The rapid acquisition of high-resolution spatial distribution of soil organic matter (SOM) at the field scale is essential for precision agriculture. The UAV imaging hyperspectral technology, with its high spatial resolution and timeliness, can fill the research gap between ground-based monitoring and remote sensing. This study aimed to test the feasibility of using UAV hyperspectral data (400–1000 nm) with a small-sized calibration sample set for mapping SOM at a 1 m resolution in typical low-relief black soil areas of Northeast China. The experiment was conducted in an approximately 20 ha field. For calibration, 20 samples were collected using a 100 × 100 m grid sampling strategy, while 20 samples were randomly collected for independent validation. UAV captured hyperspectral images with a spatial resolution of 0.05 × 0.05 m. The extracted spectra within every 1 × 1 m were then averaged to represent the spectra of that grid; this procedure was also performed across the whole field. Upon applying various spectral pretreatments, including absorbance conversion, multiple scattering correction, Savitzky–Golay smoothing filtering, and first-order differentiation, the absolute maximum values of the correlation coefficients of the spectra for SOM increased from 0.41 to 0.58. Importance analysis from the optimal random forest (RF) model showed that the characterized bands of SOM were located in the 450–600 and 750–900 nm regions. When the RF model was used, the UAV hyperspectra data (UAV-RF) were able to successfully predict SOM, with an R2 of 0.53 and RMSE of 1.48 g kg−1. The prediction accuracy was then compared with that obtained using ordinary kriging (OK) and the RF model based on proximal sensing (PS-RF) with the same number of calibration samples. However, the OK method failed to predict the SOM accuracy (RMSE = 2.17 g kg−1; R2 = 0.02) due to a low sampling density. The semi-covariance function was unable to describe the spatial variability of SOM effectively. When the sampling density was increased to 50 × 50 m, OK successfully predicted SOM, with RMSE = 1.37 g kg−1 and R2 = 0.59, and its results were comparable to those of UAV-RF. The prediction accuracy of PS-RF was generally consistent with that of UAV-RF, with RMSE values of 1.41 g kg−1 and 1.48 g kg−1 and R2 values of 0.57 and 0.53, respectively, which indicated that SOM prediction based on UAV-RF is feasible. Additionally, compared with the PS platforms, the UAV hyperspectral technology could simultaneously provide spectral information of tens or even hundreds of continuous bands and spatial information at the same time. This study provides a reference for further research and development of UAV hyperspectral techniques for fine-scale SOM mapping using a small number of samples.
Up with ultrafine aerosol particles Ultrafine aerosol particles (smaller than 50 nanometers in diameter) have been thought to be too small to affect cloud formation. Fan et al. show that this is not the case. They studied the effect of urban pollution transported into the otherwise nearly pristine atmosphere of the Amazon. Condensational growth of water droplets around the tiny particles releases latent heat, thereby intensifying atmospheric convection. Thus, anthropogenic ultrafine aerosol particles may exert a more important influence on cloud formation processes than previously believed. Science , this issue p. 411
The size-resolved properties of atmospheric black carbon (BC) importantly determine its absorption capacity and cloud condensation nuclei (CCN) ability. This study reports comprehensive vertical profiles of BC size-related properties over the Beijing area (BJ) and Continental Europe (CE). BC mass loadings over CE were in the range of clean background over BJ. For both planetary boundary layer (PBL) and lower free troposphere, the BC mass median core diameter over BJ during the cold season was 0.21 ± 0.02 μm, larger than the warm season over BJ and CE (0.18 ± 0.01 μm), which may reflect seasonal differences in emissions. The BC coatings were positively correlated with the pollution level, with background BC having a smaller coated count median diameter (0.19 ± 0.01 μm). The modeled absorption enhancement (Eabs) due to coatings was 1.23 ± 0.14 for the background but in the PBL following a linear expression (Eabs = 0.13 × MassBC,surface + 1.26). The CCN ability of BC was significantly enhanced in the polluted PBL, due to both enlarged size and increased hygroscopicity. In polluted BJ at predicted supersaturations, ∼0.08% half of the BC number could be activated, whereas the cleaner environment needs ∼0.14%. The results here suggest that the highly coated and absorbing BC can be efficiently incorporated into clouds and can exert important indirect radiative impacts over the polluted East Asia region.
Abstract. The main objective of this study is to investigate the formation and evolution mechanism of the regional haze in megacity Beijing by analyzing the process of a severe haze that occurred 20–27 September 2011. Mass concentration and size distribution of aerosol particles as well as aerosol optical properties were concurrently measured at the Beijing urban atmospheric environment monitoring station. Gaseous pollutants (SO2, NO-NO2-NOx, O3, CO) and meteorological parameters (wind speed, wind direction, and relative humidity (RH)) were simultaneously monitored. Meanwhile, aerosol spatial distribution and the height of planetary boundary layer (PBL) were retrieved from the signal of satellite and LIDAR (light detection and ranging). Results showed that high intensity of local pollutants from Beijing urban source is the fundamental cause that led to the regional haze. Meteorological factors such as higher RH, weak surface wind speed, and decreasing height of PBL played an important role on the deterioration of air quality. New particle formation was considered to be the most important factor contributing the formation of haze. In order to improve the atmospheric visibility and reduce the occurrence of the haze, the mass concentration of PM2.5 at dry condition should be less than 60 µg m−3 in Beijing according to the empirical relationship of visibility, PM2.5 mass concentration and RH. This case study may provide valuable information for the public to recognize the formation mechanism of the regional haze event over the megacity, which is also useful for the government to adopt scientific approach to forecast and eliminate the occurrence of regional haze in China.
There is limited evidence on association between air pollutants and hospital admissions, hospital cost and length of stay (LOS) among patients with diabetes mellitus (DM) and comorbid respiratory diseases (RD), especially in low- and middle-income countries (LMICs) with low levels of air pollution.Daily data on RD-DM patients were collected in Panzhihua from 2016 to 2020. A generalised additive model (GAM) was used to explore the effect of air pollutants on daily hospital admissions, LOS and hospital cost. Attributable risk was employed to estimate RD-DM's burden due to exceeding air pollution exposure, using both 0 microgrammes per cubic metre (μg/m3) and WHO's 2021 air quality guidelines as reference.For each 10 ug/m3 increase of particles with an aerodynamic diameter <2.5 micron (μm) (PM2.5), particles with an aerodynamic diameter <10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3), the admissions of RD-DM patients increased by 7.25% (95% CI = 4.26 to 10.33), 5.59% (95% CI = 3.79 to 7.42), 10.10% (95% CI = 7.29 to 12.98), 12.33% (95% CI = 8.82 to 15.95) and -2.99% (95% CI = -4.08 to -1.90); per 1 milligramme per cubic metre (mg/m3) increase of carbon monoxide (CO) corresponded to a 25.77% (95% CI = 17.88 to 34.19) increment for admissions of RD-DM patients. For LOS and hospital cost, the six air pollutants showed similar effect. Given 0 μg/m3 as the reference, NO2 showed the maximum attributable fraction of 32.68% (95% CI = 25.12 to 39.42%), corresponding to an avoidable burden of 5661 (95% CI = 3611 to 5860) patients with RD-DM.There is an association between PM2.5, PM10, SO2, NO2, and CO with increased hospital admissions, LOS and hospital cost in patients with RD-DM. Disease burden of RD-DM may be improved by formulating policies related to air pollutants exposure reduction, especially in LMICs with low levels of air pollution.
c-Jun N-terminal kinase (JNK) regulates cellular responses to various extracellular stimuli, environmental stresses, pathogen infections, and apoptotic agents. Here, a JNK1, Ec-JNK1, was identified from orange-spotted grouper, Epinephelus coioides. Ec-JNK1 has been found involving in the immune response to pathogen challenges in vivo, and the infection of Singapore grouper iridovirus (SGIV) and SGIV-induced apoptosis in vitro. SGIV infection activated Ec-JNK1, of which phosphorylation of motif TPY is crucial for its activity. Over-expressing Ec-JNK1 phosphorylated transcription factors c-Jun and promoted the infection and replication of SGIV, while partial inhibition of the phosphorylation of Ec-JNK1 showed the opposite effects by over-expressing the dominant-negative EcJNK1-Δ183-185 mutant. Interestingly, SGIV enhanced the viral infectivity by activating Ec-JNK1 which in turn drastically inhibited the antiviral responses of type 1 IFN, indicating that Ec-JNK1 could be involved in blocking IFN signaling during SGIV infection. In addition, Ec-JNK1 enhanced the activation of AP-1, p53, and NF-κB, and resulted in increasing the levels of SGIV-induced cell death. The caspase 3-dependent activation correlated with the phosphorylation of Ec-JNK1 and contributed to SGIV-induced apoptosis. Taken together, SGIV modulated the phosphorylation of Ec-JNK1 to inactivate the antiviral signaling, enhance the SGIV-induced apoptosis and activate transcription factors for efficient infection and replication. The "positive cooperativity" molecular mechanism mediated by Ec-JNK1 contributes to the successful evasion and infection of iridovirus pathogenesis.
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