Development of a Low-Cost Sensor Network for Community-Made Measurements of Air Pollution

2020 ◽  
Author(s):  
Sebastian Sandoval Campos ◽  
Fabián A. Ballesteros Higuera ◽  
Sebastián Roa Prada ◽  
Claudia I. Cáceres Becerra ◽  
Alfredo A. Díaz Claro
Keyword(s):  
Air Pollution ◽  
Sensor Network ◽  
Fossil Fuels ◽  
Low Cost ◽  
Independent Solution ◽  
Weather Conditions ◽  
Local Area ◽  
Daily Basis ◽  
Processing Plant ◽  
Communication Module

Abstract The levels of pollution present in the air have been dramatically increasing over the years due to the continuous emission of greenhouse gases such as CO2, CO, NOx and H2S, among others. The main source of these emissions is from burning fossil fuels for electricity, heat, and transportation. This represents a tremendous risk to the populations located near the emission sources where people get exposed to dangerous concentrations of such gases on a daily basis. The lack of open real-time monitoring tools makes people unaware of the damage these pollutants cause to their health. This research proposes the development and implementation of a low-cost independent solution to keep the members of a community informed about concentration levels of air pollution due to local emissions. This tool must be easily accessible to the users so that the data about the number of particulates per million of a specific gas within a zone of interest can be viewed at any time. The proposed solution consists of a sensor network, covering the widest possible area, with respect to the point of interest. The collected data is sent to a cloud server, which operates as storage center and in which the data can be latter accessed for subsequent analysis. The measurements are sent to the server by means of a wireless communication protocol, carried out by a General Packet Radio Service, GPRS, communication module connected to each station. In this way, the coverage of the network is not limited by issues such as the use of local area networks which at the same time facilitates the transportation and installation of the stations at any desired measurement site. Since each station can collect large amounts of data during a given period of time, it was necessary to implement techniques such as Big Data in order to extract important information and to identify patterns from the data such as the areas having the highest concentration of gases and possible correlations with other variables such as local weather conditions. This information could be used to support the making of decisions that benefit the communities impacted by air pollution, for example the early triggering of bad air quality alarms or the development of policies to regulate industry operation that can potentially impact the health of neighboring communities. A pilot case study was implemented in the city of Floridablanca, Colombia, to demonstrate the monitoring of the emissions of hydrogen sulfide of a big wastewater processing plant.

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 Development of a solar concentrator with tracking system

2016 ◽  
Vol 7 (2) ◽  
pp. 233-245 ◽  
Author(s):  
Flávia V. Barbosa ◽  
João L. Afonso ◽  
Filipe B. Rodrigues ◽  
José C. F. Teixeira
Keyword(s):  
Solar Energy ◽  
Fossil Fuels ◽  
Tracking System ◽  
Low Cost ◽  
Experimental Tests ◽  
Weather Conditions ◽  
Electrical Energy ◽  
Real Problem ◽  
Stored Energy ◽  
Geometrical Properties

Abstract. Solar Energy has been, since the beginning of human civilization, a source of energy that raised considerable interest, and the technology used for their exploitation has developed constantly. Due to the energetic problems which society has been facing, the development of technologies to increase the efficiency of solar systems is of paramount importance. The solar concentration is a technology that has been used for many years by the scientist, because this system enables the concentration of solar energy in a focus, which allows a significant increase in energy intensity. The receiver, placed at the focus of the concentrator, can use the stored energy to produce electrical energy through Stirling engine, for example, or to produce thermal energy by heating a fluid that can be used in a thermal cycle. The efficiency of solar concentrators can be improved with the addition of a dual axis solar tracker system which allows a significant increase in the amount of stored energy. In response to the aforementioned, this paper presents the design and construction of a solar dish concentrator with tracking system at low cost, the optical and thermal modelling of this system and a performance analysis through experimental tests. The experimental validation allows to conclude that the application of a tracking system to the concentrator is very important since a minimum delay of the solar radiation leads to important losses of system efficiency. On the other hand, it is found that the external factors can affect the final results which include the optical and geometrical properties of the collector, the absorptivity and the position of the receiver as well as the weather conditions (essentially the wind speed and clouds). Thus, the paper aims to present the benefits of this technology in a world whose the consumption of energy by fossil fuels is a real problem that society needs to face.

scholarly journals A High-Resolution Open Source Platform for Building Envelope Thermal Performance Assessment Using a Wireless Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1755 ◽  
Author(s):  
Romwald Lihakanga ◽  
Yuan Ding ◽  
Gabriela M. Medero ◽  
Samuel Chapman ◽  
George Goussetis
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Low Cost ◽  
Weather Conditions ◽  
Building Envelope ◽  
Wireless Sensor ◽  
Variation Analysis ◽  
Heat Flows

This paper presents an in-situ wireless sensor network (WSN) for building envelope thermal transmission analysis. The WSN is able to track heat flows in various weather conditions in real-time. The developed system focuses on long-term in-situ building material variation analysis, which cannot be readily achieved using current approaches, especially when the number of measurement hotspots is large. This paper describes the implementation of the proposed system using the heat flow method enabled through an adaptable and low-cost wireless network, validated via a laboratory experiment.

scholarly journals Big data and human biosensors – birthweight as a biomonitor for air pollution levels. A population based study using routine data.

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Chris Dibben ◽  
Tom Clemens
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Routine Data ◽  
Local Area ◽  
Quantitative Information ◽  
Population Based ◽  
Small Areas ◽  
Pollution Levels ◽  
Routine Data Collection ◽  
The Individual

IntroductionIn the natural sciences biomonitors, (organisms, such as pine needles, shells, lichen, that can provide quantitative information on the quality of their past and present environments) have been developed for environmental measurement. In recent year’s physiologists have started to explore if human tissue could also be used. Objectives and ApproachThe costs of collecting and processing human tissues for biomonitoring, may be too prohibitive for its use in wide scale monitoring. In contrast if biomonitors could be identified within a routine data collection process that were part of standard medical recording a low cost, widely available large sample would be available to scientists. Pregnancy is known to be effected by air pollution, therefore we explore whether birthweight, recorded in maternity records, could be a biomonitor for air pollution. ResultsWe use maternity records (~1 million births) in Scotland between 2000 and 2015. We modelled, at the individual mother level birthweight, controlling mother’s age, estimated household income, local area crime rates and area level of multiple deprivation. We then aggregate and calculate the averaged of the residuals for this model for all mothers within an intermediate datazones (small areas of around 4000 residents). These mean deviations were then compared with pollution modelled figures produced by AEA for the Scottish government averaged over the same spatial units and time period as the maternity data. "We find a relatively strong correlations (between -0.37 and -0.39) between our ‘biomonitor estimates’ of air pollution derived from the maternity records and the entirely separated modelled air pollution data." Conclusion/ImplicationsAs far as we know this is the first study to demonstrate that it may be possible to use routine health data to derive ‘biomonitors’ information. Importantly if this method proves to be reliable it will be a relatively cheap method for collecting information that is actually personally monitoring.

A new calibration system for low-cost Sensor Network in air pollution monitoring

2021 ◽  
Author(s):  
Houxin Cui ◽  
Ling Zhang ◽  
Wanxin Li ◽  
Ziyang Yuan ◽  
Mengxian Wu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Sensor Network ◽  
Low Cost ◽  
Pollution Monitoring ◽  
Calibration System ◽  
Air Pollution Monitoring

scholarly journals NB-IoT Sensor Network for Obtaining the Input Data for Hydrological Simulation Model

2021 ◽  
Vol 13 (1) ◽  
pp. 59-69
Author(s):  
Oskars Java ◽  
Aleksandrs Sigajevs ◽  
Juris Binde ◽  
Michal Kepka
Keyword(s):  
Simulation Model ◽  
Sensor Network ◽  
Input Data ◽  
Low Cost ◽  
Simulation Models ◽  
Weather Conditions ◽  
Operating Principle ◽  
Network Technology ◽  
Hydrological Simulation ◽  
Cloud Server

The article describes the choice of appropriate network technology that provides sufficient coverage to allow the sensor network to be placed even in the remote and difficult to reach locations and the data to reach the cloud server. Further it describes the components of the sensor network, the operating principle, architecture and the processing of the data obtained to convert them into the input data used in the hydrological simulation model. The NB-IoT sensor network proposed by the authors would not only collect the data needed to operate hydrological simulation models, but, for example, could provide the data needed to forecast weather conditions, particularly if the architecture of this sensor network, because of its low cost, would be widely applied around the globe, joining a unified global sensor network.

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

2016 ◽  
Vol 9 (11) ◽  
pp. 5281-5292 ◽  
Author(s):  
Wan Jiao ◽  
Gayle Hagler ◽  
Ronald Williams ◽  
Robert Sharpe ◽  
Ryan Brown ◽  
...  
Keyword(s):  
United States ◽  
Air Pollution ◽  
Carbon Monoxide ◽  
Sulfur Dioxide ◽  
Sensor Network ◽  
Correlation Coefficient ◽  
Low Cost ◽  
Southeastern United States ◽  
Sensor Technology ◽  
Very High

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 the southeastern United States. Collocation 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 the degree to which multiple identical sensors produced 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, and −0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r < 0.5) with a reference monitor and erroneously high concentration values. A wide variety of particulate matter (PM) sensors were tested with variable results – some sensors had very high agreement (e.g., r =  0.99) between identical sensors but moderate agreement with a reference PM2.5 monitor (e.g., r =  0.65). For select sensors that had moderate to strong correlation with reference monitors (r > 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in number of 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 (two nodes) and PM (four nodes) data for an 8-month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to nearby traffic emissions. Overall, this study demonstrates the performance of emerging air quality sensor technologies in a real-world setting; the variable agreement between sensors and reference monitors indicates that in situ testing of sensors against benchmark monitors should be a critical aspect of all field studies.

scholarly journals Real-Time Implementation of Greenhouse Monitoring System Based on Wireless Sensor Network

2019 ◽  
Vol 8 (2S2) ◽  
pp. 215-219
Keyword(s):  
Wireless Sensor Network ◽  
Embedded System ◽  
Real Time ◽  
Sensor Network ◽  
High Reliability ◽  
Low Cost ◽  
Economic Value ◽  
Weather Conditions ◽  
Wireless Sensor ◽  
Main Role

The climate change has brought about unpredictable weather conditions that have resulted in the global food shortage being experienced. This issue can be solved by greenhouses, they play a main role in increasing the crop yield per unit area and represent the suitable environment for off-corps yields. Managing and continuous monitoring the green house environment can be done using a wired sensor network, but the high cost, wiring complexity, fixed sensor locations and the restricted distances are the big problems of this type of a networking. To solve these problems, we implemented a real time embedded system using Wireless Sensor Network (WSN) based on ZigBee technology to control and monitor the environmental of greenhouses. The WSN can be adopted as the best solution to apply in greenhouse because of its good properties, long distances, low-cost, low power consumption, high security and high reliability. The constructed system is implemented based on simple components, ATMEGA328P microcontroller and ZigBee are represented the kernel of sensor node, collect data from various sensors and present them to a coordinating station where data can be stored and processed, then actuators will be operate depending on the processed data. The captured data will be displayed for monitoring in a real time manner. The monitor system was developed using GSM technology. The simulation results show that the system is more efficient in the manpower saving and raising the economic value of products. Furthermore, the developed system is simple, and easily installable.

scholarly journals Developing of Low-Cost Air Pollution Sensor—Measurements with the Unmanned Aerial Vehicles in Poland

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3582
Author(s):  
Sławomir Pochwała ◽  
Arkadiusz Gardecki ◽  
Piotr Lewandowski ◽  
Viola Somogyi ◽  
Stanisław Anweiler
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Three Dimensional ◽  
Weather Conditions ◽  
Microprocessor System ◽  
Data Output ◽  
Small Device ◽  
Quick Measurement ◽  
Three Dimensional Models ◽  
Pm2.5 And Pm10

This article presents the capabilities and selected measurement results from the newly developed low-cost air pollution measurement system mounted on an unmanned aerial vehicle (UAV). The system is designed and manufactured by the authors and is intended to facilitate, accelerate, and ensure the safety of operators when measuring air pollutants. It allows the creation of three-dimensional models and measurement visualizations, thanks to which it is possible to observe the location of leakage of substances and the direction of air pollution spread by various types of substances. Based on these models, it is possible to create area audits and strategies for the elimination of pollution sources. Thanks to the usage of a multi-socket microprocessor system, the combination of nine different air quality sensors can be installed in a very small device. The possibility of simultaneously measuring several different substances has been achieved at a very low cost for building the sensor unit: 70 EUR. The very small size of this device makes it easy and safe to mount it on a small drone (UAV). Because of this device, many harmful chemical compounds such as ammonia, hexane, benzene, carbon monoxide, and carbon dioxide, as well as flammable substances such as hydrogen and methane, can be detected. Additionally, a very important function is the ability to perform measurements of PM2.5 and PM10 suspended particulates. Thanks to the use of UAV, the measurement is carried out remotely by the operator, which allows us to avoid the direct exposure of humans to harmful factors. A big advantage is the quick measurement of large spaces, at different heights above the ground, in different weather conditions. Because of the three-dimensional positioning from GPS receiver, users can plot points and use colors reflecting a concentration of measured features to better visualize the air pollution. A human-friendly data output can be used to determine the mostly hazardous regions of the sampled area.

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