Particulate matter is widely known as a significant air pollutant due to its proven detrimental impact on human health. Furthermore, ultrafine particles (UFPs) are those with diameters smaller than 100 nm, which can cause numerous serious health effects. Thus, identifying the sources of UFPs is essential for formulating effective mitigation strategies. Quantifying the contributions of particle sources can be performed by measuring particle number size distributions (PNSDs) for specific size ranges. This study was conducted in the city of Belgrade, the capital of Serbia, and one of the largest cities in the Balkans peninsula, which, within the European framework, belongs to a region and urban area characterized by high levels of atmospheric particulate matter pollution. In addition, there is a lack of studies addressing UFP levels and their sources in Serbia, including Belgrade. Several criteria pollutants were measured, together with the UFPs and equivalent black carbon (BC) at the urban background site in the city of Belgrade, Serbia, for the period from February to August 2024. The particle sources were analyzed using Positive Matrix Factorization (PMF) of PNSDs along with equivalent BC, PM10, PM2.5, O3, SO2, NO, NO2 and NOx. Seven source types were identified, characterized, and quantified, including two traffic sources (separated into traffic 1 and traffic 2), mixed traffic, an urban diffuse source, nucleation and nucleation growth sources, and a biomass burning source. Traffic-related sources were found to have the most significant contribution at around 40% of total particles emitted, followed by nucleation-related sources (24%) and biomass burning (20%). This is the first study performed in Serbia and Belgrade that addresses source apportionment of PNSD, for particles in the range 10-400 nm.

Ultrafine particles (UFPs) are particles which can penetrate deeply into the respiratory system due to their small size and can translocate into the bloodstream, where they are linked to oxidative stress, inflammation, and adverse cardiovascular outcomes. Ultrafine particles can originate from direct emissions or processes of new particle formation (NPF) which we investigated in this study. New particle formation is the process by which molecular clusters form and then grow to larger particles and develop to nucleation and Aitken mode particles. This study presents a detailed analysis of ultrafine particle dynamics in the city of Belgrade, Serbia, based on high-resolution particle number size distribution (PNSD) measurements performed at an urban background site in the period from January to March 2020. A total of seven factors were identified using Positive Matrix Factorization (with contributions in brackets): three attributed to traffic, including mixed source (55%), biomass burning (26%), nucleation (11%), and urban diffuse (8%) sources. The results were obtained by measuring size-resolved number concentrations (10-400 nm) and other pollutants (NO, NO2, NOx, CO, O3, PM1, PM2.5, PM10, equivalent black carbon, organic carbon). Wind directional analysis revealed clear spatial signatures, with nucleation linked to south-western winds and primary factors associated with major local emission influences. The results provide the first combined characterization of new particle formation processes and source-resolved ultrafine particle contributions in Belgrade, offering new insights into wintertime urban exposure in Southeastern Europe.

Abstract. Substantial efforts and improvements in air quality across Europe lowered levels of air pollutants including particulate matter (PM) in the last decades. However, significant proportion of the European population still lives in areas exceeding WHO recommendations, especially in Northern Italy, Balkans and Eastern Europe, including Serbia. Targeted PM mitigation strategies require extensive air quality monitoring and modelling including source apportionment (SA) studies. In the past, numerous SA studies were conducted for Belgrade city, capital of Serbia. Nevertheless, comparisons across the results are difficult, as they encompass different datasets of pollutants contained in PM fractions such as elements and/or ions and/or PAHs. Here, the aim is to offer a broader insight on PM10 sources at an urban background site in Belgrade by including 34 species as input variables for SA (carbonous aerosols, elements, ions and specific organic tracers). For SA, the USEPA PMF 5.0 software was applied. The factor that dominated PM10 mass was biomass burning (21%), primarily during heating season, followed by ammonium sulphate (18%) and mineral dust (17%). A mixed traffic and industrial activity accounted for 15% of PM10 mass while contribution of factors from biological origin, primary biological aerosol particles and biogenic secondary organic aerosols from isoprene, was 10% each. Mixed factor of long-range transport and road salt/local combustion contributed to the PM10 mass 9%. This analysis provides more detailed perspective on the composition and sources of PM10 in Belgrade, both from anthropogenic and natural, including biological origin. These findings are valuable for defining targeted PM10 mitigation strategies.

Black carbon (BC) is an important urban air pollutant of emerging concern with documented health and climate effects, yet direct BC measurements remain unavailable at the majority of automatic monitoring stations in typical urban sensor networks. Virtual sensing has been recently proposed as a complementary approach, using statistical relationships between routinely measured air quality parameters to provide indicative estimates of unmeasured quantities. In this study, regression models based on multiple linear regression (MLR), random forests (RF) and support vector regression (SVR) are explored for BC estimation using reference grade signals from a newly established urban background pilot supersite (Ada Marina, Belgrade). Three seasonal cases were considered: heating season, non-heating season, and the complete ~1 year dataset. Predictors for the models were derived using two approaches: greedy algorithm based on stepwise linear regression, and non-greedy algorithm based on adaptive best subset selection, yielding: NOx, CO for the heating season; NO2, PM2.5 for the non-heating season and NOx, PM2.5 for the complete period. Models achieved the following R2 and RMSE performance metrics for 50/50 training/test split: heating season R2 = 0.87–0.94, RMSE = 0.65–0.95 μg/m3; non-heating season R2 = 0.57–0.60, RMSE = 0.87-0.90 μg/m3; and complete period R2 = 0.70–0.72, RMSE = 0.78–0.80 μg/m3. Model performance is discussed in the context of published BC virtual sensor results from other European cities. A network applicability assessment indicates that the majority of Belgrade monitoring stations record the predictor signals required by the developed models, supporting the potential for indicative city-wide BC estimation as a complement to direct on-site measurements.

Abstract. Carbonaceous aerosol measurements are scarce in the Western Balkans. We present year-round observations (2023 to 2025) from the urban-background site Ada Marina, Belgrade (44.790166° N, 20.4169° E; 71 m a.s.l.), combining three source apportionment (SA) approaches (organic tracers, the aethalometer model, and a novel application of non-negative matrix factorization, NNMF, to aethalometer data) with FLEXPART modelling and gridded emissions to identify source regions and transport pathways. The annual mean equivalent black carbon (eBC) concentration was 1.42 ± 1.40 µg m⁻³, indicating a moderate eBC burden, while PM₁₀ (27.7 µg m⁻³) remained below current EU and Serbian limits but above WHO guidelines and upcoming EU limits. All SA methods consistently show strong winter to summer seasonality driven by residential wood combustion (RWC). The aethalometer model attributes 64 % of eBC to solid fuel (eBCSF) in winter versus 18 % in summer, NNMF yields 51 % versus 6 %, and offline analysis of tracers show elemental carbon from biomass burning (ECBB) contributions of 48 % versus 3 %. Organic carbon (OC) shows similar seasonality, with biomass burning contributing 59 % in winter and 4 % in summer. A substantial wintertime secondary OC component linked to RWC is evident, with secondary OC (SOC) strongly correlated with ECBB and eBCSF (R² = 0.78 to 0.86), alongside enhanced brown carbon absorption (29 % annually; 44 % in winter). FLEXPART indicates pollution episodes are dominated by regional transport within Serbia and the Pannonian Basin, while correlations between modelled residential emissions and observations demonstrate strong model skill.

This article investigates the essential role of sensor network metrology in advancing the reliability and adaptability of sensor networks through a review of the state of the art and expected trends in this field. Addressing the challenges of harmonized metrological approaches, it outlines a future roadmap for the metrological assessment of real-world non-static distributed sensor networks and underlines the importance of joint efforts for a sustainable and reliable future.

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM2.5 and PM10 were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM10 (50 μg/m3) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area.

Scalable and effective calibration is a fundamental requirement for low-cost air quality (AQ) monitoring systems and will enable accurate and pervasive monitoring in cities. Suffering from environmental interferences and fabrication variance, these devices need to encompass sensor-specific and complex calibration processes for reaching a sufficient accuracy to be deployed as indicative measurement devices in AQ monitoring networks. Concept and sensor drift often force the calibration process to be frequently repeated. These issues lead to unbearable calibration costs, which denies their massive deployment when accuracy is a concern. In this work, we propose a zero transfer samples, global calibration methodology as a technological enabler for Internet of Things (IoT) AQ multisensory devices, which relies on low-cost particulate matter (PM) sensors. This methodology is based on field recorded responses from a limited number of IoT AQ multisensors units and machine learning (ML) concepts and can be universally applied to all units of the same type. A multiseason test campaign has shown that, when applied to different sensors, this methodology's performances match those of state-of-the-art methodology, which requires to derive different calibration parameters for each different unit. If confirmed, these results show that, when properly derived, a global calibration law can be exploited for a large number of networked devices with dramatic cost reduction eventually allowing massive deployment of accurate IoT AQ monitoring devices. Furthermore, this calibration model could be easily embedded onboard of the device or implemented on the edge allowing immediate access to accurate readings for personal exposure monitor applications as well as reducing long-range data transfer needs.

Кontinuirano praćenje nivoa zagađujućih materija u vazduhu kao što su suspendovane čestice od izuzetne je važnosti kako bi se mogle preduzeti odgovarajuće mere zaštite životne sredine i zdravlja ljudi. Unapređenje sistema nadzora ovih zagađujućih materija, uz korišćenje novih tehnologija kao što su senzorske mreže i kompaktni senzori, može biti od značaja za smanjenje njihovog negativnog uticaja na životnu sredinu i ljudsko zdravlje. Primenjene u senzorskim mrežama ove tehnologije omogućavaju kontinualno prikupljanje podataka sa više senzorskih jedinica, a jedna od glavnih prednosti je ta što su senzorske jedinice dostupne po pristupačnijim cenama nabavke i održavanja u odnosu na uređaje koji se koriste u tradicionalnom monitoringu. Međutim, ovim pristupačnim senzorskim mrežama često nedostaje rigorozan sistem kvaliteta i kasnija mogućnost poređenja sa postojećim referentnim stanicama za monitoring, što otežava pouzdano izveštavanje o rezultatima njihovih merenja. U ovom istraživanju akcenat je stavljen na opis dela procedura koje su korišćene za utvrđivanje stepena metrološke sledljivosti dve novouspostavljene mreže senzora za praćenje koncentracije suspendovanih čestica, od kojih je jedna bila namenjena širem gradskom području u gradu Novom Sadu, dok je druga bila namenjena istovremenom praćenju aerozagađenja u spoljašnjoj sredini i u unutrašnjem prostoru u jednoj školskoj ustanovi u Beogradu.

In this study, we describe the development of seasonal winter and summer (heating and non-heating season) land use regression (LUR) models for PM2.5 mass concentration for the city of Novi Sad, Serbia. The PM2.5 data were obtained through an extensive seasonal measurement campaign conducted at 21 locations in urban, urban/industrial, industrial and background areas in the period from February 2020–July 2021. At each location, PM2.5 samples were collected on quartz fibre filters for 10 days per season using a reference gravimetric pump. The developed heating season model had two predictors, the first can be associated with domestic heating over a larger area and the second with local traffic. These predictors contributed to the adjusted R2 of 0.33 and 0.55, respectively. The developed non-heating season model had one predictor which can be associated with local traffic, which contributed to the adjusted R2 of 0.40. Leave-one-out cross-validation determined RMSE/mean absolute error for the heating and non-heating season model were 4.04/4.80 μg/m3 and 2.80/3.17 μg/m3, respectively. For purposes of completeness, developed LUR models were also compared to a simple linear model which utilizes satellite aerosol optical depth data for PM2.5 estimation, and showed superior performance. The developed LUR models can help with quantification of differences between seasonal levels of air pollution, and, consequently, air pollution exposure and association between seasonal long-term exposure and possible health risk implications.

The aim of the study was to analyse relationship between PM2.5 and PM10 concentrations, traffic density and meteorological factors during the week with regular traffic regime and first week of COVID-19 lockdown in Novi Sad, Serbia. During the study period, which included working days and weekends, traffic emission contributions to PM were also determined. Obtained results have shown higher PM, pressure and emission contribution values, lower temperature, relative humidity values, and lower traffic counts for all vehicle categories during the COVID-19 week. A positive correlation was detected only during the first week, between PM2.5 and passenger vehicles and lightweight trucks, as well as between PM10 and all categories of vehicles. Background PM2.5 and PM10 concentrations were moderately correlated to total traffic during the first week as well. Very strong and moderate positive correlation was detected between PM2.5 and PM10 concentrations and temperature during COVID-19 week. The PM concentrations increased during COVID-19 week, but total traffic decreased by 31% on work-days and 42% on weekends, proving the impact of lockdown measures on traffic regime and intensity. Since relationship between PM2.5 and PM10 with different vehicle categories was confirmed only during first week, and PM and emission contribution concentrations were higher during COVID-19 week, a secondary emission source of PM was strongly indicated. Very strong and strong positive correlations of PM2.5 and PM10 with temperature during COVID-19 week have confirmed lower temperature impact on PM concentrations and, consequently, increased impact of heating, as an emission source, due to lockdown measures and people staying at their homes.

A novel statistical model based on a two-layer, contact and information, graph is suggested in order to study the influence of disease prevalence on voluntary general population vaccination during the COVID-19 outbreak. Details about the structure and number of susceptible, infectious, and recovered/vaccinated individuals from the contact layer are simultaneously transferred to the information layer. The ever-growing wealth of information that is becoming available about the COVID virus was modelled at each individual level by a simplified proxy predictor of the amount of disease spread. Each informed individual, a node in a heterogeneous graph, makes a decision about vaccination "motivated" by their benefit. The obtained results showed that disease information type, global or local, has a significant impact on an individual vaccination decision. A number of different scenarios were investigated. The scenarios showed that in the case of the stronger impact of globally broadcasted disease information, individuals tend to vaccinate in larger numbers at the same time when the infection has already spread within the population. If individuals make vaccination decisions based on locally available information, the vaccination rate is uniformly spread during infection outbreak duration. Prioritising elderly population vaccination leads to an increased number of infected cases and a higher reduction in mortality. The developed model accuracy allows the precise targeting of vaccination order depending on the individuals' number of social contacts. Precisely targeted vaccination, combined with pre-existing immunity, and public health measures can limit the infection to isolated hotspots inside the population, as well as significantly delay and lower the infection peak.

Changes in air pollution in the region of the city of Novi Sad due to the COVID-19 induced state of emergency were evaluated while using data from permanently operating air quality monitoring stations belonging to the national, regional, and local networks, as well as ad hoc deployed low-cost particulate matter (PM) sensors. The low-cost sensors were collocated with reference gravimetric pumps. The starting idea for this research was to determine if and to what extent a massive change of anthropogenic activities introduced by lockdown could be observed in main air pollutants levels. An analysis of the data showed that fine and coarse particulate matter, as well as SO2 levels, did not change noticeably, compared to the pre-lockdown period. Isolated larger peaks in PM pollution were traced back to the Aralkum Desert episode. The reduced movement of vehicles and reduced industrial and construction activities during the lockdown in Novi Sad led to a reduction and a more uniform profile of the PM2.5 levels during the period between morning and afternoon air pollution peak, approximately during typical working hours. Daily profiles of NO2, NO, and NOX during the state of emergency proved lower levels during most hours of the day, due to restrictions on vehicular movement. CO during the state of the emergency mainly exhibited a lower level during night. Pollutants having transportation-dominated source profiles exhibited a decrease in level, while pollutants with domestic heating source profiles mostly exhibited a constant level. Considering local sources in Novi Sad, slight to moderate air quality improvement was observed after the lockdown as compared with days before. Furthermore, PM low-cost sensors' usefulness in air quality assessment was confirmed, as they increase spatial resolution, but it is necessary to calibrate them at the deployment location.

In this work we explore the relationship between particulate matter (PM) and small ion (SI) concentration in a typical indoor elementary school environment. A range of important air quality parameters (radon, PM, SI, temperature, humidity) were measured in two elementary schools located in urban background and suburban area in Belgrade city, Serbia. We focus on an interplay between concentrations of radon, small ions (SI) and particulate matter (PM) and for this purpose, we utilize two approaches. The first approach is based on a balance equation which is used to derive approximate relation between concentration of small ions and particulate matter. The form of the obtained relation suggests physics based linear regression modelling. The second approach is more data driven and utilizes machine learning techniques, and in this approach, we develop a more complex statistical model. This paper attempts to put together these two methods into a practical statistical modelling approach that would be more useful than either approach alone. The artificial neural network model enabled prediction of small ion concentration based on radon and particulate matter measurements. Models achieved median absolute error of about 40 ions/cm3 and explained variance of about 0.7. This could potentially enable more simple measurement campaigns, where a smaller number of parameters would be measured, but still allowing for similar insights.

The current compliance networks of automatic air-quality monitoring stations in large urban environments are not sufficient to provide spatial and temporal measurement resolution for realistic assessment of personal exposure to pollutants. Small low-cost sensor platforms with greater mobility and expected lower maintenance costs, are increasingly being used as a supplement to compliance monitoring stations. However, low-cost sensor platforms usually provide data with uncertain precision. To improve the precision, these sensor platforms require in-field calibration. Our paper aims to demonstrate that data from each individual sensor system can be corrected using that sensor system's own data to achieve much improved data quality compared to a reference. However, in this procedure, there are practical difficulties such as individual sensor outputs from the multi-sensor system not being sufficiently available due to malfunctions for instance. We explore how this can be dealt with. In our opinion, this is a novel approach, of practical importance both to users and manufacturers. We present a detailed comparative analysis of Linear Regression (univariate), Multivariate Linear Regression and Artificial Neural Networks used with a specific aim of calibrating field-deployed low-cost CO and O3 sensors. For Artificial Neural Network models, the performance of three common training algorithms was compared (Levenberg-Marquardt, Resilient back-propagation and Conjugate Gradient Powell-Beale algorithm). Data for this study were obtained from two campaigns conducted with 25 multi-sensor AQMESH v.3.5 platforms used within the activities of the CITI-SENSE project. The platforms were co-located to reference gas monitors at the Automatic Monitoring Station Stari Grad, in Belgrade, Serbia. This paper demonstrates that Multivariate Linear Regression and Artificial Neural Network calibration models can improve the output signal. This improvement can be measured by changes in the median and interquartile ranges of statistical parameters used for model evaluation. Artificial Neural Networks showed the best results compared to Linear Regression and Multivariate Linear Regression models. The best predictors for CO, in addition to CO low-cost sensor data, were PM2.5 and NO2, while for O3, in addition to O3 low-cost sensor data, the most suitable input predictors were NO and aH. Based on residual error analysis, we have shown that for CO and O3, a certain range of concentrations exists in which calibrated values differ by less than 10% from the reference method results. In addition, it was noted that for all models, CO sensors consistently showed lower variability between platforms compared to O3 sensors.

In the diffraction pattern produced by a half-plane sharp edge when it obstructs the passage of a laser beam, two characteristic regions are noticeable. There is a central region, where the diffraction of laser light appears in the region of geometric shadow, while intensity oscillations are observed in the non-obstructed area. On both sides of the edge, there are also very long light traces along the normal to the edge of the obstacle. The theoretical explanation of this phenomenon is based on the Fresnel–Kirchhoff diffraction theory applied to the Gaussian beam propagation behind the obstacle. In this paper, we supplement this explanation by considering electromagnetic flow lines, which provide a more complete interpretation of the phenomenon in terms of electric and magnetic fields and flux lines; at the same time, that can be related to average photon paths.

We consider scale transformations (q, p) → (λq, λp) in phase space. They induce transformations of the Husimi functions H(q, p) defined in this space. We consider the Husimi functions for states that are arbitrary superpositions of n-particle states of a harmonic oscillator. We develop a method that allows finding so-called stretched states to which these superpositions transform under such a scale transformation. We study the properties of the stretched states and calculate their density matrices in explicit form. We establish that the density matrix structure can be described using negative binomial distributions. We find expressions for the energy and entropy of stretched states and calculate the means of the number-ofstates operator. We give the form of the Heisenberg and Robertson–Schrödinger uncertainty relations for stretched states.