This paper presents the concept and development of an autonomous DIY air quality sensor for citizen science. Large civil monitoring projects often rely on air quality calculations based on PM2.5 and PM10 dust readings in combination with some gases and do not cover the full list of air quality indicators. The authors have analyzed existing air quality calculation methodologies and attempted to conceptualize a universal AQI monitoring device for use in citizen science and by volunteers. This device is based on the available ESP32 DevKit v1 platform to which compatible sensors have been selected to monitor AQI indicators such as PM2.5 and PM10 dust particles, Ozone, Carbon Monoxide, Nitrogen Dioxide, Sulphur Dioxide, and Ammonia. The SD card module was chosen for data storage, the NB-IoT module for data transmission, and a battery pack for autonomy. The housing, sensor design components and fasteners were also selected. All components are available on the international market. Based on the selected element base, an electrical connection diagram was designed, the device's design, presented in the form of 3D models, was developed, and the assembly process was described. The cost of the device was also evaluated and compared to the price level of existing DIY devices used in citizen science.

Studying the impact of aircraft emissions on the climate and environment is a complex and important issue, especially given the increasing frequency of extreme weather events linked to climate change. In this study, we examine the potential impact of aircraft emissions from four Egyptian airports on their surrounding areas using the Graz Lagrangian dispersion model (GRAL). Furthermore, we investigate how climate change may interact with potential increases in airport capacity in the future. To analyze the dispersion of pollutants emitted by aircraft at the selected airports, we utilized the GRAL model, which incorporates various inputs related to emission sources and meteorological data. The meteorological data for the studied periods (2021, 2025, 2030, and 2035) were derived from the output of the ICTP regional climate model (RegCM4), using the RCP4.5 climate change scenario as input for the dispersion model. The emissions were calculated based on the expected emissions from several aircraft types during 2021, which served as our reference year. We assumed that airport capacity would increase over the coming years until 2035, leading to an expected rise in pollutants emitted by aircraft. Our results indicate that, based on projected rates of emissions for carbon monoxide, sulfur dioxide, and nitrogen oxides, an increase in the capacity of these airports will not result in pollutant concentrations exceeding the maximum limits established by Egyptian Environmental Law, either at the airports or in the surrounding areas.

Air pollution is a growing concern in urban areas, and fine particulate matter poses significant risks to public health. Fine particulate matter is defined as particles that are 2.5 microns or less in diameter (PM2.5). Emissions from the combustion of gasoline, oil, diesel fuel, and wood produce much of the PM2.5 pollution found in outdoor air.

This work explores the use, for the first time, of machine learning techniques to predict PM2.5 air quality levels in Tashkent, Uzbekistan. The primary goal is to develop robust predictive models that can accurately estimate PM2.5 concentrations based on environmental and temporal factors. Open-source air quality datasets from ten automated air quality monitoring stations were utilized, and additional features, such as weather conditions and seasonal trends, were implemented to improve model accuracy. A hypothesis-driven approach was adopted to test the relevance of these features and assess their impact on model performance. This study employed a range of regression models, starting with linear regression and progressively advancing to more sophisticated methods, including ensemble models such as Random Forest and Gradient Boosting.

The performance of these models was evaluated using the R² metric, with a focus on balancing accuracy and model interpretability.

Our results exhibit the great potential of machine learning in addressing urban air quality challenges and pave the way for informed environmental strategic decision making in Tashkent city and similar urban contexts.

Significant climatic changes have been observed in the Arctic over the past few decades. The ongoing increase in the Earth's surface temperature has accelerated permafrost melting and contributed to a rise in the number of wildfires. In addition, state programs aimed at reducing anthropogenic emissions of certain pollutants (e.g., NOx, SO2, CO, etc.) in the future could lead to an increase in ozone (O₃), one of the most harmful atmospheric pollutants. In the future, these and other consequences of climate change in the Arctic could become more dramatic. Therefore, assessments of changes in the climate and atmospheric composition over the next 100 years on a regional scale (~1000 km) are of great importance today. The assessment results could indicate which regions of the Earth will be most affected by future climate change and anthropogenic activity.

One of the most promising approaches for such evaluations is model downscaling, which uses the simulation results of Earth System Models (ESMs) as boundary conditions in regional-scale models. This approach takes into account both global factors of ESMs (e.g., distant pollutant transport) and local factors of regional-scale modelling (such as complex landscapes and anthropogenic activity). In the current study, the model system, which consists of the SOCOLv4 ESM and the regional tropospheric composition model WRF-Chem, is used to evaluate changes in the climate and atmospheric composition in the Russian Arctic over the next ~100 years.

The authors acknowledge Saint-Petersburg State University for research project 116234986.

Morocco has experienced notable urban and industrial growth in recent years, resulting in increasing air pollution concerns. This study investigates the characteristics of size-resolved aerosols (PM₁₀ and PM_2.5) and their sources across four distinct locations: two Mediterranean coastal cities (Tetouan and Nador) and two Atlantic coastal cities (Kenitra and Salé). Aerosol samples were collected and analyzed for elemental composition (Al, Fe, Ni, V, Cu, Cr, Zn, and Pb) using a range of analytical techniques such as Total X-ray Fluorescence (TXRF), Atomic Absorption Spectroscopy (AAS), Microwave Plasma Atomic Emission Spectrometry (MP-AES), and Instrumental Neutron Activation Analysis (INAA). Gravimetric analysis was performed to determine daily PM mass concentrations. To evaluate the effects of long-range transport, air mass back-trajectories were generated using the HYSPLIT™ model. Source apportionment was conducted using inter-elemental ratios, Positive Matrix Factorization (PMF) receptor modeling, and air mass back-trajectory statistics. The analysis of inter-elemental ratios highlighted urban emissions, largely attributable to traffic and construction activities, as the primary anthropogenic source. Contributions from long-range transport were identified by linking PM mass concentrations with air mass flow directions, demonstrating the significant impact of emissions from the Mediterranean Basin and the Atlantic Ocean on air quality in Moroccan cities. Moreover, the PMF source apportionment indicated that the contributing sources vary between PM fractions. For PM_2.5, major sources were identified as vehicle exhaust/non-exhaust emissions, regional secondary aerosols, and local anthropogenic activities. In contrast, PM₁₀ was predominantly associated with soil dust, re-suspended road dust, and fresh/aged sea salt emissions. Finally, the study highlighted that the PM_2.5/PM₁₀ ratio is site-dependent. Mediterranean coast cities presented higher PM_2.5/PM10 ratios (>0.5), indicating a significant contribution from fine anthropogenic particles. Conversely, Atlantic coastal cities displayed ratios below 0.5, suggesting a predominance of coarse particles, likely emanating from local pollution sources characteristic of those areas.

Various techniques are used to assess air quality. Basic parameters such as particulate matter (PM) and certain gases can be easily obtained from local meteorological stations. However, for more detailed data, such as heavy metal concentrations, researchers must collect and analyze samples in laboratories. Due to natural limitations, regulatory monitoring is often restricted in both spatial and temporal coverage.

Satellite imagery provides a valuable source of atmospheric and surface data. Each year, new missions with advanced sensors enhance remote sensing capabilities. Modern instruments like Sentinel-5 offer near-ready air quality data, including information on gases and aerosols. However, the Sentinel-5's orbital cycle and resolution remain limited. Meanwhile, widely used public satellite missions such as Landsat, MODIS, and Sentinel provide high-resolution data with frequent updates. Integrating in situ measurements with satellite data and machine learning techniques enhances air quality monitoring. Modeling helps fill gaps in in situ data, provides detailed assessments of specific areas, and enables a partial automation of environmental control processes.

This report reviews widely used satellite-based programs, tools for efficient data processing, and machine learning approaches for air quality estimation. It highlights the effectiveness and advantages of ML-driven remote sensing for air quality monitoring. Additionally, we discuss commercial satellite missions, firsthand experiences, and future directions for advancing air quality monitoring technologies.

Introduction
Humic-like substances (HULISs) are a group of substances with no specific chemical structure, resembling the humic substances found in soil and water. They constitute the main component of water-soluble organic matter in the atmosphere and are known to exhibit absorption properties in the ultraviolet to visible light range, potentially influencing atmospheric radiative balance. Understanding their environmental dynamics is therefore crucial. However, existing analytical methods for HULISs involve labor-intensive pretreatment steps, which can lead to reduced quantification accuracy and hinder improvements in analytical efficiency. To address this issue, this study aims to develop a system for HULIS analysis using high-performance liquid chromatography (HPLC) equipped with an integrated sample cleanup mechanism, enabling the direct analysis of atmospheric particles through their simple extraction using water.

Methods
The automated system for sample cleanup and detection consists of four HPLC pumps, an autosampler, a cleanup column (Oasis-HLB, 3 mmφ × 20 mm, Waters), a column heater, a six-port valve, and a photodiode array detector (DAD) . The following steps were performed in accordance with a time program:

1. Before sample injection, the flow path was cleaned with ultrapure water.
2. The cleanup column was conditioned with methanol and ultrapure water.
3. The system was acidified with hydrochloric acid (pH = 2).
4. After injecting the sample, the HULISs in the acidified sample were adsorbed onto the column.
5. HULISs were eluted with a 2% ammonia–methanol solution (w/w) and detected using the DAD.

Results and Conclusions

This automated pretreatment analysis system achieved highly favorable peaks. The recovery rate of a 100 mg/L standard fulvic acid solution was 91.2±0.9% with this method, under optimal conditions. We are planning to optimize the time program conditions and apply this system to actual atmospheric samples.

Introduction: The study of the atmospheric particulate matter (PM) oxidative potential (OP), a general indicator of human health risks associated with PM exposure, has become a focal point in research. However, in the Italian territory, the number of studies concerning OP is limited, especially for the fine fraction of particulate matter (PM_2.5). This study aims at investigating the impact of different natural and anthropogenic sources on PM_2.5 and its oxidative potential in different areas of Southern Italy.

Methods: Samples of PM_2.5 were collected in sites of different typology (i.e. rural, urban background, urban and industrial sites) from three regions of Southern Italy. The PM_2.5 sources were identified and characterized with the PMF5.0 receptor model, while the contribution of each source to the OP (measured with a DTT assay) was estimated with the MLR approach. The results were compared with those obtained from other similar studies performed in Northern Italy, related to different typology sites.

Results and conclusions: The PMF5.0 identified similar sources for the southern sites (biomass combustion, vehicular traffic, crustal and marine contributions, secondary inorganic aerosols and industrial emissions), with different contributions to PM_2.5 and OP. The MLR analysis highlighted that combustion sources (i.e., biomass combustion and traffic emissions) were the main contributors to the OP activity of PM_2.5. Furthermore, it was also observed that the relative contributions to OP and PM_2.5 were not comparable for all sources. The results of this study represent a contribution to a better understanding of the potential health impact of PM_2.5; of its spatial variability; and of the role of different sources.

We aimed at addressing how the weakening of the geomagnetic field affects cloud properties and climate, and whether this weakening could lead to an environmental crisis, as suggested by paleoclimatic data. To investigate this, we performed climate simulations driven by the relationship between geomagnetic field strength, atmospheric ionization rates, global electric circuit, and the cloud life cycle. The ionization rates were calculated using a model applicable to both regular and geomagnetic excursion periods. Our model modifications focused specifically on the cloud parameterization scheme, introducing a dependency of the cloud droplet coagulation on the global electric field strength. This dependency was determined as a function of fair weather vertical electric currents (J_z), which were interactively calculated from the simulated atmospheric ion concentrations and conductivity. The model results indicate that the impact of the geomagnetic field weakening on the atmospheric electrical currents is the most pronounced in the middle and low latitudes, leading to an increase in J_z of up to 20%. We also observed statistically significant changes in cloud cover and surface cooling of about 0.4K in global and annual mean surface temperatures. Local and seasonal effects are even more pronounced; for instance, substantial temperature drops of up to 2 K are observed in the Northern Hemisphere. These findings will be described in further detail during the talk. However, based on our results, we cannot conclude that the introduced mechanism would lead to a large-scale environmental crisis. This work is supported by the Swiss National Science Foundation (project Spark GECO; CRSK-2_221368).

We analyze the influence of both biogenic natural emissions and anthropogenic emissions on atmospheric chemistry and climate using the SOCOLv4 Earth climate model. To achieve our objective, we simulated climate behavior from 2015 to 2100 using two IPCC scenarios: SSP2-45 and SSP5-85. Additionally, we created an artificial scenario wherein all biogenic emissions in SSP2-45 were replaced with those from the SSP5-85 scenario. The last scenario helps elucidate the contribution of biogenic emissions to the differences observed between the climates simulated with SSP2-45 and SSP5-85. The model results indicate that the impact of using biogenic emissions from the SSP5-85 scenario instead of those from the SSP2-45 scenario for the calculation of the future climate is significant for the tropospheric ozone, potentially leading to an increase of up to 10% in the troposphere. Regionally, changes in tropospheric ozone can vary, showing positive effects in regions like Australia and South Africa, while resulting in a negative response for Russia. The distribution of the surface temperature response resembles the tropospheric ozone change pattern. We observed significant warming in South America and the high latitudes of the Northern Hemisphere, alongside cooling in parts of Russia. A more detailed description of the local and seasonal features will be presented in this talk. The Saint Petersburg State University supported this work under research grant 116234986.