Uncertainties In Emission Estimates Research Articles

Towards tree-based systems disturbance monitoring of tropical mosaic landscape using a time series ensemble learning approach

Tree clearing and degradation inside and outside forest ecosystems in Africa are important contributors to global carbon budget and emissions. Part of the uncertainties in emission estimates, among other things, is related to the non-inclusion of disturbances across all tree-based systems and limitations in the capacity of existing methodologies to detect subtle changes or degradation. Here, we present a tree-based system (TBS) disturbance monitoring approach in a mosaic landscape in East Africa. For this purpose, high- and low-intensity disturbance detection time series algorithms, namely Breaks for Additive Season and Trend monitor (BFASTmonitor), Continuous Degradation Detection (CODED), and Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr), were used. These algorithms were tested in three approaches based on reference canopy cover change data from repeated airborne laser scanning. Data processing was performed using Collection 2 Landsat (5, 7, and 8) time series in Google Earth Engine. The approaches were based on testing the accuracy of 1) the algorithms' magnitude of change outputs using multispectral information (i.e., several vegetation indices and bands); 2) multispectral ensemble learning approach applied at each of the three algorithms; and 3) combining the multiple algorithms in an ensemble learning approach. Our results showed an improvement in the accuracy of detecting a TBS disturbance from approach 1 to 3. The best result (44% omission error and 31% commission error against reference airborne laser scanning data) was obtained when BFASTmonitor and CODED were combined in an ensemble learning approach. The shortwave infrared bands were more important for detecting TBS disturbance than green, red, and near-infrared bands. The application of the best model in TBS disturbance monitoring in the region revealed that disturbances were more dominant outside forest ecosystems than inside. Improved disturbance monitoring in TBS can contribute to more accurate emission estimates in Africa and elsewhere in the tropics.

Estimating the CO2 emissions of Chinese cities from 2011 to 2020 based on SPNN-GNNWR

Global warming is a serious threat to human survival and health. Facing increasing global warming, the issue of CO2 emissions has attracted more attention. China is a major contributor of anthropogenic CO2 emissions and so it is essential to accurately estimate China's CO2 emissions and analyze their changing characteristics. This study recalculates CO2 emissions from Chinese cities from 2011 to 2020 using the SPNN-GNNWR model and multiple factors to reduce the uncertainty in emission estimates. The SPNN-GNNWR model has excellent predictions (R2: 0.925, 10-fold CV R2: 0.822) when cross-validation is used. The results indicate that the total CO2 emissions in China calculated by the model are close to those accounted for by other authorities in the world, with the total CO2 emissions increasing from 9.122 billion tonnes in 2011 to 9.912 billion tonnes in 2020. The city with the largest increase in CO2 emissions is Tianjin, and the city with the largest decrease is Beijing. The study also reveals the regional differences in CO2 emissions in Chinese mainland, including emissions, emission intensity and per capita emissions. Capturing and understanding the emissions and the related socioeconomic characteristics of different cities can help to develop effective emission mitigation strategies.

Impact of the High-Emission Event Duration and Sampling Frequency on the Uncertainty in Emission Estimates

Short duration remote sensing measurements of methane emissions from oil and gas operations are being deployed at a large scale, but interpretation of these snapshot measurements is complex due to the spatial and temporal variability of methane emissions. The accuracy and precision of annual emission estimates extrapolated from short duration measurements depend on the measurement frequency and complexity of temporal emission patterns. This work examines sampling uncertainties associated with short duration measurements of varying frequencies for methane emissions from a group of sites representing the Barnett Shale region. Routine, frequent emissions are accurately captured with minimal bias through semiannual sampling; however, infrequent high-emission rate events increase the error associated with annual emission estimates, even under the assumption of no measurement uncertainty. If emission events have a duration of ≤1 week, monthly sampling has an estimated sampling error of >15%. For quarterly sampling with emission events that persist for ≤1 month, the sampling error is >30%. There is also negative bias associated with quarterly, semiannual, and annual sampling, which suggests infrequent campaigns may be systemically underestimating emissions. The sampling error increases as the duration of the high-emission events becomes shorter, making the temporal persistence of emission events an important factor in designing measurement protocols.

A Modeling Approach for Addressing Sensitivity and Uncertainty of Estuarine Greenhouse Gas (CO2 and CH4) Dynamics

AbstractEstuaries make an important contribution to the global greenhouse gas budget. Yet modeling predictions of carbon dioxide (CO2) and methane (CH4) emissions from estuaries remain highly uncertain due to both simplified assumptions about the underpinning hydrologic and biologic processes and inadequate data availability to uniquely define parameters related to CO2 and CH4 processes. This study presents a modeling framework to quantify the sensitivity and uncertainty of predicted CO2 and CH4 concentrations and emissions, which is demonstrated through application to a subtropical urban estuary (Brisbane River, Australia). A 3D hydrodynamic‐biogeochemical model was constructed, and calibrated using the model‐independent Parameter ESTimation software (PEST) with field data sets that captured strong gradients of CO2 and CH4 concentrations and emissions along the estuary. The approach refined uncertainty in the estimation of whole‐estuary annual emissions, and enabled us to assess the sensitivity and uncertainty of CO2 and CH4 dynamics. Estuarine CO2 concentrations were most sensitive to uncertainty in riverine inputs, whereas estuarine CH4 concentrations were most sensitive to sediment production and pelagic oxidation. Over the modeled year, variance in the daily fluctuations in carbon emissions from this case‐study spanned the full range of emission rates reported for estuaries around the world, highlighting that spatially or temporally limited sampling regimes could significantly bias estuarine greenhouse gas emission estimates. The combination of targeted field campaigns with the modeling approach presented in this study can help to improve carbon budgeting in estuaries, reduce uncertainty in emission estimates, and support management strategies to reduce or offset estuary greenhouse gas emissions.

How can process-based modeling improve peat CO2 and N2O emission factors for oil palm plantations?

Oil palm plantations on peat and associated drainage generate sizeable GHG emissions. Current IPCC default emission factors (EF) for oil palm on organic soil are based on a very limited number of observations from young plantations, thereby resulting in large uncertainties in emissions estimates. To explore the potential of process-based modeling to refine oil palm peat CO2 and N2O EFs, we simulated peat GHG emissions and biogeophysical variables over 30 years in plantations of Central Kalimantan, Indonesia. The DNDC model simulated well the magnitude of C inputs (litterfall and root mortality) and dynamics of annual heterotrophic respiration and peat decomposition N2O fluxes. The modeled peat onsite CO2-C EF was lower than the IPCC default (11 Mg C ha−1 yr−1) and decreased from 7.7 ± 0.4 Mg C ha−1 yr−1 in the first decade to 3.0 ± 0.2 and 1.8 ± 0.3 Mg C ha−1 yr−1 in the second and third decades of the rotation. The modeled N2O-N EF from peat decomposition was higher than the IPCC default (1.2 kg N ha−1 yr−1) and increased from 3.5 ± 0.3 kg N ha−1 yr−1 in the first decade to 4.7–4.6 ± 0.5 kg N ha−1 yr−1 in the following ones. Modeled fertilizer-induced N2O emissions were minimal and much less than 1.6% of N inputs recommended by the IPCC in wet climates regardless of soil type. Temporal variations in EFs were strongly linked to soil C:N ratio and soil mineral N content for CO2 and fertilizer-induced N2O emissions, and to precipitation, water table level and soil NH4+ content for peat decomposition N2O emissions. These results suggest that current IPCC EFs for oil palm on organic soil could over-estimate peat onsite CO2 emissions and underestimate peat decomposition N2O emissions and that temporal variation in emissions should be considered for further improvement of EFs.

Modeling air emissions from complex facilities at detailed temporal and spatial resolution: The Methane Emission Estimation Tool (MEET)

Recent attention to methane emissions from oil and gas infrastructure has increased interest in comparing measurements with inventory emission estimates. While measurement methods typically estimate emissions over a few periods that are seconds to hours in length, current inventory methods typically produce long-term average emission estimates. This temporal mis-alignment complicates comparisons and leads to underestimates in the uncertainty of measurement methods. This study describes a new temporally and spatially resolved inventory emission model (MEET), and demonstrates the model by application to compressor station emissions – the key facility type in midstream natural gas operations The study looks at three common facility measurement methods: tracer flux methods for measuring station emissions, the use of ethane-methane ratios for source attribution of basin-scale estimates, and the behavior of continuous monitoring for leak detection at stations. Simulation results indicate that measurement methods likely underestimate uncertainties in emission estimates by failing to account for the variability in normal facility emissions and variations in ethane/methane ratios. A tracer-based measurement campaign could estimate emissions outside the 95% confidence interval of annual emissions 30% of the time, while ethane/methane ratios could be mis-estimated by as much as 50%. Use of MEET also highlights the need to improve data reporting from measurement campaigns to better capture the temporal and spatial variation in observed emissions.

MIROC4を基にした化学輸送モデルと逆解法とを用いた大気中一酸化二窒素の動態解析

Atmospheric nitrous oxide (N2O) contributes to global warming and stratospheric ozone depletion, so reducing uncertainty in estimates of emissions from different sources is important for climate policy. In this study, we simulate atmospheric N2O using an atmospheric chemistry-transport model (ACTM), and the results are first compared with the in situ measurements. Five combinations of known (a priori) N2O emissions due to natural soil, agricultural land, other human activities, and sea–air exchange are used. The N2O lifetime is 127.6 ± 4.0 yr in the control ACTM simulation (range indicates interannual variability). Regional N2O emissions are optimized using Bayesian inverse modeling for 84 partitions of the globe at monthly intervals, using measurements at 42 sites around the world covering 1997–2019. The best estimated global land and ocean emissions are 12.99 ± 0.22 TgN yr−1 and 2.74 ± 0.27 TgN yr−1, respectively, for 2000–2009, and 14.30 ± 0.20 TgN yr−1 and 2.91 ± 0.27 TgN yr−1, respectively, for 2010–2019. On regional scales, we find that the most recent ocean emission estimation, with lower emissions in the Southern Ocean regions, fits better with that predicted by the inversions. Marginally higher (lower) emissions than the inventory/model for the tropical (extratropical) land regions are estimated and validated using independent aircraft observations. Global land and ocean emission variabilities show a statistically significant correlation with El Niño Southern Oscillation (ENSO). Analysis of regional land emissions shows increases over America (Temperate North, Central, and Tropical), Central Africa, and Asia (South, East, and Southeast) between the 2000s and 2010s. Only Europe as a whole recorded a slight decrease in N2O emissions due to the chemical industry. Our inversions suggest revisions to seasonal emission variations for three of the 15 land regions (East Asia, Temperate North America, and Central Africa), and the Southern Ocean region. The terrestrial ecosystem model (Vegetation Integrative SImulator for Trace Gases) can simulate annual total emissions in agreement with the observed N2O growth rate since 1978, but the lag-time scales of N2O emissions from nitrogen fertilizer application may need to be revised.

Real-time optimal spatiotemporal sensor placement for monitoring air pollutants

Air pollution exposure assessment involves monitoring of pollutant species concentrations in the atmosphere along with their health impact assessment on the population. Often air pollutants are monitored via stationary monitoring stations. Due to the cost of sensors and land for the installation of the sensors within an urban area as well as maintenance of a monitoring network, sensors can only be installed at a limited number of locations. The sparse spatial coverage of immobile monitors can lead to errors in estimating the actual exposure of pollutants. One approach to address these limitations is dynamic sensing, a new monitoring technique that adjusts the locations of portable sensors in real time to measure the dynamic changes in air quality. The key challenge in dynamic sensing is to develop algorithms to identify the optimal sensor locations in real time in the face of inherent uncertainties in emissions estimates and the fate and transport of air pollutants. In this paper, we present an algorithmic framework to address the challenge of sensor placement in real time, given those uncertainties. Uncertainty in the system includes location and amount of pollutants as well as meteorology leading to a stochastic optimization problem. We use the novel better optimization of nonlinear uncertain systems (BONUS) algorithm to solve these problems. Fisher information (FI) is used as the objective of the optimization. We demonstrate the capability of our novel algorithm using a case study in Atlanta, Georgia. Our real-time sensor placement algorithm allows, for the first time, determination of the optimal location of sensors under the spatial–temporal variability of pollutants, which cannot be accomplished by a stationary monitoring station. We present the dynamic locations of sensors for observing concentrations of pollutants as well as for observing the impacts of these pollutants on populations.

A Remote Sensing Technique to Upscale Methane Emission Flux in a Subtropical Peatland

AbstractQuantification of methane (CH4) gas emission from peat is critical to understand CH4 budget from natural wetlands under a climate warming scenario. Previous studies have focused on prediction and mapping of CH4 emission flux using process‐based models, while application of statistical‐empirical models for upscaling spatially sparse in situ measurements is scarce. In this study, we developed an empirical remote sensing upscaling approach to estimate CH4 emission flux in the Everglades using limited in situ point‐based CH4 emission flux measurements and Landsat data during 2013–2018. We spatially and temporally linked in situ data with Landsat surface reflectance based on temporally composite data sets and developed an object‐based machine learning framework to model and map CH4 emission flux. An ensemble analysis of two machine learning models, k‐Nearest Neighbor (k‐NN) and Support Vector Machine (SVM), shows that the upscaling approach is promising for predicting CH4 emission flux with a R2 of 0.65 and 0.87 based on a fivefold cross‐validation for a dry season and wet season estimation, respectively. We generated emission flux map products that successfully revealed the spatial and temporal heterogeneity of CH4 emission within the dominant freshwater marsh ecosystem in the Everglades. We conclude that Landsat is promising for upscaling and monitoring CH4 emission flux and reducing the uncertainty in emission estimates from wetlands.

Characterization and emission factors of carbonaceous aerosols originating from coke production in China.

Coking is a substantial source of carbonaceous aerosols in China, but the emission characteristics and pollution levels of coking-produced organic carbon (OC) and elemental carbon (EC) remain unknown, causing considerable uncertainty in emission estimates. In this study, the emission factors of OC (EFOC) and EC (EFEC) of typical coking plants in Shanxi, China, were measured. The measured EFEC and EFOC from fugitive emissions (7.43 and 9.54g/t) were significantly higher than those from flue gas (1.67 and 3.71g/t). The technological conditions of coke production affect the emissions of OC and EC. For example, the total emissions from coke plants that use 3.2-m-high coke ovens were greater than those from plants that use 4.3- and 6-m-high ovens. The EFOC and EFEC for plants conducting stamp charging were considerably higher than those for plants using top charging. The stable carbon isotopes of total carbon (δ13CTC), OC (δ13COC), and EC (δ13CEC) for fly ash during coking were-23.74‰ to-24.17‰,-23.32‰ to-23.87‰, and-23.84‰ to-24.14‰, respectively, and no clear isotopic fractionation was found during coke production. Different EC/OC ratios from different emission pathways and the carbon isotope signature of coke production should be considered when investigating the sources of carbonaceous aerosols. The total estimated EC and OC emissions from coke production in China were 3.93 and 5.72Gg in 2017, and Shanxi, Hebei, and Shaanxi made the largest contributions.

Quantifying greenhouse gas emissions

The assessment of greenhouse gases (GHGs) and air pollutants emitted to and removed from the atmosphere ranks high on international political and scientific agendas. Growing international concern and cooperation regarding the climate change problem have increased the need to consider the uncertainty in inventories of GHG emissions. The approaches to address uncertainty discussed in this special issue reflect attempts to improve national inventories, not only for their own sake but also from a wider, system analytic perspective. They seek to strengthen the usefulness of national emission inventories under a compliance and/or global monitoring and reporting framework. The papers in this special issue demonstrate the benefits of including inventory uncertainty in policy analyses. The issues raised by the authors and featured in their papers, along with the role that uncertainty analysis plays in many of their arguments, highlight the challenges and the importance of dealing with uncertainty. While the Intergovernmental Panel on Climate Change (IPCC) clearly stresses the value of conducting uncertainty analyses and offers guidance on executing them, the arguments made here in favor of performing these studies go well beyond any suggestions made by the IPCC to date. Improving and conducting uncertainty analyses are needed to develop a clear understanding and informed policy. Uncertainty matters and is key to many issues related to inventorying and reducing emissions. Considering uncertainty helps to avoid situations that can create a false sense of certainty or lead to invalid views of subsystems. Dealing proactively with uncertainty allows for the generation of useful knowledge that the international community should have to hand while strengthening the 2015 Paris Agreement, which had been agreed at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC). However, considering uncertainty does not come free. Proper treatment of uncertainty is demanding because it forces us to take the step from “simple to complex” and to grasp a holistic system view. Only, thereafter, can we consider potential simplifications. That is, comprehensive treatment of uncertainty does not necessarily offer quick or easy solutions for policymakers. This special issue brings together 13 papers that resulted from the 2015 (4th) International Workshop on Uncertainty in Atmospheric Emissions, in Cracow, Poland. While they deal with many different aspects of the uncertainty in emission estimates, they are guided by the same principal question: “What GHGs shall be verified at what spatio-temporal scale to support conducive legislation at local and national scales, while ensuring effective governance at the global scale?” This question is at the heart of mitigation and adaptation. It requires an understanding of the entire system of GHG sources and sinks, their spatial characteristics and the temporal scales at which they react and interact, the uncertainty (accuracy and/or precision) with which fluxes can be measured, and last but not least, the consequences that follow from all of the aforementioned aspects, for policy actors to frame compliance and/or global monitoring and reporting agreements. This bigger system context serves as a reference for the papers in the special issue, irrespective of their spatio-temporal focus, and is used as a guide for the reader.

Satellite-derived emissions of carbon monoxide, ammonia, and nitrogen dioxide from the 2016 Horse River wildfire in the Fort McMurray area

Abstract. In May 2016, the Horse River wildfire led to the evacuation of ∼ 88 000 people from Fort McMurray and surrounding areas and consumed ∼ 590 000 ha of land in Northern Alberta and Saskatchewan. Within the plume, satellite instruments measured elevated values of CO, NH3, and NO2. CO was measured by two Infrared Atmospheric Sounding Interferometers (IASI-A and IASI-B), NH3 by IASI-A, IASI-B, and the Cross-track Infrared Sounder (CrIS), and NO2 by the Ozone Monitoring Instrument (OMI). Daily emission rates were calculated from the satellite measurements using fire hotspot information from the Moderate Resolution Imaging Spectroradiometer (MODIS) and wind information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, combined with assumptions on lifetimes and the altitude range of the plume. Sensitivity tests were performed and it was found that uncertainties of emission estimates are more sensitive to the plume shape for CO and to the lifetime for NH3 and NOx. The satellite-derived emission rates were ∼ 50–300 kt d−1 for CO, ∼ 1–7 kt d−1 for NH3, and ∼ 0.5–2 kt d−1 for NOx (expressed as NO) during the most active fire periods. The daily satellite-derived emission estimates were found to correlate fairly well (R∼0.4–0.7) with daily output from the ECMWF Global Fire Assimilation System (GFAS) and the Environment and Climate Change Canada (ECCC) FireWork models, with agreement within a factor of 2 for most comparisons. Emission ratios of NH3∕CO, NOx∕CO, and NOx∕NH3 were calculated and compared against enhancement ratios of surface concentrations measured at permanent surface air monitoring stations and by the Alberta Environment and Parks Mobile Air Monitoring Laboratory (MAML). For NH3∕CO, the satellite emission ratios of ∼ 0.02 are within a factor of 2 of the model emission ratios and surface enhancement ratios. For NOx∕CO satellite-measured emission ratios of ∼0.01 are lower than the modelled emission ratios of 0.033 for GFAS and 0.014 for FireWork, but are larger than the surface enhancement ratios of ∼0.003, which may have been affected by the short lifetime of NOx. Total emissions from the Horse River fire for May 2016 were calculated and compared against total annual anthropogenic emissions for the province of Alberta in 2016 from the ECCC Air Pollutant Emissions Inventory (APEI). Satellite-measured emissions of CO are ∼1500 kt for the Horse River fire and exceed the total annual Alberta anthropogenic CO emissions of 992.6 kt for 2016. The satellite-measured emissions during the Horse River fire of ∼30 kt of NH3 and ∼7 kt of NOx (expressed as NO) are approximately 20 % and 1 % of the magnitude of total annual Alberta anthropogenic emissions, respectively.

Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis

Tropical forest loss currently contributes 5 to 15% of anthropogenic carbon emissions to the atmosphere. The large uncertainty in emissions estimates is a consequence of many factors, including differences in definitions of forests and degradation, as well as estimation methodologies. However, a primary factor driving uncertainty is an inability to properly account for forest degradation. While remote sensing offers the only practical way of monitoring forest disturbances over large areas, and despite recent improvements in data quality and quantity and processing techniques, remote sensing approaches are still limited in their ability to detect forest degradation. In this paper, a system is presented that uses time series of Landsat data and spectral mixture analysis to detect both degradation and deforestation in forested landscapes. The Landsat data are transformed into spectral endmember fractions and are used to calculate the Normalized Degradation Fraction Index (NDFI; Souza et al., 2005). The spectrally unmixed data are used for disturbance monitoring and land cover classification via time series analysis. To assess the performance of the system, maps of deforestation and degradation were used to stratify the study area for collection of sample data to which unbiased estimators were applied to produce accuracy and area estimates of degradation and deforestation from 1990 to 2013. The approach extends previous research in spectral mixture analysis for identifying forest degradation to the temporal domain. The method was applied using the Google Earth Engine and tested in the Brazilian State of Rondônia. Degradation and deforestation were mapped with 88.0% and 93.3% User's Accuracy, and 68.1% and 85.3% Producer's Accuracy. Area estimates of degradation and deforestation were produced with margins of error of 13.9% and 5.3%, respectively, over the 24 year time period. These results indicate that for Rondônia a decreasing trend in deforestation after 2004 corresponds to an increase in degradation during the same time period.

Uncertainty in projected climate change arising from uncertain fossil-fuel emission factors

Emission inventories are widely used by the climate community, but their uncertainties are rarely accounted for. In this study, we evaluate the uncertainty in projected climate change induced by uncertainties in fossil-fuel emissions, accounting for non-CO2 species co-emitted with the combustion of fossil-fuels and their use in industrial processes. Using consistent historical reconstructions and three contrasted future projections of fossil-fuel extraction from Mohr et al we calculate CO2 emissions and their uncertainties stemming from estimates of fuel carbon content, net calorific value and oxidation fraction. Our historical reconstructions of fossil-fuel CO2 emissions are consistent with other inventories in terms of average and range. The uncertainties sum up to a ±15% relative uncertainty in cumulative CO2 emissions by 2300. Uncertainties in the emissions of non-CO2 species associated with the use of fossil fuels are estimated using co-emission ratios varying with time. Using these inputs, we use the compact Earth system model OSCAR v2.2 and a Monte Carlo setup, in order to attribute the uncertainty in projected global surface temperature change (∆T) to three sources of uncertainty, namely on the Earth system’s response, on fossil-fuel CO2 emission and on non-CO2 co-emissions. Under the three future fuel extraction scenarios, we simulate the median ∆T to be 1.9, 2.7 or 4.0 °C in 2300, with an associated 90% confidence interval of about 65%, 52% and 42%. We show that virtually all of the total uncertainty is attributable to the uncertainty in the future Earth system’s response to the anthropogenic perturbation. We conclude that the uncertainty in emission estimates can be neglected for global temperature projections in the face of the large uncertainty in the Earth system response to the forcing of emissions. We show that this result does not hold for all variables of the climate system, such as the atmospheric partial pressure of CO2 and the radiative forcing of tropospheric ozone, that have an emissions-induced uncertainty representing more than 40% of the uncertainty in the Earth system’s response.

A screening procedure to measure the effect of uncertainty in air emission estimates

Emission inventories are compiled at regional level. When these sources of information are used, uncertainty of emission estimates is never considered. In this paper, we propose an initial screening to identify whether and to what extent uncertainty related to emission inventories affects quantitative analysis used to set strategies and implement actions at regional and subregional levels. We consider the regional air emission inventory of the Piedmont region in Italy. For each pollutant and each sector, uncertainty is calculated by adapting the insurance-based method. A hybrid accounting matrix is built, three environmental themes are analyzed, and a shift-share analysis is undertaken considering jointly air emission estimates and the number of employees at regional and provincial levels. The same procedure is undertaken for data processed with and without uncertainty. Based on the obtained outcomes, few comments are drawn in order to reach some general conclusion to feed discussion on the importance of integrating and prioritizing uncertainty into decision-making at subnational level.

A technology-based mass emission factors of gases and aerosol precursor and spatial distribution of emissions from on-road transport sector in India

This study presents a new emission estimate of gaseous pollutants including CO, CO2, and NOX from on-road transport sector of India for the base year 2013. For the first time, a detailed vintage-wise on-road measured emission factors used for reducing uncertainties in emission estimates. The consumptions of diesel, gasoline, and compressed natural gas (CNG) were also estimated at the national level and disaggregated at the state level. The national average use of diesel, gasoline, and CNG and their 95% confidence interval estimated as 52 (39–66), 24 (18–30), and 1.6 (1.2–2.0) MTy−1 for the year 2013. The CO, CO2, and NOX emissions were estimated as 7349 (3220–11477) Gg y−1, 261 (179–343) Tg y−1, and 4052 (2127–5977) Gg y−1, respectively from on-road transport sector for the year 2013. New vehicles registered after 2005 emit 70–80% of national level CO2, and NOX, while rest 20–30% were emitted by old vehicles registered before 2005. Old and new vehicles both equally contributed to CO emissions. Superemitters accounted for 14% of total traffic volume, but they were responsible for 17–57% of total CO2, CO and NOX emissions. The uncertainties in emission estimates were reduced to 48–56% compared to previous estimates (62–136%). The comparison with recent studies for nationwide emission estimates from 4-wheelers indicated that use of emission factors from dynamometer studies can underestimate the emissions by 32–92% for various pollutants, while an overestimation by 20–82% was seen with the use of emission model derived emission factors. Similarly for Delhi city recent CO and NOx emission estimates for 4-wheelers based on emission factors reported from dynamometer studies were 23–89% lower than present work. The present work revealed the need for representative vintage wise emission factor database development from on-road measurement and the more comprehensive assessment of activity data through survey.

Analysis of uncertainty for N2O fluxes measured with the closed‐chamber method under field conditions: Calculation method, detection limit, and spatial variability.

AbstractThe search for agricultural practices that mitigate N2O emissions, and the validation and development of models, would benefit if the uncertainty of emission estimates would be reduced relative to current levels. This uncertainty has different sources, such as the error of the flux measurements, the error caused by spatially scaling up measurements to the field or regional level, and the error caused by estimating emissions for the time intervals between measurements by interpolation. This paper focuses on the uncertainty of flux measurements and on flux spatial variability, which is a major cause of error when measured fluxes are scaled up spatially. The analysis focuses on 2415 flux measurements made with the closed chamber method over a monitoring campaign of approximately 3 years.Statistically significant nonlinearities in the changes of N2O concentrations during chamber closure were infrequent (8.3%). Further analysis of significant non‐linear concentration changes indicates that, for positive fluxes, nonlinearity might not always be an artifact caused by chamber placement, but that it can reflect natural temporal variability of the flux during chamber placement in a significant number of cases. The analysis of the coefficients of determination (R2) and of the normalized root mean squared errors (NRMSE) of the linear regressions shows that, below emission rates of 5 g N2O‐N ha−1 d−1, the uncertainty of flux estimates strongly increases. The flux detection limit was 3.5 g N2O‐N ha−1 d−1, which is consistent with the outcome of the analysis of the R2s and NRMSEs. Flux measurements based on less than five N2O concentrations per flux led to estimates with considerably larger confidence intervals. When the number of N2O concentrations per flux was reduced from five to four, the detection limit increased to 7.5 g N2O‐N ha−1 d−1. The individual fluxes of spatially replicated plots show strong dispersion around the mean: the average coefficient of variation for fluxes above 5 g N2O‐N ha−1 d−1 was 54.3% and the data suggest that the spatial variability of fluxes correlates positively with flux magnitude.Our results suggest that, for measurements performed with the closed static chamber method, (1) linear regression might generally lead to the best estimates of the average fluxes during closure time, and that the chamber sampling strategy might be designed accordingly, (2) there is considerable potential to reduce the uncertainty for fluxes lower than 5 g N2O‐N ha−1 d−1 by employing analytical instrumentation with higher precision, (3) flux uncertainty and detection limit are strongly affected by the number of concentrations measured for each flux, and (4) large numbers of replicated chambers could be particularly beneficial if high fluxes are expected.

Uncertainties in emissions estimates of greenhouse gases and air pollutants in India and their impacts on regional air quality

Greenhouse gas and air pollutant precursor emissions have been increasing rapidly in India. Large uncertainties exist in emissions inventories and quantification of their uncertainties is essential for better understanding of the linkages among emissions and air quality, climate, and health. We use Monte Carlo methods to assess the uncertainties of the existing carbon dioxide (CO2), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOx), and particulate matter (PM) emission estimates from four source sectors for India. We also assess differences in the existing emissions estimates within the nine subnational regions. We find large uncertainties, higher than the current estimates for all species other than CO, when all the existing emissions estimates are combined. We further assess the impact of these differences in emissions on air quality using a chemical transport model. More efforts are needed to constrain emissions, especially in the Indo-Gangetic Plain, where not only the emissions differences are high but also the simulated concentrations using different inventories. Our study highlights the importance of constraining SO2, NOx, and NH3 emissions for secondary PM concentrations.

Variations of Ammonia Emissions from Cattle Operations: Effects of Air Temperature and Dietary Crude Protein Content

<abstract> <b><sc>Abstract.</sc></b> The objective of this study was to conduct a systematic review of the published literature on ammonia (NH₃) emissions from beef feedlots and dairy barns using a meta-analytical approach in order to improve the estimation of emission rates, to understand the sources of variation, and to specifically quantify the effects of air temperature and dietary crude protein (CP) on NH₃ emissions. Emissions from lagoons or other manure storage facilities were not included. The review compiled results from 91 studies (published from 1997 to 2016) and from the five dairy sites in the National Air Emissions Monitoring Study (NAEMS). Based on the results, the average NH₃ emission rate was 105.9 g d^-1 animal^-1 for beef feedlots and 125.6 g d^-1 animal^-1 for open-lot dairy barns, which were much higher than the emission factors proposed by the U.S. Environmental Protection Agency. Expressing NH₃ emissions as the NH₃-N loss as a percentage of nitrogen (N) intake can address the effect of N feed intake, can reduce uncertainties in estimation of emissions, and can be used as a key parameter in establishing emission inventories and evaluating mitigation strategies. The average NH₃-N loss as a percentage of N intake was 53.1%, 15.6%, 8.7%, and 5.1% for beef feedlots, open-lot dairy barns, free-stall dairy barns, and tie-stall dairy barns, respectively. Results showed that the measured NH₃ emission rates could be affected by measurement method. The chamber method tends to underestimate, while the N balance method tends to overestimate. Proper adjustment and validation may be needed when using the tracer ratio method in free-stall/tie-stall barns in order to avoid potential overestimation. Regression models were developed to quantify the effects of air temperature and dietary CP on emissions from various types of cattle operations. The sensitivity of NH₃ emission rate to air temperature obtained for various operations was consistent (2.33, 2.39, and 2.66 g d^-1 animal^-1 per °C, respectively, for beef feedlots, open-lot dairy barns, and free-stall/tie-stall dairy barns with natural ventilation). Higher dietary CP resulted in significantly higher NH₃ emission rates and also corresponded with higher NH₃-N loss as a percentage of N intake for beef feedlots and free-stall/tie-stall dairy barns with natural ventilation. The sensitivity of emission rates to dietary CP obtained for various types of dairy cattle operations was consistent (9.91, 12.68, and 10.40 g d^-1 animal^-1 per 1% CP, respectively, for beef feedlots, open-lot dairy barns, and free-stall/tie-stall dairy barns with natural ventilation). The average NH₃ emission rate for barns with mechanical ventilation was less than half that for barns with natural ventilation. When developing mitigating strategies, managing N intake and improving feed efficiency seem to represent the greatest opportunities.

Estimates of greenhouse gas and black carbon emissions from a major Australian wildfire with high spatiotemporal resolution

AbstractEstimates of greenhouse gases and particulate emissions are made with a high spatiotemporal resolution from the Kilmore East fire in Victoria, Australia, which burnt approximately 100,000 ha over a 12 h period. Altogether, 10,175 Gigagrams (Gg) of CO2 equivalent (CO2‐e) emissions occurred, with CO2 (∼68%) being the dominant chemical species emitted followed by CH4 (∼17%) and black carbon (BC) (∼15%). About 63% of total CO2‐e emissions were estimated to be from coarse woody debris, 22% were from surface fuels, 7% from bark, 6% from elevated fuels, and less than 2% from tree crown consumption. To assess the quality of our emissions estimates, we compared our results with previous estimates which used the Global Fire Emissions Database version 3.1 (GFEDv3.1) and the Fire INventory from the National Center for Atmospheric Research version 1.0 (FINNv1), as well as Australia's National Inventory System (and its revision). The uncertainty in emission estimates was addressed using truncated Monte Carlo analysis, which derived a probability density function for total emissions from the uncertainties in each input. The distribution of emission estimates from Monte Carlo analysis was lognormal with a mean of 10,355 Gigagrams (Gg) and a ±1 standard deviation (σ) uncertainty range of 7260–13,450 Gg. Results were in good agreement with the global data sets (when using the same burnt area), although they predicted lower total emissions by 15–37% due to underestimating fuel consumed. Emissions estimates can be improved by obtaining better estimates of fuel consumed and BC emission factors. Overall, this study presents a methodological template for high‐resolution emissions accounting and its uncertainty, enabling a step toward process‐based emissions accounting to be achieved.