Spatial modeling of global agricultural field burning emissions

Introduction

Burning of agricultural crop residue is practiced globally to remove excess residue, control pests, and produce ash fertilization. It is an important local air pollution source. The emissions from crop residue burning are included in several models. However, the results from the models differ, especially when specific areas are investigated, and it is also suspected that the emissions are underestimated. This study therefore answers the following questions: (1) Why are the results from different models so different, and (2) How can the modeling be improved?

Methodology

Three different modeling approaches were studied: FINNv1 [1] , GFEDv4 [2], and Jessica McCarty's work on agricultural fires [3]. The approaches use different fire activity data to assess the occurrence of fires. The fire activity data examined in this study were the MODIS Thermal Anomalies Product, and the MODIS Burned Area (MCD45A1) product. The fire activity data is coupled with land cover data to identify cropland fires and to achieve estimates for the area of burned land. The land cover data that the models use is MODIS MCD12Q1 with IGBP classification (UMD classification in GFED). Emission factors were from FINNv1 and McCarty's collection from literature. To assess the effect of land use data to the results, the emissions were also calculated with the IIASA hybrid cropland product. The results were calculated for and compared in Eastern Europe   to avoid differences being averaged out, which could happen on a global scale. The emissions modeled were CO, black carbon, and PM_2.5.

Results and Conclusions

The fire data used were found to have the largest effect on the modeled emissions. The difference between the emissions from the highest and the lowest estimate of burned area was 5-fold. The Burned Area product produced higher emissions, but they were also less spread out. Land cover data had a smaller impact on the emissions, and the differences were not systematic. In the future, more work is needed to identify the best possible fire data for the emission modeling. Measured fire radiative power, which was not included in this study, should be included in future assessments. The best land use data may depend on the fire data used, as the different fire data represent different quantities (e.g., fire detections, fire intensity, or area burned), and they should be assessed together. Local assessment for the emissions should be carried out in areas where measurement or other types of observation of the burnings exist. Crop-specific emission factors that would account for local differences in burning practices and their timing would systematically improve the emission estimates. 

Please note these Proceedings have received limited or no review from supervisors and IIASA program directors, and the views and results expressed therein do not necessarily represent IIASA, its National Member Organizations, or other organizations supporting the work.

Last edited: 19 August 2015