Browse Articles

An Investigative Analysis of Climate Change Using Historical and Modern Weather Data

Han et al. | Dec 02, 2013

An Investigative Analysis of Climate Change Using Historical and Modern Weather Data

Climate change is an important and contentious issue that has far-reaching implications for our future. The authors here compare primary temperature and precipitation data from almost 200 years ago against the present day. They find that the average annual temperature in Brooklyn, NY has risen significantly over this time, as has the frequency of precipitation, though not the amount of precipitation. These data stress the need for more ecologically-conscious choices in our daily lives.

Read More...

Harvesting Atmospheric Water

Greenwald et al. | Jul 10, 2020

Harvesting Atmospheric Water

The objective of this project was to test various materials to determine which ones collect the most atmospheric water when exposed to the same environmental factors. The experiment observed the effect of weather conditions, a material’s surface area and hydrophilicity on atmospheric water collection. The initial hypothesis was that hydrophobic materials with the greatest surface area would collect the most water. The materials were placed in the same outside location each night for twelve trials. The following day, the materials were weighed to see how much water each had collected. On average, ribbed plastic collected 10.8 mL of water per trial, which was over 20% more than any other material. This result partially supported the hypothesis because although hydrophobic materials collected more water, surface area did not have a significant effect on water collection.

Read More...

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.

Read More...

Discovery of the Heart in Mathematics: Modeling the Chaotic Behaviors of Quantized Periods in the Mandelbrot Set

Golla et al. | Dec 14, 2020

Discovery of the Heart in Mathematics: Modeling the Chaotic Behaviors of Quantized Periods in the Mandelbrot Set

This study aimed to predict and explain chaotic behavior in the Mandelbrot Set, one of the world’s most popular models of fractals and exhibitors of Chaos Theory. The authors hypothesized that repeatedly iterating the Mandelbrot Set’s characteristic function would give rise to a more intricate layout of the fractal and elliptical models that predict and highlight “hotspots” of chaos through their overlaps. The positive and negative results from this study may provide a new perspective on fractals and their chaotic nature, helping to solve problems involving chaotic phenomena.

Read More...

Ladder Fuel Treatments Effect Burn Area of Forest Fires in Semi-Arid High Elevation Climates

Schwartz et al. | Oct 05, 2020

Ladder Fuel Treatments Effect Burn Area of Forest Fires in Semi-Arid High Elevation Climates

In this study, the authors investigate a timely and important topic: forest fires. More specifically, they use a wildfire simulator to test how ladder fuels effect the burn area of a forest fire. Ladder fuels are fuels that cause a forest fire to rise up from the forest floor to the canopy, which may affect the overall spread. They simulated fire spread with different levels of ladder fuel treatment and found that the spread of a burn area would indeed decrease with increased ladder fuel treatment. These findings have important implications for forest and forest fire management.

Read More...

Assigning Lightning Seasons to Different Regions in the United States

Hawkins et al. | Sep 07, 2020

Assigning Lightning Seasons to Different Regions in the United States

Climate change is predicted to increase the frequency of severe thunderstorm events in coming years. In this study, the authors hypothesized that (i) the majority of severe thunderstorm events will occur in the summer months in all states examined for all years analyzed, (ii) climate change will cause an unusual number of severe thunderstorm events in winter months in all states, (iii) thundersnow would be observed in Colorado, and (iv.) there would be no difference in the number of severe thunderstorm events between states in any given year examined. They classified lightning seasons in all states observed, with the most severe thunderstorm events occurring in May, June, July, and August. Colorado, New Jersey, Washington, and West Virginia were found to have severe thunderstorm events in the winter, which could be explained by increased winter storms due to climate change (1). Overall, they highlight the importance of quantifying when lightning seasons occur to avoid lightning-related injuries or death.

Read More...

Search Articles

Search articles by title, author name, or tags

Clear all filters

Popular Tags

Browse by school level