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.

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.

scholarly journals Domain adaptation based deep calibration of low-cost PM2.5 sensors

2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Domain Adaptation ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Proposed Model ◽  
Temperature And Humidity ◽  
Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

scholarly journals KTP—Collectively Improving Air Quality in Krakow: A New Air Quality Plan for the Małopolska Region

2021 ◽  
pp. 67-78
Author(s):  
Agnieszka Włodarczyk-Gębik ◽  
Aleksandra Gabriel ◽  
Maria Dubis ◽  
Monika Machowska
Keyword(s):  
Quality Of Life ◽  
Air Pollution ◽  
Air Quality ◽  
Metropolitan Area ◽  
Decision Makers ◽  
Improve Quality ◽  
New Policies ◽  
User Centered

AbstractKTP’s project relates to the challenge of air pollution and the need to improve quality of life in Kraków and the Kraków Metropolitan Area. The aim is to improve the quality of the air by motivating citizens to change their ecological attitudes, transport and heating habits and support decision makers with relevant tools and instruments for better co-creation of local new policies with a user-centered approach.

scholarly journals Key Concerns and Drivers of Low-Cost Air Quality Sensor Use

2022 ◽  
Vol 14 (1) ◽  
pp. 584
Author(s):  
Priyanka Nadia deSouza
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Data Visualization ◽  
Low Cost ◽  
Pollution Monitoring ◽  
Quality Information ◽  
User Reviews ◽  
Air Pollution Monitoring ◽  
The Public

Low-cost sensors are revolutionizing air pollution monitoring by providing real-time, highly localized air quality information. The relatively low-cost nature of these devices has made them accessible to the broader public. Although there have been several fitness-of-purpose appraisals of the various sensors on the market, little is known about what drives sensor usage and how the public interpret the data from their sensors. This article attempts to answer these questions by analyzing the key themes discussed in the user reviews of low-cost sensors on Amazon. The themes and use cases identified have the potential to spur interventions to support communities of sensor users and inform the development of actionable data-visualization strategies with the measurements from such instruments, as well as drive appropriate ‘fitness-of-purpose’ appraisals of such devices.

Monitoring urban environmental phenomena through a wireless distributed sensor network

2018 ◽  
Vol 7 (1) ◽  
pp. 68-79 ◽  
Author(s):  
Niek Bebelaar ◽  
Robin Christian Braggaar ◽  
Catharina Marianne Kleijwegt ◽  
Roeland Willem Erik Meulmeester ◽  
Gina Michailidou ◽  
...  
Keyword(s):  
Air Quality ◽  
Built Environment ◽  
Data Quality ◽  
Real Time ◽  
Sensor Network ◽  
Environmental Information ◽  
Environmental Data ◽  
Area Network ◽  
Content Type

Purpose The purpose of this paper is to provide local environmental information to raise community’s environmental awareness, as a cornerstone to improve the quality of the built environment. Next to that, it provides environmental information to professionals and academia in the fields of urbanism and urban microclimate, making it available for reuse. Design/methodology/approach The wireless sensor network (WSN) consists of sensor platforms deployed at fixed locations in the urban environment, measuring temperature, humidity, noise and air quality. Measurements are transferred to a server via long range wide area network (LoRaWAN). Data are also processed and publicly disseminated via the server. The WSN is made interactive as to increase user involvement, i.e. people who pass by a physical sensor in the city can interact with the sensor platform and request specific environmental data in near real time. Findings Microclimate phenomena such as temperature, humidity and air quality can be successfully measured with a WSN. Noise measurements are less suitable to send over LoRaWAN due to high temporal variations. Research limitations/implications Further testing and development of the sensor modules is needed to ensure consistent measurements and data quality. Practical implications Due to time and budget limitations for the project group, it was not possible to gather reliable data for noise and air quality. Therefore, conclusions on the effect of the measurements on the built environment cannot currently be drawn. Originality/value An autonomously working low-cost low-energy WSN gathering near real-time environmental data is successfully deployed. Ensuring data quality of the measurement results is subject for upcoming research.

scholarly journals PROTOTIPE MOBILE STATION UNTUK PEREKAMAN KANDUNGAN UDARA

2018 ◽  
Vol 5 (1) ◽  
pp. 164
Author(s):  
Gusti Ketut Bella ◽  
Nyoman Putra Sastra ◽  
I G. A. K. Diafari Djuni Hartawan
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Mobile Station ◽  
Air Content ◽  
The City ◽  
Content Information ◽  
The Web ◽  
Alcohol Benzene

This study aims to monitor the air quality of Denpasar city with mobile station that can transmit air content information in mobile and real time. This mobile station was built using Wemos D1 Mini Board which is a small wifi board based on ESP8266. Information sent via the Wemos D1 board is information obtained from sensors MQ-7 and MQ-135 and DHT11. DHT11, MQ-7 and MQ-135 sensors can detect the temperature, CO and NH3 gas, NOx, alcohol, benzene, smoke and CO2. Information obtained from the sensors will be posted on a website so that people can know the level of air pollution in the city of Denpasar. This developed monitoring system has successfully displayed data in the form of a folder on the web server.

Low-cost airbox network and Gaussian plume modelling to assist air quality policy-making at the local scale: Shexian County, Hebei Province, China

2021 ◽  
Author(s):  
Jun Zhang ◽  
Arjan Hensen ◽  
Paul Seignette ◽  
Dan Yu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Low Cost ◽  
Monitoring Network ◽  
Local Scale ◽  
Hebei Province ◽  
Steel Company ◽  
Pollution Levels ◽  
Gaussian Plume ◽  
Coking Plant

&lt;p&gt;High air pollution levels pose a threat to both human health and ecosystem vitality in Hebei Province, NE China. Although air quality changes are monitored hourly with high-end equipment at the provincial scale (197 stations for 187,693 km&lt;sup&gt;2&lt;/sup&gt;) it is difficult for individual counties or cities to improve local air quality based on regional-scale information. The Sino-Dutch Technology Transfer &amp; Training Project established a monitoring network of 43 low-cost air-boxes and 11 standard meteorological stations in Shexian county, Handan city (~ 1500 km&lt;sup&gt;2&lt;/sup&gt;) to measure atmospheric concentrations of PM&lt;sub&gt;10&lt;/sub&gt;, PM&lt;sub&gt;2.5&lt;/sub&gt;, CO, SO&lt;sub&gt;2&lt;/sub&gt;, NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; at 1-min intervals from January 2020 onwards. Data from these stations were evaluated in real time using the TNO Gaussian plume model. The model provides point emission levels of PM&lt;sub&gt;10&lt;/sub&gt;, PM&lt;sub&gt;2.5 &lt;/sub&gt;and CO at 10-min intervals after calibration against measured concentrations. Based on a 2019 pollution source inventory, 21 major source areas were identified and used to derive an optimized source map for model input &amp;#8211; including a large steel company, a coal-fueled power plant, different industrial complexes (cement, coking plant for ore smelting), as well as the densely populated city centre, rural residential areas, and a busy highway. The model performs source optimization using concentration data for all 43 stations and subsequently calculates the contributions of individual sources for each monitoring station to see to what extent the source map explained observed concentrations. Full network operation started in July 2020. Based on a one-month test period (August 2020), the steel company and coking plant were estimated to contribute ~25% of the total area&amp;#8217;s PM-emissions. The central city area contributed ~10% and 17% of total PM- and CO-emissions, respectively, mostly due to construction activity and traffic. Repeating the exercise for the two provincial monitoring stations that also had high-end equipment in place in the downtown area gave inferred average urban contributions to measured concentrations as high as 60&amp;#8211;62.5% for PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt; versus 48% for CO. The steel factory contributed an estimated 9&amp;#8211;11% for PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt; at these locations and a cement factory 13% for CO. The combined results underline the importance of taking spatial variability of emission sources into explicit account in complex industrialized cities. Moreover, the combination of a low-cost airbox real-time monitoring network with emission apportionment modeling will allow local policy-makers to take proper actions towards reducing air pollution levels at the local scale.&lt;/p&gt;

scholarly journals Domain adaptation based deep calibration of low-cost PM2.5 sensors

2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Domain Adaptation ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Proposed Model ◽  
Temperature And Humidity ◽  
Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

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