基于回归模型的道路二氧化碳排放体积分数的估算与验证

Estimate and Verification of the Concentrations of on-Road Carbon Dioxide Emissions Based on Regression Model

目前，道路CO2排放量的估算研究存在数据获取难、时空分辨率低等难题．利用排放因子模型法所建立的城市道路碳排放清单，往往不能直接用于对道路实际碳排放水平的实时模拟和预测．文中利用回归分析方法，结合易于获取的空间变量（即行政街道人口密度、车道数），建立了实测道路CO2排放体积分数与相关因子之间的非线性回归模型，并分析了CO2体积分数沿道路水平垂直方向的衰减规律．基于回归模型，实现了对研究区域内全部路段早高峰时段（7：00-9：00）CO2排放体积分数的预测，经验证模型误差较小，获得了天河区CO2体积分数的空间分布．车道数对CO2排放对体积分数的影响显著．其研究结果预测效果较好，适用性广泛，有助于城市道路交通碳排放清单的建立和城市碳减排策略的制定。

Current methods for estimating on-road CO2 emissions are facing difficulties such as the lack of data and low spatial and temporal resolution. In addition, the inventory established by using emission factors often can not be directly used to simulate or predict the real on-road carbon emission level. The regression method to build a nonlin- ear regression model between measured volume fractions of on-road CO2 emissions and two easily accessible spatial variables namely population densities of subdistricts and numbers of lanes are employed. The attenuation law of CO2 volume fractions along the direction perpendicular to an open road in the horizontal plane is analyzed. By using the regression model, the on-road CO2 volume fractions of all the sections during the morning peak hours（7：00 -9：00） within the study area with small model prediction error is successfully predicted and the CO2 volume fractions＇ distri- bution map of Tianhe District is obtained. The analyzing results of the characteristics of the CO2 volume fractions＇ distribution show that the number of lanes that represent traffic volume significantly affects CO2 volume fraction. The findings connote that the achievement of better prediction of on-road CO2 volume fractions has a wider applica- bility and contributes to the establishment of urban on-road carbon emission inventory and the formulation of carbon emissions＇ reduction strategies.