Particle Dispersion Model Research Articles

Prediction and Source Contribution Analysis of PM2.5 Using a Combined FLEXPART Model and Bayesian Method over the Beijing-Tianjin-Hebei Region in China

Fine particulate matter (PM2.5) has a serious impact on human health. Forecasting PM2.5 levels and analyzing the pollution sources of PM2.5 are of great significance. In this study, the Lagrangian particle dispersion (LPD) model was developed by combining the FLEXPART model and the Bayesian inventory optimization method. The LPD model has the capacity for real-time forecasting and determination of pollution sources of PM2.5, which refers to the contribution ratio and spatial distribution of each type of pollution (industry, power, residential, and transportation). In this study, we applied the LPD model to the Beijing-Tianjin-Hebei (BTH) region to optimize the a priori PM2.5 emission inventory estimates during 15–20 March 2018. The results show that (1) the a priori estimates have a certain degree of overestimation compared with the a posteriori flux of PM2.5 for most areas of BTH; (2) after optimization, the correlation coefficient (R) between the forecasted and observed PM2.5 concentration increased by an average of approximately 10%, the root mean square error (RMSE) decreased by 30%, and the IOA (index of agreement) index increased by 16% at four observation sites (Aotizhongxin_Beijing, Beichenkejiyuanqu_Tianjin, Dahuoquan_Xintai, and Renmingongyuan_Zhangjiakou); and (3) the main sources of pollution at the four sites mainly originated from industrial and residential emissions, while power factory and transportation pollution accounted for only a small proportion. The concentration of PM2.5 forecasts and pollution sources in each type of analysis can be used as corresponding reference information for environmental governance and protection of public health.

Moisture sources of heavy precipitation in Xinjiang characterized by meteorological patterns

AbstractIn this study, the FLEXible PARTicle dispersion model (FLEXPART) is applied to determine the moisture source of heavy precipitation in Xinjiang in the wet season (April–September) from 1979 to 2018. This is investigated for different meteorological patterns of heavy precipitation categories based on the self-organizing maps (SOM) method. The SOM results suggest that there are four main meteorological patterns (N1, N2, N3 and N4) for heavy precipitation in Xinjiang. These match the strength and position of geopotential height anomalies at middle-high levels over Central Asia and indicate the anomalous activities of the Central Asia trough and vortex. Further analysis shows that the heavy precipitation is centered at the Tianshan Mountains and the Kunlun Mountains in the N1 and N3 patterns and around the Tianshan Mountains in the N2 and N4 patterns. There are four moisture source regions that contribute to each of the four meteorological patterns for heavy precipitation in Xinjiang, which are listed in descending order of their contribution rates: southern Xinjiang (29–37%), North–Central Asia (19–27%), northern Xinjiang (14–19%), and South–Central Asia (13–16%). The contribution of each source to the heavy precipitation in Xinjiang varies with the meteorological pattern. Additionally, the contribution rates of each source region match well with the precipitation-related particle aggregation before heavy precipitation days. These results help us better understand the moisture source of the heavy precipitation in Xinjiang.

Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0

Abstract. Atmospheric inversions are used to derive constraints on the net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied. In recent years, there has been an increasing demand from stakeholders for inversions at higher spatial resolution (country scale), in particular in the framework of the Paris agreement. This step up in resolution is in theory enabled by the growing availability of observations from surface in situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the bottom-up modeling community (vegetation models, fossil fuel emission inventories, etc.). However, it calls for new developments in the inverse models: diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency. In this context, we developed LUMIA, the Lund University Modular Inversion Algorithm. LUMIA is a Python library for inverse modeling built around the central idea of modularity: it aims to be a platform that enables users to construct and experiment with new inverse modeling setups while remaining easy to use and maintain. It is in particular designed to be transport-model-agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. We have constructed a first regional inversion setup using the LUMIA framework to conduct regional CO2 inversions in Europe using in situ data from surface and tall-tower observation sites. The inversions rely on a new offline coupling between the regional high-resolution FLEXPART Lagrangian particle dispersion model and the global coarse-resolution TM5 transport model. This test setup is intended both as a demonstration and as a reference for comparison with future LUMIA developments. The aims of this paper are to present the LUMIA framework (motivations for building it, development principles and future prospects) and to describe and test this first implementation of regional CO2 inversions in LUMIA.

“Military Parade Blue Skies” in Beijing: Decisive Influence of Meteorological Factors on Transport Channel and Atmospheric Pollutant Concentration Level

The severity of high atmospheric pollution has been a major social problem in northern China. To improve the air quality in the Beijing–Tianjin–Hebei (BTH) region and guarantee a suitable environment during the military parade and other celebrating activities for the 70th anniversary of the victory for anti-Fascist Warcraft in the year 2015, a series of strict air quality control policies were carried out. To analyze the reduction extents of PM2.5 and organic matter components during the control period and to examine the meteorological conditions in this region and their decisive influence on the air quality, PM2.5 samples were collected and the Lagrangian particle dispersion model FLEXPART was performed to calculate potential source locations within the BTH region. PM2.5, organic carbon (OC), elementary carbon (EC), and three species types were specifically analyzed. Although the results showed that PM2.5, OC, and EC reduced by 64.55%, 48.74%, and 60.75% during the control period, the air mass transport patterns showed great difference at certain periods, which altered the dominant transport direction of air mass and the potential source region of pollutants and organic matters. This alteration completely changed major atmospheric pollutants sources contribution and caused huge concentration changes. Parallel cases also showed that meteorological conditions could avoid massive atmospheric transported from a major emission source region to a receptor site. The meteorological conditions changed the main contribution source region in control and non-control periods and proved the air quality control measures were less necessary in some southern Hebei cities during special events periods.

Metals in coarse ambient aerosol as markers for source apportionment and their health risk assessment over an eastern coastal urban atmosphere in India.

Ambient PM10 (particulate matter with aerodynamic diameter ≤ 10µm) samples were collected and characterized from July 2012 to August 2013 with the objective to evaluate the variation in elemental concentration and use the same as markers for source apportionment and health risk assessment for the first time over Bhubaneswar, India. The yearly average mass of PM10 was 82.28µg/m3, which was ~ 37% higher than the national ambient air quality (NAAQ) standards. Maximum PM10 concentration was observed during winter season followed by post-monsoon, pre-monsoon, and monsoon months. Acid soluble components in the PM10 samples were analyzed using ICP-OES (inductive coupled plasma optical emission spectroscopy), and 19 different elements including heavy metals were determined. Enrichment factor analysis attributed the source to either crustal or non-crustal origin. Principal component analysis (PCA) revealed that crustal sources, industrial activities, and vehicular emissions were significant contributors to PM mass. The contribution of total average elemental concentration showed a seasonal variation with the lowest (11.96µg/m3) and highest (17.77µg/m3) during monsoon and winter, respectively, which is relatively less significant than the variation in total PM10 mass that ranged between 48.43µg/m3 in monsoon and 138.24µg/m3 during the winter season. This observation evidences the predominant contribution of local/regional emission sources to the metallic components in coarse PM10 mass, which is corroborated by the wind pattern studies carried out using polar plots and a Lagrangian Particle Dispersion Model (LPDM) FLEXPART. Further, carcinogenic and non-carcinogenic health risk assessments of the measured elements that find their way into the human body through different exposure pathways have been calculated using United State Environmental Protection Agency (USEPA) standards. The carcinogenic risk of most of the elements was insignificant. The potential risk assessment study revealed that regular exposure to heavy metals through the ingestion pathway caused detrimental health effects. These effects were observed to be more severe in children in comparison to adults.

A new Lagrangian in-time particle simulation module (Itpas v1) for atmospheric particle dispersion

Abstract. Trajectory models are intuitive tools for airflow studies. But in general, they are limited to non-turbulent, i.e. laminar flow, conditions. Therefore, trajectory models are not particularly suitable for investigating airflow within the turbulent atmospheric boundary layer. To overcome this, a common approach is handling the turbulent uncertainty as a random deviation from a mean path in order to create a statistic of possible solutions which envelops the mean path. This is well known as the Lagrangian particle dispersion model (LPDM). However, the decisive factor is the representation of turbulence in the model, for which widely used models such as FLEXPART and HYSPLIT use an approximation. A conceivable improvement could be the use of a turbulence parameterisation approach based on the turbulent kinetic energy (TKE) at high temporal resolution. Here, we elaborated this approach and developed the LPDM Itpas, which is coupled online to the German Weather Service's mesoscale weather forecast model COSMO. It benefits from the prognostically calculated TKE as well as from the high-frequency wind information. We demonstrate the model's applicability for a case study on agricultural particle emission in eastern Germany. The results obtained are discussed with regard to the model's ability to describe particle transport within a turbulent boundary layer. Ultimately, the simulations performed suggest that the newly introduced method based on prognostic TKE sufficiently represents the particle transport.

Firewood residential heating – local versus remote influence on the aerosol burden

Abstract. We report the first-time use of the Lagrangian particle dispersion model (LPDM) FLEXPART to simulate isotope ratios of the biomass burning tracer levoglucosan. Here, we combine the model results with observed levoglucosan concentrations and δ13C to assess the contribution of local vs. remote emissions from firewood domestic heating to the particulate matter sampled during the cold season at two measurements stations of the Environmental Agency of North Rhine-Westphalia, Germany. For the investigated samples, the simulations indicate that the largest part of the sampled aerosol is 1 to 2 d old and thus originates from local to regional sources. Consequently, ageing, also limited by the reduced photochemical activity in the dark cold season, has a minor influence on the observed levoglucosan concentration and δ13C. The retro plume ages agree well with those derived from observed δ13C (the “isotopic” ages), demonstrating that the limitation of backwards calculations to 7 d for this study does not introduce any significant bias. A linear regression analysis applied to the experimental levoglucosan δ13C vs. the inverse concentration confirms the young age of aerosol. The high variability in the observed δ13C implies that the local levoglucosan emissions are characterized by different isotopic ratios in the range of −26.3 ‰ to −21.3 ‰. These values are in good agreement with previous studies on levoglucosan source-specific isotopic composition in biomass burning aerosol. Comparison between measured and estimated levoglucosan concentrations suggests that emissions are underestimated by a factor of 2 on average. These findings demonstrate that the aerosol burden from home heating in residential areas is not of remote origin. In this work we show that combining Lagrangian modelling with isotope ratios is valuable to obtain additional insight into source apportionment. Error analysis shows that the largest source of uncertainty is limited information on isotope ratios of levoglucosan emissions. Based on the observed low extent of photochemical processing during the cold season, levoglucosan can be used under similar conditions as a conservative tracer without introducing substantial bias.

Main sources controlling atmospheric burdens of persistent organic pollutants on a national scale

National long-term monitoring programs on persistent organic pollutants (POPs) in background air have traditionally relied on active air sampling techniques. Due to limited spatial coverage of active air samplers, questions remain (i) whether active air sampler monitoring sites are representative for atmospheric burdens within the larger geographical area targeted by the monitoring programs, and thus (ii) if the main sources affecting POPs in background air across a nation are understood. The main objective of this study was to explore the utility of spatial and temporal trends in concert with multiple modelling approaches to understand the main sources affecting polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in background air across a nation. For this purpose, a comprehensive campaign was carried out in summer 2016, measuring POPs in background air across Norway using passive air sampling. Results were compared to a similar campaign in 2006 to assess possible changes over one decade. We furthermore used the Global EMEP Multi-media Modeling System (GLEMOS) and the Flexible Particle dispersion model (FLEXPART) to predict and evaluate the relative importance of primary emissions, secondary emissions, long-range atmospheric transport (LRAT) and national emissions in controlling atmospheric burdens of PCB-153 on a national scale. The concentrations in air of both PCBs and most of the targeted OCPs were generally low, with the exception of hexachlorobenzene (HCB). A limited spatial variability for all POPs in this study, together with predictions by both models, suggest that LRAT dominates atmospheric burdens across Norway. Model predictions by the GLEMOS model, as well as measured isomeric ratios, further suggest that LRAT of some POPs are dictated by secondary emissions. Our results illustrate the utility of combining observations and mechanistic modelling approaches to help identify the main factors affecting atmospheric burdens of POPs across a nation, which, in turn, may be used to inform both national monitoring and control strategies.

Real Scale Experiment for Suspended Solid Transport Analysis and Modeling of Particle Dispersion Model

Real Scale Experiment for Suspended Solid Transport Analysis and Modeling of Particle Dispersion Model

Aerosol dynamics and dispersion of radioactive particles

Abstract. In the event of a failure of a nuclear power plant with release of radioactive material into the atmosphere, dispersion modelling is used to understand how the released radioactivity is spread. For the dispersion of particles, Lagrangian particle dispersion models (LPDMs) are commonly used, in which model particles, representing the released material, are transported through the atmosphere. These model particles are usually inert and undergo only first-order processes such as dry deposition and simplified wet deposition along the path through the atmosphere. Aerosol dynamic processes including coagulation, condensational growth, chemical interactions, formation of new particles and interaction with new aerosol sources are usually neglected in such models. The objective of this study is to analyse the impact of these advanced aerosol dynamic processes if they were to be included in LPDM simulations for use in radioactive preparedness. In this investigation, a fictitious failure of a nuclear power plant is studied for three geographically and atmospherically different sites. The incident was simulated with a Lagrangian single-trajectory box model with a new simulation for each hour throughout a year to capture seasonal variability of meteorology and variation in the ambient aerosol. (a) We conclude that modelling of wet deposition by incorporating an advanced cloud parameterization is advisable, since it significantly influence simulated levels of airborne and deposited activity including radioactive hotspots, and (b) we show that inclusion of detailed ambient-aerosol dynamics can play a large role in the model result in simulations that adopt a more detailed representation of aerosol–cloud interactions. The results highlight a potential necessity for implementation of more detailed representation of general aerosol dynamic processes into LPDMs in order to cover the full range of possible environmental characteristics that can apply during a release of radionuclides into the atmosphere.

An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO2 Retrievals.

A growing constellation of satellites is providing near‐global coverage of column‐averaged CO2 observations. Launched in 2019, NASA’s OCO‐3 instrument is set to provide XCO2 observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time‐Inverted Lagrangian Transport (X‐STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO‐3 observations and future space‐based column‐observing missions. However, OCO‐3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X‐STILT model to generate upwind influence footprints with less computational expense. The method uses X‐STILT generated influence footprints from a key subset of OCO‐3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO‐3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO‐3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO2 missions are also discussed.

Simulations of anthropogenic bromoform indicate high emissions at the coast of East Asia

Abstract. Bromoform is the major by-product from chlorination of cooling water in coastal power plants. The number of power plants in East and Southeast Asian economies has increased rapidly, exceeding mean global growth. Bottom-up estimates of bromoform emissions based on few measurements appear to under-represent the industrial sources of bromoform from East Asia. Using oceanic Lagrangian analyses, we assess the amount of bromoform produced from power plant cooling-water treatment in East and Southeast Asia. The spread of bromoform is simulated as passive particles that are advected using the three-dimensional velocity fields over the years 2005/2006 from the high-resolution NEMO-ORCA0083 ocean general circulation model. Simulations are run for three scenarios with varying initial bromoform concentrations based on the range of bromoform measurements in cooling-water discharge. Comparing the modelled anthropogenic bromoform to in situ observations in the surface ocean and atmosphere, the two lower scenarios show the best agreement, suggesting initial bromoform concentrations in cooling water to be around 20–60 µg L−1. Based on these two scenarios, the model produces elevated bromoform in coastal waters of East Asia with average concentrations of 23 and 68 pmol L−1 and maximum values in the Yellow Sea, Sea of Japan and East China Sea. The industrially produced bromoform is quickly emitted into the atmosphere with average air–sea flux of 3.1 and 9.1 nmolm-2h-1, respectively. Atmospheric abundances of anthropogenic bromoform are derived from simulations with the Lagrangian particle dispersion model FLEXPART based on ERA-Interim wind fields in 2016. In the marine boundary layer of East Asia, the FLEXPART simulations show mean anthropogenic bromoform mixing ratios of 0.4–1.3 ppt, which are 2–6 times larger compared to the climatological bromoform estimate. During boreal winter, the simulations show that some part of the anthropogenic bromoform is transported by the northeasterly winter monsoon towards the tropical regions, whereas during boreal summer anthropogenic bromoform is confined to the Northern Hemisphere subtropics. Convective events in the tropics entrain an additional 0.04–0.05 ppt of anthropogenic bromoform into the stratosphere, averaged over tropical Southeast Asia. In our simulations, only about 10 % of anthropogenic bromoform is outgassed from power plants located in the tropics south of 20∘ N, so that only a small fraction of the anthropogenic bromoform reaches the stratosphere. We conclude that bromoform from cooling-water treatment in East Asia is a significant source of atmospheric bromine and might be responsible for annual emissions of 100–300 Mmol of Br in this region. These anthropogenic bromoform sources from industrial water treatment might be a missing factor in global flux estimates of organic bromine. While the current emissions of industrial bromoform provide a significant contribution to regional tropospheric budgets, they provide only a minor contribution to the stratospheric bromine budget of 0.24–0.30 ppt of Br.

Particle dispersion induced by vortical interactions in a particle-laden upward jet with a partial crossflow

Abstract

Turbulent scalar fluxes from a generalized Langevin model: Implications on mean scalar mixing and tracer particle dispersion

A Generalized Langevin Model (GLM) formulation to be used in transported joint velocity-scalar probability density function methods is recalled in order to imply a turbulent scalar-flux model where the pressure-scrambling term is in correspondence with standard Monin's return-to-isotropy term. The proposed non-constant C0 formulation is extended to seen-velocity models for particle dispersion modeling in dispersed two-phase flows. This allows us to correct the wrong turbulent scalar-flux modeling in the limit of tracer particles. Moreover, this allows us to have a more general formulation in order to consider advanced Reynolds-stress models. The cubic model of Fu, Launder, and Tselepidakis is considered, together with the model of Merci and Dick for turbulent dissipation. Results are presented for different swirling and recirculating single-phase and two-phase flows, showing the capabilities of the proposed non-constant C0 GLM formulations compared to the standard GLM.

Characterization of Aerosol Sources and Optical Properties in Siberia Using Airborne and Spaceborne Observations

Airborne backscatter lidar at 532 nm and in-situ measurements of black carbon (BC), carbon monoxide excess above background (ΔCO), and aerosol size distribution were carried out over Siberia in July 2013 and June 2017 in order to sample several kinds of aerosol sources. Aerosol types are derived using the Lagrangian FLEXible PARTicle dispersion model (FLEXPART) simulations and satellite observations. Six aerosol types could be identified in this work: (i) dusty aerosol mixture, (ii) Ob valley gas flaring emission, (iii) fresh forest fire, (iv) aged forest fire, (v) urban emissions over the Tomsk/Novosibirsk region (vi) long range transport of Northern China urban emission. The altitude range of aerosol layers is discussed for each aerosol type, showing transport above the boundary layer for long range transport of Northern China emissions or fresh forest fire. Comparisons of aerosol optical properties, BC and ΔCO are made between aged and fresh plumes for both the urban and forest fire emissions. An increase of aerosol optical depth at 532 nm (AOD532), aerosol particle size and ΔCO is found for aged forest fire plumes. Similar results are obtained when comparing the aged urban plume from Northern China with fresh urban emissions from Siberian cities. A flight above gas flaring emissions corresponds to the largest AOD532 and provides a possible range of 50–60 sr for the lidar ratio of these aerosol plumes often encountered in Siberia. Black carbon concentrations are relatively higher for the flaring plume (0.4–0.5 μμg.m−3) than for the urban plume (0.2 μμg.m−3). The largest BC concentrations are found for the fresh forest fire plume. The aerosol type identification and AOD532 provided by CALIOP Version 4.2 data products in air masses with similar origin generally agree with the results obtained from our detailed analysis of the aerosol plume origins.

Linkage between interannual variation of winter cold surge over East Asia and autumn sea ice over the Barents Sea

This paper discusses the interannual variation of winter cold surges (CSs) over East Asia (EA) from the perspective of the cold air path and its relationship with autumn Barents Sea ice concentration (BSIC). Flexible particle dispersion model for large-scale atmospheric transport process simulation is used to track the cold air path of 301 CSs in EA in winters from 1979 to 2017, and the probability density distribution of cold air path is calculated by kernel density estimation. The cold air mainly originates from the Ural Mountains, and is continuously cooled by the impact of cold land, then accumulates and strengthens near the Lake Baikal, and finally invades EA under the guidance of the northwest airflow. The autumn BSIC has a significant negative correlation with CSs over EA. The decreased autumn BSIC hinders the spread of zonal warm advection over Eurasia and makes the soil temperature near Lake Baikal drop, and the negative anomaly of soil temperature persists from autumn to winter, strengthening cold source therein, which provides suitable thermal conditions for occurring more CSs over EA. Moreover, the decreased autumn BSIC can also persist to winter, which leads to the positive anomaly of the geopotential height over the polar region. Through the propagation of Rossby wave, the negative geopotential height anomaly over EA is excited and provides the appropriate dynamic conditions for forming more CSs in EA. The simulations from the Community Atmosphere Model version 5.3 (CAM5.3) further confirm the proposed possible mechanism.

Real-time measurement of radionuclide concentrations and its impact on inverse modeling of 106Ru release in the fall of 2017

Abstract. Low concentrations of 106Ru were detected across Europe at the turn of September and October 2017. The origin of 106Ru has still not been confirmed; however, current studies agree that the release occurred probably near Mayak in the southern Urals. The source reconstructions are mostly based on an analysis of concentration measurements coupled with an atmospheric transport model. Since reasonable temporal resolution of concentration measurements is crucial for proper source term reconstruction, the standard 1-week sampling interval could be limiting. In this paper, we present an investigation of the usability of the newly developed AMARA (Autonomous Monitor of Atmospheric Radioactive Aerosol) and CEGAM (carousel gamma spectrometry) real-time monitoring systems, which are based on the gamma-ray counting of aerosol filters and allow for determining the moment when 106Ru arrived at the monitoring site within approx. 1 h and detecting activity concentrations as low as several mBq m−3 in 4 h intervals. These high-resolution data were used for inverse modeling of the 106Ru release. We perform backward runs of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric transport model driven with meteorological data from the Global Forecast System (GFS), and we construct a source–receptor sensitivity (SRS) matrix for each grid cell of our domain. Then, we use our least squares with adaptive prior covariance (LS-APC) method to estimate possible locations of the release and the source term of the release. With Czech monitoring data, the use of concentration measurements from the standard regime and from the real-time regime is compared, and a better source reconstruction for the real-time data is demonstrated in the sense of the location of the source and also the temporal resolution of the source. The estimated release location, Mayak, and the total estimated source term, 237±107 TBq, are in agreement with previous studies. Finally, the results based on the Czech monitoring data are validated with the IAEA-reported (International Atomic Energy Agency) dataset with a much better spatial resolution, and the agreement between the IAEA dataset and our reconstruction is demonstrated. In addition, we validated our findings also using the FLEXPART (FLEXible PARTicle dispersion) model coupled with meteorological analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF).

Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint

Abstract. We developed a high-resolution surface flux inversion system based on the global Eulerian–Lagrangian coupled tracer transport model composed of the National Institute for Environmental Studies (NIES) transport model (TM; collectively NIES-TM) and the FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM–FLEXPART-variational) as it applies a variational optimization to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve the best fit to atmospheric CO2 data collected by the global in situ network as a necessary step towards the capability of estimating anthropogenic CO2 emissions. We employed the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate surface flux footprints of CO2 observations at a spatial resolution of 0.1∘×0.1∘. The LPDM is coupled with a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator was implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010–2012 from in situ CO2 data included in the Observation Package (ObsPack) data set. High-resolution prior flux fields were prepared using the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, the Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange, and the Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and a separate simulation of CO2 fluxes at a daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for the terrestrial biosphere was scaled proportionally to the monthly mean gross primary production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and fine-scale vegetation productivity gradients. The resulting flux estimates improved the fit to the observations taken at continuous observation sites, reproducing both the seasonal and short-term concentration variabilities including high CO2 concentration events associated with anthropogenic emissions. The use of a high-resolution atmospheric transport in global CO2 flux inversions has the advantage of better resolving the transported mixed signals from the anthropogenic and biospheric sources in densely populated continental regions. Thus, it has the potential to achieve better separation between fluxes from terrestrial ecosystems and strong localized sources, such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as our posterior fit was better than that of the National Oceanic and Atmospheric Administration (NOAA)'s CarbonTracker for only a fraction of the monitoring sites, i.e. mostly at coastal and island locations where background and local flux signals are mixed.

Selected Persistent Organic Pollutants in Ambient Air in Turkey: Regional Sources and Controlling Factors.

As a result of its unique location, Turkey receives air masses from Europe, Russia, Middle East, and Africa, making it an important place in terms of long-range atmospheric transport (LRT) of contaminants. Atmospheric levels of 22 organochlorine pesticides (OCPs), 45 polychlorinated biphenyls (PCBs), and 14 polybrominated diphenyl ethers (PBDEs) were measured in two metropolitan cities, Istanbul and Izmir, on a weekly basis from May 2014 to May 2015. Dichlorodiphenyltrichloroethane (DDT) and its derivatives were dominant OCP species, followed by isomers of hexachlorocyclohexane (HCH) at both sites. The annual mean concentration of ∑DDX (sum of o,p'-DDT, p,p'-DDT, o,p'-DDD, p,p'-DDD, o,p'-DDE, and p,p'-DDE) was 82 pg/m3 for Istanbul and 89 pg/m3 for Izmir, while these levels were about 46 pg/m3 for ∑HCHs (sum of α-, β-, γ-, and δ-HCH) at both of the sites. At both stations, tri- and tetra-PCBs and tetra- and penta-PBDEs were dominant congeners. The temperature dependence indicates that both LRT and local contaminated areas contribute to the elevated levels. A Lagrangian particle dispersion model (FLEXPART) showed a few potential source regions in northern Africa and Middle East, southern-southwestern and eastern Europe including Russia, as well as from local domestic metropolitan areas.

Linking marine phytoplankton emissions, meteorological processes, and downwind particle properties with FLEXPART

Abstract. Marine biogenic particle contributions to atmospheric aerosol concentrations are not well understood though they are important for determining cloud optical and cloud-nucleating properties. Here we examine the relationship between marine aerosol measurements (with satellites and model fields of ocean biology) and meteorological variables during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). NAAMES consisted of four field campaigns between November 2015 and April 2018 that aligned with the four major phases of the annual phytoplankton bloom cycle. The FLEXible PARTicle (FLEXPART) Lagrangian particle dispersion model is used to spatiotemporally connect these variables to ship-based aerosol and dimethyl sulfide (DMS) observations. We find that correlations between some aerosol measurements with satellite-measured and modeled variables increase with increasing trajectory length, indicating that biological and meteorological processes over the air mass history are influential for measured particle properties and that using only spatially coincident data would miss correlative connections that are lagged in time. In particular, the marine non-refractory organic aerosol mass correlates with modeled marine net primary production when weighted by 5 d air mass trajectory residence time (r=0.62). This result indicates that non-refractory organic aerosol mass is influenced by biogenic volatile organic compound (VOC) emissions that are typically produced through bacterial degradation of dissolved organic matter, zooplankton grazing on marine phytoplankton, and as a by-product of photosynthesis by phytoplankton stocks during advection into the region. This is further supported by the correlation of non-refractory organic mass with 2 d residence-time-weighted chlorophyll a (r=0.39), a proxy for phytoplankton abundance, and 5 d residence-time-weighted downward shortwave forcing (r=0.58), a requirement for photosynthesis. In contrast, DMS (formed through biological processes in the seawater) and primary marine aerosol (PMA) concentrations showed better correlations with explanatory biological and meteorological variables weighted with shorter air mass residence times, which reflects their localized origin as primary emissions. Aerosol submicron number and mass negatively correlate with sea surface wind speed. The negative correlation is attributed to enhanced PMA concentrations under higher wind speed conditions. We hypothesized that the elevated total particle surface area associated with high PMA concentrations leads to enhanced rates of condensation of VOC oxidation products onto PMA. Given the high deposition velocity of PMA relative to submicron aerosol, PMA can limit the accumulation of secondary aerosol mass. This study provides observational evidence for connections between marine aerosols and underlying ocean biology through complex secondary formation processes, emphasizing the need to consider air mass history in future analyses.