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Diez S, Lacy S, Urquiza J, Edwards P. QUANT: a long-term multi-city commercial air sensor dataset for performance evaluation. Sci Data 2024; 11:904. [PMID: 39168987 PMCID: PMC11339295 DOI: 10.1038/s41597-024-03767-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 08/09/2024] [Indexed: 08/23/2024] Open
Abstract
The QUANT study represents the most extensive open-access evaluation of commercial air quality sensor systems to date. This comprehensive study assessed 49 systems from 14 manufacturers across three urban sites in the UK over a three-year period. The resulting open-access dataset captures high time-resolution measurements of a variety of gasses (NO, NO2, O3, CO, CO2), particulate matter (PM1, PM2.5, PM10), and key meteorological parameters (humidity, temperature, atmospheric pressure). The quality and scope of the dataset is enhanced by reference monitors' data and calibrated products from sensor manufacturers across the three sites. This publicly accessible dataset serves as a robust and transparent resource that details the methods used for data collection and procedures to ensure dataset integrity. It provides a valuable tool for a wide range of stakeholders to analyze the performance of air quality sensors in real-world settings. Policymakers can leverage this data to refine sensor deployment guidelines and develop standardized protocols, while manufacturers can utilize it as a benchmark for technological innovation and product certification. Moreover, the dataset has supported the development of a UK code of practice, and the certification of one of the participating companies, underscoring the dataset's utility and reliability.
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Affiliation(s)
- Sebastian Diez
- Centro de Investigación en Tecnologías para la Sociedad, Universidad del Desarrollo, Santiago, CP, 7550000, Chile.
- Wolfson Atmospheric Chemistry Laboratories, University of York, York, YO10 5DD, UK.
| | - Stuart Lacy
- Wolfson Atmospheric Chemistry Laboratories, University of York, York, YO10 5DD, UK
| | - Josefina Urquiza
- Grupo de Estudios de la Atmósfera y el Ambiente (GEAA), Universidad Tecnológica Nacional, Facultad Regional Mendoza (UTN-FRM), Cnel. Rodriguez 273, Mendoza, 5501, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Pete Edwards
- Wolfson Atmospheric Chemistry Laboratories, University of York, York, YO10 5DD, UK
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2
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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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Nalakurthi NVSR, Abimbola I, Ahmed T, Anton I, Riaz K, Ibrahim Q, Banerjee A, Tiwari A, Gharbia S. Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:3650. [PMID: 38894441 PMCID: PMC11175279 DOI: 10.3390/s24113650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 05/23/2024] [Accepted: 05/26/2024] [Indexed: 06/21/2024]
Abstract
The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Salem Gharbia
- Smart Earth Innovation Hub (Earth-Hub), Atlantic Technological University, F91 YW50 Sligo, Ireland; (N.V.S.R.N.); (I.A.); (T.A.); (I.A.); (K.R.); (Q.I.); (A.B.); (A.T.)
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4
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Wang Z, Yu T, Ye J, Tian L, Lin B, Leng W, Liu C. A novel low sampling rate and cost-efficient active sampler for medium/long-term monitoring of gaseous pollutants. JOURNAL OF HAZARDOUS MATERIALS 2024; 461:132583. [PMID: 37741205 DOI: 10.1016/j.jhazmat.2023.132583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 09/14/2023] [Accepted: 09/17/2023] [Indexed: 09/25/2023]
Abstract
Active sampling is a dependable approach for gaseous pollutants monitoring, offering high accuracy and precision that is unaffected by environmental factors such as wind and temperature in comparison to passive sampling. To measure long-term average concentrations while minimizing the use of materials, a reduced sampling rate is necessary. Thus, this study aims to develop a novel low sampling rate (down to 1 mL/min) and cost-efficient active sampler (LASP) for medium/long-term monitoring of gaseous pollutants. The LASP mainly consisted of a syringe pump, a Y-shaped fitting with two one-way valves, and a control unit for intermittent operation. Results showed that LASP can obtain a sampling rate of less than 1 mL/min and sampling rate exhibited a high level of stability. Daily average concentrations measurements for nitrogen dioxide and formaldehyde by LASP had normalized mean biases of 2.8% and 5.2%, respectively. These numbers were - 5.8% and 6.1% for weekly-average samplings. This study demonstrated applications of LASP in real outdoor (daily-average) and indoor (weekly-average) air quality measurements. It worked well with low noise levels, and without interfering with occupants' daily activities. LASP can assist in improving our ability to monitor air quality and pollutants emissions, thereby supporting health research and policy development. ENVIRONMENTAL IMPLICATION: Gaseous air pollution is an important hazardous factor threatening human health. Medium/long-term air quality monitoring is essential for outdoor and indoor air quality assessment and control. However, air sampler for medium/long-term sampling is lacking. This study developed a novel low sampling rate and cost-efficient active sampler and applied it to medium/long-term air sampling. The sampler can work at a sampling rate of less than 1 mL/min. This technology provides a feasible strategy for medium/long-term monitoring of gaseous air pollutants in both environments and emission hotspots.
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Affiliation(s)
- Zhiyuan Wang
- School of Energy and Environment, Southeast University, Nanjing 210096, China
| | - Tao Yu
- Wuhan Second Ship Design and Research Institute, Wuhan 430205, China
| | - Jin Ye
- School of Energy and Power, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, China
| | - Lei Tian
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Bencheng Lin
- Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China
| | - Wenjun Leng
- Wuhan Second Ship Design and Research Institute, Wuhan 430205, China
| | - Cong Liu
- School of Energy and Environment, Southeast University, Nanjing 210096, China.
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Air Quality Sensor Networks for Evidence-Based Policy Making: Best Practices for Actionable Insights. ATMOSPHERE 2022. [DOI: 10.3390/atmos13060944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
(1) Background: This work evaluated the usability of commercial “low-cost” air quality sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring station (AQMS) and subsequently deployed in Kampenhout and Sint-Niklaas (Belgium) to address real-world policy concerns: (a) what is the pollution contribution from road traffic near a school and at a central city square and (b) do local traffic interventions result in quantifiable air quality impacts? (3) Results: The considered sensor systems performed well in terms of data capture, correlation and intra-sensor uncertainty. Their accuracy was improved via local re-calibration, up to data quality levels for indicative measurements as set in the Air Quality Directive (Uexp < 50% for PM and <25% for NO2). A methodological setup was proposed using local background and source locations, allowing for quantification of the (3.1) maximum potential impact of local policy interventions and (3.2) air quality impacts from different traffic interventions with local contribution reductions of up to 89% for NO2 and 60% for NO throughout the considered 3 month monitoring period; (4) Conclusions: Our results indicate that commercial air quality sensor systems are able to accurately quantify air quality impacts from (even short-lived) local traffic measures and contribute to evidence-based policy making under the condition of a proper methodological setup (background normalization) and data quality (recurrent calibration) procedure. The applied methodology and learnings were distilled in a blueprint for air quality sensor networks for replication actions in other cities.
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Rogulski M, Badyda A, Gayer A, Reis J. Improving the Quality of Measurements Made by Alphasense NO 2 Non-Reference Sensors Using the Mathematical Methods. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22103619. [PMID: 35632025 PMCID: PMC9144097 DOI: 10.3390/s22103619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/05/2022] [Accepted: 05/07/2022] [Indexed: 06/02/2023]
Abstract
Conventional NO2 monitoring devices are relatively cumbersome, expensive, and have a relatively high-power consumption that limits their use to fixed sites. On the other hand, they offer high-quality measurements. In contrast, the low-cost NO2 sensors offer greater flexibility, are smaller, and allow greater coverage of the area with the measuring devices. However, their disadvantage is much lower accuracy. The main goal of this study was to investigate the measurement data quality of NO2-B43F Alphasense sensors. The measurement performance analysis of Alphasense NO2-B43F sensors was conducted in two research areas in Poland. Sensors were placed near fixed, professional air quality monitoring stations, carrying out measurements based on reference methods, in the following periods: July-November, and December-May. Results of the study show that without using sophisticated correction methods, the range of measured air pollution concentrations may be greater than their actual values in ambient air-measured in the field by fixed stations. In the case of summer months (with air temperature over 30 °C), the long-term mean absolute percentage error was over 150% and the sensors, using the methods recommended by the manufacturer, in the case of high temperatures could even show negative values. After applying the mathematical correction functions proposed in this article, it was possible to significantly reduce long-term errors (to 40-70% per month, regardless of the location of the measurements) and eliminate negative measurement values. The proposed method is based on the recalculation of the raw measurement, air temperature, and air RH and does not require the use of extensive analytical tools.
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Affiliation(s)
- Mariusz Rogulski
- Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, Nowowiejska 20, 00-653 Warsaw, Poland; (A.B.); (A.G.)
| | - Artur Badyda
- Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, Nowowiejska 20, 00-653 Warsaw, Poland; (A.B.); (A.G.)
| | - Anna Gayer
- Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, Nowowiejska 20, 00-653 Warsaw, Poland; (A.B.); (A.G.)
| | - Johnny Reis
- CESAM—Center for Environmental and Marine Studies & Department Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal;
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Prospects of Aloe vera and its Bioactive Compounds in Diabetes: Critical Review. JOURNAL OF PURE AND APPLIED MICROBIOLOGY 2021. [DOI: 10.22207/jpam.15.4.54] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Diabetes is a significant public health issue. The global diabetes epidemic has had a tremendous impact on India, and the disease burden has increased dramatically. Diabetes is quickly increasing in prevalence, especially in Indian cities, according to data. Therefore, an ideal drug is sought that has better safety and tolerability and the most effective control of diabetes. Many effective medications come from plant sources. Natural products like onion and garlic can effectively control diabetes. In this review, we should pay attention to Aloe vera and its bioactive compounds, that with the development of traditional medicine, Aloe vera can be used to treat various diseases. Some reports have questioned the safety and efficacy of Aloe vera or its compounds, especially at different doses, and some studies have shown no side effects. In this review we also focus on benefits on human health so that Aloe vera is part of the daily diet in many countries and appears to be non-toxic, it is necessary to investigate whether aloe vera dietary supplement can be a beneficial preventive or nutritional mitigation strategy to reduce the effects of diabetes. This review focuses on Aloe vera and its biologically active compounds that play a role in the treatment or prevention of this morbid disease: diabetes, including its underlying mechanism of blood sugar lowering properties, and herbal products that have been marketed for the treatment of diabetes or the therapeutic effect of diabetes.
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8
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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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Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics. SENSORS 2021; 21:s21144717. [PMID: 34300458 PMCID: PMC8309582 DOI: 10.3390/s21144717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/26/2021] [Accepted: 07/02/2021] [Indexed: 11/30/2022]
Abstract
With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.
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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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Relevance of Drift Components and Unit-to-Unit Variability in the Predictive Maintenance of Low-Cost Electrochemical Sensor Systems in Air Quality Monitoring. SENSORS 2021; 21:s21093298. [PMID: 34068777 PMCID: PMC8126229 DOI: 10.3390/s21093298] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 01/20/2023]
Abstract
As key components of low-cost sensor systems in air quality monitoring, electrochemical gas sensors have recently received a lot of interest but suffer from unit-to-unit variability and different drift components such as aging and concept drift, depending on the calibration approach. Magnitudes of drift can vary across sensors of the same type, and uniform recalibration intervals might lead to insufficient performance for some sensors. This publication evaluates the opportunity to perform predictive maintenance solely by the use of calibration data, thereby detecting the optimal moment for recalibration and improving recalibration intervals and measurement results. Specifically, the idea is to define confidence regions around the calibration data and to monitor the relative position of incoming sensor signals during operation. The emphasis lies on four algorithms from unsupervised anomaly detection-namely, robust covariance, local outlier factor, one-class support vector machine, and isolation forest. Moreover, the behavior of unit-to-unit variability and various drift components on the performance of the algorithms is discussed by analyzing published field experiments and by performing Monte Carlo simulations based on sensing and aging models. Although unsupervised anomaly detection on calibration data can disclose the reliability of measurement results, simulation results suggest that this does not translate to every sensor system due to unfavorable arrangements of baseline drifts paired with sensitivity drift.
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12
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Dessimond B, Annesi-Maesano I, Pepin JL, Srairi S, Pau G. Academically Produced Air Pollution Sensors for Personal Exposure Assessment: The Canarin Project. SENSORS 2021; 21:s21051876. [PMID: 33800192 PMCID: PMC7962460 DOI: 10.3390/s21051876] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/19/2021] [Accepted: 03/04/2021] [Indexed: 01/26/2023]
Abstract
The World Health Organization has estimated that air pollution is a major threat to health, causing approximately nine million premature deaths every year. Each individual has, over their lifetime, a unique exposure to air pollution through their habits, working and living conditions. Medical research requires dedicated tools to assess and understand individual exposure to air pollution in view of investigating its health effects. This paper presents portable sensors produced by the Canarin Project that provides accessible, real time personal exposure data to particulate matter. Our primary results demonstrate the use of portable sensors for the assessment of personal exposure to the different micro-environments attended by individuals, and for inspecting the short-term effects of air pollution through the example of sleep apnea. These findings underscore the necessity of obtaining contextual data in determining environmental exposure and give perspectives for the future of air pollution sensors dedicated to medical research.
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Affiliation(s)
- Boris Dessimond
- Computer Science Laboratory, University of Pierre et Marie Curie, LIP6, NPA Team, 4 Place Jussieu, 75005 Paris, France;
- EPAR Team, Medecine Faculty of Saint-Antoine, IPLESP, INSERM & Sorbonne University, 27 rue Chaligny, 75571 Paris, France;
- Correspondence:
| | - Isabella Annesi-Maesano
- EPAR Team, Medecine Faculty of Saint-Antoine, IPLESP, INSERM & Sorbonne University, 27 rue Chaligny, 75571 Paris, France;
| | - Jean-Louis Pepin
- Laboratoire HP2, Université Grenoble Alpes, Grenoble, INSERM, U1042 and CHU de 24 Grenoble, France;
| | - Salim Srairi
- Centre for Expertise and Engineering on Risks, Urban and Country Planning, Environment and Mobility—Ile de France Territorial Division, Mobility Department, 12, rue Teisserenc de Bort, 78190 Trappes-en-Yvelines, France;
| | - Giovanni Pau
- Computer Science Laboratory, University of Pierre et Marie Curie, LIP6, NPA Team, 4 Place Jussieu, 75005 Paris, France;
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Duvall R, Hagler G, Clements A, Benedict K, Barkjohn K, Kilaru V, Hanley T, Watkins N, Kaufman A, Kamal A, Reece S, Fransioli P, Gerboles M, Gillerman G, Habre R, Hannigan M, Ning Z, Papapostolou V, Pope R, Quintana P, Snyder JL. Deliberating Performance Targets: Follow-on workshop discussing PM 10, NO 2, CO, and SO 2 air sensor targets. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2021; 246:10.1016/j.atmosenv.2020.118099. [PMID: 33746555 PMCID: PMC7970457 DOI: 10.1016/j.atmosenv.2020.118099] [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: 05/21/2023]
Abstract
The use of air sensor technology is increasing worldwide for a variety of applications, however, with significant variability in data quality. The United States Environmental Protection Agency held a workshop in July 2019 to deliberate possible performance targets for air sensors measuring particles with aerodynamic diameters of 10 μm or less (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). These performance targets were discussed from the perspective of non-regulatory applications and with the sensors operating primarily in a stationary mode in outdoor environments. Attendees included representatives from multiple levels of government organizations, sensor developers, environmental nonprofits, international organizations, and academia. The workshop addressed the current lack of sensor technology requirements, discussed fit-for-purpose data quality needs, and debated transparency issues. This paper highlights the purpose and key outcomes of the workshop. While more information on performance and applications of sensors is available than in past years, the performance metrics, or parameters used to describe data quality, vary among the studies reports and there is a need for more clear and consistent approaches for evaluating sensor performance. Organizations worldwide are increasingly considering, or are in the process of developing, sensor performance targets and testing protocols. Workshop participants suggested that these new guidelines are highly desirable, would help improve data quality, and would give users more confidence in their data. Given the wide variety of uses for sensors and user backgrounds, as well as varied sensor design features (e.g., communication approaches, data tools, processing/adjustment algorithms and calibration procedures), the need for transparency was a key workshop theme. Suggestions for increasing transparency included documenting and sharing testing and performance data, detailing best practices, and sharing data processing and correction approaches.
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Affiliation(s)
- R.M. Duvall
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA
| | - G.S.W. Hagler
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA
| | - A.L. Clements
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA
| | - K. Benedict
- U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, USA
| | - K. Barkjohn
- Oak Ridge Institute for Science and Education Fellow, U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA
| | - V. Kilaru
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA
| | - T. Hanley
- U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, USA
| | - N. Watkins
- U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, USA
| | - A. Kaufman
- U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, USA
| | - A. Kamal
- U.S. Environmental Protection Agency, Office of Transportation and Air Quality, Ann Arbor, MI, USA
| | - S. Reece
- Former Oak Ridge Institute for Science and Education Fellow, U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA
| | - P. Fransioli
- Clark County Department of Air Quality, Las Vegas, NV, USA
| | - M. Gerboles
- European Commission, Joint Research Centre, Ispra, Italy
| | - G. Gillerman
- National Institute of Standards and Technology, Standards Coordination Office, Gaithersburg, MD, USA
| | - R. Habre
- University of Southern California, Keck School of Medicine, Los Angeles, CA, USA
| | - M. Hannigan
- University of Colorado-Boulder, Mechanical Engineering Department, Boulder, CO, USA
| | - Z. Ning
- Hong Kong University of Science and Technology, Hong Kong, China
| | - V. Papapostolou
- South Coast Air Quality Management District, Diamond Bar, CA, USA
| | - R. Pope
- Maricopa County Air Quality Department, Phoenix, AZ, USA
| | - P.J.E. Quintana
- San Diego State University, School of Public Health, San Diego, CA, USA
| | - J. Lam Snyder
- Sacramento Metropolitan Air Quality Management District, Sacramento, CA, USA
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14
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Landis MS, Long RW, Krug J, Colón M, Vanderpool R, Habel A, Urbanski SP. The U.S. EPA wildland fire sensor challenge: Performance and evaluation of solver submitted multi-pollutant sensor systems. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2021; 247:10.1016/j.atmosenv.2020.118165. [PMID: 33889052 PMCID: PMC8059620 DOI: 10.1016/j.atmosenv.2020.118165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Wildland fires can emit substantial amounts of air pollution that may pose a risk to those in proximity (e.g., first responders, nearby residents) as well as downwind populations. Quickly deploying air pollution measurement capabilities in response to incidents has been limited to date by the cost, complexity of implementation, and measurement accuracy. Emerging technologies including miniaturized direct-reading sensors, compact microprocessors, and wireless data communications provide new opportunities to detect air pollution in real time. The U.S. Environmental Protection Agency (EPA) partnered with other U.S. federal agencies (CDC, NASA, NPS, NOAA, USFS) to sponsor the Wildland Fire Sensor Challenge. EPA and partnering organizations share the desire to advance wildland fire air measurement technology to be easier to deploy, suitable to use for high concentration events, and durable to withstand difficult field conditions, with the ability to report high time resolution data continuously and wirelessly. The Wildland Fire Sensor Challenge encouraged innovation worldwide to develop sensor prototypes capable of measuring fine particulate matter (PM2.5), carbon monoxide (CO), carbon dioxide (CO2), and ozone (O3) during wildfire episodes. The importance of using federal reference method (FRM) versus federal equivalent method (FEM) instruments to evaluate performance in biomass smoke is discussed. Ten solvers from three countries submitted sensor systems for evaluation as part of the challenge. The sensor evaluation results including sensor accuracy, precision, linearity, and operability are presented and discussed, and three challenge winners are announced. Raw solver submitted PM2.5 sensor accuracies of the winners ranged from ~22 to 32%, while smoke specific EPA regression calibrations improved the accuracies to ~75-83% demonstrating the potential of these systems in providing reasonable accuracies over conditions that are typical during wildland fire events.
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Affiliation(s)
- Matthew S. Landis
- US EPA, Office of Research and Development, Research Triangle Park, NC, USA
| | - Russell W. Long
- US EPA, Office of Research and Development, Research Triangle Park, NC, USA
| | - Jonathan Krug
- US EPA, Office of Research and Development, Research Triangle Park, NC, USA
| | - Maribel Colón
- US EPA, Office of Research and Development, Research Triangle Park, NC, USA
| | - Robert Vanderpool
- US EPA, Office of Research and Development, Research Triangle Park, NC, USA
| | - Andrew Habel
- Jacobs Technology Inc., Research Triangle Park, NC, USA
| | - Shawn P. Urbanski
- U.S. Forest Service, Rocky Mountain Research Station, Missoula, MT, USA
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15
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Alfano B, Barretta L, Del Giudice A, De Vito S, Di Francia G, Esposito E, Formisano F, Massera E, Miglietta ML, Polichetti T. A Review of Low-Cost Particulate Matter Sensors from the Developers' Perspectives. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6819. [PMID: 33260320 PMCID: PMC7730878 DOI: 10.3390/s20236819] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 11/16/2020] [Accepted: 11/19/2020] [Indexed: 11/25/2022]
Abstract
The concerns related to particulate matter's health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device's operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R2 values lower than 0.5 are observed.
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Affiliation(s)
- Brigida Alfano
- ENEA CR-Portici, TERIN-FSD Department, P.le E. Fermi 1, 80055 Portici, Italy; (B.A.); (A.D.G.); (G.D.F.); (E.E.); (F.F.); (E.M.); (M.L.M.); (T.P.)
| | - Luigi Barretta
- Department of Physics, University of Naples Federico II, via Cinthia, 80100 Napoli, Italy;
- STmicroelectronics, via R. De Feo, Arzano, 80022 Napoli, Italy
| | - Antonio Del Giudice
- ENEA CR-Portici, TERIN-FSD Department, P.le E. Fermi 1, 80055 Portici, Italy; (B.A.); (A.D.G.); (G.D.F.); (E.E.); (F.F.); (E.M.); (M.L.M.); (T.P.)
| | - Saverio De Vito
- ENEA CR-Portici, TERIN-FSD Department, P.le E. Fermi 1, 80055 Portici, Italy; (B.A.); (A.D.G.); (G.D.F.); (E.E.); (F.F.); (E.M.); (M.L.M.); (T.P.)
| | - Girolamo Di Francia
- ENEA CR-Portici, TERIN-FSD Department, P.le E. Fermi 1, 80055 Portici, Italy; (B.A.); (A.D.G.); (G.D.F.); (E.E.); (F.F.); (E.M.); (M.L.M.); (T.P.)
| | - Elena Esposito
- ENEA CR-Portici, TERIN-FSD Department, P.le E. Fermi 1, 80055 Portici, Italy; (B.A.); (A.D.G.); (G.D.F.); (E.E.); (F.F.); (E.M.); (M.L.M.); (T.P.)
| | - Fabrizio Formisano
- ENEA CR-Portici, TERIN-FSD Department, P.le E. Fermi 1, 80055 Portici, Italy; (B.A.); (A.D.G.); (G.D.F.); (E.E.); (F.F.); (E.M.); (M.L.M.); (T.P.)
| | - Ettore Massera
- ENEA CR-Portici, TERIN-FSD Department, P.le E. Fermi 1, 80055 Portici, Italy; (B.A.); (A.D.G.); (G.D.F.); (E.E.); (F.F.); (E.M.); (M.L.M.); (T.P.)
| | - Maria Lucia Miglietta
- ENEA CR-Portici, TERIN-FSD Department, P.le E. Fermi 1, 80055 Portici, Italy; (B.A.); (A.D.G.); (G.D.F.); (E.E.); (F.F.); (E.M.); (M.L.M.); (T.P.)
| | - Tiziana Polichetti
- ENEA CR-Portici, TERIN-FSD Department, P.le E. Fermi 1, 80055 Portici, Italy; (B.A.); (A.D.G.); (G.D.F.); (E.E.); (F.F.); (E.M.); (M.L.M.); (T.P.)
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16
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Ruiter S, Kuijpers E, Saunders J, Snawder J, Warren N, Gorce JP, Blom M, Krone T, Bard D, Pronk A, Cauda E. Exploring Evaluation Variables for Low-Cost Particulate Matter Monitors to Assess Occupational Exposure. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E8602. [PMID: 33228125 PMCID: PMC7699371 DOI: 10.3390/ijerph17228602] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 11/13/2020] [Accepted: 11/13/2020] [Indexed: 01/20/2023]
Abstract
(1) Background: Small, lightweight, low-cost optical particulate matter (PM) monitors are becoming popular in the field of occupational exposure monitoring, because these devices allow for real-time static measurements to be collected at multiple locations throughout a work site as well as being used as wearables providing personal exposure estimates. Prior to deployment, devices should be evaluated to optimize and quantify measurement accuracy. However, this can turn out to be difficult, as no standardized methods are yet available and different deployments may require different evaluation procedures. To gain insight in the relevance of different variables that may affect the monitor readings, six PM monitors were selected based on current availability and evaluated in the laboratory; (2) Methods: Existing strategies that were judged appropriate for the evaluation of PM monitors were reviewed and seven evaluation variables were selected, namely the type of dust, within- and between-device variations, nature of the power supply, temperature, relative humidity, and exposure pattern (peak and constant). Each variable was tested and analyzed individually and, if found to affect the readings significantly, included in a final correction model specific to each monitor. Finally, the accuracy for each monitor after correction was calculated; (3) Results: The reference materials and exposure patterns were found to be main factors needing correction for most monitors. One PM monitor was found to be sufficiently accurate at concentrations up to 2000 µg/m3 PM2.5, with other monitors appropriate at lower concentrations. The average accuracy increased by up to three-fold compared to when the correction model did not include evaluation variables; (4) Conclusions: Laboratory evaluation and readings correction can greatly increase the accuracy of PM monitors and set boundaries for appropriate use. However, this requires identifying the relevant evaluation variables, which are heavily reliant on how the monitors are used in the workplace. This, together with the lack of current consensus on standardized procedures, shows the need for harmonized PM monitor evaluation methods for occupational exposure monitoring.
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Affiliation(s)
- Sander Ruiter
- Netherlands Organization for Applied Scientific Research (TNO), 3584 CB Utrecht, The Netherlands; (E.K.); (M.B.); (T.K.); (A.P.)
| | - Eelco Kuijpers
- Netherlands Organization for Applied Scientific Research (TNO), 3584 CB Utrecht, The Netherlands; (E.K.); (M.B.); (T.K.); (A.P.)
| | - John Saunders
- Health and Safety Executive (HSE), HSE Science and Research Centre, Harpur Hill, Buxton SK17 9JN, UK; (J.S.); (N.W.); (J.-P.G.); (D.B.)
| | - John Snawder
- Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health (NIOSH), 1090 Tusculum Avenue, Cincinnati, OH 45226, USA; (J.S.); (E.C.)
| | - Nick Warren
- Health and Safety Executive (HSE), HSE Science and Research Centre, Harpur Hill, Buxton SK17 9JN, UK; (J.S.); (N.W.); (J.-P.G.); (D.B.)
| | - Jean-Philippe Gorce
- Health and Safety Executive (HSE), HSE Science and Research Centre, Harpur Hill, Buxton SK17 9JN, UK; (J.S.); (N.W.); (J.-P.G.); (D.B.)
| | - Marcus Blom
- Netherlands Organization for Applied Scientific Research (TNO), 3584 CB Utrecht, The Netherlands; (E.K.); (M.B.); (T.K.); (A.P.)
| | - Tanja Krone
- Netherlands Organization for Applied Scientific Research (TNO), 3584 CB Utrecht, The Netherlands; (E.K.); (M.B.); (T.K.); (A.P.)
| | - Delphine Bard
- Health and Safety Executive (HSE), HSE Science and Research Centre, Harpur Hill, Buxton SK17 9JN, UK; (J.S.); (N.W.); (J.-P.G.); (D.B.)
| | - Anjoeka Pronk
- Netherlands Organization for Applied Scientific Research (TNO), 3584 CB Utrecht, The Netherlands; (E.K.); (M.B.); (T.K.); (A.P.)
| | - Emanuele Cauda
- Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health (NIOSH), 1090 Tusculum Avenue, Cincinnati, OH 45226, USA; (J.S.); (E.C.)
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17
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Tancev G, Pascale C. The Relocation Problem of Field Calibrated Low-Cost Sensor Systems in Air Quality Monitoring: A Sampling Bias. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6198. [PMID: 33143233 PMCID: PMC7662848 DOI: 10.3390/s20216198] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 10/20/2020] [Accepted: 10/26/2020] [Indexed: 11/16/2022]
Abstract
This publication revises the deteriorated performance of field calibrated low-cost sensor systems after spatial and temporal relocation, which is often reported for air quality monitoring devices that use machine learning models as part of their software to compensate for cross-sensitivities or interferences with environmental parameters. The cause of this relocation problem and its relationship to the chosen algorithm is elucidated using published experimental data in combination with techniques from data science. Thus, the origin is traced back to insufficient sampling of data that is used for calibration followed by the incorporation of bias into models. Biases often stem from non-representative data and are a common problem in machine learning, and more generally in artificial intelligence, and as such a rising concern. Finally, bias is believed to be partly reducible in this specific application by using balanced data sets generated in well-controlled laboratory experiments, although not trivial due to the need for infrastructure and professional competence.
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Affiliation(s)
- Georgi Tancev
- Swiss Federal Institute of Metrology, 3084 Bern, Switzerland;
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18
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Kephart JL, Fandiño-Del-Rio M, Williams KN, Malpartida G, Steenland K, Naeher LP, Gonzales GF, Chiang M, Checkley W, Koehler K. Nitrogen dioxide exposures from biomass cookstoves in the Peruvian Andes. INDOOR AIR 2020; 30:735-744. [PMID: 32064681 PMCID: PMC8884918 DOI: 10.1111/ina.12653] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 01/28/2020] [Accepted: 02/12/2020] [Indexed: 05/18/2023]
Abstract
BACKGROUND Household air pollution from biomass cookstoves is a major contributor to global morbidity and mortality, yet little is known about exposures to nitrogen dioxide (NO2 ). OBJECTIVE To characterize NO2 kitchen area concentrations and personal exposures among women with biomass cookstoves in the Peruvian Andes. METHODS We measured kitchen area NO2 concentrations at high-temporal resolution in 100 homes in the Peruvian Andes. We assessed personal exposure to NO2 in a subsample of 22 women using passive samplers. RESULTS Among 97 participants, the geometric mean (GM) highest hourly average NO2 concentration was 723 ppb (geometric standard deviation (GSD) 2.6) and the GM 24-hour average concentration was 96 ppb (GSD 2.6), 4.4 and 2.9 times greater than WHO indoor hourly (163 ppb) and annual (33 ppb) guidelines, respectively. Compared to the direct-reading instruments, we found similar kitchen area concentrations with 48-hour passive sampler measurements (GM 108 ppb, GSD 3.8). Twenty-seven percent of women had 48-hour mean personal exposures above WHO annual guidelines (GM 18 ppb, GSD 2.3). In univariate analyses, we found that roof, wall, and floor type, as well as higher SES, was associated with lower 24-hour kitchen area NO2 concentrations. PRACTICAL IMPLICATIONS Kitchen area concentrations and personal exposures to NO2 from biomass cookstoves in the Peruvian Andes far exceed WHO guidelines. More research is warranted to understand the role of this understudied household air pollutant on morbidity and mortality and to inform cleaner-cooking interventions for public health.
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Affiliation(s)
- Josiah L. Kephart
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Center for Global Non-Communicable Disease Research and Training, Johns Hopkins University, Baltimore, MD, USA
| | - Magdalena Fandiño-Del-Rio
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Center for Global Non-Communicable Disease Research and Training, Johns Hopkins University, Baltimore, MD, USA
| | - Kendra N. Williams
- Center for Global Non-Communicable Disease Research and Training, Johns Hopkins University, Baltimore, MD, USA
- Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Gary Malpartida
- Molecular Biology and Immunology Laboratory, Research Laboratory of Infectious Diseases, Department of Cell and Molecular Sciences, Faculty of Sciences and Philosophy, Universidad Peruana Cayetano Heredia, Lima, Perú
- Biomedical Research Unit, Asociación Benéfica PRISMA, Lima, Perú
| | - Kyle Steenland
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Luke P. Naeher
- Environmental Health Science Department, College of Public Health, University of Georgia, Athens, GA, USA
| | - Gustavo F. Gonzales
- Laboratories of Investigation and Development, Department of Biological and Physiological Sciences, Faculty of Sciences and Philosophy, Universidad Peruana Cayetano Heredia, Lima, Perú
- High Altitude Research Institute, Universidad Peruana Cayetano Heredia, Lima, Perú
| | - Marilú Chiang
- Biomedical Research Unit, Asociación Benéfica PRISMA, Lima, Perú
| | - William Checkley
- Center for Global Non-Communicable Disease Research and Training, Johns Hopkins University, Baltimore, MD, USA
- Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Program in Global Disease Epidemiology and Control, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Kirsten Koehler
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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19
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Liu X, Jayaratne R, Thai P, Kuhn T, Zing I, Christensen B, Lamont R, Dunbabin M, Zhu S, Gao J, Wainwright D, Neale D, Kan R, Kirkwood J, Morawska L. Low-cost sensors as an alternative for long-term air quality monitoring. ENVIRONMENTAL RESEARCH 2020; 185:109438. [PMID: 32276167 DOI: 10.1016/j.envres.2020.109438] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 03/02/2020] [Accepted: 03/24/2020] [Indexed: 06/11/2023]
Abstract
Low-cost air quality sensors are increasingly being used in many applications; however, many of their performance characteristics have not been adequately investigated. This study was conducted over a period of 13 months using low-cost air quality monitors, each comprising two low-cost sensors, which were subjected to a wide range of pollution sources and concentrations, relative humidity and temperature at four locations in Australia and China. The aim of the study was to establish the performance characteristics of the two low-cost sensors (a Plantower PMS1003 for PM2.5 and an Alphasense CO-B4 for carbon monoxide, CO) and the KOALA monitor as a whole under various conditions. Parameters evaluated included the inter-variability between individual monitors, the accuracy of monitors in comparison with the reference instruments, the effect of temperature and RH on the performance of the monitors, the responses of the PM2.5 sensors to different types of aerosols, and the long-term stability of the PM2.5 and CO sensors. The monitors showed high inter-correlations (r > 0.91) for both PM2.5 and CO measurements. The monitor performance varied with location, with moderate to good correlations with reference instruments for PM2.5 (0.44< R2 < 0.91) and CO (0.37< R2 < 0.90). The monitors performed well at relative humidity < 75% and high temperature conditions; however, two monitors in Beijing failed at low temperatures, probably due to electronic board failure. The PM2.5 sensor was less sensitive to marine aerosols and fresh vehicle emissions than to mixed urban background emissions, aged traffic emissions and industrial emissions. The long-term stability of the PM2.5 and CO sensors was good, while CO relative errors were affected by both high and low temperatures. Overall, the KOALA monitors performed well in the environments in which they were operated and provided a valuable contribution to long-term air quality monitoring within the elucidated limitations.
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Affiliation(s)
- Xiaoting Liu
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD, 4001, Australia
| | - Rohan Jayaratne
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD, 4001, Australia
| | - Phong Thai
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD, 4001, Australia
| | - Tara Kuhn
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD, 4001, Australia
| | - Isak Zing
- Institute for Future Environments, Queensland University of Technology, Brisbane, QLD, 4001, Australia
| | - Bryce Christensen
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD, 4001, Australia
| | - Riki Lamont
- Institute for Future Environments, Queensland University of Technology, Brisbane, QLD, 4001, Australia
| | - Matthew Dunbabin
- Institute for Future Environments, Queensland University of Technology, Brisbane, QLD, 4001, Australia
| | - Sicong Zhu
- MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing, 100044, China
| | - Jian Gao
- Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - David Wainwright
- Queensland Department of Environment and Science, GPO Box 2454, Brisbane, QLD, 4001, Australia
| | - Donald Neale
- Queensland Department of Environment and Science, GPO Box 2454, Brisbane, QLD, 4001, Australia
| | - Ruby Kan
- Office of Environment and Heritage, PO Box 29, Lidcombe, NSW, 1825, Australia
| | - John Kirkwood
- Office of Environment and Heritage, PO Box 29, Lidcombe, NSW, 1825, Australia
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD, 4001, Australia.
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Low-Cost Air Quality Sensors: One-Year Field Comparative Measurement of Different Gas Sensors and Particle Counters with Reference Monitors at Tušimice Observatory. ATMOSPHERE 2020. [DOI: 10.3390/atmos11050492] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
With attention increasing regarding the level of air pollution in different metropolitan and industrial areas worldwide, interest in expanding the monitoring networks by low-cost air quality sensors is also increasing. Although the role of these small and affordable sensors is rather supplementary, determination of the measurement uncertainty is one of the main questions of their applicability because there is no certificate for quality assurance of these non-reference technologies. This paper presents the results of almost one-year field testing measurements, when the data from different low-cost sensors (for SO2, NO2, O3, and CO: Cairclip, Envea, FR; for PM1, PM2.5, and PM10: PMS7003, Plantower, CHN, and OPC-N2, Alphasense, UK) were compared with co-located reference monitors used within the Czech national ambient air quality monitoring network. The results showed that in addition to the given reduced measurement accuracy of the sensors, the data quality depends on the early detection of defective units and changes caused by the effect of meteorological conditions (effect of air temperature and humidity on gas sensors and effect of air humidity with condensation conditions on particle counters), or by the interference of different pollutants (especially in gas sensors). Comparative measurement is necessary prior to each sensor’s field applications.
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21
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DeWitt HL, Crow WL, Flowers B. Performance evaluation of ozone and particulate matter sensors. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2020; 70:292-306. [PMID: 31961265 DOI: 10.1080/10962247.2020.1713921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 11/27/2019] [Accepted: 12/02/2019] [Indexed: 06/10/2023]
Abstract
As public awareness and concern about air quality grows, companies and researchers have begun to develop small, low-cost sensors to measure local air quality. These sensors have been used in citizen science projects, in distributed networks within cities, and in combination with public health studies on asthma and other air-quality-associated diseases. However, sensor long-term performance under different environmental conditions and pollutant levels is not fully understood. In addition, further evaluation is needed for other long-term performance trends such as performance among sensors of the same model, comparison between sensors from different companies and comparison of sensor data to federal equivalence or reference method (FEM/FRM) measurements. A 10-month evaluation of two popular particulate matter (PM) sensors, Dylos DC1100 and AirBeam, and a popular ozone (O3) sensor, Aeroqual 500, was performed as part of this study. Data from these sensors were compared to each other and to FEM/FRM data and local meteorology. The study took place at the Houston Regional Monitoring (HRM) site 3, located between the Houston Ship Channel and Houston's urban center. PM sensor performance was found to vary in time, with multivariate analysis, binning of data by meteorological parameter, and machine learning techniques able to account for some but not all performance variations. PM type (i.e., size distribution, fiber-flake-spheroid shape and black-brown-white color) likely played a role in the changing sensor performance. Triplicate individual Aeroqual O3 sensors tracked reasonably well with the FEM data for most of the measurement period but had irregular periods of O3 measurement offset. While the FEM data indicated 4 days where ozone levels were above the NAAQS, the Aeroqual ozone sensors indicated a substantially higher number of days, ranging from 9 to 16 for the three sensors.Implications: This paper evaluated the long-term performance of several commercial low-cost sensors (PM2.5 and ozone) as compared to federal equivalence method (FEM) monitors under a range of meteorological and air quality conditions. PM2.5 sensors performed well on low humidity days with winds indicative of sea salt or dust PM sources but had poor correlation with FEM data under other conditions. Two types of PM sensors were studied (Dylos 1100 and AirBeam) and data only correlated well between sensors of the same type. Sensor networks with multiple PM sensor types would not be as useful for comparative purposes as sensor networks of the same type. Relative humidity corrections alone did not increase sensor agreement with FEM to acceptable levels, specific information about PM sources and sensor response in the area measured is needed. Low-cost ozone sensors tested (Aeroqual) performed well but were biased high and overestimated days above ozone NAAQS.
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Bi J, Wildani A, Chang HH, Liu Y. Incorporating Low-Cost Sensor Measurements into High-Resolution PM 2.5 Modeling at a Large Spatial Scale. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:2152-2162. [PMID: 31927908 DOI: 10.1021/acs.est.9b06046] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Low-cost air quality sensors are promising supplements to regulatory monitors for PM2.5 exposure assessment. However, little has been done to incorporate the low-cost sensor measurements in large-scale PM2.5 exposure modeling. We conducted spatially varying calibration and developed a downweighting strategy to optimize the use of low-cost sensor data in PM2.5 estimation. In California, PurpleAir low-cost sensors were paired with air quality system (AQS) regulatory stations, and calibration of the sensors was performed by geographically weighted regression. The calibrated PurpleAir measurements were then given lower weights according to their residual errors and fused with AQS measurements into a random forest model to generate 1 km daily PM2.5 estimates. The calibration reduced PurpleAir's systematic bias to ∼0 μg/m3 and residual errors by 36%. Increased sensor bias was found to be associated with higher temperature and humidity, as well as longer operating time. The weighted prediction model outperformed the AQS-based prediction model with an improved random cross-validation (CV) R2 of 0.86, an improved spatial CV R2 of 0.81, and a lower prediction error. The temporal CV R2 did not improve due to the temporal discontinuity of PurpleAir. The inclusion of PurpleAir data allowed the predictions to better reflect PM2.5 spatial details and hotspots.
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Affiliation(s)
- Jianzhao Bi
- Department of Environmental Health, Rollins School of Public Health , Emory University , Atlanta , Georgia 30322 , United States
| | - Avani Wildani
- Department of Computer Science , Emory University , Atlanta , Georgia 30307 , United States
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health , Emory University , Atlanta , Georgia 30322 , United States
| | - Yang Liu
- Department of Environmental Health, Rollins School of Public Health , Emory University , Atlanta , Georgia 30322 , United States
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23
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Tagle M, Rojas F, Reyes F, Vásquez Y, Hallgren F, Lindén J, Kolev D, Watne ÅK, Oyola P. Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:171. [PMID: 32040639 PMCID: PMC7010625 DOI: 10.1007/s10661-020-8118-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 01/23/2020] [Indexed: 06/01/2023]
Abstract
Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM10 and PM2.5) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM10-PM2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filter-based sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM2.5 (normalized root-mean-square error 9-24%) and PM10 (10-37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM2.5 (R2 0.47-0.86) than PM10 (0.24-0.56). The correlations (R2) between the 24-h PM2.5 averages from the sensors and reference instruments were 0.63-0.87 for continuous monitoring and 0.69-0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM2.5 averages after correcting for RH.
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Affiliation(s)
- Matías Tagle
- Centro Mario Molina Chile, Antonio Bellet 292, Providencia, Santiago, Chile
| | - Francisca Rojas
- Centro Mario Molina Chile, Antonio Bellet 292, Providencia, Santiago, Chile
| | - Felipe Reyes
- Centro Mario Molina Chile, Antonio Bellet 292, Providencia, Santiago, Chile
| | - Yeanice Vásquez
- Centro Mario Molina Chile, Antonio Bellet 292, Providencia, Santiago, Chile
| | - Fredrik Hallgren
- IVL Swedish Environmental Research Institute, Aschebergsgatan 44, Gothenburg, Sweden
| | - Jenny Lindén
- IVL Swedish Environmental Research Institute, Aschebergsgatan 44, Gothenburg, Sweden
| | - Dimitar Kolev
- RISE Acreo, Research Institutes of Sweden, Lindholmspiren 7 A, Gothenburg, Sweden
| | - Ågot K Watne
- Environment Administration, City of Gothenburg, Karl Johansgatan 23, Gothenburg, Sweden
| | - Pedro Oyola
- Centro Mario Molina Chile, Antonio Bellet 292, Providencia, Santiago, Chile.
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24
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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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25
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Lim CC, Kim H, Vilcassim MJR, Thurston GD, Gordon T, Chen LC, Lee K, Heimbinder M, Kim SY. Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea. ENVIRONMENT INTERNATIONAL 2019; 131:105022. [PMID: 31362154 PMCID: PMC6728172 DOI: 10.1016/j.envint.2019.105022] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 06/26/2019] [Accepted: 07/15/2019] [Indexed: 05/04/2023]
Abstract
Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR models for street-level fine particulate matter (PM2.5) concentration levels in Seoul, South Korea. 169 h of data were collected from an approximately three week long campaign across five routes by ten volunteers sharing seven AirBeams, a low-cost ($250 per unit), smartphone-based particle counter, while geospatial data were extracted from OpenStreetMap, an open-source and crowd-generated geographical dataset. We applied and compared three statistical approaches in constructing the LUR models - linear regression (LR), random forest (RF), and stacked ensemble (SE) combining multiple machine learning algorithms - which resulted in cross-validation R2 values of 0.63, 0.73, and 0.80, respectively, and identification of several pollution 'hotspots.' The high R2 values suggest that study designs employing mobile sampling in conjunction with multiple low-cost air quality monitors could be applied to characterize urban street-level air quality with high spatial resolution, and that machine learning models could further improve model performance. Given this study design's cost-effectiveness and ease of implementation, similar approaches may be especially suitable for citizen science and community-based endeavors, or in regions bereft of air quality data and preexisting air monitoring networks, such as developing countries.
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Affiliation(s)
- Chris C Lim
- Department of Environmental Medicine, New York School of Medicine, New York, NY, United States of America.
| | - Ho Kim
- Graduate School of Public Health, Seoul National University, Seoul, South Korea
| | - M J Ruzmyn Vilcassim
- Department of Environmental Medicine, New York School of Medicine, New York, NY, United States of America
| | - George D Thurston
- Department of Environmental Medicine, New York School of Medicine, New York, NY, United States of America
| | - Terry Gordon
- Department of Environmental Medicine, New York School of Medicine, New York, NY, United States of America
| | - Lung-Chi Chen
- Department of Environmental Medicine, New York School of Medicine, New York, NY, United States of America
| | - Kiyoung Lee
- Graduate School of Public Health, Seoul National University, Seoul, South Korea
| | | | - Sun-Young Kim
- Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi, South Korea
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26
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Feinberg SN, Williams R, Hagler G, Low J, Smith L, Brown R, Garver D, Davis M, Morton M, Schaefer J, Campbell J. Examining spatiotemporal variability of urban particulate matter and application of high-time resolution data from a network of low-cost air pollution sensors. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2019; 213:579-584. [PMID: 34121907 PMCID: PMC8193829 DOI: 10.1016/j.atmosenv.2019.06.026] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Traditional air monitoring approaches using regulatory monitors have historically been used to assess regional-scale trends in air pollutants across large geographical areas. Recent advances in air pollution sensor technologies could provide additional information about nearby sources, support the siting of regulatory monitoring stations, and improve our knowledge of finer-scale spatiotemporal variation of ambient air pollutants and their associated health effects. Sensors are now being developed that are much smaller and lower cost than traditional ambient air monitoring systems and are capable of being deployed as a network to provide greater coverage of a given area. The CitySpace project conducted by the US EPA and the Shelby County Health Department included the deployment of a network of 17 sensor pods using Alphasense OPC-N2 particulate matter (PM) sensors integrated with meteorological sensors in Memphis, TN for six months. Sensor pods were collocated with a federal equivalent method (FEM) tapered element oscillating microbalance (TEOM) monitor both before and after the primary study period. Six of the sensor pods were found to meet the data quality objective (DQO) of coefficient of determination (R2) greater than 0.5 when collocated with the TEOM. Seven pods were decommissioned before the end of the study due to mechanical failure. The six pods meeting the DQO were used to examine the spatiotemporal variability of fine PM (PM2.5) across the Memphis area. One site was found to have higher relative PM2.5 concentrations when compared to the other sites in the network. The 1-min data from this sensor pod were evaluated to quantify the regional urban background and local-scale contributions to PM2.5 at that monitoring location. This method found that approximately 20% of the PM2.5 was attributed to local sources at this location, compared to 9% at a local regulatory monitoring site. Additionally, the 1-min data were combined with 1-min wind speed and wind direction data to examine potential sources in the area using the nonparametric trajectory analysis (NTA) technique. This method geographically identified local source areas that contributed to the measured concentrations at the high reading sensor location throughout the course of the study.
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Affiliation(s)
- Stephen Neil Feinberg
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830
- U.S Environmental Protection Agency (EPA), Office of Research and Development, Research Triangle Park, NC 27711
| | - Ron Williams
- U.S Environmental Protection Agency (EPA), Office of Research and Development, Research Triangle Park, NC 27711
| | - Gayle Hagler
- U.S Environmental Protection Agency (EPA), Office of Research and Development, Research Triangle Park, NC 27711
| | - Judy Low
- Shelby County Health Department, Memphis, TN 38105
| | - Larry Smith
- Shelby County Health Department, Memphis, TN 38105
| | | | | | | | | | | | - John Campbell
- General Dynamics Information Technology; Edison, NJ 08837
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27
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Chatzidiakou L, Krause A, Popoola OAM, Di Antonio A, Kellaway M, Han Y, Squires FA, Wang T, Zhang H, Wang Q, Fan Y, Chen S, Hu M, Quint JK, Barratt B, Kelly FJ, Zhu T, Jones RL. Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments. ATMOSPHERIC MEASUREMENT TECHNIQUES 2019; 12:4643-4657. [PMID: 31534556 PMCID: PMC6751078 DOI: 10.5194/amt-12-1-2019] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (NO x ), carbon monoxide (CO), ozone (O3) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed high reproducibility (meanR ¯ 2 = 0.93, min-max: 0.80-1.00) and excellent agreement with standard instrumentation (meanR ¯ 2 = 0.82, min-max: 0.54-0.99) in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies such as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at a large scale to investigate the underlying mechanisms of the effects of air pollution on health.
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Affiliation(s)
- Lia Chatzidiakou
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Anika Krause
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | | | - Andrea Di Antonio
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | | | - Yiqun Han
- MRC-PHE Centre for Environment & Health, Imperial College London and King’s College London, London, W2 1PG, UK
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- Department of Analytical, Environmental and Forensic Sciences, King’s College London, London, SE1 9NH, UK
| | | | - Teng Wang
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, 100871, China
| | - Hanbin Zhang
- MRC-PHE Centre for Environment & Health, Imperial College London and King’s College London, London, W2 1PG, UK
- Department of Analytical, Environmental and Forensic Sciences, King’s College London, London, SE1 9NH, UK
- NIHR Health Protection Research Unit in Health Impact of Environmental Hazards, King’s College London, London, SE1 9NH, UK
| | - Qi Wang
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, 100871, China
| | - Yunfei Fan
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, 100871, China
| | - Shiyi Chen
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Min Hu
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, 100871, China
| | - Jennifer K. Quint
- National Heart and Lung Institute, Imperial College London, SW3 6LR, UK
| | - Benjamin Barratt
- MRC-PHE Centre for Environment & Health, Imperial College London and King’s College London, London, W2 1PG, UK
- Department of Analytical, Environmental and Forensic Sciences, King’s College London, London, SE1 9NH, UK
- NIHR Health Protection Research Unit in Health Impact of Environmental Hazards, King’s College London, London, SE1 9NH, UK
| | - Frank J. Kelly
- MRC-PHE Centre for Environment & Health, Imperial College London and King’s College London, London, W2 1PG, UK
- Department of Analytical, Environmental and Forensic Sciences, King’s College London, London, SE1 9NH, UK
- NIHR Health Protection Research Unit in Health Impact of Environmental Hazards, King’s College London, London, SE1 9NH, UK
| | - Tong Zhu
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, 100871, China
| | - Roderic L. Jones
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
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Abstract
A growing number of companies have started commercializing low-cost sensors (LCS) that are said to be able to monitor air pollution in outdoor air. The benefit of the use of LCS is the increased spatial coverage when monitoring air quality in cities and remote locations. Today, there are hundreds of LCS commercially available on the market with costs ranging from several hundred to several thousand euro. At the same time, the scientific literature currently reports independent evaluation of the performance of LCS against reference measurements for about 110 LCS. These studies report that LCS are unstable and often affected by atmospheric conditions—cross-sensitivities from interfering compounds that may change LCS performance depending on site location. In this work, quantitative data regarding the performance of LCS against reference measurement are presented. This information was gathered from published reports and relevant testing laboratories. Other information was drawn from peer-reviewed journals that tested different types of LCS in research studies. Relevant metrics about the comparison of LCS systems against reference systems highlighted the most cost-effective LCS that could be used to monitor air quality pollutants with a good level of agreement represented by a coefficient of determination R2 > 0.75 and slope close to 1.0. This review highlights the possibility to have versatile LCS able to operate with multiple pollutants and preferably with transparent LCS data treatment.
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29
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Tryner J, Quinn C, Windom BC, Volckens J. Design and evaluation of a portable PM 2.5 monitor featuring a low-cost sensor in line with an active filter sampler. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2019; 21:1403-1415. [PMID: 31389929 DOI: 10.1039/c9em00234k] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Fine particulate air pollution (PM2.5) is a health hazard with numerous indoor and outdoor sources. Versatile monitors are needed to characterize PM2.5 sources, concentrations, and exposures in a range of locations and applications. Whereas low-cost light-scattering PM sensors provide real-time measurements with limited accuracy, gravimetric samples provide more accurate, albeit time-integrated, measurements. When used together, low-cost sensor data can be corrected to gravimetric samples. Here we describe the development of a portable PM2.5 monitor that features a low-cost sensor in line with an active filter sampler. Laboratory tests were conducted to determine (1) the accuracy and precision of PM2.5 concentrations derived from the filter sample and (2) correction factors for the low-cost sensor response to ammonium sulfate, Arizona road dust, urban particulate matter, and match smoke. Filter samples collected at 0.25 and 1.0 L min-1 had mean biases of -10% and -4%, relative to a tapered element oscillating microbalance, and a relative standard deviation (RSD) that ranged from 1% to 17%. The low-cost sensor correction factor varied with the test aerosol, sample flow rate, and between individual monitors. Gravimetric correction reduced the bias and RSD of ∼1 hour average concentrations measured by low-cost sensors in three collocated monitors. A week-long field experiment was also conducted to investigate how the monitor could be used to learn about sources of residential air pollution. Field data were used to identify: (1) pollution events resulting from cooking and use of a wood furnace and (2) variations in the number of air changes per hour inside the residence.
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Affiliation(s)
- Jessica Tryner
- Department of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, CO, USA 80523.
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30
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Clements AL, Reece S, Conner T, Williams R. Observed data quality concerns involving low-cost air sensors. ATMOSPHERIC ENVIRONMENT: X 2019; 3:100034. [PMID: 34327315 PMCID: PMC8318136 DOI: 10.1016/j.aeaoa.2019.100034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Affiliation(s)
- Andrea L. Clements
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, 27711, USA
- Corresponding author. US EPA, MD, D-343-05, USA. (A.L. Clements)
| | - Stephen Reece
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Teri Conner
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, 27711, USA
| | - Ron Williams
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, 27711, USA
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31
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Kimbrough S, Krabbe S, Baldauf R, Barzyk T, Brown M, Brown S, Croghan C, Davis M, Deshmukh P, Duvall R, Feinberg S, Isakov V, Logan R, McArthur T, Shields A. The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS): Integration of Low-Cost Sensors and Reference Grade Monitoring in a Complex Metropolitan Area. Part 1: Overview of the Project. CHEMOSENSORS (BASEL, SWITZERLAND) 2019; 7:26. [PMID: 32704490 PMCID: PMC7377253 DOI: 10.3390/chemosensors7020026] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Emissions from transportation sources can impact local air quality and contribute to adverse health effects. The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS), conducted over a 1-year period, researched emissions source characterization in the Argentine, Turner, and Armourdale, Kansas (KS) neighborhoods and the broader southeast Kansas City, KS area. This area is characterized as a near-source environment with impacts from large railyard operations, major roadways, and commercial and industrial facilities. The spatial and meteorological effects of particulate matter less than 2.5 μm (PM2.5), and black carbon (BC) pollutants on potential population exposures were evaluated at multiple sites using a combination of regulatory grade methods and instrumentation, low-cost sensors, citizen science, and mobile monitoring. The initial analysis of a subset of these data showed that mean reference grade PM2.5 concentrations (gravimetric) across all sites ranged from 7.92 to 9.34 μg/m3. Mean PM2.5 concentrations from low-cost sensors ranged from 3.30 to 5.94 μg/m3 (raw, uncorrected data). Pollution wind rose plots suggest that the sites are impacted by higher PM2.5 and BC concentrations when the winds originate near known source locations. Initial data analysis indicated that the observed PM2.5 and BC concentrations are driven by multiple air pollutant sources and meteorological effects. The KC-TRAQS overview and preliminary data analysis presented will provide a framework for forthcoming papers that will further characterize emission source attributions and estimate near-source exposures. This information will ultimately inform and clarify the extent and impact of air pollutants in the Kansas City area.
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Affiliation(s)
- Sue Kimbrough
- U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Stephen Krabbe
- U.S. Environmental Protection Agency, Region 7, 300 Minnesota Ave., Kansas City, KS 66101, USA
| | - Richard Baldauf
- U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Timothy Barzyk
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Matthew Brown
- U.S. Environmental Protection Agency, Region 7, 300 Minnesota Ave., Kansas City, KS 66101, USA
| | - Steven Brown
- U.S. Environmental Protection Agency, Region 7, 11201 Renner Blvd., Lenexa, KS 66219, USA
| | - Carry Croghan
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Michael Davis
- U.S. Environmental Protection Agency, Region 7, 300 Minnesota Ave., Kansas City, KS 66101, USA
| | - Parikshit Deshmukh
- Jacobs Technology Inc., 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Rachelle Duvall
- U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Stephen Feinberg
- ORISE Participant, U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Vlad Isakov
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Russell Logan
- Jacobs Technology Inc., 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Tim McArthur
- Science Systems and Applications, Inc., 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Amy Shields
- U.S. Environmental Protection Agency, Region 7, 11201 Renner Blvd., Lenexa, KS 66219, USA
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Kimbrough S, Krabbe S, Baldauf R, Barzyk T, Brown M, Brown S, Croghan C, Davis M, Deshmukh P, Duvall R, Feinberg S, Isakov V, Logan R, McArthur T, Shields A. The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS): Integration of Low-Cost Sensors and Reference Grade Monitoring in a Complex Metropolitan Area. Part 1: Overview of the Project. CHEMOSENSORS (BASEL, SWITZERLAND) 2019. [PMID: 32704490 DOI: 10.3390/chemosensors70200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Emissions from transportation sources can impact local air quality and contribute to adverse health effects. The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS), conducted over a 1-year period, researched emissions source characterization in the Argentine, Turner, and Armourdale, Kansas (KS) neighborhoods and the broader southeast Kansas City, KS area. This area is characterized as a near-source environment with impacts from large railyard operations, major roadways, and commercial and industrial facilities. The spatial and meteorological effects of particulate matter less than 2.5 μm (PM2.5), and black carbon (BC) pollutants on potential population exposures were evaluated at multiple sites using a combination of regulatory grade methods and instrumentation, low-cost sensors, citizen science, and mobile monitoring. The initial analysis of a subset of these data showed that mean reference grade PM2.5 concentrations (gravimetric) across all sites ranged from 7.92 to 9.34 μg/m3. Mean PM2.5 concentrations from low-cost sensors ranged from 3.30 to 5.94 μg/m3 (raw, uncorrected data). Pollution wind rose plots suggest that the sites are impacted by higher PM2.5 and BC concentrations when the winds originate near known source locations. Initial data analysis indicated that the observed PM2.5 and BC concentrations are driven by multiple air pollutant sources and meteorological effects. The KC-TRAQS overview and preliminary data analysis presented will provide a framework for forthcoming papers that will further characterize emission source attributions and estimate near-source exposures. This information will ultimately inform and clarify the extent and impact of air pollutants in the Kansas City area.
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Affiliation(s)
- Sue Kimbrough
- U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Stephen Krabbe
- U.S. Environmental Protection Agency, Region 7, 300 Minnesota Ave., Kansas City, KS 66101, USA
| | - Richard Baldauf
- U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Timothy Barzyk
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Matthew Brown
- U.S. Environmental Protection Agency, Region 7, 300 Minnesota Ave., Kansas City, KS 66101, USA
| | - Steven Brown
- U.S. Environmental Protection Agency, Region 7, 11201 Renner Blvd., Lenexa, KS 66219, USA
| | - Carry Croghan
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Michael Davis
- U.S. Environmental Protection Agency, Region 7, 300 Minnesota Ave., Kansas City, KS 66101, USA
| | - Parikshit Deshmukh
- Jacobs Technology Inc., 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Rachelle Duvall
- U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Stephen Feinberg
- ORISE Participant, U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Vlad Isakov
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Russell Logan
- Jacobs Technology Inc., 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Tim McArthur
- Science Systems and Applications, Inc., 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA
| | - Amy Shields
- U.S. Environmental Protection Agency, Region 7, 11201 Renner Blvd., Lenexa, KS 66219, USA
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Reece S, Williams R, Colón M, Southgate D, Huertas E, O'Shea M, Iglesias A, Sheridan P. Spatial-Temporal Analysis of PM 2.5 and NO₂ Concentrations Collected Using Low-Cost Sensors in Peñuelas, Puerto Rico. SENSORS (BASEL, SWITZERLAND) 2018; 18:E4314. [PMID: 30544516 PMCID: PMC6308536 DOI: 10.3390/s18124314] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 11/28/2018] [Accepted: 12/04/2018] [Indexed: 01/09/2023]
Abstract
The U.S. Environmental Protection Agency (EPA) is involved in the discovery, evaluation, and application of low-cost air quality (AQ) sensors to support citizen scientists by directly engaging with them in the pursuit of community-based interests. The emergence of low-cost (<$2500) sensors have allowed a wide range of stakeholders to better understand local AQ conditions. Here we present results from the deployment of the EPA developed Citizen Science Air Monitor (CSAM) used to conduct approximately five months (October 2016⁻February 2017) of intensive AQ monitoring in an area of Puerto Rico (Tallaboa-Encarnación, Peñuelas) with little historical data on pollutant spatial variability. The CSAMs were constructed by combining low-cost particulate matter size fraction 2.5 micron (PM2.5) and nitrogen dioxide (NO₂) sensors and distributed across eight locations with four collocated weather stations to measure local meteorological parameters. During this deployment 1 h average concentrations of PM2.5 and NO₂ ranged between 0.3 to 33.6 µg/m³ and 1.3 to 50.6 ppb, respectively. Peak concentrations were observed for both PM2.5 and NO₂ when conditions were dominated by coastal-originated winds. These results advanced the community's understanding of pollutant concentrations and trends while improving our understanding of the limitations and necessary procedures to properly interpret measurements produced by low-cost sensors.
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Affiliation(s)
- Stephen Reece
- Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
| | - Ron Williams
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
| | - Maribel Colón
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
| | | | - Evelyn Huertas
- U.S. Environmental Protection Agency, Region 2, Caribbean Environmental Protection Division, Guaynabo, PR 00968-8069, USA.
| | - Marie O'Shea
- Region 2, U.S. Environmental Protection Agency, 290 Broadway, New York, NY 10007-1866, USA.
| | - Ariel Iglesias
- Region 2, U.S. Environmental Protection Agency, 290 Broadway, New York, NY 10007-1866, USA.
| | - Patricia Sheridan
- Region 2, U.S. Environmental Protection Agency, Edison, NJ 08837-3679, USA.
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