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.

Ultrafine particles (diameter of less than 100 nm) are primary suspects for enhanced negative health effects on humans. Measuring the chemical composition and physical properties of ultrafine particles online, continuously, and accurately is particularly challenging because of their typically low mass concentration (PM_0.1) and susceptibility to interference from larger particles. The few past PM_0.1 chemical composition measurement studies have used cascade impactors and at least a daily temporal resolution. In this study, we perform, for the first time, high-temporal-resolution measurements of the composition and sources of PM_0.1 using an aerodynamic aerosol classifier (AAC) to separate PM_0.1 from larger particles, integrated with other instruments. These include a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS, for organics, sulfate, nitrate, ammonium, and chloride), a single-particle soot photometer (SP2-XR, for black carbon), and an Xact625i (for elements). Ambient PM_0.1 composition measurements were conducted in a suburban area in Greece to test the system. The hourly PM_0.1 levels varied from 0.4 to 1.5 µg m⁻³, with an average of 0.7 µg m⁻³. Most of the PM_0.1 (45 %) was organic aerosol (OA). On average, sulfates contributed 14 %, ammonium contributed 7 %, nitrate contributed 3 %, and black carbon contributed 4 % to PM_0.1. Calcium (Ca) showed a surprising high average contribution to PM_0.1 (18 %). The rest of the detected elements were Fe, K, Zn, and Ti, contributing 7 % together. Source apportionment analysis showed that most of the PM_0.1 OA during this summertime period was oxygenated OA (90 %), with 70 % being less oxidized and 20 % being more oxidized, while only 10 % was fresh hydrocarbon-like OA.

Ultrafine particles (diameter less than 100 nm) are primary suspects for enhanced negative health effects on humans. Measuring the chemical composition and physical properties of ultrafine particles on-line, continuously, and accurately is particularly challenging because of their typically low mass concentration (PM_0.1) and susceptibility to interference from larger particles. The few past PM_0.1 chemical composition measurement studies have used cascade impactors and at least daily temporal resolution. In this study we perform for the first time high temporal measurements of the composition and sources of PM_0.1 using an aerodynamic aerosol classifier (AAC) to separate PM_0.1 from larger particles. A high-resolution time of flight aerosol mass spectrometer (HR-ToF-AMS, for sulfate, nitrate, organics, chloride), a single particle soot photometer (SP2-XR, for black carbon) and an Xact625i (for elements) are also used. Ambient PM_0.1 composition measurements were conducted in a suburban area in Greece to test the system. The hourly PM_0.1 levels varied from 0.4 to 1.5 μg m^-3, with an average of 0.7 μg m^-3. Most of the PM_0.1 (45 %) was organic aerosol (OA). On average, sulfates contributed 14 %, ammonium 7 %, nitrate 3 %, and black carbon 4 % to PM_0.1. Calcium (Ca) showed a surprising high average contribution to PM_0.1 (18 %). The rest of the detected elements were Fe, K, Zn and Ti, contributing together 7 %. Source apportionment analysis showed that most of the PM_0.1 OA, during this summertime period, was oxygenated OA (90 %), with 70 % being less oxidized and 20 % being more oxidized, while only 10 % was fresh hydrocarbon-like OA.

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.