Low cost sensors and crowd-sourced data to map air pollution in urban areas

2020 ◽  
Author(s):  
Rodrigo Carbajales ◽  
Massimiliano Iurcev ◽  
Paolo Diviacco
Keyword(s):  
Air Pollution ◽  
Real Time ◽  
Urban Areas ◽  
Low Cost ◽  
Reliable Data ◽  
Crowd Sourcing ◽  
Moving Vehicle ◽  
Redundant Data ◽  
Boundary Definition ◽  
Easy Integration

<p>Low cost sensors and crowd-sourcing data could potentially revolutionise the way air pollution measurements are collected providing high density geolocated data. In fact, so far data have been collected mostly using dedicated fixed position monitoring stations. These latter rely on high quality instrumentation, well established practices and well trained personnel, which means that, due to its costs, this paradigm entails limitations in the resolution and extension of geographic sampling of an area.</p><p>The combination of low costs sensors and volunteer-based or opportunistic acquisition of data can, instead, possibly turn the cost issue into an advantage. This approach, however, introduces other limitations since low cost sensors provide less reliable data and crowd source acquisition are subjects to data gaps in space and time.</p><p>In order to overcome these issues redundant data from multiple platforms have to be made available. On one hand this allows statistics to be applied to identify and remove anomalous values, and on the other hand when multiple platforms are used, the chances to have a better coverage and more reliable data  increases.</p><p>To implement this approach OGS developed the full suite of tools that has been named COCAL that allow to follow the full path from the acquisition, transmission, storage, integration and real time visualization of the crowdsourced data.</p><p>Low cost sensors for the detection of suspended particulate matter size 2.5 and 10 µm, together with atmospheric pressure, humidity and temperature, have been combined with GPS positioning and transmission (being able to opt for GSM, WiFi or LoRaWAN transmission) unit in a black box that can be attached to any moving vehicle travelling in an area. This way large areas can be sampled with high geographic resolution.</p><p>Atmospheric data are collected in an InfluxDB database, which allows easy integration with TheThingsNetwork for LoRaWAN network management and directly with GSM and WiFi connections. Public users are provided with a real-time web interface based on OpenLayers for map visualization. Server based processing and conversion scripts generate both filtered data and aggregate data, by computing averages on a spatial and temporal grid.. Finally, automatic interpolation techniques like Inverse Distance Weighting or Natural Neighbours may provide detailed online maps with contouring and boundary definition. All products are available in near real-time through OGC compliant web services, suited for an easy integration with other repositories and services.</p>

scholarly journals Design of an IoT based Real Time Environment Monitoring System using Legacy Sensors

2018 ◽  
Vol 210 ◽  
pp. 03008
Author(s):  
Aparajita Das ◽  
Manash Pratim Sarma ◽  
Kandarpa Kumar Sarma ◽  
Nikos Mastorakis
Keyword(s):  
Air Pollution ◽  
Light Intensity ◽  
Real Time ◽  
Environmental Conditions ◽  
Low Cost ◽  
Environment Monitoring ◽  
Time Data ◽  
Graphical Information ◽  
Real Time Data ◽  
Sun Light

This paper describes the design of an operative prototype based on Internet of Things (IoT) concepts for real time monitoring of various environmental conditions using certain commonly available and low cost sensors. The various environmental conditions such as temperature, humidity, air pollution, sun light intensity and rain are continuously monitored, processed and controlled by an Arduino Uno microcontroller board with the help of several sensors. Captured data are broadcasted through internet with an ESP8266 Wi-Fi module. The projected system delivers sensors data to an API called ThingSpeak over an HTTP protocol and allows storing of data. The proposed system works well and it shows reliability. The prototype has been used to monitor and analyse real time data using graphical information of the environment.

scholarly journals REAL-TIME ENVIRONMENTAL SENSORS TO IMPROVE HEALTH IN THE SENSING CITY

2016 ◽  
Vol XLI-B2 ◽  
pp. 729-733 ◽  
Author(s):  
L. Marek ◽  
M. Campbell ◽  
M. Epton ◽  
M. Storer ◽  
S. Kingham
Keyword(s):  
Quality Of Life ◽  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Low Cost ◽  
Monitoring Network ◽  
Environmental Data ◽  
Chronic Obstructive ◽  
Actual Usage

The opportunity of an emerging smart city in post-disaster Christchurch has been explored as a way to improve the quality of life of people suffering Chronic Obstructive Pulmonary Disease (COPD), which is a progressive disease that affects respiratory function. It affects 1 in 15 New Zealanders and is the 4th largest cause of death, with significant costs to the health system. While, cigarette smoking is the leading cause of COPD, long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust can also cause and exacerbate it. Currently, we do know little what happens to the patients with COPD after they leave a doctor’s care. By learning more about patients’ movements in space and time, we can better understand the impacts of both the environment and personal mobility on the disease. This research is studying patients with COPD by using GPS-enabled smartphones, combined with the data about their spatiotemporal movements and information about their actual usage of medication in near real-time. We measure environmental data in the city, including air pollution, humidity and temperature and how this may subsequently be associated with COPD symptoms. In addition to the existing air quality monitoring network, to improve the spatial scale of our analysis, we deployed a series of low-cost Internet of Things (IoT) air quality sensors as well. The study demonstrates how health devices, smartphones and IoT sensors are becoming a part of a new health data ecosystem and how their usage could provide information about high-risk health hotspots, which, in the longer term, could lead to improvement in the quality of life for patients with COPD.

Smart Environmental Monitoring System

2019 ◽  
Vol 10 (1) ◽  
pp. 43-54
Author(s):  
Karthik Sudhakaran Menon ◽  
Brinzel Rodrigues ◽  
Akash Prakash Barot ◽  
Prasad Avinash Gharat
Keyword(s):  
Air Pollution ◽  
Real Time ◽  
Urban Areas ◽  
Real Life ◽  
Cloud Service ◽  
Threshold Value ◽  
Time Data ◽  
Multiple Sensors ◽  
Alert Message ◽  
Environmental Monitoring System

In today's world, air pollution has become a common phenomenon everywhere, especially in the urban areas, air pollution is a real-life problem. In urban areas, the increased number of hydrocarbons and diesel vehicles and the presence of industrial areas at the outskirts of the major cities are the main causes of air pollution. The problem is seriously intense within the metropolitan cities. The governments around the world are taking measure in their capability. The main aim of this project is to develop a system which may monitor and measure pollutants in the air in real time, tell the quality of air and log real-time data onto a remote server (Cloud Service). If the value of the parameters exceeds the given threshold value, then an alert message is sent with the GPS coordinates to the registered number of the authority or person so necessary actions can be taken. The Arduino board connects with Thingspeak cloud service platform using ESP8266 Wi-Fi module. The device uses multiple sensors for monitoring the parameters of the air pollution like MQ-135, MQ-7, DHT-22, sound sensor, LCD.

scholarly journals Smart Air Quality Monitoring and Sensing Device (SAQM Sensing Device)

2021 ◽  
Vol 9 (8) ◽  
pp. 2787-2792
Author(s):  
Hareetaa Mallani
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Heart Diseases ◽  
Low Cost ◽  
Power Requirement ◽  
Premature Deaths ◽  
Response Recovery ◽  
Alert Message ◽  
Mobile Measurement ◽  
Harmful Effects

Abstract: Air pollution is the biggest problem of every nation, whether it is developed or developing. Health problems have been growing at faster rate especially in urban areas of developing countries where industrialization and growing number of vehicles leads to release of lot of gaseous pollutants. Harmful effects of pollution include mild allergic reactions such as irritation of the throat, eyes and nose as well as some serious problems like bronchitis, heart diseases, pneumonia, lung and aggravated asthma. According to a survey, due to air pollution 50,000 to 100,000 premature deaths per year occur in the U.S. alone. LPG sensor is added in this system which is used mostly in houses. The system will show temperature and humidity. The system can be installed anywhere but mostly in industries and houses where gases are mostly to be found and gives an alert message when the system crosses threshold limit. The advantages of the detector, have a reliable stability, rapid response recovery and long-life features. It is affordable, userfriendly, low-cost and minimum-power requirement hardware which is appropriate for mobile measurement, as well as comprehensible data collection

scholarly journals Impacts of Modifiable Factors on Ambient Air Pollution: A Case Study of COVID-19 Shutdowns

2020 ◽  
Author(s):  
Rebecca Tanzer-Gruener ◽  
Jiayu Li ◽  
s. rose eilenberg ◽  
Allen Robinson ◽  
Albert Presto
Keyword(s):  
Air Pollution ◽  
Real Time ◽  
Low Cost ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Activity Levels ◽  
Industrial Emissions ◽  
Modifiable Factors ◽  
Business As Usual ◽  
The Impact

Modifiable sources of air pollution such as traffic, cooking, and electricity generation emissions can be modulated either by changing activity levels or source intensity. Although air pollution regulations typically target reducing emission factors rather than altering activity, the COVID-19 related closures offered a novel opportunity to observe and quantify the impact of activity levels of modifiable factors on ambient air pollution in real-time. We use data from a network of twenty-seven low-cost Real-time Affordable Multi-Pollutant (RAMP) sensor packages deployed throughout urban and suburban Pittsburgh along with data from EPA regulatory monitors. The RAMP locations were divided into four site groups based on land use (High Traffic, Urban Residential, Suburban Residential, and Industrial). Concentrations of PM2.5, CO, and NO2 following the COVID-related closures at each site group were compared to measurements from “business as usual” periods in March 2019 and 2020. Overall, PM2.5 concentrations decreased across the domain by 3 μg/m3. Intra-day variabilities of the pollutants were computed to attribute pollutant enhancements to specific emission sources (i.e. traffic and industrial emissions). There was no significant change in the industrial related intra-day variability of PM2.5 at the Industrial sites following the COVID-related closures. The morning rush hour induced CO and NO2 concentrations at the High Traffic sites were reduced by 57% and 43%, respectively, which is consistent with the observed reduction in commuter traffic (~50%). The morning rush hour PM2.5 enhancement from traffic emissions fell from ~1.5 μg/m3 to ~0 μg/m3 across all site groups. This translates to a reduction of 0.125 μg/m3 in the daily average PM2.5 concentration. If PM2.5 National Ambient Air Quality Standards (NAAQS) are tightened these calculations shed light on to what extent reductions in traffic related emissions are able to aid in meeting more stringent regulations.

scholarly journals Impacts of Modifiable Factors on Ambient Air Pollution: A Case Study of COVID-19 Shutdowns

2020 ◽  
Author(s):  
Rebecca Tanzer-Gruener ◽  
Jiayu Li ◽  
s. rose eilenberg ◽  
Allen Robinson ◽  
Albert Presto
Keyword(s):  
Air Pollution ◽  
Real Time ◽  
Low Cost ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Activity Levels ◽  
Industrial Emissions ◽  
Modifiable Factors ◽  
Business As Usual ◽  
The Impact

Modifiable sources of air pollution such as traffic, cooking, and electricity generation emissions can be modulated either by changing activity levels or source intensity. Although air pollution regulations typically target reducing emission factors rather than altering activity, the COVID-19 related closures offered a novel opportunity to observe and quantify the impact of activity levels of modifiable factors on ambient air pollution in real-time. We use data from a network of twenty-seven low-cost Real-time Affordable Multi-Pollutant (RAMP) sensor packages deployed throughout urban and suburban Pittsburgh along with data from EPA regulatory monitors. The RAMP locations were divided into four site groups based on land use (High Traffic, Urban Residential, Suburban Residential, and Industrial). Concentrations of PM2.5, CO, and NO2 following the COVID-related closures at each site group were compared to measurements from “business as usual” periods in March 2019 and 2020. Overall, PM2.5 concentrations decreased across the domain by 3 μg/m3. Intra-day variabilities of the pollutants were computed to attribute pollutant enhancements to specific emission sources (i.e. traffic and industrial emissions). There was no significant change in the industrial related intra-day variability of PM2.5 at the Industrial sites following the COVID-related closures. The morning rush hour induced CO and NO2 concentrations at the High Traffic sites were reduced by 57% and 43%, respectively, which is consistent with the observed reduction in commuter traffic (~50%). The morning rush hour PM2.5 enhancement from traffic emissions fell from ~1.5 μg/m3 to ~0 μg/m3 across all site groups. This translates to a reduction of 0.125 μg/m3 in the daily average PM2.5 concentration. If PM2.5 National Ambient Air Quality Standards (NAAQS) are tightened these calculations shed light on to what extent reductions in traffic related emissions are able to aid in meeting more stringent regulations.

Sensor Based Smart Real Time Monitoring of Patients Conditions Using Wireless Protocol

Biotechnology ◽  
2019 ◽  
pp. 720-743
Author(s):  
Jegan R. ◽  
Nimi W. S.
Keyword(s):  
Real Time ◽  
Online Monitoring ◽  
Low Cost ◽  
Health Condition ◽  
Programming Environment ◽  
Wireless Monitoring ◽  
Physiological Signal ◽  
Easy Integration ◽  
Parameters Measurement ◽  
Wireless Protocol

This article describes how physiological signal monitoring plays an important role in identifying the health condition of heart. In recent years, online monitoring and processing of biomedical signals play a major role in accurate clinical diagnosis. Therefore, there is a requirement for the developing of online monitoring systems that will be helpful for physicians to avoid mistakes. This article focuses on the method for real time acquisition of an ECG and PPG signal and it's processing and monitoring for tele-health applications. This article also presents the real time peak detection of ECG and PPG for vital parameters measurement. The implementation and design of the proposed wireless monitoring system can be done using a graphical programming environment that utilizes less power and a minimized area with reasonable speed. The advantages of the proposed work are very simple, low cost, easy integration with programming environment and continuous monitoring of physiological signals.

Assessment of heterogenity of air pollution within an urban canopy

2020 ◽  
Author(s):  
Vivien Voss ◽  
K. Heinke Schlünzen ◽  
David Grawe
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Citizen Science ◽  
Urban Areas ◽  
Hot Spot ◽  
Low Cost ◽  
Urban Canopy ◽  
Limit Values ◽  
Data Standard ◽  
The City

<p>Air pollution is an important topic within urban areas.  Limit values as given in the European Guidelines are introduced to reduce negative effects on humans and vegetation.  Exceedances of the limit values are to be assessed using measurements.  In case of found exceedances of the limit values, the local authorities need to act to reduce pollution levels. Highest values are found for several pollutants (NOx, NO2, particles) within densely build-up urban areas with traffic emissions being the major source and dispersion being very much impacted by the urban structures.  The quality assured measuring network used by the authorities is often too coarse to determine the heterogeneity in the concentration field. Low cost sample devices as employed in several citizen science projects might help to overcome the data sparsity. Volunteers measure the air quality at many sites, contribute to the measurement networks and provide the data on the web. However, the questions arising are: a) Are these data of sufficient high quality to provide results comparable to those of the quality assured networks? b) Is the network density sufficient to determine concentration patterns within the urban canopy layer? <br>One-year data from a citizen science network, which measures particulate matter (PM10, PM2.5) were compared to measurements provided by the local environmental agency, using two hot-spot areas in the city of Hamburg as an example. To determine how well the measurements agree with each other, a regression analyses was performed dependent on seasonal and diurnal cycles. Additionally, model simulations with the microscale obstacle resolving model MITRAS were performed for two characteristic building structures and different meteorological situations. The model results were used to determine local hot spots as well as areas where measurements might represent the concentration of particles for the urban quarter. The low cost sensor measurements show a general agreement to the city’s measurements, however, the values per sensor differ. Moreover, the measurements of the low-cost-sensor show an unrealistic dependence on relative humidity, resulting in over- or underestimations in certain cases. The model results clearly show that only a few sites allow measurements to be representative for a city quarter. The measurements of the citizen science project can provide a good overview about the tendencies of the air quality, but are currently not of sufficient quality to provide measurements calling for legal action.</p><p>The model results were used for the project AtMoDat. AtMoDat is an attempt to create a data standard for obstacle resolving models based on the existing Climate and Forecast (CF) conventions. A web-based survey is developed to get information on the requirements for the data standard. The next step is to extend the collection of model characteristics and eventually to provide a generic scheme.</p><p><strong>Acknowledgements</strong><br>This work contributes to project “AtMoDat” funded by the Federal Ministry of Education and Research under the funding number 16QK02C. Responsibility for the content of this publication lies with the authors.</p>

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