Understanding how traffic contributes to air pollution, especially in urban areas, is essential for designing effective strategies to reduce air pollution emissions. This study examines the hourly association between traffic volume and concentrations of two air pollution indicators (NO₂ and PM₁₀) using high-resolution data from two monitoring stations in Helsinki. A Prophet time series model was applied to forecast hourly traffic trends for 2024, which were then compared to yearly average NO₂ and PM₁₀ concentrations. Polynomial regression and cross-correlation analyses were used to capture temporal patterns and assess the strength and timing of the relationship. The results show a strong alignment between traffic and NO₂ and PM₁₀ concentrations, particularly at the traffic-heavy measuring site (Mäkelänkatu supersite), with minimal time lag observed. Root mean square error (RMSE) and polynomial fit comparisons confirmed the predictive value of traffic trends in estimating the behavior of NO₂ and PM₁₀ concentrations. These findings support the use of traffic-based proxy models as practical tools for real-time air pollution assessment and for informing targeted urban air quality interventions.

An integrated framework is introduced and applied to assess the health impact of airborne pollution with greater physiological relevance, moving beyond conventional exposure metrics. Measured particle number size distribution data was integrated with a regional respiratory tract deposition fractions to estimate total and alveolar deposited particle surface area concentrations. Land use regression modeling, combined with randomized commuting patterns, enabled the evaluation of city-specific alveolar surface area deposition doses, providing new insight into localized average exposure and its implications for public health. The results showed that although the mean street-level air pollution in Lithuania is higher than in other European cities, the urban background levels are on the same level. We found that the total respiratory deposited surface area concentration is up to 18-fold higher due to coarse particles, which also determines alveolar deposited particle surface area dose. Our findings advocate for using integrated pollution assessments and region-specific policies rather than broad diesel vehicle-targeted bans. The proposed methodology is expected to enhance traditional exposure assessments by switching to lung deposited surface area, which can be further refined by incorporating daytime activity patterns, socio-economic status, and personal health conditions.

Understanding indoor black carbon (BC) dynamics is important for assessing human exposure and informing air quality management in residential settings. This study presents a high-resolution, multi-sensor dataset collected over 24 days in a semi-occupied home in Zagreb, Croatia, designed to characterize the temporal behavior and sources of indoor BC. Indoor BC concentrations were measured at 1 min resolution using a dual-spot aethalometer, with source apportionment into biomass burning and fossil fuel components. Complementary contextual data including motion detection, door and window states, and traffic activity were collected in parallel using smart sensors and annotated experimental logs. Across the monitoring period, daily mean BC concentrations ranged from 174.7 and 1053.1 ng/m³ for biomass burning BC and between 53.2 and 880.3 ng/m³ for fossil fuel component. Statistical analyses revealed significant increases in BC concentrations during direct combustion-related activities, including scented candle burning and gas burner use. Additional BC elevations were associated with mechanical heat sources and nearby vehicle traffic, particularly affecting the fossil fuel BC component. In contrast, non-combustion activities such as brief human presence exhibited minor or inconsistent effects on indoor BC levels. This study elucidates the primary role of combustion-based indoor activities in influencing short-term BC exposure and highlights the importance of synchronized, high-resolution datasets for indoor air quality research.

Meteorological normalization is a key concept in studying anthropogenic effects on air pollutant concentrations and its temporal trends. While apparently successful in revealing anthropogenic effects and often used, there are downsides to the methods and limitations which should be taken into account when using it. This study examines the concept of meteorological normalization in the context of predicting air pollutant concentrations, particularly focusing on the method's theoretical background and implicit assumptions. We provide a rigorous analysis that outlines the conditions under which meteorological normalization can effectively estimate expected values of response variables, which are commonly time series of air pollutant concentrations such as PM$_{10}$ . Through our analysis, we identify critical assumptions, such as the independence of meteorological and non-meteorological variables, which can significantly impact the validity of the method's outcomes. We also explore potential failure modes of meteorological normalization when these assumptions are violated, offering practical examples and visualizations that highlight the limitations of this technique. Our findings suggest that while meteorological normalization can be a useful tool, careful consideration of its applicability is necessary to avoid misleading conclusions in air quality assessments.