Development of a conceptual framework for risk assessment of elevated internal temperatures in landfills.

Subsurface elevated temperatures (SETs) often occur in landfills and pose great threats to their structural and environmental integrity. Current landfill gas monitoring practices only recommend maintaining certain soil gases percentages, with no integrated strategy for predicting subsurface temperature. As a solution, this paper proposes a comprehensive risk assessment framework specific to SET mitigation. The risk model (RSET) was constructed by incorporating independent gas variables (methane, carbon dioxide, oxygen, residual nitrogen, and temperature) identified in the existing literature as SET indicators, and analyzing gas-well data from the Bridgeton Landfill. Upon identifying these gas indictors and their safety thresholds, we found a significant association (p-value < 0.05) between safe-unsafe ranges of gas variables and subsurface temperature. Temperatures above 80 °C were found to be associated with 100%, 92.3%, and only 4% of the unsafe ranges of methane, residual nitrogen, and oxygen, respectively. As the correlation between gases and temperature seemed to vary for different gas combinations, we developed the RSET by incorporating into these correlation coefficients event intensities specific to certain gas combinations, and then normalizing the RSET scale over a 0-10 range. Over the study period, we identified 22.29% of cases as medium risk at the Bridgeton Landfill and 17.7% as high risk. SETs are governed by different combinations of safe-unsafe ranges of parameters rather than any individual parameters alone. Subsequently, we used a decision tree algorithm to assess the risk types associated with RSET values. The proposed RSET can serve as a monitoring and decision-making tool for landfill authorities for managing and preventing SET incidents.

The impact of stay-at-home orders on air-quality and COVID-19 mortality rate in the United States.

Since the beginning of the pandemic in the U.S., most jurisdictions issued mitigation strategies, such as restricting businesses and population movements. This provided an opportunity to measure any positive implications on air quality and COVID-19 mortality rate during a time of limited social interactions. Four broad categories of stay-at-home orders (for states following the order for at least 40 days, for states with less than 40 days, for states with the advisory order, and the states with no stay-at-home order) were created to analyze change in air quality and mortality rate. Ground-based monitoring data for particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO) was collected during the initial country-wide lockdown period (15 March-15 June 2020). Data on confirmed COVID-19 cases and deaths were also collected to analyze the effects of the four measures on the mortality trend. Findings show air quality improvement for the states staying under lockdown longer compared to states without a stay-at-home order. All stay-at-home order categories, except states without measures were observed a decrease in PM2.5 and the core-based statistical areas (CBSAs) within the longer mitigation states had an improvement of their air quality index (AQI).