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The flux divergence approach (FDA) is a popular technique for deriving NOx emission estimates from tropospheric NO2 columns measured by the TROPOspheric Monitoring Instrument (TROPOMI) satellite sensor. An attractive aspect of the FDA is that the method simplifies three-dimensional atmospheric chemistry and transport processes into a two-dimensional (longitude–latitude) steady-state continuity equation for columns that balances local NOx emissions with the net outflow and chemical loss of NOx. Here we test the capability of the FDA to reproduce known NOx emissions from synthetic NO2 column retrievals generated with the LOTOS-EUROS chemistry transport model over the Netherlands at high spatial resolution of about 2×2 km during summer. Our results show that the FDA captures the magnitude and spatial distribution of the NOx emissions to high accuracy (absolute bias <9 %), provided that the observations represent the NO2 column in the boundary layer, that wind speed and direction are representative for the boundary layer (PBL) column, and that the high-resolution spatiotemporal variability of the NO2 lifetimes and NOx:NO2 ratio is accounted for in the inversion instead of using single fixed values. The FDA systematically overestimates NOx emissions by 15 %–60 % when using tropospheric NO2 columns as the driving observation, while using PBL NO2 columns largely overcomes this systematic error. This merely reflects the fact that the local balance between emissions and sinks of NOx occurs in the boundary layer, which is decoupled from the NO2 in the free troposphere. Based on the recommendations from this sensitivity test, we then applied the FDA using observations of NO2 columns from TROPOMI, corrected for contributions from free-tropospheric NO2 , between 1 June and 31 August 2018. The NOx emissions derived from the default TROPOMI retrievals are biased low over cities and industrialized areas. However, when the coarse 1×1 ° TM5-MP NO2 profile used in the retrieval is replaced by the high-resolution profile of LOTOS-EUROS, the TROPOMI NOx emissions are enhanced by 22 % and are in better agreement with the inventory for the Netherlands. This emphasizes the importance of using realistic high-resolution a priori NO2 profile shapes in the TROPOMI retrieval. We conclude that accurate quantitative NOx emissions estimates are possible with the FDA, but they require sophisticated, fine-scale corrections for both the NO2 observations driving the method and the estimates of the NO2 chemical lifetime and NOx:NO2 ratio. This information can be obtained from high-resolution chemistry transport model simulations at the expense of the simplicity and applicability of the FDA. [ABSTRACT FROM AUTHOR]

To evaluate the potential of an upcoming large-swath satellite for estimating surface methane (CH₄) fluxes at a weekly scale, we report the results from a series of observing system simulation experiments (OSSEs) that use an established modeling framework that includes the GEOS-Chem 3D atmospheric transport model and an ensemble Kalman filter. These experiments focus on the sensitivity of CH₄ flux estimates to systematic errors (μ) and random errors (σ) in the column average methane (XCH4) measurements. Our control test (INV_CTL) demonstrates that with median errors (μ = 1.0 ± 0.9 ppb and σ = 6.9 ± 1.6 ppb) in XCH₄ measurements over a 1000 km swath, global CH4 fluxes can be estimated with an accuracy of 5.1 ± 1.7%, with regional accuracies ranging from 3.8% to 21.6% across TransCom sub-continental regions. The northern hemisphere mid-latitudes show greater reliability and consistency across varying μ and σ levels, while tropical and boreal regions exhibit higher sensitivity due to limited high-quality observations. In σ -sensitive regions, such as the North American boreal zone, expanding the swath width from 1000 km to 3000 km significantly reduces discrepancies, while such adjustments provide limited improvements for μ -sensitive regions like North Africa. For TanSat-2 mission, with its elliptical medium Earth orbit and 1500 km swath width, the global total estimates achieved an accuracy of 3.1 ± 2.2%. Enhancing the swath width or implementing a dual-satellite configuration is proposed to further improve TanSat-2 inversion performance. [ABSTRACT FROM AUTHOR]

Atmospheric energy transport is central to the cooling of primordial magma oceans. Theoretical studies of atmospheres on lava planets have assumed that convection is the only process involved in setting the atmospheric temperature structure. This significantly influences the ability for a magma ocean to cool. It has been suggested that convective stability in these atmospheres could preclude permanent magma oceans. We develop a new 1D radiative-convective model in order to investigate when the atmospheres overlying magma oceans are convectively stable. Using a coupled interior-atmosphere framework, we simulate the early evolution of two terrestrial-mass exoplanets: TRAPPIST-1 c and HD 63433 d. Our simulations suggest that the atmosphere of HD 63433 d exhibits deep isothermal layers which are convectively stable. However, it is able to maintain a permanent magma ocean and an atmosphere depleted in |$\mathrm{H_{2}O}$|⁠. It is possible to maintain permanent magma oceans underneath atmospheres without convection. Absorption features of |$\mathrm{CO_{2}}$| and |$\mathrm{SO_{2}}$| within synthetic emission spectra are associated with mantle redox state, meaning that future observations of HD 63433 d may provide constraints on the geochemical properties of a magma ocean analogous with the early Earth. Simulations of TRAPPIST-1 c indicate that it is expected to have solidified within |$100 \,\mathrm{M}\rm {yr}$|⁠ , outgassing a thick atmosphere in the process. Cool isothermal stratospheres generated by low-molecular-weight atmospheres can mimic the emission of an atmosphere-less body. Future work should consider how atmospheric escape and chemistry modulates the lifetime of magma oceans, and the role of tidal heating in sustaining atmospheric convection. [ABSTRACT FROM AUTHOR]

Atmospheric ammonia (NH3) is a key transboundary air pollutant that contributes to the impacts of nitrogen and acidity on terrestrial ecosystems. Ammonia also contributes to the atmospheric aerosol that affects air quality. Emission inventories indicate that NH3 was predominantly emitted by agriculture over the 19th and 20th centuries but, up to now, these estimates have not been compared to long-term observations. To document past atmospheric NH3 pollution in south-eastern Europe, ammonium (NH 4+) was analysed along an ice core extracted from Mount Elbrus in the Caucasus, Russia. The NH 4+ ice-core record indicates a 3.5-fold increase in concentrations between 1750 and 1990 CE. Remaining moderate prior to 1950 CE, the increase then accelerated to reach a maximum in 1989 CE. Comparison between ice-core trends and estimated past emissions using state-of-the-art atmospheric transport modelling of submicron-scale aerosols (FLEXPART (FLEXible PARTicle dispersion) model) indicates good agreement with the course of estimated NH3 emissions from south-eastern Europe since ∼ 1750 CE, with the main contributions from south European Russia, Türkiye, Georgia, and Ukraine. Examination of ice deposited prior to 1850 CE, when agricultural activities remained limited, suggests an NH 4+ ice concentration related to natural soil emissions representing ∼ 20 % of the 1980–2009 CE NH 4+ level, a level mainly related to current agricultural emissions that almost completely outweigh biogenic emissions from natural soil. These findings on historical NH3 emission trends represent a significant contribution to the understanding of ammonia emissions in Europe over the last 250 years. [ABSTRACT FROM AUTHOR]

In order to reduce the uncertainty of aerosol radiative forcing in global climate models, we need to better understand natural aerosol sources which are important to constrain the current and pre-industrial climate. Here, we analyse particle number size distributions (PNSDs) collected during a year (2015) across four coastal and inland Antarctic research bases (Halley, Marambio, Dome C and King Sejong). We utilise k -means cluster analysis to separate the PNSD data into six main categories. "Nucleation" and "bursting" PNSDs occur 28 %–48 % of the time between sites, most commonly at the coastal sites of Marambio and King Sejong where air masses mostly come from the west and travel over extensive regions of sea ice, marginal ice and open ocean and likely arise from new particle formation. "Aitken high", "Aitken low" and "bimodal" PNSDs occur 37 %–68 % of the time, most commonly at Dome C on the Antarctic Plateau, and likely arise from atmospheric transport and ageing from aerosol originating likely in both the coastal boundary layer and free troposphere. "Pristine" PNSDs with low aerosol concentrations occur 12 %–45 % of the time, most commonly at Halley, located at low altitudes and far from the coastal melting ice and influenced by air masses from the west. Not only the sea spray primary aerosols and gas to particle secondary aerosol sources, but also the different air masses impacting the research stations should be kept in mind when deliberating upon different aerosol precursor sources across research stations. We infer that both primary and secondary components from pelagic and sympagic regions strongly contribute to the annual seasonal cycle of Antarctic aerosols. Our simultaneous aerosol measurements stress the importance of the variation in atmospheric biogeochemistry across the Antarctic region. [ABSTRACT FROM AUTHOR]

Planetary atmospheric detection is an important way to recognize the physical and chemical properties of planets that inform about their formation and evolution, and atmospheric in-situ sampling is an ideal way to obtain high-resolution information. In this paper, a scheme for in-situ sampling of planetary atmosphere based on multi-pass aeroassisted maneuvers is given, and the corresponding design method for multiple traversal trajectories through the atmosphere is proposed. The aeroassisted maneuvering scheme achieves target-area sampling by crossing the atmosphere circularly, and is able to flexibly adjust the sampling altitude, thus having the advantage of three-dimensional and wide-area sampling. The trajectory design method involves algorithms to determine key design parameters separately. Specifically, the minimum entry periapsis altitude is determined by building its mapping relationship with path constraints to satisfy the minimum flight altitude constraint. Besides, the pass number of atmospheric flights is calculated by giving the upper bound of the energy attenuation and mission-time constraints. Then, a rapid inclination correction method via bank angle reversal is given to satisfy the inclination constraint of the maneuver. In numerical simulations, three Martian atmospheric detection scenarios, designated as high-latitude region with superficial ice water, magnetic anomalies region, and the polar region enriched with atmospheric transport properties, are established, with corresponding maneuvering sampling trajectories and characteristic parameter distributions provided. This paper introduces for the first time the use of multi-pass aeroassisted maneuvers for in-situ atmospheric sampling. Simulation results demonstrate the effectiveness and general applicability of the proposed method. • Atmospheric sampling scheme by multi-pass aeroassisted maneuvers is introduced. • General trajectory design method for sampling is proposed. • Properties of the sampling process are demonstrated by different scenarios. [ABSTRACT FROM AUTHOR]

Many known rocky exoplanets are so highly irradiated that their dayside surfaces are molten, and 'silicate atmospheres', composed of rock-forming elements, are generated above these lava pools. The compositions of these 'lava planet' atmospheres are of great interest because they must be linked to the composition of the underlying rocky interiors. It may be possible to investigate these atmospheres, either by detecting them directly via emission spectroscopy or by observing the dust tails which trail the low-mass 'catastrophically evaporating planets'. In this work, we develop a simple chemical model of the lava pool–atmosphere system under mass-loss, to study its evolution. Mass-loss can occur both into space and from the day to the nightside. We show that the system reaches a steady state, where the material in the escaping atmosphere has the same composition as that melted into the lava pool from the mantle. We show that the catastrophically evaporating planets are likely to be in this evolved state. This means that the composition of their dust tails is likely to be a direct trace of the composition of the mantle material that is melted into the lava pool. We further show that, due to the strength of day-to-nightside atmospheric transport, this evolved state may even apply to relatively high-mass planets (⁠|$\gtrsim 1\mathrm \,{M}_{\oplus }$|⁠). Moreover, the low pressure of evolved atmospheres implies that non-detections may not be due to the total lack of an atmosphere. Both conclusions are important for the interpretation of future observations. [ABSTRACT FROM AUTHOR]

The largest anthropogenic emissions of carbon dioxide (CO2) come from local sources, such as cities and power plants. The upcoming Copernicus CO2 Monitoring (CO2M) mission will provide satellite images of the CO2 and NO2 plumes associated with these sources at a resolution of 2 km × 2 km and with a swath of 250 km. These images could be exploited using atmospheric-plume inversion methods to estimate local CO2 emissions at the time of the satellite overpass and their corresponding uncertainties. To support the development of the operational processing of satellite imagery of the column-averaged CO2 dry-air mole fraction (XCO2) and tropospheric-column NO2, this study evaluates data-driven inversion methods, i.e., computationally light inversion methods that directly process information from satellite images, local winds, and meteorological data, without resorting to computationally expensive dynamical atmospheric transport models. We designed an objective benchmarking exercise to analyze and compare the performance of five different data-driven inversion methods: two implementations with different complexities for the cross-sectional flux approach (CSF and LCSF), as well as one implementation each for the integrated mass enhancement (IME), divergence (Div), and Gaussian plume (GP) model inversion approaches. This exercise is based on pseudo-data experiments with simulations of synthetic true emissions, meteorological and concentration fields, and CO2M observations across a domain of 750 km × 650 km, centered on eastern Germany, over 1 year. The performance of the methods is quantified in terms of the accuracy of single-image emission estimates (from individual images) or annual-average emission estimates (from the full series of images), as well as in terms of the number of instant estimates for the city of Berlin and 15 power plants within this domain. Several ensembles of estimations are conducted using different scenarios for the available synthetic datasets. These ensembles are used to analyze the sensitivity of performance to (1) data loss due to cloud cover, (2) uncertainty in the wind, or (3) the added value of simultaneous NO2 images. The GP and LCSF methods generate the most accurate estimates from individual images. The deviations between the emission estimates and the true emissions from these two methods have similar interquartile ranges (IQRs), ranging from ∼ 20 % to ∼ 60 % depending on the scenario. When taking cloud cover into account, these methods produce 274 and 318 instant estimates, respectively, from the ∼ 500 daily images, which cover significant portions of the plumes from the sources. Filtering the results based on the associated uncertainty estimates can improve the statistics of the IME and CSF methods but does so at the cost of a large decrease in the number of estimates. Due to a reliable estimation of uncertainty and, thus, a suitable selection of estimates, the CSF method achieves similar, if not better, accuracy statistics for instant estimates compared to the GP and LCSF methods after filtering. In general, the performance of retrieving single-image estimates improves when, in addition to XCO2 data, collocated NO2 data are used to characterize the structure of plumes. With respect to the estimates of annual emissions, the root mean square errors (RMSEs) for the most realistic benchmarking scenario are 20 % (GP), 27 % (CSF), 31 % (LCSF), 55 % (IME), and 79 % (Div). This study suggests that the Gaussian plume and/or cross-sectional approaches are currently the most efficient tools for providing estimates of CO2 emissions from satellite images, and their relatively light computational cost will enable the analysis of the massive amount of data to be provided by future satellite XCO2 imagery missions. [ABSTRACT FROM AUTHOR]

We present a protocol to improve confidence in reported radon activity concentrations, facilitating direct site-to-site comparisons and integration with co-located greenhouse gas (GHG) measurements within a network of three independently managed observatories in the UK. Translating spot measurements of atmospheric GHG amount fractions into regional flux estimates ("top-down" analysis) is usually performed with atmospheric transport models (ATMs), which calculate the sensitivity of regional emissions to changes in observed GHGs at a finite number of locations. However, the uncertainty of regional emissions is closely linked to ATM uncertainties. Radon, emitted naturally from the land surface, can be used as a tracer of atmospheric transport and mixing to independently evaluate the performance of such models. To accomplish this, the radon measurements need to have a comparable precision to the GHGs at the modelled temporal resolution. Australian Nuclear Science and Technology Organisation (ANSTO) dual-flow-loop two-filter radon detectors provide output every 30 min. The measurement accuracy at this temporal resolution depends on the characterization and removal of instrumental background, the calibration procedure, and response time correction. Consequently, unless these steps are standardized, measurement precision may differ between sites. Here we describe standardized approaches regarding (1) instrument maintenance, (2) quality control of the raw data stream, (3) determination and removal of the instrumental background, (4) calibration methods, and (5) response time correction (by deconvolution). Furthermore, we assign uncertainties for each reported 30 min radon estimate (assuming these steps have been followed) and validate the final result through comparison of diurnal and sub-diurnal radon characteristics with co-located GHG measurements. While derived for a network of UK observatories, the proposed standardized protocol could be equally applied to two-filter dual-flow-loop radon observations across larger networks, such as the Integrated Carbon Observation System (ICOS) or the Global Atmosphere Watch (GAW) baseline network. [ABSTRACT FROM AUTHOR]

Quantifying emissions from megacities is important for reduction of greenhouse gases. We used atmospheric carbon dioxide (CO2) concentration data obtained at an altitude of around 250 m above the ground on TOKYO SKYTREE (TST; a 634-m-high freestanding broadcasting tower; 35.71°N, 139.81°E), which is located north of central Tokyo, Japan. To use the TST observations for estimating net CO2 fluxes from Tokyo, a global, high-resolution simulation of atmospheric CO2 transport with CO2 flux data from a global inverse analysis was performed. In the simulation, atmospheric CO2 variations were well reproduced at remote sites around Japan. The application of tagged tracers in the simulation revealed that variations of CO2 concentrations at TST were largely driven by fluxes in the southwest region of Tokyo, including the western Tokyo Bay area where huge power plants are located. Then, we performed a regression analysis of modeled and observed Tokyo-originated CO2 concentrations, both of which were derived from the simulated background concentrations, while changing the minimum wind speed used in the analysis. The removal of low wind speeds altered the slope of the regression line, and excluding wind speeds below 7 m s−1 resulted in a stabilized slope of 0.93 ± 0.08. This stabilized regression indicated that the annual net CO2 emission from Tokyo is 79.5 ± 6.6 Tg-C yr−1. Our findings demonstrate that analysis using a global high-resolution model with tagged tracers has the potential to monitor emissions changes in a megacity. [ABSTRACT FROM AUTHOR]

Ignoring the diurnal cycle in surface-to-atmosphere CO2 fluxes leads to a systematic bias in CO2 mole fraction simulations sampled at daytime because the daily mean flux systematically misses the CO2 uptake during the daytime hours. In an atmospheric inversion using daytime-selected CO2 measurements at most continental sites and not resolving diurnal cycles in the flux, this leads to systematic biases in the estimates of the annual sources and sinks of atmospheric CO2. This study focuses on quantifying the impact of this diurnal cycle effect on the annual carbon fluxes estimated with the CarboScope (CS) atmospheric inversion at regional, continental, and global scales for the period of time 2010–2020. Our analysis is based on biogenic fluxes of hourly net ecosystem exchange (NEE) obtained from the data-driven FLUXCOM-X estimates, together with global and regional atmospheric transport models. Differences between CO2 mixing ratios simulated with daily averaged and hourly NEE from FLUXCOM-X range between around - 2.5 and 7 ppm averaged annually throughout a site network across the world. These differences lead to systematic biases in CO2 flux estimates from the atmospheric inversions. Although the impact on the global total flux is negligible (around 2 % of the overall land flux of - 1.79 Pg C yr−1), we find significant biases in the annual flux budgets at continental and regional scales. For Europe, the annual mean difference in the fluxes arising indirectly from the diurnal cycle of CO2 through the boundary condition amounts to around 48 % of the annual posterior fluxes (0.31 Pg C yr−1) estimated with CarboScope-Regional (CSR). Furthermore, the differences in NEE estimates calculated with CS increase the magnitude of the flux budgets for some regions such as North American temperate forests and northern Africa by a factor of about 1.5. To the extent that FLUXCOM-X diurnal cycles are realistic at all latitudes and for the station set including many continental stations as used in our inversions here, we conclude that ignoring the diurnal variations in the land CO2 flux leads to overestimation of both CO2 sources in the tropical lands and CO2 sinks in the temperate zones. [ABSTRACT FROM AUTHOR]

The largest anthropogenic emissions of carbon dioxide (CO2) come from local sources, such as cities and power plants. The upcoming Copernicus CO2 Monitoring (CO2M) mission will provide satellite images of the CO2 and NO2 plumes associated with these sources at a resolution of 2 km × 2 km and with a swath of 250 km. These images could be exploited using atmospheric-plume inversion methods to estimate local CO2 emissions at the time of the satellite overpass and their corresponding uncertainties. To support the development of the operational processing of satellite imagery of the column-averaged CO2 dry-air mole fraction (XCO2) and tropospheric-column NO2, this study evaluates data-driven inversion methods, i.e., computationally light inversion methods that directly process information from satellite images, local winds, and meteorological data, without resorting to computationally expensive dynamical atmospheric transport models. We designed an objective benchmarking exercise to analyze and compare the performance of five different data-driven inversion methods: two implementations with different complexities for the cross-sectional flux approach (CSF and LCSF), as well as one implementation each for the integrated mass enhancement (IME), divergence (Div), and Gaussian plume (GP) model inversion approaches. This exercise is based on pseudo-data experiments with simulations of synthetic true emissions, meteorological and concentration fields, and CO2M observations across a domain of 750 km × 650 km, centered on eastern Germany, over 1 year. The performance of the methods is quantified in terms of the accuracy of single-image emission estimates (from individual images) or annual-average emission estimates (from the full series of images), as well as in terms of the number of instant estimates for the city of Berlin and 15 power plants within this domain. Several ensembles of estimations are conducted using different scenarios for the available synthetic datasets. These ensembles are used to analyze the sensitivity of performance to (1) data loss due to cloud cover, (2) uncertainty in the wind, or (3) the added value of simultaneous NO2 images. The GP and LCSF methods generate the most accurate estimates from individual images. The deviations between the emission estimates and the true emissions from these two methods have similar interquartile ranges (IQRs), ranging from ∼ 20 % to ∼ 60 % depending on the scenario. When taking cloud cover into account, these methods produce 274 and 318 instant estimates, respectively, from the ∼ 500 daily images, which cover significant portions of the plumes from the sources. Filtering the results based on the associated uncertainty estimates can improve the statistics of the IME and CSF methods but does so at the cost of a large decrease in the number of estimates. Due to a reliable estimation of uncertainty and, thus, a suitable selection of estimates, the CSF method achieves similar, if not better, accuracy statistics for instant estimates compared to the GP and LCSF methods after filtering. In general, the performance of retrieving single-image estimates improves when, in addition to XCO2 data, collocated NO2 data are used to characterize the structure of plumes. With respect to the estimates of annual emissions, the root mean square errors (RMSEs) for the most realistic benchmarking scenario are 20 % (GP), 27 % (CSF), 31 % (LCSF), 55 % (IME), and 79 % (Div). This study suggests that the Gaussian plume and/or cross-sectional approaches are currently the most efficient tools for providing estimates of CO2 emissions from satellite images, and their relatively light computational cost will enable the analysis of the massive amount of data to be provided by future satellite XCO2 imagery missions. [ABSTRACT FROM AUTHOR]

Measurements of the seasonal cycle of δ13 C of atmospheric CO2 (δ13 Ca) provide information on the global carbon cycle and the regulation of carbon and water fluxes by leaf stomatal openings on ecosystem and decadal scales. Land biosphere carbon exchange is the primary driver of δ13 Ca seasonality in the Northern Hemisphere (NH). We use isotope-enabled simulations of the Bern3D-LPX (Land surface Processes and eXchanges) Earth system model of intermediate complexity and fossil fuel emission estimates with a model of atmospheric transport to simulate atmospheric δ13 Ca at globally distributed monitoring sites. Unlike the observed growth of the seasonal amplitude of CO2 at northern sites, no significant temporal trend in the seasonal amplitude of δ13 Ca was detected at most sites, consistent with the insignificant model trends. Comparing the preindustrial (1700) and modern (1982–2012) periods, the modeled small-amplitude changes at northern sites are linked to the near-equal increase in background atmospheric CO2 and the seasonal signal of the net atmosphere–land δ13 C flux in the northern extratropical region, with no long-term temporal changes in the isotopic fractionation in these ecosystems dominated by C3 plants. The good data–model agreement in the seasonal amplitude of δ13 Ca and in its decadal trend provides implicit support for the regulation of stomatal conductance by C3 plants towards intrinsic water use efficiency growing proportionally to atmospheric CO2 over recent decades. Disequilibrium fluxes contribute little to the seasonal amplitude of the net land isotope flux north of 40° N but contribute near equally to the isotopic flux associated with growing season net carbon uptake in tropical and Southern Hemisphere (SH) ecosystems, pointing to the importance of monitoring δ13 Ca over these ecosystems. We propose applying seasonally resolved δ13 Ca observations as an additional constraint for land biosphere models and underlying processes for improved projections of the anthropogenic carbon sink. [ABSTRACT FROM AUTHOR]

Abstract Nanoplastics are suspected to pollute every environment on Earth, including very remote areas reached via atmospheric transport. We approached the challenge of measuring environmental nanoplastics by combining high-sensitivity TD-PTR-MS (thermal desorption-proton transfer reaction-mass spectrometry) with trained mountaineers sampling high-altitude glaciers (“citizen science”). Particles

Consumer-grade economical radon monitors are becoming increasingly popular in private and institutional use, in the contexts of both Citizen Science and traditional research. Although originally designed for screening indoor radon levels in view of radon regulation and decisions about mitigation or remediation—motivated by the health hazard posed by high radon concentrations—researchers are increasingly exploring their potential in some environmental studies. For long time, radon has been used as a tracer for investigating atmospheric transport processes. This paper focuses on RadonEye, currently the most sensitive among low-cost monitors available on the market, and specifically, its potential use for monitoring very low radon concentrations. It has two objectives: firstly, discussing issues of statistics of low count rates, and secondly, analyzing radon concentration time series acquired with RadonEyes outdoors and in low-radon indoor spaces. Regarding the first objective, among other things, the inference radon concentration reported to expected true is discussed. The second objective includes the application of autoregressive methods and fractal statistics to time series analysis. The overall result is that radon dynamics can be well captured using this "low-tech" approach. Statistical results are plausible; however, few results are available in the literature for comparison, particularly concerning fractal methods. The paper may therefore be seen as an incentive for further research in this direction. [ABSTRACT FROM AUTHOR]

Implicit time‐stepping for advection is applied locally in space and time where Courant numbers are large, but standard explicit time‐stepping is used for the remaining solution which is typically the majority. This adaptively implicit advection scheme facilitates efficient and robust integrations with long time‐steps while having negligible impact on the overall accuracy, and achieving monotonicity and local conservation on general meshes. A novel and important aspect for the efficiency of the approach is that only one iteration is needed each time the linear equation solver is called for solving the advection equation. The demonstration in this paper uses the second‐order Runge‐Kutta implicit/explicit time integration in combination with a second/third‐order finite‐volume spatial discretization and is tested using deformation flow tracer advection on the sphere and a fully compressible model for atmospheric flows. Tracers are advected over the poles of highly anisotropic latitude‐longitude grids with very large Courant numbers and on quasi‐uniform hexagonal and cubed‐sphere meshes with the same algorithm. Buoyant flow simulations with strong local updrafts also benefit from adaptively implicit advection. Stably stratified compressible flow simulations require a stable combination of implicit treatment of gravity and acoustic waves as well as advection in order to achieve long time‐steps. Plain Language Summary: Weather and climate prediction models take small time‐steps in order to make predictions about the future, starting from estimates of current conditions. The smaller the time‐steps are, the more of them have to be taken to make a prediction for a given time in the future. The more time‐steps that have to be taken, the more expensive the prediction is. If the time‐steps are too big, models can not only lose accuracy, they can become unstable—inaccuracies can become so large that wild oscillations are generated and the model crashes. These instabilities are often caused by the advective transport of constituents of the atmosphere by the wind. This paper describes a method—adaptive implicit advection—for calculating atmospheric transport using longer time‐steps while maintaining stability. We show that this can be achieved with minimal additional cost, and accuracy is only lost locally, where the time‐step is large relative to the flow speed and model grid size. Key Points: Implicit time‐stepping for advection enables large Courant numbersImplicit time‐stepping for advection is cheap as it combines an explicit correction with a simple first‐order accurate implicit estimateImplicit time‐stepping enables monotonicity, conservation and unconditional stability [ABSTRACT FROM AUTHOR]

A rare phenomenon—the long-range atmospheric transport of dust from the arid and semiarid territories of the Caspian Sea region through the center of the European part of Russia to its Arctic areas—was recorded in December 2023 during a field study of the physicochemical characteristics of airborne particles in Moscow and the snow composition in Arkhangelsk oblast. The analysis of the trajectories of the air mass transport, dynamics of spatial and temporal variability of the mass concentration of PM2.5 and PM10 matter in Moscow oblast, and numerical estimates and spatial distributions of near-surface concentrations and optical characteristics of airborne particles in the European part of Russia (according to the MERRA-2 reanalysis estimates) confirmed an increase in aerosol air pollution in the territories from the Caspian Sea to Arkhangelsk oblast. The snow sample taken in the area of Pinega Nature Reserve, Arkhangelsk oblast, in the spring of 2024, in the snow cover at a height of 18–20 cm (with a total snow thickness of 65 cm), was found to contain a yellowish layer of snow that fell in December 2023. The preliminary studies of the sample from this snow showed the presence of a large amount of organic suspended matter and plant residue, which in winter indicates transport of atmospheric aerosol from the southern regions of Russia. [ABSTRACT FROM AUTHOR]

Global warming has changed both the amount of global precipitation and the atmospheric capacity to retain water. In this paper, a novel definition of the long‐term Capturability of Atmospheric Water (CAW) based on horizontal atmospheric water transport is proposed, describing the ability of a certain area to intercept and convert the atmospheric water transported by horizontal moisture flux into local precipitation. The significant decrease of the CAW in Amazon and Congo rainforests and Inside Greenland indicates that these areas were having less precipitation with the same water vapor in the past 42 years, while in Asia (especially China), CAW is showing a large‐scale increasing trend, verifying the regional humidifying. Considering the change of both the CAW and the background atmospheric water simultaneously, their mismatch degree is also investigated. The positive mismatch in Qinghai Tibet Plateau, Greenland, and the Andes, suggests higher susceptibility to climate change, and in the areas of negative mismatch (Amazon, Maritime Continent, southeastern China, the Eastern United States, India, and Japan), a more stable precipitation response to climate change is expected. The proposed concept of CAW provides a novel perspective to analyze the precipitation response to climate change on a global scale. Key Points: A novel definition of long‐term Capturability of Atmospheric Water (CAW) based on horizontal transport is proposedIn the past 42 years, the CAW in the Amazon and Congo rainforests and the Greenland ice sheet has degraded significantlyThe findings provide a perspective to analyze the precipitation response to global climate change [ABSTRACT FROM AUTHOR]

Air‐sea heat and moisture fluxes modulate the surface energy balance and oceanic and atmospheric heat transport across all timescales. Spatial gradients of these fluxes, on a multitude of spatial scales, also have significant impacts on the ocean and atmosphere. Nevertheless, analysis of these gradients, and discussion regarding our ability to represent them, is relatively absent within the community. This letter discusses their importance and presents a wintertime climatology. Their sensitivity to spatiotemporal scale and choice of data set is also examined in the mid‐latitudes. A lead‐lag analysis illustrates that wintertime air‐sea heat flux gradients in the Gulf Stream can precede the North Atlantic Oscillation by ∼1 month. A lack of observations and thus validation of air‐sea heat flux gradients represents a significant gap in our understanding of how air‐sea processes affect weather and climate, and warrants increased attention from the observational and modeling communities. Plain Language Summary: The oceans impact both weather and climate by heating and cooling the lower atmosphere. Surface latent (sensible) heat flux is a quantity that measures the exchange of heat associated with evaporation of seawater (an air‐sea temperature difference). In addition to the absolute exchange, the manner in which the exchange varies spatially (the heat flux gradients) is also known to be important for the development of weather systems and longer‐term climate. Despite this, relatively little attention is paid in the literature to variability in these gradients. This study provides a brief overview of their importance and provides a wintertime climatology in these gradients. It is also illustrated that when considering gradients, the importance of specifying the spatial scale over which the gradient is calculated is critical. Although many differences exist between air‐sea heat flux data products in these gradients, there are currently almost no observations to validate them in key areas of interest, which represents a significant deficiency in our understanding of ocean‐atmosphere interactions. This is emphasized by demonstrating that these gradients in the mid‐latitudes can statistically precede variability in the North Atlantic Oscillation, the most important mode of monthly atmospheric variability in the North Atlantic. Key Points: Air‐sea heat and moisture flux gradients modulate important oceanic and atmospheric processes across a multitude of spatiotemporal scalesAir‐sea heat flux gradient variability can statistically precede mid‐latitude atmospheric variabilityNotable air‐sea heat and moisture flux gradient inconsistencies exist in data products, yet the ability to validate them remains elusive [ABSTRACT FROM AUTHOR]

Objectives: Dental clinics are one of the major producers of mercury-containing waste due to the use of dental amalgam. The atmospheric transport and persistence of mercury and its compounds in the environment, coupled with their high potential for bioaccumulation and detrimental effects on human health and ecosystems, underscore the necessity for effective management of mercury waste. Due to the lack of comprehensive and integrated guidelines for the effective management of dental amalgam waste in Iran, the objective of this study was to adapt a guideline for the management of amalgam waste in dental settings within the country. Materials and Methods: The method used was based on the adaptation principles presented by the Ministry of Health and Medical Education, and included searching and reviewing guidelines related to the management of amalgam waste in developed countries, extracting recommendations, revising the recommendations considering the local infra-structures and conditions, and receiving expert opinions and reaching consensus according to the RAND/UCLA Appropriateness Method. Results: The final guideline includes 34 recommendations in 5 areas: management of the amalgam scraps, considerations for dental equipment, management of the extracted teeth containing amalgam restorations, management of the amalgam capsules, and considerations for placement and replacement of the amalgam restorations. Conclusion: The use of this guideline in medical universities, public and private dental clinics, along with the supervisory role of the Ministry of Health and Medical Education, can be a way to minimize the environmental hazards of mercury. [ABSTRACT FROM AUTHOR]

Chinese baijiu as one of the famous distilled liquor in which fermented in open environments, with various microorganisms (i.e., bacteria, fungi, and yeast) involved in their brewing process, and created corresponding unique flavor. However, the sources of environmentally enriched microbial communities associated with liquor fermentation are still being characterized yet. Given the dependence of microbial growth and reproduction on environmental ecology, it is important to understand the correlation between baijiu fermentation microbial community and surrounding environmental ecology (i.e., temperature, humidity, wind, and precipitation). This study systematically overviewed the sources of microorganisms in the Jiang-flavor-Baijiu fermentation system. The results showed that microorganisms in baijiu brewing (i.e., mold, lactic acid bacteria, and yeast) mainly originated from surrounding environmental matrices, including the air (i.e., Yeast , Streptomyces and Bacillus), soil (i.e., Xanthomonas , Methanococcus and Comamonas) and water (i.e., Flavobacterium , Acinetobacter , and Pseudomonas) via atmospheric transport, raw material transfer and surface runoff. In addition, the unique baijiu fermentation microbial community diversity depends on local geology and meteorological conditions, highlighting that the structural stability and diversity of the microorganisms in the Baijiu brewing process dominated by local environmental ecology. We also explored the regional environmental conditions on the microbial community and found that the unique Jiang-flavor-Baijiu fermentation microbial community diversity depends on local geology and meteorological conditions. The Jiang-flavor-Baijiu workshop is located in the basin of the middle-and low latitude mountainous areas, with sufficient solar irradiation and rainfall, high air humidity, and low wind speed that favor the growth and propagation of Baijiu fermentation microorganisms. Therefore, the obtained conclusions provide new insights unraveling the key factor controlling the unique flavor of Chinese Baijiu, where protecting the ecology of baijiu brewing-regions is fundamental for maintaining the long-term quality of baijiu. [ABSTRACT FROM AUTHOR]

Arsenic, a toxic element from both anthropogenic and natural sources, reaches surface environments through atmospheric cycling and dry and wet deposition. Biomethylation volatilizes arsenic into the atmosphere and deposition cycles it back to the surface, affecting soil-plant systems. Chemical speciation of deposited arsenic is important for understanding further processing in soils and bioavailability. However, the range of atmospheric transport and source signature of arsenic species remain understudied. Here we report significant levels of methylated arsenic in precipitation, cloud water and aerosols collected under free tropospheric conditions at Pic du Midi Observatory (France) indicating long-range transport, which is crucial for atmospheric budgets. Through chemical analyses and moisture source diagnostics, we identify terrestrial and marine sources for distinct arsenic species. Estimated atmospheric deposition fluxes of methylated arsenic are similar to reported methylation rates in soils, highlighting atmospheric deposition as a significant, overlooked source of potentially bioavailable methylated arsenic species impacting plant uptake in soils. Analyses of atmospheric samples at a high-altitude site show that methylated forms of arsenic, emitted via biological processes, represent an important share of arsenic deposition. These forms are transported from both marine and terrestrial systems. [ABSTRACT FROM AUTHOR]

Airborne microplastics are emerging pollutants originating from disposable tableware, packaging materials, textiles, and other consumer goods. Microplastics vary in shape and size and exposed to external factors break down into even smaller fractions. Airborne microplastics are abundant in both urban and natural environments, including water bodies and glaciers, as particles can travel long distances. The potential toxicity of airborne microplastics cannot be underestimated. Microparticles, especially those < 10 µm, entering the human body through inhalation or ingestion have been shown to cause serious adverse health effects, such as chronic inflammation, oxidation stress, physical damage to tissues, etc. Microplastics adsorb toxic chemicals and biopolymers, forming a polymer corona on their surface, affecting their overall toxicity. In addition, microplastics can also affect carbon dynamics in ecosystems and have a serious impact on biochemical cycles. The approaches to improve sampling techniques and develop standardized methods to assess airborne microplastics are still far from being perfect. The mechanisms of microplastic intracellular and tissue transport are still not clear, and the impact of airborne microplastics on human health is not understood well. Reduced consumption followed by collection, reuse, and recycling of microplastics can contribute to solving the microplastic problem. Combinations of different filtration techniques and membrane bioreactors can be used to optimize the removal of microplastic contaminants from wastewater. In this review we critically summarize the existing body of literature on airborne microplastics, including their distribution, identification, and safety assessment. [ABSTRACT FROM AUTHOR]

Four severe nuclear accident scenarios have been identified for operating nuclear power plants (ONPPs). However, there is a research gap in predicting the mid–long-term radiation doses for these scenarios. This study aims to address this gap by proposing a novel approach for predicting the mid–long-term radiation dose in the case of a hypothetical short-term station blackout (STSBO) scenario, one of the aforementioned scenarios. Firstly, the Weather Research and Forecasting (WRF) model was coupled with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) (WRF-HYSPLIT) model to establish an atmospheric transport and diffusion model for airborne radionuclides, and the regularity of the atmospheric transport and diffusion for the airborne radionuclides was determined. Subsequently, the Residual Radioactive Material Guidelines (RESRAD) OFFSITE (RESRAD-OFFSITE) code was utilized to establish a radiation dose model for predicting the mid–long-term radiation dose resulting from the airborne radionuclides, and the evolution of the mid–long-term radiation dose was analyzed. Finally, the proposed approach was applied to an ONPP, and the results were used to predict the mid–long-term public radiation dose. The results indicated that the total radiation dose would be lower than the dose limit recommended by the International Commission on Radiological Protection (1 mSv/yr) from the second month to the 100th year after the hypothetical STSBO nuclear accident, and the total radiation dose would decrease slowly over time. Recommendations are made for offsite emergency response measures. These research findings can assist ONPPs in analyzing their environmental impacts in the event of an STSBO scenario. [ABSTRACT FROM AUTHOR]

Semi-volatile organic contaminants (SVOCs) are known for their tendency to evaporate from source regions and undergo atmospheric transport to distant areas. Cold condensation intensifies dry deposition, particle deposition, and scavenging by snow and rain, allowing SVOCs to move from the atmosphere into terrestrial and aquatic ecosystems in alpine and polar regions. However, no standardized methods exist for the sampling, laboratory processing, and instrumental analysis of persistent organic pollutants (POPs) in snow. The lack of reference methods makes these steps highly variable and prone to errors. This study critically reviews the existing literature to highlight the key challenges in the sampling phase, aiming to develop a reliable, consistent, and easily reproducible technique. The goal is to simplify this crucial step of the analysis, allowing data to be shared more effectively through standardized methods, minimizing errors. Additionally, an innovative method for laboratory processing is introduced, which uses activated carbon fibers (ACFs) as adsorbents, streamlining the analysis process. The extraction method is applied to analyze polychlorobiphenyls (PCBs) and chlorinated pesticides (α-HCH, γ-HCH, p,p′-DDE, o,p′-DDT, HCB, and PeCB). The entire procedure, from sampling to instrumental analysis, is subsequently tested on snow samples collected on the Svalbard Islands. To validate the efficiency of the new extraction system, quality control measures based on the EPA methods 1668B and 1699 for aqueous methods are employed. This study presents a new, reliable method that covers both sampling and lab analysis, tailored for detecting POPs in snow. [ABSTRACT FROM AUTHOR]

Numerical methods and simulation codes are essential for the advancement of our understanding of complex atmospheric processes. As technology and computer hardware continue to evolve, the development of sophisticated code is vital for accurate and efficient simulations. In this paper, we present the recent advancements made in the FLEXible PARTicle dispersion model (FLEXPART), a Lagrangian particle dispersion model, which has been used in a wide range of atmospheric transport studies over the past 3 decades, extending from tracing radionuclides from the Fukushima nuclear disaster, to inverse modelling of greenhouse gases, and to the study of atmospheric moisture cycles. This version of FLEXPART includes notable improvements in accuracy and computational efficiency. (1) By leveraging the native vertical coordinates of European Centre for Medium Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) instead of interpolating to terrain-following coordinates, we achieved an improvement in trajectory accuracy, leading to a ∼8 %–10 % reduction in conservation errors for quasi-conservative quantities like potential vorticity. (2) The shape of aerosol particles is now accounted for in the gravitational settling and dry-deposition calculation, increasing the simulation accuracy for non-spherical aerosol particles such as microplastic fibres. (3) Wet deposition has been improved by the introduction of a new below-cloud scheme, by a new cloud identification scheme, and by improving the interpolation of precipitation. (4) Functionality from a separate version of FLEXPART, the FLEXPART CTM (chemical transport model), is implemented, which includes linear chemical reactions. Additionally, the incorporation of Open Multi-Processing parallelisation makes the model better suited for handling large input data. Furthermore, we introduced novel methods for the input and output of particle properties and distributions. Users now have the option to run FLEXPART with more flexible particle input data, providing greater adaptability for specific research scenarios (e.g. effective backward simulations corresponding to satellite retrievals). Finally, a new user manual (https://flexpart.img.univie.ac.at/docs/ , last access: 11 September 2024) and restructuring of the source code into modules will serve as a basis for further development. [ABSTRACT FROM AUTHOR]

Remote sensing based on satellites can provide long-term, consistent, and global coverage of NO2 (an important atmospheric air pollutant) as well as other trace gases. However, satellites often miss data due to factors including but not limited to clouds, surface features, and aerosols. Moreover, as one of the longest continuous observational platforms of NO2 , the Ozone Monitoring Instrument (OMI) has suffered from missing data over certain rows since 2007, significantly reducing its spatial coverage. This work uses the OMI-based tropospheric NO2 (OMNO2) product as well as a NO2 product from the Global Ozone Monitoring Experiment-2 (GOME-2) in combination with machine learning (eXtreme Gradient Boosting – XGBoost) and spatial interpolation (data-interpolating empirical orthogonal function – DINEOF) methods to produce the 16-year global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM- NO2 ; https://doi.org/10.5281/zenodo.10968462 ; Qin et al., 2024), which increases the average global spatial coverage of NO2 from 39.5 % to 99.1 %. The HSTCM- NO2 dataset is validated using upward-looking observations of NO2 (multi-axis differential optical absorption spectroscopy – MAX-DOAS), other satellites (the Tropospheric Monitoring Instrument – TROPOMI), and reanalysis products. The comparisons show that HSTCM- NO2 maintains a good correlation with the magnitudes of other observational datasets, except for under heavily polluted conditions (> 6 × 1015 molec.cm-2). This work also introduces a new validation technique to validate coherent spatial and temporal signals (empirical orthogonal function – EOF) and confirms that HSTCM- NO2 is not only consistent with the original OMNO2 data but in some parts of the world also effectively fills in missing gaps and yields a superior result when analyzing long-range atmospheric transport of NO2. The few differences are also reported to be related to areas in which the original OMNO2 signal was very low, which has been shown elsewhere but not from this perspective, further confirming that applying a minimum cutoff to retrieved NO2 data is essential. The reconstructed data product can effectively extend the utilization value of the original OMNO2 data, and the data quality of HSTCM- NO2 can meet the needs of scientific research. [ABSTRACT FROM AUTHOR]

The global atmospheric transport of microplastics (MPs) plays a crucial role in the contamination of remote, especially higher-elevation, environments. Precipitation is considered the main source of MP pollution. Meanwhile, plastic waste generated from, for example, tourism activities can be a local source of MP pollution. In this study, we specify which of the mentioned sources of MP, global or local, have a higher impact on the pollution level in the high-elevation oligotrophic lakes of Tatra National Park in Poland. Due to its unique natural value, it is listed by UNESCO as an international biosphere reserve and meets the criteria for Natura 2000 areas. We comprehensively analyzed the morphometric and anthropogenic features of 11 lakes in terms of the contamination level, color, shape, and polymer type of the MPs found in the surface waters. MP fibers were found to be present in all studied lakes, with contamination ranging from 25 to 179 items/m3. Polypropylene, polyethylene terephthalate, and natural or semi-natural cellulose fibers—black or red in color with a length of 0.2–1.0 mm—predominated, which corresponds with other studies conducted on remote mountain ecosystems. We did not find any correlation of the number of MPs with local anthropogenic pressure characteristics. In turn, the significant correlation with lake area, coastline length, lake volume, and catchment area indicated airborne sources, including global transport of MPs to the lakes with reduced water outflow. [ABSTRACT FROM AUTHOR]

Atmospheric inverse modeling is the process of estimating emissions from atmospheric observations by minimizing a cost function, which includes a term describing the difference between simulated and observed concentrations. The minimization of this difference is typically limited by uncertainties in the atmospheric transport model rather than by uncertainties in the observations. In this study, we showcase how a temporally varying, flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation through idealized experiments using an ensemble Kalman smoother system. We use the estimation of European CH4 emissions from the in situ measurement network as an example, but we also demonstrate the additional benefits for trace gases with more localized sources, such as SF6. The uncertainty in flow-dependent transport is determined using meteorological ensemble simulations that are perturbed by physics and driven at the boundaries by an analysis ensemble from a global meteorology and a CH4 simulation. The impact of direct representation of temporally varying transport uncertainties in atmospheric inversions is then investigated in an observation system simulation experiment framework in various setups and for different flux signals. We show that the uncertainty in the transport model varies significantly in space and time and that it is generally highest during nighttime. We apply inversions using only afternoon observations, as is common practice, but also explore the option of assimilating hourly data irrespective of the hour of day using a filter based on transport uncertainty and taking into account the temporal covariances. Our findings indicate that incorporating flow-dependent uncertainties in inversion techniques leads to more accurate estimates of GHG emissions. Differences between estimated and true emissions could be reduced more effectively by 9 % to 82 %, with generally larger improvements for the SF6 inversion problem and for the more challenging setup with small flux signals. [ABSTRACT FROM AUTHOR]

Wind-borne dust supply of iron (Fe) to the oceans plays a crucial role in Earth's biogeochemical cycles. Iron, a limiting micronutrient for phytoplankton growth, is fundamental in regulating ocean primary productivity and in turn the global carbon cycle. The flux of bioavailable Fe to the open ocean affects oscillations in atmospheric CO2 due to its control on inorganic carbon fixation into organic matter that is eventually exported to the sediments. However, the nature of dust-delivered Fe to the ocean and controls on its bioavailability remain poorly constrained. To evaluate the supply of wind-borne bioavailable Fe and its potential impact on Fe-based climate feedbacks over the last 120,000 years, we examine sediment profiles from four localities that define a proximal to distal transect relative to Saharan dust inputs. Bulk δ56Fe isotope compositions (average = -0.05‰) and FeT/Al ratios suggest crustal values, thus pointing to a dominant dust origin for the sediments at all four sites. We observed no variability in grain size distribution or in bioavailable Fe supply at individual sites as a function of glacial-versus-interglacial deposition. Importantly, there is no correlation between sediment grain size and Fe bioavailability. Spatial trends do, however, suggest increasing Fe bioavailability with increasing distance of atmospheric transport, and our sediments also indicate the loss of this Fe and thus potential bioavailability utilization once deposited in the ocean. Our study underscores the significance of Fe dynamics in oceanic environments using refined speciation techniques to elucidate patterns in Fe reactivity. Such insights are crucial for understanding nutrient availability and productivity in various ocean regions, including the Southern Ocean, where wind-delivered Fe may play a pivotal role. It is expected that dust delivery on glacial-interglacial timescales would be more pronounced in these high-latitude regions. Our findings suggest that studies linking Fe availability to marine productivity should benefit significantly from refined Fe speciation approaches, which provide insights into the patterns and controls on Fe reactivity, including atmospheric processing. These insights are essential for understanding the impacts on primary production and thus carbon cycling in the oceans and consequences for the atmosphere. [ABSTRACT FROM AUTHOR]

The North Pacific is known with iron limitation for phytoplankton growth in the subarctic region and nitrogen limitation in the subtropical gyre. Although the growth rate of phytoplankton is determined by the concentration of limiting nutrient, the supply ratio of iron to nitrogen is suggested to be essential to this biogeographic pattern. However, the underlying dynamics determining the ratio remain largely unknown. We investigated mechanisms of dissolved iron (dFe) and nitrate (NO3−) transport to the euphotic zone of the North Pacific using an eddy‐resolvable biogeochemical model. We show that lateral advection and atmospheric deposition are dominant drivers for dFe transport, resulting in high Fe:N supply ratio in both subarctic and subtropical regions. Conversely, significant vertical supplies of NO3− through upwelling and diffusion processes markedly reduce the supply ratio in the subarctic region. These dynamics combined lead to high Fe:N supply ratio in the gyre and low ratio in the subarctic, ultimately driving high nitrogen fixation condition in the gyre and the iron‐limited phytoplankton growth condition in the subarctic region. Plain Language Summary: Iron is a critical trace element for the photosynthesis and nitrogen fixation of phytoplankton in the ocean. In North Pacific Subarctic region, although there is plenty of nitrate, the growth of phytoplankton is limited due to the lack of iron. In the North Pacific Subtropical Gyre (NPSG), nutrient supply to the surface is restricted due to ocean stratification, but diazotrophs can fix nitrogen from the atmosphere. However, their growth is also constrained by iron availability. Understanding how nutrients like iron reach the ocean's surface is vital for predicting the productivity of marine life. Our research employed advanced computer models to explore how dissolved iron is transported in the North Pacific. We discovered that lateral transport by ocean currents, followed by atmospheric deposition, is the primary pathway for iron delivery to the sunlit layer of the NPSG. In the Subarctic Gyre, atmospheric deposition and vertical advection are the major sources of iron. However, we found different transport patterns for nitrate, revealing that physical process‐controlled supply ratio of iron to nitrate may determine where different types of phytoplankton thrive in the surface ocean. This research helps understand the complex processes that supply nutrients to ocean surface. Key Points: Lateral transport and atmospheric deposition dominate supplies of dissolved iron (dFe) to the euphotic zone of the North PacificUpwelling and vertical diffusion control nitrate (NO3−) supply in subarctic regionLateral dFe and vertical NO3− transports determine the stoichiometric supply ratio and shape the biogeographic pattern [ABSTRACT FROM AUTHOR]

Refractory black carbon (rBC) has great potential to increase melting when deposited on snow and ice surfaces. Previous studies attributed sources and impacts of rBC in the northern Antarctic Peninsula region by investigating long-range atmospheric transport from South Hemisphere biomass burning and industrial regions or by assessing impacts from local tourism and research activities. We used high-resolution measurements of refractory rBC in a firn core collected near the northern tip of the Antarctic Peninsula, as well as atmospheric rBC from Modern-Era Retrospective Analysis for Research and Applications, Version 2, satellite measurements, modeling, burned area data, and tourism statistics, to assess combined impacts of both long-range transported rBC and locally emitted rBC. Our findings suggest that tourism activities have a regional rather than local impact and the increase in rBC concentrations during late spring-summer, influenced by tourism activities and fires in the Southern Hemisphere, can enhance ice melt. This highlights the need for strategies to reduce local and distant rBC emissions. [ABSTRACT FROM AUTHOR]

The equatorward contraction of tropical precipitation, commonly referred to as the "deep‐tropical contraction", is witnessed in the paleoclimate simulations of the early Eocene. However, the mechanism driving this contraction is still unclear. Based on the energetics framework of the Intertropical Convergence Zone (ITCZ) and the decomposition method of the latent heat flux along with the simulations of a climate system model, CESM1.2, we proposed a novel mechanism responsible for the "deep‐tropical contraction" in the early Eocene. The greenhouse gases‐induced sea surface warming amplifies the sensitivity of evaporation to surface wind speed changes through Clausius‐Clapeyron scaling, leading to an interhemispheric asymmetric enhancement of the latent heat flux. To maintain hemispheric energy balance, the cross‐equatorial atmospheric energy transport must be reduced during the solstice seasons. As a result, the solstitial location of the ITCZ shifts equatorward, causing the "deep‐tropical contraction" in the early Eocene. Plain Language Summary: The early Eocene is the warmest epoch in the last 65 million years, with a global mean temperature 9°C–23°C higher than the preindustrial period. According to state‐of‐the‐art climate models, the tropical rainfall contracted toward the equator during this extremely warm period. However, the physical mechanism causing this phenomenon remains unclear. In this study, we examined the hemispheric energy balance in the early Eocene that causes the equatorward contraction of tropical precipitation. A novel mechanism underlying this phenomenon is revealed. Based on the climate modeling of CESM1.2, we show that the GHG‐induced warmth enhances the sensitivity of evaporation to surface wind speed changes in the early Eocene. Thus, the stronger tropical trade wind in the winter hemisphere will drive out stronger latent heat flux than in the summer hemisphere. This interhemispheric asymmetric response reduces the interhemispheric heating contrast in the solstice seasons. As a result, the ascending motion in the tropical atmosphere migrates toward the equator, finally decreases the width of tropical precipitation in the early Eocene. Key Points: The "deep tropical contraction" in the early Eocene is caused by the equatorward migration of the seasonal ITCZThe equatorward migration of the solstitial ITCZ location in the early Eocene is due to decreased cross‐equatorial energy transportThe enhanced wind‐evaporation effect in the early Eocene reduces the cross‐equatorial atmospheric energy transport in the solstitial seasons [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) is a crucial greenhouse gas, and its concentration and spatiotemporal characteristics are among the principal sources of uncertainty in global warming assessments. Satellite remote sensing is a widely adopted, high-accuracy approach for monitoring atmospheric CO 2. However, limited swath width and cloud cover significantly reduce satellite observation coverage. This study addresses the temporal changes in CO 2 concentration and utilizes a machine learning-based fusion of multiple data sources to generate daily, full-coverage, 0.05° spatial resolution column-averaged CO 2 concentration data for China from 2015 to 2020. Ten-fold cross-validation yielded a determination coefficient R2 of 0.97, root mean square error of 0.92 ppm, and mean absolute error of 0.59 ppm. Compared to other datasets, this study's dataset exhibits superior accuracy and spatiotemporal detail. Using the produced CO 2 concentration data in this study, we conducted a spatiotemporal analysis of CO 2 concentrations in China. The results indicate that, in general, the Western region exhibits a higher growth rate in CO 2 concentration than the Eastern and Central regions, with areas of lower CO 2 concentration experiencing higher growth rates while regions with higher CO 2 concentration have lower growth rates. Moreover, the highest increase in CO 2 concentration occurred in 2016, with a substantial decrease in CO 2 concentration growth observed in 2018. Notably, the reduction in CO 2 concentration in the Qinghai-Tibet Plateau region during the summer is considerably smaller than in other regions, possibly due to atmospheric transport from the Indian Peninsula. [ABSTRACT FROM AUTHOR]

A study for atmospheric transport is essential for the consequence assessment of severe nuclear accidents since radionuclides could be released from the nuclear facility into the atmosphere and cause radioactive pollution in the environment. Atmospheric transport behaviors are strongly related with meteorological conditions, which can obviously influence the transport and diffusion characteristics of radioactive materials in the atmosphere; thus, it is meaningful to investigate the coupling effects between meteorological processes and transport behaviors of radioactive materials. To evaluate the influence of meteorological conditions on atmospheric transport, meteorological parameters for different seasons were first acquired by the weather research forecast model. Furthermore, atmospheric transport behaviors of radioactive materials were simulated by the meso-scale numerical model under different meteorological conditions, and numerical analyses were conducted toward transport and deposition behaviors of radioactive materials. In addition, the influence of FDDA (four-dimensional data assimilation) on meteorological parameters and atmospheric transport behaviors was researched. The present study is important for strengthening consequence assessment for severe nuclear accident and made it possible to apply the data assimilation technology in further research works. [ABSTRACT FROM AUTHOR]

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation. [ABSTRACT FROM AUTHOR]

Polychlorinated naphthalenes (PCNs) are a new type of persistent organic pollutant (POP) characterized by persistence, bioaccumulation, dioxin-like toxicity, and long-range atmospheric transport. Focusing on one type of PCN, monochlorinated naphthalenes (CN-1, CN-2), this study aimed to examine their photodegradation in the environment. In this work, CN-1 and CN-2 were employed as the model pollutants to investigate their photodegradation process under UV-C irradiation. Factors like the pH, initial concentrations of CN-1, and inorganic anions were investigated. Next, the roles of hydroxyl radicals (•OH), superoxide anion radicals (O2•−), and singlet oxygen (1O2) in the photodegradation process were discussed and proposed via theory computation. The results show that the photodegradation of CN-1 and CN-2 follows pseudo-first-order kinetics. Acidic conditions promote the photodegradation of CN-1, while the effects of pH on the photodegradation of CN-2 are not remarkable. Cl−, NO3−, and SO32− accelerate the photodegradation of CN-1, whereas the effect of SO42− and CO32− is not significant. Additionally, the contributions of •OH and O2•− to the photodegradation of CN-1 are 20.47% and 38.80%, while, for CN-2, the contribution is 16.40% and 16.80%, respectively. Moreover, the contribution of 1O2 is 15.7%. Based on DFT calculations, C4 and C6 of the CN-1 benzene ring are prioritized attack sites for •OH, while C2 and C9 of CN-2 are prioritized attack sites. [ABSTRACT FROM AUTHOR]

Surface ozone (O3) pollution has adverse impacts on the yield of winter wheat. The North China Plain (NCP), one of the globally significant primary regions for winter wheat production, has been frequently plagued by severe O3 pollution in recent years. In this study, the effects of O3 pollution on winter wheat yield and economic impact were evaluated in the NCP during the 2015–2018 seasons using the regional atmospheric chemical transport model (WRF-Chem), O3 metrics including the phytotoxic surface O3 dose above 12 nmol m−2 s−1 (POD12), and the accumulated daytime O3 above 40 ppb (AOT40). Results showed that the modeled O3, exposure-based AOT40, and flux-based POD12 increased during the winter wheat growing season from 2015 to 2018. The annual average daytime O3, exposure-based AOT40, and flux-based POD12 were 44 ppb, 5.32 ppm h, and 1.78 mmol m−2, respectively. During 2015–2018, winter wheat relative production loss averaged 10.9% with AOT40 and 14.6% with POD12. This resulted in an average annual production loss of 12.4 million metric tons, valued at approximately USD 4.5 billion. This study enhances our understanding of the spatial sensitivity of winter wheat to O3 impacts, and suggests that controlling O3 pollution during the key growth stages of winter wheat or improving its O3 tolerance will enhance food security. [ABSTRACT FROM AUTHOR]

Estimation of anthropogenic carbon dioxide (CO2) emission sources and natural sinks (i.e., CO2 fluxes) is essential for the development of climate policies. Satellite observations provide an opportunity for top-down inversion of CO2 fluxes, which can be used to improve the results of bottom-up estimation. This study proposes to develop a new top-down CO2 flux estimation method based on deep learning, as well as satellite observations, and an atmospheric chemical transport model. This method utilizes two deep learning models: the concentration correction model and the concentration–flux inversion model. The former optimizes the GEOS-Chem-simulated CO2 concentration using Orbiting Carbon Observatory-2 (OCO-2) satellite observations, while the latter establishes the complicated relationship between CO2 concentration and CO2 flux. Results showed that both deep learning models demonstrated excellent prediction performance, with a mean bias of 0.461 ppm for the concentration correction model and an annual mean correlation coefficient of 0.920 for the concentration–flux inversion model. A posterior CO2 flux was obtained through a two-step optimization process using these well-trained models. Our findings indicate that the posterior estimations of CO2 flux sources in eastern China and northern Europe have been significantly reduced compared to the prior estimations. This study provides a new perspective on top-down CO2 flux inversion using satellite observation. With advancements in deep learning algorithms and increased satellite observations, this method may become an effective approach for CO2 flux inversion in the future. [ABSTRACT FROM AUTHOR]

The extensive terrestrial ecosystems of tropical Africa are a significant store of carbon and play a key but uncertain role in the atmospheric budgets of carbon dioxide and methane. As ground-based observations in the tropics are scarce compared with other parts of the world, recent studies have instead made use of satellite observations assimilated into atmospheric chemistry and transport models to conclude that methane emissions from this geographical region have increased since 2010 as a result of increased wetland extent, accounting for up to a third of global methane growth, and that the tropical Africa region dominates net carbon emission across the tropics. These studies critically rely on the accuracy of satellite datasets, such as those from the Orbiting Carbon Observatory-2 (OCO-2), the Greenhouse gases Observing SATellite (GOSAT), and the Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI), along with results from atmospheric transport models, over a geographical region where there are little independent data to test the robustness of published results. In this paper we present the first ground-based observations of greenhouse gas column concentrations over East Africa, obtained using a portable Bruker EM27/SUN Fourier transform infrared (FTIR) spectrometer during a deployment covering the first few months of 2020 in Jinja, Uganda. We operated the instrument near autonomously by way of an automated weatherproof enclosure and observed total atmospheric column concentrations of the greenhouse gases carbon dioxide and methane, as well as carbon monoxide, a useful proxy for emissions from incomplete combustion processes in the region. We discuss the performance of the combined enclosure and spectrometer system that we deployed in Jinja to obtain these data and show comparisons of our ground-based observations with satellite datasets from OCO-2 and Orbiting Carbon Observatory-3 (OCO-3) for carbon dioxide and TROPOMI for methane and carbon monoxide, whilst also comparing our results with concentration data from the GEOS-Chem and Copernicus Atmosphere Monitoring Service (CAMS) atmospheric inversions that provide a means of increasing spatial and temporal coverage where satellite data are not available. For our measurement period, we find mean differences in XCO2 between OCO-2 and the EM27/SUN of - 0.29 % and between OCO-3 and the EM27/SUN of - 0.28 %. In the case of TROPOMI, the mean difference in XCH4 that we find between TROPOMI and the EM27/SUN is - 0.44 %, whilst for XCO the mean difference is - 5.65 %. In each of these cases, the mean difference observed between the satellite and ground-based column concentrations is either close to or within the precision and accuracy requirements for the respective missions. With regard to the model and reanalysis comparisons with the EM27/SUN column concentrations, we see mean differences from the EM27/SUN of a global GEOS-Chem inversion for XCO2 of - 0.08 %, a regional high-resolution GEOS-Chem inversion for XCH4 of - 0.22 %, and the CAMS global reanalysis for XCO of - 9.79 %. Our results demonstrate the value of ground-based observations of total column concentrations and show that the combined EM27/SUN and enclosure system employed would be suitable for acquisition of the longer-term observations needed to rigorously evaluate satellite observations and model and reanalysis calculations over tropical Africa. [ABSTRACT FROM AUTHOR]

In this study, the emissions of criteria pollutants and greenhouse gases from mobile sources in the main streets of the city of Valledupar were estimated. A vehicular characterization of the streets studied was carried out, and the IVE model was implemented to estimate the emissions generated by automobiles; subsequently, scenarios of atmospheric transport routes of pollutants were analyzed with the HYSPLIT model. The results showed that in the case of pollutant criteria, motorcycles emitted the highest amount of CO and PM10, and private cars the highest SOx and NOx emissions. For GHGs, private cars emitted the highest amount of CO2 and N2O, while motorcycles emitted the highest amount of CH4. The trajectory scenarios for the days of the study were carried out during the peak hours of vehicular circulation, implementing meteorological data from the WRF model. The trajectories were evaluated in rainy periods of 2021 and 2022 and in the dry period of 2022. In the rainy period, it was observed that the transport routes of the pollutants were directed in a greater proportion towards the NE direction and the dry period towards the SE direction. With this, it was possible to determine which areas of the city could be the most affected by vehicle emissions from the roads studied. [ABSTRACT FROM AUTHOR]

Atmospheric moisture transport is pivotal in regulating water resources over the Tibetan Plateau (TP). With the growing concerns about climate change, understanding the evolution of atmospheric moisture transport over the TP has become increasingly critical. however, the spatiotemporal distinctions of this transport remain poorly understood in the CMIP6 models. Here, we conducted a comprehensive evaluation of simulated historical atmospheric moisture transport from 33 CMIP6 models, utilizing a novel methodology that assesses the accuracy of model simulations in replicating regional atmospheric moisture transport over the TP. Our results indicate that the CMIP6 models generally succeed in reproducing the broad spatial patterns of atmospheric moisture transport. Nonetheless, substantial errors occur during the monsoon period, primarily attributable to inaccuracies in the location, movement, and intensity of the simulated Indian summer monsoon. The coarser resolution and poor representation of physical processes are potential reasons for errors in atmospheric moisture transport simulation over the TP. The Failure to simulate the terrain blocking on atmospheric moisture transport exacerbates these deficiencies, leading to significant discrepancies. Of the 33 CMIP6 models we investigated, over one-third displayed serious deficiencies in this regard. While coarser resolution and orographic gravity waves are plausible factors, they do not fully account for all the results obtained in this study. Insufficiently detailed or inaccurate topographic data used in the models may also contribute to this deficiency. This study highlights the necessity of using rigorously evaluated models to develop effective regional adaptation strategies over the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Abstract Accurately describing the distribution of CO2 in the atmosphere with atmospheric tracer transport models is essential for greenhouse gas monitoring and verification support systems to aid implementation of international climate agreements. Large deep neural networks are poised to revolutionize weather prediction, which requires 3D modeling of the atmosphere. While similar in this regard, atmospheric transport modeling is subject to new challenges. Both, stable predictions for longer time horizons and mass conservation throughout need to be achieved, while IO plays a larger role compared to computational costs. In this study we explore four different deep neural networks (UNet, GraphCast, Spherical Fourier Neural Operator and SwinTransformer) which have proven as state‐of‐the‐art in weather prediction to assess their usefulness for atmospheric tracer transport modeling. For this, we assemble the CarbonBench data set, a systematic benchmark tailored for machine learning emulators of Eulerian atmospheric transport. Through architectural adjustments, we decouple the performance of our emulators from the distribution shift caused by a steady rise in atmospheric CO2. More specifically, we center CO2 input fields to zero mean and then use an explicit flux scheme and a mass fixer to assure mass balance. This design enables stable and mass conserving transport for over 6 months with all four neural network architectures. In our study, the SwinTransformer displays particularly strong emulation skill: 90‐day R2>0.99 and physically plausible multi‐year forward runs. This work paves the way toward high resolution forward and inverse modeling of inert trace gases with neural networks.

The composition of aerosols influenced by regional pollution sources during a post-monsoon haze event was studied including the isotopic, bulk, and molecular signatures. The air mass back trajectory and fire spot analysis revealed that the Delhi aerosols were influenced by the regional post-harvest crop (rice plant) residue-burning activities during the sampling period. To better understand the atmospheric processes during such an event, three samples of 4 h duration each (Period I: from 06:00–10:00, Period II: 10:00–14:00, and Period III: 14:00–18:00 h local time) were collected during the sampling period (8th -17th November, 2019) in the daytime. The average mass concentration of PM2.5, molecular compounds including the inorganic and carbonaceous components (dicarboxylic acid class compounds), along with the stable isotopes of C and N were observed to be elevated during Period I of the study. NH4+ and SO42− were found to be the most abundant inorganic ions during Period II and III with Cl− being the dominant ion during Period I. The OC/EC, WSOC/EC ratios indicated the influence of biomass burning on Delhi aerosols with little influence of local ageing processes evident from the minimal variation observed between the three periods of study during the day. High concentrations of dicarboxylic acids than previous studies are reported with oxalic and succinic acid being the most abundant diacids, a typical behaviour observed in biomass-burning influenced aerosols with an interesting observation of terephthalic acid to be found in an appreciable amount. The δ15 N of TN and δ13 C of TC signatures clearly indicated the influence of emissions from the burning of a C3 plant on the aerosols. The results strongly suggested that the aerosols were influenced by biomass-burning activities in the neighbouring regions and were aged during the atmospheric transport over to the city of Delhi with minimal effect of local ageing processes during the study period. [ABSTRACT FROM AUTHOR]

Coastal oceans, traditionally seen as a conduit for transporting atmospheric carbon dioxide (CO2)-derived anthropogenic carbon (CANT) to open oceans, exhibit complex carbon exchanges at their interface. South China Sea (SCS) exemplifies this complexity, where interactions with the Pacific, particularly through Kuroshio intrusion, challenge the understanding of CANT source and variability in a coastal ocean. Contrary to prevailing paradigm expectations, our high-resolution, long-term data reveal that CANT in the SCS primarily originates from Pacific water injection across the Luzon Strait rather than atmospheric CO2 invasion. Over the past two decades, the SCS has experienced increasing CANT levels, with notable interannual fluctuations driven by El Niño and La Niña events influencing Kuroshio intrusion, generating anomalously high and low CANT inventories, respectively. This highlights an overlooked CANT transport pathway from open to coastal oceans, responsible for cumulative ocean acidification that has already affected coral reefs enriched in the SCS located west of the Coral Triangle. [ABSTRACT FROM AUTHOR]

Under the Paris Agreement, countries report their anthropogenic greenhouse gas emissions in national inventories, which are used to track progress towards mitigation goals, but they must be independently verified. Atmospheric observations of CO2 , interpreted using inverse methods, can potentially provide that verification. Conventional CO2 inverse methods infer natural CO2 fluxes by subtracting a priori estimates of fuel combustion from the a posteriori net CO2 fluxes, assuming that a priori knowledge for combustion emissions is better than for natural fluxes. We describe an inverse method that uses measurements of CO2 and carbon monoxide (CO), a trace gas that is co-emitted with CO2 during combustion, to report self-consistent combustion emissions and natural fluxes of CO2. We use an ensemble Kalman filter and the GEOS-Chem atmospheric transport model to explore how satellite observations of CO and CO2 collected by the TROPOspheric Monitoring Instrument (TROPOMI) and Orbiting Carbon Observatory-2 (OCO-2), respectively, can improve understanding of combustion emissions and natural CO2 fluxes across the UK and mainland Europe in 2018–2021. We assess the value of using satellite observations of CO2 , with and without CO, above what is already available from the in situ network. Using CO2 satellite observations leads to small corrections to a priori emissions that are inconsistent with in situ observations, due partly to the insensitivity of the atmospheric CO2 column to CO2 emission changes. When we introduce satellite CO observations, we find better agreement with our in situ inversion and a better model fit to atmospheric CO2 observations. Our regional mean a posteriori combustion CO2 emission ranges from 4.6–5.0 Gta-1 (1.5 %–2.4 % relative standard deviation), with all inversions reporting an overestimate for Germany's wintertime emissions. Our national a posteriori CO2 combustion emissions are highly dependent on the assumed relationship between CO2 and CO uncertainties, as expected. Generally, we find better results when we use grid-scale-based a priori CO2:CO uncertainty estimates rather than a fixed relationship between the two species. [ABSTRACT FROM AUTHOR]