Nottingham Trent University-Led Study Uses UbiBot AQS1 for Indoor Air Quality Monitoring
Research Overview
| Paper Title | Innovations in Air Quality Monitoring: Sensors, IoT and Future Research |
| Publisher | MDPI |
| Journal | Sensors |
| Publish Time | March 2025 |
| Authors / Institutions | Saim Shahid, David J. Brown, Philip Wright, Ahmad M. Khasawneh, Bryn Taylor, and Omprakash Kaiwartya; Nottingham Trent University, Cobac Security Limited, and Skyline University College |
| UbiBot Product | UbiBot AQS1 Smart Air Quality Monitor |
| Data Collected | CO₂, PM1.0, PM2.5, PM10, temperature, humidity, atmospheric pressure, VOCs, formaldehyde, and equivalent CO₂, according to the device feature comparison in the paper; the experimental analysis specifically discusses UbiBot data for CO₂ and particulate matter during indoor scenarios |
| Sampling Frequency | Not explicitly stated in the paper; the study presents time-series indoor air quality data collected during real-world experiments |
| Research Period | The paper was published in March 2025; the exact dates of the indoor experiments are not specified |
| Application Scenario | Indoor air quality monitoring, cooking-related pollution analysis, bedroom ventilation assessment, IoT-based air quality monitor comparison |
| Original Link | https://doi.org/10.3390/s25072070 |
Research Background: What Problem Did This Study Address?
Indoor and outdoor air pollution have become major public health concerns. Air quality monitoring is no longer limited to government-grade stations or laboratory instruments. With the development of low-cost sensors, IoT platforms, and wireless communication, researchers are increasingly interested in how sensor-based systems can provide more granular, real-time environmental data.
This paper reviews innovations in air quality monitoring, including pollutant types, sensor materials, IoT frameworks, calibration challenges, and future research directions. In addition to reviewing the broader research landscape, the authors conducted an experimental evaluation of three commercially available air quality monitoring devices in the UK market: UbiBot AQS1 Smart Air Quality Monitor, Temtop 1000S+ Air Quality Monitor, and Amazon Smart Air Quality Monitor.
The practical problem addressed in the experimental section is straightforward: how do commercially available IoT air quality monitors perform in typical indoor environments, and what do their readings reveal about everyday activities such as cooking and sleeping in rooms with different ventilation conditions?
The study does not claim that UbiBot alone proves a broad scientific conclusion. Instead, the researchers used UbiBot AQS1 as one of the commercial monitoring devices to collect real-world indoor air quality data. These data were then compared with readings from other devices and used to analyse how indoor pollution changes during cooking and under different bedroom ventilation conditions.
The Specific Role of UbiBot in the Study
In this study, UbiBot AQS1 was used as one of three commercially available IoT-based air quality monitoring devices evaluated under real indoor conditions. The researchers selected the devices based on availability, suitability for indoor environments, and connectivity features.
UbiBot AQS1 was not used as a standalone scientific proof tool. Its role was to collect indoor air quality data that could be compared with data from Temtop 1000S+ and Amazon Smart Air Quality Monitor. The researchers used these data to examine how different devices respond to the same indoor pollution events and to identify differences in measurement sensitivity and output values.
According to the feature comparison table in the paper, UbiBot AQS1 supports monitoring of temperature, humidity, atmospheric pressure, PM1.0, PM2.5, PM10, VOCs, formaldehyde, CO₂, equivalent CO₂, and Wi-Fi connectivity. In the experimental section, the paper specifically presents UbiBot data for CO₂ levels during cooking and UbiBot PM1.0, PM2.5, and PM10 readings during cooking.
The device was used in indoor residential scenarios. One experiment focused on cooking with a conventional gas stove in an unventilated environment. In this scenario, UbiBot recorded a sharp rise in CO₂ after cooking started. The paper also presents UbiBot particulate matter data showing that PM1.0, PM2.5, and PM10 increased rapidly to hazardous levels during cooking.
The data were used for environmental recording and comparative analysis. For example, the UbiBot readings helped show how CO₂ and particulate matter changed before ventilation, after the range hood was turned on, and after a window was opened. The researchers also compared PM2.5 trends across UbiBot, Temtop, and Amazon devices, noting that all three devices showed similar trends but different absolute values.
In this way, UbiBot’s role in the research was to provide time-series indoor air quality measurements that supported the analysis of pollution patterns, ventilation effects, and differences among commercial IoT air quality monitors.
Research Methods and Data Collection Approach
The paper combines a technical review with experimental performance evaluation. The review sections cover air pollutants, sensor design, sensing materials, IoT frameworks, calibration, and future research directions. The experimental section evaluates three commercial air quality monitoring devices in indoor environments.
The three devices compared were UbiBot AQS1 Smart Air Quality Monitor, Temtop 1000S+ Air Quality Monitor, and Amazon Smart Air Quality Monitor. The study compared their supported parameters and then analysed output data collected in real-world indoor scenarios.
Three indoor experiments were conducted:
First, the researchers monitored air quality during cooking on a conventional gas stove in an unventilated environment. During the experiment, CO₂ and particulate matter increased after cooking began. The range hood was activated at 20:10, and a window was opened later at 21:28. These changes allowed the researchers to observe how different ventilation actions affected indoor air quality.
Second, the researchers monitored a bedroom occupied by two adults with windows closed. This scenario represented a low-ventilation sleeping environment.
Third, the researchers monitored a similar bedroom setting with ventilation introduced through an open window in an adjacent room. This allowed comparison between ventilated and unventilated sleeping conditions.
The collected data were visualised using colour-coded air quality levels: green for good air quality, yellow for fair air quality, orange for poor air quality, and red for terrible air quality. The study used these time-series charts to evaluate pollutant changes and compare device responses.
Key Research Findings
The study found that typical indoor activities can lead to rapid increases in air pollutants. During cooking, CO₂ levels rose sharply after the gas stove was used. CO₂ levels began to decline after the range hood was activated, but the most significant reduction occurred after a window was opened. This suggests that window ventilation was more effective than the range hood alone in that experimental setting.
The UbiBot particulate matter chart showed that PM1.0, PM2.5, and PM10 rose rapidly during cooking and reached hazardous levels within a short time. After the range hood was turned on, particulate matter levels declined more quickly, but opening a window produced the strongest drop toward safer levels.
When comparing PM2.5 readings across UbiBot, Temtop, and Amazon devices, the paper found that all three monitors captured broadly similar trends, but their absolute readings differed. Temtop showed the highest PM2.5 values, followed by UbiBot and then Amazon. The Amazon device responded fastest to environmental changes, while UbiBot provided broader pollutant coverage in the device feature comparison.
The bedroom ventilation experiment showed that CO₂ levels increased quickly when two adults slept in a room without outdoor ventilation. In less than 30 minutes, CO₂ moved from the “fair” range to the “poor” range. When an adjacent window was open, CO₂ remained within a healthier range through the night.
The study also found that PM2.5 and PM10 were higher in the closed-window bedroom scenario and much lower when ventilation was available. Overall, the experiments showed that indoor air quality can change significantly during everyday activities and that sensor-based monitoring can help reveal these changes in real time.
What This Means for Indoor Air Quality Monitoring
This research highlights the value of real-world indoor air quality monitoring. Many pollution events occur during ordinary activities such as cooking or sleeping in poorly ventilated rooms. Without sensor data, occupants may not notice how quickly CO₂ and particulate matter can accumulate indoors.
For indoor air quality monitoring, the study suggests that continuous or time-series data are more useful than occasional spot checks. Changes in pollutant levels can happen quickly, and ventilation decisions can have immediate effects. Data from devices such as UbiBot AQS1 can help users and researchers observe these changes over time.
The study also shows that commercial air quality monitors may follow similar trends while reporting different absolute values. This means that device selection, calibration, sensor type, and interpretation of readings are important. For research and practical applications, data from low-cost or consumer-grade monitors should be used carefully, especially when comparing numerical values across devices.
For homes, kitchens, bedrooms, offices, classrooms, hospitality venues, and healthcare environments, the findings support the need for better ventilation awareness and more accessible air quality monitoring tools.
Application Value of UbiBot Devices
UbiBot AQS1 demonstrated value as a multi-parameter indoor air quality monitoring device in the study. Its main value was not that it independently diagnosed health risks, but that it collected real-world indoor environmental data that could be used for comparison and analysis.
The device’s ability to monitor multiple parameters is useful in complex indoor environments. According to the paper’s device comparison, UbiBot AQS1 covers temperature, humidity, atmospheric pressure, PM1.0, PM2.5, PM10, VOCs, formaldehyde, CO₂, and equivalent CO₂. This broader parameter coverage allows researchers or building managers to observe more than one aspect of indoor air quality.
In the cooking experiment, UbiBot data helped show how CO₂ and particulate matter changed during a real pollution event. The charts demonstrated changes before ventilation, after the range hood was turned on, and after window ventilation was introduced.
UbiBot also supported comparative evaluation. By placing it alongside other commercial monitors, the researchers could compare trends and identify differences in absolute readings. This type of comparison is valuable for understanding the practical performance of commercial IoT air quality monitors.
For applied scenarios, UbiBot’s value lies in continuous environmental recording, multi-parameter sensing, and support for indoor air quality awareness, especially in spaces where ventilation and everyday activities strongly affect pollutant levels.
Extended Application Scenarios
The monitoring approach discussed in this study can be extended to several indoor air quality scenarios:
- Residential kitchen monitoring
Used to observe particulate matter and CO₂ changes during cooking and to assess the effect of range hoods and window ventilation. - Bedroom ventilation assessment
Used to monitor CO₂ and particulate levels during sleep and evaluate whether ventilation is sufficient. - Office and meeting room monitoring
Used to track CO₂ buildup, ventilation performance, and occupancy-related air quality changes. - Schools and classrooms
Used to support ventilation decisions in spaces with high occupancy density. - Restaurants and commercial kitchens
Used to monitor cooking-related PM, CO₂, VOCs, and other indoor pollutants. - Hotels, care homes, and healthcare facilities
Used to support indoor environmental monitoring in spaces where occupant comfort and health are important. - Smart building systems
Used as part of IoT-enabled building management systems to provide real-time indoor air quality data. - Sensor comparison and calibration studies
Used alongside other air quality monitors to evaluate consistency, drift, and differences in device readings.
FAQ
1. Which UbiBot product was used in the paper?
The paper evaluated the UbiBot AQS1 Smart Air Quality Monitor as one of three commercially available air quality monitoring devices.
2. What data can UbiBot AQS1 collect according to the paper?
The device comparison table lists temperature, humidity, atmospheric pressure, PM1.0, PM2.5, PM10, VOCs, formaldehyde, CO₂, equivalent CO₂, and Wi-Fi connectivity for UbiBot AQS1.
3. What UbiBot data were specifically discussed in the experiments?
The experimental section specifically presents UbiBot CO₂ data during cooking and UbiBot PM1.0, PM2.5, and PM10 data during cooking.
4. Where was UbiBot deployed?
It was used in indoor residential environments, including a cooking scenario and comparison with other monitors in indoor air quality experiments.
5. What was the sampling frequency?
The paper does not explicitly state the sampling frequency. It presents time-series data collected during indoor experiments.
6. How long did the experiments last?
The exact experimental dates and total duration are not specified. The paper reports three indoor experiments: cooking, bedroom without ventilation, and bedroom with ventilation.
7. Did UbiBot prove that cooking causes indoor air pollution?
No. UbiBot was used to collect air quality data during cooking. The researchers used those data, together with data from other devices, to analyse changes in CO₂ and particulate matter under different ventilation conditions.
8. What did the cooking experiment show?
The experiment showed that CO₂ and particulate matter rose rapidly during cooking. The range hood helped reduce pollutant levels, but opening a window produced the most significant reduction in the reported experiment.
9. Why did the researchers compare UbiBot with other devices?
The comparison helped evaluate how different commercial air quality monitors respond to the same indoor environment. The study found similar trends but different absolute readings across devices.
10. What is the practical value of this research?
The research shows how IoT-based air quality monitors can help capture real indoor pollution patterns, support ventilation awareness, and provide data for future sensor and IoT system development.
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Nottingham Trent University-Led Study Uses UbiBot AQS1 for Indoor Air Quality Monitoring
Research Overview
| Paper Title | Innovations in Air Quality Monitoring: Sensors, IoT and Future Research |
| Publisher | MDPI |
| Journal | Sensors |
| Publish Time | March 2025 |
| Authors / Institutions | Saim Shahid, David J. Brown, Philip Wright, Ahmad M. Khasawneh, Bryn Taylor, and Omprakash Kaiwartya; Nottingham Trent University, Cobac Security Limited, and Skyline University College |
| UbiBot Product | UbiBot AQS1 Smart Air Quality Monitor |
| Data Collected | CO₂, PM1.0, PM2.5, PM10, temperature, humidity, atmospheric pressure, VOCs, formaldehyde, and equivalent CO₂, according to the device feature comparison in the paper; the experimental analysis specifically discusses UbiBot data for CO₂ and particulate matter during indoor scenarios |
| Sampling Frequency | Not explicitly stated in the paper; the study presents time-series indoor air quality data collected during real-world experiments |
| Research Period | The paper was published in March 2025; the exact dates of the indoor experiments are not specified |
| Application Scenario | Indoor air quality monitoring, cooking-related pollution analysis, bedroom ventilation assessment, IoT-based air quality monitor comparison |
| Original Link | https://doi.org/10.3390/s25072070 |
Research Background: What Problem Did This Study Address?
Indoor and outdoor air pollution have become major public health concerns. Air quality monitoring is no longer limited to government-grade stations or laboratory instruments. With the development of low-cost sensors, IoT platforms, and wireless communication, researchers are increasingly interested in how sensor-based systems can provide more granular, real-time environmental data.
This paper reviews innovations in air quality monitoring, including pollutant types, sensor materials, IoT frameworks, calibration challenges, and future research directions. In addition to reviewing the broader research landscape, the authors conducted an experimental evaluation of three commercially available air quality monitoring devices in the UK market: UbiBot AQS1 Smart Air Quality Monitor, Temtop 1000S+ Air Quality Monitor, and Amazon Smart Air Quality Monitor.
The practical problem addressed in the experimental section is straightforward: how do commercially available IoT air quality monitors perform in typical indoor environments, and what do their readings reveal about everyday activities such as cooking and sleeping in rooms with different ventilation conditions?
The study does not claim that UbiBot alone proves a broad scientific conclusion. Instead, the researchers used UbiBot AQS1 as one of the commercial monitoring devices to collect real-world indoor air quality data. These data were then compared with readings from other devices and used to analyse how indoor pollution changes during cooking and under different bedroom ventilation conditions.
The Specific Role of UbiBot in the Study
In this study, UbiBot AQS1 was used as one of three commercially available IoT-based air quality monitoring devices evaluated under real indoor conditions. The researchers selected the devices based on availability, suitability for indoor environments, and connectivity features.
UbiBot AQS1 was not used as a standalone scientific proof tool. Its role was to collect indoor air quality data that could be compared with data from Temtop 1000S+ and Amazon Smart Air Quality Monitor. The researchers used these data to examine how different devices respond to the same indoor pollution events and to identify differences in measurement sensitivity and output values.
According to the feature comparison table in the paper, UbiBot AQS1 supports monitoring of temperature, humidity, atmospheric pressure, PM1.0, PM2.5, PM10, VOCs, formaldehyde, CO₂, equivalent CO₂, and Wi-Fi connectivity. In the experimental section, the paper specifically presents UbiBot data for CO₂ levels during cooking and UbiBot PM1.0, PM2.5, and PM10 readings during cooking.
The device was used in indoor residential scenarios. One experiment focused on cooking with a conventional gas stove in an unventilated environment. In this scenario, UbiBot recorded a sharp rise in CO₂ after cooking started. The paper also presents UbiBot particulate matter data showing that PM1.0, PM2.5, and PM10 increased rapidly to hazardous levels during cooking.
The data were used for environmental recording and comparative analysis. For example, the UbiBot readings helped show how CO₂ and particulate matter changed before ventilation, after the range hood was turned on, and after a window was opened. The researchers also compared PM2.5 trends across UbiBot, Temtop, and Amazon devices, noting that all three devices showed similar trends but different absolute values.
In this way, UbiBot’s role in the research was to provide time-series indoor air quality measurements that supported the analysis of pollution patterns, ventilation effects, and differences among commercial IoT air quality monitors.
Research Methods and Data Collection Approach
The paper combines a technical review with experimental performance evaluation. The review sections cover air pollutants, sensor design, sensing materials, IoT frameworks, calibration, and future research directions. The experimental section evaluates three commercial air quality monitoring devices in indoor environments.
The three devices compared were UbiBot AQS1 Smart Air Quality Monitor, Temtop 1000S+ Air Quality Monitor, and Amazon Smart Air Quality Monitor. The study compared their supported parameters and then analysed output data collected in real-world indoor scenarios.
Three indoor experiments were conducted:
First, the researchers monitored air quality during cooking on a conventional gas stove in an unventilated environment. During the experiment, CO₂ and particulate matter increased after cooking began. The range hood was activated at 20:10, and a window was opened later at 21:28. These changes allowed the researchers to observe how different ventilation actions affected indoor air quality.
Second, the researchers monitored a bedroom occupied by two adults with windows closed. This scenario represented a low-ventilation sleeping environment.
Third, the researchers monitored a similar bedroom setting with ventilation introduced through an open window in an adjacent room. This allowed comparison between ventilated and unventilated sleeping conditions.
The collected data were visualised using colour-coded air quality levels: green for good air quality, yellow for fair air quality, orange for poor air quality, and red for terrible air quality. The study used these time-series charts to evaluate pollutant changes and compare device responses.
Key Research Findings
The study found that typical indoor activities can lead to rapid increases in air pollutants. During cooking, CO₂ levels rose sharply after the gas stove was used. CO₂ levels began to decline after the range hood was activated, but the most significant reduction occurred after a window was opened. This suggests that window ventilation was more effective than the range hood alone in that experimental setting.
The UbiBot particulate matter chart showed that PM1.0, PM2.5, and PM10 rose rapidly during cooking and reached hazardous levels within a short time. After the range hood was turned on, particulate matter levels declined more quickly, but opening a window produced the strongest drop toward safer levels.
When comparing PM2.5 readings across UbiBot, Temtop, and Amazon devices, the paper found that all three monitors captured broadly similar trends, but their absolute readings differed. Temtop showed the highest PM2.5 values, followed by UbiBot and then Amazon. The Amazon device responded fastest to environmental changes, while UbiBot provided broader pollutant coverage in the device feature comparison.
The bedroom ventilation experiment showed that CO₂ levels increased quickly when two adults slept in a room without outdoor ventilation. In less than 30 minutes, CO₂ moved from the “fair” range to the “poor” range. When an adjacent window was open, CO₂ remained within a healthier range through the night.
The study also found that PM2.5 and PM10 were higher in the closed-window bedroom scenario and much lower when ventilation was available. Overall, the experiments showed that indoor air quality can change significantly during everyday activities and that sensor-based monitoring can help reveal these changes in real time.
What This Means for Indoor Air Quality Monitoring
This research highlights the value of real-world indoor air quality monitoring. Many pollution events occur during ordinary activities such as cooking or sleeping in poorly ventilated rooms. Without sensor data, occupants may not notice how quickly CO₂ and particulate matter can accumulate indoors.
For indoor air quality monitoring, the study suggests that continuous or time-series data are more useful than occasional spot checks. Changes in pollutant levels can happen quickly, and ventilation decisions can have immediate effects. Data from devices such as UbiBot AQS1 can help users and researchers observe these changes over time.
The study also shows that commercial air quality monitors may follow similar trends while reporting different absolute values. This means that device selection, calibration, sensor type, and interpretation of readings are important. For research and practical applications, data from low-cost or consumer-grade monitors should be used carefully, especially when comparing numerical values across devices.
For homes, kitchens, bedrooms, offices, classrooms, hospitality venues, and healthcare environments, the findings support the need for better ventilation awareness and more accessible air quality monitoring tools.
Application Value of UbiBot Devices
UbiBot AQS1 demonstrated value as a multi-parameter indoor air quality monitoring device in the study. Its main value was not that it independently diagnosed health risks, but that it collected real-world indoor environmental data that could be used for comparison and analysis.
The device’s ability to monitor multiple parameters is useful in complex indoor environments. According to the paper’s device comparison, UbiBot AQS1 covers temperature, humidity, atmospheric pressure, PM1.0, PM2.5, PM10, VOCs, formaldehyde, CO₂, and equivalent CO₂. This broader parameter coverage allows researchers or building managers to observe more than one aspect of indoor air quality.
In the cooking experiment, UbiBot data helped show how CO₂ and particulate matter changed during a real pollution event. The charts demonstrated changes before ventilation, after the range hood was turned on, and after window ventilation was introduced.
UbiBot also supported comparative evaluation. By placing it alongside other commercial monitors, the researchers could compare trends and identify differences in absolute readings. This type of comparison is valuable for understanding the practical performance of commercial IoT air quality monitors.
For applied scenarios, UbiBot’s value lies in continuous environmental recording, multi-parameter sensing, and support for indoor air quality awareness, especially in spaces where ventilation and everyday activities strongly affect pollutant levels.
Extended Application Scenarios
The monitoring approach discussed in this study can be extended to several indoor air quality scenarios:
- Residential kitchen monitoring
Used to observe particulate matter and CO₂ changes during cooking and to assess the effect of range hoods and window ventilation. - Bedroom ventilation assessment
Used to monitor CO₂ and particulate levels during sleep and evaluate whether ventilation is sufficient. - Office and meeting room monitoring
Used to track CO₂ buildup, ventilation performance, and occupancy-related air quality changes. - Schools and classrooms
Used to support ventilation decisions in spaces with high occupancy density. - Restaurants and commercial kitchens
Used to monitor cooking-related PM, CO₂, VOCs, and other indoor pollutants. - Hotels, care homes, and healthcare facilities
Used to support indoor environmental monitoring in spaces where occupant comfort and health are important. - Smart building systems
Used as part of IoT-enabled building management systems to provide real-time indoor air quality data. - Sensor comparison and calibration studies
Used alongside other air quality monitors to evaluate consistency, drift, and differences in device readings.
FAQ
1. Which UbiBot product was used in the paper?
The paper evaluated the UbiBot AQS1 Smart Air Quality Monitor as one of three commercially available air quality monitoring devices.
2. What data can UbiBot AQS1 collect according to the paper?
The device comparison table lists temperature, humidity, atmospheric pressure, PM1.0, PM2.5, PM10, VOCs, formaldehyde, CO₂, equivalent CO₂, and Wi-Fi connectivity for UbiBot AQS1.
3. What UbiBot data were specifically discussed in the experiments?
The experimental section specifically presents UbiBot CO₂ data during cooking and UbiBot PM1.0, PM2.5, and PM10 data during cooking.
4. Where was UbiBot deployed?
It was used in indoor residential environments, including a cooking scenario and comparison with other monitors in indoor air quality experiments.
5. What was the sampling frequency?
The paper does not explicitly state the sampling frequency. It presents time-series data collected during indoor experiments.
6. How long did the experiments last?
The exact experimental dates and total duration are not specified. The paper reports three indoor experiments: cooking, bedroom without ventilation, and bedroom with ventilation.
7. Did UbiBot prove that cooking causes indoor air pollution?
No. UbiBot was used to collect air quality data during cooking. The researchers used those data, together with data from other devices, to analyse changes in CO₂ and particulate matter under different ventilation conditions.
8. What did the cooking experiment show?
The experiment showed that CO₂ and particulate matter rose rapidly during cooking. The range hood helped reduce pollutant levels, but opening a window produced the most significant reduction in the reported experiment.
9. Why did the researchers compare UbiBot with other devices?
The comparison helped evaluate how different commercial air quality monitors respond to the same indoor environment. The study found similar trends but different absolute readings across devices.
10. What is the practical value of this research?
The research shows how IoT-based air quality monitors can help capture real indoor pollution patterns, support ventilation awareness, and provide data for future sensor and IoT system development.
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