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Heffernan C, PenG R, Gentner DR, Koehler K, Datta A. A DYNAMIC SPATIAL FILTERING APPROACH TO MITIGATE UNDERESTIMATION BIAS IN FIELD CALIBRATED LOW-COST SENSOR AIR POLLUTION DATA. Ann Appl Stat 2023; 17:3056-3087. [PMID: 38646662 PMCID: PMC11031266 DOI: 10.1214/23-aoas1751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Low-cost air pollution sensors, offering hyper-local characterization of pollutant concentrations, are becoming increasingly prevalent in environmental and public health research. However, low-cost air pollution data can be noisy, biased by environmental conditions, and usually need to be field-calibrated by collocating low-cost sensors with reference-grade instruments. We show, theoretically and empirically, that the common procedure of regression-based calibration using collocated data systematically underestimates high air pollution concentrations, which are critical to diagnose from a health perspective. Current calibration practices also often fail to utilize the spatial correlation in pollutant concentrations. We propose a novel spatial filtering approach to collocation-based calibration of low-cost networks that mitigates the underestimation issue by using an inverse regression. The inverse-regression also allows for incorporating spatial correlations by a second-stage model for the true pollutant concentrations using a conditional Gaussian Process. Our approach works with one or more collocated sites in the network and is dynamic, leveraging spatial correlation with the latest available reference data. Through extensive simulations, we demonstrate how the spatial filtering substantially improves estimation of pollutant concentrations, and measures peak concentrations with greater accuracy. We apply the methodology for calibration of a low-cost PM2.5 network in Baltimore, Maryland, and diagnose air pollution peaks that are missed by the regression-calibration.
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Affiliation(s)
| | - Roger PenG
- Department of Statistics and Data Sciences, University of Texas, Austin
| | - Drew R. Gentner
- Department of Chemical & Environmental Engineering, Yale University
| | - Kirsten Koehler
- Department of Environmental Health and Engineering, Johns Hopkins University
| | - Abhirup Datta
- Department of Biostatistics, Johns Hopkins University
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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. Build Environ 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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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Llaguno-Munitxa M, Bou-Zeid E. Role of vehicular emissions in urban air quality: The COVID-19 lockdown experiment. Transp Res D Transp Environ 2023; 115:103580. [PMID: 36573137 PMCID: PMC9771761 DOI: 10.1016/j.trd.2022.103580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/17/2022] [Accepted: 12/18/2022] [Indexed: 06/17/2023]
Abstract
While the decrease in air pollutant concentration during the COVID-19 lockdown is well documented, neighborhood-scale and multi-city data have not yet been explored systematically to derive a generalizable quantitative link to the drop in vehicular traffic. To bridge this gap, high spatial resolution air quality and georeferenced traffic datasets were compiled for the city of London during three weeks with significant differences in traffic. The London analysis was then augmented with a meta-analysis of lower-resolution studies from 12 other cities. The results confirm that the improvement in air quality can be partially attributed to the drop of traffic density, and more importantly quantifies the elasticity (0.71 for NO2 & 0.56 for PM2.5) of their linkages. The findings can also inform on the future impacts of the ongoing shift to electric vehicles and micro-mobility on urban air quality.
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Affiliation(s)
- Maider Llaguno-Munitxa
- Faculty of Architecture, Architectural Engineering and Urban Planning, UCLouvain, Place du Levant 1, 1348 Ottignies-Louvain-la-Neuve, Belgium
| | - Elie Bou-Zeid
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
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Liu G, Moore K, Su WC, Delclos GL, Gimeno Ruiz de Porras D, Yu B, Tian H, Luo B, Lin S, Lewis GT, Craft E, Zhang K. Chemical explosion, COVID-19, and environmental justice: Insights from low-cost air quality sensors. Sci Total Environ 2022; 849:157881. [PMID: 35944636 PMCID: PMC9356636 DOI: 10.1016/j.scitotenv.2022.157881] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/20/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES To examine the impact of the Intercontinental Terminals Company (ITC) fire and COVID-19 on airborne particulate matter (PM) concentrations and the PM disproportionally affecting communities in Houston using low-cost sensors. METHODS We compared measurements from a network of low-cost sensors with a separate network of monitors from the Environmental Protection Agency (EPA) in the Houston metropolitan area from Mar 18, 2019, to Dec 31, 2020. Further, we examined the associations between neighborhood-level sociodemographic status and air pollution patterns by linking the low-cost sensor data to EPA environmental justice screening and mapping systems. FINDINGS We found increased PM levels during ITC fire and pre-COVID-19, and lower PM levels after the COVID-19 lockdown, comparable to observations from the regulatory monitors, with higher variations and a greater number of locations with high PM levels detected. In addition, the environmental justice analysis showed positive associations between higher PM levels and the percentage of minority, low-income population, and demographic index. IMPLICATION Our study indicates that low-cost sensors provide pollutant measures with higher spatial variations and a better ability to identify hot spots and high peak concentrations. These advantages provide critical information for disaster response and environmental justice studies. SYNOPSIS We used measurements from a low-cost sensor network for air pollution monitoring and environmental justice analysis to examine the impact of anthropogenic and natural disasters.
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Affiliation(s)
- Guning Liu
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX 77030, USA
| | - Katie Moore
- Clarity Movement Co., Durham, NC, USA; Environmental Defense Fund, 301 Congress Avenue, Austin, TX, USA
| | - Wei-Chung Su
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX 77030, USA; Southwest Center for Occupational and Environmental Health, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Houston, TX 77030, USA
| | - George L Delclos
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX 77030, USA; Southwest Center for Occupational and Environmental Health, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Houston, TX 77030, USA
| | - David Gimeno Ruiz de Porras
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX 77030, USA; Southwest Center for Occupational and Environmental Health, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Houston, TX 77030, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX 77030, USA
| | - Hezhong Tian
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
| | - Bin Luo
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Shao Lin
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Albany, NY, USA
| | - Grace Tee Lewis
- Environmental Defense Fund, 301 Congress Avenue, Austin, TX, USA
| | - Elena Craft
- Environmental Defense Fund, 301 Congress Avenue, Austin, TX, USA
| | - Kai Zhang
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Albany, NY, USA.
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Bakola M, Hernandez Carballo I, Jelastopulu E, Stuckler D. The impact of COVID-19 lockdown on air pollution in Europe and North America: a systematic review. Eur J Public Health 2022; 32:962-968. [PMID: 36074061 PMCID: PMC9494388 DOI: 10.1093/eurpub/ckac118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Multiple studies report reductions in air pollution associated with COVID-19 lockdowns. METHODS We performed a systematic review of the changes observed in hazardous air pollutants known or suspected to be harmful to health, including nitrogen dioxide (NO2), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and particulate matter (PM). We searched PubMed and Web of Science for studies reporting the associations of lockdowns with air pollutant changes during the COVID-19 pandemic in Europe and North America. RESULTS One hundred nine studies were identified and analyzed. Several pollutants exhibited marked and sustained reductions. The strongest was NO2 (93% of 89 estimated changes were reductions) followed by CO (88% of 33 estimated pollutant changes). All NOx and benzene studies reported significant reductions although these were based on fewer than 10 estimates. About three-quarters of PM2.5 and PM10 estimates showed reductions and few studies reported increases when domestic fuel use rose during COVID-19 lockdowns. In contrast, O3 levels rose as NOx levels fell. SO2 and ammonia (NH3) had mixed results. In general, greater reductions appeared when lockdowns were more severe, as well as where baseline pollutant levels were higher, such as at low-elevation and in densely populated areas. Substantial and robust reductions in NO2, NO, CO, CO2, PM2.5, PM10, benzene and air quality index pollution occurred in association with COVID-19 lockdowns. O3 levels tended to increase, while SO2 and NH3 had mixed patterns. CONCLUSIONS Our study shows the profound impact of human activity levels on air pollution and its potential avoidability.
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Affiliation(s)
- Maria Bakola
- Research Unit for General Medicine and Primary Health Care, Faculty of Medicine, School of Health Science, University of Ioannina, Ioannina, Greece.,Department of Public Health, Medical School, University of Patras, Patras, Greece
| | - Ireri Hernandez Carballo
- Department of Social and Political Sciences, Bocconi University, Milan, Italy.,RFF-CMCC European Institute of Economics and the Environment, Centro Euro-Mediterraneo sui Cambiamenti Climatici, Milan, Italy
| | - Eleni Jelastopulu
- Department of Public Health, Medical School, University of Patras, Patras, Greece
| | - David Stuckler
- Department of Social and Political Sciences, Bocconi University, Milan, Italy.,Department of Social & Political Sciences and Dondena Research Centre, University of Bocconi, Milan, Italy
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Macías-hernández BA, Tello-leal E. Analysis of Particulate Matter Concentration Changes before, during, and Post COVID-19 Lockdown: A Case Study from Victoria, Mexico. Atmosphere 2022; 13:827. [DOI: 10.3390/atmos13050827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The lockdown measures implemented due to the SARS-CoV-2 pandemic to reduce the epidemic curve, in most cases, have had a positive impact on air quality indices. Our study describes the changes in the concentration levels of PM2.5 and PM10 during the lockdown and post-lockdown in Victoria, Mexico, considering the following periods: before the lockdown (BL) from 16 February to 14 March, during the lockdown (DL) from 15 March to 2 May, and in the partial lockdown (PL) from 3 May to 6 June. When comparing the DL period of 2019 and 2020, we document a reduction in the average concentration of PM2.5 and PM10 of −55.56% and −55.17%, respectively. Moreover, we note a decrease of −53.57% for PM2.5 and −51.61% for PM10 in the PL period. When contrasting the average concentration between the DL periods of 2020 and 2021, an increase of 91.67% for PM2.5 and 100.00% for PM10 was identified. Furthermore, in the PL periods of 2020 and 2021, an increase of 38.46% and 31.33% was observed for PM2.5 and PM10, respectively. On the other hand, when comparing the concentrations of PM2.5 in the three periods of 2020, we found a decrease between BL and DL of −50.00%, between BL and PL a decrease of −45.83%, and an increase of 8.33% between DL and PL. In the case of PM10, a decrease of −48.00% between BL and DL, −40.00% between BL and PL, and an increase of 15.38% between the DL and PL periods were observed. In addition, we performed a non-parametric statistical analysis, where a significant statistical difference was found between the DL-2020 and DL-2019 pairs (x2 = 1.204) and between the DL-2021 and DL-2019 pairs (x2 = 0.372), with a p<0.000 for PM2.5, and the contrast between pairs of PM10 (DL) showed a significant difference between all pairs with p<0.01.
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