AWD Chamber Designer
IoT CH₄ Static Flux Chamber — Nepal Rice Field
dMRV / CH₄
Chamber Dimension Calculator
Rice Crop Parameters
Plant height
80 cm
Water depth
5 cm
Nepal AWD: 70–110 cm typical
Tall traditional varieties: up to 150 cm
AWD flooded: 0–5 cm above soil
Tall traditional varieties: up to 150 cm
AWD flooded: 0–5 cm above soil
Chamber Dimensions
Length
50 cm
Width
50 cm
Headspace
20 cm
Closure (min)
20 min
Computed Chamber Metrics
Total Height
—
cm
Base Area
—
m²
Volume (V)
—
litres
V / Area ratio
—
L / m²
Hills enclosed
—
@ 20×20 cm spacing
Headspace status
—
—
Pre-deployment Checklist
Measurement Protocol — Closure Sequence
Automated IoT Closure Sequence
T − 5 min
Fan ON. Log ambient CH₄ baseline (C₀). Confirm GPS lock and plot ID.
T = 0
Chamber sealed (manual lid or actuator). Begin logging every 15–30 s.
T = 0 → close
Log CH₄ (NGM2611), temp / RH / pressure (BME280), soil temp (DS18B20), water level (JSN-SR04T). Store to SD card with RTC timestamp.
T = close
Lid opens. Fan purges chamber. Log post-closure ambient CH₄ for drift check.
Post
Firmware computes slope (dC/dt), applies flux formula, stores result to SD, transmits via 4G/LoRa to dashboard.
Frequency by AWD Phase
Flooded
Once daily — 08:00–10:00 local time
Draining
Every 12 hours — peak emissions often occur at drainage onset
Dry phase
Once daily — emissions typically low
Re-flood ⚠
Every 6 hours for 48 h after re-wetting — sharp CH₄ pulse very likely
Harvest
Every other day until field drained
GRA/NIAES guideline: manual sampling misses peak events during AWD drainage cycles.
IoT automation captures them. This is the core scientific value of this system.
IoT automation captures them. This is the core scientific value of this system.
GRA / NIAES Flux Formula
CH₄ Flux Equation
F (mg CH₄ m⁻² h⁻¹) = dC/dt × V × M / (A × R × T) × 3600
dC/dt CH₄ concentration rise rate ppm / s ← from IoT slope fit
V Enclosed chamber volume m³ ← from dimensions
M Molar mass of CH₄ 16 g/mol
A Chamber base footprint area m² ← from dimensions
R Universal gas constant 8.314 J mol⁻¹ K⁻¹
T Chamber air temperature Kelvin ← from BME280
Why frequent IoT readings improve accuracy
Manual method uses 3 gas samples → 3 points to fit slope.IoT sensor at 15 s intervals over 20 min → ~80 points to fit slope.
More points = better linear regression = more accurate dC/dt = better flux estimate.
Non-linear accumulation (ebullition events) also becomes detectable.
Accuracy levels (per Bastviken et al. 2020)
Level 1 — Screening: Low-cost sensor + T/RH correction. Relative comparisons valid now.Level 2 — Research flux: Sensor-specific calibration + ~10–20 GC reference samples per season.
Level 3 — Registry MRV: Full-season validation vs GC/LI-COR, drift testing, QA/QC protocol.
Tags:
IoT