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Near-surface air temperature (Ta) estimation by geostationary meteorological satellites is mainly carried out under clear-sky conditions. In this study, we propose an all-weather Ta estimation method utilizing FY-4A Advanced Geostationary Radiation Imager (AGRI) and the Global Forecast System (GFS), along with additional auxiliary data. The method includes two neural-network-based Ta estimation models for clear and cloudy skies, respectively. For clear skies, AGRI LST was utilized to estimate the Ta (Ta,clear), whereas cloud top temperature and cloud top height were employed to estimate the Ta for cloudy skies (Ta,cloudy). The estimated Ta was validated using the 2020 data from 1211 stations in China, and the RMSE values of the Ta,clear and Ta,cloudy were 1.80 °C and 1.72 °C, while the correlation coefficients were 0.99 and 0.986, respectively. The performance of the all-weather Ta estimation model showed clear temporal and spatial variation characteristics, with higher accuracy in summer (RMSE = 1.53 °C) and lower accuracy in winter (RMSE = 1.88 °C). The accuracy in southeastern China was substantially better than in western and northern China. In addition, the dependence of the accuracy of the Ta estimation model for LST, CTT, CTH, elevation, and air temperature were analyzed. The global sensitivity analysis shows that AGRI and GFS data are the most important factors for accurate Ta estimation. The AGRI-estimated Ta showed higher accuracy compared to the ERA5-Land data. The proposed models demonstrated potential for Ta estimation under all-weather conditions and are adaptable to other geostationary satellites. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) is one of the most dynamic components of the atmosphere, playing a critical role in precipitation formation, the hydrological cycle, and climate change. This study used SuomiNet Global Positioning System (GPS) data from April 2021 to June 2023 in the United States to comprehensively evaluate 3 and 6 h Global Forecast System (GFS) PWV products (i.e., PWV3h and PWV6h). There was high consistency between the GFS PWV and GPS PWV data, with correlation coefficients (Rs) higher than 0.98 and a root mean square error (RMSE) of about 0.23 cm. The PWV3h product performed slightly better than PWV6h. PWV tended to be underestimated when PWV > 4 cm, and the degree of underestimation increased with increasing water vapor value. The RMSE showed obvious seasonal and diurnal variations, with the RMSE value in summer (i.e., 0.280 cm) considerably higher than in winter (i.e., 0.158 cm), and nighttime were RMSEs higher than daytime RMSEs. Clear-sky conditions showed smaller RMSEs, while cloudy-sky conditions exhibited a smaller range of monthly RMSEs and higher Rs. PWV demonstrated a clear spatial pattern, with both Rs and RMSEs decreasing with increasing elevation and latitude. Based on these temporal and spatial patterns, Back Propagation neural network and random forest (RF) models were employed, using PWV, Julian day, and geographic information (i.e., latitude, longitude, and elevation) as input data to correct the GFS PWV products. The results indicated that the RF model was more advantageous for water vapor correction, improving overall accuracy by 12.08%. In addition, the accuracy of GFS PWV forecasts during hurricane weather was also evaluated. In this extreme weather, the RMSE of the GFS PWV forecast increased comparably to normal weather, but it remained less than 0.4 cm in most cases. [ABSTRACT FROM AUTHOR]

Aerosol research is important for the protection of the ecological environment, the improvement of air quality, and as a response to climate change. In this study, a random forest (RF) estimation model of aerosol optical depth (AOD) and extinction coefficient vertical profiles was, respectively, established using Orbiting Carbon Observatory-2 (OCO-2) oxygen-A band (O2 A-band) data from China and its surrounding areas in 2016, combined with geographical information (longitude, latitude, and elevation) and viewing angle data. To address the high number of OCO-2 O2 A-band channels, principal component analysis (PCA) was employed for dimensionality reduction. The model was then applied to estimate the aerosol extinction coefficients for the region in 2017, and its validity was verified by comparing the estimated values with the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 extinction coefficients. In the comprehensive analysis of overall performance, an AOD model was initially constructed using variables, achieving a correlation coefficient (R) of 0.676. Subsequently, predictions for aerosol extinction coefficients were generated, revealing a satisfactory agreement between the predicted and the actual values in the vertical direction, with an R of 0.535 and a root mean square error (RMSE) of 0.107 km−1. Of the four seasons of the year, the model performs best in autumn (R = 0.557), while its performance was relatively lower in summer (R = 0.442). Height had a significant effect on the model, with both R and RMSE decreasing as height increased. Furthermore, the accuracy of aerosol profile inversion shows a dependence on AOD, with a better accuracy when AOD is less than 0.3 and RMSE can be less than 0.06 km−1. [ABSTRACT FROM AUTHOR]

Air pollution is a hotspot of wide concern in Chinese cities. With the worsening of air pollution, urban agglomerations face an increasingly complex environment for air quality monitoring, hindering sustainable and high-quality development in China. More effective methods for predicting air quality are urgently needed. In this study, we employed seven single models and ensemble learning algorithms and constructed a hybrid learning algorithm, the LSTM-SVR model, totaling eight machine learning algorithms, to predict the Air Quality Index in six major urban agglomerations in China. We comprehensively compared the predictive performance of the eight algorithmic models in different urban agglomerations. The results reveal that, in areas with higher levels of air pollution, the situation for model prediction is more complicated, leading to a decline in predictive accuracy. The constructed hybrid model LSTM-SVR demonstrated the best predictive performance, followed by the ensemble model RF, both of which effectively enhanced the predictive accuracy in heavily polluted areas. Overall, the predictive performance of the hybrid and ensemble models is superior to that of the single-model prediction methods. This study provides AI technological support for air quality prediction in various regions and offers a more comprehensive discussion of the performance differences between different types of algorithms, contributing to the practical application of air pollution control. [ABSTRACT FROM AUTHOR]

l-threonine is an essential amino acid in human and animal nutrition. It is widely used in food, medicine, feed, and other fields. The global market scale exceeds 700,000 tons per year. Corynebacterium glutamicum, as a chassis cell for industrial amino acid production, has the advantages of biological safety and strong environmental adaptability, and is a potential strain for the efficient production of l-threonine. This article systematically reviewed the function and application of l-threonine, the pathway of C. glutamicum to synthesize l-threonine, and the use of metabolic engineering technology to improve the production of l-threonine. [ABSTRACT FROM AUTHOR]

The escalating issue of air pollution in China's rapidly developing urban areas has prompted increased attention to the role of meteorological conditions in PM2.5 pollution. This study examines the spatiotemporal distribution of PM2.5 concentrations and their relationship with meteorological factors in six major Chinese urban agglomerations from 2017 to 2020, using daily average data. Statistical and spatial analysis techniques are employed, alongside the construction of eight machine learning models for prediction purposes. The study also compares the feature importance of various meteorological factors impacting PM2.5 concentrations. Results reveal significant regional differences in both average PM2.5 levels and meteorological influences. The Multilayer Perceptron (MLP) model demonstrates the highest prediction accuracy for PM2.5 concentrations. According to the MLP model's feature importance identification, temperature is the most significant factor affecting PM2.5 concentrations across all urban agglomerations, while wind speed and precipitation have the least impact. Contributions from air pressure and dew point temperature, however, vary among different urban agglomerations. This research considers the impact of urban agglomerations and meteorological conditions on PM2.5 and also offers valuable artificial intelligence-based insights into the key meteorological factors influencing PM2.5 concentrations in diverse regions, thereby informing the development of effective air pollution control policies. [ABSTRACT FROM AUTHOR]

It is widely known that root morphology has different response mechanisms at different phosphorus (P) levels. However, the effects of P application times on wheat (Triticum aestivum L.) root morphology and, therefore, on growth and nutrient uptake are still unclear. In this study, we investigated the feedback from the physiological indexes of wheat on different P application times after planting. Compared with the P application at planting, the shoot biomass with delayed P application for 21 days (d) increased by 11.8% (p < 0.05). Compared with the P application at planting, the shoot P uptake with delayed P application for 14–21 days increased by 38.4–71.2%, while the shoot N uptake and K uptake with delayed P application for 21 days increased by 16.0% and 14.1% (p < 0.05). Compared with the P application at planting, P use efficiency, N use efficiency, and K use efficiency with delayed P application for 21 days increased by 16.4%, 12.4%, and 12.4%. Delayed P application for 14–21 days promoted wheat shoot growth, nutrient (P, N, and K) uptake, and their use efficiencies. This is particularly important for optimizing the P fertilizer input and nutrient management for wheat growth. [ABSTRACT FROM AUTHOR]

During the early seedling stage, rice (Oryza sativa L.) must overcome low-temperature stress. While a few cold-tolerance genes have been characterized, further excavation of cold-resistance genes is still needed. In this study, we identified a cold-induced transcription factor—LUX ARRHYTHMO (LUX)—in rice. OsLUX was found to be specifically expressed in leaf blades and upregulated by both cold stress and circadian rhythm. The full-length OsLUX showed autoactivation activity, and the OsLUX protein localized throughout the entire onion cell. Overexpressing OsLUX resulted in increased cold tolerance and reduced ion leakage under cold-stress conditions during the seedling stage. In contrast, the knockout of OsLUX decreased seedling cold tolerance and showed higher ion leakage compared to the wild type. Furthermore, overexpressing OsLUX upregulated the expression levels of oxidative stress-responsive genes, which improved reactive oxygen species (ROS) scavenging ability and enhanced tolerance to chilling stress. Promoter analysis showed that the OsLUX promoter contains two dehydration-responsive element binding (DREB) motifs at positions −510/−505 (GTCGGa) and −162/−170 (cCACCGccc), which indicated that OsDREB1s and OsDREB2s probably regulate OsLUX expression by binding to the motif to respond to cold stress. Thus, OsLUX may act as a downstream gene of the DREB pathway. These results demonstrate that OsLUX serves as a positive regulatory factor of cold stress and that overexpressing OsLUX could be used in rice breeding programs to enhance abiotic stress tolerance. [ABSTRACT FROM AUTHOR]

Soil-dissolved organic matter (DOM) drives the carbon (C) and nitrogen (N) cycles in agroecosystems. Despite many studies on DOM dynamics, hardly any attention has been directed toward DOM quality, particularly DOM composition. The aim of this study was to elucidate how C and N management practices alter soil water-extractable organic matter (WEOM) in a loess soil agroecosystem. Field experiments were conducted with a winter wheat monoculture. Three N fertilization rates (0, 120, and 240 kg ha−1 year−1) were applied for 17 years (2002–2019), combined with five C practices (zero, low, and high rates of sheep manure or wheat straw) for three years (2016–2019). The results reveal that soil organic carbon (SOC) and water-extractable organic carbon (WEOC) concentrations in the topsoil (0–20 cm) were increased by organic amendments considerably but were not affected by N fertilization. The fluorescence excitation–emission matrix spectra (EEM) of WEOM were resolved to two humic-like components (C1 and C2) and two soluble microbial byproduct-like components (C3 and C4). The proportions of C1 and C2 were increased, while the proportion of C3 was decreased by both C and N management practices. In conclusion, organic amendments increased both WEOM quality and its proportion of humic-like components, whereas N fertilization increased the proportion of humic-like components without variations of WEOM quality in the topsoil of loess soil. [ABSTRACT FROM AUTHOR]