Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits.

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012-2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha-1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (-25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.

Development of Company-Specific Emission Factors with Confidence Intervals for Natural Gas Customer Meters in Southern California.

Methane is both a significant and short-lived greenhouse gas compared to CO2, and reducing methane emissions from natural gas distribution systems may offer cost-effective reduction opportunities. We report substantial new direct leak rate measurements from customer meter set assemblies (MSAs) in Southern California. In a novel way, emission factors are defined in terms of aboveground Hazardous and Nonhazardous leak categories, which take direct advantage of readily available industry leak data. We also studied leaks that were not detected as part of normal leak survey procedures. As a result, this yields company-specific emission factors that can be used to track progress in reducing methane emissions. This approach also has the advantage of explicitly accounting for the skewed or fat-tail distribution of leak rates by treating high flow rate MSA leaks separately from low flow rate MSA leaks. The Southern California Gas (SoCalGas) methane emission factors, based on 485 leak rate measurements by direct enclosure, were 4.55 (95% confidence interval: 2.32 to 7.14) kg/day for Hazardous leaks, 0.149 (0.119 to 0.183) kg/day for Nonhazardous leaks, and 0.0039 (0.0003 to 0.0198) kg/day for Non-Detected leaks. The percentage of surveyed meters with nondetected leaks was 29.1% (24.3 to 34.6%). Based on a robust Monte Carlo analysis, total leak emissions from MSAs for the SoCalGas system were reduced by 35% based on data from 2015 to 2022. These reductions were attributed to surveying a larger number of MSAs and accelerated leak repair rates. In traditional population-based emission inventories, an individual emission factor for a given asset category is multiplied by the total population of MSAs within the category. This approach simply cannot capture the reduction in leak numbers and methane emissions resulting from leak mitigation and prevention programs.

Comparing NISAR (Using Sentinel-1), USDA/NASS CDL, and Ground Truth Crop/Non-Crop Areas in an Urban Agricultural Region.

A general limitation in assessing the accuracy of land cover mapping is the availability of ground truth data. At sites where ground truth is not available, potentially inaccurate proxy datasets are used for sub-field-scale resolution investigations at large spatial scales, i.e., in the Contiguous United States. The USDA/NASS Cropland Data Layer (CDL) is a popular agricultural land cover dataset due to its high accuracy (>80%), resolution (30 m), and inclusions of many land cover and crop types. However, because the CDL is derived from satellite imagery and has resulting uncertainties, comparisons to available in situ data are necessary for verifying classification performance. This study compares the cropland mapping accuracies (crop/non-crop) of an optical approach (CDL) and the radar-based crop area (CA) approach used for the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) L- and S-band mission but using Sentinel-1 C-band data. CDL and CA performance are compared to ground truth data that includes 54 agricultural production and research fields located at USDA's Beltsville Agricultural Research Center (BARC) in Maryland, USA. We also evaluate non-crop mapping accuracy using twenty-six built-up and thirteen forest sites at BARC. The results show that the CDL and CA have a good pixel-wise agreement with one another (87%). However, the CA is notably more accurate compared to ground truth data than the CDL. The 2017-2021 mean accuracies for the CDL and CA, respectively, are 77% and 96% for crop, 100% and 94% for built-up, and 100% and 100% for forest, yielding an overall accuracy of 86% for the CDL and 96% for CA. This difference mainly stems from the CDL under-detecting crop cover at BARC, especially in 2017 and 2018. We also note that annual accuracy levels varied less for the CA (91-98%) than for the CDL (79-93%). This study demonstrates that a computationally inexpensive radar-based cropland mapping approach can also give accurate results over complex landscapes with accuracies similar to or better than optical approaches.

Machine learning-based ozone and PM2.5 forecasting: Application to multiple AQS sites in the Pacific Northwest.

Air quality in the Pacific Northwest (PNW) of the U.S has generally been good in recent years, but unhealthy events were observed due to wildfires in summer or wood burning in winter. The current air quality forecasting system, which uses chemical transport models (CTMs), has had difficulty forecasting these unhealthy air quality events in the PNW. We developed a machine learning (ML) based forecasting system, which consists of two components, ML1 (random forecast classifiers and multiple linear regression models) and ML2 (two-phase random forest regression model). Our previous study showed that the ML system provides reliable forecasts of O3 at a single monitoring site in Kennewick, WA. In this paper, we expand the ML forecasting system to predict both O3 in the wildfire season and PM2.5 in wildfire and cold seasons at all available monitoring sites in the PNW during 2017-2020, and evaluate our ML forecasts against the existing operational CTM-based forecasts. For O3, both ML1 and ML2 are used to achieve the best forecasts, which was the case in our previous study: ML2 performs better overall (R2 = 0.79), especially for low-O3 events, while ML1 correctly captures more high-O3 events. Compared to the CTM-based forecast, our O3 ML forecasts reduce the normalized mean bias (NMB) from 7.6 to 2.6% and normalized mean error (NME) from 18 to 12% when evaluating against the observation. For PM2.5, ML2 performs the best and thus is used for the final forecasts. Compared to the CTM-based PM2.5, ML2 clearly improves PM2.5 forecasts for both wildfire season (May to September) and cold season (November to February): ML2 reduces NMB (-27 to 7.9% for wildfire season; 3.4 to 2.2% for cold season) and NME (59 to 41% for wildfires season; 67 to 28% for cold season) significantly and captures more high-PM2.5 events correctly. Our ML air quality forecast system requires fewer computing resources and fewer input datasets, yet it provides more reliable forecasts than (if not, comparable to) the CTM-based forecast. It demonstrates that our ML system is a low-cost, reliable air quality forecasting system that can support regional/local air quality management.

Development of a Machine Learning Approach for Local-Scale Ozone Forecasting: Application to Kennewick, WA.

Chemical transport models (CTMs) are widely used for air quality forecasts, but these models require large computational resources and often suffer from a systematic bias that leads to missed poor air pollution events. For example, a CTM-based operational forecasting system for air quality over the Pacific Northwest, called AIRPACT, uses over 100 processors for several hours to provide 48-h forecasts daily, but struggles to capture unhealthy O3 episodes during the summer and early fall, especially over Kennewick, WA. This research developed machine learning (ML) based O3 forecasts for Kennewick, WA to demonstrate an improved forecast capability. We used the 2017-2020 simulated meteorology and O3 observation data from Kennewick as training datasets. The meteorology datasets are from the Weather Research and Forecasting (WRF) meteorological model forecasts produced daily by the University of Washington. Our ozone forecasting system consists of two ML models, ML1 and ML2, to improve predictability: ML1 uses the random forest (RF) classifier and multiple linear regression (MLR) models, and ML2 uses a two-phase RF regression model with best-fit weighting factors. To avoid overfitting, we evaluate the ML forecasting system with the 10-time, 10-fold, and walk-forward cross-validation analysis. Compared to AIRPACT, ML1 improved forecast skill for high-O3 events and captured 5 out of 10 unhealthy O3 events, while AIRPACT and ML2 missed all the unhealthy events. ML2 showed better forecast skill for less elevated-O3 events. Based on this result, we set up our ML modeling framework to use ML1 for high-O3 events and ML2 for less elevated O3 events. Since May 2019, the ML modeling framework has been used to produce daily 72-h O3 forecasts and has provided forecasts via the web for clean air agency and public use: http://ozonematters.com/. Compared to the testing period, the operational forecasting period has not had unhealthy O3 events. Nevertheless, the ML modeling framework demonstrated a reliable forecasting capability at a selected location with much less computational resources. The ML system uses a single processor for minutes compared to the CTM-based forecasting system using more than 100 processors for hours.

Operational bias correction for PM2.5 using the AIRPACT air quality forecast system in the Pacific Northwest.

A bias correction scheme based on a Kalman filter (KF) method has been developed and implemented for the AIRPACT air quality forecast system which operates daily for the Pacific Northwest. The KF method was used to correct hourly rolling 24-h average PM2.5 concentrations forecast at each monitoring site within the AIRPACT domain and the corrected forecasts were evaluated using observed daily PM2.5 24-h average concentrations from 2017 to 2018. The evaluation showed that the KF method reduced mean daily bias from approximately -50% to ±6% on a monthly averaged basis, and the corrected results also exhibited much smaller mean absolute errors typically less than 20%. These improvements were also apparent for the top 10 worst PM2.5 days during the 2017-2018 test period, including months with intensive wildfire events. Significant differences in AIRPACT performance among urban, suburban, and rural monitoring sites were greatly reduced in the KF bias correction forecasts. The daily 24-h average bias corrections for each monitoring site were interpolated to model grid points using three different interpolation schemes: cubic spline, Gaussian Kriging, and linear Kriging. The interpolated results were more accurate than the original AIRPACT forecasts, and both Kriging methods were better than the cubic spline method. The Gaussian method yielded smaller mean biases and the linear method yielded smaller absolute errors. The KF bias correction method has been implemented operationally using both Kriging interpolation methods for routine output on the AIRPACT website (http://lar.wsu.edu/airpact). This method is relatively easy to implement, but very effective to improve air quality forecast performance.Implications: Current chemical transport models, including CMAQ, used for air quality forecasting can have large errors and uncertainties in simulated PM2.5 concentrations. In this paper, we describe a relatively simple bias correction scheme applied to the AIRPACT air quality forecast system for the Pacific Northwest. The bias correction yields much more accurate and reliable PM2.5 results compared to the normal forecast system. As such, the operational bias corrected forecasts will provide a much better basis for daily air quality management by agencies within the region. The bias corrected results also highlight issues to guide further improvements to the normal forecast system.

Application of an atmospheric tracer ratio method to estimation of PM2.5 emission rates from wheat conveying operations at a wheat pile storage facility.

Particulate matter (PM) pollution is associated with adverse effects on human health and the environment. There is no designated PM2.5 emission factor for horizontal grain conveyors. Instead, in Washington state, the air permitting agency uses an emission factor for headhouse and grain handling operations to issue permits. There is concern that this factor does not accurately represent the conveyor operations and limits the size and operation of wheat pile facilities. The primary goal of this work was to estimate the PM2.5 emission rate (which can further be converted to an emission factor) from wheat conveying operations at a large wheat pile storage facility in eastern Washington using an atmospheric tracer ratio method, with CO2 gas as the tracer. The field study results yield an emission rate of 5.2[Formula: see text]1.7 grams of PM2.5 per hour and these emissions are due to the transfer point from an upper belt to a lower belt. This rate is approximately 320 times lower than the emission rate for headhouse operations which has been used previously to represent conveyor operations. The emission rate was in relatively good agreement with results of an inverse Gaussian plume model calculation of emissions using measured ambient PM2.5 levels at a very short distance downwind of the transfer point. A consistent PM2.5 to tracer gas ratio over the tests showed that PM2.5 and CO2 disperse in a similar manner and confirmed that the CO2 tracer release was a reliable simulation of the PM2.5 pollutant source over distances involved in the study (less than 10 meters). The results also indicate a need for the Environmental Protection Agency to develop a designated PM2.5 emission factor for wheat conveyance. IMPLICATIONS: There are presently no emission factors available for large wheat pile storage facilities where wheat is transferred via long horizontal conveyor belts. As a result, local and state permitting agencies use emission factors for other types of grain handling systems. In this paper, we report the first measurements of PM2.5 emission rates (that can further be converted to emission factors using a known grain rate on the conveyor) for horizontal grain conveyors used at wheat pile storage facilities. The measured emission rate is much less than the emission rate derived from the surrogate emission factor currently used for permit purposes. This has implications for the size and operation of wheat pile storage facilities.

Whole-House Emission Rates and Loss Coefficients of Formaldehyde and Other Volatile Organic Compounds as a Function of the Air Change Rate.

Whole-house emission rates and indoor loss coefficients of formaldehyde and other volatile organic compounds (VOCs) were determined from continuous measurements inside a net-zero energy home at two different air change rates (ACHs). By turning the mechanical ventilation on and off, it was demonstrated that formaldehyde concentrations reach a steady state much more quickly than other VOCs, consistent with a significant indoor loss rate attributed to surface uptake. The first order loss coefficient for formaldehyde was 0.47 ± 0.06 h-1 at 0.08 h-1 ACH and 0.88 ± 0.22 h-1 at 0.62 h-1 ACH. Loss rates for other VOCs measured were not discernible, with the exception of hexanoic acid. A factor of 5.5 increase in the ACH increased the whole-house emission rates of VOCs but by varying degrees (factors of 1.1 to 3.8), with formaldehyde displaying no significant change. The formaldehyde area-specific emission rate (86 ± 8 µg m-2 h-1) was insensitive to changes in the ACH because its large indoor loss rate muted the impact of ventilation on indoor air concentrations. These results demonstrate that formaldehyde loss rates must be taken into account to correctly estimate whole-house emission rates and that ventilation will not be as effective at reducing indoor formaldehyde concentrations as it is for other VOCs.

N2O Emissions From Two Agroecosystems: High Spatial Variability and Long Pulses Observed Using Static Chambers and the Flux-Gradient Technique.

With the addition of nitrogen (N), agricultural soils are the main anthropogenic source of N2O, but high spatial and temporal variabilities make N2O emissions difficult to characterize at the field scale. This study used flux-gradient measurements to continuously monitor N2O emissions at two agricultural fields under different management regimes in the inland Pacific Northwest of Washington State, USA. Automated 16-chamber arrays were also deployed at each site; chamber monitoring results aided the interpretation of the flux gradient results. The cumulative emissions over the six-month (1 April-30 September) monitoring period were 2.4 ± 0.7 and 2.1 ± 2 kg N2O-N/ha at the no-till and conventional till sites, respectively. At both sites, maximum N2O emissions occurred following the first rainfall event after N fertilization, and both sites had monthlong emission pulses. The no-till site had a larger N2O emission factor than the Intergovernmental Panel on Climate Change Tier 1 emission factor of 1% of the N input, while the conventional-till site's emission factor was close to 1% of the N input. However, these emission factors are likely conservative. We estimate that the global warming potential of the N2O emissions at these sites is larger than that of the no-till conversion carbon uptake. We recommend the use of chambers to investigate spatiotemporal controls as a complementary method to micrometeorological monitoring, especially in systems with high variability. Continued monitoring coupled with the use of models is necessary to investigate how changing management and environmental conditions will affect N2O emissions.

Comparison of Near-surface NO2 Pollution with Pandora Total Column NO2 during the Korea-United States Ocean Color (KORUS OC) Campaign.

Near-surface air quality (AQ) observations over coastal waters are scarce, a situation that limits our capacity to monitor pollution events at land-water interfaces. Satellite measurements of total column (TC) nitrogen dioxide (NO2) observations are a useful proxy for combustion sources but the once daily snapshots available from most sensors are insufficient for tracking the diurnal evolution and transport of pollution. Ground-based remote sensors like the Pandora Spectrometer Instrument (PSI) that have been developed to verify space-based total column NO2 and other trace gases are being tested for routine use as certified AQ monitors. The KORUS-OC (Korea-United States Ocean Color) cruise aboard the R/V Onnuri in May-June 2016 represented an opportunity to study AQ near the South Korean coast, a region affected by both local/regional and long-distance pollution sources. Using PSI data in direct-sun mode and in situ sensors for shipboard ozone, CO and NO2, we explore, for the first time, relationships between TC NO2 and surface AQ in this coastal region. Three case studies illustrate the value of the PSI as well as complexities in the surface-column NO2 relationship caused by varying meteorological conditions. Case Study 1 (25-26 May 2016) exhibited a high correlation of surface NO2 to TC NO2 measured by both PSI and Aura's Ozone Monitoring Instrument (OMI) but two other cases displayed poor relationships between in situ and TC NO2 due to decoupling of pollution layers from the surface. With suitable interpretation the PSI TC NO2 measurement demonstrates good potential for working with upcoming geostationary satellites to advance diurnal tracking of pollution.

Vehicle-Based Methane Surveys for Finding Natural Gas Leaks and Estimating Their Size: Validation and Uncertainty.

Managing leaks in urban natural gas (NG) distribution systems is important for reducing methane emissions and costly waste. Mobile surveying technologies have emerged as a new tool for monitoring system integrity, but this new technology has not yet been widely adopted. Here, we establish the efficacy of mobile methane surveys for managing local NG distribution systems by evaluating their ability to detect and locate NG leaks and quantify their emissions. In two cities, three-quarters of leak indications from mobile surveys corresponded to NG leaks, but local distribution companies' field crews did not find most of these leaks, indicating that the national CH4 activity factor for leaks in local NG distribution pipelines is underestimated by a factor of 2.4. We found the median distance between mobile-estimated leak locations and actual leak locations was 19 m. A comparison of emission quantification methods (mobile-based, surface enclosure, and tracer ratio) found that the mobile method overestimated leak magnitude for the smallest leaks but accurately estimated size for the largest leaks that are responsible for the majority of total emissions. Across leak sizes, mobile methods adequately rank relative emission rates for repair prioritization, and they are easily deployed and offer efficient spatial coverage.

Impacts of prescribed fires and benefits from their reduction for air quality, health and visibility in the Pacific Northwest of the United States.

Using a WRF-SMOKE-CMAQ modeling framework, we investigate the impacts of smoke from prescribed fires on model performance, regional and local air quality, health impacts, and visibility in protected natural environments using three different prescribed fire emission scenarios - 100% fire, no fire, and 30% fire. The 30% fire case reflects a 70% reduction in fire activities due to harvesting of logging residues for use as a feedstock for a potential aviation biofuel supply chain. Overall model performance improves for several performance metrics when fire emissions are included, especially for organic carbon, irrespective of the model goals and criteria used. This effect on model performance is more pronounced for the rural and remote IMPROVE sites for organic carbon and total PM2.5. A reduction in prescribed fire emissions (30% fire case) results in significant improvement in air quality in areas in western Oregon, northern Idaho and western Montana where most prescribed fires occur. Prescribed burning contributes to visibility impairment and a relatively large portion of protected class I areas will benefit from a reduced emission scenario. For the haziest 20% days, prescribed burning is an important source of visibility impairment and approximately 50% of IMPROVE sites in the model domain show a significant improvement in visibility for the reduced fire case. Using BenMAP, a health impact assessment tool, we show that several hundred additional deaths, several thousand upper and lower respiratory symptom cases, several hundred bronchitis cases, and more than 35,000 work day losses can be attributed to prescribed fires and these health impacts decrease by 25-30% when a 30% fire emission scenario is considered. Implications This study assesses the potential regional and local air quality, public health and visibility impacts from prescribed burning activities as well as benefits that can be achieved by a potential reduction in emissions for a scenario where biomass is harvested for conversion to biofuel. As prescribed burning activities become more frequent, they can be more detrimental for air quality and health. Forest residue based biofuel industry can be source of cleaner fuel with co-benefits of improved air quality, reduction in health impacts and improved visibility.

Assessment of methane emissions from the U.S. oil and gas supply chain.

Methane emissions from the U.S. oil and natural gas supply chain were estimated by using ground-based, facility-scale measurements and validated with aircraft observations in areas accounting for ~30% of U.S. gas production. When scaled up nationally, our facility-based estimate of 2015 supply chain emissions is 13 ± 2 teragrams per year, equivalent to 2.3% of gross U.S. gas production. This value is ~60% higher than the U.S. Environmental Protection Agency inventory estimate, likely because existing inventory methods miss emissions released during abnormal operating conditions. Methane emissions of this magnitude, per unit of natural gas consumed, produce radiative forcing over a 20-year time horizon comparable to the CO2 from natural gas combustion. Substantial emission reductions are feasible through rapid detection of the root causes of high emissions and deployment of less failure-prone systems.

Air Quality and Health Impacts of an Aviation Biofuel Supply Chain Using Forest Residue in the Northwestern United States.

Forest residue is a major potential feedstock for second-generation biofuel; however, little knowledge exists about the environmental impacts of the development and production of biofuel from such a feedstock. Using a high-resolution regional air quality model, we estimate the air quality impacts of a forest residue based aviation biofuel supply chain scenario in the Pacific Northwestern United States. Using two potential supply chain regions, we find that biomass and biofuel hauling activities will add <1% of vehicle miles traveled to existing traffic, but the biorefineries will add significant local sources of NO x and CO. In the biofuel production scenario, the regional average increase in the pollutant concentration is small, but 8-hr maximum summer time O3 can increase by 1-2 ppb and 24-hr average maximum PM2.5 by 2 µg/m3. The alternate scenario of slash pile burning increased the multiday average PM2.5 by 2-5 µg/m3 during a winter simulation. Using BenMAP, a health impact assessment tool, we show that avoiding slash pile burning results in a decrease in premature mortality as well as several other nonfatal and minor health effects. In general, we show that most air quality and health benefits result primarily from avoided slash pile burning emissions.

A semisynthetic organism engineered for the stable expansion of the genetic alphabet.

All natural organisms store genetic information in a four-letter, two-base-pair genetic alphabet. The expansion of the genetic alphabet with two synthetic unnatural nucleotides that selectively pair to form an unnatural base pair (UBP) would increase the information storage potential of DNA, and semisynthetic organisms (SSOs) that stably harbor this expanded alphabet would thereby have the potential to store and retrieve increased information. Toward this goal, we previously reported that Escherichia coli grown in the presence of the unnatural nucleoside triphosphates dNaMTP and d5SICSTP, and provided with the means to import them via expression of a plasmid-borne nucleoside triphosphate transporter, replicates DNA containing a single dNaM-d5SICS UBP. Although this represented an important proof-of-concept, the nascent SSO grew poorly and, more problematically, required growth under controlled conditions and even then was unable to indefinitely store the unnatural information, which is clearly a prerequisite for true semisynthetic life. Here, to fortify and vivify the nascent SSO, we engineered the transporter, used a more chemically optimized UBP, and harnessed the power of the bacterial immune response by using Cas9 to eliminate DNA that had lost the UBP. The optimized SSO grows robustly, constitutively imports the unnatural triphosphates, and is able to indefinitely retain multiple UBPs in virtually any sequence context. This SSO is thus a form of life that can stably store genetic information using a six-letter, three-base-pair alphabet.

The Indianapolis Flux Experiment (INFLUX): A test-bed for developing urban greenhouse gas emission measurements.

The objective of the Indianapolis Flux Experiment (INFLUX) is to develop, evaluate and improve methods for measuring greenhouse gas (GHG) emissions from cities. INFLUX's scientific objectives are to quantify CO2 and CH4 emission rates at 1 km resolution with a 10% or better accuracy and precision, to determine whole-city emissions with similar skill, and to achieve high (weekly or finer) temporal resolution at both spatial resolutions. The experiment employs atmospheric GHG measurements from both towers and aircraft, atmospheric transport observations and models, and activity-based inventory products to quantify urban GHG emissions. Multiple, independent methods for estimating urban emissions are a central facet of our experimental design. INFLUX was initiated in 2010 and measurements and analyses are ongoing. To date we have quantified urban atmospheric GHG enhancements using aircraft and towers with measurements collected over multiple years, and have estimated whole-city CO2 and CH4 emissions using aircraft and tower GHG measurements, and inventory methods. Significant differences exist across methods; these differences have not yet been resolved; research to reduce uncertainties and reconcile these differences is underway. Sectorally- and spatially-resolved flux estimates, and detection of changes of fluxes over time, are also active research topics. Major challenges include developing methods for distinguishing anthropogenic from biogenic CO2 fluxes, improving our ability to interpret atmospheric GHG measurements close to urban GHG sources and across a broader range of atmospheric stability conditions, and quantifying uncertainties in inventory data products. INFLUX data and tools are intended to serve as an open resource and test bed for future investigations. Well-documented, public archival of data and methods is under development in support of this objective.

Direct and Indirect Measurements and Modeling of Methane Emissions in Indianapolis, Indiana.

This paper describes process-based estimation of CH4 emissions from sources in Indianapolis, IN and compares these with atmospheric inferences of whole city emissions. Emissions from the natural gas distribution system were estimated from measurements at metering and regulating stations and from pipeline leaks. Tracer methods and inverse plume modeling were used to estimate emissions from the major landfill and wastewater treatment plant. These direct source measurements informed the compilation of a methane emission inventory for the city equal to 29 Gg/yr (5% to 95% confidence limits, 15 to 54 Gg/yr). Emission estimates for the whole city based on an aircraft mass balance method and from inverse modeling of CH4 tower observations were 41 ± 12 Gg/yr and 81 ± 11 Gg/yr, respectively. Footprint modeling using 11 days of ethane/methane tower data indicated that landfills, wastewater treatment, wetlands, and other biological sources contribute 48% while natural gas usage and other fossil fuel sources contribute 52% of the city total. With the biogenic CH4 emissions omitted, the top-down estimates are 3.5-6.9 times the nonbiogenic city inventory. Mobile mapping of CH4 concentrations showed low level enhancement of CH4 throughout the city reflecting diffuse natural gas leakage and downstream usage as possible sources for the missing residual in the inventory.

Expert opinion: Can different assessments be used to overcome current candidacy issues?

Since the National Institute of Health and Care Excellence (NICE) review of cochlear implantation in 2009, (NICE, 2009) there have been a number of significant changes to our understanding of the impact of severe-to-profound hearing loss on quality of life and comorbidity with life limiting illness. There have been questions about the validity of current methods of assessing candidacy for cochlear implants. There have also been significant improvements in the effectiveness of implants, the age of successful implantation and a reduction in costs. Additionally, the costs to the health and welfare system of not addressing severe-to-profound hearing loss are often not considered when assessing costs and benefits of this technology and when assessing candidacy criteria. Consideration of these changes since the NICE review suggests the need for an urgent review of the current guidance.

Cost implications for changing candidacy or access to service within a publicly funded healthcare system?

INTRODUCTION: Undue attention in the allocation of healthcare resources can be given to expenditures as opposed to expenditures avoided. This can be particularly apparent when expenditures avoided fall across different budget holders and budgetary pressures are strained. METHODS: The paper presents estimates of the potential savings attributable to the adoption of new hearing assistive technologies in Britain between 1992 and 2014 based on multivariate analyses of survey data. RESULTS: The reduction in service use among the hearing impaired between 1992 and 2014 is estimated to amount to between £53 and £92 million per annum. CONCLUSION: Issues in estimating the impact of widening candidature for cochlear implants on costs exist related to potential savings. This research begins to lay a firmer evidence base for such work as well as identifying some of the challenges.

Perspectives of adults with cochlear implants on current CI services and daily life.

This paper reports on a survey and interviews carried out with adults who have gone through the cochlear implantation pathway. It explores their experiences of current services, the assessment process for implantation, and the impact on their daily lives, including views and experiences on communication, independence and confidence. It also explores, in today's financially challenging climate, their awareness of current funding issues and the value of their implant to them.