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Smoke aerosols released from biomass burning greatly influence air quality, weather, and climate. The total particulate matter (TPM) of smoke aerosols has been demonstrated to be a linear function of fire radiative energy (FRE) during a period of biomass burning via a smoke aerosol emission coefficient (Ce). However, it remains challenging to quantify Ce appropriately through satellite observations. In this study, an innovative approach was put forward to explore Ce by establishing the relationships between FRE and TPM in two regions, the CONtiguous United States and Indonesia. Specifically, we identified 584 isolated fire clusters and smoke plumes in the CONtiguous United States and 248 in Indonesia using Moderate Resolution Imaging Spectroradiometer natural color images, and then calculated FRE from Moderate Resolution Imaging Spectroradiometer active fire product and TPM from Moderate Resolution Imaging Spectroradiometer aerosol optical depth product for each fire‐smoke matchup during Terra and Aqua overpasses. The relationships between TPM and FRE were constructed to determine Ce using an ordinary least squares regression. The results show that FRE and TPM are significantly correlated (r2 ≥ 0.63, p < 0.001) with the Ce varying across regions and fuel types. In the CONtiguous United States, forest Ce values are 21.3 and 34.1 g/MJ and savanna Ce values are 18.2 and 22.8 g/MJ for western and eastern regions, respectively; additionally, Ce is 20.9 g/MJ for grasslands and 5.0 g/MJ for shrublands. In Indonesia, Ce is 52.4 and 30.0 g/MJ for peatlands and forests, respectively. Overall, this study improves our understanding of Ce variations with fuel types and climate regions.

Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe.

Wildlife is reservoir of emerging viruses. Here we identified 27 families of mammalian viruses from 1981 wild animals and 194 zoo animals collected from south China between 2015 and 2022, isolated and characterized the pathogenicity of eight viruses. Bats harbor high diversity of coronaviruses, picornaviruses and astroviruses, and a potentially novel genus of Bornaviridae. In addition to the reported SARSr-CoV-2 and HKU4-CoV-like viruses, picornavirus and respiroviruses also likely circulate between bats and pangolins. Pikas harbor a new clade of Embecovirus and a new genus of arenaviruses. Further, the potential cross-species transmission of RNA viruses (paramyxovirus and astrovirus) and DNA viruses (pseudorabies virus, porcine circovirus 2, porcine circovirus 3 and parvovirus) between wildlife and domestic animals was identified, complicating wildlife protection and the prevention and control of these diseases in domestic animals. This study provides a nuanced view of the frequency of host-jumping events, as well as assessments of zoonotic risk.

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