Mobile monitoring of air pollution using low cost sensors to visualize spatio-temporal variation of pollutants at urban hotspots

2019 ◽  
Vol 44 ◽  
pp. 520-535 ◽  
Author(s):  
Shiva Nagendra SM ◽  
Pavan Reddy Yasa ◽  
Narayana MV ◽  
Seema Khadirnaikar ◽  
Pooja Rani
Keyword(s):  
Air Pollution ◽  
Temporal Variation ◽  
Low Cost ◽  
Mobile Monitoring ◽  
Spatio Temporal

How low-cost air pollution sensors could make homes smarter?

2021 ◽  
Author(s):  
Hamid Omidvarborna ◽  
Prashant Kumar
Keyword(s):  
Air Pollution ◽  
Indoor Air ◽  
Air Pollutants ◽  
Indoor Air Pollution ◽  
Prediction Models ◽  
Disease Risk ◽  
Low Cost ◽  
Mortality And Morbidity ◽  
Spatio Temporal ◽  
Indoor Air Pollutants

<p>The majority of people spend most of their time indoors, where they are exposed to indoor air pollutants. Indoor air pollution is ranked among the top ten largest global burden of a disease risk factor as well as the top five environmental public health risks, which could result in mortality and morbidity worldwide. The spent time in indoor environments has been recently elevated due to coronavirus disease 2019 (COVID-19) outbreak when the public are advised to stay in their place for longer hours per day to protect lives. This opens an opportunity to low-cost air pollution sensors in the real-time Spatio-temporal mapping of IAQ and monitors their concentration/exposure levels indoors. However, the optimum selection of low-cost sensors (LCSs) for certain indoor application is challenging due to diversity in the air pollution sensing device technologies. Making affordable sensing units composed of individual sensors capable of measuring indoor environmental parameters and pollutant concentration for indoor applications requires a diverse scientific and engineering knowledge, which is not yet established. The study aims to gather all these methodologies and technologies in one place, where it allows transforming typical homes into smart homes by specifically focusing on IAQ. This approach addresses the following questions: 1) which and what sensors are suitable for indoor networked application by considering their specifications and limitation, 2) where to deploy sensors to better capture Spatio-temporal mapping of indoor air pollutants, while the operation is optimum, 3) how to treat the collected data from the sensor network and make them ready for the subsequent analysis and 4) how to feed data to prediction models, and which models are best suited for indoors.</p>

scholarly journals User-Centred Design of a Final Results Report for Participants in Multi-Sensor Personal Air Pollution Exposure Monitoring Campaigns

2021 ◽  
Vol 18 (23) ◽  
pp. 12544
Author(s):  
Johanna Amalia Robinson ◽  
Rok Novak ◽  
Tjaša Kanduč ◽  
Thomas Maggos ◽  
Demetra Pardali ◽  
...  
Keyword(s):  
Air Pollution ◽  
Design Thinking ◽  
Low Cost ◽  
Personal Exposure ◽  
Final Report ◽  
Draft Report ◽  
Horizon 2020 ◽  
User Centred Design ◽  
Exposure Monitoring ◽  
Spatio Temporal

Using low-cost portable air quality (AQ) monitoring devices is a growing trend in personal exposure studies, enabling a higher spatio-temporal resolution and identifying acute exposure to high concentrations. Comprehension of the results by participants is not guaranteed in exposure studies. However, information on personal exposure is multiplex, which calls for participant involvement in information design to maximise communication output and comprehension. This study describes and proposes a model of a user-centred design (UCD) approach for preparing a final report for participants involved in a multi-sensor personal exposure monitoring study performed in seven cities within the EU Horizon 2020 ICARUS project. Using a combination of human-centred design (HCD), human–information interaction (HII) and design thinking approaches, we iteratively included participants in the framing and design of the final report. User needs were mapped using a survey (n = 82), and feedback on the draft report was obtained from a focus group (n = 5). User requirements were assessed and validated using a post-campaign survey (n = 31). The UCD research was conducted amongst participants in Ljubljana, Slovenia, and the results report was distributed among the participating cities across Europe. The feedback made it clear that the final report was well-received and helped participants better understand the influence of individual behaviours on personal exposure to air pollution.

Modelling spatio-temporal air pollution data from a mobile monitoring station

2016 ◽  
Vol 86 (13) ◽  
pp. 2546-2559 ◽  
Author(s):  
Simone Del Sarto ◽  
Maria Giovanna Ranalli ◽  
David Cappelletti ◽  
Beatrice Moroni ◽  
Stefano Crocchianti ◽  
...  
Keyword(s):  
Air Pollution ◽  
Monitoring Station ◽  
Mobile Monitoring ◽  
Spatio Temporal

Assessment of microscale spatio-temporal variation of air pollution at an urban hotspot in Madrid (Spain) through an extensive field campaign

2016 ◽  
Vol 140 ◽  
pp. 432-445 ◽  
Author(s):  
Rafael Borge ◽  
Adolfo Narros ◽  
Begoña Artíñano ◽  
Carlos Yagüe ◽  
Francisco Javier Gómez-Moreno ◽  
...  
Keyword(s):  
Air Pollution ◽  
Temporal Variation ◽  
Field Campaign ◽  
Extensive Field ◽  
Spatio Temporal

scholarly journals Spatio-temporal variation characteristics of air pollution and influencing factors in Hebei province

2021 ◽  
Vol 36 (2) ◽  
pp. 411
Author(s):  
Zhen-fang HE ◽  
Qing-chun GUO ◽  
Jia-zhen LIU ◽  
Ying-ying ZHANG ◽  
Jie LIU ◽  
...  
Keyword(s):  
Air Pollution ◽  
Temporal Variation ◽  
Influencing Factors ◽  
Hebei Province ◽  
Variation Characteristics ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Variation Characteristics of PM2.5 in the Beijing–Tianjin–Hebei Region, China, from 2013 to 2018

2019 ◽  
Vol 16 (21) ◽  
pp. 4276 ◽  
Author(s):  
Lili Wang ◽  
Qiulin Xiong ◽  
Gaofeng Wu ◽  
Atul Gautam ◽  
Jianfang Jiang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Temporal Variation ◽  
Mass Concentration ◽  
Pollution Prevention ◽  
Concentration Data ◽  
Control Plan ◽  
Variation Characteristics ◽  
Spatio Temporal ◽  
Bth Region

Air pollution, including particulate matter (PM2.5) pollution, is extremely harmful to the environment as well as human health. The Beijing–Tianjin–Hebei (BTH) Region has experienced heavy PM2.5 pollution within China. In this study, a six-year time series (January 2013–December 2018) of PM2.5 mass concentration data from 102 air quality monitoring stations were studied to understand the spatio-temporal variation characteristics of the BTH region. The average annual PM2.5 mass concentration in the BTH region decreased from 98.9 μg/m3 in 2013 to 64.9 μg/m3 in 2017. Therefore, China has achieved its Air Pollution Prevention and Control Plan goal of reducing the concentration of fine particulate matter in the BTH region by 25% by 2017. The PM2.5 pollution in BTH plain areas showed a more significant change than mountains areas, with the highest PM2.5 mass concentration in winter and the lowest in summer. The results of spatial autocorrelation and cluster analyses showed that the PM2.5 mass concentration in the BTH region from 2013–2018 showed a significant spatial agglomeration, and that spatial distribution characteristics were high in the south and low in the north. Changes in PM2.5 mass concentration in the BTH region were affected by both socio-economic factors and meteorological factors. Our results can provide a point of reference for making PM2.5 pollution control decisions.

scholarly journals User-Centred Design of a Final Results Report for Participants in Multi-Sensor Personal Air Pollution Exposure Monitoring Campaigns

2021 ◽  
Author(s):  
Johanna Amalia Robinson ◽  
Rok Novak ◽  
Tjaša Kanduč ◽  
Thomas Maggos ◽  
Demetra Pardali ◽  
...  
Keyword(s):  
Air Pollution ◽  
Design Thinking ◽  
Low Cost ◽  
Personal Exposure ◽  
Final Report ◽  
Draft Report ◽  
Horizon 2020 ◽  
User Centred Design ◽  
Exposure Monitoring ◽  
Spatio Temporal

Using low-cost portable air quality (AQ) monitoring devices is a growing trend in personal exposure studies enabling a higher spatio-temporal resolution and identifying acute exposure to high concentrations. Comprehension of results by participants is not guaranteed in exposure studies. However, information on personal exposure is multiplex, which calls for participant involvement to maximise communication output and comprehension. This study describes a user centred design (UCD) approach for preparing a final report for participants involved in a multi-sensor personal exposure monitoring study performed in seven cities within the EU Horizon 2020 ICARUS project. Using a combination of human-centred design (HCD), human-information interaction (HII) and design thinking approaches, we iteratively included participants in the framing and design of the final report. User needs were mapped using a survey (n=82), and feedback on the draft report was obtained from a focus group (n=5). User requirements were assessed and validated using a post-campaign survey (n=31). The UCD research was conducted amongst participants in Ljubljana, Slovenia and the results report was distributed among the participating cities across Europe. The feedback received made it clear that the final report was well-received and helped participants better understand the influence of individual behaviours on personal exposure to air pollution.

A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018

2020 ◽  
Vol 220 ◽  
pp. 117066 ◽  
Author(s):  
Hao Fan ◽  
Chuanfeng Zhao ◽  
Yikun Yang
Keyword(s):  
Air Pollution ◽  
Temporal Variation ◽  
Urban Air Pollution ◽  
Comprehensive Analysis ◽  
Urban Air ◽  
Spatio Temporal

scholarly journals Low-Cost Air Quality Sensing towards Smart Homes

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 453
Author(s):  
Hamid Omidvarborna ◽  
Prashant Kumar ◽  
Joe Hayward ◽  
Manik Gupta ◽  
Erick Giovani Sperandio Nascimento
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Data Processing ◽  
Low Cost ◽  
Smart Homes ◽  
Monitoring And Control ◽  
Spatio Temporal ◽  
Key Steps ◽  
And Control

The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.

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