Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands.

Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also evidence indicating that biochar promotes, rather than reduces, N2O emissions. Thus, the effect of biochar on N2O remains uncertain in constructed wetlands (CWs), and there is not a characterization metric for this effect, which increases the difficulty and inaccuracy of biochar-driven alleviation effect projections. Here, we provide new insight by utilizing machine learning-based, tree-structured Parzen Estimator (TPE) optimization assisted by a meta-analysis to estimate the potency of biochar-driven N2O mitigation. We first synthesized datasets that contained 80 studies on global biochar-amended CWs. The mitigation effect size was then calculated and further introduced as a new metric. TPE optimization was then applied to automatically tune the hyperparameters of the built extreme gradient boosting (XGBoost) and random forest (RF), and the optimum TPE-XGBoost obtained adequately achieved a satisfactory prediction accuracy for N2O flux (R2 = 91.90%, RPD = 3.57) and the effect size (R2 = 92.61%, RPD = 3.59). Results indicated that a high influent chemical oxygen demand/total nitrogen (COD/TN) ratio and the COD removal efficiency interpreted by the Shapley value significantly enhanced the effect size contribution. COD/TN ratio made the most and the second greatest positive contributions among 22 input variables to N2O flux and to the effect size that were up to 18% and 14%, respectively. By combining with a structural equation model analysis, NH4+-N removal rate had significant negative direct effects on the N2O flux. This study implied that the application of granulated biochar derived from C-rich feedstocks would maximize the net climate benefit of N2O mitigation driven by biochar for future biochar-based CWs.

Quantifying biochar-induced greenhouse gases emission reduction effects in constructed wetlands and its heterogeneity: A multi-level meta-analysis.

Zero-waste biochar is an emerging tool for carbon neutralization, but the role of biochar in reducing greenhouse gases (GHGs) emissions from CWs were controversy and uncertainty. Yet, no previous study has integrated multiple research systems to quantitatively examine biochar-mediated GHGs emission reduction potential in CWs. Here we synthesized 114 studies to quantify biochar-induced declines ability of GHGs in the CWs by using the multi-level meta-analysis, reveal the variation of GHGs emission effect in different biochar-CWs and its response relationship with biochar, and identify the moderating variables that had a strong explanatory effect on the emission reduction effect of biochar. We showed that biochar remarkably affect CO2 mitigation (p < 0.05), but has insignificant and heterogeneous effects on CH4 and N2O. Pyrolysis time, influent dissolved oxygen (DO), influent NO3--N concentration, hydraulic retention time (HRT) and wetland type can significantly affect the effect of biochar on CH4 emission reduction. Particularly, the importance of HRT and wetland type was 0.89 and 0.85, respectively. Specially, the surface batch CWs modified by biochar could significantly promote the emission of CH4 (p < 0.001), and the effect size was up to 89.59. For N2O, biochar diameter, biochar addition ratio, influent COD/TN ratio, plant name, and removal efficiency of NO3--N/TN/COD were significant moderators. Among them, influent COD/TN ratio and plant name showed a stronger explanation. Planting Cyperus alternifolius L. significantly enhanced the N2O emission reduction capacity by biochar (p < 0.001), and effect size was as low as -24.32. 700-900 °C biochar can promote CH4 flux but inhibit N2O flux. This study provides an important theoretical basis and valuable strategic guidance for more accurate estimation and improvement of synergistic emission reduction benefits between CH4 and N2O of biochar in CWs.