Performance Assessment of Two Low-Cost PM2.5 and PM10 Monitoring Networks in the Padana Plain (Italy).

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 for PM10 concentrations were compared vs. regulatory-grade stations during a full "heating season" (15 October 2022-15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM10 than PM2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R2. AirQino stations under-estimated both PM2.5 and PM10 reference concentrations (MB = -4.8 and -2.9 µg/m3, respectively), while PurpleAir stations over-estimated PM2.5 concentrations (MB = +5.4 µg/m3) and slightly under-estimated PM10 concentrations (MB = -0.4 µg/m3). PurpleAir stations were finer than AirQino at capturing the time variation of both PM2.5 and PM10 daily concentrations (R2 = 0.68-0.75 vs. 0.59-0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM2.5/PM10 ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM2.5 vs. PM10 outputs.

Direct observations of CO2 emission reductions due to COVID-19 lockdown across European urban districts.

The measures taken to contain the spread of COVID-19 in 2020 included restrictions of people's mobility and reductions in economic activities. These drastic changes in daily life, enforced through national lockdowns, led to abrupt reductions of anthropogenic CO2 emissions in urbanized areas all over the world. To examine the effect of social restrictions on local emissions of CO2, we analysed district level CO2 fluxes measured by the eddy-covariance technique from 13 stations in 11 European cities. The data span several years before the pandemic until October 2020 (six months after the pandemic began in Europe). All sites showed a reduction in CO2 emissions during the national lockdowns. The magnitude of these reductions varies in time and space, from city to city as well as between different areas of the same city. We found that, during the first lockdowns, urban CO2 emissions were cut with respect to the same period in previous years by 5% to 87% across the analysed districts, mainly as a result of limitations on mobility. However, as the restrictions were lifted in the following months, emissions quickly rebounded to their pre-COVID levels in the majority of sites.

Unveiling the changes in urban atmospheric CO2 in the time of COVID-19 pandemic: A case study of Florence (Italy).

The outbreak of COVID-19 pandemic was accompanied by global mobility restrictions and slowdown in manufacturing activities. Accordingly, cities experienced a significant decrease of CO2 emissions. In this study, continuous measurements of CO2 fluxes, atmospheric CO2 concentrations and δ13C-CO2 values were performed in the historical center of Florence (Italy) before, during and after the almost two-month long national lockdown. The temporal trends of the analyzed parameters, combined with the variations in emitting source categories (from inventory data), evidenced a fast response of flux measurements to variations in the strength of the emitting sources. Similarly, the δ13C-CO2 values recorded the change in the prevailing sources contributing to urban atmospheric CO2, confirming the effectiveness of carbon isotopic data as geochemical tracers for identifying and quantifying the relative contributions of emitting sources. Although the direct impact of restriction measurements on CO2 concentrations was less clear due to seasonal trends and background fluctuations, an in-depth analysis of the daily local CO2 enhancement with respect to the background values revealed a progressive decrease throughout the lockdown phase at the end of the heating season (>10 ppm), followed by a net increase (ca. 5 ppm) with the resumption of traffic. Finally, the investigation of the shape of the frequency distribution of the analyzed variables revealed interesting aspects concerning the dynamics of the systems.

The role of emissions and meteorology in driving CO2 concentrations in urban areas.

A multi-year dataset of measurements of CO2 concentrations, eddy covariance fluxes, and meteorological parameters over the city centre of Florence (Italy) has been analysed to assess the role of anthropogenic emissions and meteorology in controlling urban CO2 concentrations. The latter exhibited a negative correlation with air temperature, wind speed, solar radiation, and sensible heat flux and a positive one with relative humidity and emissions. A linear and an artificial neural network (ANN) model have been developed and validated for short-term modelling of 3-h CO2 concentrations. The ANN model performed better, with mean bias of 0.58 ppm, root mean square error within 30 ppm, and r2=0.49. Data clustering through the self-organized maps allowed to disentangle the role played by emissions and meteorological parameters in influencing CO2 concentrations. Sensitivity analysis of CO2 concentrations revealed a primary role played by the meteorological parameters, particularly wind speed. These results highlighted that (i) emission reduction actions at local urban scale should be better tied to actual and expected meteorological conditions and (ii) those actions alone have limited effects (e.g. a 20% emission reduction would result in a 3% CO2 concentrations reduction). For all these reasons, large-scale policies would be needed.

Quantifying road traffic impact on air quality in urban areas: A Covid19-induced lockdown analysis in Italy.

Covid19-induced lockdown measures caused modifications in atmospheric pollutant and greenhouse gas emissions. Urban road traffic was the most impacted, with 48-60% average reduction in Italy. This offered an unprecedented opportunity to assess how a prolonged (∼2 months) and remarkable abatement of traffic emissions impacted on urban air quality. Six out of the eight most populated cities in Italy with different climatic conditions were analysed: Milan, Bologna, Florence, Rome, Naples, and Palermo. The selected scenario (24/02/2020-30/04/2020) was compared to a meteorologically comparable scenario in 2019 (25/02/2019-02/05/2019). NO2, O3, PM2.5 and PM10 observations from 58 air quality and meteorological stations were used, while traffic mobility was derived from municipality-scale big data. NO2 levels remarkably dropped over all urban areas (from -24.9% in Milan to -59.1% in Naples), to an extent roughly proportional but lower than traffic reduction. Conversely, O3 concentrations remained unchanged or even increased (up to 13.7% in Palermo and 14.7% in Rome), likely because of the reduced O3 titration triggered by lower NO emissions from vehicles, and lower NOx emissions over typical VOCs-limited environments such as urban areas, not compensated by comparable VOCs emissions reductions. PM10 exhibited reductions up to 31.5% (Palermo) and increases up to 7.3% (Naples), while PM2.5 showed reductions of ∼13-17% counterbalanced by increases up to ∼9%. Higher household heating usage (+16-19% in March), also driven by colder weather conditions than 2019 (-0.2 to -0.8 °C) may partly explain primary PM emissions increase, while an increase in agriculture activities may account for the NH3 emissions increase leading to secondary aerosol formation. This study confirmed the complex nature of atmospheric pollution even when a major emission source is clearly isolated and controlled, and the need for consistent decarbonisation efforts across all emission sectors to really improve air quality and public health.

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.

Industrial point source CO2 emission strength estimation with aircraft measurements and dispersion modelling.

CO2 remains the greenhouse gas that contributes most to anthropogenic global warming, and the evaluation of its emissions is of major interest to both research and regulatory purposes. Emission inventories generally provide quite reliable estimates of CO2 emissions. However, because of intrinsic uncertainties associated with these estimates, it is of great importance to validate emission inventories against independent estimates. This paper describes an integrated approach combining aircraft measurements and a puff dispersion modelling framework by considering a CO2 industrial point source, located in Biganos, France. CO2 density measurements were obtained by applying the mass balance method, while CO2 emission estimates were derived by implementing the CALMET/CALPUFF model chain. For the latter, three meteorological initializations were used: (i) WRF-modelled outputs initialized by ECMWF reanalyses; (ii) WRF-modelled outputs initialized by CFSR reanalyses and (iii) local in situ observations. Governmental inventorial data were used as reference for all applications. The strengths and weaknesses of the different approaches and how they affect emission estimation uncertainty were investigated. The mass balance based on aircraft measurements was quite succesful in capturing the point source emission strength (at worst with a 16% bias), while the accuracy of the dispersion modelling, markedly when using ECMWF initialization through the WRF model, was only slightly lower (estimation with an 18% bias). The analysis will help in highlighting some methodological best practices that can be used as guidelines for future experiments.

Legal immigrants: invasion of alien microbial communities during winter occurring desert dust storms.

BACKGROUND: A critical aspect regarding the global dispersion of pathogenic microorganisms is associated with atmospheric movement of soil particles. Especially, desert dust storms can transport alien microorganisms over continental scales and can deposit them in sensitive sink habitats. In winter 2014, the largest ever recorded Saharan dust event in Italy was efficiently deposited on the Dolomite Alps and was sealed between dust-free snow. This provided us the unique opportunity to overcome difficulties in separating dust associated from "domestic" microbes and thus, to determine with high precision microorganisms transported exclusively by desert dust. RESULTS: Our metagenomic analysis revealed that sandstorms can move not only fractions but rather large parts of entire microbial communities far away from their area of origin and that this microbiota contains several of the most stress-resistant organisms on Earth, including highly destructive fungal and bacterial pathogens. In particular, we provide first evidence that winter-occurring dust depositions can favor a rapid microbial contamination of sensitive sink habitats after snowmelt. CONCLUSIONS: Airborne microbial depositions accompanying extreme meteorological events represent a realistic threat for ecosystem and public health. Therefore, monitoring the spread and persistence of storm-travelling alien microbes is a priority while considering future trajectories of climatic anomalies as well as anthropogenically driven changes in land use in the source regions.

WhiteRef: a new tower-based hyperspectral system for continuous reflectance measurements.

Proximal sensing is fundamental to monitor the spatial and seasonal dynamics of ecosystems and can be considered as a crucial validation tool to upscale in situ observations to the satellite level. Linking hyperspectral remote sensing with carbon fluxes and biophysical parameters is critical to allow the exploitation of spatial and temporal extensive information for validating model simulations at different scales. In this study, we present the WhiteRef, a new hyperspectral system designed as a direct result of the needs identified during the EUROSPEC ES0903 Cost Action, and developed by Fondazione Edmund Mach and the Institute of Biometeorology, CNR, Italy. The system is based on the ASD FieldSpec Pro spectroradiometer and was designed to acquire continuous radiometric measurements at the Eddy Covariance (EC) towers and to fill a gap in the scientific community: in fact, no system for continuous spectral measurements in the Short Wave Infrared was tested before at the EC sites. The paper illustrates the functioning of the WhiteRef and describes its main advantages and disadvantages. The WhiteRef system, being based on a robust and high quality commercially available instrument, has a clear potential for unattended continuous measurements aiming at the validation of satellites' vegetation products.