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

Expression of the antiapoptotic protein bcl-2 is not dependent on the tumor suppressor p53 protein in Indian breast carcinoma

Tissue homeostasis and the maintenance of cell populations depend on a delicate balance between the rates of cell proliferation and cell death. Programmed cell death or apoptosis is believed to play a major role in physiological processes which, when defective, could contribute to the pathogenesis and progression of tumors. A role for altered programmed cell death in cancer stems from the description of alterations of tumor-associated genes involved in the regulation of apoptosis such as p53 and bcl-2. The p53 gene promotes apoptosis in cells with genetic damage, while bcl-2 is an antiapoptotic gene. It is therefore possible that the balance between p53 and bcl-2 may have significant implications for the pathobiology of breast cancer. This study was therefore undertaken to evaluate the expression of these two proteins with opposite functions and their relation to the total growth fraction of the tumor as measured by PCNA immunoreactivity. A significant correlation was observed between expression of p53 and PCNA. In contrast, bcl-2 expression did not correlate with the expression of p53. There was also no correlation observed between expression of bcl-2 and PCNA. A significant correlation was observed between expression of p53 and the grade of the tumor and stage of the disease. Our results thus support the hypothesis that accumulation of p53 is associated with a high tumor proliferation rate, an association that might be expected in view of the role of wild-type p53 as a negative regulator of cell proliferation. Another important observation was the lack of relationship between bcl-2 expression and PCNA immunoreactivity, supporting the hypothesis that bcl-2 is not a major regulator of proliferation

Technological Innovation Driven by Big Data

Large-scale research evaluations, as well as the acquisition and analysis of massive amounts of data, in order to follow trends and models form the organization. As the world around us continues to change, many entrepreneurs are interested in learning how big data might help them enhance their businesses. While businesses have traditionally analyzed data, recent technical advancements have spawned new entrants and unlocked the potential of big data. The extent of the consequences of big data, according to an SNS research, is presently $57 billion and expected to rise further.</jats:p

Transforming growth factor beta related to extent of tumor angiogenesis but not apoptosis or proliferation in breast carcinoma

Background: Recent investigations have demonstrated the clinical significance of intralesional mean vessel density (ILVD), as a marker of tumor angiogenesis. The role of growth factors in mediating angiogenesis has also been well documented. Transforming growth factor beta (TGF/&#946;) belongs to a family of polypeptides with diverse biological functions. Very few studies however have looked at the role of this growth factor in relation to angiogenesis. This study analyzed the significance of TGF&#946; in relation to CD34, an endothelial cell marker, the extent of apoptosis, and tissue proliferation defined by Ki67 expression in breast cancer. Methods: The extent of apoptosis was defined by morphological criteria and the Tdt-mediated dUTP biotin nick end labelling (TUNEL) assay. Immunocyfochemistry was performed to measure TGF&#946;, CD34 and Ki67 expression. Results An inverse association was observed between TGF&#946; expression and ILVD as evident by CD34 labelling (r=-0.31182, p=0.00005). TGF&#946; expression did not correlate with either TUNEL reactivity or Ki67 expression. CD34 and TGF&#946; expression also had no relationship with histopathological grade. No correlation was observed between CD34 expression and apoptosis. However a statistically significant correlation was observed between CD34 and Ki67 expression. Conclusions: These results suggest that breast cancer cells synthesize TGF&#946; that, through paracrine mechanisms, may inhibit proliferation of vascular endothelium rather than their own growth Moreover the data also suggest that decreased expression of TGF&#946; was associated with on increase in neovascularization, which in turn would increase the tumor proliferative fraction