The APi-TOF (Tofwerk AG, Switzerland) is a high-resolution mass spectrometer capable of identifying ambient atmospheric ions and naturally occurring ion clusters with high mass accuracy and resolving power. The APi-TOF was equipped with a second-generation MION-2 inlet (Karsa Ltd., Finland), which allows rapid switching of reagent ions based on the chemical ionisation (CI)-inlet principle and ambient naturally charged ions (Rissanen et al., 2019). In this campaign, the MION-2 inlet was operated exclusively in negative polarity and operated with a 15 min temporal resolution, switching between ambient ion mode for anion measurements and nitrate-based CI-mode for neutral compounds. The MION-APi-TOF sampled ambient air at 0.8 Lmin-1 through a critical orifice (0.3 mm diameter). The sampled air was ionised in the MION source, and subsequently the gas-phase ions were transferred to the TOF chamber, where the charged compounds were separated according to their mass-to-charge ratio (m/z) with a resolving power of about 9000 Th/Th. Data analysis was performed using the MATLAB-based tofTools package developed by Junninen et al. (2010). The Csat values for individual HOMs were calculated based on their molecular composition using the parametrisation by Mohr et al. (2019).

Online measurements of VOCs were conducted using a high-resolution PTR-ToF-MS 4000 (Ionicon Analytik GmbH, Austria) following the methodology described by Desservettaz et al. (2023). The drift tube was operated at E/N=128Td, 2.2 mbar, 630 V, and 85 °C. Ambient air was sampled at 0.21 Lmin-1 through a heated 2 m long PEEK line with a PTFE filter, minimizing losses by using inert materials (PTFE, PEEK, glass). Mass spectra was recorded every 30 s and averaged to 10 min. Background signals were acquired every two hours with VOC-free air from a platinum catalyst, and calibrations were performed bi-weekly with certified standards (Apel-Riemer Environmental, USA). Data were processed with the Ionicon Data Analyzer (IDA) and matched against the GLOVOCS database (Yáñez-Serrano et al., 2016), yielding ∼76 reliably quantified VOCs after applying detection limits, calibration corrections, and quality filtering. Formaldehyde was excluded due to humidity-related detection artefacts (Vlasenko et al., 2010).

The A11 nCNC (Airmodus Oy, Finland) is composed of a particle size magnifier (PSM; Model A10) coupled with a CPC (Model A20) (Vanhanen et al., 2011). The sample inlet tube was approximately 60 cm long. The PSM was operated in scanning saturator flow mode (0.1–1.3 Lmin-1), corresponding to a cutoff diameter of ∼1.1-2.5nm. To minimize artefacts, the setup included an inlet system performing background (zero) checks three times daily (12 min each, equivalent to three full scans) and a core sampling piece to reduce line losses of sub-3 nm particles (Baalbaki et al., 2021). A dilution with dry particle-free air behind the inlet head was employed to ensure the dew point temperatures<20°C, following ACTRIS standard procedures for in-situ aerosol sampling and analyses (Lehtipalo et al., 2022).

Ion and particle size distributions were measured using a NAIS (Airel Ltd., Estonia). This instrument measures ion and naturally charged particle size distributions in the diameter range of 0.8–42 nm (109 size bins obtained from 25 electrometers) (Mirme and Mirme, 2013; Manninen et al., 2016), with measurements performed in both positive and negative polarities. The total particle size distribution was measured by charging aerosols using a corona charger, followed by size separation and detection carried out by two multichannel electrical mobility analyzer columns operated in parallel. Concentrations of particles with sizes smaller than ∼2nm were excluded due to known artefacts from the corona charger. Raw counts were inverted using the NAIS SPECTOPS algorithm and corrected for inlet losses (Gormley and Kennedy, 1948). Further details of the NAIS setup at CAO-AMX are available in Deot et al. (2025).

The SMPS is composed of a TSI 3083 long differential mobility analyzer (DMA) and a TSI 3789 CPC. It measures particle size distribution between 10 and 777 nm (61 size bins). The aerosol and sheath flows were checked weekly and set to 1.0 and 4.8 Lmin-1, respectively. The SMPS sampled ambient air using a 3 m long sample inlet tube. Aerosol sample drying was achieved using a Nafion dryer (typically RH<40%), following the ACTRIS standard procedures for in situ aerosol sampling and analyses, and charge neutralization was achieved using a TSI radioactive (85Kr) neutralizer (model 3077). The raw data from the SMPS was inverted using TSI's Aerosol Instrument Manager (AIM, version 11.5) software. The nano-DMA SMPS, consisting of a TSI 3085 DMA and a TSI water-based 3786 CPC, was also operated simultaneously, with a sample flow rate of 0.3 Lmin-1. The nano-DMA SMPS sampled ambient air using a 2 m long sample inlet tube. It measures particle size distribution between 3.1 and 105.5 nm (99 size bins). Additionally, a TSI butanol-based standalone 3750 CPC (D50=7nm) was also operated, with a sample flow rate of 1 Lmin-1.

BC mass concentrations were measured using a seven-wavelength aethalometer (AE33, Magee Scientific, USA) at 1 min resolution. Ambient aerosol was sampled through a PM1 inlet at 5 Lmin-1 after drying with a Nafion dryer. BC concentrations were derived at 880 nm using a mass absorption cross-section of 7.77 m2g-1 (Petzold et al., 2013). This wavelength was chosen for calculating BC concentration because absorption by other aerosols is negligible at this wavelength (Drinovec et al., 2015). The aethalometer uses Teflon-coated glass fiber tape, and aerosols are collected onto two parallel spots for optical absorption measurements, which provide real-time compensation for the filter loading effect.

The Vaisala Ceilometer CL51, in operation since 2021 and part of the E-PROFILE network , provides vertical profiles of aerosols and clouds (Münkel and Roininen, 2010). It employs an eye-safe InGaAs diode-laser lidar (910±10nm, 110 ns pulses, 6.5 kHz) in a vertical or near-vertical direction, detecting aerosols and clouds from ∼300m–15 km with 10 m resolution. Backscatter profiles are processed with the BL-VIEW software, which applies a gradient method with a “cloud and precipitation filter” to remove spurious signals (Münkel and Roininen, 2010; Emeis et al., 2007). The software combines gradient and idealized backscatter approaches to automatically estimate planetary boundary layer (PBL) height at 16 s temporal and 10 m vertical resolution. In this study, we retained Level 3 boundary layer height data with a “good” quality control index.

Collocated (within a 50 m radius) trace gas measurements, including NOx, CO, and O3, as well as meteorological parameters, including air temperature, relative humidity, solar radiation, wind speed, and wind direction, were obtained from the national air quality monitoring network operated by the DLI of Cyprus. O3 was measured with UV photometric monitors (Ecotech 9810B). CO was measured with non-dispersive IR (NDIR) spectroscopy monitors (Ecotech 9830B). NOx was measured with the chemiluminescence technique. Solar radiation was measured with a Kipp & Zonen CMP22 solar irradiance sensor. Further details of DLI instrumentation are available in Vrekoussis et al. (2022). In addition, aerosol optical depth (AOD) was obtained from the AERONET sun photometers operated at the CAO-AMX site. All data are reported in Coordinated Universal Time (UTC). Local time in Cyprus is UTC+3 during daylight saving time (Eastern European Summer Time, late March to late October). Only quality-assured and quality-controlled datasets, based on standard procedures and instrument-specific data quality flags, were used in this study. Air-mass backward trajectories were computed using NOAA's Air Resources Laboratory (ARL), Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) PC-version model, version 4.4 (Draxler and Rolph, 2010; Stein et al., 2015), incorporating meteorological data from the Global Data Assimilation System (GDAS) with a spatial resolution of 0.25°×0.25° and a temporal resolution of 1 h (Draxler and Rolph, 2010). Two-day hourly backward air-mass trajectories, initialized at 500 m above the ground level, were used in this analysis. We further used ERA5, a fifth-generation reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), 0.25° gridded data of vertical velocity and specific humidity at hourly resolution on pressure levels from 1000–550 hPa (Hersbach et al., 2023).

The median diurnal variation of particle size distributions and aerosol properties during two consecutive events, an NPF event on 6 April followed by an NPS event on 7 April, are shown in Fig. 3. A sharp increase in sub-3 nm particle number concentrations (nCNC; Fig. 3a), followed by a typical “banana-shaped” aerosol growth pattern in the particle size distribution (Fig. 3b–d), was observed during the NPF event. In contrast, the NPS event exhibited a rapid decrease in nanoparticle mode diameter (<20nm) without any preceding evidence of particle formation or growth. During the NPF event, an approximately 2 h time lag between the peak number concentrations of sub-3 nm particles (nCNC) and >7nm particles (CPC) clearly indicate particle growth, whereas the nearly simultaneous peaks observed during the NPS event suggest the absence of particle growth (Fig. 3e). While the concentrations of ions with diameters 2.0–2.3 nm, indicative of local intermediate ion (2–7 nm) formation (Tuovinen et al., 2024), increased sharply during the NPF event, they remained negligible during the NPS event (Fig. 3e). This indicates the absence of local NPF processes during the NPS event. The number concentrations of nucleation mode particles (N10-25nm) and total particles (N10-777nm) were comparable on 6 and 7 April. In contrast, both the total condensation sink (CS_{10−777 nm}) and particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) were approximately twofold lower during the NPS event than during the NPF event (Fig. 3f). While a lower CS generally favors the growth of small clusters or newly formed particles by reducing vapour losses to pre-existing particles (Kulmala et al., 2001), it also indicates cleaner atmospheric conditions with a limited availability of condensing vapours. Consistently, black carbon (BC) mass concentrations were slightly lower during the NPS event, suggesting a minimal influence from local anthropogenic sources such as fossil fuel combustion or biomass burning. This further suggests the absence of anthropogenic sources of nanoparticles upwind of the site. Two other consecutive NPF-NPS cases (10–11 and 20–21 April) showed a similar temporal evolution of particle size distributions and aerosol properties (Figs. S3 and S4), except for higher pre-existing particle concentrations during the NPS event in the 10–11 April case.

Meteorological conditions were largely comparable during the NPF event on 6 April and the NPS event on 7 April (Fig. 4). The diurnal temperature variation was similar, with mean temperatures of 20.1 and 19.9 °C (averaged over 06:00–12:00 UTC) during the NPF and NPS events, respectively. Relative humidity also showed little difference, with mean values of 30.4 % during the NPF event and 30.7 % during the NPS event. Wind speeds were similar for both events, although wind direction differed, being northeasterly on 6 April and northwesterly on 7 April during the same period (Fig. 4b). Boundary layer height and solar radiation (Fig. 4c), which are key factors influencing new particle formation and growth (Kulmala et al., 2004; Deot et al., 2025), likewise exhibited no substantial differences between the two events. Overall, local meteorological conditions were similar during the NPF and NPS events, with wind direction being the notable difference. A comparable temporal evolution of meteorological parameters was also observed for the two additional consecutive NPF-NPS cases (Fig. S5), suggesting that the contrasting particle behavior was unlikely to be primarily driven by local meteorological variability but rather by variations in air-mass history (discussed next).

Previous studies have shown that variations in air-mass origin, transport pathways, atmospheric dilution, vertical mixing, and the mixing of different air-masses can strongly influence the occurrence and evolution of NPF events (Kulmala et al., 2004; Kanawade et al., 2012; Nilsson et al., 2001; Hussein et al., 2009; Tunved et al., 2004). Air-mass trajectory analysis reveals distinct transport pathways and air-mass characteristics between the two cases, particularly on 6 and 7 April (Fig. 5a). During the NPF event, air masses traveled slowly near the ocean surface and largely remained within the PBL. In contrast, during the NPS event, the air masses traveled fast and descended from the free troposphere to near-surface levels during the passage over the Western Taurus mountain range (Fig. 5a). A similar contrast in air-mass history between NPF and NPS events was observed for the 10–11 April case (Fig. S6a). While the air masses traveled within the PBL during both the NPF and NPS events for the 20–21 April case (Fig. S7a), air masses traveled fast near the ocean surface on 21 April (NPS event).

To further assess the dynamic state of the lower atmosphere, vertical velocity and specific humidity were examined (Fig. 5b and c). Stronger low-level subsidence, accompanied by lower specific humidity (i.e., drier air), was evident in the upwind region of the measurement site on NPS event days (Fig. 5c), indicating the intrusion of drier air masses from higher altitudes. Such enhanced vertical mixing and entrainment of cleaner, drier free tropospheric air are indicative of atmospheric dilution and are consistent with the observed lower near-surface aerosol mass (Fig. 6a and b) and reduced columnar aerosol loading (Fig. 6c), as well as lower NOx (Fig. 6d) and carbon monoxide (CO; Fig. 6e). Fast-moving air-masses accompanied by strong winds can enhance turbulent mixing, leading to dilution of particle concentrations and changes in particle size distributions (Shi et al., 1999), which is evident during NPS events (Figs. 5a, S6 and S7). Furthermore, the observed nucleation mode particles may originate from NPF occurring upwind of the measurement site and subsequently advected to the site, or from overlapping nucleation modes arising from distinct nearby anthropogenic sources (Hakala et al., 2023; Kivekäs et al., 2016). As discussed earlier, anthropogenic sources upwind of the measurement site are minimal (Fig. 3f); thus, the advection of nucleation-mode particles directly from primary emissions is unlikely to explain the observed nucleation mode particles during the NPS event.

The origin of the observed nanoparticles during NPS event days can be constrained into three physically consistent scenarios: the particles were either transported from nearby anthropogenic sources or formed upwind of the measurement site and subsequently advected to the site or formed locally but failed to grow. The low BC mass concentrations (Figs. 3f and 6a), together with reduced NOx (Fig. 6d) and CO (Fig. 6e) levels, suggest that contributions from overlapping nucleation modes arising from nearby anthropogenic sources (Hakala et al., 2023) are unlikely to explain the observed nucleation mode particles during the NPS event. Instead, these particles are more likely to be formed upwind of the site and subsequently transported to the observation location. It is therefore critical to constrain the spatial extent of over which particle formation occurs. Two approaches are commonly used to estimate this extent. The first, and more resource-intensive, approach relies on simultaneous measurements of particle size distributions at multiple stationary sites along the air-mass transport pathway. The second approach is based on hourly calculated backward air-mass trajectories. As no simultaneous measurements were available at the upwind locations of the measurement site, the spatial extent of the observed NPF and NPS events was estimated based on backward air-mass trajectory analysis, following the methodology of Hussein et al. (2009). This analysis reveals similar spatial scales for both event types (NPF and NPS; approximately 50–200 km, Fig. S8), with NPS events occurring along a similar air-mass path. This suggests that particle formation occurring upwind of the measurement site on NPS event days is plausible. In our recent study, using simultaneous measurements from a low-altitude site (CAO-AMX) and a high-altitude site (Troodos, CAO-TRO), we showed that approximately half of the observed NPF events at both sites occurred concurrently along the same air-mass pathway, under the influence of an evolving planetary boundary layer height (Deot et al., 2025). The lower number concentrations of negative ions in 2.0–2.3 nm size range (Fig. 6g), sub-3 nm neutral particles (Fig. 6 h), and negative particles in the 2.5–7 nm (Fig. 6i) and 7–25 nm (Fig. 6j) size range on NPS event days can be attributed to reduced levels of condensing vapours (discussed in Sect. 3.2.3), together with a lower condensation sink (Fig. 6k) and a lower particle volume concentration (Fig. 6l).

Volatile organic compound (VOC) mixing ratios and their early oxidation products were examined to identify the dominant chemical regimes associated with NPF and NPS events. VOCs at the CAO-AMX site are controlled by local temperature and continental transport, with long-term observations indicating the onset of local biogenic emissions in mid-March each year (Garg et al., 2026). Mixing ratios of benzene and toluene (anthropogenic tracers), as well as biogenic tracers such as isoprene and monoterpenes (MTs), were comparable between the 2 d (Fig. 7a). Methyl vinyl ketone (MVK) and methacrolein (MACR), first-generation OH-initiated oxidation products of isoprene, showed similar behavior on both days; accordingly, the (MVK + MACR) / isoprene ratio, a proxy for the extent of isoprene oxidation, was also comparable (Fig. 7b). Plant chamber studies (Kiendler-Scharr et al., 2009) and field observations (Kanawade et al., 2011) have shown that isoprene can suppress biogenic NPF at ground level, thereby dampening aerosol cooling effects (Lee et al., 2016), with the extent of suppression depending on the relative contributions of isoprene- and monoterpene-derived carbon. To compare the relative OH-initiated oxidation rates of isoprene and MTs, we calculated the ratio (kIso-OH×[Isoprene])/(kMT-OH×[MT]), where kIso-OH=1.01×10-10 and kα-pinene-OH=5.36×10-11cm3molec.-1s-1 represent the reaction rate coefficients of isoprene and alpha-pinene with OH radical, respectively (Finlayson-Pitts and Pitts, 2000). This ratio was slightly higher during the NPF event (Fig. 7b), indicating an isoprene-dominated OH-reactivity regime that favors the formation of more volatile, lower oxygen-to-carbon (O/C) oxidation products, which are less favorable for biogenic NPF. In contrast, the lower ratio observed during the NPS event rules out isoprene-driven suppression of biogenic NPF under those conditions; nevertheless, NPF did not occur at the site on 7 April. Curtius et al. (2024) showed that organonitrates formed from OH-initiated oxidation of isoprene in the presence of nitrogen oxides drive NPF in the upper troposphere over the Amazon. Consistently, concentrations of isoprene-derived hydroxy hydroperoxy nitrate (C5H10O4(ONO2)) measured by MION-APi-TOF were comparable on both days, indicating that the initial stages of isoprene oxidation proceeded similarly during the two events (Fig. 7c). In contrast, peak concentrations of isoprene-derived dinitrates (C5H10O2(ONO2)2) were slightly higher during the NPF event than during the NPS event, indicating a stronger influence of high-NOx driven chemistry during the NPF event. This interpretation is supported by the reactive nitrogen compounds (NOy) concentrations, which were also slightly higher during the NPF event than during the NPS event (Fig. 7c). Further, acetaldehyde (C2H4O), a marker of atmospheric oxidation, also exhibited similar mixing ratios on both days, indicating comparable overall oxidation levels (Fig. 7c).

Next, we examined neutral aerosol precursors, including sulfuric acid (SA), methanesulfonic acid (MSA), iodic acid (IA), and HOMs, measured using the nitrate mode of the MION-APi-TOF. SA concentrations were approximately five-fold higher during the NPF event than during the NPS event (Fig. 7d). Nevertheless, SA concentrations during the NPS event (∼4×106molec.cm-3) were sufficient to trigger NPF under atmospherically relevant ammonia mixing ratios of 100 parts per trillion by volume (pptv) and dimethylamine mixing ratios of 40 pptv (Kirkby et al., 2011; Kürten et al., 2018). Concurrently, HOMs concentrations increased during the NPF event, but decreased during the NPS event (Fig. 7d), whereas MSA and IA concentrations were comparable on both days (Fig. 7d). Negative ion measurements from APi-TOF further showed that concentrations of bisulfate monomers (HSO4-) and dimers (H2SO4⋅HSO4-) were also similar on both days (Fig. 7e), indicating comparable levels of ionised SA clusters in the ambient air. No indication of higher multimers or ammonia-containing clusters was detected.

Chamber experiments have demonstrated that initial nanoparticle growth is dominated by extremely low-volatility organics (ELVOC; approximately C*<10-4.5µgm-3, volatility bin<-4), whereas growth at larger sizes is increasingly supported by more abundant, slightly higher-volatility vapours in the low-volatility organic compounds (LVOC) range (C*≈10-4.5-10-0.5; -4>volatility bin<0) as the Kelvin barrier falls with increasing diameter (Tröstl et al., 2016). We, therefore, examined differences in organic vapour concentrations relevant to early cluster formation and subsequent growth between the two event types (Fig. 7f). Organic vapour concentrations were higher during the NPS event than during the NPF event across all volatility bins (Fig. 7f), indicating that particle growth was not limited by the availability of condensable vapours. Instead, these conditions point to an environment less favorable for stable initial cluster formation and subsequent growth, and more favorable to net evaporation of condensed material. Fast-moving air-masses enhance turbulent mixing, leading to dilution of particle concentrations, precursor gases, and changes in particle size distributions (Shi et al., 1999). This dilution lowers organic aerosol concentrations and reduces the absorptive capacity of the particle phase. As a result, gas-particle equilibrium shifts towards the vapour phase, enhancing evaporation of semi-volatile species from particle surfaces to re-establish equilibrium (Pankow, 1994; Zhang and Wexler, 2004). This repartitioning can cause aerosol mass to decrease more rapidly than expected from dilution alone, reflecting the dynamic response of SVOCs to changing ambient conditions (Donahue et al., 2014). Under ambient conditions, SVOCs partition between the gas and particle phases, whereas LVOCs and ELVOCs exist primarily, or entirely, in the particle phase (Barsanti et al., 2017). Consistent with this framework, the observed reduction in HOM concentrations (Figs. 7d, S9, and S10) and the absence of particle growth suggest that dilution associated with the entrainment of cleaner, drier free tropospheric air into the boundary layer (Figs. 5, S6 and S7) played a key role during NPS events. The similar chemical environment (Figs. 7, S9 and S10) and local meteorological conditions (Figs. 4 and S5), together associated with the contrasting particle evolution (Figs. 3, S3 and S4), indicate that the observed differences in particle behavior were not primarily driven by low condensing vapours but are instead associated with atmospheric dilution.

The higher concentrations of LVOCs and SVOCs observed during the NPS event (Fig. 7f) would, in principle, be expected to condense onto existing particles and promote growth; however, condensation onto the smallest particles is likely suppressed by the strong curvature (Kelvin) effect. This inhibition limits nanoparticle growth despite the availability of condensable material, preventing particles from growing to larger sizes. Consistent with this interpretation, the shift in the modal diameter of the volume size distribution from larger to smaller sizes (Fig. S11), together with the low total particle volume concentrations (Fig. 6l) and elevated LVOC and SVOC concentrations (Fig. 7f), points to the evaporation of condensed material during NPS event days.