The wide-range applications of isotope analysis make isotope measurement approaches under attentive focus. Off-axis integrated cavity output spectroscopy technology (OA-ICOS) is the most advanced isotope analysis method; however, further studies are still needed to avoid signal noise and improve accuracy. Zero-phase low pass filtering multivariate Fourier Decomposition Method (FDM) was applied for data analysis in the present study, which has its unique advantage to fix up rapid but seasonal changes for nonlinear and non-stationary time series data. In the present study, δ13C content in gaseous CO2 sample were measured by OA-ICOS at ambient temperature. The experimental data treated by FDM showed less signal fluctuant and clearer value change tendency than what showed in raw data, whereas the data density kept same with that of raw data. In the meantime, the experimental results suggested that it is flexible to decide the variance explanation rate by simply change the order of an FDM filter. This approach meets up with the requirements of different practical application scenarios of isotope analysis, which enhances the feasibility for OA-ICOS application in real-time environmental monitoring field.

The release of leachates from intact coal ash impoundments is a concern due to the enrichment and mobilization of toxic elements such as arsenic (As) and selenium (Se). This study aims to explore the intrinsic properties of coal fly ash that correlate with the relative leachability of As and Se. We performed leaching experiments with 52 fly ash samples collected from 15 different U.S. power plants and representing coal feedstocks from the three major domestic regions. We assessed the mobilization potential of As and Se in fly ash based on standardized leaching protocols and performed multivariate and lasso regression analyses to explore correlations of leachable As and Se contents with characteristics such as major element contents, loss on ignition, and pH. The results of regression models indicated that major elements (Fe, Ca, and Al) for a wide range of fly ashes can serve as predictor variables for the leaching potential of As but not for Se. LOI and pH were not important predictive variables in the models. Both regression approaches resulted in relatively strong fits for leachable As (correlation coefficient R 2 = 0.78 for both models) compared to models for leachable Se (R 2 = 0.49). Overall, these results suggest that correlation models combined with on-site elemental analysis with portable analyzers may enable a screening method for leachable As in coal ash.

Coal combustion byproducts are known to be enriched in arsenic (As) and selenium (Se). This enrichment is a concern during the handling, disposal, and reuse of the ash as both...