Cluster Analysis. However, the applied methods did not allow for the full identification of the relationship between the wastewater quality parameters as well as the differences and similarities in the wastewater composition from individual parts of the city, which could simplify and reduce the measurement campaigns in the future. Therefore, in the case of large urban catchments, it is necessary to introduce other solutions to control the wastewater composition.

Development of design rainfalls: A review

Design rainfalls are essential parameters in designing and assessing combined and stormwater sewers and are still used today. They create idealized intensity profiles with statistically determined parameters. Their gradual development is based on the development of computer technology, measuring instruments, and measurement campaigns’ progress. However, climate change is causing changes in precipitation events’ characteristics, so it is important to focus on reassessing them and adapting to current conditions. The paper summarizes the knowledge about the development of design rains to reveal their potential for the future.

Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks

Paving the path toward the fifth generation (5G) of wireless networks with a huge increase in the number of user equipment has strengthened public concerns on human exposure to radio-frequency electromagnetic fields (RF EMFs). This requires an assessment and monitoring of RF EMF exposure, in an almost continuous way. Particular interest goes to the uplink (UL) exposure, assessed through the transmission power of the mobile phone, due to its close proximity to the human body. However, the UL transmit (TX) power is not provided by the off-the-shelf modem and RF devices. In this context, we first conduct measurement campaigns in a multi-floor indoor environment using a drive test solution to record both downlink (DL) and UL connection parameters for Long Term Evolution (LTE) networks. Several usage services (including WhatsApp voice calls, WhatsApp video calls, and file uploading) are investigated in the measurement campaigns. Then, we propose an artificial neural network (ANN) model to estimate the UL TX power, by exploiting easily available parameters such as the DL connection indicators and the information related to an indoor environment. With those easy-accessed input features, the proposed ANN model is able to obtain an accurate estimation of UL TX power with a mean absolute error (MAE) of 1.487 dB.

On-Line Water Consumption Monitoring as a Tool for Optimal Management of Water Distribution Network

Monitoring and computer modeling of water networks is an effective tool supporting water management in the city. The aim of this paper is to present the results of measurement campaigns carried out in Polanica-Zdrój, preceding the construction, calibration and validation of the hydrodynamic model of the water supply network. The obtained results showed that the changed conditions of the functioning of the local community during a pandemic also significantly affect the way water is used. Daily distribution of water consumption by residents of multi-family housing has been significantly “flattened” due to remote work and extended time at home. This confirms the necessity to conduct on-line measurements in order to calibrate the model reliably and effectively use the results of computer simulations.

Seasonality of the Airborne Ambient Soot Predominant Emission Sources Determined by Raman Microspectroscopy and Thermo-Optical Method

Raman microspectroscopy and thermo-optical-transmittance (TOT) method were used to study airborne ambient soot collected at the suburban air monitoring station in southern Poland during the residential heating (January-February) and non-heating (June–July) seasons of 2017. Carbonaceous material constituted on average 47.2 wt.% of PM2.5 during the heating season and 26.9 wt.% in the non-heating season. Average concentrations of OC (37.5 ± 11.0 μg/m3) and EC (5.3 ± 1.1 μg/m3) during the heating season were significantly higher than those in the non-heating season (OC = 2.65 ± 0.78 μg/m3, and EC = 0.39 ± 0.18 μg/m3). OC was a chief contributor to the TC mass concentration regardless of the season. All Raman parameters indicated coal combustion and biomass burning were the predominant sources of soot in the heating season. Diesel soot, which is structurally less ordered than soot from other sources, was dominant during the non-heating season. The D1 and G bands area ratio (D1A/GA) was the most sensitive Raman parameter that discriminated between various soot sources, with D1A/GA > 1 for diesel soot, and less than 1 for soot from coal and wood burning. Due to high daily variability of both TOT and Raman spectroscopy data, single-day measurements can be inconclusive regarding the soot source apportionment. Long-time measurement campaigns are recommended.

Characterizing the Impact of Doppler Effects on Body-Centric LoRa Links with SDR

Long-range, low-power wireless technologies such as LoRa have been shown to exhibit excellent performance when applied in body-centric wireless applications. However, the robustness of LoRa technology to Doppler spread has recently been called into question by a number of researchers. This paper evaluates the impact of static and dynamic Doppler shifts on a simulated LoRa symbol detector and two types of simulated LoRa receivers. The results are interpreted specifically for body-centric applications and confirm that, in most application environments, pure Doppler effects are unlikely to severely disrupt wireless communication, confirming previous research, which stated that the link deteriorations observed in a number of practical LoRa measurement campaigns would mainly be caused by multipath fading effects. Yet, dynamic Doppler shifts, which occur as a result of the relative acceleration between communicating nodes, are also shown to contribute to link degradation. This is especially so for higher LoRa spreading factors and larger packet sizes.

Model assisted identification of N2O mitigation strategies for full-scale reject water treatment plants

In a 3-year research project, a new approach to forecast biological N2O formation and emission at high-strength reject water treatment has been developed (ASM3/1_N2OISAH). It was calibrated by extensive batch-tests and finally evaluated by long-term measurement campaigns realized at three WWTPs with different process configurations for nitrogen removal of reject water. To enable a model application with common full-scale data, the nitritation connected supplementary processes are not depicted in the model. Instead, within the new model approach the N2O formation is linked to the NH4-N oxidation rate by defining specific formation factors [N2O-Nform/NH4-Nox], depending on three minor parameters, the concentrations of NO2 and O2 as well as the NH4 load. A comparison between the measured and the modeled N2O concentrations in the liquid and gas phase at the full-scale treatment plants prove the ability of the proposed modelling approach to represent the observed trends of N2O formation, emission and reduction using the standard parameter set of kinetics and formation factors. Thus, enabling a reliable estimation of the N2O emissions for different operational conditions. The measurements indicate that a formation of N2O by AOB cannot completely be avoided. However, a considerable reduction of the formed N2O was observed in an anoxic environment. Applying the model, operational settings and mitigation strategies can now be identified without extensive measurement campaigns. For further enhancement of the model, first results for kinetics of N2O reduction kinetics by denitrification processes were determined in laboratory-scale batch tests.

Humidity in Power Converters of Wind Turbines—Field Conditions and Their Relation with Failures

Power converters in wind turbines exhibit frequent failures, the causes of which have remained unexplained for years. Field-experience based research has revealed that power- and thermal-cycling induced fatigue effects in power electronics do not contribute significantly to the field failures observed. Instead, clear seasonal failure patterns point to environmental influences, in particular to humidity, as a critical stressor and likely driver of converter failure. In addition to the electrical operating conditions, it is therefore important to also identify and characterize the climatic conditions that power converters in wind turbines are exposed to, both as a contribution to root-cause analysis and as a basis for the derivation of suitable test procedures for reliability qualification of components and systems. This paper summarizes the results of field-measurement campaigns in 31 wind turbines of seven different manufacturers spread over three continents. The temperature and humidity conditions inside the converter cabinets are characterized and related to the environmental conditions of the turbines and to their operation. The cabinet-internal climate is found to be subject to pronounced seasonal variations. In addition to the site-specific ambient climatic conditions and the operation of the turbines, the converter cooling concept is identified to significantly influence the climatic conditions inside the power cabinets.