Quantification estimates in distribution. Quantification estimates are also useful when studied in distribution, as long-term aggregates of quantification estimates can be used for annualized emissions reporting at the site-level. To compare quantification estimates in distribution, we bin the vendor-provided quantification estimates (averaged up to a 30-minute frequency) and the estimates from the DLQ algorithm. Similar to the near real time quantification comparison, we compare vendor-provided estimates against vendor-provided estimates and DLQ estimates against DLQ estimates. Note that this procedure again allows us to separate the influence of the sensor platform and the quantification algorithm. Comparisons are made using distributional averages and empirical cumulative distribution functions. Confidence intervals for the distributional averages are calculated by randomly sampling 50% of the distribution 1,000 times, taking the mean of each sample, and then selecting the 0.025 and 0.975 quantiles as the 95% interval.