Black carbon BC assessment is a new topic of low importance in scientific research in Morocco. The following study aims to understand the temporal variation BC concentrations, their correlation with the meteorological variables, and to estimate the contribution of fossil fuel and biomass combustion to BC and nitrogen dioxide (NO2) during the 2020 autumn season. For this purpose and during seven weeks, in-situ measurements of equivalent black carbon (eBC) and NO2 were conducted simultaneously at an urban background monitoring site in Sale, Morocco. Contribution of fossil fuel (BCff) and biomass burning (BCwb) to eBC was apportioned based on the spectral dependence of the absorption of BC. The average eBC concentration observed was 1.9 ± 1.5 µg/m3. It was found that eBC and NO2 are strongly correlated (r=0.6) and typically emitted from local sources within Sale city. Fossil fuel combustion, mainly from road transport, is the largest contributor to ambient eBC and NO2 concentrations. However, wood combustion makes a significant contribution to the observed eBC of around 28%. The relationship between meteorological variables and BC levels was not significate and likely don’t contribute to daily variations.

Climate and weather conditions have a profound influence on human comfort and discomfort sense. In addition, the impact of emissions and human activities on air quality seems to be scientifically indisputable. The maintenance of low levels of environmental nuisance in areas of high environmental and cultural interest, such as some Greek islands, is becoming increasingly important. Thus, exploring the combination of the effect of air quality and climate comfort in a high-traffic area falls within the scope of the principles and practices of sustainable development of such areas. The current study aims to shed some light on this field for the case of Rhodes city, located in eastern Mediterranean, during the summer of 2021. For the analysis, measurements of the concentration of pollutants (PM_2.5, NO_X and O₃) and meteorological recordings (wind speed, wind direction and temperature) from a mobile air quality system located in the center of Rhodes city were conducted. Furthermore, meteorological data from the ERA5 reanalysis (wind speed, temperature, relative humidity, precipitation, cloud cover and height of boundary layer) over a geographical domain around Rhodes Island are also included in the study. Results show that climate conditions and emissions are closely linked to traffic and tourism activities, which in turn affect the variability of pollutant concentrations. The calculation of the discomfort index shows that during the period of higher levels of air pollution, more than half of the population of Rhodes city feels discomfort while, the holiday climate index values show that the climatic conditions are suitable for tourist activities. In conclusion, this study could enhance our understanding of climate comfort and air quality by providing some evidence of the benefits of implementing a sustainable development policy in such tourist areas.

Tropical cyclones are extreme hydrometeorological events whose impact causes human, material and economic losses. The inaccuracies in the forecast of the trajectory of these phenomena often lead to inefficient decisions, such as unnecessary evacuation. This research proposes the combination of the three forecasting tools Numerical Tools for Hurricane Forecast (NTHF), Sistema de Pronóstico Inmediato (SisPI) and Sistema de Predicción Numérica Océano-Atmósfera (SPNOA) in the generation of ensemble forecasting systems, with the aim of improving the trajectory forecasts of tropical cyclones. Three variants were used for the construction of the time lagged ensemble sets, and for their evaluation the best track and historical errors (2016-2020) of the National Hurricane Center (NHC) were used. The ensembles lead to improved track forecasts for tropical cyclones. Position errors vary from case to case, but ensembles generally tend to be more accurate than independent forecasts. Compared to the historical errors of the NHC, the results obtained are promising because they are superior in some cases.

Since 1976, the mean annual temperature over the territory of Russia has been increasing at a rate of 0.47^oC per decade (in the Arctic at 1^oC per decade). This has laid the ground for shifts in biome boundaries and for large-scale ecosystem restructuring. In fact, based on their temperature parameters, biome boundaries common for the late last century should now shift 400 to 500 km northwards in the Arctic and 200 to 300 km northwards in other climate zones. Climate change projections indicate, that these boundaries will likely shift another 200 to 500 km to the north. Most biomes found in the territory of Russia stretch for about 500 km from north to south. In terms of mean annual temperatures practically no biomes in Russia will stay within their last century’s temperature boundaries by the end of 21^st century. The following ecosystems are the most vulnerable to adverse climate change: Arctic (substantial temperature rise), mountain (a large variety of climate-related hazards), steppe (temperature rise-driven aridization) and the Far East (added impacts of extreme precipitation and strong winds). Creation of protected areas has become a priority measure for the adaptation of ecosystems to climate change. On average, federal protected areas account for 7.6 percent of a biome territory across the country; in two biomes, they exceed 70 percent; in five biomes, no federal protected areas have been established. A large share of protected areas is common for mountainous and poorly developed Arctic regions. For the purpose of effective adaptation to climate change, it is advisable to increase the total territory covered by all category protected areas to 17 percent of each biome.

Achieving the overall goal of carbon neutrality in the middle of 21th century requires comprehensive information about anthropogenic and natural emission and uptake of greenhouse gases (GHG) in different biomes of the world. The area of Northern Eurasia is represented by extremely diverse plant communities and soil types situated within various natural zones including tundra, forest-tundra, forest, wetland and grasslands, and their contributions into the global and regional atmospheric GHG budgets are still very poorly investigated. Information about GHG fluxes in this area can be also very important because the largest part of Northern Eurasia is underlined by continuous permafrost. The thawing of permafrost due to global warming may result in sharp increase of GHG emission into the atmosphere that can have a significant impact on the climate. The relevant information on GHG fluxes in those ecosystems could obviously serve as a basis for a reliable prediction of future climate change and mitigation measures.

The main goal of the study was to obtain new experimental data on seasonal variability of carbon dioxide (CO₂) and methane (CH₄) fluxes in a subarctic palsa mire, as well as to assess the sensitivity of these CO₂ and CH₄ fluxes to changes in environmental conditions. Experimental area is situated at the boundary between northern taiga and forest-tundra ecotone at the North of Central Siberia in Turukhansky district of Krasnoyarsk Krai of Russia. Vegetation of the peatland is a mosaic of perennial frost mounds and peat plateau with a height of 1-1.5 m and flat, mostly unfrozen hollows, 150-200 m wide. Lichens and feather mosses in combination with Betula nana L., Ledum palustre L., Rubus chamaemorus L. and bare peat as well, occupy the mounds, while various species of Carex, feather mosses and herbs are common within the hollows. The CO₂ and CH₄ turbulent fluxes at our site were continuously measured using the eddy covariance method. Intensive field campaigns were conducted from the late winter (early May) to fall (early October) since 2016 (from snow to snow).

The results of field measurements of 2017 and 2018 years showed that the mean daily CO₂ uptake rates significantly exceeded CO₂ emissions for the period between mid-June to the end of August for both years, i.e. the palsa mire ecosystem in the growing season served as a sink of CO₂ from the atmosphere. Maximum CO₂ uptake rate (about 4.5 gC m^-2 d^-1) were observed in July mainly due to high incoming photosynthetically active radiation (PAR) and optimal air temperature and soil moisture conditions. In the late August the balance between CO₂ uptake and CO₂ emissions was close to zero. The net CO₂ fluxes in September were positive showing the net efflux of CO₂ into the atmosphere. Temporal variability of CH₄ fluxes was relatively high and varied between -28 to 74 mgC m^-2 d^-1. Such variability was mainly associated with weather conditions, peat aeration, vegetation growth and functioning, nutrient level, peat temperature and microbial processes responsible for net release of CH₄.

The field measurements provided by V.Zyrianov and A. Panov were founded by the joint grant of RFBR and Krasnoyarsk Regional Science Foundation (20-45-242908). Landscape description conducted by A.Prokushkin was supported by the grant of the Russian Science Foundation (20-17-00043). The data analysis supervised by A.Olchev was supported the grant of the Russian Science Foundation (22-17-00073).

Adequate prediction of the spatial and temporal redistribution of greenhouse gas (GhG) within the atmospheric surface layer requires sophisticated process-based models describing the GhG atmospheric transfer, anthropogenic emission and GhG release and uptake by various terrestrial and marine ecosystems. The three-dimensional hydrodynamic model developed in this study allows describing the turbulent transfer of carbon dioxide (CO₂) within the atmospheric surface layer taking into account the horizontal land surface heterogeneity including complex topography, mosaic vegetation and soil properties. The model is based on the ”one and a half" E-w closure scheme for the system of the averaged Navier-Stokes and continuity equations, allowing to obtain the established spatial distribution of the average wind speed and turbulent exchange coefficient patterns taking into account horizontal vegetation, soil and surface topography heterogeneity. The spatial distribution of CO₂ within the atmospheric surface layer is described using the diffusion-reaction-advection equation (or system of equations) taking into account the spatial distribution of the natural CO₂ sources and sinks. The model considers carbon dioxide released by soil and non-photosynthetic parts of plants, and vegetation uptake by plant leaves due to photosynthesis processes in daylight hours. The obtained spatial pattern of CO₂ distribution is used to describe the spatial patterns of turbulent and advective CO₂ fluxes. The developed model was applied to describe the spatial wind and atmospheric CO₂ flux distribution in a non-uniform forest peatland ecosystem in Central part of European Russia. The modeling results were compared with results of CO₂ flux measurements conducted using the eddy covariance technique and showed very good agreement.

The study was supported by grant of Moscow State University (121051400081-7).

Iran is a semi-arid and arid country in the Middle East region which has faced a water shortage crisis from early times. Hence, various elements of the hydrological cycle should be studied deeply and accurately in this country. Among the various water cycle elements, investigations on the main moisture providing sources of precipitation (as the most important input of water cycle) is so scarce. Moisture for precipitation in Iran is mainly provided by the Red Sea, the Caspian Sea, and the Persian Gulf during the dry period (May to October), while the Arabian Sea, the Persian Gulf, and the Mediterranean Sea provide large amounts of moisture during the wet period (November to April) obtained by FLEXPART v9.0 model. Understanding the role and importance of each moisture source influencing Iran’s precipitation has great application in climatological models to predict droughts across the country. Nowadays, machine learning techniques have improved significantly for simulation and determining the correlation between various parameters in large and complicated data sets due to their high accuracy. In this study, the role and fractional importance of various water bodies providing moisture for Iran’s precipitation has been determined for 35 years (1981-2015) based on the (E-P) values obtained by the FLEXPART model and various machine learning models (artificial neural network (ANN), deep neural network (DNN), decision tree and random forest) and analytical hierarchy process (AHP) and Fuzzy AHP models. The results show that in the wet period, the Arabian Sea in all the developed machine learning and AHP models play the dominant role and its fractional importance varies from 52% to 28.2% of the total importance in decision tree and AHP models, respectively. During the dry period, the Arabian Sea, with 57.8 % and 24.3% of the total importance in AHP and DNN models, respectively, play a dominant role. However, the Mediterranean Sea with 32.83% based on the random forest model, the Black Sea with 24.3% based on the ANN model, and the Indian Ocean with 31.19% of the total importance based on the Fuzzy AHP model’s influence Iran’s precipitation during the dry period.

This study aims to use the regional climate model (RegCM4) to examine the influence of climate change on potential evapotranspiration (PET) of Egypt under two future scenarios. To address such a topic, the calculated PET is first corrected in the historical period with respect to the long-term gridded PET data (Climate Research Unit; CRU) using a linear regression model (LRM) between RegCM4 and CRU. After that, the LRM is used to correct the two future scenarios Representative Concentration Pathways (RCP4.5 and 8.5) of the period 2006-2100. The RegCM4 was downscaled by the medium resolution of the Earth System Model of the Max Planck Institute (MPI-ESM-MR) with 50 km horizontal grid spacing over Middle East and North Africa (MENA) and then nested over Egypt with 20 km horizontal grid spacing. The results showed that the RegCM4 is able to capture the monthly variability of PET with respect to the CRU; furthermore the RegCM4 overestimates/underestimates the PET depending on the location under consideration. Also, the simulated PET was notably improved when the LRM was used. Such improvement is indicated by a low mean bias and a high standard deviation ratio (close to unity) between the corrected PET and CRU. In addition, the future PET projects a strong increased trend under the RCP8.5 scenario; meanwhile the future PET projects a weak increased trend under the RCP4.5 scenario.

This study elucidates the variation of PM_2.5 at Delhi, during the northeast monsoon (NEM) and the southwest monsoon (SWM) of 2014-2019. The seasonal concentrations were observed as during NEM (2014: 178 ± 56 µg/m³, 2015: 125 ± 56 µg/m³, 2016: 71 ± 32 µg/m³, 2017: 144 ± 50 µg/m³, 2018: 77 ± 49 µg/m³, 2019: 83 ± 43 µg/m³) and SWM (2014: 61 ± 18 µg/m³, 2015: 54 ± 23 µg/m³, 2017: 37 ± 20 µg/m³, 2019: 48. ± 13 µg/m³). Further, the elemental composition was achieved by using wavelength dispersive X-ray Fluorescence (WD-XRF). During NEM it was observed that Na, K, and Br contributed dominatingly, whereas, Si, K, and Cr dominated during the SWM season. Moreover, the trajectory profile was adopted to study the long-range transport over the site. Major air parcels were observed from the Sahara Desert (SD), Arabian Sea (AS), and Bay of Bengal (BOB) for both seasons (NEM and SWM), thus significantly affecting the loading of mass concentration at the site Delhi. We have also calculated the hazard quotient (HQ) and hazard index (HI) of elements over Delhi during this period

Air pollution, especially particulate matter (PMx), is one of the most serious environmental threats worldwide. It is challenging in terms of both public health, impact on climate, and the reduction of visibility. The assessment of spatial variability of PMx allows us to understand better the processes causing the smog episodes, and may also be an additional element for the validation of the results of dispersion models. The study presents the results of measurements of basic meteorological parameters and air pollution, involving the multi-sensor system and conducted in a vertical profile. A Matrice 600 hexacopter with installed environmental head was used as the measurement platform. This system is a prototype solution, developed in cooperation between the University of Wrocław and the Optimum Tymiński company. This system enables us to measure the concentrations of PM2.5, PM10, air temperature and humidity. Additionally, all flight parameters are recorded. All data are registered with 1 second time resolution. The measuring head is placed at the top of the device, above the rotors plane. Measurements were carried out in Wrocław (Poland), both in the warm and cold season in 2019-2020, in areas with different land use and emission structure. The main objective of the research was to analyze the variability of the concentration of particulate matter and temperature gradients in the vertical profile up to a height of 350 m above the ground. The mobile data were supplemented by information obtained at the Meteorological Observatory of the University of Wrocław (e.g. sodar echos from acoustic sounding system). In-situ measurements involving UAVs as a mobile platform and high-resolution sensors indicate the usefulness of this method in atmospheric research, providing reliable information on temporal and spatial variability of air quality and temperature. These data can complement both traditional and remote sensing measurements.