Rapid industrialization and technological advancements have contributed to a significant rise in air pollution, posing severe threats to both human health and the environment. Real-time monitoring of air quality is crucial for understanding the extent of pollution and implementing effective mitigation strategies. This research aims to provide a cost-effective and versatile solution for air pollution monitoring, addressing the limitations of existing expensive and complex systems.
Literature Review:
This section reviews the impact of air pollution on human health and the environment, highlighting the need for comprehensive monitoring systems. It discusses various air pollution monitoring technologies, emphasizing the advantages of solid-state semiconductor gas sensors. The role of IoT in enabling efficient data collection and transmission is also explored.
System Design:
The system comprises an array of semiconductor gas sensors for detecting CO, NO2, SO2, and O3. These sensors are connected to signal conditioning circuits and an Arduino Mega microcontroller, which acts as the Smart Transducer Interface Module (STIM). The STIM manages actuator interfaces, supports Transducer Electronic Data Sheets (TEDS), communicates with the Network Capable Application Processor (NCAP), and adheres to the IEEE 1451.2 Transducer Independent Interface (TII) standard. The NCAP, implemented using NodeMCU and ATmega328P, communicates with the STIM via TII and transmits data to a central server using GPRS technology. The system also includes a data backup mechanism using a microSD card.
Sensor Organization:
Detailed descriptions of the digital gas sensors used for CO, NO2, SO2, and O3 detection are provided, emphasizing their key features such as fast response time, low power consumption, high repeatability, and improved range.
Implementation:
The implementation details of the STIM and NCAP are discussed, including the use of flash memory for TEDS storage and the communication protocols between STIM and NCAP. The GUI section of NCAP is responsible for displaying STIM details, gas concentrations, and system status, as well as allowing user interaction. The system offers web access through GPRS and data visualization on the "slpiot" open-source platform.
Power Supply:
The system is designed for low power consumption, utilizing an 11.1V 5200mAh lithium-polymer battery. It can be charged using solar or external sources.
Data Quality and Comparison:
The accuracy of the system's measurements is validated by comparing them to data from standard air quality monitoring stations.
Conclusion:
The research demonstrates the successful application of open technologies and gas sensor modules for air pollution monitoring. The use of open standards allows for easy integration of additional sensors, open hardware enables further development and modifications, open software provides flexibility for system replication, and open data facilitates research and analysis by various stakeholders.
M. A. L. S. K. Manchanayaka, J. P. D. Wijesekara, C. -Y. Yang, C. Premachandra, M. F. M. Firdhous and B. H. Sudantha, "Open, IoT powered Environmental Air Pollution Monitoring Framework for Traffic Management (ieeexplore.ieee.org)," 2021 6th International Conference on Information Technology Research (ICITR), Moratuwa, Sri Lanka, 2021, pp. 1-7, doi: 10.1109/ICITR54349.2021.9657315.Abstract: An IoT-enabled Environmental Ambient Air Pollution Monitoring System was developed using open technologies including open hardware, open software, and open standards. It can detect and measure the concentrations of four major air pollutant gases. The system measures concentrations of air pollutant gases such as NO2, CO, SO2, and O3 using gas sensor modules. The system was developed to comply with IEEE 1451 standard, where the Smart Transducer Interface Module (STIM) was implemented using the Arduino Mega controller. Network Capable Application Processor (NCAP) was implemented using a NodeMCU and ATmega328P controller which is connected to the main board, STIM via the Transducer Independent Interface (TII). The system measures the concentrations of pollutant gases in every 10 seconds and collects all data upto 10 minutes. The measured concentration levels averaged to 10 min intervals and would be sent to the central server with the timestamp and data quality index. For the implementation of a fully open framework, the pollutant data made available free, and anyone can access data and they could be used for a relevant purpose considering the quality index. keywords: Gases;Transducers;Atmospheric measurements;Air pollution;Software;Pollution measurement;Indexes;IoT;air pollution monitoring;gas sensors;IEEE 1451 standards;smart transducers;STIM;electronic data sheet;traffic data;Arduino;NodeMCU,URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9657315&isnumber=9657162
A Ciência & Ensino é uma publicação semestral destinada a professores de ciências do ensino fundamental e médio e seus formadores.
An IoT-Based Open Air Pollution Monitoring System
por Beatris Thorne (2024-05-12)
Introduction:Rapid industrialization and technological advancements have contributed to a significant rise in air pollution, posing severe threats to both human health and the environment. Real-time monitoring of air quality is crucial for understanding the extent of pollution and implementing effective mitigation strategies. This research aims to provide a cost-effective and versatile solution for air pollution monitoring, addressing the limitations of existing expensive and complex systems.
Literature Review:
This section reviews the impact of air pollution on human health and the environment, highlighting the need for comprehensive monitoring systems. It discusses various air pollution monitoring technologies, emphasizing the advantages of solid-state semiconductor gas sensors. The role of IoT in enabling efficient data collection and transmission is also explored.
System Design:
The system comprises an array of semiconductor gas sensors for detecting CO, NO2, SO2, and O3. These sensors are connected to signal conditioning circuits and an Arduino Mega microcontroller, which acts as the Smart Transducer Interface Module (STIM). The STIM manages actuator interfaces, supports Transducer Electronic Data Sheets (TEDS), communicates with the Network Capable Application Processor (NCAP), and adheres to the IEEE 1451.2 Transducer Independent Interface (TII) standard. The NCAP, implemented using NodeMCU and ATmega328P, communicates with the STIM via TII and transmits data to a central server using GPRS technology. The system also includes a data backup mechanism using a microSD card.
Sensor Organization:
Detailed descriptions of the digital gas sensors used for CO, NO2, SO2, and O3 detection are provided, emphasizing their key features such as fast response time, low power consumption, high repeatability, and improved range.
Implementation:
The implementation details of the STIM and NCAP are discussed, including the use of flash memory for TEDS storage and the communication protocols between STIM and NCAP. The GUI section of NCAP is responsible for displaying STIM details, gas concentrations, and system status, as well as allowing user interaction. The system offers web access through GPRS and data visualization on the "slpiot" open-source platform.
Power Supply:
The system is designed for low power consumption, utilizing an 11.1V 5200mAh lithium-polymer battery. It can be charged using solar or external sources.
Data Quality and Comparison:
The accuracy of the system's measurements is validated by comparing them to data from standard air quality monitoring stations.
Conclusion:
The research demonstrates the successful application of open technologies and gas sensor modules for air pollution monitoring. The use of open standards allows for easy integration of additional sensors, open hardware enables further development and modifications, open software provides flexibility for system replication, and open data facilitates research and analysis by various stakeholders.
M. A. L. S. K. Manchanayaka, J. P. D. Wijesekara, C. -Y. Yang, C. Premachandra, M. F. M. Firdhous and B. H. Sudantha, "Open, IoT powered Environmental Air Pollution Monitoring Framework for Traffic Management (ieeexplore.ieee.org)," 2021 6th International Conference on Information Technology Research (ICITR), Moratuwa, Sri Lanka, 2021, pp. 1-7, doi: 10.1109/ICITR54349.2021.9657315.Abstract: An IoT-enabled Environmental Ambient Air Pollution Monitoring System was developed using open technologies including open hardware, open software, and open standards. It can detect and measure the concentrations of four major air pollutant gases. The system measures concentrations of air pollutant gases such as NO2, CO, SO2, and O3 using gas sensor modules. The system was developed to comply with IEEE 1451 standard, where the Smart Transducer Interface Module (STIM) was implemented using the Arduino Mega controller. Network Capable Application Processor (NCAP) was implemented using a NodeMCU and ATmega328P controller which is connected to the main board, STIM via the Transducer Independent Interface (TII). The system measures the concentrations of pollutant gases in every 10 seconds and collects all data upto 10 minutes. The measured concentration levels averaged to 10 min intervals and would be sent to the central server with the timestamp and data quality index. For the implementation of a fully open framework, the pollutant data made available free, and anyone can access data and they could be used for a relevant purpose considering the quality index. keywords: Gases;Transducers;Atmospheric measurements;Air pollution;Software;Pollution measurement;Indexes;IoT;air pollution monitoring;gas sensors;IEEE 1451 standards;smart transducers;STIM;electronic data sheet;traffic data;Arduino;NodeMCU,URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9657315&isnumber=9657162