Traffic-Related Air Pollution and Cognitive Function in a Cohort of Older Men
Melinda C. Power; Marc G. Weisskopf; Stacey Alexeeff; Brent A. Coull; Avron Spiro; Joel Schwartz · 2010 · Environmental Health Perspectives
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Traffic-related particles induce oxidative stress and may exert adverse effects on central nervous system function, which could manifest as cognitive impairment. OBJECTIVE: We assessed the association between black carbon (BC), a marker of traffic-related air pollution, and cognition in older men. METHODS: A total of 680 men (mean ± SD, 71 ± 7 years of age) from the U.S. Department of Veterans Affairs Normative Aging Study completed a battery of seven cognitive tests at least once between 1996 and 2007. We assessed long-term exposure to traffic-related air pollution using a validat
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Is CO 2 an Indoor Pollutant? Direct Effects of Low-to-Moderate CO 2 Concentrations on Human Decision-Making Performance
Negative / Null Result ReportIngestion of Nanoplastics and Microplastics by Pacific Oyster Larvae
Negative / Null Result ReportHealth Effects of Chronic Arsenic Exposure
Negative / Null Result ReportSevere Air Pollution and Labor Productivity: Evidence from Industrial Towns in China
Negative / Null Result ReportLand-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers
Negative / Null Result ReportAssociation Between Changes in Air Pollution Levels During the Beijing Olympics and Biomarkers of Inflammation and Thrombosis in Healthy Young Adults
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1289/ehp.1002767