Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models

Global measurements of the elemental composition of fine particulate matter across several urban locations by the Surface Particulate Matter Network reveal an enhanced fraction of anthropogenic dust compared to natural dust sources, especially over Asia. We develop a global simulation of anthropogenic fugitive, combustion, and industrial dust which, to our knowledge, is partially missing or strongly underrepresented in global models. We estimate 2-16 μg/m3 increase in fine particulate mass concentration across East and South Asia by including anthropogenic fugitive, combustion, and industrial dust emissions. A simulation including anthropogenic fugitive, combustion, and industrial dust emissions increases the correlation from 0.06 to 0.66 of simulated fine dust in comparison with Surface Particulate Matter Network measurements at 13 globally dispersed locations, and reduces the low bias by 10% in total fine particulate mass in comparison with global in situ observations. Global population-weighted PM2.5 increases by 2.9 μg/m3 (10%). Our assessment ascertains the urgent need of including this underrepresented fine anthropogenic dust source into global bottom-up emission inventories and global models

SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications

Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM2.5) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short-and long-term exposure to PM2.5 at local-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which ground-level aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health-effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM2.5 and PM10, are highly autonomous. Hourly PM2.5 concentrations are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, water-soluble ions, and metals. These measurements provide, in a variety of regions around the world, the key data required to evaluate and enhance satellite-based PM2.5 estimates used for assessing the health effects of aerosols. Mean PM2.5 concentrations across sites vary by more than 1 order of magnitude. Our initial measurements indicate that the ratio of AOD to ground-level PM2.5 is driven temporally and spatially by the vertical profile in aerosol scattering. Spatially this ratio is also strongly influenced by the mass scattering efficiency.Fil: Snider, G.. Dalhousie University Halifax; CanadáFil: Weagle, C. L.. Dalhousie University Halifax; CanadáFil: Martin, R. V.. Dalhousie University Halifax; Canadá. University of Cambridge; Reino UnidoFil: van Donkelaar, A.. Dalhousie University Halifax; CanadáFil: Conrad, K.. Dalhousie University Halifax; CanadáFil: Cunningham, D.. Dalhousie University Halifax; CanadáFil: Gordon, C.. Dalhousie University Halifax; CanadáFil: Zwicker, M.. Dalhousie University Halifax; CanadáFil: Akoshile, C.. University of Ilorin; NigeriaFil: Artaxo, P.. Governo Do Estado de Sao Paulo; BrasilFil: Anh, N. X.. Vietnam Academy of Science and Technology. Institute of Geophysics; VietnamFil: Brook, J.. University of Toronto; CanadáFil: Dong, J.. Tsinghua University; ChinaFil: Garland, R. M.. North-West University; SudáfricaFil: Greenwald, R.. Rollins School of Public Health; Estados UnidosFil: Griffith, D.. Council for Scientific and Industrial Research; SudáfricaFil: He, K.. Tsinghua University; ChinaFil: Holben, B. N.. NASA Goddard Space Flight Center; Estados UnidosFil: Kahn, R.. NASA Goddard Space Flight Center; Estados UnidosFil: Koren, I.. Weizmann Institute Of Science Israel; IsraelFil: Lagrosas, N.. Manila Observatory, Ateneo de Manila University campus; FilipinasFil: Lestari, P.. Institut Teknologi Bandung; IndonesiaFil: Ma, Z.. Rollins School of Public Health; Estados UnidosFil: Vanderlei Martins, J.. University of Maryland; Estados UnidosFil: Quel, Eduardo Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rudich, Y.. Weizmann Institute Of Science Israel; IsraelFil: Salam, A.. University Of Dhaka; BangladeshFil: Tripathi, S. N.. Indian Institute Of Technology, Kanpur; IndiaFil: Yu, C.. Rollins School of Public Health; Estados UnidosFil: Zhang, Q.. Tsinghua University; ChinaFil: Zhang, Y.. Tsinghua University; ChinaFil: Brauer, M.. University of British Columbia; CanadáFil: Cohen, A.. Health Effects Institute; Estados UnidosFil: Gibson, M. D.. Dalhousie University Halifax; CanadáFil: Liu, Y.. Rollins School of Public Health; Estados Unido

Characterizing the Atmospheric Mn Cycle and Its Impact on Terrestrial Biogeochemistry

The role of manganese (Mn) in ecosystem carbon (C) biogeochemical cycling is gaining increasing attention. While soil Mn is mainly derived from bedrock, atmospheric deposition could be a major source of Mn to surface soils, with implications for soil C cycling. However, quantification of the atmospheric Mn cycle, which comprises emissions from natural (desert dust, sea salts, volcanoes, primary biogenic particles, and wildfires) and anthropogenic sources (e.g., industrialization and land-use change due to agriculture), transport, and deposition, remains uncertain. Here, we use compiled emission data sets for each identified source to model and quantify the atmospheric Mn cycle by combining an atmospheric model and in situ atmospheric concentration measurements. We estimated global emissions of atmospheric Mn in aerosols (<10 mu m in aerodynamic diameter) to be 1,400 Gg Mn year(-1). Approximately 31% of the emissions come from anthropogenic sources. Deposition of the anthropogenic Mn shortened Mn "pseudo" turnover times in 1-m-thick surface soils (ranging from 1,000 to over 10,000,000 years) by 1-2 orders of magnitude in industrialized regions. Such anthropogenic Mn inputs boosted the Mn-to-N ratio of the atmospheric deposition in non-desert dominated regions (between 5 x 10(-5) and 0.02) across industrialized areas, but that was still lower than soil Mn-to-N ratio by 1-3 orders of magnitude. Correlation analysis revealed a negative relationship between Mn deposition and topsoil C density across temperate and (sub)tropical forests, consisting with atmospheric Mn deposition enhancing carbon respiration as seen in in situ biogeochemical studies

Constraining Present-Day Anthropogenic Total Iron Emissions Using Model and Observations

Iron emissions from human activities, such as oil combustion and smelting, affect the Earth's climate and marine ecosystems. These emissions are difficult to quantify accurately due to a lack of observations, particularly in remote ocean regions. In this study, we used long-term, near-source observations in areas with a dominance of anthropogenic iron emissions in various parts of the world to better estimate the total amount of anthropogenic iron emissions. We also used a statistical source apportionment method to identify the anthropogenic components and their sub-sources from bulk aerosol observations in the United States. We find that the estimates of anthropogenic iron emissions are within a factor of 3 in most regions compared to previous inventory estimates. Under- or overestimation varied by region and depended on the number of sites, interannual variability, and the statistical filter choice. Smelting-related iron emissions are overestimated by a factor of 1.5 in East Asia compared to previous estimates. More long-term iron observations and the consideration of the influence of dust and wildfires could help reduce the uncertainty in anthropogenic iron emissions estimates.Human activities, such as smelting and oil combustion, release smoke and particles into the atmosphere. These particles often contain iron, which not only absorbs sunlight, contributing to atmospheric warming, but also serves as a nutrient for phytoplankton in various ocean regions. However, the precise extent of human-induced iron emissions remains uncertain due to a lack of comprehensive monitoring data. In this study, we leverage a global data set of iron observations to refine our estimates of iron emissions attributed to human activities. Additionally, we examine other co-released substances, such as carbon and nickel, to identify specific emission sources of iron. We employ statistical techniques to distinguish human-caused iron emissions from those originating from natural sources like dust and wildfires. Moreover, we utilize iron oxide observations to constrain emissions originating from East Asia and Norway, which are estimated to originate largely from smelting emissions. Through the analysis of long-term data sets, we provide lower and upper bounds to human-caused iron emissions. Furthermore, we investigate the impact of reduced observation numbers and a sparse network on the range of estimated iron emissions. Our findings highlight the critical role of observation quality in accurately assessing iron emissions from human activities.Anthropogenic total iron emissions are constrained to a factor of 3 in most global regions using long-term aerosol observations The number of sites, interannual variability, and site selection filter can affect the model-observation comparison uncertainty by 15%-50% Smelting-related emissions are constrained to a factor of 1.5 using iron oxide observations from East Asi

AERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition

Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations - and, especially, particle composition - to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit similar to 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers

AERO-MAP: A data compilation and modelling approach to understand spatial variability in fine and coarse mode aerosol composition

Aerosol particles are an important part of the Earth system, but their concentrations are spatially and temporally heterogeneous, as well as variable in size and composition. Particles can interact with incoming solar radiation and outgoing long wave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the surface albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial datasets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modelling community. Here we present a new compilation of PM2.5 and PM10 aerosol observations, focusing on the spatial variability across different observational stations, including composition, and demonstrate a method for comparing the datasets to model output. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations, and especially particle composition to understand the current distribution of particles. Most climate models exclude 10–30 % of the aerosol particles in both PM2.5 and PM10 size fractions across large swaths of the globe in their current configurations, with ammonium nitrate and agricultural dust aerosol being the most important omitted aerosol types

AERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition

Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers.</p