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Runway crossing behind an aircraft has attracted a large amount of interest in recent years. In this work, based on a turbofan engine model, three-dimensional models of the nozzle and inlet are established, and the effects of the jet of the aircraft in front on the inlet of the aircraft behind are studied under different operating conditions using numerical simulations. The results show that at the same distance between the two aircraft, the total pressure decreases as the exit flow rate of the inlet increases, but different flow rates do not lead to different component distributions. For the same flow rate at the exit of the inlet, when the distance increases, the distortion index of the total pressure at the exit of the inlet decreases, and the contents of the component of the airflow entering the inlet from the jet decrease, but those entering the inlet from the far field increase. Through component and isotope identification, the main components of the airflow entering the inlet are oxygen and nitrogen. Based on these results, when studying the effect of the exhaust jet from an aircraft on an aircraft behind, only the maximum idle engine speed needs to be considered, and the effect of the jet components on the inlet can be ignored. The inlet distortion caused by the exhaust flow is more severe in crosswind environments.

The advancement of analytical techniques, such as comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC–MS), enables the efficient separation of complex organics. Developing innovative methods for data processing and analysis is crucial to unlock the full potential of GC×GC–MS in understanding intricate chemical mixtures. In this study, we proposed an innovative method for the semi-automated identification and quantification of complex organic mixtures using GC×GC–MS. The method was formulated based on self-constructed mass spectrum patterns and the traversal algorithms and was applied to organic vapor and aerosol samples collected from the tailpipe emissions of heavy-duty diesel vehicles and the ambient atmosphere. Thousands of compounds were filtered, speciated, and clustered into 26 categories, including aliphatic and cyclic hydrocarbons, aromatic hydrocarbons, aliphatic oxygenated species, phenols and alkylphenols, and heteroatom-containing species. The identified species accounted for over 80 % of all the eluted chromatographic peaks at the molecular level. A comprehensive analysis of quantification uncertainty was undertaken. Using representative compounds, quantification uncertainties were found to be less than 37.67 %, 22.54 %, and 12.74 % for alkanes, polycyclic aromatic hydrocarbons (PAHs), and alkyl-substituted benzenes, respectively, across the GC×GC space, excluding the first and the last time intervals. From a source apportionment perspective, adamantane was clearly isolated as a potential tracer for heavy-duty diesel vehicle (HDDV) emissions. The systematic distribution of nitrogen-containing compounds in oxidized and reduced valences was discussed, and many of them served as critical tracers for secondary nitrate formation processes. The results highlighted the benefits of developing self-constructed models for the enhanced peak identification, automated cluster analysis, robust uncertainty estimation, and source apportionment and achieving the full potential of GC×GC–MS in atmospheric chemistry.

Understanding the near-ground vertical and temporal photochemical O3 formation mechanism is important to mitigate O3 pollution. Here, we measured the vertical profiles of O3 and its precursors at six different heights, ranging from 5 to 335 m, using a newly built vertical observation system in the Pearl River Delta (PRD) region of China. The net photochemical ozone production rate (P(O3)net) and O3 formation sensitivities at various heights were diagnosed using an observation-based model coupled with the Master Chemical Mechanism (MCM v3.3.1). Moreover, to assess model performance and identify the causative factors behind O3 pollution episodes, the P(O3)net was measured at 5 m above ground level with a custom-built detection system. In total, three O3 pollution episodes and two non-episodes were captured. The identified O3 pollution episodes were found to be jointly influenced by both photochemical production and physical transport, with local photochemical reactions playing a major role. The high index of agreement (IOA) calculated by comparing the modelled and measured P(O3)net values indicated the rationality of investigating the vertical and temporal variability in O3 formation mechanisms using model results. However, the measured P(O3)net values were generally higher than the modelled P(O3)net values, particularly under high-NOx conditions, which may indicate a potential underestimation of total RO2 by the model. Throughout the measurement period, the contribution of different reaction pathways to O3 production remained consistent across various heights, with HO2 + NO as the major O3 production pathway, followed by RO2 + NO. We observed that P(O3)net decreased with an increase in measurement height, which was primarily attributed to the reduction in O3 precursors, such as oxygenated volatile organic compounds (OVOCs) and non-methane hydrocarbons (NMHCs). The O3 formation regimes were similar at different heights during both episodes and non-episodes, either being located in the VOC-sensitive regime or in the transition regime that is more sensitive to VOCs. Diurnally, photochemical O3 formation typically remained in the VOC-sensitive regime during the morning and noon, but it transitioned to the transition regime and was more sensitive to VOCs in the afternoon at around 16:00 LT (local time). Vertical and temporal photochemical O3 formation is most sensitive to OVOCs, suggesting that targeting specific VOCs for control measures is more practical and feasible at the observation site. The vertical temporal analysis of O3 formation mechanisms near the ground surface in this study provides critical foundational knowledge that can be used to formulate effective short-term emergency and long-term control strategies to combat O3 pollution in the PRD region of China.

In the intricate tapestry of Yunnan Province's biodiversity, the Lanping black-boned sheep (LPBB) emerges as a captivating enigma, distinguished by its profound melanin pigmentation adorning both its skin and its internal organs. Initially cataloged in the 1950s within the confines of Lanping County, this exceptional mammalian species presents a scarcity and uniqueness that extends beyond its geographic origins. Here, we collected 100 blood samples from Lanping black-boned sheep along with 50 samples each from Lanping normal sheep (LPN) and Huize normal sheep (HZN), all sourced from Yunnan Province. Our investigation focused on the association between the platelet-derived growth factor receptor-like gene (PDGFRL) polymorphism and the distinctive melanin characteristics observed in Lanping black-boned sheep. Utilizing UV–visible spectrophotometry, we assessed the melanin indexes present, such as tyrosinase activity and true melanin in the sheep blood, and the results demonstrated a significant elevation in melanin indexes for Lanping black-boned sheep compared to the control group (P<0.05). We also identified three synonymous mutation sites within a partial 1128 bp exon fragment of the gene-encoding PDGFRL (EX2-G408A, EX5-T184C, and EX5-G222T). Notably, Lanping black-boned sheep, harboring genotypes GG, TT, and GG at these specific sites, showcased a pronounced surge in tyrosinase activity, eumelanin / total melanin ratios, and plasma colorimetric values when contrasted with the control group (P<0.05). The discernment of GG, TT, and GG as the prevailing genotypes at their respective genetic loci in Lanping black-boned sheep heralds a breakthrough in our understanding of the genetic markers associated with black pigmentation. However, all three loci are silent mutations and do not alter the phenotypic changes. Whether they affect changes in melanin content through other metabolic pathways requires further study. In conclusion, the PDGFRL gene was silenced by mutations in our study and affected blood melanin levels. However, the gene did not undergo a missense mutation that altered the phenotypic changes, and the exact channel through which the changes in melanin content were affected needs to be further verified.

The present investigation focuses on analyzing the microstructure and tensile properties of room temperature quenching and partitioning (RT Q&P) steel manufactured using laser powder bed fusion (LPBF). The as-built specimen reveals a dual-phase microstructure composed of retained austenite (RA) grains within the martensite matrix, with the absence of the conventional Mn banded microstructure. However, the as-built sample demonstrates poor tensile properties, exhibiting only a 2.5% total elongation, largely due to significant residual stress. To mitigate this issue, the as-built sample undergoes tempering at low temperatures (300, 350, and 400 °C) for 10 min. This treatment results in a reduction of the average compressive residual stresses from −214.4 to −132.1 MPa, and a transformation of the residual stresses in RA from a compressive state of −52.4 MPa to a tensile state of 136.4 MPa after tempering at 350 °C. Furthermore, the volume fraction of RA increases by approximately 10%, and there is a slight increase in the C content after tempering. The tempered RT Q&P steel demonstrates an ultra-high tensile strength ranging from 1.3 to 1.5 GPa, coupled with a substantial total elongation of 10–15%. This exceptional combination of strength and ductility is attributed to the transformation-induced plasticity (TRIP) effect and the reduction of residual stresses. Notably, this study marks the first confirmation that strong and ductile RT Q&P steel can be produced through the LPBF process.

The climate of the Tibetan Plateau (TP) has experienced substantial changes in recent decades as a result of the location's susceptibility to global climate change. The changes observed across the TP are closely associated with regional land–atmosphere interactions. Current models and satellites struggle to accurately depict the interactions; therefore, critical field observations on land–atmosphere interactions outlined here provide necessary independent validation data and fine-scale process insights for constraining reanalysis products, remote sensing retrievals, and land surface model parameterizations. Scientific data sharing is crucial for the TP since in situ observations are rarely available under these harsh conditions. However, field observations are currently dispersed among individuals or groups and have not yet been integrated for comprehensive analysis. This has prevented a better understanding of the interactions, the unprecedented changes they generate, and the substantial ecological and environmental consequences they bring about. In this study, we collaborated with different agencies and organizations to present a comprehensive dataset for hourly measurements of surface energy balance components, soil hydrothermal properties, and near-surface micrometeorological conditions spanning up to 17 years (2005–2021). This dataset, derived from 12 field stations covering a variety of typical TP landscapes, provides the most extensive in situ observation data available for studying land–atmosphere interactions on the TP to date in terms of both spatial coverage and duration. Three categories of observations are provided in this dataset: meteorological gradient data (met), soil hydrothermal data (soil), and turbulent flux data (flux). To assure data quality, a set of rigorous data-processing and quality control procedures are implemented for all observation elements (e.g., wind speed and direction at different height) in this dataset. The operational workflow and procedures are individually tailored to the varied types of elements at each station, including automated error screening, manual inspection, diagnostic checking, adjustments, and quality flagging. The hourly raw data series; the quality-assured data; and supplementary information, including data integrity and the percentage of correct data on a monthly scale, are provided via the National Tibetan Plateau Data Center (https://doi.org/10.11888/Atmos.tpdc.300977, Ma et al., 2023a). With the greatest number of stations covered, the fullest collection of meteorological elements, and the longest duration of observations and recordings to date, this dataset is the most extensive hourly land–atmosphere interaction observation dataset for the TP. It will serve as the benchmark for evaluating and refining land surface models, reanalysis products, and remote sensing retrievals, as well as for characterizing fine-scale land–atmosphere interaction processes of the TP and underlying influence mechanisms.

Volatile chemical products (VCPs) have become an important source of reactive organic gases (ROGs) in urban areas worldwide. Industrial activities can also utilize a large number of VCPs and emit many organic gases into the atmosphere. Due to multiple sampling and measurement challenges, only a subset of ROG species is usually measured for many industrial VCP sources. This study aims to investigate the emissions of ROGs from five industrial VCP sources in the Pearl River Delta (PRD) region of China, including the shoemaking, plastic surface coating, furniture coating, printing, and ship coating industries. A more comprehensive speciation of ROG emissions from these industrial VCP sources was developed by the combination of proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) and the gas chromatography–mass spectrometer/flame ionization detector (GC–MS/FID). Our study identified oxygenated ROG species (OVOCs) as representative ROGs emitted from these sources, which are highly related to specific chemicals used during industrial activities. Moreover, mass spectra similarity analysis revealed significant dissimilarities among the ROG emissions from industrial activities, indicating substantial variations between different industrial VCP sources. Except for the ship coating industry utilizing solvent-borne coatings, the proportions of OVOCs range from 67 % to 96 % in total ROG emissions and 72 % to 97 % in total OH reactivity (OHR) for different industrial sources, while the corresponding contributions of OVOCs in the ship coating industry are only 16 ± 3.5 % and 15 ± 3.6 %. The industrial VCP sources associated with solvent-borne coatings exhibited a higher ozone formation potential (OFP), reaching as high as 5.5 and 2.7 g O3 g−1 ROGs for the ship coating and furniture coating industries, primarily due to contributions from aromatics. We find that a few species can contribute the majority of the ROG emissions and also their OHR and OFP from various industrial VCP sources. Our results suggest that ROG treatment devices may have limited effectiveness for all ROGs, with treatment efficiencies ranging from −12 % to 68 %. Furthermore, we found that ambient measurements in industrial areas have been significantly impacted by industrial VCP sources, and ROG pairs (e.g., methyl ethyl ketone (MEK) / C8 aromatics ratio) can be utilized as reliable evidence by using high-time-resolution ROG measurements from PTR-ToF-MS. Our study demonstrated the importance of measuring a large number of ROGs using PTR-ToF-MS for characterizing ROG emissions from industrial VCP sources.

Formic acid is the most abundant organic acid in the troposphere and has significant environmental and climatic impacts. Isocyanic acid poses severe threats to human health and could be formed through the degradation of formic acid. However, the lack of vertical observation information has strongly limited the understanding of their sources, particularly in urban regions with complex pollutant emissions. To address this issue, we assessed the impact of long tubes on the measurement uncertainties of formic and isocyanic acids and found that the tubing impact was negligible. Then, we conducted continuous (27 d) vertical gradient measurements (five heights between 5–320 m) of formic and isocyanic acids using long tubes based on a tall tower in Beijing, China, in the summer of 2021. Results show that the respective mean mixing ratios of formic and isocyanic acids were 1.3 ± 1.3 ppbv and 0.28 ± 0.16 ppbv at 5 m and were 2.1 ± 1.9 ppbv and 0.43 ± 0.21 ppbv at 320 m during the campaign. The mixing ratios of formic and isocyanic acids were substantially enhanced in the daytime and correlated with the diurnal change of ozone. Upon sunrise, the mixing ratios of formic and isocyanic acids at different heights simultaneously increased, even in the residual layer. In addition, positive vertical gradients were observed for formic and isocyanic acids throughout the day. The positive vertical gradients of formic and isocyanic acids in the daytime imply the enhancement of their secondary formation in urban regions aloft, predominantly due to the enhancements of oxygenated volatile organic compounds. Furthermore, the afternoon peaks and positive vertical gradients of formic and isocyanic acids in the nighttime also indicate their minor contributions from primary emissions from ground-level sources. The formation pathway of isocyanic acid through HCOOH–CH3NO–HNCO was enhanced with height but only accounted for a tiny fraction of its ambient abundance. The abundance and source contributions of formic and isocyanic acids in the atmospheric boundary layer may be highly underestimated when being derived from their ground-level measurements. With the aid of numerical modeling techniques, future studies could further identify key precursors that drive the rapid formation of formic and isocyanic acids and quantitatively assess the impacts of the enhanced formation of the two acids aloft on their budgets at ground level.

Litter size is one of the key factors affecting the efficiency of sheep breeding, and previous studies found that the HRG, FETUB, and GUCY1A1 genes were closely related to litter size in sheep. This experiment aims to explore the polymorphisms of the g.405442728A>G locus of the HRG gene, the g.421655951C>T locus of the FETUB gene, and the g.414050897G>C locus of the GUCY1A1 gene and their association with sheep litter size. The MassARRAY® single-nucleotide polymorphism (SNP) genotyping technique was used to detect the polymorphisms of these loci in five sheep breeds, i.e., Small-tailed Han sheep, Hu sheep, Cele black sheep, Sunite sheep, and Bamei mutton sheep. In addition, the association between the polymorphisms of these genes and the litter size of Small-tailed Han sheep was also analyzed. The results showed that the g.405442728A>G locus of the HRG gene was moderately polymorphic (0.25 < PIC <0.5) in both monotocous and polytocous sheep breeds; the g.421655951C>T locus of the FETUB gene was lowly polymorphic (PIC <0.25) in five sheep breeds; the g.414050897G>C locus of GUCY1A1 showed moderately polymorphism in Small-tailed Han sheep (0.25 ≤ PIC <0.5) and low polymorphism in four other sheep breeds (PIC <0.25). The chi-squared test results showed that the g.405442728A>G locus of the HRG gene was in the Hardy–Weinberg equilibrium state in five sheep breeds (P>0.05). The g.421655951 C>T locus of the FETUB gene and the g.414050897G>C locus of the GUCY1A1 gene were in the Hardy–Weinberg equilibrium state in Small-tailed Han sheep (P>0.05) and in the Hardy–Weinberg disequilibrium state in other sheep breeds (P<0.05). The association analysis showed that the g.405442728A>G locus of the HRG gene and the g.421655951C>T locus of the FETUB gene had a significant impact on the litter size of sheep (P<0.05), while the g.414050897G>C locus of the GUCY1A1 gene had no significant impact on the litter size (P>0.05). In summary, the HRG gene and the FETUB gene can be used as potential molecular markers for the selection of the litter size in sheep.

Numerous studies on prolific sheep breeds have shown that the transforming growth factor beta (TGF-β) superfamily members, including bone morphogenetic protein receptor type 1B (BMPR1B), bone morphogenetic protein 15 (BMP15) and growth differentiation factor 9 (GDF9), are the essential regulators of ovulation rate and litter size. In total, 11 known mutations (1 in BMPR1B, 6 in BMP15 and 4 in GDF9) are able to increase the ovulation rate and litter size, respectively. In this study, the genomic DNA was isolated from 512 high-prolificacy sheep (including the Small-tailed Han, Hu and Wadi sheep breeds) and 164 low-prolificacy sheep (Tan sheep), and genotyping of the specific mutations of the three fecundity-related genes was carried out by sequencing. The results showed that the FecB mutation in BMPR1B was detected in all four sheep breeds, and the frequency of B allele was significantly higher in the high-prolificacy breeds than that in the low-prolificacy breed (P). A novel mutation, c.T755C (named S1), was found in BMP15 from the four sheep breeds. However, known mutations such as FecXI, FecXH, FecXB, FecXG, FecXL and FecXR were not detected in these breeds. Three known loci (G1, G3 and G4) and a new mutation, c.A1515G (named S2), were found in GDF9, and the other three known mutations (FecGH (G8), FecGE and FecTT) were not detected in all four sheep breeds. The genotype distribution at the G1 and G4 loci had significant differences between the low-prolificacy sheep breed and the other three high-prolificacy sheep breeds. There was no difference in the genotype distribution at the G1 and G4 loci between the three high-prolificacy sheep breeds. Haplotype analysis of the four polymorphic loci in GDF9 suggested that H4 (GGAA) was the preponderant haplotype in the three high-prolificacy sheep breeds, but H1 (GGGG) was in the low-prolificacy sheep breed. These results preliminarily showed that the BMPR1B and GDF9 might be major genes influencing the prolificacy of Chinese sheep breeds.

In the context of global warming, an increase in atmospheric aridity and global dryland expansion under the future climate has been expected in previous studies. However, this conflicts with observed greening over drylands and the insignificant increase in hydrological and ecological aridity from the ecohydrology perspective. Combining climatic, hydrological, and vegetation data, this study evaluated global dryland aridity changes at meteorological stations from 2003 to 2019. A decoupling between atmospheric, hydrological, and vegetation aridity was found. Atmospheric aridity represented by the vapor pressure deficit (VPD) increased, hydrological aridity indicated by machine-learning-based precipitation minus evapotranspiration (P − ET) data did not change significantly, and ecological aridity represented by the leaf area index (LAI) decreased. P − ET showed nonsignificant changes in most of the dominant combinations of the VPD, LAI, and P − ET. This study highlights the added value of using station-scale data to assess dryland change as a complement to results based on coarse-resolution reanalysis data and land surface models.

In order to solve the problems of low efficiency, high cost and long cycle of the traditional 1:10000 basic geographic information data production method (aerial photography combined with field mapping), this paper proposes a 1:10000 rapid mapping method based on GF-7 satellite data. It solves the key technical difficulties of automatic information recognition, extraction, integration and mapping of water body, building, road, vegetation, contour, and other elements. And an automatic software system of “image -> patch -> data -> map” is developed, which realizes the rapid production of 1:10000 basic geographic information data products and improves the ability of high-precision basic geographic information resource construction.

Dense matching plays an important role in 3D modeling from satellite images. Its purpose is to establish pixel-by-pixel correspondences between two stereo images. The most well-known algorithm is the semi-global matching (SGM), which can generate high-quality 3D models with high computational efficiency. Due to the complex coverage and imaging condition, SGM cannot cope with these situation well. In recent years, deep learning-based stereo matching has attracted wide attention and shown overwhelming benefits over traditional algorithms in terms of precision and completeness. However, existing models are usually evaluated by using close-ranging datasets. Thus, this study investigates the recent deep learning models and evaluate their performance on both close-ranging and satellite image datasets. The results demonstrate that deep learning network can better adapt to the satellite dataset than the typical SGM. Meanwhile, the generalization ability of deep learning-based models is still low for the real application at recent time.

Seasonal variation and influencing factors of river water isotopes were investigated in the Xiangjiang River basin located in the East Asian monsoon region. This investigation involved comprehensive sampling of daily precipitation and river water with a 5 d interval as well as observing hydrometeorological factors spanning 13 hydrological years from January 2010 to December 2022, combined with the temporal and spatial correlation analyses based on linear regression and the isotopic Atmospheric Water Balance Model. Key findings are as follows: river water δ2H (δ2HR) exhibited significant seasonal variation, with the most positive and negative values occurring in the spring flood period and summer drought period, respectively, in alignment with those observed in precipitation. The correlations of the δ2HR with corresponding hydrometeorological factors with a 5 d interval were commonly weak due to the seasonality of precipitation isotopes and mixing of various water bodies within the basin, but the changes in the runoff (ΔR) and δ2HR (Δδ2HR) between two contiguous samplings with 5 d or higher intervals showed significant responses to the corresponding accumulated precipitation and evaporation. Prolonged rainless intervals with high evaporation rates in 2013 and 2022 as well as significant precipitation events in major flood periods in 2011 and 2017 had a significant impact on the δ2HR and runoff discharge. However, the most positive δ2HR values were primarily influenced by precipitation input with the most enriched isotopes in the spring flood period, while the moderately isotope-depleted precipitation during limited wetness conditions led to the most negative δ2HR. The spatial correlation analysis between water isotopes and hydrometeorological factors at the observing site and in the surrounding regions supported the representation of the Changsha site in the Xiangjiang River basin. These results underscore the potential of Δδ2HR as a proxy that reflects the seasonal variations in local environments, while caution is advised when interpreting extreme isotopic signals in river water. Overall, this study provides insights into the seasonal variation, extreme signal interpreting, and controlling factors of δ2HR in the study area, which was valuable for paleoclimate reconstruction and establishment of isotope hydrologic models.

Most phosphorus (P) in soils is unavailable for direct biological uptake, as it is locked within primary or secondary mineral particles, adsorbed to mineral surfaces, or immobilized inside of organic material. Deciphering the composition of different P forms in soil is critical for understanding P bioavailability and its underlying dynamics. However, widely used global estimates of different soil P forms are based on a dataset containing few measurements in which many regions or soil types are unrepresented. This poses a major source of uncertainty in assessments that rely on these estimates to quantify soil P constraints on biological activity controlling global food production and terrestrial carbon balance. To address this issue, we consolidated a database of six major soil P “forms” containing 1857 entries from globally distributed (semi-)natural soils and 11 related environmental variables. These six different forms of P (labile inorganic P (Pi), labile organic P (Po), moderately labile Pi, moderately labile Po, primary mineral P, and occluded P) were measured using a sequential P fractionation method. As they do not represent precise forms of specific discrete P compounds in the soil but rather resemble operational pools, we will now refer to them as P pools. In order to quantify the relative importance of 11 soil-forming variables in predicting soil P pool concentrations and then make further predictions at the global scale, we trained random forest regression models for each of the P pools and captured observed variation with R2 higher than 60 %. We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH and only secondarily by total soil P concentration. When expressed in relative values (proportion of total P), the model showed that soil pH is generally the most important predictor for proportions of all soil P pools, alongside the prominent influences of soil organic carbon, total P concentration, soil depth, and biome. These results suggest that, while concentration values of P pools logically strongly depend on soil total P concentration, the relative values of the different pools are modulated by other soil properties and the environmental context. Using the trained random forest models, we predicted soil P pools' distributions in natural systems at a resolution of 0.5∘×0.5∘. Our global maps of different P pools in soils as well as the pools' underlying drivers can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system.

Current process-based research mainly uses box models to evaluate photochemical ozone production and destruction rates, and it is unclear to what extent the photochemical reaction mechanisms are elucidated. Here, we modified and improved a net photochemical ozone production rate (NPOPR, P(O3)net) detection system based on the current dual-channel reaction chamber technique, which makes the instrument applicable to different ambient environments, and its various operating indicators were characterized, i.e., “airtightness”, light transmittance, wall losses of the reaction and reference chambers, conversion rate of O3 to NO2, air residence time, and performance of the reaction and reference chambers. The limits of detection of the NPOPR detection system were determined to be 0.07, 1.4, and 2.3 ppbv h−1 at sampling flow rates of 1.3, 3, and 5 L min−1, respectively. We further applied the NPOPR detection system to field observations at an urban site in the Pearl River Delta (China). During the observation period, the maximum value of P(O3)net was 34.1 ppbv h−1, which was ∼ 0 ppbv h−1 at night within the system detection error and peaked at approximately noon local time. The daytime (from 06:00–18:00 LT) average value of P(O3)net was 12.8 (± 5.5) ppbv h−1. We investigated the detailed photochemical O3 formation mechanism in the reaction and reference chambers of the NPOPR detection system using a zero-dimensional box model. We found that the photochemical reactions in the reaction chamber were very close to those in ambient air, but there was not zero chemistry in the reference chamber because the reaction related to the production and destruction of RO2 (= HO2 + RO2) continued in the reference chamber, which led to a small amount of P(O3)net. Therefore, the P(O3)net measured here can be regarded as the lower limit of the real P(O3)net in the atmosphere; however, the measured P(O3)net was still ∼ 7.5 to 9.3 ppbv h−1 higher than the modeled P(O3)net value depending on different modeling methods, which may be due to the inaccurate estimation of HO2 / RO2 radicals in the modeling study. Short-lived intermediate measurements coupled with direct P(O3)net measurements are needed in the future to better understand O3 photochemistry. Our results show that the NPOPR detection system can achieve high temporal resolution and continuous field observations, which helps us to better understand photochemical O3 formation and provides a key scientific basis for continuous improvement of air quality in China.

The energy and water vapor exchange between the land surface and atmospheric boundary layer plays a critical role in regional climate simulations. This paper implemented a hybrid data assimilation and machine learning framework (DA-ML method) into the Weather Research and Forecasting (WRF) model to optimize surface soil and vegetation conditions. The hybrid method can integrate remotely sensed leaf area index (LAI), multi-source soil moisture (SM) observations, and land surface models (LSMs) to accurately describe regional climate and land–atmosphere interactions. The performance of the hybrid method on the regional climate was evaluated in the Heihe River basin (HRB), the second-largest endorheic river basin in Northwest China. The results show that the estimated sensible (H) and latent heat (LE) fluxes from the WRF (DA-ML) model agree well with the large aperture scintillometer (LAS) observations. Compared to the WRF (open loop – OL), the WRF (DA-ML) model improved the estimation of evapotranspiration (ET) and generated a spatial distribution consistent with the ML-based watershed ET (ETMap). The proposed WRF (DA-ML) method effectively reduces air warming and drying biases in simulations, particularly in the oasis region. The estimated air temperature and specific humidity from WRF (DA-ML) agree well with the observations. In addition, this method can simulate more realistic oasis–desert boundaries, including wetting and cooling effects and wind shield effects within the oasis. The oasis–desert interactions can transfer water vapor to the surrounding desert in the lower atmosphere. In contrast, the dry and hot air over the desert is transferred to the oasis from the upper atmosphere. The results show that the integration of LAI and SM will induce water vapor intensification and promote precipitation in the upstream of the HRB, particularly on windward slopes. In general, the proposed WRF (DA-ML) model can improve climate modeling by implementing detailed land characterization information in basins with complex underlying surfaces.

Water-soluble metals are known to produce greater adverse human health outcomes than their water-insoluble forms. Although the concentrations of water-soluble aerosol metals are usually limited by atmospheric processes that convert water-insoluble metals to water-soluble forms, factors that control the solubilities of aerosol metals in different environments remain poorly understood. In this study, we investigated the abundance and fractional solubilities of different metals in size-fractionated aerosols collected at an urban site in Hong Kong and identified the factors that modulated metal solubilities in fine aerosols. The concentrations of total and water-soluble metals in fine and coarse aerosols were the highest during the winter and spring seasons due to the long-range transport of air masses by northerly prevailing winds from emission sources located in continental areas north of Hong Kong. The study-averaged metal fractional solubilities spanned a wide range for both fine (7.8 % to 71.2 %) and coarse (0.4 % to 47.9 %) aerosols, but higher fractional solubilities were typically observed for fine aerosols. Sulfate was found to be strongly associated with both the concentrations of water-soluble Cr, Fe, Co, Cu, Pb, and Mn and their fractional solubilities in fine aerosols, which implied that sulfate-driven acid processing likely played an important role in the dissolution of the water-insoluble forms for these six metals. Further analyses revealed that these strong associations were due to sulfate providing both the acidic environment and liquid water reaction medium needed for the acid dissolution process. Thus, the variability in the concentrations of water-soluble Cr, Fe, Co, Cu, Pb, and Mn and their fractional solubilities were driven by both the aerosol acidity levels and liquid water concentrations, which in turn were controlled by sulfate. These results highlight the roles that sulfate plays in the acid dissolution of metals in fine aerosols in Hong Kong. Our findings will likely also apply to other urban areas in South China, where sulfate is the dominant acidic and hygroscopic component in fine aerosols.

Global warming has significantly impacted the hydrological processes and ecological environment in permafrost regions. Mean residence time (MRT) is a fundamental catchment descriptor that provides hydrological information regarding storage, flow pathways, and water source within a particular catchment. However, water stable isotopes and MRT have rarely been investigated due to limited data collection in the high-altitude permafrost regions. This study uses the long-term stable isotopic observations to identify runoff components and applied the sine-wave exponential model to estimate water MRT in a high-altitude permafrost catchment (5300 m a.s.l.) in the central Tibetan Plateau (TP). We found that the isotope composition in precipitation, stream, and supra-permafrost water exhibited obvious seasonal variability. The freeze–thaw process of the permafrost active layer and direct input of precipitation significantly modified the stable isotope compositions in supra-permafrost and stream water. The hydrograph separation revealed that precipitation and supra-permafrost water accounted for 35 ± 2 % and 65 ± 2 % of the total discharge of stream water, respectively. MRT for stream and supra-permafrost water was estimated at 100 and 255 d, respectively. Such shorter MRTs of supra-permafrost and stream water (compared to the non-permafrost catchments) might reflect the unique characteristics of the hydrological process in permafrost catchments. Moreover, the MRT of supra-permafrost water was more sensitive to environmental change than that of stream water. Climate and vegetation factors affected the MRT of stream and supra-permafrost water mainly by changing the thickness of the permafrost active layer. Our results suggest that climate warming might retard the rate of water cycle in permafrost regions. Overall, our study expands our understanding of hydrological processes in high-altitude permafrost catchments under global warming.

Cycloalkanes are important trace hydrocarbons existing in the atmosphere, and they are considered a major class of intermediate volatile organic compounds (IVOCs). Laboratory experiments showed that the yields of secondary organic aerosols (SOAs) from oxidation of cycloalkanes are higher than acyclic alkanes with the same carbon number. However, measurements of cycloalkanes in the atmosphere are still challenging at present. In this study, we show that online measurements of cycloalkanes can be achieved using proton transfer reaction time-of-flight mass spectrometry with NO+ chemical ionization (NO+ PTR-ToF-MS). Cyclic and bicyclic alkanes are ionized with NO+ via hydride ion transfer, leading to major product ions of CnH2n-1+ and CnH2n-3+, respectively. As isomers of cycloalkanes, alkenes undergo association reactions with major product ions of CnH2n ⚫ (NO)+, and concentrations of 1-alkenes and trans-2-alkenes in the atmosphere are usually significantly lower than cycloalkanes (about 25 % and %, respectively), as a result inducing little interference with cycloalkane detection in the atmosphere. Calibrations of various cycloalkanes show similar sensitivities associated with small humidity dependence. Applying this method, cycloalkanes were successfully measured at an urban site in southern China and during a chassis dynamometer study of vehicular emissions. Concentrations of both cyclic and bicyclic alkanes are significant in urban air and vehicular emissions, with comparable cyclic alkanes / acyclic alkanes ratios between urban air and gasoline vehicles. These results demonstrate that NO+ PTR-ToF-MS provides a new complementary approach for the fast characterization of cycloalkanes in both ambient air and emission sources, which can be helpful to fill the gap in understanding the importance of cycloalkanes in the atmosphere.

Here we present a field measurement of ClNO2 (nitryl chloride) and N2O5 (dinitrogen pentoxide) by a time-of-flight chemical ionization mass spectrometer (ToF-CIMS) with the Filter Inlet for Gas and AEROsols (FIGAERO) at a regional site in the Pearl River Delta during a photochemical pollution season from 26 September to 17 November 2019. Three patterns of air masses are sampled during this campaign, including the dominating air masses from the north and northeast urban regions (Type A), the southeast coast (Type B), and the South China Sea (Type C). The concentration of ClNO2 and N2O5 was observed to be much higher in Type A and B than in Type C, indicating that the urban nighttime chemistry is more active than the background marine regions. The N2O5 uptake coefficient and ClNO2 production yield were estimated based on the field measurement, and the performance of the previously derived parameterizations was assessed. The nighttime ClNO2 correlated with particulate chloride and the mass concentration of fine particles (most likely due to aerosol surface area) suggested that the ClNO2 formation was limited by the N2O5 uptake at this site. By examining the relationship between particulate chloride and other species, we implied that anthropogenic emissions (e.g., biomass burning) rather than sea salt particles dominate the origin of particulate chloride, although the site was only about 100 km away from the ocean. A box model with detailed chlorine chemistry is used to investigate the impacts of ClNO2 chemistry on atmospheric oxidation. Model simulations showed that the chlorine radical liberated by ClNO2 photolysis during the next day had a slight increase in concentrations of OH, HO2, and RO2 radicals, as well as minor contributions to RO2 radical and O3 formation (< 5 %, on daytime average), in all the three types of air masses. Relatively high contributions were observed in Type A and B. The overall low contributions of ClNO2 to atmospheric oxidation are consistent with those reported recently from wintertime observations in China (including Shanghai, Beijing, Wangdu, and Mt. Tai). This may be attributed to the following: (1) relatively low particle mass concentration limited ClNO2 formation; (2) other reactions channels, like nitrous acid (HONO), oxygenated volatile organic compounds (OVOCs, including formaldehyde), and ozone photolysis had a more significant radical formation rate during the ozone pollution episodes and weakened the ClNO2 contribution indirectly. The results provided scientific insights into the role of nighttime chemistry in photochemical pollution under various scenarios in coastal areas.

Tailpipe emissions from three heavy-duty diesel vehicles (HDDVs), complying with varying emission standards and installed with diverse aftertreatment technologies, are collected at a certified chassis dynamometer laboratory. The HDDV-emitted intermediate-volatility and semi-volatile organic compound (I/SVOC) emission and the gas–particle partitioning of the I/SVOCs are investigated. Over 4000 compounds are identified and grouped into 21 categories. The dominant compound groups of particulate I/SVOCs are alkanes and phenolic compounds. For HDDVs without aftertreatment devices, i.e., diesel oxidation catalysts (DOCs) and diesel particulate filters (DPFs), the emitted I/SVOCs partition dramatically into the gas phase (accounting for ∼ 93 % of the total I/SVOC mass), with a few exceptions: hopane, four-ring polycyclic aromatic hydrocarbons (PAH4rings), and five-ring polycyclic aromatic hydrocarbons (PAH5rings). For HDDVs with DPFs and DOCs, the particulate fractions are reduced to a negligible level (i.e., less than 2 %). Nevertheless, 50 % of the total two-ring PAH mass is detected in the particle phase, which is much higher than the high-molecular-weight PAHs, arising from the positive sampling artifact of quartz filter absorbing organic vapors. The positive sampling artifact of quartz filter absorbing organic vapors is clearly observed, and uncertainties are discussed and quantified. Particulate I/SVOCs at low-speed, middle-speed, and high-speed phases are collected and analyzed separately. The emission factor (EF) distribution of the speciated organic aerosol (OA) on a two-dimensional volatility basis set (2D-VBS) space reveals that the fractions of OA with oxygen to carbon (O : C) ratios > 0.3 (0.4, 0.5) are 18.2 % (11.5 %, 9.5 %), 23 % (15.4 %, 13.6 %), and 29.1 % (20.6 %, 19.1 %) at the low-speed, middle-speed, and high-speed stages. These results help to resolve complex organic mixtures and trace the evolution of OA.

Fungi and bacteria are the two dominant groups of soil microbial communities worldwide. By controlling the turnover of soil organic matter, these organisms directly regulate the cycling of carbon between the soil and the atmosphere. Fundamental differences in the physiology and life history of bacteria and fungi suggest that variation in the biogeography of relative abundance of soil fungi versus bacteria could drive striking differences in carbon decomposition and soil organic matter formation between different biomes. However, a lack of global and predictive information on the distribution of these organisms in terrestrial ecosystems has prevented the inclusion of relative abundance of soil fungi versus bacteria and the associated processes in global biogeochemical models. Here, we used a global-scale dataset of >3000 distinct observations of abundance of soil fungi versus bacteria in the surface topsoil (up to 15 cm) to generate the first quantitative and high-spatial-resolution (1 km2) explicit map of soil fungal proportion, defined as fungi/fungi + bacteria, across terrestrial ecosystems. We reveal striking latitudinal trends where fungal dominance increases in cold and high-latitude environments with large soil carbon stocks. There was a strong nonlinear response of fungal dominance to the environmental gradient, i.e., mean annual temperature (MAT) and net primary productivity (NPP). Fungi dominated in regions with low MAT and NPP and bacteria dominated in regions with high MAT and NPP, thus representing slow vs. fast soil energy channels, respectively, a concept with a long history in soil ecology. These high-resolution models provide the first steps towards representing the major soil microbial groups and their functional differences in global biogeochemical models to improve predictions of soil organic matter turnover under current and future climate scenarios. Raw datasets and global maps generated in this study are available at https://doi.org/10.6084/m9.figshare.19556419 (Yu, 2022).

Volatile organic compounds (VOCs) are key precursors of ozone and particulate matter, which are the two dominant air pollutants in urban environments. However, compositions and sources of VOCs in urban air aloft have rarely been reported so far. To address this matter, highly time-resolved measurements of VOCs were made by a proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) at a 450 m platform on the Canton Tower in Guangzhou, China. A combination of in situ measurements and modeling techniques was used to characterize variations in and sources of VOCs. Five sources were identified from positive matrix factorization (PMF) analysis, namely daytime mixed (e.g., biogenic emissions and secondary formation), visitor-related (e.g., human breath, cooking, and volatilization of ethanol-containing products), vehicular–industrial, regional transport, and volatile chemical product (VCP) (i.e., volatilization of personal care products), contributing on average to 21 %, 30 %, 28 %, 10 %, and 11 % of total VOC (TVOC) mixing ratios, respectively. We observe that contributions of the visitor-related source, mainly composed of ethanol, followed with the variation in visitor numbers on the tower well. The VCP-dominated source only had an average contribution of ∼5.7 ppb during the campaign, accounting for a small fraction (11 %) of TVOC mixing ratios but a large fraction (22 %) of the total OH reactivity. However, large fractions of reactive VOC species, e.g., monoterpenes (49 %), were attributed to the VCP-dominated source, indicating important contributions of VCPs to ambient concentrations of these species in urban environments. Vertical profiles of air pollutants (namely NOx, ozone, Ox, and PM2.5), measured at 5, 118, 168, and 488 m, exhibited more evident gradients at night than in the daytime owing to the stronger stability of the nocturnal boundary layer. Mixing ratios of VOC species during the nighttime generally decreased with time when the 450 m platform was located in the nocturnal residual layer and markedly increased when impacted by emissions at ground level. The results in this study demonstrated composition characteristics and sources of VOCs in urban air aloft, which could provide valuable implications in making strategies for control of VOCs and secondary air pollutants.

Vehicular emissions are an important source for volatile organic compounds (VOCs) in urban and downwind regions. In this study, we conducted a chassis dynamometer study to investigate VOC emissions from vehicles using gasoline, diesel, and liquefied petroleum gas (LPG) as fuel. Time-resolved VOC emissions from vehicles are chemically characterized by a proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) with high frequency. Our results show that emission factors of VOCs generally decrease with the improvement of emission standards for gasoline vehicles, whereas variations in emission factors for diesel vehicles with emission standards are more diverse. Mass spectrum analysis of the PTR-ToF-MS suggests that cold starts significantly influence VOC emissions of gasoline vehicles, while the influences are less important for diesel vehicles. Large differences in VOC emissions between gasoline and diesel vehicles are observed with emission factors of most VOC species from diesel vehicles being higher than gasoline vehicles, especially for most oxygenated volatile organic compounds (OVOCs) and heavier aromatics. These results indicate quantification of heavier species by the PTR-ToF-MS may be important in the characterization of vehicular exhausts. Our results suggest that VOC pairs (e.g., C14 aromatics / toluene ratio) could potentially provide good indicators for distinguishing emissions from gasoline and diesel vehicles. The fractions of OVOCs in total VOC emissions are determined by combining measurements of hydrocarbons from canisters and online observations of the PTR-ToF-MS. We show that OVOCs contribute 9.4 % ± 5.6 % of total VOC emissions for gasoline vehicles, while the fractions are significantly higher for diesel vehicles (52 %–71 %), highlighting the importance of detecting these OVOC species in diesel emissions. Our study demonstrated that the large number of OVOC species measured by the PTR-ToF-MS are important in the characterization of VOC emissions from vehicles.

This paper presents a brief overview of photogrammetry and remote sensing technologies and applications in the Chang’e-4 mission. The developed technologies, such as image matching, block adjustment, 3D mapping, spectral analysis and mineral retrieval, have been applied in topographic mapping, rover localization, and rover path planning, and directly supported daily surface operations of the Yutu-2 rover. The technologies and products have also been supportive in various scientific investigations, such as topographic evolution of Von Kármán crater, multi-scale crater morphologies, mineral composition of lunar deep materials, regolith maturity and space weathering effects, etc.

Ambient nitrate has been of increasing concern in PM2.5, while there are still large uncertainties in quantifying the formation of nitrate aerosol. The formation pathways of nitrate aerosol at an urban site and a suburban site in the Pearl River Delta (PRD) are investigated using an observation-constrained box model. Throughout the campaigns, aerosol pollution episodes were constantly accompanied with the increase in nitrate concentrations and fractions at both urban and suburban sites. The simulations demonstrate that chemical reactions in the daytime and at night both contributed significantly to formation of nitrate in the boundary layer at the two sites. However, nighttime reactions predominantly occurred aloft in the residual layer at the urban site, and downward transport from the residual layer in the morning is an important source (53 %) for surface nitrate at the urban site, whereas similar amounts of nitrate were produced in the nocturnal boundary layer and residual layer at the suburban site, which results in little downward transport of nitrate from the residual layer to the ground at the suburban site. We show that nitrate formation was in the volatile-organic-compound-limited (VOC-limited) regime at the urban site, and in the transition regime at the suburban site, identical to the response of ozone at both sites. The reduction of VOC emissions can be an efficient approach to mitigate nitrate in both urban and suburban areas through influencing hydroxyl radical (OH) and N2O5 production, which will also be beneficial for the synergistic control of regional ozone pollution. The results highlight that the relative importance of nitrate formation pathways and ozone can be site-specific, and the quantitative understanding of various pathways of nitrate formation will provide insights for developing nitrate and ozone mitigation strategies.

Developing accurate and efficient modeling techniques for streamflow at the tens-of-kilometers spatial scale and multi-year temporal scale is critical for evaluating and predicting the impact of climate- and human-induced discharge variations on river hydrodynamics. However, achieving such a goal is challenging because of limited surveys of streambed hydraulic roughness, uncertain boundary condition specifications, and high computational costs. We demonstrate that accurate and efficient three-dimensional (3-D) hydrodynamic modeling of natural rivers at 30 km and 5-year scales is feasible using the following three techniques within OpenFOAM, an open-source computational fluid dynamics platform: (1) generating a distributed hydraulic roughness field for the streambed by integrating water-stage observation data, a rough wall theory, and a local roughness optimization and adjustment strategy; (2) prescribing the boundary condition for the inflow and outflow by integrating precomputed results of a one-dimensional (1-D) hydraulic model with the 3-D model; and (3) reducing computational time using multiple parallel runs constrained by 1-D inflow and outflow boundary conditions. Streamflow modeling for a 30 km long reach in the Columbia River (CR) over 58 months can be achieved in less than 6 d using 1.1 million CPU hours. The mean error between the modeled and the observed water stages for our simulated CR reach ranges from −16 to 9 cm (equivalent to approximately ±7 % relative to the average water depth) at seven locations during most of the years between 2011 and 2019. We can reproduce the velocity distribution measured by the acoustic Doppler current profiler (ADCP). The correlation coefficients of the depth-averaged velocity between the model and ADCP measurements are in the range between 0.71 and 0.83 at 75 % of the survey cross sections. With the validated model, we further show that the relative importance of dynamic pressure versus hydrostatic pressure varies with discharge variations and topography heterogeneity. Given the model's high accuracy and computational efficiency, the model framework provides a generic approach to evaluate and predict the impacts of climate- and human-induced discharge variations on river hydrodynamics at tens-of-kilometers and decadal scales.

A hybrid Reynolds-averaged Navier-Stokes (RANS) large eddy simulation (LES) method is applied in this work. It called very-large-eddy simulation (VLES) turbulence closure model. The aim of this present study is firstly to validate the accuracy of this method for a specific engineering application (a trapped vortex combustor) and secondly to describe its flow characteristics. The trapped vortex combustor is a new concept that utilizes a large recirculation vortex to stabilize the flame. An accurate prediction of the turbulent flow is meaningful for the trapped vortex combustor. The time-averaged velocity, root-mean-square (rms) velocity, and flow pattern are compared with the experimental data. And the LES model, RANS BSL k-ω model, and RANS k-ɛ model are also applied for the simulation with different mesh resolutions. The results show that the VLES BSL k-ω model provides improved accuracy for velocity prediction. The classical large vortex structure for the trapped vortex combustor is captured qualitatively by the VLES BSL k-ω model also. In addition, the vortex breakdown and processing vortex cone are visualized using the Q-criterion. Furthermore, the VLES BSL k-ω model is not sensitive to the gird resolution. The VLES method is able to predict the turbulent flow of trapped vortex combustor relatively well‎.

In the summer of 2018, a comprehensive field campaign, with measurements on HONO and related parameters, was conducted at the foot (150 m a.s.l.) and the summit of Mt. Tai (1534 m a.s.l.) in the central North China Plain (NCP). With the implementation of a 0-D box model, the HONO budget with six additional sources and its role in radical chemistry at the foot station were explored. We found that the model default source, NO + OH, could only reproduce 13 % of the observed HONO, leading to a strong unknown source strength of up to 3 ppbv h−1. Among the additional sources, the NO2 uptake on the ground surface dominated (∼ 70 %) nighttime HONO formation, and its photo-enhanced reaction dominated (∼ 80 %) daytime HONO formation. Their contributions were sensitive to the mixing layer height (MLH) used for the parameterizations, highlighting the importance of a reasonable MLH for exploring ground-level HONO formation in 0-D models and the necessity of gradient measurements. A ΔHONO/ΔNOx ratio of 0.7 % for direct emissions from vehicle exhaust was inferred, and a new method to quantify its contribution to the observations was proposed and discussed. Aerosol-derived sources, including the NO2 uptake on the aerosol surface and the particulate nitrate photolysis, did not lead to significant HONO formation, with their contributions lower than NO + OH. HONO photolysis in the early morning initialized the daytime photochemistry at the foot station. It was also a substantial radical source throughout the daytime, with contributions higher than O3 photolysis to OH initiation. Moreover, we found that OH dominated the atmospheric oxidizing capacity in the daytime, while modeled NO3 appeared to be significant at night. Peaks of modeled NO3 time series and average diurnal variation reached 22 and 9 pptv, respectively. NO3-induced reactions contribute 18 % of nitrate formation potential (P(HNO3)) and 11 % of the isoprene (C5H8) oxidation throughout the whole day. At night, NO3 chemistry led to 51 % and 44 % of P(HNO3) or the C5H8 oxidation, respectively, implying that NO3 chemistry could significantly affect nighttime secondary organic and inorganic aerosol formation in this high-O3 region. Considering the severe O3 pollution in the NCP and the very limited NO3 measurements, we suggest that besides direct measurements of HOx and primary HOx precursors (O3, HONO, alkenes, etc.), NO3 measurements should be conducted to understand the atmospheric oxidizing capacity and air pollution formation in this and similar regions.

Previous studies have indicated that FLT3, NLRP5, and TGIF1 play a pivotal role in sheep fecundity. Nevertheless, little is known about the association of the polymorphisms of these genes with litter size (LS). In this study, the selected single-nucleotide polymorphisms (SNPs) were genotyped using a Sequenom MassARRAY® platform, and the distribution of different genotypes of the SNPs in the seven sheep breeds (Small-tailed Han, Hu, Cele Black, Suffolk, Tan, Prairie Tibetan, and Sunite sheep) were analyzed. The reliability of the estimated allele frequency for all seven SNPs was at least 0.9545. Given the association of the TGIF1 g.37866222C > T polymorphism with LS in Small-tailed Han sheep (p), fecundity differences might be caused by the change in amino acid from proline (Pro) to serine (Ser), which has an impact on secondary, tertiary protein structures with concomitant TGIF1 functionality changes. The FLT3 rs421947730 locus has a great effect on the LS (p), indicating that the locus of FLT3 in synergy with KILTG is likely to facilitate ovarian follicle maturation and ovulation. Moreover, NLRP5 rs426897754 is associated with the LS of the second and third parities (p). We speculate that a synonymous variant of NLRP5 may be involved in folliculogenesis accompanied by BMP15, FSHR, BMPR1B, AMH, and GDF9, resulting in the different fecundity of Small-tailed Han sheep. Our studies provide valuable genetic markers for sheep breeding.

Soil represents the largest phosphorus (P) stock in terrestrial ecosystems. Determining the amount of soil P is a critical first step in identifying sites where ecosystem functioning is potentially limited by soil P availability. However, global patterns and predictors of soil total P concentration remain poorly understood. To address this knowledge gap, we constructed a database of total P concentration of 5275 globally distributed (semi-)natural soils from 761 published studies. We quantified the relative importance of 13 soil-forming variables in predicting soil total P concentration and then made further predictions at the global scale using a random forest approach. Soil total P concentration varied significantly among parent material types, soil orders, biomes, and continents and ranged widely from 1.4 to 9630.0 (median 430.0 and mean 570.0) mg kg−1 across the globe. About two-thirds (65 %) of the global variation was accounted for by the 13 variables that we selected, among which soil organic carbon concentration, parent material, mean annual temperature, and soil sand content were the most important ones. While predicted soil total P concentrations increased significantly with latitude, they varied largely among regions with similar latitudes due to regional differences in parent material, topography, and/or climate conditions. Soil P stocks (excluding Antarctica) were estimated to be 26.8 ± 3.1 (mean ± standard deviation) Pg and 62.2 ± 8.9 Pg (1 Pg = 1 × 1015 g) in the topsoil (0–30 cm) and subsoil (30–100 cm), respectively. Our global map of soil total P concentration as well as the underlying drivers of soil total P concentration can be used to constraint Earth system models that represent the P cycle and to inform quantification of global soil P availability. Raw datasets and global maps generated in this study are available at https://doi.org/10.6084/m9.figshare.14583375 (He et al., 2021).

MXenes are a two-dimensional (2D) class of transition metal carbides, nitrides, and carbonitrides that have been synthesized and developed for diverse purposes, such as energy storage devices. MXenes feature exceptional mechanical strength, outstanding hydrophilicity, and astounding dispersion quality in comparison to other 2D materials, making them perfect for the creation of films/membranes with specified morphologies and tunable nanochannels which is important for active anode material. For metal-ion and Li–S batteries, 2D MXene-based films (MFs) have received significant attention due to their outstanding ion storage, electron transport and ionic selectivity properties. In terms of flexibility, tailorability, and functionality, MFs outperform conventional electrode materials and structures, making them ideal for flexible, portable, and highly integrated devices for metal-ion and Li–S batteries. This review outlines recent advancements and well-developed methodologies in the design and manufacture of MFs for metal-ion (MIBs), (including Lithium-, Sodium-, and Potassium-ions) and lithium-sulfur (Li–S) batteries. The main design and principles of MFs-based microstructures, fabrication strategies, and deep study of metal matrix active materials, existing difficulties and promising future of MFs for MIBs and Li–S are given special attention.

The circadian rhythm is a biological rhythm that is closely related to the rhythmic expression of a series of clock genes. Results from several studies have indicated that clock genes are associated with the estrous cycle in female animals. Until now, the relationship between estrus cycle transition and clock gene expression in reproductive-axis-related tissues has remained unknown in Small-tailed Han (STH) sheep. This study was conducted to analyze the expression patterns of six canonical clock genes (Clock, BMAL1, Per1, Per2, Cry1, and Cry2) in the follicle phase and luteal phase of STH sheep. We found that all six genes were expressed in the brain, cerebellum, hypothalamus, pituitary, ovary, uterus, and oviduct in follicle and luteal phases. The results indicated that Clock expression was significantly higher in the cerebellum, hypothalamus, and uterus of the luteal phase than that of the follicle phase, whereas BMAL1 expression was significantly higher in the hypothalamus of the luteal phase than that of the follicle phase. Per1 expression was significantly higher in the brain, cerebellum, hypothalamus, and pituitary of the luteal phase than that of the follicle phase, and Per2 expression was significantly higher in the hypothalamus, pituitary, and uterus of the luteal phase than that of the follicle phase. Cry1 expression was significantly higher in the brain, cerebellum, and hypothalamus of the luteal phase than that of the follicle phase, whereas Cry2 expression was significantly higher in the pituitary of the luteal phase than that of the follicle phase. The clock gene expression in all tissues was different between follicle and luteal phases, but all clock gene mRNA levels were found to exhibit higher expression among seven tissues in the luteal phase. Our results suggest that estrous cycles may be associated with clock gene expression in the STH sheep. This is the first study to systematically analyze the expression patterns of clock genes of different estrous cycle in ewes, which could form a basis for further studies to develop the relationship between clock genes and the estrous cycle.

Background: When and where to apply the biological modulations is effective to promote healing in the anterior cruciate ligament (ACL) reconstruction remains unclear. Purpose: To perform a systematic review of preclinical animal studies on biological modulation in anterior cruciate ligament reconstruction (ACLR) concerning the time and site of delivery. Study design: Systematic review of controlled laboratory studies. Methods: PubMed, Ovid, and Scopus were searched until December 2020 using a combination of keywords and their synonym to retrieve all animal studies about biological modulation in ACLR. Studies that assessed mechanical strength after ACLR and compared with negative control were included. The methodological quality of animal studies was evaluated. Results: 33 studies were included in this review and the majority reported mechanical strength improvement. 79 ​% of studies applied the biological modulations intra-operatively with different delivery systems used. For 21 ​% of post-operative delivery studies, intermittent delivery was tried. 21 of the included studies directly applied the biological modulations in the bone tunnels, 5 studies applied intra-articularly while 7 studies applied both in the bone tunnels and intra-articular part. Biological modulations applied intra-operatively and those applied in both parts showed better mechanical strength increase. A shift of the failure mode of pull-out from the bone tunnel in the early healing phase, to mid-substance rupture in the later phase was observed in most studies. Conclusion: The improvement of the mechanical strength depends on how the biological modulations (delivery phase, delivery site, delivery form) are applied. The intra-operative delivery showed an overall higher mechanical strength increase and bone tunnel only delivery or intra-articular and bone tunnel both delivery are preferred than intra-articular only delivery. In addition, intra-articular and bone tunnel both delivery can have better mechanical strength increase for a long follow-up time. Thus, intra-operative application with a carrier to control release rate in both parts should be recommended. Further studies are needed to achieve a better healing outcome and more attention should be given to the intra-articular remodeling of the graft along with the tendon bone healing to increase the final mechanical strength. The Translational potential of this article: Here, a systematic review of preclinical evidence of the time, site and the method the biological modulations being applied for ACLR to improve the graft healing would be performed. After reviewing the available studies, a choice of when and where to apply the biological modulations can achieve better mechanical strength after ACLR can be obtained. It provides evidence for both researchers and clinicians to decide when and where to apply the biological modulations can achieve their best effectiveness for ACLR before implementing. Promoting graft healing with targeted time and targeted site may reduce the risk of graft failure, safeguard return to sport.

Recently, various models have been developed, including the fractional Brownian motion (fBm), to analyse the stochastic properties of geodetic time series together with the estimated geophysical signals. The noise spectrum of these time series is generally modelled as a mixed spectrum, with a sum of white and coloured noise. Here, we are interested in modelling the residual time series after deterministically subtracting geophysical signals from the observations. This residual time series is then assumed to be a sum of three stochastic processes, including the family of Lévy processes. The introduction of a third stochastic term models the remaining residual signals and other correlated processes. Via simulations and real time series, we identify three classes of Lévy processes, namely Gaussian, fractional and stable. In the first case, residuals are predominantly constituted of short-memory processes. The fractional Lévy process can be an alternative model to the fBm in the presence of long-term correlations and self-similarity properties. The stable process is here restrained to the special case of infinite variance, which can be only satisfied in the case of heavy-tailed distributions in the application to geodetic time series. Therefore, the model implies potential anxiety in the functional model selection, where missing geophysical information can generate such residual time series.

Urban air pollution has tremendous spatial variability at scales ranging from kilometers to meters due to unevenly distributed emission sources, complex flow patterns, and photochemical reactions. However, high-resolution air quality information is not available through traditional approaches such as ground-based measurements and regional air quality models (with typical resolution > 1 km). Here we develop a 10 m resolution air quality model for traffic-related CO pollution based on the Parallelized Large-Eddy Simulation Model (PALM). The model performance is evaluated with measurements obtained from sensors deployed on a taxi platform, which collects data with a comparable spatial resolution to our model. The very high resolution of the model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network. The model results (0.92 ± 0.40 mg m−3) agree well with the measurements (0.90 ± 0.58 mg m−3, n=114 502). The model has similar spatial patterns to those of the measurements, and the r2 value of a linear regression between model and measurement data is 0.50 ± 0.07 during non-rush hours with middle and low wind speeds. A non-linear relationship is found between average modeled concentrations and wind speed with higher concentrations under calm wind speeds. The modeled concentrations are also 20 %–30 % higher in streets that align with the wind direction within ∼ 20∘. We find that streets with higher buildings downwind have lower modeled concentrations at the pedestrian level, and similar effects are found for the variability in building heights (including gaps between buildings). The modeled concentrations also decay fast in the first ∼ 50 m from the nearest highway and arterial road but change slower further away. This study demonstrates the potential of large-eddy simulation in urban air quality modeling, which is a vigorous part of the smart city system and could inform urban planning and air quality management.

The study was designed to explore the effect of a commercial yeast cell wall product (YP) on chicken intestinal IgA response and cecum microbiome after oral vaccination. Chickens were fed with YP during the experiments and orally immunized with live Newcastle disease virus (NDV) vaccine at 2 wk of age. Then, the animals were sacrificed, and samples were collected to measure the indicators of hemagglutination inhibition (HI), IgA response, IgA + cells, and cecum microbiome populations. The results showed that supplement of YP significantly enhanced serum NDV HI titer, intestinal NDV-specific secretory IgA, and intestinal IgA + cells. The sequencing results revealed that obviously increased relative abundance of Ruminococcaceae and decreased population of Bacteroidaceae in cecum were found in YP group. In summary, YP supplementation in diet enhanced intestinal IgA response to NDV vaccination by oral route and modulated the cecum microbiota to the advantage of the host in chickens.

Streamflow forecasting is a crucial component in the management and control of water resources. Decomposition-based approaches have particularly demonstrated improved forecasting performance. However, direct decomposition of entire streamflow data with calibration and validation subsets is not practical for signal component prediction. This impracticality is due to the fact that the calibration process uses some validation information that is not available in practical streamflow forecasting. Unfortunately, independent decomposition of calibration and validation sets leads to undesirable boundary effects and less accurate forecasting. To alleviate such boundary effects and improve the forecasting performance in basins lacking meteorological observations, we propose a two-stage decomposition prediction (TSDP) framework. We realize this framework using variational mode decomposition (VMD) and support vector regression (SVR) and refer to this realization as VMD-SVR. We demonstrate experimentally the effectiveness, efficiency and accuracy of the TSDP framework and its VMD-SVR realization in terms of the boundary effect reduction, computational cost, and overfitting, in addition to decomposition and forecasting outcomes for different lead times. Specifically, four comparative experiments were conducted based on the ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA), discrete wavelet transform (DWT), boundary-corrected maximal overlap discrete wavelet transform (BCMODWT), autoregressive integrated moving average (ARIMA), SVR, backpropagation neural network (BPNN) and long short-term memory (LSTM). The TSDP framework was also compared with the wavelet data-driven forecasting framework (WDDFF). Results of experiments on monthly runoff data collected from three stations at the Wei River show the superiority of the VMD-SVR model compared to benchmark models.

Identification of various emission sources and quantification of their contributions comprise an essential step in formulating scientifically sound pollution control strategies. Most previous studies have been based on traditional offline filter analysis of aerosol major components (usually inorganic ions, elemental carbon – EC, organic carbon – OC, and elements). In this study, source apportionment of PM2.5 using a positive matrix factorization (PMF) model was conducted for urban Shanghai in the Yangtze River Delta region, China, utilizing a large suite of molecular and elemental tracers, together with water-soluble inorganic ions, OC, and EC from measurements conducted at two sites from 9 November to 3 December 2018. The PMF analysis with inclusion of molecular makers (i.e., MM-PMF) identified 11 pollution sources, including 3 secondary-source factors (i.e., secondary sulfate; secondary nitrate; and secondary organic aerosol, SOA, factors) and 8 primary sources (i.e., vehicle exhaust, industrial emission and tire wear, industrial emission II, residual oil combustion, dust, coal combustion, biomass burning, and cooking). The secondary sources contributed 62.5 % of the campaign-average PM2.5 mass, with the secondary nitrate factor being the leading contributor. Cooking was a minor contributor (2.8 %) to PM2.5 mass while a significant contributor (11.4 %) to the OC mass. Traditional PMF analysis relying on major components alone (PMFt) was unable to resolve three organics-dominated sources (i.e., biomass burning, cooking, and SOA source factors). Utilizing organic tracers, the MM-PMF analysis determined that these three sources combined accounted for 24.4 % of the total PM2.5 mass. In PMFt, this significant portion of PM mass was apportioned to other sources and thereby was notably biasing the source apportionment outcome. Backward trajectory and episodic analysis were performed on the MM-PMF-resolved source factors to examine the variations in source origins and composition. It was shown that under all episodes, secondary nitrate and the SOA factor were two major source contributors to the PM2.5 pollution. Our work has demonstrated that comprehensive hourly data of molecular markers and other source tracers, coupled with MM-PMF, enables examination of detailed pollution source characteristics, especially organics-dominated sources, at a timescale suitable for monitoring episodic evolution and with finer source breakdown.

Nucleation of atmospheric vapours produces more than half of global cloud condensation nuclei and so has an important influence on climate. Recent studies show that monoterpene (C10H16) oxidation yields highly oxygenated products that can nucleate with or without sulfuric acid. Monoterpenes are emitted mainly by trees, frequently together with isoprene (C5H8), which has the highest global emission of all organic vapours. Previous studies have shown that isoprene suppresses new-particle formation from monoterpenes, but the cause of this suppression is under debate. Here, in experiments performed under atmospheric conditions in the CERN CLOUD chamber, we show that isoprene reduces the yield of highly oxygenated dimers with 19 or 20 carbon atoms – which drive particle nucleation and early growth – while increasing the production of dimers with 14 or 15 carbon atoms. The dimers (termed C20 and C15, respectively) are produced by termination reactions between pairs of peroxy radicals (RO2⚫) arising from monoterpenes or isoprene. Compared with pure monoterpene conditions, isoprene reduces nucleation rates at 1.7 nm (depending on the isoprene ∕ monoterpene ratio) and approximately halves particle growth rates between 1.3 and 3.2 nm. However, above 3.2 nm, C15 dimers contribute to secondary organic aerosol, and the growth rates are unaffected by isoprene. We further show that increased hydroxyl radical (OH⚫) reduces particle formation in our chemical system rather than enhances it as previously proposed, since it increases isoprene-derived RO2⚫ radicals that reduce C20 formation. RO2⚫ termination emerges as the critical step that determines the highly oxygenated organic molecule (HOM) distribution and the corresponding nucleation capability. Species that reduce the C20 yield, such as NO, HO2 and as we show isoprene, can thus effectively reduce biogenic nucleation and early growth. Therefore the formation rate of organic aerosol in a particular region of the atmosphere under study will vary according to the precise ambient conditions.

The calibration microwave radiometer (CMR) on board the Haiyang-2A (HY-2A) satellite provides wet tropospheric delay correction for altimetry data, which can also contribute to the understanding of climate system and weather processes. The ground-based global navigation satellite system (GNSS) provides precise precipitable water vapor (PWV) with high temporal resolution and could be used for calibration and monitoring of the CMR data, and shipborne GNSS provides accurate PWV over open oceans, which can be directly compared with uncontaminated CMR data. In this study, the HY-2A CMR water vapor product is validated using ground-based GNSS observations of 100 International GNSS Service (IGS) stations along the global coastline and 56 d shipborne GNSS observations over the Indian Ocean. The processing strategy for GNSS data and CMR data is discussed in detail. Special efforts were made in the quality control and reconstruction of contaminated CMR data. The validation result shows that HY-2A CMR PWV agrees well with ground-based GNSS PWV with 2.67 mm as the root mean square (rms) within 100 km. Geographically, the rms is 1.12 mm in the polar region and 2.78 mm elsewhere. The PWV agreement between HY-2A and shipborne GNSS shows a significant correlation with the distance between the ship and the satellite footprint, with an rms of 1.57 mm for the distance threshold of 100 km. Ground-based GNSS and shipborne GNSS agree with HY-2A CMR well.

The Chang'e-4 successfully landed on the far side of the moon in January 2019. By the 12th lunar day, its Yutu-2 rover had achieved a breakthrough travel distance of greater than 300 m. A visible and near-infrared imaging spectrometer (VNIS), consisting of a visible and near-infrared (VNIR) imaging spectrometer and a shortwave infrared (SWIR) spectrometer was used for detecting mineralogical compositions of lunar-surface materials. Because VNIS is fixed on the front of the rover, and the field-of-view (FOV) of VNIR and SWIR are small (8.5° and 3.6° respectively), approaching and accurately pointing at the specific science target depend completely on the precise control of the moving rover.In this paper, a successful method of VNIS target detection based on vision measurement is proposed. First, the accurate position of the target is calculated via navigation camera imaging. Then, the moving path is planned by considering the terrain environment, illumination, communication condition, and other constraints. After the rover moves to the designed position, the binocular imaging of the hazard-avoidance cameras are activated, the detection direction and forward distance are calculated according to the images, and the FOV trajectory of the VINS is predicted while moving. Finally, by choosing the required moving control parameters, the imaging field of the VINS accurately cover the detected targets visually.These methods have been verified many times, and the results show that they are effective and feasible. The research results based on the VNIS data have successfully revealed the material composition on the far side of the moon and have deepened human understanding of its formation and evolution.

Studies on ecological successions have a long tradition and have strongly contributed to the understanding of community assembly, niche theory, and ecosystem structure and functionality. Reports on ecological successions are however mostly restricted to one or two taxonomic groups, neglecting the mutual influences and dependencies between multiple taxonomic groups that are the building blocks of diverse communities. We introduce the Alpine research platform Ödenwinkel to promote observational and experimental research on the emergence of multidiversity and ecosystem complexity. We established n= 140 permanent plots along the successional gradient of the forefield of the Ödenwinkelkees glacier at the end of the Stubachtal valley in the Hohe Tauern range (Hohe Tauern National Park, Land Salzburg, Austria). In summer 2019 we completed a first full inventory of biotic and abiotic characteristics of these plots covering the diversity and composition of vascular plants, bryophytes, arthropods, and other animals, bacteria and fungi as well as some geomorphologic properties. In this paper we introduce the design of the research platform and show first results. While focusing on the diversity and composition of vascular plants along the successional gradient, we also provide data on the diversity of animals, bacteria, and fungi. The Ödenwinkel platform will be available as a long-term ecological research site where researchers from various disciplines can contribute to the accumulation of knowledge on ecological successions and on how interactions between various taxonomic groups structure ecological complexity in this Alpine environment.

On January 3, 2019, the Chang'e-4 (CE-4) probe successfully landed in the Von Kármán crater inside the South Pole-Aitken (SPA) basin. With the support of a relay communication satellite "Queqiao" launched in 2018 and located at the Earth-Moon L2 liberation point, the lander and the Yutu-2 rover carried out in-situ exploration and patrol surveys, respectively, and were able to make a series of important scientific discoveries. Owing to the complexity and unpredictability of the lunar surface, teleoperation has become the most important control method for the operation of the rover. Computer vision is an important technology to support the teleoperation of the rover. During the powered descent stage and lunar surface exploration, teleoperation based on computer vision can effectively overcome many technical challenges, such as fast positioning of the landing point, high-resolution seamless mapping of the landing site, localization of the rover in the complex environment on the lunar surface, terrain reconstruction, and path planning. All these processes helped achieve the first soft landing, roving, and in-situ exploration on the lunar farside. This paper presents a high-precision positioning technology and positioning results of the landing point based on multi-source data, including orbital images and CE-4 descent images. The method and its results have been successfully applied in an actual engineering mission for the first time in China, providing important support for the topographical analysis of the landing site and mission planning for subsequent teleoperations. After landing, a 0.03 m resolution DOM was generated using the descent images and was used as one of the base maps for the overall rover path planning. Before each movement, the Yutu-2 rover controlled its hazard avoidance cameras (Hazcam), navigation cameras (Navcam), and panoramic cameras (Pancam) to capture stereo images of the lunar surface at different angles. Local digital elevation models (DEMs) with a 0.02 m resolution were routinely produced at each waypoint using the Navcam and Hazcam images. These DEMs were then used to design an obstacle recognition method and establish a model for calculating the slope, aspect, roughness, and visibility. Finally, in combination with the Yutu-2 rover mobility characteristics, a comprehensive cost map for path search was generated.By the end of the first 12 lunar days, the Yutu-2 rover has been working on the lunar farside for more than 300 days, greatly exceeding the projected service life. The rover was able to overcome the complex terrain on the lunar farside, and travelled a total distance of more than 300 m, achieving the "double three hundred" breakthrough. In future manned lunar landing and exploration of Mars by China, computer vision will play an integral role to support science target selection and scientific investigations, and will become an extremely important core technology for various engineering tasks.

Lanping black-boned sheep was first discovered in the 1950s in Lanping county of China and characterized by black pigmentation on skin and internal organs. Due to the novel and unique trait, the genetic background of Lanping black-boned sheep is of great interest. Here, we genotyped genome-wide SNPs (single nucleotide polymorphisms) of Lanping black-boned sheep and Lanping normal sheep using Illumina OvineSNP50 BeadChip to investigate the genetic diversity and genetic origin of Lanping black-boned sheep. We also downloaded a subset SNP dataset of two Tibet-lineage sheep breeds and four other sheep breeds from the International Sheep Genomics Consortium (ISGC) as a reference for interpreting. Lanping black-boned sheep had a lower genetic diversity level when compared to seven other sheep breeds. Principal component analysis (PCA) showed that Lanping black-boned sheep and Lanping normal sheep were clustered into the Asian group, but there was no clear separation between the two breeds. Structure analysis demonstrated a high ancestry coefficient in Lanping black-boned sheep and Lanping normal sheep. However, the two populations were separated into two distinct branches in a neighbor-joining (NJ) tree. We further evaluated the genetic divergence using population FST, which showed that the genetic differentiation that existed between Lanping black-boned sheep and Lanping normal sheep was higher than that between Tibet sheep and Changthangi sheep, which revealed that Lanping black-boned sheep is a different breed from Lanping normal sheep on the genetic level. In addition, structure analysis and NJ tree showed that Lanping black-boned sheep had a relatively close relation with Tibet sheep. The results reported herein are a first step toward understanding the genetic background of Lanping black-boned sheep, and it will provide informative knowledge on the unique genetic resource conservation and mechanism of novel breed formation.

The effect of salinity on primary photochemical reactions (using JIP-test) in six sweet sorghum genotypes was tested. An increase in salt concentrations induced significantly the accumulation of proline and caused a decline in leaf osmotic potential. Except for 100 mM NaCl concentration, salinity significantly decreased chlorophyll content and photosynthetic efficiency of plants. Increasing salinity led to a higher accumulation of QB-nonreducing PSII reaction centers. K-step in OJIP fluorescence transient was observed for the most sensitive genotypes under the high NaCl concentration. The studied sorghum genotypes responded differently to salinity stress. Thus, the study helps understand the plant tolerance mechanisms of different sweet sorghum genotypes to increasing salinity stress. The study also confirmed that the use of JIP-test is suitable for the identification of sorghum genotypes according to their growth under salinity stress.

Satellite-based InSAR (Interferometric Synthetic Aperture Radar) provides an effective way to measure large-scale land surface motions. Currently, the atmospheric phase delay is one of the most critical issues in InSAR deformation monitoring. Generic Atmospheric Correction Online Service (GACOS) is a free, globally available and easy-to-implement tool to generate high-resolution zenith total delay maps, which could be used for InSAR atmospheric delay correction. The mean velocity could then be estimated by stacking multiple GACOS-corrected interferograms. We applied the proposed GACOS-corrected InSAR stacking method in the North China Plain and analysed its performance. Within the 549 interferograms, more than 85 % gained positive correction performances. The correlation between the phase-dZTD indicator and the performance reached 0.89, demonstrating a significant relationship. Deformation maps revealed by InSAR stacking with and without GACOS corrections showed that GACOS could mainly remove the topography-related and long wavelength signals.

Single-school and multi-school district divisions are the two main ways to balance educational resources for enrollment in primary and secondary schools. A hybrid heuristic algorithm (M-ILS-SA) for school district division is proposed based on the combination of a Multi-Start Iterative Local Search (M-ILS) algorithm and a Simulated Annealing (SA) algorithm. According to the principle of “school grouping first and student assigning second”, a K-Medoids model is first used to implement school grouping. Then, the initial solution for each run of ILS that starts is generated by the region growth algorithm. After completing the neighborhood search, the SA algorithm is finally used to choose the optimal solution from the historically generated school districts identified by ILS. The experimental results show that the proposed M-ILS-SA algorithm can effectively reduce the total elapsed time and the number of over-enrolled students in each school district, and ensure spatial continuity in both single-school and multi-school district divisions.

Study region: The Niger River basin (NRB) at Niamey, West Africa. Study focus: Two approaches to estimate the cross-section of rivers based solely on satellite imagery (or digital elevation model, DEM) and altimetry for a large data-deficient region are proposed. These approaches are the ''lateral-method'' (LM), which directly provides the cross-sections, and the mixed method (MM), which incorporates a DEM with topography/bathymetry. The LM consists of following the river water lateral evolution on either side of a centerline using daily Planet Scope imagery, while the riverbed is estimated using satellite altimeters. The MM is based on the riverbed estimated from altimetry during the recession period, and the banks' heights are extracted from a DEM. River discharge was estimated at four cross-sections over a reach of 30 km at Niamey. An evaluation of the estimated discharge using in-situ discharge at Niamey hydrometric station presents a Nash-Sutcliffe efficiency of 0.93 and a relative bias of 3.3 %. New hydrological insights for the region: The proposed method is feasible for other rivers in the Sahelian zone and supports a better understanding of surface water hydrology. For example, the estimated discharge at four cross-sections at a river reach of 30 km at Niamey was used to observe downstream flow gains. Nevertheless, the role of uncertainties and other contributions (e.g., evaporation losses) on the observed gains deserves further investigation.