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Urban air pollution is one of the major challenges that cities around the world face. Particulate matter (PM), nitrogen dioxide (NO 2 ), volatile organic compounds (VOCs), and other pollutants are many times over the recommended airborne exposure, generating a strong impact on human health and city well-being. Considering Bucharest as a case study, this study aimed to investigate the patterns of particulate matter and nitrogen dioxide concentrations. Multiyear data from the Romanian National Air Quality Monitoring Network were used to investigate spatial and temporal variability. All air pollutants presented a typical bimodal trend during the day, with specific double peaks corresponding to the morning rush hours and nighttime. Spatial variability in NO 2  concentrations was observed, with almost double the concentration values in the city center during midday compared with those for the background and industrial areas. A weekly pattern of PM was noticed, with lower concentrations during the weekends in comparison with those during weekdays, more pronounced in the case of PM 10  compared with the case of PM 2.5 . The fine particle fraction presented monthly and seasonal variability, with higher levels during the cold months compared with the warm months, mainly corresponding to the increased household heating. The estimated proportion of mortality attributable to annual exposure to an air PM 2.5  above 5  μ g/m 3  in Bucharest ranged between 7.55% and 8.26%, with the maximum from 2021. By contrast, the estimated proportion of mortality attributable to PM 10  and NO 2  above 10  μ g/m 3  was significantly lower, with values around 4%. The results are useful in supporting environmental planning measures to decrease urban air pollution. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) has become a major concern in terms of human health and climate impact. In particular, the source apportionment (SA) of organic aerosols (OA) present in submicron particles (PM 1) has gained relevance as an atmospheric research field due to the diversity and complexity of its primary sources and secondary formation processes. Moreover, relatively simple but robust instruments such as the Aerosol Chemical Speciation Monitor (ACSM) are now widely available for the near-real-time online determination of the composition of the non-refractory PM 1. One of the most used tools for SA purposes is the source-receptor positive matrix factorisation (PMF) model. Even though the recently developed rolling PMF technique has already been used for OA SA on ACSM datasets, no study has assessed its added value compared to the more common seasonal PMF method using a practical approach yet. In this paper, both techniques were applied to a synthetic dataset and to nine European ACSM datasets in order to spot the main output discrepancies between methods. The main advantage of the synthetic dataset approach was that the methods' outputs could be compared to the expected "true" values, i.e. the original synthetic dataset values. This approach revealed similar apportionment results amongst methods, although the rolling PMF profile's adaptability feature proved to be advantageous, as it generated output profiles that moved nearer to the truth points. Nevertheless, these results highlighted the impact of the profile anchor on the solution, as the use of a different anchor with respect to the truth led to significantly different results in both methods. In the multi-site study, while differences were generally not significant when considering year-long periods, their importance grew towards shorter time spans, as in intra-month or intra-day cycles. As far as correlation with external measurements is concerned, rolling PMF performed better than seasonal PMF globally for the ambient datasets investigated here, especially in periods between seasons. The results of this multi-site comparison coincide with the synthetic dataset in terms of rolling–seasonal similarity and rolling PMF reporting moderate improvements. Altogether, the results of this study provide solid evidence of the robustness of both methods and of the overall efficiency of the recently proposed rolling PMF approach. [ABSTRACT FROM AUTHOR]