Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments

Abstract. The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed high reproducibility (mean R‾2=0.93, min–max: 0.80–1.00) and excellent agreement with standard instrumentation (mean R‾2=0.82, min–max: 0.54–0.99) in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies such as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at a large scale to investigate the underlying mechanisms of the effects of air pollution on health.

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Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments

10.5194/amt-2019-158 ◽

2019 ◽

Author(s):

Lia Chatzidiakou ◽

Anika Krause ◽

Olalekan A. M. Popoola ◽

Andrea Di Antonio ◽

Mike Kellaway ◽

...

Keyword(s):

Air Pollution ◽

Large Scale ◽

Daily Life ◽

Low Cost ◽

Personal Exposure ◽

High Variability ◽

Health Studies ◽

Underlying Mechanisms ◽

Exposure Metrics ◽

Standard Instrumentation

Abstract. The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed excellent agreement with standard instrumentation in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at large scale to investigate the underlying mechanisms of the effects of air pollution on health.

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182312544 ◽

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.

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Building Sensor Grid Architecture for Large-Scale Air Pollution Data Management

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.831.276 ◽

2013 ◽

Vol 831 ◽

pp. 276-281

Author(s):

Ya Jie Ma ◽

Zhi Jian Mei ◽

Xiang Chuan Tian

Keyword(s):

Air Pollution ◽

Data Management ◽

High Performance ◽

Large Scale ◽

Low Cost ◽

Physical World ◽

Sensor Nodes ◽

Grid Architecture ◽

Autonomous Sensor ◽

Efficient Data

Large-scale sensor networks are systems that a large number of high-throughput autonomous sensor nodes are distributed over wide areas. Much attention has paid to provide efficient data management in such systems. Sensor grid provides low cost and high performance computing to physical world data perceived through sensors. This article analyses the real-time sensor grid challenges on large-scale air pollution data management. A sensor grid architecture for pollution data management is proposed. The processing of the service-oriented grid management is described in psuedocode. A simulation experiment investigates the performance of the data management for such a system.

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Effects of AIR pollution on cardiopuLmonary disEaSe in urban and peri-urban reSidents in Beijing: protocol for the AIRLESS study

Atmospheric Chemistry and Physics ◽

10.5194/acp-20-15775-2020 ◽

2020 ◽

Vol 20 (24) ◽

pp. 15775-15792

Author(s):

Yiqun Han ◽

Wu Chen ◽

Lia Chatzidiakou ◽

Anika Krause ◽

Li Yan ◽

...

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Large Scale ◽

Personal Exposure ◽

Ambient Air ◽

Ambient Air Pollution ◽

Urban Residents ◽

Cardiopulmonary Disease ◽

Pollution Levels ◽

Large Scale Field

Abstract. Beijing, as a representative megacity in China, is experiencing some of the most severe air pollution episodes in the world, and its fast urbanization has led to substantial urban and peri-urban disparities in both health status and air quality. Uncertainties remain regarding the possible causal links between individual air pollutants and health outcomes, with spatial comparative investigations of these links lacking, particularly in developing megacities. In light of this challenge, Effects of AIR pollution on cardiopuLmonary disEaSe in urban and peri-urban reSidents in Beijing (AIRLESS) was initiated, with the aim of addressing the complex issue of relating multi-pollutant exposure to cardiopulmonary outcomes. This paper presents the novel methodological framework employed in the project, namely (1) the deployment of two panel studies from established cohorts in urban and peri-urban Beijing, with different exposure settings regarding pollution levels and diverse sources; (2) the collection of detailed measurements and biomarkers of participants from a nested case (hypertensive) and control (healthy) study setting; (3) the assessment of indoor and personal exposure to multiple gaseous pollutants and particulate matter at unprecedented spatial and temporal resolution with validated novel sensor technologies; (4) the assessment of ambient air pollution levels in a large-scale field campaign, particularly the chemical composition of particulate matter. Preliminary results showed that there is a large difference between ambient and personal air pollution levels, and the differences varied between seasons and locations. These large differences were reflected on the different health responses between the two panels.

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Exposures to Air Pollution and Noise from Multi-Modal Commuting in a Chinese City

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16142539 ◽

2019 ◽

Vol 16 (14) ◽

pp. 2539 ◽

Cited By ~ 4

Author(s):

Yisi Liu ◽

Bowen Lan ◽

Jeff Shirai ◽

Elena Austin ◽

Changhong Yang ◽

...

Keyword(s):

Air Pollution ◽

Personal Exposure ◽

Urban Traffic ◽

The Other ◽

Noise Levels ◽

Mixed Effect ◽

Urban Core ◽

Effect Model ◽

Transportation Modes ◽

Health Studies

Background: Modern urban travel includes mixtures of transit options, which potentially impact individual pollution exposures and health. This study aims to investigate variations in traffic-related air pollution and noise levels experienced in traffic in Chengdu, China. Methods: Real-time PM2.5, black carbon (BC), and noise levels were measured for four transportation modes (car, bus, subway, and shared bike) on scripted routes in three types of neighborhoods (urban core, developing neighborhood, and suburb). Each mode of transportation in each neighborhood was sampled five times in summer and winter, respectively. After quality control, mixed effect models were built for the three pollutants separately. Results: Air pollutants had much higher concentrations in winter. Urban Core had the highest PM2.5 and BC concentrations across seasons compared to the other neighborhoods. The mixed effect model indicated that car commutes were associated with lower PM2.5 (−34.4 μg/m3; 95% CI: −47.5, −21.3), BC (−2016.4 ng/m3; 95% CI: −3383.8, −648.6), and noise (−9.3 dBA; 95% CI: −10.5, −8.0) levels compared with other modes; subway commutes had lower PM2.5 (−11.9 μg/m3; 95% CI: 47.5, −21.3), but higher BC (2349.6 ng/m3; 95% CI: 978.1, 3722.1) and noise (3.0 dBA; 95% CI: 1.7, 4.3) levels than the other three modes of transportation. Conclusion: Personal exposure to air pollution and noise vary by season, neighborhood, and transportation modes. Exposure models accounting for environmental, meteorological, and behavioral factors, and duration of mixed mode commuting may be useful for health studies of urban traffic microenvironments.

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City Wide Participatory Sensing of Air Quality

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.773778 ◽

2021 ◽

Vol 9 ◽

Author(s):

Andrew Rebeiro-Hargrave ◽

Pak Lun Fung ◽

Samu Varjonen ◽

Andres Huertas ◽

Salla Sillanpää ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Low Cost ◽

Age Groups ◽

Personal Exposure ◽

Urban Environments ◽

Sensor Data ◽

Automated System ◽

Participatory Sensing ◽

Good Correspondence

Air pollution is a contributor to approximately one in every nine deaths annually. Air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality stations are expensive to maintain resulting in sparse coverage and data is not readily available to citizens. This can be resolved by city-wide participatory sensing of air quality fluctuations using low-cost sensors. We introduce new concepts for participatory sensing: a voluntary community-based monitoring data forum for stakeholders to manage air pollution interventions; an automated system (cyber-physical system) for monitoring outdoor air quality and indoor air quality; programmable platform for calibration and generating virtual sensors using data from low-cost sensors and city monitoring stations. To test our concepts, we developed a low-cost sensor to measure particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) with GPS. We validated our approach in Helsinki, Finland, with participants carrying the sensor for 3 months during six data campaigns between 2019 and 2021. We demonstrate good correspondence between the calibrated low-cost sensor data and city’s monitoring station measurements. Data analysis of their personal exposure was made available to the participants and stored as historical data for later use. Combining the location of low cost sensor data with participants public profile, we generate proxy concentrations for black carbon and lung deposition of particles between districts, by age groups and by the weekday.

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Effects of AIR pollution on cardiopuLmonary disEaSe in urban and peri-urban reSidents in Beijing: protocol for the AIRLESS study

10.5194/acp-2020-208 ◽

2020 ◽

Cited By ~ 2

Author(s):

Yiqun Han ◽

Wu Chen ◽

Lia Chatzidiakou ◽

Li Yan ◽

Hanbin Zhang ◽

...

Keyword(s):

Air Pollution ◽

Urban Areas ◽

Personal Exposure ◽

Urban Residents ◽

Susceptible Population ◽

Cardiopulmonary Disease ◽

Outdoor Environments ◽

Detailed Assessment ◽

Underlying Mechanisms ◽

Indoor And Outdoor Environments

Abstract. Beijing, as a representative megacity in China, is experiencing some of the most severe air pollution in the world, and its fast urbanization has led to a substantial urban and peri-urban disparities in both health status and air quality. Uncertainties remain regarding the possible causal links between individual air pollutants and health outcomes, with spatial comparative investigations of these links lacking, particularly in developing megacities. In light of this challenge, Effects of AIR pollution on cardiopuLmonary disEaSe in urban and peri-urban reSidents in Beijing (AIRLESS) was initiated with the aim of addressing the complex issue of multipollutant exposures on cardiopulmonary outcomes. The two panel studies deployed included urban and peri-urban elderly Beijing residents recruited from two established cohorts. The project was strengthened further through the measurement of an extensive range of ambient and indoor pollutants during two intensive monitoring campaigns undertaken simultaneously with the collection of health data from the panel participants. This paper presents the novel elements and methodology deployed in the AIRLESS project that addressed gaps in current understanding: namely (1) contrast of the exposure to air pollution in peri-urban and urban areas in a developing megacity, (2) a nested case (hypertensive) – control (healthy) study design to identify potential susceptible population, (3) the detailed assessments of personal exposure to air pollution in diverse indoor and outdoor environments using miniaturised portable platforms; (4) detailed assessment of the chemical composition of particulate matter; and (5) a rich collection of biological markers to understand the underlying mechanisms of health responses to air pollution.

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Assessment of PM2.5 Exposure during Cycle Trips in The Netherlands Using Low-Cost Sensors

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18116007 ◽

2021 ◽

Vol 18 (11) ◽

pp. 6007

Author(s):

Joost Wesseling ◽

Wouter Hendricx ◽

Henri de Ruiter ◽

Sjoerd van Ratingen ◽

Derko Drukker ◽

...

Keyword(s):

Air Pollution ◽

The Netherlands ◽

Low Cost ◽

Fine Particulate Matter ◽

Monitoring Network ◽

Personal Exposure ◽

Sensor Data ◽

Background Concentrations ◽

Fine Particulate

Air pollution, especially fine particulate matter (PM2.5), is a major environmental risk factor for human health in Europe. Monitoring of air quality takes place using expensive reference stations. Low-cost sensors are a promising addition to this official monitoring network as they add spatial and temporal resolution at low cost. Moreover, low-cost sensors might allow for better characterization of personal exposure to PM2.5. In this study, we use 500 dust (PM2.5) sensors mounted on bicycles to estimate typical PM2.5 levels to which cyclists are exposed in the province of Utrecht, the Netherlands, in the year 2020. We use co-located sensors at reference stations to calibrate and validate the mobile sensor data. We estimate that the average exposure to traffic related PM2.5, on top of background concentrations, is approximately 2 μg/m3. Our results suggest that cyclists close to major roads have a small, but consistently higher exposure to PM2.5 compared to routes with less traffic. The results allow for a detailed spatial representation of PM2.5 concentrations and show that choosing a different cycle route might lead to a lower exposure to PM2.5. Finally, we conclude that the use of mobile, low-cost sensors is a promising method to estimate exposure to air pollution.

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GeoAir—A Novel Portable, GPS-Enabled, Low-Cost Air-Pollution Sensor: Design Strategies to Facilitate Citizen Science Research and Geospatial Assessments of Personal Exposure

Sensors ◽

10.3390/s21113761 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3761

Author(s):

Yoo Min Park ◽

Sinan Sousan ◽

Dillon Streuber ◽

Kai Zhao

Keyword(s):

Air Pollution ◽

Air Quality ◽

Low Cost ◽

Rapid Evolution ◽

Personal Exposure ◽

Quality Data ◽

Portable Devices ◽

Design Strategies ◽

Laboratory Test Results ◽

Voc Sensor

The rapid evolution of air sensor technologies has offered enormous opportunities for community-engaged research by enabling citizens to monitor the air quality at any time and location. However, many low-cost portable sensors do not provide sufficient accuracy or are designed only for technically capable individuals by requiring pairing with smartphone applications or other devices to view/store air quality data and collect location data. This paper describes important design considerations for portable devices to ensure effective citizen engagement and reliable data collection for the geospatial analysis of personal exposure. It proposes a new, standalone, portable air monitor, GeoAir, which integrates a particulate matter (PM) sensor, volatile organic compound (VOC) sensor, humidity and temperature sensor, LTE-M and GPS module, Wi-Fi, long-lasting battery, and display screen. The preliminary laboratory test results demonstrate that the PM sensor shows strong performance when compared to a reference instrument. The VOC sensor presents reasonable accuracy, while further assessments with other types of VOC are needed. The field deployment and geo-visualization of the field data illustrate that GeoAir collects fine-grained, georeferenced air pollution data. GeoAir can be used by all citizens regardless of their technical proficiency and is widely applicable in many fields, including environmental justice and health disparity research.

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Nationwide estimation of personal exposure to air pollution using activity-based field-agent modelling

10.5194/egusphere-egu21-7081 ◽

2021 ◽

Author(s):

Oliver Schmitz ◽

Meng Lu ◽

Kees de Hoogh ◽

Nicole Probst-Hensch ◽

Ayoung Jeong ◽

...

Keyword(s):

Air Pollution ◽

Exposure Assessment ◽

Large Scale ◽

Communicable Disease ◽

Personal Exposure ◽

Space Time ◽

Gps Tracking ◽

Individual Mobility ◽

Activity Schedule ◽

The Impact

Estimating personal exposure to air pollution is important in investigating the impact of air pollution on chronic diseases such as diabetes or cardiovascular disease. Long-term personal exposures estimates from large cohorts are required to reliably identify the relation between chronic air pollution exposure and non-communicable disease outcomes. Using e.g. yearly averaged concentrations at fixed locations such as the home address may result in incomplete quantification of personal exposure as persons move in space and time. An appropriate estimation involves mapping of space-time variation of concentrations as well as incorporating several activities of individuals at different locations and the mobility of individuals along their space-time paths. While for small surveys detailed information is often available (e.g. home and work address, GPS tracking data and travel mode), this abundance of data is not available for large-scale personal exposure assessment. Thus, for large-scale exposure assessment the first challenge is the design of model representations of individual mobility for which parameters can be identified with relatively limited observational data on individual mobility. The second challenge is the execution of such large-scale models over large populations.We address these challenges by developing a modelling framework on top of Campo (https://campo.computationalgeography.org) that combines the space-time mapping of pollution and activity-based mobility simulation of individuals. To represent data sparse information on individuals, we use personal activity schedules. Air pollution is based on land use regression models. Our modelling approach contains the following key components: a) an activity schedule generator allowing to express the type, location and duration of an individual's activity as a function of a person's profile defined by e.g. age, gender or occupation, and b) a spatial context generator providing the location of an individual during a particular activity. Activities cover residence in certain areas (home, work, leisure) or along routes using different travel modes (car, bicycle, on foot), and c) an exposure estimator. Exposure estimation is subsequently the combination of the spatial contexts for each activity with air pollution concentrations at corresponding times.Using these decoupled but interacting components provides the flexibility to express a broad range of representative time spans and spatial residences, required e.g. to represent uncertainty of unknown work locations or travelled routes. We present concepts and the model using a nationwide cohort from Switzerland.

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