1
|
Wiora A, Wiora J, Kasprzyk J. Indication Variability of the Particulate Matter Sensors Dependent on Their Location. SENSORS (BASEL, SWITZERLAND) 2024; 24:1683. [PMID: 38475219 DOI: 10.3390/s24051683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/20/2024] [Accepted: 03/01/2024] [Indexed: 03/14/2024]
Abstract
Particulate matter (PM) suspended in the air significantly impacts human health. Those of anthropogenic origin are particularly hazardous. Poland is one of the countries where the air quality during the heating season is the worst in Europe. Air quality in small towns and villages far from state monitoring stations is often much worse than in larger cities where they are located. Their residents inhale the air containing smoke produced mainly by coal-fired stoves. In the frame of this project, an air quality monitoring network was built. It comprises low-cost PMS7003 PM sensors and ESP8266 microcontrollers with integrated Wi-Fi communication modules. This article presents research results on the influence of the PM sensor location on their indications. It has been shown that the indications from sensors several dozen meters away from each other can differ by up to tenfold, depending on weather conditions and the source of smoke. Therefore, measurements performed by a network of sensors, even of worse quality, are much more representative than those conducted in one spot. The results also indicated the method of detecting a sudden increase in air pollutants. In the case of smokiness, the difference between the mean and median indications of the PM sensor increases even up to 400 µg/m3 over a 5 min time window. Information from this comparison suggests a sudden deterioration in air quality and can allow for quick intervention to protect people's health. This method can be used in protection systems where fast detection of anomalies is necessary.
Collapse
Affiliation(s)
- Alicja Wiora
- Department of Measurements and Control Systems, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
| | - Józef Wiora
- Department of Measurements and Control Systems, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
| | - Jerzy Kasprzyk
- Department of Measurements and Control Systems, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
| |
Collapse
|
2
|
Jain S, Gardner‐Frolick R, Martinussen N, Jackson D, Giang A, Zimmerman N. Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results From a Community-Partnered Low-Cost Sensor Deployment. GEOHEALTH 2024; 8:e2023GH000935. [PMID: 38361590 PMCID: PMC10867477 DOI: 10.1029/2023gh000935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/14/2023] [Accepted: 12/19/2023] [Indexed: 02/17/2024]
Abstract
The Strathcona neighborhood in Vancouver is particularly vulnerable to environmental injustice due to its close proximity to the Port of Vancouver, and a high proportion of Indigenous and low-income households. Furthermore, local sources of air pollutants (e.g., roadways) can contribute to small-scale variations within communities. The aim of this study was to assess hyperlocal air quality patterns (intra-neighborhood variability) and compare them to average Vancouver concentrations (inter-neighborhood variability) to identify possible disparities in air pollution exposure for the Strathcona community. Between April and August 2022, 11 low-cost sensors (LCS) were deployed within the neighborhood to measure PM2.5, NO2, and O3 concentrations. The collected 15-min concentrations were down-averaged to daily concentrations and compared to greater Vancouver region concentrations to quantify the exposures faced by the community relative to the rest of the region. Concentrations were also estimated at every 25 m grid within the neighborhood to quantify the distribution of air pollution within the community. Using population information from census data, cumulative hazard indices (CHIs) were computed for every dissemination block. We found that although PM2.5 concentrations in the neighborhood were lower than regional Vancouver averages, daily NO2 concentrations and summer O3 concentrations were consistently higher. Additionally, although CHIs varied daily, we found that CHIs were consistently higher in areas with high commercial activity. As such, estimating CHI for dissemination blocks was useful in identifying hotspots and potential areas of concern within the neighborhood. This information can collectively assist the community in their advocacy efforts.
Collapse
Affiliation(s)
- Sakshi Jain
- Department of Mechanical EngineeringUniversity of British ColumbiaVancouverBCCanada
| | | | - Nika Martinussen
- Institute for Resources Environment and SustainabilityUniversity of British ColumbiaVancouverBCCanada
| | - Dan Jackson
- Strathcona Residents AssociationVancouverBCCanada
| | - Amanda Giang
- Department of Mechanical EngineeringUniversity of British ColumbiaVancouverBCCanada
- Institute for Resources Environment and SustainabilityUniversity of British ColumbiaVancouverBCCanada
| | - Naomi Zimmerman
- Department of Mechanical EngineeringUniversity of British ColumbiaVancouverBCCanada
| |
Collapse
|
3
|
Zafra-Pérez A, Boente C, García-Díaz M, Gómez-Galán JA, de la Campa AS, de la Rosa JD. Aerial monitoring of atmospheric particulate matter produced by open-pit mining using low-cost airborne sensors. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166743. [PMID: 37659558 DOI: 10.1016/j.scitotenv.2023.166743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/09/2023] [Accepted: 08/30/2023] [Indexed: 09/04/2023]
Abstract
Mining is an economic activity that entails the production and displacement of significant amounts of atmospheric particulate matter (PM) during operations involving intense earthcrushing or earthmoving. As high concentrations of PM may have adverse effects on human health, it is necessary to monitor and control the fugitive emissions of this pollutant. This paper presents an innovative methodology for the online monitoring of PM10 concentrations in air using a low-cost sensor (LCS, <300 USD) onboard an unmanned aerial vehicle. After comprehensive calibration, the LCS was horizontally flown over seven different areas of the large Riotinto copper mine (Huelva, Spain) at different heights to study the PM10 distribution at different longitudes and altitudes. The flights covered areas of zero activity, intense mining, drilling, ore loading, waste discharge, open stockpiling, and mineral processing. In the zero-activity area, the resuspension of PM10 was very low, with a weak wind speed (3.6 m/s). In the intense-mining area, unhealthy concentrations of PM10 (>51 μgPM10/m3) could be released, and the PM10 can reach surrounding populations through long-distance transport driven by several processes being performed simultaneously. Strong dilution was also observed at high altitudes (> 50 m). Mean concentrations were found to be 22-89 μgPM10/m3, with peaks ranging from 86 to 284 μgPM10/m3. This study demonstrates the potential applicability of airborne LCSs in the high-resolution online monitoring of PM in mining, thus supporting environmental managers during decision-making against fugitive emissions in a cost-effective manner.
Collapse
Affiliation(s)
- Adrián Zafra-Pérez
- CIQSO-Center for Research in Sustainable Chemistry, Associate Unit CSIC-University of Huelva "Atmospheric Pollution", Campus El Carmen s/n, 21007 Huelva, Spain
| | - Carlos Boente
- Departamento de Ingeniería Geológica y Minera, E.T.S.I. Minas y Energía de Madrid, Universidad Politécnica de Madrid, C/ Ríos Rosas 21, Madrid, 28003, Spain.
| | - Manuel García-Díaz
- Department of Fluid Mechanics, University of Oviedo, C/Wifredo Ricart, Gijón 33204, Spain
| | - Juan Antonio Gómez-Galán
- Department of Electronic Engineering, Computers and Automation, University of Huelva, Huelva 21007, Spain
| | - Ana Sánchez de la Campa
- CIQSO-Center for Research in Sustainable Chemistry, Associate Unit CSIC-University of Huelva "Atmospheric Pollution", Campus El Carmen s/n, 21007 Huelva, Spain; Department of Earth Sciences, Faculty of Experimental Sciences, University of Huelva, Huelva 21007, Spain
| | - Jesús D de la Rosa
- CIQSO-Center for Research in Sustainable Chemistry, Associate Unit CSIC-University of Huelva "Atmospheric Pollution", Campus El Carmen s/n, 21007 Huelva, Spain; Department of Earth Sciences, Faculty of Experimental Sciences, University of Huelva, Huelva 21007, Spain
| |
Collapse
|
4
|
Bulot FMJ, Russell HS, Rezaei M, Johnson MS, Ossont SJ, Morris AKR, Basford PJ, Easton NHC, Mitchell HL, Foster GL, Loxham M, Cox SJ. Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution-Part B-Particle Number Concentrations. SENSORS (BASEL, SWITZERLAND) 2023; 23:7657. [PMID: 37688113 PMCID: PMC10490673 DOI: 10.3390/s23177657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/30/2023] [Accepted: 07/31/2023] [Indexed: 09/10/2023]
Abstract
Low-cost Particulate Matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors, but some models of sensors also report Particle Number Concentrations (PNCs) segregated into different PM size ranges. In this study, eight units each of Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors' to different particle size distributions to quantify and better understand the low-cost sensors performance across a range of relevant environmental ranges. The PNCs reported by the sensors were analysed to characterise sensor-reported particle size distribution, to determine whether sensor-reported PNCs can follow the transient variations of PM observed by the reference instruments and to determine the relative impact of different variables on the performances of the sensors. This study shows that the Alphasense OPC-R1 reported at least five size ranges independently from each other, that the Sensirion SPS30 reported two size ranges independently from each other and that all the size ranges reported by the Plantower PMS5003 were not independent of each other. It demonstrates that all sensors tested here could track the fine temporal variation of PNCs, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it shows that particle size distribution and composition are more impactful on sensor measurements than relative humidity.
Collapse
Affiliation(s)
- Florentin Michel Jacques Bulot
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK; (P.J.B.); (H.L.M.); (S.J.C.)
- Southampton Marine and Maritime Institute, University of Southampton, Southampton SO16 7QF, UK; (N.H.C.E.); (M.L.)
| | - Hugo Savill Russell
- Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark;
- AirScape UK, London W1U 6TQ, UK;
- Department of Environmental Science, Atmospheric Measurement, Aarhus University, DK-4000 Roskilde, Denmark
- Department of Chemistry, University of Copenhagen, DK-2100 Copenhagen, Denmark;
| | - Mohsen Rezaei
- Department of Chemistry, University of Copenhagen, DK-2100 Copenhagen, Denmark;
| | - Matthew Stanley Johnson
- AirScape UK, London W1U 6TQ, UK;
- Department of Chemistry, University of Copenhagen, DK-2100 Copenhagen, Denmark;
| | | | | | - Philip James Basford
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK; (P.J.B.); (H.L.M.); (S.J.C.)
| | - Natasha Hazel Celeste Easton
- Southampton Marine and Maritime Institute, University of Southampton, Southampton SO16 7QF, UK; (N.H.C.E.); (M.L.)
- School of Ocean and Earth Science, National Oceanography Centre, University of Southampton, Southampton SO14 3ZH, UK;
| | - Hazel Louise Mitchell
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK; (P.J.B.); (H.L.M.); (S.J.C.)
| | - Gavin Lee Foster
- School of Ocean and Earth Science, National Oceanography Centre, University of Southampton, Southampton SO14 3ZH, UK;
| | - Matthew Loxham
- Southampton Marine and Maritime Institute, University of Southampton, Southampton SO16 7QF, UK; (N.H.C.E.); (M.L.)
- Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK
- National Institute for Health Research, Southampton Biomedical Research Centre, Southampton SO16 6YD, UK
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - Simon James Cox
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK; (P.J.B.); (H.L.M.); (S.J.C.)
- Southampton Marine and Maritime Institute, University of Southampton, Southampton SO16 7QF, UK; (N.H.C.E.); (M.L.)
| |
Collapse
|
5
|
Considine EM, Braun D, Kamareddine L, Nethery RC, deSouza P. Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:10.1021/acs.est.2c06626. [PMID: 36623253 PMCID: PMC10329730 DOI: 10.1021/acs.est.2c06626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
U.S. Environmental Protection Agency (EPA) air quality (AQ) monitors, the "gold standard" for measuring air pollutants, are sparsely positioned across the U.S. Low-cost sensors (LCS) are increasingly being used by the public to fill in the gaps in AQ monitoring; however, LCS are not as accurate as EPA monitors. In this work, we investigate factors impacting the differences between an individual's true (unobserved) exposure to air pollution and the exposure reported by their nearest AQ instrument (which could be either an LCS or an EPA monitor). We use simulations based on California data to explore different combinations of hypothetical LCS placement strategies (e.g., at schools or near major roads), for different numbers of LCS, with varying plausible amounts of LCS device measurement errors. We illustrate how real-time AQ reporting could be improved (or, in some cases, worsened) by using LCS, both for the population overall and for marginalized communities specifically. This work has implications for the integration of LCS into real-time AQ reporting platforms.
Collapse
Affiliation(s)
- Ellen M. Considine
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, 02215, USA
| | - Leila Kamareddine
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Rachel C. Nethery
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Priyanka deSouza
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, Colorado, 80202, USA
| |
Collapse
|
6
|
Air Quality Sensor Networks for Evidence-Based Policy Making: Best Practices for Actionable Insights. ATMOSPHERE 2022. [DOI: 10.3390/atmos13060944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
(1) Background: This work evaluated the usability of commercial “low-cost” air quality sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring station (AQMS) and subsequently deployed in Kampenhout and Sint-Niklaas (Belgium) to address real-world policy concerns: (a) what is the pollution contribution from road traffic near a school and at a central city square and (b) do local traffic interventions result in quantifiable air quality impacts? (3) Results: The considered sensor systems performed well in terms of data capture, correlation and intra-sensor uncertainty. Their accuracy was improved via local re-calibration, up to data quality levels for indicative measurements as set in the Air Quality Directive (Uexp < 50% for PM and <25% for NO2). A methodological setup was proposed using local background and source locations, allowing for quantification of the (3.1) maximum potential impact of local policy interventions and (3.2) air quality impacts from different traffic interventions with local contribution reductions of up to 89% for NO2 and 60% for NO throughout the considered 3 month monitoring period; (4) Conclusions: Our results indicate that commercial air quality sensor systems are able to accurately quantify air quality impacts from (even short-lived) local traffic measures and contribute to evidence-based policy making under the condition of a proper methodological setup (background normalization) and data quality (recurrent calibration) procedure. The applied methodology and learnings were distilled in a blueprint for air quality sensor networks for replication actions in other cities.
Collapse
|
7
|
Urban Air Quality Assessment by Fusing Spatial and Temporal Data from Multiple Study Sources Using Refined Estimation Methods. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11060330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
In urban environmental management and public health evaluation efforts, there is an urgent need for fine-grained urban air quality monitoring. However, the high price and sparse distribution of air quality monitoring equipment make it difficult to develop effective and comprehensive fine-scale monitoring at the city scale. This has also led to air quality estimation methods based on incomplete monitoring data, which lack the ability to detect urban air quality differences within a neighborhood. To address this problem, this study proposes a refined urban air quality estimation method that fuses multisource spatio-temporal data. Based on the fact that urban air quality is easily affected by social activities, this method integrates meteorological data with urban social activity data to form a comprehensive environmental data set. It uses the spatio-temporal feature extraction model to extract the multi-source spatio-temporal features of the comprehensive environmental data set. Finally, the improved cascade forest algorithm is used to fit the relationship between the multisource spatio-temporal features and the air quality index (AQI) to construct an air quality estimation model, and the model is used to estimate the hourly PM2.5 index in Beijing on a 1 km × 1 km grid. The results show that the estimation model has excellent performance, and its goodness-of-fit (R2) and root mean square error (RMSE) reach 0.961 and 17.47, respectively. This method effectively achieves the assessment of urban air quality differences within a neighborhood and provides a new strategy for preventing information fragmentation and improving the effectiveness of information representation in the data fusion process.
Collapse
|
8
|
Khreis H, Johnson J, Jack K, Dadashova B, Park ES. Evaluating the Performance of Low-Cost Air Quality Monitors in Dallas, Texas. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031647. [PMID: 35162669 PMCID: PMC8835131 DOI: 10.3390/ijerph19031647] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/24/2022] [Accepted: 01/24/2022] [Indexed: 02/01/2023]
Abstract
The emergence of low-cost air quality sensors may improve our ability to capture variations in urban air pollution and provide actionable information for public health. Despite the increasing popularity of low-cost sensors, there remain some gaps in the understanding of their performance under real-world conditions, as well as compared to regulatory monitors with high accuracy, but also high cost and maintenance requirements. In this paper, we report on the performance and the linear calibration of readings from 12 commercial low-cost sensors co-located at a regulatory air quality monitoring site in Dallas, Texas, for 18 continuous measurement months. Commercial AQY1 sensors were used, and their reported readings of O3, NO2, PM2.5, and PM10 were assessed against a regulatory monitor. We assessed how well the raw and calibrated AQY1 readings matched the regulatory monitor and whether meteorology impacted performance. We found that each sensor’s response was different. Overall, the sensors performed best for O3 (R2 = 0.36–0.97) and worst for NO2 (0.00–0.58), showing a potential impact of meteorological factors, with an effect of temperature on O3 and relative humidity on PM. Calibration seemed to improve the accuracy, but not in all cases or for all performance metrics (e.g., precision versus bias), and it was limited to a linear calibration in this study. Our data showed that it is critical for users to regularly calibrate low-cost sensors and monitor data once they are installed, as sensors may not be operating properly, which may result in the loss of large amounts of data. We also recommend that co-location should be as exact as possible, minimizing the distance between sensors and regulatory monitors, and that the sampling orientation is similar. There were important deviations between the AQY1 and regulatory monitors’ readings, which in small part depended on meteorology, hindering the ability of the low-costs sensors to present air quality accurately. However, categorizing air pollution levels, using for example the Air Quality Index framework, rather than reporting absolute readings, may be a more suitable approach. In addition, more sophisticated calibration methods, including accounting for individual sensor performance, may further improve performance. This work adds to the literature by assessing the performance of low-cost sensors over one of the longest durations reported to date.
Collapse
Affiliation(s)
- Haneen Khreis
- Texas A&M Transportation Institute (TTI), Texas A&M University System, Bryan, TX 77807, USA; (J.J.); (B.D.); (E.S.P.)
- Center for Advancing Research in Transportation Emissions, Energy, and Health (CARTEEH), Texas A&M University System, Bryan, TX 77807, USA
- Correspondence:
| | - Jeremy Johnson
- Texas A&M Transportation Institute (TTI), Texas A&M University System, Bryan, TX 77807, USA; (J.J.); (B.D.); (E.S.P.)
| | - Katherine Jack
- The Nature Conservancy, Texas Chapter, San Antonio, TX 78215, USA;
| | - Bahar Dadashova
- Texas A&M Transportation Institute (TTI), Texas A&M University System, Bryan, TX 77807, USA; (J.J.); (B.D.); (E.S.P.)
| | - Eun Sug Park
- Texas A&M Transportation Institute (TTI), Texas A&M University System, Bryan, TX 77807, USA; (J.J.); (B.D.); (E.S.P.)
| |
Collapse
|
9
|
Saini J, Dutta M, Marques G. ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment. SENSORS 2022; 22:s22031008. [PMID: 35161754 PMCID: PMC8838659 DOI: 10.3390/s22031008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 01/22/2022] [Accepted: 01/25/2022] [Indexed: 01/27/2023]
Abstract
Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset.
Collapse
Affiliation(s)
- Jagriti Saini
- National Institute of Technical Teacher’s Training and Research, Chandigarh 160019, India;
- Correspondence: (J.S.); (G.M.)
| | - Maitreyee Dutta
- National Institute of Technical Teacher’s Training and Research, Chandigarh 160019, India;
| | - Gonçalo Marques
- ESTGOH, Polytechnic of Coimbra, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal
- Correspondence: (J.S.); (G.M.)
| |
Collapse
|
10
|
Novak R, Petridis I, Kocman D, Robinson JA, Kanduč T, Chapizanis D, Karakitsios S, Flückiger B, Vienneau D, Mikeš O, Degrendele C, Sáňka O, García Dos Santos-Alves S, Maggos T, Pardali D, Stamatelopoulou A, Saraga D, Persico MG, Visave J, Gotti A, Sarigiannis D. Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:11614. [PMID: 34770131 PMCID: PMC8583633 DOI: 10.3390/ijerph182111614] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/26/2021] [Accepted: 11/01/2021] [Indexed: 11/17/2022]
Abstract
Use of a multi-sensor approach can provide citizens with holistic insights into the air quality of their immediate surroundings and their personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from seven European cities, discusses the data fusion and harmonization of a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants. Harmonizing the data streams identified issues with the sensor devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate visualizations and reports, and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization of a diverse set of multi-sensor data streams considerably improved the quality and quantity of distilled data that a research participant received. Though automation considerably accelerated the production of the reports, manual and structured double checks are strongly recommended.
Collapse
Affiliation(s)
- Rok Novak
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (D.K.); (J.A.R.); (T.K.)
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
| | - Ioannis Petridis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (I.P.); (D.C.); (S.K.); (D.S.)
| | - David Kocman
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (D.K.); (J.A.R.); (T.K.)
| | - Johanna Amalia Robinson
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (D.K.); (J.A.R.); (T.K.)
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
| | - Tjaša Kanduč
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (D.K.); (J.A.R.); (T.K.)
| | - Dimitris Chapizanis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (I.P.); (D.C.); (S.K.); (D.S.)
| | - Spyros Karakitsios
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (I.P.); (D.C.); (S.K.); (D.S.)
- HERACLES Research Centre on the Exposome and Health, Center for Interdisciplinary Research and Innovation, 54124 Thessaloniki, Greece
| | - Benjamin Flückiger
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, CH-4051 Basel, Switzerland; (B.F.); (D.V.)
- University of Basel, CH-4001 Basel, Switzerland
| | - Danielle Vienneau
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, CH-4051 Basel, Switzerland; (B.F.); (D.V.)
- University of Basel, CH-4001 Basel, Switzerland
| | - Ondřej Mikeš
- RECETOX, Faculty of Science, Masaryk University, 62500 Brno, Czech Republic; (O.M.); (C.D.); (O.S.)
| | - Céline Degrendele
- RECETOX, Faculty of Science, Masaryk University, 62500 Brno, Czech Republic; (O.M.); (C.D.); (O.S.)
- LCE, CNRS, Aix-Marseille University, 13003 Marseille, France
| | - Ondřej Sáňka
- RECETOX, Faculty of Science, Masaryk University, 62500 Brno, Czech Republic; (O.M.); (C.D.); (O.S.)
| | - Saul García Dos Santos-Alves
- Department of Atmospheric Pollution, National Environmental Health Centre, Institute of Health Carlos III, 28220 Madrid, Spain;
| | - Thomas Maggos
- Atmospheric Chemistry and Innovative Technologies Laboratory, INRASTES, NCSR “Demokritos”, Aghia Paraskevi, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Demetra Pardali
- Atmospheric Chemistry and Innovative Technologies Laboratory, INRASTES, NCSR “Demokritos”, Aghia Paraskevi, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Asimina Stamatelopoulou
- Atmospheric Chemistry and Innovative Technologies Laboratory, INRASTES, NCSR “Demokritos”, Aghia Paraskevi, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Dikaia Saraga
- Atmospheric Chemistry and Innovative Technologies Laboratory, INRASTES, NCSR “Demokritos”, Aghia Paraskevi, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Marco Giovanni Persico
- Department of Science, Technology and Society, University School of Advanced Study IUSS, 27100 Pavia, Italy; (M.G.P.); (J.V.)
- Eucentre Foundation, Via A. Ferrata, 1, 27100 Pavia, Italy;
| | - Jaideep Visave
- Department of Science, Technology and Society, University School of Advanced Study IUSS, 27100 Pavia, Italy; (M.G.P.); (J.V.)
- Eucentre Foundation, Via A. Ferrata, 1, 27100 Pavia, Italy;
| | - Alberto Gotti
- Eucentre Foundation, Via A. Ferrata, 1, 27100 Pavia, Italy;
| | - Dimosthenis Sarigiannis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (I.P.); (D.C.); (S.K.); (D.S.)
- HERACLES Research Centre on the Exposome and Health, Center for Interdisciplinary Research and Innovation, 54124 Thessaloniki, Greece
- Department of Science, Technology and Society, University School of Advanced Study IUSS, 27100 Pavia, Italy; (M.G.P.); (J.V.)
| |
Collapse
|
11
|
Cho H, Baek Y. Practical Particulate Matter Sensing and Accurate Calibration System Using Low-Cost Commercial Sensors. SENSORS (BASEL, SWITZERLAND) 2021; 21:6162. [PMID: 34577369 PMCID: PMC8472837 DOI: 10.3390/s21186162] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/09/2021] [Accepted: 09/09/2021] [Indexed: 11/24/2022]
Abstract
Air pollution is a social problem, because the harmful suspended materials can cause diseases and deaths to humans. Specifically, particulate matters (PM), a form of air pollution, can contribute to cardiovascular morbidity and lung diseases. Nowadays, humans are exposed to PM pollution everywhere because it occurs in both indoor and outdoor environments. To purify or ventilate polluted air, one need to accurately monitor the ambient air quality. Therefore, this study proposed a practical particulate matter sensing and accurate calibration system using low-cost commercial sensors. The proposed system basically uses noisy and inaccurate PM sensors to measure the ambient air pollution. This paper mainly deals with three types of error caused in the light scattering method: short-term noise, part-to-part variation, and temperature and humidity interferences. We propose a simple short-term noise reduction method to correct measurement errors, an auto-fitting calibration for part-to-part repeatability to pinpoint the baseline of the signal that affects the performance of the system, and a temperature and humidity compensation method. This paper also contains the experiment setup and performance evaluation to prove the superiority of the proposed methods. Based on the evaluation of the performance of the proposed system, part-to-part repeatability was less than 2 μg/m3 and the standard deviation was approximately 1.1 μg/m3 in the air. When the proposed approaches are used for other optical sensors, it can result in better performance.
Collapse
Affiliation(s)
- Hyuntae Cho
- School of Digital Media Engineering, Tongmyong University, Busan 48520, Korea
| | - Yunju Baek
- School of Computer Science and Engineering, Pusan National University, Busan 46241, Korea;
| |
Collapse
|
12
|
Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics. SENSORS 2021; 21:s21144717. [PMID: 34300458 PMCID: PMC8309582 DOI: 10.3390/s21144717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/26/2021] [Accepted: 07/02/2021] [Indexed: 11/30/2022]
Abstract
With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.
Collapse
|
13
|
Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches. ATMOSPHERE 2021. [DOI: 10.3390/atmos12020179] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.
Collapse
|