Assessment of seasonal variations in particulate matter accumulation and elemental composition in urban tree species.

Outdoor air pollution in urban areas, especially particulate matter (PM), is harmful to human health. Urban trees and shrubs provide crucial ecosystem services such as air pollution mitigation by acting as natural filters. However, urban greenery comprises a particular biodiversity, and different plant species vary in their capacity to accumulate PM. Twenty-two plant species were analyzed and selected according to their leaf traits, the different fractions of PM accumulated on the leaves (large - PML, coarse - PMC, and fine - PMF) and their chemical composition. The study was conducted in four city zones: urban traffic (UT), urban background (UB), industrial (IND), and rural (RUR), comparing winter (W) and summer (S) seasons. The average PM levels in the air and accumulated on the leaves were higher in W than in S season. During both seasons, the highest PM accumulated on the leaves was recorded at the UT zone. Nine species were selected as the most suitable for accumulating PML, seven as the most efficient for accumulating PMC, and six for accumulating PMF. The leaf area and leaf roundness were correlated negatively with PM accumulation. The evergreen species L. nobilis was indicated as suitable for dealing with air pollution based on PM10 and PM2.5 values recorded in the air. Regarding the PM element and metal composition, L. nobilis, Photinia x fraseri, Olea europaea, Quercus ilex and Nerium oleander were selected as species with notable elements and metal accumulation. In summary, the study identified species with higher PM accumulation capacity and assessed the seasonal PM accumulation patterns in different city zones, providing insights into the species interactions with PM and their potential for monitoring and coping with air pollution.

Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment.

The Arctic is an important natural laboratory that is extremely sensitive to climatic changes and its monitoring is, therefore, of great importance. Due to the environmental extremes it is often hard to deploy sensors and observations are limited to a few sparse observation points limiting the spatial and temporal coverage of the Arctic measurement. Given these constraints the possibility of deploying a rugged network of low-cost sensors remains an interesting and convenient option. The present work validates for the first time a low-cost sensor array (AIRQino) for monitoring basic meteorological parameters and atmospheric composition in the Arctic (air temperature, relative humidity, particulate matter, and CO2). AIRQino was deployed for one year in the Svalbard archipelago and its outputs compared with reference sensors. Results show good agreement with the reference meteorological parameters (air temperature (T) and relative humidity (RH)) with correlation coefficients above 0.8 and small absolute errors (≈1 °C for temperature and ≈6% for RH). Particulate matter (PM) low-cost sensors show a good linearity (r2 ≈ 0.8) and small absolute errors for both PM2.5 and PM10 (≈1 µg m-3 for PM2.5 and ≈3 µg m-3 for PM10), while overall accuracy is impacted both by the unknown composition of the local aerosol, and by high humidity conditions likely generating hygroscopic effects. CO2 exhibits a satisfying agreement with r2 around 0.70 and an absolute error of ≈23 mg m-3. Overall these results, coupled with an excellent data coverage and scarce need of maintenance make the AIRQino or similar devices integrations an interesting tool for future extended sensor networks also in the Arctic environment.

Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors.

A low-cost air quality station has been developed for real-time monitoring of main atmospheric pollutants. Sensors for CO, CO2, NO2, O3, VOC, PM2.5 and PM10 were integrated on an Arduino Shield compatible board. As concerns PM2.5 and PM10 sensors, the station underwent a laboratory calibration and later a field validation. Laboratory calibration has been carried out at the headquarters of CNR-IBIMET in Florence (Italy) against a TSI DustTrak reference instrument. A MATLAB procedure, implementing advanced mathematical techniques to detect possible complex non-linear relationships between sensor signals and reference data, has been developed and implemented to accomplish the laboratory calibration. Field validation has been performed across a full "heating season" (1 November 2016 to 15 April 2017) by co-locating the station at a road site in Florence where an official fixed air quality station was in operation. Both calibration and validation processes returned fine scores, in most cases better than those achieved for similar systems in the literature. During field validation, in particular, for PM2.5 and PM10 mean biases of 0.036 and 0.598 µg/m³, RMSE of 4.056 and 6.084 µg/m³, and R² of 0.909 and 0.957 were achieved, respectively. Robustness of the developed station, seamless deployed through a five and a half month outdoor campaign without registering sensor failures or drifts, is a further key point.