1
|
Feng Z, Zheng L, Ren B, Liu D, Huang J, Xue N. Feasibility of low-cost particulate matter sensors for long-term environmental monitoring: Field evaluation and calibration. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:174089. [PMID: 38897458 DOI: 10.1016/j.scitotenv.2024.174089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/05/2024] [Accepted: 06/16/2024] [Indexed: 06/21/2024]
Abstract
Low-cost sensor networks offer the potential to reduce monitoring costs while providing high-resolution spatiotemporal data on pollutant levels. However, these sensors come with limitations, and many aspects of their field performance remain underexplored. During October to December 2023, this study deployed two identical low-cost sensor systems near an urban standard monitoring station to record PM2.5 and PM10 concentrations, along with environmental temperature and humidity. Our evaluation of the monitoring performance of these sensors revealed a broad data distribution with a systematic overestimation; this overestimation was more pronounced in PM10 readings. The sensors showed good consistency (R2 > 0.9, NRMSE<5 %), and normalization residuals were tracked to assess stability, which, despite occasional environmental influences, remained generally stable. A lateral comparison of four calibration models (MLR, SVR, RF, XGBoost) demonstrated superior performance of RF and XGBoost over others, particularly with RF showing enhanced effectiveness on the test set. SHAP analysis identified sensor readings as the most critical variable, underscoring their pivotal role in predictive modeling. Relative humidity consistently proved more significant than dew point and temperature, with higher RH levels typically having a positive impact on model outputs. The study indicates that, with appropriate calibration, sensors can supplement the sparse networks of regulatory-grade instruments, enabling dense neighborhood-scale monitoring and a better understanding of temporal air quality trends.
Collapse
Affiliation(s)
- Zikang Feng
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, People's Republic of China
| | - Lina Zheng
- Jiangsu Engineering Research Center for Dust Control and Occupational Protection, China University of Mining and Technology, Xuzhou, People's Republic of China; School of Safety Engineering, China University of Mining and Technology, Xuzhou, People's Republic of China; Institute of Occupational Health, China University of Mining and Technology, Xuzhou, People's Republic of China.
| | - Bilin Ren
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, People's Republic of China
| | - Dou Liu
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, People's Republic of China
| | - Jing Huang
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, People's Republic of China
| | - Ning Xue
- Joycontrol (Shanghai) Environment Technology Co., Ltd, Shanghai, People's Republic of China
| |
Collapse
|
2
|
Stojanović DB, Kleut D, Davidović M, Živković M, Ramadani U, Jovanović M, Lazović I, Jovašević-Stojanović M. Data Evaluation of a Low-Cost Sensor Network for Atmospheric Particulate Matter Monitoring in 15 Municipalities in Serbia. SENSORS (BASEL, SWITZERLAND) 2024; 24:4052. [PMID: 39000831 PMCID: PMC11244021 DOI: 10.3390/s24134052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/11/2024] [Accepted: 06/17/2024] [Indexed: 07/16/2024]
Abstract
Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM2.5 and PM10 were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM10 (50 μg/m3) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area.
Collapse
Affiliation(s)
- Danka B. Stojanović
- VIDIS Centre, Vinča Institute of Nuclear Sciences—National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia; (D.K.); (M.D.); (M.Ž.); (U.R.); (M.J.); (I.L.); (M.J.-S.)
| | | | | | | | | | | | | | | |
Collapse
|
3
|
Gualtieri G, Brilli L, Carotenuto F, Cavaliere A, Giordano T, Putzolu S, Vagnoli C, Zaldei A, Gioli B. Performance Assessment of Two Low-Cost PM 2.5 and PM 10 Monitoring Networks in the Padana Plain (Italy). SENSORS (BASEL, SWITZERLAND) 2024; 24:3946. [PMID: 38931730 PMCID: PMC11207606 DOI: 10.3390/s24123946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 06/10/2024] [Accepted: 06/16/2024] [Indexed: 06/28/2024]
Abstract
Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 for PM10 concentrations were compared vs. regulatory-grade stations during a full "heating season" (15 October 2022-15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM10 than PM2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R2. AirQino stations under-estimated both PM2.5 and PM10 reference concentrations (MB = -4.8 and -2.9 μg/m3, respectively), while PurpleAir stations over-estimated PM2.5 concentrations (MB = +5.4 μg/m3) and slightly under-estimated PM10 concentrations (MB = -0.4 μg/m3). PurpleAir stations were finer than AirQino at capturing the time variation of both PM2.5 and PM10 daily concentrations (R2 = 0.68-0.75 vs. 0.59-0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM2.5/PM10 ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM2.5 vs. PM10 outputs.
Collapse
Affiliation(s)
- Giovanni Gualtieri
- National Research Council, Institute of Bioecomony (CNR-IBE), Via Caproni 8, 50145 Firenze, Italy; (L.B.); (F.C.); (T.G.); (S.P.); (C.V.); (A.Z.); (B.G.)
| | - Lorenzo Brilli
- National Research Council, Institute of Bioecomony (CNR-IBE), Via Caproni 8, 50145 Firenze, Italy; (L.B.); (F.C.); (T.G.); (S.P.); (C.V.); (A.Z.); (B.G.)
| | - Federico Carotenuto
- National Research Council, Institute of Bioecomony (CNR-IBE), Via Caproni 8, 50145 Firenze, Italy; (L.B.); (F.C.); (T.G.); (S.P.); (C.V.); (A.Z.); (B.G.)
| | - Alice Cavaliere
- National Research Council, Institute of Polar Sciences (CNR-ISP), Via P. Gobetti 101, 40129 Bologna, Italy;
| | - Tommaso Giordano
- National Research Council, Institute of Bioecomony (CNR-IBE), Via Caproni 8, 50145 Firenze, Italy; (L.B.); (F.C.); (T.G.); (S.P.); (C.V.); (A.Z.); (B.G.)
| | - Simone Putzolu
- National Research Council, Institute of Bioecomony (CNR-IBE), Via Caproni 8, 50145 Firenze, Italy; (L.B.); (F.C.); (T.G.); (S.P.); (C.V.); (A.Z.); (B.G.)
| | - Carolina Vagnoli
- National Research Council, Institute of Bioecomony (CNR-IBE), Via Caproni 8, 50145 Firenze, Italy; (L.B.); (F.C.); (T.G.); (S.P.); (C.V.); (A.Z.); (B.G.)
| | - Alessandro Zaldei
- National Research Council, Institute of Bioecomony (CNR-IBE), Via Caproni 8, 50145 Firenze, Italy; (L.B.); (F.C.); (T.G.); (S.P.); (C.V.); (A.Z.); (B.G.)
| | - Beniamino Gioli
- National Research Council, Institute of Bioecomony (CNR-IBE), Via Caproni 8, 50145 Firenze, Italy; (L.B.); (F.C.); (T.G.); (S.P.); (C.V.); (A.Z.); (B.G.)
| |
Collapse
|
4
|
Ribalta C, Garrandes F, Bermon S, Adami PE, Ibarrola-Ulzurrun E, Rivas I, Viana M. Dynamic and stationary monitoring of air pollutant exposures and dose during marathons. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:171997. [PMID: 38565357 DOI: 10.1016/j.scitotenv.2024.171997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/07/2024] [Accepted: 03/24/2024] [Indexed: 04/04/2024]
Abstract
Marathon running significantly increases breathing volumes and, consequently, air pollution inhalation doses. This is of special concern for elite athletes who ventilate at very high rates. However, race organizers and sport governing bodies have little guidance to support events scheduling to protect runners. A key limitation is the lack of hyper-local, high temporal resolution air quality data representative of exposure along the racecourse. This work aimed to understand the air pollution exposures and dose inhaled by athletes, by means of a dynamic monitoring methodology designed for road races. Air quality monitors were deployed during three marathons, monitoring nitrogen dioxide (NO2), ozone (O3), particulate matter (PMx), air temperature, and relative humidity. One fixed monitor was installed at the Start/Finish line and one mobile monitor followed the women elite runner pack. The data from the fixed monitors, deployed prior the race, described daily air pollution trends. Mobile monitors in combination with heatmap analysis facilitated the hyper-local characterization of athletes' exposures and helped identify local hotspots (e.g., areas prone to PM resuspension) which should be preferably bypassed. The estimation of inhaled doses disaggregated by gender and ventilation showed that doses inhaled by last finishers may be equal or higher than those inhaled by first finishers for O3 and PMx, due to longer exposures as well as the increase of these pollutants over time (e.g., 58.2 ± 9.6 and 72.1 ± 23.7 μg of PM2.5 for first and last man during Rome marathon). Similarly, men received significantly higher doses than women due to their higher ventilation rate, with differences of 31-114 μg for NO2, 79-232 μg for O3, and 6-41 μg for PMx. Finally, the aggregated data obtained during the 4 week- period prior the marathon can support better race scheduling by the organizers and provide actionable information to mitigate air pollution impacts on athletes' health and performance.
Collapse
Affiliation(s)
- Carla Ribalta
- Federal Institute for Occupational Safety and Health (BAuA), 10317 Berlin, Germany; The National Research Center for Work Environment (NRCWE), 2100, Copenhagen, Denmark.
| | - Fréderic Garrandes
- Health and Science Department, World Athletics, 98000, Monaco; Laboratoire Motricité Humaine Expertise Sport Santé (LAMHESS), Université Côte d'Azur, 06000 Nice, France
| | - Stéphane Bermon
- Health and Science Department, World Athletics, 98000, Monaco; Laboratoire Motricité Humaine Expertise Sport Santé (LAMHESS), Université Côte d'Azur, 06000 Nice, France
| | - Paolo Emilio Adami
- Health and Science Department, World Athletics, 98000, Monaco; Laboratoire Motricité Humaine Expertise Sport Santé (LAMHESS), Université Côte d'Azur, 06000 Nice, France
| | | | - Ioar Rivas
- Barcelona Institute for Global Health (ISGlobal) 08003, Barcelona, Spain
| | - Mar Viana
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain
| |
Collapse
|
5
|
Biagi R, Ferrari M, Venturi S, Sacco M, Montegrossi G, Tassi F. Development and machine learning-based calibration of low-cost multiparametric stations for the measurement of CO 2 and CH 4 in air. Heliyon 2024; 10:e29772. [PMID: 38720758 PMCID: PMC11076643 DOI: 10.1016/j.heliyon.2024.e29772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/20/2024] [Accepted: 04/15/2024] [Indexed: 05/12/2024] Open
Abstract
The pressing issue of atmospheric pollution has prompted the exploration of affordable methods for measuring and monitoring air contaminants as complementary techniques to standard methods, able to produce high-density data in time and space. The main challenge of this low-cost approach regards the in-field accuracy and reliability of the sensors. This study presents the development of low-cost stations for high-time resolution measurements of CO2 and CH4 concentrations calibrated via an in-field machine learning-based method. The calibration models were built based on measurements parallelly performed with the low-cost sensors and a CRDS analyzer for CO2 and CH4 as reference instrument, accounting for air temperature and relative humidity as external variables. To ensure versatility across locations, diversified datasets were collected, consisting of measurements performed in various environments and seasons. The calibration models, trained with 70 % for modeling, 15 % for validation, and 15 % for testing, demonstrated robustness with CO2 and CH4 predictions achieving R2 values from 0.8781 to 0.9827 and 0.7312 to 0.9410, and mean absolute errors ranging from 3.76 to 1.95 ppm and 0.03 to 0.01 ppm, for CO2 and CH4, respectively. These promising results pave the way for extending these stations to monitor additional air contaminants, like PM, NOx, and CO through the same calibration process, integrating them with remote data transmission modules to facilitate real-time access, control, and processing for end-users.
Collapse
Affiliation(s)
- R. Biagi
- Department of Earth Sciences, University of Florence, Via G. La Pira 4, 50121, Firenze, Italy
| | - M. Ferrari
- Department of Earth Sciences, University of Florence, Via G. La Pira 4, 50121, Firenze, Italy
| | - S. Venturi
- Department of Earth Sciences, University of Florence, Via G. La Pira 4, 50121, Firenze, Italy
- Institute of Geosciences and Earth Resources (IGG), National Research Council of Italy (CNR), Via G. La Pira 4, 50121, Firenze, Italy
- Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Palermo, Via Ugo La Malfa 153, Palermo, 90146, Italy
| | - M. Sacco
- Department of Physics and Astronomy, University of Florence, Via Sansone 1, 50019, Sesto Fiorentino, Firenze, Italy
| | - G. Montegrossi
- Institute of Geosciences and Earth Resources (IGG), National Research Council of Italy (CNR), Via G. La Pira 4, 50121, Firenze, Italy
| | - F. Tassi
- Department of Earth Sciences, University of Florence, Via G. La Pira 4, 50121, Firenze, Italy
- Institute of Geosciences and Earth Resources (IGG), National Research Council of Italy (CNR), Via G. La Pira 4, 50121, Firenze, Italy
| |
Collapse
|
6
|
Kairuz-Cabrera D, Hernandez-Rodriguez V, Schalm O, Martinez A, Laso PM, Alejo-Sánchez D. Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment. SENSORS (BASEL, SWITZERLAND) 2024; 24:2729. [PMID: 38732833 PMCID: PMC11086090 DOI: 10.3390/s24092729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/19/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024]
Abstract
In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.
Collapse
Affiliation(s)
- David Kairuz-Cabrera
- Faculty of Electrical Engineering, Central University Marta Abreu of Las Villas (UCLV), Santa Clara 54830, Cuba; (D.K.-C.); (V.H.-R.); (A.M.); (D.A.-S.)
| | - Victor Hernandez-Rodriguez
- Faculty of Electrical Engineering, Central University Marta Abreu of Las Villas (UCLV), Santa Clara 54830, Cuba; (D.K.-C.); (V.H.-R.); (A.M.); (D.A.-S.)
| | | | - Alain Martinez
- Faculty of Electrical Engineering, Central University Marta Abreu of Las Villas (UCLV), Santa Clara 54830, Cuba; (D.K.-C.); (V.H.-R.); (A.M.); (D.A.-S.)
| | - Pedro Merino Laso
- French Maritime Academy (ENSM), 76600 Le Havre, France;
- Arts et Métiers Institute of Technology, École navale, IRENAV EA 3634, BCRM de Brest, CC 600, 29240 Brest cedex 9, France
| | - Daniellys Alejo-Sánchez
- Faculty of Electrical Engineering, Central University Marta Abreu of Las Villas (UCLV), Santa Clara 54830, Cuba; (D.K.-C.); (V.H.-R.); (A.M.); (D.A.-S.)
| |
Collapse
|
7
|
Koziel S, Pietrenko-Dabrowska A, Wojcikowski M, Pankiewicz B. Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO 2 sensor using machine learning. Sci Rep 2024; 14:9152. [PMID: 38644408 PMCID: PMC11033258 DOI: 10.1038/s41598-024-59993-6] [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: 02/06/2024] [Accepted: 04/17/2024] [Indexed: 04/23/2024] Open
Abstract
Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.
Collapse
Affiliation(s)
- Slawomir Koziel
- Engineering Optimization and Modeling Center, Reykjavik University, 102, Reykjavik, Iceland.
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland.
| | - Anna Pietrenko-Dabrowska
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland
| | - Marek Wojcikowski
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland
| | - Bogdan Pankiewicz
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland
| |
Collapse
|
8
|
Corona J, Tondini S, Gallichi Nottiani D, Scilla R, Gambaro A, Pasut W, Babich F, Lollini R. Environmental Quality bOX (EQ-OX): A Portable Device Embedding Low-Cost Sensors Tailored for Comprehensive Indoor Environmental Quality Monitoring. SENSORS (BASEL, SWITZERLAND) 2024; 24:2176. [PMID: 38610386 PMCID: PMC11014031 DOI: 10.3390/s24072176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/10/2024] [Accepted: 03/15/2024] [Indexed: 04/14/2024]
Abstract
The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users' health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of air temperature, relative humidity, and CO2 concentration, lacking consideration of other important parameters due to budget constraints and the complexity of multi-dimensional signal analyses. In this paper, we introduce the Environmental Quality bOX (EQ-OX) system, which was designed for the simultaneous monitoring of quantities of some of the main IEQs with a low level of uncertainty and an affordable cost. Up to 15 parameters can be acquired at a time. The system embeds only low-cost sensors (LCSs) within a compact case, enabling vast-scale monitoring campaigns in residential and office buildings. The results of our laboratory and field tests show that most of the selected LCSs can match the accuracy required for indoor campaigns. A lightweight data processing algorithm has been used for the benchmark. Our intent is to estimate the correlation achievable between the detected quantities and reference measurements when a linear correction is applied. Such an approach allows for a preliminary assessment of which LCSs are the most suitable for a cost-effective IEQ monitoring system.
Collapse
Affiliation(s)
- Jacopo Corona
- Institute for Renewable Energy, Eurac Research, 39100 Bolzano, Italy
| | - Stefano Tondini
- Center for Sensing Solutions, Eurac Research, 39100 Bolzano, Italy (R.S.)
- Photonics Integration, Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Duccio Gallichi Nottiani
- Environmental Sciences, Informatics and Statistics Department, University Ca’ Foscari, 30172 Venezia, Italy (A.G.)
- Dipartimento di Ingegneria e Architettura, Università di Parma, 43124 Parma, Italy
| | - Riccardo Scilla
- Center for Sensing Solutions, Eurac Research, 39100 Bolzano, Italy (R.S.)
| | - Andrea Gambaro
- Environmental Sciences, Informatics and Statistics Department, University Ca’ Foscari, 30172 Venezia, Italy (A.G.)
| | - Wilmer Pasut
- Environmental Sciences, Informatics and Statistics Department, University Ca’ Foscari, 30172 Venezia, Italy (A.G.)
- College of Engineering, University of Korea, Seoul 06591, Republic of Korea
| | - Francesco Babich
- Institute for Renewable Energy, Eurac Research, 39100 Bolzano, Italy
| | - Roberto Lollini
- Institute for Renewable Energy, Eurac Research, 39100 Bolzano, Italy
| |
Collapse
|
9
|
Ilenič A, Pranjić AM, Zupančič N, Milačič R, Ščančar J. Fine particulate matter (PM 2.5) exposure assessment among active daily commuters to induce behaviour change to reduce air pollution. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169117. [PMID: 38065488 DOI: 10.1016/j.scitotenv.2023.169117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/14/2023] [Accepted: 12/03/2023] [Indexed: 01/18/2024]
Abstract
Fine particulate matter (PM2.5), a detrimental urban air pollutant primarily emitted by traffic and biomass burning, poses disproportionately significant health risks at relatively limited exposure during commuting. Previous studies have mainly focused on fixed locations when assessing PM2.5 exposure, while neglecting pedestrians and cyclists, who often experience higher pollution levels. In response, this research aimed to independently validate the effectiveness of bicycle-mounted low-cost sensors (LCS) adopted by citizens, evaluate temporal and spatial PM2.5 exposure, and assess associated health risks in Ljubljana, Slovenia. The LCS quality assurance results, verified by co-location field tests by air quality monitoring stations (AQMS), showed comparable outcomes with an average percentage difference of 21.29 %, attributed to humidity-induced nucleation effects. The colder months exhibited the highest air pollution levels (μ = 32.31 μg/m3) due to frequent thermal inversions and weak wind circulation, hindering vertical air mixing and the adequate dispersion of pollutants. Additionally, PM2.5 levels in all sampling periods were lowest in the afternoon (μ = 12.09 μg/m3) and highest during the night (μ = 61.00 μg/m3) when the planetary boundary layer thins, leading to the trapping of pollutants near the surface, thus significantly affecting diurnal and seasonal patterns. Analysis of exposure factors revealed that cyclists were approximately three times more exposed than pedestrians. However, the toxicological risk assessment indicated a minimal potential risk of PM2.5 exposure. The collaborative integration of data from official AQMS and LCS can enhance evidence-based policy-making processes and facilitates the realignment of effective regulatory frameworks to reduce urban air pollution.
Collapse
Affiliation(s)
- Anja Ilenič
- Slovenian National Building and Civil Engineering Institute (ZAG), Dimičeva ulica 12, 1000 Ljubljana, Slovenia; Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Alenka Mauko Pranjić
- Slovenian National Building and Civil Engineering Institute (ZAG), Dimičeva ulica 12, 1000 Ljubljana, Slovenia.
| | - Nina Zupančič
- University of Ljubljana, Faculty of Natural Sciences and Engineering, Aškerčeva 12, 1000 Ljubljana, Slovenia; ZRC SAZU Ivan Rakovec Institute of Paleontology, Novi trg 2, 1000 Ljubljana, Slovenia
| | - Radmila Milačič
- Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia; Institute Jožef Stefan, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Janez Ščančar
- Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia; Institute Jožef Stefan, Jamova cesta 39, 1000 Ljubljana, Slovenia
| |
Collapse
|
10
|
Bi J, Burnham D, Zuidema C, Schumacher C, Gassett AJ, Szpiro AA, Kaufman JD, Sheppard L. Evaluating low-cost monitoring designs for PM 2.5 exposure assessment with a spatiotemporal modeling approach. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 343:123227. [PMID: 38147948 PMCID: PMC10922961 DOI: 10.1016/j.envpol.2023.123227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 12/15/2023] [Accepted: 12/23/2023] [Indexed: 12/28/2023]
Abstract
Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 μg/m3]) compared to the model with all LCM measurements (0.84 [0.9 μg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 μg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 μg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 μg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.
Collapse
Affiliation(s)
- Jianzhao Bi
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA.
| | - Dustin Burnham
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Christopher Zuidema
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Cooper Schumacher
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Amanda J Gassett
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, Seattle, USA
| | - Joel D Kaufman
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA; Department of Medicine, University of Washington, Seattle, USA; Department of Epidemiology, University of Washington, USA
| | - Lianne Sheppard
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA; Department of Biostatistics, University of Washington, Seattle, USA
| |
Collapse
|
11
|
Mitchell HL, Cox SJ, Lewis HG. Calibration of a Low-Cost Methane Sensor Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:1066. [PMID: 38400226 PMCID: PMC10892608 DOI: 10.3390/s24041066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/23/2024] [Accepted: 02/01/2024] [Indexed: 02/25/2024]
Abstract
In order to combat greenhouse gas emissions, the sources of these emissions must be understood. Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection module based around the TGS2611-E00 sensor. The manufacturer provides sensitivity characteristics for methane concentrations above 300 ppm, but lower concentrations are typical in outdoor settings. This study investigates the potential to calibrate these sensors for lower methane concentrations using machine learning. Models of varying complexity, accounting for temperature and humidity variations, were trained on over 50,000 calibration datapoints, spanning 0-200 ppm methane, 5-30 °C and 40-80% relative humidity. Interaction terms were shown to improve model performance. The final selected model achieved a root-mean-square error of 5.1 ppm and an R2 of 0.997, demonstrating the potential for the NGM2611-E13 sensor to measure methane concentrations below 200 ppm.
Collapse
Affiliation(s)
- Hazel Louise Mitchell
- Computational Engineering and Design Group, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | | | | |
Collapse
|
12
|
Jain S, Gardner‐Frolick R, Martinussen N, Jackson D, Giang A, Zimmerman N. Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results From a Community-Partnered Low-Cost Sensor Deployment. GEOHEALTH 2024; 8:e2023GH000935. [PMID: 38361590 PMCID: PMC10867477 DOI: 10.1029/2023gh000935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/14/2023] [Accepted: 12/19/2023] [Indexed: 02/17/2024]
Abstract
The Strathcona neighborhood in Vancouver is particularly vulnerable to environmental injustice due to its close proximity to the Port of Vancouver, and a high proportion of Indigenous and low-income households. Furthermore, local sources of air pollutants (e.g., roadways) can contribute to small-scale variations within communities. The aim of this study was to assess hyperlocal air quality patterns (intra-neighborhood variability) and compare them to average Vancouver concentrations (inter-neighborhood variability) to identify possible disparities in air pollution exposure for the Strathcona community. Between April and August 2022, 11 low-cost sensors (LCS) were deployed within the neighborhood to measure PM2.5, NO2, and O3 concentrations. The collected 15-min concentrations were down-averaged to daily concentrations and compared to greater Vancouver region concentrations to quantify the exposures faced by the community relative to the rest of the region. Concentrations were also estimated at every 25 m grid within the neighborhood to quantify the distribution of air pollution within the community. Using population information from census data, cumulative hazard indices (CHIs) were computed for every dissemination block. We found that although PM2.5 concentrations in the neighborhood were lower than regional Vancouver averages, daily NO2 concentrations and summer O3 concentrations were consistently higher. Additionally, although CHIs varied daily, we found that CHIs were consistently higher in areas with high commercial activity. As such, estimating CHI for dissemination blocks was useful in identifying hotspots and potential areas of concern within the neighborhood. This information can collectively assist the community in their advocacy efforts.
Collapse
Affiliation(s)
- Sakshi Jain
- Department of Mechanical EngineeringUniversity of British ColumbiaVancouverBCCanada
| | | | - Nika Martinussen
- Institute for Resources Environment and SustainabilityUniversity of British ColumbiaVancouverBCCanada
| | - Dan Jackson
- Strathcona Residents AssociationVancouverBCCanada
| | - Amanda Giang
- Department of Mechanical EngineeringUniversity of British ColumbiaVancouverBCCanada
- Institute for Resources Environment and SustainabilityUniversity of British ColumbiaVancouverBCCanada
| | - Naomi Zimmerman
- Department of Mechanical EngineeringUniversity of British ColumbiaVancouverBCCanada
| |
Collapse
|
13
|
Porwisiak P, Werner M, Kryza M, ApSimon H, Woodward H, Mehlig D, Gawuc L, Szymankiewicz K, Sawiński T. Application of ADMS-Urban for an area with a high contribution of residential heating emissions - model verification and sensitivity study for PM 2.5. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:168011. [PMID: 37871816 DOI: 10.1016/j.scitotenv.2023.168011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/06/2023] [Accepted: 10/20/2023] [Indexed: 10/25/2023]
Abstract
Air pollution poses a significant risk to both human health and the environment in the contemporary world. Among the various pollutants, particulate matter with a diameter <2.5 μm (PM2.5) is regarded as the most hazardous. It has been implicated in over four million global fatalities in 2019 alone. This research paper divulges the outcomes of modelling the spatial-temporal fluctuations of PM2.5 concentrations within the confines of Wroclaw, a city situated in Poland, Central Europe. The model's output was evaluated through comparison with collected data from two government-operated monitoring stations within the city. For this study, we used the ADMS-Urban model and tested two different sources of background data (low-cost sensors and the EMEP MSC-W atmospheric chemistry transport model). The statistical analysis conducted in the paper indicates that the model reproduces the temporal variability of PM2.5. The conclusions from this research indicate that the average annual PM2.5 concentration within Wroclaw is 13.8 μg/m3, with the concentration peaking in the month of March. The spatial distribution reveals the highest PM2.5 concentrations primarily in the southern and western zones of the city, with additional elevated concentrations observed sporadically throughout the city. The study unveils that 1.3 % of Wroclaw's area experiences PM2.5 concentrations exceeding the EU's annual limit of 20 μg/m3. When considered in relation to the WHO's suggested annual average level of 5 μg/m3, Wroclaw city experiences exceedances throughout. When background concentrations are excluded from the model, the annual average PM2.5 concentration across the city is noted to be reduced by >50 %. A thorough investigation of the city's emission structure, taking into account only emissions from the city without background, indicates that the residential sector contributes about 77.3 % of the total annual average PM2.5 concentration in Wroclaw. The transportation and industrial sectors account for nearly 19.5 % and 3.2 %, respectively.
Collapse
Affiliation(s)
- Paweł Porwisiak
- Faculty of Earth Sciences and Environmental Management, University of Wrocław, Kosiby 8, 51-621 Wroclaw, Poland.
| | - Małgorzata Werner
- Faculty of Earth Sciences and Environmental Management, University of Wrocław, Kosiby 8, 51-621 Wroclaw, Poland
| | - Maciej Kryza
- Faculty of Earth Sciences and Environmental Management, University of Wrocław, Kosiby 8, 51-621 Wroclaw, Poland
| | - Helen ApSimon
- Centre for Environmental Policy, Imperial College London, London SW7 1NE, UK
| | - Huw Woodward
- Centre for Environmental Policy, Imperial College London, London SW7 1NE, UK
| | - Daniel Mehlig
- Centre for Environmental Policy, Imperial College London, London SW7 1NE, UK
| | - Lech Gawuc
- Institute of Environmental Protection-National Research Institute, Krucza 5/11D, 00-548 Warsaw, Poland
| | - Karol Szymankiewicz
- Institute of Environmental Protection-National Research Institute, Krucza 5/11D, 00-548 Warsaw, Poland
| | - Tymoteusz Sawiński
- Faculty of Earth Sciences and Environmental Management, University of Wrocław, Kosiby 8, 51-621 Wroclaw, Poland
| |
Collapse
|
14
|
Feldman A, Kendler S, Marshall J, Kushwaha M, Sreekanth V, Upadhya AR, Agrawal P, Fishbain B. Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:480-487. [PMID: 38104325 PMCID: PMC10785748 DOI: 10.1021/acs.est.3c04495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 11/05/2023] [Accepted: 11/06/2023] [Indexed: 12/19/2023]
Abstract
Mobile monitoring provides robust measurements of air pollution. However, resource constraints often limit the number of measurements so that assessments cannot be obtained in all locations of interest. In response, surrogate measurement methodologies, such as videos and images, have been suggested. Previous studies of air pollution and images have used static images (e.g., satellite images or Google Street View images). The current study was designed to develop deep learning methodologies to infer on-road pollutant concentrations from videos acquired with dashboard cameras. Fifty hours of on-road measurements of four pollutants (black carbon, particle number concentration, PM2.5 mass concentration, carbon dioxide) in Bengaluru, India, were analyzed. The analysis of each video frame involved identifying objects and determining motion (by segmentation and optical flow). Based on these visual cues, a regression convolutional neural network (CNN) was used to deduce pollution concentrations. The findings showed that the CNN approach outperformed several other machine learning (ML) techniques and more conventional analyses (e.g., linear regression). The CO2 prediction model achieved a normalized root-mean-square error of 10-13.7% for the different train-validation division methods. The results here thus contribute to the literature by using video and the relative motion of on-screen objects rather than static images and by implementing a rapid-analysis approach enabling analysis of the video in real time. These methods can be applied to other mobile-monitoring campaigns since the only additional equipment they require is an inexpensive dashboard camera.
Collapse
Affiliation(s)
- Alon Feldman
- Department
of Mathematics, Technion−Israel Institute
of Technology, Haifa 3200003, Israel
| | - Shai Kendler
- Department
of Environmental, Water and Agricultural Engineering, Faculty of Civil
& Environmental Engineering, Technion−Israel
Institute of Technology, Haifa 3200003, Israel
- Environmental
Physics Department, Israel Institute for
Biological Research, Ness Ziona 7410001, Israel
| | - Julian Marshall
- Department
of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | | | - V. Sreekanth
- Center for
Study of Science, Technology & Policy, Bengaluru 560094, India
| | - Adithi R. Upadhya
- ILK
Laboratories, Bengaluru 560046, India
- Department
of Public Health, Policy & Systems, University of Liverpool, Liverpool L69 3GF, England
| | - Pratyush Agrawal
- Center for
Study of Science, Technology & Policy, Bengaluru 560094, India
| | - Barak Fishbain
- Department
of Environmental, Water and Agricultural Engineering, Faculty of Civil
& Environmental Engineering, Technion−Israel
Institute of Technology, Haifa 3200003, Israel
| |
Collapse
|
15
|
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.
Collapse
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.
| |
Collapse
|
16
|
Govea J, Gaibor-Naranjo W, Sanchez-Viteri S, Villegas-Ch W. Integration of Data and Predictive Models for the Evaluation of Air Quality and Noise in Urban Environments. SENSORS (BASEL, SWITZERLAND) 2024; 24:311. [PMID: 38257404 PMCID: PMC10820565 DOI: 10.3390/s24020311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024]
Abstract
This work addresses assessing air quality and noise in urban environments by integrating predictive models and Internet of Things technologies. For this, a model generated heat maps for PM2.5 and noise levels, incorporating traffic data from open sources for precise contextualization. This approach reveals significant correlations between high pollutant/noise concentrations and their proximity to industrial zones and traffic routes. The predictive models, including convolutional neural networks and decision trees, demonstrated high accuracy in predicting pollution and noise levels, with correlation values such as R2 of 0.93 for PM2.5 and 0.90 for noise. These findings highlight the need to address environmental issues in urban planning comprehensively. Furthermore, the study suggests policies based on the quantitative results, such as implementing low-emission zones and promoting green spaces, to improve urban environmental management. This analysis offers a significant contribution to scientific understanding and practical applicability in the planning and management of urban environments, emphasizing the relevance of an integrated and data-driven approach to inform effective policy decisions in urban environmental management.
Collapse
Affiliation(s)
- Jaime Govea
- Escuela de Ingeniería en Ciberseguridad, Faculatad de Ingenierías y Ciencias Aplicadas, Universidad de Las Américas, Quito 170125, Ecuador;
| | - Walter Gaibor-Naranjo
- Carrera de Ciencias de la Computación, Universidad Politécnica Salesiana, Quito 170105, Ecuador;
| | | | - William Villegas-Ch
- Escuela de Ingeniería en Ciberseguridad, Faculatad de Ingenierías y Ciencias Aplicadas, Universidad de Las Américas, Quito 170125, Ecuador;
| |
Collapse
|
17
|
Chen S, Cerruti M, Ghandi M, Tsao LL, Sermeno R. Determine the impact of Emotive Intelligent Spaces on children's behavioural and cognitive outcomes. COGENT EDUCATION 2023; 10:2281850. [PMID: 38282646 PMCID: PMC10822668 DOI: 10.1080/2331186x.2023.2281850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 11/07/2023] [Indexed: 01/30/2024]
Abstract
This study aims to investigate the impact of a novel environmental intervention-Emotive Intelligent Spaces (EIS) on young children's self-regulation and working memory using a single-subject reversal design (ABAB). EIS is a semi-private space with coloured lights that could adapt to each child's preferred colour based on the child's self-reported emotional state. A total of 29 three-to-seven-year-old participants completed the experiment from fall 2020 to summer 2021. Self-regulation was measured by the Head-Toes-Knees-Shoulders task; working memory was measured by the Woodcock-Johnson Numbers Reversed subset. Children's age was controlled as a covariate. Descriptive statistics indicated that the group means of self-regulation scores were higher in the intervention conditions. However, the group means of working memory scores were lower in the intervention conditions. We conducted repeated measure ANCOVA for the main analysis, and results showed no statistically significant differences in children's self-regulation and working memory scores between baseline and intervention conditions. It is recommended that future studies should take the illuminance level into consideration of the intervention effect. Further, our study implies that avoiding visual overstimulation in the classroom (e.g. heavily decorated walls) may create an optimal level of visual arousal and promote focused attention.
Collapse
Affiliation(s)
- Shiyi Chen
- Margaret Ritchie School of Family and Consumer Sciences, College of Agriculture and Life Sciences, University of Idaho, Moscow, ID, USA
| | - Minyoung Cerruti
- School of Design and Construction, Voiland College of Engineering and Architecture, Washington State University, Pullman, Washington, USA
| | - Mona Ghandi
- School of Design and Construction, Voiland College of Engineering and Architecture, Washington State University, Pullman, Washington, USA
| | - Ling-Ling Tsao
- Margaret Ritchie School of Family and Consumer Sciences, College of Agriculture and Life Sciences, University of Idaho, Moscow, ID, USA
| | - Rebecca Sermeno
- Margaret Ritchie School of Family and Consumer Sciences, College of Agriculture and Life Sciences, University of Idaho, Moscow, ID, USA
| |
Collapse
|
18
|
Dai X, Shang W, Liu J, Xue M, Wang C. Achieving better indoor air quality with IoT systems for future buildings: Opportunities and challenges. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 895:164858. [PMID: 37343873 DOI: 10.1016/j.scitotenv.2023.164858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/26/2023] [Accepted: 06/11/2023] [Indexed: 06/23/2023]
Abstract
With the development of IoT technology and low-cost indoor air quality (IAQ) sensors, the IoT-based IAQ monitoring platform has garnered significant research interest and demonstrated its potential in enhancing IAQ management. This study presents a comprehensive review of previous research on the development and application of IoT-based IAQ platforms in different built environments. It offers detailed insights into the design and implementation of recent IoT-based IAQ platforms. The findings indicate that the IoT-based IAQ platforms are able to provide reliable information for IAQ monitoring. To ensure quality control of the IoT-based IAQ platform, it is suggested to replace the sensors every 4-6 months for reliable monitoring. In another aspect, integrating data-driven technology into the platform is crucial for IAQ prediction and efficient control of ventilation systems, leveraging the wealth of data available from the IoT platform. According to recent studies that applied data-driven algorithms for IAQ management, it can be confirmed that the data-driven algorithms are able to prompt IAQ by providing either more information or a control strategy. However, it should be noted that only 9.1 % of the developed platforms integrated data-driven models for IAQ management. Based on our findings, current challenges and further opportunities are discussed. Future studies should focus on integrating data-driven algorithms into IoT-based IAQ platforms and developing digital twins that can be used for real building IAQ management. However, there is obvious tension between controlling ventilation for energy efficiency versus better air quality. It is important to make a balance between energy efficiency and better air quality according to the current situations of specific built environments. Also, the next generation of IoT-based IAQ platforms should include occupants in the loop to create a more occupant-centric IAQ management approach.
Collapse
Affiliation(s)
- Xilei Dai
- Department of the Built Environment, College of Design and Engineering, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
| | - Wenzhe Shang
- Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Junjie Liu
- Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China.
| | - Min Xue
- Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Congcong Wang
- School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
| |
Collapse
|
19
|
Pei Z, Balitskiy M, Thalman R, Kelly KE. Laboratory Performance Evaluation of a Low-Cost Electrochemical Formaldehyde Sensor. SENSORS (BASEL, SWITZERLAND) 2023; 23:7444. [PMID: 37687899 PMCID: PMC10490822 DOI: 10.3390/s23177444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 08/23/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023]
Abstract
Formaldehyde is a known human carcinogen and an important indoor and outdoor air pollutant. However, current strategies for formaldehyde measurement, such as chromatographic and optical techniques, are expensive and labor intensive. Low-cost gas sensors have been emerging to provide effective measurement of air pollutants. In this study, we evaluated eight low-cost electrochemical formaldehyde sensors (SFA30, Sensirion®, Staefa, Switzerland) in the laboratory with a broadband cavity-enhanced absorption spectroscopy as the reference instrument. As a group, the sensors exhibited good linearity of response (R2 > 0.95), low limit of detection (11.3 ± 2.07 ppb), good accuracy (3.96 ± 0.33 ppb and 6.2 ± 0.3% N), acceptable repeatability (3.46% averaged coefficient of variation), reasonably fast response (131-439 s) and moderate inter-sensor variability (0.551 intraclass correlation coefficient) over the formaldehyde concentration range of 0-76 ppb. We also systematically investigated the effects of temperature and relative humidity on sensor response, and the results showed that formaldehyde concentration was the most important contributor to sensor response, followed by temperature, and relative humidity. The results suggest the feasibility of using this low-cost electrochemical sensor to measure formaldehyde concentrations at relevant concentration ranges in indoor and outdoor environments.
Collapse
Affiliation(s)
- Zheyuan Pei
- Department of Chemical Engineering, University of Utah, Salt Lake City, UT 84112, USA; (Z.P.); (M.B.)
| | - Maxim Balitskiy
- Department of Chemical Engineering, University of Utah, Salt Lake City, UT 84112, USA; (Z.P.); (M.B.)
| | - Ryan Thalman
- Department of Chemistry, Snow College, Ephraim, UT 84627, USA;
| | - Kerry E. Kelly
- Department of Chemical Engineering, University of Utah, Salt Lake City, UT 84112, USA; (Z.P.); (M.B.)
| |
Collapse
|
20
|
Won WS, Noh J, Oh R, Lee W, Lee JW, Su PC, Yoon YJ. Enhancing the reliability of particulate matter sensing by multivariate Tobit model using weather and air quality data. Sci Rep 2023; 13:13150. [PMID: 37573439 PMCID: PMC10423292 DOI: 10.1038/s41598-023-40468-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/10/2023] [Indexed: 08/14/2023] Open
Abstract
Low-cost particulate matter (PM) sensors have been widely used following recent sensor-technology advancements; however, inherent limitations of low-cost monitors (LCMs), which operate based on light scattering without an air-conditioning function, still restrict their applicability. We propose a regional calibration of LCMs using a multivariate Tobit model with historical weather and air quality data to improve the accuracy of ambient air monitoring, which is highly dependent on meteorological conditions, local climate, and regional PM properties. Weather observations and PM2.5 (fine inhalable particles with diameters ≤ 2.5 μm) concentrations from two regions in Korea, Incheon and Jeju, and one in Singapore were used as training data to build a visibility-based calibration model. To validate the model, field measurements were conducted by an LCM in Jeju and Singapore, where R2 and the error after applying the model in Jeju improved (from 0.85 to 0.88) and reduced by 44% (from 8.4 to 4.7 μg m-3), respectively. The results demonstrated that regional calibration involving air temperature, relative humidity, and other local climate parameters can efficiently correct the bias of the sensor. Our findings suggest that the proposed post-processing using the Tobit model with regional weather and air quality data enhances the applicability of LCMs.
Collapse
Affiliation(s)
- Wan-Sik Won
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
- Department of Aerospace Industrial and Systems Engineering, Hanseo University, Taean, Chungcheongnam-do, 32158, Republic of Korea
| | - Jinhong Noh
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Rosy Oh
- Department of Mathematics, Korea Military Academy, Seoul, 01805, Republic of Korea
| | - Woojoo Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jong-Won Lee
- Observer Foundation, Seoul, 04050, Republic of Korea
| | - Pei-Chen Su
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
| | - Yong-Jin Yoon
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
| |
Collapse
|
21
|
Alfonso Albarracín KY, Altamar Consuegra A, Aguilar-Arias J. Particulate matter 10 µm (PM 10), 2.5 µm (PM 2.5) datasets gathered by direct measurement, low-cost sensor and by public air quality stations in Fontibón, Bogotá D.C., Colombia. Data Brief 2023; 49:109323. [PMID: 37456118 PMCID: PMC10344790 DOI: 10.1016/j.dib.2023.109323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Concentration of particulate matter directly affects air quality and human health. Three sources of information were used in this work to generate datasets on this matter at the Fontibón county in Bogota D.C., Colombia. The first source was a Davis AirLinkⓇ low-cost sensor air quality readings for PM2.5, PM10 and meteorological variables. The sensor was installed in the referred area, collecting air quality readings for PM2.5, PM10, as well as temperature, relative humidity, dew point, wet bulb, and heat index as meteorological variables during the months of May to August 2022. The second source was collecting by direct measurement the PM10 particles using a TischⓇ Hi- Vol equipment, evaluated the concentration of particulate matter PM10 in the same place for 27 days. Finally, raw data was provided by the Bogotá's Environmental District Bureau (SDA), validating in this work the data readings for the years 2021 and 2022 from the two meteorological stations located in the same county, named "Fontibón" and "Móvil Fontibón", including Air quality data for PM2.5, PM10, Carbon Monoxide (CO), Ozone, Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2) and the meteorological variables wind speed, wind direction, temperature, precipitation, relative humidity (RH) and Barometric pressure. A Machine Learning model was made to perform the mining and completeness of the missing data with an iterative imputation and with a regression model, and the Pearson, Spearman and Kendall correlation coefficients were calculated, using Python language.
Collapse
Affiliation(s)
| | | | - Jaime Aguilar-Arias
- Chemical and Environmental Engineering Department, Universidad Nacional de Colombia. Sede Bogotá D.C. 111321, Colombia
| |
Collapse
|
22
|
Baker M, Gollier F, Melzer JE, McLeod E. Lensfree Air-Quality Monitoring of Fine and Ultrafine Particulate Matter Using Vapor-Condensed Nanolenses. ACS APPLIED NANO MATERIALS 2023; 6:11166-11174. [PMID: 37744874 PMCID: PMC10516119 DOI: 10.1021/acsanm.3c01154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/31/2023] [Indexed: 09/26/2023]
Abstract
Current commercial air-quality monitoring devices lack a large dynamic range, especially at the small, ultrafine size scale. Furthermore, there is a low density of air-quality monitoring stations, reducing the precision with which local particulate matter hazards can be tracked. Here, we show a low-cost, lensfree, and portable air-quality monitoring device (LPAQD) that can detect and measure micron-sized particles down to 100 nm-sized particles, with the capability to track and measure particles in real time throughout a day and the ability to accurately measure particulate matter densities as low as 3 μg m-3. A vapor-condensed film is deposited onto the coverslip used to collect particles before the LPAQD is deployed at outdoor monitoring sites. The vapor-condensed film increases the scattering cross section of particles smaller than the pixel size, enabling the sub-pixel and sub-diffraction-limit-sized particles to be detected. The high dynamic range, low cost, and portability of this device can enable citizens to monitor their own air quality to hopefully impact user decisions that reduce the risk for particulate matter-related diseases.
Collapse
Affiliation(s)
- Maryam Baker
- University of Arizona, Wyant College of Optical Sciences, Tucson, Arizona 85721, United States
| | - Florian Gollier
- University of Arizona, Wyant College of Optical Sciences, Tucson, Arizona 85721, United States
| | - Jeffrey E. Melzer
- University of Arizona, Wyant College of Optical Sciences, Tucson, Arizona 85721, United States
| | - Euan McLeod
- University of Arizona, Wyant College of Optical Sciences, Tucson, Arizona 85721, United States
| |
Collapse
|
23
|
Araújo T, Silva L, Aguiar A, Moreira A. Calibration Assessment of Low-Cost Carbon Dioxide Sensors Using the Extremely Randomized Trees Algorithm. SENSORS (BASEL, SWITZERLAND) 2023; 23:6153. [PMID: 37448003 DOI: 10.3390/s23136153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023]
Abstract
As the monitoring of carbon dioxide is an important proxy to estimate the air quality of indoor and outdoor environments, it is essential to obtain trustful data from CO2 sensors. However, the use of widely available low-cost sensors may imply lower data quality, especially regarding accuracy. This paper proposes a new approach for enhancing the accuracy of low-cost CO2 sensors using an extremely randomized trees algorithm. It also reports the results obtained from experimental data collected from sensors that were exposed to both indoor and outdoor environments. The indoor experimental set was composed of two metal oxide semiconductors (MOS) and two non-dispersive infrared (NDIR) sensors next to a reference sensor for carbon dioxide and independent sensors for air temperature and relative humidity. The outdoor experimental exposure analysis was performed using a third-party dataset which fit into our goals: the work consisted of fourteen stations using low-cost NDIR sensors geographically spread around reference stations. One calibration model was trained for each sensor unit separately, and, in the indoor experiment, it managed to reduce the mean absolute error (MAE) of NDIR sensors by up to 90%, reach very good linearity with MOS sensors in the indoor experiment (r2 value of 0.994), and reduce the MAE by up to 98% in the outdoor dataset. We have found in the outdoor dataset analysis that the exposure time of the sensor itself may be considered by the algorithm to achieve better accuracy. We also observed that even a relatively small amount of data may provide enough information to perform a useful calibration if they contain enough data variety. We conclude that the proper use of machine learning algorithms on sensor readings can be very effective to obtain higher data quality from low-cost gas sensors either indoors or outdoors, regardless of the sensor technology.
Collapse
Affiliation(s)
- Tiago Araújo
- Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN), Parnamirim 59124-455, Brazil
- Algoritmi Research Centre, University of Minho, 4800-058 Guimarães, Portugal
| | - Lígia Silva
- CTAC Research Centre, University of Minho, 4800-058 Guimarães, Portugal
| | - Ana Aguiar
- Telecommunications Institute, Engineering Faculty, University of Porto, 4200-465 Porto, Portugal
| | - Adriano Moreira
- Algoritmi Research Centre, University of Minho, 4800-058 Guimarães, Portugal
| |
Collapse
|
24
|
Volckens J, Haynes EN, Croisant SP, Cui Y, Errett NA, Henry HF, Horney JA, Kwok RK, Magzamen S, Rappold AG, Ravichandran L, Reinlib L, Ryan PH, Shaughnessy DT. Health Research in the Wake of Disasters: Challenges and Opportunities for Sensor Science. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:65002. [PMID: 37389972 PMCID: PMC10312369 DOI: 10.1289/ehp12270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/24/2023] [Accepted: 06/15/2023] [Indexed: 07/02/2023]
Abstract
BACKGROUND Disaster events adversely affect the health of millions of individuals each year. They create exposure to physical, chemical, biological, and psychosocial hazards while simultaneously exploiting community and individual-level vulnerabilities that allow such exposures to exert harm. Since 2013, the National Institute of Environmental Health Sciences (NIEHS) has led the development of the Disaster Research Response (DR2) program and infrastructure; however, research exploring the nature and effects of disasters on human health is lacking. One reason for this research gap is the challenge of developing and deploying cost-effective sensors for exposure assessment during disaster events. OBJECTIVES The objective of this commentary is to synergize the consensus findings and recommendations from a panel of experts on sensor science in support of DR2. METHODS The NIEHS convened the workshop, "Getting Smart about Sensors for Disaster Response Research" on 28 and 29 July 2021 to discuss current gaps and recommendations for moving the field forward. The workshop invited full discussion from multiple viewpoints, with the goal of identifying recommendations and opportunities for further development of this area of research. The panel of experts included leaders in engineering, epidemiology, social and physical sciences, and community engagement, many of whom had firsthand experience with DR2. DISCUSSION The primary finding of this workshop is that exposure science in support of DR2 is severely lacking. We highlight unique barriers to DR2, such as the need for time-sensitive exposure data, the chaos and logistical challenges that ensue from a disaster event, and the lack of a robust market for sensor technologies in support of environmental health science. We highlight a need for sensor technologies that are more scalable, reliable, and versatile than those currently available to the research community. We also recommend that the environmental health community renew efforts in support of DR2 facilitation, collaboration, and preparedness. https://doi.org/10.1289/EHP12270.
Collapse
Affiliation(s)
- John Volckens
- Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado, USA
| | - Erin N. Haynes
- Department of Epidemiology and Environmental Health, University of Kentucky, Lexington, Kentucky, USA
| | - Sharon P. Croisant
- Department of Preventive Medicine & Community Health, University of Texas Medical Branch, Galveston, Texas, USA
| | - Yuxia Cui
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | - Nicole A. Errett
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Heather F. Henry
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | | | - Richard K. Kwok
- National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Sheryl Magzamen
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Ana G. Rappold
- Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Chapel Hill, North Carolina, USA
| | - Lingamanaidu Ravichandran
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | - Les Reinlib
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | - Patrick H. Ryan
- Department of Environmental Health, University of Cincinnati Medical Center, Cincinnati, Ohio, USA
| | - Daniel T. Shaughnessy
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| |
Collapse
|
25
|
Bush T, Bartington S, Pope FD, Singh A, Thomas GN, Stacey B, Economides G, Anderson R, Cole S, Abreu P, Leach FCP. The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK. BUILDING AND ENVIRONMENT 2023; 237:110330. [PMID: 37124118 PMCID: PMC10121078 DOI: 10.1016/j.buildenv.2023.110330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 05/03/2023]
Abstract
Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM10, PM2.5) from January 2020 to September 2021. Measurements of PM10 and PM2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM10 and PM2.5 were reduced by 9-10 μg/m3 below typical peak levels experienced in recent years, mean daily PM10 and PM2.5 concentrations were only ∼1 μg/m3 lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM10 and PM2.5 levels to within WHO health-based guidelines and to achieve compliance with PM2.5 targets developed under the Environment Act 2021.
Collapse
Affiliation(s)
- Tony Bush
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
- Apertum Consulting, Harwell, Oxfordshire, UK
| | - Suzanne Bartington
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Francis D Pope
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Ajit Singh
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - G Neil Thomas
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Brian Stacey
- Ricardo Energy and Environment, The Gemini Building, Fermi Avenue, Harwell, Didcot, OX11 0QR, UK
| | - George Economides
- Oxfordshire County Council, County Hall, New Road, Oxford, OX1 1ND, UK
| | - Ruth Anderson
- Oxfordshire County Council, County Hall, New Road, Oxford, OX1 1ND, UK
| | - Stuart Cole
- Oxfordshire County Council, County Hall, New Road, Oxford, OX1 1ND, UK
| | - Pedro Abreu
- Oxford City Council, Town Hall, St Aldate's, Oxford, OX1 1BX, UK
| | - Felix C P Leach
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| |
Collapse
|
26
|
Puga A, Yalin A. Ozone Detection via Deep-Ultraviolet Cavity-Enhanced Absorption Spectroscopy with a Laser Driven Light Source. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23114989. [PMID: 37299716 DOI: 10.3390/s23114989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/18/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
We present a novel sensing approach for ambient ozone detection based on deep-ultraviolet (DUV) cavity-enhanced absorption spectroscopy (CEAS) using a laser driven light source (LDLS). The LDLS has broadband spectral output which, with filtering, provides illumination between ~230-280 nm. The lamp light is coupled to an optical cavity formed from a pair of high-reflectivity (R~0.99) mirrors to yield an effective path length of ~58 m. The CEAS signal is detected with a UV spectrometer at the cavity output and spectra are fitted to yield the ozone concentration. We find a good sensor accuracy of <~2% error and sensor precision of ~0.3 ppb (for measurement times of ~5 s). The small-volume (<~0.1 L) optical cavity is amenable to a fast response with a sensor (10-90%) response time of ~0.5 s. Demonstrative sampling of outdoor air is also shown with favorable agreement against a reference analyzer. The DUV-CEAS sensor compares favorably against other ozone detection instruments and may be particularly useful for ground-level sampling including that from mobile platforms. The sensor development work presented here can also inform of the possibilities of DUV-CEAS with LDLSs for the detection of other ambient species including volatile organic compounds.
Collapse
Affiliation(s)
- Anthony Puga
- Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA
| | - Azer Yalin
- Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA
| |
Collapse
|
27
|
Li J, Crooks J, Murdock J, de Souza P, Hohsfield K, Obermann B, Stockman T. A nested machine learning approach to short-term PM 2.5 prediction in metropolitan areas using PM 2.5 data from different sensor networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 873:162336. [PMID: 36813194 DOI: 10.1016/j.scitotenv.2023.162336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/26/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Many predictive models for ambient PM2.5 concentrations rely on ground observations from a single monitoring network consisting of sparsely distributed sensors. Integrating data from multiple sensor networks for short-term PM2.5 prediction remains largely unexplored. This paper presents a machine learning approach to predict ambient PM2.5 concentration levels at any unmonitored location several hours ahead using PM2.5 observations from nearby monitoring sites from two sensor networks and the location's social and environmental properties. Specifically, this approach first applies a Graph Neural Network and Long Short-Term Memory (GNN-LSTM) network to time series of daily observations from a regulatory monitoring network to make predictions of PM2.5. This network produces feature vectors to store aggregated daily observations as well as dependency characteristics to predict daily PM2.5. The daily feature vectors are then set as the precondition of the hourly level learning process. The hourly level learning again uses a GNN-LSTM network based on daily dependency information and hourly observations from a low-cost sensor network to produce spatiotemporal feature vectors capturing the combined dependency described by daily and hourly observations. Finally, the spatiotemporal feature vectors from the hourly learning process and social-environmental data are merged and used as the input to a single-layer Fully Connected (FC) network to output the predicted hourly PM2.5 concentrations. To demonstrate the benefits of this novel prediction approach, we have conducted a case study using data collected from two sensor networks in Denver, CO, during 2021. Results show that the utilization of data from two sensor networks improves the overall performance of predicting fine-level, short-term PM2.5 concentrations compared to other baseline models.
Collapse
Affiliation(s)
- Jing Li
- Department of Geography and the Environment, University of Denver, United States of America.
| | - James Crooks
- Division of Biostatistics and Bioinformatics, National Jewish Health, United States of America; Department of Epidemiology, Colorado School of Public Health, United States of America
| | - Jennifer Murdock
- Department of Geography and the Environment, University of Denver, United States of America
| | - Priyanka de Souza
- Department of Urban and Regional Planning, University of Colorado - Denver, United States of America; CU Population Center, University of Colorado - Boulder, United States of America
| | - Kirk Hohsfield
- University of Colorado, School of Medicine, United States of America
| | - Bill Obermann
- Department of Public Health and Environment, City and County of Denver, United States of America
| | - Tehya Stockman
- Department of Public Health and Environment, City and County of Denver, United States of America; Civil, Environmental and Architectural Engineering Department, University of Colorado - Boulder, United States of America
| |
Collapse
|
28
|
McCarron A, Semple S, Braban CF, Swanson V, Gillespie C, Price HD. Public engagement with air quality data: using health behaviour change theory to support exposure-minimising behaviours. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:321-331. [PMID: 35764891 PMCID: PMC10234807 DOI: 10.1038/s41370-022-00449-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 05/26/2022] [Accepted: 05/27/2022] [Indexed: 06/03/2023]
Abstract
Exposure to air pollution prematurely kills 7 million people globally every year. Policy measures designed to reduce emissions of pollutants, improve ambient air and consequently reduce health impacts, can be effective, but are generally slow to generate change. Individual actions can therefore supplement policy measures and more immediately reduce people's exposure to air pollution. Air quality indices (AQI) are used globally (though not universally) to translate complex air quality data into a single unitless metric, which can be paired with advice to encourage behaviour change. Here we explore, with reference to health behaviour theories, why these are frequently insufficient to instigate individual change. We examine the health behaviour theoretical steps linking air quality data with reduced air pollution exposure and (consequently) improved public health, arguing that a combination of more 'personalised' air quality data and greater public engagement with these data will together better support individual action. Based on this, we present a novel framework, which, when used to shape air quality interventions, has the potential to yield more effective and sustainable interventions to reduce individual exposures and thus reduce the global public health burden of air pollution.
Collapse
Affiliation(s)
- Amy McCarron
- Biological and Environmental Sciences, University of Stirling, Stirling, UK.
| | - Sean Semple
- Institute of Social Marketing and Health, University of Stirling, Stirling, UK
| | | | | | | | - Heather D Price
- Biological and Environmental Sciences, University of Stirling, Stirling, UK
| |
Collapse
|
29
|
Mohamad Jamil PAS, Mohammad Yusof NAD, Karuppiah K, Rasdi I, How V, Mohd Tamrin SB, Samsudin MH, Sambasivam S, Almutairi NS. Concept Development and Field Testing of Wireless Outdoor Indicator System for Use in Monitoring Exposures at Work among Malaysian Traffic Police. TOXICS 2023; 11:385. [PMID: 37112612 PMCID: PMC10147009 DOI: 10.3390/toxics11040385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/24/2023] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
Real-time exposure air monitoring is essential to protect the respiratory health of the Malaysian traffic police. However, the data from monitoring stations have been inadequate to provide accurate information about their exposure. This report describes the conceptual design of a wireless exposure indicator system, and then evaluates the field performance of the system by collocation. The study tested the accuracy of particulate matter size 2.5 (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2) by comparing the measurements from the prototype with the measurements from reference instruments. The field testing found that the data tested were significantly correlated with each other (PM2.5-rs = 0.207, p = 0.019; NO2-rs = 0.576, p = 0.02 and CO-rs = 0.545, p = 0.04). The prototype proved to be successful as it can compute and transmit real-time monitoring data on the level of exposure to harmful air.
Collapse
Affiliation(s)
- Putri Anis Syahira Mohamad Jamil
- Department of Environmental Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, Kampar 31900, Malaysia
| | - Nur Athirah Diyana Mohammad Yusof
- Engineering and Technology Department, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia Kuala Lumpur, Kuala Lumpur 54100, Malaysia
| | - Karmegam Karuppiah
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
| | - Irniza Rasdi
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
| | - Vivien How
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
| | - Shamsul Bahri Mohd Tamrin
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
| | - Muhammad Hasnolhadi Samsudin
- Department of Construction Management, Faculty of Engineering and Technology, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
| | - Sivasankar Sambasivam
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
| | - Nayef Shabbab Almutairi
- Department Public Health, Al-Lith College of Health Sciences, Umm Al-Qura University, P.O. Box 3712, Mecca 21955, Saudi Arabia
| |
Collapse
|
30
|
Wang J, Du W, Lei Y, Chen Y, Wang Z, Mao K, Tao S, Pan B. Quantifying the dynamic characteristics of indoor air pollution using real-time sensors: Current status and future implication. ENVIRONMENT INTERNATIONAL 2023; 175:107934. [PMID: 37086491 DOI: 10.1016/j.envint.2023.107934] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/12/2023] [Accepted: 04/12/2023] [Indexed: 05/03/2023]
Abstract
People generally spend most of their time indoors, making indoor air quality be of great significance to human health. Large spatiotemporal heterogeneity of indoor air pollution can be hardly captured by conventional filter-based monitoring but real-time monitoring. Real-time monitoring is conducive to change air assessment mode from static and sparse analysis to dynamic and massive analysis, and has made remarkable strides in indoor air evaluation. In this review, the state of art, strengths, challenges, and further development of real-time sensors used in indoor air evaluation are focused on. Researches using real-time sensors for indoor air evaluation have increased rapidly since 2018, and are mainly conducted in China and the USA, with the most frequently investigated air pollutants of PM2.5. In addition to high spatiotemporal resolution, real-time sensors for indoor air evaluation have prominent advantages in 3-dimensional monitoring, pollution peak and source identification, and short-term health effect evaluation. Huge amounts of data from real-time sensors also facilitate the modeling and prediction of indoor air pollution. However, challenges still remain in extensive deployment of real-time sensors indoors, including the selection, performance, stability, as well as calibration of sensors. In future, sensors with high performance, long-term stability, low price, and low energy consumption are welcomed. Furthermore, more target air pollutants are also expected to be detected simultaneously by real-time sensors in indoor air monitoring.
Collapse
Affiliation(s)
- Jinze Wang
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Wei Du
- Yunnan Provincial Key Laboratory of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science & Engineering, Kunming University of Science & Technology, Kunming 650500, China.
| | - Yali Lei
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Yuanchen Chen
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, China
| | - Zhenglu Wang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - Kang Mao
- State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, China
| | - Shu Tao
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Bo Pan
- Yunnan Provincial Key Laboratory of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science & Engineering, Kunming University of Science & Technology, Kunming 650500, China
| |
Collapse
|
31
|
Kim SY, Blanco MN, Bi J, Larson TV, Sheppard L. Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs. ENVIRONMENTAL RESEARCH 2023; 223:115451. [PMID: 36764437 PMCID: PMC9992293 DOI: 10.1016/j.envres.2023.115451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/10/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms. OBJECTIVES We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies. METHODS We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs. RESULTS Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility. DISCUSSION Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.
Collapse
Affiliation(s)
- Sun-Young Kim
- Department of Cancer AI and Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
| | - Magali N Blanco
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Jianzhao Bi
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Timothy V Larson
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA
| |
Collapse
|
32
|
Suriano D, Prato M. An Investigation on the Possible Application Areas of Low-Cost PM Sensors for Air Quality Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:3976. [PMID: 37112317 PMCID: PMC10143454 DOI: 10.3390/s23083976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/30/2023] [Accepted: 04/11/2023] [Indexed: 06/19/2023]
Abstract
In recent years, the availability on the market of low-cost sensors (LCSs) and low-cost monitors (LCMs) for air quality monitoring has attracted the interest of scientists, communities, and professionals. Although the scientific community has raised concerns about their data quality, they are still considered a possible alternative to regulatory monitoring stations due to their cheapness, compactness, and lack of maintenance costs. Several studies have performed independent evaluations to investigate their performance, but a comparison of the results is difficult due to the different test conditions and metrics adopted. The U.S. Environmental Protection Agency (EPA) tried to provide a tool for assessing the possible uses of LCSs or LCMs by publishing guidelines to assign suitable application areas for each of them on the basis of the mean normalized bias (MNB) and coefficient of variance (CV) indicators. Until today, very few studies have analyzed LCS performance by referring to the EPA guidelines. This research aimed to understand the performance and the possible application areas of two PM sensor models (PMS5003 and SPS30) on the basis of the EPA guidelines. We computed the R2, RMSE, MAE, MNB, CV, and other performance indicators and found that the coefficient of determination (R2) ranged from 0.55 to 0.61, while the root mean squared error (RMSE) ranged from 11.02 µg/m3 to 12.09 µg/m3. Moreover, the application of a correction factor to include the humidity effect produced an improvement in the performance of the PMS5003 sensor models. We also found that, based on the MNB and CV values, the EPA guidelines assigned the SPS30 sensors to the "informal information about the presence of the pollutant" application area (Tier I), while PMS5003 sensors were assigned to the "supplemental monitoring of regulatory networks" area (Tier III). Although the usefulness of the EPA guidelines is acknowledged, it appears that improvements are necessary to increase their effectiveness.
Collapse
|
33
|
Che W, Zhang Y, Lin C, Fung YH, Fung JCH, Lau AKH. Impacts of pollution heterogeneity on population exposure in dense urban areas using ultra-fine resolution air quality data. J Environ Sci (China) 2023; 125:513-523. [PMID: 36375934 DOI: 10.1016/j.jes.2022.02.041] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 02/18/2022] [Accepted: 02/22/2022] [Indexed: 06/16/2023]
Abstract
Traditional air quality data have a spatial resolution of 1 km or above, making it challenging to resolve detailed air pollution exposure in complex urban areas. Combining urban morphology, dynamic traffic emission, regional and local meteorology, physicochemical transformations in air quality models using big data fusion technology, an ultra-fine resolution modeling system was developed to provide air quality data down to street level. Based on one-year ultra-fine resolution data, this study investigated the effects of pollution heterogeneity on the individual and population exposure to particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3) in Hong Kong, one of the most densely populated and urbanized cities. Sharp fine-scale variabilities in air pollution were revealed within individual city blocks. Using traditional 1 km average to represent individual exposure resulted in a positively skewed deviation of up to 200% for high-end exposure individuals. Citizens were disproportionally affected by air pollution, with annual pollutant concentrations varied by factors of 2 to 5 among 452 District Council Constituency Areas (DCCAs) in Hong Kong, indicating great environmental inequities among the population. Unfavorable city planning resulted in a positive spatial coincidence between pollution and population, which increased public exposure to air pollutants by as large as 46% among districts in Hong Kong. Our results highlight the importance of ultra-fine pollutant data in quantifying the heterogeneity in pollution exposure in the dense urban area and the critical role of smart urban planning in reducing exposure inequities.
Collapse
Affiliation(s)
- Wenwei Che
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Yumiao Zhang
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Changqing Lin
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
| | - Yik Him Fung
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Jimmy C H Fung
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China; Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Alexis K H Lau
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| |
Collapse
|
34
|
Bouillon L, Gros V, Abboud M, El Hafyani H, Zeitouni K, Alage S, Languille B, Bonnaire N, Naude JM, Srairi S, Campos Y Sansano A, Kauffmann A. NO 2, BC and PM Exposure of Participants in the Polluscope Autumn 2019 Campaign in the Paris Region. TOXICS 2023; 11:206. [PMID: 36976970 PMCID: PMC10051186 DOI: 10.3390/toxics11030206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/10/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
The Polluscope project aims to better understand the personal exposure to air pollutants in the Paris region. This article is based on one campaign from the project, which was conducted in the autumn of 2019 and involved 63 participants equipped with portable sensors (i.e., NO2, BC and PM) for one week. After a phase of data curation, analyses were performed on the results from all participants, as well as on individual participants' data for case studies. A machine learning algorithm was used to allocate the data to different environments (e.g., transportation, indoor, home, office, and outdoor). The results of the campaign showed that the participants' exposure to air pollutants depended very much on their lifestyle and the sources of pollution that may be present in the vicinity. Individuals' use of transportation was found to be associated with higher levels of pollutants, even when the time spent on transport was relatively short. In contrast, homes and offices were environments with the lowest concentrations of pollutants. However, some activities performed in indoor air (e.g., cooking) also showed a high levels of pollution over a relatively short period.
Collapse
Affiliation(s)
- Laura Bouillon
- Laboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL), UMR CNRS-CEA-UVSQ, 91191 Gif-Sur-Yvette, France
| | - Valérie Gros
- Laboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL), UMR CNRS-CEA-UVSQ, 91191 Gif-Sur-Yvette, France
| | - Mohammad Abboud
- Laboratoire DAVID, Université Saint-Quentin-en-Yvelines, 78035 Versailles, France
| | - Hafsa El Hafyani
- Laboratoire DAVID, Université Saint-Quentin-en-Yvelines, 78035 Versailles, France
| | - Karine Zeitouni
- Laboratoire DAVID, Université Saint-Quentin-en-Yvelines, 78035 Versailles, France
| | - Stéphanie Alage
- Laboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL), UMR CNRS-CEA-UVSQ, 91191 Gif-Sur-Yvette, France
| | | | - Nicolas Bonnaire
- Laboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL), UMR CNRS-CEA-UVSQ, 91191 Gif-Sur-Yvette, France
| | - Jean-Marc Naude
- Cerema, Île-De-France, Département Mobilité, 78190 Trappes-en-Yvelines, France
| | - Salim Srairi
- Cerema, Île-De-France, Département Mobilité, 78190 Trappes-en-Yvelines, France
| | | | | |
Collapse
|
35
|
Hasan MH, Yu H, Ivey C, Pillarisetti A, Yuan Z, Do K, Li Y. Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal. ACS OMEGA 2023; 8:5917-5924. [PMID: 36816698 PMCID: PMC9933490 DOI: 10.1021/acsomega.2c07734] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 01/16/2023] [Indexed: 05/31/2023]
Abstract
Low-cost air quality (LCAQ) sensors are increasingly being used for community air quality monitoring. However, data collected by low-cost sensors contain significant noise, and proper calibration of these sensors remains a widely discussed, but not yet fully addressed, area of concern. In this study, several LCAQ sensors measuring nitrogen dioxide (NO2) and ozone (O3) were deployed in six cities in the United States (Atlanta, GA; New York City, NY; Sacramento, CA; Riverside, CA; Portland, OR; Phoenix, AZ) to evaluate the impacts of different climatic and geographical conditions on their performance and calibration. Three calibration methods were applied, including regression via linear and polynomial models and random forest methods. When signals from carbon monoxide (CO) sensors were included in the calibration models for NO2 and O3 sensors, model performance generally increased, with pronounced improvements in selected cities such as Riverside and New York City. Such improvements may be due to (1) temporal co-variation between concentrations of CO and NO2 and/or between CO and O3; (2) different performance levels of low-cost CO, NO2, and O3 sensors; and (3) different impacts of environmental conditions on sensor performance. The results showed an innovative approach for improving the calibration of NO2 and O3 sensors by including CO sensor signals into the calibration models. Community users of LCAQ sensors may be able to apply these findings further to enhance the data quality of their deployed NO2 and O3 monitors.
Collapse
Affiliation(s)
- Md Hasibul Hasan
- Department
of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, Florida32816, United States
| | - Haofei Yu
- Department
of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, Florida32816, United States
| | - Cesunica Ivey
- Department
of Civil and Environmental Engineering, The University of California, Berkeley, Berkeley, California94720, United States
| | - Ajay Pillarisetti
- Environmental
Health Sciences, School of Public Health, University of California, Berkeley, California94720, United States
| | - Ziyang Yuan
- Sailbri
Cooper, Inc., Tigard, Oregon97223, United States
| | - Khanh Do
- Department
of Chemical and Environmental Engineering, University of California, Riverside, California92521, United States
| | - Yi Li
- Sailbri
Cooper, Inc., Tigard, Oregon97223, United States
| |
Collapse
|
36
|
Graça D, Reis J, Gama C, Monteiro A, Rodrigues V, Rebelo M, Borrego C, Lopes M, Miranda AI. Sensors Network as an Added Value for the Characterization of Spatial and Temporal Air Quality Patterns at the Urban Scale. SENSORS (BASEL, SWITZERLAND) 2023; 23:1859. [PMID: 36850456 PMCID: PMC9967040 DOI: 10.3390/s23041859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 02/03/2023] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
Within the scope of the Aveiro STEAM City project, an air quality monitoring network was installed in the city of Aveiro (Portugal), to evaluate the potential of sensors to characterize spatial and temporal patterns of air quality in the city. The network consists of nine sensors stations with air quality sensors (PM10, PM2.5, NO2, O3 and CO) and two meteorological stations, distributed within selected locations in the city of Aveiro. The analysis of the data was done for a one-year measurement period, from June 2020 to May 2021, using temporal profiles, statistical comparisons with reference stations and Air Quality Indexes (AQI). The analysis of sensors data indicated that air quality variability exists for all pollutants and stations. The majority of the study area is characterized by good air quality, but specific areas-associated with hotspot traffic zones-exhibit medium, poor and bad air quality more frequently. The daily patterns registered are significantly different between the affected and non-affected road traffic sites, mainly for PM and NO2 pollutants. The weekly profile, significative deltas are found between week and weekend: NO2 is reduced on the weekends at traffic sites, but PM10 is higher in specific areas during winter weekends, which is explained by residential combustion sources.
Collapse
|
37
|
deSouza P, Barkjohn K, Clements A, Lee J, Kahn R, Crawford B, Kinney P. An analysis of degradation in low-cost particulate matter sensors. ENVIRONMENTAL SCIENCE: ATMOSPHERES 2023; 3:521-536. [PMID: 37234229 PMCID: PMC10208317 DOI: 10.1039/d2ea00142j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Low-cost sensors (LCS) are increasingly being used to measure fine particulate matter (PM2.5) concentrations in cities around the world. One of the most commonly deployed LCS is the PurpleAir with ~ 15,000 sensors deployed in the United States, alone. PurpleAir measurements are widely used by the public to evaluate PM2.5 levels in their neighborhoods. PurpleAir measurements are also increasingly being integrated into models by researchers to develop large-scale estimates of PM2.5. However, the change in sensor performance over time has not been well studied. It is important to understand the lifespan of these sensors to determine when they should be serviced or replaced, and when measurements from these devices should or should not be used for various applications. This paper fills this gap by leveraging the fact that: (1) Each PurpleAir sensor is comprised of two identical sensors and the divergence between their measurements can be observed, and (2) There are numerous PurpleAir sensors within 50 meters of regulatory monitors allowing for the comparison of measurements between these instruments. We propose empirically derived degradation outcomes for the PurpleAir sensors and evaluate how these outcomes change over time. On average, we find that the number of 'flagged' measurements, where the two sensors within each PurpleAir sensor disagree, increases with time to ~ 4% after 4 years of operation. Approximately 2 percent of all PurpleAir sensors were permanently degraded. The largest fraction of permanently degraded PurpleAir sensors appeared to be in the hot and humid climate zone, suggesting that sensors in these locations may need to be replaced more frequently. We also find that the bias of PurpleAir sensors, or the difference between corrected PM2.5 levels and the corresponding reference measurements, changed over time by -0.12 μg/m3(95% CI: -0.13 μg/m3, -0.10 μg/m3) per year. The average bias increases dramatically after 3.5 years. Further, climate zone is a significant modifier of the association between degradation outcomes and time.
Collapse
Affiliation(s)
- Priyanka deSouza
- Department of Urban and Regional Planning, University of Colorado Denver, Denver CO, 80202, USA
- CU Population Center, University of Colorado Boulder, Boulder CO, 80302, USA
| | - Karoline Barkjohn
- Office of Research and Development, US Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Andrea Clements
- Office of Research and Development, US Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Jenny Lee
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Ralph Kahn
- NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
| | - Ben Crawford
- Department of Geography and Environmental Sciences, University of Colorado Denver, 80202, USA
| | - Patrick Kinney
- Boston University School of Public Health, Boston, MA, 02118 USA
| |
Collapse
|
38
|
Hodoli CG, Coulon F, Mead MI. Source identification with high-temporal resolution data from low-cost sensors using bivariate polar plots in urban areas of Ghana. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 317:120448. [PMID: 36457223 DOI: 10.1016/j.envpol.2022.120448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 10/07/2022] [Accepted: 10/14/2022] [Indexed: 06/17/2023]
Abstract
The emergence of low-cost sensors for atmospheric observations presents a new opportunity for identifying atmospheric emission sources based on high-resolution data reporting. Low-cost sensors have been widely assessed for use in source monitoring and identification of hotspots of key atmospheric species in advanced countries (e.g., for CO, NOx, CO2, SO2, O3, VOCs and PM (PM10, PM2.5 including emerging PM1). In contrast, research in recent years has focused on their utility for real-time monitoring, understanding precision and associated calibration requirements in technologically lagging environments. This leads to limited evidence on the utility of high-resolution data from low-cost sensor networks for air pollution source identification in Ghana and more widely across the African continent. In this paper, we demonstrate the potential of low-cost sensors for emission source apportionment in urban areas of Ghana when used with analytical tools such as sectoral and cluster analysis. With a 14-week dataset from a low-cost sensor deployment study at Cape Coast in the Central Region of Ghana, we aimed to identify sources of particulate matter (PM2.5 and PM10). PM pollution was local (associated with increased PM at wind speeds of ≤2 m s-1). High levels of PM during this study were associated with transport from the NNE. For coarse PM, hourly levels as high as 125 μg m-3 were observed at higher wind speeds (7-8 m s-1) indicating the importance of meteorology in the transport of PM. This study suggests that low-cost sensors could be deployed to (1) augment any existing sparsely distributed air quality monitoring and (2) undertake air quality monitoring for source apportionment studies in areas with no existing air quality observational capability (with appropriate calibration and operation in both cases). The urban landscape monitored in this study is typical of both Ghana and wider areas across Sub-Saharan Africa demonstrating the reproducibility of this study.
Collapse
Affiliation(s)
- C Gameli Hodoli
- Cranfield University, School of Water, Energy and Environment, Cranfield, MK43 0AL, UK; University of Environment and Sustainable Development, School of Built Environment, PMB, Somanya, Eastern Region, Ghana
| | - F Coulon
- Cranfield University, School of Water, Energy and Environment, Cranfield, MK43 0AL, UK
| | - M I Mead
- Cranfield University, School of Water, Energy and Environment, Cranfield, MK43 0AL, UK; MRC Centre for Environment and Health, Environmental Research Group, Imperial College London, W12 0BZ, UK.
| |
Collapse
|
39
|
Ali S, Alam F, Arif KM, Potgieter J. Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:854. [PMID: 36679650 PMCID: PMC9862378 DOI: 10.3390/s23020854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
The advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.
Collapse
Affiliation(s)
- Sharafat Ali
- Department of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand
| | - Fakhrul Alam
- Department of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand
| | - Khalid Mahmood Arif
- Department of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand
| | - Johan Potgieter
- Massey Agrifood Digital Lab., Massey University, Palmerston North 4410, New Zealand
| |
Collapse
|
40
|
Lacey SD, Gilardi E, Müller E, Merckling C, Saint-Girons G, Botella C, Bachelet R, Pergolesi D, El Kazzi M. Integration of Li 4Ti 5O 12 Crystalline Films on Silicon Toward High-Rate Performance Lithionic Devices. ACS APPLIED MATERIALS & INTERFACES 2023; 15:1535-1544. [PMID: 36576942 DOI: 10.1021/acsami.2c17073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The growth of crystalline Li-based oxide thin films on silicon substrates is essential for the integration of next-generation solid-state lithionic and electronic devices including on-chip microbatteries, memristors, and sensors. However, growing crystalline oxides directly on silicon typically requires high temperatures and oxygen partial pressures, which leads to the formation of undesired chemical species at the interface compromising the crystal quality of the films. In this work, we employ a 2 nm gamma-alumina (γ-Al2O3) buffer layer on Si substrates in order to grow crystalline thin films of Li4Ti5O12 (LTO), a well-known active material for lithium-ion batteries. The ultrathin γ-Al2O3 layer enables the formation of a stable heterostructure with sharp interfaces and drastically improves the LTO crystallographic and electrochemical properties. Long-term galvanostatic cycling of 50 nm LTO films in liquid-based half-cells demonstrates a high capacity retention of 91% after 5000 cycles at 100 C. Rate capability tests showcase a specific charge of 56 mA h g-1 at an exceptional C-rate of 5000 C (15 mA cm-2). Moreover, with sub-millisecond current pulse tests, the reported thin-film heterostructure exhibits rapid Li-ion (de)intercalation, which could lead to fast switching timescales in resistive memory devices and electrochemical transistors.
Collapse
Affiliation(s)
- Steven D Lacey
- Electrochemistry Laboratory, Paul Scherrer Institut, Villigen PSI CH-5232, Switzerland
- National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Lausanne CH-1015, Switzerland
| | - Elisa Gilardi
- National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Lausanne CH-1015, Switzerland
- Multiscale Materials Experiments Laboratory, Paul Scherrer Institut, Villigen PSI CH-5232, Switzerland
| | - Elisabeth Müller
- Electron Microscopy Facility, Paul Scherrer Institut, Villigen PSI CH-5232, Switzerland
| | - Clement Merckling
- Imec, Kapeldreef 75, Leuven 3001, Belgium
- KU Leuven, Material Engineering, Kasteelpark Arenberg 44, Leuven 3001, Belgium
| | - Guillaume Saint-Girons
- Institut des Nanotechnologies de Lyon (INL-CNRS UMR 5270), Université de Lyon, Ecole Centrale de Lyon, Ecully Cedex 69134, France
| | - Claude Botella
- Institut des Nanotechnologies de Lyon (INL-CNRS UMR 5270), Université de Lyon, Ecole Centrale de Lyon, Ecully Cedex 69134, France
| | - Romain Bachelet
- Institut des Nanotechnologies de Lyon (INL-CNRS UMR 5270), Université de Lyon, Ecole Centrale de Lyon, Ecully Cedex 69134, France
| | - Daniele Pergolesi
- National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Lausanne CH-1015, Switzerland
- Multiscale Materials Experiments Laboratory, Paul Scherrer Institut, Villigen PSI CH-5232, Switzerland
| | - Mario El Kazzi
- Electrochemistry Laboratory, Paul Scherrer Institut, Villigen PSI CH-5232, Switzerland
| |
Collapse
|
41
|
Schilt U, Barahona B, Buck R, Meyer P, Kappani P, Möckli Y, Meyer M, Schuetz P. Low-Cost Sensor Node for Air Quality Monitoring: Field Tests and Validation of Particulate Matter Measurements. SENSORS (BASEL, SWITZERLAND) 2023; 23:794. [PMID: 36679602 PMCID: PMC9862273 DOI: 10.3390/s23020794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/12/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
Air pollution is still a major public health issue, which makes monitoring air quality a necessity. Mobile, low-cost air quality measurement devices can potentially deliver more coherent data for a region or municipality than stationary measurement stations are capable of due to their improved spatial coverage. In this study, air quality measurements obtained during field tests of our low-cost air quality sensor node (sensor-box) are presented and compared to measurements from the regional air quality monitoring network. The sensor-box can acquire geo-tagged measurements of several important pollutants, as well as other environmental quantities such as light and sound. The field test consists of sensor-boxes mounted on utility vehicles operated by municipalities located in Central Switzerland. Validation is performed against a measurement station that is part of the air quality monitoring network of Central Switzerland. Often not discussed in similar studies, this study tests and discusses several data filtering methods for the removal of outliers and unfeasible values prior to further analysis. The results show a coherent measurement pattern during the field tests and good agreement to the reference station during the side-by-side validation test.
Collapse
Affiliation(s)
- Ueli Schilt
- School of Engineering and Architecture, Lucerne University of Applied Sciences and Arts, CH-6048 Horw, Switzerland
| | - Braulio Barahona
- School of Engineering and Architecture, Lucerne University of Applied Sciences and Arts, CH-6048 Horw, Switzerland
| | - Roger Buck
- School of Engineering and Architecture, Lucerne University of Applied Sciences and Arts, CH-6048 Horw, Switzerland
| | - Patrick Meyer
- School of Engineering and Architecture, Lucerne University of Applied Sciences and Arts, CH-6048 Horw, Switzerland
| | - Prince Kappani
- School of Engineering and Architecture, Lucerne University of Applied Sciences and Arts, CH-6048 Horw, Switzerland
| | - Yannis Möckli
- School of Engineering and Architecture, Lucerne University of Applied Sciences and Arts, CH-6048 Horw, Switzerland
| | | | - Philipp Schuetz
- School of Engineering and Architecture, Lucerne University of Applied Sciences and Arts, CH-6048 Horw, Switzerland
| |
Collapse
|
42
|
Peck A, Handy RG, Sleeth DK, Schaefer C, Zhang Y, Pahler LF, Ramsay J, Collingwood SC. Aerosol Measurement Degradation in Low-Cost Particle Sensors Using Laboratory Calibration and Field Validation. TOXICS 2023; 11:56. [PMID: 36668782 PMCID: PMC9862639 DOI: 10.3390/toxics11010056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/22/2022] [Accepted: 12/25/2022] [Indexed: 06/17/2023]
Abstract
Increasing concern over air pollution has led to the development of low-cost sensors suitable for wide-scale deployment and use by citizen scientists. This project investigated the AirU low-cost particle sensor using two methods: (1) a comparison of pre- and post-deployment calibration equations for 24 devices following use in a field study, and (2) an in-home comparison between 3 AirUs and a reference instrument, the GRIMM 1.109. While differences (and therefore some sensor degradation) were found in the pre- and post-calibration equation comparison, absolute value changes were small and unlikely to affect the quality of results. Comparison tests found that while the AirU did tend to underestimate minimum and overestimate maximum concentrations of particulate matter, ~88% of results fell within ±1 μg/m3 of the GRIMM. While these tests confirm that low-cost sensors such as the AirU do experience some sensor degradation over multiple months of use, they remain a valuable tool for exposure assessment studies. Further work is needed to examine AirU performance in different environments for a comprehensive survey of capability, as well as to determine the source of sensor degradation.
Collapse
Affiliation(s)
- Angela Peck
- Occupational and Environmental Health, Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UT 84108, USA
| | - Rodney G. Handy
- Occupational and Environmental Health, Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UT 84108, USA
| | - Darrah K. Sleeth
- Occupational and Environmental Health, Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UT 84108, USA
| | - Camie Schaefer
- Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UT 84108, USA
| | - Yue Zhang
- Department of Internal Medicine, University of Utah, Salt Lake City, UT 84108, USA
| | - Leon F. Pahler
- Occupational and Environmental Health, Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UT 84108, USA
| | - Joemy Ramsay
- Occupational and Environmental Health, Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UT 84108, USA
| | | |
Collapse
|
43
|
Castellani B, Bartington S, Wistow J, Heckels N, Ellison A, Van Tongeren M, Arnold SR, Barbrook-Johnson P, Bicket M, Pope FD, Russ TC, Clarke CL, Pirani M, Schwannauer M, Vieno M, Turnbull R, Gilbert N, Reis S. Mitigating the impact of air pollution on dementia and brain health: Setting the policy agenda. ENVIRONMENTAL RESEARCH 2022; 215:114362. [PMID: 36130664 DOI: 10.1016/j.envres.2022.114362] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/13/2022] [Accepted: 09/14/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Emerging research suggests exposure to high levels of air pollution at critical points in the life-course is detrimental to brain health, including cognitive decline and dementia. Social determinants play a significant role, including socio-economic deprivation, environmental factors and heightened health and social inequalities. Policies have been proposed more generally, but their benefits for brain health have yet to be fully explored. OBJECTIVE AND METHODS Over the course of two years, we worked as a consortium of 20+ academics in a participatory and consensus method to develop the first policy agenda for mitigating air pollution's impact on brain health and dementia, including an umbrella review and engaging 11 stakeholder organisations. RESULTS We identified three policy domains and 14 priority areas. Research and Funding included: (1) embracing a complexities of place approach that (2) highlights vulnerable populations; (3) details the impact of ambient PM2.5 on brain health, including current and historical high-resolution exposure models; (4) emphasises the importance of indoor air pollution; (5) catalogues the multiple pathways to disease for brain health and dementia, including those most at risk; (6) embraces a life course perspective; and (7) radically rethinks funding. Education and Awareness included: (8) making this unrecognised public health issue known; (9) developing educational products; (10) attaching air pollution and brain health to existing strategies and campaigns; and (11) providing publicly available monitoring, assessment and screening tools. Policy Evaluation included: (12) conducting complex systems evaluation; (13) engaging in co-production; and (14) evaluating air quality policies for their brain health benefits. CONCLUSION Given the pressing issues of brain health, dementia and air pollution, setting a policy agenda is crucial. Policy needs to be matched by scientific evidence and appropriate guidelines, including bespoke strategies to optimise impact and mitigate unintended consequences. The agenda provided here is the first step toward such a plan.
Collapse
Affiliation(s)
- Brian Castellani
- Durham Research Methods Centre, Durham University, Stockton Road, Durham, DH1 3LE, United Kingdom; Centre for the Evaluation of Complexity Across the Nexus, University of Surrey, Guildford, GU2 7XH, United Kingdom; Wolfson Research Institute for Health and Wellbeing, Durham University, Stockton Road, DH1 3LE, United Kingdom; Department of Sociology, Durham University, Stockton Road, Durham, DH1 3LE, United Kingdom.
| | - Suzanne Bartington
- Institute of Applied Health Research, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Jonathan Wistow
- Wolfson Research Institute for Health and Wellbeing, Durham University, Stockton Road, DH1 3LE, United Kingdom; Department of Sociology, Durham University, Stockton Road, Durham, DH1 3LE, United Kingdom
| | - Neil Heckels
- Research and Innovation Services, Durham University, Stockton Road, Durham, DH1 3LE, United Kingdom
| | - Amanda Ellison
- Wolfson Research Institute for Health and Wellbeing, Durham University, Stockton Road, DH1 3LE, United Kingdom; Department of Psychology, Durham University, Stockton Road, Durham, DH1 3LE, United Kingdom
| | - Martie Van Tongeren
- Centre for Occupational and Environmental Health, School of Health Sciences, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Steve R Arnold
- School of Earth & Environment, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Pete Barbrook-Johnson
- Centre for the Evaluation of Complexity Across the Nexus, University of Surrey, Guildford, GU2 7XH, United Kingdom; Environmental Change Institute, School of Geography and the Environment, University of Oxford, United Kingdom
| | - Martha Bicket
- Centre for the Evaluation of Complexity Across the Nexus, University of Surrey, Guildford, GU2 7XH, United Kingdom
| | - Francis D Pope
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Tom C Russ
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom; Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, United Kingdom
| | - Charlotte L Clarke
- Department of Sociology, Durham University, Stockton Road, Durham, DH1 3LE, United Kingdom; School of Health in Social Science, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, United Kingdom
| | - Monica Pirani
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, W2 1PG, London, United Kingdom
| | - Matthias Schwannauer
- School of Health in Social Science, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, United Kingdom
| | - Massimo Vieno
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, United Kingdom
| | - Rachel Turnbull
- Academic Health Sciences Network, North East and North Cumbria, Nuns' Moor Road, Newcastle Upon Tyne NE4 5PL, United Kingdom
| | - Nigel Gilbert
- Centre for the Evaluation of Complexity Across the Nexus, University of Surrey, Guildford, GU2 7XH, United Kingdom
| | - Stefan Reis
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, United Kingdom; University of Exeter Medical School, European Centre for Environment and Health, Knowledge Spa, Truro, TR1 3HD, United Kingdom; The University of Edinburgh, School of Chemistry, Level 3, Murchison House, 10 Max Born Crescent, The King's Buildings, West Mains Road, Edinburgh, EH9 3BF, United Kingdom
| |
Collapse
|
44
|
Zhang C, Hu Y, Adams MD, Liu M, Li B, Shi T, Li C. Natural and human factors influencing urban particulate matter concentrations in central heating areas with long-term wearable monitoring devices. ENVIRONMENTAL RESEARCH 2022; 215:114393. [PMID: 36150440 DOI: 10.1016/j.envres.2022.114393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
In northern China, central heating, as an important source of urban particulate matter (UPM), causes more than half of the air pollution during the heating season and has significant spatial-temporal heterogeneity. Owing to the limitations of stationary air monitoring networks, few studies distinguish between heating/non-heating seasons and few have been conducted in urban areas. However, fixed monitoring cannot accurately capture the dynamic exposure of residents to UPM, and there is a lack of comprehensive evaluation of the factors affecting UPM. Therefore, this study used wearable Sniffer 4D equipment to monitor the concentrations of UPM (PM1, PM2.5, and PM10) in selected typical areas of Shenyang City from March 2019 to February 2020. A random forest model was combined with land use and point-of-interest data to analyze the contributions and marginal effects of multiple influences on UPM, in both heating and non-heating seasons. The results showed that in the eastern part of the study area, UPM showed completely opposite spatial distribution characteristics during the two seasons. The concentrations of UPM were higher during the heating season than during the non-heating season. The results indicated that temperature and humidity were important factors in diffusing UPM. The production and operation of boilers were important for the production of UPM. In two-dimensional landscape pattern indices, the percentage of forest and Shannon diversity index were the first and second most important factors, respectively. The three-dimensional pattern of buildings had important effects on the transport and diffusion of UPM (landscape height range >100, floor area ratio >1.3, and landscape volume density >5). Wearable devices could monitor the real situation of residents' exposure to UPM and quantify the factors influencing the spatial-temporal distribution of UPM in an ecological sense. These results provide a scientific basis for urban planning and for health risk reduction for residents.
Collapse
Affiliation(s)
- Chuyi Zhang
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang, 110016, China; College of Resources and Environment, University of Chinese Academy of Sciences, No. 19, Yuquan Road, Beijing, 100049, China; Department of Geography & Planning, University of Toronto, 3359 Mississauga Road, Mississauga, ON, L5L 1C6, Canada
| | - Yuanman Hu
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang, 110016, China
| | - Matthew D Adams
- Department of Geography & Planning, University of Toronto, 3359 Mississauga Road, Mississauga, ON, L5L 1C6, Canada
| | - Miao Liu
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang, 110016, China
| | - Binglun Li
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang, 110016, China
| | - Tuo Shi
- College of Life Science, Shenyang Normal University, No. 253 Huanghe North Street, Shenyang, 110034, China
| | - Chunlin Li
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang, 110016, China.
| |
Collapse
|
45
|
Sayago I, Santos JP, Sánchez-Vicente C. The Effect of Rare Earths on the Response of Photo UV-Activate ZnO Gas Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:8150. [PMID: 36365849 PMCID: PMC9658068 DOI: 10.3390/s22218150] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
In this work, ZnO nanoparticle resistive sensors decorated with rare earths (REs; including Er, Tb, Eu and Dy) were used at room temperature to detect atmospheric pollutant gases (NO2, CO and CH4). Sensitive films were prepared by drop casting from aqueous solutions of ZnO nanoparticles (NPs) and trivalent RE ions. The sensors were continuously illuminated by ultraviolet light during the detection processes. The effect of photoactivation of the sensitive films was studied, as well as the influence of humidity on the response of the sensors to polluting gases. Comparative studies on the detection properties of the sensors showed how the presence of REs increased the response to the gases detected. Low concentrations of pollutant gases (50 ppb NO2, 1 ppm CO and 3 ppm CH4) were detected at room temperature. The detection mechanisms were then discussed in terms of the possible oxidation-reduction (redox) reaction in both dry and humid air atmospheres.
Collapse
|
46
|
Russell HS, Frederickson LB, Kwiatkowski S, Emygdio APM, Kumar P, Schmidt JA, Hertel O, Johnson MS. Enhanced Ambient Sensing Environment-A New Method for Calibrating Low-Cost Gas Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:7238. [PMID: 36236337 PMCID: PMC9571921 DOI: 10.3390/s22197238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/13/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
Accurate calibration of low-cost gas sensors is, at present, a time consuming and difficult process. Laboratory calibration and field calibration methods are currently used, but laboratory calibration is generally discounted due to poor transferability, and field methods requiring several weeks are standard. The Enhanced Ambient Sensing Environment (EASE) method described in this article, is a hybrid of the two, combining the advantages of a laboratory calibration with the increased accuracy of a field calibration. It involves calibrating sensors inside a duct, drawing in ambient air with similar properties to the site where the sensors will operate, but with the added feature of being able to artificially increases or decrease pollutant levels, thus condensing the calibration period required. Calibration of both metal-oxide (MOx) and electrochemical (EC) gas sensors for the measurement of NO2 and O3 (0-120 ppb) were conducted in EASE, laboratory and field environments, and validated in field environments. The EC sensors performed marginally better than MOx sensors for NO2 measurement and sensor performance was similar for O3 measurement, but the EC sensor nodes had less node inter-node variability and were more robust. For both gasses and sensor types the EASE calibration outperformed the laboratory calibration, and performed similarly to or better than the field calibration, whilst requiring a fraction of the time.
Collapse
Affiliation(s)
- Hugo Savill Russell
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
- Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
- AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark
| | - Louise Bøge Frederickson
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
- Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
- AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark
| | | | - Ana Paula Mendes Emygdio
- Global Center for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Surrey GU2 7XH, UK
| | - Prashant Kumar
- Global Center for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Surrey GU2 7XH, UK
| | | | - Ole Hertel
- Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
- Department of Ecoscience, Aarhus University, DK-4000 Roskilde, Denmark
| | - Matthew Stanley Johnson
- AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark
- Department of Chemistry, University of Copenhagen, DK-2100 Copenhagen Ø, Denmark
| |
Collapse
|
47
|
Guo Q, Ren M, Wu S, Sun Y, Wang J, Wang Q, Ma Y, Song X, Chen Y. Applications of artificial intelligence in the field of air pollution: A bibliometric analysis. Front Public Health 2022; 10:933665. [PMID: 36159306 PMCID: PMC9490423 DOI: 10.3389/fpubh.2022.933665] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/11/2022] [Indexed: 01/25/2023] Open
Abstract
Background Artificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution. Methods All literature on the application of AI to air pollution was searched from the Web of Science database. CiteSpace 5.8.R1 was used to analyze countries/regions, institutions, authors, keywords and references cited, and to reveal hot spots and frontiers of AI in atmospheric pollution. Results Beginning in 1994, publications on AI in air pollution have increased in number, with a surge in research since 2017. The leading country and institution were China (N = 524) and the Chinese Academy of Sciences (N = 58), followed by the United States (N = 455) and Tsinghua University (N = 33), respectively. In addition, the United States (0.24) and the England (0.27) showed a high degree of centrality. Most of the identified articles were published in journals related to environmental science; the most cited journal was Atmospheric Environment, which reached nearly 1,000 citations. There were few collaborations among authors, institutions and countries. The hot topics were machine learning, air pollution and deep learning. The majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods, low-cost air quality sensors, indoor air quality, and thermal comfort. Conclusion Researches in the field of AI and air pollution are expanding rapidly in recent years. The majority of scholars are from China and the United States, and the Chinese Academy of Sciences is the dominant research institution. The United States and the England contribute greatly to the development of the cooperation network. Cooperation among research institutions appears to be suboptimal, and strengthening cooperation could greatly benefit this field of research. The prediction of air pollutant concentrations, particularly PM2.5, low-cost air quality sensors, and thermal comfort are the current research hotspot.
Collapse
Affiliation(s)
- Qiangqiang Guo
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Mengjuan Ren
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Shouyuan Wu
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Yajia Sun
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Jianjian Wang
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Qi Wang
- Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada,McMaster Health Forum, McMaster University, Hamilton, ON, Canada
| | - Yanfang Ma
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China
| | - Xuping Song
- School of Public Health, Lanzhou University, Lanzhou, China,Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China,Lanzhou University Institute of Health Data Science, Lanzhou, China,World Health Organization Collaborating Center for Guideline Implementation and Knowledge Translation, Lanzhou, China,*Correspondence: Xuping Song
| | - Yaolong Chen
- School of Public Health, Lanzhou University, Lanzhou, China,Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China,Lanzhou University Institute of Health Data Science, Lanzhou, China,World Health Organization Collaborating Center for Guideline Implementation and Knowledge Translation, Lanzhou, China,Yaolong Chen
| |
Collapse
|
48
|
Mahajan S. Design and development of an open-source framework for citizen-centric environmental monitoring and data analysis. Sci Rep 2022; 12:14416. [PMID: 36002580 PMCID: PMC9402591 DOI: 10.1038/s41598-022-18700-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022] Open
Abstract
Cities around the world are struggling with environmental pollution. The conventional monitoring approaches are not effective for undertaking large-scale environmental monitoring due to logistical and cost-related issues. The availability of low-cost and low-power Internet of Things (IoT) devices has proved to be an effective alternative to monitoring the environment. Such systems have opened up environment monitoring opportunities to citizens while simultaneously confronting them with challenges related to sensor accuracy and the accumulation of large data sets. Analyzing and interpreting sensor data itself is a formidable task that requires extensive computational resources and expertise. To address this challenge, a social, open-source, and citizen-centric IoT (Soc-IoT) framework is presented, which combines a real-time environmental sensing device with an intuitive data analysis and visualization application. Soc-IoT has two main components: (1) CoSense Unit—a resource-efficient, portable and modular device designed and evaluated for indoor and outdoor environmental monitoring, and (2) exploreR—an intuitive cross-platform data analysis and visualization application that offers a comprehensive set of tools for systematic analysis of sensor data without the need for coding. Developed as a proof-of-concept framework to monitor the environment at scale, Soc-IoT aims to promote environmental resilience and open innovation by lowering technological barriers.
Collapse
Affiliation(s)
- Sachit Mahajan
- Computational Social Science, ETH Zurich, 8092, Zürich, Switzerland.
| |
Collapse
|
49
|
Comparative Study on the Use of Some Low-Cost Optical Particulate Sensors for Rapid Assessment of Local Air Quality Changes. ATMOSPHERE 2022. [DOI: 10.3390/atmos13081218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Official air quality (AQ) stations are sporadically located in cities to monitor the anthropogenic pollutant levels. Consequently, their data cannot be used for further locations to estimate hidden changes in AQ and local emissions. Low-cost sensors (LCSs) of particulate matter (PM) in a network can help in solving this problem. However, the applicability of LCSs in terms of analytical performance requires careful evaluation. In this study, two types of pocket-size LCSs were tested at urban, suburban and background sites in Budapest, Hungary, to monitor PM1, PM2.5, PM10, and microclimatic parameters at high resolutions (1 s to 5 min). These devices utilize the method of laser irradiation and multi-angle light scattering on air-suspended particulates. A research-grade AQ monitor was applied as a reference. The LCSs showed acceptable accuracy for PM species in indoor/outdoor air even without calibration. Low PM readings (<10 μg/m3) were generally handicapped by higher bias, even between sensors of the same type. The relative humidity (RH) slightly affected the PM readings of LCSs at RHs higher than 85%, necessitating field calibration. The air quality index was calculated to classify the extent of air pollution and to make predictions for human health effects. The LCSs were useful for detecting peaks stemming from emissions of motor vehicular traffic and residential cooking/heating activities.
Collapse
|
50
|
Li X, Baumgartner J, Harper S, Zhang X, Sternbach T, Barrington‐Leigh C, Brehmer C, Robinson B, Shen G, Zhang Y, Tao S, Carter E. Field measurements of indoor and community air quality in rural Beijing before, during, and after the COVID-19 lockdown. INDOOR AIR 2022; 32:e13095. [PMID: 36040277 PMCID: PMC9538603 DOI: 10.1111/ina.13095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/15/2022] [Accepted: 07/31/2022] [Indexed: 06/15/2023]
Abstract
The coronavirus (COVID-19) lockdown in China is thought to have reduced air pollution emissions due to reduced human mobility and economic activities. Few studies have assessed the impacts of COVID-19 on community and indoor air quality in environments with diverse socioeconomic and household energy use patterns. The main goal of this study was to evaluate whether indoor and community air pollution differed before, during, and after the COVID-19 lockdown in homes with different energy use patterns. Using calibrated real-time PM2.5 sensors, we measured indoor and community air quality in 147 homes from 30 villages in Beijing over 4 months including periods before, during, and after the COVID-19 lockdown. Community pollution was higher during the lockdown (61 ± 47 μg/m3 ) compared with before (45 ± 35 μg/m3 , p < 0.001) and after (47 ± 37 μg/m3 , p < 0.001) the lockdown. However, we did not observe significantly increased indoor PM2.5 during the COVID-19 lockdown. Indoor-generated PM2.5 in homes using clean energy for heating without smokers was the lowest compared with those using solid fuel with/without smokers, implying air pollutant emissions are reduced in homes using clean energy. Indoor air quality may not have been impacted by the COVID-19 lockdown in rural settings in China and appeared to be more impacted by the household energy choice and indoor smoking than the COVID-19 lockdown. As clean energy transitions occurred in rural households in northern China, our work highlights the importance of understanding multiple possible indoor sources to interpret the impacts of interventions, intended or otherwise.
Collapse
Affiliation(s)
- Xiaoying Li
- Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Department of Civil and Environmental EngineeringColorado State UniversityFort CollinsColoradoUSA
| | - Jill Baumgartner
- Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Institute for Health and Social PolicyMcGill UniversityMontrealQuebecCanada
| | - Sam Harper
- Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Institute for Health and Social PolicyMcGill UniversityMontrealQuebecCanada
| | - Xiang Zhang
- Department of GeographyMcGill UniversityMontrealQuebecCanada
| | - Talia Sternbach
- Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Institute for Health and Social PolicyMcGill UniversityMontrealQuebecCanada
| | - Christopher Barrington‐Leigh
- Institute for Health and Social PolicyMcGill UniversityMontrealQuebecCanada
- Bieler School of EnvironmentMcGill UniversityMontrealQuebecCanada
| | - Collin Brehmer
- Department of Civil and Environmental EngineeringColorado State UniversityFort CollinsColoradoUSA
| | - Brian Robinson
- Department of GeographyMcGill UniversityMontrealQuebecCanada
| | - Guofeng Shen
- Laboratory for Earth Surface Processes, Sino‐French Institute for Earth System Science, College of Urban and Environmental SciencesPeking UniversityBeijingChina
| | - Yuanxun Zhang
- College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijingChina
- CAS Center for Excellence in Regional Atmospheric EnvironmentChinese Academy of SciencesXiamenChina
| | - Shu Tao
- Laboratory for Earth Surface Processes, Sino‐French Institute for Earth System Science, College of Urban and Environmental SciencesPeking UniversityBeijingChina
| | - Ellison Carter
- Department of Civil and Environmental EngineeringColorado State UniversityFort CollinsColoradoUSA
| |
Collapse
|