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Zhi mu (Anemarrhena asphodeloides Bunge.) is an important medicinal plant in China, and its growth is greatly threatened by the fungal disease anthracnose, which can be caused by a number of species of Colletotrichum. Studying the phytopathogen and its sensitivities to fungicides will help to control anthracnose on zhi mu. Ten isolates of Colletotrichum were collected from zhi mu in Hebei, Anhui and Shanxi Provinces in China for this study. The isolates were identified as C. gloeosporioides, C. liriopes and C. spaethianum based on multi‐locus phylogenetic analyses, including ACT, CHS‐1, GADPH, HIS3 and ITS, micromorphological characteristics and the characteristics of colonies. Koch's postulates were confirmed by inoculating the leaves of zhi mu in vitro. The levels of virulence of 10 isolates varied significantly. Isolate YC‐1 was the most virulent, while BZ‐2 was the least virulent. The sensitivity of isolates YC‐1, BY‐1 and BY‐2 (C. spaethianum, C. gloeosporioides and C. liriopes respectively) to fungicides were examined. The EC50 values of BY‐1, BY‐2, and YC‐1 to tebuconazole, difenoconazole, prochloraz, fludioxonil, pyraclostrobin, carbendazim and azoxystrobin confirm that all of the isolates were markedly sensitive to the seven fungicides tested, strongly suggesting that these fungicides could be used to control anthracnose on zhi mu in the field. [ABSTRACT FROM AUTHOR]

PM 2.5 is the main component of most haze, and the presence of high concentrations of PM 2.5 in the air for an extended time can cause serious effects on human health, so there is an urgent need for research work related to PM 2.5. Traditional PM 2.5 monitoring uses ground-based monitoring stations with low spatial resolution. Other studies have retrieved the Moderate Resolution Imaging Spectroradiometer aerosol optical depth product by the dark-target algorithm. However, the estimated PM 2.5 concentration on the ground will produce missing values, which will lead to the reduction of spatial and temporal resolution. Based on this, this study proposes a machine learning algorithm to estimate PM 2.5 by using the fifth generation reanalysis (ERA5) data set published by the European Center for Medium-range Weather Forecasts (ECMWF). In this study, two different methods of back propagation neural network (BPNN) and random forest (RF) were used to develop the models. Firstly, the meteorological parameters (precipitable water vapor, water vapor pressure and relative humidity, etc.) and pollution parameters (O 3 , CO, NO 2 , SO 2 , PM 10 , and PM 2.5) were used to establish PM 2.5 model in 2021. The results showed that the R2 and RMSE for BPNN and RF were 0.94/0.96 and 10.37/8.77 µg/m3, respectively. Then, due to the lack and the low spatial resolution of the pollution parameters, using only the ERA5 meteorological data with the high spatiotemporal resolution to develop the PM 2.5 model in winter, the R2 of the RF model (0.93) was 0.05 higher and the RMSE (12.50 µg/m3) was 4.19 µg/m3 lower than that of the BPNN model, which indicates that it is feasible to develop the PM 2.5 model using only meteorological parameters. Finally, using the RF model of the second stage and ERA5 meteorological data with a spatial resolution of 0.05° (obtained by cubic spline interpolation) to generate the hourly PM 2.5 map of Beijing and compare it with China High Air Pollutants dataset, the R2 and RMSE of Beijing were 0.78 µg/m3 and 14.85 µg/m3, respectively. On this basis, it is found that the areas with high PM 2.5 concentration are close to the areas with serious pollution in Hebei Province by analyzing the PM 2.5 map of Beijing, and area transport and human activities are important sources of air pollution. [ABSTRACT FROM AUTHOR]

Remote sensing retrieval of foliar biochemical traits via inversion of radiative transfer models is hampered by the uncertainties arising from the ill-posed problem of insufficient remote sensing information for the inversion. The use of leaf transmittance in addition to leaf reflectance in retrieving leaf traits could be a key step to alleviate this issue. To achieve this objective, our study introduces a novel theoretical framework for the simultaneous retrieval of leaf directional-hemispherical reflectance and transmittance, leveraging multi-angular optical observations at the canopy level. This framework partitions the canopy's spectral reflectance in the range of 400 nm to 2500 nm into two distinct components, each aligning with the contributions of 1st-order and multiple-order scatterings of radiation in the canopy. At a specific illumination-observation (I O) angle, these two scattering components are assumed to be determined by leaf hemispherical reflectance and scattering albedo (i.e., the sum of leaf reflectance and transmittance). The average leaf directional-hemispherical reflectance and transmittance can be estimated from combined canopy reflectance observed under different I O angles. The proposed framework was implemented using a single site data collected on August 19th, 2018, in Saihanba National Forest Park, Hebei, China. A comprehensive dataset was compiled, including hyperspectral images of a dense white birch canopy acquired at 8 different illumination-observation (I O) angles. This dataset was augmented with corresponding canopy structural traits, including leaf area index, diameter at breast height, crown radius, tree number, and domain size. Additionally, terrain features such as slope and aspect, along with leaf spectra and background spectra, were included in this extensive dataset. The geometrical-optical model GOST2 was utilized to establish the relationships between canopy reflectance and leaf reflectance and transmittance. Initially, the performance of GOST2 was evaluated, followed by the construction of a leaf spectral dataset generated by PROSPECT-D and input into GOST2 to construct the LUT. Subsequently, leaf reflectance and transmittance were estimated for different combinations of I O angles. The results demonstrated a close agreement between the canopy reflectance factors modelled by GOST2 and the observations obtained from UAV. Overall, leaf reflectance was more accurately retrieved than leaf transmittance across the entire spectrum of interest, primarily due to the significant influence of the 1st-order scattering in canopy reflectance. In the near-infrared region, where the magnitudes of 1st- and multiple-order scatterings were similar, the proposed framework produced satisfactory leaf transmittance with a relative error of <10% when compared to the measured reference data. Furthermore, the results indicated that the absolute error of model simulations should be less than half of the multiple-order scattering, which is particularly stringent for wavelengths characterized by strong absorption features. The proposed framework holds potential for reducing uncertainties in various fields, including global mappings of leaf biochemical traits. • A framework is developed for simultaneous retrieval of leaf DHR and DHT. • The framework is tested using multiangle UAV data collected over a forest. • The retrieval of leaf DHT is based on multiple scattering of the canopy. • The retrieved leaf transmittance is more reliable in near-infrared. • Including leaf transmittance can improve leaf traits estimation. [ABSTRACT FROM AUTHOR]

Background: Quantifying CO2 emissions from cities is of great importance because cities contribute more than 70% of the global total CO2 emissions. As the largest urbanized megalopolis region in northern China, the Beijing-Tianjin-Hebei (Jing-Jin-Ji, JJJ) region (population: 112.7 million) is under considerable pressure to reduce carbon emissions. Despite the several emission inventories covering the JJJ region, a comprehensive evaluation of the CO2 emissions at the prefectural city scale in JJJ is still limited, and this information is crucial to implementing mitigation strategies. Results: Here, we collected and analyzed 8 published emission inventories to assess the emissions and uncertainty at the JJJ city level. The results showed that a large discrepancy existed in the JJJ emissions among downscaled country-level emission inventories, with total emissions ranging from 657 to 1132 Mt CO2 (or 849 ± 214 for mean ± standard deviation (SD)) in 2012, while emission estimates based on provincial-level data estimated emissions to be 1038 and 1056 Mt. Compared to the mean emissions of city-data-based inventories (989 Mt), provincial-data-based inventories were 6% higher, and national-data-based inventories were 14% lower. Emissions from national-data-based inventories were 53–75% lower in the high-emitting industrial cities of Tangshan and Handan, while they were 47–160% higher in Beijing and Tianjin than those from city-data-based inventories. Spatially, the emissions pattern was consistent with the distribution of urban areas, and urban emissions in Beijing contributed 50–70% of the total emissions. Higher emissions from Beijing and Tianjin resulted in lower estimates of prefectural cities in Hebei for some national inventories. Conclusions: National-level data-based emission inventories produce large differences in JJJ prefectural city-level emission estimates. The city-level statistics data-based inventories produced more consistent estimates. The consistent spatial distribution patterns recognized by these inventories (such as high emissions in southern Beijing, central Tianjin and Tangshan) potentially indicate areas with robust emission estimates. This result could be useful in the efficient deployment of monitoring instruments, and if proven by such measurements, it will increase our confidence in inventories and provide support for policy makers trying to reduce emissions in key regions. [ABSTRACT FROM AUTHOR]