AN APPROACH TO ESTIMATE GLOBAL BIOMASS BURNING EMISSIONS OF ORGANIC AND BLACK CARBON FROM MODIS FIRE RADIATIVE POWER

Biomass burning is an important global phenomenon affecting atmospheric composition with significant implications for climatic forcing. Wildland fire is the main global source of fine primary carbonaceous aerosols in the form of organic carbon (OC) and black carbon (BC), but uncertainty in aerosol emission estimates from biomass burning is still rather large. Application of satellite based measures of fire radiative power (FRP) has been demonstrated to offer an alternative approach to estimate biomass consumed with the potential to estimate the associated emissions from fires. To date, though, no study has derived integrated FRP (referred to as fire radiative energy or FRE) at a global scale, in part due to limitations in temporal or spatial resolution of satellite sensors. The main objective of this research was to quantify global biomass burning emissions of organic and black carbon aerosols and the corresponding effect on planetary radiative forcing. The approach is based on the geophysical relationship between the flux of FRE emitted, biomass consumed, and aerosol emissions. Aqua and Terra MODIS observations were used to estimate FRE using a simple model to parameterize the fire diurnal cycle based on the long term ratio between Terra and Aqua MODIS FRP and cases of diurnal satellite measurements of FRP made by the geostationary sensor SEVIRI, precessing sensor VIRS, and high latitude (and thus high overpass frequency) observations by MODIS. Investigation of the atmospheric attenuation of MODIS channels using a parametric model based on the MODTRAN radiative transfer model indicates a small bias in FRE estimates which was accounted for. Accuracy assessment shows that the FRE estimates are precise (R2 = 0.85), but may be underestimated. Global estimates of FRE show that Africa and South America dominate biomass burning, accounting for nearly 70% of the annual FRE generated. The relationship between FRE and OCBC estimates made with a new MODIS-derived inversion product of daily integrated biomass burning aerosol emissions was explored. The slope of the relationship within each of several biomes yielded a FRE-based emission factor. The biome specific emission factors and FRE monthly data were used to estimate OCBC emissions from fires on a global basis for 2001 to 2007. The annual average was 17.23 Tg which was comparable to previously published values, but slightly lower. The result in terms of global radiative forcing suggests a cooling effect at both the top-of-atmosphere (TOA) and surface approaching almost -0.5 K which implies that biomass burning aerosols could dampen the warming effect of green house gas emissions. An error budget was developed to explore the sources and total uncertainty in the OCBC estimation. The results yielded an uncertainty value of 58% with specific components of the process warranting future consideration and improvement. The uncertainty estimate does not demonstrate a significant improvement over current methods to estimate biomass burning aerosols, but given the simplicity of the approach should allow for refinements to be made with relative ease

Health And Empires: Implications For Political Development On The Health Of The Inhabitants Of Great Moravia (9Th--10Th Centuries)

Thesis (M.A.) University of Alaska Fairbanks, 2012The early medieval period was a time of great change in Europe. Politically thee empires ruled Europe: Charlemagne's Carolingian Empire, the Holy Roman Empire and the Byzantine Empire. During this time early cities began to form in Europe, and new patterns of settlement developed. Great Moravia was a state level society in the southeastern region of the Czech Republic during the late 9th and early 10th centuries. This thesis explores the impact of urban development on the health of its inhabitants. In order to do this, rural (Josefov and Lahovice) and urban (Mikulcice-Kostelisko) skeletal populations were examined for cribra orbitalia, porotic hyperostosis, and linear enamel hypoplasia (LEH). Cribra orbitalia had a consistently low frequency in all populations. This suggests that anemia (often due to chronic parasitic infection and subsequent malnutrition) was present, but at a low level. LEH frequency was significantly higher, with more age of occurrence variation in the urban population. The results of this thesis suggest that despite the advantages of greater wealth and access to greater amount of food (and food varieties) urban populations were under more stress than rural populations. These results have implications about the impact of urban development and migration in modern developing nations

The Science and Application of Satellite Based Fire Radiative Energy

The accurate measurement of ecosystem biomass is of great importance in scientific, resource management and energy sectors. In particular, biomass is a direct measurement of carbon storage within an ecosystem and of great importance for carbon cycle science and carbon emission mitigation. Remote Sensing is the most accurate tool for global biomass measurements because of the ability to measure large areas. Current biomass estimates are derived primarily from ground-based samples, as compiled and reported in inventories and ecosystem samples. By using remote sensing technologies, we are able to scale up the sample values and supply wall to wall mapping of biomass

Transporting treatment effects from difference-in-differences studies

Difference-in-differences (DID) is a popular approach to identify the causal effects of treatments and policies in the presence of unmeasured confounding. DID identifies the sample average treatment effect in the treated (SATT). However, a goal of such research is often to inform decision-making in target populations outside the treated sample. Transportability methods have been developed to extend inferences from study samples to external target populations; these methods have primarily been developed and applied in settings where identification is based on conditional independence between the treatment and potential outcomes, such as in a randomized trial. This paper develops identification and estimators for effects in a target population, based on DID conducted in a study sample that differs from the target population. We present a range of assumptions under which one may identify causal effects in the target population and employ causal diagrams to illustrate these assumptions. In most realistic settings, results depend critically on the assumption that any unmeasured confounders are not effect measure modifiers on the scale of the effect of interest. We develop several estimators of transported effects, including a doubly robust estimator based on the efficient influence function. Simulation results support theoretical properties of the proposed estimators. We discuss the potential application of our approach to a study of the effects of a US federal smoke-free housing policy, where the original study was conducted in New York City alone and the goal is extend inferences to other US cities

Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence.

Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approaches and illustrate differences with examples from the literature on education and health. Across disciplines, all methods to test a hypothesized causal relationship involve unverifiable assumptions, and rarely is there clear justification for exclusive reliance on one method. Each method entails trade-offs between statistical power, internal validity, measurement quality, and generalizability. The choice between confounder-control and instrument-based methods should be guided by these tradeoffs and consideration of the most important limitations of previous work in the area. Our goals are to foster common understanding of the methods available for causal inference in population health research and the tradeoffs between them; to encourage researchers to objectively evaluate what can be learned from methods outside one's home discipline; and to facilitate the selection of methods that best answer the investigator's scientific questions