Abstract The near-roadway pollutant dispersion model RLINE was evaluated for prediction of nitrogen oxides (NOx) and black carbon (BC) concentrations. Model predictions were compared with continuous, yearlong measurements from two near-roadway sites in the San Francisco Bay Area. Heavy-duty diesel trucks were a significant source of NOx and BC at both sites. Characterization of temporal variations in heavy-duty truck activity on diurnal, weekly, and seasonal scales were included in this study; truck traffic and emissions are not well-correlated with passenger vehicle or total traffic volumes. For both pollutants, more than 90% of predicted 24-h average concentrations were within a factor of two of observations at both near-roadway monitoring sites. The model responds appropriately to seasonal variations in meteorology and day-of-week variations in emissions. RLINE model performance for NOx was better overall than for BC. Reducing uncertainties in emission factors would help to improve model performance for BC.

中文翻译：

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使用 RLINE 评估柴油相关空气污染的近道路浓度

摘要 评估了近路污染物扩散模型 RLINE，用于预测氮氧化物 (NOx) 和黑碳 (BC) 浓度。模型预测与旧金山湾区两个靠近道路站点的连续、为期一年的测量结果进行了比较。重型柴油卡车是两个地点的氮氧化物和碳排放的重要来源。本研究包括了重型卡车活动在日、周和季节尺度上的时间变化特征；卡车交通和排放与乘用车或总交通量没有很好的相关性。对于这两种污染物，超过 90% 的预测 24 小时平均浓度在两个近道路监测点观测值的两倍以内。该模型对气象的季节性变化和排放量的每周变化做出了适当的响应。NOx 的 RLINE 模型性能总体上优于 BC。减少排放因子的不确定性将有助于提高 BC 的模型性能。