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Impact of Biomass Burning on Arctic Aerosol Composition
Get PDFEmissions from biomass burning (BB) occurring at midlatitudes can reach the Arctic, where they influence the remote aerosol population. By using measurements of levoglucosan and black carbon, we identify seven BB events reaching Svalbard in 2020. We find that most of the BB events are significantly different to the rest of the year (nonevents) for most of the chemical and physical properties. Aerosol mass and number concentrations are enhanced by up to 1 order of magnitude during the BB events. During BB events, the submicrometer aerosol bulk composition changes from an organic- and sulfate-dominated regime to a clearly organic-dominated regime. This results in a significantly lower hygroscopicity parameter κ for BB aerosol (0.4 ± 0.2) compared to nonevents (0.5 ± 0.2), calculated from the nonrefractory aerosol composition. The organic fraction in the BB aerosol showed no significant difference for the O:C ratios (0.9 ± 0.3) compared to the year (0.9 ± 0.6). Accumulation mode particles were present during all BB events, while in the summer an additional Aitken mode was observed, indicating a mixture of the advected air mass with locally produced particles. BB tracers (vanillic, homovanillic, and hydroxybenzoic acid, nitrophenol, methylnitrophenol, and nitrocatechol) were significantly higher when air mass back trajectories passed over active fire regions in Eastern Europe, indicating agricultural and wildfires as sources. Our results suggest that the impact of BB on the Arctic aerosol depends on the season in which they occur, and agricultural and wildfires from Eastern Europe have the potential to disturb the background conditions the most.publishedVersio
Negative correlation between soil salinity and soil organic carbon variability
Get PDFSoil organic carbon (SOC) is vital for terrestrial ecosystems, affecting biogeochemical processes, and soil health. It is known that soil salinity impacts SOC content, yet the specific direction and magnitude of SOC variability in relation to soil salinity remain poorly understood. Analyzing 43,459 mineral soil samples (SOC < 150 g kg−1) collected across different land covers since 1992, we approximate a soil salinity increase from 1 to 5 dS m−1 in croplands would be associated with a decline in mineral soils SOC from 0.14 g kg−1 above the mean predicted SOC (= 18.47 g kg−1) to 0.46 g kg−1 below (~−430%), while for noncroplands, such decline is sharper, from 0.96 above = 35.96 g kg−1 to 4.99 below (~−620%). Although salinity’s significance in explaining SOC variability is minor (<6%), we estimate a one SD increase in salinity of topsoil samples (0 to 7 cm) correlates with respective declines of ~4.4% and ~9.26%, relative to and. The decline in croplands is greatest in vegetation/cropland mosaics while lands covered with evergreen needle-leaved trees are estimated with the highest decline in noncroplands. We identify soil nitrogen, land cover, and precipitation Seasonality Index as the most significant parameters in explaining the SOC’s variability. The findings provide insights into SOC dynamics under increased soil salinity, improving understanding of SOC stock responses to land degradation and climate warming.publishedVersio
Forecasting the Exceedances of PM2.5 in an Urban Area
Get PDFParticular matter (PM) constitutes one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5 μm, PM2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, lung cancer, etc. Accurate air-quality forecasting on a regional scale enables local agencies to design and apply appropriate policies (e.g., meet specific emissions limitations) to tackle the problem of air pollution. Under this framework, low-cost sensors have recently emerged as a valuable tool, facilitating the spatiotemporal monitoring of air pollution on a local scale. In this study, we present a deep learning approach (long short-term memory, LSTM) to forecast the intra-day air pollution exceedances across urban and suburban areas. The PM2.5 data used in this study were collected from 12 well-calibrated low-cost sensors (Purple Air) located in the greater area of the Municipality of Thermi in Thessaloniki, Greece. The LSTM-based methodology implements PM2.5 data as well as auxiliary data, meteorological variables from the Copernicus Atmosphere Monitoring Service (CAMS), which is operated by ECMWF, and time variables related to local emissions to enhance the air pollution forecasting performance. The accuracy of the model forecasts reported adequate results, revealing a correlation coefficient between the measured PM2.5 and the LSTM forecast data ranging between 0.67 and 0.94 for all time horizons, with a decreasing trend as the time horizon increases. Regarding air pollution exceedances, the LSTM forecasting system can correctly capture more than 70.0% of the air pollution exceedance events in the study region. The latter findings highlight the model’s capabilities to correctly detect possible WHO threshold exceedances and provide valuable information regarding local air quality.publishedVersio
A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks
Get PDFTop-down approaches, such as atmospheric inversions, are a promising tool for evaluating emission estimates based on activity-data. In particular, there is a need to examine carbon budgets at subnational scales (e.g. state/province), since this is where the climate mitigation policies occur. In this study, the subnational scale anthropogenic CO2 emissions are estimated using a high-resolution global CO2 inverse model. The approach is distinctive with the use of continuous atmospheric measurements from regional/urban networks along with background monitoring data for the period 2015–2019 in global inversion. The measurements from several urban areas of the U.S., Europe and Japan, together with recent high-resolution emission inventories and data-driven flux datasets were utilized to estimate the fossil emissions across the urban areas of the world. By jointly optimizing fossil fuel and natural fluxes, the model is able to contribute additional information to the evaluation of province–scale emissions, provided that sufficient regional network observations are available. The fossil CO2 emission estimates over the U.S. states such as Indiana, Massachusetts, Connecticut, New York, Virginia and Maryland were found to have a reasonable agreement with the Environmental Protection Agency (EPA) inventory, and the model corrects the emissions substantially towards the EPA estimates for California and Indiana. The emission estimates over the United Kingdom, France and Germany are comparable with the regional inventory TNO–CAMS. We evaluated model estimates using independent aircraft observations, while comparison with the CarbonTracker model fluxes confirms ability to represent the biospheric fluxes. This study highlights the potential of the newly developed inverse modeling system to utilize the atmospheric data collected from the regional networks and other observation platforms for further enhancing the ability to perform top-down carbon budget assessment at subnational scales and support the monitoring and mitigation of greenhouse gas emissions.publishedVersio
Optical and Microphysical Properties of the Aerosols during a Rare Event of Biomass-Burning Mixed with Polluted Dust
Get PDFA rare event of mixed biomass-burning and polluted dust aerosols was observed over Athens, Greece (37.9° N, 23.6° E), during 21–26 May 2014. This event was studied using a synergy of a 6-wavelength elastic-Raman-depolarization lidar measurements, a CIMEL sun photometer, and in situ instrumentation. The FLEXPART dispersion model was used to identify the aerosol sources and quantify the contribution of dust and black carbon particles to the mass concentration. The identified air masses were found to originate from Kazakhstan and Saharan deserts, under a rare atmospheric pressure system. The lidar ratio (LR) values retrieved from the Raman lidar ranged within 25–89 sr (355 nm) and 35–70 sr (532 nm). The particle linear depolarization ratio (δaer) ranged from 7 to 28% (532 nm), indicating mixing of dust with biomass-burning particles. The aerosol optical depth (AOD) values derived from the lidar ranged from 0.09–0.43 (355 nm) to 0.07–0.25 (532 nm). An inversion algorithm was used to derive the mean aerosol microphysical properties (mean effective radius (reff), single scattering albedo (SSA), and mean complex refractive index (m)) inside selected atmospheric layers. We found that reff was 0.12–0.51 (±0.04) µm, SSA was 0.94–0.98 (±0.19) (at 532 nm), while m ranged between 1.39 (±0.05) + 0.002 (±0.001)i and 1.63 (±0.05) + 0.008 (±0.004)i. The polarization lidar photometer networking (POLIPHON) algorithm was used to estimate the vertical profile of the mass concentration for the dust and non-dust components. A mean mass concentration of 15 ± 5 μg m−3 and 80 ± 29 μg m−3 for smoke and dust was estimated for selected days, respectively. Finally, the retrieved aerosol microphysical properties were compared with column-integrated sun photometer CIMEL data with good agreementOptical and Microphysical Properties of the Aerosols during a Rare Event of Biomass-Burning Mixed with Polluted DustpublishedVersio
Effect of Long-Range Transported Fire Aerosols on Cloud Condensation Nuclei Concentrations and Cloud Properties at High Latitudes
Get PDFActive vegetation fires in south-eastern (SE) Europe resulted in a notable increase in the number concentration of aerosols and cloud condensation nuclei (CCN) particles at two high latitude locations—the SMEAR IV station in Kuopio, Finland, and the Zeppelin Observatory in Svalbard, high Arctic. During the fire episode aerosol hygroscopicity κ slightly increased at SMEAR IV and at the Zeppelin Observatory κ decreased. Despite increased κ in high CCN conditions at SMEAR IV, the aerosol activation diameter increased due to the decreased supersaturation with an increase in aerosol loading. In addition, at SMEAR IV during the fire episode, in situ measured cloud droplet number concentration (CDNC) increased by a factor of ∼7 as compared to non-fire periods which was in good agreement with the satellite observations (MODIS, Terra). Results from this study show the importance of SE European fires for cloud properties and radiative forcing in high latitudes.publishedVersio
Archetypes of Spatial Concentration Variability of Organic Contaminants in the Atmosphere: Implications for Identifying Sources and Mapping the Gaseous Outdoor Inhalation Exposome
Get PDFWhereas inhalation exposure to organic contaminants can negatively impact human health, knowledge of their spatial variability in the ambient atmosphere remains limited. We analyzed the extracts of passive air samplers deployed at 119 unique sites in Southern Canada between 2019 and 2022 for 353 organic vapors. Hierarchical clustering of the obtained data set revealed four archetypes of spatial concentration variability in the outdoor atmosphere, which are indicative of common sources and similar atmospheric dispersion behavior. “Point Source” signatures are characterized by elevated concentration in the vicinity of major release locations. A “Population” signature applies to compounds whose air concentrations are highly correlated with population density, and is associated with emissions from consumer products. The “Water Source” signature applies to substances with elevated levels in the vicinity of water bodies from which they evaporate. Another group of compounds displays a “Uniform” signature, indicative of a lack of major sources within the study area. We illustrate how such a data set, and the derived spatial patterns, can be applied to support the identification of sources, the quantification of atmospheric emissions, the modeling of air quality, and the investigation of potential inequities in inhalation exposure.publishedVersio
Estimating surface NO2 concentrations over Europe using Sentinel-5P TROPOMI
Get PDFSatellite observations from instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) show significant potential for monitoring the spatiotemporal variability of NO2, however they typically provide vertically integrated measurements over the tropospheric column. In this study, we introduce a machine learning approach entitled ‘S-MESH’ (Satellite and ML-based Estimation of Surface air quality at High resolution) that allows for estimating daily surface NO2 concentrations over Europe at 1 km spatial resolution based on eXtreme gradient boost (XGBoost) model using primarily observation-based datasets over the period 2019–2021. Spatiotemporal datasets used by the model include TROPOMI NO2 tropospheric vertical column density, night light radiance from the Visible Infrared Imaging Radiometer Suite (VIIRS), Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer (MODIS), observations of air quality monitoring stations from the European Environment Agency database and modeled meteorological parameters such as planetary boundary layer height, wind velocity, temperature. The overall model evaluation shows a mean absolute error of 7.77 μg/m3, a median bias of 0.6 μg/m3 and a Spearman rank correlation of 0.66. The model performance is found to be influenced by NO2 concentration levels, with the most reliable predictions at concentration levels of 10–40 μg/m3 with a bias ofpublishedVersio
Surface warming in Svalbard may have led to increases in highly active ice-nucleating particles
Get PDFThe roles of Arctic aerosols as ice-nucleating particles remain poorly understood, even though their effects on cloud microphysics are crucial for assessing the climate sensitivity of Arctic mixed-phase clouds and predicting their response to Arctic warming. Here we present a full-year record of ice-nucleating particle concentrations over Svalbard, where surface warming has been anomalously faster than the Arctic average. While the variation of ice-nucleating particles active at around −30 °C was relatively small, those active at higher temperatures (i.e., highly active ice-nucleating particles) tended to increase exponentially with rising surface air temperatures when the surface air temperatures rose above 0 °C and snow/ice-free barren and vegetated areas appeared in Svalbard. The aerosol population relevant to their increase was largely characterized by dust and biological organic materials that likely originated from local/regional terrestrial sources. Our results suggest that highly active ice-nucleating particles could be actively released from Arctic natural sources in response to surface warming.publishedVersio
