Decision-support tool development for eggshell-based wastewater technology application using meta-analysis and qualitative comparative analysis

One of the recent trends in sustainable approach to wastewater treatment is biowaste utilization, particularly eggshell waste. Due to its abundance as a waste stream, and potential for modifications, various applications of eggshell in wastewater treatment have been explored in the past 2 decades or more but are yet to be used by the broader industry or in a large-scale setting. One challenge was the lack of systematic organization of the growing body of research into a practical tool that may accelerate its innovation. This study served as the first structured synthesis of eggshell in wastewater treatment data into a decision-support tool through the combination of meta-analysis and qualitative comparative analysis approach. A total of 332 data points from 211 total retrieved studies in terms of modification type, contaminant class, wastewater sample source, treatment process, and low to high range of operational conditions (pH, initial concentration of contaminants, dosage, temperature, contact time) simplified and contextualized the analysis. The overall significant pooled effect size of 0.83 (95 % CI 0.77–0.89) suggested eggshell-based technology's high efficiency and reliability. The most critical factor identified was the interaction of modification type and contaminant class emphasizing context-dependent solutions. Qualitative comparative analysis found sufficient solutions for high removal efficiency. The research found the broad applicability of modified eggshell waste for a sustainable wastewater treatment application given its versatility and economic viability. The findings were successfully translated into a decision-support tool designed to provide practical, and evidence-based information for researchers, engineers, and innovators optimizing ES use for specific wastewater treatment goals or research problems, available at https://zrolda.shinyapps.io/ES-DST/ . • The combined meta-analysis and QCA was useful in developing a decision support tool. • .ES-modifications show a statistically significant 83 % removal efficiency for contaminants in wastewater across all studies. • The interaction of modification and contaminant type highlights the need for tailored ES solutions in wastewater treatment. • The decision-support tool is available at https://zrolda.shinyapps.io/ES-DST/ .