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Whilst there is a consensus that aerosol particles and primary biological aerosol particles (PBAPs) play an important role in regulating the global climate, with aerosols-PBAPs research increasing lately in Brazil, information summarizing the available knowledge is limited. Here, we present a systematic review of research published during the last 35-years on aerosols-PBAPs in Brazil. A total of 212 studies encompassing 474 cases met the selection criteria. The Amazon rainforest was the most studied biome represented by 72% of cases, followed by the Atlantic Forest with 18%. Studies focusing on the Amazon rainforest mostly studied climate-related issues and aerosol physics, with less than 5% examining the biological identity of aerosols, whereas outside the Amazon rainforest this number reached 16%. In addition, more than half of the cases within Amazon (55%) were held at seven sampling sites only, but conclusions were mainly extrapolated to the entire biome. On the other hand, research beyond Amazon has mostly addressed temporal and biological characterization of PBAPs, and not only is it scattered, but also scarce. Regarding sampling effort, most cases (72%) have had less than 100 days of sampling, and 60% of them spanned less than half a year of study, confining research to one or two seasons at the most. Consequently, while research from different countries that conduce their studies within Brazil and scientific fields are focusing on the same area, inconsistences are slowing the progress of this research topic. We argue that scientists from different fields of research (e.g., biologists, physicists) and countries should work together to produce more detailed and complete assessments of aerosols-PBAPs in the country as a whole, particularly on regards to their biological identity, given their importance to global climate regulation.

The current socio-economic and political scenario is posing serious challenges to energy usage, mobility, residential and industrial activities, which may impact local emission patterns, fossil fuel vs alternative fuels usage and air quality in unknown ways. On top of this, winter 2022-23 is being exceptionally warm across the European continent.The investigation of the interactions between air quality, meteorological factors and emissions sources at high spatio-temporal resolution plays a crucial role to detect areas and periods characterized by critical conditions, especially in complex urban environments. Data at such fine resolution can today be obtained through dense low-cost sensor networks integrating sparse official air quality stations. This research aims to assess meteorological forcing, emission proxies (i.e., population density, CO2 local enhancement, land-use) and particulate matter concentrations (PMs) for the last three winter seasons (2020-2023) characterized by contrasting conditions. The study was focused on the Lucca plain (central Italy), an area characterized by heterogeneous emission sources and classified among the most polluted in the region. Sixteen low-cost stations (“AirQino”), equipped with high-frequency sensors to detect PM10, PM2.5, air-temperature, relative humidity (RH), and CO2 concentration were installed on an area spanning approximately 10 x 20 km. Local land use surrounding the measurement stations was obtained from the Corine Land Cover. Mixing Layer Height (Hmix) and wind data were obtained from a WRF-CALMET coupled model. Population density was computed with demographic data from the Global Human Settlement database. For CO2 data, the nocturnal enhancement normalized by wind speed with respect the minimum midday values was used as an emission proxy (ΔCO2). Population density and land use including fractions of residential, industrial, and agricultural areas were computed in a 1-km radius surrounding each station.Preliminary comparisons between winter 2021/2022 and 2022/23 were performed to assess the effects of energy crisis and climatic conditions on air-quality. Different multivariate regression models were developed to investigate the inference between Hmix, air-temperature, RH, ΔCO2, population density, land use and PMs concentrations.Most recent analysis shows a consistent and robust positive air temperature anomaly in November-December 2022 vs 2021 (+ 1.25 °C). CO2 enhancements were on average 20% lower (-19.0 to +3.7 ppm in 2022 vs 2021 respectively) suggesting a relevant emission reduction occurring at the landscape scale. However, these reductions did not always translate into PM10 and PM2.5 reductions and air quality improvements. Among the 16 stations, 11 exhibited a decrease in PM2.5 concentration (-21.60 % on average) and only 9 in PM10 (-21.55 % on average). The locations where air quality did not improve or was even worse this year are characterized by the lowest population density and lowest presence of residential neighborhoods, being mostly in rural or rural/industrial contexts. This suggests that alternative fuels such as wood biomass are likely replacing natural gas in peri-urban areas for domestic heating, generating a negative impact on air quality that could be much worst when usual lower temperature conditions will be met. A complete multivariate analysis to confirm these patterns will be presented.

Whilst fungi are a large fraction of primary biological aerosol particles (PBAPs) and their impact on global climate has been widely recognised, few studies have empirically assessed fungal vertical profiles and diversity relating those with rainfall. Here, we show the results of fungal PBAPs before and after a rainfall event during a fieldwork campaign using a hot-air balloon over a mixed land-use context at the Brazilian Atlantic Forest biodiversity hotspot. Four flights of c. 1 hour each were performed in the early morning from 8th until 11th of March 2022, and data were collected at three sampling heights (0, 150 and 300 m). Rainfall estimation using IMERG data indicated the precipitation event was of 15–20 mm and ERA5/ECMWF data highlighted that most of the airborne samples were taken above the boundary layer height. After the rainfall, the concentration of fungal spores at the ground level remained unchanged, whereas it was reduced to between 2- and 2.5-fold for the 150 and the 300 m heights, respectively. This was also accompanied by a reduction in the number of Pink-CFU, indicating a major drop in fungal PBAPs at higher altitudes associated with the rain. In addition, total spore concentration indicated Cladosporium sp. as dominant at all sampling heights, accounting for more than 80% of all spores, whereas Aspergillus/Penicillium-like represented less than 20%. Our results show the effects of rainfall and altitude on the concentration of fungal PBAPs, indicating how wet removal impacts fungi vertical profiles which has knock-on-effects on cloud and precipitation formation.

Lakes in the Alpine region are recognised as critical CH4 emitters, but a robust characterisation of the magnitude and variability of CH4 fluxes is still needed. We developed a mobile platform for CH4 eddy covariance (EC) flux measurements to tackle this gap. Our approach was shown to be well suited to catch all CH4 emission pathways and overcome the limitations of other methods (e.g., gradient-based). This is by surpassing their local nature and thus being suited for characterising the variability of the within-lake emissions, primarily because of CH4 emissions by ebullition stochasticity. The mobile system was deployed at nine lakes across a latitudinal transect in the Alps and validated by comparing the measured fluxes with a fixed EC station and to chambers and boundary layer estimates. Methane fluxes were explained by water turbidity, dissolved organic carbon, dissolved nitrogen, elevation, particulate organic carbon, and total phosphorus. The highest fluxes and most substantial seasonal variability were found in a shallow low-altitude lake in the Southern Alps. Additionally, the mobile EC permitted to resolve the spatial structure of fluxes at the selected lakes. Finally, we demonstrated the usability of our novel mobile system to characterise intra- and inter-lake variability of fluxes. We suggest that characterising the intra-lake emission heterogeneity and a deeper understanding of inter-lake emission magnitude differences is fundamental for a solid estimate of freshwater CH4 budgets.Key PointsCH4 emissions from alpine lakes are recognised to be an important component to the global methane budget but they are poorly characterizedWe developed and validated a mobile eddy covariance platform for capturing CH4 fluxes across lakes in the alpine region for two yearsA robust statistical model based on a fewin-situphysicochemical and biological parameters can be generally used to predict CH4 fluxes

International audience; 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.

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 CO

Low-cost air quality stations can provide useful data that can offer a complete picture of urban air quality dynamics, especially when integrated with daily measurements from reference air quality stations. However, the success of such deployment depends on the measurement accuracy and the capability of resolving spatial and temporal gradients within a spatial domain. In this work, an ensemble of three low-cost stations named “AirQino” was deployed to monitor particulate matter (PM) concentrations over three different sites in an area affected by poor air quality conditions. Data of PM2.5 and PM10 concentrations were collected for about two years following a protocol based on field calibration and validation with a reference station. Results indicated that: (i) AirQino station measurements were accurate and stable during co-location periods over time (R2 = 0.5–0.83 and RMSE = 6.4–11.2 μg m−3, valid data: 87.7–95.7%), resolving current spatial and temporal gradients, (ii) spatial variability of anthropogenic emissions was mainly due to extensive use of wood for household heating, (iii) the high temporal resolution made it possible to detect time occurrence and strength of PM10 concentration peaks, (iv) the number of episodes above the 1-h threshold of 90 μg m−3 and their persistence were higher under urban and industrial sites compared to the rural area.

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., Graphical abstract Unlabelled Image

The worldwide restrictions imposed to contain the spread of Coronavirus (COVID-19) disease markedly affected social and economic systems, undeniably disrupting people’s habits. At the same time, the reduction of industrial and commercial activities and limitation of movements led to significant decline in most greenhouse gas (GHG) emissions, improving urban air quality. Nevertheless, worldwide CO2 emission reduction was not accompanied by detectable CO2 concentration decreasing, that continued to grow at a global scale. The relationship between emission rate and urban atmospheric GHG concentrations represents a fundamental tool for monitoring activities aimed at indicating strategies to reduce and buffer GHG concentrations in the urban atmosphere. Generally, the occurrence of many different GHG sources (e.g. industry activities, domestic heating) in urban areas does not allow to evaluate the efficiency of short-term interventions on a specific source of contamination to mitigate urban air pollution (i.e. traffic restriction or reduction of energy use). The COVID-19 lockdown has provided a unique opportunity to empirically evaluate the effect on CO2 urban plume of both total and sector-specific anthropogenic emission cutting related to traffic dramatic decrease, followed by the stop of the domestic heating and the progressive resumption of urban normal functions at the end of the lockdown period.In Italy, the first country in Europe to adopt stringent restrictions, the lockdown (mainly consisting of movement limitation of all people and restrictions involving commercial and industrial sectors) was established from March 9, 2020, during and until the end of the heating season, to May 4, 2020, when vehicular traffic and economic activity progressively resumed. In this study, real-time data of concentration and carbon isotopic composition of CO2 at ground level (2 m height) and of eddy covariance (EC) CO2 flux at ~33 m above the ground level were measured in the historical center of Florence (Italy), from April 2 to June 4, 2020 and from February 1 to June 4, 2020, respectively. As expected, a clear stepwise decrease in CO2 fluxes occurred, evidencing a rapid response of the EC measurements to drop in the urban emissions related to COVID-19-containment measures and domestic heating switch-off. Accordingly, during the observation period a relatively small decrease (i.e. few ppm) in the CO2 concentrations at both ground level and 33 m height was recorded. Moreover, an overall increasing trend of 13C/12C ratios of CO2 and daily CO2-enhancement was observed concomitantly with the gradual easing of severe COVID-19 restrictions.These trends highlighted that the COVID-19-related short-term (few months) drastic reduction of anthropogenic emission caused, at a local scale, a rapid response of CO2 urban plume. Hence, the COVID-19 crisis made us aware of the importance of our actions to fight the CO2-related climate change, although a worldwide CO2 atmospheric concentration reduction requires a radical and long-lasting CO2 emission cutting and lifestyle changes from each of us.

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.

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.

Clouds containing supercooled water are important for both climate and weather, but our knowledge of which aerosol particle types nucleate ice in these clouds is far from complete. Combustion aerosols have strong anthropogenic sources, and if these aerosol types were to nucleate ice in clouds, they might exert a climate forcing. Here, we quantified the atmospheric ice‐nucleating particle (INP) concentrations during the United Kingdom's annual Bonfire Night celebrations, which are characterized by large amounts of combustion aerosol from bonfires and fireworks. We used three immersion mode techniques covering more than 6 orders of magnitude in INP concentration over the temperature range from −10°C to homogeneous freezing. We found no observable systematic change in the INP concentration on three separate nights, despite more than a factor of 10 increase in aerosol number concentrations, up to a factor of 10 increase in PM10 concentration, and more than a factor of 100 increase in black carbon (BC) mass concentration relative to pre‐event levels. This implies that BC and other combustion aerosol such as ash did not compete with the INPs present in the background air. Furthermore, the upper limit of the ice‐active site surface density, ns(T), of BC generated in these events was shown to be consistent with several other recent laboratory studies, showing a very low ice‐nucleating activity of BC. We conclude that combustion aerosol particles similar to those emitted on Bonfire Night are at most of secondary importance for the INP population relevant for mixed‐phase clouds in typical midlatitude terrestrial locations. © 2020 American Geophysical Union. ISSN:0148-0227 ISSN:2169-897X

Air pollution is currently one of the main issues affecting urbanized areas worldwide. Local administrations monitor these harmful gases by means of reference monitoring stations provided by regional/national environmental protection agencies. These stations, however, have limitations due to coarse spatial coverage of the whole municipality, low time-frequency, and high costs. In this framework, the National Research Council of Italy (CNR-IBE) and the Tuscany Region Environmental Protection Agency (ARPAT) agreed to an initiative to create a low-cost network aimed at monitoring air quality over an Italian urban area. The rural town of Capannori, located in the Tuscany region (Italy), was chosen as a testing area since it lies within a critical area both affected by a variety of emission sources and weather conditions unfavourable to pollutant dispersion. The air quality analysis was carried out by means of several innovative low-cost stations named AIRQino, equipped with sensors for collecting air pollution (PM2.5, PM10, NO2, O3, CO, CO2) and meteorological parameters (air temperature and relative humidity). Concentrations of PM were mainly considered in this work for providing indicative air quality measurements to supplement fixed measurements collected by the official urban monitoring network. This work, still ongoing, has two main objectives: (i) to show the robustness of AIRQino at measuring PM concentrations; (ii) to investigate the PM concentrations dynamics at higher spatial and time scale distribution compared to the reference station.

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., Graphical abstract Image 1, Highlights • Air quality impacts of Covid19 lockdown over 6 major Italian cities were assessed. • Daily observations of air quality, meteorological parameters and mobility were used. • Against a dramatic road traffic reduction (48–60%), NO2 levels reduced by 24.9–59.1%. • O3 levels remained basically unchanged or slightly increased (up to 11.4–14.7%). • PM daily concentrations slightly decreased, at most by 17% (PM2.5) and 32.1% (PM10)., Main finding A 2-month urban traffic ban extended to the whole Italy only significantly reduced NO2 levels, while O3, PM2.5 and PM10 concentrations were affected to a minor extent.

As the basis of livestock feeding and related performances, pastures evolution and dynamics need to be carefully monitored and assessed, particularly in the Alps where the effects of land abandonment are further amplified by climate change. As such, increases in temperature associated with changes in precipitation patterns and quantity are leading to modifications of grassland extent and composition with consequences on the pastoral systems. This study applied a machine learning approach (Random Forest) and GIS techniques to map the suitability of seven pasture macro types most representative of the Italian Alps and simulated the impact of climate change on their dynamics according to two future scenarios (RCP4.5, 8.5), two time-slices (2011&ndash, 2040, 2041&ndash, 2070), and three RCMs (Aladin, CMCC, ICTP). Results indicated that (i) the methodology was robust to map the current suitability of pasture macro types (mean accuracy classification = 98.7%), so as to predict the expected alterations due to climate change, (ii) future climate will likely reduce current extend of suitable pasture (&minus, 30% on average) and composition, especially for most niche ecosystems (i.e., pastures dominated by Carex firma and Festuca gr. Rubra), (iii) areas suited to hardier but less palatable pastures (i.e., dominated by Nardus stricta and xeric species) will expand over the Alps in the near future. These impacts will likely determine risks for biodiversity loss and decreases of pastoral values for livestock feeding, both pivotal aspects for maintaining the viability and profitability of the Alpine pastoral system as a whole.

In forests with dense mixed canopies, laser scanning is often the only effective technique to acquire forest inventory attributes, rather than structure-from-motion optical methods. This study investigates the potential of laser scanner data collected with a low-cost unmanned aerial vehicle laser scanner (UAV-LS), for individual tree crown (ITC) delineation to derive forest biometric parameters, over two-layered dense mixed forest stands in central Italy. A raster-based local maxima region growing algorithm (itcLiDAR) and a point cloud-based algorithm (li2012) were applied to isolate individual tree crowns, compute height and crown area, estimate the diameter at breast height (DBH) and the above ground biomass (AGB) of individual trees. To maximize the level of detection rate, the ITC algorithm parameters were tuned varying 1350 setting combinations and matching the segmented trees with field measured trees. For each setting, the delineation accuracy was assessed by computing the detection rate, the omission and commission errors over three forest plots. Segmentation using itcLiDAR showed detection rates between 40% and 57%, while ITC delineation was successful at segmenting trees with DBH larger than 10 cm (detection rate ~78%), while failed to detect trees with smaller DBH (detection rate ~37%). The performance of li2012 was quite lower with the higher detection rate equal to 27%. Errors and goodness-of-fit between field-surveyed and flight-derived biometric parameters (AGB and tree height) were species-dependent, with higher error and lower r2 for shorter species that constitute the lowermost layer of the forest. Overall, while the application of UAV-LS to delineate tree crowns and estimate biometric parameters is satisfactory, its accuracy is affected by the presence of a multilayered and multispecies canopy that will require specific approaches and algorithms to better deal with the added complexity.

A multi-year dataset of measurements of CO

Lakes are considered an important natural source of methane (CH4). However, direct measurements of lake-atmosphere gas exchange are still sparse especially in the Alpine region. To overcome this shortcoming, we designed a mobile eddy covariance (EC) station to measure CO2, CH4, and energy fluxes at various lakes in the Alps. EC measurements were compared to flux measurements using floating chambers and related to abiotic and biotic factors like temperature, lake morphometry, dissolved components and trophic status.During the first year, measurements were conducted at 9 lakes at different elevations ranging from 200 to 1900 m.a.s.l. to capture the spatial variability. The following year, measurements were repeated more frequently at three contrasting lakes to capture the seasonal trends of the fluxes.The results indicate that all lakes were supersaturated with CH4. However, there was a high variability in the magnitude of CH4 emissions between lakes with generally higher emissions from warmer lakes at low elevation. In particular, the lake at the lowest elevation, Lake Caldaro, had highest dissolved CH4 concentrations and emissions and showed a clear seasonal trend with emissions peaking during the hot summer months. In contrast, the lake at the highest elevation, Lake Zoccolo, showed low CH4 concentrations and emissions with highest concentrations in fall when the water level was low.

The Arctic is one critical environment for monitoring climate change as well as variations in background concentrations of atmospheric components. The associated logistic difficulties, though, make hard to deploy an extensive monitoring network of sensors, limiting long time-series to only sparse and costly point observations. Low-cost sensors are experiencing a widespread employment in research and monitoring applications and could be an interesting tool to deploy spatialized monitoring networks even in extreme environments. In this context, two CNR Labs (IBE and ISAC) made a long-term deployment of a prototypal low-cost sensor for atmospheric composition monitoring in the polar research village of Ny-Ålesund (Svalbard, Norway). In about one year of measurements the low-cost sensor showed: i) a good consistency in the data series with minimal data loss, ii) no significant requirements for maintenance and iii) the capability of capturing the main atmospheric trends of the Arctic lower troposphere.

A linear and an artificial neural network (ANN) statistical model have been developed and validated for short-term forecasting of PM10 hourly concentrations in the city of Brescia (Italy). PM10 observed concentrations were biased by less than 1% by each model, though the ANN outperformed the linear model, as exhibiting NRMSE of 0.48 vs. 0.53, and r2 of 0.57 vs. 0.48. The self-organizing maps (SOMs) showed that both models predictions exhibit the same clustering as the observations, with the ANN at worst capable of under-estimating clustered PM10 peak concentrations by 5.8 μg/m3. In Brescia, PM10 most critical conditions were detected in wintertime in the early morning or late afternoon under unfavourable meteorological conditions, i.e. reduced advection enhancing PM10 stagnation, and lack of precipitations capable of reducing PM10 resuspension. Under these conditions, PM10 accumulation is driven by local anthropogenic emissions ascribing to two main sources: heating plants, responsible of emissions of primary PM10 (mostly PM2.5, likely resulting from wood and biomass burning); and road traffic (basically diesel vehicles), mainly responsible of emissions of secondary PM10 precursors (mostly NOx), and secondly of primary PM10 emissions. The SOM analysis clearly indicated that PM10 most critical conditions are driven by the secondary rather primary PM10 component.

A minute fraction of atmospheric particles exert a disproportionate effect on the phase of mixed-phase clouds by acting as ice-nucleating particles (INPs). To understand the effects of these particles on weather and climate, both now and into the future, we must first develop a quantitative understanding of the major INP sources worldwide. Previous work has demonstrated that aerosols such as desert dusts are globally important INPs, but the role of biogenic INPs is unclear, with conflicting evidence for their importance. Here, we show that at a temperate site all INPs active above −18 °C at concentrations >0.1 L−1 are destroyed on heating, consistent with these INPs being of biological origin. Furthermore, we show that a global model of desert dust INPs dramatically underestimates the measured INP concentrations, but is consistent with the thermally-stable component. Notably, the heat sensitive INPs are active at temperatures where shallow cloud layers in Northern Europe are frequently observed to glaciate. Hence, we suggest that biogenic material is important for primary ice production in this region. The prevalence of heat sensitive, most likely biogenic, INPs in this region highlights that, as a community, we need to quantify the sources and transport of these particles as well as determine their atmospheric abundance across the globe and at cloud altitudes.

Lakes have a controversial climate footprint. In fact, they are a sink of CO2 but at the same time they can be an important source of CH4. Indeed, a global synthesis of methane emission data (Bastv...

Unmanned aerial vehicles (UAVs) or remotely piloted aircraft systems are new platforms that have been increasingly used over the last decade in Europe to collect data for forest research, thanks to the miniaturization and cost reduction of GPS receivers, inertial navigation system, computers, and, most of all, sensors for remote sensing. In this review, after describing the regulatory framework for the operation of UAVs in the European Union (EU), an overview of applications in forest research is presented, followed by a discussion of the results obtained from the analysis of different case studies. Rotary-wing and fixed-wing UAVs are equally distributed among the case studies, while ready-to-fly solutions are preferred over self-designed and developed UAVs. Most adopted technologies are visible-red, green, and blue, multispectral in visible and near-infrared, middle-infrared, thermal infrared imagery, and lidar. The majority of current UAV-based applications for forest research aim to inventory resources, map diseases, classify species, monitor fire and its effects, quantify spatial gaps, and estimate post-harvest soil displacement. Successful implementation of UAVs in forestry depends on UAV features, such as flexibility of use in flight planning, low cost, reliability and autonomy, and capability of timely provision of high-resolution data. Unfortunately, the fragmented regulations among EU countries, a result of the lack of common rules for operating UAVs in Europe, limit the chance to operate within Europe's boundaries and prevent research mobility and exchange opportunities. Nevertheless, the applications of UAVs are expanding in different domains, and the use of UAVs in forestry will increase, possibly leading to a regular utilization for small-scale monitoring purposes in Europe when recent technologies (i.e. hyperspectral imagery and lidar) and methodological approaches will be consolidated.

The insurance industry reports a pronounced intensification, at the global level, of weather-related events such as droughts, windstorms and hailstorms. As an efficient quantification tool, improved capacities can be built adopting innovative remote sensing methods to map vegetation damage spatial distribution, to quantify its intensity and impact. New airborne LiDAR (Light Detection and Ranging) sensors provide high vertical resolution data, which are potentially suitable not only for forest canopies but also for monitoring shorter crop canopies (e.g. corn − Zea mays L.) for crop injury and lodging assessment. To evaluate the potential of LiDAR metrics to map corn canopy height and hail defoliation, a flight campaign was organized in 2014 in Wampersdorf (Austria) in a cropland area affected by a hailstorm. Ground-truth observations were carried out in 16 plots, where defoliation was assessed both visually (observed range from 0% to 70%) and using a biophysical parameter-based method. The performance of both traditional and newly-introduced metrics (i.e. Canopy Metric, Ground Metric) was assessed at different sampling point densities. The results showed the ability of LiDAR data to map both corn canopy height and defoliation (predicted vs. observed regression: R 2 = 0.69 for both canopy height and defoliation; point density 5 and 42 points/m 2 , respectively). The presented approach has distinct advantages compared to previous remote sensing methods and has a clear application potential for farmers and insurance industries. Larger-scale studies are needed to verify its best implementation strategies and to investigate its economic and logistic benefits.

Air pollution is currently one of main issues affecting urbanized areas worldwide. Industrial activities, road traffic and heating systems are main emission sources significantly increasing the levels of atmospheric pollutants as particulate matter, ozone, and nitrogen oxides. Local administrations monitor these harmful gases by means of reference monitoring stations provided by regional/national environmental protection agencies. These stations, however, have limitations due to the little presence over the whole municipality, low time-frequency, and high costs. In this framework, CNR-IBE, University of Florence – Department of Agriculture, food, environment, and forestry (DAGRI), Tuscany Region Environmental Protection Agency (ARPAT) and epidemiologists of the Pisa University agreed an initiative to create an environmental “living lab” aimed at assessing the impacts due to anthropogenic activities on air quality and thus on population exposure. Two study areas located in the Tuscany region (Italy) were chosen: the rural town of Capannori, and the city of Florence. The town of Capannori was selected since it lies within a critical area both affected by a variety of emission sources and winter weather conditions unfavourable to pollutant dispersion. The city of Florence was chosen for assessing air quality in urban areas following a possible traffic reduction due to creation of new urban tramway lines. The air quality analysis was carried out by means of a monitoring network comprising innovative low-cost stations (named AIRQino). PM concentrations were mainly considered for providing indicative air quality measurements. The preliminary results indicated that: i) low-cost stations, after calibration and validation against more than one-year observations from a reference air quality station, confirmed their reliability in measuring air quality data; ii) AIRQino data can supplement air quality information from reference stations and may be used to help traffic regulation actions at urban scale.

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 &ldquo, heating season&rdquo, (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 &micro, g/m3, RMSE of 4.056 and 6.084 &micro, g/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.

Monitoring the Arctic regions allows understanding the impact of global warming on Earth's climate but some processes affecting the climate change cannot be fully discerned because data are sparse and even missing due to difficulties in collecting them in hazardous area. This is particularly true in the proximity of the arctic calving glaciers that induce changes in the physicochemical and biological properties of the seawater. Unmanned marine and aerial vehicles allow the in-situ observing of such processes. In June 2017 CNR-ISSIA, in collaboration with other institutes of the CNR (IAMC, ISMAR, ISAC, IBIMET) and UNITUS, has conducted a measurement campaign in the Kongsfjord, a small fiord in Svalbard. A customised octocopter (OTTO) capable of carrying various types of heavy payloads was used. A set of low cost and high-resolution sensors were used to analyse the column of air in order to monitor atmospheric parameters and gases (humidity, temperature, CO, CO2, O3, NO2). A thermal camera was also installed on the drone to reconstruct a thermal map of the sea/ice in the area surveyed by the Unmanned Marine Vehicle PROTEUS equipped with underwater sensor. The measurements performed allowed to fully characterise the state of air-sea interface near marine glacier fronts. A preliminary data analysis to correlate the different measured quantities shows interesting results but further experiment need to be done to achieve a complete understanding of the processes.