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Ammonia (NH3) has major effects on the environment and climate. In situ measurements of NH3 concentrations taken between June 2009 and July 2020 at an urban site in Beijing were analyzed to study its long-term behavior, responses to meteorological conditions, and influences on the formation of secondary inorganic aerosols (SIAs). The 11-year average NH3 mixing ratio was 26.9±19.3 ppb (median 23.5 ppb). The annual average NH3 mixing ratio increased from 2009 to 2017 by 50 % and then decreased by 49 % from 2017 to 2020. Notably, the long-term trend for NH3 at the ground level did not align with the trends derived from satellite observations and emission estimates. The NH3 concentration exhibited a stronger correlation with the daily variation in water vapor (H2O) concentration than with air temperature. Thermodynamic modeling revealed the nonlinear response of SIAs to NH3, with increased sensitivity when its concentration was reduced to 40 % of the initial level. Although reducing NH3 concentrations can improve air quality during winter, controlling acid gas concentrations has a greater effect than controlling NH3 concentrations on reducing SIA concentrations, until NH3 and acidic gas concentrations are reduced below 80 % of their current levels. Nevertheless, the increased mass proportion of ammonium salts in SIAs during the observation period indicates that future control measures for NH3 concentrations may need to be prioritized in Beijing.

The New Baseline Surface Radiation (NBSR) system was established at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station in 2013 to observe nine broadband radiation components, i.e. the global, direct, diffuse, and upwelling shortwave irradiance (GSWI, DSWI, DifSWI, and UpSWI); the photosynthetically active radiation (PAR); the ultraviolet irradiance (UVAI and UVBI); and the down- and upwelling longwave irradiance (DnLWI and UpLWI). To test the 1 min raw radiometric data, a Hybrid Algorithm for Radiation Data Quality Control (HARDQC) is presented in this study based on well-established methods, together with the solar irradiance dataset and the spectral features of the instrument bands. Subsequently, a NBSR dataset, which consists of radiation data at multiple timescales (i.e. 1 min, hourly, daily, monthly, monthly average hourly, and monthly average daily) over 2013–2022, is established and evaluated. Results show that more than 98.7 % of all radiation components passed the physical possibility test. The percentages of those that passed the extremely rare test are greater than 98.6 % for all radiation components except for the DnLWI (97.1 %). The percentages of those that passed the comparison test are greater than 83.3 % (GSWI), 78.3 % (DSWI), 81.7 % (DifSWI), 93.1 % (UpSWI), 88.9 % (PAR), 95.6 % (UVAI), 96.3 % (UVBI), 99.8 % (DnLWI), and 99.7 % (UpLWI), respectively. Due to data logger faults, removal of the instruments for calibration, and lightning strikes, some apparent data gaps in the upwelling radiation components (January 2015–August 2017) and all radiation components (December 2018; July to September 2021) were detected. Despite the existence of a few imperfections in the NBSR dataset, it is still reliable to apply it in many fields such as the validation of satellite products and numerical models, the investigation of relationships between radiation and atmospheric composition, and the detection of changes in the surface fluxes. The dataset described in this paper is available at https://doi.org/10.1594/PANGAEA.963330 (Quan et al., 2023b).

Fine particulate matter (PM2.5) samples were collected between November 2020 and October 2021 at the Minzu University of China in Beijing, and the n-alkyl nitrate concentrations in the PM2.5 samples were determined to investigate n-alkyl nitrate pollution and formation mechanisms. C9–C16 n-alkyl nitrate standards were synthesized, and the n-alkyl nitrate concentrations in PM2.5 were determined by gas chromatography triple quadrupole mass spectrometry. Temporal trends in and correlations between particulate-bound n-alkyl nitrate, ozone, PM2.5, and nitrogen dioxide concentrations were investigated to assess the relationships between particulate-bound n-alkyl nitrate concentrations and gas-phase homogeneous reactions in the photochemical process and speculate the particulate-bound n-alkyl nitrates' formation mechanisms. The n-alkyl nitrate concentrations in the PM2.5 samples were 9.67–2730 pg m−3, and the mean was 578 pg m−3. The n-alkyl nitrate homologue group concentrations increased as the carbon chain length increased; i.e., long-chain n-alkyl nitrates contributed more than short-chain n-alkyl nitrates to the total n-alkyl nitrate concentrations in PM2.5. The n-alkyl nitrate concentrations clearly varied seasonally and diurnally, the concentrations decreasing in the order winter > spring > autumn > summer and the mean concentrations being higher at night than in the day. The particulate-bound n-alkyl nitrate and ozone concentrations significantly negatively correlated despite gas-phase alkyl nitrate and ozone concentrations previously being found to positively correlate. This indicated that long-chain alkyl nitrates may not be produced during gas-phase homogeneous reactions. The particulate-bound n-alkyl nitrate concentrations followed the same trends as and significantly positively correlated with the PM2.5 and nitrogen dioxide concentrations. Nitrogen dioxide is an important contributor of nitrates in particulate matter. This indicated that particulate-bound n-alkyl nitrates may form through non-homogeneous reactions between alkanes and nitrates on particulate matter surfaces. As secondary pollutants, particulate-bound alkyl nitrates are important components of PM2.5 during haze events and strongly affect haze pollution and atmospheric visibility.

Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmental monitoring stations and their operational forecasts in advance using inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions and cannot solely be used as the operational forecast. In contrast, deterministic chemical transport models (CTMs), which simulate the full life cycles of air pollutants, provide predictions that are continuous in the 3D field. Despite their benefits, CTM predictions are typically biased, particularly on a fine scale, owing to the complex error sources due to the emission, transport, and removal of pollutants. In this study, we proposed a fusion of site-available machine learning prediction, which is from our regional feature selection-based machine learning model (RFSML v1.0), and a CTM prediction. Compared to the normal pure machine learning model, the fusion system provides a gridded prediction with relatively high accuracy. The prediction fusion was conducted using the Bayesian-theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented superior performance compared to the pure CTM. Moreover, covariance inflation further enhanced the fused prediction, particularly in cases of severe underestimation.

The most common cause of autosomal recessive familial Parkinson’s disease (PD) are mutations in the PRKN/PARK2 gene encoding an E3 ubiquitin protein-ligase PARKIN. We report the generation of an iPSC cell line from the fibroblasts of a male PD patient carrying a common missense variant in exon 7 (p.Arg275Trp), and a 133 kb deletion encompassing exon 8, using transiently-present Sendai virus. The established line displays typical human primed iPSC morphology and expression of pluripotency-associated markers, normal karyotype without SNP array-detectable copy number variations and can give rise to derivatives of all three embryonic germ layers. We envisage the usefulness of this iPSC line, carrying a common and well-studied missense mutation in the RING1 domain of the PARKIN protein, for the elucidation of PARKIN-dependent mechanisms of PD using in vitro and in vivo models.

The characteristics of n-alkanes and the contributions of various sources of fine particulate matter (PM2.5) in the atmosphere in Beijing were investigated. PM2.5 samples were collected at Minzu University of China between November 2020 and October 2021, and n-alkanes in the samples were analyzed by gas chromatography mass spectrometry. A positive matrix factorization analysis model and source indices (the main carbon peaks, carbon preference indices, and plant wax contribution ratios) were used to identify the sources of n-alkanes, to determine the contributions of different sources, and to explain the differences. The n-alkane concentrations were 4.51–153 ng m−3 (mean 32.7 ng m−3), and the particulate-bound n-alkane and PM2.5 concentrations varied in parallel. There were marked seasonal and diurnal differences in the n-alkane concentrations (p). The n-alkane concentrations in the different seasons decreased in the order of winter > spring > summer > fall. The mean concentration of each homolog was higher at night than in the day in all seasons. Particulate-bound n-alkanes were supplied by common anthropogenic and biogenic sources, and fossil fuel combustion was the dominant contributor. The positive matrix factorization model results indicated five sources of n-alkanes in PM2.5, which were coal combustion, diesel vehicle emissions, gasoline vehicle emissions, terrestrial plant release, and mixed sources. Vehicle emissions were the main sources of n-alkanes, contributing 57.6 %. The sources of PM2.5 can be indicated by n-alkanes (i.e., using n-alkanes as organic tracers). Vehicle exhausts strongly affect PM2.5 pollution. Controlling vehicle exhaust emissions is key to controlling n-alkanes and PM2.5 pollution in Beijing.

Satellite-based aerosol optical depth (AOD) has gained popularity as a powerful data source for calibrating aerosol models and correcting model errors through data assimilation. However, simulated airborne particle mass concentrations are not directly comparable to satellite-based AODs. For this, an AOD operator needs to be developed that can convert the simulated mass concentrations into model AODs. The AOD operator is most sensitive to the input of the particle size and chemical composition of aerosols. Furthermore, assumptions regarding particle size vary significantly amongst model AOD operators. More importantly, satellite retrieval algorithms rely on different size assumptions. Consequently, the differences between the simulations and observations do not always reflect the actual difference in aerosol amount. In this study, the sensitivity of the AOD operator to aerosol properties has been explored. We conclude that, to avoid inconsistencies between the AOD operator and retrieved properties, a common understanding of the particle size is required. Accordingly, we designed a hybrid assimilation methodology (hybrid AOD assimilation) that includes two sequentially conducted procedures. First, aerosol size in the model operator has been brought closer to the assumption of the satellite retrieval algorithm via assimilation of Ångström exponents. This ensures that the model AOD operator is more consistent with the AOD retrieval. The second step in the methodology concerns optimization of aerosol mass concentrations through direct assimilation of AOD (standard AOD assimilation). The hybrid assimilation method is tested over the European domain using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue products. The corrections made to the model aerosol size information are validated through a comparison with the ground-based Aerosol Robotic Network (AERONET) optical product. The increments in surface aerosol mass concentration that occur due to either the standard AOD assimilation analysis or the hybrid AOD assimilation analysis are evaluated against independent ground PM2.5 observations. The standard analysis always results in relatively accurate posterior AOD distributions; however, the corrections are hardly transferred into better aerosol mass concentrations due to the uncertainty in the AOD operator. In contrast, the model AOD and mass concentration states are considerably more accurate when using the hybrid methodology.

Ozone (O3) is a secondary pollutant in the atmosphere formed by photochemical reactions that endangers human health and ecosystems. O3 has aggravated in Asia in recent decades and will vary in the future. In this study, to quantify the impacts of future climate change on O3 pollution, near-surface O3 concentrations over Asia in 2020–2100 are projected using a machine learning (ML) method along with multi-source data. The ML model is trained with combined O3 data from a global atmospheric chemical transport model and real-time observations. The ML model is then used to estimate future O3 with meteorological fields from multi-model simulations under various climate scenarios. The near-surface O3 concentrations are projected to increase by 5 %–20 % over South China, Southeast Asia, and South India and less than 10 % over North China and the Gangetic Plains under the high-forcing scenarios in the last decade of 21st century, compared to the first decade of 2020–2100. The O3 increases are primarily owing to the favorable meteorological conditions for O3 photochemical formation in most Asian regions. We also find that the summertime O3 pollution over eastern China will expand from North China to South China and extend into the cold season in a warmer future. Our results demonstrate the important role of a climate change penalty on Asian O3 in the future, which provides implications for environmental and climate strategies of adaptation and mitigation.

The co-polluted days by ozone (O3) and PM2.5 (particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less) (O3–PM2.5PDs) were frequently observed in the Beijing–Tianjin–Hebei (BTH) region in warm seasons (April–October) of 2013–2020. We applied the 3-D global chemical transport model (GEOS-Chem) to investigate the chemical and physical characteristics of O3–PM2.5PDs by composited analyses of such days that were captured by both the observations and the model. Model results showed that, when O3–PM2.5PDs occurred, the concentrations of hydroxyl radical and total oxidant, sulfur oxidation ratio, and nitrogen oxidation ratio were all high, and the concentrations of sulfate at the surface were the highest among all pollution types. We also found unique features in vertical distributions of aerosols during O3–PM2.5PDs; concentrations of PM2.5 decreased with altitude near the surface but remained stable at 975–819 hPa. Process analyses showed that secondary aerosols (nitrate, ammonium, and sulfate) had strong chemical productions at 913–819 hPa, which were then transported downward, resulting in the quite uniform vertical profiles at 975–819 hPa on O3–PM2.5PDs. The weather patterns for O3–PM2.5PDs were characterized by anomalous high-pressure system at 500 hPa as well as strong southerlies and high RH at 850 hPa. The latter resulted in the strong chemical productions around 850 hPa on O3–PM2.5PDs. The physical and chemical characteristics of O3–PM2.5PDs are quite different from those of polluted days by either O3 alone or PM2.5 alone and have important implications for air quality management.

Hydrological simulations are a main method of quantifying the contribution rate (CR) of climate change (CC) and human activities (HAs) to watershed streamflow changes. However, the uncertainty of hydrological simulations is rarely considered in current research. To fill this research gap, based on the Soil and Water Assessment Tool (SWAT) model, in this study, we propose a new framework to quantify the CR of CC and HAs based on the posterior histogram distribution of hydrological simulations. In our new quantitative framework, the uncertainty of hydrological simulations is first considered to quantify the impact of “equifinality for different parameters”, which is common in hydrological simulations. The Lancang River (LR) basin in China, which has been greatly affected by HAs in the past 2 decades, is then selected as the study area. The global gridded monthly sectoral water use data set (GMSWU), coupled with the dead capacity data of the large reservoirs within the LR basin and the Budyko hypothesis framework, is used to compare the calculation result of the novel framework. The results show that (1) the annual streamflow at Yunjinghong station in the Lancang River basin changed abruptly in 2005, which was mainly due to the construction of the Xiaowan hydropower station that started in October 2004. The annual streamflow and annual mean temperature time series from 1961 to 2015 in the LR basin showed significant decreasing and increasing trends at the α= 0.01 significance level, respectively. The annual precipitation showed an insignificant decreasing trend. (2) The results of quantitative analysis using the new framework showed that the reason for the decrease in the streamflow at Yunjinghong station was 42.6 % due to CC, and the remaining 57.4 % was due to HAs, such as the construction of hydropower stations within the study area. (3) The comparison with the other two methods showed that the CR of CC calculated by the Budyko framework and the GMSWU data was 37.2 % and 42.5 %, respectively, and the errors of the calculations of the new framework proposed in this study were within 5 %. Therefore, the newly proposed framework, which considers the uncertainty of hydrological simulations, can accurately quantify the CR of CC and HAs to streamflow changes. (4) The quantitative results calculated by using the simulation results with the largest Nash–Sutcliffe efficiency coefficient (NSE) indicated that CC was the dominant factor in streamflow reduction, which was in opposition to the calculation results of our new framework. In other words, our novel framework could effectively solve the calculation errors caused by the “equifinality for different parameters” of hydrological simulations. (5) The results of this case study also showed that the reduction in the streamflow in June and November was mainly caused by decreased precipitation and increased evapotranspiration, while the changes in the streamflow in other months were mainly due to HAs such as the regulation of the constructed reservoirs. In general, the novel quantitative framework that considers the uncertainty of hydrological simulations presented in this study has validated an efficient alternative for quantifying the CR of CC and HAs to streamflow changes.

With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models).

The passive suppression of combustion instability by quarter wavelength tube was hereby studied to absorb the oscillation pressure with large amplitudes caused by combustion instability. The suppression effects of quarter wavelength tube on combustion instability were systematically analyzed by combining the acoustic numerical simulation and the experimental research methods. Firstly, the influence of quarter wavelength tube on the acoustic characteristics of the system was analyzed using acoustic numerical simulation; and then, the acoustic absorption characteristics to external acoustic disturbance and the suppression effects on the self-excited combustion instability were experimentally studied. The results show that the quarter wavelength tube can effectively absorb the acoustic pressure when the dominant frequency of acoustic pressure is close to the resonance frequency of the system, and can effectively suppress the combustion instability under acoustic resonance. However, given that the quarter wavelength tube adds adjoint dominant frequencies after eliminating the original resonant frequency of the system, and the combustion instability is stabilized on the adjoint dominant frequencies, combustion instability suppression is different from noise suppression. In addition, the diameter of wavelength tube exercises obvious effects on the above characteristics. All these make it necessary to determine the best parameters and the maximum suppression efficiency by combining numerical simulation and experiments. The research results of this paper provide theoretical and technical supports for the suppression of combustion instability by the quarter wavelength tube.

After a long device enhancement phase, scientific operation resumed in 2022. The main new device components are the water cooling of all plasma facing components and the new water-cooled high heat flux divertor units. Water cooling allowed for the first long-pulse operation campaign. A maximum discharge length of 8 min was achieved with a total heating energy of 1.3 GJ. Safe divertor operation was demonstrated in attached and detached mode. Stable detachment is readily achieved in some magnetic configurations but requires impurity seeding in configurations with small magnetic pitch angle within the edge islands. Progress was made in the characterization of transport mechanisms across edge magnetic islands: Measurement of the potential distribution and flow pattern reveals that the islands are associated with a strong poloidal drift, which leads to rapid convection of energy and particles from the last closed flux surface into the scrape-off layer. Using the upgraded plasma heating systems, advanced heating scenarios were developed, which provide improved energy confinement comparable to the scenario, in which the record triple product for stellarators was achieved in the previous operation campaign. However, a magnetic configuration-dependent critical heating power limit of the electron cyclotron resonance heating was observed. Exceeding the respective power limit leads to a degradation of the confinement.

Tides play an important role in ocean energy transfer and mixing, and provide major energy for maintaining thermohaline circulation. This study proposes a new explicit tidal scheme and assesses its performance in a global ocean model. Instead of using empirical specifications of tidal amplitudes and frequencies, the new scheme directly uses the positions of the moon and sun in a global ocean model to incorporate tides. Compared with the traditional method that has specified tidal constituents, the new scheme can better simulate the diurnal and spatial characteristics of the tidal potential of spring and neap tides as well as the spatial patterns and magnitudes of major tidal constituents (K1 and M2). It significantly reduces the total errors of eight tidal constituents (with the exception of N2 and Q1) in the traditional explicit tidal scheme, in which the total errors of K1 and M2 are reduced by 21.85 % and 32.13 %, respectively. Relative to the control simulation without tides, both the new and traditional tidal schemes can lead to better dynamic sea level (DSL) simulation in the North Atlantic, reducing significant negative biases in this region. The new tidal scheme also shows smaller positive bias than the traditional scheme in the Southern Ocean. The new scheme is suited to calculate regional distributions of sea level height in addition to tidal mixing.

Last spring, super dust storms reappeared in East Asia after being absent for one and a half decades. The event caused enormous losses in both Mongolia and China. Accurate simulation of such super sandstorms is valuable for the quantification of health damage, aviation risks, and profound impacts on the Earth system, but also to reveal the climatic driving force and the process of desertification. However, accurate simulation of dust life cycles is challenging, mainly due to imperfect knowledge of emissions. In this study, the emissions that lead to the 2021 spring dust storms are estimated through assimilation of MODIS AOD and ground-based PM10 concentration data simultaneously. With this, the dust concentrations during these super storms could be reproduced and validated with concentration observations. The multi-observation assimilation is also compared against emission inversion that assimilates AOD or PM10 concentration measurements alone, and the added values are analyzed. The emission inversion results reveal that wind-blown dust emissions originated from both China and Mongolia during spring 2021. Specifically, 19.9×106 and 37.5×106 t of particles were released in the Chinese and Mongolian Gobi, respectively, during these severe dust events. By source apportionment it was revealed that the Mongolian Gobi poses more severe threats to the densely populated regions of the Fenwei Plain (FWP) and the North China Plain (NCP) located in northern China than does the Chinese Gobi. It was estimated that 63 % of the dust deposited in FWP was due to transnational transport from Mongolia. For NCP, the long-distance transport dust from Mongolia contributes about 69 % to the dust deposition.

This study aimed to improve runoff simulations and explore deep soil hydrological processes for a watershed in the center of the Loess Plateau (LP), China. This watershed, the Wuding River Basin (WRB), has very complex topography, with soil depths ranging from 0 to 197 m. The hydrological model used for our simulations was Community Land Model version 5 (CLM5) developed by the National Center for Atmospheric Research. Actual soil depths and river channels were incorporated into CLM5 to realistically represent the physical features of the WRB. Through sensitivity tests, CLM5 with 150 soil layers with the observed variable soil depths produced the most reasonable results and was adopted for this study. Our results showed that CLM5 with actual soil depths significantly suppressed unrealistic variations of the simulated subsurface runoff when compared to the default simulations. In addition, when compared with the default version with 20 soil layers, CLM5 with 150 soil layers slightly improved runoff simulations but generated simulations with much smoother vertical water flows that were consistent with the uniform distribution of soil textures in our study watershed. The runoff simulations were further improved by the addition of river channels to CLM5, where the seasonal variability of the simulated runoff was reasonably captured. Moreover, the magnitude of the simulated runoff remarkably decreased with increased soil evaporation by lowering the soil water content threshold, which triggers surface resistance. The lowered threshold was consistent with the loess soil, which has a high sand component. Such soils often generate stronger soil evaporation than soils dominated by clay. Finally, with the above changes in CLM5, the simulated total runoff matched very closely with observations. When compared with those for the default runoff simulations, the correlation coefficient, root mean square error, and Nash–Sutcliffe coefficient for the improved simulations changed dramatically from 0.02, 10.37 mm, and −12.34 to 0.62, 1.8 mm, and 0.61. The results in this study provide strong physical insight for further investigation of hydrological processes in complex terrain with deep soils.

Abstract The inodilator levosimendan, in clinical use for over two decades, has been the subject of extensive clinical and experimental evaluation in various clinical settings beyond its principal indication in the management of acutely decompensated chronic heart failure. Critical care and emergency medicine applications for levosimendan have included postoperative settings, septic shock, and cardiogenic shock. As the experience in these areas continues to expand, an international task force of experts from 15 countries (Austria, Belgium, China, Croatia, Finland, France, Germany, Greece, Hungary, Italy, the Netherlands, Spain, Sweden, Switzerland, and the USA) reviewed and appraised the latest additions to the database of levosimendan use in critical care, considering all the clinical studies, meta-analyses, and guidelines published from September 2019 to November 2021. Overall, the authors of this opinion paper give levosimendan a “should be considered” recommendation in critical care and emergency medicine settings, with different levels of evidence in postoperative settings, septic shock, weaning from mechanical ventilation, weaning from veno-arterial extracorporeal membrane oxygenation, cardiogenic shock, and Takotsubo syndrome, in all cases when an inodilator is needed to restore acute severely reduced left or right ventricular ejection fraction and overall haemodynamic balance, and also in the presence of renal dysfunction/failure.

The absence of a compiled large-scale catchment characteristics dataset is a key obstacle limiting the development of large-sample hydrology research in China. We introduce the first large-scale catchment attribute dataset in China. We compiled diverse data sources, including soil, land cover, climate, topography, and geology, to develop the dataset. The dataset also includes catchment-scale 31-year meteorological time series from 1990 to 2020 for each basin. Potential evapotranspiration time series based on Penman's equation are derived for each basin. The 4911 catchments included in the dataset cover all of China. We introduced several new indicators that describe the catchment geography and the underlying surface differently from previously proposed datasets. The resulting dataset has a total of 125 catchment attributes and includes a separate HydroMLYR (hydrology dataset for machine learning in the Yellow River Basin) dataset containing standardized weekly averaged streamflow for 102 basins in the Yellow River Basin. The standardized streamflow data should be able to support machine learning hydrology research in the Yellow River Basin. The dataset is freely available at https://doi.org/10.5281/zenodo.5729444 (Zhen et al., 2021). In addition, the accompanying code used to generate the dataset is freely available at https://github.com/haozhen315/CCAM-China-Catchment-Attributes-and-Meteorology-dataset (last access: 26 November 2021) and supports the generation of catchment characteristics for any custom basin boundaries. Compiled data for the 4911 basins covering all of China and the open-source code should be able to support the study of any selected basins rather than being limited to only a few basins.

Soil evaporation is a key process in the water cycle and can be conveniently quantified using δ2H and δ18O in bulk surface soil water (BW). However, recent research shows that soil water in larger pores evaporates first and differs from water in smaller pores in δ2H and δ18O, which disqualifies the quantification of evaporation from BW δ2H and δ18O. We hypothesized that BW had different isotopic compositions from evaporating water (EW). Therefore, our objectives were to test this hypothesis first and then evaluate whether the isotopic difference alters the calculated evaporative water loss. We measured the isotopic composition of soil water during two continuous evaporation periods in a summer maize field. Period I had a duration of 32 d, following a natural precipitation event, and period II lasted 24 d, following an irrigation event with a 2H-enriched water. BW was obtained by cryogenically extracting water from samples of 0–5 cm soil taken every 3 d; EW was derived from condensation water collected every 2 d on a plastic film placed on the soil surface. The results showed that when event water was heavier than pre-event BW, δ2H of BW in period II decreased, with an increase in evaporation time, indicating heavy water evaporation. When event water was lighter than the pre-event BW, δ2H and δ18O of BW in period I and δ18O of BW in period II increased with increasing evaporation time, suggesting light water evaporation. Moreover, relative to BW, EW had significantly smaller δ2H and δ18O in period I and significantly smaller δ18O in period II (p). These observations suggest that the evaporating water was close to the event water, both of which differed from the bulk soil water. Furthermore, the event water might be in larger pores from which evaporation takes precedence. The soil evaporative water losses derived from EW isotopes were compared with those from BW. With a small isotopic difference between EW and BW, the evaporative water losses in the soil did not differ significantly (p>0.05). Our results have important implications for quantifying evaporation processes using water stable isotopes. Future studies are needed to investigate how soil water isotopes partition differently between pores in soils with different pore size distributions and how this might affect soil evaporation estimation.

As a major alkaline gas in the atmosphere, NH3 significantly impacts atmospheric chemistry, ecological environment, and biodiversity. Gridded NH3 emission inventories can significantly affect the accuracy of model concentrations and play a crucial role in the refinement of mitigation strategies. However, several uncertainties are still associated with existing NH3 emission inventories in China. Therefore, in this study, we focused on improving fertilizer-application-related NH3 emission inventories. We comprehensively evaluated the dates and times of fertilizer application to the major crops that are cultivated in China, improved the spatial allocation methods for NH3 emissions from croplands with different rice types, and established a gridded NH3 emission inventory for mainland China with a resolution of 5 min × 5 min in 2016. The results showed that the atmospheric NH3 emissions in mainland China amounted to 12.11 Tg, with livestock waste (44.8 %) and fertilizer application (38.6 %) being the two main NH3 emission sources in China. Obvious spatial variability in NH3 emissions was also identified, and high emissions were predominantly concentrated in North China. Further, NH3 emissions tended to be high in summer and low in winter, and the ratio for the July–January period was 3.08. Furthermore, maize and rice fertilization in summer was primarily responsible for the increase in NH3 emissions in China, and the evaluation of the spatial and temporal accuracy of the NH3 emission inventory established in this study using the WRF-Chem and ground-station- and satellite-based observations showed that it was more accurate than other inventories.

When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timing or to uncertainties in the transport. Though many studies have been conducted on the calibration or correction of dust simulations, most of these focus on intensity solely and leave the position errors mainly unchanged. In this paper, a grid-distorted data assimilation, which consists of an image-morphing method and an ensemble-based variational assimilation, is designed for realigning a simulated dust plume to correct the position error. This newly developed grid-distorted data assimilation has been applied to a dust storm event in May 2017 over East Asia. Results have been compared for three configurations: a traditional assimilation configuration that focuses solely on intensity correction, a grid-distorted data assimilation that focuses on position correction only and the hybrid assimilation that combines these two. For the evaluated case, the position misfit in the simulations is shown to be dominant in the results. The traditional emission inversion only slightly improves the dust simulation, while the grid-distorted data assimilation effectively improves the dust simulation and forecasting. The hybrid assimilation that corrects both position and intensity of the dust load provides the best initial condition for forecasting of dust concentrations.

ABSTRACT One lung ventilation (OLV) often results in trauma to the unventilated contralateral lung. This study aims to evaluate the effects of different OLV regimens on the injury of the unventilated contralateral lung to identify the best conditions for OLV. Forty rabbits were divided into five groups: a sham group, OLV group I (fraction of inspired oxygen (FIO2) 1.0, tidal volume (VT) 8mL/kg, respiratory rate (R) 40 breaths/min and inspiratory/expiratory ratio (I:E) 1:2), OLV group II (FIO2=1.0, VT 8mL/kg, R 40 breaths/min, I:E 1:2, and positive end-expiratory pressure (PEEP) 5 cm H2O), OLV group III (FIO2 1.0, VT 6mL/kg, R 40 breaths/min, I:E 1:2 and PEEP 5 cm H2O) and OLV group IV (FIO2 0.8, VT 6mL/kg, R 40 breaths/min, I:E 1:2 and PEEP 5 cm H2O). Animals from all OLV groups received two-lung ventilation (TLV) to establish a baseline, followed by one of the indicated OLV regimens. The rabbits in the sham group were intubated through trachea and ventilated with fresh air. Arterial blood gas samples were collected, lung injury parameters were evaluated, and the concentrations of TNF-α and IL-8 in bronchoalveolar lavage fluid (BALF) and pulmonary surfactant protein A (SPA) in the unventilated lung were also measured. In OLV group I, the unventilated left lung had higher TNF-α, IL-8 and lung injury score but lower SPA than the ventilated right lung. In OLV groups I to III, the concentrations of TNF-α, IL-8 and lung injury score in the left lung decreased but SPA increased. No differences in these parameters between OLV groups III and IV were observed. Strategic ventilation designed for OLV groups III and IV reduced OLV-induced injury of the non-ventilated contralateral lung in rabbits.

The second Cabauw Intercomparison of Nitrogen Dioxide measuring Instruments (CINDI-2) took place in Cabauw (the Netherlands) in September 2016 with the aim of assessing the consistency of multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of tropospheric species (NO2, HCHO, O3, HONO, CHOCHO and O4). This was achieved through the coordinated operation of 36 spectrometers operated by 24 groups from all over the world, together with a wide range of supporting reference observations (in situ analysers, balloon sondes, lidars, long-path DOAS, direct-sun DOAS, Sun photometer and meteorological instruments). In the presented study, the retrieved CINDI-2 MAX-DOAS trace gas (NO2, HCHO) and aerosol vertical profiles of 15 participating groups using different inversion algorithms are compared and validated against the colocated supporting observations, with the focus on aerosol optical thicknesses (AOTs), trace gas vertical column densities (VCDs) and trace gas surface concentrations. The algorithms are based on three different techniques: six use the optimal estimation method, two use a parameterized approach and one algorithm relies on simplified radiative transport assumptions and analytical calculations. To assess the agreement among the inversion algorithms independent of inconsistencies in the trace gas slant column density acquisition, participants applied their inversion to a common set of slant columns. Further, important settings like the retrieval grid, profiles of O3, temperature and pressure as well as aerosol optical properties and a priori assumptions (for optimal estimation algorithms) have been prescribed to reduce possible sources of discrepancies. The profiling results were found to be in good qualitative agreement: most participants obtained the same features in the retrieved vertical trace gas and aerosol distributions; however, these are sometimes at different altitudes and of different magnitudes. Under clear-sky conditions, the root-mean-square differences (RMSDs) among the results of individual participants are in the range of 0.01–0.1 for AOTs, (1.5–15) ×1014molec.cm-2 for trace gas (NO2, HCHO) VCDs and (0.3–8)×1010molec.cm-3 for trace gas surface concentrations. These values compare to approximate average optical thicknesses of 0.3, trace gas vertical columns of 90×1014molec.cm-2 and trace gas surface concentrations of 11×1010molec.cm-3 observed over the campaign period. The discrepancies originate from differences in the applied techniques, the exact implementation of the algorithms and the user-defined settings that were not prescribed. For the comparison against supporting observations, the RMSDs increase to a range of 0.02–0.2 against AOTs from the Sun photometer, (11–55)×1014molec.cm-2 against trace gas VCDs from direct-sun DOAS observations and (0.8–9)×1010molec.cm-3 against surface concentrations from the long-path DOAS instrument. This increase in RMSDs is most likely caused by uncertainties in the supporting data, spatiotemporal mismatch among the observations and simplified assumptions particularly on aerosol optical properties made for the MAX-DOAS retrieval. As a side investigation, the comparison was repeated with the participants retrieving profiles from their own differential slant column densities (dSCDs) acquired during the campaign. In this case, the consistency among the participants degrades by about 30 % for AOTs, by 180 % (40 %) for HCHO (NO2) VCDs and by 90 % (20 %) for HCHO (NO2) surface concentrations. In former publications and also during this comparison study, it was found that MAX-DOAS vertically integrated aerosol extinction coefficient profiles systematically underestimate the AOT observed by the Sun photometer. For the first time, it is quantitatively shown that for optimal estimation algorithms this can be largely explained and compensated by considering biases arising from the reduced sensitivity of MAX-DOAS observations to higher altitudes and associated a priori assumptions.

It is necessary to acquire the accurate information of vehicle driving states for the implementation of automobile active safety control. To this end, this paper proposes an effective co-estimation method based on an unscented Kalman filter (UKF) algorithm to accurately predict the sideslip angle, yaw rate, and longitudinal speed of a ground vehicle. First, a 3 degrees-of-freedom (DOFs) nonlinear vehicle dynamics model is established as the nominal control plant. Then, based on CarSim software, the simulation results of the front steer angle and longitudinal and lateral acceleration are obtained under a variety of working conditions, which are regarded as the pseudo-measured values. Finally, the joint simulation of vehicle state estimation is realized in the MATLAB/Simulink environment by using the pseudo-measured values and UKF algorithm concurrently. The results show that the proposed UKF-based vehicle driving state estimation method is effective and more accurate in different working scenarios compared with the EKF-based estimation method.

Abstract Background Annually > 10% of patients with atrial fibrillation on oral anticoagulation undergo invasive procedures. Optimal peri-procedural management of anticoagulation, as judged by major bleeding and thromboembolic events, especially in the elderly, is still debated. Methods Procedures from 1442 patients were evaluated. Peri-procedural edoxaban management was guided only by the experience of the attending physician. The primary safety outcome was the rate of major bleeding. Secondary outcomes included the peri-procedural administration of edoxaban, other bleeding events, and the main efficacy outcome, a composite of acute coronary syndrome, non-hemorrhagic stroke, transient ischemic attack, systemic embolic events, deep vein thrombosis, pulmonary embolism, and mortality. Results Of the 1442 patients, 280 (19%) were 65, and seven in patients aged > 75 years. Conclusion Despite increased bleeding risk factors in the elderly, bleeding rates were small and similar across all age groups. However, there was a trend toward more thromboembolic complications with advancing age. Further efforts to identify the optimal management to reduce ischemic complications are needed. Trial registration: NCT# 02950168, October 31, 2016

Emission inversion using data assimilation fundamentally relies on having the correct assumptions about the emission background error covariance. A perfect covariance accounts for the uncertainty based on prior knowledge and is able to explain differences between model simulations and observations. In practice, emission uncertainties are constructed empirically; hence, a partially unrepresentative covariance is unavoidable. Concerning its complex parameterization, dust emissions are a typical example where the uncertainty could be induced from many underlying inputs, e.g., information on soil composition and moisture, land cover and erosive wind velocity, and these can hardly be taken into account together. This paper describes how an adjoint model can be used to detect errors in the emission uncertainty assumptions. This adjoint-based sensitivity method could serve as a supplement of a data assimilation inverse modeling system to trace back the error sources in case large observation-minus-simulation residues remain after assimilation based on empirical background covariance. The method follows an application of a data assimilation emission inversion for an extreme severe dust storm over East Asia (Jin et al., 2019b). The assimilation system successfully resolved observation-minus-simulation errors using satellite AOD observations in most of the dust-affected regions. However, a large underestimation of dust in Northeast China remained despite the fact that the assimilated measurements indicated severe dust plumes there. An adjoint implementation of our dust simulation model is then used to detect the most likely source region for these unresolved dust loads. The backward modeling points to the Horqin desert as the source region, which was indicated as a non-source region by the existing emission scheme. The reference emission and uncertainty are then reconstructed over the Horqin desert by assuming higher surface erodibility. After the emission reconstruction, the emission inversion is performed again, and the posterior dust simulations and reality are now in much closer harmony. Based on our results, it is advised that emission sources in dust transport models include the Horqin desert as a more active source region.

Objective: We aimed to better understand the clinical application and economics of five commonly used combined oral short-acting contraceptives (COCs) by analyzing big data collected from an obstetrics and gynecology hospital in China. The COCs studied included desogestrel ethinyl estradiol tablets (DSE), which was administered at a dose of 20 or 30 μg ethinyl estradiol, ethinyl estradiol cyproterone tablets (ECP), drospirenone ethinyl estradiol tablets (DRE; 21 pills/box), and drospirenone ethinyl estradiol tablets (II; 28 pills/box). Methods: This retrospective study included patients who were prescribed COCs from 2014-2018 in our obstetrics and gynecology hospital, which is affiliated with Fudan University. We analyzed patient characteristics, clinical indications, drug costs, and types of drugs often prescribed with COCs to identify factors influencing medication choice and use. Results: Data from 127,183 patients using COCs was analyzed. The most commonly prescribed COCs was ECP, accounting for 64.16% of all COCs use, while DRE was the second most commonly prescribed (23.13%). Most patients were 21- to 30-year-old (56.82%). ECP, DSE (30 μg), and DRE were most commonly used in the treatment of menstrual disorders, while DRE (II) was more frequently used for contraception. The second most common indication for DSE (30 μg) use was endometriosis, while it was polycystic ovary syndrome (PCOS) for ECP and DRE. DSE (20 μg) was used by only two patients in our study. The per capita cost of either dose of DSE was low: 34.95 ± 5.34 RMB for the 30 µg dose and 62.56 ± 0.00 RMB for the 20 µg dose. ECP was the second most affordable at a cost of 82.81 ± 10.63 RMB, while DRE and DRE (II) were considerably more expensive at 186.88 ± 23.88 and 265.98 ± 22.12 RMB, respectively. The rank of the total cost of therapeutic drugs per capita was similar to that of COCs per capita. Either dose of DSE was cheapest, followed ECP. Again, DRE and DRE (II) were the most expensive. Metformin hydrochloride was the mostly commonly drug prescribed in conjunction with ECP; 39.89% of patients took both medications. Spironolactone tablets, other hormonal agents, and various Chinese patent medicines were also commonly prescribed with a COC. Conclusion: The main clinical indication for the prescription of COCs in our hospital has not been contraception, rather menstrual disorders and conditions characterized by excess androgen (e.g. PCOS). ECP entered the market the earliest in China, it is approved for the greatest number clinical indications, and it appears in the medical insurance catalogue of China. Thus, it is the most widely used COCs in China. In addition to contraception, it is also widely used to treat polycystic ovary syndrome and endometriosis. However, the 30 µg dose of DSE is the most affordable in regards to total list price, and it is used for menstrual disorders, endometriosis, and contraception.Key MessagesCOCs are widely used in obstetrics and gynecology hospitals. They are not only a reliable method of contraception, but in China they are even more widely used to treat or manage a variety of other conditions. A total of five COCs are commonly used; each has advantages and disadvantages in regards to cost, when they were introduced in China, their presence in the medical insurance catalog, and the conditions they are commonly used to treat. At present, there are few published studies focusing on the clinical applications and economics of COCs in actual practice. In this study, we used “big” clinical data to explore the indications for use and drug economy of these key COCs. We thereby aimed to understand the decision-making process that occurs between doctor and patient to provide a theoretical basis for optimal treatment and resource allocation, while reducing the economic burden.Based on this data, we determined that compared to the available alternatives, ECP is the most commonly prescribed form of COC, which is most familiar to frontline Gynecologists and Obstetricians while possessing the widest range of approved clinical indications. Although the 30µg dose of DSE is the most affordable, ECP runs a close second. Combined with its other attributes, it is not surprising ECP is widely prescribed.

We carried out 19 city-circle-around car multi-axis differential optical absorption spectroscopy (MAX-DOAS) experiments on the 6th Ring Road of Beijing in January, September, and October 2014. The tropospheric vertical column densities (VCDs) of NO2 were retrieved from measured spectra by the MAX-DOAS technique and used to estimate the emissions of NOx (≡NO+NO2) from urban Beijing during the experimental periods. The offline LAPS-WRF-CMAQ model system was used to simulate the wind fields by assimilation of observational data and calculate the NO2-to-NOx concentration ratios, both of which are also needed for the estimation of NOx emissions. The NOx emissions in urban Beijing for the different months derived from the car MAX-DOAS measurements in this study were compared to the multi-resolution emission inventory in China for 2012 (MEIC 2012). Our car MAX-DOAS measurements showed higher NO2 VCD in January than in the other two months. The wind field had obvious impacts on the spatial distribution of NO2 VCD, with the mean NO2 VCD along the 6th Ring Road typically being higher under the southerly wind than under the northerly wind. In addition to the seasonal difference, the journey-to-journey variations of estimated NOx emission rates (ENOx) were large even within the same month, mainly due to uncertainties in the calculations of wind speed, the ratio of NO2 and NOx concentration, and the decay rate of NOx from the emission sources to the measured positions under different meteorological conditions. The ranges of ENOx during the heating and non-heating periods were 22.6×1025 to 31.3×1025 and 9.6×1025 to 12.0×1025 molec. s−1, respectively. The average ENOx values in the heating and non-heating periods were 26.9±6.1×1025 molec. s−1 and 11.0±1.2×1025 molec. s−1, respectively. The uncertainty range of ENOx was 20 %–52 %. The monthly emission rates from MEIC 2012 are found to be lower than the estimated ENOx, particularly in January. Our results provide important information and datasets for the validation of satellite products and also show how car MAX-DOAS measurements can be used effectively for dynamic monitoring and updating of the NOx emissions from megacities such as Beijing.

We present the inter-comparison of delta slant column densities (SCDs) and vertical profiles of nitrous acid (HONO) derived from measurements of different multi-axis differential optical absorption spectroscopy (MAX-DOAS) instruments and using different inversion algorithms during the Second Cabauw Inter-comparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) in September 2016 at Cabauw, the Netherlands (51.97∘ N, 4.93∘ E). The HONO vertical profiles, vertical column densities (VCDs), and near-surface volume mixing ratios are compared between different MAX-DOAS instruments and profile inversion algorithms for the first time. Systematic and random discrepancies of the HONO results are derived from the comparisons of all data sets against their median values. Systematic discrepancies of HONO delta SCDs are observed in the range of ±0.3×1015 molec. cm−2, which is half of the typical random discrepancy of 0.6×1015 molec. cm−2. For a typical high HONO delta SCD of 2×1015 molec. cm−2, the relative systematic and random discrepancies are about 15 % and 30 %, respectively. The inter-comparison of HONO profiles shows that both systematic and random discrepancies of HONO VCDs and near-surface volume mixing ratios (VMRs) are mostly in the range of ∼±0.5×1014 molec. cm−2 and ∼±0.1 ppb (typically ∼20 %). Further we find that the discrepancies of the retrieved HONO profiles are dominated by discrepancies of the HONO delta SCDs. The profile retrievals only contribute to the discrepancies of the HONO profiles by ∼5 %. However, some data sets with substantially larger discrepancies than the typical values indicate that inappropriate implementations of profile inversion algorithms and configurations of radiative transfer models in the profile retrievals can also be an important uncertainty source. In addition, estimations of measurement uncertainties of HONO dSCDs, which can significantly impact profile retrievals using the optimal estimation method, need to consider not only DOAS fit errors, but also atmospheric variability, especially for an instrument with a DOAS fit error lower than ∼3×1014 molec. cm−2. The MAX-DOAS results during the CINDI-2 campaign indicate that the peak HONO levels (e.g. near-surface VMRs of ∼0.4 ppb) often appeared in the early morning and below 0.2 km. The near-surface VMRs retrieved from the MAX-DOAS observations are compared with those measured using a co-located long-path DOAS instrument. The systematic differences are smaller than 0.15 and 0.07 ppb during early morning and around noon, respectively. Since true HONO values at high altitudes are not known in the absence of real measurements, in order to evaluate the abilities of profile inversion algorithms to respond to different HONO profile shapes, we performed sensitivity studies using synthetic HONO delta SCDs simulated by a radiative transfer model with assumed HONO profiles. The tests indicate that the profile inversion algorithms based on the optimal estimation method with proper configurations can reproduce the different HONO profile shapes well. Therefore we conclude that the features of HONO accumulated near the surface derived from MAX-DOAS measurements are expected to represent the ambient HONO profiles well.

The authors report a case of leiomyoma that developed from an aplastic uterus in a 38-year-old Chinese female with a typical Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome. She had normal female secondary sexual characteristics, normal external genitalia with discernible vagina, primary amenorrhea, and infertility with asymptomatic a left adnexal mass. Pelvic MRI showed a mass of 3.7 cm in the left pelvis and a mass of 1.5 × 1.4 × 2.6 cm3 in the right pelvis without a normal functioning uterus and vagina between the bladder and rectum. Laparoscopic exploration showed that the bilateral rudimentary uteri located laterally in the pelvis and they had a constant caudal relationship with their paired ovary. The bilateral fallopian tubes and ovaries were normal. A leiomyoma developed from the left rudimentary uteri. Removal of the left leiomyoma, bilateral rudimentary uteri and fallopian tubes was performed with preservation of bilateral ovaries. The histologic diagnosis showed that the bilateral rudimentary uteri had no clear structures of uterine cavity and endometrium, and the bizarre leiomyoma developed from the left rudimentary uterus without necrosis or mitosis.

Mt. Waliguan Observatory (WLG) is a World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) global baseline station in China. WLG is located at the northeastern part of the Tibetan Plateau (36∘17′ N, 100∘54′ E, 3816 m a.s.l.) and is representative of the pristine atmosphere over the Eurasian continent. We made long-term ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements at WLG during the period 2012–2015. In this study, we retrieve the differential slant column densities (dSCDs) and estimate the tropospheric background mixing ratios of different trace gases, including NO2, SO2, HCHO, and BrO, using the measured spectra at WLG. Averaging of 10 original spectra is found to be an “optimum option” for reducing both the statistical error of the spectral retrieval and systematic errors in the analysis. The dSCDs of NO2, SO2, HCHO, and BrO under clear-sky and low-aerosol-load conditions are extracted from measured spectra at different elevation angles at WLG. By performing radiative transfer simulations with the model TRACY-2, we establish approximate relationships between the trace gas dSCDs at 1∘ elevation angle and the corresponding average tropospheric background volume mixing ratios. Mixing ratios of these trace gases in the lower troposphere over WLG are estimated to be in a range of about 7 ppt (January) to 100 ppt (May) for NO2, below 0.5 ppb for SO2, between 0.4 and 0.9 ppb for HCHO, and lower than 0.3 ppt for BrO. The chemical box model simulations constrained by the NO2 concentration from our MAX-DOAS measurements show that there is a little net ozone loss (−0.8 ppb d−1) for the free-tropospheric conditions and a little net ozone production (0.3 ppb d−1) for the boundary layer conditions over WLG during summertime. Our study provides valuable information and data sets for further investigating tropospheric chemistry in the background atmosphere and its links to anthropogenic activities.

In September 2016, 36 spectrometers from 24 institutes measured a number of key atmospheric pollutants for a period of 17 d during the Second Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) that took place at Cabauw, the Netherlands (51.97∘ N, 4.93∘ E). We report on the outcome of the formal semi-blind intercomparison exercise, which was held under the umbrella of the Network for the Detection of Atmospheric Composition Change (NDACC) and the European Space Agency (ESA). The three major goals of CINDI-2 were (1) to characterise and better understand the differences between a large number of multi-axis differential optical absorption spectroscopy (MAX-DOAS) and zenith-sky DOAS instruments and analysis methods, (2) to define a robust methodology for performance assessment of all participating instruments, and (3) to contribute to a harmonisation of the measurement settings and retrieval methods. This, in turn, creates the capability to produce consistent high-quality ground-based data sets, which are an essential requirement to generate reliable long-term measurement time series suitable for trend analysis and satellite data validation. The data products investigated during the semi-blind intercomparison are slant columns of nitrogen dioxide (NO2), the oxygen collision complex (O4) and ozone (O3) measured in the UV and visible wavelength region, formaldehyde (HCHO) in the UV spectral region, and NO2 in an additional (smaller) wavelength range in the visible region. The campaign design and implementation processes are discussed in detail including the measurement protocol, calibration procedures and slant column retrieval settings. Strong emphasis was put on the careful alignment and synchronisation of the measurement systems, resulting in a unique set of measurements made under highly comparable air mass conditions. The CINDI-2 data sets were investigated using a regression analysis of the slant columns measured by each instrument and for each of the target data products. The slope and intercept of the regression analysis respectively quantify the mean systematic bias and offset of the individual data sets against the selected reference (which is obtained from the median of either all data sets or a subset), and the rms error provides an estimate of the measurement noise or dispersion. These three criteria are examined and for each of the parameters and each of the data products, performance thresholds are set and applied to all the measurements. The approach presented here has been developed based on heritage from previous intercomparison exercises. It introduces a quantitative assessment of the consistency between all the participating instruments for the MAX-DOAS and zenith-sky DOAS techniques.

We present different methods for in-field elevation calibration of MAX-DOAS (Multi AXis Differential Optical Absorption Spectroscopy) instruments that were applied and inter-compared during the second Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2). One necessary prerequisite of consistent MAX-DOAS retrievals is a precise and accurate calibration of the elevation angles of the different measuring systems. Therefore, different methods for this calibration were applied to several instruments during the campaign, and the results were inter-compared. This work first introduces and explains the different methods, namely far- and near-lamp measurements, white-stripe scans, horizon scans and sun scans, using data and results for only one (mainly the Max Planck Institute for Chemistry) instrument. In the second part, the far-lamp measurements and the horizon scans are examined for all participating groups. Here, the results for both methods are first inter-compared for the different instruments; secondly, the two methods are compared amongst each other. All methods turned out to be well-suited for the calibration of the elevation angles of MAX-DOAS systems, with each of them having individual advantages and drawbacks. Considering the results of this study, the systematic uncertainties of the methods can be estimated as ±0.05∘ for the far-lamp measurements and the sun scans, ±0.25∘ for the horizon scans, and around ±0.1∘ for the white-stripe and near-lamp measurements. When comparing the results of far-lamp and horizon-scan measurements, a spread of around 0.9∘ in the elevation calibrations is found between the participating instruments for both methods. This spread is of the order of a typical field of view (FOV) of a MAX-DOAS instrument and therefore affecting the retrieval results. Further, consistent (wavelength dependent) offsets of 0.32∘ and 0.40∘ between far-lamp measurements and horizon scans are found, which can be explained by the fact that, despite the flat topography around the measurement site, obstacles such as trees might mark the visible horizon during daytime. The observed wavelength dependence can be explained by surface albedo effects. Lastly, the results are discussed and recommendations for future campaigns are given.

We improved lake mixing process simulations by applying a vertical mixing scheme, K profile parameterization (KPP), in the Community Land Model (CLM) version 4.5, developed by the National Center for Atmospheric Research. Vertical mixing of the lake water column can significantly affect heat transfer and vertical temperature profiles. However, the current vertical mixing scheme in CLM requires an arbitrarily enlarged eddy diffusivity to enhance water mixing. The coupled CLM-KPP considers a boundary layer for eddy development, and in the lake interior water mixing is associated with internal wave activity and shear instability. We chose a lake in Arctic Alaska and a lake on the Tibetan Plateau to evaluate this improved lake model. Results demonstrated that CLM-KPP reproduced the observed lake mixing and significantly improved lake temperature simulations when compared to the original CLM. Our newly improved model better represents the transition between stratification and turnover. This improved lake model has great potential for reliable physical lake process predictions and better ecosystem services.

Data assimilation algorithms rely on a basic assumption of an unbiased observation error. However, the presence of inconsistent measurements with nontrivial biases or inseparable baselines is unavoidable in practice. Assimilation analysis might diverge from reality since the data assimilation itself cannot distinguish whether the differences between model simulations and observations are due to the biased observations or model deficiencies. Unfortunately, modeling of observation biases or baselines which show strong spatiotemporal variability is a challenging task. In this study, we report how data-driven machine learning can be used to perform observation bias correction for data assimilation through a real application, which is the dust emission inversion using PM10 observations. PM10 observations are considered unbiased; however, a bias correction is necessary if they are used as a proxy for dust during dust storms since they actually represent a sum of dust particles and non-dust aerosols. Two observation bias correction methods have been designed in order to use PM10 measurements as proxy for the dust storm loads under severe dust conditions. The first one is the conventional chemistry transport model (CTM) that simulates life cycles of non-dust aerosols. The other one is the machine-learning model that describes the relations between the regular PM10 and other air quality measurements. The latter is trained by learning using 2 years of historical samples. The machine-learning-based non-dust model is shown to be in better agreement with observations compared to the CTM. The dust emission inversion tests have been performed, through assimilating either the raw measurements or the bias-corrected dust observations using either the CTM or machine-learning model. The emission field, surface dust concentration, and forecast skill are evaluated. The worst case is when we directly assimilate the original observations. The forecasts driven by the a posteriori emission in this case even result in larger errors than the reference prediction. This shows the necessities of bias correction in data assimilation. The best results are obtained when using the machine-learning model for bias correction, with the existing measurements used more precisely and the resulting forecasts close to reality.

Dissolved inorganic nitrogen (DIN), including nitrate, nitrite and ammonium, frequently acts as the limitation for primary productivity. Our study focused on the transport and transformation of DIN in a tropical estuary, i.e., the Rajang River estuary, in Borneo, Malaysia. Three cruises were conducted in August 2016 and February–March and September 2017, covering both dry and wet seasons. Before entering the coastal delta, decomposition of the terrestrial organic matter and the subsequent soil leaching was assumed to be the main source of DIN in the river water. In the estuary, decomposition of dissolved organic nitrogen was an additional DIN source, which markedly increased DIN concentrations in August 2016 (dry season). In the wet season (February 2017), ammonium concentrations showed a relatively conservative distribution during the mixing, and the nitrate addition was weak. La Niña events induced high precipitations and discharge rates, decreased reaction intensities of ammonification and nitrification. Hence similar distribution patterns of DIN species in the estuary were found in September 2017 (end of the dry season). The magnitude of riverine DIN flux varied between 77.2 and 101.5 t N d−1, which might be an important support for the coastal primary productivity.

In order to meet the high precision requirement in various mechanical applications, ball screws are being lapped as a finishing process to improve the travel variation and surface quality. However, the existing manufacturing method is labor intensive. It depends on an operator's skill and experience and is very complex, time consuming with low production efficiency. The aim of this study is finding a modified lapping method to improve the lapping effect, increase efficiency and change the present labor-intensive situation. In addition, the effects of main lapping factors on the surface quality of a ball screw will be investigated, such as friction torque, rotational speed, abrasive particulate size and lapping time. A new lapping tool was installed on a specially designed friction torque test machine, which can control the rotation of a screw and monitor the friction torque applied on the screw dynamically. A set of orthogonal experiments were conducted on the test machine and the travel variation, surface roughness and residual stress of tested screws were measured after lapping. The measurement results of these three parameters were used to evaluate the lapping effect. Compared with the conventional lapping method, the lapping time of the present method was remarkably shorter, with only tens of minutes to get a good lapping result.

Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90) performed better than LiDAR-metrics method (OA = 79.86 %, Kc = 0.81) and spectral-metircs method (OA = 71.26, Kc = 0.69) in terms of classification accuracy, which indicated that the advanced method of data processing and sensitive feature selection are critical for improving the accuracy of crown-level tree species classification.