Amyotrophic lateral sclerosis (ALS) is a rapidly fatal progressive neurological disorder characterized by the degeneration of motor neurons in the brain and spinal cord. Environmental exposures likely play an important role in ALS pathogenesis. However, due to difficulty recruiting large, representative samples of newly diagnosed ALS cases, these environmental risk factors remain largely unidentified. The proposed work will address this knowledge gap by comprehensively searching for new candidate environmental risk factors and then formally testing their association with ALS risk using a large population-based administrative dataset with >70,000 incident ALS cases from the U.S. We will systematically assess the nationwide relationship between ALS in 2002-2019 and nearly 200 air pollutants at the census tract level. Specifically, we will consider all “air toxics” and “criteria pollutants” as estimated by the U.S. Environmental Protection Agency, as well as fine particulate matter (PM2.5) and ultrafine diesel and jet aircraft emissions. In addition, we will agnostically search for geographic clusters of ALS to identify novel environmental risk factors for further examination in our study. We will prioritize candidates that might underlie the known association between military service and ALS. Based on our pilot statistical analyses we anticipate examining estuarine exposures using geographic indicators of naturally-derived volatiles, ultrafine particulate, and biotoxins, along with several disease vectors. Throughout our work we will account for correlated environmental exposures, demographics, and tobacco and alcohol use. Our pilot dataset that we constructed using the methods proposed here demonstrates the expected associations for these factors, medical risk factors, and lead. We will leverage our large nationwide dataset to test for interaction between the set of top exposures including lead. This will allow us to cross multi- exposure associations with the NIEHS-funded Comparative Toxicogenomics Database to identify potential biological mechanisms underlying ALS. We will enhance our screening of this database through in vitro experimental studies in which we compare the toxicity of selected chemicals on motor neuron subtypes, which recapitulate aspects of ALS-relevant neuronal vulnerability. We will develop regression models with summed concentrations of chemicals weighted by effects on relevant genes. We will attempt to replicate findings in a dataset with >50,000 additional incident ALS cases. For the most recent cases of all ages we will examine the above exposures in relation to ALS progression while also accounting for medical risk factors and treatment from a specialist or multidisciplinary ALS center.