Narayan Kanishka B, Smith Steven J, Fioletov Vitali E, McLinden Chris A
Joint Global Change Research Institute, Pacific Northwest National Lab, Washington D.C. 20740, United States.
Air Quality Research Division, Environment and Climate Change Canada, Toronto M3H5T4, Canada.
Environ Sci Technol. 2023 Aug 1;57(30):11134-11143. doi: 10.1021/acs.est.2c07056. Epub 2023 Jul 19.
Satellite remote sensing is a promising method of monitoring emissions that may be missing in inventories, but the accuracy of these estimates is often not clear. We demonstrate here a comprehensive evaluation of errors in anthropogenic sulfur dioxide (SO) emission estimates from NASA's OMI point source catalog for the contiguous US by comparing emissions from the catalog with high-quality emission inventory data over different dimensions including size of individual sources, aggregate vs individual source errors, and potential bias in individual source estimates over time. For sources that are included in the catalog, we find that errors in aggregate (sum of error for all included sources) are relatively low. Errors for individual sources in any given year can be substantial, however, with over- or underestimates in terms of total error ranging from -80 to 110 kt (roughly 10-90th percentile). We find that these errors are not necessarily random over time and that there can be consistently positive or negative biases for individual sources. We did not find any overall statistical relationship between the degree of isolation of a source and bias, either at a 40 or 70 km scales. For a sub-set of sources where inventory emissions over a radius of 70 km around an OMI detection are larger than twice the emissions within 40 km, the OMI value is consistently overestimated. We find, as expected, that emission sources not included in the catalog are the largest aggregate source of difference between the satellite estimates and inventories, especially in more recent years where source emission magnitudes have been decreasing and note that trends in satellite detections do not necessarily track trends in total emissions. We find that the OMI-based SO emissions are accurate in aggregate, when summed over a number of sources, but must be interpreted more cautiously at the individual source level. Similar analyses would be valuable for other satellite emission estimates; however, in many cases, the appropriate high-quality reference data may need to be generated.
卫星遥感是一种很有前景的监测排放的方法，这些排放可能在排放清单中缺失，但这些估算的准确性往往并不明确。我们在此通过比较美国国家航空航天局（NASA）的对流层监测仪器（OMI）点源目录中人为二氧化硫（SO）排放估算值与高质量排放清单数据在不同维度上的差异，对美国本土OMI点源目录中人为二氧化硫排放估算的误差进行了全面评估，这些维度包括单个源的规模、总误差与单个源误差，以及单个源估算随时间的潜在偏差。对于目录中包含的源，我们发现总误差（所有包含源的误差总和）相对较低。然而，任何给定年份中单个源的误差可能很大，总误差的高估或低估范围在-80至110千吨之间（大致为第10至90百分位数）。我们发现这些误差随时间不一定是随机的，单个源可能存在持续的正偏差或负偏差。在40公里或70公里尺度上，我们均未发现源的孤立程度与偏差之间存在任何总体统计关系。对于OMI探测周围70公里半径范围内的清单排放大于40公里范围内排放两倍的一部分源，OMI值一直被高估。正如预期的那样，我们发现目录中未包含的排放源是卫星估算值与清单之间差异的最大总体来源，尤其是在近年来源排放规模一直在下降的情况下，并指出卫星探测趋势不一定跟踪总排放趋势。我们发现，基于OMI的SO排放在多个源求和时总体上是准确的，但在单个源层面必须更谨慎地解读。类似的分析对于其他卫星排放估算也将是有价值的；然而，在许多情况下，可能需要生成合适的高质量参考数据。
