Department of Geography and Environment, Boston University, Boston, MA 02215, USA.
Environ Pollut. 2012 Nov;170:113-23. doi: 10.1016/j.envpol.2012.06.003. Epub 2012 Jul 7.
On-road emissions are a major contributor to rising concentrations of atmospheric greenhouse gases. In this study, we applied a downscaling methodology based on commonly available spatial parameters to model on-road CO(2) emissions at the 1 × 1 km scale for the Boston, MA region and tested our approach with surface-level CO(2) observations. Using two previously constructed emissions inventories with differing spatial patterns and underlying data sources, we developed regression models based on impervious surface area and volume-weighted road density that could be scaled to any resolution. We found that the models accurately reflected the inventories at their original scales (R(2) = 0.63 for both models) and exhibited a strong relationship with observed CO(2) mixing ratios when downscaled across the region. Moreover, the improved spatial agreement of the models over the original inventories confirmed that either product represents a viable basis for downscaling in other metropolitan regions, even with limited data.
道路排放是大气温室气体浓度上升的主要原因。在这项研究中，我们应用了一种基于常见空间参数的降尺度方法，对马萨诸塞州波士顿地区的道路 CO2 排放进行了 1×1 公里尺度的建模，并利用地面 CO2 观测对我们的方法进行了测试。我们使用了两个具有不同空间模式和基础数据源的先前构建的排放清单，开发了基于不透水面面积和体积加权道路密度的回归模型，这些模型可以扩展到任何分辨率。我们发现，这些模型在原始尺度上准确地反映了清单数据（两个模型的 R2 均为 0.63），并且在整个区域进行降尺度时，与观测到的 CO2 混合比具有很强的关系。此外，模型相对于原始清单的空间一致性的提高证实，即使数据有限，这两种产品都可以作为在其他大都市区进行降尺度的可行基础。
