Abstract
Besides primary emissions, transport and chemical production of particles in the atmosphere are crucial for both air quality and climate. We performed continuous measurements of meteorological conditions, concentrations of trace gases, oxidants and condensable vapors as well as basic characteristics of clusters, ions and aerosol particles in Hyytiälä (a forestry site), Finland, and Beijing (an urban site), China, from 2018 to 2022. Regarding air pollution and as sources of climate-active constituents, Hyytiälä and Beijing represent contrasting locations, motivating a detailed comparison between the two locations. We show our main findings on such comparison for selected variables, and over different time scales. Our results provide strong evidence that NPF is associated with sulphuric acid and highly oxygenated organic molecules (HOMs) in Hyytiälä, while in Beijing NPF is associated with sulphuric acid dimers indicative of sulfuric acid-base (ammonia/amines) clustering. The median particle growth rates were quite similar at the two sites (4–6 nm/h), although their dependence on particle size differed. Our results demonstrate the importance of continuous and comprehensive atmospheric observations and illustrate that we can learn much by comparing such measurements between two sites with different emission and pollution characteristics.
Similar content being viewed by others
Introduction
Direct anthropogenic and natural emissions as well as airborne production affect air quality and climate. Airborne production is due to subsequent chemical/physical processes and transport of the emitted compounds in the atmosphere. Air pollutants, including aerosol particles and various trace gases like ozone, have been estimated to cause several million premature deaths of humans per year globally1,2,3. The overall adverse health effects of air pollution are significant even at relatively low pollutant concentration levels, or at substantially lower levels than previously thought4. Atmospheric aerosols, via aerosol-radiation and especially aerosol-cloud interactions, constitute the largest uncertainty factor in estimating the radiative forcing due to anthropogenic emission5, thereby seriously limiting our ability to quantify the ongoing climate change and to predict its future behavior.
Although operating largely over different spatial and temporal scales, the health and climate influences of aerosols and other pollutants are interconnected together through common emission sources and various feedback mechanisms involving the atmosphere, oceans, cryosphere, and various land and oceanic ecosystems. There are strong indications, for example, that reduced aerosol and their precursor emissions tend to cause positive radiative forcing via aerosol-radiation and aerosol-cloud interactions, thereby having a warming effect on climate6,7. Similarly, changes in climatic conditions influence air pollution by altering the formation, dispersion, and removal of air pollutants8,9 and by influencing the strength of natural emissions to the atmosphere10,11. Enhanced natural aerosol production in forested environments due to climate change appears to enhance the forest carbon uptake12,13, whereas the net influence of anthropogenic aerosols on the ecosystem carbon uptake remains controversial14,15. However, both the plausible climate penalty/gain of air pollution control and the influences of climate change on air pollution remain poorly quantified. This is largely due to the complicated and often non-linear associations and feedbacks between the emissions of different pollutants, their atmospheric concentrations and other properties, and corresponding air quality and climate effects in different atmospheric environments7,16,17,18.
Emissions of gaseous compounds and aerosol particles are under continuous changes as a result of urbanization, other changes in anthropogenic activities, and pressures to control the emissions. Kulmala et al.19. suggested the existence of three different pollution regimes for atmospheric particulate matter (PM), each characterized by different balances between emissions and air pollution control, and having different processes dictating the PM concentration levels: 1) primary dominating regime, corresponding to high primary PM emissions and concentrations, 2) secondary dominating regime, characterized by yet relatively high PM precursor emissions, leading to enhanced secondary PM production and partial replacement of primary PM with secondary PM, and 3) clean regime, in which decreased PM precursor emissions have resulted in an improved air quality. In terms of O3 pollution, it has been common to differentiate between the VOCs (volatile organic compounds) and NOx (nitrogen oxides) limited regimes20, along with a transition regime in between21. Studies during the past decade or so have recognized a connection between O3 and PM pollution22,23,24, leading to the definition of an aerosol-inhibited regime, in which reductions in PM concentrations may lead to increases in O3 concentrations23,25.
While it is clear that most of the regions, or atmospheric environments, can be categorized into one of PM or O3 regimes at a given period, it is equally clear that any region can move from one regime to another over time. From a climate perspective, we may think that any region or environment can be classified into one of the following three categories: 1) anthropogenic dominating region, in which emissions of climate-active constituents are dominated by anthropogenic activities, 2) natural dominating region, in which natural emissions dominate over anthropogenic ones, yet the region itself is affected by anthropogenic pollution, and 3) pristine region, which resemble pre-industrial conditions in the sense that the region is affected minimally by anthropogenic activities. While pristine conditions appear rare in the present-day continental atmosphere26, many regions might be alternating between the first two source-categories of climate-active constituents over a year.
To our knowledge, there are no studies that have comprehensively investigated atmospheric chemistry over an extended period in contrasting environments with respect to both PM and O3 pollution or as a source area for climate-active constituents. Comparing the processes in contrasting environments enables us to identify the key processes in different environments (biogenic/anthropogenic emissions dominating). In addition, it enables to identify the similarities in processes that are independent of the local (or transported) emission characteristics as well as climate and geographical conditions and characteristics. This will help us to improve our understanding on the formation of aerosols and haze. Here we consider two such environments. The first of these is Beijing in China which, as a result of active air pollution control during the past decade27,28, is likely to belong to a secondary dominating regime in terms of PM pollution and either VOC- or aerosol-inhibited O3 regime25,29,30. In addition, Beijing is clearly an anthropogenic dominating region for climate-active constituents. The second environment is Hyytiälä in Finland which, during the extended summer period (March-September) and for a pre-selected air mass transport sector, can be considered to belong to clean PM regime, NOx-limited O3 regime, and natural dominating region for climate-active constituents31,32,33.
The main aim of this paper is to provide an overview on the similarities and differences in air pollution- and climate-relevant atmospheric constituents, in their time evolution as air masses age, and in processes driving such time evolution between the two contrasting environments: Beijing and Hyytiälä. We note that several of our findings, discussed briefly below, will be investigated in more detail in separate papers focusing on more specific aspects of this issue.
Results
Overview of the differences between Beijing and Hyytiälä
Hyytiälä, although not a pristine environment, is much cleaner than Beijing. In Hyytiälä biogenic emissions dominate most of the time, whereas in Beijing anthropogenic emissions dominate over natural ones in practice all the time. In Fig. 1 we show the ratios of medians between Beijing and Hyytiälä (B/H) for different variables. In general, most of the gas and particle phase pollutants showed significant differences, attributed to the influence of meteorological conditions, sources, processes, and sinks at the two sites.
These variables included boundary layer height, radiation conditions (i.e. ultraviolet radiation A and B), trace gases (i.e. CO, SO2, O3, NOx, VOCs, sulfuric acid, highly oxygenated organic molecules and their clusters), aerosol number (with the size range as a subscript), mass concentrations and compositions (i.e. organics, sulfate, nitrate, ammonium, chloride, and black carbon) of particulate pollutants. Also, the formation and growth rates of aerosol particles are included. Hourly mean datasets from 2018 to 2022 were used.
Compared with Hyytiälä, Beijing is a warmer (the median value of 16.2 °C in Beijing and 5.2 °C in Hyytiälä) environment but has typically lower relative humidities (the median value of 40% in Beijing and 89.8% in Hyytiälä) when considering the whole study period. It can be easily seen that the B/H ratios for anthropogenic variables are high, while those associated with biogenic emissions are rather small. Nitrogen and sulfur compounds are the major inorganic compounds associated with human activities. Additionally, the B/H ratios for nitrogen-containing compounds – like NOx (39.5), NO2 (29.5) and particulate NO3 (35.5) concentrations– are higher than B/H ratios for sulfur-containing compounds – like SO2 (21.1), H2SO4 (5.5) and particulate SO4 (5.5). This is consistent with the previous studies showing that the sulfur-containing compounds decreased significantly after 2013 due to the clean air actions in Beijing, while the level of NOx remained relatively high27,34. Organic compounds are divided into two groups: Particulate organics and VOCs. In Beijing, most of the anthropogenic VOCs, such as benzene, isoprene, and toluene, are 6.8 to 26.0 times higher than in Hyytiälä. In contrast, the B/H ratio of monoterpenes is only 0.8. The possible reasons could be that in both Hyytiälä and Beijing, biogenic emissions were the major source of VOCs, while anthropogenic emissions played an important role in the other VOCs in Beijing. In Hyytiälä, monoterpenes are the major contributors to secondary aerosol formation35,36.
For few variables the ratio of medians is near unity, e.g. HOM (total) and O3. Although the total HOM concentrations were comparable at both sites, the components were different. In Beijing, HOM is formed in the presence of NOx, resulting in a relatively high B/H ratio of nitrogen-containing compound (CHON) (9.0). Specially, there is almost no HOM dimers in Beijing, or the concentrations were below the detection limit, as RO2 cross-reactions are almost entirely suppressed by the reaction of RO2 + NO37. In the case of O3, the ratio is near unity, because O3 dynamics is non-linear and therefore median O3 values tell only little on diurnal or seasonal O3 concentrations.
The formation rate of 3 nm aerosol particles is much higher (ca. 100-1000 times) in Beijing than in Hyytiälä. This can be attributed to the different formation mechanisms between these two sites, and the much more abundant base concentrations in Beijing, which was discussed in the Section “Discussion” for aerosol particles. On the other hand, growth rates of freshly formed particles are relatively similar at both sites. To better understand the differences and similarities of the studied variables between the two sites, we will analyze them in more details (see Section “Results” for trace gases and for aerosol particles).
Meteorological conditions and boundary layer dynamics
Besides chemical and physical processes, meteorology affects the concentrations, composition and dynamics of trace gases, clusters and aerosol particles. The seasonal variations and differences of the two sites are evident in Fig. 2 for temperature, relative humidity and UVB radiation (median diurnal cycles shown in Supplementary Fig. 1–3), whereas Fig. 3 shows the median diurnal cycle of BLH in both sites (time series shown in Supplementary Fig. 4). Hyytiälä is experiencing much colder winter temperatures with milder summer heat than Beijing (Fig. 2 and Supplementary Fig. 1). In addition, in Hyytiälä, the RH is typically lowest during the summer, and in winter the RH is mostly in the range 75–100% (Fig. 2 and Supplementary Fig. 2). However, in Beijing, the most humid season is typically summer with hot and humid conditions, and the winter is rather dry with temperatures close to 0 °C. The UVB radiation is substantially higher in Beijing in all the seasons (Fig. 2 and Supplementary Fig. 3).
Hourly time series of (a, b) temperature, (c, d) relative humidity and (e, f) UVB radiation for Hyytiälä (left panel, blue lines) and Beijing (right panel, red lines).
The median boundary layer height in different seasons are shown for (a, b) Spring, (c, d) Summer, (e, f) Autumn, (g, h) Winter) for Hyytiälä (blue lines) and Beijing (red lines). The shaded areas are 95/5 and 75/25 percentiles. Hourly mean datasets from 2018 to 2022 were used.
The seasonal variations of BLH are strong in both sites (Fig. 3 and Supplementary Fig. 4). In Hyytiälä, the median BLH is the largest during the summer (up to approximately 1500 m), slightly lower during the spring, and clearly the lowest in winter. In Beijing, the median BLH is the largest during the spring months (up to approximately 1500 m), with lower values in other seasons. However, there is still clear boundary layer evolution in all the seasons, which is different from Hyytiälä where no clear evolution of the BLH in winter can be observed. The mechanism for the low BLH in winter is different between the two sites. In Hyytiälä, it is due to the very weak diurnal cycle of incoming solar radiation in winter months and more stable daytime surface layer than in other seasons38,39. In Beijing, however, the winter is the most polluted season, and since aerosols are affecting the boundary layer evolution through the radiative effect of aerosols40,41,42,43, the BLH is lower than in the other seasons.
Gas phase concentrations
Ozone: diurnal and seasonal variation
In Fig. 4 time series of O3 concentrations are given for Hyytiälä and Beijing. From 2018 to 2022, O3 at both sites showed overall decreasing trends (-1.25 ppb/year in Beijing and -0.97 ppb/year in Hyytiälä). Considering that the median concentration of O3 is around 30 ppb at both sites, the slight fluctuation of O3 could not be really interpreted as the decrease of O3 pollution. Longer time observations might be needed for a more reasonable analysis on the variation trend of O3. It can be seen that both seasonal and diurnal variations are much stronger in Beijing than those in Hyytiälä. Since O3 is one of the main oxidants, it is clear that the dynamics of chemical processes in the atmosphere are different in both places. As reported in the previous studies44,45,46,47, O3 in Beijing was mainly driven by the photochemical reactions, and hence showed pronounced seasonal and diurnal variations. O3 in Beijing is higher in summer and lower in winter, corresponding to the variation of solar radiation and enhanced titration of O3 because of higher NO in winter44. The diurnal variations of O3 in Beijing showed very low concentrations during nighttime because gaseous O3 was titrated by nitrogen oxide. However, in Hyytiälä, the variation of O3 was rather small, indicating that O3 concentrations were mainly affected by transport process and that the formation of O3 was limited due to low NOx emissions33,48,49. As a result, corresponding to the photochemical reactions, the B/H ratios of O3 increased from early morning to the late afternoon (Fig. 4b).
a Shows the hourly mean time series of O3 concentrations in Beijing (red) and Hyytiälä (blue). b Shows the diurnal variations of O3 concentrations in Beijing (red) and Hyytiälä (blue), respectively. The shaded areas are 75/25 percentiles. The ratios of O3 concentration between Beijing and Hyytiälä (line with black circles) are also shown. Hourly mean datasets from 2018 to 2022 were used.
The variation in O3 concentrations within the boundary layer tends to be higher in more polluted places than in cleaner environments, as well as in places where photochemistry is stronger. Such feature is particularly evident in diurnal variations. Non-linear dynamics make it still difficult to estimate how changes in emission (emission control) affect O3 levels in different environments, and therefore profound observations are needed.
Other gaseous pollutants and VOCs
In Figs. 5 and 6 we show the seasonal and diurnal variation of NO, NOx, SO2 and CO concentrations. In general, all these gaseous pollutants related to anthropogenic emissions were higher in Beijing than in Hyytiälä. Vehicle emissions are one of the major contributors to NO, and NOx50,51. In Beijing, these two compounds showed decreasing trends during these five years, which could be associated with the strict emission standard there. The diurnal variations of NO and NOx showed the highest values in the early morning attributed to the influence of traffic emissions and boundary layer dynamics. In Hyytiälä with no significant traffic emissions, NO and NOx had much lower concentrations, and these concentrations were relatively stable, or even increased slightly, over time. NO concentrations in Hyytiälä showed higher values during daytime, likely indicating fresh traffic emissions during the working hours, even though NO emissions from trees might also contribute to this52. The diurnal shape of the NOx concentration in Hyytiälä was similar to that in Beijing, but with considerably less variability.
Concentrations of (a) NO, (b) NOx, (c) SO2, and (d) CO in Beijing (red) and Hyytiälä (blue) are shown, respectively. Hourly mean datasets from 2018 to 2022 were used.
Concentrations of (a) NO, (b) NOx, (c) SO2, and (d) CO in Beijing (red) and Hyytiälä (blue) are shown, respectively. The ratios of the variables between Beijing and Hyytiälä were also shown (the line with the black circle). The shaded areas are 75/25 percentiles. Hourly mean datasets from 2018 to 2022 were used.
Due to the clean air actions in Beijing, SO2 emissions have been reduced significantly within the Beijing area27,34, with most of the SO2 originating from the regional transport53,54,55. Typically, SO2 concentrations in Beijing were high in the early morning due to the evolution of the urban boundary layer, which enables the downward mixing of transported pollutants from the residual layer to the surface boundary layer. This is similar to the previous observations in cities in China, such as in Nanjing, Shijiazhuang and Beijing43,56,57. SO2 concentrations in Hyytiälä were in general very low, often even lower than the detection limit, but they seemed to have a decreasing trend during the five years similar to Beijing. Although it is hard to reach a firm conclusion on the reason for such a trend in Hyytiälä, it could be that the background concentration of SO2 in Finland decreased during these years as a result of the shift of the energy structure and the EU directives on reducing SO2 from ship emissions58. Such a decrease affected the sulfuric acid concentrations as well as NPF event frequencies and intensities59.
CO concentrations were higher in winter and lower in summer at both sites, although the background concentration was much higher in Beijing than in Hyytiälä. CO may also have been influenced by traffic emissions, and hence the diurnal variation of CO in Beijing was similar to that of NOx. In Hyytiälä, the CO concentration did not have a pronounced diurnal cycle.
Overall, the diurnal concentration ratios of the compounds associated with traffic emissions (NO, NOx and CO) between Beijing and Hyytiälä showed high peaks in the early morning, corresponding to the influence of boundary layer dynamics and traffic activities in urban Beijing. The SO2 concentration ratios between Beijing and Hyytiälä showed the highest values during the daytime due to the pronounced downward mixing of SO2 in Beijing from the residual layer aloft which enhances the daytime near surface SO2.
The time series of concentrations of biogenic and anthropogenic VOCs, and their diurnal variations, are given in Supplementary Fig. 5. Excluding monoterpenes with somewhat higher concentrations in Hyytiälä than in Beijing due to their biogenic origin, VOC concentrations were about an order of magnitude higher in Beijing. Toluene and benzene have a clear anthropogenic origin, and therefore their B/H ratios are around 10. In Beijing, there are substantial isoprene emissions from the vegetation in large urban parks, and therefore the B/H ratio had a clear diurnal variation being the highest around noon.
Precursors for NPF and growth
Sulphuric acid and HOMs are crucial precursors for atmospheric NPF and early growth of newly-formed aerosol particles60,61. The time series of sulfuric acid and HOMs concentrations are shown in Fig. 7, and their median diurnal variations are presented in Fig. 8 together with the B/H ratios. While the diurnal cycles of these compounds, driven by photochemical reactions, were similar between the two sites, sulfuric acid concentration was generally higher in Beijing than in Hyytiälä. In fact, the sulfuric acid concentration was significantly higher in Beijing than in Hyytiälä in winter, while it was somehow comparable between the two sites in summer. The dominant source of sulfuric acid is usually the reaction between SO2 and OH radical, and its main sink is condensation onto a pre-existing particle population62. In summer, the solar radiation is generally strong at both sites, inducing high OH levels and high sulfuric acid production. Although SO2 concentrations were much lower in Hyytiälä compared with Beijing, so were also the values of the condensation sink (see Section “Results” for air mass aging), resulting in comparable sulfuric concentrations in these two sites during summer. In winter, the solar radiation is extremely low in Hyytiälä, leading to a very limited sulfuric acid source.
Concentrations of (a) sulfuric acid and (b) highly oxygenated organic molecules (HOMs) in Beijing (red) and Hyytiälä (blue) are shown, respectively. Hourly mean datasets from 2018 to 2022 were used.
Concentrations of (a) sulfuric acid and (b) HOMs in Beijing (red) and Hyytiälä (blue) are shown, respectively. The ratios of the variables between Beijing and Hyytiälä are also shown (the line with black circle). The shaded areas are 75/25 percentiles. Hourly mean datasets from 2018 to 2022 were used.
The differences in HOMs concentrations between Beijing and Hyytiälä were much smaller than those of sulfuric acid. HOMs concentrations peaked during the daytime at both sites, but these peaks were less pronounced than those of sulfuric acid, partly due to the complicated oxidation chemistry associated with HOMs production63. In Hyytiälä, HOMs were mainly from the oxidation of biogenic VOCs, while in Beijing HOMs could be more contributed by anthropogenic64 VOCs. Thus, the chemical compounds and volatility could show pronounced differences, which is worth to be further analyzed.
Atmospheric aerosol particles
Concentration time series over the five years
In Fig. 9, size-segregated number concentrations are given over the whole measurement period (3–7 nm, 7–25 nm, 25–100 nm and 100–1000 nm) and in Supplementary Fig. 6 for sub 3 nm particles. Similar to the earlier observations at these two sites65,66, on average the highest number concentrations were observed for the nucleation mode (<25 nm), followed by Aitken mode (25–100 nm) and then the accumulation mode (100–1000 nm). The number concentrations of particles in different size ranges were higher in Beijing than in Hyytiälä by roughly an order of magnitude, with smaller-size particles having larger B/H ratios (Fig. 1, see also Section “Results” for air mass aging). Total particle number concentrations in Hyytiälä are typically higher in spring and summer and clearly the lowest in winter31, whereas in Beijing they are higher in winter than in summer67.
a–d Show the particle number concentrations in the size range of 3–7 nm measured by the NAIS, 7–25 nm, 25–100 nm, 100–1000 nm measured by the DMPS and PSD in Beijing (red) and Hyytiälä (blue), along with their ratio (black dots), respectively.The hourly mean datasets from 2018 to 2022 were used.
Similar to the size-segregated number concentrations, aerosol mass concentrations were around ten times higher in Beijing compared with Hyytiälä (Fig. 10). While the PM measurements were based on PM2.5 in Being and PM1 in Hyytiälä, we consider that a comparison between these two datasets is appropriate as PM1 was estimated to contribute, on average, around 80% of the PM2.5 in Hyytiälä31. In Beijing, although the contribution of PM1 to PM2.5 was found to change with the relative humidity, the PM1/PM2.5 ratio was generally high (around 70% on average)68. In Hyytiälä, the main contributor to the PM mass was organic material, showing high concentrations in summer associated with the formation of the biogenic secondary organic aerosols69. In Beijing, the highest PM2.5 concentrations were observed during the winter and spring, when haze pollution was significantly influenced by anthropogenic emissions. These emissions were associated with secondary inorganic aerosols, particularly ammonium nitrate, as well as secondary organic aerosols originating from both solid-fuel combustion and aqueous processes, likely involving aromatic compounds35,70,71,72.
a Time evolution of daily PM concentrations in Beijing (red) and Hyytiälä (blue). The chemical compositions of non-refractory PM, including organics, sulfate, nitrate, ammonium, chloride and black carbon are also illustrated in (b) Beijing and (c) Hyytiälä. Daily mean datasets from 2018 to 2022 were used.
Influence of air mass aging on the evolution of atmospheric aerosol particles
One of the key ways to examine atmospheric processes, integrations and feedbacks in aging air masses, and particularly differences in such aging between clean and polluted environments, is to utilized the concept of time over land (TOL, clean air mass sector) in Hyytiälä and the concept of time over populated land (TOPL, populated air mass sector) in Beijing.
In both Beijing and Hyytiälä, the transport time of a measured air mass over land was important in determining particle number size distributions (Fig. 11). With the increase of TOL/TOPL, particles shifted to larger size in both sites, especially during the first 20 hours. At low TOL/TOPL, the air masses are relatively fresh with a low condensation sink (CS) (Fig. 12), favorable for the occurrence of NPF (discussed in Section “Results” for new particle formation and growth). The existing particle population at low TOL/TOPL is dominated by small particles from NPF in Hyytiälä and from both NPF and primary emissions in Beijing. With the aging of the air masses (i.e., the increasing TOL/TOPL), the accumulation of particulate pollutants, together with the growth of pre-existing particles, leads to a significant increase of CS (Fig. 12), suppressing the occurrence of NPF53,73 (discussed in Sec. 3.4.3). As a result, the flux of particles to small sizes decreases compared with the existing particle population. The shift of particle number size distribution to larger sizes is, therefore, a result of a balance between the continuous growth of pre-existing particle population of different-age particles on one hand, and sources of small particles from either NPF or primary emissions on the other hand. The former dominates at low TOL/TOPL and the latter at large TOL/TOPL, causing a net shift in the particle size. Interestingly, such a shift reminds typical growth events associated with regional NPF and, based on our calculations, the average shift of the particle size was 2.8 nm/TOPL hour in Beijing and 1.7 nm/TOL hour in Hyytiälä.
a Particle number size distributions as a function of time over populated land (TOPL) in Beijing and (b) particle number size distributions as a function of time over land (TOL) in Hyytiälä. The hourly mean datasets from 2018 to 2022 were used.
Condensation sink (CS) as a function of time over land (TOL) in Hyytiälä (blue) and time over populated land (TOPL) in Beijing (red). The shaded areas are 75/25 percentiles. The hourly mean datasets from 2018 to 2022 were used.
The relative rate at which the condensation sink increases with an increasing TOL/TOPL is the highest during the first 20 hours of TOL/TOPL (Fig. 12). At longer transport times over land, CS is about 10 times larger in Beijing compared with Hyytiälä, quite similar to the differences in PM, accumulation mode and total particle number concentrations between these two sites.
Figure 13 illustrates the evolution of particle number concentrations in different size ranges as a function of TOL/TOPL in Beijing and Hyytiälä. Concentrations of nucleation mode (N3-7, N7-25), particles were the highest at relatively low values of TOL/TOPL at both sites. In Beijing, a monotonic decrease in the number concentration of both nucleation and Aitken mode particles with an increasing TOPL was observed, as one might expect due to the very rapid increase of CS from a median of 0.006 s‒1 in the beginning close to 0.02 s‒1 already after a few hours of TOPL (Fig. 12). Such an increase seemed to be sufficient to slow down NPF. In Hyytiälä, we observe first increases of particle number concentrations in all sub-100 nm size ranges with an increasing TOL, then a maximum first for N3-7, second for N7-25 and finally for N25-100 between about 20 and 30 h of TOL, following a monotonic decrease thereafter, similarly to reported by Tunved et al.74. Compared with Beijing, NPF clearly keeps active for longer transport times over land in Hyytiälä to shape the sub-100 nm particle size range, which is understandable based on much lower overall levels of CS in Hyytiälä compared with Beijing. The number concentrations of accumulation mode particles increased monotonically with an increasing TOL/TOPL in both Beijing and Hyytiälä. This is consistent with the lower removal rates of accumulation mode particles from the atmosphere compared with sub-100 nm particles75, their continuous production by both emissions and particle growth from smaller sizes, and the almost complete lack of particle growth out of the accumulation mode.
Particle number concentration as a function of time over land (TOL) in Hyytiälä (blue) and time over populated land (TOPL) in Beijing (red) are shown, including (a) 3–7 nm measured by NAIS, (b) 7–25 nm measured by DMPS and PSD, (c) 25–100 nm measured by DMPS and PSD, and (d) 100–1000 nm measured by DMPS and PSD, and (e) 3–1000 nm by combining NAIS and DMPS/PSD. Within each box, the median (middle horizontal line), mean (solid circles), 25 and 75 percentiles (lower and upper ends of the box), and 10 and 90 percentiles (lower and upper whiskers) are shown. The ratios of median number concentrations between Beijing and Hyytiälä are also shown as line with black circles. The hourly mean datasets from 2018 to 2022 were used.
Concentrations of sub-3 nm particles showed weak dependence on TOL/TOPL, and relatively minor differences between Beijing and Hyytiälä (Supplementary Fig. 7). The only notable features are their high concentrations in Beijing at the lowest TOPL and a weak concentration maximum before 20 h of TOL in Hyytiälä, somewhat similar to what was observed for nucleation mode particles at low values of TOL/TOPL.
Figure 14 shows how the concentrations of the main PM2.5 chemical components and the mass fractions evolve with air mass aging, and Supplementary Fig. 8 shows the same for different seasons. In Beijing, almost all components of PM2.5 increased with an increasing TOPL. The chemical composition of PM2.5 changed dramatically during TOPL < 30 h, characterized by increasing contributions of secondary inorganic aerosols and especially nitrate, after which the composition remained relatively stable. The rapid increase in mass and the significant increase in CS occurs at the same time windows in TOL/TOPL, indicating that in Hyytiälä organics are mainly responsible for CS enhancement and that in Beijing besides organics also nitrates play a crucial role. Overall, the increasing trend of PM2.5 and its components with an increasing TOPL was the highest in winter and least pronounced in summer (Supplementary Fig. 8). It can be noted that the contribution of secondary inorganic aerosols increased continuously in winter and reached 60% when the air masses were exposed to the anthropogenic emissions for >80 h. Contrary to Beijing, in Hyytiälä organics were the dominant chemical components of PM for all values of TOL. The exposure of measured air masses to biogenic precursor emissions for organic aerosols is expected to be the highest in summer, during which we observed that the contribution of organics increased continuously to reach about 80% at TOL = 80 h (Supplementary Fig. 8). In winter, the low temperature and solar radiation suppressed the formation of biogenic secondary aerosols, while sulfate from regional transport was found to play a role in the accumulation of particle mass69.
The evolution of particle chemical composition, including organics, sulfate, nitrate, ammonium, chloride, and black carbon, as (a) a function of time over populated land (TOPL) in Beijing and (b) time over land (TOL) in Hyytiälä. Hourly mean data from 2018 to 2022 were used.
New particle formation and growth and its connection with air mass aging and underlying mechanisms
Figure 15 shows the diurnal evolution of particle number size distributions averaged over five ranking percentile bins at both sites. The ranking was based on particle number concentrations in the size range 2.5–5 nm (N2.5-5), so that higher values of N2.5-5 correspond to higher ranking values (see Section “Materials and methods” for nanoparticle ranking). The days having a larger N2.5-5 have a larger ranking percentile and, on average, tend to have more intense NPF and more probable occurrence of a visible regional NPF event76. At both sites, we can see very clear signs of typical regional NPF at the highest ranking bin (80–100%), and in Beijing, the influence of regional NPF remains visually apparent also down to the next ranking bin (60–80%). It should be noted that in Beijing, an unknown fraction of the growing nanoparticle population may originate from primary sources, especially traffic. At high ranking percentiles, however, the appearance of these particles around noon, rather than during the rush hours, suggests dominant contribution by photochemical NPF as the origin of these particles.
Data are grouped into 20% intervals based on the ranking of nanoparticle concentrations N2.5-5 measured by NAIS measurements in Beijing (left column) and Hyytiälä (right column), including ranking 0 – 20% (a, b), 20 – 40% (c, d), 40 – 60% (e, f), 60 – 80% (g, h), and 80 – 100% (i, j). The hourly mean datasets from 2018 – 2022 were used.
In Fig. 16 we have combined ranking with TOL and TOPL. At both sites, the median value of the ranking percentile decreased monotonically as a function of TOL/TOPL, indicating more intense NPF for lower values of TOL/TOPL. Such a behavior is expected: with increasing values of TOL and TOPL, the measured air mass has been exposed to more extensive emissions of both biogenic and anthropogenic emissions, thereby accumulating high particle loadings and CS (Figs. 12 and 14), which tend to suppress NPF. It can be noted that the ranking value decreased from 0.8 to 0.2 that during the first 40 h of TOPL in Beijing, which is a considerably larger decline that observed in Hyytiälä during the first 40 h of TOL. This suggests that compared with Hyytiälä, the factors suppressing NPF (essentially the magnitude of CS) start to dominate over the factors favoring NPF (precursor concentrations etc.) much faster in Beijing as the measured air mass age during their transport over land.
The evolution of the particle number size distribution and nanoparticle ranking as a function of (a) time over populated land (TOPL) in Beijing, and (b) time over land (TOL) in Hyytiälä. Hourly mean datasets from 2018 to 2022 were used.
The intermediate ion concentrations in the size range 2.0-2.3 nm (Ions2-2.3) are shown in Fig. 17. This size range has been shown to be a good indication of local NPF77 in Hyytiälä. In Beijing, Ions2-2.3 tended to be higher in winter compared to summer, suggesting local particle production to be more effective in winter than in summer. This is somewhat counter-intuitive finding because particle loadings, acting as the sink for gaseous precursors, were generally lower in summer than in winter. However, a possible explanation could be that air masses in summer were typically coming from south78, having sufficient water vapor to promote the formation of clouds which suppresses NPF79,80. Furthermore, also the higher temperatures in summer suppress the formation of sulfuric acid (SA) dimers/clusters81. On the contrary, in winter air masses in Beijing are coming mainly from north78, with low TOPL and low PM loadings. Hyytiälä shows a different pattern. Ions2-2.3 were low in winter due to the lack of solar radiation, while higher values of Ions2-2.3 suggestive of stronger local NPF intensities were observed in summer (Fig. 17).
The negative ions in the size range 2.0‒2.3 nm in Beijing (red) and Hyytiälä (blue) are shown. The shaded areas are 75/25 percentiles. The hourly mean datasets from 2018 to 2022 were used.
The formation rates of 3–7 nm aerosol particles are given as a function of sulfuric acid dimers in Beijing (Fig. 18a) and as a function of sulfuric acid times HOM concentrations in Hyytiälä (Fig. 18b). The formation rates as function of sulfuric acid concentration and HOMs concentration for Hyytiälä are given in Supplementary Fig. 9. The obtained values agree with values presented by Aliaga et al.76.
The particle formation rate (J3) in both Beijing (red) and Hyytiälä (blue) are shown. Within each box, the median (middle horizontal line), mean (solid circles), 25 and 75 percentiles (lower and upper ends of the box), and 10 and 90 percentiles (lower and upper whiskers) are shown. Line with black cycles are the ratios of J3 between Beijing and Hyytiälä. Hourly mean datasets within the NPF active time window (10:00 to 14:00) were used, covering the period from 2018 to 2022 in Hyytiälä and the period from 2018 to 2020 in Beijing.
In general, the formation rate of 3 nm particles (J3) increased with an increasing rank value in both Beijing and Hyytiälä (Fig. 18), and the obtained values of J3 agree with those by Aliaga et al.76. for Hyytiälä. The particle formation rates were much higher in Beijing than in Hyytiälä, being about two orders of magnitude higher for high ranking values and several hundred time higher at low ranking values. This demonstrates a substantially more intense NPF taking place in Beijing compared with Hyytiälä.
In Beijing, J3 showed a clear positive association with the sulfuric acid dimer concentration (Fig. 19a). In Hyytiälä, J3 increased as a function of the sulfuric acid concentration times the HOMs concentration when these concentrations were high (Fig. 19b).
a Formation rate of 3 nm (J3) particles versus sulfuric acid dimer concentration in Beijing. b Formation rate of 3 nm (J3) particles versus sulfuric acid concentration multiplied by highly oxygenated organic molecules (HOMs) concentration in Hyytiälä. The data points are coloured by nanoparticle ranking values. Hourly mean datasets within the NPF active time window (10:00 to 14:00) were used, covering the period from 2018 to 2022 in Hyytiälä and the period from 2018 to 2020 in Beijing.
We have summarized the potential formation mechanisms of new atmospheric aerosol particles as a function of ranking (Figs. 20 and 21). Earlier studies have reported that the nucleation process was driven by SA, HOMs and the base in Hyytiälä82, and by SA dimers with amines and ammonia in Beijing83. Actually, the concentration of the product SA×HOM is increasing clearly until rank 0.4 and then staying rather constant, indicating clear increase of precursors as a function of the rank for low values of the rank, consistent with the quiet NPF discussed by Kulmala et al.84. The concentration product SA×HOM showed a slightly decreasing trend with an increasing value of rank for ranks larger than about 0.7, which seems to be in conflict with the continuously increasing of J3 with the rank. In Beijing, neither SA + HOM concentration nor SA×HOM concentrations change notably as a function of the rank. On the other hand, the SA dimer concentration increased with an increasing of rank, indicating the important role SA dimer in particle formation in Beijing83.
Within each box, the median (middle horizontal line), mean (solid circles), 25 and 75 percentiles (lower and upper ends of the box), and 10 and 90 percentiles (lower and upper whiskers) are shown. The ratios of median values between Beijing and Hyytiälä are also shown. Hourly mean datasets within the NPF active time window (10:00 to 14:00) from 2018 to 2022 were used.
Within each box, the median (middle horizontal line), mean (solid circles), 25 and 75 percentiles (lower and upper ends of the box), and 10 and 90 percentiles (lower and upper whiskers) are shown. Hourly mean datasets within the NPF active time window (10:00 to 14:00) from 2018 to 2022 were used.
Unlike traditional NPF analysis, which only provides particle formation rates for very limited fraction of days, ranking gives it for all the days. Therefore, we now have a realistic distribution of the strength of NPF all the time (excluding nights).
Figure 22 shows that the particle growth rate (GR) varies by roughly over a factor of five when HOM concentrations vary by over two orders of magnitude. The median values of GR were around 4‒6 nm h-1 for the nucleation mode at both sites, but showed different size dependencies: the growth rate of 3‒7 nm particles was slightly higher in Beijing, while that of 7‒25 nm particles was slightly higher in Hyytiälä. In general, higher growth rates were observed at higher HOM concentrations, which is expected due to the generally dominant role of oxidized organic compounds in the growth of nucleation mode particles in a continental atmosphere85. Similar to our earlier observations at these two sites19,86, condensable vapor concentrations seemed to have a weaker influence on GR than one would theoretically expect based on our current understanding on vapor condensation the ambient atmosphere. Theoretically, the GR depends linearly on the sum of such condensable vapor concentrations whose saturation vapor concentration is well below its ambient vapor concentration. In practice, the total concentration of 107 cm-3 of such vapors corresponds to GR of 1 nm/h. Therefore, the concentration between 106 and 109 cm-3 would mean growth rates of 0.1–100 nm/h, and as seen from our Fig. 22 the variation in GR is much less.
a Growth rate (GR) in the size ranges of 3‒7 nm and from 7-25 nm in Beijing (red) and Hyytiälä (blue). Panels (b, d) show the GR from 3 to 7 nm versus highly oxygenated organic molecules (HOMs) concentration in Beijing (red) and Hyytiälä (blue), respectively. Panels (c, e) show the GR from 7 to 25 nm versus highly oxygenated organic molecules (HOMs) concentration in Beijing (red) and Hyytiälä (blue), respectively. Daily datasets within the NPF active time window (10:00 to 14:00) from 2018 to 2020 and from 2018 to 2022 were used in Beijing and Hyytiälä, respectively.
The survival of newly borne particles are shown in Supplementary Fig. 10. The survival probability is surprising high also in Beijing, supporting the results presented by Kulmala et al.73.: even at very high (larger than 100) values of the survival parameter, growing particle will survive from coagulation into the pre-existing particle population.
Discussion
The atmosphere and Earth surface are tightly interlinked and connected with each other. The fluxes (emissions) from the surface affect the composition of air parcels, and subsequently the chemical reactions in the atmosphere. Atmospheric chemistry affects the concentration of gases and oxidants, guiding the formation and loss processes of vapors, clusters and aerosol particles. Actually, the chemistry, physics and meteorology are heavily interlinked in the atmosphere. To understand these processes, interlinks and feedbacks, we have performed comprehensive observations in Hyytiälä and in Beijing.
The analyzed data set consists of five years. In both sites, we observed over 1000 different variables. First, we compared median concentrations for both places and calculated their ratios. As expected, the anthropogenic ones dominated in Beijing and the biogenic ones in Hyytiälä. Particularly NO, NOx and aerosol nitrate were much higher in Beijing than in Hyytiälä.
There were several variables for which the ratio between Beijing and Hyytiälä is around unity as annual medians, such as the O3 concentration and particle growth rate. However, the seasonal and diurnal variations of O3 in Beijing were much higher than in Hyytiälä. On the other hand, the growth rate of aerosol particles was surprisingly similar in both places and did not vary as much as condensation vapor concentrations. When it comes to the weather, the most humid season in Hyytiälä is winter, whereas in Beijing wintertime is typically rather dry (though it can be quite humid during haze pollution periods). The seasonal differences in boundary layer dynamics in Beijing showed clear feedback between pollution and boundary layer height, since pollution levels in Beijing, although improving significantly, are still high, especially in wintertime. The strong seasonal differences of BLH in Hyytiälä are due to the very different amounts of incoming solar radiation in different seasons as well as the more stable surface layer during the winter.
In deeper analyses, we utilized time over land (TOL) and time over populated land (TOPL) as a method to find out how biogenic and anthropogenic emissions affect concentrations of gaseous compounds and aerosol particles, and aerosol chemical composition. In Hyytiälä, we used the clean sector only to minimize the anthropogenic influences on the air masses to properly examine the effect of biogenic emissions. In both places, we can see that the production of new aerosol particles occurs mainly at small values of TOL/TOPL, and mass and condensation sink increases as a function of TOL/TOPL.
Furthermore, we analyzed new particle formation and growth using the ranking method. The higher the ranking, the more new particles are being produced. The ranking decreases with increasing valuesTOL/TOPL, since the probability to form new particles goes down as air masses age. In Hyytiälä, new particle formation is related to sulfuric acid and highly oxidized molecules (HOMs) and in Beijing to sulfuric acid dimers. At both sites, bases (amines and ammonia) also contribute to NPF.
Around 70 hours air mass transport time over clean land contributes to about 2 µg m-3 of particulate mass, and for the same time over populated landscape it contributes to more than 40 µg m-3 of mass and about 10 times higher condensation sink. The formation rates of new particles are more than hundreds of times larger in Beijing than in Hyytiälä, whereas the growth rates of recently formed particles are almost the same (median 4–6 nm h-1) between the two sites under certain condensation vapor levels,
Atmospheric processes and interactions with the Earth’s surface are crucial for air quality and climate. Furthermore, they affect food production and water supply. Actually, several pollutants like O3 and particular matter are mainly airborne and their dynamics are non-linear. To understand the whole system, we need several tools, including comprehensive observations, lab experiments, remote sensing and multi-scale models. Here we show how the comprehensive observations give insight into atmospheric chemistry and atmospheric aerosol dynamics. Although Hyytiälä is not the cleanest place in the world and Beijing is not the most polluted one, they are in these ends. Therefore, when finding out similarities in dynamics we can assume that they are globally valid. On the other hand, we will learn also from differences. In the future we can utilize these unique datasets to find out pathways for a cleaner atmosphere.
Additionally, these massive datasets could help the modelling community in evaluating and understanding model simulation results. Observational datasets for model evaluation are usually very fragmented, limiting our capability of understating what is going right/wrong in models. These collaborative efforts are forming the next level of observational datasets based on the SMEAR concept, bringing our modelling prediction capability to another level of understanding, from emissions to meteorology to concentrations to final removal processes of atmospheric constituent.
The Major Challenge is to understand airborne production (traditionally called secondary), which is often dominating atmospheric composition, but it is difficult to master. There are very non-linear processes and complex coupling with meteorology and climate. As one example, in order to fix haze, we need to start with understanding clustering. Furthermore, clustering is heavily interlinked with O3 production and concentrations.
In air pollution point of view, Beijing is an airborne-dominating area and from a climate point of view, one of anthropogenic emission dominating, on the other hand, Hyytiälä is a typically clean and biogenic dominating area (Table 1).
Actually, we can utilize Chinese Gigacity as a living lab for haze investigations and then apply the results in new megacities in Global South like in India and Nigeria. It is predicted that Africa, particularly western Africa, will be the most populated area in the world after 50-75 years87. The knowledge obtained here can be used for other gigacities in the world, and also to find out answers to several potentially crucial scientific and societal questions.
Materials and Methods
To understand the atmospheric processes in these clean and polluted environments, we conducted the long-term continuous measurements in two SMEAR-type stations (Station for Measuring Earth Surface - Atmosphere Relationships, Fig. 23). SMEAR II observations in Hyytiälä have started in 1995, while BUCT/AHL observations started in 2018. Currently, comprehensive observations are performed in both sites. Here we compare general findings using observations from 1 January 2018 to 31 December 2022 (BUCT/AHL measurements from 6 February 2018 onwards), when both stations were operating continuously.
a Locations of SMEAR II (Blue) and BUCT/AHL (Red) stations. b The definition of clean air mass transport sector (blue shaded area) for SMEAR II. c The definition of the populated area with high anthropogenic emissions (red shaded area) for BUCT/AHL. In (b, c), the transition from yellow to red represents increasing population density and urbanization. Map data: (a) base map from Google Maps (retrieved 1 May 2024), and (b, c) base map and population data from the Global Human Settlement Layer for 2020 (GHSL100, CC BY 4.0).
Sampling sites and instruments
SMEAR II, located in Hyytiälä in the boreal forests of Finland (61°50′N, 24°17′E), is selected as the representative of the clean environments88,89. Aerosol and Haze Lab (BUCT/AHL), located on the west campus of Beijing University of Chemical and Technology in Beijing, China (39°94′N, 116°30′E), is a typical pollution environment surrounded by local residential emissions such as traffic, cooking activities, and other domestic emissions29,90.
Comprehensive measurements were performed in both stations to obtain detailed information on meteorological conditions and gas and particle phase pollutants (Table S1). To ensure that these instruments performed steadily, inter-comparisons and/or instrument calibration were conducted during the observations. The detailed information of the instruments’ operation and calibration is described in our previous studies28,29,31,50,53,62,88,91,92,93,94.
In general, meteorological parameters in BUCT/AHL, including temperature (T), relative humidity (RH), and radiation including ultraviolet radiation (UVB) were measured with a weather station (AWS310, Vaisala Inc.). The boundary layer height (BLH) was measured using a ceilometer (CL51, Vaisala). In the SMEAR II station, temperature was measured with Pt100 inside a ventilated custom-made radiation shield, relative humidity using a Rotronic MP102H RH sensor, and UVB radiation using a Solar Light SL501A pyranometer. Boundary layer height in SMEAR II was calculated from HALO Photonics StreamLine LiDAR using the method described in Manninen et al.38.
Trace gases, i.e., CO, SO2, O3, NOx, were measured in BUCT/AHL using Thermo instruments (Thermo 48i, 43i-TLE, 49i, 42i, respectively) and in SMEAR II using Teledyne API 300EU, and Thermo TEI 43i-TLE, TEI 49 C, and TEI 42iTL, respectively. Single Photon Ionization Mass Spectrometer (SPI-MS)85 and proton transfer quadrupole mass spectrometer (PTR-MS; Ionicon GmbH)92,95 were then deployed to obtain the volatile organic compounds (VOCs) in BUCT/AHL and SMEAR II, respectively. The time-of-flight chemical ionization mass spectrometers (ToF-CIMS, Aerodyne Research Inc.)96 with nitrate (NO3-) as the reagent ion were used to characterize the neutral sulfuric acid (H2SO4), highly oxygenated organic molecules (HOMs, see supplementary text S1), and their clusters.
In addition, both particle number size distributions (PNSD) and particle chemical compositions were measured. The PNSDs in the size range from 1 to 1000 nm were obtained by a series of instruments including a Particle Size Magnifier (PSM; Airmodus Ltd)97, neutral cluster, and air ion spectrometer (NAIS; Airel Ltd)98,99, and Differential Mobility Particle Sizers (DMPS)100 and Particle Size Distribution spectrometer (PSD)101,102. NAIS could also provide the ion number size distributions (INSD) in the size range from 0.8 to 42 nm. The black carbon (BC) and non-refractory chemical compositions, i.e., organics (Org), sulfate (SO4), nitrate (NO3), ammonium (NH4), and chloride (Chl), were then measured by the seven-wavelength Aethalometer (AE33; Aerosol Magee Scientific)103 and online Chemical Speciation Monitor (ACSM, Aerodyne Research Inc.)104, respectively for fine particles (NR-PM2.5) in Beijing and submicron aerosols (NR-PM1) in Hyytiälä.
Air mass accumulation
HYSPLIT model was used to investigate air mass transport and dispersion processes105. At both sites, we investigated the air mass history by calculating at least 72 h back trajectories for each hour of the study period (2018–2022) using the HYSPLIT model. To better understand the influence of biogenic and anthropogenic emissions on the evolution of gas and particle phase pollutants, the time over (clean) land (TOL) and time over populated land (TOPL) concepts were then used.
TOL is the time that air originating from either the Atlantic Ocean or Arctic Ocean travelled over a boreal forest before arriving at SMEAR II, which was first introduced in Tunved et al.74. and later e.g., in Petäjä et al.32. After getting the trajectories from the HYSPLIT model, three steps were then used to calculate TOL:
1. Define the clean sector. As shown in Fig. 23b, three air mass transport sectors were considered in this study, i.e., clean, anthropogenic I, and anthropogenic II sectors. Only in clean sector, air mass was transported over the boreal forest with very limited population density and was found to be the sector associated with the occurrence of NPF in Hyytiälä79. Thus, the air mass was mainly influenced by biogenic emissions.
2. Find the trajectories spent at least 90% of their time in the clean sector. The 90% criterion is aimed to minimize the effect of adjacent sectors on passing air masses. In other words, once the air mass spent a longer time in the other two sectors, it may have been affected by anthropogenic emissions.
3. Calculate TOL. The time that the selected trajectories spent over the land within the clean sector were then counted as TOL.
Similarly, we define the TOPL as the time that the air mass spent over the densely populated area before arriving BUCT/AHL106. To calculate TOPL, we need to:
1. Define the densely populated area with strong anthropogenic emissions. Here, the populated area was in line with the gigacity area107. As shown in Fig. 23c, the populated/gigacity area is the triangle confined roughly by the lines Shanghai-Nanjing-Xi’an-Beijing-Shanghai. About 10% of the global population is living within the gigacity area, and hence it was characterized by the high population density and strong anthropogenic emissions.
2. Calculate TOPL. The time that the trajectories spent over the selected populated area were then counted as TOPL. Therefore, the air mass was exposed to more anthropogenic emissions in gigacity areas with longer TOPL.
The risk associated with TOPL is that the air mass could be affected by human activities also outside the gigacity area. However, the risk could be limited because of the reasons below: in the view of the terrain, the north, west, and south region of the gigacity area is surrounded by a series of high mountains (i.e. Yin, Taihang, Dabie, respectively), while the east region of the gigacity is the Bohai Sea and the Huanghai Sea. In the view of emissions, the population density outside the gigacity area is much lower (Fig. 23c), indicating the anthropogenic emissions related to human activities were much less. Our results illustrated in the results and discussion section then confirmed that the TOPL concept well-tracked the evolution of air pollutants with the accumulation of anthropogenic emissions.
There are two widely used models to obtain information on the origin of the air masses, HYSPLIT and FLEXPART105,108,109. They are both Lagrangian models that can be run backwards to estimate the footprints of the air masses110. In previous studies of TOL and TOPL, we have used HYSPLIT in SMEAR II32,74 and FLEXPART in Beijing106. In this study, we used the HYSPLIT model for both sites for the comparison of the results.
Nanoparticle ranking
The nanoparticle ranking analysis was used to characterize new particle formation (NPF). Traditionally, we classified NPF events based on the measurements for individual days111, which tends to be time-consuming and subjective. The details of calculations of the general characters related to NPF are given in Supplementary Text 2. Most recently, Kulmala et al.84. found that NPF still occurred with lower intensities in these days without clear nucleation and growth signals in the traditional view. Therefore, a new alternative approach (i.e. nanoparticle ranking) was proposed to quantify NPF events even with low intensity76,112.
The method can be divided into 4 steps:
1. Determine the size range of the ranking particle. To well track the NPF strength, particles in a certain size range which is sensitive to the intensity of NPF should be determined. In this study, the size range was selected to be 2.5–5 nm, which has been proven to be successful in the previous study76. The lower boundary of particle size is set to 2.5 nm in order to avoid the interference from the continuous mode of molecular clusters113, while the higher boundary is chosen as 5 nm to minimize the influence of ultrafine particles from primary sources, e.g. traffic emissions65.
2. Obtain the time series of number concentration in the size range of 2.5–5 nm (N2.5-5). The PNSD from the NAIS negative polarity was used to calculate the N2.5-5. In order to reduce the noise and outliers, N2.5-5 was then rolling-mediated using a time window of 6 h. The smoothed N2.5-5 could be used to represent the intensity of NPF since the time series of N2.5-5 before and after smoothing tracked well with each other (r2 > 0.9).
3. Determine the daily active period. The active period refers to the general time window observing the occurrence of NPF, which could be determined based on the diurnal evolution of N2.5-5.
4. Rank days based on the daily peak of N2.5-5 during the active period. The peak of N2.5-5 was set as the 90th percentile of the data during the active time window for each day. Then all days were ranked according to the daily peak. Consequently, each day was assigned a percentile rank (ranging from 0 to 100%).
Data availability
The SMEAR II data are available at the SmartSMEAR data repository (https://smear.avaa.csc.fi/, SmartSMEAR, 2023). The AHL/BUCT data are available from the authors upon reasonable request.
References
Chowdhury, S., Pozzer, A., Dey, S., Klinginuellei, K. & Lelieveld, J. Changing risk factors that contribute to premature mortality from ambient air pollution between 2000 and 2015. Environ Res Lett 15 https://doi.org/10.1088/1748-9326/ab8334 (2020).
Du, W., Chen, D. A., Petäjä, T. & Kulmala, M. Air pollution: a more serious health problem than COVID-19 in 2020. Boreal Environ. Res 26, 105–116 (2021).
Landrigan, P. J. et al. The Lancet Commission on pollution and health. Lancet 391, 462–512, https://doi.org/10.1016/S0140-6736(17)32345-0 (2018).
Sigsgaard, T. & Hoffmann, B. Assessing the health burden from air pollution. Science 384, 33–34, https://doi.org/10.1126/science.abo3801 (2024).
IPCC: Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report, edited by: Masson-Delmotte, V. et al. (Cambridge University Press, 2021). https://doi.org/10.1017/9781009157896.
Partanen, A. I., Landry, J. S. & Matthews, H. D. Climate and health implications of future aerosol emission scenarios. Environ Res Lett 13 https://doi.org/10.1088/1748-9326/aaa511 (2018).
Persad, G. G., Samset, B. H. & Wilcox, L. J. Aerosols must be included in climate risk assessments. Nature 611, 662–664, https://doi.org/10.1038/d41586-022-03763-9 (2022).
East, J. D., Monier, E. & G-Menendez, F. Characterizing and quantifying uncertainty in projections of climate change impacts on air quality. Environ Res Lett 17 https://doi.org/10.1088/1748-9326/ac8d17 (2022).
Wang, R. et al. Stringent Emission Controls Are Needed to Reach Clean Air Targets for Cities in China under a Warming Climate. Environ Sci Technol https://doi.org/10.1021/acs.est.1c08403 (2022).
Carslaw, K. S. et al. Large contribution of natural aerosols to uncertainty in indirect forcing. Nature 503, 67–71, https://doi.org/10.1038/nature12674 (2013).
Scott, C. E. et al. Substantial large-scale feedbacks between natural aerosols and climate. Nat. Geosci. 11, 44–48, https://doi.org/10.1038/s41561-017-0020-5 (2018).
Ezhova, E. et al. Direct effect of aerosols on solar radiation and gross primary production in boreal and hemiboreal forests. Atmos. Chem. Phys. 18, 17863–17881, https://doi.org/10.5194/acp-18-17863-2018 (2018).
Kulmala, M. et al. CO2-induced terrestrial climate feedback mechanism: From carbon sink to aerosol source and back. Boreal Environ. Res 19, 122–131 (2014).
He, L. et al. The weekly cycle of photosynthesis in Europe reveals the negative impact of particulate pollution on ecosystem productivity. Proc. Natl Acad. Sci. USA 120, e2306507120, https://doi.org/10.1073/pnas.2306507120 (2023).
Zhou, H. et al. Distinguishing the impacts of natural and anthropogenic aerosols on global gross primary productivity through diffuse fertilization effect. Atmos. Chem. Phys. 22, 693–709, https://doi.org/10.5194/acp-22-693-2022 (2022).
Jia, H. L. & Quaas, J. Nonlinearity of the cloud response postpones climate penalty of mitigating air pollution in polluted regions. Nat. Clim. Change 13, 943–950, https://doi.org/10.1038/s41558-023-01775-5 (2023).
Rasmussen, D. J., Hu, J. L., Mahmud, A. & Kleeman, M. J. The Ozone-Climate Penalty: Past, Present, and Future. Environ. Sci. Technol. 47, 14258–14266, https://doi.org/10.1021/es403446m (2013).
Samset, B. H. How cleaner air changes the climate. Science 360, 148–150, https://doi.org/10.1126/science.aat1723 (2018).
Kulmala, M. et al. The contribution of new particle formation and subsequent growth to haze formation. Environ. Sci.-Atmos. 2, 352–361, https://doi.org/10.1039/d1ea00096a (2022).
Seinfeld, J. H. & Pandis, S. N. Atmospheric chemistry and physics: from air pollution to climate change. Third edition. edn, (John Wiley & Sons, Inc., 2016).
Chen, X. K. et al. Data- and Model-Based Urban O3 Responses to NOx Changes in China and the United States. J Geophys Res-Atmos 128 https://doi.org/10.1029/2022JD038228 (2023).
Ding, A. J. et al. Ozone and fine particle in the western Yangtze River Delta: an overview of 1 yr data at the SORPES station. Atmos. Chem. Phys. 13, 5813–5830, https://doi.org/10.5194/acp-13-5813-2013 (2013).
Huang, X. et al. Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. Natl Sci Rev 8 https://doi.org/10.1093/nsr/nwaa137 (2021).
Wang, T. et al. Ground-level ozone pollution in China: a synthesis of recent findings on influencing factors and impacts. Environ Res Lett 17 https://doi.org/10.1088/1748-9326/ac69fe (2022).
Ivatt, P. D., Evans, M. J. & Lewis, A. C. Suppression of surface ozone by an aerosol-inhibited photochemical ozone regime. Nat. Geosci. 15, 536–540, https://doi.org/10.1038/s41561-022-00972-9 (2022).
Hamilton, D. S. et al. Occurrence of pristine aerosol environments on a polluted planet. P Natl Acad. Sci. USA 111, 18466–18471, https://doi.org/10.1073/pnas.1415440111 (2014).
Li, W. J. et al. Air quality improvement in response to intensified control strategies in Beijing during 2013-2019. Sci. Total Environ. 744, 140776, https://doi.org/10.1016/j.scitotenv.2020.140776 (2020).
Guo, Y. et al. Measurement report: The 4-year variability and influence of the Winter Olympics and other special events on air quality in urban Beijing during wintertime. Atmos. Chem. Phys. 23, 6663–6690, https://doi.org/10.5194/acp-23-6663-2023 (2023).
Kulmala, M. et al. Is reducing new particle formation a plausible solution to mitigate particulate air pollution in Beijing and other Chinese megacities? Faraday Discuss. 226, 334–347, https://doi.org/10.1039/D0FD00078G (2021).
Li, Y. et al. Significant Reductions in Secondary Aerosols after the Three-Year Action Plan in Beijing Summer. Environ. Sci. Technol. 57, 15945–15955, https://doi.org/10.1021/acs.est.3c02417 (2023).
Neefjes, I. et al. 25 years of atmospheric and ecosystem measurements in a boreal forest - Seasonal variation and responses to warm and dry years. Boreal Environ. Res 27, 1–31 (2022).
Petäjä, T. et al. Influence of biogenic emissions from boreal forests on aerosol-cloud interactions. Nat. Geosci. 15, 42–47, https://doi.org/10.1038/s41561-021-00876-0 (2022).
Ciarelli, G. et al. On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest. Geosci. Model Dev. 17, 545–565, https://doi.org/10.5194/gmd-17-545-2024 (2024).
Wang, Y. H. et al. Contrasting trends of PM2.5 and surface-ozone concentrations in China from 2013 to 2017. Natl Sci. Rev. 7, 1331–1339, https://doi.org/10.1093/nsr/nwaa032 (2020).
Daellenbach, K. R. et al. Substantial contribution of transported emissions to organic aerosol in Beijing. Nature Geoscience https://doi.org/10.1038/s41561-024-01493-3 (2024).
Mohr, C. et al. Ambient observations of dimers from terpene oxidation in the gas phase: Implications for new particle formation and growth. Geophys Res Lett. 44, 2958–2966, https://doi.org/10.1002/2017gl072718 (2017).
Yan, C. et al. Size-dependent influence of NOx on the growth rates of organic aerosol particles. Sci. Adv. 6, eaay4945, https://doi.org/10.1126/sciadv.aay4945 (2020).
Manninen, A. J., Marke, T., Tuononen, M. & O’Connor, E. J. Atmospheric Boundary Layer Classification With Doppler Lidar. J. Geophys Res-Atmos. 123, 8172–8189, https://doi.org/10.1029/2017jd028169 (2018).
Sinclair, V. A. et al. Boundary-layer height and surface stability at Hyytiälä, Finland, in ERA5 and observations. Atmos. Meas. Tech. 15, 3075–3103, https://doi.org/10.5194/amt-15-3075-2022 (2022).
Ding, A. J. et al. Enhanced haze pollution by black carbon in megacities in China. Geophys Res Lett. 43, 2873–2879, https://doi.org/10.1002/2016gl067745 (2016).
Petäjä, T. et al. Enhanced air pollution via aerosol-boundary layer feedback in China. Sci. Rep.-Uk 6, 18998, https://doi.org/10.1038/srep18998 (2016).
Kulmala, M. et al. Aerosols, Clusters, Greenhouse Gases, Trace Gases and Boundary-Layer Dynamics: on Feedbacks and Interactions. Bound-Lay. Meteorol. 186, 475–503, https://doi.org/10.1007/s10546-022-00769-8 (2023).
Wang, J. D. et al. Black-carbon-induced regime transition of boundary layer development strongly amplifies severe haze. One Earth 6, 751–759, https://doi.org/10.1016/j.oneear.2023.05.010 (2023).
Ding, A. J., Wang, T., Thouret, V., Cammas, J. P. & Nédélec, P. Tropospheric ozone climatology over Beijing: analysis of aircraft data from the MOZAIC program. Atmos. Chem. Phys. 8, 1–13, https://doi.org/10.5194/acp-8-1-2008 (2008).
Tang, G., Li, X., Wang, Y., Xin, J. & Ren, X. Surface ozone trend details and interpretations in Beijing, 2001-2006. Atmos. Chem. Phys. 9, 8813–8823, https://doi.org/10.5194/acp-9-8813-2009 (2009).
Zhang, Q. et al. Variations of ground-level O3 and its precursors in Beijing in summertime between 2005 and 2011. Atmos. Chem. Phys. 14, 6089–6101, https://doi.org/10.5194/acp-14-6089-2014 (2014).
Chen, S. Y. et al. The trend of surface ozone in Beijing from 2013 to 2019: Indications of the persisting strong atmospheric oxidation capacity. Atmos. Environ. 242, 117801, https://doi.org/10.1016/j.atmosenv.2020.117801 (2020).
Laurila, T. Effects of Environmental Conditions and Transport on Surface Ozone. Geophysica 32, 167–193 (1996).
Lyubovtseva, Y. S. et al. Seasonal variations of trace gases, meteorological parameters, and formation of aerosols in boreal forests. Boreal Environ. Res 10, 493–510 (2005).
Deng, C. J. et al. Seasonal Characteristics of New Particle Formation and Growth in Urban Beijing. Environ. Sci. Technol. 54, 8547–8557, https://doi.org/10.1021/acs.est.0c00808 (2020).
Wang, J. et al. Vehicle emission and atmospheric pollution in China: problems, progress, and prospects. Peerj 7, e6932, https://doi.org/10.7717/peerj.6932 (2019).
Hari, P. et al. Ultraviolet light and leaf emission of NOx. Nature 422, 134–134, https://doi.org/10.1038/422134a (2003).
Du, W. et al. Influence of Aerosol Chemical Composition on Condensation Sink Efficiency and New Particle Formation in Beijing. Environ. Sci. Tech. Let. 9, 375–382, https://doi.org/10.1021/acs.estlett.2c00159 (2022).
Wu, Y. et al. The high-resolution estimation of sulfur dioxide (SO2) concentration, health effect and monetary costs in Beijing. Chemosphere 241, 125031, https://doi.org/10.1016/j.chemosphere.2019.125031 (2020).
Tan, Q. X. et al. Increasing impacts of the relative contributions of regional transport on air pollution in Beijing: Observational evidence. Environ. Pollut. 292, 118407, https://doi.org/10.1016/j.envpol.2021.118407 (2022).
Xu, Z. N. et al. Impact of Biomass Burning and Vertical Mixing of Residual-Layer Aged Plumes on Ozone in the Yangtze River Delta, China: A Tethered-Balloon Measurement and Modeling Study of a Multiday Ozone Episode. J. Geophys Res-Atmos. 123, 11786–11803, https://doi.org/10.1029/2018jd028994 (2018).
Huang, X. et al. Chemical Boundary Layer and Its Impact on Air Pollution in Northern China. Environ. Sci. Tech. Let. 7, 826–832, https://doi.org/10.1021/acs.estlett.0c00755 (2020).
Van Roy, W. et al. International maritime regulation decreases sulfur dioxide but increases nitrogen oxide emissions in the North and Baltic Sea. Commun. Earth Environ. 4, 391, https://doi.org/10.1038/s43247-023-01050-7 (2023).
Li, X. Y. et al. Over 20 years of observations in the boreal forest reveal a decreasing trend of atmospheric new particle formation. Boreal Environ. Res 29, 35–52 (2024).
Lee, S. H. et al. New Particle Formation in the Atmosphere: From Molecular Clusters to Global Climate. J. Geophys Res-Atmos. 124, 7098–7146, https://doi.org/10.1029/2018jd029356 (2019).
Stolzenburg, D. et al. Atmospheric nanoparticle growth. Rev. Mod. Phys. 95, 045002, https://doi.org/10.1103/RevModPhys.95.045002 (2023).
Dada, L. et al. Sources and sinks driving sulfuric acid concentrations in contrasting environments: implications on proxy calculations. Atmos. Chem. Phys. 20, 11747–11766, https://doi.org/10.5194/acp-20-11747-2020 (2020).
Bianchi, F. et al. Highly Oxygenated Organic Molecules (HOM) from Gas-Phase Autoxidation Involving Peroxy Radicals: A Key Contributor to Atmospheric Aerosol. Chem. Rev. 119, 3472–3509, https://doi.org/10.1021/acs.chemrev.8b00395 (2019).
Guo, Y. et al. Seasonal variation in oxygenated organic molecules in urban Beijing and their contribution to secondary organic aerosol. Atmos. Chem. Phys. 22, 10077–10097, https://doi.org/10.5194/acp-22-10077-2022 (2022).
Zhou, Y. et al. Variation of size-segregated particle number concentrations in wintertime Beijing. Atmos. Chem. Phys. 20, 1201–1216, https://doi.org/10.5194/acp-20-1201-2020 (2020).
Kulmala, M. et al. On the mode-segregated aerosol particle number concentration load: contributions of primary and secondary particles in Hyytiälä and Nanjing. Boreal Environ. Res 21, 319–331 (2016).
Wu, Z. J. et al. Particle number size distribution in the urban atmosphere of Beijing, China. Atmos. Environ. 42, 7967–7980, https://doi.org/10.1016/j.atmosenv.2008.06.022 (2008).
Sun, Y. L. et al. Chemical Differences Between PM1 and PM2.5 in Highly Polluted Environment and Implications in Air Pollution Studies. Geophys Res Lett. 47, e2019GL086288, https://doi.org/10.1029/2019GL086288 (2020).
Heikkinen, L. et al. Long-term sub-micrometer aerosol chemical composition in the boreal forest: inter- and intra-annual variability. Atmos. Chem. Phys. 20, 3151–3180, https://doi.org/10.5194/acp-20-3151-2020 (2020).
Sun, Y. L. et al. A chemical cocktail during the COVID-19 outbreak in Beijing, China: Insights from six-year aerosol particle composition measurements during the Chinese New Year holiday. Sci. Total Environ. 742, 140739, https://doi.org/10.1016/j.scitotenv.2020.140739 (2020).
Elser, M. et al. New insights into PM2.5 chemical composition and sources in two major cities in China during extreme haze events using aerosol mass spectrometry. Atmos. Chem. Phys. 16, 3207–3225, https://doi.org/10.5194/acp-16-3207-2016 (2016).
Huang, R. J. et al. High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514, 218–222, https://doi.org/10.1038/nature13774 (2014).
Kulmala, M., Kerminen, V. M., Petäjä, T., Ding, A. J. & Wang, L. Atmospheric gas-to-particle conversion: why NPF events are observed in megacities? Faraday Discuss. 200, 271–288, https://doi.org/10.1039/c6fd00257a (2017).
Tunved, P. et al. High natural aerosol loading over boreal forests. Science 312, 261–263, https://doi.org/10.1126/science.1123052 (2006).
Petroff, A. & Zhang, L. Development and validation of a size-resolved particle dry deposition scheme for application in aerosol transport models. Geosci. Model Dev. 3, 753–769, https://doi.org/10.5194/gmd-3-753-2010 (2010).
Aliaga, D. et al. Nanoparticle ranking analysis: determining new particle formation (NPF) event occurrence and intensity based on the concentration spectrum of formed (sub-5nm) particles. Aerosol Res. 1, 81–92, https://doi.org/10.5194/ar-1-81-2023 (2023).
Tuovinen, S., Lampilahti, J., Kerminen, V. M. & Kulmala, M. Intermediate ions as indicator for local new particle formation. Aerosol Res. 2, 93–105, https://doi.org/10.5194/ar-2-93-2024 (2024).
Panagi, M. et al. Investigating the regional contributions to air pollution in Beijing: a dispersion modelling study using CO as a tracer. Atmos. Chem. Phys. 20, 2825–2838, https://doi.org/10.5194/acp-20-2825-2020 (2020).
Dada, L. et al. Long-term analysis of clear-sky new particle formation events and nonevents in Hyytiälä. Atmos. Chem. Phys. 17, 6227–6241, https://doi.org/10.5194/acp-17-6227-2017 (2017).
Dada, L. et al. Refined classification and characterization of atmospheric new-particle formation events using air ions. Atmos. Chem. Phys. 18, 17883–17893, https://doi.org/10.5194/acp-18-17883-2018 (2018).
Cai, R. L. et al. The missing base molecules in atmospheric acid-base nucleation. Natl Sci Rev 9 https://doi.org/10.1093/nsr/nwac137 (2022).
Lehtipalo, K. et al. Multicomponent new particle formation from sulfuric acid, ammonia, and biogenic vapors. Sci. Adv. 4, eaau5363, https://doi.org/10.1126/sciadv.aau5363 (2018).
Cai, R. L. et al. An indicator for sulfuric acid-amine nucleation in atmospheric environments. Aerosol Sci. Tech. 55, 1059–1069, https://doi.org/10.1080/02786826.2021.1922598 (2021).
Kulmala, M. et al. Quiet New Particle Formation in the Atmosphere. Frontiers in Environmental Science 10 https://doi.org/10.3389/fenvs.2022.912385 (2022).
Gao, W. et al. Development of portable single photon ionization time-of-flight mass spectrometer combined with membrane inlet. Int J. Mass Spectrom. 334, 8–12, https://doi.org/10.1016/j.ijms.2012.09.003 (2013).
Kulmala, M. et al. Direct link between the characteristics of atmospheric new particle formation and Continental Biosphere-Atmosphere-Cloud-Climate (COBACC) feedback loop. Boreal Environ. Res 28, 1–13 (2023).
Hoornweg, D. & Pope, K. Population predictions for the world’s largest cities in the 21st century. Environ. Urban 29, 195–216, https://doi.org/10.1177/0956247816663557 (2017).
Hari, P. & Kulmala, M. Station for measuring ecosystem-atmosphere relations (SMEAR II). Boreal Environ. Res 10, 315–322 (2005).
Hari, P. et al. Conceptual design of a measurement network of the global change. Atmos. Chem. Phys. 16, 1017–1028, https://doi.org/10.5194/acp-16-1017-2016 (2016).
Liu, Y. et al. Continuous and comprehensive atmospheric observations in Beijing: a station to understand the complex urban atmospheric environment. Big Earth Data 4, 295–321 (2020).
Aalto, P. et al. Physical characterization of aerosol particles during nucleation events. Tellus B 53, 344–358 (2001).
Taipale, R. et al. Technical Note: Quantitative long-term measurements of VOC concentrations by PTR-MS - measurement, calibration, and volume mixing ratio calculation methods. Atmos. Chem. Phys. 8, 6681–6698, https://doi.org/10.5194/acp-8-6681-2008 (2008).
Nieminen, T. et al. Trends in atmospheric new-particle formation: 16 years of observations in a boreal-forest environment. Boreal Environ. Res 19, 191–214 (2014).
Cai, J. et al. Size-segregated particle number and mass concentrations from different emission sources in urban Beijing. Atmos. Chem. Phys. 20, 12721–12740, https://doi.org/10.5194/acp-20-12721-2020 (2020).
Lindinger, W., Hansel, A. & Jordan, A. On-line monitoring of volatile organic compounds at pptv levels by means of proton-transfer-reaction mass spectrometry (PTR-MS) - Medical applications, food control and environmental research. Int J. Mass Spectrom. 173, 191–241, https://doi.org/10.1016/S0168-1176(97)00281-4 (1998).
Jokinen, T. et al. Atmospheric sulphuric acid and neutral cluster measurements using CI-APi-TOF. Atmos. Chem. Phys. 12, 4117–4125, https://doi.org/10.5194/acp-12-4117-2012 (2012).
Vanhanen, J. et al. Particle Size Magnifier for Nano-CN Detection. Aerosol Sci. Tech. 45, 533–542, https://doi.org/10.1080/02786826.2010.547889 (2011).
Mirme, S. & Mirme, A. The mathematical principles and design of the NAIS - a spectrometer for the measurement of cluster ion and nanometer aerosol size distributions. Atmos. Meas. Tech. 6, 1061–1071, https://doi.org/10.5194/amt-6-1061-2013 (2013).
Manninen, H. E., Mirme, S., Mirme, A., Petäjä, T. & Kulmala, M. How to reliably detect molecular clusters and nucleation mode particles with Neutral cluster and Air Ion Spectrometer (NAIS). Atmos. Meas. Tech. 9, 3577–3605, https://doi.org/10.5194/amt-9-3577-2016 (2016).
Schiavina M. et al. GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) https://doi.org/10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE (2023).
Liu, J. Q., Jiang, J. K., Zhang, Q., Deng, J. G. & Hao, J. M. A spectrometer for measuring particle size distributions in the range of 3 nm to 10 μm. Front Env Sci. Eng. 10, 63–72, https://doi.org/10.1007/s11783-014-0754-x (2016).
Deng, C. J. et al. Measurement report: Size distributions of urban aerosols down to 1 nm from long-term measurements. Atmos. Chem. Phys. 22, 13569–13580, https://doi.org/10.5194/acp-22-13569-2022 (2022).
Luoma, K. et al. Effects of different correction algorithms on absorption coefficient - a comparison of three optical absorption photometers at a boreal forest site. Atmos. Meas. Tech. 14, 6419–6441, https://doi.org/10.5194/amt-14-6419-2021 (2021).
Fröhlich, R. et al. The ToF-ACSM: a portable aerosol chemical speciation monitor with TOFMS detection. Atmos. Meas. Tech. 6, 3225–3241, https://doi.org/10.5194/amt-6-3225-2013 (2013).
Stein, A. F. et al. NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System. Bull. Am. Meteorological Soc. 96, 2059–2077, https://doi.org/10.1175/Bams-D-14-00110.1 (2015).
Hakala, S. et al. Observed coupling between air mass history, secondary growth of nucleation mode particles and aerosol pollution levels in Beijing. Environ. Sci.-Atmos. 2, 146–164, https://doi.org/10.1039/d1ea00089f (2022).
Kulmala, M. et al. Opinion: Gigacity – a source of problems or the new way to sustainable development. Atmos. Chem. Phys. 21, 8313–8322, https://doi.org/10.5194/acp-21-8313-2021 (2021).
Stohl, A., Forster, C., Frank, A., Seibert, P. & Wotawa, G. Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2. Atmos. Chem. Phys. 5, 2461–2474, https://doi.org/10.5194/acp-5-2461-2005 (2005).
Pisso, I. et al. The Lagrangian particle dispersion model FLEXPART version 10.4. Geosci. Model Dev. 12, 4955–4997, https://doi.org/10.5194/gmd-12-4955-2019 (2019).
Ding, A. J. et al. Transport of north China air pollution by midlatitude cyclones: Case study of aircraft measurements in summer 2007. J. Geophys Res-Atmos. 114, D08304, https://doi.org/10.1029/2008jd011023 (2009).
Dal Maso, M. et al. Formation and growth of fresh atmospheric aerosols: eight years of aerosol size distribution data from SMEAR II, Hyytiälä, Finland. Boreal Environ. Res 10, 323–336 (2005).
Kulmala, M. et al. Opinion: A paradigm shift in investigating the general characteristics of atmospheric new particle formation using field observations. Aerosol Res. 2, 49–58, https://doi.org/10.5194/ar-2-49-2024 (2024).
Kulmala, M. et al. Toward direct measurement of atmospheric nucleation. Science 318, 89–92, https://doi.org/10.1126/science.1144124 (2007).
Acknowledgements
We acknowledge the following projects: ACCC Flagship funded by the Academy of Finland grant number 337549, Academy professorship funded by the Academy of Finland (grant no. 302958), Academy of Finland projects no. 325656, 311932, 334792, 316114, 325647, 325681, 333397, 328616, 357902, 345510, 347782, “Quantifying carbon sink, CarbonSink+ and their interaction with air quality” INAR project funded by Jane and Aatos Erkko Foundation, “Gigacity” project funded by Wihuri foundation, European Research Council (ERC) project ATM-GTP Contract No. 742206, and European Union via Non-CO2 Forcers and their Climate, Weather, Air Quality and Health Impacts (FOCI), and CRiceS (No 101003826), RI-URBANS (101036245), EMME-CARE (856612) and FORCeS (821205). The Technology Industries of Finland Centennial foundation via urbaani ilmanlaatu 2.0 -project is gratefully acknowledged. University of Helsinki support via ACTRIS-HY is acknowledged. Support of the technical and scientific staff in Hyytiälä and BUCT/AHL are acknowledged. L.D. acknowledges funding from the Swiss National Science Foundation (grant number 216181).
Author information
Authors and Affiliations
Contributions
M.K., W.D., F.B., V.-M.K. and T.K. designed the research; M.X., Y.W., F.Z., Y.Z., Z.W., C.H., J.X., W.M., Y.G., X.C., T.L., J.L., H.P., G.Z., K.C., Z.Z., W.Z., S.G., W.Z., Q.Y., I.Y., L.A. collected the data; W.D., T.Z., Z.Z., W.F., and T.K. analyzed the data with help from M.K., X.R., M.X., Q.Z., C.Y., I.Y., D.A., R.C., M.A., L.A., P.S.-A., S.T., J.C., K.D., L.D., V.-M.K.; M.K., W.D., K.D., L.D., V.M.-K. and T.K. wrote the paper with inputs from all the authors.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Kulmala, M., Wei, D., Zhang, X. et al. Understanding atmospheric processes: insights from the comparison between Beijing and Hyytiälä. npj Clean Air 1, 26 (2025). https://doi.org/10.1038/s44407-025-00020-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s44407-025-00020-x
