Name: Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers (Video) | JoVE Journal
Uploaded: 2025-12-12T14:50:00.000+00:00
Duration: 7 min 12 s
Description: Explore facade-level CO2 monitoring under urban heat island conditions using low-cost sensor data loggers. This protocol enables scalable environmental assessment for building energy performance and ventilation strategies.

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5 minutes

1. Sensor Calibration and Setup
<img alt="CO2 monitor with LEDs indicating air quality levels: danger, safe, enhanced; safety equipment." class="xfigimg" src="/files/ftp_upload/69307/69307fig1.jpg" data-altupdatedat="1789444946000" data-alt="Figure 1"> 
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. <a href="https://www.jove.com/files/ftp_upload/69307/69307fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol>
	<li>Prepare sensor modules.
	<ol>
		<li>Assemble the sensor module (Figure 1) using an NDIR CO₂ sensor and a combined temperature-humidity probe.</li>
		<li>Configure the microcontroller-based data logger with a real-time clock module and secure digital (SD) card shield.</li>
		<li>Set the logging frequency to one reading every 10 min.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>Test sensor function and perform calibration in open air.
	<ol>
		<li>Place all CO2 and temperature-humidity sensors in a well-ventilated outdoor area (Figure 2).</li>
		<li>Connect to a stable power source, preferably a 230 V AC-to-DC adapter or a battery bank rated for continuous 24 h operation.</li>
		<li>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).</li>
		<li>NOTE: CO₂, temperature, and humidity sensors with integrated data logging are commercially available and offer a cost-effective option for standardized environmental monitoring applications.</li>
	</ol>
	</li>
</ol>
<img alt="CO2 and temperature monitoring setup showing graphs and sensor on a 4ft ladder in a garden setting." class="xfigimg" src="/files/ftp_upload/69307/69307fig2.jpg" data-altupdatedat="1789444946000" data-alt="Figure 2"> 
Figure 2: Sensor calibration setup. A 230 V AC-powered sensor module configured for calibration to ensure measurement consistency across all deployed sensors. <a href="https://www.jove.com/files/ftp_upload/69307/69307fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Site selection and deployment of sensor modules
<ol>
	<li>Identify contrasting thermal zones.
	<ol>
		<li>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.</li>
		<li>Install sensors on multiple façades where applicable to capture thermal and pollutant variation.</li>
	</ol>
	</li>
	<li>Mount sensors.
	<ol>
		<li>Mount the sensor module (Figure 3) using stainless steel brackets fixed with masonry screws or harness ropes anchored to window grills or wall hooks.</li>
		<li>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.</li>
		<li>Record the directional orientation (e.g., east-facing façade adjacent to road) and nearby obstructions (e.g., HVAC units, walls).</li>
	</ol>
	</li>
	<li>Configure power supply and logging.
	<ol>
		<li>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.</li>
		<li>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.</li>
	</ol>
	</li>
</ol>
<img alt="Air monitoring setup diagram and installation showing perforated bucket design for sensor accuracy." class="xfigimg" src="/files/ftp_upload/69307/69307fig3.jpg" data-altupdatedat="1789444946000" data-alt="Figure 3"> 
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. <a href="https://www.jove.com/files/ftp_upload/69307/69307fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Data collection
<ol>
	<li>Begin data acquisition.
	<ol>
		<li>Power the logger to begin recording and capture the diurnal measurement cycle.</li>
		<li>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.</li>
		<li>Verify that timestamps remain synchronized across all sensors before and after deployment.</li>
		<li>Record start and stop times manually in a field logbook to provide redundant backup documentation.</li>
	</ol>
	</li>
</ol>
4. Data retrieval and visualization
<img alt="Environmental monitoring dashboard; data charts for CO2, temperature, humidity; trend graph below." class="xfigimg" src="/files/ftp_upload/69307/69307fig4.jpg" data-altupdatedat="1789444946000" data-alt="Figure 4"> 
Figure 4: Monitoring dashboard interface. A snapshot of the cloud-based dashboard showing real-time and historical sensor data visualizations. <a href="https://www.jove.com/files/ftp_upload/69307/69307fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol>
	<li>Retrieve data.
	<ol>
		<li>Safely eject the SD card and insert it into a card reader; copy the CSV data files into the designated project folder.</li>
		<li>For Wi-Fi-enabled loggers, connect via the local SSID and download logs through the IP-based dashboard (Figure 4).</li>
		<li>Process the retrieved data using a spreadsheet or data analysis software to prepare for visualization.</li>
	</ol>
	</li>
	<li>Generate plots.
	<ol>
		<li>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.</li>
		<li>Label the x-axis as "Time," the primary y-axis as "CO2 (ppm)/Temperature (°C)," and the secondary y-axis as "Relative Humidity (%)."</li>
		<li>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.</li>
	</ol>
	</li>
</ol>
5. Quality control
<ol>
	<li>Perform data validation.
	<ol>
		<li>Identify and remove outliers or data points flagged due to timestamp irregularities or device communication errors.</li>
	</ol>
	</li>
	<li>Recalibrate if needed.
	<ol>
		<li>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.</li>
	</ol>
	</li>
</ol>
6. Optional: seasonal or multisite deployment
<ol>
	<li>Extend the field of study.
	<ol>
		<li>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.</li>
		<li>Conduct seasonal monitoring during summer, winter, and monsoon periods to evaluate temporal and diurnal variability in CO2 and temperature.</li>
	</ol>
	</li>
	<li>Perform seasonal trend analysis.
	<ol>
		<li>Analyse results across localities and seasons to identify recurring periods of elevated CO2 and thermal stress.</li>
		<li>Generate comparative visualizations to assess the influence of building orientation, land use, and façade composition on CO2 and temperature variation.</li>
	</ol>
	</li>
</ol>
7. Troubleshooting
<ol>
	<li>Power interruption.
	<ol>
		<li>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.</li>
		<li>Ensure the battery bank is fully charged before redeployment to avoid data loss.</li>
	</ol>
	</li>
	<li>Logger failure or incomplete data.
	<ol>
		<li>If no data is recorded, verify wire continuity using a circuit tester and confirm power delivery with a voltage meter.</li>
		<li>Use a digital multimeter to check sensor terminal output (5 V DC).</li>
		<li>Replace corrupted SD cards or reset the microcontroller, then repeat the baseline calibration (step 1.2) before redeployment.</li>
	</ol>
	</li>
	<li>Damaged conduit or loose wiring.
	<ol>
		<li>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.</li>
		<li>Verify connection integrity using a continuity tester before restoring power.</li>
	</ol>
	</li>
	<li>Sensor drift or inaccurate readings.
	<ol>
		<li>If data deviates beyond ±3% (±10 ppm for CO₂), recalibrate the sensors using the open-air baseline procedure described in step 1.2.</li>
		<li>Record stabilized baseline readings to confirm accuracy before resuming data collection.</li>
	</ol>
	</li>
	<li>Loss of wireless data transmission.
	<ol>
		<li>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.</li>
		<li>As a backup, remove the SD card and retrieve CSV data logs manually.</li>
	</ol>
	</li>
</ol>

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<li>Pioppi, B., Pigliautile, I., Pisello, A. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Human-centric+microclimate+analysis+of+Urban+Heat+Island%3A+Wearable+sensing+and+data-driven+techniques+for+identifying+mitigation+strategies+in+New+York+City%5BTitle%5D)+AND+%22Urban+Clim%22%5BJournal%5D)">Human-centric microclimate analysis of Urban Heat Island: Wearable sensing and data-driven techniques for identifying mitigation strategies in New York City.</a> Urban Clim. 34, 100716(2020).</li>
<li>Schiano-Phan, R., Weber, F., Santamouris, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+mitigative+potential+of+urban+environments+and+their+microclimates%5BTitle%5D)+AND+%22Buildings%22%5BJournal%5D)">The mitigative potential of urban environments and their microclimates.</a> Buildings. 5 (3), 783-801 (2015).</li>
<li>Fadhil, M., Hamoodi, M. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mitigating+urban+heat+island+effects+in+urban+environments%3A+Strategies+and+tools%5BTitle%5D)+AND+%22IOP+Conf+Ser+Earth+Environ+Sci%22%5BJournal%5D)">Mitigating urban heat island effects in urban environments: Strategies and tools.</a> IOP Conf Ser Earth Environ Sci. 1229, 012025(2023).</li>
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<li>Hong, C., Wang, Y., Gu, Z., Yu, C. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cool+fa%C3%A7ades+to+mitigate+urban+heat+island+effects%5BTitle%5D)+AND+%22Indoor+Built+Environ%22%5BJournal%5D)">Cool façades to mitigate urban heat island effects.</a> Indoor Built Environ. 31 (10), 2373-2377 (2022).</li>
<li>Cvar, N., Trilar, J., Kos, A., Volk, M., Duh, E. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+use+of+IoT+technology+in+smart+cities+and+smart+villages%3A+similarities%2C+differences%2C+and+future+prospects%5BTitle%5D)+AND+%22Sensors%22%5BJournal%5D)">The use of IoT technology in smart cities and smart villages: similarities, differences, and future prospects.</a> Sensors. 20 (14), 3897(2020).</li>
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The authors have nothing to disclose.

A critical step in this protocol is the baseline calibration and synchronization of CO&#8322;, 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...

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 cover1,2,3. Elevated urban temperatures alter localized microclimates, influencing wind flow, humidity, and the retention of atmospheric CO21,4. In densely built environments, thermal stratification reduces natural air mixing, thereby intensifying CO&#8322; accumulation at the pedestrian level, which in turn impacts both air quality and public health5,6.
Urban morphology strongly influences microclimatic behavior and CO2&#160;dynamics, especially at the building and neighborhood scales2. Variations in solar exposure between shaded and sun-exposed fa&#231;ades 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 fa&#231;ade or building scale remains limited5,6.
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 risks4,7,8. To address this gap, the present study introduces a sensor-based protocol for spatial and temporal monitoring of CO2&#160;fluctuations in urban microenvironments. Low-cost NDIR CO2&#160;sensors and thermo-hygrometers were deployed across sun-exposed and shaded fa&#231;ades to evaluate the influence of thermal exposure and airflow on CO2&#160;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 fa&#231;ade-level microclimates, this approach identifies critical environmental gradients that influence air chemistry under real-world built conditions1,2,5. The findings are relevant to multiple domains, including urban climatology, air quality modeling, sustainable architecture, and HVAC design. Specifically, identifying CO2-rich fa&#231;ades, vehicular emission hotspots, and poorly ventilated zones can inform the placement of air intakes, guide passive ventilation strategies, and support climate-responsive urban design4,7. With the adoption of mitigation strategies such as green roofs, reflective coatings, vegetated fa&#231;ades, and high-SRI materials, accurate fa&#231;ade-level monitoring provides quantitative evidence to validate performance and inform long-term climate-resilient interventions3,8.

<table><tbody><tr><td>Arduino IDE</td><td>Arduino</td><td>v2.x</td><td>For programming and debugging ESP32/Arduino boards</td></tr><tr><td>CO&lt;sub&gt;2&lt;/sub&gt; Sensor, NDIR (Non-Dispersive Infrared)</td><td>Sensirion</td><td>SCD40 / SCD41</td><td>Used for real-time ambient CO&lt;sub&gt;2&lt;/sub&gt; monitoring</td></tr><tr><td>Data Logging Microcontroller (ESP32)</td><td>Espressif</td><td>ESP32 DevKitC</td><td>For logging sensor data; supports Wi-Fi and SD card</td></tr><tr><td>Humidity and Temperature Sensor</td><td>Sensirion</td><td>SHT21</td><td>For ambient temperature and relative humidity measurement</td></tr><tr><td>MicroSD Card (32 GB or higher)</td><td>SanDisk / Kingston</td><td>Various</td><td>Stores time-series CO&lt;sub&gt;2&lt;/sub&gt;, temperature, and humidity data</td></tr><tr><td>MicroSD Card Module</td><td>Generic Supplier</td><td>N/A</td><td>Interface between ESP32 and SD card for data logging</td></tr><tr><td>Power Adapter (AC to 5V DC)</td><td>Generic Supplier</td><td>N/A</td><td>Converts 230 V AC to stable 5 V DC power</td></tr><tr><td>Python Software Environment</td><td>Python.org</td><td>v3.10 (pandas 1.5, matplotlib 3.7)</td><td>For advanced data analysis, visualization, and automation</td></tr><tr><td>Rainproof Sensor Housing</td><td>Polypropylene/3D Printed</td><td>N/A</td><td>Perforated casing to protect electronics from rain and dust</td></tr><tr><td>Rope or Mounting Harness</td><td>Generic</td><td>N/A</td><td>Used for secure fa&amp;ccedil;ade mounting at required height</td></tr><tr><td>SD Card Reader/Writer</td><td>Any USB Interface</td><td>N/A</td><td>For transferring data from SD card to PC</td></tr><tr><td>Excel&amp;nbsp;</td><td>Microsoft</td><td>N/A</td><td>For post-processing time-series data</td></tr><tr><td>Calc</td><td>LibreOffice</td><td>N/A</td><td>Spreadsheet Software, For post-processing time-series data</td></tr><tr><td>USB Cable (Micro/Type-C)</td><td>Generic</td><td>N/A</td><td>Used for programming and powering ESP32 module</td></tr></tbody></table>

fa&#231;ade-level monitoring of co2 variability under urban heat island conditions using low-cost sensor data loggers

Fa&#231;ade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

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 fa&#231;ade-level spatial resolution necessary to inform building-level interventions.
This study presents a reproducible, low-cost protocol for fa&#231;ade-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 fa&#231;ades-to capture diurnal variations over a 24 h cycle. Calibration against outdoor baseline CO2&#160;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&#160;concentrations in densely built zones compared to adjacent green fa&#231;ades, 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 &#176;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 PM&#8322;.&#8325;, PM&#8321;&#8320;, and VOCs, enabling a comprehensive assessment of urban air quality. By providing fa&#231;ade-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.

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 ...

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.

Urbanization and rapid land-use change have intensified the urban heat island effect (UHIE), leading to elevated ambient temperatures, altered humidity, ...

The research examines how urban microclimate impacts outdoor/indoor CO2 interplay, and also takes a look at how realtime ventilation gets impacted in an operational building. The main challenge is lack of adequate data, correlating urban heat island effect and microclimate, especially while designing a building for energy performance and health. To begin, assemble the s.ensor module using a non-dispersive infrared carbon dioxide sensor and a combined temperature and humidity probe. Secure both sensors together as a single functional module. Configure the microcontroller based data logger by connecting a realtime clock module and installing the secure digital card shield.Ensure that all components are firmly seated on the microcontroller. Set the logging frequency to one reading every 10 minutes using the data logger configuration interface. Confirm that the selected interval is correctly saved in the system settings.To test sensor function and perform calibration, place all carbon dioxide and temperature humidity sensors in a well-ventilated outdoor area. Arrange the sensors so that they're equally exposed to ambient air. Now, connect the sensor modules to a stable power source, such as a 230-volt alternating current to direct current adapter.Allow the sensors to stabilize for 10 minutes. Then, record the baseline carbon dioxide concentration of approximately 410 parts per million to verify consistency across devices. Now, place the assembled sensor module and data logger inside a weather-resistant housing made of polypropylene.Ensure that all wiring is fully insulated and that all connectors are firmly secured. Identify contrasting thermal zones within the locality around the building or zone of interest. Install sensor modules on multiple facades where applicable to capture thermal and pollutant variation.After mounting the sensor module, confirm that it is securely attached to the facade. Maintain a mounting height between one and two meters above the local floor level for each facade. Adjust the exact height is needed based on accessibility, available supporting infrastructure, and facade geometry.Now, record the directional orientation of each sensor location, such as an east facing facade adjacent to a road, and document nearby obstructions including heating, ventilation, and air conditioning units or walls. Verify sensor output by observing live readings on the display or logger interface. Confirm that the connection is stable and that active data recording is underway.Allow the data logger to operate continuously for 24 hours with a data logging interval of 10 minutes to capture full-day variations in carbon dioxide concentration, temperature, and humidity. Verify that timestamps remain synchronized across all sensors before deployment and again after deployment. Now, record the start time and stop time manually in a field logbook to provide redundant backup documentation.For wireless fidelity-enabled loggers, connect to the local service set identifier and download the log files through the internet protocol-based dashboard. Process the retrieved data using a spreadsheet or data analysis software to prepare it for visualization. Next, generate a multi-variable line plot showing carbon dioxide concentration and temperature on the primary vertical axis and relative humidity on the secondary vertical axis to allow direct trend comparison.Label the horizontal axis as time. Add a legend to distinguish variables and annotate peak values, typically between 11 and 15 hours for intersite comparison. Perform data validation by reviewing the collected dataset for anomalies.Identify and remove outliers or data points flagged due to timestamp irregularities or device communication errors. Finally, recalibrate sensors if post-experiment analysis indicates sensor drift exceeding 3%or a variation of more than 10 parts per million. Repeat the baseline calibration procedure in open air conditions and reacquire data under identical environmental conditions.External carbon dioxide concentrations were significantly higher at concrete-dominant facades compared to adjacent green facades across all monitored cities. In Chennai, elevated carbon dioxide levels at the east facade coincided with higher local temperatures near the sensor compared to the shaded facade. In Pune, the difference in carbon dioxide concentration between facades occurred despite only a one degree Celsius increase in temperature at the concrete facade.Green zones consistently exhibited lower temperatures, higher humidity, and faster carbon dioxide dispersion rates compared to concretized facades across all sites. Our studies highlight that urban heat island effect has a direct impact on the outdoor CO2 and air quality, and this translates into an increased CO2 buildup indoors. And this is true for naturally ventilated buildings as well.Our protocol is low-cost, scalable, field-ready, and visually reproducible across building typologies and climatic conditions. Our future studies will focus on community level air quality index, energy efficiency in buildings, climate adaptive ventilation strategies, and cognitive performance of the building occupants.

faade-level-monitoring-co2-variability-under-urban-heat-island

Research

JoVE Journal

Environment

Surface renewal is a micrometeorological method that is being used increasingly to determine energy fluxes, but its technical complexity makes it inaccessible to a broad audience. We describe the steps needed to set up and calibrate a surface renewal field station, to acquire and process data, and to correctly interpret results.

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

With the advancement of technology and the rise in end-user expectations, the need and use of higher temporal resolution data for pollutant load estimation has increased. This protocol describes a method for continuous in situ water quality monitoring to obtain higher temporal resolution data for informed water resource management decisions.

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Sublethal doses of pesticides may affect colonies in ways that are difficult to detect using only periodic or visual methods. Methods to measure total adult bee mass, brood, and food resources by weighing hives and hive parts, photographing frames, and installing sensors, are provided. Data analysis is also addressed.

Monitoring Colony-level Effects of Sublethal Pesticide Exposure on Honey Bees

Understanding the ecosystem services and processes provided by vernal ponds and the impacts of anthropogenic activities on their ability to provide these services requires intensive hydrologic monitoring. This sampling protocol using in-situ monitoring equipment was developed to understand the impact of anthropogenic activities on water levels and quality.

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

With the advent of small, low-cost environmental sensors, it is now possible to deploy high-density networks of sensors to measure hyper localized temperature variation. Here, we provide a detailed methodology for constructing a compact version of a previously described custom-fabricated radiation shield for use with inexpensive thermochrons.

Construction of a Compact Low-Cost Radiation Shield for Air-Temperature Sensors in Ecological Field Studies

This paper presents a protocol that enables instrumentation of random wound electric coils with fiber Bragg grating (FBG) thermal sensors for the purpose of distributed condition monitoring of internal thermal hot spots.

Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing

Accurately measuring temperature and water content of the upper 5 mm of the soil surface can improve our understanding of environmental controls on biological, chemical, and physical processes. Here we describe a protocol for manufacturing, calibrating, and conducting measurements with soil surface temperature and moisture sensors.

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Fast and precise leaf area index (LAI) estimation in terrestrial ecosystems is crucial for a wide range of ecological studies and calibrating remote sensing products. Presented here is the protocol for using the new LP 110 optical device for taking ground-based in situ LAI measurements.

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

We detail the consistent, high-quality procedures used throughout air and biological sampling processes at Indian field sites during a large randomized controlled trial. Insights gathered from the oversight of applications of innovative technologies, adapted for exposure assessment in rural regions, enable better field data collection practices with more reliable outcomes.

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

The determination of soil water content is a critical mission requirement for many state and federal agencies. This protocol synthesizes multi-agency efforts to measure soil water content using buried in situ sensors.

In Situ Soil Moisture Sensors in Undisturbed Soils

Explore facade-level CO2 monitoring under urban heat island conditions using low-cost sensor data loggers. This protocol enables scalable environmental assessment for building energy performance and ventilation strategies.

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

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