We consider the buoyancy-driven rise and interaction between two coaxially placed bubbles of equal size and constant volume that are initially stationary inside an elastoviscoplastic material. We simulate the material using the Saramito extension of the Herschel-Bulkley constitutive model, and we fit its properties to a 0.1% aqueous Carbopol solution. The interplay between plasticity, viscoelasticity, and inertia is investigated. We observe that a “bridge” of shear stresses develops, which connects the leading and the trailing bubble, decreasing the drag force on the latter and initiating their approach. The solidlike behavior of the material preserves stresses generated by the passage of the leading bubble and makes the material “softer” for the trailing bubble. At the same time the normal stresses primarily extend the bubbles, but their finite distance eventually causes the leading bubble to adopt a hydrodynamically less favorable shape that slows it down, further promoting the approach. Moreover, we examine the effect of the geometric characteristics and the material properties. Increasing the initial distance between the bubbles delays their approach, which, however, is inevitable. Increasing the radius results in a transition from the elastoplastic to a mixed elastoplastic-inertial regime and a delayed approach. Increasing elasticity or the shear and extension thinning of the material decreases the approach time. On the contrary, an increased viscosity delays their approach. Finally, varying the yield stress induces a nonmonotonic effect. Sufficiently small values of yield stress delay the approach compared to intermediate values, because it reduces the elastic response. Raising the yield stress slightly above the entrapment conditions of a single bubble, the bubbles still interact and move.

This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluation of the model. The results reveal that utilizing voltage signals instead of the manufacturer’s calibration equations diminishes variability among identical sensors. Moreover, the latter approach enhances calibration efficiency for CO, NO, NO₂, and O₃ sensors while incorporating voltage signals from all sensors in the ML algorithm, taking advantage of cross-sensitivity to improve calibration performance. The Random Forest ML algorithm is a promising solution for calibrating similar devices for use in urban areas.

Indoor air quality (IAQ) is influenced by a wide range of chemical, biological and physical agents that can negatively impact physical, immunological and mental health. Adverse health effects depend on the type and concentration of pollutants, duration of exposure, and individual susceptibility. The availability of data on IAQ is limited, as are standardized approaches for evaluating its health impact. This expert review aims to describe the most important indoor air determinants affecting health, and present the IDEAL cluster, which comprises seven EU‐funded scientific projects on the topic of IAQ and human health. Across the IDEAL projects, knowledge is generated on exposure to a wide range of indoor air pollutants, including well‐known hazards and more explorative chemical and microbiological determinants. The projects will also contribute to the implementation of low‐cost and/or real‐time sensors on IAQ, as well as advanced chemical and microbiological analyses, and evaluate various interventions to improve IAQ. Several of them focus on particularly vulnerable groups. Raising public awareness and implementing measures to reduce pollutant levels are essential for safeguarding health, particularly in urban areas with elevated pollution levels.

Monitoring indoor air quality in schools is essential, particularly as children are highly vulnerable to air pollution. This study evaluates the performance of the low-cost sensor-based air quality monitoring system ENSENSIA, during a 3-week campaign in an elementary school classroom in Athens, Greece. The system measured PM_2.5, CO, NO, NO₂, O₃, and CO₂. High-end instrumentation provided the reference concentrations. The aim was to assess the sensors’ performance in estimating the average day-to-day exposure, capturing temporal variations and the degree of agreement among different sensor units, with particular attention to the impact of machine learning (ML) calibration. Using the factory calibration settings, the CO₂ and PM_2.5 sensors showed strong inter-unit consistency for hourly averaged values. The other sensors, however, exhibited inter-unit variability, with differences in the reported average day-to-day concentrations ranging from 20% to 160%. ML-based calibration was investigated for the CO, NO, NO₂, and O₃ sensors using measurements by reference instruments for training and evaluation. Among the eleven ML algorithms tested, the Support Vector Regression performed better for the calibration of the CO, NO₂, and O₃ sensors. The NO sensor was better calibrated using the Elastic Net algorithm. The inter-unit variability was reduced by a factor of two after the ML calibration. The daily average error compared to the reference measured was also reduced by approximately 15–50% depending upon the sensor.

Motor vehicles remain a significant contributor to urban air pollution, emitting compounds in both the gas and particulate phases. While emissions under hot driving conditions have decreased due to improvements in exhaust after-treatment systems, cold starts continue to contribute disproportionately to total vehicle emissions. Furthermore, gas-phase organic compounds emitted during cold starts can be oxidized in the atmosphere, leading to the formation of secondary organic aerosol (SOA).To enhance our understanding of cold-start emissions and their potential to form SOA, measurements of approximately 21,000 cold starts were carried out inside an underground parking garage from November 29 to December 24, 2024. The garage consists of 250 parking spaces and is located beneath a shopping mall in Patras, Greece. The particle phase was measured by a scanning mobility particle sizer (SMPS) and further characterized using a high-resolution aerosol mass spectrometer (HR-ToF-AMS) and a high-resolution proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) coupled to a CHARON inlet. Black carbon was quantified by an Aethalometer (AE33). Gas-phase composition was measured with the PTR-MS, while inorganic trace gases including NO, NO2, CO, CO2, O3, and SO2 were continuously monitored. Tenax tubes were also collected from the garage ambient air for offline GC-MS analysis. Additionally, the traffic flow was recorded at both the entrance and exit of the garage. The SOA formation from vehicle emissions was investigated using an oxidation flow reactor (OFR) under controlled OH exposures by varying combinations of the reactor’s UV lamps.The measurements of the traffic and the concentrations were combined to derive average emissions of gas and particulate phase pollutants. The estimated cold start emission factors were then compared to those reported in the Dutch Εmission Inventory. Within the OFR, organic aerosol concentrations increased from four to eleven times depending on OH exposure, with the production of highly oxygenated organic compounds. Aromatic compounds were identified as the dominant precursors of SOA.

Transportation emissions can be a significant source of secondary organic aerosol (SOA) both in urban areas and regionally. Aromatic hydrocarbons and large alkanes are expected to be the major SOA precursors. However, there are still major uncertainties in understanding SOA from vehicle exhaust in different timescales.To address these uncertainties, this study focuses on measuring SOA formation from vehicle emissions in a real-world environment. The SOA formation from vehicle emissions in a large underground parking structure was investigated using an oxidation flow reactor (OFR). The organic vapors in the study were dominated by cold start emissions. The air in the parking structure was continuously fed to the OFR. The OH exposure was controlled by varying combinations of the OFR’s UV lamps. Α scanning mobility particle sizer (SMPS) was used to continuously measure the SOA formation inside the OFR. A high-resolution aerosol mass spectrometer and a high-resolution proton-transfer-reaction time-of-flight mass spectrometer coupled to a CHARON inlet were used to characterize the particulate phase alternating sampling between the parking garage and the OFR every 20 minutes. The PTR-MS also monitored the gas phase, while quartz filters were collected for offline analysis. Trace gases (NO, NO2, CO, CO2, O3, and SO2) and black carbon were quantified using dedicated instruments that continuously sampled air from the parking garage.Significant SOA formation was observed increasing the organic aerosol levels several times. The SOA increased with the intensity of UV lamp exposure in the OFR. The results of the measurements can be used for the parameterization of SOA formation from the vehicle emissions at both intermediate and longer timescales.