scholarly journals LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6225
Author(s):  
Ernesto González ◽  
Juan Casanova-Chafer ◽  
Alfonso Romero ◽  
Xavier Vilanova ◽  
Jan Mitrovics ◽  
...  

During the few last years, indoor and outdoor Air Quality Monitoring (AQM) has gained a lot of interest among the scientific community due to its direct relation with human health. The Internet of Things (IoT) and, especially, Wireless Sensor Networks (WSN) have given rise to the development of wireless AQM portable systems. This paper presents the development of a LoRa (short for long-range) based sensor network for AQM and gas leakage events detection. The combination of both a commercial gas sensor and a resistance measurement channel for graphene chemoresistive sensors allows both the calculation of an Air Quality Index based on the concentration of reducing species such as volatile organic compounds (VOCs) and CO, and it also makes possible the detection of NO2, which is an important air pollutant. The graphene sensor tested with the LoRa nodes developed allows the detection of NO2 pollution in just 5 min as well as enables monitoring sudden changes in the background level of this pollutant in the atmosphere. The capability of the system of detecting both reducing and oxidizing pollutant agents, alongside its low-cost, low-power, and real-time monitoring features, makes this a solution suitable to be used in wireless AQM and early warning systems.

2017 ◽  
Author(s):  
Michael Mueller ◽  
Jonas Meyer ◽  
Christoph Hueglin

Abstract. This study focuses on the investigation and quantification of low-cost sensor performance in application fields such as the extension of traditional air quality monitoring networks or the replacement of diffusion tubes. For this, sensor units consisting of two boxes featuring NO2 and O3 low-cost sensors and wireless data transfer were engineered. The sensor units were initially operated at air quality monitoring sites for three months for performance analysis and initial calibration. Afterwards, they were relocated and operated within a sensor network consisting of six locations for more than one year. Our analyses show that the employed O3 and NO2 sensors can be accurate to 2–5 and 5–7 ppb, respectively, during the first three months of operation. This accuracy, however, could not be maintained during their operation within the sensor network related to changes in sensor behaviour. Hence, the low-cost sensors in our configuration do not reach the accuracy level of NO2 diffusion tubes. Tests in the laboratory revealed that changes in relative humidity can impact the signal of the employed NO2 sensors similarly as changes in ambient NO2 concentration. All the employed low-cost sensors need to be individually calibrated. Best performance of NO2 sensors is achieved when the calibration models include also time dependent parameters accounting for changes in sensor response over time. Accordingly, an effective procedure for continuous data control and correction is essential for obtaining meaningful data. It is demonstrated that linking the measurements from low-cost sensors to the high quality measurements from routine air quality monitoring stations is an effective procedure for both tasks provided that time periods can be identified when pollutant concentrations can be accurately predicted at sensor locations.


2017 ◽  
Vol 10 (10) ◽  
pp. 3783-3799 ◽  
Author(s):  
Michael Mueller ◽  
Jonas Meyer ◽  
Christoph Hueglin

Abstract. This study focuses on the investigation and quantification of low-cost sensor performance in application fields such as the extension of traditional air quality monitoring networks or the replacement of diffusion tubes. For this, sensor units consisting of two boxes featuring NO2 and O3 low-cost sensors and wireless data transfer were engineered. The sensor units were initially operated at air quality monitoring sites for 3 months for performance analysis and initial calibration. Afterwards, they were relocated and operated within a sensor network consisting of six locations for more than 1 year. Our analyses show that the employed O3 and NO2 sensors can be accurate to 2–5 and 5–7 ppb, respectively, during the first 3 months of operation. This accuracy, however, could not be maintained during their operation within the sensor network related to changes in sensor behaviour. For most of the O3 sensors a decrease in sensitivity was encountered over time, clearly impacting the data quality. The NO2 low-cost sensors in our configuration exhibited better performance but did not reach the accuracy level of NO2 diffusion tubes (∼ 2 ppb for uncorrected 14-day average concentrations). Tests in the laboratory revealed that changes in relative humidity can impact the signal of the employed NO2 sensors similarly to changes in ambient NO2 concentration. All the employed low-cost sensors need to be individually calibrated. Best performance of NO2 sensors is achieved when the calibration models also include time-dependent parameters accounting for changes in sensor response over time. Accordingly, an effective procedure for continuous data control and correction is essential for obtaining meaningful data. It is demonstrated that linking the measurements from low-cost sensors to the high-quality measurements from routine air quality monitoring stations is an effective procedure for both tasks provided that time periods can be identified when pollutant concentrations can be accurately predicted at sensor locations.


2020 ◽  
Author(s):  
Yongmi Park ◽  
Ho-Sun Park ◽  
Wonsik Choi

<p> </p><p>As urbanization has spread, increased energy consumption, complicated built environments, and dense road networks cause spatiotemporal heterogeneity of air pollutant distributions even in an intra-community scale. High spatiotemporal heterogeneity of air pollutant distributions can affect pedestrian and/or traffic users’ exposure to air pollutants according to where and when they are, potentially forming air pollution hotspots. Thus, it is important to understand the characteristics of spatiotemporal distributions in air pollutants in various micro-built environments in populated urban areas. However, current air quality monitoring performed by the government cannot capture these highly heterogeneous distributions of air pollutants due to the limitations of financial and human resources. In this respect, cost-effective sensors have great potential to build highly spatially dense air quality monitoring networks to address the low spatial resolution issue of conventional air quality monitoring stations.</p><p>In this study, we built a highly dense air quality monitoring network consisting of 30 sets of sensor nodes in an 800 m ´ 800 m spatial domain to understand the characteristics of air pollutant distributions in various urban microenvironments. The domain includes urban street canyon with moderate traffic, a mixture of high and low buildings with high traffic, an open space with minimal traffic, and others. The sensor node consists of sensors (for CO, NO<sub>2</sub>, O<sub>3</sub>, PM<sub>2.5</sub>, and PM<sub>10</sub>, temperature, and humidity) and communication/data storage parts (wifi, interface for smartphone connection, and SD card). We also conducted inter-sensor comparison among sensor nodes and intercomparison tests between the sensor node and conventional reference instruments.</p><p>Intra-community air quality monitoring with a sensor network was conducted for a couple of weeks in two distinct weather conditions (humid and hot summer and dry and cold winter) in 2017 and 2018. During the observation periods, the concentration distribution analyses for air pollutants (except CO, PM) showed significant heterogeneity in their distributions in space. In addition, the correlation analysis with the meteorological factors showed that CO concentrations were affected by wind speed (winter, R<sup>2</sup>=0.22-0.25), but the other air pollutants were not directly correlated. We also examined the effects of land-use and building configuration on air pollution distributions. More details concerning these results are presented.</p><p>Keywords: Sensor network, low-cost sensor, spatial heterogeneity, micro-built environments</p>


Author(s):  
Mare Srbinovska ◽  
Aleksandra Krkoleva Mateska ◽  
Vesna Andova ◽  
Maja Celeska Krstevska ◽  
Tomislav Kartalov

Author(s):  
Maja Celeska Krstevska ◽  
Mare Srbinovska ◽  
Tomislav Kartalov ◽  
Vesna Andova ◽  
Aleksandra Krkoleva Mateska

Author(s):  
A. Hernández-Gordillo ◽  
S. Ruiz-Correa ◽  
V. Robledo-Valero ◽  
C. Hernández-Rosales ◽  
S. Arriaga

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