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Occurrence of antimicrobial-resistant bovine mastitis bacteria in Sakon Nakhon, Thailand
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Document Title
Occurrence of antimicrobial-resistant bovine mastitis bacteria in Sakon Nakhon, Thailand
Author
Camsing A., Phetburom N., Chopjitt P., Pumhirunroj B., Patikae P., Watwiengkam N., Yongkiettrakul S., Kerdsin A., Boueroy P.
Affiliations
Thammasat University, SIIT, Pathum Thani, 12120, Thailand; JAIST, School of Knowledge Science, Nomi, 923-1211, Japan; JAIST, Division of Advanced Science and Technology, Nomi, 923-1211, Japan; JAIST, Intelligence Research Area, Knowledge Science, Research Centre for Interpretable AI, International Research Center for Materials Informatics (Excellent Core), Nomi, 923-1211, Japan; Thammasat University, School of ICT at Sirindhorn International Institute of Technology, Pathum Thani, 12120, Thailand; National Science and Technology Development Agency, Artificial Intelligence Research Group, National Electronics and Computer Technology Center, Pathum Thani, 12120, Thailand
Type
Article
Source Title
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ISSN
19391404
Year
2024
Volume
17
Page
8203-8212
Open Access
All Open Access, Gold
Publisher
Institute of Electrical and Electronics Engineers Inc.
DOI
10.1109/JSTARS.2024.3384964
Abstract
This study introduces a novel method for estimating hourly concentrations of particulate matter 2.5 ?m (PM2.5) using satellite data. The pollution control department of Thailand collected hourly PM2.5 data nationwide in 2020. NASA's Earth Observing System Data and Information System encompasses all moderate resolution imaging spectroradiometer satellite data. We employed aerosol optical depth (AOD), land surface temperature (LST), normalized difference vegetation index (NDVI), and elevation (EV) in our analysis. The approach incorporates a weighted sum contrast log-linear regression model that integrates satellite data, allowing for the examination of small-scale hourly variations in PM2.5 concentrations. The results reveal a high correlation between hourly PM2.5 levels and AOD, LST, NDVI, EV, time, and week of the year in terms of spatial distribution, with an R2 value of 53.8%. The mean hourly PM2.5 concentration was 23.1 ?g/m3, displaying elevated concentrations during the dry season (November to March) and peak hours (8 to 11 a.m. and 8 to 12 p.m.). Positive correlations between AOD and PM2.5, especially when AOD exceeded 0.52, and between LST and PM2.5, particularly when LST exceeded 33.9 ?C, along with NDVI ranging from -0.08 to 0.18 and EV above 67.9 m, resulted in higher PM2.5 levels than the overall mean. The proposed model proved valuable for interpretation and practical application, offering comparable estimated hourly PM2.5 concentrations at a 1-km resolution with monitoring stations. This suggests that researchers or policymakers may use the model to understand hourly PM2.5 fluctuations and their impact on human health and the environment. ? 2008-2012 IEEE.
License
CC BY-NC-ND
Rights
Authors
Publication Source
WoS