Delhi, the capital of India, suffers from heavy local emissions as well as regional transport of air pollutants, resulting in severe aerosol loadings. To determine the sources of these pollutants, we have quantified the mass concentrations of 26 elements in airborne particles, measured by an online X-ray fluorescence spectrometer with time resolution between 30 min and 1 h. Measurements of PM10 and PM2.5 (particulate matter <10 μm and < 2.5 μm) were conducted during two consecutive winters (2018 and 2019) in Delhi. On average, 26 elements from Al to Pb made up ~25% and ~19% of the total PM10 mass (271 μg m-3 and 300 μg m-3) in 2018 and 2019, respectively. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, non-exhaust, an S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). All factors were resolved in both size ranges (but varying relative concentrations), comprising the following contributions to the elemental PM10 mass (in % average for 2018 and 2019): Cl-Br-Se (41.5%, 36.9%), dust (27.6%, 28.7%), non-exhaust (16.2%, 13.7%), S-rich (6.9%, 9.2%), SFC1 + SFC2 (4%, 7%), Pb-Sn-Se (2.3%, 1.66%), Cu-Cd-Pb (0.67%, 2.2%) and Cr-Ni-Mn (0.57%, 0.47%). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the Pb-Sn-Se, Cl-Br-Se and SFC2 factors prevailed for northwest winds (Pakistan, Punjab, Haryana and Delhi), while the Cu-Cd-Pb and S-rich factors originated from east (Nepal and Uttar Pradesh) and the Cr-Ni-Mn factor from northeast (Uttar Pradesh). In contrast, SFC1, dust and non-exhaust were not associated with any specific wind direction.

Net vertical air mass export by thermally driven flows from the atmospheric boundary layer (ABL) to the free troposphere (FT) above deep Alpine valleys was investigated. The vertical export of pollutants above mountainous terrain is presently poorly represented in global chemistry transport models (GCTMs) and needs to be quantified. Air mass budgets were calculated using aircraft observations obtained in deep Alpine valleys. The results show that on average 3 times the valley air mass is exported vertically per day under fair weather conditions. During daytime the type of valleys investigated in this study can act as an efficient "air pump" that transports pollutants upward. The slope wind system within the valley plays an important role in redistributing pollutants. Nitrogen oxide emissions in mountainous regions are efficiently injected into the FT. This could enhance their ozone (O₃) production efficiency and thus influences tropospheric pollution budgets. Once lifted to the FT above the Alps pollutants are transported horizontally by the synoptic flow and are subject to European pollution export. Forward trajectory studies show that under fair weather conditions two major pathways for air masses above the Alps dominate. Air masses moving north are mixed throughout the whole tropospheric column and further transported eastward towards Asia. Air masses moving south descend within the subtropical high pressure system above the Mediterranean.

A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a 1-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zurich, Switzerland. The measured organic aerosol mass spectra were analyzed by PMF using a small (14 d) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainties of the PMF solutions were estimated. Factor–tracer correlations for averaged seasonal results from the rolling window analysis are higher than those retrieved from conventional PMF analyses of individual seasons, highlighting the improved performance of the rolling window algorithm for long-term data. In this study four to five factors were tested for every PMF window. Factor profiles for primary organic aerosol from traffic (HOA), cooking (COA) and biomass burning (BBOA) were constrained. Secondary organic aerosol was represented by either the combination of semi-volatile and low-volatility organic aerosol (SV-OOA and LV-OOA, respectively) or by a single OOA when this separation was not robust. This scheme led to roughly 40 000 PMF runs. Full visual inspection of all these PMF runs is unrealistic and is replaced by predefined user-selected criteria, which allow factor sorting and PMF run acceptance/rejection. The selected criteria for traffic (HOA) and BBOA were the correlation with equivalent black carbon from traffic (eBC_tr) and the explained variation of $m/z$ 60, respectively. COA was assessed by the prominence of a lunchtime concentration peak within the diurnal cycle. SV-OOA and LV-OOA were evaluated based on the fractions of $m/z$ 43 and 44 in their respective factor profiles. Seasonal pre-tests revealed a non-continuous separation of OOA into SV-OOA and LV-OOA, in particular during the warm seasons. Therefore, a differentiation between four-factor solutions (HOA, COA, BBOA and OOA) and five-factor solutions (HOA, COA, BBOA, SV-OOA and LV-OOA) was also conducted based on the criterion for SV-OOA. HOA and COA contribute between 0.4–0.7 µg m⁻³ (7.8 %–9.0 %) and 0.7–1.2 µg m⁻³ (12.2 %–15.7 %) on average throughout the year, respectively. BBOA shows a strong yearly cycle with the lowest mean concentrations in summer (0.6 µg m⁻³, 12.0 %), slightly higher mean concentrations during spring and fall (1.0 and 1.5 µg m⁻³, or 15.6 % and 18.6 %, respectively), and the highest mean concentrations during winter (1.9 µg m⁻³, 25.0 %). In summer, OOA is separated into SV-OOA and LV-OOA, with mean concentrations of 1.4 µg m⁻³ (26.5 %) and 2.2 µg m⁻³ (40.3 %), respectively. For the remaining seasons the seasonal concentrations of SV-OOA, LV-OOA and OOA range from 0.3 to 1.1 µg m⁻³ (3.4 %–15.9 %), from 0.6 to 2.2 µg m⁻³ (7.7 %–33.7 %) and from 0.9 to 3.1 µg m⁻³ (13.7 %–39.9 %), respectively. The relative PMF errors modeled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average ±34 %, ±27 %, ±30 %, ±11 %, ±25 % and ±12 %, respectively.

An Analysis of Covariance (ANCOVA) was used to derive the influence of the meteorological variability on the daily maximum ozone concentrations at 12 low-elevation sites north of the Alps in Switzerland during the four seasons in the 1992–2002 period. The afternoon temperature and the morning global radiation were the variables that accounted for most of the meteorological variability in summer and spring, while other variables that can be related to vertical mixing and dilution of primary pollutants (afternoon global radiation, wind speed, stability or day of the week) were more significant in winter. In addition, the number of days after a frontal passage was important to account for ozone build-up in summer and ozone destruction in winter. The statistical model proved to be a robust tool for reducing the impact of the meteorological variability on the ozone concentrations. The explained variance of the model, averaged over all stations, ranged from 60.2% in winter to 71.9% in autumn. The year-to-year variability of the seasonal medians of daily ozone maxima was reduced by 85% in winter, 60% in summer, and 50% in autumn and spring after the meteorological adjustment. For most stations, no significantly negative trends (at the 95% confidence level) of the summer medians of daily O₃ or O_x (O₃+NO₂) maxima were found despite the significant reduction in the precursor emissions in Central Europe. However, significant downward trends in the summer 90th percentiles of daily O_x maxima were observed at 6 sites in the region around Zürich (on average −0.73 ppb yr^-1 for those sites). The lower effect of the titration by NO as a consequence of the reduced emissions could partially explain the significantly positive O₃ trends in the cold seasons (on average 0.69 ppb yr^-1 in winter and 0.58 ppb yr^-1 in autumn). The increase of O_x found for most stations in autumn (on average 0.23 ppb yr^-1) and winter (on average 0.39 ppb yr^-1) could be due to increasing European background ozone levels, in agreement with other studies. The statistical model was also able to explain the very high ozone concentrations in summer 2003, the warmest summer in Switzerland for at least ~150 years. On average, the measured daily ozone maximum was 15 ppb (nearly 29%) higher than in the reference period summer 1992–2002, corresponding to an excess of 5 standard deviations of the summer means of daily ozone maxima in that period.

The Xact 625 Ambient Metals Monitor was tested during a 3-week field campaign at the rural, traffic-influenced site Härkingen in Switzerland during the summer of 2015. The field campaign encompassed the Swiss National Day fireworks event, providing increased concentrations and unique chemical signatures compared to non-fireworks (or background) periods. The objective was to evaluate the data quality by intercomparison with other independent measurements and test its applicability for aerosol source quantification. The Xact was configured to measure 24 elements in PM₁₀ with 1 h time resolution. Data quality was evaluated for 10 24 h averages of Xact data by intercomparison with 24 h PM₁₀ filter data analysed with ICP-OES for major elements, ICP-MS for trace elements, and gold amalgamation atomic absorption spectrometry for Hg. Ten elements (S, K, Ca, Ti, Mn, Fe, Cu, Zn, Ba, Pb) showed excellent correlation between the compared methods, with r² values ≥ 0.95. However, the slopes of the regressions between Xact 625 and ICP data varied from 0.97 to 1.8 (average 1.28) and thus indicated generally higher Xact elemental concentrations than ICP for these elements. Possible reasons for these differences are discussed, but further investigations are needed. For the remaining elements no conclusions could be drawn about their quantification for various reasons, mainly detection limit issues. An indirect intercomparison of hourly values was performed for the fireworks peak, which brought good agreement of total masses when the Xact data were corrected with the regressions from the 24 h value intercomparison. The results demonstrate that multi-metal characterization at high-time-resolution capability of Xact is a valuable and practical tool for ambient monitoring.