Inversion System Research Articles

Efficient Defect-Driven Cation Exchange beyond the Nanoscale Semiconductors toward Antibacterial Functionalization.

Defect engineering is an exciting tool for customizing semiconductors' structural and optoelectronic properties. Elaborating programmable methodologies to circumvent energy constraints in multievent inversions expands our understanding of the mechanisms governing the functionalization of nanomaterials. Herein, we introduce a novel strategy based on defect incorporation and solution rationalization, which triggers energetically unfavorable cation exchange reactions in extended solids. Using Sb2X3 + Ag (I) → Ag: Sb2X3 (X= S, Se) as a system to model, we demonstrate that incorporating chalcogen vacancies and AgSbVX complex defects into initial thin films (TFs) is crucial for activating long-range solid-state ion diffusion. Additional regulation of the Lewis acidity of auxiliary chemicals provides an exceptional conversion yield of the Ag precursor into a solid-state product up to 90%, simultaneously transforming upper matrix layers into AgSbX2. The proposed strategy enables tailoring radiative recombination processes, offers efficiency to invert TFs at moderate temperatures quickly, and yields structures of large areas with substantial antibacterial activity in visible light for a particular inversion system. Similar customization can be applied to most sulfides/selenides with controlled reaction yields.

Development and deployment of a mid-cost CO2 sensor monitoring network to support atmospheric inverse modeling for quantifying urban CO2 emissions in Paris

Abstract. To effectively monitor highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. These dense measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or to separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm for hourly mean measurements. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision cavity ring-down spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12:00–17:00 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects for integrating mid-cost measurements along with high-precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area.

China’s methane emissions derived from the inversion of GOSAT observations with a CMAQ and EnKS-based regional data assimilation system

At present, inversions at higher temporal and spatial resolution tend to be in increasing demand. The resolution and precision of methane (CH4) inversions depend on quality of observations, transport model, and inversion scheme. Currently, most inversion-based estimates of CH4 in China use a global atmospheric transport model to relate emissions to observations, or a Lagrangian model to quantify the receptor-to-source sensitivity. Taking advantage of the ability that regional transport models have in mesoscale simulation, a regional inversion system was developed to infer China’s CH4 emissions. The CMAQ (Community Multi-scale Air Quality) model was configured for forward simulation of CH4, including processes of emission, transport, diffusion, and chemical transformation. Furthermore, the Ensemble Kalman Smoother was extended to assimilate satellite observations with joint optimization scheme of concentrations and emissions to reduce the impact of initial uncertainty. We found that the posterior annual estimated emissions (53.30 Tg a−1) in 2020 were closer to the official reported figure (53.57 Tg a−1) than to the prior (63.80 Tg a−1), with a downward correction of 16.45% in prior estimates based on extrapolation of the bottom-up inventory, which likely led to overestimation. Moreover, under current observational coverage, monthly posterior estimates reflected region-dependent responses to local conditions. Generally, the regional assimilation system estimated annual and monthly CH4 emissions well, attributable to reliable CMAQ simulation, joint assimilation scheme, and careful selection of satellite retrievals. In addition, evaluation of the posterior estimates indicates that inversion delivers reasonable improvements, but amelioration of uncertainties in prior information and observations is still needed.

Long‐Term Maintenance of Complex Chromosomal Inversion Polymorphism in Drosophila mediopunctata

ABSTRACTNatural selection is known to favor specific gene combinations, thereby shaping the evolution of recombination rates, often through epistatic interactions. However, the dynamics of these interacting factors within natural populations remain poorly understood. In this study, we investigate the long‐term maintenance of a complex polymorphism involving linked, nonoverlapping chromosomal inversions in a natural population of Drosophila mediopunctata. Remarkably, even after 30 years—equivalent to roughly 340 generations—two major features have remained unexpectedly stable: the linkage disequilibrium (LD) between inversions, which deviates significantly from the theoretical prediction of decay, and a consistent seasonal cycle pattern of heterozygous excess and homozygous deficiencies. We explored the roles of recombination suppression, epistatic selection, and overdominance in maintaining this stability, examining their alignment with previously described patterns. Our findings reveal that moderate selection coefficients, such as s = 0.0407, are sufficient to maintain the observed LD for the most common haplotypes, albeit leading to an unstable equilibrium. Simulations further reveal that the introduction of overdominance stabilizes the system, enabling the long‐term persistence of this complex inversion polymorphism across various frequency scenarios. The stability of this system appears to hinge on a delicate balance between LD, recombination rates, and selective pressures, with overdominance playing a critical role. Our findings highlight the significance of epistatic interactions and selective pressures in shaping evolutionary pathways in natural populations and offer a compelling example of natural selection acting on a complex inversion polymorphism, providing valuable insights into the evolutionary dynamics governing inversion systems.

Speaker-independent speech inversion for recovery of velopharyngeal port constriction degreea).

For most of his illustrious career, Ken Stevens focused on examining and documenting the rich detail about vocal tract changes available to listeners underlying the acoustic signal of speech. Current approaches to speech inversion take advantage of this rich detail to recover information about articulatory movement. Our previous speech inversion work focused on movements of the tongue and lips, for which "ground truth" is readily available. In this study, we describe acquisition and validation of ground-truth articulatory data about velopharyngeal port constriction, using both the well-established measure of nasometry plus a novel technique-high-speed nasopharyngoscopy. Nasometry measures the acoustic output of the nasal and oral cavities to derive the measure nasalance. High-speed nasopharyngoscopy captures images of the nasopharyngeal region and can resolve velar motion during speech. By comparing simultaneously collected data from both acquisition modalities, we show that nasalance is a sufficiently sensitive measure to use as ground truth for our speech inversion system. Further, a speech inversion system trained on nasalance can recover known patterns of velopharyngeal port constriction shown by American English speakers. Our findings match well with Stevens' own studies of the acoustics of nasal consonants.

Physics-guided deep learning-based inversion for airborne electromagnetic data

SUMMARY The Earth's subsurface structure provides critical insights into sustainable resource management and geologic evolution. The airborne electromagnetic (AEM) method is an efficient data acquisition technique and can be used to image the underground resistivity structure with high spatial resolution. However, inversion of the increasingly huge volume of AEM data poses a heavy computational burden. In this study, we develop a hybrid deep learning-based approach by using the physics-guided neural network (PGNN) which incorporates the governing physical laws into the loss function to solve the AEM inverse problem. The PGNN integrates the strength of data-driven method for representation learning with electromagnetic laws and allows for the underlying physical constraints to be strictly satisfied. We validate the effectiveness of our approach using both synthetic and field datasets. Compared with the classic Gauss–Newton method, our PGNN inversion system shows strong robustness against multiple noise sources and reduces the risk of being trapped in local extrema. Moreover, the PGNN-inverted results are physically more consistent with the AEM observations compared to the purely data-driven approach. Application to the field AEM data from Northern Australia demonstrates that the PGNN-based inversion framework effectively estimates the subsurface electrical properties with considerable lateral continuity and significantly higher efficiency, completing the inversion of more than 2734000 AEM soundings taking only minutes on a common PC. Our proposed PGNN-based method shows great promise for large-scale underground resistivity imaging, and the well-identified subsurface resistivity structure can effectively improve our understanding of resource distributions and geological hazards.

A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system

Abstract. Accurate assessment of the size and distribution of carbon dioxide (CO2) sources and sinks is important for efforts to understand the carbon cycle and support policy decisions regarding climate mitigation actions. Satellite retrievals of the column-averaged dry-air mole fractions of CO2 (XCO2) have been widely used to infer spatial and temporal variations in carbon fluxes through atmospheric inversion techniques. In this study, we present a global spatially resolved terrestrial and ocean carbon flux dataset for 2015–2022. The dataset was generated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through the assimilation of Orbiting Carbon Observatory-2 (OCO-2) XCO2 retrievals. We describe the carbon budget, interannual variability, and seasonal cycle for the global scale and a set of TransCom regions. The 8-year mean net biosphere exchange and ocean carbon fluxes were −2.22 ± 0.75 and −2.32 ± 0.18 Pg C yr−1, absorbing approximately 23 % and 24 % of contemporary fossil fuel CO2 emissions, respectively. The annual mean global atmospheric CO2 growth rate was 5.17 ± 0.68 Pg C yr−1, which is consistent with the National Oceanic and Atmospheric Administration (NOAA) measurement (5.24 ± 0.59 Pg C yr−1). Europe has the largest terrestrial sink among the 11 TransCom land regions, followed by Boreal Asia and Temperate Asia. The dataset was evaluated by comparing posterior CO2 simulations with Total Carbon Column Observing Network (TCCON) retrievals as well as Observation Package (ObsPack) surface flask observations and aircraft observations. Compared with CO2 simulations using the unoptimized fluxes, the bias and root mean square error (RMSE) in posterior CO2 simulations were largely reduced across the full range of locations, confirming that the GONGGA system improves the estimates of spatial and temporal variations in carbon fluxes by assimilating OCO-2 XCO2 data. This dataset will improve the broader understanding of global carbon cycle dynamics and their response to climate change. The dataset can be accessed at https://doi.org/10.5281/zenodo.8368846 (Jin et al., 2023a).

Inversion of extinction coefficient for scattering media based on range-gated detection

The extinction coefficient is an important parameter for describing light transmitting properties in the scattering medium. However, the single-point detection way and the large inversion error of the existing techniques cannot satisfy the need for spatial analysis and data application. A novel approach for inversion of extinction coefficient based on range-gated detection utilizing backward scattering of medium is proposed, and an inversion algorithm for extinction coefficient is established utilizing the backscattering signal of multiple adjacent spatial slices of the medium. The method can simultaneously invert transverse multiple-points extinction coefficient and longitudinal profile of extinction coefficient. Further, experiments are conducted in the scattering medium including fog and smoke based on the fabricated range-gated extinction-coefficient detection and inversion system, and the results demonstrate that inversion with the error less than 5% can be achieved at different detection distances, different concentrations and different kinds of scattering mediums. This approach offers a convenient, rapid and accurate means to acquire extinction coefficients, laying the foundation for the development of efficient environmental monitoring and high-quality defogging imaging.

Trends and drivers of anthropogenic NOx emissions in China since 2020

Nitrogen oxides (NOx), significant contributors to air pollution and climate change, form aerosols and ozone in the atmosphere. Accurate, timely, and transparent information on NOx emissions is essential for decision-making to mitigate both haze and ozone pollution. However, a comprehensive understanding of the trends and drivers behind anthropogenic NOx emissions from China—the world's largest emitter—has been lacking since 2020 due to delays in emissions reporting. Here we show a consistent decline in China's NOx emissions from 2020 to 2022, despite increased fossil fuel consumption, utilizing satellite observations as constraints for NOx emission estimates through atmospheric inversion. This reduction is corroborated by data from two independent spaceborne instruments: the TROPOspheric Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI). Notably, a reduction in transport emissions, largely due to the COVID-19 lockdowns, slightly decreased China's NOx emissions in 2020. In subsequent years, 2021 and 2022, reductions in NOx emissions were driven by the industry and transport sectors, influenced by stringent air pollution controls. The satellite-based inversion system developed in this study represents a significant advancement in the real-time monitoring of regional air pollution emissions from space.

Multiparameter shallow-seismic waveform inversion based on the Jensen–Shannon divergence

SUMMARY Seismic full-waveform inversion (FWI) or waveform inversion (WI) has gained extensive attention as a cutting-edge imaging method, which is expected to reveal the high-resolution images of complex geological structures. In this paper, we regard each 1-D signal in the inversion system as a 1-D probability distribution, then use the Jensen–Shannon divergence from information theory to measure the discrepancy between the predicted and observed signals, and finally implement a novel 2-D multiparameter shallow-seismic WI (MSWI). Essentially, the novel approach achieves an implicit weighting along the time-axis for each 1-D adjoint source defined by the classical WI (CWI), thus enhancing the extra illumination for a deeper medium compared with the CWI. By evaluating the inversion results of the two-layer model and fault model, the reconstruction accuracy for S-wave velocity and density of the new method is increased by about 30 and 20 per cent compared with that of the CWI under the same conditions, respectively. The reconstruction performance for P-wave velocity of these two methods is almost equal. In addition, the new 2-D MSWI is also resilient to white Gaussian noise in the data. Numerically, the inversion system has almost the strongest sensitivities to the S-wave velocity and density, performing the poorest sensitivity to the P-wave velocity. Finally, we test the novel method with a detection case for a power tunnel.

Detection of Chinese Spring Festival in Beijing using in-situ CO2 observations and atmospheric inversion

Anthropogenic CO2 emissions inventories in urban areas are highly uncertain and lack fine-scale temporal information. Here, we use atmospheric CO2 observations from 6 sites and an ensemble-based "top-down" inversion system to estimate the spatiotemporal dynamics of CO2 emissions in the world's largest urban agglomeration of Beijing-Tianjin-Hebei (JJJ). Relative to the period before the Chinese Spring Festival (CSF) in 2019, we showed, for the first time, a 13.9% reduction and a 14.3% increase in emissions during and after the CSF, respectively, which is missed by a monthly emissions inventory. Moreover, the emissions reduction occurred in most of the JJJ areas. The exact timing of emissions dynamics inferred from the CO2 observations is consistent with a NO2 observation and a near-real-time daily “bottom-up” estimate. Here, we showed the advantages of "top-down" inversion on a fine temporal scale (e.g., daily to weekly) emissions assessment and its potential applications in refined emissions management and reductions.

Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data

Abstract. The constellation of Earth-observing satellites has now produced atmospheric greenhouse gas concentration estimates covering a period of several years. Their global coverage is providing additional information on the global carbon cycle. These products can be combined with complex inversion systems to infer the magnitude of carbon sources and sinks around the globe. Multiple factors, including the atmospheric transport model and satellite product aggregation method, can impact such flux estimates. Analysis of variance (ANOVA) is a well-established statistical framework for estimating common signals while partitioning variability across factors in the analysis of experiments. Functional ANOVA extends this approach with a statistical model that incorporates spatiotemporal correlation for each ANOVA component. The approach is illustrated on inversion experiments with different satellite retrieval aggregation methods and identifies consistent significant patterns in flux increments that span large spatial scales. Functional ANOVA identifies these patterns while accounting for the uncertainty at small spatial scales that is attributed to differences in the aggregation method. Functional ANOVA is also applied to a recent flux model intercomparison project (MIP), and the relative magnitudes of inversion system effects and data source (satellite versus in situ) are similar but exhibit slightly different importance for fluxes over different continents. In all examples, the unexplained residual variability is locally sizable in magnitude but with limited spatial and temporal correlation. These common behaviors across flux inversion experiments demonstrate the diagnostic capability for functional ANOVA to simultaneously distinguish the spatiotemporal coherence of carbon cycle processes and algorithmic factors.

Frequency encoded tristate Pauli X-gate using SOA assisted photonic band gap crystal

The quantum optical tristate Pauli-X gate can broadly be used due to its very fast speed of operation up to THz order, very fast information processing rate, and a wide domain of application. This Pauli X-gate is also important as it can undergo inversion operation. In this proposed scheme, the quantum tristate Pauli-X logic gate has been constructed by a closely packed square lattice of 2D air photonic band gap crystal (PhCs) composed of GaAsInP-dopped rods. The photonic band gap crystal-based tristate Pauli-X gate is performed also as an all-optical reversible quantum logic gate which has the character of low-powered output, very fast speed and a densely packed design structure. This photonic crystal-based structure is also verified by simulation experiment that this proposed scheme performs as the path of two input signals are cross-exchanged to one another at the output, retaining the path of the other input signal same. In this paper, the frequency encoding technique is used in all the designs to establish the logic of all-optical tristate Pauli X-gate towards the need of obtaining a three-input-three-output inversion system. The frequency encoding technique and the photonic crystal-based three Semiconductor Optical Amplifiers (pc-SOAs) are used here in all the schemes to establish the logic of the tristate Pauli-X gate scheme. Tristate Pauli-X gates have been designed and analyzed by Finite-Difference Time-Domain (FDTD) and Plane Wave Expansion (PWE) techniques. The Plane Wave Expansion (PWE) technique is used here in each system for determining the frequency range of band gap structure of transverse electric (TE) mode in the units of μm-1. All of these devices perform within the frequency range 0.629818≤ω2πc≤0.888037 and 1.17551≤ω2πc ≤ 1.34921 μm-1. The wafer dimension of each photonic band gap crystal-based device is 19.20 μm (length) × 16.20 μm (width) providing a very fast response time and very fast information handling (bit rate) capacity, which is found good for these THz Photonic crystal devices. Simulation results show that the proposed gate provides a response period of 0.291 ps and a bit rate of 3.44 Tbps, operating with a low input power of 3.45 mW. The reversible gate exhibits a response period of 0.289 ps and a bit rate of 3.46 Tbps, operating also with a low input power of 2.70 mW. Additionally, the alternative Pauli-X gate demonstrates a response time of 0.291 ps and a bit rate of 3.44 Tbps, operating with a low input power of 2.83 mW.

Lateral Rock Characteristic Evidence of Hydrocarbon Restoration from Post-Stack Impedance Inversion: A Case Study of ZED-field Offshore Niger Delta

Lateral predictions of rock properties that are descriptive of a reservoir have been studied using a model-established seismic inversion approach that was used to convert input seismic data into an acoustic impedance structure to optimize hydrocarbon restoration in the field. This was actualized by integrating wireline logs and 3D post stack seismic data procured from ZED-Field offshore Niger Delta. The inversion system employed in this study comprises of forward modelling of reflection coefficients starting with a low-frequency impedance model induced from well logs and convolution of the reflection coefficients with the source wavelet extracted from the seismic input. P-impedance analysis at reservoir C5000 delineated from the control well gave a near perfect correlation of 0.998109 (≈ 99.8 %) between the original P-impedance log, initial P-impedance model log and inverted P-impedance log with an estimated error of 0.0616932 which is about (6.16%). Seismic inversion analysis realized an acoustic impedance structure with P-impedance values ranging from 3801 to 11073.0m/s*g/cc and having an overall increase with depth tendency. Specifically, a low-impedance structure was observed at the reservoir window that can be profiled laterally elsewhere from the existing wells. Impedance slices extracted from the impedance volume at the top and base of reservoir C5000 clearly showed low impedance values away from the existing wells which are evidence of new hydrocarbon prospects (hydrocarbon-charged sands) in the field that can be explored for improved hydrocarbon recovery and field development. Therefore, the realized attribute could supply litho-fluids knowledge inside the current reservoirs, and likewise aid in defining probable hydrocarbon regions of low impedance that can be tested with the input seismic to boost the interpretation of reservoir attributes with feasible integration to seismic stratigraphy for improved reservoir prediction away from the extant wells. This information can be invaluable in delineating more prospective reservoir zones in the field, and thereby enhancing optimum field development which aids in reservoir management decisions.

Towards fast focal mechanism inversion of shallow crustal earthquakes in the Chinese mainland

We have developed an automatic regional focal mechanism inversion system based on the Earthquake Rapid Report (ERR) system and the real-time three-component seismic waveform stream of 1 000 broadband seismic stations provided by the China Earthquake Networks Center (CENC). The system can rapidly provide a double couple solution and centroid depth within 5–15 ​min after receiving earthquake information from the ERR system. The data processing is triggered by earthquake information obtained from the ERR system. The system is capable of determining the focal mechanism of all shallow-depth earthquakes in the Chinese mainland with a magnitude of 5.5–6.5. It utilizes waveform data recorded by seismic stations located within 500 ​km from the epicenter, enabling the reporting of a focal mechanism solution within 5–15 ​min of an earthquake occurrence. Additionally, the system can assign a corresponding grade (A B C) to the focal mechanism solution. We processed a total of 301 earthquakes that occurred from 2021 to June 2022, and after the quality control, 166 of them were selected. These selected solutions were manually checked, and 160 of them were compiled in a focal mechanism catalog. This catalog can be conveniently downloaded online via the Internet. The automatic focal mechanism solution of earthquakes in eastern China exhibits a good agreement with that provided by the Global Centroid Moment Tensor (GCMT), when available. The average Kagan angle between this catalog and GCMT is 22°, and the average difference in MW is 0.17. Furthermore, compared with GCMT, the minimum magnitude of our catalog has been reduced from approximately 5.0 to 4.0. The correlation between the centroid depth and crustal thickness in the Chinese mainland confirms the distribution of the centroid depth.

Anthropogenic CO2 emission estimates in the Tokyo metropolitan area from ground-based CO2 column observations

Abstract. Urban areas are responsible for more than 40 % of global energy-related carbon dioxide (CO2) emissions. The Tokyo metropolitan area (TMA), Japan, one of the most populated regions in the world, includes various emission sources, such as thermal power plants, automobile traffic, and residential facilities. In order to infer a top–down emission estimate, we conducted an intensive field campaign in the TMA from February to April 2016 to measure column-averaged dry-air mole fractions of CO2 (XCO2) with three ground-based Fourier transform spectrometers (one IFS 125HR and two EM27/SUN spectrometers). At two urban sites (Saitama and Sodegaura), measured XCO2 values were generally larger than those at a rural site (Tsukuba) by up to 9.5 ppm, and average diurnal variations increased toward evening. To simulate the XCO2 enhancement (ΔXCO2) resulting from emissions at each observation site, we used the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by meteorological fields at a horizontal resolution of ∼1 km from the Weather Research and Forecasting (WRF) model, which was coupled with anthropogenic (large point source and area source) CO2 emissions and biogenic fluxes. Although some of the diurnal variation of ΔXCO2 was not reproduced and plumes from nearby large point sources were not captured, primarily because of a transport modeling error, the WRF–STILT simulations using prior fluxes were generally in good agreement with the observations (mean bias, 0.30 ppm; standard deviation, 1.31 ppm). By combining observations with high-resolution modeling, we developed an urban-scale inversion system in which spatially resolved CO2 emission fluxes at >3 km resolution and a scaling factor of large point source emissions were estimated on a monthly basis by using Bayesian inference. The XCO2 simulation results from the posterior CO2 fluxes were improved (mean bias, −0.03 ppm; standard deviation, 1.21 ppm). The prior and posterior total CO2 emissions in the TMA are 1.026 ± 0.116 and 1.037 ± 0.054 Mt-CO2 d−1 at the 95 % confidence level, respectively. The posterior total CO2 emissions agreed with emission inventories within the posterior uncertainty, demonstrating that the EM27/SUN spectrometer data can constrain urban-scale monthly CO2 emissions.

Preliminary Estimations of Mars Atmospheric and Ionospheric Profiles from Tianwen-1 Radio Occultation One-Way, Two-Way, and Three-Way Observations

The radio occultation method, one of the methods used to provide planetary atmospheric profiles with high vertical resolution, was applied to China’s first Mars mission, Tianwen-1. We carried out observations based on the Chinese Deep Space Network, and one-way, two-way, and three-way modes were used for Doppler observations from the Tianwen-1 spacecraft. We successfully obtained effective observations from Tianwen-1 on 22 and 25 March 2022. An inversion system developed for Tianwen-1 radio occultation observations enabled the derivation of neutral atmospheric density, pressure, temperature, and electron density profiles of Mars. Utilizing one-way tracking data, Martian ionospheric electron density profiles were retrieved at latitudes between 68.7 and 70.7 degrees (N). However, the presence of strong random walk noise in one-way tracking data led to poor inversion results. Meanwhile, Martian ionospheric electron density and neutral atmosphere profiles were extracted from two-way and three-way tracking data at latitudes between 55.1 and 57.0 degrees (S) on 22 March and at latitudes between 62.8 and 63.4 degrees (S) on 25 March. Importantly, our inversion results from Tianwen-1 maintained consistency with results from the Mars Express and the Chapman theory (mainly in the M2 layer). Through two days’ observation experiments, we established and verified the occultation solution system and prepared for the follow-up occultation plans.

A novel tetra-primer ARMS-PCR approach for the molecular karyotyping of chromosomal inversion 2Ru in the main malaria vectors Anopheles gambiae and Anopheles coluzzii

BackgroundChromosomal inversion polymorphisms have been associated with adaptive behavioral, physiological, morphological and life history traits in the two main Afrotropical malaria vectors, Anopheles coluzzii and Anopheles gambiae. The understanding of the adaptive value of chromosomal inversion systems is constrained by the feasibility of cytological karyotyping. In recent years in silico and molecular approaches have been developed for the genotyping of most widespread inversions (2La, 2Rb and 2Rc). The 2Ru inversion, spanning roughly 8% of chromosome 2R, is commonly polymorphic in West African populations of An. coluzzii and An. gambiae and shows clear increases in frequency with increasing rainfall seasonally and geographically. The aim of this work was to overcome the constraints of currently available cytological and high-throughput molecular assays by developing a simple PCR assay for genotyping the 2Ru inversion in individual specimens of both mosquito species.MethodsWe designed tetra-primer amplification refractory mutation system (ARMS)-PCR assays based on five tag single-nucleotide polymorphisms (SNPs) previously shown to be strongly correlated with 2Ru inversion orientation. The most promising assay was validated against laboratory and field samples of An. coluzzii and An. gambiae karyotyped either cytogenetically or molecularly using a genotyping-in-thousands by sequencing (GT-seq) high-throughput approach that employs targeted sequencing of multiplexed PCR amplicons.ResultsA successful assay was designed based on the tag SNP at position 2R, 31710303, which is highly predictive of the 2Ru genotype. The assay, which requires only one PCR, and no additional post-PCR processing other than electrophoresis, produced a clear banding pattern for 98.5% of the 454 specimens tested, which is a 96.7% agreement with established karyotyping methods. Sequences were obtained for nine of the An. coluzzii specimens manifesting 2Ru genotype discrepancies with GT-seq. Possible sources of these discordances are discussed.ConclusionsThe tetra-primer ARMS-PCR assay represents an accurate, streamlined and cost-effective method for the molecular karyotyping of the 2Ru inversion in An. coluzzii and An. gambiae. Together with approaches already available for the other common polymorphic inversions, 2La, 2Rb and 2Rc, this assay will allow investigations of the adaptive value of the complex set of inversion systems observed in the two major malaria vectors in the Afrotropical region.Graphical

Evaluating the Ability of the Pre-Launch TanSat-2 Satellite to Quantify Urban CO2 Emissions

TanSat-2, the next-generation Chinese greenhouse gas monitoring satellite for measuring carbon dioxide (CO2), has a new city-scale observing mode. We assess the theoretical capability of TanSat-2 to quantify integrated urban CO2 emissions over the cities of Beijing, Jinan, Los Angeles, and Paris. A high-resolution emission inventory and a column-averaged CO2 (XCO2) transport model are used to build an urban CO2 inversion system. We design a series of numerical experiments describing this observing system to evaluate the impacts of sampling patterns and XCO2 measurement errors on inferring urban CO2 emissions. We find that the correction in systematic and random flux errors is correlated with the signal-to-noise ratio of satellite measurements. The reduction in systematic flux errors for the four cities are sizable, but are subject to unbiased satellite sampling and favorable meteorological conditions (i.e., less cloud cover and lower wind speed). The corresponding correction to the random flux error is 19–28%. Even though clear-sky satellite data from TanSat-2 have the potential to reduce flux errors for cities with high CO2 emissions, quantifying urban emissions by satellite-based measurements is subject to additional limitations and uncertainties.

Urban-scale variational flux inversion for CO Using TROPOMI total-column retrievals: A case study of Tehran

Emission inventories of trace gases and aerosols are a key input for air pollution modelling, yet are subject to significant uncertainties. The aim of this study is to refine urban-scale, gridded estimates of carbon monoxide (CO) emissions for Tehran, Iran; this was done using an inverse-modelling approach. We assimilated total column mixing ratios of CO retrieved from the TROPOspheric Monitoring Instrument (TROPOMI) using a four-dimensional variational assimilation system based on the Community Multi-Scale Air Quality Model. The inversion system allows for two representations of the temporal profile of emissions at each grid-point: scaling a fixed diurnal profile, or with separate, constant estimates spanning four diurnal time-categories. We evaluate the inversion capability by applying independent surface measurements.We find that the model forced with prior emissions substantially underestimates day-to-day variation in satellite data. However, the posterior emissions show significant improvements in simulating the assimilated data, reducing the bias of modelled mixing ratios by 72% to 91% when using categorised temporal variation in the system and by 35% to 83% with a fixed temporal variation. Comparisons with surface measurements indicate that the posterior emissions also show some improvements in simulating surface observations, although not in all cases. For example, in February 2019, the magnitude of the bias is reduced from 1.1 ppm to 0.9 ppm and 0.2 ppm using the categorised and fixed temporal variation schemes, respectively. The posterior results are sensitive to the specification of temporal emission patterns, and the spatiotemporal pattern of the posterior varies depending on whether we assume the fixed or categorised temporal variation. It is worth noting that TROPOMI samples only once per day, so it cannot define the full diurnal cycle of emissions. Finally, the inability to match simultaneously the surface and column-integrated observations may be due to issues in modelling the vertical tracer transport, however, this was not evaluated in the present work. Notwithstanding these weaknesses, the approach shows considerable promise for regions lacking local inventories.