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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.

HONO plays a crucial role as a precursor to OH radicals in the tropospheric atmosphere. The incongruity between HONO concentration and NOx emissions during the COVID-19 pandemic remains puzzling. Here, we show evidence from field observations of 10 sites in China where there was a noticeable increase in NH3 concentrations during the COVID-19 pandemic. In addition to the meteorological conditions, the significant decrease in sulfate and nitrate concentrations enhanced the conversion of NH4+ to NH3. Sensitivity analysis indicated that the decrease in anion concentrations (especially sulfate and nitrate) and the increase in cation concentrations during the COVID-19 pandemic led to an increase in particle pH. In other words, changes in the excess ammonia drove changes in particle pH that may consequently have impacted the rate of HONO formation. The calculation of reaction rates indicates that during the epidemic, the increase in pH may promote the generation of HONO by facilitating redox reactions, which highlights the importance of coordinating the control of SO2, NOx, and NH3 emissions.

Accurate localization of multi-scattering features of cable-stayed bridges in multi-band Synthetic Aperture Radar (SAR) imagery is crucial for intelligent recognition of bridge targets within images, as well as for precise water level extraction. This study focuses on the Badong Yangtze River Bridge, utilizing Unmanned Aerial Vehicle (UAV) LiDAR data of the bridge, and analyzes the multi-scattering characteristics of different bridge structural targets based on Geometric Optics (GO) methods and the Range-Doppler principle. Furthermore, the study integrates LiDAR data of the bridge's cable-stays to examine their multi-scattering phenomena, finding that the undulations of the Yangtze River's surface waves significantly contribute to the pronounced double scattering features of the bridge's cable-stays. Additionally, statistical analysis of multi-source SAR data indicates that this phenomenon is not directly correlated with radar wavelength, implying no direct connection to surface roughness. Utilizing LiDAR point cloud data from the bridge's street lamps, this paper proposes a novel method for estimating water level elevation by identifying the center position of spots formed by double scattering from lamp posts. The results show that using TerraSAR ascending and descending orbit images, this method achieves a water level elevation accuracy of approximately 0.2 meters.

Due to the intricate distortion and reflection geometry of the SAR signal, it is typically difficult to determine the multiple scattering of large artificial objects in SAR images. This work presents a scattering point path tracking model that utilizes the real three-dimensional dimensions of targets, based on the geometric optics method. Three different artificial structures, including light poles, cable stayed bridges, and power transmission lines, are carefully analysed in time-series SAR images with their simulated multiple scattering results. The results demonstrate that the routes determined by the model are consistent with the multiple scattering features on SAR images. Moreover, the time-series data demonstrate that ripples in the water's surface have a significant impact on the multi-scattering features of power lines and bridges. The double scattering features of the light pole provides a novel approach to the process of permanent scatterers (PS) in urban areas. The instances presented in this study demonstrate the effectiveness of the scattering point path tracking model in identifying the various artificial facility targets on different reflective surfaces. It will be a useful tool for deciphering the multiple scattering of large artificial structures when their 3D model is known.

In this work, to comprehensively analyze the flow field characteristics of a normal slot plasma synthetic jet actuator, three-dimensional simulation models are established for both normal slot and normal orifice actuators. A detailed comparative analysis of the three-dimensional flow field characteristics of these two actuators is performed. The results indicate that the motion shockwaves and jets generated by the normal slot actuator cover a larger and more uniform region, showing planar characteristics and excellent flow control uniformity. The total pressure ratio for the normal slot actuator is 3.59, significantly higher than the value of 3.50 for the normal orifice actuator, indicating lower pressure loss in the former. Additionally, the normal slot has a larger average exit Mach number (Ma), indicating a stronger flow control capability. It also achieves the peak Ma in a shorter time, indicating a faster momentum output response. Therefore, compared with the normal orifice actuator, the normal slot actuator has better potential for flow control.

We quantify 2019 annual mean methane emissions in the contiguous US (CONUS) at 0.25° × 0.3125° resolution by inverse analysis of atmospheric methane columns measured by the Tropospheric Monitoring Instrument (TROPOMI). A gridded version of the US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) serves as the basis for the prior estimate for the inversion. We optimize emissions and quantify observing system information content for an eight-member inversion ensemble through analytical minimization of a Bayesian cost function. We achieve high resolution with a reduced-rank characterization of the observing system that optimally preserves information content. Our optimal (posterior) estimate of anthropogenic emissions in CONUS is 30.9 (30.0–31.8) Tg a−1, where the values in parentheses give the spread of the ensemble. This is a 13 % increase from the 2023 GHGI estimate for CONUS in 2019. We find emissions for livestock of 10.4 (10.0–10.7) Tg a−1, for oil and gas of 10.4 (10.1–10.7) Tg a−1, for coal of 1.5 (1.2–1.9) Tg a−1, for landfills of 6.9 (6.4–7.5) Tg a−1, for wastewater of 0.6 (0.5–0.7), and for other anthropogenic sources of 1.1 (1.0–1.2) Tg a−1. The largest increase relative to the GHGI occurs for landfills (51 %), with smaller increases for oil and gas (12 %) and livestock (11 %). These three sectors are responsible for 89 % of posterior anthropogenic emissions in CONUS. The largest decrease (28 %) is for coal. We exploit the high resolution of our inversion to quantify emissions from 70 individual landfills, where we find emissions are on median 77 % larger than the values reported to the EPA's Greenhouse Gas Reporting Program (GHGRP), a key data source for the GHGI. We attribute this underestimate to overestimated recovery efficiencies at landfill gas facilities and to under-accounting of site-specific operational changes and leaks. We also quantify emissions for the 48 individual states in CONUS, which we compare to the GHGI's new state-level inventories and to independent state-produced inventories. Our posterior emissions are on average 27 % larger than the GHGI in the largest 10 methane-producing states, with the biggest upward adjustments in states with large oil and gas emissions, including Texas, New Mexico, Louisiana, and Oklahoma. We also calculate emissions for 95 geographically diverse urban areas in CONUS. Emissions for these urban areas total 6.0 (5.4–6.7) Tg a−1 and are on average 39 (27–52) % larger than a gridded version of the 2023 GHGI, which we attribute to underestimated landfill and gas distribution emissions.

Satellite-derived spatiotemporal patterns of nitrogen oxide (NOx) emissions can improve accuracy of emission inventories to better support air quality and climate research and policy studies. In this study, we develop a new method by coupling the chemical transport Model-Independent SATellite-derived Emission estimation Algorithm for Mixed-sources (MISATEAM) with a divergence method to map high-resolution NOx emissions across US cities using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric nitrogen dioxide (NO2) retrievals. The accuracy of the coupled method is validated through application to synthetic NO2 observations from the NASA-Unified Weather Research and Forecasting (NU-WRF) model, with a horizontal spatial resolution of 4 km × 4 km for 33 large and mid-size US cities. Validation reveals excellent agreement between inferred and NU-WRF-provided emission magnitudes (R= 0.99, normalized mean bias, NMB = −0.01) and a consistent spatial pattern when comparing emissions for individual grid cells (R=0.88±0.06). We then develop a TROPOMI-based database reporting annual emissions for 39 US cities at a horizontal spatial resolution of 0.05° × 0.05° from 2018 to 2021. This database demonstrates a strong correlation (R= 0.90) with the National Emission Inventory (NEI) but reveals some bias (NMB = −0.24). There are noticeable differences in the spatial patterns of emissions in some cities. Our analysis suggests that uncertainties in TROPOMI-based emissions and potential misallocation of emissions and/or missing sources in bottom-up emission inventories both contribute to these differences.

Multifaceted regime shifts of Earth's surface are ongoing dramatically and – in turn – considerably alter the global carbon budget, energy balance and biogeochemical cycles. Sustainably managing terrestrial ecosystems necessitates a deeper comprehension of the diverse and dynamic nature of multicomponent information within these environments. However, comprehensive records of global-scale fractional vegetation and soil information that encompass these structural and functional complexities remain limited. Here, we provide a globally comprehensive record of monthly vegetation and soil fractions during the period 2001–2022 using a spatiotemporally adaptive spectral mixture analysis framework. This product is designed to continuously represent Earth's terrestrial surface as a percentage of five physically meaningful vegetation and soil endmembers, including photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), bare soil (BS), ice or snow (IS) and dark surface (DA), with high accuracy and low uncertainty compared to the previous vegetation index and vegetation continuous-field product as well as traditional fully constrained linear spectral mixture models. We also adopt nonparametric seasonal Mann–Kendall tested fractional dynamics to identify shifts based on interactive changes in these fractions. Our results – superior to previous portrayals of the greening planet – not only report a +9.35 × 105 km2 change in photosynthetic vegetation, but also explore decreases in nonphotosynthetic vegetation (−2.19 × 105 km2), bare soil (−5.14 × 105 km2) and dark surfaces (−2.27 × 105 km2). In addition, interactive changes in these fractions yield multifaceted regime shifts with important implications, such as a simultaneous increase in PV and NPV in central and southwestern China during afforestation activities, an increase in PV in cropland of China and India due to intensive agricultural development, a decrease in PV and an increase in BS in tropical zones resulting from deforestation. These advantages emphasize that our dataset provides locally relevant information on multifaceted regime shifts at the required scale, enabling scalable modeling and effective governance of future terrestrial ecosystems. The data about five fractional surface vegetation and soil components are available in the Science Data Bank (https://doi.org/10.57760/sciencedb.13287, Sun and Sun, 2023).

ABSTRACT: The objective of this study was to evaluate the effects of partially replacing soybean meal (SBM) with algal sources on in vitro ruminal fermentation. Using 6 fermenters in a 3 × 3 replicated Latin square with 3 periods of 10 d each, we tested 3 treatments: a control diet (CRT) with SBM at 17.8% of the diet dry matter (DM); and 50% SBM biomass replacement with either Chlorella pyrenoidosa (CHL); or Spirulina platensis (SPI). The basal diet was formulated to meet the requirements of a 680-kg Holstein dairy cow producing 45 kg/d of milk with 3.5% fat and 3% protein. All diets had a similar nutritional composition (16.0% CP; 34.9% NDF; 31.0% starch, DM basis) and fermenters were provided with 106 g DM/d split into 2 portions. After 7 d of adaptation, samples were collected for 3 d of each period for analyses of ruminal fermentation at 0, 1, 2, 4, 6, and 8 h after morning feeding for evaluation of the ruminal fermentation kinetics. For the evaluation of the daily production of total metabolites and for the evaluation of nutrient degradability, samples from the effluent containers were collected daily. Statistical analysis was performed with the MIXED procedure of SAS with treatment, time, and their interactions considered as fixed effects; day, square, and fermenter were considered as random effects. Orthogonal contrasts (CRT vs. algae; and CHL vs. SPI) were used to depict the treatment effect, and significance was declared when P ≤ 0.05. Fermenters that received algae-based diets had a greater propionate molar concentration and molar proportion when compared with the fermenters fed CRT diets. In addition, those algae-fed fermenters had lower branched short-chain fatty acids (BSCFA) and isoacids (IA), which are biomarkers of ruminal protein degradation, along with lower ammonia (NH3-N) concentration and greater nonammonia nitrogen (NAN). When contrasting with fermenters fed SPI-diets, fermenters fed based CHL-diets had a lower molar concentration of BSCFA and IA, along with lower NH3-N concentration and flow, and greater NAN, bacterial nitrogen flow, and efficiency of nitrogen utilization. Those results indicate that CHL protein may be more resistant to ruminal degradation, which would increase efficiency of nitrogen utilization. In summary, partially replacing SBM with algae biomass, especially with CHL, is a promising strategy to improve the efficiency of nitrogen utilization, due to the fact that fermenters fed CHL-based diets resulted in a reduction in BSCFA and IA, which are markers of protein degradation, and it would improve the efficiency of nitrogen utilization. However, further validation using in vivo models are required.

The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper.

To enhance the timeliness and accuracy of spatial prediction of coseismic landslides, we propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software named Mat.LShazard V1.0. Based on this software, we evaluate the applicability of this improved spatial prediction strategy in six earthquake events that have occurred near the Sichuan–Yunnan region, including the Wenchuan, Ludian, Lushan, Jiuzhaigou, Minxian, and Yushu earthquakes. The results indicate that in the first stage (immediately after the quake event), except for the 2013 Minxian earthquake, the area under the curve (AUC) values of the modeling performance are above 0.8. Among them, the AUC value of the Wenchuan earthquake is the highest, reaching 0.947. The prediction results in the first stage can meet the requirements of emergency rescue by immediately obtaining the overall predicted information of the possible coseismic landslide locations in the quake-affected area. In the second and third stages, with the improvement of landslide data quality, the prediction ability of the model based on the entire landslide database is gradually improved. Based on the entire landslide database, the AUC value of the six events exceeds 0.9, indicating a very high prediction accuracy. For the second and third stages, the predicted landslide area (Ap) is relatively consistent with the observed landslide area (Ao). However, based on the incomplete landslide data in the meizoseismal area, Ap is much smaller than Ao. When the prediction model based on complete landslide data is built, Ap is nearly identical to Ao. This study provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages of emergency rescue, temporary resettlement, and late reconstruction after a major earthquake.

There have been consistent efforts to improve the spatiotemporal representations of biogenic/anthropogenic emission sources for photochemical transport modeling for better accuracy of local/regional air quality forecasts. While biogenic emissions, bi-directional NH3 from fertilizer applications, and point source plume rise are dynamically coupled in the Community Multiscale Air Quality (CMAQ) “inline”, there are still known meteorology-induced emissions sectors (e.g., on-road mobile sources, residential heating, and livestock waste), with little or no accounting for the meteorological impacts in the currently operational chemical and aerosol forecasts, but they are represented with static, not weather-aware annual or monthly county total emissions and standard monthly, weekly, or daily temporal allocation profiles to disaggregate them on finer timescales for the hourly air quality forecasts. It often results in poor forecasting performance due to the poor spatiotemporal representations of precursor pollutants during high ozone and PM2.5 episodes. The main focus of this study is to develop a dynamic inline coupler within the CMAQ system for the on-road mobile emission sector that requires significant computational resources in the current modeling application. To improve their accuracy and spatiotemporal representations, we developed the inline coupler module called CMAQ-MetEmis (for meteorology-induced emission sources within CMAQ version 5.3.2 modeling system). It can dynamically estimate meteorology-induced hourly gridded on-road mobile emissions within the CMAQ, using simulated meteorology without any computational burden to the CMAQ modeling system. To understand the impacts of meteorology-driven on-road mobile emissions on local air quality, the CMAQ is applied over the continental U.S. for 2 months (January and July 2019) for two emissions scenarios, namely (a) “static” on-road vehicle emissions based on static temporal profiles and (b) inline CMAQ-MetEmis on-road vehicle emissions. Overall, the CMAQ-MetEmis coupler allows us to dynamically simulate on-road vehicle emissions from the MOtor Vehicle Emission Simulator (MOVES) on-road emission model for CMAQ, with a better spatiotemporal representation based on the simulated meteorology inputs when compared to the static scenario. The domain total of daily volatile organic compound (VOC) emissions from the inline scenario shows that the largest impacts are from the local meteorology, which is approximately 10 % lower than the ones from the static scenario. In particular, the major difference in the VOC estimates was shown over the California region. These local meteorology impacts on the on-road vehicle emissions via CMAQ-MetEmis revealed an improvement in the hourly NO2, daily maximum ozone, and daily average PM2.5 patterns, with a higher agreement and correlation with daily ground observations.

Terrestrial carbon cycle models are routinely used to determine the response of the land carbon sink under expected future climate change, yet these predictions remain highly uncertain. Increasing the realism of processes in these models may help with predictive skill, but any such addition should be confronted with observations and evaluated in the context of the aggregate behavior of the carbon cycle. Here, two formulations for leaf area index (LAI) phenology are coupled to the same terrestrial biosphere model: one is climate agnostic, and the other incorporates direct environmental controls on both timing and growth. Each model is calibrated simultaneously to observations of LAI, net ecosystem exchange (NEE), and biomass using the CARbon DAta-MOdel fraMework (CARDAMOM) and validated against withheld data, including eddy covariance estimates of gross primary productivity (GPP) and ecosystem respiration (Re) across six ecosystems from the tropics to high latitudes. Both model formulations show similar predictive skill for LAI and NEE. However, with the addition of direct environmental controls on LAI, the integrated model explains 22 % more variability in GPP and Re and reduces biases in these fluxes by 58 % and 77 %, respectively, while also predicting more realistic annual litterfall rates due to changes in carbon allocation and turnover. We extend this analysis to evaluate the inferred climate sensitivity of LAI and NEE with the new model and show that the added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. This highlights the benefit of process complexity when inferring underlying processes from Earth observations and representing the climate response of the terrestrial carbon cycle.

Global climate change leads to simultaneous changes in multiple environmental drivers in the marine realm. Although physiological characterization of coccolithophores has been studied under climate change, there is limited knowledge on the biochemical responses of this biogeochemically important phytoplankton group to changing multiple environmental drivers. Here, we investigate the interactive effects of reduced phosphorus availability (4 to 0.4 µmol L−1), elevated pCO2 concentrations (426 to 946 µatm), and increasing light intensity (40 to 300 µmol photons m−2 s−1) on elemental content and macromolecules of the cosmopolitan coccolithophore Emiliania huxleyi. Reduced phosphorus availability reduces particulate organic nitrogen (PON) and protein contents per cell under 40 µmol photons m−2 s−1 but not under 300 µmol photons m−2 s−1. Reduced phosphorus availability and elevated pCO2 concentrations act synergistically to increase particulate organic carbon (POC) and carbohydrate contents per cell under 300 µmol photons m−2 s−1 but not under 40 µmol photons m−2 s−1. Reduced phosphorus availability, elevated pCO2 concentrations, and increasing light intensity act synergistically to increase the allocation of POC to carbohydrates. Under elevated pCO2 concentrations and increasing light intensity, enhanced carbon fixation could increase carbon storage in the phosphorus-limited regions of the oceans where E. huxleyi dominates the phytoplankton assemblages. In each type of light intensity, elemental-carbon-to-phosphorus (C:P) and nitrogen-to-phosphorus (N:P) ratios decrease with increasing growth rate. These results suggest that coccolithophores could reallocate chemical elements and energy to synthesize macromolecules efficiently, which allows them to regulate their elemental content and growth rate to acclimate to changing environmental conditions.

Flow control methods have been gradually applied to improve the flow field characteristics of axial compressors. However, most of the current research is focused on the cascade, and few studies have taken a compressor as the research object. Therefore, a 3.5-stage transonic axial compressor is adopted to explore the characteristics of different flow control schemes. The effects of the micro-vortex generator (MVG), segmented boundary layer suction (BLS), and combined technology (COM) on the MVG and BLS are compared by the numerical simulation method. The research results are summarized as follows. The excessive accumulated low-energy fluid in the blade passage induces the occurrence of a corner stall in the last stage stator of an axial compressor. At this time, the compressor can still work, but the performance has accelerated deterioration. The flow characteristics are effectively improved when the MVG is introduced, the stall margin improvement ΔSM and the peak efficiency improvement Δηe of the last stage are 2.1% and 1.02%, respectively. Moreover, the BLS shows advantages in removing the three-dimensional reverse flow and decreasing the total pressure loss compared with the MVG, the ΔSM of the last stage is 2.69%, and the Δηe is 1.83%. When the combined technology is applied, it shows a significant advantage in delaying the occurrence of a corner stall, and the stall margin of the last stage is improved by 2.71%. Based on a quantitative analysis method using loss sources, the three flow control schemes show significant advantages in reducing the secondary flow loss sources and the wake loss sources. Above all, the BLS shows significant advantages in reducing the total pressure loss, and the COM shows an advantage in expanding the stable operating range. The research results will provide a reference for studies of flow control methods related to reducing flow losses and widening stability margins.

The tropospheric concentrations of nitrogen dioxide (NO2) and formaldehyde (HCHO) have high spatio-temporal variability, and in situ observations of these trace gases are still scarce, especially in remote background areas. We made four similar circling journeys of mobile multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements in the Three Rivers' Source region over the Tibetan Plateau in summer (18–30 July) 2021 for the first time. The differential slant column densities (DSCDs) of NO2 and HCHO were retrieved from the measured spectra, with very weak absorptions along the driving routes. The tropospheric NO2 and HCHO vertical column densities (VCDs) were calculated from their DSCDs by the geometric approximation method, and they were further filtered to form reliable data sets by eliminating the influences of sunlight shelters and the vehicle's vibration and bumpiness. The observational data show that the tropospheric NO2 and HCHO VCDs decreased with the increasing altitude of the driving route, whose background levels ± standard deviations were 0.40 ± 1.13×1015 molec. cm−2 for NO2 and 2.27±1.66×1015 molec. cm−2 for HCHO in July 2021 over the Three Rivers' Source region. The NO2 VCDs show similar geographical distribution patterns between the different circling journeys, but the levels of the HCHO VCDs are different between the different circling journeys. The elevated NO2 VCDs along the driving routes usually corresponded to enhanced transport emissions from the towns crossed. However, the spatial distributions of the HCHO VCDs depended significantly on natural and meteorological conditions, such as surface temperature. By comparing TROPOMI satellite products and mobile MAX-DOAS results, we found that TROPOMI NO2 and HCHO VCDs have large positive offsets in the background atmosphere over the main area of the Three Rivers' Source. Our study provides valuable data sets and information of NO2 and HCHO over the Tibetan Plateau, benefitting the scientific community in investigating the spatio-temporal evolution of atmospheric composition in the background atmosphere at high altitudes, validating and improving the satellite products over mountain terrains, and evaluating the model's ability to simulate atmospheric chemistry over the Tibetan Plateau.

Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM2.5) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM2.5 concentrations near the source region but higher AOD and PM2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %–30 % near the source regions and 5 %–10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (>35 µg m−3) predictions. Higher plume heights generally produce larger downwind PM2.5 exceedance areas. The PM2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM2.5 concentration and predicted PM2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events.

Abstract Earth system models (ESMs) typically simplify the representation of land surface spectral albedo to two values, which correspond to the photosynthetically active radiation (PAR, 400–700 nm) and the near infrared (NIR, 700–2,500 nm) spectral bands. However, the availability of hyperspectral observations now allows for a more direct retrieval of ecological parameters and reduction of uncertainty in surface reflectance. To investigate sensitivity and quantify biases of incorporating hyperspectral albedo information into ESMs, we examine how shortwave soil albedo affects surface radiative forcing and simulations of the carbon and water cycles. Results reveal that the use of two broadband values to represent soil albedo can introduce systematic radiative‐forcing differences compared to a hyperspectral representation. Specifically, we estimate soil albedo biases of ±0.2 over desert areas, which can result in spectrally integrated radiative forcing divergences of up to 30 W m−2, primarily due to discrepancies in the blue (404–504 nm) and far‐red (702–747 nm) regions. Furthermore, coupled land‐atmosphere simulations indicate a significant difference in net solar flux at the top of the atmosphere (>3.3 W m−2), which can impact global energy fluxes, rainfall, temperature, and photosynthesis. Finally, simulations show that considering the hyperspectrally resolved soil reflectance leads to increased maximum daily temperatures under current and future CO2 concentrations.

One of the major undetermined problems in evaporation (ET) retrieval using thermal infrared remote sensing is the lack of a physically based ground heat flux (G) model and its integration within the surface energy balance (SEB) equation. Here, we present a novel approach based on coupling a thermal inertia (TI)-based mechanistic G model with an analytical surface energy balance model, Surface Temperature Initiated Closure (STIC, version STIC1.2). The coupled model is named STIC-TI. The model is driven by noon–night (13:30 and 01:30 local time) land surface temperature, surface albedo, and a vegetation index from MODIS Aqua in conjunction with a clear-sky net radiation sub-model and ancillary meteorological information. SEB flux estimates from STIC-TI were evaluated with respect to the in situ fluxes from eddy covariance measurements in diverse ecosystems of contrasting aridity in both the Northern Hemisphere and Southern Hemisphere. Sensitivity analysis revealed substantial sensitivity of STIC-TI-derived fluxes due to the land surface temperature uncertainty. An evaluation of noontime G (Gi) estimates showed 12 %–21 % error across six flux tower sites, and a comparison between STIC-TI versus empirical G models also revealed the substantially better performance of the former. While the instantaneous noontime net radiation (RNi) and latent heat flux (LEi) were overestimated (15 % and 25 %), sensible heat flux (Hi) was underestimated (22 %). Overestimation (underestimation) of LEi (Hi) was associated with the overestimation of net available energy (RNi−Gi) and use of unclosed surface energy balance flux measurements in LEi (Hi) validation. The mean percent deviations in Gi and Hi estimates were found to be strongly correlated with satellite day–night view angle difference in parabolic and linear pattern, and a relatively weak correlation was found between day–night view angle difference versus LEi deviation. Findings from this parameter-sparse coupled G–ET model can make a valuable contribution to mapping and monitoring the spatiotemporal variability of ecosystem water stress and evaporation using noon–night thermal infrared observations from future Earth observation satellite missions such as TRISHNA, LSTM, and SBG.

The deposition of fluvial sediments in tectonically active areas is mainly controlled by tectonics, climate, and associated Earth surface processes; consequently, fluvial sediments can provide a valuable record of changes in regional climate and tectonic activity. In this study, we conducted a detailed analysis of the grain-size distribution in modern fluvial sediments from the upper Min River, eastern Tibet. These data, combined with information on regional climate, vegetation, hydrology, geomorphology, lithology, and fault slip rate, indicate that modern regional tectonic activity along upper Min River can be divided into three segments. Specifically, fluvial sediments in the Minjiangyuan–Diexi segment are dominated by silts (< 63 µm, 70.2 %), agreeing with low runoff, low rainfall, and high vegetation cover and revealing a windblown origin influenced by the arid and windy climate. These observations are consistent with the low hillslope angle and low relief, all indicating weak activity along the Minjiang Fault. The coarse-grained fraction (> 250 µm) of fluvial sediments in the Diexi–Wenchuan and Wenchuan–Dujiangyan segments increases stepwise downstream, although runoff and rainfall do not change significantly. These patterns correlate well with increases in both regional relief and hillslope angles. Together, these observations imply that regional tectonic activity along the Maoxian–Wenchuan Fault becomes more pervasive downstream along the Min River. The occurrence of well-sorted and well-rounded pebbles of fluvial sediments downstream of Dujiangyan must be related to the long-time scouring and sorting by rivers. This study marks the first development of a new research approach that can characterize regional tectonic activity by analysis of grain-size distribution of fluvial sediments collected from tectonically active regions.

The nitrate depletion and HNO3 release in internally mixed nitrate and dicarboxylic acid (DCA) particles have been widely detected in field and laboratory studies. Nevertheless, considerable discrepancies are still present among these measurements, and the influencing factors for this acid-displacement reaction have not yet been elucidated. In this work, the hygroscopic growth and chemical composition evolution of mixtures of NaNO3 and DCAs, i.e. oxalic acid (OA), malonic acid (MA), and glutaric acid (GA), were measured using attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and vacuum FTIR techniques. The nitrate depletion from NaNO3–OA mixtures was observed in both the measurements, owing to the relatively higher acidity of OA compared to MA and GA. At the same time, the NaNO3 phase state was found to act as a key regulator of nitrate depletion. Amorphous NaNO3 solids at relative humidity (RH) <5 % were inert to liquid OA. With increasing RH, the mixtures experienced three interesting stages of phase changes showing different HNO3 release rates; e.g. at around 15 % RH, the slow HNO3 release was detected by the vacuum IR spectra, potentially indicating the transformation of amorphous solids to semisolid NaNO3. In the second stage (sudden RH increase from ∼15 % to 61 %), the HNO3 release rate was increased by about an order of magnitude. When NaNO3 deliquescence occurred in the third stage, this displacement reaction proceeded due to more available NO3- ion formation. Compared to OA, MA and GA reacted with nitrate only in vacuum FTIR measurement, while in ATR-FTIR measurement, the mixtures tended to be effloresced completely without nitrate depletion. Further, the influences of ambient pressure, chemical composition, and water activity on HNO3 release rates were estimated via Maxwell steady-state diffusive mass transfer equation. The results showed that weaker acidity of MA and GA as well as relatively lower HNO3 diffusion rate in the ambient gas phase mainly contributed to the unobserved nitrate depletion in ATR-FTIR measurement. Our findings reveal that chemical component, phase state, and water activity of particles, as well as HNO3 gas-phase diffusion, play crucial roles in HNO3 release from nitrate and DCA mixtures. This work may provide a new perspective on nitrate depletion in the ageing processes during transport of tropospheric aerosols.

China is one of the major forest countries in the world, and the accurate estimation of its forest biomass carbon (C) pool is critical for evaluating the country's C budget and ecosystem services of forests. Although several studies have estimated China's forest biomass using national forest inventory data, most of them were limited to the period of 2004–2008. In this study, we extended our estimation to the most recent period of 2014–2018. Using datasets of eight inventory periods from 1977 to 2018 and the continuous biomass expansion factor method, we estimated that the total biomass C pool and average biomass C density in Chinese forests increased from 4717 Tg C (1 Tg = 1012 g) in the period of 1977–1981 to 7975 Tg C in the period of 2014–2018 and 38.2 Mg C ha−1 to 45.8 Mg C ha−1 (1 Mg = 106 g), respectively, with a net increase of 3258 Tg C and an annual sink of 88.0 Tg C yr−1. Over the most recent 10 years (2009–2018), the average national forest biomass C density and C sink were 44.6 Mg C ha−1 and 154.8 Tg C yr−1, respectively, much larger than those of 39.6 Mg C ha−1 and 63.3 Tg C yr−1 in the period 1977–2008. These pronounced increases were largely attributed to afforestation practices, forest growth, and environmental changes. Our results have documented the importance of ecological restoration practices, provided an essential basis for assessing ecosystem services, and helped to achieve China's C neutrality target.

Aqueous reactions may turn precursors into light-absorbing and toxic products, leading to air quality deterioration and adverse health effects. In this study, we comprehensively investigated eugenol photooxidation (a representative biomass-burning-emitted, highly substituted phenolic compound) in the bulk aqueous phase with direct photolysis, a hydroxyl radical (OH), and an organic triplet excited state (3C∗). Results show that the degradation rates of eugenol followed the order of 3C∗> OH > direct photolysis. During the 3C∗-initiated oxidation, different reactive oxygen species (ROS), including 3C∗, OH, 1O2, and O2⚫-, can participate in the oxidation of eugenol, quenching experiments verified 3C∗ was the most important one, while, during OH-initiated oxidation, O2⚫- was a more important ROS than OH for degrading eugenol. The rate constants under saturated O2, air, and N2 followed the order of kO2>kAir>kN2 for both direct photolysis and OH-initiated oxidation but changed to kAir>kN2>kO2 for 3C∗-mediated oxidation. pH and dissolved oxygen (DO) levels both decreased during oxidation, indicating the formation of acids and the participation of DO in oxidation. Ultraviolet and visible (UV-vis) light absorption spectra of the reaction products showed a clear absorbance enhancement in the 300–400 nm range for all three sets of experiments, and new fluorescence at excitation/emission =250/ (400–500) nm appeared, suggesting the formation of new chromophores and fluorophores (brown carbon species). These species were likely attributed to humic-like substances (HULIS), as shown by the increases in HULIS concentrations during oxidation. Large mass yields of products (140 %–197 %) after 23 h of illumination were obtained, and high oxidation degrees of these products were also observed. Correspondingly, a series of oxygenated compounds were identified, and a detailed reaction mechanism with functionalization as a dominant pathway was proposed. At last, the dithiothreitol (DTT) assay was applied to assess the oxidation potential of the reaction products, and the end products of all three sets of experiments showed higher DTT consumption rates than those of eugenol, indicating that more toxic species were produced upon aqueous oxidation. Overall, our results from using eugenol as a model compound, underscore the potential importance of the aqueous processing of biomass burning emissions in secondary organic aerosol (SOA) formation.

To improve the compressor performances at variable conditions, in this research, we used numerical simulation software to analyze and discuss the effects of three kinks of a wavy blade-end on an axial compressor cascade. One of the wavy blade-ends was a traditional structure with a wavy leading edge and a trailing edge (WBE_P). The other two wavy blades were new structures with a straight trailing edge and a wavy leading edge (WBE_R, WBE_S). The difference between WBE_R and WBE_S was that the blade profile used to form the wavy blade was different. For WBE_R, we obtained the wavy leading edge by rotating the element at different heights, and the trailing edge was the center of a circle. For WBE_S, we obtained the wavy leading edge by using a new element with a different camber angle. Because of the new geometry involving some discussions of a variety of parameters, we further discussed the geometric parameters and structure to guide the design and optimization of the blade. We drew some conclusions at the stable condition (6° incidence). For the first case (WBE_P), when the wavelength of the wavy blade-end was increased to 0.3 times the blade height, the total pressure loss was enhanced. When the wavelength was 0.2 times the blade height, the amplitude was 0.06 times the pitch, the minimum area of blocked range (Ab) and the total pressure loss coefficient ζ could be obtained, and the ζ and Ab were decreased by 5.49%, and 12.72% compared with the baseline. Furthermore, to weaken the influence of changed blade profile on the exit airflow angle, we proposed WBE_P and WBE_R with new structures. We further improved the flow-field characteristics of WBE_P by WBE_R, and the ζ and Ab decreased by 5.84% and 13.79% compared with the baseline. Additionally, the stall point was delayed from 7.5° to 7.7° incidence. Therefore, the wavy blade-end applied in the leading edge had a greater contribution to improving the flow characteristic, and the straight trailing edge was beneficial to maintaining the exit flow angle. WBE_S with a smoother blade surface did not show an advantage compared with WBE_R. WBE_R with a wavy blade-end leading edge and a straight trailing edge showed an advantage in improving the cascade performance, which was suitable for use in the stator from the last stage of a high-load axial compressor.

We use optimal estimation (OE) to quantify methane fluxes based on total column CH4 data from the Greenhouse Gases Observing Satellite (GOSAT) and the GEOS-Chem global chemistry transport model. We then project these fluxes to emissions by sector at 1∘ resolution and then to each country using a new Bayesian algorithm that accounts for prior and posterior uncertainties in the methane emissions. These estimates are intended as a pilot dataset for the global stock take in support of the Paris Agreement. However, differences between the emissions reported here and widely used bottom-up inventories should be used as a starting point for further research because of potential systematic errors of these satellite-based emissions estimates. We find that agricultural and waste emissions are ∼ 263 ± 24 Tg CH4 yr−1, anthropogenic fossil emissions are 82 ± 12 Tg CH4 yr−1, and natural wetland/aquatic emissions are 180 ± 10 Tg CH4 yr−1. These estimates are consistent with previous inversions based on GOSAT data and the GEOS-Chem model. In addition, anthropogenic fossil estimates are consistent with those reported to the United Nations Framework Convention on Climate Change (80.4 Tg CH4 yr−1 for 2019). Alternative priors can be easily tested with our new Bayesian approach (also known as prior swapping) to determine their impact on posterior emissions estimates. We use this approach by swapping to priors that include much larger aquatic emissions and fossil emissions (based on isotopic evidence) and find little impact on our posterior fluxes. This indicates that these alternative inventories are inconsistent with our remote sensing estimates and also that the posteriors reported here are due to the observing and flux inversion system and not uncertainties in the prior inventories. We find that total emissions for approximately 57 countries can be resolved with this observing system based on the degrees-of-freedom for signal metric (DOFS > 1.0) that can be calculated with our Bayesian flux estimation approach. Below a DOFS of 0.5, estimates for country total emissions are more weighted to our choice of prior inventories. The top five emitting countries (Brazil, China, India, Russia, USA) emit about half of the global anthropogenic budget, similar to our choice of prior emissions but with the posterior emissions shifted towards the agricultural sector and less towards fossil emissions, consistent with our global posterior results. Our results suggest remote-sensing-based estimates of methane emissions can be substantially different (although within uncertainty) than bottom-up inventories, isotopic evidence, or estimates based on sparse in situ data, indicating a need for further studies reconciling these different approaches for quantifying the methane budget. Higher-resolution fluxes calculated from upcoming satellite or aircraft data such as the Tropospheric Monitoring Instrument (TROPOMI) and those in formulation such as the Copernicus CO2M, MethaneSat, or Carbon Mapper can be incorporated into our Bayesian estimation framework for the purpose of reducing uncertainty and improving the spatial resolution and sectoral attribution of subsequent methane emissions estimates.

Abstract Background The onset of delirium after major surgery is associated with worse in-hospital outcomes for major surgical patients. Best practice recommends assessing surgical patients for delirium risk factors and this includes screening for cognitive impairment. The Mini-Cog© is a short instrument which has been shown to predict postoperative delirium (POD) and other complications in elderly patients undergoing major elective surgery. The primary aim of this study was to ascertain whether a positive preoperative Mini-Cog© is associated with postoperative delirium in elective colorectal surgery patients at high-risk of mortality due to age or comorbidity. Secondary outcomes were 90-day mortality and length of stay. Methods This is a retrospective analysis of data gathered prospectively between October 2015 and December 2017. Baseline data were collected at a preoperative screening clinic, and postoperative data during daily ward rounds by the Perioperative Medicine team at The York Hospital. Results Three hundred nineteen patients were included in the final analysis, of which 52 (16%) were found to be cognitively impaired on the Mini-Cog©. Older patients (median difference 10 years, p < 0.001) and patients with cognitive impairment (OR 3.04, 95%CI 1.15 to 8.03, p = 0.019) were more likely to develop postoperative delirium in univariate analysis; however, cognitive impairment (OR 0.492, 95%CI 0.177 to 1.368, p = 0.174) loses its significance when controlled for by confounding factors in a logistic regression model. Cognitive impairment (OR 4.65, 95%CI 1.36 to 15.9, p = 0.02), frailty (OR 7.28, 95%CI 1.92 to 27.58, p = 0.009), American Society of Anesthesiologists (ASA) grade (OR 5.95, 95%CI 1.54 to 22.94, p = 0.006) and age (median difference 10 years, p = 0.002) were significantly associated with 90-day mortality in univariate analysis. Sex was the only factor significantly associated with length of stay in the multiple regression model, with males having a 3-day longer average length of stay than females (OR = 2.94, 95%CI 0.10–5.78). Conclusions Mini-Cog© is not independently associated with post-operative delirium in high-risk elective colorectal surgery patients in this cohort. Mini-Cog© shows promise as a possible predictor of 90-day mortality. Larger studies exploring preoperative cognitive status and postoperative confusion and mortality could improve risk-stratification for surgery and allocation of resources to those patients at higher risk.

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways are highly uncertain. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: (1) the ebullition bubble growth volume threshold approach (EBG) and (2) the modified ebullition concentration threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model–data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 % to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization.

Land–atmosphere carbon and water exchanges have large uncertainty in terrestrial biosphere models (TBMs). Using observations to reduce TBM structural and parametric errors and uncertainty is a critical priority for both understanding and accurately predicting carbon and water fluxes. Recent implementations of the Bayesian CARbon DAta–MOdel fraMework (CARDAMOM) have yielded key insights into ecosystem carbon and water cycling. CARDAMOM estimates parameters for an associated TBM of intermediate complexity (Data Assimilation Linked Ecosystem Carbon – DALEC). These CARDAMOM analyses – informed by co-located C​​​​​​​ and H2O flux observations – have exhibited considerable skill in both representing the variability of assimilated observations and predicting withheld observations. CARDAMOM and DALEC have been continuously developed to accommodate new scientific challenges and an expanding variety of observational constraints. However, so far there has been no concerted effort to globally and systematically validate CARDAMOM performance across individual model–data fusion configurations. Here we use the FLUXNET 2015 dataset – an ensemble of 200+ eddy covariance flux tower sites – to formulate a concerted benchmarking framework for CARDAMOM carbon (photosynthesis and net C exchange) and water (evapotranspiration) flux estimates (CARDAMOM-FluxVal version 1.0). We present a concise set of skill metrics to evaluate CARDAMOM performance against both assimilated and withheld FLUXNET 2015 photosynthesis, net CO2 exchange, and evapotranspiration estimates. We further demonstrate the potential for tailored CARDAMOM evaluations by categorizing performance in terms of (i) individual land-cover types, (ii) monthly, annual, and mean fluxes, and (iii) length of assimilation data. The CARDAMOM benchmarking system – along with the CARDAMOM driver files provided – can be readily repeated to support both the intercomparison between existing CARDAMOM model configurations and the formulation, development, and testing of new CARDAMOM model structures.

We quantify methane emissions and their 2010–2017 trends by sector in the contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric methane observations. The inversion uses as a prior estimate the national anthropogenic emission inventories for the three countries reported by the US Environmental Protection Agency (EPA), Environment and Climate Change Canada (ECCC), and the Instituto Nacional de Ecología y Cambio Climático (INECC) in Mexico to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as an evaluation of these inventories in terms of their magnitudes and trends. Emissions are optimized with a Gaussian mixture model (GMM) at 0.5∘×0.625∘ resolution and for individual years. Optimization is done analytically using lognormal error forms. This yields closed-form statistics of error covariances and information content on the posterior (optimized) estimates, allows better representation of the high tail of the emission distribution, and enables construction of a large ensemble of inverse solutions using different observations and assumptions. We find that GOSAT and in situ observations are largely consistent and complementary in the optimization of methane emissions for North America. Mean 2010–2017 anthropogenic emissions from our base GOSAT + in situ inversion, with ranges from the inversion ensemble, are 36.9 (32.5–37.8) Tg a−1 for CONUS, 5.3 (3.6–5.7) Tg a−1 for Canada, and 6.0 (4.7–6.1) Tg a−1 for Mexico. These are higher than the most recent reported national inventories of 26.0 Tg a−1 for the US (EPA), 4.0 Tg a−1 for Canada (ECCC), and 5.0 Tg a−1 for Mexico (INECC). The correction in all three countries is largely driven by a factor of 2 underestimate in emissions from the oil sector with major contributions from the south-central US, western Canada, and southeastern Mexico. Total CONUS anthropogenic emissions in our inversion peak in 2014, in contrast to the EPA report of a steady decreasing trend over 2010–2017. This reflects offsetting effects of increasing emissions from the oil and landfill sectors, decreasing emissions from the gas sector, and flat emissions from the livestock and coal sectors. We find decreasing trends in Canadian and Mexican anthropogenic methane emissions over the 2010–2017 period, mainly driven by oil and gas emissions. Our best estimates of mean 2010–2017 wetland emissions are 8.4 (6.4–10.6) Tg a−1 for CONUS, 9.9 (7.8–12.0) Tg a−1 for Canada, and 0.6 (0.4–0.6) Tg a−1 for Mexico. Wetland emissions in CONUS show an increasing trend of +2.6 (+1.7 to +3.8)% a−1 over 2010–2017 correlated with precipitation.

Although air quality in the United States has improved remarkably in the past decades, ground-level ozone (O3) often rises in exceedance of the national ambient air quality standard in nonattainment areas, including the Long Island Sound (LIS) and its surrounding areas. Accurate prediction of high-ozone episodes is needed to assist government agencies and the public in mitigating harmful effects of air pollution. In this study, we have developed a suite of potential forecast improvements, including dynamic boundary conditions, rapid emission refresh and chemical data assimilation, in a 3 km resolution Community Multiscale Air Quality (CMAQ) modeling system. The purpose is to evaluate and assess the effectiveness of these forecasting techniques, individually or in combination, in improving forecast guidance for two major air pollutants: surface O3 and nitrogen dioxide (NO2). Experiments were conducted for a high-O3 episode (28–29 August 2018) during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, which provides abundant observations for evaluating model performance. The results show that these forecast system updates are useful in enhancing the capability of this 3 km forecasting model with varying effectiveness for different pollutants. For O3 prediction, the most significant improvement comes from the dynamic boundary conditions derived from the NOAA operational forecast system, National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (R) from 0.81 to 0.93 and reduces the root mean square error (RMSE) from 14.97 to 8.22 ppbv, compared to that with the static boundary conditions (BCs). The NO2 from all high-resolution simulations outperforms that from the operational 12 km NAQFC simulation, regardless of the BCs used, highlighting the importance of spatially resolved emission and meteorology inputs for the prediction of short-lived pollutants. The effectiveness of improved initial concentrations through optimal interpolation (OI) is shown to be high in urban areas with high emission density. The influence of OI adjustment, however, is maintained for a longer period in rural areas, where emissions and chemical transformation make a smaller contribution to the O3 budget than that in high-emission areas. Following the assessment of individual updates, the forecasting system is configured with dynamic boundary conditions, optimal interpolation of initial concentrations and emission adjustment, to simulate a high-ozone episode during the 2018 LISTOS field campaign. The newly developed forecasting system significantly reduces the bias of surface NO2 prediction. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), this system is able to reproduce the NO2 VCD with a higher correlation (0.74), lower normalized mean bias (40 %) and normalized mean error (61 %) than NAQFC (0.57, 45 % and 76 %, respectively). The 3 km system captures magnitude and timing of surface O3 peaks and valleys better. In comparison with lidar, O3 profile variability of the vertical O3 is captured better by the new system (correlation coefficient of 0.71) than by NAQFC (correlation coefficient of 0.54). Although the experiments are limited to one pollution episode over the Long Island Sound, this study demonstrates feasible approaches to improve the predictability of high-O3 episodes in contemporary urban environments.

Atmospheric aerosols consisting of organic and inorganic components may undergo liquid–liquid phase separation (LLPS) and liquid–solid phase transitions during ambient relative humidity (RH) fluctuation. However, the knowledge of dynamic phase evolution processes for mixed organic–inorganic particles is scarce. Here we present a universal and visualized observation of LLPS, efflorescence and deliquescence transitions as well as hygroscopic growth of laboratory-generated mixed 1, 2, 6-hexanetriol / ammonium sulfate (AS) particles with different organic–inorganic mole ratios (OIR = 1:4, 1:2, 1:1, 2:1 and 4:1) with high time resolution (0.5 s) using an optical microscope operated with a video camera. The optical images suggest that an inner AS solution phase is surrounded by an outer organic-rich phase after LLPS for all mixed particles. The LLPS mechanism for particles with different OIRs is found to be distinct; meanwhile, multiple mechanisms may dominate successively in individual particles with a certain OIR, somewhat inconsistently with previously reported observations. More importantly, another phase separation in the inner AS solution phase, defined as secondary LLPS here, is observed for OIR = 1:1, 1:2 and 1:4 particles. The secondary LLPS may be attributed to the formation of more concentrated AS inclusions in the inner phase and becomes more obvious with decreasing RH and increasing AS mole fraction. Furthermore, the changes in size and number of AS inclusions during LLPS are quantitatively characterized, which further illustrate the equilibrium partitioning process of organic and inorganic components. These experimental results have significant implications for the revelation of complex phase transitions of internally mixed atmospheric particles and evaluation of liquid–liquid and liquid–solid equilibria in thermodynamic models.

This work reports the synthesis of a series of shape memory polyimides by polycondensation of 2,2′-bis[4-(3-aminophenoxy)phenyl]hexafluoropropane (m-6FBAPP), 4,4′-(hexafluoroisopropylidene) (6FDA) and PA-terminated OAPS. The influence upon shape memory properties by the introduction of PA-terminated OAPS was investigated systematically in both experiments and simulations. PA-terminated OAPS acted as both a plasticizer and a rigid cage structure, preventing excessive movement of the polymer chain. Thus, both strain and shape recovery values of the resulting polyimide composites strongly depended on the content of PA-terminated OAPS. The shape memory polyimide composites showed high strain and recovery values equal to 247 and 94.3%, respectively. Additionally, these polyimide composites showed multiple shape memory effects and excellent shape memory cycle stability.

A rigid-body dynamic model of multi-stage planetary roller screw mechanism (multi-stage PRSM) is proposed in this paper. The structure of multi-stage PRSM is introduced and the motion analysis is presented. The total kinetic energy of the mechanism is calculated. The rotation of the screws and carriers is chosen as generalized degrees of freedom. The generalized forces and motion equations of multi-stage PRSM are derived using the Lagrange method. The transient and steady-state behaviours of multi-stage PRSM are simulated, followed by an analysis of the influence of friction coefficients and thread pitches on the motion and forces acting on the multi-stage PRSM. Taking a two-stage PRSM as an example, the simulation results show that the friction coefficient between screw #1 and screw #2 has a slight effect on efficiency and rotational velocity ratios of carriers to screws. When the sum of the pitches of screws is a constant, the axial component of contact force between screw #1 and roller #1 decreases with the increase in the pitch of screw #1.

We conduct a global inverse analysis of 2010–2018 GOSAT observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes anthropogenic methane emissions and their 2010–2018 trends on a 4∘×5∘ grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane). We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. We verify our inversion results with independent methane observations from the TCCON and NOAA networks. Our inversion successfully reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high for China (coal) and Russia (oil and gas) and too low for Venezuela (oil and gas) and the US (oil and gas). We show large 2010–2018 increases in anthropogenic methane emissions over South Asia, tropical Africa, and Brazil, coincident with rapidly growing livestock populations in these regions. We do not find a significant trend in anthropogenic emissions over regions with high rates of production or use of fossil methane, including the US, Russia, and Europe. Our results indicate that the peak methane growth rates in 2014–2015 are driven by low OH concentrations (2014) and high fire emissions (2015), while strong emissions from tropical (Amazon and tropical Africa) and boreal (Eurasia) wetlands combined with increasing anthropogenic emissions drive high growth rates in 2016–2018. Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.

We use satellite (GOSAT) and in situ (GLOBALVIEWplus CH4 ObsPack) observations of atmospheric methane in a joint global inversion of methane sources, sinks, and trends for the 2010–2017 period. The inversion is done by analytical solution to the Bayesian optimization problem, yielding closed-form estimates of information content to assess the consistency and complementarity (or redundancy) of the satellite and in situ data sets. We find that GOSAT and in situ observations are to a large extent complementary, with GOSAT providing a stronger overall constraint on the global methane distributions, but in situ observations being more important for northern midlatitudes and for relaxing global error correlations between methane emissions and the main methane sink (oxidation by OH radicals). The in-situ-only and the GOSAT-only inversions alone achieve 113 and 212 respective independent pieces of information (DOFS) for quantifying mean 2010–2017 anthropogenic emissions on 1009 global model grid elements, and respective DOFS of 67 and 122 for 2010–2017 emission trends. The joint GOSAT+ in situ inversion achieves DOFS of 262 and 161 for mean emissions and trends, respectively. Thus, the in situ data increase the global information content from the GOSAT-only inversion by 20 %–30 %. The in-situ-only and GOSAT-only inversions show consistent corrections to regional methane emissions but are less consistent in optimizing the global methane budget. The joint inversion finds that oil and gas emissions in the US and Canada are underestimated relative to the values reported by these countries to the United Nations Framework Convention on Climate Change (UNFCCC) and used here as prior estimates, whereas coal emissions in China are overestimated. Wetland emissions in North America are much lower than in the mean WetCHARTs inventory used as a prior estimate. Oil and gas emissions in the US increase over the 2010–2017 period but decrease in Canada and Europe. The joint inversion yields a global methane emission of 551 Tg a−1 averaged over 2010–2017 and a methane lifetime of 11.2 years against oxidation by tropospheric OH (86 % of the methane sink).

Artificial rain is explored as a remedy for climate change caused farmland drought and bushfires. Increasing the ion density in the open air is an efficient way to generate charged nuclei from atmospheric aerosols and induce precipitation or eliminate fog. Here we report on the development of a large commercial-installation-scale atmospheric ion generator based on corona plasma discharges, experimental monitoring, and numerical modeling of the parameters and range of the atmospheric ions, as well as the application of the generated ions to produce charged aerosols and induce precipitation at the scale of a large cloud chamber. The coverage area of the ions generated by the large corona discharge installation with the 7.2 km long wire electrode and applied voltage of −90 kV is studied under prevailing weather conditions including wind direction and speed. By synergizing over 300 000 localized corona discharge points, we demonstrate a substantial decrease in the decay of ions compared to a single corona discharge point in the open air, leading to large-scale (30 m ×23 m ×90 m) ion coverage. Once aerosols combine with the generated ions, charged nuclei are produced. Higher wind speed has led to larger areas covered by the plasma-generated ions. The cloud chamber experiments (relative humidity 130±10  %) suggest that charged aerosols generated by ions with a density of ∼104 cm−3 can accelerate the settlement of moisture by 38 %. These results are promising for the development of large-scale installations for the effective localized control of atmospheric phenomena.

Mineral dust particles play an important role in the Earth system, imposing a variety of effects on air quality, climate, human health, and economy. Accurate forecasts of dust events are highly desirable to provide an early warning and inform the decision-making process. East Asia is one of the largest dust sources in the world. This study applies and evaluates four widely used regional air quality models to simulate dust storms in northeastern China. Three dust schemes in the Weather Research and Forecasting model with Chemistry (WRF-Chem) (version 3.9.1), two schemes in both CHIMERE (version 2017r4) and CMAQ (version 5.2.1), and one scheme in CAMx (version 6.50) were applied to a dust event during 4–6 May 2015 in northeastern China. Most of these models were able to capture this dust event with the exception of CAMx, which has no dust source map covering the study area; hence, another dust source mask map was introduced to replace the default one for the subsequent simulation. Although these models reproduced the spatial pattern of the dust plume, there were large discrepancies between predicted and observed PM10 concentrations in each model. In general, CHIMERE had relatively better performance among all simulations with default configurations. After parameter tuning, WRF-Chem with the Air Force Weather Agency (AFWA) scheme using a seasonal dust source map from Ginoux et al. (2012) showed the best performance, followed by WRF-Chem with the UOC_Shao2004 scheme, CHIMERE, and CMAQ. The performance of CAMx had significantly improved by substituting the default dust map and removing the friction velocity limitation. This study suggested that the dust source maps should be carefully selected on a regional scale or replaced with a new one constructed with local data. Moreover, further study and measurement of sandblasting efficiency of different soil types and locations should be conducted to improve the accuracy of estimated vertical dust fluxes in air quality models.

Predicting future changes in ecosystem services is not only highly desirable but is also becoming feasible as several forces (e.g., available big data, developed data assimilation (DA) techniques, and advanced cyber-infrastructure) are converging to transform ecological research into quantitative forecasting. To realize ecological forecasting, we have developed an Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models. EcoPAD (v1.0) is a web-based software system that automates data transfer and processing from sensor networks to ecological forecasting through data management, model simulation, data assimilation, forecasting, and visualization. It facilitates interactive data–model integration from which the model is recursively improved through updated data while data are systematically refined under the guidance of model. EcoPAD (v1.0) relies on data from observations, process-oriented models, DA techniques, and the web-based workflow. We applied EcoPAD (v1.0) to the Spruce and Peatland Responses Under Climatic and Environmental change (SPRUCE) experiment in northern Minnesota. The EcoPAD-SPRUCE realizes fully automated data transfer, feeds meteorological data to drive model simulations, assimilates both manually measured and automated sensor data into the Terrestrial ECOsystem (TECO) model, and recursively forecasts the responses of various biophysical and biogeochemical processes to five temperature and two CO2 treatments in near-real time (weekly). Forecasting with EcoPAD-SPRUCE has revealed that mismatches in forecasting carbon pool dynamics are more related to model (e.g., model structure, parameter, and initial value) than forcing variables, opposite to forecasting flux variables. EcoPAD-SPRUCE quantified acclimations of methane production in response to warming treatments through shifted posterior distributions of the CH4:CO2 ratio and the temperature sensitivity (Q10) of methane production towards lower values. Different case studies indicated that realistic forecasting of carbon dynamics relies on appropriate model structure, correct parameterization, and accurate external forcing. Moreover, EcoPAD-SPRUCE stimulated active feedbacks between experimenters and modelers to identify model components to be improved and additional measurements to be taken. It has become an interactive model–experiment (ModEx) system and opens a novel avenue for interactive dialogue between modelers and experimenters. Altogether, EcoPAD (v1.0) acts to integrate multiple sources of information and knowledge to best inform ecological forecasting.

The workpiece contour errors from previous process affect current and subsequent process. In order to improve the uniformity of workpiece allowance distribution, a stepwise workpiece matching adjustment method with contact inspection is developed. This method includes two stepwise registration processes. At first, some pairs of measured points on the theoretical and actual surfaces are selected to build the corresponding local coordinate systems, and then the rough matching matrixes are obtained by the coordinate systems alignment. During the fine matching process, the objective function based on the least square method is established by the measured point sets adjusted through the rough matching. The fine matching matrixes can be obtained by self-adaptive differential evolution algorithm. The posture adjustment can be realized by transforming coordinate systems of 5 axis machine tool, of which adjusted values can be calculated by the matching matrixes and the machine tool topology. At last, some experiments were presented to demonstrate the performance of the method.

Abstract The large-scale popularization of electric vehicles (EVs) brings the potential for grid frequency regulation. Considering the characteristics of fast response and adjustment of EVs, two control strategies of automatic generation control (AGC) with EVs are proposed responding to two high frequency regulating signals extracted from area control error (ACE) and area regulation requirement (ARR) by a digital filter, respectively. In order to dispatch regulation task to EVs, the capacity of regulation is calculated based on maximum V2G power and the present V2G power of EVs. Finally, simulations based on a two-area interconnected power system show that the proposed approaches can significantly suppress frequency deviation and reduce the active power output of traditional generation units.

There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA) transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM) based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.

Hyperspectral data has characteristics of multiple bands and continuous, large amount of data, redundancy, and non-destructive. These characteristics make it possible to use hyperspectral data to study cultural relics. In this paper, the hyperspectral imaging technology is adopted to recognize the bottom images of an ancient tomb located in Shanxi province. There are many black remains on the bottom surface of the tomb, which are suspected to be some meaningful texts or paintings. Firstly, the hyperspectral data is preprocessing to get the reflectance of the region of interesting. For the convenient of compute and storage, the original reflectance value is multiplied by 10000. Secondly, this article uses three methods to extract the symbols at the bottom of the ancient tomb. Finally we tried to use morphology to connect the symbols and gave fifteen reference images. The results show that the extraction of information based on hyperspectral data can obtain a better visual experience, which is beneficial to the study of ancient tombs by researchers, and provides some references for archaeological research findings.

Plant carbon (C) content is one of the most important plant traits and is critical to the assessment of global C cycle and ecological stoichiometry; however, the global variations in plant C content remain poorly understood. In this study, we conducted a global analysis of the plant C content by synthesizing data from 4318 species to document specific values and their variation of the C content across plant organs and life forms. Plant organ C contents ranged from 45.0 % in reproductive organs to 47.9 % in stems at global scales, which were significantly lower than the widely employed canonical value of 50 %. Plant C content in leaves (global mean of 46.9 %) was higher than that in roots (45.6 %). Across life forms, woody plants exhibited higher C content than herbaceous plants. Conifers, relative to broad-leaved woody species, had higher C content in roots, leaves, and stems. Plant C content tended to show a decrease with increasing latitude. The life form explained more variation of the C content than climate. Our findings suggest that specific C content values of different organs and life forms developed in our study should be incorporated into the estimations of regional and global vegetation biomass C stocks.

Background. Gymnocypris firmispinatus Wu et Wu, 1988 is a small fish with the maximum total length of 242 mm, which occurs only in the Jinsha River and its tributaries (China), at altitudes above 2000 m. Nowadays, G. firmispinatus had been placed in a dangerous situation, due to hydropower station and excessive fishing. However, attempts to develop an effective population management strategy have been obstructed by lack of basic biological information, especially for age estimation. As we known, sectioned otoliths provide the most precise age estimates of many Schizothoracinae fishes, vertebrae and opercular bones were also used for age determination of some fish species. This work aimed to assess the suitability of three calcified structures (otolith, vertebra, and opercular bone) for age estimation of G. firmispinatus. Materials and methods. The annulus characteristics of otolith, vertebra, and opercular bone were described. Annual periodicity was verified by marginal increment ratio (MIR) analysis in otoliths and edge analysis in vertebrae and opercular bones. Otoliths, vertebrae, and opercular bones were examined to determine which structure produced the most precise and accurate age estimates in G. firmispinatus, mainly by scoring for readability on a five-point scale and calculating the index of the mean percentage error (IMPE). Results. Microscopically, otoliths and vertebrae showed a typical pattern of opaque-and translucent-zones, attributed to slow and fast growth periods. Annuli formed between March and May once a year in otoliths and vertebrae. Forty-four percent of the opercular bones were scored as unreadable. The value of IMPE between the two counts was lowest for otoliths (4.25%), followed by vertebrae (6.21%) and opercular bones (11.58%). The IMPE value of vertebra vs. otolith was 11.05%, while that of opercular bone vs. otolith was 23.03%. Vertebrae and otoliths matched closely only for the first 4 years of life, while opercular bones appeared to underestimate the age of G. firmispinatus. Conclusion. Annuli in the otoliths and vertebrae both formed once a year up to age 8, and otoliths would be the most precise structure for age determination in G. firmispinatus. Vertebrae and otoliths matched closely up to age 4, while opercular bones could not be used for age estimation for the species.

Axial slot CTs were designed and applied on Rotor 67 to understand the physical mechanisms responsible for the improvement of the stall margin. Unsteady Reynolds-averaged Navier-Stokes was applied in addition to steady Reynolds-averaged Navier-Stokes to simulate the flow field of the rotor. The results show that aerodynamic performance and the rotor stability were improved. Stall margin improvement (SMI) improved by 26.85% after the CT covering 50% of the axial tip chord was applied, whereas peak efficiency (PE) decreased the least. The main reason for the rotor stall in the solid casing is the blockage caused by tip leakage flow. After axial slot CTs were applied, the tip leakage flow in the front part of the chord was obviously reduced, and the majority of the blockages in the tip region were removed. The absolute value of the axial momentum before 45% axial chord in CT_50 was reduced by 50%, whereas the maximum tangential momentum value of CT_50 was decreased by 70% relative to the solid casing. CT_50 configuration was located across the shock wave; thus, it can fully utilize the pressure gradient to bleed and remove the blockage region, and the across flow is considerably depressed.

Axial slot CTs were designed and applied on Rotor 67 to understand the physical mechanisms responsible for the improvement of the stall margin. Unsteady Reynolds-averaged Navier-Stokes was applied in addition to steady Reynolds-averaged Navier-Stokes to simulate the flow field of the rotor. The results show that aerodynamic performance and the rotor stability were improved. Stall margin improvement (SMI) improved by 26.85% after the CT covering 50% of the axial tip chord was applied, whereas peak efficiency (PE) decreased the least. The main reason for the rotor stall in the solid casing is the blockage caused by tip leakage flow. After axial slot CTs were applied, the tip leakage flow in the front part of the chord was obviously reduced, and the majority of the blockages in the tip region were removed. The absolute value of the axial momentum before 45% axial chord in CT_50 was reduced by 50%, whereas the maximum tangential momentum value of CT_50 was decreased by 70% relative to the solid casing. CT_50 configuration was located across the shock wave; thus, it can fully utilize the pressure gradient to bleed and remove the blockage region, and the across flow is considerably depressed.