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Gilardi L, Marconcini M, Metz-Marconcini A, Esch T, Erbertseder T. Long-term exposure and health risk assessment from air pollution: impact of regional scale mobility. Int J Health Geogr 2023; 22:11. [PMID: 37208713 PMCID: PMC10196305 DOI: 10.1186/s12942-023-00333-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 05/08/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND The negative effect of air pollution on human health is widely reported in recent literature. It typically involves urbanized areas where the population is concentrated and where most primary air pollutants are produced. A comprehensive health risk assessment is therefore of strategic importance for health authorities. METHODS In this study we propose a methodology to perform an indirect and retrospective health risk assessment of all-cause mortality associated with long-term exposure to particulate matter less than 2.5 microns (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in a typical Monday to Friday working week. A combination of satellite-based settlement data, model-based air pollution data, land use, demographics and regional scale mobility, allowed to examine the effect of population mobility and pollutants daily variations on the health risk. A Health Risk Increase (HRI) metric was derived on the basis of three components: hazard, exposure and vulnerability, utilizing the relative risk values from the World Health Organization. An additional metric, the Health Burden (HB) was formulated, which accounts for the total number of people exposed to a certain risk level. RESULTS The effect of regional mobility patterns on the HRI metric was assessed, resulting in an increased HRI associated with all three stressors when considering a dynamic population compared to a static one. The effect of diurnal variation of pollutants was only observed for NO2 and O3. For both, the HRI metric resulted in significantly higher values during night. Concerning the HB parameter, we identified the commuting flows of the population as the main driver in the resulting metric. CONCLUSIONS This indirect exposure assessment methodology provides tools to support policy makers and health authorities in planning intervention and mitigation measures. The study was carried out in Lombardy, Italy, one of the most polluted regions in Europe, but the incorporation of satellite data makes our approach valuable for studying global health.
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Affiliation(s)
- Lorenza Gilardi
- German Remote Sensing Data Center, Department Atmosphere (DFD-ATM), German Aerospace Center (DLR), Münchener Str. 20, 82234, Weßling, Germany.
| | - Mattia Marconcini
- German Remote Sensing Data Center, Department Land Surface Dynamics (DFD-LAX), German Aerospace Center (DLR), Münchener Str. 20, 82234, Weßling, Germany
| | - Annekatrin Metz-Marconcini
- German Remote Sensing Data Center, Department Land Surface Dynamics (DFD-LAX), German Aerospace Center (DLR), Münchener Str. 20, 82234, Weßling, Germany
| | - Thomas Esch
- German Remote Sensing Data Center, Department Land Surface Dynamics (DFD-LAX), German Aerospace Center (DLR), Münchener Str. 20, 82234, Weßling, Germany
| | - Thilo Erbertseder
- German Remote Sensing Data Center, Department Atmosphere (DFD-ATM), German Aerospace Center (DLR), Münchener Str. 20, 82234, Weßling, Germany
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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: 1] [Impact Index Per Article: 1.0] [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.
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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
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Breen MS, Xu Y, Christopher Frey H, Breen M, Isakov V. Microenvironment Tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation using smartphone data. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:407-415. [PMID: 36526873 DOI: 10.1038/s41370-022-00514-w] [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: 05/31/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 06/03/2023]
Abstract
BACKGROUND A critical aspect of air pollution exposure assessments is determining the time spent in various microenvironments (ME), which can have substantially different pollutant concentrations. We previously developed and evaluated a ME classification model, called Microenvironment Tracker (MicroTrac), to estimate time of day and duration spent in eight MEs (indoors and outdoors at home, work, school; inside vehicles; other locations) based on input data from global positioning system (GPS) loggers. OBJECTIVE In this study, we extended MicroTrac and evaluated the ability of using geolocation data from smartphones to determine the time spent in the MEs. METHOD We performed a panel study, and the MicroTrac estimates based on data from smartphones and GPS loggers were compared to 37 days of diary data across five participants. RESULTS The MEs were correctly classified for 98.1% and 98.3% of the time spent by the participants using smartphones and GPS loggers, respectively. SIGNIFICANCE Our study demonstrates the extended capability of using ubiquitous smartphone data with MicroTrac to help reduce time-location uncertainty in air pollution exposure models for epidemiologic and exposure field studies.
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Affiliation(s)
- Michael S Breen
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
| | - Yadong Xu
- Center for Public Health and Environmental Assessment, ORAU/U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - H Christopher Frey
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC, USA
| | - Miyuki Breen
- Center for Public Health and Environment Assessment, ORISE/U.S. Environmental Protection Agency, Chapel Hill, NC, 27514, USA
| | - Vlad Isakov
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
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Brouwer D, Keretetse G, Nelson G. Quantitative Self-Assessment of Exposure to Solvents among Formal and Informal Nail Technicians in Johannesburg, South Africa. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20085459. [PMID: 37107741 PMCID: PMC10139043 DOI: 10.3390/ijerph20085459] [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: 03/04/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/11/2023]
Abstract
Participatory research, including self-assessment of exposure (SAE), can engage study participants and reduce costs. The objective of this study was to investigate the feasibility and reliability of a SAE regime among nail technicians. The study was nested in a larger study, which included exposure assessment supervised by experts, i.e., controlled assessment of exposure (CAE). In the SAE approach, ten formal and ten informal nail technicians were verbally instructed to use a passive sampler and complete an activity sheet. Each participant conducted measurements on three consecutive days, whereafter the expert collected the passive samplers. Sixty samples were, thus, analyzed for twenty-one volatile organic compounds (VOCs). The reported concentrations of 11 VOCs were converted into total VOC (TVOC) concentrations, adjusted for their respective emission rates (adj TVOC) to allow comparison within and between nail technician categories (formal vs informal), as well as assessment regimes (SAE versus CAE), using the data from the main study. In total, 57 SAE and 58 CAE results were compared, using a linear mixed-effects model. There were variations in individual VOC concentrations, especially for the informal sector participants. The major contributors to the adj TVOC concentrations were acetone and 2-propanol for the formal category, whereas ethyl- and methyl methacrylate contributed most to the informal nail technicians' total exposures. No significant differences in adj TVOC-concentrations were observed between the assessment regimes, but significantly higher exposures were recorded in the formal technicians. The results show that the SAE approach is feasible in the informal service sector and can extend an exposure dataset to enable reliable estimates for scenarios with substantial exposure variations.
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Kouis P, Michanikou A, Galanakis E, Michaelidou E, Dimitriou H, Perez J, Kinni P, Achilleos S, Revvas E, Stamatelatos G, Zacharatos H, Savvides C, Vasiliadou E, Kalivitis N, Chrysanthou A, Tymvios F, Papatheodorou SI, Koutrakis P, Yiallouros PK. Responses of schoolchildren with asthma to recommendations to reduce desert dust exposure: Results from the LIFE-MEDEA intervention project using wearable technology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 860:160518. [PMID: 36573449 DOI: 10.1016/j.scitotenv.2022.160518] [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: 09/28/2022] [Revised: 11/14/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
Current public health recommendations for desert dust storms (DDS) events focus on vulnerable population groups, such as children with asthma, and include advice to stay indoors and limit outdoor physical activity. To date, no scientific evidence exists on the efficacy of these recommendations in reducing DDS exposure. We aimed to objectively assess the behavioral responses of children with asthma to recommendations for reduction of DDS exposure. In two heavily affected by DDS Mediterranean regions (Cyprus & Crete, Greece), schoolchildren with asthma (6-11 years) were recruited from primary schools and were randomized to control (business as usual scenario) and intervention groups. All children were equipped with pedometer and GPS sensors embedded in smartwatches for objective real-time data collection from inside and outside their classroom and household settings. Interventions included the timely communication of personal DDS alerts accompanied by exposure reduction recommendations to both the parents and school-teachers of children in the intervention group. A mixed effect model was used to assess changes in daily levels of time spent, and steps performed outside classrooms and households, between non-DDS and DDS days across the study groups. The change in the time spent outside classrooms and homes, between non-DDS and DDS days, was 37.2 min (pvalue = 0.098) in the control group and -62.4 min (pvalue < 0.001) in the intervention group. The difference in the effects between the two groups was statistically significant (interaction pvalue < 0.001). The change in daily steps performed outside classrooms and homes, was -495.1 steps (pvalue = 0.350) in the control group and -1039.5 (pvalue = 0.003) in the intervention group (interaction pvalue = 0.575). The effects on both the time and steps performed outside were more profound during after-school hours. To summarize, among children with asthma, we demonstrated that timely personal DDS alerts and detailed recommendations lead to significant behavioral changes in contrast to the usual public health recommendations.
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Affiliation(s)
- Panayiotis Kouis
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus
| | - Antonis Michanikou
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus
| | | | | | - Helen Dimitriou
- Medical School, University of Crete, Heraklion, Crete, Greece
| | - Julietta Perez
- Medical School, University of Crete, Heraklion, Crete, Greece
| | - Paraskevi Kinni
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus
| | - Souzana Achilleos
- Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, Cyprus; Cyprus International Institute for Environmental & Public Health, Cyprus University of Technology, Limassol, Cyprus
| | | | | | | | - Chrysanthos Savvides
- Air Quality and Strategic Planning Section, Department of Labour Inspection, Ministry of Labour and Social Insurance, Nicosia, Cyprus
| | - Emily Vasiliadou
- Air Quality and Strategic Planning Section, Department of Labour Inspection, Ministry of Labour and Social Insurance, Nicosia, Cyprus
| | - Nikos Kalivitis
- Department of Chemistry, University of Crete, Heraklion, Crete, Greece
| | | | | | - Stefania I Papatheodorou
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Petros Koutrakis
- Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, USA
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Chatzidiakou L, Krause A, Kellaway M, Han Y, Li Y, Martin E, Kelly FJ, Zhu T, Barratt B, Jones RL. Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution. Environ Health 2022; 21:125. [PMID: 36482402 PMCID: PMC9733291 DOI: 10.1186/s12940-022-00939-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 11/07/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity. METHODS We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants. RESULTS Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs. CONCLUSIONS Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies.
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Affiliation(s)
- Lia Chatzidiakou
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
| | - Anika Krause
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
- Institute for Chemistry, University of Potsdam, Karl-Liebknecht-Straße 24-25, 14476 Potsdam, Germany
| | | | - Yiqun Han
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ London, UK
| | - Yilin Li
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
| | - Elizabeth Martin
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
| | - Frank J. Kelly
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ London, UK
| | - Tong Zhu
- BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Center for Environment and Health, Peking University, 100871 Beijing, China
| | - Benjamin Barratt
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ London, UK
| | - Roderic L. Jones
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
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Vosburgh DJH, Cauda E, O’Shaughnessy PT, Sheehan MJ, Park JH, Anderson K. Direct-reading instruments for aerosols: A review for occupational health and safety professionals part 1: Instruments and good practices. JOURNAL OF OCCUPATIONAL AND ENVIRONMENTAL HYGIENE 2022; 19:696-705. [PMID: 36197119 PMCID: PMC10679882 DOI: 10.1080/15459624.2022.2132255] [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] [Indexed: 06/16/2023]
Abstract
With advances in technology, there are an increasing number of direct-reading instruments available to occupational health and safety professionals to evaluate occupational aerosol exposures. Despite the wide array of direct-reading instruments available to professionals, the adoption of direct-reading technology to monitor workplace exposures has been limited, partly due to a lack of knowledge on how the instruments operate, how to select an appropriate instrument, and challenges in data analysis techniques. This paper presents a review of direct-reading aerosol instruments available to occupational health and safety professionals, describes the principles of operation, guides instrument selection based on the workplace and exposure, and discusses data analysis techniques to overcome these barriers to adoption. This paper does not cover all direct-reading instruments for aerosols but only those that an occupational health and safety professional could use in a workplace to evaluate exposures. Therefore, this paper focuses on instruments that have the most potential for workplace use due to their robustness, past workplace use, and price with regard to return on investment. The instruments covered in this paper include those that measure aerosol number concentration, mass concentration, and aerosol size distributions.
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Affiliation(s)
- Donna J. H. Vosburgh
- Department of Occupational & Environmental Safety & Health, University of Wisconsin-Whitewater, Whitewater, Wisconsin
| | - Emanuele Cauda
- Pittsburgh Mining Research Division, National Institute for Occupational Safety and Health, Pittsburgh, Pennsylvania
| | | | - Maura J. Sheehan
- Department of Health, West Chester University, West Chester, Pennsylvania
| | - Jae Hong Park
- School of Health Sciences, Purdue University, West Lafayette, Indiana
| | - Kimberly Anderson
- Respiratory Health Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia
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Russell HS, Kappelt N, Fessa D, Frederickson LB, Bagkis E, Apostolidis P, Karatzas K, Schmidt JA, Hertel O, Johnson MS. Particulate air pollution in the Copenhagen metro part 2: Low-cost sensors and micro-environment classification. ENVIRONMENT INTERNATIONAL 2022; 170:107645. [PMID: 36434885 DOI: 10.1016/j.envint.2022.107645] [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: 06/19/2022] [Revised: 10/12/2022] [Accepted: 11/16/2022] [Indexed: 06/16/2023]
Abstract
In this study fine particulate matter (PM2.5) levels throughout the Copenhagen metro system are measured for the first time and found to be ∼10 times the roadside levels in Copenhagen. In this Part 2 article, low-cost sensor (LCS) nodes designed for personal-exposure monitoring are tested against a conventional mid-range device (TSI DustTrak), and gravimetric methods. The nodes were found to be effective for personal exposure measurements inside the metro system, with R2 values of > 0.8 at 1-min and > 0.9 at 5-min time-resolution, with an average slope of 1.01 in both cases, in comparison to the reference, which is impressive for this dynamic environment. Micro-environment (ME) classification techniques are also developed and tested, involving the use of auxiliary sensors, measuring light, carbon dioxide, humidity, temperature and motion. The output from these sensors is used to distinguish between specific MEs, namely, being aboard trains travelling above- or under- ground, with 83 % accuracy, and determining whether sensors were aboard a train or stationary at a platform with 92 % accuracy. This information was used to show a 143 % increase in mean PM2.5 concentration for underground sections relative to overground, and 22 % increase for train vs. platform measurements. The ME classification method can also be used to improve calibration models, assist in accurate exposure assessment based on detailed time-activity patterns, and facilitate field studies that do not require personnel to record time-activity diaries.
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Affiliation(s)
- Hugo S Russell
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark; AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
| | - Niklas Kappelt
- AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Department of Chemistry, Copenhagen University, DK-2100 Copenhagen, Denmark
| | - Dafni Fessa
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
| | - Louise B Frederickson
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark; AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
| | - Evangelos Bagkis
- Environmental Informatics Research Group, School of Mechanical Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
| | - Pantelis Apostolidis
- Environmental Informatics Research Group, School of Mechanical Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
| | - Kostas Karatzas
- Environmental Informatics Research Group, School of Mechanical Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
| | | | - 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 S Johnson
- AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Department of Chemistry, Copenhagen University, DK-2100 Copenhagen, Denmark.
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