The Use of the Internet of Things for Estimating Personal Pollution Exposure

Araño, Keith April; Sun, Shengjing; Ordieres‐Meré, Joaquín; Gong, Bing

doi:10.3390/ijerph16173130

Cited by 15 publications

(11 citation statements)

References 64 publications

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“…Two [ 26 , 27 ] were excluded following EC4 as they were review papers or theoretical analysis (EC3, EC4). Furthermore, 20 studies [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ] were removed due to missing information about the design methodology and the type of sensors used for measuring IAQ parameters (IC4, EC3). In addition, five more studies [ 48 , 49 , 50 , 51 , 52 ] were excluded as they were focused on thermal comfort parameters only or had no relevant details about IAQ sensors (IC2, IC3, EC3).…”

Section: Methodsmentioning

confidence: 99%

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

Saini

Dutta

Marques

2020

IJERPH

129

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Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015–2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO2, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

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Section: Methodsmentioning

confidence: 99%

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

Saini

Dutta

Marques

2020

IJERPH

129

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“…Moreover, two studies [89,120] were rejected because they were limited to a monitoring system design and no prediction system was implemented. One paper [9] had a relevant abstract, but the authors did not specify the prediction methods. Similarly, authors in [108] did not specify the AI method used for prediction.…”

Section: Study Selectionmentioning

confidence: 99%

Indoor air quality prediction systems for smart environments: A systematic review

Saini

Dutta

Marques

2020

AIS

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Air quality is a critical matter of concern in terms of the impact on public health and well-being. Although the consequences of poor air quality are more severe in developing countries, they also have a critical impact in developed countries. Healthcare costs due to air pollution reach $150 billion in the USA, whereas particulate matter causes 412,000 premature deaths in Europe, every year. According to the Environmental Protection Agency (EPA), indoor air pollutant levels can be up to 100 times higher in comparison to outdoor air quality. Indoor air quality (IAQ) is in the top five environmental risks to global health and well-being. The research community explored the scope of artificial intelligence (AI) in the past years to deal with this problem. The IAQ prediction systems contribute to smart environments where advanced sensing technologies can create healthy living conditions for building occupants. This paper reviews the applications and potential of AI for the prediction of IAQ to enhance building environment and public health. The results show that most of the studies analyzed incorporate neural networks-based models and the preferred evaluation metrics are RMSE, R 2 score and error rate. Furthermore, 66.6% of the studies include CO2 sensors for IAQ assessment. Temperature and humidity parameters are also included in 90.47% and 85.71% of the proposed methods, respectively. This study also presents some limitations of the current research activities associated with the evaluation of the impact of different pollutants based on different geographical conditions and living environments. Moreover, the use of reliable and calibrated sensor networks for real-time data collection is also a significant challenge.

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“…Although the use of sensor-based monitoring devices in exposure assessment is still controversial, the application of low-cost sensors has already shifted paradigms in air pollution monitoring and exposure assessment. As such, the application of these technologies will continue to grow, including supplementing atmospheric surveillance networks and expanding communication with the community [39,40].…”

Section: Sensor-based Air Monitoring and Internet Of Things Technologymentioning

confidence: 99%

Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment

Yang

Park

Cho

et al. 2020

Toxics

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Human exposure to air pollution is a major public health concern. Environmental policymakers have been implementing various strategies to reduce exposure, including the 10th-day-no-driving system. To assess exposure of an entire population of a community in a highly polluted area, pollutant concentrations in microenvironments and population time–activity patterns are required. To date, population exposure to air pollutants has been assessed using air monitoring data from fixed atmospheric monitoring stations, atmospheric dispersion modeling, or spatial interpolation techniques for pollutant concentrations. This is coupled with census data, administrative registers, and data on the patterns of the time-based activities at the individual scale. Recent technologies such as sensors, the Internet of Things (IoT), communications technology, and artificial intelligence enable the accurate evaluation of air pollution exposure for a population in an environmental health context. In this study, the latest trends in published papers on the assessment of population exposure to air pollution were reviewed. Subsequently, this study proposes a methodology that will enable policymakers to develop an environmental health surveillance system that evaluates the distribution of air pollution exposure for a population within a target area and establish countermeasures based on advanced exposure assessment.

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The Use of the Internet of Things for Estimating Personal Pollution Exposure

Cited by 15 publications

References 64 publications

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

Indoor air quality prediction systems for smart environments: A systematic review

Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment

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