Constructed wetlands (CWs) are increasingly recognized as multifunctional Nature-based Solutions capable of simultaneously addressing wastewater treatment and biomass valorization challenges within the Water-Energy-Food nexus. However, the seasonal variability of plant biomass composition, which is intrinsically linked to species-specific phenological stages, remains poorly investigated in relation to its impact on anaerobic digestion (AD) performance. This study evaluates the biochemical methane potential (BMP) of four common CW macrophytes ( Iris pseudacorus , Phragmites australis , Carex spp. and Typha latifolia ) harvested from a full-scale CW at three phenological stages (spring, summer, autumn), to assess the influence of harvesting time on biogas production potential and quality. Physico-chemical characterization revealed significant seasonal shifts in dry matter content, C/N ratio, and biochemical properties of the biomasses, which translated into marked differences in methane yields and biogas composition. Autumn-harvested feedstocks exhibited the highest cumulative methane yields (up to 357 mL CH₄/gVS), attributed to a favourable balance of moisture content, moderate lignification, and optimal C/N ratios. Furthermore, autumn samples showed the highest methane enrichment (up to 61% CH₄), suggesting improved process stability and microbial conversion efficiency. These findings demonstrate that the timing of CW biomass harvesting is a critical parameter in optimizing AD-based energy recovery, and supports the integration of CWs within circular bioresource management strategies aimed at enhancing system multifunctionality and resource-use efficiency. • Integrating CWs with bioenergy systems supports circular economy goals. • Harvesting time strongly influences AD performance of CW-derived biomass. • Summer-harvested biomass yields the highest cumulative methane. • Autumn-harvested biomass produces biogas with the highest methane concentration. • Seasonal biomass characteristics are key to AD efficiency and resource optimization.
Mancuso et al. (Tue,) studied this question.