Increasing but Variable Trend of Surface Ozone in the Yangtze River Delta Region of China

Surface ozone (O-3) increased by similar to 20% in the Yangtze River Delta (YRD) region of China during 2014-2020, but the aggravating trend is highly variable on interannual time and city-level space scales. Here, we employed multiple air quality observations and numerical simulation to describe the increasing but variable trend of O-3 and to reveal the main driving factors behind it. In 2014-2017, the governmental air pollution control action plan was mostly against PM2.5 (mainly to control the emissions of SO2, NOx, and primary PM2.5) and effectively reduced the PM2.5 concentration by 18%-45%. However, O-3 pollution worsened in the same period with an increasing rate of 4.9 mu g m(-3) yr(-1), especially in the Anhui province, where the growth rate even reached 14.7 mu g m(-3) yr(-1). After 2018, owing to the coordinated prevention and control of both PM2.5 and O-3, volatile organic compound (VOC) emissions in the YRD region has also been controlled with a great concern, and the O-3 aggravating trend in the same period has been obviously alleviated (1.1 mu g m(-3) yr(-1)). We further combined the precursor concentration and the corresponding O-3 formation regime to explain the observed trend of O-3 in 2014-2020. The leading O-3 formation regime in 2014-2017 is diagnosed as VOC-limited (21%) or mix-limited (58%), with the help of a simulated indicator HCHO/NOy. Under such condition, the decreasing NO2 (2.8% yr(-1)) and increasing VOCs (3.6% yr(-1)) in 2014-2017 led to a rapid increment of O-3. With the continuous reduction in NOx emission and further in ambient NOx/VOCs, the O-3 production regime along the Yangtze River has been shifting from VOC-limited to mix-limited, and after 2018, the mix-limited regime has become the dominant O-3 formation regime for 55% of the YRD cities. Consequently, the decreases of both NOx (3.3% yr(-1)) and VOCs (7.7% yr(-1)) in 2018-2020 obviously slowed down the aggravating trend of O-3. Our study argues that with the implementation of coordinated regional reduction of NOx and VOCs, an effective O-3 control is emerging in the YRD region.Peer reviewe

On-road emission inventory and its potential impact on secondary pollutant in China

After 2013, the environmental protection department in China has significantly reduced on-road emission through the upgrade of emission standards, the improvement of fuel quality and economic tools. However, the specific effect of the control policies on emission and air quality is still difficult to quantify. This is mainly due to the data shortage on vehicle emission factors and vehicle activities. In this research, we developed the 2008-2018 on-road emissions inventory based on Emission Inventory Preparation Guide (GEI) and existing vehicle activity database. Our estimates suggest that CO and PM2.5 showed a relatively significant decrease, by 66.2% and 58.8%. However, the trend of NOx (5.8%) and NMVOC (-4.8%) was relatively stable. The Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Sichuan Basin (SCB) regions all showed a uniform trend especially in NOx. For Beijing-Tianjin-Hebei, the significant decline in NOx might be caused by earlier implementations in emission standard and fuel quality. In addition to this, we designed additional evaporation emission scenarios to verify the application of GEI in quantify emission impact on secondary pollutant (PM2.5 and O3). The results indicate that evaporation emission contributed to Maximum Daily Average 8-hour (MDA8) O3 concentration by about 3.5%, for Beijing, Shanghai and Nanjing. This value can reach up to 5.9%, 5.3% and 7.3%, but the impact on PM2.5is extremely limited. Our results indicate the feasibility of GEI in improving and lowering the technical barrier of on-road emission inventory establishment at the same time and its further application in quantifying on-road emission contribution to air quality. Besides that, it shows a strong potential in on-road policy environmental assessment and short-term air quality assessment

Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change

Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change

Effect of Hydrofluoric Acid Etching on Performance of Si/C Composite as Anode Material for Lithium-Ion Batteries

The effect of hydrofluoric acid (HF) etching on the performance of Si/C anode was extensively studied in terms of the structural stability, morphology, element distribution, and electrochemical properties. XRD results show that the diffraction peaks of silicon got weakened after being etched by HF. SEM images reveal that the morphology of the composite became coarse after being etched by HF. EDS mapping illustrates the distribution of elements before and after HF etching. Electrochemical studies show that HF etching can improve the cycling performance of Si/C composite but exhibit a deleterious effect on capacity. The results indicate that HF etching could be a promising method for enhancing the performance of silicon-based materials

Alterations of Endogenous Hormones, Antioxidant Metabolism, and Aquaporin Gene Expression in Relation to γ-Aminobutyric Acid-Regulated Thermotolerance in White Clover

Persistent high temperature decreases the yield and quality of crops, including many important herbs. White clover (Trifolium repens) is a perennial herb with high feeding and medicinal value, but is sensitive to temperatures above 30 °C. The present study was conducted to elucidate the impact of changes in endogenous γ-aminobutyric acid (GABA) level by exogenous GABA pretreatment on heat tolerance of white clover, associated with alterations in endogenous hormones, antioxidant metabolism, and aquaporin-related gene expression in root and leaf of white clover plants under high-temperature stress. Our results reveal that improvement in endogenous GABA level in leaf and root by GABA pretreatment could significantly alleviate the damage to white clover during high-temperature stress, as demonstrated by enhancements in cell membrane stability, photosynthetic capacity, and osmotic adjustment ability, as well as lower oxidative damage and chlorophyll loss. The GABA significantly enhanced gene expression and enzyme activities involved in antioxidant defense, including superoxide dismutase, catalase, peroxidase, and key enzymes of the ascorbic acid–glutathione cycle, thus reducing the accumulation of reactive oxygen species and the oxidative injury to membrane lipids and proteins. The GABA also increased endogenous indole-3-acetic acid content in roots and leaves and cytokinin content in leaves, associated with growth maintenance and reduced leaf senescence under heat stress. The GABA significantly upregulated the expression of PIP1-1 and PIP2-7 in leaves and the TIP2-1 expression in leaves and roots under high temperature, and also alleviated the heat-induced inhibition of PIP1-1, PIP2-2, TIP2-2, and NIP1-2 expression in roots, which could help to improve the water transportation and homeostasis from roots to leaves. In addition, the GABA-induced aquaporins expression and decline in endogenous abscisic acid level could improve the heat dissipation capacity through maintaining higher stomatal opening and transpiration in white clovers under high-temperature stress

Effect of Bi³⁺ Doping on the Electronic Structure and Thermoelectric Properties of (Sr_0.889-xLa_0.111Bi_x)TiO_2.963: First-Principles Calculations

The electronic structure and thermoelectric properties of Bi3+-doped (Sr0.889-xLa0.111Bix)TiO2.963 were studied by the first principles method. Doping Bi3+ can increase the cell parameters, cell asymmetry and band gap. With increasing Bi3+ content, the asymmetry of DOS relative to the Fermi level increases, which results in an enhanced Seebeck coefficient, increasing carrier mobility and decreasing carrier concentration. An appropriate Bi3+-doping concentration (7.4–14.8%) can increase the lattice distortion and reduce the lattice thermal conductivity of the material. An appropriate Bi3+-doping concentration (7.4%) can effectively optimize the electrical transport performance and improve the thermoelectric properties of strontium titanate. The optimal Bi3+-doping concentration is 7.4%, and Sr0.815La0.111Bi0.074TiO2.963 obtains a maximum ZT of 0.48. This work shows the mechanism of Bi3+ doping in enhancing the thermoelectric properties of strontium titanate

Detection of Common Anatomical Landmarks and Vertical Trajectories for Freehand Pedicle Screw Placement

Objective It is clinically important for pedicle screws to be placed quickly and accurately. Misplacement of pedicle screws results in various complications. However, the incidence of complications varies greatly due to the different professional titles of physicians and surgical experience. Therefore, physicians must minimize pedicle screw dislocation. This study aims to compare the three nail placement methods in this study, and explore which method is the best for determining the anatomical landmarks and vertical trajectories. Methods This study involved 70 patients with moderate idiopathic scoliosis who had undergone deformity correction surgery between 2018 and 2021. Two spine surgeons used three techniques (preoperative computed tomography scan [CTS], visual inspection‐X‐freehand [XFH], and intraoperative detection [ID] of anatomical landmarks) to locate pedicle screws. The techniques used include visual inspection for 287 screws in 21 patients, preoperative planning for 346 screws in 26 patients, and intraoperative probing for 309 screws in 23 patients. Observers assessed screw conditions based on intraoperative CT scans (Grade A, B, C, D). Results There were no significant differences between the three groups in terms of age, sex, and degree of deformity. We found that 68.64% of screws in the XFH group, 67.63% in the CTS group, and 77.99% in the ID group were placed within the pedicle margins (grade A). On the other hand, 6.27% of screws in the XFH group, 4.33% in the CTS group, and 6.15% in the ID group were considered misplaced (grades C and D). The results show that the total amount of upper thoracic pedicle screws was fewer, meanwhile their placement accuracy was lower. The three methods used in this study had similar accuracy in intermediate physicians (P > 0.05). Compared with intermediate physicians, the placement accuracy of three techniques in senior physicians was higher. The intraoperative detection group was better than the other two groups in the good rate and accuracy of nail placement (P < 0.05). Conclusion Intraoperative common anatomical landmarks and vertical trajectories were beneficial to patients with moderate idiopathic scoliosis undergoing surgery. It is an optimal method for clinical application

Is the efficacy of satellite-based inversion of SO2 emission model dependent?

AbstractSatellite-based inverse modeling has the potential to drive aerosol precursor emissions, but its efficacy for improving chemistry transport models (CTMs) remains elusive because of its likely inherent dependence on the error characteristics of a specific CTM used for the inversion. This issue is quantitively assessed here by using three CTMs. We show that SO2 emissions from global GEOS-Chem adjoint model and OMI SO2 data, when combined with spatial variation of bottom-up emissions, can largely improve WRF-Chem and WRF-CMAQ forecast of SO2 and aerosol optical depth (in reference to moderate resolution imaging spectroradiometer data) in China. This suggests that the efficacy of satellite-based inversion of SO2 emission appears to be high for CTMs that use similar or identical emission inventories. With the advent of geostationary air quality monitoring satellites in next 3 years, this study argues that an era of using top-down approach to rapidly update emission is emerging for regional air quality forecast, especially over Asia having highly varying emissions