scholarly journals Development of an IoT based Air Quality Monitoring System

2019 ◽  
Vol 8 (10S) ◽  
pp. 23-28
Keyword(s):  
Air Quality ◽  
Web Application ◽  
Large Scale ◽  
Environmental Parameters ◽  
Air Pollutant ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Quality Data ◽  
Indoor Environments ◽  
Http Protocol

The proposed work aims for a large-scale air pollutant monitoring for ambient and indoor environments. This system is developed to measure various environmental parameters. Sourceof pollutants can be identified by analyzing the data collected from the various sensor nodes, so that air quality can be monitored by applying engineering science and data. This is achieved by installing multiple sensor stations in various locations such as hospitals, factories, Offices, streets and weather stations. These sensor stations measure the environmental parameters such as PM2.5, PM10, Sulphate (SOx), Nitrate (NOx), Ozone(O3), Volatile Organic Compounds (VOC), Temperature and Humidity. The sensor stations communicate with cloud over HTTP protocol. Each station has ESP 8266 smart controller which captures the sensor data and creates forms theJavaScript Object Notation (JSON) data packets that mainly consists of sensor data along with node address. These packets will be sent to the cloud over HTTP protocol. The user can access the air quality data from the web application.

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 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.

Applications of Wireless Sensor Networks

2010 ◽  
pp. 1076-1090 ◽  
Author(s):  
Corinna Schmitt ◽  
Georg Carle
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Radio Communication ◽  
Power Resources ◽  
Limited Power

Today the researchers want to collect as much data as possible from different locations for monitoring reasons. In this context large-scale wireless sensor networks are becoming an active topic of research (Kahn1999). Because of the different locations and environments in which these sensor networks can be used, specific requirements for the hardware apply. The hardware of the sensor nodes must be robust, provide sufficient storage and communication capabilities, and get along with limited power resources. Sensor nodes such as the Berkeley-Mote Family (Polastre2006, Schmitt2006) are capable of meeting these requirements. These sensor nodes are small and light devices with radio communication and the capability for collecting sensor data. In this chapter the authors review the key elements for sensor networks and give an overview on possible applications in the field of monitoring.

scholarly journals Distributed Fusion of Sensor Data in a Constrained Wireless Network

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1006 ◽  
Author(s):  
Charikleia Papatsimpa ◽  
Jean-Paul Linnartz
Keyword(s):  
Large Scale ◽  
Sensor Nodes ◽  
Sensor Data ◽  
A Posteriori ◽  
Likelihood Ratios ◽  
Process Data ◽  
Local Data ◽  
Fusion Algorithm ◽  
Distributed Information ◽  
Iot Devices

Smart buildings with connected lighting and sensors are likely to become one of the first large-scale applications of the Internet of Things (IoT). However, as the number of interconnected IoT devices is expected to rise exponentially, the amount of collected data will be enormous but highly redundant. Devices will be required to pre-process data locally or at least in their vicinity. Thus, local data fusion, subject to constraint communications will become necessary. In that sense, distributed architectures will become increasingly unavoidable. Anticipating this trend, this paper addresses the problem of presence detection in a building as a distributed sensing of a hidden Markov model (DS-HMM) with limitations on the communication. The key idea in our work is the use of a posteriori probabilities or likelihood ratios (LR) as an appropriate “interface” between heterogeneous sensors with different error profiles. We propose an efficient transmission policy, jointly with a fusion algorithm, to merge data from various HMMs running separately on all sensor nodes but with all the models observing the same Markovian process. To test the feasibility of our DS-HMM concept, a simple proof-of-concept prototype was used in a typical office environment. The experimental results show full functionality and validate the benefits. Our proposed scheme achieved high accuracy while reducing the communication requirements. The concept of DS-HMM and a posteriori probabilities as an interface is suitable for many other applications for distributed information fusion in wireless sensor networks.

scholarly journals Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM

2020 ◽  
Vol 10 (6) ◽  
pp. 1953 ◽  
Author(s):  
Songzhou Li ◽  
Gang Xie ◽  
Jinchang Ren ◽  
Lei Guo ◽  
Yunyun Yang ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Air Pollutant ◽  
Quality Data ◽  
Coefficient Of Determination ◽  
Series Data ◽  
The Past ◽  
Lstm Network ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN–LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.

Comparison of Modeled-to-Monitored PM2.5 Exposure Concentrations Resulting from Transportation Emissions in a Near-Road Community

2020 ◽  
Vol 2674 (12) ◽  
pp. 130-143
Author(s):  
Mayra Chavez ◽  
Wen-Whai Li
Keyword(s):  
Air Quality ◽  
Meteorological Data ◽  
Temporal Variations ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Spatial And Temporal Variations ◽  
Quality Data ◽  
Background Concentrations ◽  
Regional Background ◽  
Transportation Emissions

Residents living in near-road communities are exposed to traffic-related air pollutants, which can adversely affect their health. Near-road communities are expected to observe significant spatial and temporal variations in pollutant concentrations. Determining these variations in the surrounding areas can help raise awareness among government agencies of these underserved communities living near highways. This study conducted traffic and air quality measurements along with emission and dispersion modeling of the exposure to transportation emissions of a near-road urban community adjacent to the US 54 highway (US 54), with annual average daily traffic (AADT) of 107,237. The objectives of this study were (i) to develop spatial and temporal patterns of pollutant concentration variation and (ii) to apportion the differences in exposure concentrations to background concentrations and those that are contributed from major highways. It was observed that: (a) particulate matter (PM2.5) in near-road communities is dominated by the regional background concentrations which account for more than 85% of the pollution; and (b) only near-road receptors are affected by the traffic-related air pollutant emissions from major highways while spatial and temporal variations of PM2.5 concentrations in near-road communities are less influenced by local traffic, subsiding rapidly to negligible concentrations at 300 m from the road. Modeled PM2.5 concentrations were compared with monitored data. For better air quality impact assessments, higher quality data such as time-specific traffic volume and fleet information as well as site-specific meteorological data could help yield more accurate concentration predictions. Modeled-to-monitored comparison shows that air quality in near-road communities is dominated by regional background concentrations.

scholarly journals Mobility Restrictions and Air Quality under COVID-19 Pandemic in São Paulo, Brazil

2020 ◽  
Author(s):  
Edmilson D. Freitas ◽  
Sergio A. Ibarra-Espinosa ◽  
Mario E. Gavidia-Calderón ◽  
Amanda Rehbein ◽  
Sameh A. Abou Rafee ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Large Scale ◽  
Hospital Admissions ◽  
Air Pollutant ◽  
Government Officials ◽  
Social Unrest ◽  
The Public ◽  
Containment Measure ◽  
Mobility Reduction

Social distancing policies put in place during COVID-19 epidemic in addition to helping to limit the spread of the disease also contributed to improving urban air quality. Here we show a decrease in air pollutant concentration as a consequence of mobility reduction in São Paulo during the containment measure which began on 22nd March 2020. When comparing to foregoing weeks to equivalent periods of 2019, the concentration of most air pollutants sharply decreased in the first days of mobility restriction, to then increase again after government officials downplayed the threat of the disease. This trend is also followed by a decrease in hospital admissions by SARS-influenza. Therefore, despite the great economic and social unrest caused by the pandemic, this unique situation shows that large-scale mobility reduction policy had a significant impact on air quality, benefiting, directly and indirectly, the public health system.

Respiratory Symptoms and Spirometric Observations in Relation to Atmospheric Pollutants in a Sample of Urban Population

2000 ◽  
Vol 12 (2) ◽  
pp. 58-64 ◽  
Author(s):  
K. Satish Kumar ◽  
C.E. Prasad ◽  
N. Balakrishna ◽  
K. Visweswara Rao ◽  
P. Uma Maheswara Reddy
Keyword(s):  
Air Quality ◽  
Respiratory Symptoms ◽  
Ambient Air ◽  
Air Pollutant ◽  
Control Measures ◽  
Atmospheric Pollutants ◽  
Quality Data ◽  
Ambient Air Quality ◽  
Group I ◽  
The Mean

The prevalence of respiratory problems and the ventilatory functions in subjects belonging to three sample areas with different levels of pollution was studied to ascertain if there is any association between air pollutant levels and abnormal ventilatory functions. The predominant activity existing in that area served as the basis for stratification of the city into industrial (Group I), commercial (Group II) and residential (Group III) areas. Ambient air quality data of suspended particulate matter SPM, SO2 and NOx of the three sample areas were measured using standard methods. 216 men included in the study were administered the American Thoracic Society - Division of Lung Diseases ATS-DLD respiratory questionnaire, clinically examined and subjected to routine laboratory investigations. Spirometry and salbutamol reversibility tests were performed as per the ATS guidelines 1991. The mean and peak levels of SPM in the commercial area and the peak levels in the residential area were higher than the National Ambient Air Quality Standards (NAAQS). The mean and peak levels of NOx and SO2 in all the three areas were lower than the NAAQS. A high prevalence of ∼ 30-50% of respiratory symptoms was reported in the present study. Respiratory and ventilatory abnormalities were higher in the commercial areas, which are associated with the higher mean and peak levels of SO 2 and the peak levels of NOx. The pollution control measures should also aim at the peak levels of pollutants as they have been shown to exacerbate the respiratory symptoms in the present study. Asia Pac J Public Health 2000;12(2): 58-64

scholarly journals The influence of COVID-19 preventive measures on the air quality in Abu Dhabi (United Arab Emirates)

2021 ◽  
Author(s):  
Oriol Teixidó ◽  
Aurelio Tobías ◽  
Jordi Massagué ◽  
Ruqaya Mohamed ◽  
Rashed Ekaabi ◽  
...  
Keyword(s):  
Air Quality ◽  
United Arab Emirates ◽  
Preventive Measures ◽  
Dust Storms ◽  
Air Pollutant ◽  
Anthropogenic Sources ◽  
Abu Dhabi ◽  
Quality Data ◽  
Gaseous Pollutants ◽  
Pollutant Concentrations

AbstractThe preventive and cautionary measures taken by the UAE and Abu Dhabi governments to reduce the spread of the coronavirus disease (COVID-19) and promote social distancing have led to a reduction of mobility and a modification of economic and social activities. This paper provides statistical analysis of the air quality data monitored by the Environment Agency – Abu Dhabi (EAD) during the first 10 months of 2020, comparing the different stages of the preventive measures. Ground monitoring data is compared with satellite images and mobility indicators. The study shows a drastic decrease during lockdown in the concentration of the gaseous pollutants analysed (NO2, SO2, CO, and C6H6) that aligns with the results reported in other international cities and metropolitan areas. However, particulate matter (PM10 and PM2.5) averaged concentrations followed a markedly different trend from the gaseous pollutants, indicating a larger influence from natural events (sand and dust storms) and other anthropogenic sources. The ozone (O3) levels increased during the lockdown, showing the complexity of O3 formation. The end of lockdown led to an increase of the mobility and the air pollution; however, air pollutant concentrations remained in lower levels than during the same period of 2019. The results in this study show the large impact of human activities on the quality of air and present an opportunity for policymakers and decision-makers to design stimulus packages to overcome the economic slow-down, with strategies to accelerate the transition to resilient, low-emission economies and societies more connected to the nature that protect human health and the environment.

Efficient Multi-Level Human Motion Tracking Based on Compressive Infrared Sensing Paradigm

2013 ◽  
Vol 278-280 ◽  
pp. 988-993 ◽  
Author(s):  
Xiao Mu Luo ◽  
Dong Hui Liu ◽  
Hao Chen ◽  
Jia Ming Hong ◽  
Tong Liu ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Large Scale ◽  
Human Motion ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Human Motion Tracking ◽  
Low Level ◽  
Infrared Sensing ◽  
Level Sensor ◽  
Multi Level

Multi-level human motion tracking and analysis is still an open question in person surveillance, especially with constrained computational and communication resources. In this paper, we propose a sensing paradigm which could address this challenge efficiently and effectively. The proposed paradigm mainly includes two components. First, we design a compressive infrared sensing model, which can sample and encode multi-level human motion into low-level sensor data directly, without the mediate process of scene recovery. Second, we employ lightweight data processing algorithms to detect and segment human motion at different levels, and decode the location information adaptively. We used self-developed pyroelectric infrared (PIR) sensor nodes to construct a wireless distributed network, and conducted experiments in real office environment. The experimental results showed that the proposed paradigm could track human motion at two levels robustly, and the computational and communication burden is low (5×1 sensor data stream at 5 Hz for processing). Our paradigm bridges the gap between the low-level sensor data and the high-level analysis for large-scale automated surveillance, and could serve as useful guidance for system design if needed.

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