Greenhouse Gas Emission Monitoring and Air Quality Index Project
Air quality monitoring is often done with expensive fixed stations or single-parameter point sensors. Under this TÜBİTAK 1501-funded R&D project, HEFA Teknoloji A.Ş. developed a LoRaWAN-connected multi-parameter air quality monitoring device and web interface capable of simultaneously monitoring NO₂, SO₂, CO, CO₂, CH₄, O₃, PM₁₀, PM₂.₅ and meteorological parameters. The project output is an end-to-end system comprising hardware, embedded firmware, cloud infrastructure and an AI-assisted air quality index prediction model.
What We Built
Multi-Parameter Sensor Infrastructure
- Electrochemical sensors: Alphasense 4-electrode electrochemical sensors providing ppb-level sensitivity for NO₂, SO₂, CO and O₃. Cross-sensitivity, linearity and temperature tests were performed and regression functions developed.
- NDIR sensor: NDIR sensor with optical measurement principle for CO₂. Long-term stability and high accuracy were the selection rationale.
- PM sensor: Optical particle counter module based on laser scattering for PM₁₀ and PM₂.₅.
- Meteorology module: temperature, humidity, wind speed and direction, pressure measurement.
PCB Design — Mixed Signal Layout
- Mixed signal layout rules were rigorously applied for co-locating analog sensor outputs, high-resolution ADC and digital communication blocks on the same board. Analog and digital sections are separated; internal layers in the multi-layer PCB were selected as solid GND planes.
Communication Infrastructure
- LoRaWAN Class A selected as primary communication protocol. Data is transferred to the cloud via The Things Network (TTN) integration.
- GSM module: backup communication for standalone operation where LoRaWAN gateway infrastructure is unavailable.
Web-Based Monitoring Interface
- Dashboard supporting per-parameter graphical views, historical data queries and threshold breach notifications.
AI-Assisted Air Quality Index Prediction
- Model taking instantaneous sensor measurements, meteorological parameters and historical data patterns as input to produce air quality forecasts.
Technical Specifications
| Parameter | Value |
|---|---|
| Monitored gases | NO₂, SO₂, CO, CO₂, CH₄, O₃ |
| Particulate measurement | PM₁₀, PM₂.₅ |
| Meteorology | Temperature, humidity, pressure |
| Gas sensor technologies | Electrochemical (NO₂, SO₂, CO, O₃), NDIR (CO₂) |
| Primary communication | LoRaWAN Class A |
| Backup communication | GSM |
| Network infrastructure | The Things Network (TTN) |
| PCB design | Mixed signal layout |
| Data visualisation | Web-based dashboard |
| AI component | Air quality index prediction model |
| Project support | TÜBİTAK 1501 |
Use Cases
- Urban Air Quality Monitoring Networks — Municipalities, environmental agencies, low-cost distributed network via LoRaWAN
- Industrial Site Monitoring — Factory perimeter, organised industrial zones, port emission monitoring
- Research and Measurement Campaigns — Universities, research institutions
Frequently Asked Questions
Why was LoRaWAN Class A chosen over Class C?
Is TTN integration suitable for commercial applications?
Why was NDIR instead of electrochemical sensor used for CO₂?
Does the AI model run in real time?
How complex is it to scale the system?
This project encompasses a broad R&D process covering multi-parameter sensor integration, mixed signal PCB design, LoRaWAN/GSM communication infrastructure, cloud-connected data platform and AI model development. If you need environmental monitoring, emission tracking or multi-parameter sensor system development, we can evaluate your technical requirements together. end-to-end sensor product development service for more information.
Contact Us