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The authors studied the relationship between air quality and weather conditions (wind speed, precipitation, and temperature) in the Washington D.C. area.

This study hypothesized that a machine learning model could accurately predict the severity of California wildfires and determine the most influential meteorological factors. It utilized a custom dataset with information from the World Weather Online API and a Kaggle dataset of wildfires in California from 2013-2020. The developed algorithms classified fires into seven categories with promising accuracy (around 55 percent). They found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This tool could vastly improve the efficiency and preparedness of firefighters as they deal with wildfires.

Droughts have a wide range of effects, from ecosystems failing and crops dying, to increased illness and decreased water quality. Drought prediction is important because it can help communities, businesses, and governments plan and prepare for these detrimental effects. This study predicts drought conditions by using predictable weather patterns in machine learning models.

In this study the authors sought to understand the influence of implicit social pressure on prosocial behavior (voluntary acts that benefit someone else) in adolescents age 12–19. They considered how participants divided money between themselves and four teammate they did a school project with. Despite being shown examples of how to divide money (social pressure), their results showed that both boys and girls were unaffected by prosocial, neutral, and antisocial pressures.

Pediatric asthma remains a significant health issue for Dallas students. This study examined the relationship between microclimatic parameters, vegetation, and pediatric asthma vulnerability (PAV) in urban schools.

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.

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.