Strong Point Sources Research Articles

Performance and sensitivity of column-wise and pixel-wise methane retrievals for imaging spectrometers

Abstract. Strong methane point source emissions generate large atmospheric concentrations that can be detected and quantified with infrared remote sensing and retrieval algorithms. Two standard and widely used retrieval algorithms for one class of observing platform, imaging spectrometers, include pixel-wise and column-wise approaches. In this study, we assess the performance of both approaches using the airborne imaging spectrometer (Global Airborne Observatory) observations of two extensive controlled-release experiments. We find that the column-wise retrieval algorithm is sensitive to the flight line length and can have a systematic low bias with short flight lines, which is not present in the pixel-wise retrieval algorithm. However, the pixel-wise retrieval is very computationally expensive, and the column-wise retrieval algorithms can produce good results when the flight line length is sufficiently long. Lastly, this study examines the methane plume detection performance of the Global Airborne Observatory with a column-wise retrieval algorithm and finds minimum detection limits of between 9 of 10 kg h−1 and 90 % probability of detection between 10 and 45 kg h−1. These results present a framework of rules for guiding proper concentration retrieval selection given conditions at the time of observation in order to ensure robust detection and quantification.

Quantifying strong point sources emissions of CO2 using spaceborne LiDAR: Method development and potential analysis

Accurate reporting of point source emissions of CO2 is fundamental to addressing climate change. Currently, bottom-up verification methods based on inventory statistics face significant challenges in this area. Satellite remote sensing has emerged as a promising approach for cost-effective global-scale verification of point source emissions, with spaceborne LiDAR offering high spatial resolution ideal for this purpose. However, the inversion of CO2 emissions from spaceborne LiDAR CO2 concentration observations requires urgent attention, as existing methods heavily rely on prior information in diffusion models and the accuracy of meteorological data. In this work, a novel emission inversion method based on genetic algorithms and trust-region techniques is proposed to estimate CO2 emissions from point sources using spaceborne LiDAR observations. A comparison between the CO2 emission rates calculated from actual airborne LiDAR data (as a prototype of spaceborne LiDAR) and emission inventories for the Suizhong power plant showed a deviation of less than 7.0%. Observing system simulation experiment (OSSE) demonstrated that using DQ-1 (spaceborne LiDAR) observation data as input, the relative error of emission rates would be less than 0.6% when the distance between the emission source and the observation footprint is less than 10 km. Furthermore, the developed model mitigates the impact of uncertainties in meteorological data and IPDA (Integrated-Path Differential Absorption) LiDAR measurements on the final emission quantification. The proposed approach is expected to enable DQ-1 to provide affordable and accurate carbon verification services for over 20.0% of the world's strong point source emissions.

Retrieving CH4-emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model

Abstract. There are plenty of monitoring methods to quantify gas emission rates based on gas concentration measurements around the strong sources. However, there is a lack of quantitative models to evaluate methane emission rates from coal mines with less prior information. In this study, we develop a genetic algorithm–interior point penalty function (GA-IPPF) model to calculate the emission rates of large point sources of CH4 based on concentration samples. This model can provide optimized dispersion parameters and self-calibration, thus lowering the requirements for auxiliary data accuracy. During the Carbon Dioxide and Methane Mission (CoMet) pre-campaign, we retrieve CH4-emission rates from a ventilation shaft in Pniówek coal mine (Silesia coal mining region, Poland) based on the data collected by an unmanned aerial vehicle (UAV)-based AirCore system and a GA-IPPF model. The concerned CH4-emission rates are variable even on a single day, ranging from 621.3 ± 19.8 to 1452.4 ± 60.5 kg h−1 on 18 August 2017 and from 348.4 ± 12.1 to 1478.4 ± 50.3 kg h−1 on 21 August 2017. Results show that CH4 concentration data reconstructed by the retrieved parameters are highly consistent with the measured ones. Meanwhile, we demonstrate the application of GA-IPPF in three gas control release experiments, and the accuracies of retrieved gas emission rates are better than 95.0 %. This study indicates that the GA-IPPF model can quantify the CH4-emission rates from strong point sources with high accuracy.

Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the United States

Understanding, prioritizing, and mitigating methane (CH4) emissions requires quantifying CH4 budgets from facility scales to regional scales with the ability to differentiate between source sectors. We deployed a tiered observing system for multiple basins in the United States (San Joaquin Valley, Uinta, Denver-Julesburg, Permian, Marcellus). We quantify strong point source emissions (>10 kg CH4 h-1) using airborne imaging spectrometers, attribute them to sectors, and assess their intermittency with multiple revisits. We compare these point source emissions to total basin CH4 fluxes derived from inversion of Sentinel-5p satellite CH4 observations. Across basins, point sources make up on average 40% of the regional flux. We sampled some basins several times across multiple months and years and find a distinct bimodal structure to emission timescales: the total point source budget is split nearly in half by short-lasting and long-lasting emission events. With the increasing airborne and satellite observing capabilities planned for the near future, tiered observing systems will more fully quantify and attribute CH4 emissions from facility to regional scales, which is needed to effectively and efficiently reduce methane emissions.

Third-Generation Calibrations for MeerKAT Observation

Superclusters and galaxy clusters offer a wide range of astrophysical science topics with regards to studying the evolution and distribution of galaxies, intra-cluster magnetization mediums, cosmic ray accelerations and large scale diffuse radio sources all in one observation. Recent developments in new radio telescopes and advanced calibration software have completely changed data quality that was never possible with old generation telescopes. Hence, radio observations of superclusters are a very promising avenue to gather rich information of a large-scale structure (LSS) and their formation mechanisms. These newer wide-band and wide field-of-view (FOV) observations require state-of-the-art data analysis procedures, including calibration and imaging, in order to provide deep and high dynamic range (DR) images with which to study the diffuse and faint radio emissions in supercluster environments. Sometimes, strong point sources hamper the radio observations and limit the achievement of a high DR. In this paper, we have shown the DR improvements around strong radio sources in the MeerKAT observation of the Saraswati supercluster by applying newer third-generation calibration (3GC) techniques using CubiCal and killMS software. We have also calculated the statistical parameters to quantify the improvements around strong radio sources. This analysis advocates for the use of new calibration techniques to maximize the scientific returns from new-generation telescopes.

Simultaneous temporal, spatial and size-resolved measurements of aerosol particles in closed indoor environments applying mobile filters in various use-cases

This paper focuses on simultaneous, time- and space-resolved measurements of particle size distributions in three different closed indoor environments (small office room, elementary-school classroom, and seminar room) applying mobile air filters in four scenarios (decay curves, filtration while people are present, a temporal strong point source, impact of filter orientation & cross-flow ventilation). The experiments reveal, that mobile indoor air filters, equipped with high-performance filter media (HEPA - quality), remove particles in the investigated rooms in relevant submicron size classes (x < 1 μm) efficiently and uniformly over time.For the description of the local decrease in particle concentration a simple mathematical model based on a transient continuous stirred tank reactor was applied. The local decay curves obtained in the different room-types were compared to simulated ones assuming ideal mixing of the indoor air. The real-room scenarios show a slower particle decay than the predicted ones assuming ideal mixing of the indoor air. The experiments reported in this contribution demonstrate, that indoor air filters, operated with a filtration rate of 3.5 h−1 and positioned correctly, are capable of lowering the particle concentration in all relevant size classes in real-world closed indoor environments slowly over time (e.g. a reduction in particle concentration of 50 % after 30 min in a classroom w/o particle sources). In the investigated set-ups, at filtration rates above 9 h−1, the filters’ performance is close to cross-flow window-ventilation. The experiments reveal, that mobile air filters cannot avoid close distance transmission of submicron aerosol particles from one person to another. Therefore, they do not replace any of the well-known methods to avoid aerosol-driven infection (like wearing an efficient face mask correctly, limiting the number of people and time of stay in closed indoor environments, frequent ventilation). Mobile air filter devices may represent an additional component in an entire prevention strategy, especially when rooms cannot be ventilated regularly, efficiently or the constellation of people in groups changes frequently (e.g. waiting areas).

Superresolution in interferometric imaging of strong thermal sources

Imaging using interferometer arrays based on the Van Cittert--Zernike theorem has been widely used in astronomical observation. Recently it was shown that superresolution can be achieved in this system for imaging two weak thermal point sources. Using quantum estimation theory, we consider the fundamental quantum limit of resolving the transverse separation of two strong thermal point sources using interferometer arrays, and show that the resolution is not limited by the longest baseline. We propose measurement techniques using linear beam splitters and photon-number-resolving detection to achieve our bound. Our results demonstrate that superresolution for resolving two thermal point sources of any strength can be achieved in interferometer arrays.

High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations

Abstract. We demonstrate the capability of the Sentinel-2 MultiSpectral Instrument (MSI) to detect and quantify anomalously large methane point sources with fine pixel resolution (20 m) and rapid revisit rates (2–5 d). We present three methane column retrieval methods that use shortwave infrared (SWIR) measurements from MSI spectral bands 11 (∼ 1560–1660 nm) and 12 (∼ 2090–2290 nm) to detect atmospheric methane plumes. The most successful is the multi-band–multi-pass (MBMP) method, which uses a combination of the two bands and a non-plume reference observation to retrieve methane columns. The MBMP method can quantify point sources down to about 3 t h−1 with a precision of ∼ 30 %–90 % (1σ) over favorable (quasi-homogeneous) surfaces. We applied our methods to perform high-frequency monitoring of strong methane point source plumes from a well-pad device in the Hassi Messaoud oil field of Algeria (October 2019 to August 2020, observed every 2.5 d) and from a compressor station in the Korpezhe oil and gas field of Turkmenistan (August 2015 to November 2020, observed every 5 d). The Algerian source was detected in 93 % of cloud-free scenes, with source rates ranging from 2.6 to 51.9 t h−1 (averaging 9.3 t h−1) until it was shut down by a flare lit in August 2020. The Turkmen source was detected in 40 % of cloud-free scenes, with variable intermittency and a 9-month shutdown period in March–December 2019 before it resumed; source rates ranged from 3.5 to 92.9 t h−1 (averaging 20.5 t h−1). Our source-rate retrievals for the Korpezhe point source are in close agreement with GHGSat-D satellite observations for February 2018 to January 2019, but provide much higher observation density. Our methods can be readily applied to other satellite instruments with coarse SWIR spectral bands, such as Landsat-7 and Landsat-8. High-frequency satellite-based detection of anomalous methane point sources as demonstrated here could enable prompt corrective action to help reduce global methane emissions.

Parameterizing the Impact of Seawater Temperature and Irradiance on Dimethylsulfide (DMS) in the Great Barrier Reef and the Contribution of Coral Reefs to the Global Sulfur Cycle

AbstractBiogenic emissions of dimethylsulfide (DMS) are an important source of sulfur to the atmosphere, with implications for aerosol formation and cloud albedo over the ocean. Natural aerosol sources constitute the largest uncertainty in estimates of aerosol radiative forcing and climate and thus, an improved understanding of DMS sources is needed. Coral reefs are strong point sources of DMS; however, this coral source of biogenic sulfur is not explicitly included in climatologies or in model simulations. Consequently, the role of coral reefs in local and regional climate remains uncertain. We aim to improve the representation of tropical coral reefs in DMS databases by calculating a climatology of seawater DMS concentration (DMSw) and sea‐air flux in the Great Barrier Reef (GBR), Australia. DMSw is calculated from remotely sensed observations of sea surface temperature and photosynthetically active radiation using a multiple linear regression model derived from field observations of DMSw in the GBR. We estimate that coral reefs and lagoon waters in the GBR (∼347,000 km2) release 0.03–0.05 Tg yr−1 of DMS (0.02 Tg yr−1 of sulfur). Based on this estimate, global tropical coral reefs (∼600,000 km2) could emit 0.08 Tg yr−1 of DMS (0.04 Tg yr−1 of sulfur), with the potential to influence the local radiative balance.

Searching for proto-planets with MUSE

Context. Protoplanetary disks contain structures such as gaps, rings, and spirals, which are thought to be produced by the interaction between the disk and embedded protoplanets. However, only a few planet candidates are found orbiting within protoplanetary disks, and most of them are being challenged as having been confused with disk features. Aims. The VLT/MUSE discovery of PDS 70 c demonstrated a powerful way of searching for still-forming protoplanets by targeting accretion signatures with medium-resolution integral field spectroscopy. We aim to discover more proto-planetary candidates with MUSE, with a secondary aim of improving the high-resolution spectral differential imaging (HRSDI) technique by analyzing the instrumental residuals of MUSE. Methods. We analyzed MUSE observations of five young stars with various apparent brightnesses and spectral types. We applied the HRSDI technique to perform high-contrast imaging. The detection limits were estimated using fake planet injections. Results. With a 30 min integration time, MUSE can reach 5σ detection limits in apparent Hα line flux down to 10−14 and 10−15 erg s−1 cm−2 at 0.075′′ and 0.25′′, respectively. In addition to PDS 70 b and c, we did not detect any clear accretion signatures in PDS 70, J1850-3147, and V1094 Sco down to 0.1′′. MUSE avoids the small sample statistics problem by measuring the noise characteristics in the spatial direction at multiple wavelengths. We detected two asymmetric atomic jets in HD 163296 with a very high spatial resolution (down to 8 au) and medium spectral resolution (R ~ 2500). Conclusions. The HRSDI technique when applied to MUSE data allows us to reach the photon noise limit at small separations (i.e., &lt;0.5′′). With the combination of high-contrast imaging and medium spectral resolution, MUSE can achieve fainter detection limits in apparent line flux than SPHERE/ZIMPOL by a factor of ~5. MUSE has some instrumental issues that limit the contrast that appear in cases with strong point sources, which can be either a spatial point source due to high Strehl observations or a spectral point source due to a high line-to-continuum ratio. We modified the HRSDI technique to better handle the instrumental artifacts and improve the detection limits. To avoid the instrumental effects altogether, we suggest faint young stars with relatively low Hα line-to-continuum ratio to be the most suitable targets for MUSE to search for potential protoplanets.

An inversion method for estimating strong point carbon dioxide emissions using a differential absorption Lidar

Anthropogenic carbon emissions increase the atmospheric concentration of carbon dioxide, resulting in global warming and climate change. To complement the existing carbon emission verification system, it is crucial to develop a novel measurement-based estimation method for determining the carbon emissions produced by strong point sources. This study determines the feasibility of measuring point source carbon emissions using differential absorption Lidar (DIAL). First, for measuring carbon emissions, an estimation method is introduced that utilizes a Gaussian dispersion model with range-resolved carbon dioxide concentrations for the inversion of carbon emissions produced at specific localized point sources. On this basis, a sensitivity analysis is conducted to study the quantitative effect of different parameters on the performance of the proposed method. The inversion method yields less than 0.01% error in ideal conditions. Considering the uncertainties in the relevant input parameters, we found that the errors in the estimates of point source carbon emissions can be maintained below 5.8% using hypothetical equipment with a configuration identical to that of the planned DIAL. Such a novel framework for estimating point source carbon emissions is important for monitoring, reporting, and verifying anthropogenic carbon emissions.

Using remote sensing to detect, validate, and quantify methane emissions from California solid waste operations

Solid waste management represents one of the largest anthropogenic methane emission sources. However, precise quantification of landfill and composting emissions remains difficult due to variety of site-specific factors that contribute to landfill gas generation and effective capture. Remote sensing is an avenue to quantify process-level emissions from waste management facilities. The California Methane Survey flew the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) over 270 landfills and 166 organic waste facilities repeatedly during 2016–2018 to quantify their contribution to the statewide methane budget. We use representative methane retrievals from this campaign to present three specific findings where remote sensing enabled better landfill and composting methane monitoring: (1) Quantification of strong point source emissions from the active face landfills that are difficult to capture by in situ monitoring or landfill models, (2) emissions that result from changes in landfill infrastructure (design, construction, and operations), and (3) unexpected large emissions from two organic waste management methods (composting and digesting) that were originally intended to help mitigate solid waste emissions. Our results show that remotely-sensed emission estimates reveal processes that are difficult to capture in biogas generation models. Furthermore, we find that airborne remote sensing provides an effective avenue to study the temporally changing dynamics of landfills. This capability will be further improved with future spaceborne imaging spectrometers set to launch in the 2020s.

Study of SO Pollution in the Middle East Using MERRA‐2, CAMS Data Assimilation Products, and High‐Resolution WRF‐Chem Simulations

AbstractOil recovery, power generation, water desalination, gas flaring, and traffic are the main contributors to SO emissions in the Middle East (ME). Satellite observations suggest that the traditional emission inventories do not account for multiple SO emission sources in the ME. This study aims to evaluate the most frequently used SO emission data sets over the ME by comparing high‐resolution regional model simulations and meteorology/chemistry assimilation products, MERRA‐2 and CAMS, with satellite and available ground‐based air‐quality observations. Here, we employ the WRF‐Chem‐3.7.1 regional meteorology‐chemistry model and conduct simulations for the period 2015–2016 with 10 km grid spacing using HTAP‐2.2 emission data sets and the new OMI‐HTAP data, which is based on the combination of the near‐surface SO emissions taken from the HTAP‐2.2 inventory with strong (&gt;30 kt/year) SO point sources obtained from the satellite Ozone Monitoring Instrument (OMI) observations. We find that conventional emission inventories (EDGAR‐4.2, MACCity, and HTAP‐2.2) have uncertainties in the location and magnitude of SO sources in the ME and significantly underestimate SO emissions in the Arabian Gulf. The WRF‐Chem, run in conjunction with the new OMI‐HTAP emissions, improves comparisons between the satellite and ground‐based SO observations. Our simulations show that SO surface concentrations in Jeddah and Riyadh frequently exceed European air‐quality limits. The ME generates about 10% of global anthropogenic SO emissions, on par with India. Therefore, the development of effective emission controls and improvement of air‐quality monitoring in the ME are urgently needed.

Gamma-Ray Emission of 60Fe and 26Al Radioactivity in Our Galaxy

Abstract The isotopes 60Fe and 26Al originate from massive stars and their supernovae, reflecting ongoing nucleosynthesis in the Galaxy. We studied the gamma-ray emission from these isotopes at characteristic energies 1173, 1332, and 1809 keV with over 15 yr of SPI data, finding a line flux in 60Fe combined lines of and the Al line flux of above the background and continuum emission for the whole sky. Based on the exponential disk grid maps, we characterize the emission extent of 26Al to find scale parameters and kpc; however, the 60Fe lines are too weak to spatially constrain the emission. Based on a point-source model test across the Galactic plane, the 60Fe emission would not be consistent with a single strong point source in the Galactic center or somewhere else, providing a hint of a diffuse nature. We carried out comparisons of emission morphology maps using different candidate source tracers for both 26Al and 60Fe emissions and suggest that the 60Fe emission is more likely to be concentrated toward the Galactic plane. We determine the 60Fe/26Al γ-ray flux ratio at 18.4% ± 4.2% when using a parameterized spatial morphology model. Across the range of plausible morphologies, it appears possible that 26Al and 60Fe are distributed differently in the Galaxy. Using the best-fitting maps for each of the elements, we constrain flux ratios in the range 0.2–0.4. We discuss the implications for massive star models and their nucleosynthesis.

How to avoid X'es around point sources in maximum likelihood CMB maps

The maximum likelihood estimator for CMB map-making is optimal and unbiased as long as the data model is correct, but in practice it rarely is, with model errors including sub-pixel structure and instrumental problems like time-variable gain and pointing errors. In the presence of such errors, the solution is biased, with the local error in each pixel leaking outwards along the scanning pattern by a noise correlation length. The most important sources of such leakage are strong point sources, and for common scanning patterns the leakage manifests as an X around each such source. I discuss why this happens, and present several old and new methods for mitigating and/or eliminating this leakage, along with a small stand-alone TOD simulator and map-maker in Python that implements them.

California's methane super-emitters.

Methane is a powerful greenhouse gas and is targeted for emissions mitigation by the US state of California and other jurisdictions worldwide1,2. Unique opportunities for mitigation are presented by point-source emitters-surface features or infrastructure components that are typically less than 10 metres in diameter and emit plumes of highly concentrated methane3. However, data on point-source emissions are sparse and typically lack sufficient spatial and temporal resolution to guide their mitigation and to accurately assess their magnitude4. Here we survey more than 272,000 infrastructure elements in California using an airborne imaging spectrometer that can rapidly map methane plumes5-7. We conduct five campaigns over several months from 2016 to 2018, spanning the oil and gas, manure-management and waste-management sectors, resulting in the detection, geolocation and quantification of emissions from 564 strong methane point sources. Our remote sensing approach enables the rapid and repeated assessment of large areas at high spatial resolution for a poorly characterized population of methane emitters that often appear intermittently and stochastically. We estimate net methane point-source emissions in California to be 0.618 teragrams per year (95 per cent confidence interval 0.523-0.725), equivalent to 34-46 per cent of the state's methane inventory8 for 2016. Methane 'super-emitter' activity occurs in every sector surveyed, with 10 per cent of point sources contributing roughly 60 per cent of point-source emissions-consistent with a study of the US Four Corners region that had a different sectoral mix9. The largest methane emitters in California are a subset of landfills, which exhibit persistent anomalous activity. Methane point-source emissions in California are dominated by landfills (41 per cent), followed by dairies (26 per cent) and the oil and gas sector (26 per cent). Our data have enabled the identification of the 0.2 per cent of California's infrastructure that is responsible for these emissions. Sharing these data with collaborating infrastructure operators has led to the mitigation ofanomalous methane-emission activity10.

Interaction of SN Ib 2004dk with a Previously Expelled Envelope.

The interaction between the expanding supernova (SN) ejecta with the circumstellar material (CSM) that was expelled from the progenitor prior to explosion is a long-sought phenomenon, yet observational evidence is scarce. Here we confirm a new example: SN 2004dk, originally a hydrogen-poor, helium-rich Type Ib SN that reappeared as a strong Hα-emitting point source on narrowband Hα images. We present follow-up optical spectroscopy that reveals the presence of a broad Hα component with full width at half maximum of ~ 290 km s-1 in addition to the narrow Hα+[N ii] emission features from the host galaxy. Such a broad component is a clear sign of an ejecta-CSM interaction. We also present observations with the XMM-Newton Observatory, the Swift satellite, and the Chandra X-ray Observatory that span 10 days to 15 years after discovery. The detection of strong radio, X-ray, and Hα emission years after explosion allows various constraints to be put on pre-SN mass-loss processes. We present a wind-bubble model in which the CSM is "pre-prepared" by a fast wind interacting with a slow wind. Much of the outer density profile into which the SN explodes corresponds to no steady-state mass-loss process. We estimate that the shell of compressed slow wind material was ejected ~1400 yr prior to explosion, perhaps during carbon burning, and that the SN shock had swept up about 0.04 M ⊙ of material. The region emitting the Hα has a density of order 10-20 g cm-3.

Detection of Strong NOX Emissions from Fine-scale Reconstruction of the OMI Tropospheric NO2 Product

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.

An Eigenvector-Based Method of Radio Array Calibration and Its Application to the Tianlai Cylinder Pathfinder

Abstract We propose an eigenvector-based formalism for the calibration of radio interferometer arrays. In the presence of a strong dominant point source, the complex gains of the array can be obtained by taking the first eigenvector of the visibility matrix. We use the stable principle component analysis method to help separate outliers and noise from the calibrator signal to improve the performance of the method. This method can be applied with poorly known beam model of the antenna, is insensitive to outliers or imperfections in the data, and has low computational complexity. It thus is particularly suitable for the initial calibration of the array, which can serve as the initial point for more accurate calibrations. We demonstrate this method by applying it to the cylinder pathfinder of the Tianlai experiment, which aims to measure the dark energy equation of state using the baryon acoustic oscillation features in the large-scale structure by making intensity mapping observation of the redshifted 21 cm emission of the neutral hydrogen (H i). The complex gain of the array elements and the beam profile in the east–west direction (short axis of the cylinder) are successfully obtained by applying this method to the transit data of bright radio sources.

The Expansion of the Young Supernova Remnant 0509-68.7 (N103B)

Abstract We present a second epoch of Chandra observations of the Type Ia Large Magellanic Cloud supernova remnant (SNR) 0509-68.7 (N103B) obtained in 2017. When combined with the earlier observations from 1999, we have a 17.4 year baseline with which we can search for evidence of the remnant’s expansion. Although the lack of strong point source detections makes absolute image alignment at the necessary accuracy impossible, we can measure the change in the diameter and the area of the remnant, and find that it has expanded by an average velocity of 4170 (2860, 5450) km s−1. This supports the picture of this being a young remnant; this expansion velocity corresponds to an undecelerated age of 850 years, making the real age somewhat younger, consistent with results from light echo studies. Previous infrared observations have revealed high densities in the western half of the remnant, likely from circumstellar material, so it is probable that the real expansion velocity is lower on that side of the remnant and higher on the eastern side. A similar scenario is seen in Kepler’s SNR. N103B joins the rare class of Magellanic Cloud SNRs with measured proper motions.