The purpose of this study was to evaluate the effect of traffic volume on ambient black carbon (BC) concentration in an inner-city neighborhood "hot spot" while accounting for modifying effects of weather and time. Continuous monitoring was conducted for 12 months at the Baltimore Traffic Study site surrounded by major urban streets that together carry over 150,000 vehicles per day. Outdoor BC concentration was measured with an Aethalometer; vehicles were counted pneumatically on two nearby streets. Meteorological data were also obtained. Missing data were imputed and all data were normalized to a 5-min observational interval (n = 105,120). Time-series modeling accounted for autoregressively (AR) correlated errors. This study found that outdoor BC was positively correlated at a statistically significant level with neighborhood-level vehicle counts, which contributed at a rate of 66 +/- 10 (SE) ng/m3 per 100 vehicles every 5 min. Winds from the SW-S-SE quarter were associated with the greatest increases in BC (376-612 ng/m3). These winds would have entrained BC from Baltimore's densely trafficked central business district, as well as a nearby interstate highway. The strong influence of wind direction implicates atmospheric transport processes in determining BC exposure. Dew point, mixing height, wind speed, season, and workday were also statistically significant predictors. Background exposure to BC was estimated to be 905 ng/m3. The optimal, statistically significant representation of BC's autocorrelation was AR([1:6]) x 288 x 2016, where the short-term AR factor (lags 1-6) indicated that BC concentrations are correlated for up to 30 min, and the AR factors for lags 288 and 2016 indicate longer-term autocorrelations at diurnal and weekly cycles, respectively. It was concluded that local exposure to BC from mobile sources is substantially modified by meteorological and temporal conditions, including atmospheric transport processes. BC concentration also demonstrates statistically significant autocorrelation at several time scales.