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Qin X, Wei P, Ning Z, Gali NK, Ghadikolaei MA, Wang Y. Dissecting PM sensor capabilities: A combined experimental and theoretical study on particle sizing and physicochemical properties. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 356:124354. [PMID: 38862097 DOI: 10.1016/j.envpol.2024.124354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/21/2024] [Accepted: 06/08/2024] [Indexed: 06/13/2024]
Abstract
Recent advancements in particulate matter (PM) optical measurement technology have enhanced the characterization of particle size distributions (PSDs) across various temporal and spatial scales, offering a more detailed analysis than traditional PM mass concentration monitoring. This study employs field experiments, laboratory tests, and model simulations to evaluate the influence of physicochemical characteristics of particulate matter (PM) on the performance of a compact, multi-channel PM sizing sensor. The sensor is integrated within a mini air station (MAS) designed to detect particles across 52 channels. The field experiments highlighted the sensor's ability to track hygroscopicity parameter κ-values across particle sizes, noting an increasing trend with particle size. The sensor's capability in identifying the size and mass concentration of different PM types, including ammonium nitrate, sodium chloride, smoke, incense, and silica dust particles, was assessed through laboratory tests. Laboratory comparisons with the Aerodynamic Particle Sizer (APS) showed high consistency (R2 > 0.96) for various PM sources, supported by Kolmogorov-Smirnov tests confirming the sensor's capability to match APSsize distributions. Model simulations further elucidated the influence of particle refractive index and size distributions on sensor performance, leading to optimized calibrant selection and application-specific recommendations. These comprehensive evaluations underscore the critical interplay between the chemical composition and physical properties of PM, significantly advancing the application and reliability of optical PM sensors in environmental monitoring.
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Affiliation(s)
- Xiaoliang Qin
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China; Atmospheric Research Center, Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, China
| | - Peng Wei
- College of Geography and Environment, Shandong Normal University, Jinan, China
| | - Zhi Ning
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China; Atmospheric Research Center, Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, China.
| | - Nirmal Kumar Gali
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Meisam Ahmadi Ghadikolaei
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Ya Wang
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
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2
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Stampfer O, Zuidema C, Allen RW, Fox J, Sampson P, Seto E, Karr CJ. Practical considerations for using low-cost sensors to assess wildfire smoke exposure in school and childcare settings. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2024:10.1038/s41370-024-00677-8. [PMID: 38730039 DOI: 10.1038/s41370-024-00677-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND More frequent and intense wildfires will increase concentrations of smoke in schools and childcare settings. Low-cost sensors can assess fine particulate matter (PM2.5) concentrations with high spatial and temporal resolution. OBJECTIVE We sought to optimize the use of sensors for decision-making in schools and childcare settings during wildfire smoke to reduce children's exposure to PM2.5. METHODS We measured PM2.5 concentrations indoors and outdoors at four schools in Washington State during wildfire smoke in 2020-2021 using low-cost sensors and gravimetric samplers. We randomly sampled 5-min segments of low-cost sensor data to create simulations of brief portable handheld measurements. RESULTS During wildfire smoke episodes (lasting 4-19 days), median hourly PM2.5 concentrations at different locations inside a single facility varied by up to 49.6 µg/m3 (maximum difference) during school hours. Median hourly indoor/outdoor ratios across schools ranged from 0.22 to 0.91. Within-school differences in concentrations indicated that it is important to collect measurements throughout a facility. Simulation results suggested that making handheld measurements more often and over multiple days better approximates indoor/outdoor ratios for wildfire smoke. During a period of unstable air quality, PM2.5 over the next hour indoors was more highly correlated with the last 10-min of data (mean R2 = 0.94) compared with the last 3-h (mean R2 = 0.60), indicating that higher temporal resolution data is most informative for decisions about near-term activities indoors. IMPACT STATEMENT As wildfires continue to increase in frequency and severity, staff at schools and childcare facilities are increasingly faced with decisions around youth activities, building use, and air filtration needs during wildfire smoke episodes. Staff are increasingly using low-cost sensors for localized outdoor and indoor PM2.5 measurements, but guidance in using and interpreting low-cost sensor data is lacking. This paper provides relevant information applicable for guidance in using low-cost sensors for wildfire smoke response.
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Affiliation(s)
- Orly Stampfer
- University of Washington Department of Environmental and Occupational Health Sciences, 4225 Roosevelt Way NE, Seattle, WA, 98105, USA.
| | - Christopher Zuidema
- University of Washington Department of Environmental and Occupational Health Sciences, 4225 Roosevelt Way NE, Seattle, WA, 98105, USA
| | - Ryan W Allen
- Simon Fraser University Faculty of Health Sciences, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Julie Fox
- Washington State Department of Health, 101 Israel Rd. S.E., Tumwater, WA, 98501, USA
| | - Paul Sampson
- University of Washington Department of Statistics; B-313 Padelford Hall, Seattle, WA, 98195, USA
| | - Edmund Seto
- University of Washington Department of Environmental and Occupational Health Sciences, 4225 Roosevelt Way NE, Seattle, WA, 98105, USA
| | - Catherine J Karr
- University of Washington Department of Environmental and Occupational Health Sciences, 4225 Roosevelt Way NE, Seattle, WA, 98105, USA
- University of Washington Department of Pediatrics, 4245 Roosevelt Way NE, Seattle, WA, 98105, USA
- Northwest Pediatric Environmental Health Specialty Unit, 4225 Roosevelt Way NE, Seattle, WA, 98105, USA
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3
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Lee YM, Lin GY, Le TC, Hong GH, Aggarwal SG, Yu JY, Tsai CJ. Characterization of spatial-temporal distribution and microenvironment source contribution of PM 2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques. ENVIRONMENTAL RESEARCH 2024; 244:117906. [PMID: 38101720 DOI: 10.1016/j.envres.2023.117906] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 12/07/2023] [Accepted: 12/08/2023] [Indexed: 12/17/2023]
Abstract
Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 by the LCS network. The calibrated PM2.5 were shown to agree with reference PM2.5 measured by the BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 μg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM2.5. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies.
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Affiliation(s)
- Yi-Ming Lee
- Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Guan-Yu Lin
- Department of Environmental Science and Engineering, Tunghai University, Taichung, Taiwan.
| | - Thi-Cuc Le
- Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Gung-Hwa Hong
- Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Shankar G Aggarwal
- Environmental Sciences & Biomedical Metrology Division, CSIR-National Physical Laboratory, New Delhi, India
| | - Jhih-Yuan Yu
- Division Chief, Department of Environmental Monitoring and Information Management, Environmental Protection Administration, Taiwan
| | - Chuen-Jinn Tsai
- Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
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4
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Bi J, Burnham D, Zuidema C, Schumacher C, Gassett AJ, Szpiro AA, Kaufman JD, Sheppard L. Evaluating low-cost monitoring designs for PM 2.5 exposure assessment with a spatiotemporal modeling approach. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 343:123227. [PMID: 38147948 PMCID: PMC10922961 DOI: 10.1016/j.envpol.2023.123227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 12/15/2023] [Accepted: 12/23/2023] [Indexed: 12/28/2023]
Abstract
Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 μg/m3]) compared to the model with all LCM measurements (0.84 [0.9 μg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 μg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 μg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 μg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.
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Affiliation(s)
- Jianzhao Bi
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA.
| | - Dustin Burnham
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Christopher Zuidema
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Cooper Schumacher
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Amanda J Gassett
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, Seattle, USA
| | - Joel D Kaufman
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA; Department of Medicine, University of Washington, Seattle, USA; Department of Epidemiology, University of Washington, USA
| | - Lianne Sheppard
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA; Department of Biostatistics, University of Washington, Seattle, USA
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5
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Bekbulat B, Agrawal P, Allen RW, Baum M, Boldbaatar B, Clark LP, Galsuren J, Hystad P, L’Orange C, Vakacherla S, Volckens J, Marshall JD. Application of an Ultra-Low-Cost Passive Sampler for Light-Absorbing Carbon in Mongolia. SENSORS (BASEL, SWITZERLAND) 2023; 23:8977. [PMID: 37960676 PMCID: PMC10647794 DOI: 10.3390/s23218977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/29/2023] [Accepted: 11/02/2023] [Indexed: 11/15/2023]
Abstract
Low-cost, long-term measures of air pollution concentrations are often needed for epidemiological studies and policy analyses of household air pollution. The Washington passive sampler (WPS), an ultra-low-cost method for measuring the long-term average levels of light-absorbing carbon (LAC) air pollution, uses digital images to measure the changes in the reflectance of a passively exposed paper filter. A prior publication on WPS reported high precision and reproducibility. Here, we deployed three methods to each of 10 households in Ulaanbaatar, Mongolia: one PurpleAir for PM2.5; two ultrasonic personal aerosol samplers (UPAS) with quartz filters for the thermal-optical analysis of elemental carbon (EC); and two WPS for LAC. We compared multiple rounds of 4-week-average measurements. The analyses calibrating the LAC to the elemental carbon measurement suggest that 1 µg of EC/m3 corresponds to 62 PI/month (R2 = 0.83). The EC-LAC calibration curve indicates an accuracy (root-mean-square error) of 3.1 µg of EC/m3, or ~21% of the average elemental carbon concentration. The RMSE values observed here for the WPS are comparable to the reported accuracy levels for other methods, including reference methods. Based on the precision and accuracy results shown here, as well as the increased simplicity of deployment, the WPS may merit further consideration for studying air quality in homes that use solid fuels.
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Affiliation(s)
- Bujin Bekbulat
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (B.B.); (L.P.C.)
| | - Pratyush Agrawal
- Center for Study of Science, Technology & Policy, Bengaluru 560095, Karnataka, India; (P.A.); (S.V.)
| | - Ryan W. Allen
- Department of Health Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada;
| | | | - Buyantushig Boldbaatar
- School of Public Health, Mongolian National University of Medical Sciences, Ulaanbaatar 14210, Mongolia; (B.B.); (J.G.)
| | - Lara P. Clark
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (B.B.); (L.P.C.)
| | - Jargalsaikhan Galsuren
- School of Public Health, Mongolian National University of Medical Sciences, Ulaanbaatar 14210, Mongolia; (B.B.); (J.G.)
| | - Perry Hystad
- Department of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA;
| | - Christian L’Orange
- Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA; (C.L.); (J.V.)
| | - Sreekanth Vakacherla
- Center for Study of Science, Technology & Policy, Bengaluru 560095, Karnataka, India; (P.A.); (S.V.)
| | - John Volckens
- Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA; (C.L.); (J.V.)
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (B.B.); (L.P.C.)
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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.
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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.)
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Won WS, Noh J, Oh R, Lee W, Lee JW, Su PC, Yoon YJ. Enhancing the reliability of particulate matter sensing by multivariate Tobit model using weather and air quality data. Sci Rep 2023; 13:13150. [PMID: 37573439 PMCID: PMC10423292 DOI: 10.1038/s41598-023-40468-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/10/2023] [Indexed: 08/14/2023] Open
Abstract
Low-cost particulate matter (PM) sensors have been widely used following recent sensor-technology advancements; however, inherent limitations of low-cost monitors (LCMs), which operate based on light scattering without an air-conditioning function, still restrict their applicability. We propose a regional calibration of LCMs using a multivariate Tobit model with historical weather and air quality data to improve the accuracy of ambient air monitoring, which is highly dependent on meteorological conditions, local climate, and regional PM properties. Weather observations and PM2.5 (fine inhalable particles with diameters ≤ 2.5 μm) concentrations from two regions in Korea, Incheon and Jeju, and one in Singapore were used as training data to build a visibility-based calibration model. To validate the model, field measurements were conducted by an LCM in Jeju and Singapore, where R2 and the error after applying the model in Jeju improved (from 0.85 to 0.88) and reduced by 44% (from 8.4 to 4.7 μg m-3), respectively. The results demonstrated that regional calibration involving air temperature, relative humidity, and other local climate parameters can efficiently correct the bias of the sensor. Our findings suggest that the proposed post-processing using the Tobit model with regional weather and air quality data enhances the applicability of LCMs.
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Affiliation(s)
- Wan-Sik Won
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
- Department of Aerospace Industrial and Systems Engineering, Hanseo University, Taean, Chungcheongnam-do, 32158, Republic of Korea
| | - Jinhong Noh
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Rosy Oh
- Department of Mathematics, Korea Military Academy, Seoul, 01805, Republic of Korea
| | - Woojoo Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jong-Won Lee
- Observer Foundation, Seoul, 04050, Republic of Korea
| | - Pei-Chen Su
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
| | - Yong-Jin Yoon
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
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8
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Kosmopoulos G, Salamalikis V, Wilbert S, Zarzalejo LF, Hanrieder N, Karatzas S, Kazantzidis A. Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain. SENSORS (BASEL, SWITZERLAND) 2023; 23:6541. [PMID: 37514835 PMCID: PMC10383866 DOI: 10.3390/s23146541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/10/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023]
Abstract
Low-cost sensors (LCSs) for particulate matter (PM) concentrations have attracted the interest of researchers, supplementing their efforts to quantify PM in higher spatiotemporal resolution. The precision of PM mass concentration measurements from PMS 5003 sensors has been widely documented, though limited information is available regarding their size selectivity and number concentration measurement accuracy. In this work, PMS 5003 sensors, along with a Federal Referral Methods (FRM) sampler (Grimm spectrometer), were deployed across three sites with different atmospheric profiles, an urban (Germanou) and a background (UPat) site in Patras (Greece), and a semi-arid site in Almería (Spain, PSA). The LCSs particle number concentration measurements were investigated for different size bins. Findings for particles with diameter between 0.3 and 10 μm suggest that particle size significantly affected the LCSs' response. The LCSs could accurately detect number concentrations for particles smaller than 1 μm in the urban (R2 = 0.9) and background sites (R2 = 0.92), while a modest correlation was found with the reference instrument in the semi-arid area (R2 = 0.69). However, their performance was rather poor (R2 < 0.31) for coarser aerosol fractions at all sites. Moreover, during periods when coarse particles were dominant, i.e., dust events, PMS 5003 sensors were unable to report accurate number distributions (R2 values < 0.47) and systematically underestimated particle number concentrations. The results indicate that several questions arise concerning the sensors' capabilities to estimate PM2.5 and PM10 concentrations, since their size distribution did not agree with the reference instruments.
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Affiliation(s)
- Georgios Kosmopoulos
- Laboratory of Atmospheric Physics, Department of Physics, University of Patras, GR 26500 Patras, Greece
| | | | - Stefan Wilbert
- Institute of Solar Research, German Aerospace Center (DLR), Paseo de Almería 73, 04001 Almería, Spain
| | - Luis F Zarzalejo
- Renewable Energy Division, CIEMAT Energy Department, Avenida Complutense, 40, 28040 Madrid, Spain
| | - Natalie Hanrieder
- Institute of Solar Research, German Aerospace Center (DLR), Paseo de Almería 73, 04001 Almería, Spain
| | - Stylianos Karatzas
- Civil Engineering Department, University of Patras, GR 26500 Patras, Greece
| | - Andreas Kazantzidis
- Laboratory of Atmospheric Physics, Department of Physics, University of Patras, GR 26500 Patras, Greece
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9
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Bulot FM, Ossont SJ, Morris AK, Basford PJ, Easton NH, Mitchell HL, Foster GL, Cox SJ, Loxham M. Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance. Heliyon 2023; 9:e15943. [PMID: 37187904 PMCID: PMC10176080 DOI: 10.1016/j.heliyon.2023.e15943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/03/2023] [Accepted: 04/27/2023] [Indexed: 05/17/2023] Open
Abstract
Particulate Matter (PM) low-cost sensors (LCS) present a cost-effective opportunity to improve the spatiotemporal resolution of airborne PM data. Previous studies focused on PM-LCS-reported hourly data and identified, without fully addressing, their limitations. However, PM-LCS provide measurements at finer temporal resolutions. Furthermore, government bodies have developed certifications to accompany new uses of these sensors, but these certifications have shortcomings. To address these knowledge gaps, PM-LCS of two models, 8 Sensirion SPS30 and 8 Plantower PMS5003, were collocated for one year with a Fidas 200S, MCERTS-certified PM monitor and were characterised at 2 min resolution, enabling replication of certification processes, and highlighting their limitations and improvements. Robust linear models using sensor-reported particle number concentrations and relative humidity, coupled with 2-week biannual calibration campaigns, achieved reference-grade performance, at median PM2.5 background concentration of 5.5 μg/m3, demonstrating that, with careful calibration, PM-LCS may cost-effectively supplement reference equipment in multi-nodes networks with fine spatiotemporality.
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Affiliation(s)
- Florentin M.J. Bulot
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK
- Southampton Marine and Maritime Institute, University of Southampton, Southampton, UK
- Corresponding author. University of Southampton, Southampton, UK.
| | | | | | - Philip J. Basford
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK
| | - Natasha H.C. Easton
- Southampton Marine and Maritime Institute, University of Southampton, Southampton, UK
- National Oceanography Centre, Southampton, UK
- Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
- School of Ocean and Earth Science, National Oceanography Centre, University of Southampton, UK
| | - Hazel L. Mitchell
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK
| | - Gavin L. Foster
- Southampton Marine and Maritime Institute, University of Southampton, Southampton, UK
- Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
- School of Ocean and Earth Science, National Oceanography Centre, University of Southampton, UK
| | - Simon J. Cox
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK
| | - Matthew Loxham
- Southampton Marine and Maritime Institute, University of Southampton, Southampton, UK
- School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health Research Southampton Biomedical Research Centre, Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
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10
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Wallace L, Zhao T. Spatial Variation of PM 2.5 Indoors and Outdoors: Results from 261 Regulatory Monitors Compared to 14,000 Low-Cost Monitors in Three Western States over 4.7 Years. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094387. [PMID: 37177591 PMCID: PMC10181715 DOI: 10.3390/s23094387] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/24/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023]
Abstract
Spatial variation of indoor and outdoor PM2.5 within three states for a five-year period is studied using regulatory and low-cost PurpleAir monitors. Most of these data were collected in an earlier study (Wallace et al., 2022 Indoor Air 32:13105) investigating the relative contribution of indoor-generated and outdoor-infiltrated particles to indoor exposures. About 260 regulatory monitors and ~10,000 outdoor and ~4000 indoor PurpleAir monitors are included. Daily mean PM2.5 concentrations, correlations, and coefficients of divergence (COD) are calculated for pairs of monitors at distances ranging from 0 (collocated) to 200 km. We use a transparent and reproducible open algorithm that avoids the use of the proprietary algorithms provided by the manufacturer of the sensors in PurpleAir PA-I and PA-II monitors. The algorithm is available on the PurpleAir API website under the name "PM2.5_alt". This algorithm is validated using several hundred pairs of regulatory and PurpleAir monitors separated by up to 0.5 km. The PM2.5 spatial variation outdoors is homogeneous with high correlations to at least 10 km, as shown by the COD index under 0.2. There is also a steady improvement in outdoor PM2.5 concentrations with increasing distance from the regulatory monitors. The spatial variation of indoor PM2.5 is not homogeneous even at distances < 100 m. There is good agreement between PurpleAir outdoor monitors located <100 m apart and collocated Federal Equivalent Methods (FEM).
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Affiliation(s)
- Lance Wallace
- Independent Researcher, 428 Woodley Way, Santa Rosa, CA 95409, USA
| | - Tongke Zhao
- Independent Researcher, Milpitas, CA 95035, USA
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11
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Kim SY, Blanco MN, Bi J, Larson TV, Sheppard L. Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs. ENVIRONMENTAL RESEARCH 2023; 223:115451. [PMID: 36764437 PMCID: PMC9992293 DOI: 10.1016/j.envres.2023.115451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/10/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms. OBJECTIVES We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies. METHODS We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs. RESULTS Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility. DISCUSSION Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.
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Affiliation(s)
- Sun-Young Kim
- Department of Cancer AI and Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
| | - Magali N Blanco
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Jianzhao Bi
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Timothy V Larson
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA
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12
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Lin JJY, Buehler C, Datta A, Gentner DR, Koehler K, Zamora ML. Laboratory and field evaluation of a low-cost methane sensor and key environmental factors for sensor calibration. ENVIRONMENTAL SCIENCE: ATMOSPHERES 2023; 3:683-694. [PMID: 37063944 PMCID: PMC10100561 DOI: 10.1039/d2ea00100d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 02/19/2023] [Indexed: 02/23/2023]
Abstract
Low-cost sensors enable finer-scale spatiotemporal measurements within the existing methane (CH4) monitoring infrastructure and could help cities mitigate CH4 emissions to meet their climate goals. While initial studies of low-cost CH4 sensors have shown potential for effective CH4 measurement at ambient concentrations, sensor deployment remains limited due to questions about interferences and calibration across environments and seasons. This study evaluates sensor performance across seasons with specific attention paid to the sensor's understudied carbon monoxide (CO) interferences and environmental dependencies through long-term ambient co-location in an urban environment. The sensor was first evaluated in a laboratory using chamber calibration and co-location experiments, and then in the field through two 8 week co-locations with a reference CH4 instrument. In the laboratory, the sensor was sensitive to CH4 concentrations below ambient background concentrations. Different sensor units responded similarly to changing CH4, CO, temperature, and humidity conditions but required individual calibrations to account for differences in sensor response factors. When deployed in-field, co-located with a reference instrument near Baltimore, MD, the sensor captured diurnal trends in hourly CH4 concentration after corrections for temperature, absolute humidity, CO concentration, and hour of day. Variable performance was observed across seasons with the sensor performing well (R 2 = 0.65; percent bias 3.12%; RMSE 0.10 ppm) in the winter validation period and less accurately (R 2 = 0.12; percent bias 3.01%; RMSE 0.08 ppm) in the summer validation period where there was less dynamic range in CH4 concentrations. The results highlight the utility of sensor deployment in more variable ambient CH4 conditions and demonstrate the importance of accounting for temperature and humidity dependencies as well as co-located CO concentrations with low-cost CH4 measurements. We show this can be addressed via Multiple Linear Regression (MLR) models accounting for key covariates to enable urban measurements in areas with CH4 enhancement. Together with individualized calibration prior to deployment, the sensor shows promise for use in low-cost sensor networks and represents a valuable supplement to existing monitoring strategies to identify CH4 hotspots.
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Affiliation(s)
- Joyce J Y Lin
- Johns Hopkins University Bloomberg School of Public Health, Environmental Health and Engineering Baltimore MD 21205-2103 USA
| | - Colby Buehler
- SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University New Haven CT 06520 USA
- Chemical and Environmental Engineering, Yale University New Haven CT 06520 USA
| | - Abhirup Datta
- Johns Hopkins University Bloomberg School of Public Health, Department of Biostatistics Baltimore MD 21205-2103 USA
| | - Drew R Gentner
- SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University New Haven CT 06520 USA
- Chemical and Environmental Engineering, Yale University New Haven CT 06520 USA
| | - Kirsten Koehler
- Johns Hopkins University Bloomberg School of Public Health, Environmental Health and Engineering Baltimore MD 21205-2103 USA
- SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University New Haven CT 06520 USA
| | - Misti Levy Zamora
- Johns Hopkins University Bloomberg School of Public Health, Environmental Health and Engineering Baltimore MD 21205-2103 USA
- SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University New Haven CT 06520 USA
- Department of Public Health Sciences, UConn School of Medicine, University of Connecticut Health Center Farmington CT USA 06032-1941
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13
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Glenn K, He J, Rochlin R, Teng S, Hecker JG, Novosselov I. Assessment of aerosol persistence in ICUs via low-cost sensor network and zonal models. Sci Rep 2023; 13:3992. [PMID: 36899063 PMCID: PMC10006437 DOI: 10.1038/s41598-023-30778-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 03/01/2023] [Indexed: 03/12/2023] Open
Abstract
The COVID-19 pandemic raised public awareness about airborne particulate matter (PM) due to the spread of infectious diseases via the respiratory route. The persistence of potentially infectious aerosols in public spaces and the spread of nosocomial infections in medical settings deserve careful investigation; however, a systematic approach characterizing the fate of aerosols in clinical environments has not been reported. This paper presents a methodology for mapping aerosol propagation using a low-cost PM sensor network in ICU and adjacent environments and the subsequent development of the data-driven zonal model. Mimicking aerosol generation by a patient, we generated trace NaCl aerosols and monitored their propagation in the environment. In positive (closed door) and neutral-pressure (open door) ICUs, up to 6% or 19%, respectively, of all PM escaped through the door gaps; however, the outside sensors did not register an aerosol spike in negative-pressure ICUs. The K-means clustering analysis of temporospatial aerosol concentration data suggests that ICU can be represented by three distinct zones: (1) near the aerosol source, (2) room periphery, and (3) outside the room. The data suggests two-phase plume behavior: dispersion of the original aerosol spike throughout the room, followed by an evacuation phase where "well-mixed" aerosol concentration decayed uniformly. Decay rates were calculated for positive, neutral, and negative pressure operations, with negative-pressure rooms clearing out nearly twice as fast. These decay trends closely followed the air exchange rates. This research demonstrates the methodology for aerosol monitoring in medical settings. This study is limited by a relatively small data set and is specific to single-occupancy ICU rooms. Future work needs to evaluate medical settings with high risks of infectious disease transmission.
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Affiliation(s)
- K Glenn
- Department of Mechanical Engineering, University of Washington, Seattle, USA
| | - J He
- Department of Mechanical Engineering, University of Washington, Seattle, USA
| | - R Rochlin
- Department of Mechanical Engineering, University of Washington, Seattle, USA
| | - S Teng
- Department of Mechanical Engineering, University of Washington, Seattle, USA
| | - J G Hecker
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, USA
| | - I Novosselov
- Department of Mechanical Engineering, University of Washington, Seattle, USA.
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14
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Hasan MH, Yu H, Ivey C, Pillarisetti A, Yuan Z, Do K, Li Y. Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal. ACS OMEGA 2023; 8:5917-5924. [PMID: 36816698 PMCID: PMC9933490 DOI: 10.1021/acsomega.2c07734] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 01/16/2023] [Indexed: 05/31/2023]
Abstract
Low-cost air quality (LCAQ) sensors are increasingly being used for community air quality monitoring. However, data collected by low-cost sensors contain significant noise, and proper calibration of these sensors remains a widely discussed, but not yet fully addressed, area of concern. In this study, several LCAQ sensors measuring nitrogen dioxide (NO2) and ozone (O3) were deployed in six cities in the United States (Atlanta, GA; New York City, NY; Sacramento, CA; Riverside, CA; Portland, OR; Phoenix, AZ) to evaluate the impacts of different climatic and geographical conditions on their performance and calibration. Three calibration methods were applied, including regression via linear and polynomial models and random forest methods. When signals from carbon monoxide (CO) sensors were included in the calibration models for NO2 and O3 sensors, model performance generally increased, with pronounced improvements in selected cities such as Riverside and New York City. Such improvements may be due to (1) temporal co-variation between concentrations of CO and NO2 and/or between CO and O3; (2) different performance levels of low-cost CO, NO2, and O3 sensors; and (3) different impacts of environmental conditions on sensor performance. The results showed an innovative approach for improving the calibration of NO2 and O3 sensors by including CO sensor signals into the calibration models. Community users of LCAQ sensors may be able to apply these findings further to enhance the data quality of their deployed NO2 and O3 monitors.
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Affiliation(s)
- Md Hasibul Hasan
- Department
of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, Florida32816, United States
| | - Haofei Yu
- Department
of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, Florida32816, United States
| | - Cesunica Ivey
- Department
of Civil and Environmental Engineering, The University of California, Berkeley, Berkeley, California94720, United States
| | - Ajay Pillarisetti
- Environmental
Health Sciences, School of Public Health, University of California, Berkeley, California94720, United States
| | - Ziyang Yuan
- Sailbri
Cooper, Inc., Tigard, Oregon97223, United States
| | - Khanh Do
- Department
of Chemical and Environmental Engineering, University of California, Riverside, California92521, United States
| | - Yi Li
- Sailbri
Cooper, Inc., Tigard, Oregon97223, United States
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15
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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.
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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
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16
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Lin X, Luo J, Liao M, Su Y, Lv M, Li Q, Xiao S, Xiang J. Wearable Sensor-Based Monitoring of Environmental Exposures and the Associated Health Effects: A Review. BIOSENSORS 2022; 12:1131. [PMID: 36551098 PMCID: PMC9775571 DOI: 10.3390/bios12121131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 11/24/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Recent advances in sensor technology have facilitated the development and use of personalized sensors in monitoring environmental factors and the associated health effects. No studies have reviewed the research advancement in examining population-based health responses to environmental exposure via portable sensors/instruments. This study aims to review studies that use portable sensors to measure environmental factors and health responses while exploring the environmental effects on health. With a thorough literature review using two major English databases (Web of Science and PubMed), 24 eligible studies were included and analyzed out of 16,751 total records. The 24 studies include 5 on physical factors, 19 on chemical factors, and none on biological factors. The results show that particles were the most considered environmental factor among all of the physical, chemical, and biological factors, followed by total volatile organic compounds and carbon monoxide. Heart rate and heart rate variability were the most considered health indicators among all cardiopulmonary outcomes, followed by respiratory function. The studies mostly had a sample size of fewer than 100 participants and a study period of less than a week due to the challenges in accessing low-cost, small, and light wearable sensors. This review guides future sensor-based environmental health studies on project design and sensor selection.
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Affiliation(s)
- Xueer Lin
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Jiaying Luo
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Minyan Liao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Yalan Su
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Mo Lv
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Qing Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China
| | - Shenglan Xiao
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Jianbang Xiang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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17
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Iyer SR, Balashankar A, Aeberhard WH, Bhattacharyya S, Rusconi G, Jose L, Soans N, Sudarshan A, Pande R, Subramanian L. Modeling fine-grained spatio-temporal pollution maps with low-cost sensors. NPJ CLIMATE AND ATMOSPHERIC SCIENCE 2022; 5:76. [PMID: 36254321 PMCID: PMC9555706 DOI: 10.1038/s41612-022-00293-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 08/30/2022] [Indexed: 06/16/2023]
Abstract
The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.
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Affiliation(s)
- Shiva R. Iyer
- Department of Computer Science, New York University, New York, NY USA
| | | | | | - Sujoy Bhattacharyya
- Columbia University, New York, NY USA
- Evidence for Policy Design (EPoD) at the Institute for Financial Management and Research (IFMR), New Delhi, New Delhi India
| | - Giuditta Rusconi
- Evidence for Policy Design (EPoD) at the Institute for Financial Management and Research (IFMR), New Delhi, New Delhi India
- State Secretariat for Education, Research and Innovation (SERI), Bern, Switzerland
| | - Lejo Jose
- Kai Air Monitoring Pvt Ltd, Gautam Buddha Nagar, UP India
| | - Nita Soans
- Kai Air Monitoring Pvt Ltd, Gautam Buddha Nagar, UP India
| | - Anant Sudarshan
- Department of Economics, University of Chicago, Chicago, IL USA
| | - Rohini Pande
- Department of Economics, Yale University, New Haven, CT USA
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18
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Capozzi V, Raia L, Cretella V, De Vivo C, Cucciniello R. The Impact of Meteorological Conditions and Agricultural Waste Burning on PM Levels: A Case Study of Avellino (Southern Italy). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12246. [PMID: 36231548 PMCID: PMC9566629 DOI: 10.3390/ijerph191912246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/07/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
In this work, the effect of the meteorological conditions and the agricultural waste burning on PM air pollution levels has been investigated in the city of Avellino, located in the Sabato Valley (southern Italy). Avellino has been described among the most polluted towns in Italy in terms of particulate matter (PM) during the last 10 years. The main aim of this study was to analyze the air quality data collected in Avellino and its surroundings during September 2021. In this period, the air quality in the Sabato Valley has been adversely affected by agricultural practices, which represent a significant source of PM. The impact of agricultural waste burning on PM levels in Avellino has been determined through an integrated monitoring network, consisting of two fixed urban reference stations and by several low-cost sensors distributed in the Sabato Valley. In the considered period, the two reference stations recorded several exceedances of the daily average PM10 legislative limit value (50 µg m-3) in addition to high concentrations of PM2.5. Moreover, we provide a detailed description of the event that took place on 25 September 2021, when the combined effect of massive agricultural practices and very stable atmospheric conditions produced a severe pollution episode. Results show PM exceedances in Avellino concurrent with high PM values in the areas bordering the city due to agricultural waste burning and adverse meteorological conditions, which inhibit PM dispersion in the atmosphere.
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Affiliation(s)
- Vincenzo Capozzi
- Department of Science and Technology, University of Naples “Parthenope”, Centro Direzionale di Napoli—Isola C4, 80143 Naples, Italy
| | - Letizia Raia
- Department of Chemistry and Biology “Adolfo Zambelli”, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy
| | - Viviana Cretella
- Department of Science and Technology, University of Naples “Parthenope”, Centro Direzionale di Napoli—Isola C4, 80143 Naples, Italy
| | - Carmela De Vivo
- Department of Science and Technology, University of Naples “Parthenope”, Centro Direzionale di Napoli—Isola C4, 80143 Naples, Italy
| | - Raffaele Cucciniello
- Department of Chemistry and Biology “Adolfo Zambelli”, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy
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19
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Anastasiou E, Vilcassim MJR, Adragna J, Gill E, Tovar A, Thorpe LE, Gordon T. Feasibility of low-cost particle sensor types in long-term indoor air pollution health studies after repeated calibration, 2019-2021. Sci Rep 2022; 12:14571. [PMID: 36028517 PMCID: PMC9411839 DOI: 10.1038/s41598-022-18200-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 08/08/2022] [Indexed: 11/09/2022] Open
Abstract
Previous studies have explored using calibrated low-cost particulate matter (PM) sensors, but important research gaps remain regarding long-term performance and reliability. Evaluate longitudinal performance of low-cost particle sensors by measuring sensor performance changes over 2 years of use. 51 low-cost particle sensors (Airbeam 1 N = 29; Airbeam 2 N = 22) were calibrated four times over a 2-year timeframe between 2019 and 2021. Cigarette smoke-specific calibration curves for Airbeam 1 and 2 PM sensors were created by directly comparing simultaneous 1-min readings of a Thermo Scientific Personal DataRAM PDR-1500 unit with a 2.5 µm inlet. Inter-sensor variability in calibration coefficient was high, particularly in Airbeam 1 sensors at study initiation. Calibration coefficients for both sensor types trended downwards over time to < 1 at final calibration timepoint [Airbeam 1 Mean (SD) = 0.87 (0.20); Airbeam 2 Mean (SD) = 0.96 (0.27)]. We lost more Airbeam 1 sensors (N = 27 out of 56, failure rate 48.2%) than Airbeam 2 (N = 2 out of 24, failure rate 8.3%) due to electronics, battery, or data output issues. Evidence suggests degradation over time might depend more on particle sensor type, rather than individual usage. Repeated calibrations of low-cost particle sensors may increase confidence in reported PM levels in longitudinal indoor air pollution studies.
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Affiliation(s)
- Elle Anastasiou
- Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, New York, NY, 10016, USA
| | - M J Ruzmyn Vilcassim
- Department of Environmental Health Sciences, University of Alabama at Birmingham School of Public Health, Birmingham, AL, 205-934-8927, USA
| | - John Adragna
- Department of Environmental Science, New York University Grossman School of Medicine, 341 East 25th Street, New York, NY, 10010, USA
| | - Emily Gill
- Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, New York, NY, 10016, USA
| | - Albert Tovar
- Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, New York, NY, 10016, USA
| | - Lorna E Thorpe
- Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, New York, NY, 10016, USA
| | - Terry Gordon
- Department of Environmental Science, New York University Grossman School of Medicine, 341 East 25th Street, New York, NY, 10010, USA.
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20
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Zheng H, Krishnan V, Walker S, Loomans M, Zeiler W. Laboratory evaluation of low-cost air quality monitors and single sensors for monitoring typical indoor emission events in Dutch daycare centers. ENVIRONMENT INTERNATIONAL 2022; 166:107372. [PMID: 35777114 DOI: 10.1016/j.envint.2022.107372] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/13/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Daycare centers (DCCs) are where infants and toddlers (0-4 years old) spend the most time besides their homes. Given their higher susceptibility to the effects of air pollutants, as compared to older children and adults, indoor air quality (IAQ) is regarded as an essential parameter to monitor in DCCs. Recent advances in IAQ monitoring technologies have enabled the deployment of low-cost air quality monitors (LCMs) and single sensors (LCSs) to continuously monitor various indoor environments, and their performance testing should also be performed in the intended indoor applications. To our knowledge, there is no study evaluating the application of LCMs/LCSs in DCCs scenarios yet. Therefore, this study is aimed to assess the response of five types of LCMs (previously not tested) and five LCSs to typical DCCs emission activities in detecting multiple IAQ parameters, i.e., particulate matter, carbon dioxide, total volatile organic compounds, temperature, and relative humidity. These LCMs/LCSs were compared to outcomes from research-grade instruments (RGIs). All the experiments were performed in a climate chamber, where three kinds of typical activities (background; arts-and-crafts; cleaning; [in a total of 32 events]) were simulated by recruited subjects at two typical indoor climatic conditions (cool and dry [20 ± 1 °C & 40 ± 10%], warm and humid [26 ± 1 °C & 70 ± 5%]). Results showed that tested LCMs had the ability to capture DCCs activities by simultaneously monitoring multiple IAQ parameters, and LCMs/LCSs revealed a strong correlation with RGIs in most events (R2 values from 0.7 to 1), but, for some events, the magnitude of responses varied widely. Sensirion SCD41, an emerging CO2 sensor built on the photoacoustic sensing principle, had a more accurate performance than all tested NDIR-based CO2 sensors/monitors. In general, the study implies that the selection of LCMs/LCSs for a specific application of interest should be based on emission characteristics and space conditions.
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Affiliation(s)
- Hailin Zheng
- Department of the Built Environment, Eindhoven University of Technology, Eindhoven, the Netherlands.
| | - Vinayak Krishnan
- Department of the Built Environment, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Shalika Walker
- Department of the Built Environment, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Marcel Loomans
- Department of the Built Environment, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Wim Zeiler
- Department of the Built Environment, Eindhoven University of Technology, Eindhoven, the Netherlands
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21
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Nauwelaerts SJD, De Cremer K, Bustos Sierra N, Gand M, Van Geel D, Delvoye M, Vandermassen E, Vercauteren J, Stroobants C, Bernard A, Saenen ND, Nawrot TS, Roosens NHC, De Keersmaecker SCJ. Assessment of the Feasibility of a Future Integrated Larger-Scale Epidemiological Study to Evaluate Health Risks of Air Pollution Episodes in Children. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148531. [PMID: 35886381 PMCID: PMC9323067 DOI: 10.3390/ijerph19148531] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/08/2022] [Accepted: 07/10/2022] [Indexed: 02/07/2023]
Abstract
Air pollution exposure can lead to exacerbation of respiratory disorders in children. Using sensitive biomarkers helps to assess the impact of air pollution on children’s respiratory health and combining protein, genetic and epigenetic biomarkers gives insights on their interrelatedness. Most studies do not contain such an integrated approach and investigate these biomarkers individually in blood, although its collection in children is challenging. Our study aimed at assessing the feasibility of conducting future integrated larger-scale studies evaluating respiratory health risks of air pollution episodes in children, based on a qualitative analysis of the technical and logistic aspects of a small-scale field study involving 42 children. This included the preparation, collection and storage of non-invasive samples (urine, saliva), the measurement of general and respiratory health parameters and the measurement of specific biomarkers (genetic, protein, epigenetic) of respiratory health and air pollution exposure. Bottlenecks were identified and modifications were proposed to expand this integrated study to a higher number of children, time points and locations. This would allow for non-invasive assessment of the impact of air pollution exposure on the respiratory health of children in future larger-scale studies, which is critical for the development of policies or measures at the population level.
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Affiliation(s)
- Sarah J. D. Nauwelaerts
- Transversal Activities in Applied Genomics, Sciensano, 1050 Brussels, Belgium; (S.J.D.N.); (M.G.); (D.V.G.); (M.D.); (E.V.); (N.H.C.R.)
- Centre for Toxicology and Applied Pharmacology, University Catholique de Louvain, 1200 Brussels, Belgium;
| | - Koen De Cremer
- Platform Chromatography and Mass Spectrometry, Sciensano, 1050 Brussels, Belgium;
| | | | - Mathieu Gand
- Transversal Activities in Applied Genomics, Sciensano, 1050 Brussels, Belgium; (S.J.D.N.); (M.G.); (D.V.G.); (M.D.); (E.V.); (N.H.C.R.)
| | - Dirk Van Geel
- Transversal Activities in Applied Genomics, Sciensano, 1050 Brussels, Belgium; (S.J.D.N.); (M.G.); (D.V.G.); (M.D.); (E.V.); (N.H.C.R.)
| | - Maud Delvoye
- Transversal Activities in Applied Genomics, Sciensano, 1050 Brussels, Belgium; (S.J.D.N.); (M.G.); (D.V.G.); (M.D.); (E.V.); (N.H.C.R.)
| | - Els Vandermassen
- Transversal Activities in Applied Genomics, Sciensano, 1050 Brussels, Belgium; (S.J.D.N.); (M.G.); (D.V.G.); (M.D.); (E.V.); (N.H.C.R.)
| | - Jordy Vercauteren
- Unit Air, Vlaamse Milieumaatschappij, 2000 Antwerpen, Belgium; (J.V.); (C.S.)
| | | | - Alfred Bernard
- Centre for Toxicology and Applied Pharmacology, University Catholique de Louvain, 1200 Brussels, Belgium;
| | - Nelly D. Saenen
- Centre for Environmental Sciences, Hasselt University, 3590 Diepenbeek, Belgium; (N.D.S.); (T.S.N.)
| | - Tim S. Nawrot
- Centre for Environmental Sciences, Hasselt University, 3590 Diepenbeek, Belgium; (N.D.S.); (T.S.N.)
- Department of Public Health and Primary Care, KU Leuven, 3000 Leuven, Belgium
| | - Nancy H. C. Roosens
- Transversal Activities in Applied Genomics, Sciensano, 1050 Brussels, Belgium; (S.J.D.N.); (M.G.); (D.V.G.); (M.D.); (E.V.); (N.H.C.R.)
| | - Sigrid C. J. De Keersmaecker
- Transversal Activities in Applied Genomics, Sciensano, 1050 Brussels, Belgium; (S.J.D.N.); (M.G.); (D.V.G.); (M.D.); (E.V.); (N.H.C.R.)
- Correspondence:
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22
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Wallace L, Zhao T, Klepeis NE. Calibration of PurpleAir PA-I and PA-II Monitors Using Daily Mean PM2.5 Concentrations Measured in California, Washington, and Oregon from 2017 to 2021. SENSORS 2022; 22:s22134741. [PMID: 35808235 PMCID: PMC9269269 DOI: 10.3390/s22134741] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/13/2022] [Accepted: 06/21/2022] [Indexed: 12/04/2022]
Abstract
Large quantities of real-time particle data are becoming available from low-cost particle monitors. However, it is crucial to determine the quality of these measurements. The largest network of monitors in the United States is maintained by the PurpleAir company, which offers two monitors: PA-I and PA-II. PA-I monitors have a single sensor (PMS1003) and PA-II monitors employ two independent PMS5003 sensors. We determine a new calibration factor for the PA-I monitor and revise a previously published calibration algorithm for PA-II monitors (ALT-CF3). From the PurpleAir API site, we downloaded 83 million hourly average PM2.5 values in the PurpleAir database from Washington, Oregon, and California between 1 January 2017 and 8 September 2021. Daily outdoor PM2.5 means from 194 PA-II monitors were compared to daily means from 47 nearby Federal regulatory sites using gravimetric Federal Reference Methods (FRM). We find a revised calibration factor of 3.4 for the PA-II monitors. For the PA-I monitors, we determined a new calibration factor (also 3.4) by comparing 26 outdoor PA-I sites to 117 nearby outdoor PA-II sites. These results show that PurpleAir PM2.5 measurements can agree well with regulatory monitors when an optimum calibration factor is found.
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Affiliation(s)
- Lance Wallace
- Independent Researcher, Santa Rosa, CA 95049, USA
- Correspondence:
| | - Tongke Zhao
- Independent Researcher, Milpitas, CA 95035, USA;
| | - Neil E. Klepeis
- Department of American Indian Studies, San Diego State University (SDSU), San Diego, CA 92182, USA;
- Education, Training, and Research, Inc. (ETR), Scotts Valley, CA 95066, USA
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23
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Band-Sensitive Calibration of Low-Cost PM2.5 Sensors by LSTM Model with Dynamically Weighted Loss Function. SUSTAINABILITY 2022. [DOI: 10.3390/su14106120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Particulate matter has become one of the major issues in environmental sustainability, and its accurate measurement has grown in importance recently. Low-cost sensors (LCS) have been widely used to measure particulate concentration, but concerns about their accuracy remain. Previous research has shown that LCS data can be successfully calibrated using various machine learning algorithms. In this study, for better calibration, dynamic weight was introduced to the loss function of the LSTM model to amplify the loss, especially in a specific band. Our results showed that the dynamically weighted loss function resulted in better calibration in the specific band, where the model accepts the loss more sensitively than outside of the band. It was also confirmed that the dynamically weighted loss function can improve the calibration of the LSTM model in terms of both overall performance and local performance in bands. In a test case, the overall calibration performance was improved by about 12.57%, from 3.50 to 3.06, in terms of RMSE. The local calibration performance in the band improved from 4.25 to 3.77. Such improvements were achieved by varying coefficients of the dynamic weight. The results from different bands also indicated that having more data in a band will guarantee better improvement.
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24
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Wallace L. Intercomparison of PurpleAir Sensor Performance over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5. SENSORS 2022; 22:s22072755. [PMID: 35408369 PMCID: PMC9002513 DOI: 10.3390/s22072755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 12/04/2022]
Abstract
Low-cost particle sensors are now used worldwide to monitor outdoor air quality. However, they have only been in wide use for a few years. Are they reliable? Does their performance deteriorate over time? Are the algorithms for calculating PM2.5 concentrations provided by the sensor manufacturers accurate? We investigate these questions using continuous measurements of four PurpleAir monitors (8 sensors) under normal conditions inside and outside a home for 1.5–3 years. A recently developed algorithm (called ALT-CF3) is compared to the two existing algorithms (CF1 and CF_ATM) provided by the Plantower manufacturer of the PMS 5003 sensors used in PurpleAir PA-II monitors. Results. The Plantower CF1 algorithm lost 25–50% of all indoor data due in part to the practice of assigning zero to all concentrations below a threshold. None of these data were lost using the ALT-CF3 algorithm. Approximately 92% of all data showed precision better than 20% using the ALT-CF3 algorithm, but only approximately 45–75% of data achieved that level using the Plantower CF1 algorithm. The limits of detection (LODs) using the ALT-CF3 algorithm were mostly under 1 µg/m3, compared to approximately 3–10 µg/m3 using the Plantower CF1 algorithm. The percentage of observations exceeding the LOD was 53–92% for the ALT-CF3 algorithm, but only 16–44% for the Plantower CF1 algorithm. At the low indoor PM2.5 concentrations found in many homes, the Plantower algorithms appear poorly suited.
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25
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Huang J, Kwan MP, Cai J, Song W, Yu C, Kan Z, Yim SHL. Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research. SENSORS 2022; 22:s22062381. [PMID: 35336552 PMCID: PMC8948698 DOI: 10.3390/s22062381] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 02/04/2023]
Abstract
This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM1, PM2.5, and PM10) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, increasing the aggregation level of the temporal units (i.e., from 5-s to 30 min intervals) increases the correlation between the PM concentrations obtained by the AirBeam2 sensors, while it does not significantly improve the accuracy and bias of the data. Lastly, our results indicate that using a machine learning model (i.e., random forest) for the calibration of PM concentrations collected on sunny days generates better results than those obtained with multiple linear models. These findings have important implications for researchers when designing environmental exposure studies based on low-cost PM sensors.
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Affiliation(s)
- Jianwei Huang
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China; (J.H.); (J.C.); (W.S.); (C.Y.); (Z.K.)
| | - Mei-Po Kwan
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China; (J.H.); (J.C.); (W.S.); (C.Y.); (Z.K.)
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
- Correspondence:
| | - Jiannan Cai
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China; (J.H.); (J.C.); (W.S.); (C.Y.); (Z.K.)
| | - Wanying Song
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China; (J.H.); (J.C.); (W.S.); (C.Y.); (Z.K.)
| | - Changda Yu
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China; (J.H.); (J.C.); (W.S.); (C.Y.); (Z.K.)
| | - Zihan Kan
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China; (J.H.); (J.C.); (W.S.); (C.Y.); (Z.K.)
| | - Steve Hung-Lam Yim
- Asian School of the Environment, Nanyang Technological University, Singapore 639798, Singapore;
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 639798, Singapore
- Earth Observatory of Singapore, Nanyang Technological University, Singapore 639798, Singapore
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26
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Lagesse B, Wang S, Larson TV, Kim AA. Performing indoor PM 2.5 prediction with low-cost data and machine learning. FACILITIES 2022. [DOI: 10.1108/f-05-2021-0046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The paper aims to develop a particle matter (PM2.5) prediction model for open-plan office space using a variety of data sources. Monitoring of PM2.5 levels is not widely applied in indoor settings. Many reliable methods of monitoring PM2.5 require either time-consuming or expensive equipment, thus making PM2.5 monitoring impractical for many settings. The goal of this paper is to identify possible low-cost, low-effort data sources that building managers can use in combination with machine learning (ML) models to approximate the performance of much more costly monitoring devices.
Design/methodology/approach
This study identified a variety of data sources, including freely available, public data, data from low-cost sensors and data from expensive, high-quality sensors. This study examined a variety of neural network architectures, including traditional artificial neural networks, generalized recurrent neural networks and long short-term memory neural networks as candidates for the prediction model. The authors trained the selected predictive model using this data and identified data sources that can be cheaply combined to approximate more expensive data sources.
Findings
The paper identified combinations of free data sources such as building damper percentages and weather data and low-cost sensors such as Wi-Fi-based occupancy estimator or a Plantower PMS7003 sensor that perform nearly as well as predictions made based on nephelometer data.
Originality/value
This work demonstrates that by combining low-cost sensors and ML, indoor PM2.5 monitoring can be performed at a drastically reduced cost with minimal error compared to more traditional approaches.
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27
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Learning Calibration Functions on the Fly: Hybrid Batch Online Stacking Ensembles for the Calibration of Low-Cost Air Quality Sensor Networks in the Presence of Concept Drift. ATMOSPHERE 2022. [DOI: 10.3390/atmos13030416] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Deployment of an air quality low-cost sensor network (AQLCSN), with proper calibration of low-cost sensors (LCS), offers the potential to substantially increase the ability to monitor air pollution. However, to leverage this potential, several drawbacks must be ameliorated, thus the calibration of such sensors is becoming an essential component in their use. Commonly, calibration takes place in a laboratory environment using gasses of known composition to measure the response and a linear calibration is often reached. On site calibration is a promising complementary technique where an LCS and a reference instrument are collocated with the former being calibrated to match the measurements of the latter. In a scenario where an AQLCSN is already operational, both calibration approaches are resource and time demanding procedures to be implemented as frequently repeated actions. Furthermore, sensors are sensitive to the local meteorology and adaptation is a slow process making relocation a complex and expensive option. We concentrate our efforts in keeping the LCS positions fixed and propose to blend a genetic algorithm (GA) with a hybrid stacking (HS) ensemble into the GAHS framework. GAHS employs a combination of batch machine learning algorithms and regularly updated online machine learning calibration function(s) for the whole network when a small number of reference instruments are present. Furthermore, we introduce the concept of spatial online learning to achieve better spatial generalization. The frameworks are tested for the case of Thessaloniki where a total of 33 devices are installed. The AQLCSN is calibrated on the basis of on-site matching with high quality observations from three reference station measurements. The O3 LCS are successfully calibrated for 8–10 months and the PM10 LCS calibration is evaluated for 13–24 months showing a strong seasonal dependence on their ability to correctly capture the pollution levels.
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28
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Bi J, Carmona N, Blanco MN, Gassett AJ, Seto E, Szpiro AA, Larson TV, Sampson PD, Kaufman JD, Sheppard L. Publicly available low-cost sensor measurements for PM 2.5 exposure modeling: Guidance for monitor deployment and data selection. ENVIRONMENT INTERNATIONAL 2022; 158:106897. [PMID: 34601393 PMCID: PMC8688284 DOI: 10.1016/j.envint.2021.106897] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/24/2021] [Accepted: 09/22/2021] [Indexed: 05/12/2023]
Abstract
High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM2.5 in epidemiological studies. Using sparse regulatory PM2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models' accuracy in predicting PM2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort's residential locations). In this study, we used the PM2.5 measurements from a publicly available commercial low-cost PM2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 "gold-standard" monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R2 and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 μg/m3 to 0.92 and 1.63 μg/m3, respectively. The external spatial validation R2 and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 μg/m3 to 0.79 and 0.88 μg/m3, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model's prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.
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Affiliation(s)
- Jianzhao Bi
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, USA.
| | - Nancy Carmona
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Magali N Blanco
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Amanda J Gassett
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Edmund Seto
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Timothy V Larson
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Paul D Sampson
- Department of Statistics, University of Washington, Seattle, WA, USA
| | - Joel D Kaufman
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Medicine, University of Washington, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Lianne Sheppard
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA
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29
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Huang CH, He J, Austin E, Seto E, Novosselov I. Assessing the value of complex refractive index and particle density for calibration of low-cost particle matter sensor for size-resolved particle count and PM2.5 measurements. PLoS One 2021; 16:e0259745. [PMID: 34762676 PMCID: PMC8584671 DOI: 10.1371/journal.pone.0259745] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 10/25/2021] [Indexed: 11/19/2022] Open
Abstract
Low-cost optical scattering particulate matter (PM) sensors report total or size-specific particle counts and mass concentrations. The PM concentration and size are estimated by the original equipment manufacturer (OEM) proprietary algorithms, which have inherent limitations since particle scattering depends on particles' properties such as size, shape, and complex index of refraction (CRI) as well as environmental parameters such as temperature and relative humidity (RH). As low-cost PM sensors are not able to resolve individual particles, there is a need to characterize and calibrate sensors' performance under a controlled environment. Here, we present improved calibration algorithms for Plantower PMS A003 sensor for mass indices and size-resolved number concentration. An aerosol chamber experimental protocol was used to evaluate sensor-to-sensor data reproducibility. The calibration was performed using four polydisperse test aerosols. The particle size distribution OEM calibration for PMS A003 sensor did not agree with the reference single particle sizer measurements. For the number concentration calibration, the linear model without adjusting for the aerosol properties and environmental conditions yields an absolute error (NMAE) of ~ 4.0% compared to the reference instrument. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM's mass concentrations estimates with NMAE within 5.0%. The calibration algorithms developed in this study can be used in indoor air quality monitoring, occupational/industrial exposure assessments, or near-source monitoring scenarios where field calibration might be challenging.
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Affiliation(s)
- Ching-Hsuan Huang
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, United States of America
| | - Jiayang He
- Department of Mechanical Engineering, College of Engineering, University of Washington, Seattle, Washington, United States of America
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, United States of America
| | - Edmund Seto
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, United States of America
| | - Igor Novosselov
- Department of Mechanical Engineering, College of Engineering, University of Washington, Seattle, Washington, United States of America
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30
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Reisen F, Cooper J, Powell JC, Roulston C, Wheeler AJ. Performance and Deployment of Low-Cost Particle Sensor Units to Monitor Biomass Burning Events and Their Application in an Educational Initiative. SENSORS (BASEL, SWITZERLAND) 2021; 21:7206. [PMID: 34770510 PMCID: PMC8588471 DOI: 10.3390/s21217206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 10/21/2021] [Accepted: 10/26/2021] [Indexed: 11/16/2022]
Abstract
Biomass burning smoke is often a significant source of airborne fine particles in regional areas where air quality monitoring is scarce. Emerging sensor technology provides opportunities to monitor air quality on a much larger geographical scale with much finer spatial resolution. It can also engage communities in the conversation around local pollution sources. The SMoke Observation Gadget (SMOG), a unit with a Plantower dust sensor PMS3003, was designed as part of a school-based Science, Technology, Engineering and Mathematics (STEM) project looking at smoke impacts in regional areas of Victoria, Australia. A smoke-specific calibration curve between the SMOG units and a standard regulatory instrument was developed using an hourly data set collected during a peat fire. The calibration curve was applied to the SMOG units during all field-based validation measurements at several locations and during different seasons. The results showed strong associations between individual SMOG units for PM2.5 concentrations (r2 = 0.93-0.99) and good accuracy (mean absolute error (MAE) < 2 μg m-3). Correlations of the SMOG units to reference instruments also demonstrated strong associations (r2 = 0.87-95) and good accuracy (MAE of 2.5-3.0 μg m-3). The PM2.5 concentrations tracked by the SMOG units had a similar response time as those measured by collocated reference instruments. Overall, the study has shown that the SMOG units provide relevant information about ambient PM2.5 concentrations in an airshed impacted predominantly by biomass burning, provided that an adequate adjustment factor is applied.
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Affiliation(s)
- Fabienne Reisen
- CSIRO Oceans & Atmosphere, Private Bag 1, Aspendale, VIC 3195, Australia; (J.C.); (J.C.P.); (C.R.)
| | - Jacinta Cooper
- CSIRO Oceans & Atmosphere, Private Bag 1, Aspendale, VIC 3195, Australia; (J.C.); (J.C.P.); (C.R.)
| | - Jennifer C. Powell
- CSIRO Oceans & Atmosphere, Private Bag 1, Aspendale, VIC 3195, Australia; (J.C.); (J.C.P.); (C.R.)
| | - Christopher Roulston
- CSIRO Oceans & Atmosphere, Private Bag 1, Aspendale, VIC 3195, Australia; (J.C.); (J.C.P.); (C.R.)
| | - Amanda J. Wheeler
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3000, Australia;
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS 7000, Australia
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Assessment and Calibration of a Low-Cost PM2.5 Sensor Using Machine Learning (HybridLSTM Neural Network): Feasibility Study to Build an Air Quality Monitoring System. ATMOSPHERE 2021. [DOI: 10.3390/atmos12101306] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Commercially available low-cost air quality sensors have low accuracy. The improved accuracy of low-cost PM2.5 sensors allows the use of low-cost sensor systems to reasonably investigate PM2.5 emissions from industrial activities or to accurately estimate individual exposure to PM2.5. In this work, we developed a new PM2.5 calibration model (HybridLSTM) by combining a deep neural network (DNN) optimized in calibration problems and a long short-term memory (LSTM) neural network optimized in time-dependent characteristics to improve the performance of conventional calibration algorithms of low-cost PM sensors. The PM2.5 concentrations, temperature and humidity by low-cost sensors and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmarks (multiple linear regression model (MLR), DNN model) and low-cost sensor results. The gravimetric measurements were used as reference data to evaluate sensor accuracy. For root-mean-square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41–60% of error when compared with the raw data of low-cost sensors, reduced 30–51% of error when compared with the MLR model and reduced 8–40% of error when compared with the MLR model. R2 of HybridLSTM, DNN, MLR and raw data were 93, 90, 80 and 59%, respectively. HybridLSTM showed the state-of-the-art calibration performance for a low-cost PM sensor. In other words, the proposed ML model has state-of-the-art calibration performance among the tested calibration algorithms.
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Nieckarz Z, Zoladz JA. New Calibration System for Low-Cost Suspended Particulate Matter Sensors with Controlled Air Speed, Temperature and Humidity. SENSORS 2021; 21:s21175845. [PMID: 34502737 PMCID: PMC8434339 DOI: 10.3390/s21175845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/25/2021] [Accepted: 08/27/2021] [Indexed: 11/16/2022]
Abstract
This paper presents a calibration system for low-cost suspended particulate matter (PM) sensors, consisting of reference instruments, enclosed space in a metal pipe (volume 0.145 m3), a duct fan, a controller and automated control software. The described system is capable of generating stable and repeatable concentrations of suspended PM in the air duct. In this paper, as the final result, we presented the process and effects of calibration of two low-cost air pollution stations—university measuring stations (UMS)—developed and used in the scientific project known as Storm&DustNet, implemented at the Jagiellonian University in Kraków (Poland), for the concentration range of PM from a few up to 240 µg·m–3. Finally, we postulate that a device of this type should be available for every system composed of a large number of low-cost PM sensors.
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Affiliation(s)
- Zenon Nieckarz
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, ul. Łojasiewicza 11, 30-348 Kraków, Poland
- Correspondence: ; Tel.: +48-12-664-4864
| | - Jerzy A. Zoladz
- Faculty of Health Sciences, Jagiellonian University Medical College, ul. Michałowskiego 12, 31-126 Kraków, Poland;
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Xiang J, Hao J, Austin E, Shirai J, Seto E. Residential cooking-related PM 2.5: Spatial-temporal variations under various intervention scenarios. BUILDING AND ENVIRONMENT 2021; 201:108002. [PMID: 34177073 PMCID: PMC8224830 DOI: 10.1016/j.buildenv.2021.108002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Some cooking events can generate high levels of hazardous PM2.5. This study assesses the dispersion of cooking-related PM2.5 throughout a naturally-ventilated apartment in the US, examines the dynamic process of cooking-related emissions, and demonstrates the impact of different indoor PM2.5 mitigating strategies. We conducted experiments with a standardized pan-frying cooking procedure under seven scenarios, involving opening kitchen windows, using a range hood, and utilizing a portable air cleaner (PAC) in various indoor locations. Real-time PM2.5 concentrations were measured in the open kitchen, living room, bedroom (door closed), and outdoor environments. Decay-related parameters were estimated, and time-resolved PM2.5 emission rates for each experiment were determined using a dynamic model. Results show that the 1-min mean PM2.5 concentrations in the kitchen and living room peaked 1-7 min after cooking at levels of 200-1400 μg/m3, which were more than 9 times higher than the peak bedroom levels. Mean (standard deviation) kt for the kitchen, ranging from 0.58 (0.02) to 6.62 (0.34) h-1, was generally comparable to that of the living room (relative difference < 20%), but was 1-5 times larger than that of the bedroom. The range of PM2.5 full-decay time was between 1-10 h for the kitchen and living room, and from 0 to > 6 h for the bedroom. The PM2.5 emission rates during and 5 min after cooking were 2.3 (3.4) and 5.1 (3.9) mg/min, respectively. Intervention strategies, including opening kitchen windows and using PACs either in the kitchen or living room, can substantially reduce indoor PM2.5 levels and the related full-decay time. For scenarios involving a PAC, placing it in the kitchen (closer to the source) resulted in better efficacy.
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Affiliation(s)
- Jianbang Xiang
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, 98195, United States
| | - Jiayuan Hao
- Department of Biostatistics, Harvard University, Cambridge, MA, 02138, United States
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, 98195, United States
| | - Jeff Shirai
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, 98195, United States
| | - Edmund Seto
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, 98195, United States
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Won WS, Oh R, Lee W, Ku S, Su PC, Yoon YJ. Hygroscopic properties of particulate matter and effects of their interactions with weather on visibility. Sci Rep 2021; 11:16401. [PMID: 34385551 PMCID: PMC8361198 DOI: 10.1038/s41598-021-95834-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 07/26/2021] [Indexed: 11/09/2022] Open
Abstract
The hygroscopic property of particulate matter (PM) influencing light scattering and absorption is vital for determining visibility and accurate sensing of PM using a low-cost sensor. In this study, we examined the hygroscopic properties of coarse PM (CPM) and fine PM (FPM; PM2.5) and the effects of their interactions with weather factors on visibility. A censored regression model was built to investigate the relationships between CPM and PM2.5 concentrations and weather observations. Based on the observed and modeled visibility, we computed the optical hygroscopic growth factor, \documentclass[12pt]{minimal}
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\begin{document}$$f\left( {RH} \right)$$\end{document}fRH, and the hygroscopic mass growth, \documentclass[12pt]{minimal}
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\begin{document}$$GM_{VIS}$$\end{document}GMVIS, which were applied to PM2.5 field measurement using a low-cost PM sensor in two different regions. The results revealed that the CPM and PM2.5 concentrations negatively affect visibility according to the weather type, with substantial modulation of the interaction between the relative humidity (RH) and PM2.5. The modeled \documentclass[12pt]{minimal}
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\begin{document}$$f\left( {RH} \right)$$\end{document}fRH agreed well with the observed \documentclass[12pt]{minimal}
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\begin{document}$$f\left( {RH} \right)$$\end{document}fRH in the RH range of the haze and mist. Finally, the RH-adjusted PM2.5 concentrations based on the visibility-derived hygroscopic mass growth showed the accuracy of the low-cost PM sensor improved. These findings demonstrate that in addition to visibility prediction, relationships between PMs and meteorological variables influence light scattering PM sensing.
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Affiliation(s)
- Wan-Sik Won
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Rosy Oh
- Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Korea
| | - Woojoo Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, 08826, Korea
| | - Sungkwan Ku
- Department of Aviation Industrial and System Engineering, Hanseo University, Seosan-si, Chungcheongnam-do, 32158, Korea
| | - Pei-Chen Su
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
| | - Yong-Jin Yoon
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore. .,Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Korea.
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Shaffer RM, Blanco MN, Li G, Adar SD, Carone M, Szpiro AA, Kaufman JD, Larson TV, Larson EB, Crane PK, Sheppard L. Fine Particulate Matter and Dementia Incidence in the Adult Changes in Thought Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2021; 129:87001. [PMID: 34347531 PMCID: PMC8336685 DOI: 10.1289/ehp9018] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
BACKGROUND Air pollution may be associated with elevated dementia risk. Prior research has limitations that may affect reliability, and no studies have evaluated this question in a population-based cohort of men and women in the United States. OBJECTIVES We evaluated the association between time-varying, 10-y average fine particulate matter (PM2.5) exposure and hazard of all-cause dementia. An additional goal was to understand how to adequately control for age and calendar-time-related confounding through choice of the time axis and covariate adjustment. METHODS Using the Adult Changes in Thought (ACT) population-based prospective cohort study in Seattle, we linked spatiotemporal model-based PM2.5 exposures to participant addresses from 1978 to 2018. Dementia diagnoses were made using high-quality, standardized, consensus-based protocols at biennial follow-ups. We conducted multivariable Cox proportional hazards regression to evaluate the association between time-varying, 10-y average PM2.5 exposure and time to event in a model with age as the time axis, stratified by apolipoprotein E (APOE) genotype, and adjusted for sex, education, race, neighborhood median household income, and calendar time. Alternative models used calendar time as the time axis. RESULTS We report 1,136 cases of incident dementia among 4,166 individuals with nonmissing APOE status. Mean [mean ± standard deviation (SD)] 10-y average PM2.5 was 10.1 (±2.9) μg/m3. Each 1-μg/m3 increase in the moving average of 10-y PM2.5 was associated with a 16% greater hazard of all-cause dementia [1.16 (95% confidence interval: 1.03, 1.31)]. Results using calendar time as the time axis were similar. DISCUSSION In this prospective cohort study with extensive exposure data and consensus-based outcome ascertainment, elevated long-term exposure to PM2.5 was associated with increased hazard of all-cause dementia. We found that optimal control of age and time confounding could be achieved through use of either age or calendar time as the time axis in our study. Our results strengthen evidence on the neurodegenerative effects of PM2.5. https://doi.org/10.1289/EHP9018.
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Affiliation(s)
- Rachel M. Shaffer
- Department of Environmental and Occupational Health Sciences, University of Washington Seattle School of Public Health, Seattle, Washington, USA
| | - Magali N. Blanco
- Department of Environmental and Occupational Health Sciences, University of Washington Seattle School of Public Health, Seattle, Washington, USA
| | - Ge Li
- VA Northwest Network Mental Illness Research, Education, and Clinical Center, Virginia Puget Sound Health Care System, Seattle, Washington, USA
- Geriatric Research, Education, and Clinical Center, Virginia Puget Sound Health Care System, Seattle, Washington, USA
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA
| | - Sara D. Adar
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Marco Carone
- Department of Biostatistics, University of Washington Seattle School of Public Health, Seattle, Washington, USA
| | - Adam A. Szpiro
- Department of Biostatistics, University of Washington Seattle School of Public Health, Seattle, Washington, USA
| | - Joel D. Kaufman
- Department of Environmental and Occupational Health Sciences, University of Washington Seattle School of Public Health, Seattle, Washington, USA
- Departments of Medicine and Epidemiology, University of Washington Seattle School of Public Health, Seattle, Washington, USA
| | - Timothy V. Larson
- Department of Environmental and Occupational Health Sciences, University of Washington Seattle School of Public Health, Seattle, Washington, USA
- Department of Civil & Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Eric B. Larson
- School of Medicine, University of Washington, Seattle, Washington, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Paul K. Crane
- School of Medicine, University of Washington, Seattle, Washington, USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington Seattle School of Public Health, Seattle, Washington, USA
- Department of Biostatistics, University of Washington Seattle School of Public Health, Seattle, Washington, USA
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Barkjohn KK, Gantt B, Clements AL. Development and Application of a United States wide correction for PM 2.5 data collected with the PurpleAir sensor. ATMOSPHERIC MEASUREMENT TECHNIQUES 2021; 4:10.5194/amt-14-4617-2021. [PMID: 34504625 PMCID: PMC8422884 DOI: 10.5194/amt-14-4617-2021] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
PurpleAir sensors, which measure particulate matter (PM), are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10,000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12,000 24-hour averaged PM2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM2.5 measurements were collected across diverse regions of the United States (U.S.), including 16 states. Consistent with previous evaluations, under typical ambient and smoke impacted conditions, the raw data from PurpleAir sensors overestimate PM2.5 concentrations by about 40% in most parts of the U.S. A simple linear regression reduces much of this bias across most U.S. regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 μg m-3 to 3 μg m-3 with an average FRM or FEM concentration of 9 μg m-3. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the U.S. in the AirNow Fire and Smoke Map (fire.airnow.gov) and has the potential to be successfully used in other air quality and public health applications.
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Affiliation(s)
- Karoline K. Barkjohn
- Office of Research and Development, U.S. Environmental Protection Agency 109 T.W. Alexander Drive Research Triangle Park, NC 27711
| | - Brett Gantt
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive Research Triangle Park, NC 27711
| | - Andrea L. Clements
- Office of Research and Development, U.S. Environmental Protection Agency 109 T.W. Alexander Drive Research Triangle Park, NC 27711
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Zuidema C, Schumacher CS, Austin E, Carvlin G, Larson TV, Spalt EW, Zusman M, Gassett AJ, Seto E, Kaufman JD, Sheppard L. Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study. SENSORS 2021; 21:s21124214. [PMID: 34205429 PMCID: PMC8234435 DOI: 10.3390/s21124214] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/16/2021] [Accepted: 06/16/2021] [Indexed: 11/30/2022]
Abstract
We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO2; NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.
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Affiliation(s)
- Christopher Zuidema
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Cooper S. Schumacher
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Graeme Carvlin
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Timothy V. Larson
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 18195, USA
| | - Elizabeth W. Spalt
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Marina Zusman
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Amanda J. Gassett
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Edmund Seto
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
| | - Joel D. Kaufman
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
- Department of Medicine, University of Washington, Seattle, WA 18195, USA
- Department of Epidemiology, University of Washington, Seattle, WA 18195, USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA; (C.Z.); (C.S.S.); (E.A.); (G.C.); (T.V.L.); (E.W.S.); (M.Z.); (A.J.G.); (E.S.); (J.D.K.)
- Department of Biostatistics, University of Washington, Seattle, WA 18795, USA
- Correspondence:
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Xiang J, Huang CH, Shirai J, Liu Y, Carmona N, Zuidema C, Austin E, Gould T, Larson T, Seto E. Field measurements of PM 2.5 infiltration factor and portable air cleaner effectiveness during wildfire episodes in US residences. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 773:145642. [PMID: 33592483 PMCID: PMC8026580 DOI: 10.1016/j.scitotenv.2021.145642] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 01/13/2021] [Accepted: 01/31/2021] [Indexed: 05/04/2023]
Abstract
Wildfires have frequently occurred in the western United States (US) during the summer and fall seasons in recent years. This study measures the PM2.5 infiltration factor in seven residences recruited from five dense communities in Seattle, Washington, during a 2020 wildfire episode and evaluates the impacts of HEPA-based portable air cleaner (PAC) use on reducing indoor PM2.5 levels. All residences with windows closed went through an 18-to-24-h no filtration session, with five of seven following that period with an 18-to-24-h filtration session. Auto-mode PACs, which automatically adjust the fan speed based on the surrounding PM2.5 levels, were used for the filtration session. 10-s resolved indoor PM2.5 levels were measured in each residence's living room, while hourly outdoor levels were collected from the nearest governmental air quality monitoring station to each residence. Additionally, a time-activity diary in minute resolution was collected from each household. With the impacts of indoor sources excluded, indoor PM2.5 mass balance models were developed to estimate the PM2.5 indoor/outdoor (I/O) ratios, PAC effectiveness, and decay-related parameters. Among the seven residences, the mean infiltration factor ranged from 0.33 (standard deviation [SD]: 0.06) to 0.76 (SD: 0.05). The use of auto-mode PAC led to a 48%-78% decrease of indoor PM2.5 levels after adjusting for outdoor PM2.5 levels and indoor sources. The mean (SD) air exchange rates ranged from 0.30 (0.13) h-1 to 1.41 (3.18) h-1 while the PM2.5 deposition rate ranged from 0.10 (0.54) h-1 to 0.49 (0.47) h-1. These findings suggest that staying indoors, a common protective measure during wildfire episodes, is insufficient to prevent people's excess exposure to wildfire smoke, and provides quantitative evidence to support the utilization of auto-mode PACs during wildfire events in the US.
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Affiliation(s)
- Jianbang Xiang
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States.
| | - Ching-Hsuan Huang
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States
| | - Jeff Shirai
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States
| | - Yisi Liu
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States
| | - Nancy Carmona
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States
| | - Christopher Zuidema
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States
| | - Timothy Gould
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, United States
| | - Timothy Larson
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, United States
| | - Edmund Seto
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States
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Venkatraman Jagatha J, Klausnitzer A, Chacón-Mateos M, Laquai B, Nieuwkoop E, van der Mark P, Vogt U, Schneider C. Calibration Method for Particulate Matter Low-Cost Sensors Used in Ambient Air Quality Monitoring and Research. SENSORS (BASEL, SWITZERLAND) 2021; 21:3960. [PMID: 34201377 PMCID: PMC8228976 DOI: 10.3390/s21123960] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/04/2021] [Accepted: 06/06/2021] [Indexed: 11/24/2022]
Abstract
Over the last decade, manufacturers have come forth with cost-effective sensors for measuring ambient and indoor particulate matter concentration. What these sensors make up for in cost efficiency, they lack in reliability of the measured data due to their sensitivities to temperature and relative humidity. These weaknesses are especially evident when it comes to portable or mobile measurement setups. In recent years many studies have been conducted to assess the possibilities and limitations of these sensors, however mostly restricted to stationary measurements. This study reviews the published literature until 2020 on cost-effective sensors, summarizes the recommendations of experts in the field based on their experiences, and outlines the quantile-mapping methodology to calibrate low-cost sensors in mobile applications. Compared to the commonly used linear regression method, quantile mapping retains the spatial characteristics of the measurements, although a common correction factor cannot be determined. We conclude that quantile mapping can be a useful calibration methodology for mobile measurements given a well-elaborated measurement plan assures providing the necessary data.
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Affiliation(s)
- Janani Venkatraman Jagatha
- Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany; (A.K.); (C.S.)
| | - André Klausnitzer
- Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany; (A.K.); (C.S.)
| | - Miriam Chacón-Mateos
- Department of Flue Gas Cleaning and Air Quality Control, Institute of Combustion and Power Plant Technology (IFK), University of Stuttgart, Pfaffenwaldring 23, 70569 Stuttgart, Germany; (M.C.-M.); (B.L.); (U.V.)
| | - Bernd Laquai
- Department of Flue Gas Cleaning and Air Quality Control, Institute of Combustion and Power Plant Technology (IFK), University of Stuttgart, Pfaffenwaldring 23, 70569 Stuttgart, Germany; (M.C.-M.); (B.L.); (U.V.)
| | - Evert Nieuwkoop
- Netherlands Organisation for Applied Scientific Research, Anna van Buerenplein 1, 2595 DA The Hague, The Netherlands; (E.N.); (P.v.d.M.)
| | - Peter van der Mark
- Netherlands Organisation for Applied Scientific Research, Anna van Buerenplein 1, 2595 DA The Hague, The Netherlands; (E.N.); (P.v.d.M.)
| | - Ulrich Vogt
- Department of Flue Gas Cleaning and Air Quality Control, Institute of Combustion and Power Plant Technology (IFK), University of Stuttgart, Pfaffenwaldring 23, 70569 Stuttgart, Germany; (M.C.-M.); (B.L.); (U.V.)
| | - Christoph Schneider
- Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany; (A.K.); (C.S.)
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Methodology for Addressing Infectious Aerosol Persistence in Real-Time Using Sensor Network. SENSORS 2021; 21:s21113928. [PMID: 34200380 PMCID: PMC8201307 DOI: 10.3390/s21113928] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/28/2021] [Accepted: 05/29/2021] [Indexed: 01/31/2023]
Abstract
Human exposure to infectious aerosols results in the transmission of diseases such as influenza, tuberculosis, and COVID-19. Most dental procedures generate a significant number of aerosolized particles, increasing transmission risk in dental settings. Since the generation of aerosols in dentistry is unavoidable, many clinics have started using intervention strategies such as area-filtration units and extraoral evacuation equipment, especially under the relatively recent constraints of the pandemic. However, the effectiveness of these devices in dental operatories has not been studied. Therefore, the ability of dental personnel to efficiently position and operate such instruments is also limited. To address these challenges, we utilized a real-time sensor network for assessment of aerosol dynamics during dental restoration and cleaning producers with and without intervention. The strategies tested during the procedures were (i) local area High-Efficiency Particle Air (HEPA) filters and (ii) Extra-Oral Suction Device (EOSD). The study was conducted at the University of Washington School of Dentistry using a network of 13 fixed sensors positioned within the operatory and one wearable sensor worn by the dental operator. The sensor network provides time and space-resolved particulate matter (PM) data. Three-dimensional (3D) visualization informed aerosol persistence in the operatory. It was found that area filters did not improve the overall aerosol concentration in dental offices in a significant way. A decrease in PM concentration by an average of 16% was observed when EOSD equipment was used during the procedures. The combination of real-time sensors and 3D visualization can provide dental personnel and facility managers with actionable feedback to effectively assess aerosol transmission in medical settings and develop evidence-based intervention strategies.
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Kirwa K, Szpiro AA, Sheppard L, Sampson PD, Wang M, Keller JP, Young MT, Kim SY, Larson TV, Kaufman JD. Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Curr Environ Health Rep 2021; 8:113-126. [PMID: 34086258 PMCID: PMC8278964 DOI: 10.1007/s40572-021-00310-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models. RECENT FINDINGS Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.
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Affiliation(s)
- Kipruto Kirwa
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA.
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA, USA
| | - Lianne Sheppard
- Departments of Biostatistics and Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA
| | - Paul D Sampson
- Department of Statistics, University of Washington School of Public Health, Seattle, WA, USA
| | - Meng Wang
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, NY, USA
| | - Joshua P Keller
- Department of Statistics, Colorado State University, Fort Collins, CO, USA
| | - Michael T Young
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA
| | - Sun-Young Kim
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Timothy V Larson
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Joel D Kaufman
- Departments of Environmental and Occupational Health Sciences, Epidemiology, and Medicine, University of Washington, Seattle, WA, USA
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Liang L. Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges. ENVIRONMENTAL RESEARCH 2021; 197:111163. [PMID: 33887275 DOI: 10.1016/j.envres.2021.111163] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 03/29/2021] [Accepted: 04/06/2021] [Indexed: 06/12/2023]
Abstract
Low-cost sensors (LCSs) are widely acknowledged for bringing a paradigm shift in supplemental traditional air monitoring by air regulatory agencies. However, there is concern regarding its data quality and performance stability, which has greatly restricted its large-scale applications. Knowing the recent techniques, progress, and challenges of LCS calibration is of immense significance to promote the field of environmental monitoring. By summarizing the published evidence, this review shows that the global sensor market is rapidly expanding due to the surging needs, but the calibration efforts have been focused on a limited selection of sensors. Relative humidity correction, regression, and machine learning are the three mainstream calibration techniques. Although there is no one-size-fits-all solution, a feature of the latest research tendency is machine learning. The duration of calibration is largely neglected in the experiment design, but it is found to affect the performance of different calibration methods, especially those that are data-driven. Geographically, China and the United States gained the most research attention in the sensor calibration field, but the spatial mismatch between particulate matter hotspots and calibration sites is quite evident for the rest of the world. Incomplete and unevenly distributed research footprints could limit the large-scale test of method generalizability, as well as diminish the monitoring capacity in underserved areas that suffer greater environmental justice crises. In general, model performance is enhanced by including the key influencing factors, but the degree of improvement is not evidently related to the number of explanatory variables. Overall, studies prove the critical importance of field calibration before sensor deployment, but more studies are needed to establish experiment protocols that can be customized to specific needs.
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Affiliation(s)
- Lu Liang
- Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA.
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Hua J, Zhang Y, de Foy B, Mei X, Shang J, Zhang Y, Sulaymon ID, Zhou D. Improved PM 2.5 concentration estimates from low-cost sensors using calibration models categorized by relative humidity. AEROSOL SCIENCE AND TECHNOLOGY 2021; 55:600-613. [PMID: 0 DOI: 10.1080/02786826.2021.1873911] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 12/24/2020] [Accepted: 01/04/2021] [Indexed: 05/21/2023]
Affiliation(s)
- Jinxi Hua
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Yuanxun Zhang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Sciences, Xiamen, China
| | - Benjamin de Foy
- Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, Missouri, USA
| | - Xiaodong Mei
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Jing Shang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
- Institute of Urban Meteorology, China Meteorological Administration, Beijing China
| | - Yang Zhang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Ishaq Dimeji Sulaymon
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Dandan Zhou
- Zhongke YunTian Environmental Protection Technology Company, Jining, China
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Bi J, Wallace LA, Sarnat JA, Liu Y. Characterizing outdoor infiltration and indoor contribution of PM 2.5 with citizen-based low-cost monitoring data. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 276:116763. [PMID: 33631689 DOI: 10.1016/j.envpol.2021.116763] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/15/2021] [Accepted: 02/15/2021] [Indexed: 06/12/2023]
Abstract
Epidemiological research on the adverse health outcomes due to PM2.5 exposure frequently relies on measurements from regulatory air quality monitors to provide ambient exposure estimates, whereas personal PM2.5 exposure may deviate from ambient concentrations due to outdoor infiltration and contributions from indoor sources. Research in quantifying infiltration factors (Finf), the fraction of outdoor PM2.5 that infiltrates indoors, has been historically limited in space and time due to the high costs of monitor deployment and maintenance. Recently, the growth of openly accessible, citizen-based PM2.5 measurements provides an unprecedented opportunity to characterize Finf at large spatiotemporal scales. In this analysis, 91 consumer-grade PurpleAir indoor/outdoor monitor pairs were identified in California (41 residential houses and 50 public/commercial buildings) during a 20-month period with around 650000 h of paired PM2.5 measurements. An empirical method was developed based on local polynomial regression to estimate site-specific Finf. The estimated site-specific Finf had a mean of 0.26 (25th, 75th percentiles: [0.15, 0.34]) with a mean bootstrap standard deviation of 0.04. The Finf estimates were toward the lower end of those reported previously. A threshold of ambient PM2.5 concentration, approximately 30 μg/m3, below which indoor sources contributed substantially to personal exposures, was also identified. The quantified relationship between indoor source contributions and ambient PM2.5 concentrations could serve as a metric of exposure errors when using outdoor monitors as an exposure proxy (without considering indoor-generated PM2.5), which may be of interest to epidemiological research. The proposed method can be generalized to larger geographical areas to better quantify PM2.5 outdoor infiltration and personal exposure.
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Affiliation(s)
- Jianzhao Bi
- Department of Environmental & Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
| | - Lance A Wallace
- United States Environmental Protection Agency (Retired), Santa Rosa, CA, USA
| | - Jeremy A Sarnat
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
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Lu Y, Giuliano G, Habre R. Estimating hourly PM 2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study. ENVIRONMENTAL RESEARCH 2021; 195:110653. [PMID: 33476665 DOI: 10.1016/j.envres.2020.110653] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/14/2020] [Accepted: 12/17/2020] [Indexed: 05/21/2023]
Abstract
Predicting PM2.5 concentrations at a fine spatial and temporal resolution (i.e., neighborhood, hourly) is challenging. Recent growth in low cost sensor networks is providing increased spatial coverage of air quality data that can be used to supplement data provided by monitors of regulatory agencies. We developed an hourly, 500 × 500 m gridded PM2.5 model that integrates PurpleAir low-cost air sensor network data for Los Angeles County. We developed a quality control scheme for PurpleAir data. We included spatially and temporally varying predictors in a random forest model with random oversampling of high concentrations to predict PM2.5. The model achieved high prediction accuracy (10-fold cross-validation (CV) R2 = 0.93, root mean squared error (RMSE) = 3.23 μg/m3; spatial CV R2 = 0.88, spatial RMSE = 4.33 μg/m3; temporal CV R2 = 0.90, temporal RMSE = 3.85 μg/m3). Our model was able to predict spatial and diurnal patterns in PM2.5 on typical weekdays and weekends, as well as non-typical days, such as holidays and wildfire days. The model allows for far more precise estimates of PM2.5 than existing methods based on few sensors. Taking advantage of low-cost PM2.5 sensors, our hourly random forest model predictions can be combined with time-activity diaries in future studies, enabling geographically and temporally fine exposure estimation for specific population groups in studies of acute air pollution health effects and studies of environmental justice issues.
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Affiliation(s)
- Yougeng Lu
- Department of Urban Planning and Spatial Analysis, University of Southern California, Los Angeles, CA, USA
| | - Genevieve Giuliano
- Department of Urban Planning and Spatial Analysis, University of Southern California, Los Angeles, CA, USA
| | - Rima Habre
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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Saini J, Dutta M, Marques G. Sensors for indoor air quality monitoring and assessment through Internet of Things: a systematic review. ENVIRONMENTAL MONITORING AND ASSESSMENT 2021; 193:66. [PMID: 33452599 DOI: 10.1007/s10661-020-08781-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 11/30/2020] [Indexed: 06/12/2023]
Abstract
The growing populations around the world are closely associated with rising levels of air pollution. The impact is not restricted to outdoor areas. Moreover, the health of building occupants is also deteriorating due to poor indoor air quality. As per the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Therefore, numerous companies have started the development of low-cost sensors to monitor indoor air pollution with the Internet of Things-based applications. However, due to the close association of air pollution levels to the mortality and morbidity rates, communities face several limitations while selecting sensors to address this public health challenge. The main contribution of this systematic review is to present a qualitative and quantitative evaluation of low-cost sensors while providing deep insights into the selection criteria for adequate monitoring. The authors in this paper discussed studies published after the year 2015, and it includes an analysis of papers published in the English language only. Moreover, this study highlights crucial research questions, states answers, and provides recommendations for future research studies. The outcomes of this paper will be useful for students, researchers, and industry members concerning the upcoming research and manufacturing activities. The results show that 28 studies (70%) include indoor thermal comfort assessment, 26 (65%) and 12 (30%) studies include CO2 and CO sensors, respectively. In total, 32 (45.7%) out of 71 sensors (whose prices are available) discussed in this study are available in a price below the US $20 over online marketplaces. Furthermore, the authors conclude that 77.5% of the analyzed literature does not include calibration details, and the accuracy specification is missing for 39.4% sensors.
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Affiliation(s)
- Jagriti Saini
- National Institute of Technical Teacher's Training and Research, Chandigarh, 160019, India.
| | - Maitreyee Dutta
- National Institute of Technical Teacher's Training and Research, Chandigarh, 160019, India
| | - Goncalo Marques
- Polytechnic of Coimbra, ESTGOH, 3400-124, Oliveira do Hospital, Portugal
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Considine EM, Reid CE, Ogletree MR, Dye T. Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 268:115833. [PMID: 33120139 DOI: 10.1016/j.envpol.2020.115833] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 10/09/2020] [Accepted: 10/10/2020] [Indexed: 06/11/2023]
Abstract
Low-cost air quality sensors can help increase spatial and temporal resolution of air pollution exposure measurements. These sensors, however, most often produce data of lower accuracy than higher-end instruments. In this study, we investigated linear and random forest models to correct PM2.5 measurements from the Denver Department of Public Health and Environment (DDPHE)'s network of low-cost sensors against measurements from co-located U.S. Environmental Protection Agency Federal Equivalence Method (FEM) monitors. Our training set included data from five DDPHE sensors from August 2018 through May 2019. Our testing set included data from two newly deployed DDPHE sensors from September 2019 through mid-December 2019. In addition to PM2.5, temperature, and relative humidity from the low-cost sensors, we explored using additional temporal and spatial variables to capture unexplained variability in sensor measurements. We evaluated results using spatial and temporal cross-validation techniques. For the long-term dataset, a random forest model with all time-varying covariates and length of arterial roads within 500 m was the most accurate (testing RMSE = 2.9 μg/m3 and R2 = 0.75; leave-one-location-out (LOLO)-validation metrics on the training set: RMSE = 2.2 μg/m3 and R2 = 0.93). For on-the-fly correction, we found that a multiple linear regression model using the past eight weeks of low-cost sensor PM2.5, temperature, and humidity data plus a near-highway indicator predicted each new week of data best (testing RMSE = 3.1 μg/m3 and R2 = 0.78; LOLO-validation metrics on the training set: RMSE = 2.3 μg/m3 and R2 = 0.90). The statistical methods detailed here will be used to correct low-cost sensor measurements to better understand PM2.5 pollution within the city of Denver. This work can also guide similar implementations in other municipalities by highlighting the improved accuracy from inclusion of variables other than temperature and relative humidity to improve accuracy of low-cost sensor PM2.5 data.
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Affiliation(s)
| | - Colleen E Reid
- Department of Geography, University of Colorado Boulder, USA.
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Spatial calibration and PM 2.5 mapping of low-cost air quality sensors. Sci Rep 2020; 10:22079. [PMID: 33328536 PMCID: PMC7745024 DOI: 10.1038/s41598-020-79064-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 11/04/2020] [Indexed: 01/05/2023] Open
Abstract
The data quality of low-cost sensors has received considerable attention and has also led to PM2.5 warnings. However, the calibration of low-cost sensor measurements in an environment with high relative humidity is critical. This study proposes an efficient calibration and mapping approach based on real-time spatial model. The study carried out spatial calibration, which automatically collected measurements of low-cost sensors and the regulatory stations, and investigated the spatial varying pattern of the calibrated low-cost sensor data. The low-cost PM2.5 sensors are spatially calibrated based on reference-grade measurements at regulatory stations. Results showed that the proposed spatial regression approach can explain the variability of the biases from the low-cost sensors with an R-square value of 0.94. The spatial calibration and mapping algorithm can improve the bias and decrease to 39% of the RMSE when compared to the nonspatial calibration model. This spatial calibration and real-time mapping approach provide a useful way for local communities and governmental agencies to adjust the consistency of the sensor network for improved air quality monitoring and assessment.
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49
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Johnston JE, Juarez Z, Navarro S, Hernandez A, Gutschow W. Youth Engaged Participatory Air Monitoring: A 'Day in the Life' in Urban Environmental Justice Communities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:E93. [PMID: 31877745 PMCID: PMC6981490 DOI: 10.3390/ijerph17010093] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 12/12/2019] [Accepted: 12/18/2019] [Indexed: 11/17/2022]
Abstract
Air pollution in Southern California does not impact all communities equally; communities of color are disproportionately burdened by poor air quality and more likely to live near industrial facilities and freeways. Government regulatory monitors do not have the spatial resolution to provide air quality information at the neighborhood or personal scale. We describe the A Day in the Life program, an approach to participatory air monitoring that engages youth in collecting data that they can then analyze and use to take action. Academics partnered with Los Angeles-based youth environmental justice organizations to combine personal air monitoring, participatory science, and digital storytelling to build capacity to address local air quality issues. Eighteen youth participants from four different neighborhoods wore portable personal PM2.5 (fine particles <2.5 µm in diameter) monitors for a day in each of their respective communities, documenting and mapping their exposure to PM2.5 during their daily routine. Air monitoring was coupled with photography and videos to document what they experienced over the course of their day. The PM2.5 exposure during the day for participants averaged 10.7 µg/m3, although the range stretched from <1 to 180 µg/m3. One-third of all measurements were taken <300 m from a freeway. Overall, we demonstrate a method to increase local youth-centered understanding of personal exposures, pollution sources, and vulnerability to air quality.
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Affiliation(s)
- Jill E. Johnston
- Division of Environmental Health, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA; (Z.J.); (W.G.)
| | - Zully Juarez
- Division of Environmental Health, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA; (Z.J.); (W.G.)
| | | | - Ashley Hernandez
- Communities for a Better Environment, Los Angeles, CA 90089, USA;
| | - Wendy Gutschow
- Division of Environmental Health, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA; (Z.J.); (W.G.)
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