Among the various daily activities, commuting to school is considered as an important activity for the students. While commute, students are exposed to elevated particle concentrations that make significant contributions to their daily personal exposures. Present study compares the student exposure towards particle number concentration (PNC) and inhaled doses while commuting from different transports during morning (MT) and afternoon (AT) trips. PNC was measured from four different transport modes: two-wheeler (2W), three-wheeler (3W), passenger car (PC) and school bus (SB) during summer and winter seasons. In summer season, average total PNC was highest for 3W and SB during MT (5.53E+07 # m⁻³) and AT (5.29E+07 # m⁻³) trips respectively. However, in winter season, 3W has the highest average total PNC during MT (1.04E+08 # m⁻³) and AT (7.92E+07 # m⁻³) trips. Ventilation settings greatly influence the in-cabin PNC and found to be highest under window open (WO) scenario for PC and least under WO and window closed scenarios for SB during MT and AT trips respectively. The PNC exposure varies significantly with student seating positions inside SB and followed different trends during MT (front > middle > rear) and AT (rear > front > middle) trips. The mixed-effect multivariate linear regression model was used to estimate the effects of multiple predictor variables that influence the PNC measured from different transport modes while commuting. Background concentration (PNC) and meteorological variables (wind speed, ambient temperature and relative humidity) were important predictors of PNC measured while commuting and explained the variability between 0.04–27.31%.

在各种日常活动中，上下班被认为是学生的一项重要活动。在上下班途中，学生会接触到浓度升高的微粒，这对他们的日常个人接触做出了重大贡献。本研究比较了学生在早晨（MT）和下午（AT）出行途中从不同交通工具上下班时所面临的颗粒物浓度（PNC）和吸入剂量暴露。PNC是从四种不同的运输方式进行测量的：夏季和冬季，两轮车（2W），三轮车（3W），乘用车（PC）和校车（SB）。在夏季，MT（5.53E + 07＃m ^-3）和AT（5.29E + 07＃m ^-3）期间，3W和SB的平均总PNC最高。）旅行。但是，在冬季，在MT（1.04E + 08＃m ^-3）和AT（7.92E + 07＃m ^-3）期间，3W的平均总PNC最高。）旅行。在PC的开窗（WO）场景下，通风设置对车内PNC的影响最大，在MT和AT行程期间，通气设置分别在PC的开窗（WO）方案和SB的WO和关窗方案下最高。PNC的暴露随SB内部学生座位位置的不同而有很大差异，并且在MT（前>中>后）和AT（后>前>中）旅行期间遵循不同的趋势。混合效应多元线性回归模型用于估计通勤时影响从不同运输方式测得的PNC的多个预测变量的影响。背景浓度（PNC）和气象变量（风速，环境温度和相对湿度）是通勤时测量PNC的重要预测指标，并解释了0.04–27.31％之间的变化。