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Ctvrtlik, Radim, Jan Tomastik, Lukas Vaclavek, Ben D. Beake, Adrian J. Harris, Alberto Sanchez Martin, Michal Hanak, and Petr Abrham. "High-Resolution Acoustic Emission Monitoring in Nanomechanics." JOM 71, no. 10 (August 5, 2019): 3358–67. http://dx.doi.org/10.1007/s11837-019-03700-8.
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Chen, Wenjian, Yi Wang, Xuan Li, Wei Gao, Shiwei Ma, Yuanyuan Duan, and Xiaopeng Shao. "Hazardous Gas Emission Monitoring Based on High-Resolution Images." Journal of Spectroscopy 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/2698025.
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Air pollution presents unprecedentedly severe challenges to humans today. Various measures have been taken to monitor pollution from gas emissions and the changing atmosphere, of which imaging is of crucial importance. By images of target scenes, intuitional judgments and in-depth data are achievable. However, due to the limitations of imaging devices, effective and efficient monitoring work is often hindered by low-resolution target images. To deal with this problem, a superresolution reconstruction method was proposed in this study for high-resolution monitoring images. It was based on the idea of sparse representation. Particularly, multiple dictionary pairs were trained according to the gradient features of samples, and one optimal pair of dictionaries was chosen to reconstruct by judging the weighting of the information in different directions. Furthermore, the K-means singular value decomposition algorithm was used to train the dictionaries and the orthogonal matching pursuit algorithm was employed to calculate the sparse coding coefficients. Finally, the experiment’s results demonstrated its advantages in both visual fidelity and numerical measures.
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Chen, Xiaochun, Jianhui Li, Min Jia, Shaobo Chen, Shangxuan Zhang, Xin Bo, Xue Feng, and Guangxia Dong. "High Spatial Resolution Emission Inventory of Air Pollutants and Carbon in China’s Independent Coking Industry." Atmosphere 14, no. 2 (February 9, 2023): 348. http://dx.doi.org/10.3390/atmos14020348.
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China is the largest producer and exporter of coke globally, which means that it is very important to understand the characteristics of air pollutants and carbon emissions from China’s independent coking industry. This study was the first to establish a bottom-up inventory of the air pollutants and carbon emissions of China’s independent coking industry during 2001–2018 based on continuous emission monitoring system online monitoring data and unit-based corporate information. Based on the developed emission inventory, four scenarios were established to analyze potential emissions reduction of air pollutants and carbon dioxide (CO2) in future. The emissions of particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), black carbon (BC) and organic carbon (OC) decreased by 62.11%, 63.41%, 72.85%, 63.41% and 63.41%, respectively. CO2, carbon monoxide (CO), volatile organic compounds (VOCs) and nitrogen oxides (NOX) emissions increased by 355.51%, 355.51%, 355.51% and 99.74%, respectively. In 2018, PM10, PM2.5, SO2, NOx, BC, OC, CO, VOCs and CO2 emissions were, respectively. 45.20, 16.91, 63.84, 117.71, 5.07, 5.92, 554.91, 1026.58 Gg, and 176.88 Tg. Shanxi province made the greatest contributions to the total emissions of air pollutants and CO2 from this industry by 25.01%. The emission source that contributed most to PM2.5 (SO2 and NOX) emissions was coke pushing (quenching and the coke oven chimney respectively) in 2018. Under the ULE scenario (2018–2035), PM2.5 and SO2 emissions will reduce by more than 30%. Under the PCP scenario, PM2.5 and SO2 emissions will reduce by more than 55%. Under the CBP scenario, CO2 emissions will peak at 197.99 Tg in 2025 and decrease to 70% of the peak in 2035. The results showed the emission characteristics of air pollutants and CO2, future emission with several scenarios and cooperative reduction potential in China’s independent coking industry, which provides scientific support for the development of pollution control strategies.
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Huang, Qing, Hao Han, Zhan Yi Zhang, Bo Guang Wang, and Chun Lin Zhang. "The Methodology to Establish a High Temporal Resolution Emission Inventory for Tunnel Source in City." Advanced Materials Research 955-959 (June 2014): 1380–83. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.1380.
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Tunnel source is an important emission source in urban microenvironment, and the influence of emission pollutants from tunnel source to air quality in surrounding area could not be ignored. In this study the monitoring data in the entrance and the exit of the tunnel was used to calculate the emission amount from tunnel source. Then the methodology to establish the tunnel source emission inventory with a high temporal resolution was discussed in this paper. This research would provide basis for the establishment of the emission inventory in urban microenvironment, and provide a more realistic emissions inventory to Air Quality Model.
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Bandiera, Rino, Paola Focardi, Aldo Altamore, Corinne Rossi, and Otmar Stahl. "High-resolution emission line profiles in blue luminous stars." International Astronomical Union Colloquium 113 (1989): 279–80. http://dx.doi.org/10.1017/s0252921100004620.
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Emission lines are often observed in high luminosity stars and provide evidence of the presence of extended stellar envelopes. Ha is the most frequently observed across the H-R diagram, but lines of Hel or Fell are also found in emission in these stars. They could be used as diagnostics of the structure of their outer atmospheres and winds. High resolution (1/dl ~ 105) high S/N profiles of Ha and Hel 5876 in the galactic LBVs η Car, AG and HR Car, and in the LMC star S22 have been obtained with the ESO CAT-CES during 1984-87, and are described in Figs.1-5. We find that these stars show a large variety of profiles with narrow and broad emissions, wide or multiple blue-shifted absorptions. The profiles are largely variable. Once, a kind of inverse P Cyg profile was observed in HR Car (Fig.4). These results indicate the presence of large scale phenomena and high velocity fields which are dramatically variable in time. Continuous HIRES monitoring of these stars is urgently needed.
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Schuit, Berend J., Joannes D. Maasakkers, Pieter Bijl, Gourav Mahapatra, Anne-Wil van den Berg, Sudhanshu Pandey, Alba Lorente, et al. "Automated detection and monitoring of methane super-emitters using satellite data." Atmospheric Chemistry and Physics 23, no. 16 (September 19, 2023): 9071–98. http://dx.doi.org/10.5194/acp-23-9071-2023.
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Abstract. A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called super-emitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit, providing daily global coverage of methane mixing ratios at a resolution of up to 7×5.5 km2, enabling the detection of these super-emitters. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a convolutional neural network to detect plume-like structures in the methane data and subsequently apply a support vector classifier to distinguish the emission plumes from retrieval artifacts. The models are trained on pre-2021 data and subsequently applied to all 2021 observations. We detect 2974 plumes in 2021, with a mean estimated source rate of 44 t h−1 and 5–95th percentile range of 8–122 t h−1. These emissions originate from 94 persistent emission clusters and hundreds of transient sources. Based on bottom-up emission inventories, we find that most detected plumes are related to urban areas and/or landfills (35 %), followed by plumes from gas infrastructure (24 %), oil infrastructure (21 %), and coal mines (20 %). For 12 (clusters of) TROPOMI detections, we tip and cue the targeted observations and analysis of high-resolution satellite instruments to identify the exact sources responsible for these plumes. Using high-resolution observations from GHGSat, PRISMA, and Sentinel-2, we detect and analyze both persistent and transient facility-level emissions underlying the TROPOMI detections. We find emissions from landfills and fossil fuel exploitation facilities, and for the latter, we find up to 10 facilities contributing to one TROPOMI detection. Our automated TROPOMI-based monitoring system in combination with high-resolution satellite data allows for the detection, precise identification, and monitoring of these methane super-emitters, which is essential for mitigating their emissions.
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Zhao, Y., L. P. Qiu, R. Y. Xu, F. J. Xie, Q. Zhang, Y. Y. Yu, C. P. Nielsen, et al. "Advantages of a city-scale emission inventory for urban air quality research and policy: the case of Nanjing, a typical industrial city in the Yangtze River Delta, China." Atmospheric Chemistry and Physics 15, no. 21 (November 12, 2015): 12623–44. http://dx.doi.org/10.5194/acp-15-12623-2015.
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Abstract. With most eastern Chinese cities facing major air quality challenges, there is a strong need for city-scale emission inventories for use in both chemical transport modeling and the development of pollution control policies. In this paper, a high-resolution emission inventory (with a horizontal resolution of 3 × 3 km) of air pollutants and CO2 for Nanjing, a typical large city in the Yangtze River Delta, is developed, incorporating the best available information on local sources. Emission factors and activity data at the unit or facility level are collected and compiled using a thorough on-site survey of major sources. Over 900 individual plants, which account for 97 % of the city's total coal consumption, are identified as point sources, and all of the emission-related parameters including combustion technology, fuel quality, and removal efficiency of air pollution control devices (APCD) are analyzed. New data-collection approaches including continuous emission monitoring systems and real-time monitoring of traffic flows are employed to improve spatiotemporal distribution of emissions. Despite fast growth of energy consumption between 2010 and 2012, relatively small interannual changes in emissions are found for most air pollutants during this period, attributed mainly to benefits of growing APCD deployment and the comparatively strong and improving regulatory oversight of the large point sources that dominate the levels and spatial distributions of Nanjing emissions overall. The improvement of this city-level emission inventory is indicated by comparisons with observations and other inventories at larger spatial scale. Relatively good spatial correlations are found for SO2, NOx, and CO between the city-scale emission estimates and concentrations at nine state-operated monitoring sites (R = 0.58, 0.46, and 0.61, respectively). The emission ratios of specific pollutants including BC to CO, OC to EC, and CO2 to CO compare well to top-down constraints from ground observations. The interannual variability and spatial distribution of NOx emissions are consistent with NO2 vertical column density measured by the Ozone Monitoring Instrument (OMI). In particular, the Nanjing city-scale emission inventory correlates better with satellite observations than the downscaled Multi-resolution Emission Inventory for China (MEIC) does when emissions from power plants are excluded. This indicates improvement in emission estimation for sectors other than power generation, notably industry and transportation. A high-resolution emission inventory may also provide a basis to consider the quality of instrumental observations. To further improve emission estimation and evaluation, more measurements of both emission factors and ambient levels of given pollutants are suggested; the uncertainties of emission inventories at city scale should also be fully quantified and compared with those at national scale.
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Kulenkampff, Johannes, Marion Gründig, Abdelhamid Zakhnini, and Johanna Lippmann-Pipke. "Geoscientific process monitoring with positron emission tomography (GeoPET)." Solid Earth 7, no. 4 (August 18, 2016): 1217–31. http://dx.doi.org/10.5194/se-7-1217-2016.
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Abstract. Transport processes in geomaterials can be observed with input–output experiments, which yield no direct information on the impact of heterogeneities, or they can be assessed by model simulations based on structural imaging using µ-CT. Positron emission tomography (PET) provides an alternative experimental observation method which directly and quantitatively yields the spatio-temporal distribution of tracer concentration. Process observation with PET benefits from its extremely high sensitivity together with a resolution that is acceptable in relation to standard drill core sizes. We strongly recommend applying high-resolution PET scanners in order to achieve a resolution on the order of 1 mm. We discuss the particularities of PET applications in geoscientific experiments (GeoPET), which essentially are due to high material density. Although PET is rather insensitive to matrix effects, mass attenuation and Compton scattering have to be corrected thoroughly in order to derive quantitative values. Examples of process monitoring of advection and diffusion processes with GeoPET illustrate the procedure and the experimental conditions, as well as the benefits and limits of the method.
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Wu, Xiaomeng, Daoyuan Yang, Ruoxi Wu, Jiajun Gu, Yifan Wen, Shaojun Zhang, Rui Wu, et al. "High-resolution mapping of regional traffic emissions using land-use machine learning models." Atmospheric Chemistry and Physics 22, no. 3 (February 10, 2022): 1939–50. http://dx.doi.org/10.5194/acp-22-1939-2022.
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Abstract. On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on road traffic monitoring datasets in the Beijing–Tianjin–Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than other machine learning models on most occasions in this study. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. NOx, fine particulate matter (PM2.5), and black carbon (BC) emissions from heavy-duty trucks (HDTs) generally have a higher emission intensity on the highways connecting to regional ports. The model found a general reduction in light-duty passenger vehicles when traffic restrictions were implemented but a much more spatially heterogeneous impact on HDTs, with some road links experiencing up to 40 % increases in the HDT traffic volume. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, thereby providing a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.
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Zhao, Y., L. Qiu, R. Xu, F. Xie, Q. Zhang, Y. Yu, C. P. Nielsen, et al. "Advantages of city-scale emission inventory for urban air quality research and policy: the case of Nanjing, a typical industrial city in the Yangtze River Delta, China." Atmospheric Chemistry and Physics Discussions 15, no. 13 (July 9, 2015): 18691–746. http://dx.doi.org/10.5194/acpd-15-18691-2015.
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Abstract. With most eastern Chinese cities facing major air quality challenges, there is a strong need for city-scale emission inventories for use in both chemical transport modeling and the development of pollution control policies. In this paper, a high-resolution emission inventory of air pollutants and CO2 for Nanjing, a typical large city in the Yangtze River Delta, is developed incorporating the best available information on local sources. Emission factors and activity data at the unit or facility level are collected and compiled using a thorough onsite survey of major sources. Over 900 individual plants, which account for 97 % of the city's total coal consumption, are identified as point sources, and all of the emission-related parameters including combustion technology, fuel quality, and removal efficiency of air pollution control devices (APCD) are analyzed. New data-collection approaches including continuous emission monitoring systems and real-time monitoring of traffic flows are employed to improve spatiotemporal distribution of emissions. Despite fast growth of energy consumption between 2010 and 2012, relatively small inter-annual changes in emissions are found for most air pollutants during this period, attributed mainly to benefits of growing APCD deployment and the comparatively strong and improving regulatory oversight of the large point sources that dominate the levels and spatial distributions of Nanjing emissions overall. The improvement of this city-level emission inventory is indicated by comparisons with observations and other inventories at larger spatial scale. Relatively good spatial correlations are found for SO2, NOx, and CO between the city-scale emission estimates and concentrations at 9 state-opertated monitoring sites (R = 0.58, 0.46, and 0.61, respectively). The emission ratios of specific pollutants including BC to CO, OC to EC, and CO2 to CO compare well to top-down constraints from ground observations. The inter-annual variability and spatial distribution of NOx emissions are consistent with NO2 vertical column density measured by the Ozone Monitoring Instrument (OMI). In particular, the Nanjing city-scale emission inventory correlates better with satellite observations than the downscaled Multi-resolution Emission Inventory for China (MEIC) does when emissions from power plants are excluded. This indicates improvement in emission estimation for sectors other than power generation, notably industry and transportation. High-resolution emission inventory may also provide a basis to consider the quality of instrumental observations. To further improve emission estimation and evaluation, more measurements of both emission factors and ambient levels of given pollutants are suggested; the uncertainties of emission inventories at city scale should also be fully quantified and compared with those at national scale.
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Michlmayr, Gernot, Athena Chalari, Andy Clarke, and Dani Or. "Fiber-optic high-resolution acoustic emission (AE) monitoring of slope failure." Landslides 14, no. 3 (December 10, 2016): 1139–46. http://dx.doi.org/10.1007/s10346-016-0776-5.
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Yao, Lu, Dongxu Yang, Zhe Jiang, Yi Liu, Lixu Chen, Longfei Tian, Janne Hakkarainen, Zhaonan Cai, Jing Wang, and Xiaoyu Ren. "Towards Supporting Satellite Design Through the Top-Down Approach: A General Model for Assessing the Ability of Future Satellite Missions to Quantify Point Source Emissions." Remote Sensing 16, no. 23 (November 30, 2024): 4503. https://doi.org/10.3390/rs16234503.
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Monitoring and accurately quantifying greenhouse gas (GHG) emissions from point sources via satellite measurements is crucial for validating emission inventories. Numerous studies have applied varied methods to estimate emission intensities from both natural and anthropogenic point sources, highlighting the potential of satellites for point source quantification. To promote the development of the space-based GHG monitoring system, it is pivotal to assess the satellite’s capacity to quantify emissions from distinct sources before its design and launch. However, no universal method currently exists for quantitatively assessing the ability of satellites to quantify point source emissions. This paper presents a parametric conceptual model and database for efficiently evaluating the quantification capabilities of satellites and optimizing their technical characteristics for particular detection missions. Using the model and database, we evaluated how well various satellites can detect and quantify GHG emissions. Our findings indicate that accurate estimation of point source emissions requires both high spatial resolution and measurement precision. The requirement for satellite spatial resolution and measurement precision to achieve unbiased emission estimation gradually decreases with increasing emission intensity. The model and database developed in this study can serve as a reference for harmonious satellite configuration that balances measurement precision and spatial resolution. Furthermore, to progress the evaluation model of satellites for low-intensity emission point sources, it is imperative to implement a more precise simulation model and estimate method with a refined mask-building approach.
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Liu, Fei, Steffen Beirle, Joanna Joiner, Sungyeon Choi, Zhining Tao, K. Emma Knowland, Steven J. Smith, et al. "High-resolution mapping of nitrogen oxide emissions in large US cities from TROPOMI retrievals of tropospheric nitrogen dioxide columns." Atmospheric Chemistry and Physics 24, no. 6 (March 25, 2024): 3717–28. http://dx.doi.org/10.5194/acp-24-3717-2024.
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Abstract. Satellite-derived spatiotemporal patterns of nitrogen oxide (NOx) emissions can improve accuracy of emission inventories to better support air quality and climate research and policy studies. In this study, we develop a new method by coupling the chemical transport Model-Independent SATellite-derived Emission estimation Algorithm for Mixed-sources (MISATEAM) with a divergence method to map high-resolution NOx emissions across US cities using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric nitrogen dioxide (NO2) retrievals. The accuracy of the coupled method is validated through application to synthetic NO2 observations from the NASA-Unified Weather Research and Forecasting (NU-WRF) model, with a horizontal spatial resolution of 4 km × 4 km for 33 large and mid-size US cities. Validation reveals excellent agreement between inferred and NU-WRF-provided emission magnitudes (R= 0.99, normalized mean bias, NMB = −0.01) and a consistent spatial pattern when comparing emissions for individual grid cells (R=0.88±0.06). We then develop a TROPOMI-based database reporting annual emissions for 39 US cities at a horizontal spatial resolution of 0.05° × 0.05° from 2018 to 2021. This database demonstrates a strong correlation (R= 0.90) with the National Emission Inventory (NEI) but reveals some bias (NMB = −0.24). There are noticeable differences in the spatial patterns of emissions in some cities. Our analysis suggests that uncertainties in TROPOMI-based emissions and potential misallocation of emissions and/or missing sources in bottom-up emission inventories both contribute to these differences.
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Barré, Jérôme, Ilse Aben, Anna Agustí-Panareda, Gianpaolo Balsamo, Nicolas Bousserez, Peter Dueben, Richard Engelen, et al. "Systematic detection of local CH<sub>4</sub> anomalies by combining satellite measurements with high-resolution forecasts." Atmospheric Chemistry and Physics 21, no. 6 (April 1, 2021): 5117–36. http://dx.doi.org/10.5194/acp-21-5117-2021.
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Abstract. In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local CH4 concentration anomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget and by biases in the satellite retrieval data. The method uses high-resolution (7 km × 7 km) retrievals of total column CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are combined with high-resolution CH4 forecasts (∼ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) at close to the satellite's native resolution at appropriate time. Investigating these departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the synoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered departures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or overreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to local satellite retrieval biases linked to albedo and scattering issues.
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Weinberg, Irving N., David Beylin, Valera Zavarzin, Steve Yarnall, Pavel Y. Stepanov, Edward Anashkin, Deepa Narayanan, Sergei Dolinsky, Kathrin Lauckner, and Lee P. Adler. "Positron Emission Mammography: High-Resolution Biochemical Breast Imaging." Technology in Cancer Research & Treatment 4, no. 1 (February 2005): 55–60. http://dx.doi.org/10.1177/153303460500400108.
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Positron emission mammography (PEM) provides images of biochemical activity in the breast with spatial resolution matching individual ducts (1.5 mm full-width at half-maximum). This spatial resolution, supported by count efficiency that results in high signal-to-noise ratio, allows confident visualization of intraductal as well as invasive breast cancers. Clinical trials with a full-breast PEM device have shown high clinical accuracy in characterizing lesions identified as suspicious on the basis of conventional imaging or physical examination (sensitivity 93%, specificity 83%, area under the ROC curve of 0.93), with high sensitivity preserved (91%) for intraductal cancers. Increased sensitivity did not come at a cost of reduced specificity. Considering that intraductal cancer represents more than 30% of reported cancers, and is the form of cancer with the highest probability of achieving surgical cure, it is likely that the use of PEM will complement anatomic imaging modalities in the areas of surgical planning, high-risk monitoring, and minimally invasive therapy. The quantitative nature of PET promises to assist researchers interested studying the response of putative cancer precursors ( e.g., atypical ductal hyperplasia) to candidate prevention agents.
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Guevara, Marc, Santiago Enciso, Carles Tena, Oriol Jorba, Stijn Dellaert, Hugo Denier van der Gon, and Carlos Pérez García-Pando. "A global catalogue of CO2 emissions and co-emitted species from power plants, including high-resolution vertical and temporal profiles." Earth System Science Data 16, no. 1 (January 15, 2024): 337–73. http://dx.doi.org/10.5194/essd-16-337-2024.
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Abstract. We present a high-resolution global emission catalogue of CO2 and co-emitted species (NOx, SO2, CO, CH4) from thermal power plants for the year 2018. The construction of the database follows a bottom-up approach, which combines plant-specific information with national energy consumption statistics and fuel-dependent emission factors for CO2 and emission ratios for co-emitted species (e.g. the amount of NOx emitted relative to CO2: NOx/CO2). The resulting catalogue contains annual emission information for more than 16 000 individual facilities at their exact geographical locations. Each facility is linked to a country- and fuel-dependent temporal profile (i.e. monthly, day of the week and hourly) and a plant-level vertical profile, which were derived from national electricity generation statistics and plume rise calculations that combine stack parameters with meteorological information. The combination of the aforementioned information allows us to derive high-resolution spatial and temporal emissions for modelling purposes. Estimated annual emissions were compared against independent plant- and country-level inventories, including Carbon Monitoring for Action (CARMA), the Global Infrastructure emission Database (GID) and the Emissions Database for Global Atmospheric Research (EDGAR), as well as officially reported emission data. Overall good agreement is observed between datasets when comparing the CO2 emissions. The main discrepancies are related to the non-inclusion of auto-producer or heat-only facilities in certain countries due to a lack of data. Larger inconsistencies are obtained when comparing emissions from co-emitted species due to uncertainties in the fuel-, country- and region-dependent emission ratios and gap-filling procedures. The temporal distribution of emissions obtained in this work was compared against traditional sector-dependent profiles that are widely used in modelling efforts. This highlighted important differences and the need to consider country dependencies when temporally distributing emissions. The resulting catalogue (https://doi.org/10.24380/0a9o-v7xe, Guevara et al., 2023) is developed in the framework of the Prototype System for a Copernicus CO2 service (CoCO2) European Union (EU)-funded project to support the development of the Copernicus CO2 Monitoring and Verification Support capacity (CO2MVS).
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Dowd, Emily, Alistair J. Manning, Bryn Orth-Lashley, Marianne Girard, James France, Rebecca E. Fisher, Dave Lowry, et al. "First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK." Atmospheric Measurement Techniques 17, no. 5 (March 18, 2024): 1599–615. http://dx.doi.org/10.5194/amt-17-1599-2024.
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Abstract. Atmospheric methane (CH4) is the second-most-important anthropogenic greenhouse gas and has a 20-year global warming potential 82 times greater than carbon dioxide (CO2). Anthropogenic sources account for ∼ 60 % of global CH4 emissions, of which 20 % come from oil and gas exploration, production and distribution. High-resolution satellite-based imaging spectrometers are becoming important tools for detecting and monitoring CH4 point source emissions, aiding mitigation. However, validation of these satellite measurements, such as those from the commercial GHGSat satellite constellation, has so far not been documented for active leaks. Here we present the monitoring and quantification, by GHGSat's satellites, of the CH4 emissions from an active gas leak from a downstream natural gas distribution pipeline near Cheltenham, UK, in the spring and summer of 2023 and provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys. We also use a Lagrangian transport model, the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME), to estimate the flux from both satellite- and ground-based observation methods and assess the leak's contribution to observed concentrations at a local tall tower site (30 km away). We find GHGSat's emission estimates to be in broad agreement with those made from the in situ measurements. During the study period (March–June 2023) GHGSat's emission estimates are 236–1357 kg CH4 h−1, whereas the mobile surface measurements are 634–846 kg CH4 h−1. The large variability is likely down to variations in flow through the pipe and engineering works across the 11-week period. Modelled flux estimates in NAME are 181–1243 kg CH4 h−1, which are lower than the satellite- and mobile-survey-derived fluxes but are within the uncertainty. After detecting the leak in March 2023, the local utility company was contacted, and the leak was fixed by mid-June 2023. Our results demonstrate that GHGSat's observations can produce flux estimates that broadly agree with surface-based mobile measurements. Validating the accuracy of the information provided by targeted, high-resolution satellite monitoring shows how it can play an important role in identifying emission sources, including unplanned fugitive releases that are inherently challenging to identify, track, and estimate their impact and duration. Rapid, widespread access to such data to inform local action to address fugitive emission sources across the oil and gas supply chain could play a significant role in reducing anthropogenic contributions to climate change.
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Wolf, Tobias, Lasse H. Pettersson, and Igor Esau. "A very high-resolution assessment and modelling of urban air quality." Atmospheric Chemistry and Physics 20, no. 2 (January 20, 2020): 625–47. http://dx.doi.org/10.5194/acp-20-625-2020.
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Abstract. Urban air quality is one of the most prominent environmental concerns for modern city residents and authorities. Accurate monitoring of air quality is difficult due to intrinsic urban landscape heterogeneity and superposition of multiple polluting sources. Existing approaches often do not provide the necessary spatial details and peak concentrations of pollutants, especially at larger distances from monitoring stations. A more advanced integrated approach is needed. This study presents a very high-resolution air quality assessment with the Parallelized Large-Eddy Simulation Model (PALM), capitalising on local measurements. This fully three-dimensional primitive-equation hydrodynamical model resolves both structural details of the complex urban surface and turbulent eddies larger than 10 m in size. We ran a set of 27 meteorological weather scenarios in order to assess the dispersion of pollutants in Bergen, a middle-sized Norwegian city embedded in a coastal valley. This set of scenarios represents typically observed weather conditions with high air pollution from nitrogen dioxide (NO2) and particulate matter (PM2.5). The modelling methodology helped to identify pathways and patterns of air pollution caused by the three main local air pollution sources in the city. These are road vehicle traffic, domestic house heating with wood-burning fireplaces and ships docked in the harbour area next to the city centre. The study produced vulnerability maps, highlighting the most impacted districts for each weather and emission scenario. Overall, the largest contribution to air pollution over inhabited areas in Bergen was caused by road traffic emissions for NO2 and wood-burning fireplaces for PM2.5 pollution. The effect of emission from ships in the port was mostly restricted to the areas close to the harbour and moderate in comparison. However, the results have contributed to implementation of measures to reduce emissions from ships in Bergen harbour, including provision of shore power.
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Józsa, Viktor, Réka A. Kardos, Diána Kiss, Gergely Kiss-Albert, Zoltán Bozóki, and Helga Huszár. "High-resolution pollutant emission monitoring of turbulent combustion using the photoacoustic technique." Results in Engineering 24 (December 2024): 103586. https://doi.org/10.1016/j.rineng.2024.103586.
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Feng, Sha, Thomas Lauvaux, Sally Newman, Preeti Rao, Ravan Ahmadov, Aijun Deng, Liza I. Díaz-Isaac, et al. "Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO<sub>2</sub> emissions." Atmospheric Chemistry and Physics 16, no. 14 (July 22, 2016): 9019–45. http://dx.doi.org/10.5194/acp-16-9019-2016.
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Abstract. Megacities are major sources of anthropogenic fossil fuel CO2 (FFCO2) emissions. The spatial extents of these large urban systems cover areas of 10 000 km2 or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO2 emission product, Hestia-LA, to simulate atmospheric CO2 concentrations across the LA megacity at spatial resolutions as fine as ∼ 1 km. We evaluated multiple WRF configurations, selecting one that minimized errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May–June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO2 emission products to evaluate the impact of the spatial resolution of the CO2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO2 concentrations. We find that high spatial resolution in the fossil fuel CO2 emissions is more important than in the atmospheric model to capture CO2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO2 fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO2 fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO2 emissions monitoring in the LA megacity requires FFCO2 emissions modelling with ∼ 1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.
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van’t Veen, Sofie G. M. van’t, Jonas Rolighed, Jane R. Laugesen, Gitte Blicher-Mathiesen, and Brian Kronvang. "High Spatial Resolution Nitrogen Emission and Retention Maps of Three Danish Catchments Using Synchronous Measurements in Streams." Water 15, no. 3 (January 27, 2023): 498. http://dx.doi.org/10.3390/w15030498.
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We investigated the utility of using synchronous measurements to create nitrogen (N) emission and retention maps of agricultural areas. Total N (TN) emissions from agricultural areas in three different Danish pilot catchments (1800–3737 ha) and within sub-catchments (100–1200 ha) were determined by a source apportionment approach. Intensive daily (main gauging stations) and fortnightly (synchronous stations) monitoring of discharge, TN, and nitrate-N (NO3-N) concentrations was conducted for two years. The groundwater N retention was calculated as the difference between a model-calculated NO3-N leaching from agricultural fields and the calculated agricultural N emission. The average annual N leaching and N emission in the three catchments amounted to 68, 48, and 58 kg N/ha and 6, 30, and 40 kg N/ha, respectively. The N retention in groundwater in the three catchments, calculated based on either TN or NO3-N emissions, amounted to 26 and 44%, 44 and 57%, and 93 and 97%, respectively, with large variations within two of the main catchments. From this study, we conclude that synchronous measurements in streams provide a good opportunity for developing local N emission and N retention maps. However, NO3-N should be used when dealing with N retention calculation at the finer resolution scale of 100–300 ha catchments.
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Pan, Jun, Ying Wang, Xiaoliang Qin, Nirmal Kumar Gali, Qingyan Fu, and Zhi Ning. "Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network." Toxics 12, no. 10 (October 19, 2024): 760. http://dx.doi.org/10.3390/toxics12100760.
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Cargo terminals, as pivotal hubs of mechanical activities, maritime shipping, and land transportation, are significant sources of air pollutants, exhibiting considerable spatiotemporal heterogeneity due to the complex and irregular nature of emissions. This study employed a high-density air sensor network with 17 sites across four functional zones in two Shanghai cargo terminals to monitor NO and NO2 concentrations with high spatiotemporal resolution post sensor data validation against regulatory monitoring stations. Notably, NO and NO2 concentrations within the terminal surged during the night, peaking at 06:00 h, likely due to local regulations on heavy-duty diesel trucks. Spatial analysis revealed the highest NO concentrations in the core operational areas and adjacent roads, with significantly lower levels in the outer ring, indicating strong emission sources and limited dispersion. Employing the lowest percentile method for baseline extraction from high-resolution data, this study identified local emissions as the primary source of NO, constituting over 80% of total emissions. Elevated background concentrations of NO2 suggested a gradual oxidation of NO into NO2, with local emissions contributing to 32–70% of the total NO2 concentration. These findings provide valuable insights into the NO and NO2 emission characteristics across different terminal areas, aiding decision-makers in developing targeted emission control policies.
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Qu, Ge, Yusheng Shi, Yongliang Yang, Wen Wu, and Zhitao Zhou. "Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion." Atmosphere 15, no. 10 (October 18, 2024): 1247. http://dx.doi.org/10.3390/atmos15101247.
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Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.
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Ramacher, Martin Otto Paul, Anastasia Kakouri, Orestis Speyer, Josefine Feldner, Matthias Karl, Renske Timmermans, Hugo Denier van der Gon, Jeroen Kuenen, Evangelos Gerasopoulos, and Eleni Athanasopoulou. "The UrbEm Hybrid Method to Derive High-Resolution Emissions for City-Scale Air Quality Modeling." Atmosphere 12, no. 11 (October 26, 2021): 1404. http://dx.doi.org/10.3390/atmos12111404.
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As cities are growing in size and complexity, the estimation of air pollution exposure requires a detailed spatial representation of air pollution levels, rather than homogenous fields, provided by global- or regional-scale models. A critical input for city-scale modeling is a timely and spatially resolved emission inventory. Bottom–up approaches to create urban-scale emission inventories can be a demanding and time-consuming task, whereas local emission rates derived from a top–down approach may lack accuracy. In the frame of this study, the UrbEm approach of downscaling gridded emission inventories is developed, investing upon existing, open access, and credible emission data sources. As a proof-of-concept, the regional anthropogenic emissions by Copernicus Atmospheric Monitoring Service (CAMS) are handled with a top–down approach, creating an added-value product of anthropogenic emissions of trace gases and particulate matter for any city (or area) of Europe, at the desired spatial resolution down to 1 km. The disaggregation is based on contemporary proxies for the European area (e.g., Global Human Settlement population data, Urban Atlas 2012, Corine, OpenStreetMap data). The UrbEm approach is realized as a fully automated software tool to produce a detailed mapping of industrial (point), (road-) transport (line), and residential/agricultural/other (area) emission sources. Line sources are of particular value for air quality studies at the urban scale, as they enable explicit treatment of line sources by models capturing among others the street canyon effect and offer an overall better representation of the critical road transport sector. The UrbEm approach is an efficient solution for such studies and constitutes a fully credible option in case high-resolution emission inventories do not exist for a city (or area) of interest. The validity of UrbEm is examined through the evaluation of high-resolution air pollution predictions over Athens and Hamburg against in situ measurements. In addition to a better spatial representation of emission sources and especially hotspots, the air quality modeling results show that UrbEm outputs, when compared to a uniform spatial disaggregation, have an impact on NO2 predictions up to 70% for urban regions with complex topographies, which corresponds to a big improvement of model accuracy (FAC2 > 0.5), especially at the source-impacted sites.
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Kong, Hao, Jintai Lin, Ruixiong Zhang, Mengyao Liu, Hongjian Weng, Ruijing Ni, Lulu Chen, Jingxu Wang, Yingying Yan, and Qiang Zhang. "High-resolution (0.05° × 0.05°) NO<sub><i>x</i></sub> emissions in the Yangtze River Delta inferred from OMI." Atmospheric Chemistry and Physics 19, no. 20 (October 15, 2019): 12835–56. http://dx.doi.org/10.5194/acp-19-12835-2019.
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Abstract. Emission datasets of nitrogen oxides (NOx) at high horizontal resolutions (e.g., 0.05∘×0.05∘) are crucial for understanding human influences at fine scales, air quality studies, and pollution control. Yet high-resolution emission data are often missing or contain large uncertainties especially for the developing regions. Taking advantage of long-term satellite measurements of nitrogen dioxide (NO2), here we develop a computationally efficient method of estimating NOx emissions in major urban areas at the 0.05∘×0.05∘ resolution. The top-down inversion method accounts for the nonlinear effects of horizontal transport, chemical loss, and deposition. We construct a two-dimensional Peking University High-resolution Lifetime-Emission-Transport model (PHLET), its adjoint model (PHLET-A), and a satellite conversion matrix approach to relate emissions, lifetimes, simulated NO2, and satellite NO2 data. The inversion method is applied to the summer months of 2012–2015 in the Yangtze River Delta (YRD; 29–34∘ N, 118–123∘ E) area, a major polluted region of China, using the NO2 vertical column density data from the Peking University Ozone Monitoring Instrument NO2 product (POMINO). A systematic analysis of inversion errors is performed, including using an independent test based on GEOS-Chem simulations. Across the YRD area, the summer average emissions obtained in this work range from 0 to 15.3 kg km−2 h−1, and the lifetimes (due to chemical loss and deposition) range from 0.6 to 3.3 h. Our emission dataset reveals fine-scale spatial information related to nighttime light, population density, road network, maritime shipping, and land use (from a Google Earth photo). We further compare our emissions with multiple inventories. Many of the fine-scale emission structures are not well represented or not included in the widely used Multi-scale Emissions Inventory of China (MEIC).
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Hilton, M., A. H. Lettington, and C. W. Wilson. "Gas Turbine Exhaust Emissions Monitoring Using Nonintrusive Infrared Spectroscopy." Journal of Engineering for Gas Turbines and Power 120, no. 3 (July 1, 1998): 514–18. http://dx.doi.org/10.1115/1.2818175.
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Infrared (IR) spectra of the exhaust emissions from a static gas turbine engine have been studied using Fourier Transform (FT) spectroscopic techniques. Passive detection of the infrared emission from remote (range ∼ 3 m) hot exhaust gases was obtained nonintrusively using a high spectral resolution (0.25 cm−1) FTIR spectrometer. Remote gas temperatures were determined from their emission spectra using the total radiant flux method or by analysis of rotational line structure. The HITRAN database of atmospheric species was used to model the emission from gas mixtures at the relevant temperatures. The spatial distribution of molecular species across a section transverse to the exhaust plume ∼10 cm downstream of the jet pipe nozzle was studied using a tomographic reconstruction procedure. Spectra of the infrared emission from the plume were taken along a number of transverse lines of sight from the centerline of the engine outwards. A mathematical matrix inversion technique was applied to reconstruct the molecular concentrations of CO and CO2 in concentric regions about the centerline. Quantitative measurement of the molecular species concentrations determined nonintrusively were compared with results from conventional extractive sampling techniques.
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Yang, Daoyuan, Shaojun Zhang, Tianlin Niu, Yunjie Wang, Honglei Xu, K. Max Zhang, and Ye Wu. "High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets." Atmospheric Chemistry and Physics 19, no. 13 (July 11, 2019): 8831–43. http://dx.doi.org/10.5194/acp-19-8831-2019.
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Abstract. On-road vehicle emissions are a major contributor to elevated air pollution levels in populous metropolitan areas. We developed a link-level emissions inventory of vehicular pollutants, called EMBEV-Link (Link-level Emission factor Model for the BEijing Vehicle fleet), based on multiple datasets extracted from the extensive road traffic monitoring network that covers the entire municipality of Beijing, China (16 400 km2). We employed the EMBEV-Link model under various traffic scenarios to capture the significant variability in vehicle emissions, temporally and spatially, due to the real-world traffic dynamics and the traffic restrictions implemented by the local government. The results revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in the urban area (i.e., within the Fifth Ring Road) and during rush hours, both associated with the passenger vehicle traffic. By contrast, considerable fractions of nitrogen oxides (NOx), fine particulate matter (PM2.5) and black carbon (BC) emissions were present beyond the urban area, as heavy-duty trucks (HDTs) were not allowed to drive through the urban area during daytime. The EMBEV-Link model indicates that nonlocal HDTs could account for 29 % and 38 % of estimated total on-road emissions of NOx and PM2.5, which were ignored in previous conventional emission inventories. We further combined the EMBEV-Link emission inventory and a computationally efficient dispersion model, RapidAir®, to simulate vehicular NOx concentrations at fine resolutions (10 m × 10 m in the entire municipality and 1 m × 1 m in the hotspots). The simulated results indicated a close agreement with ground observations and captured sharp concentration gradients from line sources to ambient areas. During the nighttime when the HDT traffic restrictions are lifted, HDTs could be responsible for approximately 10 µg m−3 of NOx in the urban area. The uncertainties of conventional top-down allocation methods, which were widely used to enhance the spatial resolution of vehicle emissions, are also discussed by comparison with the EMBEV-Link emission inventory.
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Tang, Tao, Lili Zhang, Hao Zhu, Xiaotong Ye, Donghao Fan, Xingyu Li, Haoran Tong, and Shenshen Li. "Quantifying Urban Daily Nitrogen Oxide Emissions from Satellite Observations." Atmosphere 15, no. 4 (April 21, 2024): 508. http://dx.doi.org/10.3390/atmos15040508.
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Urban areas, characterized by dense anthropogenic activities, are among the primary sources of nitrogen oxides (NOx), impacting global atmospheric conditions and human health. Satellite observations, renowned for their continuity and global coverage, have emerged as an effective means to quantify pollutant emissions. Previous bottom-up emission inventories exhibit considerable discrepancies and lack a comprehensive and reliable database. To develop a high-precision emission inventory for individual cities, this study utilizes high-resolution single-pass observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite to quantify the emission rates of NOx. The Exponentially Modified Gaussian (EMG) model is validated for estimating NOx emission strength using real plumes observed in satellite single-pass observations, demonstrating good consistency with existing inventories. Further analysis based on the results reveals the existence of a weekend effect and seasonal variations in NOx emissions for the majority of the studied cities.
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Lee, Jae-Hyeong, Sang-Hyun Lee, and Hyun Cheol Kim. "Detection of Strong NOX Emissions from Fine-scale Reconstruction of the OMI Tropospheric NO2 Product." Remote Sensing 11, no. 16 (August 9, 2019): 1861. http://dx.doi.org/10.3390/rs11161861.
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Satellite-retrieved atmospheric NO2 column products have been widely used in assessing bottom-up NOX inventory emissions emitted from large cities, industrial facilities, and power plants. However, the satellite products fail to quantify strong NOX emissions emitted from the sources less than the satellite’s pixel size, with significantly underestimating their emission intensities (smoothing effect). The poor monitoring of the emissions makes it difficult to enforce pollution restriction regulations. This study reconstructs the tropospheric NO2 vertical column density (VCD) of the Ozone Monitoring Instrument (OMI)/Aura (13 × 24 km2 pixel resolution at nadir) over South Korea to a fine-scale product (grid resolution of 3 × 3 km2) using a conservative spatial downscaling method, and investigates the methodological fidelity in quantifying the major Korean area and point sources that are smaller than the satellite’s pixel size. Multiple high-fidelity air quality models of the Weather Research and Forecast-Chemistry (WRF-Chem) and the Weather Research and Forecast/Community Multiscale Air Quality modeling system (WRF/CMAQ) were used to investigate the downscaling uncertainty in a spatial-weight kernel estimate. The analysis results showed that the fine-scale reconstructed OMI NO2 VCD revealed the strong NOX emission sources with increasing the atmospheric NO2 column concentration and enhanced their spatial concentration gradients near the sources, which was accomplished by applying high-resolution modeled spatial-weight kernels to the original OMI NO2 product. The downscaling uncertainty of the reconstructed OMI NO2 product was inherent and estimated by 11.1% ± 10.6% at the whole grid cells over South Korea. The smoothing effect of the original OMI NO2 product was estimated by 31.7% ± 13.1% for the 6 urbanized area sources and 32.2% ± 17.1% for the 13 isolated point sources on an effective spatial resolution that is defined to reduce the downscaling uncertainty. Finally, it was found that the new reconstructed OMI NO2 product had a potential capability in quantifying NOX emission intensities of the isolated strong point sources with a good correlation of R = 0.87, whereas the original OMI NO2 product failed not only to identify the point sources, but also to quantify their emission intensities (R = 0.30). Our findings highlight a potential capability of the fine-scale reconstructed OMI NO2 product in detecting directly strong NOX emissions, and emphasize the inherent methodological uncertainty in interpreting the reconstructed satellite product at a high-resolution grid scale.
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Zhou, Shangli, Hengjing He, Leping Zhang, Wei Zhao, and Fei Wang. "A Data-Driven Method to Monitor Carbon Dioxide Emissions of Coal-Fired Power Plants." Energies 16, no. 4 (February 7, 2023): 1646. http://dx.doi.org/10.3390/en16041646.
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Reducing CO2 emissions from coal-fired power plants is an urgent global issue. Effective and precise monitoring of CO2 emissions is a prerequisite for optimizing electricity production processes and achieving such reductions. To obtain the high temporal resolution emissions status of power plants, a lot of research has been done. Currently, typical solutions are utilizing Continuous Emission Monitoring System (CEMS) to measure CO2 emissions. However, these methods are too expensive and complicated because they require the installation of a large number of devices and require periodic maintenance to obtain accurate measurements. According to this limitation, this paper attempts to provide a novel data-driven method using net power generation to achieve near-real-time monitoring. First, we study the key elements of CO2 emissions from coal-fired power plants (CFPPs) in depth and design a regression and physical variable model-based emission simulator. We then present Emission Estimation Network (EEN), a heterogeneous network-based deep learning model, to estimate CO2 emissions from CFPPs in near-real-time. We use artificial data generated by the simulator to train it and apply a few real-world datasets to complete the adaptation. The experimental results show that our proposal is a competitive approach that not only has accurate measurements but is also easy to implement.
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Beckman, J. E., R. J. García López, R. Rebolo, and L. Crivellari. "Ca II H High-Resolution Spectral Monitoring of Active Late-type Dwarfs." International Astronomical Union Colloquium 130 (1991): 463–65. http://dx.doi.org/10.1017/s0252921100080155.
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AbstractWe have monitored Ca II H with a spectral resolution of 8 × 104, in asimple of late-type MS stars over a four-year period. The high resolution enables us to add information on velocity fields to the usual flux monitoring. We detect changes in wavelength of different parts of the Ca II H feature, which can be interpreted as velocity fields in the lower chromosphere, with downflow and upflow of order 0.5 km s−1, depending on the star. Flux variations in Ca II H emission can be ascribed, via velocity tagging, to long-term change in plage cover rather than short-term modulation by (incompletely sampled) rotation cycles.
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Zhan, Tingting, Hao Chen, and Lei Li. "Numerical study of in situ acoustic emission monitoring for small-scale hydraulic fracturing." Journal of Geophysics and Engineering 19, no. 4 (July 9, 2022): 615–29. http://dx.doi.org/10.1093/jge/gxac036.
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Abstract Reservoir monitoring is necessary to achieve safe hydrocarbon extraction. It requires monitoring small acoustic emission (AE) events, assisting in determining the exact location, extension and direction of potential damage as early as possible. However, microcracks cannot be detected by the microseismic monitoring networks due to the limitations of frequency range and sensitivity. In contrast, the in situ AE monitoring system extends the detection range to higher frequencies and can detect very small deformation processes with high resolution and sensitivity. It provides detailed insights into ongoing deformation processes. However, the receivers of an in situ AE monitoring system need to be very close to hydraulic fracturing experiments due to the fast decay of high-frequency signals. In this work, by constructing four in situ AE monitoring models, the imaging effects of the interferometric imaging method for three different kHz-level frequency sources at different distances and orientations relative to the monitoring well are investigated. The numerical results show that: the higher the source frequency, the higher the imaging resolution; when the vertical orientation coverage of the source by the monitoring system is incomplete, the closer the source to the borehole axis, the worse the imaging resolution and location accuracy; when the vertical orientation coverage of the source by the monitoring system is complete, the imaging resolution and location accuracy are both improved, especially when the vertical azimuthal coverage angle is large. The integrity of the orientation coverage of the source by the monitoring system plays a critical role in improving source location accuracy.
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Wang, Yilong, Philippe Ciais, Grégoire Broquet, François-Marie Bréon, Tomohiro Oda, Franck Lespinas, Yasjka Meijer, et al. "A global map of emission clumps for future monitoring of fossil fuel CO<sub>2</sub> emissions from space." Earth System Science Data 11, no. 2 (May 17, 2019): 687–703. http://dx.doi.org/10.5194/essd-11-687-2019.
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Abstract. A large fraction of fossil fuel CO2 emissions emanate from “hotspots”, such as cities (where direct CO2 emissions related to fossil fuel combustion in transport, residential, commercial sectors, etc., excluding emissions from electricity-producing power plants, occur), isolated power plants, and manufacturing facilities, which cover a small fraction of the land surface. The coverage of all high-emitting cities and point sources across the globe by bottom-up inventories is far from complete, and for most of those covered, the uncertainties in CO2 emission estimates in bottom-up inventories are too large to allow continuous and rigorous assessment of emission changes (Gurney et al., 2019). Space-borne imagery of atmospheric CO2 has the potential to provide independent estimates of CO2 emissions from hotspots. But first, what a hotspot is needs to be defined for the purpose of satellite observations. The proposed space-borne imagers with global coverage planned for the coming decade have a pixel size on the order of a few square kilometers and a XCO2 accuracy and precision of <1 ppm for individual measurements of vertically integrated columns of dry-air mole fractions of CO2 (XCO2). This resolution and precision is insufficient to provide a cartography of emissions for each individual pixel. Rather, the integrated emission of diffuse emitting areas and intense point sources is sought. In this study, we characterize area and point fossil fuel CO2 emitting sources which generate coherent XCO2 plumes that may be observed from space. We characterize these emitting sources around the globe and they are referred to as “emission clumps” hereafter. An algorithm is proposed to identify emission clumps worldwide, based on the ODIAC global high-resolution 1 km fossil fuel emission data product. The clump algorithm selects the major urban areas from a GIS (geographic information system) file and two emission thresholds. The selected urban areas and a high emission threshold are used to identify clump cores such as inner city areas or large power plants. A low threshold and a random walker (RW) scheme are then used to aggregate all grid cells contiguous to cores in order to define a single clump. With our definition of the thresholds, which are appropriate for a space imagery with 0.5 ppm precision for a single XCO2 measurement, a total of 11 314 individual clumps, with 5088 area clumps, and 6226 point-source clumps (power plants) are identified. These clumps contribute 72 % of the global fossil fuel CO2 emissions according to the ODIAC inventory. The emission clumps is a new tool for comparing fossil fuel CO2 emissions from different inventories and objectively identifying emitting areas that have a potential to be detected by future global satellite imagery of XCO2. The emission clump data product is distributed from https://doi.org/10.6084/m9.figshare.7217726.v1.
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Liu, C. C., P. Y. Tseng, and C. Y. Chen. "The application of FORMOSAT-2 high-temporal- and high-spatial resolution imagery for monitoring open straw burning and carbon emission detection." Natural Hazards and Earth System Sciences 13, no. 3 (March 6, 2013): 575–82. http://dx.doi.org/10.5194/nhess-13-575-2013.
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Abstract. Rice is produced in more than 95 countries worldwide and is a staple food for over half of the world's population. Rice is also a major food crop of Taiwan. There are numerous rice crops planted on the western plains of Taiwan, and, after the harvest season, the left-over straw is often burned on-site. The air pollutants from the burning emissions include CO2, CO, CH4 and other suspended particles, most of these being the greenhouse gases which cause global climate change. In this study FORMOSAT-2 satellite images and ground-truth data from 2008 and 2009 are used to conduct supervised classification and calculate the extent of the straw burning areas. It was found that 10% of the paddies in the study area were burned after harvest during this 2-yr period. On this pro rata basis, we calculated the overall carbon emissions from the burning of the straw. The findings showed that these few farmers produced up to 34 000 tons of carbon emissions in 2008, and 40 000 tons in 2009. The study results indicate that remotely sensed images can be used to efficiently evaluate the important characteristics for carbon emission detection. It also provides quantitative results that are relevant to tracking sources of transport pollution, postharvest burning, and Asian dust in Taiwan.
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Strandgren, Johan, David Krutz, Jonas Wilzewski, Carsten Paproth, Ilse Sebastian, Kevin R. Gurney, Jianming Liang, Anke Roiger, and André Butz. "Towards spaceborne monitoring of localized CO<sub>2</sub> emissions: an instrument concept and first performance assessment." Atmospheric Measurement Techniques 13, no. 6 (June 3, 2020): 2887–904. http://dx.doi.org/10.5194/amt-13-2887-2020.
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Abstract. The UNFCCC (United Nations Framework Convention on Climate Change) requires the nations of the world to report their carbon dioxide (CO2) emissions. The independent verification of these reported emissions is a cornerstone for advancing towards the emission accounting and reduction measures agreed upon in the Paris Agreement. In this paper, we present the concept and first performance assessment of a compact spaceborne imaging spectrometer with a spatial resolution of 50×50 m2 that could contribute to the “monitoring, verification and reporting” (MVR) of CO2 emissions worldwide. CO2 emissions from medium-sized power plants (1–10 Mt CO2 yr−1), currently not targeted by other spaceborne missions, represent a significant part of the global CO2 emission budget. In this paper we show that the proposed instrument concept is able to resolve emission plumes from such localized sources as a first step towards corresponding CO2 flux estimates. Through radiative transfer simulations, including a realistic instrument noise model and a global trial ensemble covering various geophysical scenarios, it is shown that an instrument noise error of 1.1 ppm (1σ) can be achieved for the retrieval of the column-averaged dry-air mole fraction of CO2 (XCO2). Despite a limited amount of information from a single spectral window and a relatively coarse spectral resolution, scattering by atmospheric aerosol and cirrus can be partly accounted for in the XCO2 retrieval, with deviations of at most 4.0 ppm from the true abundance for two-thirds of the scenes in the global trial ensemble. We further simulate the ability of the proposed instrument concept to observe CO2 plumes from single power plants in an urban area using high-resolution CO2 emission and surface albedo data for the city of Indianapolis. Given the preliminary instrument design and the corresponding instrument noise error, emission plumes from point sources with an emission rate down to the order of 0.3 Mt CO2 yr−1 can be resolved, i.e., well below the target source strength of 1 Mt CO2 yr−1. This leaves a significant margin for additional error sources, like scattering particles and complex meteorology, and shows the potential for subsequent CO2 flux estimates with the proposed instrument concept.
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de Foy, Benjamin, and James J. Schauer. "An improved understanding of NOx emissions in South Asian megacities using TROPOMI NO2 retrievals." Environmental Research Letters 17, no. 2 (January 21, 2022): 024006. http://dx.doi.org/10.1088/1748-9326/ac48b4.
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Abstract Identifying air pollutant emissions has played a key role in improving air quality and hence the health of billions of people around the world. Central to this effort are the development of emission inventories and the mapping of air pollution using satellite remote sensing. The TROPOspheric Monitoring Instrument (TROPOMI) has been providing high resolution vertical column densities of nitrogen dioxide since late October 2018. Using the flux divergence method and a Gaussian Mixture Model, we identify peak emission hotspots over four cities in South Asia: Dhaka, Kolkata, Delhi and Lahore. We analyze data from November 2018 to March 2021 and focus on the three dry seasons (November to March) for which retrievals are available. The retrievals are shown to have sufficient spatial resolution to identify individual point and area sources. We further analyze the length scale and eccentricities of the hotspots to better characterize the emission sources. The TROPOMI emission estimates are compared with the EDGAR global emission inventory and the REAS regional inventory. This reveals areas of agreement but also significant discrepancies that should enable improvements and refinements of the inventories in the future. For example, urban emissions are underestimated while power generation emissions are overestimated. Some areas of light manufacturing cause significant signatures in TROPOMI retrievals but are mostly missing from the inventories. The spatial resolution of the TROPOMI instrument is now sufficient to provide detailed feedback to developers of emission inventories as well as to inform policy decisions at the urban to regional scale.
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Schade, Gunnar W., and Mitchell L. Gregg. "Testing HYSPLIT Plume Dispersion Model Performance Using Regional Hydrocarbon Monitoring Data during a Gas Well Blowout." Atmosphere 13, no. 3 (March 17, 2022): 486. http://dx.doi.org/10.3390/atmos13030486.
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A gas well blowout in south central Texas in November 2019 that lasted for 20 days provided a unique opportunity to test the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model’s plume dispersion against hydrocarbon air monitoring data at two nearby state monitoring stations. We estimated daily blowout hydrocarbon emission rates from satellite measurement-based results on methane emissions in conjunction with previously reported composition data of the local hydrocarbon resource. Using highly elevated hydrocarbon mixing ratios observed during several days at the two downwind monitoring stations, we calculated excess abundances above expected local background mixing ratios. Subsequent comparisons to HYSPLIT plume dispersion model outputs, generated using High-Resolution Rapid Refresh (HRRR) or North American Mesoscale (NAM) forecast meteorological input data, showed that the model generally reproduces both the timing and magnitude of the plume in various meteorological conditions. Absolute hydrocarbon mixing ratios could typically be reproduced within a factor of two. However, when lower emission rate estimates provided by the company in charge of the well were used, downwind hydrocarbon observations could not be reproduced. Overall, our results suggest that HYSPLIT, in combination with high-resolution meteorological input data, is a useful tool to accurately forecast chemical plume dispersion and potential human exposure in disaster situations.
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Mao, Junjie, M. Mehdipour, J. S. Kaastra, E. Costantini, C. Pinto, G. Branduardi-Raymont, E. Behar, et al. "Photoionized emission and absorption features in the high-resolution X-ray spectra of NGC 3783." Astronomy & Astrophysics 621 (January 2019): A99. http://dx.doi.org/10.1051/0004-6361/201833191.
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Our Swift monitoring program triggered two joint XMM-Newton, NuSTAR, and HST observations on 11 and 21 December 2016 targeting NGC 3783 because its soft X-ray continuum was heavily obscured. Consequently, emission features, including the O VII radiative recombination continuum, stand out above the diminished continuum. We focus on the photoionized emission features in the December 2016 Reflection Grating Spectrometer (RGS) spectra, and compare them to the time-averaged RGS spectrum obtained in 2000–2001 when the continuum was unobscured. A two-phase photoionized plasma is required to account for the narrow emission features. These narrow emission features are weakly varying between 2000–2001 and December 2016. We also find a statistically significant broad emission component in the time-averaged RGS spectrum in 2000–2001. This broad emission component is significantly weaker in December 2016, suggesting that the obscurer is farther away than the X-ray broad-line region. In addition, by analyzing the archival high-resolution X-ray spectra, we find that nine photoionized absorption components with different ionization parameters and kinematics are required for the warm absorber in X-rays.
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Wang, J. M., C. H. Jeong, N. Zimmerman, R. M. Healy, D. K. Wang, F. Ke, and G. J. Evans. "Plume-based analysis of vehicle fleet air pollutant emissions and the contribution from high emitters." Atmospheric Measurement Techniques 8, no. 8 (August 13, 2015): 3263–75. http://dx.doi.org/10.5194/amt-8-3263-2015.
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Abstract. An automated identification and integration method has been developed for in-use vehicle emissions under real-world conditions. This technique was applied to high-time-resolution air pollutant measurements of in-use vehicle emissions performed under real-world conditions at a near-road monitoring station in Toronto, Canada, during four seasons, through month-long campaigns in 2013–2014. Based on carbon dioxide measurements, over 100 000 vehicle-related plumes were automatically identified and fuel-based emission factors for nitrogen oxides; carbon monoxide; particle number; black carbon; benzene, toluene, ethylbenzene, and xylenes (BTEX); and methanol were determined for each plume. Thus the automated identification enabled the measurement of an unprecedented number of plumes and pollutants over an extended duration. Emission factors for volatile organic compounds were also measured roadside for the first time using a proton transfer reaction time-of-flight mass spectrometer; this instrument provided the time resolution required for the plume capture technique. Mean emission factors were characteristic of the light-duty gasoline-dominated vehicle fleet present at the measurement site, with mean black carbon and particle number emission factors of 35 mg kg fuel−1 and 7.5 × 1014 # kg fuel−1, respectively. The use of the plume-by-plume analysis enabled isolation of vehicle emissions, and the elucidation of co-emitted pollutants from similar vehicle types, variability of emissions across the fleet, and the relative contribution from heavy emitters. It was found that a small proportion of the fleet (< 25 %) contributed significantly to total fleet emissions: 100, 100, 81, and 77 % for black carbon, carbon monoxide, BTEX, and particle number, respectively. Emission factors of a single pollutant may help classify a vehicle as a high emitter; however, regulatory strategies to more efficiently target multi-pollutant mixtures may be better developed by considering the co-emitted pollutants as well.
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Wang, J. M., C. H. Jeong, N. Zimmerman, R. M. Healy, D. K. Wang, F. Ke, and G. J. Evans. "Plume-based analysis of vehicle fleet air pollutant emissions and the contribution from high emitters." Atmospheric Measurement Techniques Discussions 8, no. 3 (March 18, 2015): 2881–912. http://dx.doi.org/10.5194/amtd-8-2881-2015.
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Abstract. An automated identification and integration method has been developed to investigate in-use vehicle emissions under real-world conditions. This technique was applied to high time resolution air pollutant measurements of in-use vehicle emissions performed under real-world conditions at a near-road monitoring station in Toronto, Canada during four seasons, through month-long campaigns in 2013–2014. Based on carbon dioxide measurements, over 100 000 vehicle-related plumes were automatically identified and fuel-based emission factors for nitrogen oxides; carbon monoxide; particle number, black carbon; benzene, toluene, ethylbenzene, and xylenes (BTEX); and methanol were determined for each plume. Thus the automated identification enabled the measurement of an unprecedented number of plumes and pollutants over an extended duration. Emission factors for volatile organic compounds were also measured roadside for the first time using a proton transfer reaction time-of-flight mass spectrometer; this instrument provided the time resolution required for the plume capture technique. Mean emission factors were characteristic of the light-duty gasoline dominated vehicle fleet present at the measurement site, with mean black carbon and particle number emission factors of 35 mg kg−1 and 7.7 × 1014 kg−1, respectively. The use of the plume-by-plume analysis enabled isolation of vehicle emissions, and the elucidation of co-emitted pollutants from similar vehicle types, variability of emissions across the fleet, and the relative contribution from heavy emitters. It was found that a small proportion of the fleet (< 25%) contributed significantly to total fleet emissions; 95, 93, 76, and 75% for black carbon, carbon monoxide, BTEX, and particle number, respectively. Emission factors of a single pollutant may help classify a vehicle as a high emitter. However, regulatory strategies to more efficiently target multi-pollutants mixtures may be better developed by considering the co-emitted pollutants as well.
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Danjou, Alexandre, Grégoire Broquet, Andrew Schuh, François-Marie Bréon, and Thomas Lauvaux. "Optimal selection of satellite XCO2 images for urban CO2 emission monitoring." Atmospheric Measurement Techniques 18, no. 2 (January 29, 2025): 533–54. https://doi.org/10.5194/amt-18-533-2025.
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Abstract. There is a growing interest in estimating urban CO2 emission from spaceborne imagery of the CO2 column-average dry-air mole fraction (XCO2). Emission estimation methods have been widely tested and applied to actual or synthetic images. However, there is still a lack of objective criteria for selecting images that are worth processing. This study analyzes the performances of an automated method for estimating urban emissions as a function of the targeted cities and of the atmospheric conditions. It uses synthetic data experiments with synthetic truth and 9920 synthetic satellite images of XCO2 over 31 of the largest cities across the world generated with a global adaptive-mesh model, the Ocean–Land–Atmosphere Model (OLAM), zoomed in at high resolution over these cities. We use a decision tree learning method applied to this ensemble of synthetic images to define criteria based on these emission and atmospheric conditions for the selection of suitable satellite images. We show that our automated method for the emission estimation, based on a Gaussian plume model, manages to produce estimates for 92 % of the synthetic images. Our learning method identifies two criteria, the wind direction's spatial variability and the targeted city's emission budget, that discriminate images whose processing yields reasonable emission estimates from those whose processing yields large errors. Images corresponding to low spatial variability in wind direction (less than 12°) and to high urban emissions (greater than 2.1 kt CO2 h−1) account for 47 % of the images, and their processing yields relative errors in the emission estimates with a median value of −7 % and an interquartile range (IQR) of 56 %. Images corresponding to a high spatial variability in wind direction or to low urban emissions account for 53 % of our images, and their processing yield relative errors in the emission estimates with a median value of −31 % and an IQR of 99 %. Despite such efficient filtering, the accuracy of the estimates corresponding to the former group of images varies widely from city to city.
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Lin, J. T. "Satellite constraint for emissions of nitrogen oxides from anthropogenic, lightning and soil sources over East China on a high-resolution grid." Atmospheric Chemistry and Physics Discussions 11, no. 11 (November 7, 2011): 29807–43. http://dx.doi.org/10.5194/acpd-11-29807-2011.
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Abstract. Vertical column densities (VCDs) of tropospheric nitrogen dioxide (NO2) retrieved from space provide valuable information to estimate emissions of nitrogen oxides (NOx) inversely. Accurate emission attribution to individual sources, important both for understanding the global biogeochemical cycling of nitrogen and for emission control, remains difficult. This study presents a regression-based multi-step inversion approach to estimate emissions of NOx from anthropogenic, lightning and soil sources individually for 2006 over East China on a 0.25° long × 0.25° lat grid, employing the DOMINO product version 2 retrieved from the Ozone Monitoring Instrument. The nested GEOS-Chem model for East Asia is used to simulate the seasonal variations of different emission sources and impacts on VCDs of NO2 for the inversion purpose. Sensitivity tests are conducted to evaluate key assumptions embedded in the inversion process. The inverse estimate suggests annual budgets of about 7.1 TgN (±38%), 0.22 TgN (±46%), and 0.40 TgN (±48%) for the a posteriori anthropogenic, lightning and soil emissions, respectively, each about 24% higher than the respective a priori values. The enhancements in anthropogenic emissions are largest in cities and areas with extensive use of coal, particularly in the north in winter, as evident on the high-resolution grid. Derived soil emissions are consistent with recent bottom-up estimates. They are each less than 6% of anthropogenic emissions annually, increasing to about 13% for July. Overall, anthropogenic emissions are found to be the dominant source of NOx over East China with important implications for nitrogen control.
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Kinakh, V., T. Oda, R. Bun, and O. Novitska. "Mitigating geolocation errors in nighttime light satellite data and global CO2 emission gridded data." Mathematical Modeling and Computing 8, no. 2 (2021): 304–16. http://dx.doi.org/10.23939/mmc2021.02.304.
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Accurate geospatial modeling of greenhouse gas (GHG) emissions is an essential part of the future of global GHG monitoring systems. Our previous work found a systematic displacement in the high-resolution carbon dioxide (CO2) emission raster data of the Open-source Data Inventory for Anthropogenic CO2 (ODIAC) emission product. It turns out this displacement is due to geolocation bias in the Defense Meteorological Satellite Program (DMSP) nighttime lights (NTL) data products, which are used as a spatial emission proxy for estimating non-point source emissions distributions in ODIAC. Mitigating such geolocation error (~1.7 km), which is on the same order of the size of the carbon observing satellites field of view, is especially critical for the spatial analysis of emissions from cities. In this paper, there is proposed a method to mitigate the geolocation bias in DMSP NTL data that can be applied to DMSP NTL-based geospatial products, such as ODIAC. To identify and characterize the geolocation bias, we used the OpenStreetMap repository to define city boundaries for a large number of global cities. Assumption is that the total emissions within the city boundaries are at the maximum if there is no displacement (geolocation bias) in NTL data. Therefore, it is necessary to find an optimal vector (distance and angle) that maximizes the ODIAC total emissions within cities by shifting the emission fields. In the process of preparing annual composites of the nighttime stable lights data, some pixels of the DMSP data corresponding to water bodies were zeroed, which due to the geolocation bias unreasonably distorted the ODIAC emission fields. Hence, an original approach for restoring data in such pixels is considered using elimination of the factor that distorted the ODIAC emission fields. It is also proposed a bias correction method for shifted high-resolution emission fields in ODIAC. The bias correction was applied to multiple cities from the different continents. It is shown that the bias correction to the emission data (elimination of geolocation error in non-point emission source fields) increases the total CO2 emissions within city boundaries by 4.76% on average, due to reduced emissions from non-urban areas to which these emissions were likely to be erroneously attributed.
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Puliafito, S. Enrique, Tomás R. Bolaño-Ortiz, Rafael P. Fernandez, Lucas L. Berná, Romina M. Pascual-Flores, Josefina Urquiza, Ana I. López-Noreña, and María F. Tames. "High-resolution seasonal and decadal inventory of anthropogenic gas-phase and particle emissions for Argentina." Earth System Science Data 13, no. 10 (October 29, 2021): 5027–69. http://dx.doi.org/10.5194/essd-13-5027-2021.
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Abstract. This work presents the integration of a gas-phase and particulate atmospheric emission inventory (AEI) for Argentina in high spatial resolution (0.025∘×0.025∘; approx. 2.5 km×2.5 km) considering monthly variability from 1995 to 2020. The new inventory, called GEAA-AEIv3.0M, includes the following activities: energy production, fugitive emissions from oil and gas production, industrial fuel consumption and production, transport (road, maritime, and air), agriculture, livestock production, manufacturing, residential, commercial, and biomass and agricultural waste burning. The following species, grouped by atmospheric reactivity, are considered: (i) greenhouse gases (GHGs) – CO2, CH4, and N2O; (ii) ozone precursors – CO, NOx (NO+NO2), and non-methane volatile organic compounds (NMVOCs); (iii) acidifying gases – NH3 and SO2; and (iv) particulate matter (PM) – PM10, PM2.5, total suspended particles (TSPs), and black carbon (BC). The main objective of the GEAA-AEIv3.0M high-resolution emission inventory is to provide temporally resolved emission maps to support air quality and climate modeling oriented to evaluate pollutant mitigation strategies by local governments. This is of major concern, especially in countries where air quality monitoring networks are scarce, and the development of regional and seasonal emissions inventories would result in remarkable improvements in the time and space chemical prediction achieved by air quality models. Despite distinguishing among different sectoral and activity databases as well as introducing a novel spatial distribution approach based on census radii, our high-resolution GEAA-AEIv3.0M shows equivalent national-wide total emissions compared to the Third National Communication of Argentina (TNCA), which compiles annual GHG emissions from 1990 through 2014 (agreement within ±7.5 %). However, the GEAA-AEIv3.0M includes acidifying gases and PM species not considered in TNCA. Temporal comparisons were also performed against two international databases: Community Emissions Data System (CEDS) and EDGAR HTAPv5.0 for several pollutants; for EDGAR it also includes a spatial comparison. The agreement was acceptable within less than 30 % for most of the pollutants and activities, although a >90 % discrepancy was obtained for methane from fuel production and fugitive emissions and >120 % for biomass burning. Finally, the updated seasonal series clearly showed the pollution reduction due to the COVID-19 lockdown during the first quarter of year 2020 with respect to same months in previous years. Through an open-access data repository, we present the GEAA-AEIv3.0M inventory as the largest and more detailed spatial resolution dataset for the Argentine Republic, which includes monthly gridded emissions for 12 species and 15 stors between 1995 and 2020. The datasets are available at https://doi.org/10.17632/d6xrhpmzdp.2 (Puliafito et al., 2021), under a CC-BY 4 license.
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Lonsdale, Chantelle R., and Kang Sun. "Nitrogen oxides emissions from selected cities in North America, Europe, and East Asia observed by the TROPOspheric Monitoring Instrument (TROPOMI) before and after the COVID-19 pandemic." Atmospheric Chemistry and Physics 23, no. 15 (August 8, 2023): 8727–48. http://dx.doi.org/10.5194/acp-23-8727-2023.
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Abstract. Nitrogen oxides (NOx=NO+NO2) emissions are estimated in three regions in the Northern Hemisphere, generally located in North America, Europe, and East Asia, by calculating the directional derivatives of NO2 column amounts observed by the TROPOspheric Monitoring Instrument (TROPOMI) with respect to the horizontal wind vectors. We present monthly averaged emissions from 1 May 2018 to 31 January 2023 to capture variations before and after the COVID-19 pandemic. We focus on a diverse collection of 54 cities, 18 in each region. A spatial resolution of 0.04∘ resolves intracity emission variations and reveals NOx emission hotspots at city cores, industrial areas, and sea ports. For each selected city, post-COVID-19 changes in NOx emissions are estimated by comparing monthly and annually averaged values to the pre-COVID-19 year of 2019. While emission reductions are initially found during the first outbreak of COVID-19 in early 2020 in most cities, the cities' paths diverge afterwards. We group the selected cities into four clusters according to their normalized annual NOx emissions in 2019–2022 using an unsupervised learning algorithm. All but one of the selected North American cities fall into cluster 1 characterized by weak emission reduction in 2020 (−7 % relative to 2019) and an increase in 2022 by +5 %. Cluster 2 contains mostly European cities and is characterized by the largest reduction in 2020 (−31 %), whereas the selected East Asian cities generally fall into clusters 3 and 4, with the largest impacts in 2022 (−25 % and −37 %). This directional derivative approach has been implemented in object-oriented, open-source Python and is available publicly for high-resolution and low-latency emission estimation for different regions, atmospheric species, and satellite instruments.
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Pillai, Dhanyalekshmi, Michael Buchwitz, Christoph Gerbig, Thomas Koch, Maximilian Reuter, Heinrich Bovensmann, Julia Marshall, and John P. Burrows. "Tracking city CO<sub>2</sub> emissions from space using a high-resolution inverse modelling approach: a case study for Berlin, Germany." Atmospheric Chemistry and Physics 16, no. 15 (August 2, 2016): 9591–610. http://dx.doi.org/10.5194/acp-16-9591-2016.
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Abstract. Currently, 52 % of the world's population resides in urban areas and as a consequence, approximately 70 % of fossil fuel emissions of CO2 arise from cities. This fact, in combination with large uncertainties associated with quantifying urban emissions due to lack of appropriate measurements, makes it crucial to obtain new measurements useful to identify and quantify urban emissions. This is required, for example, for the assessment of emission mitigation strategies and their effectiveness. Here, we investigate the potential of a satellite mission like Carbon Monitoring Satellite (CarbonSat) which was proposed to the European Space Agency (ESA) to retrieve the city emissions globally, taking into account a realistic description of the expected retrieval errors, the spatiotemporal distribution of CO2 fluxes, and atmospheric transport. To achieve this, we use (i) a high-resolution modelling framework consisting of the Weather Research Forecasting model with a greenhouse gas module (WRF-GHG), which is used to simulate the atmospheric observations of column-averaged CO2 dry air mole fractions (XCO2), and (ii) a Bayesian inversion method to derive anthropogenic CO2 emissions and their errors from the CarbonSat XCO2 observations. We focus our analysis on Berlin, Germany using CarbonSat's cloud-free overpasses for 1 reference year. The dense (wide swath) CarbonSat simulated observations with high spatial resolution (approximately 2 km × 2 km) permits one to map the city CO2 emission plume with a peak enhancement of typically 0.8–1.35 ppm relative to the background. By performing a Bayesian inversion, it is shown that the random error (RE) of the retrieved Berlin CO2 emission for a single overpass is typically less than 8–10 Mt CO2 yr−1 (about 15–20 % of the total city emission). The range of systematic errors (SEs) of the retrieved fluxes due to various sources of error (measurement, modelling, and inventories) is also quantified. Depending on the assumptions made, the SE is less than about 6–10 Mt CO2 yr−1 for most cases. We find that in particular systematic modelling-related errors can be quite high during the summer months due to substantial XCO2 variations caused by biogenic CO2 fluxes at and around the target region. When making the extreme worst-case assumption that biospheric XCO2 variations cannot be modelled at all (which is overly pessimistic), the SE of the retrieved emission is found to be larger than 10 Mt CO2 yr−1 for about half of the sufficiently cloud-free overpasses, and for some of the overpasses we found that SE may even be on the order of magnitude of the anthropogenic emission. This indicates that biogenic XCO2 variations cannot be neglected but must be considered during forward and/or inverse modelling. Overall, we conclude that a satellite mission such as CarbonSat has high potential to obtain city-scale CO2 emissions as needed to enhance our current understanding of anthropogenic carbon fluxes, and that CarbonSat-like satellites should be an important component of a future global carbon emission monitoring system.
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Zhu, Hanxiong, Kexi Pan, Yong Liu, Zheng Chang, Ping Jiang, and Yongfu Li. "Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data." Sustainability 11, no. 17 (August 31, 2019): 4766. http://dx.doi.org/10.3390/su11174766.
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In this study, we create a high-resolution (1 km x 1 km) carbon emission spatially gridded dataset in Shanghai for 2010 to 2015 to help researchers understand the spatial pattern of urban CO2 emissions and facilitate exploration of their driving forces. First, we conclude that high spatial agglomeration, CO2 emissions centralized along the river and coastline, and a structure with three circular layers are the three notable temporal–spatial characteristics of Shanghai fossil fuel CO2 emissions. Second, we find that large point sources are the leading factors that shaped the temporal–spatial characteristics of Shanghai CO2 emission distributions. The changes of CO2 emissions in each grid during 2010–2015 indicate that the energy-controlling policies of large point emission sources have had positive effects on CO2 reduction since 2012. The changes suggest that targeted policies can have a disproportionate impact on urban emissions. Third, area sources bring more uncertainties to the forecasting of carbon emissions. We use the Geographical Detector method to identify these leading factors that influence CO2 emissions emitted from area sources. We find that Shanghai’s circular layer structure, population density, and population activity intensity are the leading factors. This result implied that urban planning has a large impact on the distribution of urban CO2 emissions. At last, we find that unbalanced development within the city will lead to different leading impact factors for each circular layer. Factors such as urban development intensity, traffic land, and industrial land have stronger power to determine CO2 emissions in the areas outside the Outer Ring, while factors such as population density and population activity intensity have stronger impacts in the other two inner areas. This research demonstrates the potential utility of high-resolution carbon emission data to advance the integration of urban planning for the reduction of urban CO2 emissions and provide information for policymakers to make targeted policies across different areas within the city.
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Zhang, Xiumei, Ronald van der A, Jieying Ding, Xin Zhang, and Yan Yin. "Significant contribution of inland ships to the total NOx emissions along the Yangtze River." Atmospheric Chemistry and Physics 23, no. 9 (May 17, 2023): 5587–604. http://dx.doi.org/10.5194/acp-23-5587-2023.
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Abstract. Despite the large number of domestic inland river vessels in China, information on inland ship emissions is very limited, since legislation for shipping emission control is limited and there is no monitoring infrastructure. Taking the Yangtze River in the region of Nanjing as a research area, we compiled a ship emission inventory based on real-time information received from automatic identification system (AIS) signals combined with ship-related data provided by the China Classification Society (CCS) database. The total ship emissions we derived for the Jiangsu section of the Yangtze River from September 2018 to August 2019 for NOx, SO2, PM10 and PM2.5 were 83.5, 0.04, 0.006 and 0.005 kt yr−1, respectively. This ship emission inventory we constructed was compared with the Multi-resolution Emission Inventory for China (MEIC), the Shipping Emission Inventory Model (SEIM) and the satellite-derived emissions using the Daily Emissions Constrained by Satellite Observations (DECSO) algorithm. The results show a consistent spatial distribution, with riverine cities having high NOx pollution. With this comparison we analyzed the relative impact of ship emissions on densely populated regions along the river. Inland ship emissions of NOx are shown to contribute significantly, accounting for at least 40 % of air pollution close to the river.
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Lin, J. T. "Satellite constraint for emissions of nitrogen oxides from anthropogenic, lightning and soil sources over East China on a high-resolution grid." Atmospheric Chemistry and Physics 12, no. 6 (March 23, 2012): 2881–98. http://dx.doi.org/10.5194/acp-12-2881-2012.
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Abstract. Vertical column densities (VCDs) of tropospheric nitrogen dioxide (NO2) retrieved from space provide valuable information to estimate emissions of nitrogen oxides (NOx) inversely. Accurate emission attribution to individual sources, important both for understanding the global biogeochemical cycling of nitrogen and for emission control, remains difficult. This study presents a regression-based multi-step inversion approach to estimate emissions of NOx from anthropogenic, lightning and soil sources individually for 2006 over East China on a 0.25° long × 0.25° lat grid, employing the DOMINO product version 2 retrieved from the Ozone Monitoring Instrument. The inversion is done gridbox by gridbox to derive the respective emissions, taking advantage of differences in seasonality between anthropogenic and natural sources. Lightning and soil emissions are combined together for any given gridbox due to their similar seasonality; and their different spatial distributions are used implicitly for source separation to some extent. The nested GEOS-Chem model for East Asia is used to simulate the seasonal variations of different emission sources and impacts on VCDs of NO2 for the inversion purpose. Sensitivity tests are conducted to evaluate key assumptions embedded in the inversion process. The inverse estimate suggests annual budgets of about 7.1 TgN (±39%), 0.21 TgN (±61%), and 0.38 TgN (±65%) for the a posteriori anthropogenic, lightning and soil emissions, respectively, about 18–23% higher than the respective a priori values. The enhancements in anthropogenic emissions are largest in cities and areas with extensive use of coal, particularly in the north in winter, as evident on the high-resolution grid. Derived soil emissions are consistent with recent bottom-up estimates. They are less than 6% of anthropogenic emissions annually, increasing to about 13% for July. Derived lightning emissions are about 3% of anthropogenic emissions annually and about 10% in July. Overall, anthropogenic emissions are found to be the dominant source of NOx over East China with important implications for nitrogen control.
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R, Sai Sathish, Yordan Kostov, and Govind Rao. "High-resolution surface plasmon coupled resonant filter for monitoring of fluorescence emission from molecular multiplexes." Applied Physics Letters 94, no. 22 (June 2009): 223113. http://dx.doi.org/10.1063/1.3149828.
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