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Opejin A, Park YM. Assessing bias in personal exposure estimates when indoor air quality is ignored: A comparison between GPS-enabled mobile air sensor data and stationary outdoor sensor data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 950:175249. [PMID: 39098424 DOI: 10.1016/j.scitotenv.2024.175249] [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: 04/29/2024] [Revised: 06/21/2024] [Accepted: 08/01/2024] [Indexed: 08/06/2024]
Abstract
Neglecting indoor air quality in exposure assessments may lead to biased exposure estimates and erroneous conclusions about the health impacts of exposure and environmental health disparities. This study assessed these biases by comparing two types of personal exposure estimates for 100 individuals: one derived from real-time particulate matter (PM2.5) measurements collected both indoors and outdoors using a low-cost portable air monitor (GeoAir2.0) and the other from PurpleAir sensor network data collected exclusively outdoors. The PurpleAir measurement data were used to create smooth air pollution surfaces using geostatistical methods. To obtain mobility-based exposure estimates, both sets of air pollution data were combined with the individuals' GPS tracking data. Paired-sample t-tests were then performed to examine the differences between these two estimates. This study also investigated whether GeoAir2.0- and PurpleAir-based estimates yielded consistent conclusions about gender and economic disparities in exposure by performing Welch's t-tests and ANOVAs and comparing their t-values and F-values. The study revealed significant discrepancies between GeoAir2.0- and PurpleAir-based estimates, with PurpleAir data consistently overestimating exposure (t = 5.94; p < 0.001). It also found that females displayed a higher average exposure than males (15.65 versus. 8.55 μg/m3) according to GeoAir2.0 data (t = 4.654; p = 0.055), potentially due to greater time spent indoors engaging in pollution-generating activities traditionally associated with females, such as cooking. This contrasted with the PurpleAir data, which indicated higher exposure for males (43.78 versus. 46.26 μg/m3) (t = 3.793; p = 0.821). Additionally, GeoAir2.0 data revealed significant economic disparities (F = 7.512; p < 0.002), with lower-income groups experiencing higher exposure-a disparity not captured by PurpleAir data (F = 0.756; p < 0.474). These findings highlight the importance of considering both indoor and outdoor air quality to reduce bias in exposure estimates and more accurately represent environmental disparities.
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Affiliation(s)
- Abdulahi Opejin
- Department of Geography, Planning, and Environment, East Carolina University, 1000 E. 5th St., Greenville, NC 27858, USA.
| | - Yoo Min Park
- Department of Geography, Sustainability, Community, and Urban Studies, University of Connecticut, 215 Glenbrook Rd., Storrs, CT 06269, USA.
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2
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Manchanda C, Harley RA, Marshall JD, Turner AJ, Apte JS. Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:12563-12574. [PMID: 38950186 PMCID: PMC11256762 DOI: 10.1021/acs.est.3c10829] [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: 12/22/2023] [Revised: 06/07/2024] [Accepted: 06/10/2024] [Indexed: 07/03/2024]
Abstract
Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.
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Affiliation(s)
- Chirag Manchanda
- Department
of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
| | - Robert A. Harley
- Department
of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
| | - Julian D. Marshall
- Department
of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Alexander J. Turner
- Department
of Atmospheric Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Joshua S. Apte
- Department
of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
- School
of Public Health, University of California, Berkeley, California 94720, United States
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3
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de Souza P, Anenberg S, Makarewicz C, Shirgaokar M, Duarte F, Ratti C, Durant JL, Kinney PL, Niemeier D. Quantifying Disparities in Air Pollution Exposures across the United States Using Home and Work Addresses. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:280-290. [PMID: 38153403 DOI: 10.1021/acs.est.3c07926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
While human mobility plays a crucial role in determining ambient air pollution exposures and health risks, research to date has assessed risks on the basis of almost solely residential location. Here, we leveraged a database of ∼128-144 million workers in the United States and published ambient PM2.5 data between 2011 and 2018 to explore how incorporating information on both workplace and residential location changes our understanding of disparities in air pollution exposure. In general, we observed higher workplace exposures relative to home exposures, as well as increased exposures for nonwhite and less educated workers relative to the national average. Workplace exposure disparities were higher among racial and ethnic groups and job types than by income, education, age, and sex. Not considering workplace exposures can lead to systematic underestimations in disparities in exposure among these subpopulations. We also quantified the error in assigning workers home instead of a weighted home-and-work exposure. We observed that biases in associations between PM2.5 and health impacts by using home instead of home-and-work exposure were the highest among urban, younger populations.
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Affiliation(s)
- Priyanka de Souza
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, Colorado 80202, United States
- CU Population Center, University of Colorado Boulder, Boulder, Colorado 80302, United States
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Susan Anenberg
- Milken Institute School of Public Health, George Washington University, Washington, D.C. 20037, United States
| | - Carrie Makarewicz
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, Colorado 80202, United States
| | - Manish Shirgaokar
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, Colorado 80202, United States
| | - Fabio Duarte
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Carlo Ratti
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - John L Durant
- Department of Civil and Environmental Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Patrick L Kinney
- Boston University School of Public Health, Boston, Massachusetts 02118, United States
| | - Deb Niemeier
- Department of Civil and Environmental Engineering, University of Maryland, College Park, Maryland 20742, United States
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4
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Sabedotti MES, O'Regan AC, Nyhan MM. Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:19637-19648. [PMID: 37972280 DOI: 10.1021/acs.est.3c05000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Unprecedented levels of urbanization have escalated urban environmental health issues, including increased air pollution in cities globally. Strategies for mitigating air pollution, including green urban planning, are essential for sustainable and healthy cities. State-of-the-art research investigating urban greenspace and pollution metrics has accelerated through the use of vast digital data sets and new analytical tools. In this study, we examined associations between Google Street View-derived urban greenspace levels and Google Air View-derived air quality, where both have been resolved in extremely high resolution, accuracy, and scale along the entire road network of Dublin City. Particulate matter of size fraction less than 2.5 μm (PM2.5), nitrogen dioxide, nitric oxide, carbon monoxide, and carbon dioxide were quantified using 5,030,143 Google Air View measurements, and greenspace was quantified using 403,409 Google Street View images. Significant (p < 0.001) negative associations between urban greenspace and pollution were observed. For example, an interquartile range increase in the Green View Index was associated with a 7.4% [95% confidence interval: -13.1%, -1.3%] decrease in NO2 at the point location spatial resolution. We provide insights into how large-scale digital data can be harnessed to elucidate urban environmental interactions that will have important planning and policy implications for sustainable future cities.
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Affiliation(s)
- Maria E S Sabedotti
- Discipline of Civil, Structural & Environmental Engineering, School of Engineering & Architecture, University College Cork, Cork T12 K8AF, Ireland
- MaREI, the SFI Research Centre for Energy, Climate & Marine, University College Cork, Ringaskiddy, CorkP43 C573, Ireland
- Environmental Research Institute, University College Cork, Lee Rd, Sundays, Well, Cork T23 XE10, Ireland
| | - Anna C O'Regan
- Discipline of Civil, Structural & Environmental Engineering, School of Engineering & Architecture, University College Cork, Cork T12 K8AF, Ireland
- MaREI, the SFI Research Centre for Energy, Climate & Marine, University College Cork, Ringaskiddy, CorkP43 C573, Ireland
- Environmental Research Institute, University College Cork, Lee Rd, Sundays, Well, Cork T23 XE10, Ireland
| | - Marguerite M Nyhan
- Discipline of Civil, Structural & Environmental Engineering, School of Engineering & Architecture, University College Cork, Cork T12 K8AF, Ireland
- MaREI, the SFI Research Centre for Energy, Climate & Marine, University College Cork, Ringaskiddy, CorkP43 C573, Ireland
- Environmental Research Institute, University College Cork, Lee Rd, Sundays, Well, Cork T23 XE10, Ireland
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5
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O'Regan AC, Nyhan MM. Towards sustainable and net-zero cities: A review of environmental modelling and monitoring tools for optimizing emissions reduction strategies for improved air quality in urban areas. ENVIRONMENTAL RESEARCH 2023; 231:116242. [PMID: 37244499 DOI: 10.1016/j.envres.2023.116242] [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: 02/09/2023] [Revised: 04/20/2023] [Accepted: 05/25/2023] [Indexed: 05/29/2023]
Abstract
Climate change is a defining challenge for today's society and its consequences pose a great threat to humanity. Cities are major contributors to climate change, accounting for over 70% of global greenhouse gas emissions. With urbanization occurring at a rapid rate worldwide, cities will play a key role in mitigating emissions and addressing climate change. Greenhouse gas emissions are strongly interlinked with air quality as they share emission sources. Consequently, there is a great opportunity to develop policies which maximize the co-benefits of emissions reductions on air quality and health. As such, a narrative meta-review is conducted to highlight state-of-the-art monitoring and modelling tools which can inform and monitor progress towards greenhouse gas emission and air pollution reduction targets. Urban greenspace will play an important role in the transition to net-zero as it promotes sustainable and active transport modes. Therefore, we explore advancements in urban greenspace quantification methods which can aid strategic developments. There is great potential to harness technological advancements to better understand the impact of greenhouse gas reduction strategies on air quality and subsequently inform the optimal design of these strategies going forward. An integrated approach to greenhouse gas emission and air pollution reduction will create sustainable, net-zero and healthy future cities.
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Affiliation(s)
- Anna C O'Regan
- Discipline of Civil, Structural & Environmental Engineering, School of Engineering & Architecture, University College Cork, Cork, Ireland; MaREI, The SFI Research Centre for Energy, Climate & Marine, University College Cork, Ringaskiddy, Cork, P43 C573, Ireland; Environmental Research Institute, University College Cork, Lee Rd, Sunday's Well, Cork, T23 XE10, Ireland
| | - Marguerite M Nyhan
- Discipline of Civil, Structural & Environmental Engineering, School of Engineering & Architecture, University College Cork, Cork, Ireland; MaREI, The SFI Research Centre for Energy, Climate & Marine, University College Cork, Ringaskiddy, Cork, P43 C573, Ireland; Environmental Research Institute, University College Cork, Lee Rd, Sunday's Well, Cork, T23 XE10, Ireland.
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6
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Tryner J, Quinn C, Molina Rueda E, Andales MJ, L'Orange C, Mehaffy J, Carter E, Volckens J. AirPen: A Wearable Monitor for Characterizing Exposures to Particulate Matter and Volatile Organic Compounds. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023. [PMID: 37450410 PMCID: PMC10373498 DOI: 10.1021/acs.est.3c02238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Exposure to air pollution is a leading risk factor for disease and premature death, but technologies for assessing personal exposure to particulate and gaseous air pollutants, including the timing and location of such exposures, are limited. We developed a small, quiet, wearable monitor, called the AirPen, to quantify personal exposures to fine particulate matter (PM2.5) and volatile organic compounds (VOCs). The AirPen combines physical sample collection (PM onto a filter and VOCs onto a sorbent tube) with a suite of low-cost sensors (for PM, VOCs, temperature, pressure, humidity, light intensity, location, and motion). We validated the AirPen against conventional personal sampling equipment in the laboratory and then conducted a field study to measure at-work and away-from-work exposures to PM2.5 and VOCs among employees at an agricultural facility in Colorado, USA. The resultant sampling and sensor data indicated that personal exposures to benzene, toluene, ethylbenzene, and xylenes were dominated by a specific workplace location. These results illustrate how the AirPen can be used to advance our understanding of personal exposure to air pollution as a function of time, location, source, and activity, even in the absence of detailed activity diary data.
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Affiliation(s)
- Jessica Tryner
- Department of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, Colorado 80523, United States
| | - Casey Quinn
- Department of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, Colorado 80523, United States
| | - Emilio Molina Rueda
- Department of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, Colorado 80523, United States
| | - Marie J Andales
- Department of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, Colorado 80523, United States
| | - Christian L'Orange
- Department of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, Colorado 80523, United States
| | - John Mehaffy
- Department of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, Colorado 80523, United States
| | - Ellison Carter
- Department of Civil and Environmental Engineering, Colorado State University, 1372 Campus Delivery, Fort Collins, Colorado 80523, United States
| | - John Volckens
- Department of Mechanical Engineering, Colorado State University, 1374 Campus Delivery, Fort Collins, Colorado 80523, United States
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7
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Dehhaghi S, Hasankhani H, Taheri A. Spatiotemporal variations, photochemical characteristics, health risk assessment and mid pandemic changes of ambient BTEX in a west Asian metropolis. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2023; 37:1-17. [PMID: 37362845 PMCID: PMC10218775 DOI: 10.1007/s00477-023-02476-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/13/2023] [Indexed: 06/28/2023]
Abstract
This study examined the concentration of BTEX in Tehran from 2018 to 2020 in five monitoring stations with different backgrounds, which has been accomplished using the combination of passive sampling and GC-FID method. The total concentration of BTEX was estimated to be 65.39 (µg/m3), with a higher average concentration in 2019-2020 (77.79 µg/m3) compared to 2018-2019 (53.48 µg/m3) due to the leaping concentration of Toluene in the pandemic era. Despite a Benzene concentration decline in recent years, the average annual concentration of Benzene (5.66 µg/m3) at five stations remained higher than the EU commission and India standards (5 µg/m3) as well as Japan and Iraq thresholds (3 µg/m3). Toluene dominated other species in terms of concentrations, mass distribution (~0.6%), followed by m,p-Xylene (~0.2%), Benzene (~0.05-0.1) and Ethylbenzene (< 0.05). The evidence regarding seasonal changes of BTEX in 2019 shows the maximum concentration of these compounds in autumn, which is probably due to heavier traffic compared to other seasons. In contrast, in the first half of 2020 (which encompasses the start of the pandemic period and urban lockdown), point sources seem to play a prominent role in concentration fluctuations, as confirmed by changes in interspecies relationships and lower traffic congestion. The highest mean concentrations were observed in high-traffic, residential and suburban sites, respectively. The study reveals that m,p-Xylene possess the highest Ozone formation potential (~109.46), followed by Toluene (~85.34), o-Xylene (~46.87), Ethylbenzene (~13.52) and Benzene (~2.61). Health risk assessment results indicated the high carcinogenic risk of Benzene (mean = 3.6 × 10-6) and the acceptable non-carcinogenic risk of BTEX (hazard index~0.03 < specified limit of 1). Finally, the estimated weighted exposures of BTEX emphasized that residents near the high-traffic districts are more exposed to BTEX. Supplementary Information The online version contains supplementary material available at 10.1007/s00477-023-02476-3.
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Affiliation(s)
- Sam Dehhaghi
- Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
| | | | - Ahmad Taheri
- Tehran Air Quality Control Company, Tehran Municipality, Tehran, Iran
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8
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Comparison of static and dynamic exposures to air pollution, noise, and greenness among seniors living in compact-city environments. Int J Health Geogr 2023; 22:3. [PMID: 36709304 PMCID: PMC9884423 DOI: 10.1186/s12942-023-00325-8] [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: 11/02/2022] [Accepted: 01/13/2023] [Indexed: 01/29/2023] Open
Abstract
GPS technology and tracking study designs have gained popularity as a tool to go beyond the limitations of static exposure assessments based on the subject's residence. These dynamic exposure assessment methods offer high potential upside in terms of accuracy but also disadvantages in terms of cost, sample sizes, and types of data generated. Because of that, with our study we aim to understand in which cases researchers need to use GPS-based methods to guarantee the necessary accuracy in exposure assessment. With a sample of 113 seniors living in Barcelona (Spain) we compare their estimated daily exposures to air pollution (PM2.5, PM10, NO2), noise (dB), and greenness (NDVI) using static and dynamic exposure assessment techniques. Results indicate that significant differences between static and dynamic exposure assessments are only present in selected exposures, and would thus suggest that static assessments using the place of residence would provide accurate-enough values across a number of exposures in the case of seniors. Our models for Barcelona's seniors suggest that dynamic exposure would only be required in the case of exposure to smaller particulate matter (PM2.5) and exposure to noise levels. The study signals to the need to consider both the mobility patterns and the built environment context when deciding between static or dynamic measures of exposure assessment.
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9
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Lyu Y, Wang W, Wu Y, Zhang J. How does digital economy affect green total factor productivity? Evidence from China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159428. [PMID: 36244487 DOI: 10.1016/j.scitotenv.2022.159428] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 05/22/2023]
Abstract
To date, digital economy has become an emerging driving force of economic growth in various countries. To further explore the green value of digital economy, full array polygon graphic index method and Global Malmquist productivity index are used to evaluate China's digital economy development and green total factor productivity respectively. Based on a comprehensive explanation of the influence mechanism, spatial econometric model and intermediary effect model are constructed to test the spatial spillover effect and transmission mechanism between digital economy and green total factor productivity. The results show that digital economy has a positive direct impact and spatial spillover effect on green total factor productivity with the significant U-shaped characteristics, and these effects mainly come from the promotion of green technology progress by digital economy. Industrial structure upgrading and factor market distortion respectively account for 22 % and 5.875 % of the impact of digital economy on green total factor efficiency, which means that they are the two primary channels for digital economy to influence green total factor productivity. Results of heterogeneity analysis show that digital economy is the key factor for resource-based cities to break the "resource curse", but the unbalanced development of digital economy leads to the "digital gap" between the central and peripheral cities.
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Affiliation(s)
- Yanwei Lyu
- School of Business, Shandong University, Weihai, 264209, China.
| | - Wenqiang Wang
- School of Business, Shandong University, Weihai, 264209, China
| | - You Wu
- School of Business, Shandong University, Weihai, 264209, China
| | - Jinning Zhang
- School of Business, Shandong University, Weihai, 264209, China.
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10
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Zhu X, Zhang B, Yuan H. Digital economy, industrial structure upgrading and green total factor productivity--Evidence in textile and apparel industry from China. PLoS One 2022; 17:e0277259. [PMID: 36331964 PMCID: PMC9635753 DOI: 10.1371/journal.pone.0277259] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Abstract
According to the standard of GB/T4754-2017 Classification of National Economic Industry and the characteristics of the textile and apparel industry, the textile and apparel industry is divided into three categories: textile industry, clothing industry and chemical fiber manufacturing industry. Based on the panel data of the textile and apparel industry from 2010 to 2019, this paper measures green total factor productivity (GTFP) by using the unexpected output super efficiency SBM model and the ML index. On this basis, this paper empirically tests the impact of digital economy on the GTFP of textile and apparel industry, and the dual intermediary effects of rationalization of industrial structure and advanced industrial structure are discussed. The results show that: (1) The GTFP of the textile and apparel industry shows a fluctuating upward trend, but it is in a state of low growth. (2) Digital economy has a significant effect on promoting the GTFP. Among them, it has a positive effect on the improvement of GTFP in textile industry, but has no obvious effect on the clothing industry, and has a restraining effect on the chemical fiber manufacturing industry. (3) In the process of the impact of digital economy on GTFP, the rationalization of industrial structure has a partial intermediary effect, and the level of effect reaches 35.81%, while the advancement of industrial structure does not necessarily have a "structural dividend", and its influence on GTFP is non-linear. This paper enriches the research on the influencing factors of GTFP, and is also an effective supplement to the research on digital economy. The conclusions provide a reliable empirical basis for digital economy to help the textile and apparel industry pollution control, and also provide policy references for giving full play to the green value of digital economy.
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Affiliation(s)
- Xiangmei Zhu
- School of Economics and Management, North University of China, Taiyuan, Shanxi, China
| | - Bin Zhang
- School of Economics and Management, North University of China, Taiyuan, Shanxi, China
- * E-mail:
| | - Hui Yuan
- School of Economics and Management, North University of China, Taiyuan, Shanxi, China
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11
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Xu Y, Yi L, Cabison J, Rosales M, O'Sharkey K, Chavez TA, Johnson M, Lurmann F, Pavlovic N, Bastain TM, Breton CV, Wilson JP, Habre R. The impact of GPS-derived activity spaces on personal PM 2.5 exposures in the MADRES cohort. ENVIRONMENTAL RESEARCH 2022; 214:114029. [PMID: 35932832 DOI: 10.1016/j.envres.2022.114029] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 07/22/2022] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND In-utero exposure to particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) is associated with low birth weight and health risks later in life. Pregnant women are mobile and locations they spend time in contribute to their personal PM2.5 exposures. Therefore, it is important to understand how mobility and exposures encountered within activity spaces contribute to personal PM2.5 exposures during pregnancy. METHODS We collected 48-h integrated personal PM2.5 samples and continuous geolocation (GPS) data for 213 predominantly Hispanic/Latina pregnant women in their 3rd trimester in Los Angeles, CA. We also collected questionnaires and modeled outdoor air pollution and meteorology in their residential neighborhood. We calculated three GPS-derived activity space measures of exposure to road networks, greenness (NDVI), parks, traffic volume, walkability, and outdoor PM2.5 and temperature. We used bivariate analyses to screen variables (GPS-extracted exposures in activity spaces, individual characteristics, and residential neighborhood exposures) based on their relationship with personal, 48-h integrated PM2.5 concentrations. We then built a generalized linear model to explain the variability in personal PM2.5 exposure and identify key contributing factors. RESULTS Indoor PM2.5 sources, parity, and home ventilation were significantly associated with personal exposure. Activity-space based exposure to roads was associated with significantly higher personal PM2.5 exposure, while greenness was associated with lower personal PM2.5 exposure (β = -3.09 μg/m3 per SD increase in NDVI, p-value = 0.018). The contribution of outdoor PM2.5 to personal exposure was positive but relatively lower (β = 2.05 μg/m3 per SD increase, p-value = 0.016) than exposures in activity spaces and the indoor environment. The final model explained 34% of the variability in personal PM2.5 concentrations. CONCLUSIONS Our findings highlight the importance of activity spaces and the indoor environment on personal PM2.5 exposures of pregnant women living in Los Angeles, CA. This work also showcases the multiple, complex factors that contribute to total personal PM2.5 exposure.
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Affiliation(s)
- Yan Xu
- Spatial Sciences Institute, University of Southern California, USA.
| | - Li Yi
- Spatial Sciences Institute, University of Southern California, USA.
| | - Jane Cabison
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - Marisela Rosales
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - Karl O'Sharkey
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - Thomas A Chavez
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - Mark Johnson
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | | | | | - Theresa M Bastain
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - Carrie V Breton
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - John P Wilson
- Spatial Sciences Institute, University of Southern California, USA; Department of Population and Public Health Sciences, University of Southern California, USA; Department of Civil & Environmental Engineering, Computer Science, and Sociology, University of Southern California, USA.
| | - Rima Habre
- Spatial Sciences Institute, University of Southern California, USA; Department of Population and Public Health Sciences, University of Southern California, USA.
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12
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Yu YT, Xiang S, Zhang T, You Y, Si S, Zhang S, Wu Y. Evaluation of City-Scale Disparities in PM 2.5 Exposure Using Hyper-Localized Taxi-Based Mobile Monitoring. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:13584-13594. [PMID: 36124860 DOI: 10.1021/acs.est.2c02354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Efforts have been directed to pollution control of fine particles (PM2.5) because exposure to PM2.5 could result in adverse health effects. However, PM2.5 exposure disparities persisted even with largely declined concentrations. Here, we applied taxi-based measurements to characterize hyper-localized PM2.5 exposures (30 m resolution) in Xi'an in December 2019 and July 2020. A big data set was derived from the taxi-based measurements (∼6 × 106 hourly PM2.5) and was used to evaluate the performance of existing regulatory measurements in urban and rural regions. Results from regulatory measurements tend to alleviate PM2.5 exposure disparities compared with taxi-based measurements. The taxi-based measurements reported higher population-weighted average (PWA) exposure in December 2019 (90 μg/m3) and July 2020 (27 μg/m3) compared to regulatory measurements (76 and 24 μg/m3 in December and July, respectively) with a wider range of relative disparities. Results indicate that the urban region would be overrepresented by regulatory measurements, where regulatory measurements reported that 60.0-84.7% of inhabitants were exposed to PM2.5 higher than PWA, while taxi-based ones reported a smaller portion (22.6-35.2%). Significant seasonal variability in PM2.5 exposure levels was found by taxi-based measurements but not regulatory measurements. The results highlight the need for providing complementary measurements (e.g., low-cost) for exposure assessment because the rural regions could be disproportionately exposed and overlooked by existing regulatory measurements.
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Affiliation(s)
- Yu Ting Yu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
| | - Sheng Xiang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
| | - Tong Zhang
- Department of Atmospheric Environment, Xi'an Ecological Environment Bureau, Xi'an 710021, PR China
| | - Yan You
- National Observation and Research Station of Coastal Ecological Environments in Macao, Macao SAR 999078, China
- Environmental Research Institute, Macau University of Science and Technology, Macao SAR 999078, China
| | - Shuchun Si
- School of Physics, Shandong University, Jinan 250100, P.R. China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
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13
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Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NO 2 Exposure. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105872. [PMID: 35627409 PMCID: PMC9141847 DOI: 10.3390/ijerph19105872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 11/17/2022]
Abstract
Previous studies on exposure disparity have focused more on spatial variation but ignored the temporal variation of air pollution; thus, it is necessary to explore group disparity in terms of spatio-temporal variation to assist policy-making regarding public health. This study employed the dynamic land use regression (LUR) model and mobile phone signal data to illustrate the variation features of group disparity in Shanghai. The results showed that NO2 exposure followed a bimodal, diurnal variation pattern and remained at a high level on weekdays but decreased on weekends. The most critical at-risk areas were within the central city in areas with a high population density. Moreover, women and the elderly proved to be more exposed to NO2 pollution in Shanghai. Furthermore, the results of this study showed that it is vital to focus on land-use planning, transportation improvement programs, and population agglomeration to attenuate exposure inequality.
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14
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Assessing personal travel exposure to on-road PM 2.5 using cellphone positioning data and mobile sensors. Health Place 2022; 75:102803. [PMID: 35443227 DOI: 10.1016/j.healthplace.2022.102803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 03/29/2022] [Accepted: 04/05/2022] [Indexed: 11/21/2022]
Abstract
PM2.5 pollution imposes substantial health risks on urban residents. Previous studies mainly focused on assessing peoples' exposures at static locations, such as homes or workplaces. There has been a scarcity of research that quantifies the dynamic PM2.5 exposures of people when they travel in cities. To address this gap, we use cellphone positioning data and PM2.5 concentration data collected from smart sensors along roads in Guangzhou, China, to assess personal travel exposure to on-road PM2.5. First, we extract the trips of cellphone users from their trajectories and use the shortest path algorithm to calculate their travel routes on the road network. Second, the travel exposure of each user is estimated by associating their movement patterns with PM2.5 concentrations on roads. The result shows that most users' average travel exposures per hour fall within the range of 20 ug/m3 to 75 ug/m3. Travel exposure varies across users, and 54.0% of users experience low travel exposure throughout the day, 25.5% of users experience high travel exposure in the evening, and 20.5% of users experience high travel exposure in the afternoon. Furthermore, the impacts of on-road PM2.5 on urban populations are uneven across roads. More attention should be given to roads with high PM2.5 concentrations and traffic flows in each period, such as Huan Shi Middle Road in the morning, Inner Ring Road in the afternoon, and Xinjiao Middle Road in the evening. The findings in this study can contribute to a more in-depth understanding of the relationship between air pollution and the travel activities of urban populations.
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15
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Furey RP, Merrill NH, Sawyer JP, Mulvaney KK, Mazzotta MJ. Evaluating water quality impacts on visitation to coastal recreation areas using data derived from cell phone locations. PLoS One 2022; 17:e0263649. [PMID: 35476786 PMCID: PMC9045601 DOI: 10.1371/journal.pone.0263649] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 01/24/2022] [Indexed: 11/22/2022] Open
Abstract
Linking human behavior to environmental quality is critical for effective natural resource management. While it is commonly assumed that environmental conditions partially explain variation in visitation to coastal recreation areas across space and time, scarce and inconsistent visitation observations challenge our ability to reveal these connections. With the ubiquity of mobile phone usage, novel sources of digitally derived data are increasingly available at a massive scale. Applications of mobile phone locational data have been effective in research on urban-centric human mobility and transportation, but little work has been conducted on understanding behavioral patterns surrounding dynamic natural resources. We present an application of cell phone locational data to estimate the effects of beach closures, based on measured bacteria threshold exceedances, on visitation to coastal access points. Our results indicate that beach closures on Cape Cod, MA, USA have a significant negative effect on visitation at those beaches with closures, while closures at a sample of coastal access points elsewhere in New England have no detected impact on visitation. Our findings represent geographic mobility patterns for over 7 million unique coastal visits and suggest that closures resulted in approximately 1,800 (0.026%) displaced visits for Cape Cod during the summer season of 2017. We demonstrate the potential for human-mobility data derived from mobile phones to reveal the scale of use and behavior in response to changes in dynamic natural resources. Potential future applications of passively collected geocoded data to human-environmental systems are vast.
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Affiliation(s)
- Ryan P. Furey
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Atlantic Coastal Environmental Sciences Division, Narragansett, Rhode Island, United States of America
- Oak Ridge Associated Universities (ORAU), Oak Ridge, Tennessee, United States of America
- * E-mail:
| | - Nathaniel H. Merrill
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Atlantic Coastal Environmental Sciences Division, Narragansett, Rhode Island, United States of America
| | - Josh P. Sawyer
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Atlantic Coastal Environmental Sciences Division, Narragansett, Rhode Island, United States of America
- Oak Ridge Associated Universities (ORAU), Oak Ridge, Tennessee, United States of America
| | - Kate K. Mulvaney
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Atlantic Coastal Environmental Sciences Division, Narragansett, Rhode Island, United States of America
| | - Marisa J. Mazzotta
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Atlantic Coastal Environmental Sciences Division, Narragansett, Rhode Island, United States of America
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16
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Fascista A. Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives. SENSORS (BASEL, SWITZERLAND) 2022; 22:1824. [PMID: 35270970 PMCID: PMC8914857 DOI: 10.3390/s22051824] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/11/2022] [Accepted: 02/22/2022] [Indexed: 01/04/2023]
Abstract
Fighting Earth's degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today's society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.
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Affiliation(s)
- Alessio Fascista
- Department of Engineering, University of Salento, Via Monteroni, 73100 Lecce, Italy
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17
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Wang J, Wang W, Ran Q, Irfan M, Ren S, Yang X, Wu H, Ahmad M. Analysis of the mechanism of the impact of internet development on green economic growth: evidence from 269 prefecture cities in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:9990-10004. [PMID: 34510353 DOI: 10.1007/s11356-021-16381-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/02/2021] [Indexed: 05/22/2023]
Abstract
As the digital economy develops rapidly and the network information technology advances, new development models represented by the network economy have emerged, which have a crucial impact on green economic growth. However, the relevant previous studies lacked the role of analyzing the direct and indirect effects of internet development on green economic growth at the prefecture-level city level. For this purpose, this paper aims to examine the intrinsic mechanism of the impact of internet development on green economic growth and provide empirical support for cities and regions in China to increase internet construction. Furthermore, the mixed model (EBM), which includes both radial and non-radial distance functions, is applied to calculate the green economic growth index. Fixed effect model and mediation effect model are also employed to test influence mechanisms of the internet development on green economic growth using panel data of 269 prefecture-level cities in China from 2004 to 2019. The statistical results reveal that internet development has contributed significantly to green economic growth. When the internet development level increases by 1 unit, the green economic growth level increases by an average of 5.0372 units. However, regional heterogeneity is evident between internet development and green economic growth, that is, the promoting effect of internet development on green economic growth is gradually enhanced from the eastern region to the western region. We also find that internet development guides industrial structure upgrading improves environmental quality and accelerates enterprise innovation, which indirectly contributes to green economic growth. And internet development mainly achieves green economic growth through enterprise innovation. Based on the above findings, we concluded that policymakers should not only strengthen the guiding role of social actors to promote the stable development of the internet industry, but also foster the construction of the three models of "internet+industry integration," "internet+environmental governance," and "internet+enterprise innovation" to promote green economic growth.
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Affiliation(s)
- Jianlong Wang
- School of Economics and Management, Xinjiang University, Urumqi, 830047, China
- Center for Innovation Management Research of Xinjiang, Urumqi, 830047, China
| | - Weilong Wang
- School of Economics and Management, Xinjiang University, Urumqi, 830047, China
- Center for Innovation Management Research of Xinjiang, Urumqi, 830047, China
| | - Qiying Ran
- School of Economics and Management, Xinjiang University, Urumqi, 830047, China
- Center for Innovation Management Research of Xinjiang, Urumqi, 830047, China
| | - Muhammad Irfan
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China
- Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China
| | - Siyu Ren
- School of Economics, Nankai University, Tianjin, 300071, China
| | - Xiaodong Yang
- School of Economics and Management, Xinjiang University, Urumqi, 830047, China.
- Center for Innovation Management Research of Xinjiang, Urumqi, 830047, China.
| | - Haitao Wu
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China.
- Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China.
| | - Munir Ahmad
- School of Economics, Zhejiang University, Hangzhou, 310058, China
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18
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Roberts H, Helbich M. Multiple environmental exposures along daily mobility paths and depressive symptoms: A smartphone-based tracking study. ENVIRONMENT INTERNATIONAL 2021; 156:106635. [PMID: 34030073 DOI: 10.1016/j.envint.2021.106635] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/07/2021] [Accepted: 05/06/2021] [Indexed: 06/12/2023]
Abstract
Few studies go beyond the residential environment in assessments of the environment-mental health association, despite multiple environments being encountered in daily life. This study investigated 1) the associations between multiple environmental exposures and depressive symptoms, both in the residential environment and along the daily mobility path, 2) examined differences in the strength of associations between residential- and mobility-based models, and 3) explored sex as a moderator. Depressive symptoms of 393 randomly sampled adults aged 18-65 were assessed using the Patient Health Questionnaire (PHQ-9). Respondents were tracked via global positioning systems- (GPS) enabled smartphones for up to 7 days. Exposure to green space (normalized difference vegetation index (NDVI)), blue space, noise (Lden) and air pollution (particulate matter (PM2.5)) within 50 m and 100 m of each residential address and GPS point was computed. Multiple linear regression analyses were conducted separately for the residential- and mobility-based exposures. Wald tests were used to assess if the coefficients differed across models. Interaction terms were entered in fully adjusted models to determine if associations varied by sex. A significant negative relationship between green space and depressive symptoms was found in the fully adjusted residential- and mobility-based models using the 50 m buffer. No significant differences were observed in coefficients across models. None of the interaction terms were significant. Our results suggest that exposure to green space in the immediate environment, both at home and along the daily mobility path, is associated with a reduction in depressive symptoms. Further research is required to establish the utility of dynamic approaches to exposure assessment in studies on the environment and mental health.
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Affiliation(s)
- Hannah Roberts
- Department of Human Geography and Spatial Planning, Utrecht University, the Netherlands.
| | - Marco Helbich
- Department of Human Geography and Spatial Planning, Utrecht University, the Netherlands
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19
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Yu H, Shao XT, Liu SY, Pei W, Kong XP, Wang Z, Wang DG. Estimating dynamic population served by wastewater treatment plants using location-based services data. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2021; 43:4627-4635. [PMID: 33928448 DOI: 10.1007/s10653-021-00954-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 04/20/2021] [Indexed: 06/12/2023]
Abstract
Wastewater-based epidemiology is a useful approach to estimate population-level exposure to a wide range of substances (e.g., drugs, chemicals, biological agents) by wastewater analysis. An important uncertainty in population normalized loads generated is related to the size and variability of the actual population served by wastewater treatment plants (WWTPs). Here, we built a population model using location-based services (LBS) data to estimate dynamic consumption of illicit drugs. First, the LBS data from Tencent Location Big Data and resident population were used to train a linear population model for estimating population (r2 = 0.92). Then, the spatiotemporal accuracy of the population model was validated. In terms of temporal accuracy, we compared the model-based population with the time-aligned ammonia nitrogen (NH4-N) population within the WWTP of SEG, showing a mean squared error of < 10%. In terms of spatial accuracy, we estimated the model-based population of 42 WWTPs in Dalian and compared it with the NH4-N and design population, indicating good consistency overall (5% less than NH4-N and 4% less than design). Furthermore, methamphetamine consumption and prevalence based on the model were calculated with an average of 111 mg/day/1000 inhabitants and 0.24%, respectively, and dynamically displayed on a visualization system for real-time monitoring. Our study provided a dynamic and accurate population for estimating the population-level use of illicit drugs, much improving the temporal and spatial trend analysis of drug use. Furthermore, accurate information on drug use could be used to assess population health risks in a community.
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Affiliation(s)
- Han Yu
- College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China
| | - Xue-Ting Shao
- College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China
| | - Si-Yu Liu
- College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China
| | - Wei Pei
- College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China
| | - Xiang-Peng Kong
- College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China
| | - Zhuang Wang
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, No. 219 Ningliu Road, Nanjing, 210044, China
| | - De-Gao Wang
- College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China.
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20
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Lu Y. Beyond air pollution at home: Assessment of personal exposure to PM 2.5 using activity-based travel demand model and low-cost air sensor network data. ENVIRONMENTAL RESEARCH 2021; 201:111549. [PMID: 34153337 DOI: 10.1016/j.envres.2021.111549] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/13/2021] [Accepted: 06/15/2021] [Indexed: 06/13/2023]
Abstract
Assessing personal exposure to air pollution is challenging due to the limited availability of human movement data and the complexity of modeling air pollution at high spatiotemporal resolution. Most health studies rely on residential estimates of outdoor air pollution instead which introduces exposure measurement error. Personal exposure for 100,784 individuals in Los Angeles County was estimated by integrating human movement data simulated from the Southern California Association of Governments (SCAG) activity-based travel demand model with hourly PM2.5 predictions from my 500 m gridded model incorporating low-cost sensor monitoring data. Individual exposures were assigned considering PM2.5 levels at homes, workplaces, and other activity locations. These dynamic exposures were compared to the residence-based exposures, which do not consider human movement, to examine the degree of exposure estimation bias. The results suggest that exposures were underestimated by 13% (range 5-22%) on average when human movement was not considered, and much of the error was eliminated by accounting for work location. Exposure estimation bias increased for people who exhibited higher mobility levels, especially for workers with long commute distances. Overall, the personal exposures of workers were underestimated by 22% (5-61%) relative to their residence-based exposures. For workers who commute >20 miles, their exposure levels can be at most underestimated by 61%. Omitting mobility resulted in underestimating exposures for people who reside in areas with cleaner air but work in more polluted areas. Similarly, exposures were overestimated for people living in areas with poorer air quality and working in cleaner areas. These could lead to differential estimation biases across racial, ethnic and socioeconomic lines that typically correlate with where people live and work and lead to important exposure and health disparities. This study demonstrates that ignoring human movement and spatiotemporal variability of air pollution could lead to differential exposure misclassification potentially biasing health risk assessments. These improved dynamic approaches can help planners and policymakers identify disadvantaged populations for which exposures are typically misrepresented and might lead to targeted policy and planning implications.
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Affiliation(s)
- Yougeng Lu
- Department of Urban Planning and Spatial Analysis, University of Southern California, USA.
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21
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Li X, Yang T, Zeng Z, Li X, Zeng G, Liang J, Xiao R, Chen X. Underestimated or overestimated? Dynamic assessment of hourly PM 2.5 exposure in the metropolitan area based on heatmap and micro-air monitoring stations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 779:146283. [PMID: 33752001 DOI: 10.1016/j.scitotenv.2021.146283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/22/2021] [Accepted: 03/01/2021] [Indexed: 06/12/2023]
Abstract
Spatio-temporal distributions of air pollution and population are two important factors influencing the patterns of mortality and diseases. Past studies have quantified the adverse effects of long-term exposure to air pollution. However, the dynamic changes of air pollution levels and population mobility within a day are rarely taken into consideration, especially in metropolitan areas. In this study, we use the high-resolution PM2.5 data from the micro-air monitoring stations, and hourly population mobility simulated by the heatmap based on Location Based Service (LBS) big data to evaluate the hourly active PM2.5 exposure in a typical Chinese metropolis. The dynamic "active population exposure" is compared spatiotemporally with the static "census population exposure" based on census data. The results show that over 12 h on both study periods, 45.83% of suburbs' population-weighted exposure (PWE) is underestimated, while 100% of rural PWE and more than 34.78% of downtown's PWE are overestimated, with the relative difference reaching from -11 μg/m3 to 7 μg/m3. More notably, the total PWE of the active population at morning peak hours on weekdays is worse than previously realized, about 12.41% of people are exposed to PM2.5 over 60 μg/m3, about twice as much as that in census scenario. The commuters who live in the suburbs and work in downtown may suffer more from PM2.5 exposure and uneven environmental resource distribution. This study proposes a new approach of calculating population exposure which can also be extended to quantify other environmental issues and related health burdens.
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Affiliation(s)
- Xin Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
| | - Tao Yang
- School of Architecture, Hunan University, Changsha 410082, PR China.
| | - Zhuotong Zeng
- Department of Dermatology, Second Xiangya Hospital, Central South University, Changsha 410011, PR China.
| | - Xiaodong Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
| | - Guangming Zeng
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
| | - Jie Liang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
| | - Rong Xiao
- Department of Dermatology, Second Xiangya Hospital, Central South University, Changsha 410011, PR China.
| | - Xuwu Chen
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
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22
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O’Regan AC, Hunter RF, Nyhan MM. "Biophilic Cities": Quantifying the Impact of Google Street View-Derived Greenspace Exposures on Socioeconomic Factors and Self-Reported Health. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:9063-9073. [PMID: 34159777 PMCID: PMC8277136 DOI: 10.1021/acs.est.1c01326] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 05/30/2023]
Abstract
According to the biophilia hypothesis, humans have evolved to prefer natural environments that are essential to their thriving. With urbanization occurring at an unprecedented rate globally, urban greenspace has gained increased attention due to its environmental, health, and socioeconomic benefits. To unlock its full potential, an increased understanding of greenspace metrics is urgently required. In this first-of-a-kind study, we quantified street-level greenspace using 751 644 Google Street View images and computer vision methods for 125 274 locations in Ireland's major cities. We quantified population-weighted exposure to greenspace and investigated the impact of greenspace on health and socioeconomic determinants. To investigate the association between greenspace and self-reported health, a negative binomial regression analysis was applied. While controlling for other factors, an interquartile range increase in street-level greenspace was associated with a 2.78% increase in self-reported "good or very good" health [95% confidence interval: 2.25-3.31]. Additionally, we observed that populations in upper quartiles of greenspace exposure had higher levels of income and education than those in lower quartiles. This study provides groundbreaking insights into how urban greenspace can be quantified in unprecedented resolution, accuracy, and scale while also having important implications for urban planning and environmental health research and policy.
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Affiliation(s)
- Anna C. O’Regan
- Discipline of Civil, Structural &
Environmental Engineering, School of Engineering & Architecture, University College Cork, Cork, Ireland
- MaREI
Centre for Energy, Climate & Marine and Environmental Research
Institute, University College Cork, Cork, Ireland
| | - Ruth F. Hunter
- Centre
for Public Health, Queen’s University
Belfast, Belfast BT12 6BA, Northern Ireland, United Kingdom
| | - Marguerite M. Nyhan
- Discipline of Civil, Structural &
Environmental Engineering, School of Engineering & Architecture, University College Cork, Cork, Ireland
- MaREI
Centre for Energy, Climate & Marine and Environmental Research
Institute, University College Cork, Cork, Ireland
- Harvard
T.H. Chan School of Public Health, Harvard
University, Boston, Massachusetts 02215, United States
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23
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Assessment of the Dynamic Exposure to PM 2.5 Based on Hourly Cell Phone Location and Land Use Regression Model in Beijing. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18115884. [PMID: 34070868 PMCID: PMC8199116 DOI: 10.3390/ijerph18115884] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/24/2021] [Accepted: 05/26/2021] [Indexed: 12/04/2022]
Abstract
The spatiotemporal locations of large populations are difficult to clearly characterize using traditional exposure assessment, mainly due to their complicated daily intraurban activities. This study aimed to extract hourly locations for the total population of Beijing based on cell phone data and assess their dynamic exposure to ambient PM2.5. The locations of residents were located by the cellular base stations that were keeping in contact with their cell phones. The diurnal activity pattern of the total population was investigated through the dynamic spatial distribution of all of the cell phones. The outdoor PM2.5 concentration was predicted in detail using a land use regression (LUR) model. The hourly PM2.5 map was overlapped with the hourly distribution of people for dynamic PM2.5 exposure estimation. For the mobile-derived total population, the mean level of PM2.5 exposure was 89.5 μg/m3 during the period from 2013 to 2015, which was higher than that reported for the census population (87.9 μg/m3). The hourly activity pattern showed that more than 10% of the total population commuted into the center of Beijing (e.g., the 5th ring road) during the daytime. On average, the PM2.5 concentration at workplaces was generally higher than in residential areas. The dynamic PM2.5 exposure pattern also varied with seasons. This study exhibited the strengths of mobile location in deriving the daily spatiotemporal activity patterns of the population in a megacity. This technology would refine future exposure assessment, including either small group cohort studies or city-level large population assessments.
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Glodeanu A, Gullón P, Bilal U. Social inequalities in mobility during and following the COVID-19 associated lockdown of the Madrid metropolitan area in Spain. Health Place 2021; 70:102580. [PMID: 34022543 PMCID: PMC8328947 DOI: 10.1016/j.healthplace.2021.102580] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 12/18/2022]
Abstract
Spain has been one of the most affected regions by the COVID-19 worldwide, and Madrid its most affected city. In response to this, the Spanish government enacted a strict lockdown in late March 2020, that was gradually eased until June 2020. We explored differentials in mobility by area-level deprivation in the functional area of Madrid, before, during, and after the COVID-19 lockdown. We used cell phone-derived mobility indicators (% of the population leaving their area) from the National Institute of Statistics (INE), and a composite measure of deprivation from the Spanish Society of Epidemiology (SEE). We computed changes in mobility with respect to pre-pandemic levels, and explored spatial patterns and associations with deprivation. We found that levels of mobility before COVID-19 were slightly higher in areas with lower deprivation. The economic hibernation period resulted in very strong declines in mobility, most acutely in low deprivation areas. These differences weakened during the re-opening, and levels of mobility were similar by deprivation once the lockdown was completely lifted. Given the existence of important socioeconomic differentials in COVID-19 exposure, it is key to ensure that these interventions do not widen existing social inequalities.
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Affiliation(s)
- Adrián Glodeanu
- Faculty of Geography and History, Universidad Complutense, Madrid, Spain.
| | - Pedro Gullón
- Public Health and Epidemiology Research Group, School of Medicine and Health Sciences, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain; School of Global, Urban and Social Studies, RMIT University, Melbourne, Australia
| | - Usama Bilal
- Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, PA, USA; Department of Epidemiology and Biostatistics, Drexel Dornsife School of Public Health, Philadelphia, PA, USA
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25
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Is Compact Urban Form Good for Air Quality? A Case Study from China Based on Hourly Smartphone Data. LAND 2021. [DOI: 10.3390/land10050504] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In previous studies, planners have debated extensively whether compact development can improve air quality in urban areas. Most of them estimated pollution exposure with stationary census data that linked exposures solely to residential locations, therefore overlooking residents’ space–time inhalation of air pollutants. In this study, we conducted an air pollution exposure assessment by scrutinizing one-hour resolution population distribution maps derived from hourly smartphone data and air pollutant concentrations derived from inverse distance weighted interpolation. We selected Wuhan as the study area and used Pearson correlation analysis to explore the effect of compactness on population-weighted concentrations. The results showed that even if a compact urban form helps to reduce pollution concentrations by decreasing vehicle traveling miles and tailpipe emissions, higher levels of building density and floor area ratios may increase population-weighted exposure. With regard to downtown areas with high population density, compact development may locate more people in areas with excessive air pollution. In all, reducing density in urban public centers and developing a polycentric urban structure may aid in the improvement of air quality in cities with compact urban forms.
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26
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Ashayeri M, Abbasabadi N, Heidarinejad M, Stephens B. Predicting intraurban PM 2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns. ENVIRONMENTAL RESEARCH 2021; 196:110423. [PMID: 33157105 DOI: 10.1016/j.envres.2020.110423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 08/14/2020] [Accepted: 10/31/2020] [Indexed: 05/28/2023]
Abstract
Urban areas contribute substantially to human exposure to ambient air pollution. Numerous statistical prediction models have been used to estimate ambient concentrations of fine particulate matter (PM2.5) and other pollutants in urban environments, with some incorporating machine learning (ML) algorithms to improve predictive power. However, many ML approaches for predicting ambient pollutant concentrations to date have used principal component analysis (PCA) with traditional regression algorithms to explore linear correlations between variables and to reduce the dimensionality of the data. Moreover, while most urban air quality prediction models have traditionally incorporated explanatory variables such as meteorological, land use, transportation/mobility, and/or co-pollutant factors, recent research has shown that local emissions from building infrastructure may also be useful factors to consider in estimating urban pollutant concentrations. Here we propose an enhanced ML approach for predicting urban ambient PM2.5 concentrations that hybridizes cascade and PCA methods to reduce the dimensionality of the data-space and explore nonlinear effects between variables. We test the approach using different durations of time series air quality datasets of hourly PM2.5 concentrations from three air quality monitoring sites in different urban neighborhoods in Chicago, IL to explore the influence of dynamic human-related factors, including mobility (i.e., traffic) and building occupancy patterns, on model performance. We test 9 state-of-the-art ML algorithms to find the most effective algorithm for modeling intraurban PM2.5 variations and we explore the relative importance of all sets of factors on intraurban air quality model performance. Results demonstrate that Gaussian-kernel support vector regression (SVR) was the most effective ML algorithm tested, improving accuracy by 118% compared to a traditional multiple linear regression (MLR) approach. Incorporating the enhanced approach with SVR algorithm increased model performance up to 18.4% for yearlong and 98.7% for month-long hourly datasets, respectively. Incorporating assumptions for human occupancy patterns in dominant building typologies resulted in improvements in model performance by between 4% and 37%. Combined, these innovations can be used to improve the performance and accuracy of urban air quality prediction models compared to conventional approaches.
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Affiliation(s)
- Mehdi Ashayeri
- College of Architecture, Illinois Institute of Technology, Chicago, IL, USA
| | - Narjes Abbasabadi
- College of Architecture, Illinois Institute of Technology, Chicago, IL, USA
| | - Mohammad Heidarinejad
- Department of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Brent Stephens
- Department of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, IL, USA.
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27
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Lu Y, Giuliano G, Habre R. Estimating hourly PM 2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study. ENVIRONMENTAL RESEARCH 2021; 195:110653. [PMID: 33476665 DOI: 10.1016/j.envres.2020.110653] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/14/2020] [Accepted: 12/17/2020] [Indexed: 05/21/2023]
Abstract
Predicting PM2.5 concentrations at a fine spatial and temporal resolution (i.e., neighborhood, hourly) is challenging. Recent growth in low cost sensor networks is providing increased spatial coverage of air quality data that can be used to supplement data provided by monitors of regulatory agencies. We developed an hourly, 500 × 500 m gridded PM2.5 model that integrates PurpleAir low-cost air sensor network data for Los Angeles County. We developed a quality control scheme for PurpleAir data. We included spatially and temporally varying predictors in a random forest model with random oversampling of high concentrations to predict PM2.5. The model achieved high prediction accuracy (10-fold cross-validation (CV) R2 = 0.93, root mean squared error (RMSE) = 3.23 μg/m3; spatial CV R2 = 0.88, spatial RMSE = 4.33 μg/m3; temporal CV R2 = 0.90, temporal RMSE = 3.85 μg/m3). Our model was able to predict spatial and diurnal patterns in PM2.5 on typical weekdays and weekends, as well as non-typical days, such as holidays and wildfire days. The model allows for far more precise estimates of PM2.5 than existing methods based on few sensors. Taking advantage of low-cost PM2.5 sensors, our hourly random forest model predictions can be combined with time-activity diaries in future studies, enabling geographically and temporally fine exposure estimation for specific population groups in studies of acute air pollution health effects and studies of environmental justice issues.
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Affiliation(s)
- Yougeng Lu
- Department of Urban Planning and Spatial Analysis, University of Southern California, Los Angeles, CA, USA
| | - Genevieve Giuliano
- Department of Urban Planning and Spatial Analysis, University of Southern California, Los Angeles, CA, USA
| | - Rima Habre
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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28
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Qi M, Hankey S. Using Street View Imagery to Predict Street-Level Particulate Air Pollution. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:2695-2704. [PMID: 33539080 DOI: 10.1021/acs.est.0c05572] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Land-use regression (LUR) models are frequently applied to estimate spatial patterns of air pollution. Traditional LUR often relies on fixed-site measurements and GIS-derived variables with limited spatial resolution. We present an approach that leverages Google Street View (GSV) imagery to predict street-level particulate air pollution (i.e., black carbon [BC] and particle number [PN] concentrations). We developed empirical models based on mobile monitoring data and features extracted from ∼52 500 GSV images using a deep learning model. We tested theory- and data-driven feature selection methods as well as models using images within varying buffer sizes (50-2000 m). Compared to LUR models with traditional variables, our models achieved similar model performance using the street-level predictors while also identifying additional potential hotspots. Adjusted R2 (10-fold CV R2) with integrated feature selection was 0.57-0.64 (0.50-0.57) and 0.65-0.73 (0.61-0.66) for BC and PN models, respectively. Models using only features near the measurement locations (i.e., GSV images within 250 m) explained ∼50% of air pollution variability, indicating PN and BC are strongly affected by the street-level built environment. Our results suggest that GSV imagery, processed with computer vision techniques, is a promising data source to develop LUR models with high spatial resolution and consistent predictor variables across administrative boundaries.
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Affiliation(s)
- Meng Qi
- School of Public and International Affairs, Virginia Tech, 140 Otey Street, Blacksburg, Virginia 24061, United States
| | - Steve Hankey
- School of Public and International Affairs, Virginia Tech, 140 Otey Street, Blacksburg, Virginia 24061, United States
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29
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WITHDRAWN—Administrative Duplicate Publication Kernel-based estimation of individual location densities from smartphone data. STAT MODEL 2020. [DOI: 10.1177/1471082x17870331] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
SAGE Publishing regrets that due to an administrative error, this article was accidentally published Online First and in Volume 20 Issue 6 with different DOIs. There was no duplication of the article in the printed and online version of Volume 20 Issue 6. The correct and citable version of the article remains: Francesco F and Paci L (2020) Kernel-based estimation of individual location densities from smartphone data. Statistical Modelling, 20, 6, 617–633. DOI 10.1177/1471082X19870331
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Guo H, Zhan Q, Ho HC, Yao F, Zhou X, Wu J, Li W. Coupling mobile phone data with machine learning: How misclassification errors in ambient PM2.5 exposure estimates are produced? THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 745:141034. [PMID: 32758750 DOI: 10.1016/j.scitotenv.2020.141034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 07/03/2020] [Accepted: 07/15/2020] [Indexed: 05/06/2023]
Abstract
BACKGROUND Most studies relying on time-activity diary or traditional air pollution modelling approach are insufficient to suggest the impacts of ignoring individual mobility and air pollution variations on misclassification errors in exposure estimates. Moreover, very few studies have examined whether such impacts differ across socioeconomic groups. OBJECTIVES We aim to examine how ignoring individual mobility and PM2.5 variations produces misclassification errors in ambient PM2.5 exposure estimates. METHODS We developed a geo-informed backward propagation neural network model to estimate hourly PM2.5 concentrations in terms of remote sensing and geospatial big data. Combining the estimated PM2.5 concentrations and individual trajectories derived from 755,468 mobile phone users on a weekday in Shenzhen, China, we estimated four types of individual total PM2.5 exposures during weekdays at multi-temporal scales. The estimate ignoring individual mobility, PM2.5 variations or both was compared with the hypothetical error-free estimate using paired sample t-test. We then quantified the exposure misclassification error using Pearson correlation analysis. Moreover, we examined whether the misclassification error differs across different socioeconomic groups. Taking findings of ignoring individual mobility as an example, we further investigated whether such findings are robust to the different selections of time. RESULTS We found that the estimate ignoring PM2.5 variations, individual mobility or both was statistically different from the hypothetical error-free estimate. Ignoring both factors produced the largest exposure misclassification error. The misclassification error was larger in the estimate ignoring PM2.5 variations than that ignoring individual mobility. People with high economic status suffered from a larger exposure misclassification error. The findings were robust to the different selections of time. CONCLUSIONS Ignoring individual mobility, PM2.5 variations or both leads to misclassification errors in ambient PM2.5 exposure estimates. A larger misclassification error occurs in the estimate neglecting PM2.5 variations than that ignoring individual mobility, which is seldom reported before.
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Affiliation(s)
- Huagui Guo
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, PR China.
| | - Qingming Zhan
- School of Urban Design, Wuhan University, Wuhan 430072, PR China.
| | - Hung Chak Ho
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China.
| | - Fei Yao
- School of GeoSciences, The University of Edinburgh, Edinburgh EH9 3FF, United Kingdom.
| | - Xingang Zhou
- College of Architecture and Urban Planning, Tongji University, Shanghai 200092, PR China.
| | - Jiansheng Wu
- Key Laboratory for Urban Habitat Environmental Science and Technology, Shenzhen Graduate School, Peking University, Shenzhen 518055, PR China; Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, PR China.
| | - Weifeng Li
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, PR China.
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31
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Klous G, Kretzschmar MEE, Coutinho RA, Heederik DJJ, Huss A. Prediction of human active mobility in rural areas: development and validity tests of three different approaches. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2020; 30:1023-1031. [PMID: 31772295 DOI: 10.1038/s41370-019-0194-6] [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: 05/19/2019] [Revised: 09/27/2019] [Accepted: 10/15/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND/AIM Active mobility may play a relevant role in the assessment of environmental exposures (e.g. traffic-related air pollution, livestock emissions), but data about actual mobility patterns are work intensive to collect, especially in large study populations, therefore estimation methods for active mobility may be relevant for exposure assessment in different types of studies. We previously collected mobility patterns in a group of 941 participants in a rural setting in the Netherlands, using week-long GPS tracking. We had information regarding personal characteristics, self-reported data regarding weekly mobility patterns and spatial characteristics. The goal of this study was to develop versatile estimates of active mobility, test their accuracy using GPS measurements and explore the implications for exposure assessment studies. METHODS We estimated hours/week spent on active mobility based on personal characteristics (e.g. age, sex, pre-existing conditions), self-reported data (e.g. hours spent commuting per bike) or spatial predictors such as home and work address. Estimated hours/week spent on active mobility were compared with GPS measured hours/week, using linear regression and kappa statistics. RESULTS Estimated and measured hours/week spent on active mobility had low correspondence, even the best predicting estimation method based on self-reported data, resulted in a R2 of 0.09 and Cohen's kappa of 0.07. A visual check indicated that, although predicted routes to work appeared to match GPS measured tracks, only a small proportion of active mobility was captured in this way, thus resulting in a low validity of overall predicted active mobility. CONCLUSIONS We were unable to develop a method that could accurately estimate active mobility, the best performing method was based on detailed self-reported information but still resulted in low correspondence. For future studies aiming to evaluate the contribution of home-work traffic to exposure, applying spatial predictors may be appropriate. Measurements still represent the best possible tool to evaluate mobility patterns.
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Affiliation(s)
- Gijs Klous
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
- Institute for Risk Assessment Sciences, Division Environmental Epidemiology and Veterinary Public Health, Utrecht University, Utrecht, The Netherlands.
| | - Mirjam E E Kretzschmar
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Roel A Coutinho
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Dick J J Heederik
- Institute for Risk Assessment Sciences, Division Environmental Epidemiology and Veterinary Public Health, Utrecht University, Utrecht, The Netherlands
| | - Anke Huss
- Institute for Risk Assessment Sciences, Division Environmental Epidemiology and Veterinary Public Health, Utrecht University, Utrecht, The Netherlands
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32
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Finazzi F, Paci L. Kernel-based estimation of individual location densities from smartphone data. STAT MODEL 2020. [DOI: 10.1177/1471082x19870331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Localizing people across space and over time is a relevant and challenging problem in many modern applications. Smartphone ubiquity gives the opportunity to collect useful individual data as never before. In this work, the focus is on location data collected by smartphone applications. We propose a kernel-based density estimation approach that exploits cyclical spatio-temporal patterns of people to estimate the individual location density at any time, uncertainty included. Model parameters are estimated by maximum likelihood cross-validation. Unlike classic tracking methods designed for high spatio-temporal resolution data, the approach is suitable when location data are sparse in time and are affected by non-negligible errors. The approach is applied to location data collected by the Earthquake Network citizen science project which carries out a worldwide earthquake early warning system based on smartphones. The approach is parsimonious and is suitable to model location data gathered by any location-aware smartphone application.
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Affiliation(s)
- Francesco Finazzi
- Department of Management, Information and Production Engineering, University of Bergamo, Bergamo, Italy
| | - Lucia Paci
- Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy
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33
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Guo H, Li W, Yao F, Wu J, Zhou X, Yue Y, Yeh AGO. Who are more exposed to PM2.5 pollution: A mobile phone data approach. ENVIRONMENT INTERNATIONAL 2020; 143:105821. [PMID: 32702593 DOI: 10.1016/j.envint.2020.105821] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 05/18/2020] [Accepted: 05/18/2020] [Indexed: 05/17/2023]
Abstract
BACKGROUND Few studies have examined exposure disparity to ambient air pollution outside North America and Europe. Moreover, very few studies have investigated exposure disparity in terms of individual-level data or at multi-temporal scales. OBJECTIVES This work aims to examine the associations between individual- and neighbourhood-level economic statuses and individual exposure to PM2.5 across multi-temporal scales. METHODS The study population included 742,220 mobile phone users on a weekday in Shenzhen, China. A geo-informed backward propagation neural network model was developed to estimate hourly PM2.5 concentrations by the use of remote sensing and geospatial big data, which were then combined with individual trajectories to estimate individual total exposure during weekdays at multi-temporal scales. Coupling the estimated PM2.5 exposure with housing price, we examined the associations between individual- and neighbourhood-level economic statuses and individual exposure using linear regression and two-level hierarchical linear models. Furthermore, we performed five sensitivity analyses to test the robustness of the two-level effects. RESULTS We found positive associations between individual- and neighbourhood-level economic statuses and individual PM2.5 exposure at a daytime, daily, weekly, monthly, seasonal or annual scale. Findings on the effects of the two-level economic statuses were generally robust in the five sensitivity analyses. In particular, despite the insignificant effects observed in three of newly selected time periods in the sensitivity analysis, individual- and neighbourhood-level economic statuses were still positively associated with individual total exposure during each of other newly selected periods (including three other seasons). CONCLUSIONS There are statistically positive associations of individual PM2.5 exposure with individual- and neighbourhood-level economic statuses. That is, people living in areas with higher residential property prices are more exposed to PM2.5 pollution. Findings emphasize the need for public health intervention and urban planning initiatives targeting socio-economic disparity in ambient air pollution exposure, thus alleviating health disparities across socioeconomic groups.
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Affiliation(s)
- Huagui Guo
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, PR China.
| | - Weifeng Li
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, PR China.
| | - Fei Yao
- School of GeoSciences, The University of Edinburgh, Edinburgh EH9 3FF, United Kingdom.
| | - Jiansheng Wu
- Key Laboratory for Urban Habitat Environmental Science and Technology, Shenzhen Graduate School, Peking University, Shenzhen 518055, PR China; Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, PR China.
| | - Xingang Zhou
- College of Architecture and Urban Planning, Tongji University, Shanghai 200092, PR China.
| | - Yang Yue
- Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518052, PR China.
| | - Anthony G O Yeh
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, PR China.
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34
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Huang L, Mao F, Zang L, Zhang Y, Zhang Y, Zhang T. Estimation of hourly PM 1 concentration in China and its application in population exposure analysis. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 273:115720. [PMID: 33508630 DOI: 10.1016/j.envpol.2020.115720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 09/11/2020] [Accepted: 09/23/2020] [Indexed: 06/12/2023]
Abstract
Particulate pollution is closely related to public health. PM1 (particles with an aerodynamic size not larger than 1 μm) is much more harmful than particles with larger sizes because it goes deeper into the body and hence arouses social concern. However, the sparse and unevenly distributed ground-based observations limit the understanding of spatio-temporal distributions of PM1 in China. In this study, hourly PM1 concentrations in central and eastern China were retrieved based on a random forest model using hourly aerosol optical depth (AOD) from Himawari-8, meteorological and geographic information as inputs. Here the spatiotemporal autocorrelation of PM1 was also considered in the model. Experimental results indicate that although the performance of the proposed model shows diurnal, seasonal and spatial variations, it is relatively better than others, with a determination coefficient (R2) of 0.83 calculated based on the 10-fold cross validation method. Geographical map implies that PM1 pollution level in Beijing-Tianjin-Hebei is much higher than in other regions, with the mean value of ∼55 μg/m3. Based on the exposure analysis, we found about 75% of the population lives in an environment with PM1 higher than 35 μg/m3 in the whole study area. The retrieval dataset in this study is of great significance for further exploring the impact of PM1 on public health.
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Affiliation(s)
- Li Huang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
| | - Feiyue Mao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
| | - Lin Zang
- Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, 430079, China.
| | - Yunquan Zhang
- School of Public Health, Medical College, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Yi Zhang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
| | - Taixin Zhang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
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35
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Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment. TOXICS 2020; 8:toxics8030074. [PMID: 32962012 PMCID: PMC7560317 DOI: 10.3390/toxics8030074] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 09/12/2020] [Accepted: 09/17/2020] [Indexed: 01/14/2023]
Abstract
Human exposure to air pollution is a major public health concern. Environmental policymakers have been implementing various strategies to reduce exposure, including the 10th-day-no-driving system. To assess exposure of an entire population of a community in a highly polluted area, pollutant concentrations in microenvironments and population time–activity patterns are required. To date, population exposure to air pollutants has been assessed using air monitoring data from fixed atmospheric monitoring stations, atmospheric dispersion modeling, or spatial interpolation techniques for pollutant concentrations. This is coupled with census data, administrative registers, and data on the patterns of the time-based activities at the individual scale. Recent technologies such as sensors, the Internet of Things (IoT), communications technology, and artificial intelligence enable the accurate evaluation of air pollution exposure for a population in an environmental health context. In this study, the latest trends in published papers on the assessment of population exposure to air pollution were reviewed. Subsequently, this study proposes a methodology that will enable policymakers to develop an environmental health surveillance system that evaluates the distribution of air pollution exposure for a population within a target area and establish countermeasures based on advanced exposure assessment.
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36
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Yu X, Ivey C, Huang Z, Gurram S, Sivaraman V, Shen H, Eluru N, Hasan S, Henneman L, Shi G, Zhang H, Yu H, Zheng J. Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data. ENVIRONMENT INTERNATIONAL 2020; 141:105772. [PMID: 32416372 DOI: 10.1016/j.envint.2020.105772] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 03/24/2020] [Accepted: 04/26/2020] [Indexed: 06/11/2023]
Abstract
One major source of uncertainty in accurately estimating human exposure to air pollution is that human subjects move spatiotemporally, and such mobility is usually not considered in exposure estimation. How such mobility impacts exposure estimates at the population and individual level, particularly for subjects with different levels of mobility, remains under-investigated. In addition, a wide range of methods have been used in the past to develop air pollutant concentration fields for related health studies. How the choices of methods impact results of exposure estimation, especially when detailed mobility information is considered, is still largely unknown. In this study, by using a publicly available large cell phone location dataset containing over 35 million location records collected from 310,989 subjects, we investigated the impact of individual subjects' mobility on their estimated exposures for five chosen ambient pollutants (CO, NO2, SO2, O3 and PM2.5). We also estimated exposures separately for 10 groups of subjects with different levels of mobility to explore how increased mobility impacted their exposure estimates. Further, we applied and compared two methods to develop concentration fields for exposure estimation, including one based on Community Multiscale Air Quality (CMAQ) model outputs, and the other based on the interpolated observed pollutant concentrations using the inverse distance weighting (IDW) method. Our results suggest that detailed mobility information does not have a significant influence on mean population exposure estimate in our sample population, although impacts can be substantial at the individual level. Additionally, exposure classification error due to the use of home-location data increased for subjects that exhibited higher levels of mobility. Omitting mobility could result in underestimation of exposures to traffic-related pollutants particularly during afternoon rush-hour, and overestimate exposures to ozone especially during mid-afternoon. Between CMAQ and IDW, we found that the IDW method generates smooth concentration fields that were not suitable for exposure estimation with detailed mobility data. Therefore, the method for developing air pollution concentration fields when detailed mobility data were to be applied should be chosen carefully. Our findings have important implications for future air pollution health studies.
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Affiliation(s)
- Xiaonan Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Cesunica Ivey
- Department of Chemical and Environmental Engineering, University of California Riverside, Riverside, CA, USA
| | - Zhijiong Huang
- Inisitute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | | | | | - Huizhong Shen
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Naveen Eluru
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Samiul Hasan
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Lucas Henneman
- T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
| | - Guoliang Shi
- College of Environmental Science and Engineering, Nankai University, Tianjin, China
| | - Hongliang Zhang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, China
| | - Haofei Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA.
| | - Junyu Zheng
- Inisitute for Environmental and Climate Research, Jinan University, Guangzhou, China
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Lei X, Chen R, Wang C, Shi J, Zhao Z, Li W, Bachwenkizi J, Ge W, Sun L, Li S, Cai J, Kan H. Necessity of personal sampling for exposure assessment on specific constituents of PM 2.5: Results of a panel study in Shanghai, China. ENVIRONMENT INTERNATIONAL 2020; 141:105786. [PMID: 32428842 DOI: 10.1016/j.envint.2020.105786] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Revised: 03/21/2020] [Accepted: 04/30/2020] [Indexed: 06/11/2023]
Abstract
Many epidemiological studies have evaluated the health risks of ambient fine particulate matter (PM2.5). However, few studies have investigated the potential exposure misclassification caused by using ambient PM2.5 concentrations as proxy for individual exposure to PM2.5 in regions with high-level of air pollution. This study aimed to compare the differences between personal and ambient PM2.5 constituent concentrations, and to predict the personal exposure of sixteen PM2.5 constituents. We collected 141 72-h personal exposure filter samples from a panel of 36 healthy non-smoking college students in Shanghai, China. We then used the liner mixed effects models to predict personal constituent-specific exposure using ambient observations and several possible influencing factors including time-activity patterns, temporal variables, and meteorological conditions. The final model of each component was further evaluated by determination coefficient (R2) and root mean square error (RMSE) from leave-one-out-cross-validation (LOOCV). We observed ambient concentrations were higher than personal concentrations for all PM2.5 components except for Mn, Fe, Ca, and V. Especially, ambient NH4+, As, and NO3- concentrations were 3.65, 5.65 and 7.33-fold higher than their corresponding personal concentrations, respectively. The ambient level was the strongest predictor of their corresponding personal PM2.5 components with the highest marginal R2 (RM2: 0.081 ~ 0.901), meteorological conditions (RM2: 0.000 ~ 0.357), time-activity pattern (RM2: 0.000 ~ 0.083) and temporal indicators (RM2: 0.031 ~ 0.562) were also important predictors. Our final models predicted at least 50% of the variance of all personal PM2.5 constituents and even over 90% for K, Pb, and SO42-. LOOCV analysis showed that R2 and RMSE ranged from 0.251 to 0.907 and 0.000 to 0.092 μg/m3, respectively. Our results showed that ambient concentration of most PM2.5 constituents along with time-activity patterns, temporal variables, and meteorological conditions, could adequately predict personal exposure concentration. Prediction models of individual PM2.5 constituent may help to improve the accuracy of exposure measurement in future epidemiological studies.
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Affiliation(s)
- Xiaoning Lei
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China
| | - Cuicui Wang
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China
| | - Jingjin Shi
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China
| | - Zhuohui Zhao
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China
| | - Weihua Li
- Key Laboratory of Reproduction Regulation of National Population and Family Planning Commission, Shanghai Institute of Planned Research, Institute of Reproduction and Development, Fudan University, Shanghai, China
| | - Jovine Bachwenkizi
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China
| | - Wenzhen Ge
- Regeneron Pharmaceuticals Inc., NY 10591, USA
| | - Li Sun
- School of the Environment, Nanjing University, Nanjing 210023, China
| | - Shanqun Li
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jing Cai
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China; Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China.
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China; Key Laboratory of Reproduction Regulation of National Population and Family Planning Commission, Shanghai Institute of Planned Research, Institute of Reproduction and Development, Fudan University, Shanghai, China.
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38
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Gariazzo C, Carlino G, Silibello C, Renzi M, Finardi S, Pepe N, Radice P, Forastiere F, Michelozzi P, Viegi G, Stafoggia M. A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 724:138102. [PMID: 32268284 DOI: 10.1016/j.scitotenv.2020.138102] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 03/13/2020] [Accepted: 03/20/2020] [Indexed: 06/11/2023]
Abstract
Cities are severely affected by air pollution. Local emissions and urban structures can produce large spatial heterogeneities. We aim to improve the estimation of NO2, O3, PM2.5 and PM10 concentrations in 6 Italian metropolitan areas, using chemical-transport and machine learning models, and to assess the effect on population exposure by using information on urban population mobility. Three years (2013-2015) of simulations were performed by the Chemical-Transport Model (CTM) FARM, at 1 km resolution, fed by boundary conditions provided by national-scale simulations, local emission inventories and meteorological fields. A downscaling of daily air pollutants at higher resolution (200 m) was then carried out by means of a machine learning Random-Forest (RF) model, considering CTM and spatial-temporal predictors, such as population, land-use, surface greenness and vehicular traffic, as input. RF achieved mean cross-validation (CV) R2 of 0.59, 0.72, 0.76 and 0.75 for NO2, PM10, PM2.5 and O3, respectively, improving results from CTM alone. Mean concentration fields exhibited clear geographical gradients caused by climate conditions, local emission sources and photochemical processes. Time series of population weighted exposure (PWE) were estimated for two months of the year 2015 and for five cities, by combining population mobility data (derived from mobile phone traffic volumes data), and concentration levels from the RF model. PWE_RF metric better approximated the observed concentrations compared with the predictions from either CTM alone or CTM and RF combined, especially for pollutants exhibiting strong spatial gradients, such as NO2. 50% of the population was estimated to be exposed to NO2 concentrations between 12 and 38 μg/m3 and PM10 between 20 and 35 μg/m3. This work supports the potential of machine learning methods in predicting air pollutant levels in urban areas at high spatial and temporal resolutions.
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Affiliation(s)
- Claudio Gariazzo
- Occupational and Environmental Medicine, Epidemiology and Hygiene Department, Italian Workers' Compensation Authority (INAIL), Monte Porzio Catone (RM), Italy.
| | | | | | - Matteo Renzi
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Italy
| | | | | | | | - Francesco Forastiere
- CNR Institute of Biomedicine and Molecular Immunology "Alberto Monroy", National Research Council Palermo, Italy; Environmental Research Group, King's College, London, UK
| | - Paola Michelozzi
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Italy
| | - Giovanni Viegi
- CNR Institute of Biomedicine and Molecular Immunology "Alberto Monroy", National Research Council Palermo, Italy
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Italy
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Rivellese F, Prediletto E. ACE2 at the centre of COVID-19 from paucisymptomatic infections to severe pneumonia. Autoimmun Rev 2020; 19:102536. [PMID: 32251718 PMCID: PMC7195011 DOI: 10.1016/j.autrev.2020.102536] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 03/31/2020] [Indexed: 01/08/2023]
Affiliation(s)
- Felice Rivellese
- Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.
| | - Edoardo Prediletto
- Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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40
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Ramacher MOP, Karl M. Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO 2 and PM 2.5 Pollution in Urban Areas. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E2099. [PMID: 32235712 PMCID: PMC7142857 DOI: 10.3390/ijerph17062099] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/19/2020] [Accepted: 03/20/2020] [Indexed: 01/13/2023]
Abstract
To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen's health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO2 and 19% more to PM2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO2 and PM2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO2 and 15% for PM2.5). Results for population-weighted exposure revealed exposure to PM2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from -12% to +7% for NO2 and PM2.5. The developed "dynamic transport approach" is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.
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41
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The Impact of Ambient Fine Particulate Matter on Consumer Expenditures. SUSTAINABILITY 2020. [DOI: 10.3390/su12051855] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Airborne particulate matter suspended from industrial facilities, power plants, and automobiles is detrimental to health. Growing concerns about the increasing level of airborne particulate matter have led many industrialized nations to advocate for the transformation of the energy market and investment in sustainable energy products. At the other end, consumers have made individual adjustments and attempted to reduce the exposure to the particulate matter. In this paper, we focus on the effect of ambient air pollution on consumer expenditures based on scanner panel data on consumers’ debit and credit card transactions. A series of empirical analyses found robust evidence that the increased level of particulate matter led to considerable disruption in total consumer expenditures with significant heterogeneity across categories. Our findings suggest that consumers alter their spending behaviors in an attempt to reduce the risk of exposures to particulate matter. Such an estimated effect of air pollution is qualitatively different from those of other macroeconomic factors and provides important guidance for policy interventions and practical decisions aimed at sustaining economic growth.
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Crouse DL, Erickson AC, Christidis T, Pinault L, van Donkelaar A, Li C, Meng J, Martin RV, Tjepkema M, Hystad P, Burnett R, Pappin A, Brauer M, Weichenthal S. Evaluating the Sensitivity of PM2.5–Mortality Associations to the Spatial and Temporal Scale of Exposure Assessment. Epidemiology 2020; 31:168-176. [DOI: 10.1097/ede.0000000000001136] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Tayarani M, Rowangould G. Estimating exposure to fine particulate matter emissions from vehicle traffic: Exposure misclassification and daily activity patterns in a large, sprawling region. ENVIRONMENTAL RESEARCH 2020; 182:108999. [PMID: 31855700 DOI: 10.1016/j.envres.2019.108999] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 11/11/2019] [Accepted: 12/02/2019] [Indexed: 06/10/2023]
Abstract
Vehicle traffic is responsible for a significant portion of toxic air pollution in urban areas that has been linked to a wide range of adverse health outcomes. Most vehicle air quality analyses used for transportation planning and health effect studies estimate exposure from the measured or modeled concentration of an air pollutant at a person's home. This study evaluates exposure to fine particulate matter from vehicle traffic and the magnitude and cause of exposure misclassification that result from not accounting for population mobility during the day in a large, sprawling region. We develop a dynamic exposure model by integrating activity-based travel demand, vehicle emission, and air dispersion models to evaluate the magnitude, components and spatial patterns of vehicle exposure misclassification in the Atlanta, Georgia metropolitan area. Overall, we find that population exposure estimates increase by 51% when population mobility is accounted for. Errors are much larger in suburban and rural areas where exposure is underestimated while exposure may be overestimated near high volume roadways and in the urban core. Exposure while at work and traveling account for much of the error. We find much larger errors than prior studies, all of which have focused on more compact urban regions. Since many people spend a large part of their day away from their homes and vehicle emissions are known to create "hotspots" along roadways, home-based exposure is unlikely to be a robust estimator of a person's actual exposure. Accounting for population mobility in vehicle emission exposure studies may reveal more effective mitigation strategies, important differences in exposure between population groups with different travel patterns, and reduce exposure misclassification in health studies.
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Affiliation(s)
- Mohammad Tayarani
- School of Civil & Environmental Engineering, Cornell University, Ithaca, NY, USA
| | - Gregory Rowangould
- University of Vermont, Department of Civil and Environmental Engineering, Votey Hall, 33 Colchester Ave., Burlington, VT, 05405, USA.
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44
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Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies. ATMOSPHERE 2020. [DOI: 10.3390/atmos11020122] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
An accurate assessment of pollutants’ exposure and precise evaluation of the clinical outcomes pose two major challenges to the contemporary environmental health research. The common methods for exposure assessment are based on residential addresses and are prone to many biases. Pollution levels are defined based on monitoring stations that are sparsely distributed and frequently distanced far from residential addresses. In addition, the degree of an association between outdoor and indoor air pollution levels is not fully elucidated, making the exposure assessment all the more inaccurate. Clinical outcomes’ assessment, on the other hand, mostly relies on the access to medical records from hospital admissions and outpatients’ visits in clinics. This method differentiates by health care seeking behavior and is therefore, problematic in evaluation of an onset, duration, and severity of an outcome. In the current paper, we review a number of novel solutions aimed to mitigate the aforementioned biases. First, a hybrid satellite-based modeling approach provides daily continuous spatiotemporal estimations with improved spatial resolution of 1 × 1 km2 and 200 × 200 m2 grid, and thus allows a more accurate exposure assessment. Utilizing low-cost air pollution sensors allowing a direct measurement of indoor air pollution levels can further validate these models. Furthermore, the real temporal-spatial activity can be assessed by GPS tracking devices within the individuals’ smartphones. A widespread use of smart devices can help with obtaining objective measurements of some of the clinical outcomes such as vital signs and glucose levels. Finally, human biomonitoring can be efficiently done at a population level, providing accurate estimates of in-vivo absorbed pollutants and allowing for the evaluation of body responses, by biomarkers examination. We suggest that the adoption of these novel methods will change the research paradigm heavily relying on ecological methodology and support development of the new clinical practices preventing adverse environmental effects on human health.
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45
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Ben Y, Ma F, Wang H, Hassan MA, Yevheniia R, Fan W, Li Y, Dong Z. A spatio-temporally weighted hybrid model to improve estimates of personal PM 2.5 exposure: Incorporating big data from multiple data sources. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 253:403-411. [PMID: 31325885 DOI: 10.1016/j.envpol.2019.07.034] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 07/07/2019] [Accepted: 07/07/2019] [Indexed: 06/10/2023]
Abstract
An accurate estimation of population exposure to particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) is crucial to hazard assessment and epidemiology. This study integrated annual data from 1146 in-home air monitors, air quality monitoring network, public applications, and traffic smart cards to determine the pattern of PM2.5 concentrations and activities in different microenvironments (including outdoors, indoors, subways, buses, and cars). By combining massive amounts of signaling data from cell phones, this study applied a spatio-temporally weighted model to improve the estimation of PM2.5 exposure. Using Shanghai as a case study, the annual average indoor PM2.5 concentration was estimated to be 29.3 ± 27.1 μg/m3 (n = 365), with an average infiltration factor of 0.63. The spatio-temporally weighted PM2.5 exposure was estimated to be 32.1 ± 13.9 μg/m3 (n = 365), with indoor PM2.5 contributing the most (85.1%), followed by outdoor (7.6%), bus (3.7%), subway (3.1%), and car (0.5%). However, considering that outdoor PM2.5 makes a significant contribution to indoor PM2.5, outdoor PM2.5 was responsible for most of the exposure in Shanghai. A heatmap of PM2.5 exposure indicated that the inner-city exposure index was significantly higher than that of the outskirts city, which demonstrated that the importance of spatial differences in population exposure estimation.
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Affiliation(s)
- YuJie Ben
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China; State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - FuJun Ma
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Hao Wang
- School of Space and Environment, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China
| | | | | | - WenHong Fan
- School of Space and Environment, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China
| | - Yubiao Li
- School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China
| | - ZhaoMin Dong
- School of Space and Environment, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China.
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46
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Wei P, Ning Z, Westerdahl D, Lam YF, Louie PKK, Sharpe R, Williams R, Hagler G. Solar-powered air quality monitor applied under subtropical conditions in Hong Kong: Performance evaluation and application for pollution source tracking. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2019; 214:1-116825. [PMID: 34434068 PMCID: PMC8384116 DOI: 10.1016/j.atmosenv.2019.116825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Air monitoring is desirable in many places to understand dynamic pollution trends and sources and improve knowledge of population exposure. While highly miniaturized low cost sensor technology is quickly evolving, there is also a need for the advancement of mid-tier systems that are closer to reference-grade technologies in their longevity and performance, but also feature compactness that requires less significant infrastructure. This project evaluated the performance of a prototype solar-powered air monitoring system known as a Village Green Project (VGP) system with wireless data transmission that was deployed on a school rooftop in Hong Kong and operated for over one year. The system provided highly time-resolved and long-term data utilizing mid-tier cost ozone, PM2.5 and meteorological instruments. It operated with very minimal maintenance but shading by a nearby building reduced solar radiation, thus battery run time, over the 16-months measurement period, approximately 330,000 1-min observations were recorded (data completeness of ~62%). The monitoring data were evaluated by comparison with a nearby Hong Kong Environment Protection Department (EPD) station and exhibited good performance for 1-h resolution (R 2 = 0.74 for PM2.5 and R 2 = 0.76 for ozone). Furthermore as a demonstration, a nonparametric regression (NPR) model was applied for identifying the location of pollution source, combining air pollution and meteorological measurements. In addition, based on the high time-resolution wind data, local-scale back-trajectories were calculated as an input for receptor-oriented Nonparametric Trajectory Analysis (NTA) model. The combination of the VGP air monitoring system and NTA model identified apparent local sources in urban area. The demonstration was largely successful and operational improvements are clearly suggested to insure better siting and configurations to insure adequate power and air flow.
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Affiliation(s)
- Peng Wei
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology
| | - Zhi Ning
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology
| | - Dane Westerdahl
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology
| | - Yun Fat Lam
- School of Energy and Environment, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region
| | - Peter K K Louie
- Environmental Protection Department, the Government of the Hong Kong Special Administration Region
| | - Robert Sharpe
- Arcadis, Inc., Greensboro, North Carolina, United States
| | - Ronald Williams
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Research Triangle Park, North Carolina, United States
| | - Gayle Hagler
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Research Triangle Park, North Carolina, United States
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47
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Cromar KR, Duncan BN, Bartonova A, Benedict K, Brauer M, Habre R, Hagler GSW, Haynes JA, Khan S, Kilaru V, Liu Y, Pawson S, Peden DB, Quint JK, Rice MB, Sasser EN, Seto E, Stone SL, Thurston GD, Volckens J. Air Pollution Monitoring for Health Research and Patient Care. An Official American Thoracic Society Workshop Report. Ann Am Thorac Soc 2019; 16:1207-1214. [PMID: 31573344 PMCID: PMC6812167 DOI: 10.1513/annalsats.201906-477st] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Air quality data from satellites and low-cost sensor systems, together with output from air quality models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much-needed air quality information in countries without them. Each of these technologies has strengths and limitations that need to be considered when integrating them to develop a robust and diverse global air quality monitoring network. To address these issues, the American Thoracic Society, the U.S. Environmental Protection Agency, the National Aeronautics and Space Administration, and the National Institute of Environmental Health Sciences convened a workshop in May 2017 to bring together global experts from across multiple disciplines and agencies to discuss current and near-term capabilities to monitor global air pollution. The participants focused on four topics: 1) current and near-term capabilities in air pollution monitoring, 2) data assimilation from multiple technology platforms, 3) critical issues for air pollution monitoring in regions without a regulatory-quality stationary monitoring network, and 4) risk communication and health messaging. Recommendations for research and improved use were identified during the workshop, including a recognition that the integration of data across monitoring technology groups is critical to maximizing the effectiveness (e.g., data accuracy, as well as spatial and temporal coverage) of these monitoring technologies. Taken together, these recommendations will advance the development of a global air quality monitoring network that takes advantage of emerging technologies to ensure the availability of free, accessible, and reliable air pollution data and forecasts to health professionals, as well as to all global citizens.
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48
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Song Y, Huang B, He Q, Chen B, Wei J, Mahmood R. Dynamic assessment of PM 2.5 exposure and health risk using remote sensing and geo-spatial big data. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 253:288-296. [PMID: 31323611 DOI: 10.1016/j.envpol.2019.06.057] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 06/12/2019] [Accepted: 06/13/2019] [Indexed: 05/12/2023]
Abstract
In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM2.5 estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM2.5 concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM2.5 observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM2.5 concentration greater than 80 μg/m3 in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM2.5 were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM2.5 estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated.
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Affiliation(s)
- Yimeng Song
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Bo Huang
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong.
| | - Qingqing He
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, China
| | - Bin Chen
- Department of Land, Air and Water Resources, University of California, Davis, CA, 95616, USA
| | - Jing Wei
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Rashed Mahmood
- Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan, Hubei, China
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49
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Wu Y, Song G. The Impact of Activity-Based Mobility Pattern on Assessing Fine-Grained Traffic-Induced Air Pollution Exposure. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16183291. [PMID: 31500255 PMCID: PMC6765945 DOI: 10.3390/ijerph16183291] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 08/26/2019] [Accepted: 09/05/2019] [Indexed: 11/28/2022]
Abstract
Quantifying the air pollution and health impacts of transportation plans provides decision makers with valuable information that can help to target interventions. However, a large number of environmental epidemiological research assumes exposures of static populations at residential locations and does not consider the human activity patterns, which may lead to significant estimation errors. This study uses an integrated modeling framework to predict fine-grained air pollution exposures occurring throughout residents’ activity spaces. We evaluate concentrations of fine particulate matter (PM2.5) under a regional transportation plan for Sacramento, California, using activity-based travel demand model outputs, vehicle emission, and air dispersion models. We use predicted air pollution exposures at the traffic analysis zone (TAZ) level to estimate residents’ exposure accounting for their movements throughout the day to assess the impact of activity-based mobility pattern on air pollution exposure. Results of PM2.5 exposures estimated statically (at residential locations) versus dynamically (over residents’ activity-based mobility) demonstrates that the two methods yield statistically significant different results (p < 0.05). In addition, the comparison conducted in different age groups shows that the difference between these two approaches is greater among youth and working age residents, whereas seniors show a similar pattern using both approaches due to their lower rates of travel activity.
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Affiliation(s)
- Yizheng Wu
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China.
| | - Guohua Song
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China.
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Yu X, Stuart AL, Liu Y, Ivey CE, Russell AG, Kan H, Henneman LRF, Sarnat SE, Hasan S, Sadmani A, Yang X, Yu H. On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 252:924-930. [PMID: 31226517 DOI: 10.1016/j.envpol.2019.05.081] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 05/15/2019] [Accepted: 05/15/2019] [Indexed: 05/18/2023]
Abstract
Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.
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Affiliation(s)
- Xiaonan Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Amy L Stuart
- College of Public Health, University of South Florida, Tampa, FL, USA; Department of Civil & Environmental Engineering, University of South Florida, Tampa, FL, USA
| | - Yang Liu
- Department of Environmental Health, Emory University, Atlanta, GA, USA
| | - Cesunica E Ivey
- Department of Chemical and Environmental Engineering, University of California Riverside, Riverside, CA, USA
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Haidong Kan
- School of Public Health, Fudan University, Shanghai, China
| | - Lucas R F Henneman
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | | | - Samiul Hasan
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Anwar Sadmani
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Xuchao Yang
- Institute of Island & Coastal Ecosystem, Zhejiang University, Hangzhou, Zhejiang, China
| | - Haofei Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA.
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