Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Summary

A simple, effective environmental monitoring protocol to measure temperature, humidity, wind speed, and carbon dioxide variations across urban surfaces with differing sunlight exposure influenced by the urban heat island effect.

Abstract

Urbanization and rapid land-use change have intensified the urban heat island effect (UHIE), leading to elevated ambient temperatures, altered humidity, and increased accumulation of carbon dioxide (CO₂) in dense urban environments. These microclimatic changes not only influence thermal comfort but also affect outdoor air quality and, consequently, indoor ventilation dynamics. Current monitoring approaches, such as satellite remote sensing and city-scale air quality stations, provide valuable insights but lack the façade-level spatial resolution necessary to inform building-level interventions.

This study presents a reproducible, low-cost protocol for façade-specific monitoring of CO₂, temperature, and humidity using non-dispersive infrared (NDIR)-based sensors integrated with microcontroller data loggers. Sensor modules are deployed on contrasting thermal zones-sun-exposed and shaded façades-to capture diurnal variations over a 24 h cycle. Calibration against outdoor baseline CO₂ concentrations ensures measurement accuracy and comparability among sensors. The method was validated across four Indian cities with distinct urban forms. Results consistently revealed higher CO₂ concentrations in densely built zones compared to adjacent green façades, with differences ranging from 19 ppm (Pune, India) to 78 ppm (Chennai, India). These gradients were further associated with localized temperature rises of up to 2 °C and reduced relative humidity, confirming the role of UHIE in exacerbating pollutant retention.

The proposed protocol offers a scalable, field-friendly approach for urban microclimate assessment, HVAC optimization, and environmental health research. Beyond CO₂, the methodology can be extended to capture additional air quality parameters such as PM₂.₅, PM₁₀, and VOCs, enabling a comprehensive assessment of urban air quality. By providing façade-level, real-time insights, this monitoring framework supports data-driven decision-making for sustainable building design, intelligent ventilation systems, and long-term climate-resilient urban planning.

Introduction

The urban heat island effect (UHIE) is a persistent climatic phenomenon in which urban regions experience elevated temperatures compared with their rural surroundings, primarily due to anthropogenic heat emissions, dense surface materials, and diminished vegetation cover^1,2,3. Elevated urban temperatures alter localized microclimates, influencing wind flow, humidity, and the retention of atmospheric CO₂^1,4. In densely built environments, thermal stratification reduces natural air mixing, thereby intensifying CO₂ accumulation at the pedestrian level, which in turn impacts both air quality and public health^5,6.

Urban morphology strongly influences microclimatic behavior and CO₂ dynamics, especially at the building and neighborhood scales². Variations in solar exposure between shaded and sun-exposed façades generate measurable gradients in temperature, humidity, and pollutant concentration. While satellite and remote-sensing approaches effectively capture broad-scale UHIE patterns, in situ monitoring at the façade or building scale remains limited^5,6.

This fine-grained resolution is increasingly critical as climate change amplifies UHIE intensity. Cities require cost-effective and scalable methods to monitor and mitigate thermal and air quality risks^4,7,8. To address this gap, the present study introduces a sensor-based protocol for spatial and temporal monitoring of CO₂ fluctuations in urban microenvironments. Low-cost NDIR CO₂ sensors and thermo-hygrometers were deployed across sun-exposed and shaded façades to evaluate the influence of thermal exposure and airflow on CO₂ retention. Data were collected from both high-density built-up zones and green-shaded areas, allowing assessment of land-use impacts on emission patterns.

By focusing on façade-level microclimates, this approach identifies critical environmental gradients that influence air chemistry under real-world built conditions^1,2,5. The findings are relevant to multiple domains, including urban climatology, air quality modeling, sustainable architecture, and HVAC design. Specifically, identifying CO₂-rich façades, vehicular emission hotspots, and poorly ventilated zones can inform the placement of air intakes, guide passive ventilation strategies, and support climate-responsive urban design^4,7. With the adoption of mitigation strategies such as green roofs, reflective coatings, vegetated façades, and high-SRI materials, accurate façade-level monitoring provides quantitative evidence to validate performance and inform long-term climate-resilient interventions^3,8.

Protocol

1. Sensor Calibration and Setup

Figure 1: AC-powered sensor module housing. A 230 V AC-powered sensor unit containing CO₂, temperature, and humidity sensors, used for continuous microclimatic monitoring. Please click here to view a larger version of this figure.

1. Prepare sensor modules.
   1. Assemble the sensor module (Figure 1) using an NDIR CO₂ sensor and a combined temperature-humidity probe.
   2. Configure the microcontroller-based data logger with a real-time clock module and secure digital (SD) card shield.
   3. Set the logging frequency to one reading every 10 min.
   4. Place the assembly inside weather-resistant housing, typically an enclosure made of polypropylene, and ensure that all wiring is insulated and connectors are firmly secured.
2. Test sensor function and perform calibration in open air.
   1. Place all CO₂ and temperature-humidity sensors in a well-ventilated outdoor area (Figure 2).
   2. Connect to a stable power source, preferably a 230 V AC-to-DC adapter or a battery bank rated for continuous 24 h operation.
   3. Allow sensors to stabilize for 5-10 min, and record baseline CO₂ levels (approximately 410 ppm ± 3% ) to verify consistency across devices (within ±3% or ±10 ppm).
   4. NOTE: CO₂, temperature, and humidity sensors with integrated data logging are commercially available and offer a cost-effective option for standardized environmental monitoring applications.

Figure 2: Sensor calibration setup. A 230 V AC-powered sensor module configured for calibration to ensure measurement consistency across all deployed sensors. Please click here to view a larger version of this figure.

2. Site selection and deployment of sensor modules

1. Identify contrasting thermal zones.
   1. Select two or more contrasting urban typologies within the locality, typically around the building or zone of interest: (a) high-density built-up area, (b) adjacent low-density green or shaded area, (c) road-facing façade, and (d) façade with outdoor HVAC heat-rejection units.
   2. Install sensors on multiple façades where applicable to capture thermal and pollutant variation.
2. Mount sensors.
   1. Mount the sensor module (Figure 3) using stainless steel brackets fixed with masonry screws or harness ropes anchored to window grills or wall hooks.
   2. Maintain a standard mounting height of 1-2 m above the local floor level for each façade on the floor. The exact height may vary between floors or buildings depending on accessibility, supporting infrastructure available, and façade geometry.
   3. Record the directional orientation (e.g., east-facing façade adjacent to road) and nearby obstructions (e.g., HVAC units, walls).
3. Configure power supply and logging.
   1. Ensure continuous power supply. Connect the module to a 230 V AC-to-12 V DC adapter with surge protection, or use a lithium-ion battery bank rated for at least 24 h of operation.
   2. Verify sensor output by observing live readings on the display or logger interface to confirm a stable connection and active data recording.
      CAUTION: Handle 230 V AC equipment with insulated gloves and avoid contact with live wires during setup.

Figure 3: Field-deployed sensor enclosure. A rain-protected sensor housing installed on a building façade in Chennai to collect CO₂, temperature, and humidity data. Please click here to view a larger version of this figure.

3. Data collection

1. Begin data acquisition.
   1. Power the logger to begin recording and capture the diurnal measurement cycle.
   2. Allow the logger to operate continuously for 24 h with a data-logging interval of 10 min to capture full-day variations in CO₂ concentration, temperature, and humidity.
   3. Verify that timestamps remain synchronized across all sensors before and after deployment.
   4. Record start and stop times manually in a field logbook to provide redundant backup documentation.

4. Data retrieval and visualization

Figure 4: Monitoring dashboard interface. A snapshot of the cloud-based dashboard showing real-time and historical sensor data visualizations. Please click here to view a larger version of this figure.

1. Retrieve data.
   1. Safely eject the SD card and insert it into a card reader; copy the CSV data files into the designated project folder.
   2. For Wi-Fi-enabled loggers, connect via the local SSID and download logs through the IP-based dashboard (Figure 4).
   3. Process the retrieved data using a spreadsheet or data analysis software to prepare for visualization.
2. Generate plots.
   1. Generate a multi-variable line plot showing CO₂ concentration and temperature on the primary y-axis and relative humidity (RH) on the secondary y-axis to allow direct trend comparison.
   2. Label the x-axis as "Time," the primary y-axis as "CO₂ (ppm)/Temperature (°C)," and the secondary y-axis as "Relative Humidity (%)."
   3. Add a legend to distinguish variables and annotate peak values (typically between 11:00-15:00) for inter-site comparison.
      NOTE: Refer to Figure 5, Figure 6, Figure 7, and Figure 8 for representative plots of CO₂ concentration versus time.

5. Quality control

1. Perform data validation.
   1. Identify and remove outliers or data points flagged due to timestamp irregularities or device communication errors.
2. Recalibrate if needed.
   1. If post-experiment analysis indicates sensor drift exceeding 3% (±10 ppm), repeat the baseline calibration described in step 1.2 and reacquire data under identical conditions.

6. Optional: seasonal or multisite deployment

1. Extend the field of study.
   1. Expand deployment by installing sensors on all façades and at multiple vertical levels (e.g., ground, mid, and rooftop) to capture vertical thermal and CO₂ gradients.
   2. Conduct seasonal monitoring during summer, winter, and monsoon periods to evaluate temporal and diurnal variability in CO₂ and temperature.
2. Perform seasonal trend analysis.
   1. Analyse results across localities and seasons to identify recurring periods of elevated CO₂ and thermal stress.
   2. Generate comparative visualizations to assess the influence of building orientation, land use, and façade composition on CO₂ and temperature variation.

7. Troubleshooting

1. Power interruption.
   1. If a power outage occurs during deployment, reconnect the module to a stable 230 V AC-to-5 V DC adapter or use a lithium-ion battery bank providing at least 320 Wh capacity to ensure uninterrupted 24 h operation.
   2. Ensure the battery bank is fully charged before redeployment to avoid data loss.
2. Logger failure or incomplete data.
   1. If no data is recorded, verify wire continuity using a circuit tester and confirm power delivery with a voltage meter.
   2. Use a digital multimeter to check sensor terminal output (5 V DC).
   3. Replace corrupted SD cards or reset the microcontroller, then repeat the baseline calibration (step 1.2) before redeployment.
3. Damaged conduit or loose wiring.
   1. If the protective conduit is damaged or wiring exposed, disconnect power immediately and replace the conduit with UV-resistant PVC tubing matching the cable diameter, with a length 1.5 times the exposed section to ensure full coverage and clearance on both sides.
   2. Verify connection integrity using a continuity tester before restoring power.
4. Sensor drift or inaccurate readings.
   1. If data deviates beyond ±3% (±10 ppm for CO₂), recalibrate the sensors using the open-air baseline procedure described in step 1.2.
   2. Record stabilized baseline readings to confirm accuracy before resuming data collection.
5. Loss of wireless data transmission.
   1. If Wi-Fi transmission fails, check the logger service set identifier (SSID) configuration and router range.Reset the microcontroller and re-enter Wi-Fi credentials.
   2. As a backup, remove the SD card and retrieve CSV data logs manually.

Results

The recorded data reveal a consistent cross-city pattern: external CO₂ concentrations are significantly higher in areas with high concretization compared to adjacent green zones.

In Chennai (Figure 9), a difference of 78 ppm was observed between the concrete-dominant east façade (490 ppm) and the shaded green north façade (412 ppm). This contrast was intensified by the presence of outdoor HVAC condenser units on the east façade, which released waste heat into the ...

Discussion

A critical step in this protocol is the baseline calibration and synchronization of CO₂, temperature, and humidity sensors. Accurate calibration in shaded, open-air settings is essential to ensure inter-sensor consistency and minimize drift during deployment¹. Incorrect placement or uneven solar exposure can introduce false gradients; therefore, maintaining a mounting height of 1-2 m and ensuring adequate distance from localized heat sources such as HVAC condenser units are important conside...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Arduino IDE	Arduino	v2.x	For programming and debugging ESP32/Arduino boards
CO2 Sensor, NDIR (Non-Dispersive Infrared)	Sensirion	SCD40 / SCD41	Used for real-time ambient CO2 monitoring
Data Logging Microcontroller (ESP32)	Espressif	ESP32 DevKitC	For logging sensor data; supports Wi-Fi and SD card
Humidity and Temperature Sensor	Sensirion	SHT21	For ambient temperature and relative humidity measurement
MicroSD Card (32 GB or higher)	SanDisk / Kingston	Various	Stores time-series CO2, temperature, and humidity data
MicroSD Card Module	Generic Supplier	N/A	Interface between ESP32 and SD card for data logging
Power Adapter (AC to 5V DC)	Generic Supplier	N/A	Converts 230 V AC to stable 5 V DC power
Python Software Environment	Python.org	v3.10 (pandas 1.5, matplotlib 3.7)	For advanced data analysis, visualization, and automation
Rainproof Sensor Housing	Polypropylene/3D Printed	N/A	Perforated casing to protect electronics from rain and dust
Rope or Mounting Harness	Generic	N/A	Used for secure façade mounting at required height
SD Card Reader/Writer	Any USB Interface	N/A	For transferring data from SD card to PC
Excel	Microsoft	N/A	For post-processing time-series data
Calc	LibreOffice	N/A	Spreadsheet Software, For post-processing time-series data
USB Cable (Micro/Type-C)	Generic	N/A	Used for programming and powering ESP32 module