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Many studies have reported associations between respiratory symptoms and resident proximity to traffic. However, only a few have documented information about the relationship between traffic volume and air quality in local areas. This study investigates the impact of traffic volume on air quality at different geographical locations in the state of South Carolina using multilevel linear mixed models and Grey Systems. Historical traffic volume and air quality data between 2006 and 2016 are obtained from the South Carolina Department of Transportation (SCDOT) and the United States Environmental Protection Agency (EPA) monitoring stations. The data are used to develop prediction models that relate Air Quality Index (AQI) to traffic volume for selected counties and schools. For the counties, two models are developed, one with Ozone (O3) and one with PM2:5 as the dependent variable. For the schools, only one model is developed, with O3 as the dependent variable. The number of counties and schools studied are limited by the availability of air monitoring stations dedicated to measuring O3 and PM2:5. Several types of models were investigated. They include linear regression model (LM), linear mixed-effect regression model (LMER), Grey Systems (GM), error corrected GM (EGM), Grey Verhulst (GV), error corrected GV (EGV), and LMER + EGM. The LM model produced the least accurate estimate while the LMER + EGM model produced the most accurate estimate (average RMSE is less than 5%). The models’ estimates suggest that air quality in South Carolina will continue to get worse in the coming years due to increasing AADT. An interesting finding of this study is that some counties and schools will have higher levels of O3 or PM2:5 when AADT decreases. This finding suggests that there are other factors, other than AADT, that influence the air quality in these counties and schools. 

αβ and γδ T cell receptors (TCRs) are highly diverse antigen receptors that define two evolutionarily conserved T cell lineages. We describe a population of γμTCRs found exclusively in non-eutherian mammals that consist of a two-domain (Vγ-Cγ) γ-chain paired to a three-domain (Vμ-Vμj-Cμ) μ-chain. γμTCRs were characterized by restricted diversity in the Vγ and Vμj domains and a highly diverse unpaired Vμ domain. Crystal structures of two distinct γμTCRs revealed the structural basis of the association of the γμTCR heterodimer. The Vμ domain shared the characteristics of a single-domain antibody within which the hypervariable CDR3μ loop suggests a major antigen recognition determinant. We define here the molecular basis underpinning the assembly of a third TCR lineage, the γμTCR.