Army-funded research identified a new chemistry approach that could remove micropollutants from the environment.

As temperatures rise, the risk of devastating forest fires is increasing. Researchers at the Technical University of Munich (TUM) are using artificial intelligence to estimate the long-term impact that an increased number of forest fires will have on forest ecosystems. 

A new method for seeing through ice sheets using radio signals from the sun could enable cheap, low-power and widespread monitoring of ice sheet evolution and contribution to sea-level rise.

Just 25 cities globally are responsible for 52 percent of urban greenhouse gas emissions, according to a new study that highlights the role that cities must play in reaching the goals of the 2015 Paris Climate Agreement.

Children exposed to elevated levels of air pollution may be more likely to have poor inhibitory control during late childhood and poor academic skills in early adolescence, including spelling, reading comprehension, and math skills. Difficulty with inhibition in late childhood was found to be a precursor to later air pollution-related academic problems. Interventions that target inhibitory control might improve outcomes.

Direct farm marketing efforts, such as farmers markets and roadside stands, are more successful in communities with more nonprofits, social enterprises and creative industries, according to a team including Cornell researchers, who created a nationwide database of assets to help municipalities craft community-specific development plans.

A new wearable device turns the touch of a finger into a source of power for small electronics and sensors. 

A world-first trial has shown that nose drops of modified 'friendly' bacteria protect against meningitis.

As typical social and academic interaction screeched to a halt last year, many young people began experiencing declines in mental health, a problem that appeared to be worse for those whose connections to family and friends weren’t as tight, a new study has found.

Writing in the July 12, 2021 online issue of Nature Communications, researchers at University of California San Diego School of Medicine describe a new approach that uses machine learning to hunt for disease targets and then predicts whether a drug is likely to receive FDA approval.