scholarly journals Sensor network for PM2.5 measurements on an academic campus area

2019 ◽  
Vol 116 ◽  
pp. 00004
Author(s):  
Marek Badura ◽  
Izabela Sówka ◽  
Piotr Batog ◽  
Piotr Szymański ◽  
Łukasz Dąbrowski
Keyword(s):  
Air Quality ◽  
Sensor Network ◽  
Optical Sensors ◽  
Low Cost ◽  
Fine Particulate Matter ◽  
Sensor Nodes ◽  
Spatial And Temporal Variability ◽  
The Public ◽  
Dense Networks ◽  
Wide Scale

Fine particulate matter (PM2.5) pose a serious threat to health. Therefore it should be monitored to assess its health impacts and to take actions to reduce its pollution. However, the traditional regulatory measuring stations are not able to capture the spatial and temporal variability of PM2.5 concentrations. The opportunity to improve the resolution of PM2.5 data is based on dense networks of miniaturized low-cost sensors. The article presents the sensor network for campus area of Wrocław University of Science and Technology. This system consists of 20 sensor nodes, distributed both on a narrow scale (14 devices on the main campus area) and on a wide scale (devices on campuses in distant parts of the city). Sensor devices have been equipped with optical sensors A003 from Plantower company and with heated inlets. Dedicated website with a map is used to present the up-to-date information about air quality to the public. Messages on air quality are based on air quality index, calculated every 15 minutes. The article demonstrates also few results of preliminary measurements, when episodes of elevated PM2.5 concentrations were observed. Sensor nodes proved to be an useful tool to monitor the changes of air pollution during such events.

Stand-alone low-cost sensor network in the inner city of Munich for modeling urban air pollutants

2021 ◽  
Author(s):  
Adrian Wenzel ◽  
Jia Chen ◽  
Florian Dietrich ◽  
Sebastian T. Thekkekara ◽  
Daniel Zollitsch ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Sensor Network ◽  
Air Pollutants ◽  
Low Cost ◽  
Air Pollutant ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Small Scale ◽  
Urban Air

<p>Modeling urban air pollutants is a challenging task not only due to the complicated, small-scale topography but also due to the complex chemical processes within the chemical regime of a city. Nitrogen oxides (NOx), particulate matter (PM) and other tracer gases, e.g. formaldehyde, hold information about which chemical regime is present in a city. As we are going to test and apply chemical models for urban pollution – especially with respect to spatial and temporally variability – measurement data with high spatial and temporal resolution are critical.</p><p>Since governmental monitoring stations of air pollutants such as PM, NOx, ozone (O<sub>3</sub>) or carbon monoxide (CO) are large and costly, they are usually only sparsely distributed throughout a city. Hence, the official monitoring sites are not sufficient to investigate whether small-scale variability and its integrated effects are captured well by models. Smart networks consisting of small low-cost air pollutant sensors have the ability to provide the required grid density and are therefore the tool of choice when it comes to setting up or validating urban modeling frameworks. Such sensor networks have been established and run by several groups, achieving spatial and temporal high-resolution concentration maps [1, 2].</p><p>After having conducted a measurement campaign in 2016 to create a high-resolution NO<sub>2</sub> concentration map for Munich [3], we are currently setting up a low-cost sensor network to measure NOx, PM, O<sub>3</sub> and CO concentrations as well as meteorological parameters [4]. The sensors are stand-alone, so that they do not demand mains supply, which gives us a high flexibility in their deployment. Validating air quality models not only requires dense but also high-accuracy measurements. Therefore, we will calibrate our sensor nodes on a weekly basis using a mobile reference instrument and apply the gathered sensor data to a Machine Learning model of the sensor nodes. This will help minimize the often occurring drawbacks of low-cost sensors such as sensor drift, environmental influences and sensor cross sensitivities.</p><p> </p><p>[1] Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, https://doi.org/10.5194/amt-11-3717-2018, 2018</p><p>[2] Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937–1946, https://doi.org/10.5194/amt-11-1937-2018, 2018</p><p>[3] Zhu, Y., Chen, J., Bi, X., Kuhlmann, G., Chan, K. L., Dietrich, F., Brunner, D., Ye, S., and Wenig, M.: Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities, Atmos. Chem. Phys., 20, 13241–13251, https://doi.org/10.5194/acp-20-13241-2020, 2020</p><p>[4] Zollitsch, D., Chen, J., Dietrich, F., Voggenreiter, B., Setili, L., and Wenig, M.: Low-Cost Air Quality Sensor Network in Munich, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020</p>

scholarly journals Intra–Community Scale Variability of Air Quality in the Center of a Megacity in South Korea: A High-Density Cost-Effective Sensor Network

2021 ◽  
Vol 11 (19) ◽  
pp. 9105
Author(s):  
Yongmi Park ◽  
Ho-Seon Park ◽  
Subin Han ◽  
Kyucheol Hwang ◽  
Seunghyun Lee ◽  
...  
Keyword(s):  
Air Quality ◽  
South Korea ◽  
Sensor Network ◽  
Temporal Variability ◽  
Urban Areas ◽  
Pearson Correlation ◽  
Urban Canopy ◽  
Air Pollutant ◽  
Sensor Nodes ◽  
Spatial And Temporal Variability

To investigate the spatial and temporal variability of air quality (CO, NO2, O3, and PM2.5) with a high spatial resolution in various adjacent micro-environments, 30 sets of sensor-nodes were deployed within an 800 × 800 m monitoring domain in the center of the largest megacity (Seoul) in South Korea. The sensor network was operated in summer and winter. The daily variation in air pollutant concentrations revealed a similar trend, with discernible concentration differences among monitoring sub-sites and a government-operated air quality monitoring station. These differences in pollutant levels (except PM2.5) among the sub-sites were pronounced in the daytime with high volumes of traffic. The coefficient of divergence and Pearson correlation coefficient showed that spatial and temporal variability was more significant in summer than winter. Ozone displayed the greatest spatial variability, with little temporal variability among the sub-sites and a negative correlation with NO2, implying that ozone concentrations were primarily determined by vehicular NOX emissions due to NO titration effects under the urban canopy. The PM2.5 concentration displayed homogeneous spatial and temporal distributions over the entire monitoring period, implying that PM2.5 monitoring with at least a 1 × 1 km resolution is sufficient to examine the spatial and temporal heterogeneity in urban areas.

scholarly journals Evolution of Wireless Sensor Network for Air Quality Measurements

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 342 ◽  
Author(s):  
Patricia Arroyo ◽  
Jesús Lozano ◽  
José Suárez
Keyword(s):  
Air Quality ◽  
Sensor Network ◽  
Low Cost ◽  
Principal Component ◽  
Solar Panels ◽  
Success Rates ◽  
Sampling System ◽  
Air Quality Measurements ◽  
Zigbee Protocol ◽  
Benzene Toluene

This study addresses the development of a wireless gas sensor network with low cost, small size, and low consumption nodes for environmental applications and air quality detection. Throughout the article, the evolution of the design and development of the system is presented, describing four designed prototypes. The final proposed prototype node has the capacity to connect up to four metal oxide (MOX) gas sensors, and has high autonomy thanks to the use of solar panels, as well as having an indirect sampling system and a small size. ZigBee protocol is used to transmit data wirelessly to a self-developed data cloud. The discrimination capacity of the device was checked with the volatile organic compounds benzene, toluene, ethylbenzene, and xylene (BTEX). An improvement of the system was achieved to obtain optimal success rates in the classification stage with the final prototype. Data processing was carried out using techniques of pattern recognition and artificial intelligence, such as radial basis networks and principal component analysis (PCA).

scholarly journals Optical particulate matter sensors in PM2.5 measurements in atmospheric air

2018 ◽  
Vol 44 ◽  
pp. 00006 ◽  
Author(s):  
Marek Badura ◽  
Piotr Batog ◽  
Anetta Drzeniecka-Osiadacz ◽  
Piotr Modzel
Keyword(s):  
Particulate Matter ◽  
Temporal Resolution ◽  
Optical Sensors ◽  
Low Cost ◽  
Monitoring Systems ◽  
Atmospheric Air ◽  
The Public ◽  
Good Operation

Monitoring systems are needed to obtain information about particulate matter (PM) concentrations and to make such information accessible to the public. Small, low-cost, optical sensors could be used to improve the spatial and temporal resolution of PM data. The paper presents results of collocated comparison of four low-cost PM sensors and TEOM analyser, conducted from 20-08-2017 to 24-12-2017 in Wrocław, Poland. Plantower PMS7003 and Nova Fitness SDS011 sensors proved to be the best in terms of precision and were linearly correlated with TEOM data. Alphasense OPC-N2 sensors exhibited only moderate precision and linearity. Winsen ZH03A sensors had low repeatability between units and only one copy demonstrated good operation possibilities. All tested sensors had a bias in relation to PM2.5 concentrations obtained from TEOM.

scholarly journals NanoSen-AQM: From Sensors to Users

2020 ◽  
Vol 16 (04) ◽  
pp. 51
Author(s):  
Pedro Lucas ◽  
Jorge Silva ◽  
Filipe Araujo ◽  
Catarina Silva ◽  
Paulo Gil ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Sensor Data ◽  
Quality Data ◽  
Easy Access ◽  
The Public ◽  
Nano Sensors ◽  
The Cost ◽  
Air Quality Data ◽  
Monitoring Platform

With the raising of environmental concerns regarding pollution, interest in monitoring air quality is increasing. However, air pollution data is mostly originated from a limited number of government-owned sensors, which can only capture a small fraction of reality. Improving air quality coverage in-volves reducing the cost of sensors and making data widely available to the public. To this end, the NanoSen-AQM project proposes the usage of low-cost nano-sensors as the basis for an air quality monitoring platform, capa-ble of collecting, aggregating, processing, storing, and displaying air quality data. Being an end-to-end system, the platform allows sensor owners to manage their sensors, as well as define calibration functions, that can im-prove data reliability. The public can visualize sensor data in a map, define specific clusters (groups of sensors) as favorites and set alerts in the event of bad air quality in certain sensors. The NanoSen-AQM platform provides easy access to air quality data, with the aim of improving public health.

scholarly journals DOS Attacks on TCP/IP Layers in WSN

2013 ◽  
Vol 1 (2013) ◽  
pp. 40-45
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Low Cost ◽  
Denial Of Service ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Renewable Energy Resources ◽  
Dos Attack ◽  
Dos Attacks

The emergence of sensor networks as one of the dominant technology trends in the coming decades has posed numerous unique challenges on their security to researchers. These networks are likely to be composed of thousands of tiny sensor nodes, which are low-cost devices equipped with limited memory, processing, radio, and in many cases, without access to renewable energy resources. While the set of challenges in sensor networks are diverse, we focus on security of Wireless Sensor Network in this paper. First, we propose some of the security goal for Wireless Sensor Network. To perform any task in WSN, the goal is to ensure the best possible utilization of sensor resources so that the network could be kept functional as long as possible. In contrast to this crucial objective of sensor network management, a Denial of Service (DoS) attack targets to degrade the efficient use of network resources and disrupts the essential services in the network. DoS attack could be considered as one of th

scholarly journals Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-cost sensor performance in a suburban environment in the southeastern United States

2016 ◽  
Author(s):  
Wan Jiao ◽  
Gayle Hagler ◽  
Ronald Williams ◽  
Robert Sharpe ◽  
Ryan Brown ◽  
...  
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Sulfur Dioxide ◽  
Sensor Network ◽  
Correlation Coefficient ◽  
Low Cost ◽  
Ambient Air ◽  
Sensor Technology ◽  
Sensor Performance

Abstract. Advances in air pollution sensor technology have enabled the development of small and low cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low cost, continuous and commercially-available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ~ 2 km area in Southeastern U.S. Co-location of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as whether multiple identical sensors reproduced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, −0.25 to 0.76, −0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r  0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2adj-orig = 0.57, R2adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (2 nodes) and PM (4 nodes) data for an 8 month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to near-by traffic emissions. Overall, this study demonstrates a straightforward methodology for establishing low-cost air quality sensor performance in a real-world setting and demonstrates the feasibility of deploying a local sensor network to measure ambient air quality trends.

scholarly journals The BErkeley Atmospheric CO<sub>2</sub> Observation Network: field calibration and evaluation of low-cost air quality sensors

2018 ◽  
Vol 11 (4) ◽  
pp. 1937-1946 ◽  
Author(s):  
Jinsol Kim ◽  
Alexis A. Shusterman ◽  
Kaitlyn J. Lieschke ◽  
Catherine Newman ◽  
Ronald C. Cohen
Keyword(s):  
Air Quality ◽  
San Francisco ◽  
Low Cost ◽  
A Priori ◽  
Sensor Nodes ◽  
Bay Area ◽  
Attractive Option ◽  
Global Background ◽  
Zero Offset ◽  
The Individual

Abstract. The newest generation of air quality sensors is small, low cost, and easy to deploy. These sensors are an attractive option for developing dense observation networks in support of regulatory activities and scientific research. They are also of interest for use by individuals to characterize their home environment and for citizen science. However, these sensors are difficult to interpret. Although some have an approximately linear response to the target analyte, that response may vary with time, temperature, and/or humidity, and the cross-sensitivity to non-target analytes can be large enough to be confounding. Standard approaches to calibration that are sufficient to account for these variations require a quantity of equipment and labor that negates the attractiveness of the sensors' low cost. Here we describe a novel calibration strategy for a set of sensors, including CO, NO, NO2, and O3, that makes use of (1) multiple co-located sensors, (2) a priori knowledge about the chemistry of NO, NO2, and O3, (3) an estimate of mean emission factors for CO, and (4) the global background of CO. The strategy requires one or more well calibrated anchor points within the network domain, but it does not require direct calibration of any of the individual low-cost sensors. The procedure nonetheless accounts for temperature and drift, in both the sensitivity and zero offset. We demonstrate this calibration on a subset of the sensors comprising BEACO2N, a distributed network of approximately 50 sensor “nodes”, each measuring CO2, CO, NO, NO2, O3 and particulate matter at 10 s time resolution and approximately 2 km spacing within the San Francisco Bay Area.

Wireless Sensor Network for Performance Monitoring of Electrical Machine

2015 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Priyanka Ranaware ◽  
N.D. Dhoot
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Low Cost ◽  
Monitoring Network ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Electrical Machine ◽  
Prototype System ◽  
Monitoring And Control

<p class="Default">This paper proposes a novel industrial wireless sensor network for industrial machine condition monitoring. To avoid unexpected equipment failures and obtain higher accuracy in diagnostic and prognostic for the health condition of a motor, efficient and comprehensive data collecting, monitoring, and control play an important role to improve the system more reliable and effective. A novel wireless data collection for health monitoring system of electric machine based on wireless sensor network is proposed and developed in this paper. The unique characteristics of ZigBee networks such as low power, low cost, and high flexibility make them ideal for this application. The proposed system consists of wireless sensor nodes which are organized into a monitoring network by ZigBee protocols. A base station and wireless nodes have been developed to form a prototype system. Various sensors have the capability to monitor physiological as well as environmental conditions. Therefore proposed system provides a flexible solution that makes our living spaces more intelligent.</p>

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