EVE: a low-cost, modular, end-to-end monitoring pipeline for environmental variables and GHG in rewetted peatlands

Abstract

Rewetted peatlands exhibit strong small-scale, spatio-temporal variability in their greenhouse gas (GHG; CO₂, CH₄ and N₂O) emissions. Those are shaped by water table dynamics, vegetation structure, and microclimate. Capturing “hotspots” and “hot moments” across heterogeneous peatlands typically requires dense instrumentation. However, conventional monitoring solutions remain expensive, difficult to scale, and often depend on commercial, vendor-locked systems. We present the Environmental Variables Explorer (EVE) as a low-cost, modular, open-source alternative that enables researchers to build, repair, adapt, and self-host their monitoring stack without vendor lock-in.EVE is a platform blueprint rather than a single device. It combines low-power microcontroller nodes with power-saving duty cycling and two interoperable end-to-end, full user controlled workflows. The first, offline workflow, provides robust timestamped local storage (RTC + FRAM) with Bluetooth retrieval via a custom Android app - suited for remote sites. The second, online workflow, uses an ESP32 IoT node to upload measurements via Wi-Fi to a self-hosted PHP/MySQL backend that provides a web dashboard, API access, data visualization and data export (as CSV file) on inexpensive shared hosting. Critically, the offline-online duality provides a “fallback” logic for intermittently connected peatland environments and supports gradual scaling from single devices to multi-site networks.Building on EVE’s user-controlled pipeline, we present a pathway toward transferable near-real-time analytics by adding chamber-based GHG modules (low-cost CO₂/CH₄ sensing and chamber automation/sampling workflows. Integrating data-driven models (Random Forest and related methods) to estimate flux dynamics and annual budgets across 2-3 sites. Explicitly comparing high-end versus minimal low-cost inputs. By releasing hardware designs, firmware, backend code, and build documentation, this work aims to lower barriers for peatland and other scientists to deploy reproducible monitoring networks and to move toward shared, community-driven approaches for scalable GHG observation and modeling.