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Antibiotic resistance genes (ARGs) are commonly detected in the atmosphere, but questions remain regarding their sources and relative contributions, bacterial hosts, and corresponding human health risks. Here, we conducted a qPCR- and metagenomics-based investigation of inhalable fine particulate matter (PM2.5) at a large wastewater treatment plant (WWTP) and in the ambient air of Hong Kong, together with an in-depth analysis of published data of other potential sources in the area. PM2.5 was observed with increasing enrichment of total ARGs along the coastal–urban–WWTP gradient and clinically relevant ARGs commonly identified in urban and WWTP sites, illustrating anthropogenic impacts on the atmospheric accumulation of ARGs. With certain kinds of putative antibiotic-resistant pathogens detected in urban and WWTP PM2.5, a comparable proportion of ARGs that co-occurred with MGEs was found between the atmosphere and WWTP matrices. Despite similar emission rates of bacteria and ARGs within each WWTP matrix, about 11–13% of the bacteria and >57% of the relevant ARGs in urban and WWTP PM2.5 were attributable to WWTPs. Our study highlights the importance of WWTPs in disseminating bacteria and ARGs to the ambient air from a quantitative perspective and, thus, the need to control potential sources of inhalation exposure to protect the health of urban populations.
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Image and Spectrum based Deep Feature Analysis for Particle Matter Estimation with Weather Information
Air pollution is a major global risk to human health and environment. Particle matter (PM) with diameters less than 2.5 micrometers (PM2.5) is more harmful to human health than other air pollutants because it can penetrate deeply into lungs and damage human respiratory system. A new image-based deep feature analysis method is presented in this paper for PM2.5 concentration estimation. Firstly, low level and high level features are extracted from images and their spectrums by a deep learning neural network, and then regression models are created using the extracted deep features to estimate the PM2.5 concentrations, which are future refined by the collected weather information. The proposed method was evaluated using a PM2.5 dataset with 1460 photos and the experimental results demonstrated that our method outperformed other state-of-the-art methods.
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- Award ID(s):
- 1726500
- PAR ID:
- 10114065
- Date Published:
- Journal Name:
- Proceedings of IEEE International Conference on Image Processing 2019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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