Machine learning model for event-based prognostics in gas circulator condition monitoring

Gas circulator (GC) units are an important rotating asset used in the Advanced Gas-cooled Reactor (AGR) design, facilitating the flow of CO2 gas through the reactor core. The ongoing maintenance and examination of these machines is important for operators in order to maintain safe and economic generation. GCs experience a dynamic duty cycle with periods of non-steady state behavior at regular refuelling intervals, posing a unique analysis problem for reliability engineers. In line with the increased data volumes and sophistication of available the technologies, the investigation of predictive and prognostic measurements has become a central interest in rotating asset condition monitoring. However, many of the state-of-the-art approaches finding success deal with the extrapolation of stationary time series feeds, with little to no consideration of more-complex but expected events in the data. In this paper we demonstrate a novel modelling approach for examining refuelling behaviors in GCs, with a focus on estimating their health state from vibration data. A machine learning model was constructed using the operational history of a unit experiencing an eventual inspection-based failure. This new approach to examining GC condition is shown to correspond well with explicit remaining useful life (RUL) measurements of the case study, improving on the existing rudimentary extrapolation methods often employed in rotating machinery health monitoring

Impact of meteorology and emissions on methane trends, 1990–2004

Over the past century, atmospheric methane (CH4) rose dramatically before leveling off in the late 1990s. The processes controlling this trend are poorly understood, limiting confidence in projections of future CH4. The MOZART-2 global tropospheric chemistry model qualitatively captures the observed CH4 trend (increasing in the early 1990s and then leveling off) with constant emissions. From 1991–1995 to 2000–2004, the CH4 lifetime versus tropospheric OH decreases by 1.6%, reflecting increases in OH and temperature. The rise in OH stems from an increase in lightning NOx as parameterized in the model. A simulation including annually varying anthropogenic and wetland CH4 emissions, as well as the changes in meteorology, best reproduces the observed CH4 distribution, trend, and seasonal cycles. Projections of future CH4 abundances should consider climate-driven changes in CH4 sources and sinks

Effect of regional precursor emission controls on long-range ozone transport – Part 1: Short-term changes in ozone air quality

Observations and models demonstrate that ozone and its precursors can be transported between continents and across oceans. We model the influences of 10% reductions in anthropogenic nitrogen oxide (NOx) emissions from each of nine world regions on surface ozone air quality in that region and all other regions. In doing so, we quantify the relative importance of long-range transport between all source-receptor pairs, for direct short-term ozone changes. We find that for population-weighted concentrations during the three-month "ozone-season", the strongest inter-regional influences are from Europe to the Former Soviet Union, East Asia to Southeast Asia, and Europe to Africa. The largest influences per unit of NOx reduced, however, are seen for source regions in the tropics and Southern Hemisphere, which we attribute mainly to greater sensitivity to changes in NOx in the lower troposphere, and secondarily to increased vertical convection to the free troposphere in tropical regions, allowing pollutants to be transported further. Results show, for example, that NOx reductions in North America are ~20% as effective per unit NOx in reducing ozone in Europe during summer, as NOx reductions from Europe itself. Reducing anthropogenic emissions of non-methane volatile organic compounds (NMVOCs) and carbon monoxide (CO) by 10% in selected regions, can have as large an impact on long-range ozone transport as NOx reductions, depending on the source region. We find that for many source-receptor pairs, the season of greatest long-range influence does not coincide with the season when ozone is highest in the receptor region. Reducing NOx emissions in most source regions causes a larger decrease in export of ozone from the source region than in ozone production outside of the source region

Co-benefits of global, domestic, and sectoral greenhouse gas mitigation for US air quality and human health in 2050

Policies to reduce greenhouse gas (GHG) emissions can bring ancillary benefits of improved air quality and reduced premature mortality, in addition to slowing climate change. Here we study the co-benefits of global and domestic GHG mitigation on US air quality and human health in 2050 at fine resolution using dynamical downscaling, and quantify for the first time the co-benefits from foreign GHG mitigation. Relative to a reference scenario, global GHG reductions in RCP4.5 avoid 16000 PM2.5-related all-cause deaths yr-1 (90% confidence interval, 11700-20300), and 8000 (3600-12400) O3-related respiratory deaths yr-1 in the US in 2050. Foreign GHG mitigation avoids 15% and 62% of PM2.5- and O3-related total avoided deaths, highlighting the importance of foreign GHG mitigation on US human health benefits. GHG mitigation in the US residential sector brings the largest co-benefits for PM2.5-related deaths (21% of total domestic co-benefits), and industry for O3 (17%). Monetized benefits, for avoided deaths from ozone, PM2.5, and heat stress from a related study, are $148 ($96-201) per ton CO2 at high valuation and $49 ($32-67) at low valuation, of which 36% are from foreign GHG reductions. These benefits likely exceed the marginal cost of GHG reductions in 2050. The US gains significantly greatermore » co-benefits when coordinating GHG reductions with foreign countries. Similarly, previous studies estimating co-benefits locally or regionally may greatly underestimate the full co-benefits of coordinated global actions.« les

Effect of regional precursor emission controls on long-range ozone transport – Part 2: Steady-state changes in ozone air quality and impacts on human mortality

Large-scale changes in ozone precursor emissions affect ozone directly in the short term, and also affect methane, which in turn causes long-term changes in ozone that affect surface ozone air quality. Here we assess the effects of changes in ozone precursor emissions on the long-term change in surface ozone via methane, as a function of the emission region, by modeling 10% reductions in anthropogenic nitrogen oxide (NOx) emissions from each of nine world regions. Reductions in NOx emissions from all world regions increase methane and long-term surface ozone. While this long-term increase is small compared to the intra-regional short-term ozone decrease, it is comparable to or larger than the short-term inter-continental ozone decrease for some source-receptor pairs. The increase in methane and long-term surface ozone per ton of NOx reduced is greatest in tropical and Southern Hemisphere regions, exceeding that from temperate Northern Hemisphere regions by roughly a factor of ten. We also assess changes in premature ozone-related human mortality associated with regional precursor reductions and long-range transport, showing that for 10% regional NOx reductions, the strongest inter-regional influence is for emissions from Europe affecting mortalities in Africa. Reductions of NOx in North America, Europe, the Former Soviet Union, and Australia are shown to reduce more mortalities outside of the source regions than within. Among world regions, NOx reductions in India cause the greatest number of avoided mortalities per ton, mainly in India itself. Finally, by increasing global methane, NOx reductions in one hemisphere tend to cause long-term increases in ozone concentration and mortalities in the opposite hemisphere. Reducing emissions of methane, and to a lesser extent carbon monoxide and non-methane volatile organic compounds, alongside NOx reductions would avoid this disbenefit

The effect of grid resolution on estimates of the burden of ozone and fine particulate matter on premature mortality in the USA

Assessments of human health impacts associated with outdoor air pollution often use air quality models to represent exposure, but involve uncertainties due to coarse model resolution. Here we quantify how estimates of mortality in the United States attributable to ozone (O3) and fine particulate matter (PM2.5) at coarse resolution differ from those at finer resolution. Using the finest modeled concentrations (12 km), we estimate that 66,000 (95% CI, 39,300 – 84,500) all-cause and 21,400 (5,600 – 34,200) respiratory deaths per year are attributable to PM2.5 and O3 concentrations above low-concentration thresholds, respectively. Using model results at 36 km resolution gives mortality burdens that are 11% higher for PM2.5 and 12% higher for O3 than the 12 km estimates, suggesting a modest positive bias. We also scale modeled concentrations at 12 km to coarser resolutions by simple averaging, and repeat the mortality assessment at multiple resolutions from 24 to 408 km, including the resolutions of global models; in doing so, we account for the effect of resolution on population exposure. Coarse grid resolutions produce mortality estimates that are substantially biased low for PM2.5 (30–40% lower than the 12 km estimate at >250 km resolution), but less than 6% higher for O3 at any resolution. Mortality estimates for primary PM2.5 species show greater bias at coarse resolution than secondary species. These results suggest that coarse resolution global models (>100 km) are likely biased low for PM2.5 health effects. For ozone, biases due to coarse resolution may be much smaller, and the effect on modeled chemistry likely dominates

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters can be used to locate spectral targets by modeling scene background as either structured (geometric) with a set of endmembers (basis vectors) or as unstructured (stochastic) with a covariance or correlation matrix. These matrices are often calculated using all available pixels in a data set. In unstructured background research, various techniques for improving upon scene-wide methods have been developed, each involving either the removal of target signatures from the background model or the segmentation of image data into spatial or spectral subsets. Each of these methods increase the detection signal-to-background ratio (SBR) and the multivariate normality (MVN) of the data from which background statistics are calculated, thus increasing separation between target and non-target species in the detection statistic and ultimately improving thresholded target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This paper provides a review and comparison of methods in target exclusion, spatial subsetting and spectral pre-clustering, and introduces a new technique which combines these methods. The analysis provides insight into the merit of employing unstructured background characterization techniques, as well as limitations for their practical application

Co-benefits of global and regional greenhouse gas mitigation for US air quality in 2050

Policies to mitigate greenhouse gas (GHG) emissions will not only slow climate change but can also have ancillary benefits of improved air quality. Here we examine the co-benefits of both global and regional GHG mitigation for US air quality in 2050 at fine resolution, using dynamical downscaling methods, building on a previous global co-benefits study (West et al., 2013). The co-benefits for US air quality are quantified via two mechanisms: through reductions in co-emitted air pollutants from the same sources and by slowing climate change and its influence on air quality, following West et al. (2013). Additionally, we separate the total co-benefits into contributions from domestic GHG mitigation vs. mitigation in foreign countries. We use the Weather Research and Forecasting (WRF) model to dynamically downscale future global climate to the regional scale and the Sparse Matrix Operator Kernel Emissions (SMOKE) program to directly process global anthropogenic emissions to the regional domain, and we provide dynamical boundary conditions from global simulations to the regional Community Multi-scale Air Quality (CMAQ) model. The total co-benefits of global GHG mitigation from the RCP4.5 scenario compared with its reference are estimated to be higher in the eastern US (ranging from 0.6 to 1.0 µg m−3) than the west (0–0.4 µg m−3) for fine particulate matter (PM2.5), with an average of 0.47 µg m−3 over the US; for O3, the total co-benefits are more uniform at 2–5 ppb, with a US average of 3.55 ppb. Comparing the two mechanisms of co-benefits, we find that reductions in co-emitted air pollutants have a much greater influence on both PM2.5 (96 % of the total co-benefits) and O3 (89 % of the total) than the second co-benefits mechanism via slowing climate change, consistent with West et al. (2013). GHG mitigation from foreign countries contributes more to the US O3 reduction (76 % of the total) than that from domestic GHG mitigation only (24 %), highlighting the importance of global methane reductions and the intercontinental transport of air pollutants. For PM2.5, the benefits of domestic GHG control are greater (74 % of total). Since foreign contributions to co-benefits can be substantial, with foreign O3 benefits much larger than those from domestic reductions, previous studies that focus on local or regional co-benefits may greatly underestimate the total co-benefits of global GHG reductions. We conclude that the US can gain significantly greater domestic air quality co-benefits by engaging with other nations to control GHGs.</html

Spatial and temporal patterns of gastrointestinal illness and their relationship with precipitation across the state of North Carolina

INTRODUCTION: The quality of drinking water quality in the US is among the best in the world; however, pathogens are present in source waters that are used for drinking water. Water and floodwaters specifically can spread pathogens within watersheds by mobilizing pathogens and transporting them. Previous research has identified an association between gastrointestinal (GI) illness and heavy precipitation (Currieroet al., 2001 and Figure 1). OVERALL STUDY OBJECTIVE: This study analyzes patterns of GI illness and their relationship with various demographic variables and precipitation across the state of North Carolina from 2008-2012