Assessing the Influence of Köppen–Geiger Climate Classification on Microalgae Productivity Simulations Using Long-Term Meteorological Data

Title: Assessing the Influence of Köppen–Geiger Climate Classification on Microalgae Productivity Simulations Using Long-Term Meteorological Data

Introduction:
This study examines how meteorological parameters influence biomass productivity under limiting conditions, using simulations as a complementary tool. These simulations also provide insights for investments aligned with circular economy objectives.

Objectives:
To develop a numerical simulation applied to wastewater treatment using microalgae.

Methodology:
Case-related study.

Results:
Twelve locations from the Renuwal-Cyted network (Red Iberoamericana para el Tratamiento de Efluentes con Microalgas 320RT0005) were analyzed: Bahía Blanca and Buenos Aires (Argentina), Santa Cruz do Sul, Bauru and Fortaleza (Brazil), Cartago (Costa Rica), Madrid and Almería (Spain), Querétaro (Mexico), Lisboa and Faro (Portugal), and Umeå (Sweden). These sites span diverse climatologies classified under different Köppen–Geiger categories.
The study first applies a climate adjustment of photosynthetically active radiation (PAR) using the site adaptation technique to produce PAR estimates. In the second phase, PAR and temperature data are used to generate Growth Meteorological Sequences (GMS) for each season, following a methodology based on Finkelstein–Schafer statistics that incorporates climatic indices, weighting coefficients and persistence criteria. The GMS represent the most probable long-term meteorological sequence for each season and support the identification of likely production scenarios for feasibility assessments and decision-making.

Conclusions:
PAR models showed the greatest errors in Oceanic, Hemiboreal and Tropical climates, with the Hemiboreal climate presenting the highest mean bias error. Conversely, Cold Arid, Hot Arid and Mediterranean climates yielded better model performance. Cold Arid and Hot Arid climates were distinct enough to differently influence both PAR measurement and modelling.

We are grateful to Rita Valenzuela (Ciemat, Spain), Germán Buitrón Méndez (UNAM, Mexico), Cintia Gómez (UAL, Spain) and Maritza Guerrero (ITCR, Costa Rica) for their collaborations.