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By means of available ice nucleating particle (INP) parameterization schemes we compute profiles of dust INP number concentration utilizing Polly-XT and CALIPSO lidar observations during the INUIT-BACCHUS-ACTRIS 2016 campaign. The polarization-lidar photometer networking (POLIPHON) method is used to separate dust and non-dust aerosol backscatter, extinction, mass concentration, particle number concentration (for particles with radius > 250 nm) and surface area concentration. The INP final products are compared with aerosol samples collected from unmanned aircraft systems (UAS) and analyzed using the ice nucleus counter FRIDGE.

Aerosol absorption profiling is crucial for radiative transfer calculations and climate modelling. Here, we utilize the synergy of lidar with sun-photometer measurements to derive the absorption coefficient and single scattering albedo profiles during the ACTRIS-2 campaigns held in Germany, Greece and Cyprus. The remote sensing techniques are compared with in situ measurements in order to harmonize and validate the different methodologies and reduce the absorption profiling uncertainties.

Trees play a crucial role in mitigating climate change by absorbing C O 2 and providing biophysical cooling. The European Commission's climate policies underscore the importance of forest monitoring systems to achieve substantial greenhouse gas reductions by 2030. In Cyprus, an EU member state located in the Eastern Mediterranean, and a climate change hot-spot, increasingly impacted by forest fires and more arid conditions, the absence of a comprehensive tree monitoring system hinders effective carbon stock assessment and land-based mitigation strategies. The exact tree population inside and outside forests is currently unknown. Artificial Intelligence is a powerful tool that can enable the development of tree monitoring systems by applying machine learning models to high-resolution image data. This study presents a deep learning neural network model applied to high resolution (10 cm) airborne images collected during the year 2019, to generate segmented tree crowns and the number of individual trees over selected areas of Cyprus, including a large national forest park, a forest park in the capital city, and a small urban area, encompassing a total studied area of 107 k m 2 . The model, previously applied in Denmark and Finland was completely re-tuned using local annotations to account for Cyprus's specific conditions and achieved an overall accuracy of 90 % and 93 % to estimate the area covered by tree crowns and the number of trees, respectively. The results are regressed against coarser resolution tree cover maps to predict the area covered by tree crowns at a national level. The accuracy of the tree cover maps created by this study is compared to those of existing global tree cover maps, such as the Copernicus products. This work lays the foundation for establishing a tree-level inventory for Cyprus using airborne remote-sensing. [ABSTRACT FROM AUTHOR]

We present top-down global gridded emissions of NOx for the year 2022. This dataset is constructed from retrievals of tropospheric vertical column densities of NO2 by the TROPOMI spaceborne instrument associated with winds and atmospheric composition data from ECMWF reanalyses, using an improved version of a mass-balance atmospheric inversion. The emissions are provided with a spatial resolution of 0.0625°×0.0625° and deliver a detailed overview of the distribution of emissions. They allow the identification of intense area sources and isolated emitters, and the quantification of their associated emissions. At global level, the emissions obtained are consistent with the EDGARv6.1 bottom-up inventory, although there are differences at regional level, particularly in emerging countries and countries with low observation densities. The three largest emitting countries, China, the United States and India, are 11, 16 and 6 % lower than EDGAR estimates. Uncertainties remain high, and a quantitative analysis of emissions over several averaging periods indicates that averaging emissions uniformly across the year may be sufficient to obtain estimates consistent with annual averages, in regions of the world with high retrieval densities. This dataset is designed to be updated with a low latency to help policymakers monitor emissions and implement energy savings and clean air quality policies. The data can be accessed at https://doi.org/10.5281/zenodo.13957837 as monthly files (Rey-Pommier et al., 2024). [ABSTRACT FROM AUTHOR]

The contents of water-soluble major's ions (MSA, Cl−, NO3−, SO42−, Na+, NH4+, K+, Mg2+, and Ca2+) in the PM10 particle fraction were investigated thanks to detailed measurements of the main chemical constituents of PM10 in remote coastal areas in Bou-Ismail; in the South-West of the Mediterranean Sea (Algeria), during a 2-year period; from July 2011 to August 2013, under the framework of the ChArMEx project (Chemistry-Aerosol Mediterranean Experiment, http://charmex.lsce.ipsl.fr). The total water-soluble ion concentrations in PM10 at the Bou-Ismail measurement station varied from 3.3 µg/m3 (July 2011) to 49.6 µg/m3 (March 2012). The annual mean mass concentrations of ions in the PM10 particulate fraction were Cl− > Na+ > SO42− > Mg2+ > NO3− > Ca2+ > K+ > NH4+ > Oxalate. The change in potassium nss-K + concentrations in PM10 over the course of a year reveals that biomass burning (BB) has an effect on three separate seasons: the beginning of winter (February and March), the end of summer (August), and the autumn (September and October). The origin periods of biomass burning BB identified employing the mapping of hotspots and fires during periods of August and September 2011, 2012, and 2013 underlined the important fires in the surrounding areas of the Mediterranean Sea (Sardinia Islands from Italy, Corsica from France). [ABSTRACT FROM AUTHOR]

In this study, we present a new approach for the determination of polarization parameters of the Nicosia Cimel CE376 lidar system, using the Polly XT in Limassol as a reference instrument. The method is applied retrospectively to the measurements obtained during the 2021 Cyprus Fall Campaign. Lidar depolarization measurements represent valuable information for aerosol typing and for the quantification of some specific aerosol types such as dust and volcanic ash. An accurate characterization is required for quality measurements and to remove instrumental artifacts. In this article, we use the Polly XT , a widely used depolarization lidar, as our reference to evaluate the CE376 system's gain ratio and channel cross-talk. We use observations of transported dust from desert regions for this approach, with layers in the free troposphere. Above the boundary layer and the highest terrain elevation of the region, we can expect that, for long-range transport of aerosols, local effects should not affect the aerosol mixture enough for us to expect similar depolarization properties at the two stations (separated by ∼ 60 km). Algebraic equations are used to derive polarization parameters from the comparison of the volume depolarization ratio measured by the two systems. The applied methodology offers a promising opportunity to evaluate the polarization parameters of a lidar system, in cases where a priori knowledge of the cross-talk parameters is not available, or to transfer the polarization parameters from one system to the other. [ABSTRACT FROM AUTHOR]

Source apportionment of PM 2.5 was performed using positive matrix factorization (PMF) based on detailed chemical composition data from 24 h filter samples collected over a 3-month period (August–November 2020) at an urban site in Montréal, a Canadian city with a population of approximately 4×106 people. This source apportionment study, which examined the main contributing sources to PM 2.5 using a larger suite of organic molecular markers than other Canadian studies, is the first of its sort in Canada. A focus of this study was on quantifying previously unresolved sources of PM 2.5 through the inclusion in the PMF analysis of additional organic molecular markers beyond those measured typically by the Canadian government's National Air Pollution Surveillance Program (NAPS). The organic species included in the PMF model were comprised of six n -alkanes, two fatty acids, one dicarboxylic acid, two biogenic secondary organic aerosol (SOA) tracers, and hopane. Secondary inorganic aerosols (SIAs) and SOAs were the dominant components and constituted 39 % of the measured PM 2.5 mass, while the local primary anthropogenic sources, namely traffic exhaust, road dust, industrial, and cooking emissions, contributed 23 %. The chemical transport model GEOS-Chem revealed that ammonium sulfate concentrations in Montréal are strongly influenced by both local sources in Quebec and transboundary input from the United States, with the transboundary input exceeding the local emissions for SOA. Co and Cr(VI) presented an elevated cancer risk, highlighting that more attention should be given to these trace metals, which were associated with industrial emissions by the PMF analysis. Furthermore, the results showed that industrial emissions were minor contributors to the total PM 2.5 mass concentration but were the largest contributors to Co and Cr(VI) concentrations. Thus, the health hazards associated with this source cannot be entirely established by the PM 2.5 mass concentration alone. This study highlights that, when evaluating air quality in Montréal and other urban regions, the prioritization of sources for mitigation strategies will diverge if one considers total PM 2.5 mass concentration or the concentration of individual particulate-bound contaminants. Furthermore, the large transboundary contribution from the United States to total PM 2.5 levels suggests that future municipal, provincial, and federal monitoring and regulations would be more effective if they focus on specific high-risk contaminants (e.g., Co and Cr(VI) rather than total PM 2.5). [ABSTRACT FROM AUTHOR]