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Differential mobility particle size spectrometers (DMPSs) are widely used to measure the aerosol number size distribution. Especially during new particle formation (NPF), the dynamics of the ultrafine size distribution determine the significance of the newly formed particles within the atmospheric system. A precision quantification of the size distribution and derived quantities such as new particle formation and growth rates is therefore essential. However, size-distribution measurements in the sub-10 nm range suffer from high particle losses and are often derived from only a few counts in the DMPS system, making them subject to very high counting uncertainties. Here we show that a CPC (modified Airmodus A20) with a significantly higher aerosol optics flow rate compared to conventional ultrafine CPCs can greatly enhance the counting statistics in that size range. Using Monte Carlo uncertainty estimates, we show that the uncertainties of the derived formation and growth rates can be reduced from 10 %–20 % down to 1 % by deployment of the high statistics CPC on a strong NPF event day. For weaker events and hence lower number concentrations, the counting statistics can result in a complete breakdown of the growth rate estimate with relative uncertainties as high as 40 %, while the improved DMPS still provides reasonable results at 10 % relative accuracy. In addition, we show that other sources of uncertainty are present in CPC measurements, which might become more important when the uncertainty from the counting statistics is less dominant. Altogether, our study shows that the analysis of NPF events could be greatly improved by the availability of higher counting statistics in the used aerosol detector of DMPS systems.

Aerosol particles are a complex component of the atmospheric system which influence climate directly by interacting with solar radiation, and indirectly by contributing to cloud formation. The variety of their sources, as well as the multiple transformations they may undergo during their transport (including wet and dry deposition), result in significant spatial and temporal variability of their properties. Documenting this variability is essential to provide a proper representation of aerosols and cloud condensation nuclei (CCN) in climate models. Using measurements conducted in 2016 or 2017 at 62 ground-based stations around the world, this study provides the most up-to-date picture of the spatial distribution of particle number concentration (Ntot) and number size distribution (PNSD, from 39 sites). A sensitivity study was first performed to assess the impact of data availability on Ntot's annual and seasonal statistics, as well as on the analysis of its diel cycle. Thresholds of 50 % and 60 % were set at the seasonal and annual scale, respectively, for the study of the corresponding statistics, and a slightly higher coverage (75 %) was required to document the diel cycle. Although some observations are common to a majority of sites, the variety of environments characterizing these stations made it possible to highlight contrasting findings, which, among other factors, seem to be significantly related to the level of anthropogenic influence. The concentrations measured at polar sites are the lowest (∼ 102 cm−3) and show a clear seasonality, which is also visible in the shape of the PNSD, while diel cycles are in general less evident, due notably to the absence of a regular day–night cycle in some seasons. In contrast, the concentrations characteristic of urban environments are the highest (∼ 103–104 cm−3) and do not show pronounced seasonal variations, whereas diel cycles tend to be very regular over the year at these stations. The remaining sites, including mountain and non-urban continental and coastal stations, do not exhibit as obvious common behaviour as polar and urban sites and display, on average, intermediate Ntot (∼ 102–103 cm−3). Particle concentrations measured at mountain sites, however, are generally lower compared to nearby lowland sites, and tend to exhibit somewhat more pronounced seasonal variations as a likely result of the strong impact of the atmospheric boundary layer (ABL) influence in connection with the topography of the sites. ABL dynamics also likely contribute to the diel cycle of Ntot observed at these stations. Based on available PNSD measurements, CCN-sized particles (considered here as either >50 nm or >100 nm) can represent from a few percent to almost all of Ntot, corresponding to seasonal medians on the order of ∼ 10 to 1000 cm−3, with seasonal patterns and a hierarchy of the site types broadly similar to those observed for Ntot. Overall, this work illustrates the importance of in situ measurements, in particular for the study of aerosol physical properties, and thus strongly supports the development of a broad global network of near surface observatories to increase and homogenize the spatial coverage of the measurements, and guarantee as well data availability and quality. The results of this study also provide a valuable, freely available and easy to use support for model comparison and validation, with the ultimate goal of contributing to improvement of the representation of aerosol–cloud interactions in models, and, therefore, of the evaluation of the impact of aerosol particles on climate.

We present a comparison between three absorption photometers that measured the absorption coefficient (σabs) of ambient aerosol particles in 2012–2017 at SMEAR II (Station for Measuring Ecosystem–Atmosphere Relations II), a measurement station located in a boreal forest in southern Finland. The comparison included an Aethalometer (AE31), a multi-angle absorption photometer (MAAP), and a particle soot absorption photometer (PSAP). These optical instruments measured particles collected on a filter, which is a source of systematic errors, since in addition to the particles, the filter fibers also interact with light. To overcome this problem, several algorithms have been suggested to correct the AE31 and PSAP measurements. The aim of this study was to research how the different correction algorithms affected the derived optical properties. We applied the different correction algorithms to the AE31 and PSAP data and compared the results against the reference measurements conducted by the MAAP. The comparison between the MAAP and AE31 resulted in a multiple-scattering correction factor (Cref) that is used in AE31 correction algorithms to compensate for the light scattering by filter fibers. Cref varies between different environments, and our results are applicable to a boreal environment. We observed a clear seasonal cycle in Cref, which was probably due to variations in aerosol optical properties, such as the backscatter fraction and single-scattering albedo, and also due to variations in the relative humidity (RH). The results showed that the filter-based absorption photometers seemed to be rather sensitive to the RH even if the RH was kept below the recommended value of 40 %. The instruments correlated well (R≈0.98), but the slopes of the regression lines varied between the instruments and correction algorithms: compared to the MAAP, the AE31 underestimated σabs only slightly (the slopes varied between 0.96–1.00) and the PSAP overestimated σabs only a little (the slopes varied between 1.01–1.04 for a recommended filter transmittance >0.7). The instruments and correction algorithms had a notable influence on the absorption Ångström exponent: the median absorption Ångström exponent varied between 0.93–1.54 for the different algorithms and instruments.

The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth–atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 µg m−3) into three sub-categories – low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) – proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-year-long time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments.

The occurrence frequency of regional atmospheric new aerosol particle formation and consecutive growth events (fNPF) were studied with respect to vegetation activity, aerosol properties, air pollutants and meteorological data in Budapest over the time interval from 2008 to 2018. The data set evaluated contained results of in situ measurements on the land surface that were mostly performed at the Budapest platform for Aerosol Research and Training Laboratory, of satellite-based products recorded by MODIS on Terra and of modelled vegetation emission-related properties from an advanced regional biogeochemical model. The annual mean relative occurrence frequencies were considerable (with an overall mean of 21 %), remained at a constant level (with an overall SD of 5 %) and did not exhibit tendentious change over the years. The shape of the distributions of monthly mean fNPF exhibited large variability from year to year, while the overall average distribution already possessed a characteristic pattern. The structure of the new particle formation (NPF) occurrence distributions was compared to those of environmental variables including concentrations of gas-phase H2SO4, SO2, O3, NO, NO2, CO, PM10 mass and NH3; particle numbers in the size fractions of 6–1000, 6–100 and 100–1000 nm; condensation sink; air temperature (T); relative humidity (RH); wind speed (WS); atmospheric pressure (P); global solar radiation (GRad); gross primary production (GPP) of vegetation; leaf area index (LAI); and stomatal conductance (SCT). There were no evident systematic similarities between fNPF on the one hand and all of the variables studied on the other hand, except for H2SO4 and perhaps NH3. The spring maximum in the NPF occurrence frequency distribution often overlapped with the time intervals of positive T anomaly in vegetated territories. The link between the potential heat stress exerted on plants in sultry summer intervals and the summer fNPF minimum could not be proven. The relevance of environmental variables was assessed by their ratios on NPF event days and on non-event days. The gas-phase H2SO4 concentration showed the largest monthly ratios, followed by O3. The WS, biogenic precursor gases and SO2 can generally favour NPF events, although their influence seemed to be constrained. An association between the fNPF and vegetation growth dynamics was clearly identified.

The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II is well known among atmospheric scientists due to the immense amount of observational data it provides of the Earth–atmosphere interface. Moreover, SMEAR II plays an important role for the large European research infrastructure, enabling the large scientific community to tackle climate- and air-pollution-related questions, utilizing the high-quality long-term data sets recorded at the site. So far, this well-documented site was missing the description of the seasonal variation in aerosol chemical composition, which helps understanding the complex biogeochemical and physical processes governing the forest ecosystem. Here, we report the sub-micrometer aerosol chemical composition and its variability, employing data measured between 2012 and 2018 using an Aerosol Chemical Speciation Monitor (ACSM). We observed a bimodal seasonal trend in the sub-micrometer aerosol concentration culminating in February (2.7, 1.6, and 5.1 µg m−3 for the median, 25th, and 75th percentiles, respectively) and July (4.2, 2.2, and 5.7 µg m−3 for the median, 25th, and 75th percentiles, respectively). The wintertime maximum was linked to an enhanced presence of inorganic aerosol species (ca. 50 %), whereas the summertime maximum (ca. 80 % organics) was linked to biogenic secondary organic aerosol (SOA) formation. During the exceptionally hot months of July of 2014 and 2018, the organic aerosol concentrations were up to 70 % higher than the 7-year July mean. The projected increase in heat wave frequency over Finland will most likely influence the loading and chemical composition of aerosol particles in the future. Our findings suggest strong influence of meteorological conditions such as radiation, ambient temperature, and wind speed and direction on aerosol chemical composition. To our understanding, this is the longest time series reported describing the aerosol chemical composition measured online in the boreal region, but the continuous monitoring will also be maintained in the future.

Aerosol particles are essential constituents of the Earth's atmosphere, impacting the earth radiation balance directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. In contrast to most greenhouse gases, aerosol particles have short atmospheric residence times, resulting in a highly heterogeneous distribution in space and time. There is a clear need to document this variability at regional scale through observations involving, in particular, the in situ near-surface segment of the atmospheric observation system. This paper will provide the widest effort so far to document variability of climate-relevant in situ aerosol properties (namely wavelength dependent particle light scattering and absorption coefficients, particle number concentration and particle number size distribution) from all sites connected to the Global Atmosphere Watch network. High-quality data from almost 90 stations worldwide have been collected and controlled for quality and are reported for a reference year in 2017, providing a very extended and robust view of the variability of these variables worldwide. The range of variability observed worldwide for light scattering and absorption coefficients, single-scattering albedo, and particle number concentration are presented together with preliminary information on their long-term trends and comparison with model simulation for the different stations. The scope of the present paper is also to provide the necessary suite of information, including data provision procedures, quality control and analysis, data policy, and usage of the ground-based aerosol measurement network. It delivers to users of the World Data Centre on Aerosol, the required confidence in data products in the form of a fully characterized value chain, including uncertainty estimation and requirements for contributing to the global climate monitoring system.

Aerosol optical properties (AOPs) describe the ability of aerosols to scatter and absorb radiation at different wavelengths. Since aerosol particles interact with the sun's radiation, they impact the climate. Our study focuses on the long-term trends and seasonal variations of different AOPs measured at a rural boreal forest site in northern Europe. To explain the observed variations in the AOPs, we also analyzed changes in the aerosol size distribution. AOPs of particles smaller than 10 µm (PM10) and 1 µm (PM1) have been measured at SMEAR II, in southern Finland, since 2006 and 2010, respectively. For PM10 particles, the median values of the scattering and absorption coefficients, single-scattering albedo, and backscatter fraction at λ=550 nm were 9.8 Mm−1, 1.3 Mm−1, 0.88, and 0.14. The median values of scattering and absorption Ångström exponents at the wavelength ranges 450–700 and 370–950 nm were 1.88 and 0.99, respectively. We found statistically significant trends for the PM10 scattering and absorption coefficients, single-scattering albedo, and backscatter fraction, and the slopes of these trends were −0.32 Mm−1, −0.086 Mm−1, 2.2×10-3, and 1.3×10-3 per year. The tendency for the extensive AOPs to decrease correlated well with the decrease in aerosol number and volume concentrations. The tendency for the backscattering fraction and single-scattering albedo to increase indicates that the aerosol size distribution consists of fewer larger particles and that aerosols absorb less light than at the beginning of the measurements. The trends of the single-scattering albedo and backscattering fraction influenced the aerosol radiative forcing efficiency, indicating that the aerosol particles are scattering the radiation more effectively back into space.

Characterizing aerosol chemical composition in response to meteorological changes and atmospheric chemistry is important to gain insights into new particle formation mechanisms. A BAECC (Biogenic Aerosols – Effects on Clouds and Climate) campaign was conducted during the spring 2014 at the SMEAR II station (Station for Measuring Forest Ecosystem–Aerosol Relations) in Finland. The particles were characterized by a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). A PBL (planetary boundary layer) dilution model was developed to assist interpreting the measurement results. Right before nucleation events, the mass concentrations of organic and sulfate aerosol species were both decreased rapidly along with the growth of PBL heights. However, the mass fraction of sulfate aerosol of the total aerosol mass was increased, in contrast to a decrease for the organic mass fraction. Meanwhile, an increase in LVOOA (low-volatility oxygenated organic aerosol) mass fraction of the total organic mass was observed, in distinct comparison to a reduction of SVOOA (semi-volatile OOA) mass fraction. Our results demonstrate that, at the beginning of nucleation events, the observed sulfate aerosol mass was mainly driven by vertical turbulent mixing of sulfate-rich aerosols between the residual layer and the newly formed boundary layer, while the condensation of sulfuric acid (SA) played a minor role in interpreting the measured sulfate mass concentration. For the measured organic aerosols, their temporal profiles were mainly driven by dilution from PBL development, organic aerosol mixing in different boundary layers and/or partitioning of organic vapors, but accurate measurements of organic vapor concentrations and characterization on the spatial aerosol chemical composition are required. In general, the observed aerosol particles by AMS are subjected to joint effects of PBL dilution, atmospheric chemistry and aerosol mixing in different boundary layers. During aerosol growth periods in the nighttime, the mass concentrations of organic aerosols and organic nitrate aerosols were both increased. The increase in SVOOA mass correlated well with the calculated increase in condensed HOMs' (highly oxygenated organic molecules) mass. To our knowledge, our results are the first atmospheric observations showing a connection between increase in SVOOA and condensed HOMs during the nighttime.

Aerosol–cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Here we present a data set – ready to be used for model validation – of long-term observations of CCN number concentrations, particle number size distributions and chemical composition from 12 sites on 3 continents. Studied environments include coastal background, rural background, alpine sites, remote forests and an urban surrounding. Expectedly, CCN characteristics are highly variable across site categories. However, they also vary within them, most strongly in the coastal background group, where CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behaviour, most continental stations exhibit very similar activation ratios (relative to particles > 20 nm) across the range of 0.1 to 1.0 % supersaturation. At the coastal sites the transition from particles being CCN inactive to becoming CCN active occurs over a wider range of the supersaturation spectrum. Several stations show strong seasonal cycles of CCN number concentrations and particle number size distributions, e.g. at Barrow (Arctic haze in spring), at the alpine stations (stronger influence of polluted boundary layer air masses in summer), the rain forest (wet and dry season) or Finokalia (wildfire influence in autumn). The rural background and urban sites exhibit relatively little variability throughout the year, while short-term variability can be high especially at the urban site. The average hygroscopicity parameter, κ, calculated from the chemical composition of submicron particles was highest at the coastal site of Mace Head (0.6) and lowest at the rain forest station ATTO (0.2–0.3). We performed closure studies based on κ–Köhler theory to predict CCN number concentrations. The ratio of predicted to measured CCN concentrations is between 0.87 and 1.4 for five different types of κ. The temporal variability is also well captured, with Pearson correlation coefficients exceeding 0.87. Information on CCN number concentrations at many locations is important to better characterise ACI and their radiative forcing. But long-term comprehensive aerosol particle characterisations are labour intensive and costly. Hence, we recommend operating migrating-CCNCs to conduct collocated CCN number concentration and particle number size distribution measurements at individual locations throughout one year at least to derive a seasonally resolved hygroscopicity parameter. This way, CCN number concentrations can only be calculated based on continued particle number size distribution information and greater spatial coverage of long-term measurements can be achieved.

Process-based land surface models are important tools for estimating global wetland methane (CH4) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH4 fluxes (FCH4) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model-observation disagreements are mainly at multi-day time scales (32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales

Abstract It is widely acknowledged that the intensity, frequency and duration of heatwaves are increasing worldwide, including the Arctic. However, less attention has been paid to the land area affected by heatwaves. Here, using atmospheric reanalysis and global climate models, we show that the area covered by heatwaves is substantially expanding in the terrestrial Arctic. Compared to the mid-20th century, the total land area affected by severe heatwaves in the Arctic has doubled, the area of extreme heatwaves has tripled, and the area of very extreme heatwaves has quadrupled. Furthermore, climate model projections suggest that the extent of heatwaves will continue to increase in the 21st century, but with large regional differences in heatwave magnitudes due to summer intraseasonal temperature variability. Our findings underscore the growing vulnerability of the Arctic region to extreme heat, potentially leading to severe impacts on both ecosystems and societies.

Abstract Background Loneliness is common among older adults in institutional settings. It leads to adverse effects on health and wellbeing, for which nature contact with peers in turn may have positive impact. However, the effects of nature engagement among older adults have not been studied in randomised controlled trials (RCT). The “Friends in Nature” (FIN) group intervention RCT for lonely older adults in Helsinki assisted living facilities (ALFs) aims to explore the effects of peer-related nature experiences on loneliness and health-related quality of life (HRQoL). In this study we aim describe the participants’ baseline characteristics of the RCT, feasibility of FIN intervention and intervention participants’ feedback on the FIN. Methods Lonely participants were recruited from 22 ALFs in Helsinki area, Finland, and randomised into two groups: 1) nature-based social intervention once a week for nine weeks (n = 162) and 2) usual care (n = 157). Demographics, diagnoses and medication use were retrieved from medical records, and baseline cognition, functioning, HRQoL, loneliness and psychological wellbeing were assessed. Primary trial outcomes will be participants’ loneliness (De Jong Giervald Loneliness Scale) and HRQoL (15D). Results The mean age of participants was 83 years, 73% were female and mean Minimental State Examination of 21 points. The participants were living with multiple co-morbidities and/or disabilities. The intervention and control groups were comparable at baseline. The adherence with intervention was moderate, with a mean attendance of 6.8 out of the nine sessions. Of the participants, 14% refused, fell ill or were deceased, and therefore, participated three sessions or less. General subjective alleviation of loneliness was achieved in 57% of the intervention participants. Of the respondents, 96% would have recommended a respective group intervention to other older adults. Intervention participants appreciated their nature excursions and experiences. Conclusions We have successfully randomised 319 lonely residents in assisted living facilities into a trial about the effects of nature experiences in a group-format. The feedback from participants was favourable. The trial will provide important information about possibilities of alleviating loneliness with peer-related nature-based experiences in frail residents. Trial registration ClinicalTrials.gov, ID: NCT05507684. Registration 19/08/2022.

Monitoring the spatial distribution and trends in surface greenhouse gas (GHG) fluxes, as well as flux attribution to natural and anthropogenic processes, is essential to track progress under the Paris Agreement and to inform its global stocktake. This study updates earlier syntheses (Petrescu et al., 2020, 2021, 2023), provides a consolidated synthesis of CH4 emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU), and is expanded to include seven additional countries with large anthropogenic and/or natural emissions (the USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of the Congo (DR Congo)). Our aim is to demonstrate the use of different emission estimates to help improve national GHG emission inventories for a diverse geographical range of stakeholders. We use updated national GHG inventories (NGHGIs) reported by Annex I parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2023 and the latest available biennial update reports (BURs) reported by non-Annex I parties. Comparing NGHGIs with other approaches highlights that different system boundaries are a key source of divergence. A key system boundary difference is whether anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are partitioned. Over the studied period, the total CH4 emission estimates in the EU, the USA, and Russia show a steady decreasing trend since 1990, while for the non-Annex I emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH4 emissions have generally increased. Quantitatively, in the EU the mean of 2015–2020 anthropogenic UNFCCC NGHGIs (15±1.8 Tg CH4 yr−1) and the mean of the BU CH4 emissions (17.8 (16–19) Tg CH4 yr−1) generally agree on the magnitude, while inversions show higher emission estimates (medians of 21 (19–22) Tg CH4 yr−1 and 24 (22–25) Tg CH4 yr−1 for the three regional and six global inversions, respectively), as they include natural emissions, which for the EU were quantified at 6.6 Tg CH4 yr−1 (Petrescu et al., 2023). Similarly, for the other Annex I parties in this study (the USA and Russia), the gap between the BU anthropogenic and total TD emissions is partly explained by the natural emissions. For the non-Annex I parties, anthropogenic CH4 estimates from UNFCCC BURs show large differences compared to the other global-inventory-based estimates and even more compared to atmospheric ones. This poses an important potential challenge to monitoring the progress of the global CH4 pledge and the global stocktake. Our analysis provides a useful baseline to prepare for the influx of inventories from non-Annex I parties as regular reporting starts under the enhanced transparency framework of the Paris Agreement. By systematically comparing the BU and TD methods, this study provides recommendations for more robust comparisons of available data sources and hopes to steadily engage more parties in using observational methods to complement their UNFCCC inventories, as well as considering their natural emissions. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, future development needs to resolve knowledge gaps in the BU and TD approaches and to better quantify the remaining uncertainty. TD methods may emerge as a powerful tool to help improve NGHGIs of CH4 emissions, but further confidence is needed in the comparability and robustness of the estimates. The referenced datasets related to figures are available at https://doi.org/10.5281/zenodo.12818506 (Petrescu et al., 2024).

Abstract Polarization management plays a key role in various applications, such as optical communications, imaging, and sensing. It not only mitigates detrimental effects (e.g., polarization mode dispersion in optical communication) but also enables advanced functionalities, such as polarization multiplexing and optical isolation. Herein, we review the state-of-the-art approaches for on-chip polarization management. Additionally, we discuss strategies for developing non-reciprocal photonic devices and the challenges associated with monolithic integration in photonics circuits.

OBJECTIVE: This study aimed to investigate (i) the risk of work disability (>10-day sickness absence spell or disability pension) due to common mental disorders (CMD) among social workers compared with other health and social care, education, and non-human service professionals and (ii) whether the risk was mediated by job stress. METHODS: A cohort of 16 306 public sector professionals in Finland was followed using survey data from baseline (2004 or if not available, 2008) on job stress [job strain or effort-reward imbalance (ERI)] and register data on work disability due to CMD from baseline through 2011. A Cox proportional hazards model was used to analyze the risk of work disability due to CMD between three occupation-pairs in a counterfactual setting, controlling for age, sex, job contract, body mass index, alcohol risk use, smoking, and physical inactivity. RESULTS: Social workers’ job stress was at higher level only when compared to education professionals. Thus, the mediation hypothesis was analyzed comparing social workers to education professionals. Social workers had a higher risk of work disability due to CMD compared with education professionals [hazard ratio (HR) 2.08, 95% confidence interval (CI) 1.58–2.74]. This HR was partly mediated by job strain (24%) and ERI (12%). Social workers had a higher risk of work disability than non-human service professionals (HR 1.54, 95% CI 1.13–2.09), but not compared with other health and social care professionals. CONCLUSIONS: Job stress partly mediated the excess risk of work disability among social workers only in comparison with education professionals.

Abstract Tracer antibodies, which are labelled with fluorescent or other type of reporter molecules, are widely employed in diagnostic immunoassays. Time-resolved fluorescence immunoassay (TRFIA), recognized as one of the most sensitive immunoassay techniques, utilizes tracers labelled with lanthanide ion (Ln) chelates. The conventional approach for conjugating isothiocyanate (ITC) Ln-chelates to antibodies involves random chemical targeting of the primary amino group of Lys residues, requiring typically overnight exposure to an elevated pH of 9–9.3 and leading to heterogeneity. Moreover, efforts to enhance the sensitivity of the assays by introducing a higher number of Ln-chelates per tracer antibody are associated with an elevated risk of targeting critical amino acid residues in the binding site, compromising the binding properties of the antibody. Herein, we report a method to precisely label recombinant antibodies with a defined number of Ln-chelates in a well-controlled manner by employing the SpyTag/SpyCatcher protein ligation technology. We demonstrate the functionality of the method with a full-length recombinant antibody (IgG) as well as an antibody fragment by producing site-specifically labelled antibodies for TRFIA for cardiac troponin I (cTnI) detection with a significant improvement in assay sensitivity compared to that with conventionally labelled tracer antibodies. Overall, our data clearly illustrates the benefits of the site-specific labelling strategy for generating high-performing tracer antibodies for TRF immunoassays.

Abstract As major terrestrial carbon sinks, forests play an important role in mitigating climate change. The relationship between the seasonal uptake of carbon and its allocation to woody biomass remains poorly understood, leaving a significant gap in our capacity to predict carbon sequestration by forests. Here, we compare the intra-annual dynamics of carbon fluxes and wood formation across the Northern hemisphere, from carbon assimilation and the formation of non-structural carbon compounds to their incorporation in woody tissues. We show temporally coupled seasonal peaks of carbon assimilation (GPP) and wood cell differentiation, while the two processes are substantially decoupled during off-peak periods. Peaks of cambial activity occur substantially earlier compared to GPP, suggesting the buffer role of non-structural carbohydrates between the processes of carbon assimilation and allocation to wood. Our findings suggest that high-resolution seasonal data of ecosystem carbon fluxes, wood formation and the associated physiological processes may reduce uncertainties in carbon source-sink relationships at different spatial scales, from stand to ecosystem levels.

Secondary aerosol particle production via new particle formation (NPF) has been shown to be a major contributor to the global aerosol load. NPF has also been observed frequently in urban environments. Here, we investigate the effect of regional NPF on urban aerosol load under well-defined atmospheric conditions. The Carpathian Basin, the largest orogenic basin in Europe, represents an excellent opportunity for exploring these interactions. Based on long-term observations, we revealed that NPF seen in a central large city of the basin (Budapest) and its regional background occur in a consistent and spatially coherent way as a result of a joint atmospheric phenomenon taking place on large horizontal scales. We found that NPF events at the urban site are usually delayed by > 1 h relative to the rural site or even inhibited above a critical condensational sink level. The urban processes require higher formation rates and growth rates to be realized, by mean factors of 2 and 1.6, respectively, than the regional events. Regional- and urban-type NPF events sometimes occur jointly with multiple onsets, while they often exhibit dynamic and timing properties which are different for these two event types.