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Full-Text Articles in Physical Sciences and Mathematics
Characteristics And Assessing Biological Risks Of Airborne Bacteria In Waste Sorting Plant, Abbas Norouzian Baghani, Somayeh Golbaz, Gholamreza Ebrahimzadeh, Marcelo I. Guzman, Mahdieh Delikhoon, Mehdi Jamshidi Rastani, Abdullah Barkhordari, Ramin Nabizadeh
Characteristics And Assessing Biological Risks Of Airborne Bacteria In Waste Sorting Plant, Abbas Norouzian Baghani, Somayeh Golbaz, Gholamreza Ebrahimzadeh, Marcelo I. Guzman, Mahdieh Delikhoon, Mehdi Jamshidi Rastani, Abdullah Barkhordari, Ramin Nabizadeh
Chemistry Faculty Publications
Examining the concentration and types of airborne bacteria in waste paper and cardboard sorting plants (WPCSP) is an urgent matter to inform policy makers about the health impacts on exposed workers. Herein, we collected 20 samples at 9 points of a WPCSP every 6 winter days, and found that the most abundant airborne bacteria were positively and negatively correlated to relative humidity and temperature, respectively. The most abundant airborne bacteria (in units of CFU m−3) were: Staphylococcus sp. (72.4) > Micrococcus sp. (52.2) > Bacillus sp. (30.3) > Enterococcus sp. (24.0) > Serratia marcescens (20.1) > E. coli (19.1) > Pseudomonas sp. (16.0) > Nocardia …
Assessing Machine Learning Utility In Predicting Hydrologic And Nitrate Dynamics In Karst Agroecosystems, Timothy Mcgill
Assessing Machine Learning Utility In Predicting Hydrologic And Nitrate Dynamics In Karst Agroecosystems, Timothy Mcgill
Theses and Dissertations--Biosystems and Agricultural Engineering
Seasonal hypoxia in the Gulf of Mexico and harmful algal blooms experienced in many inland freshwater bodies is partially driven due to excessive nitrogen loading seen from agricultural watersheds. Within the Mississippi/Atchafalaya River Basin, many areas are underlain with karst features, and efforts to reduce nitrogen contributions from these areas have had varying success, due to lacking a complete understanding of nutrient dynamics in karst agricultural systems. To improve the understanding of nitrogen cycling in these systems, 35 months of high resolution in situ water quality and atmospheric data were collected and fed into a two-hidden layer extreme learning machine …