Emission Estimates Research Articles

Estimating Total Methane Emissions from the Denver-Julesburg Basin Using Bottom-Up Approaches

Methane is a powerful greenhouse gas with a 25 times higher 100-year warming potential than carbon dioxide and is a target for mitigation to achieve climate goals. To control and curb methane emissions, estimates are required from the sources and sectors which are typically generated using bottom-up methods. However, recent studies have shown that national and international bottom-up approaches can significantly underestimate emissions. In this study, we present three bottom-up approaches used to estimate methane emissions from all emission sectors in the Denver-Julesburg basin, CO, USA. Our data show emissions generated from all three methods are lower than historic measurements. A Tier 1/2 approach using IPCC emission factors estimated 2022 methane emissions of 358 Gg (0.8% of produced methane lost by the energy sector), while a Tier 3 EPA-based approach estimated emissions of 269 Gg (0.2%). Using emission factors informed by contemporary and region-specific measurement studies, emissions of 212 Gg (0.2%) were calculated. The largest difference in emissions estimates were a result of using the Mechanistic Air Emissions Simulator (MAES) for the production and transport of oil and gas in the DJ basin. The MAES accounts for changes to regulatory practice in the DJ basin, which include comprehensive requirements for compressors, pneumatics, equipment leaks, and fugitive emissions, which were implemented to reduce emissions starting in 2014. The measurement revealed that normalized gas loss is predicted to have been reduced by a factor of 20 when compared to 10-year-old normalization loss measurements and a factor of 10 less than a nearby oil and production area (Delaware basin, TX); however, we suggest that more measurements should be made to ensure that the long-tail emission distribution has been captured by the modeling. This study suggests that regulations implemented by the Colorado Department of Public Health and Environment could have reduced emissions by a factor of 20, but contemporary regional measurements should be made to ensure these bottom-up calculations are realistic.

Identifying optimal number of driving cycles to represent diverse driving conditions

Driving cycle is one of the main inputs of vehicle emission modeling. However, the variability of driving cycles due to fluctuations in weather conditions is one of the primary sources of uncertainty in vehicle emission estimation. This study aims to identify and determine an optimal number of driving cycles that can correctly represent driving patterns in diverse weather conditions. First, a multivariate multiple regression model is developed to determine the most important weather factors affecting the driving patterns. Then, similar weather conditions are identified according to these factors using unsupervised machine learning. Next, two driving cycles are constructed for diverse weather types, one for weekdays and one for weekends. Afterward, descriptive analysis and a similarity matrix are employed to determine how similar the generated driving cycles are in different weather types. Finally, 15 driving cycles are identified to represent driving patterns in diverse driving conditions.

Estimating the Impact of a 2017 Smoke Plume on Surface Climate Over Northern Canada With a Climate Model, Satellite Retrievals, and Weather Forecasts

AbstractIn August 2017, a smoke plume from wildfires in British Columbia and the Northwest Territories recirculated and persisted over northern Canada for over two weeks. We compared a full‐factorial set of NASA Goddard Institute for Space Studies ModelE simulations of the plume to satellite retrievals of aerosol optical depth and carbon monoxide, finding that ModelE performance was dependent on the model configuration, and more so on the choice of injection height approach, aerosol scheme and biomass burning emissions estimates than to the choice of horizontal winds for nudging. In particular, ModelE simulations with free‐tropospheric smoke injection, a mass‐based aerosol scheme and comparatively high fire NOx emissions led to unrealistically high aerosol optical depth. Using paired simulations with and without fire emissions, we estimated that for 16 days over an 850,000 km2 region, the smoke decreased planetary boundary layer heights by between 253 and 547 m, decreased downward shortwave radiation by between 52 and 172 Wm−2, and decreased surface temperature by between 1.5°C and 4.9°C, the latter spanning an independent estimate from operational weather forecasts of a 3.7°C cooling. The strongest surface climate effects were for ModelE configurations with more detailed aerosol microphysics that led to a stronger first indirect effect.

A real-time estimation method of carbon emission for cement corporations based on load identification

Abstract Accurate carbon emission estimation is the basis for assessing the carbon emissions of various regions and industries and formulating reasonable and feasible emission reduction programs. This paper presents a generalized real-time corporate carbon footprint measurement method. The total carbon emissions are categorized into direct emissions and indirect emissions. For the former, a load identification method based on CNN-BLSTM is proposed to monitor the state of the devices, and the carbon emissions are directly calculated in combination with the carbon emission intensity of the devices; for the latter, accurate estimation of carbon emissions is achieved through the application of marginal carbon emission factor. By utilizing this analytical model, the estimated carbon emissions are more accurate than those of other methods, which can better grasp the carbon emissions of enterprises and promote energy saving and emission reduction.

Development of the prototype for the SPARC hard X-ray monitor.

The SPARC tokamak will be equipped with a hard X-ray (HXR) monitor system capable of measuring the bremsstrahlung emission from runaway electrons with photon energies in excess of about 100keV. This diagnostic will detect the formation of runaway electron beams during plasma start-up and inform the plasma control system to terminate the discharge early to protect the machine. In this work, we present a 0D estimate of the HXR emission in SPARC during plasma start-up. Then we discuss the characterization of a prototype of the HXR monitor. The detector mounts a 1 × 1-in.2 LaBr3 inorganic scintillator coupled with a photomultiplier tube and has been tested with γ-ray sources to find its dynamic range. Finally, two possible modes of operation for spectroscopic and current mode measurements on SPARC are proposed.

Estimating global 0.1° scale gridded anthropogenic CO2 emissions using TROPOMI NO2 and a data-driven method

Satellite remote sensing is a promising approach for monitoring global CO2 emissions. However, existing satellite-based CO2 observations are too coarse to meet the requirements of fine-scale global mapping. We propose a novel data-driven method to estimate global anthropogenic CO2 emissions at a 0.1° scale, which integrates emissions inventories and satellite data while bypassing the inadequate accuracy of CO2 observations. Due to the co-emitted anthropogenic emissions of nitrogen oxides (NOx = NO + NO2) and CO2, high-resolution NO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) are employed to map the global anthropogenic emissions at a global 0.1° scale. We construct the driving features from NO2 data and also incorporate gridded CO2/NOx emission ratios and NOx/NO2 conversion ratios as driving data to describe co-emissions. Both ratios are predicted using a long short-term memory (LSTM) neural network (with an R2 of 0.984 for the CO2/NOx emission ratio and an R2 of 0.980 for the NOx/NO2 conversion ratio). The data-driven model for estimating anthropogenic CO2 emissions is implemented by random forest regression (RFR) and trained using the Emissions Database for Global Atmospheric Research (EDGAR). The satellite-based anthropogenic CO2 emission dataset at a global 0.1° scale agrees well with the national CO2 emission inventories (an R2 of 0.998 with Global Carbon Budget (GCB) and an R2 of 0.996 with EDGAR) and consistent with city-level emission estimates from Carbon Monitor Cities (CMC) with the R2 of 0.824. This data-driven method based on satellite-observed NO2 provides a new perspective for fine-resolution anthropogenic CO2 emissions estimation.

An Evaluation of the Dust Emission Characteristics of Typical Underlying Surfaces in an Aeolian Region in the Middle Reaches of the Yarlung Zangbo River on the Qinghai–Tibet Plateau

Some of the most severe aeolian damage occurs along the middle reaches of the Yarlung Zangbo River in Tibet. Dust emission amounts (DEAs) are often used to assess aeolian damage; however, the research on DEAs in this area is currently almost blank. This article uses field-measured wind speed data from 2021 to 2022 in the Shannan wide valley area, combined with the Gillette dust emission estimation model to quantitatively determine the contributions of three surface types (riverbank quicksand area, foothill sand dunes, and the river floodplain vegetation area) to DEAs in the research area. The influence of surface characteristics on DEAs is analyzed and discussed. The results show the following: (1) The threshold friction velocity (u*t) in the riverbank quicksand area, foothill sand dunes, and the river floodplain vegetation area is 30.6 cm/s, 71.2 cm/s, and 85.6 cm/s, respectively, the threshold velocity (ut) is 6.1 m/s, 7.0 m/s, and 7.5 m/s, respectively, and the vegetation area is 2.8 times and 1.3 times that of the quicksand area, respectively. (2) The DEAs were in the following order: the riverbank quicksand area (652.9 t/km2) > foothill sand dunes (326.5 t/km2) > the river floodplain vegetation area (107.8 t/km2), the riverbank quicksand area is about 6.1 times that of the river floodplain vegetation area, and DEAs are a significant seasonal distribution: winter (44.7%) > spring (28.3%) > autumn (15.7%) > summer (11.3%). (3) The DEAs from the dusty weather were in the following order: blowing sand (60.2%) > sandstorms (28.6%) > gusty winds (11.2%). (4) The DEAs increase with the increase in the average wind speed greater than 6.1 m/s, but the increase rate is obviously different, which showed that Changguo and Azha are greater than Sangyesi, Duopazhang, Sangri, and Senburi. At approximately the same average wind speed greater than 6.1 m/s, the DEAs in the quicksand area are much greater than in the vegetation area.

Estimation of Ammonia Emission Inventory Using Life Cycle Assessment Based on Livestock Manure Flow: A Case Study of the Manure Management Sector in Korea

Ammonia is one of the precursor gases in the formation of particulate matter (PM) that reacts with nitrogen oxides and sulfur oxides in the atmosphere. Based on the Clean Air Policy Support System (CAPSS) of Korea, the annual ammonia emissions amounted to 261,207 tons in 2020 and the agricultural source (manure management sector) contributes the highest proportion of the ammonia inventory. However, the methodology for the study of ammonia emissions in Korea has some limitations regarding the representativeness of the sites selected and the reliability of the measurement method. In this study, we aimed to recalculate the ammonia emissions from the livestock industry in Korea using the UK’s estimation method, which uses the life cycle assessment of livestock manure. Three major animal types, i.e., cattle (beef cattle and dairy cows), pigs and chickens, and three major processes based on the manure flow, i.e., housing, manure storage and treatment and land application processes, were considered. The total ammonia emissions were estimated to be approximately 33% higher than the official ammonia emissions stated by the CAPSS. For the manure flow, the ammonia emissions were the highest from land application processes. The ammonia emissions from dairy cow and poultry manure were much higher than those stated by the CAPSS, while the emissions from beef cattle and pig manure showed similar levels. The methodology used in this study can offer an alternative approach to the ammonia emission estimation of the manure management sector in the agriculture industry of Korea. Korean emission factors based on the manure flow should be developed and applied in the future.

An Analysis of Greenhouse Gas Emissions in Electrolysis for Certifying Clean Hydrogen

The drive for carbon neutrality has led to legislative measures targeting reduced greenhouse gas emissions across the transportation, construction, and industry sectors. Renewable energy sources, especially solar and wind power, play a pivotal role in this transition. However, their intermittent nature necessitates effective storage solutions. Green hydrogen and ammonia have gained attention for their potential to store renewable energy while producing minimal emissions. Despite their theoretical promise of zero greenhouse gas emissions during production, real-world emissions vary based on system configurations and lifecycle assessments, highlighting the need for detailed evaluations of their environmental impact. Therefore, in this study, calculations were performed for the actual amount of produced greenhouse gas emissions that are associated with the production of green hydrogen using electrolysis, from raw material extraction and processing to hydrogen production, with these assessed from well-to-gate emission estimates. Emissions were also evaluated based on various types of renewable energy sources in South Korea, as well as hydrogen production volumes, capacities, and types. Using these data, the following factors were examined in this study: carbon dioxide emissions from the manufacturing stage of electrolysis equipment production, the correlation between materials and carbon dioxide emissions, and process emissions. Current grades of clean hydrogen were verified, and the greenhouse gas reduction effects of green hydrogen were confirmed. These findings are significant against the backdrop of a country such as South Korea, where the proportion of renewable energy in total electricity production is very low at 5.51%. Based on the domestic greenhouse gas emission efficiency standard of 55 kWh/kgH2, it was found that producing 1 kg of hydrogen emits 0.076 kg of carbon dioxide for hydropower, 0.283 kg for wind power, and 0.924 kg for solar power. The carbon dioxide emissions for AWE and PEM stacks were 8434 kg CO2 and 3695 kg CO2, respectively, demonstrating that an alkaline water electrolysis (AWE) system emits about 2.3 times more greenhouse gasses than a proton exchange membrane (PEM) system. This indicates that the total carbon dioxide emissions of green hydrogen are significantly influenced by the type of renewable energy and the type of electrolysis used.

A bottom-up approach for greenhouse gas emission estimation at the community level: A case study in Japan

Greenhouse gas (GHG) emission mitigation and climate change adaptation in urban communities are urgent. There is enormous potential to improve the GHG inventory at the community level. This research aims to develop a community-scale GHG emission inventory and improve its accuracy and consistency through a bottom-up approach. This study covers direct and indirect emissions categorized into three scopes and selects the Honjo Waseda community in Japan as a case study community. Energy consumption from different sectors (the residential and commercial, transport, and waste management sectors) is evaluated and calculated through Residential Energy Estimation based on the Daily Activities (REEDA) database, National Census, and first-hand field data. The outcome revealed that the residential and commercial sector emissions rank highest in the total GHG emissions of the community, with a value of 39 %. Translated into the scope concept, the emissions under scopes 2 and 3 account for 39 % and 61 %, respectively. Furthermore, a sensitivity analysis of the implementation of green technologies to reduce GHG emissions is also conducted. Mechanical biological treatment (MBT) implementation could yield the largest emission reduction (18 %–22 %), followed by implementing electric vehicles, ultralightweight vehicles, photovoltaic systems, and ground heat utilization.

Emission analysis based on mixed traffic flow and license plate recognition model

With the rising ownership of new energy vehicles (NEVs), accurate road traffic emission estimations are crucial. This study combined mixed traffic flow and license plate recognition (LPR) using improved 3D convolutional neural network (3D-CNN) and Transformer models. The 3D-CNN model is designed for vehicle localization and traffic flow data acquisition, while the Transformer-based LPR model accurately recognizes license plates, distinguishing NEVs from conventional vehicles. The overall model is validated by the training and test datasets, and then with field application along a primary arterial segment in the South 2nd Ring Road, Xi’an, China. The results demonstrate capability for the traffic emission approximation of the mixed traffic flow including new energy vehicles, revealing that mixed traffic flow identification plays an important role in road emission approximation. Results and procedures of the study may provide benchmark for the subsequent research and the verification of related transportation policies.

Bayesian inversion of HFC-134a emissions in southern China from a new AGAGE site: Results from an observing system simulation experiment

The abundance of hydrofluorocarbons (HFCs) in the atmosphere is increasing and is of significant importance to the Earth's system. It is thus crucial to investigate the spatial distribution of HFC emissions. However, inversion modeling, a commonly used approach, is susceptible to errors from a-priori emissions, inversion grids, observations, and other parameters. In this study, we conduct Observing System Simulation Experiment (OSSE) to evaluate the impact of inversion parameters on HFC emission estimates, focusing on HFC-134a at the newly established Xichong (XCG) AGAGE site in Shenzhen, China. We regard the EDGAR (v7) dataset as a reference for “true” emissions and simulate the “true” atmosphere using the GEOS-Chem model. Our OSSE indicates that conducting inversions in the medium-sensitivity region with source-receptor sensitivity larger than 6.0 (nmol mol−1)/(mol m−2 s−1) minimizes the bias between a-posteriori and “true” total emissions to 0.58 %–1.88 %. In the high-sensitivity region, enhancing the spatial resolution of inversion grids lowers this error from −27.07 % to +0.36 %. The accuracy of a-priori spatial distribution crucially influences that of the a-posteriori: the higher correlation coefficient between a-priori and “true” emissions, the better a-posteriori agree with the “true” emissions, as evidenced by a reduced root mean square error (RMSE) of the a-posteriori vs. “true” emissions from 1.44 × 10−9 g m−2 s−1 (GDP-based) to 8.78 × 10−10 g m−2 s−1 (VIIRS-based). Furthermore, selecting regularization parameters and balancing instrumental and a-priori errors are also important. Our OSSE setups allow for parameter testing during the inversion, offering a framework to assess the regional representativeness of future HFC measurement sites.

Estimating the dynamics of greenhouse gas emission during black soldier fly larvae growth under controlled environmental conditions

Edible insects are considered a sustainable alternative protein source. Several studies have indicated that greenhouse gas (GHG) emissions from insects are generally low. These data were usually recorded in closed static chambers, which did not reflect the actual rearing conditions. On the other hand, open respiration chambers need high amounts of substrate and insects, making the analyses time consuming and expensive. In the present study, a novel small-scale open gas exchange recording system for evaluation of GHG emissions from insects is presented. The device consists of four independent respiration chambers, each one equipped with a pump for supplying air, a flow meter and a septum for sampling the flowing air. A validation experiment was carried out using black soldier fly larvae as model organisms and moistened chicken feed (dry matter 40%) as rearing substrate. Three independent runs were conducted, each with two replicates, using 200 neonates larvae and 300 g of substrate. Moistened chicken feed (300 g) without larvae was used as control. Throughout the 12 days rearing period, gas sampling occurred every 12 h. Data interpolation considering a linear tendency between two successive sampling points was applied and overall amounts of gases were quantified as area under the curve. Irrespective of the run, CO2 emissions in the insect-substrate systems increased during the initial 7 d of experiment, consistently followed by a rapid decrease. Multiple cross correlation analysis showed high reproducibility (r ≥ 0.9), while significant differences in the CO2 emissions between insects-substrate system (range 7.76–11.88 g g−1 dry insects) and sole substrate (range 0.20–0.33 g g−1 initial dry substrate) were observed. Very limited N2O (≤1.13 mg g−1 dry insects) and CH4 (≤1.33 mg g−1 dry insects) emissions were detected in this experiment, indicating that the performed black soldier fly larvae rearing did not result in significant production of these GHGs. Overall, the experiment showed that the novel device may be particularly suited for quick and reliable estimation of GHG emissions from edible insects.

Laboratory Evaluation of Wear Particle Emissions and Suspended Dust in Tire–Asphalt Concrete Pavement Friction

This study aims to evaluate the tire–road-wear particles (TRWPs) and suspended dust generated based on the nominal maximum aggregate size (NMAS) of the polymer-modified stone mastic asphalt (SMA) mixtures indoors. The SMA mixtures containing styrene butadiene styrene (SBS) polymer and the NMASs of 19, 13, 10, 8, and 6 mm were used. Dust was generated from the wear of the tires and the pavement inside the indoor chamber by using the laboratory tire–road-wear particle generation and evaluation tester (LTRWP tester) developed by Korea Expressway Corporation (KEC). In this method, a cylindrical asphalt-mixture specimen rotates in the center, and a load is applied using three tires on the sides of the test specimen. During the test, a digital sensor was used to measure the concentration for each particle size. After the test was completed, the dust was collected and weighed. According to the test results, the generated TRWP emissions were reduced by approximately 0.15 g as the NMAS of the SMA mixture decreased by 1 mm. TRWP emissions decreased by 20% when using the 6 mm SMA mixture compared to the 13 mm SMA mixture. For practical application, a predicted equation of TRWP emissions estimation was developed by using the concentration of suspended dust measured by the digital sensor in the LTRWP tester. LTRWP can be used as an indoor test method to evaluate pavement and tire materials to reduce the amount of dust generated from tire and pavement wear.

Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach.

An accurate understanding of uncertainty is needed to properly interpret methane emission estimates from upstream oil and gas sources in a variety of contexts, from component-level measurements to yearly jurisdiction-wide inventories. To characterize measurement uncertainty, we examine controlled release (CR) data from five different technology providers including quantitative gas imaging (QOGI), tunable diode laser-absorption spectroscopy (TDLAS); and airborne near-infrared hyperspectral (NIR HS) imaging. We introduce a novel empirical method to develop probability distributions of measurements given a true emission rate using the CR data. The approach includes flexible likelihoods which capture complex relationships in the data. An algorithm which provides the distribution of the true emission rate given a measurement is also developed, which synthesizes the measurement with the CR data and external information about the possible true emission rate. The results show that flexible models that accommodate complex nonlinear behavior are needed to adequately model measurement error. We also show that measurement error can vary under different conditions. We demonstrate that measurement uncertainty can be reduced by performing repeated measurements. A limitation of the study is that the collected CR data is collected under controlled conditions that may differ from those in industrial settings. As new CR data become available, the models presented in this paper can be refit to consider more diverse scenarios. The methodology can be extended to explicitly model different conditions to improve performance.

Evaluating NOx stack plume emissions using a high-resolution atmospheric chemistry model and satellite-derived NO2 columns

Abstract. This paper presents large-eddy simulations with atmospheric chemistry of four large point sources world-wide, focusing on the evaluation of NOx (NO + NO2) emissions with the TROPOspheric Monitoring Instrument (TROPOMI). We implemented a condensed chemistry scheme to investigate how the emitted NOx (95 % as NO) is converted to NO2 in the plume. To use NOx as a proxy for CO2 emission, information about its atmospheric lifetime and the fraction of NOx present as NO2 is required. We find that the chemical evolution of the plumes depends strongly on the amount of NOx that is emitted, as well as on wind speed and direction. For large NOx emissions, the chemistry is pushed in a high-NOx chemical regime over a length of almost 100 km downwind of the stack location. Other plumes with lower NOx emissions show a fast transition to an intermediate-NOx chemical regime, with short NOx lifetimes. Simulated NO2 columns mostly agree within 20 % with TROPOMI, signalling that the emissions used in the model were approximately correct. However, variability in the simulations is large, making a one-to-one comparison difficult. We find that temporal wind speed variations should be accounted for in emission estimation methods. Moreover, results indicate that common assumptions about the NO2 lifetime (≈ 4 h) and NOx:NO2 ratios (≈ 1.3) in simplified methods that estimate emissions from NO2 satellite data need revision.

Emissions of HCFC-22 and HCFC-142b in China during 2018-2021 Inferred from Inverse Modeling.

Hydrochlorofluorocarbons (HCFCs) are transitional substitutes for chlorofluorocarbons (CFCs). However, they still have the capacity to be ozone-depleting substances (ODSs). Therefore, they are scheduled to be phased out in China by 2030 under the Montreal Protocol. The emission estimates of HCFC-22 (CHClF2) and HCFC-142b (CH3CClF2) in China using atmospheric observations are lacking after 2017, making it hard to understand the effectiveness of the phase-out process of HCFCs in China. Here, we use flask and in situ measurements of HCFC-22 and HCFC-142b during 2018-2021 and inverse modeling to determine the emission magnitude and changes in China. It was determined that China's emissions were 172 ± 40, 154 ± 39, 160 ± 22, and 155 ± 33 Gg yr-1 of HCFC-22 and 8.3 ± 1.8, 7.8 ± 1.6, 7.4 ± 1.7, and 7.9 ± 1.7 Gg yr-1 of HCFC-142b from 2018 to 2021, respectively. Top-down estimates show that HCFC-22 emissions in China were stable, while HCFC-142b emissions were decreasing during 2013-2021, although both substances were in the stage of being phased out during 2013-2021. This study reveals that 46 and 39% of the global HCFC-22 and HCFC-142b emissions, respectively, cannot be traced to certain countries in 2020. We suggest that more studies on HCFC emissions around the world in the future are needed to better safeguard the ozone layer recovery and climate mitigation by ensuring compliance with the Montreal Protocol during HCFC phase-out processes.

Spatiotemporal heterogeneity of lake carbon dioxide flux leads to substantial uncertainties in regional upscaling estimates

Limited field samplings result in significant uncertainties in regional and global estimates of lake carbon dioxide (CO2) emissions. However, quantitative analysis of uncertainty in regional lake CO2 emission estimates remains unclear. In this study, we utilized satellite data to estimate carbon dioxide flux from 113 eastern China lakes, revealing substantial spatial and temporal variations in flux, averaging 18.07 ± 81.83 mg m−2 d−1. Additionally, satellite-estimated total CO2 effluxes indicated previous upscaling studies had overestimated total CO2 effluxes from these studied lakes by approximately 3–11 times, primarily due to substantial variations in lake CO2 fluxes. Insufficient sampling resolution resulted in considerable uncertainty in upscaling estimations. Temporal variations in carbon dioxide contributed greater upscaling uncertainties than spatial variations in carbon dioxide. To capture the dynamics of lake CO2, increasing the number of sampling points and events is necessary as lake size decreases and trophic state increases. Finally, we propose a prediction for the optimal sampling resolution based on lake area and trophic state, recommending an average of 5 points per lake and bi-monthly sampling as the ideal resolution for similar shallow eutrophic lakes. This approach has been validated as effective in lakes across North America and Europe. We believe that future global-scale lake carbon budget estimates would benefit from field observations conducted at more reasonable sampling points and frequency.

Managing synthetic N-fertilizer emissions in India: Insights from field surveys across 102 districts

The use of fixed emission factors (EFs), combined with insufficient temporal distribution, leads to substantial uncertainties in current emission inventories for India, the world’s second-largest producer and consumer of synthetic N-fertilizers. Our study aimed to improve the NH3 and N2O emission estimates by utilizing crop-specific district-level activity data and refined EFs tailored to Indian conditions. In this study, a comprehensive NH3 and N2O emission inventory (EI) is methodically developed at 0.1° * 0.1° spatial and monthly temporal resolution for the year 2018-19 considering 52 crops. The data for developing this inventory is aggregated through detailed field surveys, conducted across 102 districts of 14 states, and relevant government databases. EFs have been adjusted for the Indian context by refining them to reflect local conditions through consideration of ambient temperature, application rate, and other factors. Further, upon preparing an EI for FA, a spectrum of mitigation strategies are evaluated to assess their effectiveness in reducing emissions. Yearly total NH3 and N2O emissions amount to 3.15 Tg and 138.53 Gg, with urea fertilizer as the dominant contributor accounting for 93.85% and 96.44% of emissions, respectively. Key crops such as rice, wheat, maize, sugarcane, and cotton collectively represent approximately 82% of the total N consumption. The state of Uttar Pradesh emerges as the largest emitter, contributing 706.5 Gg and 25.31 Gg of NH3 and N2O emissions, respectively. Conversely, PB and HR exhibit the highest NH3 emissions per capita. Temporally, NH3 emissions peak in August, while N2O emissions peak in July, with both pollutants reaching their nadir in February. Among the array of mitigation strategies assessed in this study, ‘adhering to recommended fertilizer doses’ and ‘incorporating urease inhibitors’ demonstrated substantial potential for reducing emissions. The current study aids policymaking to mitigate the environmental and health impacts of atmospheric emissions from synthetic N-fertilizers. Future researchers can adopt this study as a benchmark to improve Indian FA emission estimates, which helps in promoting sustainable agricultural practices and contributes to climate change mitigation efforts.

Using metal oxide gas sensors to estimate the emission rates and locations of methane leaks in an industrial site: assessment with controlled methane releases

Abstract. Fugitive methane (CH4) emissions occur in the whole chain of oil and gas production, including from extraction, transportation, storage, and distribution. Such emissions are usually detected and quantified by conducting surveys as close as possible to the source location. However, these surveys are labour-intensive, are costly, and fail to not provide continuous emissions monitoring. The deployment of permanent sensor networks in the vicinity of industrial CH4 emitting facilities would overcome the limitations of surveys by providing accurate emission estimates, thanks to continuous sampling of emission plumes. Yet high-precision instruments are too costly to deploy in such networks. Low-cost sensors using a metal oxide semiconductor (MOS) are presented as a cheap alternative for such deployments due to their compact dimensions and to their sensitivity to CH4. In this study, we demonstrate the ability of two types of MOS sensors (TGS 2611-C00 and TGS 2611-E00) manufactured by Figaro® to reconstruct a CH4 signal, as measured by a high-precision reference gas analyser, during a 7 d controlled release campaign conducted by TotalEnergies® in autumn 2019 near Pau, France. We propose a baseline voltage correction linked to atmospheric CH4 background variations per instrument based on an iterative comparison of neighbouring observations, i.e. data points. Two CH4 mole fraction reconstruction models were compared: multilayer perceptron (MLP) and second-degree polynomial. Emission estimates were then computed using an inversion approach based on the adjoint of a Gaussian dispersion model. Despite obtaining emission estimates comparable with those obtained using high-precision instruments (average emission rate error of 25 % and average location error of 9.5 m), the application of these emission estimates is limited to adequate environmental conditions. Emission estimates are also influenced by model errors in the inversion process.