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A land use regression model to predict emissions from oil and gas production using machine learning

Cao et al. | Mar 24, 2023

A land use regression model to predict emissions from oil and gas production using machine learning

Emissions from oil and natural gas (O&G) wells such as nitrogen dioxide (NO2), volatile organic compounds (VOCs), and ozone (O3) can severely impact the health of communities located near wells. In this study, we used O&G activity and wind-carried emissions to quantify the extent to which O&G wells affect the air quality of nearby communities, revealing that NO2, NOx, and NO are correlated to O&G activity. We then developed a novel land use regression (LUR) model using machine learning based on O&G prevalence to predict emissions.

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The Protective Effects of Panax notoginseng Saponin on the Blood-Brain Barrier via the Nrf2/ARE Pathway in bEnd3 Cells

Yang et al. | Apr 06, 2016

The Protective Effects of <i>Panax notoginseng</i> Saponin on the Blood-Brain Barrier via the Nrf2/ARE Pathway in bEnd3 Cells

Disruption of the blood-brain barrier (BBB) is related to many neurological disorders, and can be caused by oxidative stress to cerebral microvascular endothelial cells (CMECs) composing the BBB. The authors of the paper investigated the protective effects of the total saponins in the leaves of Panax notoginseng (LPNS) on oxidative-stress-induced damage in a mouse cerebral microvascular endothelial cell line.

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Predicting asthma-related emergency department visits and hospitalizations with machine learning techniques

Chatterjee et al. | Oct 25, 2021

Predicting asthma-related emergency department visits and hospitalizations with machine learning techniques

Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.

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