ASU Scholarship Showcase

A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations.

We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO₂ and CH₄ fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data.

Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO₂ proxy measurements such as radiocarbon in CO₂ and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

Errors in the specification or utilization of fossil fuel CO₂ emissions within carbon budget or atmospheric CO₂ inverse studies can alias the estimation of biospheric and oceanic carbon exchange. A key component in the simulation of CO₂ concentrations arising from fossil fuel emissions is the spatial distribution of the emission near coastlines. Regridding of fossil fuel CO₂ emissions (FFCO₂) from fine to coarse grids to enable atmospheric transport simulations can give rise to mismatches between the emissions and simulated atmospheric dynamics which differ over land or water. For example, emissions originally emanating from the land are emitted from a grid cell for which the vertical mixing reflects the roughness and/or surface energy exchange of an ocean surface. We test this potential "dynamical inconsistency" by examining simulated global atmospheric CO₂ concentration driven by two different approaches to regridding fossil fuel CO₂ emissions. The two approaches are as follows: (1) a commonly used method that allocates emissions to grid cells with no attempt to ensure dynamical consistency with atmospheric transport and (2) an improved method that reallocates emissions to grid cells to ensure dynamically consistent results. Results show large spatial and temporal differences in the simulated CO₂ concentration when comparing these two approaches. The emissions difference ranges from −30.3 TgC grid cell^-1 yr^-1 (−3.39 kgC m^-2 yr^-1) to +30.0 TgC grid cell^-1 yr^-1 (+2.6 kgC m^-2 yr^-1) along coastal margins. Maximum simulated annual mean CO₂ concentration differences at the surface exceed ±6 ppm at various locations and times. Examination of the current CO₂ monitoring locations during the local afternoon, consistent with inversion modeling system sampling and measurement protocols, finds maximum hourly differences at 38 stations exceed ±0.10 ppm with individual station differences exceeding −32 ppm. The differences implied by not accounting for this dynamical consistency problem are largest at monitoring sites proximal to large coastal urban areas and point sources. These results suggest that studies comparing simulated to observed atmospheric CO₂ concentration, such as atmospheric CO₂ inversions, must take measures to correct for this potential problem and ensure flux and dynamical consistency.

High-resolution, global quantification of fossil fuel CO[subscript 2] emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high-resolution fossil fuel CO[subscript 2] emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long-term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long-term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter-term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO[subscript 2] emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO[subscript 2] emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set.

Objective: To estimate the absolute wealth of households using data from demographic and health surveys.

Methods: We developed a new metric, the absolute wealth estimate, based on the rank of each surveyed household according to its material assets and the assumed shape of the distribution of wealth among surveyed households. Using data from 156 demographic and health surveys in 66 countries, we calculated absolute wealth estimates for households. We validated the method by comparing the proportion of households defined as poor using our estimates with published World Bank poverty headcounts. We also compared the accuracy of absolute versus relative wealth estimates for the prediction of anthropometric measures.

Findings: The median absolute wealth estimates of 1 403 186 households were 2056 international dollars per capita (interquartile range: 723-6103). The proportion of poor households based on absolute wealth estimates were strongly correlated with World Bank estimates of populations living on less than 2.00 United States dollars per capita per day (R-2=0.84). Absolute wealth estimates were better predictors of anthropometric measures than relative wealth indexes.

Conclusion: Absolute wealth estimates provide new opportunities for comparative research to assess the effects of economic resources on health and human capital, as well as the long-term health consequences of economic change and inequality.

Background: Most excess deaths that occur during extreme hot weather events do not have natural heat recorded as an underlying or contributing cause. This study aims to identify the specific individuals who died because of hot weather using only secondary data. A novel approach was developed in which the expected number of deaths was repeatedly sampled from all deaths that occurred during a hot weather event, and compared with deaths during a control period. The deaths were compared with respect to five factors known to be associated with hot weather mortality. Individuals were ranked by their presence in significant models over 100 trials of 10,000 repetitions. Those with the highest rankings were identified as probable excess deaths. Sensitivity analyses were performed on a range of model combinations. These methods were applied to a 2009 hot weather event in greater Vancouver, Canada.

Results: The excess deaths identified were sensitive to differences in model combinations, particularly between univariate and multivariate approaches. One multivariate and one univariate combination were chosen as the best models for further analyses. The individuals identified by multiple combinations suggest that marginalized populations in greater Vancouver are at higher risk of death during hot weather.

Conclusions: This study proposes novel methods for classifying specific deaths as expected or excess during a hot weather event. Further work is needed to evaluate performance of the methods in simulation studies and against clinically identified cases. If confirmed, these methods could be applied to a wide range of populations and events of interest.

Background: Prior studies have shown that using uterotonics to augment or induce labor before arrival at comprehensive Emergency Obstetric and Neonatal Care (CEmONC) settings (henceforth, “outside uterotonics”) may contribute to perinatal mortality in low- and middle-income countries. We estimate its effect on perinatal mortality in rural Bangladesh.

Methods: Using hospital records (23986 singleton term births, Jan 1, 2009-Dec 31, 2015) from rural Bangladesh, we use a logistic regression model to estimate the increased risk of perinatal death from uterotonics administered outside a CEmONC facility.

Results: Among term births (≥37 weeks gestation), the risk of perinatal death adjusted for key confounders is significantly increased among women reporting uterotonic use outside of CEmONC (OR = 3 · 0, 95 % CI = 2 · 4,3 · 7). This increased risk is particularly high for fresh stillbirths (OR = 4 · 0, 95 % CI = 3 · 0,5 · 3) and intrapartum-related causes of early neonatal deaths (birth asphyxia) (OR = 3 · 1, 95 % CI = 2 · 2,4 · 5).

Conclusions: In this sample, outside uterotonic use was associated with substantially increased risk of fresh stillbirths, deaths due to birth asphyxia, and all perinatal deaths. In settings of high uterotonic use outside of controlled settings, substantial improvement in both stillbirth and early neonatal mortality may be made by reducing such use.

Background: Improving perinatal health is the key to achieving the Millennium Development Goal for child survival. Recently, several reviews suggest that scaling up available effective perinatal interventions in an integrated approach can substantially reduce the stillbirth and neonatal death rates worldwide. We evaluated the effect of packaged interventions given in pregnancy, delivery and post-partum periods through integration of community- and facility-based services on perinatal mortality.

Methods: This study took advantage of an ongoing health and demographic surveillance system (HDSS) and a new Maternal, Neonatal and Child Health (MNCH) Project initiated in 2007 in Matlab, Bangladesh in half (intervention area) of the HDSS area. In the other half, women received usual care through the government health system (comparison area). The MNCH Project strengthened ongoing maternal and child health services as well as added new services. The intervention followed a continuum of care model for pregnancy, intrapartum, and post-natal periods by improving established links between community- and facility-based services. With a separate pre-post samples design, we compared the perinatal mortality rates between two periods--before (2005-2006) and after (2008-2009) implementation of MNCH interventions. We also evaluated the difference-of-differences in perinatal mortality between intervention and comparison areas.

Results: Antenatal coverage, facility delivery and cesarean section rates were significantly higher in the post- intervention period in comparison with the period before intervention. In the intervention area, the odds of perinatal mortality decreased by 36% between the pre-intervention and post-intervention periods (odds ratio: 0.64; 95% confidence intervals: 0.52-0.78). The reduction in the intervention area was also significant relative to the reduction in the comparison area (OR 0.73, 95% CI: 0.56-0.95; P = 0.018).

Conclusion: The continuum of care approach provided through the integration of service delivery modes decreased the perinatal mortality rate within a short period of time. Further testing of this model is warranted within the government health system in Bangladesh and other low-income countries.

Atmospheric radiocarbon (¹⁴C) represents an important observational constraint on emissions of fossil-fuel derived carbon into the atmosphere due to the absence of ¹⁴C in fossil fuel reservoirs. The high sensitivity and precision that accelerator mass spectrometry (AMS) affords in atmospheric ¹⁴C analysis has greatly increased the potential for using such measurements to evaluate bottom-up emissions inventories of fossil fuel CO₂(CO₂ff), as well as those for other co-emitted species. Here we use observations of ¹⁴CO₂ and a series of primary hydrocarbons and combustion tracers from discrete air samples collected between June 2009 and September 2010 at the National Oceanic and Atmospheric Administration Boulder Atmospheric Observatory (BAO; Lat: 40.050° N, Lon: 105.004° W) to derive emission ratios of each species with respect to CO₂ff. The BAO tower is situated at the boundary of the Denver metropolitan area to the south and a large industrial and agricultural region to the north and east, making it an ideal location to study the contrasting mix of emissions from the activities in each region. The species considered in this analysis are carbon monoxide (CO), methane (CH₄), acetylene (C₂H₂), benzene (C₆H₆), and C₃–C₅ alkanes. We estimate emissions for a subset of these species by using the Vulcan high resolution CO2ff emission data product as a reference. We find that CO is overestimated in the 2008 National Emissions Inventory (NEI08) by a factor of ~2. A close evaluation of the inventory suggests that the ratio of CO emitted per unit fuel burned from on-road gasoline vehicles is likely over-estimated by a factor of 2.5. Using a wind-directional analysis of the data, we find enhanced concentrations of CH₄, relative to CO₂ff, in air influenced by emissions to the north and east of the BAO tower when compared to air influenced by emissions in the Denver metro region to the south. Along with enhanced CH₄, the strongest enhancements of the C₃–C₅ alkanes are also found in the north and east wind sector, suggesting that both the alkane and CH₄ enhancements are sourced from oil and gas fields located to the northeast, though it was not possible to rule out the contribution of non oil and gas CH₄ sources.

The Arctic, even more so than other parts of the world, has warmed substantially over the past few decades. Temperature and humidity influence the rate of development, survival and reproduction of pathogens and thus the incidence and prevalence of many infectious diseases. Higher temperatures may also allow infected host species to survive winters in larger numbers, increase the population size and expand their habitat range. The impact of these changes on human disease in the Arctic has not been fully evaluated. There is concern that climate change may shift the geographic and temporal distribution of a range of infectious diseases. Many infectious diseases are climate sensitive, where their emergence in a region is dependent on climate-related ecological changes. Most are zoonotic diseases, and can be spread between humans and animals by arthropod vectors, water, soil, wild or domestic animals. Potentially climate-sensitive zoonotic pathogens of circumpolar concern include Brucella spp., Toxoplasma gondii, Trichinella spp., Clostridium botulinum, Francisella tularensis, Borrelia burgdorferi, Bacillus anthracis, Echinococcus spp., Leptospira spp., Giardia spp., Cryptosporida spp., Coxiella burnetti, rabies virus, West Nile virus, Hantaviruses, and tick-borne encephalitis viruses.