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Morocco’s economy has recently seen an unprecedented growth, which made the country emerge into the 5th strongest economy of the African continent. As other countries at the same stage of development, Morocco is also undergoing rapid urbanization, which results in increased anthropogenic air-pollution levels. The geographical location of the country makes it vulnerable to both short and longterm impacts of climate change. Despite the elevated risk and vulnerability, the current air pollution and climate change monitoring strategy of the country needs improvement. The present paper introduces a novel strategy to improve the air quality monitoring system in the capital combined with developing a project-based climate education curriculum, and promises to raise public awareness to the risks related to air pollution and climate change. The sustainability of the project is guaranteed by the low cost of instruments and the educational aspect which ensures that future experts are well-trained for the maintenance of the system that we propose to install.

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.

Measurements of cloud condensation nuclei (CCN) concentrations between 0.2-1.0% supersaturation and aerosol size distribution were performed at an urban background site of Athens during HygrA-CD. The site is affected by local and long-range transported emissions as portrayed by the external mixing of the particles, as the larger ones appear to be more hygroscopic and more CCN-active than smaller ones. Activation fractions at all supersaturations exhibit a diurnal variability with minimum values around noon, which are considerably lower than unity. This reinforces the conclusion that the aerosol is mostly externally mixed between “fresher”, less hygroscopic components with more aged, CCN active constituents.

Airborne campaigns dedicated to satellite validation are crucial for the effective global aerosol monitoring. CALIPSO is currently the only active remote sensing satellite mission, acquiring the vertical profiles of the aerosol backscatter and extinction coefficients. Here we present a method for CALIPSO evaluation from combining lidar and in-situ airborne measurements. The limitations of the method have to do mainly with the in-situ instrumentation capabilities and the hydration modelling. We also discuss the future implementation of our method in the ICE-D campaign (Cape Verde, August 2015).

We describe a novel biosafety aerosol chamber equipped with state-of-theart instrumentation for bubble-bursting aerosol generation, size distribution measurement, and condensation-growth collection to minimize sampling artifacts when measuring virus infectivity in aerosol particles. Using this facility, we investigated the effect of relative humidity (RH) in very clean air without trace gases (except ~400 ppm CO2) on the preservation of influenza A virus (IAV) infectivity in saline aerosol particles. We characterized infectivity in terms of 99%-inactivation time, t99, a metric we consider most relevant to airborne virus transmission. The viruses remained infectious for a long time, namely t99 > 5 h, if RH < 30% and the particles effloresced. Under intermediate conditions of humidity (40% < RH < 70%), the loss of infectivity was the most rapid (t99 ∾ 15-20 min, and up to t99 ∾ 35 min at 95% RH). This is more than an order of magnitude faster than suggested by many previous studies of aerosol-borne IAV, possibly due to the use of matrices containing organic molecules, such as proteins, with protective effects for the virus. We tested this hypothesis by adding sucrose to our aerosolization medium and, indeed, observed protection of IAV at intermediate RH (55%). Interestingly, the t99 of our measurements are also systematically lower than those in 1-mL droplet measurements of organic-free saline solutions, which cannot be explained by particle size effects alone. [ABSTRACT FROM AUTHOR]

China has long-term high PM2.5 levels, and its oxidative potential (OP) is worth studying as it may unravel the impacts of aerosol pollution on public health better than PM2.5 alone. OP refers to the ability of PM2.5 to induce oxidative stress (OS). OP and PM2.5 are influenced by meteorological factors, anthropogenic emission sources, and atmospheric aging. Although their impact on PM2.5 has been studied, OP measurements only recently became available and on a limited scale, as they require considerable technical expertise and resources. For this, the joint relationship between PM2.5 and OP for a wide range of meteorological conditions and emission profiles remain elusive. Towards this, we estimated PM2.5 and OP over China using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from the Weather Research and Forecasting (WRF) model. It was found that higher values of PM2.5 and OP were primarily concentrated in urban agglomerations in the central and eastern regions of China, while lower values were found in the western and northeastern regions. Furthermore, the probability density function revealed that about 40 % of areas in China had annual average PM2.5 concentrations exceeding the Chinese concentration limit. For OP, 36 % of the regions have OP below 1 nmolmin-1m-3 , 41 % have OP between 1 and 2 nmolmin-1m-3 , and 23 % have OP above 2 nmolmin-1m-3 , which are in line with previous measurement studies. Analysis of the simulations indicates that meteorological conditions contributed 46 % and 65 % to PM2.5 concentrations and OP variability, respectively, while anthropogenic emissions contributed 54 % and 35 % to PM2.5 concentrations and OP variability, respectively. The emission sensitivity analysis also highlighted the fact that PM2.5 and OP levels are mostly determined by secondary aerosol formation and biomass burning. [ABSTRACT FROM AUTHOR]

Given the importance of aerosols and clouds and their interactions in the climate system, it is imperative that the global Earth system models accurately represent processes associated with them. This is an important prerequisite if we are to narrow the uncertainties in future climate projections. In practice, this means that continuous model evaluations and improvements grounded in observations are necessary. Numerous studies in the past few decades have shown both the usability and the limitations of utilizing satellite-based observations in understanding and evaluating aerosol–cloud interactions, particularly under varying meteorological and satellite sensor sensitivity paradigms. Furthermore, the vast range of spatio-temporal scales at which aerosol and cloud processes occur adds another dimension to the challenges faced when evaluating climate models. In this context, the aim of this study is two-fold. (1) We evaluate the most recent, significant changes in the representation of aerosol and cloud processes implemented in the EC-Earth3-AerChem model in the framework of the EU project FORCeS compared with its previous CMIP6 version (Coupled Model Intercomparison Project Phase 6; https://pcmdi.llnl.gov/CMIP6/ , last access: 13 February 2019). We focus particularly on evaluating cloud physical properties and radiative effects, wherever possible, using a satellite simulator. We report on the overall improvements in the EC-Earth3-AerChem model. In particular, the strong warm bias chronically seen over the Southern Ocean is reduced significantly. (2) A statistical, maximum covariance analysis is carried out between aerosol optical depth (AOD) and cloud droplet (CD) effective radius based on the recent EC-Earth3-AerChem/FORCeS simulation to understand to what extent the Twomey effect can manifest itself in the larger spatio-temporal scales. We focus on the three oceanic low-level cloud regimes that are important due to their strong net cooling effect and where pollution outflow from the nearby continent is simultaneously pervasive. We report that the statistical covariability between AOD and CD effective radius is indeed dominantly visible even at the climate scale when the aerosol amount and composition are favourably preconditioned to allow for aerosol–cloud interactions. Despite this strong covariability, our analysis shows a strong cooling/warming in shortwave cloud radiative effects at the top of the atmosphere in our study regions associated with an increase/decrease in CD effective radius. This cooling/warming can be attributed to the increase/decrease in low cloud fraction, in line with previous observational studies. [ABSTRACT FROM AUTHOR]

Aerosol–cloud interactions in mixed-phase clouds (MPCs) are one of the most uncertain drivers of the hydrological cycle and climate change. A synergy of in situ, remote-sensing and modelling experiments were used to determine the source of ice-nucleating particles (INPs) for MPCs at Mount Helmos in the eastern Mediterranean. The influences of boundary layer turbulence, vertical aerosol distributions and meteorological conditions were also examined. When the observation site is in the free troposphere (FT), approximately 1 in ×106 aerosol particles serve as INPs around -25 °C. The INP abundance spans 3 orders of magnitude and increases in the following order: marine aerosols; continental aerosols; and, finally, dust plumes. Biological particles are important INPs observed in continental and marine aerosols, whereas they play a secondary, although important, role during Saharan dust events. Air masses in the planetary boundary layer (PBL) show both enriched INP concentrations and a higher proportion of INPs to total aerosol particles, compared with cases in the FT. The presence of precipitation/clouds enriches INPs in the FT but decreases INPs in the PBL. Additionally, new INP parameterizations are developed that incorporate the ratio of fluorescent-to-nonfluorescent or coarse-to-fine particles and predict >90 % of the observed INPs within an uncertainty range of a factor of 10; these new parameterizations exhibit better performance than current widely used parameterizations and allow ice formation in models to respond to variations in dust and biological particles. The improved parameterizations can help MPC formation simulations in regions with various INP sources or different regions with prevailing INP sources. [ABSTRACT FROM AUTHOR]

The purpose of this study is to understand the drivers of cloud droplet formation in orographic clouds. We used a combination of modeling, in situ, and remote sensing measurements at the high-altitude Helmos Hellenic Atmospheric Aerosol and Climate Change ((HAC) 2) station, which is located at the top of Mt. Helmos (1314 m above sea level), Greece, during the Cloud–AerosoL InteractionS in the Helmos Background TropOsphere (CALISHTO) campaign in fall 2021 (https://calishto.panacea-ri.gr/ , last access: 1 August 2024) to examine the origins of the aerosols (i.e., local aerosol from the planetary boundary layer (PBL) or long-range-transported aerosol from the free-tropospheric layer (FTL) contributing to the cloud condensation nuclei (CCN)), their characteristics (hygroscopicity, size distribution, and mixing state), and the vertical velocity distributions and resulting supersaturations. We found that the characteristics of the PBL aerosol were considerably different from FTL aerosol and use the aerosol particle number and equivalent mass concentration of the black carbon (eBC) in order to determine when (HAC) 2 was within the FTL or PBL based on time series of the height of the PBL. During the (HAC) 2 cloud events we sample a mixture of interstitial aerosol and droplet residues, which we characterize using a new approach that utilizes the in situ droplet measurements to determine time periods when the aerosol sample is purely interstitial. From the dataset we determine the properties (size distribution and hygroscopicity) of the pre-cloud, activated, and interstitial aerosol. The hygroscopicity of activated aerosol is found to be higher than that of the interstitial or pre-cloud aerosol. A series of closure studies with the droplet parameterization shows that cloud droplet concentration (Nd) and supersaturation can be predicted to within 25 % of observations when the aerosol size distributions correspond to pre-cloud conditions. The analysis of the characteristic supersaturation of each aerosol population indicates that droplet formation in clouds is aerosol-limited when formed in FTL air masses – hence droplet formation is driven by aerosol variations, while clouds formed in the PBL tend to be velocity-limited and droplet variations are driven by fluctuations in vertical velocity. Given that the cloud dynamics do not vary significantly between air masses, the variation in aerosol concentration and type is mostly responsible for these shifts in cloud microphysical state and sensitivity to aerosol. With these insights, the remote sensing of cloud droplets in such clouds can be used to infer either CCN spectra (when in the FTL) or vertical velocity (when in the PBL). In conclusion, we show that a coordinated measurement of aerosol and cloud properties, together with the novel analysis approaches presented here, allows for the determination of the drivers of droplet formation in orographic clouds and their sensitivity to aerosol and vertical velocity variations. [ABSTRACT FROM AUTHOR]

Remote sensing has been a key resource for developing extensive and detailed datasets for studying and constraining aerosol-cloud-climate interactions. However, aerosol-cloud collocation challenges, algorithm limitations, as well as difficulties in unraveling dynamic from aerosol-related effects on cloud microphysics, have long challenged precise retrievals of cloud droplet number concentrations. By combining a series of remote sensing techniques and in situ measurements at ground level, we developed a semiautomated approach that can address several retrieval issues for a robust estimation of cloud droplet number for non-precipitating Planetary Boundary Layer (PBL) clouds. The approach is based on satellite retrievals of the PBL cloud droplet number (Ndsat) using the geostationary meteorological satellite data of the Optimal Cloud Analysis (OCA) product, which is obtained by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The parameters of the retrieval are optimized through closure with droplet number obtained from a combination of ground-based remote sensing data and in situ observations at ground level. More specifically, the remote sensing data are used to retrieve cloud-scale vertical velocity, and the in situ aerosol measurements at ground level were used constrain as input to a state-of-the-art droplet activation parameterization to predict the respective Cloud Condensation Nuclei (CCN) spectra, cloud maximum supersaturation and droplet number concentration (Nd), accounting for the effects of vertical velocity distribution and lateral entrainment. Closure studies between collocated Nd and Nd sat are then used to evaluate exising droplet spectral width parameters used for the retrieval of droplet number, and determine the optimal values for retrieval. This methodology, used to study aerosol-cloud interactions for non-precipitating clouds formed over the Athens Metropolitan Area (AMA), Greece, during the springtime period from March to May 2020, shows that droplet closure can be achieved to within ±33.4%, comparable to the level of closure obtained in many in situ studies. Given this, the ease of applying this approach with satellite data obtained from SEVIRI with high temporal (15 min) and spatial resolution (3.6 km Ã--4.6 km), opens the possibility of continuous and reliable Ndsat, giving rise to high value datasets for aerosol-cloud-climate interaction studies. [ABSTRACT FROM AUTHOR]

Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement: Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months. [ABSTRACT FROM AUTHOR]

The Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISHTO) campaign took place in autumn 2021 at the NCSR Demokritos background high-altitude Helmos Hellenic Atmospheric Aerosol and Climate Change station (HAC) 2 to study the interactions between aerosols and clouds. The current study presents the chemical characterization of the non-refractory (NR) PM 1 aerosol fraction using a time-of-flight aerosol chemical speciation monitor (ToF-ACSM). A comparative offline aerosol filter analysis by a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) showed consistent results regarding the species determined. Source apportionment applied on both datasets (ACSM-ToF and offline AMS analysis on filter extracts) yielded the same factors for the organic aerosol (one primary and two secondary factors). Additionally, the positive matrix factorization (PMF) model was applied on the total PM 1 fraction by the ToF-ACSM (including both organic and inorganic ions). Five different types were identified, including a primary organic factor; ammonium nitrate; ammonium sulfate; and two secondary organic aerosols, one more oxidized and one less oxidized. The prevailing atmospheric conditions at the station, i.e., cloud presence, influence of emissions from the planetary boundary layer (PBL), and air mass origin, were also incorporated in the study. The segregation between PBL and free-troposphere (FT) conditions was made by combining data from remote sensing and in situ measurement techniques. The types of air masses arriving at the site were grouped as continental, marine, dust, and marine–dust based on back-trajectory data. Significant temporal variability in the aerosol characteristics was observed throughout the campaign; in September, air masses from within the PBL were sampled most of the time, resulting in much higher mass concentrations compared to October and November when concentrations were reduced by a factor of 5. Both in-cloud and FT measurement periods resulted in much lower concentration levels, while a similar composition was observed in PBL and FT conditions. We take advantage of using a recently developed "virtual-filtering" technique to separate interstitial from activated aerosol sampled from a PM 10 inlet during cloudy periods. This allows the determination of the chemical composition of the interstitial aerosol during in-cloud periods. Ammonium sulfate, the dominant PMF factor in all conditions, contributed more when air masses were arriving at (HAC) 2 during dust events, while a higher secondary organic aerosol contribution was observed when air masses arrived from continental Europe. [ABSTRACT FROM AUTHOR]

This paper presents the findings from a collaborative interlaboratory comparison exercise designed to assess oxidative potential (OP) measurements conducted by 20 laboratories worldwide. This study represents an innovative effort as the first exercise specifically aimed at harmonising this type of OP assay, setting a new benchmark in the field. Over the last decade, there has been a noticeable increase in OP studies, with numerous research groups investigating the effects of exposure to air pollution particles through the evaluation of OP levels. However, the absence of standardised methods for OP measurements has resulted in variability in results across different groups, rendering meaningful comparisons challenging. To address this issue, this study engages in an international effort to compare OP measurements using a simplified method (with a dithiothreitol (DTT) assay). Here, we quantify the OP in liquid samples to focus on the protocol measurement itself, while future ILCs should aim to assess the full-chain process, including the sample extraction. We analyse the similarities and discrepancies observed in the results, identifying the critical parameters (such as the instrument used, the use of a simplified protocol, the delivery and analysis time) that could influence OP measurements, and provide recommendations for future studies and interlaboratory comparisons. Even if other crucial aspects, such as sampling PM methods, sample storage, extraction methods and conditions, and the evaluation of other OP assays, still need to be standardised. This collaborative approach enhances the robustness of the OP-DTT assay and paves the way for future studies to build on a unified framework. This pioneering work concludes that interlaboratory comparisons provide essential insights into the OP metric and are crucial to move toward the harmonisation of OP measurements. [ABSTRACT FROM AUTHOR]

Accurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties and processes. Here, we introduce a machine‐learning (ML) approach, called Random Forest SIP (RaFSIP), to parameterize SIP in stratiform MPCs. RaFSIP is trained on 16 grid points with 10‐km horizontal spacing derived from a 2‐year simulation with the Weather Research and Forecasting (WRF) model, including explicit SIP microphysics. Designed for a temperature range of 0 to −25°C, RaFSIP simplifies the description of rime splintering, ice‐ice collisional break‐up, and droplet‐shattering using only a limited set of inputs. RaFSIP was evaluated offline before being integrated into WRF, demonstrating its stable online performance in a 1‐year simulation keeping the same model setup as during training. Even when coupled with the 50‐km grid spacing domain of WRF, RaFSIP reproduces ICNC predictions within a factor of 3 when compared to simulations with explicit SIP microphysics. The coupled WRF‐RaFSIP scheme replicates regions of enhanced SIP and accurately maps ICNCs and liquid water content, particularly at temperatures above −10°C. Uncertainties in RaFSIP minimally impact surface cloud radiative forcing in the Arctic, resulting in radiative biases under 3 Wm−2 compared to simulations with detailed microphysics. Although the performance of RaFSIP in convective clouds remains untested, its adaptable nature allows for data set augmentation to address this aspect. This framework opens possibilities for GCM simplification and process description through physics‐guided ML algorithms. Plain Language Summary: Being able to correctly simulate the amount of ice and liquid in clouds is essential for accurate predictions of the cloud radiative forcing in the climatologically sensitive polar regions. A number of collisional processes between ice and liquid particles in clouds, known as secondary ice production, can significantly enhance the ice crystal number concentrations contained in them. This enhancement is often accompanied by a decrease in the cloud liquid water content, resulting in less opaque clouds to incoming solar radiation, which, in turn, can cause a cloud‐induced warming at the surface. Currently most global climate models are missing the description of the most important secondary ice production processes, which can lead to a biased radiative impact of clouds at the surface. To address this, we propose using a machine learning algorithm trained on high‐resolution model outputs to include the effect of ice multiplication in large‐scale climate models. The machine learning framework effectively captures the physical processes underlying secondary ice production in stratiform clouds using only a few inputs readily available in model frameworks. This approach has the potential to improve model predictions bringing them closer to the observed cloud phase partitioning. Key Points: A random‐forest parameterization for secondary ice production is developed using outputs from a 10‐km horizontal grid spacing simulationCloud phase partitioning agrees within a factor of 3, with radiative biases below 3 Wm−2 compared to the detailed microphysics simulationThe scheme can be adjusted to coarser resolutions typical of climate models without losing computational efficiency and numerical stability [ABSTRACT FROM AUTHOR]

Aerosol-cloud interactions, and particularly ice crystals in mixed-phase clouds (MPC), stand as a key source of uncertainty in climate change assessments. State-of-the-art laboratory-based parameterizations were introduced into a global chemistry-transport model to investigate the contribution of mineral dust, marine primary organic aerosol (MPOA), and terrestrial primary biological aerosol particles (PBAP) to ice nucleating particles (INP) in MPC. INP originating from PBAP (INPPBAP) are found to be the primary source of INP at low altitudes between -10°C and -20°C, particularly in the tropics, with a pronounced peak in the Northern Hemisphere (NH) during boreal summer. INPPBAP contributes about 27% (in the NH) and 30% (in the SH) of the INP population. Dust-derived INP (INPD) show a prominent presence at high altitudes in all seasons, dominating at temperatures below -25°C, constituting 68% of the INP average column burden. MPOA-derived INP (INPMPOA) dominate in the Southern Hemisphere (SH), particularly at subpolar and polar latitudes at low altitudes for temperatures below -16°C, representing approximately 46% of INP population in the SH. When evaluated against available global observational INP data, the model achieves its highest predictability across all temperature ranges when both INPD and INPMPOA are included. The additional introduction of INPPBAP slightly reduces model skills for temperatures lower than -16oC; however, INPPBAP are the main contributors to warm-temperature ice nucleation events. Therefore, consideration of dust and marine and terrestrial bioaerosol as INP precursors is required to simulate ice nucleation in climate models. In this respect, emissions, ice-nucleating activity of each particle type and its evolution during atmospheric transport require further investigations. [ABSTRACT FROM AUTHOR]

Given the importance of aerosols, clouds and their interactions in the climate system, it is imperative that the global Earth system models accurately represent processes associated with them. This is an important prerequisite if we were to narrow the uncertainties in future climate projections. In practice, this means that the continuous model evaluations and improvements grounded in observations are necessary. Numerous studies in the last few decades have shown both the usability and the limitations of utilizing satellite-based observations in understanding and evaluating aerosol-cloud interactions, particularly under varying meteorological and satellite sensor sensitivity paradigms. Furthermore, the vast range of spatio-temporal scales at which aerosol and cloud processes occur adds another dimension to the challenges while evaluating climate models. In this context, the aim of this study is two-fold. 1) We evaluate the most recent, significant changes in the representation of aerosol and cloud processes implemented in the EC-Earth3-AerChem model in the framework of the EU project FORCeS compared to its previous CMIP6 version. We focus particularly on evaluating cloud physical properties and radiative effects, wherever possible, using a satellite simulator. We report overall improvements in EC-Earth3-AerChem model. In particular, the strong warm bias chronically seen over the Southern Ocean is reduced significantly. 2) A statistical, maximum covariance analysis is carried out between aerosol optical depth (AOD) and cloud droplet (CD) effective radius based on the recent EC-Earth3-AerChem/FORCeS simulation to understand to what extent the Twomey effect can manifest itself in the larger spatio-temporal scales. We focus on the three oceanic low-level cloud regimes that are important due to their strong net cooling effect and where pollution outflow from the nearby continent is simultaneously pervasive. We report that the statistical covariability between AOD and CD effective radius is indeed dominantly visible even at the climate scale when the aerosol amount and composition are favourably preconditioned for allowing aerosol-cloud interactions. Despite this strong covariability, our analysis shows a strong cooling/warming in shortwave cloud radiative effects at the top of the atmosphere in our study regions associated with an increase/decrease in CD effective radius. And this cooling/warming can be attributed to the increase/decrease in low cloud fraction, in line with the previous observational studies. [ABSTRACT FROM AUTHOR]

Aerosol-cloud interactions in mixed-phase clouds (MPCs) are one of the most uncertain drivers of the hydrological cycle and climate change. A synergy of in-situ, remote sensing and modelling experiments was used to determine the source of ice nucleating particles (INPs) for MPCs at Mount Helmos in the Eastern Mediterranean. The influences of boundary layer turbulence, vertical aerosol distributions and meteorological conditions were also examined. When the observation site is in the Free Troposphere (FT), approximately 1 in 106 aerosol particles serve as INPs. The INP abundance spans three orders of magnitude and increases following the order of marine aerosols, continental aerosols, and finally, dust plumes. Biological particles are important INPs observed in continental and marine aerosols, whereas they play a secondary yet important role even during Saharan dust events. Air masses in the planetary boundary layer (PBL) show both enriched INP concentrations and higher proportion of INPs in comparison to total aerosol particles, different from cases in the FT. The presence of precipitations/clouds enriches INPs in the FT but decreases INPs in the PBL. Additionally, new INP parameterizations, incorporating the ratio of fluorescent-to-nonfluorescent or coarse-to-fine particles and predicting >90% of the observed INPs within an uncertainty range of a factor of 10, exhibit better performance than current widely-used parameterizations, and allow ice formation in models to respond to variations in dust and biological particles. The improved parameterizations can help MPC formation simulations in regions with various INP sources or different regions with prevailing INP source. [ABSTRACT FROM AUTHOR]

This study explores the differences in performance and results by various versions of the ISORROPIA thermodynamic module implemented within the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. Three different versions of the module were used, ISORROPIA II v1, ISORROPIA II v2.3, and ISORROPIA-lite. First, ISORROPIA II v2.3 replaced ISORROPIA II v1 in EMAC to improve pH predictions close to neutral conditions. The newly developed ISORROPIA-lite has been added to EMAC alongside ISORROPIA II v2.3. ISORROPIA-lite is more computationally efficient and assumes that atmospheric aerosols exist always as supersaturated aqueous (metastable) solutions, while ISORROPIA II includes the option to allow for the formation of solid salts at low RH conditions (stable state). The predictions of EMAC by employing all three aerosol thermodynamic models were compared to each other and evaluated against surface measurements from three regional observational networks in the polluted Northern Hemisphere (Interagency Monitoring of Protected Visual Environments (IMPROVE), European Monitoring and Evaluation Programme (EMEP), and Acid Deposition Monitoring Network of East Asia (EANET)). The differences between ISORROPIA II v2.3 and ISORROPIA-lite were minimal in all comparisons with the normalized mean absolute difference for the concentrations of all major aerosol components being less than 11 % even when different phase state assumptions were used. The most notable differences were lower aerosol concentrations predicted by ISORROPIA-lite in regions with relative humidity in the range of 20 % to 60 % compared to the predictions of ISORROPIA II v2.3 in stable mode. The comparison against observations yielded satisfactory agreement especially over the USA and Europe but higher deviations over East Asia, where the overprediction of EMAC for nitrate was as high as 4 µ g m -3 (∼20%). The mean annual aerosol pH predicted by ISORROPIA-lite was on average less than a unit lower than ISORROPIA II v2.3 in stable mode, mainly for coarse-mode aerosols over the Middle East. The use of ISORROPIA-lite accelerated EMAC by nearly 5 % compared to the use of ISORROPIA II v2.3 even if the aerosol thermodynamic calculations consume a relatively small fraction of the EMAC computational time. ISORROPIA-lite can therefore be a reliable and computationally efficient alternative to the previous thermodynamic module in EMAC. [ABSTRACT FROM AUTHOR]

While the number and types of indoor air pollutants is rising, much is suspected but little is known about the impact of their potentially synergistic interactions, upon human health. Gases, particulate matter, organic compounds but also allergens and viruses, fall within the 'pollutant' definition. Distinct populations, such as children and allergy and asthma sufferers are highly susceptible, while a low socioeconomic background is a further susceptibility factor; however, no specific guidance is available. We spend most of our time indoors; for children, the school environment is of paramount importance and potentially amenable to intervention. The interactions between some pollutant classes have been studied. However, a lot is missing with respect to understanding interactions between specific pollutants of different classes in terms of concentrations, timing and sequence, to improve targeting and upgrade standards. SynAir‐G is a European Commission‐funded project aiming to reveal and quantify synergistic interactions between different pollutants affecting health, from mechanisms to real life, focusing on the school setting. It will develop a comprehensive and responsive multipollutant monitoring system, advance environmentally friendly interventions, and disseminate the generated knowledge to relevant stakeholders in accessible and actionable formats. The aim of this article it to put forward the SynAir‐G hypothesis, and describe its background and objectives. [ABSTRACT FROM AUTHOR]

China has long-term high PM2.5 levels, and its Oxidative Potential (OP) is worth studying asit may unravel the impacts of aerosol pollution on public health better than PM2.5 alone. OP and PM2.5 are influenced by meteorological factors, anthropogenic emissions sources and atmospheric aging. Although their impact on PM2.5 have been studied, OP measurements only recently became available and on a limited scale, as they require considerable technical expertise and resources. For this, the joint relationship between PM2.5 and OP for a wide range of meteorological conditions and emissions profiles remain elusive. Towards this, we estimated PM2.5 and OP over China using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from WRF weather forecast model. It was found that higher values of PM2.5 and OP were primarily concentrated in urban agglomerations in the central and eastern regions of China, while lower values were found in the western and northeastern regions. Furthermore, the probability density function revealed that about 40% of areas in China had an annual average PM2.5 concentrations exceeding the Chinese concentrations limit; 36% of the regions have OP below 1 41% have OP between 1 and, and 23% have OP above 2. Analysis of the simulations indicate that meteorological conditions and anthropogenic emission contributed 46% (65%) and 54% (35%) to the PM2.5 concentration (OP) variability. The emission sensitivity analysis also highlighted PM2.5 and OP levels are mostly determined by secondary aerosol formation and biomass burning. [ABSTRACT FROM AUTHOR]

China has long-term high PM2.5 levels, and its Oxidative Potential (OP) is worth studying as it may unravel the impacts of aerosol pollution on public health better than PM2.5 alone. OP and PM2.5 are influenced by meteorological factors, anthropogenic emissions sources and atmospheric aging. Although their impact on PM2.5 have been studied, OP measurements only recently became available and on a limited scale, as they require considerable technical expertise and resources. For this, the joint relationship between PM2.5 and OP for a wide range of meteorological conditions and emissions profiles remain elusive. Towards this, we estimated PM2.5 and OP over China using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from WRF weather forecast model. It was found that higher values of PM2.5 and OP were primarily concentrated in urban agglomerations in the central and eastern regions of China, while lower values were found in the western and northeastern regions. Furthermore, the probability density function revealed that about 40 % of areas in China had an annual average PM2.5 concentrations exceeding the Chinese concentrations limit; 36 % of the regions have OP below 1 푛푚표푙 푚푖푛−1 푚−3, 41 % have OP between 1 and 2 푛푚표푙 푚푖푛−1 푚−3, and 23 % have OP above 2 푛푚표푙 푚푖푛−1 푚−3. Analysis of the simulations indicate that meteorological conditions and anthropogenic emission contributed 46 % (65 %) and 54 % (35 %) to the PM2.5 concentration (OP) variability. The emission sensitivity analysis also highlighted PM2.5 and OP levels are mostly determined by secondary aerosol formation and biomass burning. [ABSTRACT FROM AUTHOR]