Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign

Lidar observations were analysed to characterize atmospheric pollen at four EARLINET (European Aerosol Research Lidar Network) stations (Hohenpeißenberg, Germany;Kuopio, Finland;Leipzig, Germany;and Warsaw, Poland) during the ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure) COVID-19 campaign in May 2020. The reanalysis (fully quality-assured) lidar data products, after the centralized and automatic data processing with the Single Calculus Chain (SCC), were used in this study, focusing on particle backscatter coefficients at 355 and 532 nm and particle linear depolarization ratios (PDRs) at 532 nm. A novel method for the characterization of the pure pollen depolarization ratio was presented, based on the non-linear least square regression fitting using lidar-derived backscatter-related Ångström exponents (BAEs) and PDRs. Under the assumption that the BAE between 355 and 532 nm should be zero (±0.5) for pure pollen, the pollen depolarization ratios were estimated: for Kuopio and Warsaw stations, the pollen depolarization ratios at 532 nm were of 0.24 (0.19–0.28) during the birch-dominant pollen periods, whereas for Hohenpeißenberg and Leipzig stations, the pollen depolarization ratios of 0.21 (0.15–0.27) and 0.20 (0.15–0.25) were observed for periods of mixture of birch and grass pollen. The method was also applied for the aerosol classification, using two case examples from the campaign periods;the different pollen types (or pollen mixtures) were identified at Warsaw station, and dust and pollen were classified at Hohenpeißenberg station.

Smart Face Mask with an Integrated Heat Flux Sensor for Fast and Remote People's Healthcare Monitoring.

The COVID-19 pandemic has highlighted a large amount of challenges to address. To combat the spread of the virus, several safety measures, such as wearing face masks, have been taken. Temperature controls at the entrance of public places to prevent the entry of virus carriers have been shown to be inefficient and inaccurate. This paper presents a smart mask that allows to monitor body temperature and breathing rate. Body temperature is measured by a non-invasive dual-heat-flux system, consisting of four sensors separated from each other with an insulating material. Breathing rate is obtained from the temperature changes within the mask, measured with a thermistor located near the nose. The system communicates by means of long-range (LoRa) backscattering, leading to a reduction in average power consumption. It is designed to establish the relative location of the smart mask from the signal received at two LoRa receivers installed inside and outside an access door. Low-cost LoRa transceivers with WiFi capabilities are used in the prototype to collect information and upload it to a server. Accuracy in body temperature measurements is consistent with measurements made with a thermistor located in the armpit. The system allows checking the correct placement of the mask based on the recorded temperatures and the breathing rate measurements. Besides, episodes of cough can be detected by sudden changes in thermistor temperature.