Air

Air quality forecasts play a crucial role in informing the public about atmospheric pollutant levels that pose risks to human health and the environment. The accuracy of these forecasts strongly depends on the quality and resolution of the input data used in the modelling process. At HungaroMet, the Hungarian Meteorological Service, the CHIMERE chemical transport model is used to provide two-day air quality forecasts for the territory of Hungary. This study compares two configurations of the CHIMERE model: the current operational setup, which uses climatological averages from the LMDz-INCA database for boundary conditions, and a test configuration that incorporates real-time boundary conditions from the CAMS global forecast. The primary objective of this work was to assess how the use of real-time versus climatological boundary conditions affects modelled concentrations of key pollutants, including NO₂, O₃, PM₁₀, and PM_2.5. The model results were evaluated against observational data from the Hungarian Air Quality Monitoring Network using a range of statistical metrics. The results indicate that the use of real-time boundary conditions, particularly for aerosol-type pollutants, improves the accuracy of PM₁₀ forecasts. This improvement is most significant under meteorological conditions that favour the long-range transport of particulate matter, such as during Saharan dust or wildfire episodes. These findings highlight the importance of incorporating dynamic, up-to-date boundary data, especially for particulate matter forecasting—given the increasing frequency of transboundary dust events. Full article

This project assesses the feasibility of employing mobile air pollutant concentration monitoring along fixed routes within an urban community to evaluate near-road exposure. Continuous mobile air monitoring measurements of four pollutants (PM_2.5, PM₁₀, NO₂, and O₃) were collected using high-quality air monitors paired with a GPS device to track coordinates and vehicle speed. Simultaneous near-road measurements of the same pollutants were taken at two stationary sites to establish correlations with the mobile air monitoring data. The results indicate that pollutant concentrations recorded by mobile air monitors align closely with those from near-road stationary sites. This study demonstrated strong concordance between mobile and stationary monitoring for particulate matter concentrations, with PM_2.5 and PM₁₀ showing high correlation coefficients (R² = 0.74 and 0.75, respectively). Ozone (O₃) exhibited particularly consistent spatial distributions across all measurement platforms—mobile, near-road, and community stationary sites—as reflected in even stronger correlations (R² = 0.93 and 0.89 for the two near-road sites). These robust associations suggest that mobile monitoring could serve as a viable alternative to stationary approaches for O₃ assessment. In contrast, nitrogen dioxide (NO₂) measurements displayed greater variability, with mobile concentrations consistently exceeding near-road stationary values and demonstrating weaker correlation (R² = 0.19), indicating potential limitations in mobile NO₂ monitoring reliability. This study highlights that mobile air pollutant monitoring in less congested communities can effectively capture exposure concentrations representative of both the community and near-road receptors represented by stationary air monitoring sites. Future research should explore how mobile air monitoring data can be utilized in exposure and health assessments, as well as how this technique can be applied in areas where stationary monitoring is impractical or prohibited due to cost or access limitations. Full article

This study investigates the performance of different demand-controlled ventilation strategies for improving indoor air quality while optimizing energy efficiency. The experimental research was conducted at the Indoor Live Lab at the University of Coimbra using a smart window equipped with mechanical ventilation boxes, occupancy sensors, and a real-time CO₂ monitoring system. Several occupancy-based and CO2-based ventilation control strategies were implemented and tested to dynamically adjust ventilation rates according to real-time indoor conditions, including (1) occupancy period-based control, (2) occupancy level-based control, (3) ON-OFF CO₂-based control, (4) multi-level CO₂-based control, and (5) modulating CO₂-based control. The results indicate that intelligent control strategies can significantly reduce energy consumption while maintaining indoor air quality within acceptable limits. Among the CO₂-based controls, strategy 5 achieved optimal performance, reducing energy consumption by 60% compared to the simple ON-OFF strategy, while maintaining satisfactory indoor air quality. Regarding occupancy-based strategies, strategy 2 showed 58% energy savings compared to the simple occupancy period-based control, but with greater CO₂ concentration fluctuation. The results demonstrate that intelligent DCV systems can simultaneously reduce ventilation energy use by 60% and maintain compliant indoor air quality levels, with modulating CO₂-based control proving most effective. The findings highlight the potential of integrating sensor-based ventilation controls in office spaces to achieve energy savings, enhance occupant comfort, and contribute to the development of smarter, more sustainable buildings. Future research should explore the integration of predictive analytics and multi-pollutant sensing to further optimize demand-controlled ventilation performance. Full article

Soldering is a common engineering practice that releases airborne particulate matter (PM), contributing to significant long-term respiratory risk. The health impact of this exposure is significant, with up to 22% of soldering workers worldwide being diagnosed with conditions such as occupational asthma, restrictive lung disease, and bronchial obstruction. Studies have reported that soldering can produce PM_2.5 concentrations up to 10 times higher than the U.S. Environmental Protection Agency’s (EPA) 24 h exposure limit of 35.0 μg/m³—posing significant respiratory and cognitive health risks under chronic exposure. These hazards remain underappreciated by novice engineers in academic and entry-level industrial environments, where safety practices are often informal or inconsistently applied. Air purification systems offer a mitigation approach; however, performance varies significantly with model and placement, and independent validation is limited. This study uses an indoor air quality monitoring system consisting of six AeroSpec sensors to measure PM_2.5–10 concentrations during soldering sessions conducted with and without commercial air purifiers. Tests were conducted with and without a selection of commercial air purifiers, and measurements were recorded under consistent spatial and temporal conditions. Datasets were analyzed to evaluate purifier effectiveness and the influence of placement on pollutant distribution. The findings provide independent validation of air purifier capabilities and offer evidence-based suggestions for minimizing particulate exposure and improving safety in laboratory soldering environments. Full article

Benzo[a]pyrene is an important indicator of polycyclic aromatic hydrocarbons pollution that exhibits complex atmospheric dynamics influenced by meteorological factors and suspended particulate matter (SPM). Herein, the factors influencing B(a)P concentration were elucidated by analyzing the monthly environmental data for Kyoto, Japan, from 2001 to 2021 using an improved association rule algorithm. Results revealed that B(a)P concentrations were 1.3–3 times higher in cold seasons than in warm seasons and SPM concentrations were lower in cold seasons. The clustering performance was enhanced by optimizing the K-means method using the sum of squared error. The efficiency and reliability of the traditional Apriori algorithm were enhanced by restructuring its candidate itemset generation process, specifically by (1) generating C₂ exclusively from frequent itemset L₁ to avoid redundant database scans and (2) implementing the iterative pruning of nonfrequent subsets during L_k → C_k+1 transitions, adding the lift parameter, and eliminating invalid rules. Strong association rules revealed that B(a)P concentrations ≤ 0.185 ng/m³ were associated with specific meteorological conditions, including humidity ≤ 58%, wind speed ≥ 2 m/s, temperature ≥ 12.3 °C, and pressure ≤ 1009.2 hPa. Among these, changes in pressure had the most substantial impact on the confidence of the association rules, followed by humidity, wind speed, and temperature. Under the influence of high SPM concentrations, favorable meteorological conditions further accelerated pollutant dispersion. B(a)P concentration increased with increasing pressure, decreasing temperature, and decreasing wind speed. Principal component analysis confirmed the robustness and accuracy of our optimized association rule approach in quantifying complex, nonlinear relationships, while providing granular, interpretable insights beyond the traditional methods. Full article

This research focuses on monitoring and analyzing air pollutant emissions, mainly from passenger vehicles, at a busy urban intersection with 19 traffic lanes at the junction of Thivon Avenue and Iera Odos, located in the Egaleo municipality, an urban region of Athens, Greece. To collect data, a monitoring study was conducted specifically on the four central traffic streams of this specific intersection. On each segment of the road, a specific length was assigned through which vehicles pass at an average speed in order for their emissions to be estimated. For each vehicle, the engine type (gas or diesel) and engine displacement were taken into account to calculate the predicted mass of vehicle emissions. These measurements were conducted separately for each segment and recorded during three signal phases (from green to red) for two weekdays and one non-working day. This approach allows pollutant levels to be monitored at various hours and under various traffic conditions. The analysis revealed not only the overall quantity of emissions from vehicles but also their fluctuations throughout the day and traffic conditions, comparing them with the regulatory limits set by the EU. Significant findings regarding the impact of traffic on air quality are highlighted. Full article

It is crucial to assess health impacts of PM_2.5, especially from wildfire smoke, to ensure proper planning for healthcare services. Studies often focus on respiratory conditions; fewer estimate the additional burden of cardiovascular complications. This study aims to extend previous work on the impacts of wildfire smoke and associated PM_2.5 on health in Southern Oregon by expanding study areas and timeframes, including cardiovascular conditions, and applying improved and novel air quality measurement data. This study adopts a case-crossover approach using conditional Poisson regression to analyze time stratified patient counts while controlling for mean temperature. Every 10 μg/m³ increase in PM_2.5 is associated with a 1.6% increase in same-day hospital or emergency room admission rates for respiratory conditions (OR = 1.0157; 95% CI: 1.0024–1.0287) and no significant increase in admission rates for cardiovascular conditions. A 10 μg/m³ increase in PM_2.5 lasting fifteen days is associated with a 6.5% increase in hospital or emergency room admission rates for respiratory conditions (OR = 1.0645; 95% CI: 1.0400–1.0894) and a 4.9% increase in hospital or emergency room admission rates for cardiovascular conditions (OR = 1.0493; 95% CI: 1.0265–1.0723). As the duration of poor air quality increases, the risk of negative respiratory and cardiovascular health outcomes increases. Full article

A project to assess air pollution at the National Archeological Museum in Naples was carried out. The main goal of the project was to develop and test a reliable yet simple monitoring system to be adopted at the same time in several exposition rooms. Nitrogen dioxide, hydrogen chloride, nitrous acid, and sulphur dioxide were the chemical species addressed by the technique. Monitoring was simultaneously performed in five rooms, and pollutant concentrations were determined using two passive samplers. The sampling time was approximately one month per period. In addition to passive samplers, environmental data loggers were used to obtain temperature and relative humidity data. Results show high concentrations of nitrogen dioxide inside rooms, which were consistent with those found in outdoor environments and are close to the values calculated considering the air exchange rates, estimated through time gradients of ambient temperature. The minimum values were recorded in a basement room that had a low ventilation rate. The conversion of nitrogen dioxide to real surfaces produces nitric acid and nitrous acid. Large amounts of nitrous acid, up to 15 µg/m³, were found in exposition rooms, with maximum values in the basement room, where the air exchange rate is limited, and the surface-to-volume ratio is the highest among the monitored rooms. Data analysis demonstrated that the system could discriminate between nitrous acid and nitrogen dioxide. The results show that, for the first time, passive samplers can overcome the problem of mutual interference between nitrogen-containing species. Nitrates and nitrites found in the alkaline passive sampler were generally found not to be interfered by nitrogen dioxide. Nitric acid was also found in the gas phase, likely generated by dissociation of ammonium nitrate in particulate matter. Hydrogen chloride and sulphur dioxide were present at few µg/m³. Nitrous acid is the most relevant acidic species found indoors. The presence of pollutants was discussed in terms of the reliability of the analytical procedure and its significance for indoor air pollution. Full article

The air we breathe contains contaminants such as particulate matter (PM), carbon dioxide (${\mathrm{CO}}_2$), nitrogen dioxide (${\mathrm{NO}}_2$), and nitric oxide (NO), which, when inhaled, bring about several changes in the autonomous responses of our body. Our previous work showed that we can use the human body as a sensor by making use of autonomous responses (or biometrics), such as changes in electrical activity in the brain, measured via electroencephalogram (EEG) and physiological changes, including skin temperature, galvanic skin response (GSR), and blood oxygen saturation (${\mathrm{SpO}}_2$). These biometrics can be used to estimate pollutants, in particularly ${\mathrm{PM}}_1$ and ${\mathrm{CO}}_2$, with high degree of accuracy using machine learning. Our previous work made use of the Welch method (WM) to obtain a power spectral density (PSD) from the time series of EEG data. In this study, we introduce a novel approach for obtaining a PSD from the EEG time series, developed by Astrapi, called the Astrapi Spectrum Analyzer (ASA). The physiological responses of a participant cycling outdoors were measured using a biometric suite, and ambient ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, and NO were measured simultaneously. We combined physiological responses with the PSD from the EEG time series using both the WM and the ASA to estimate the inhaled concentrations of ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, and NO. This work shows that the PSD obtained from the ASA, when combined with other physiological responses, provides much better results (RMSE = 9.28 ppm in an independent test set) in estimating inhaled ${\mathrm{CO}}_2$ compared to making use of the same physiological responses and the PSD obtained by the WM (RMSE = 17.55 ppm in an independent test set). Small improvements were also seen in the estimation of ${\mathrm{NO}}_2$ and NO when using physiological responses and the PSD from the ASA, which can be further confirmed with a large number of dataset. Full article

The transportation sector has a significant impact on climate change, as it is responsible for 20% of the global greenhouse gas (GHG) emissions. This paper evaluates the role of electric vehicles (EVs) in achieving Europe’s ambitious target of carbon neutrality by 2050. The limitations of internal combustion engines (ICEs) along with the recent advancements, such as Euro 6 standards, are examined with a pseudo–lifecycle analysis (pseudo-LCA). While ICEs remain cost-effective initially, their higher long-term cost and environmental impact make them unsustainable. The benefits of EVs, including high energy efficiency, minimal maintenance, and reduced GHG emissions, are stated. However, challenges such as range limitations, charging infrastructure, and the environmental cost of battery production persist. Hybrid electric vehicles (HEVs) are highlighted as transitional technologies, offering improved thermal efficiency and reduced emissions, enhancing air quality in both urban and rural areas. The analysis extends to the use of alternative fuels, such as bioethanol, biodiesel, and hydrogen. These provide interim solutions but face scalability and sustainability issues. Policy interventions, including subsidies, tax incentives, and investments in renewable energy, are crucial factors for EV adoption. As EVs are pivotal to decarbonization, integrating renewable energy and addressing systemic challenges are essential for a sustainable transition. Full article

The goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report on Particulate Matter (PM) concentrations. This system leverages diverse low-cost PM sensors, enhanced by machine learning for sensor calibration, with LoRaWAN connectivity for long-range data transmission. Sensors are GPS-enabled, allowing precise geospatial mapping of collected data, which can be integrated with urban air quality forecasting models and operational forecasting systems. To achieve energy self-sufficiency, the system uses a small-scale solar-powered solution, allowing it to operate independently from the grid, making it both cost-effective and suitable for remote locations. This novel approach leverages multiple operational modes based on power availability to optimize energy efficiency and prevent downtime. By dynamically adjusting system behavior according to power conditions, it ensures continuous operation while conserving energy during periods of reduced supply. This innovative strategy significantly enhances performance and resource management, improving system reliability and sustainability. This IoT network provides localized real-time air quality data, which has significant public health benefits, especially for vulnerable populations in densely populated urban environments. The project demonstrates the synergy between IoT sensor data, machine learning-enhanced calibration, and forecasting methods, contributing to scientific understanding of microenvironments, human exposure, and public health impacts of urban air quality. In addition, this study emphasizes open source design principles, promoting transparency, data quality, and reproducibility by exploring cost-effective sensor calibration techniques and adhering to open data standards. The next iteration of the sensors will include edge processing for short-term air quality forecasts. This work underscores the transformative role of low-cost sensor networks in urban air quality monitoring, advancing equitable policy development and empowering communities to address pollution challenges. Full article

Tobacco smoking and electronic cigarette (EC) use are associated with elevated levels of particulate matter (PM) and nicotine in indoor environments. This study assessed filtration and nicotine capture efficiency of untreated and citric acid-treated high efficiency particulate air (HEPA) filters from two manufacturers, “on-brand” (original) and “off-brand” (replacement). When challenged with salt aerosol, the filtration efficiency (FE) (Mean ± RSD) of original HEPA filters (99.9% ± 0.1) was significantly higher than replacements (94.4% ± 1.7), but both were significantly below the HEPA designation of 99.97%. No significant differences in FE were observed between treated and untreated HEPA filters. All filters had lower FE for EC aerosol compared to salt aerosol, especially among replacement filters. Nicotine capture efficiency was significantly higher in citric acid-treated HEPA filters for originals (99.4% ± 0.22) and replacements (99.0% ± 1.07) compared to untreated originals (57.4% ± 2.33) and replacements (42.0% ± 14.20). This study demonstrated that our citric acid treatment of HEPA filters was effective and efficient at capturing airborne nicotine and did not affect the FE for PM. Use of citric acid-treated HEPA filters would be an effective exposure reduction strategy for both nicotine and PM in indoor settings. Full article

This study aims to determine the optimal frequency for monitoring airborne pollutants in densely populated urban areas to effectively capture their temporal variations. While environmental organizations worldwide typically update air quality data hourly, there is no global consensus on the ideal monitoring frequency to adequately resolve pollutant (particulate matter) time series. By applying temporal variogram analysis to particulate matter (PM) data over time, we identified specific measurement intervals that accurately reflect fluctuations in pollution levels. Using January 2023 air quality data from the Joppa neighborhood of Dallas, Texas, USA, temporal variogram analysis was conducted on three distinct days with varying ${\mathrm{PM}}_{2.5}$ (particulate matter of size ≤ $2.5 \mathsf{\mu }\mathrm{m}$ in diameter) pollution levels. For the most polluted day, the optimal sampling interval for ${\mathrm{PM}}_{2.5}$ was determined to be 12.25 s. This analysis shows that highly polluted days are associated with shorter sampling intervals, highlighting the need for highly granular observations to accurately capture variations in PM levels. Using the variogram analysis results from the most polluted day, we trained machine learning models that can predict the sampling time using meteorological parameters. Feature importance analysis revealed that humidity, temperature, and wind speed could significantly impact the measurement time for ${\mathrm{PM}}_{2.5}$. The study also extends to the other size fractions measured by the air quality monitor. Our findings highlight how local conditions influence the frequency required to reliably track changes in air quality. Full article

Indoor air pollution constitutes a public health problem due to the long time that individuals spend in enclosed spaces every day. The present study aims to investigate the level of volatile organic compounds (VOCs) in indoor air in households in Senegal, and to assess health risks related to residents’ exposure. Of the 17 VOCs identified, 16 were detected in Medina accommodations versus 14 in Darou Khoudoss. Toluene levels reached 70.9 μg/m³ in Medina and 18.5 μg/m³ in Darou Khoudoss, which were the highest compared to other compounds. The sum of Benzene, Toluene, Ethylbenzene, o-Xylene, and 1,2,4-trimethylbenzene concentrations were two times higher in Medina (79.57 µg/m³ versus 37.1 µg/m³). Furthermore, VOCs were found at higher levels in living rooms compared to other living spaces. The highest benzene and acetone concentrations were estimated at 13.6 µg/m³ and 8.4 µg/m³, respectively, in households where incense was burnt daily, while the highest formaldehyde levels were observed in households using incense seasonally (6.8 µg/m³). As regards the health risks associated with exposure of residents, the lifetime cancer risks were all above the WHO tolerable limit (10⁻⁵–10⁻⁶). Exposure to benzene (8.5 µg/m³) associated with a lifetime risk of leukemia (51.3 per million people exposed) was higher in Darou Khoudoss, while the risk of nasopharyngeal cancer (600 per million people exposed) associated with exposure to formaldehyde (4.23 µg/m³) was higher in Medina. Full article

Museums play a vital role in preserving cultural heritage and for this reason, they require strict indoor environmental controls. Balancing indoor environmental quality with reduced energy consumption poses significant challenges. Over the course of a year (2023), indoor microclimate conditions, atmospheric pollutant concentrations (O₃, TVOC, CO, CO₂, particulate matter), and energy use were monitored at the Archaeological Museum of Kavala. Maximum daily fluctuations in relative humidity were 15% in summertime, while air temperature variations reached 2.0 °C, highlighting unstable microclimatic conditions. Particulate matter was the primary threat to the preservation of artworks, followed by indoor O₃ and NO₂, whose concentrations exceeded recommended limits for cultural conservation. In 2023, the Energy Use Intensity (EUI) was 86.1 kWh m⁻², a value that is significantly correlated with the number of visitors and the outdoor air temperature. Every person visiting the museum was assigned an average of 7.7 kWh of energy. During the hottest days and when the museum was crowded, the maximum amount of energy was consumed. Over the past decade (2013–2023), the lowest EUI was recorded during the COVID-19 pandemic at 53 kWh m⁻². Energy consumption is linked to indoor environmental quality; thus, both must be continuously monitored. Full article

Particulate matter (PM) is a critical component of urban air pollution, with severe implications for human health and environmental ecosystems. This study investigates the capacity of green roofs at the Warsaw University Library to mitigate air pollution by analyzing the retention of PM and associated trace elements (TEs) across eight perennial plant species during spring, summer, and autumn. The results highlight significant interspecies variability and seasonal trends in PM retention, with peak levels observed in summer due to increased foliage density and ambient pollution. Sedum spectabile and Spiraea japonica emerged as the most effective species for PM capture, owing to their wax-rich surfaces and dense foliage, while Betula pendula demonstrated a high retention of TEs like manganese and zinc. Seasonal shifts from surface-bound PM (_SPM) to wax-bound PM (_WPM) in autumn underline the importance of adaptive plant traits for sustained pollutant capture. These findings underscore the critical role of green roofs in urban air quality management, emphasizing the need for species-specific strategies to maximize year-round phytoremediation efficacy. Expanding the implementation of diverse vegetation on green roofs can significantly enhance their environmental and public health benefits. Full article

Indoor air quality (IAQ) significantly impacts human health, particularly in enclosed spaces where people spend most of their time. This study evaluates the performance of low-cost IAQ sensors, focusing on their ability to measure carbon dioxide (CO₂) and particulate matter (PM) under real-world conditions. Measurements provided by these sensors were verified against calibrated reference equipment. The study utilized two commercial devices from inBiot and Kaiterra, comparing their outputs to a reference sensor across a range of CO₂ concentrations (500–1200 ppm) and environmental conditions (21–25 °C, 27–92% RH). Data were analyzed for relative error, temporal stability, and reproducibility. Results indicate strong correlation between low-cost sensors (LCSs) and the reference sensor at lower CO₂ concentrations, with minor deviations at higher levels. Environmental conditions had minimal impact on sensor performance, highlighting robustness to temperature and humidity within the tested ranges. For PM measurements, low-cost sensors effectively tracked trends, but inaccuracies increased with particle concentration. Overall, these findings support the feasibility of using low-cost sensors for non-critical IAQ monitoring, offering an affordable alternative for tracking CO₂ and PM trends. Additionally, LCSs can assess long-term exposure to contaminants, providing insights into potential health risks and useful information for non-expert users. Full article

It is well known that carbonyl compounds play an important role in air pollution and the formation of secondary pollutants, such as peroxyacetyl nitrates (PAN). Additionally, airborne carbonyls have been described as cytotoxic, mutagenic and carcinogenic. In this research, several carbonyl compounds, including aldehydes and ketones, as well as ozone, were monitored during a campaign conducted in July and September-October 2023 at Golf Ciudad Real, a golf course located in a non-industrial area of a south-central province in Spain. Extraction and analysis were carried out following procedures outlined by Radiello^®. Analyses were performed using HPLC-DAD and UV-Visible spectrophotometry. Ozone shows seasonal variation (temperature-dependent) concentrations displaying lower values in September/October. Among all the identified carbonyls, butanal was the most abundant, accounting for 40% of the total concentration. The C₁/C₂ and C₂/C₃ ratios were also calculated to provide information about the main emissions sources of the analyzed carbonyl compounds, indicating that mainly anthropogenic sources contribute to air quality in the area. The data were further supported by Quantitative Structure-Activity Relationship (QSAR) models using the ProtoPRED online server, which employs in silico methods based on European Chemicals Agency (ECHA) regulations to assess the (eco)toxicity of the measured carbonyl compounds. Full article

Because PM_2.5 concentrations are not regularly monitored in Mbarara, Uganda, this study was implemented to test whether correlations exist between weather parameters and PM_2.5 concentration, which could then be used to estimate PM_2.5 concentrations. PM_2.5 was monitored for 24 h periods once every week for eight months, while weather parameters were monitored every day. The mean dry and wet season PM_2.5 concentrations were 70.1 and 39.4 µg/m³, respectively. Diurnal trends for PM_2.5 levels show bimodal peaks in the morning and evening. The univariate regression analysis between PM_2.5 and meteorological factors for the 24 h averages yields a significant correlation with air pressure when all data are considered, and when the data are separated by season, there is a significant correlation between PM_2.5 concentration and wind speed in the dry season. A strong correlation is seen between diurnal variations in PM_2.5 concentration and most weather parameters, but our analysis suggests that in modeling PM_2.5 concentrations, the importance of these meteorological factors is mainly due to their correlation with underlying causes including diurnal changes in the atmospheric boundary layer height and changes in sources both hourly and seasonally. While additional measurements are needed to confirm the results, this study contributes to the knowledge of short-term and seasonal variation in PM_2.5 concentration in Mbarara and forms a basis for modeling short-term variation in PM_2.5 concentration and determining the effect of seasonal and diurnal sources on PM_2.5 concentration. Full article