ABSTRACT
Artificially prepared microbial spores have excellent electromagnetic attenuation properties due to their special composition and structure. At present, studies on the optical properties of microbial spores have mainly focused on those with a single band or a single germplasm, which has limitations and cannot reveal the optical properties comprehensively. In this paper, 3 kinds of laboratory-prepared microbial spores were selected for compounding, and the spectral reflectivities of single-germplasm biospores and compound biospores were measured in the wavebands of 0.25–2.4 and 3–15 μm. The complex refractive indices (CRIs) were calculated in combination with the Kramers–Kronig (K-K) algorithm. Relying on the smoke box broadband test system, the transmittance of single-germplasm bioaerosols and compound bioaerosols from the ultraviolet (UV) band to the far-infrared (FIR) band was measured, and the mass extinction coefficients were calculated. The results indicate that the trend of the complex refractive indices of the compound spores is consistent with that of the single-germplasm spores with a larger particle size. For the single-germplasm bioaerosols, the lowest transmittance values were 2.21, 5.70 and 6.27% in the visible (VIS), near-infrared (NIR) and middle-infrared (FIR) bands, and the mass extinction coefficients reached 1.15, 0.87 and 0.84 m2/g, respectively. When AO and BB spores were compounded at 4:1, the extinction performance of the bioaerosols somewhat improved in all wavebands. These results can help to comprehensively analyze the optical properties of bioaerosols and provide ideas for the development of new extinction materials.
ABSTRACT
The scholarly literature on the links between Artificial Intelligence and the United Nations’ Sustainable Development Goals is burgeoning as climate change and the biotic crisis leading to mass extinction of species are raising concerns across the globe. With a focus on Sustainable Development Goals 14 (Life below Water) and 15 (Life on Land), this paper explores the opportunities of Artificial Intelligence applications in various domains of wildlife, ocean and land conservation. For this purpose, we develop a conceptual framework on the basis of a comprehensive review of the literature and examples of Artificial Intelligence-based approaches to protect endangered species, monitor and predict animal behavior patterns, and track illegal or unsustainable wildlife trade. Our findings provide scholars, governments, environmental organizations, and entrepreneurs with a much-needed taxonomy and real-life examples of Artificial Intelligence opportunities for tackling the grand challenge of rapidly decreasing biological diversity, which has severe implications for global food security, nature, and humanity.