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Hackländer J, Parente L, Ho YF, Hengl T, Simoes R, Consoli D, Şahin M, Tian X, Jung M, Herold M, Duveiller G, Weynants M, Wheeler I. Land potential assessment and trend-analysis using 2000-2021 FAPAR monthly time-series at 250 m spatial resolution. PeerJ 2024; 12:e16972. [PMID: 38495753 PMCID: PMC10944167 DOI: 10.7717/peerj.16972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/29/2024] [Indexed: 03/19/2024] Open
Abstract
The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.
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Affiliation(s)
- Julia Hackländer
- OpenGeoHub, Wageningen, Netherlands
- Wageningen University and Research, Wageningen, Netherlands
| | | | | | | | | | | | | | - Xuemeng Tian
- OpenGeoHub, Wageningen, Netherlands
- Wageningen University and Research, Wageningen, Netherlands
| | - Martin Jung
- Biodiversity, Ecology and Conservation Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
| | - Martin Herold
- Wageningen University and Research, Wageningen, Netherlands
- Helmholtz GFZ German Research Centre for Geosciences, Remote Sensing and Geoinformatics, Potsdam, Germany
| | | | - Melanie Weynants
- Max Planck Institute for Biogeochemistry (MPI-BGC), Jena, Germany
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Bi J, Burnham D, Zuidema C, Schumacher C, Gassett AJ, Szpiro AA, Kaufman JD, Sheppard L. Evaluating low-cost monitoring designs for PM 2.5 exposure assessment with a spatiotemporal modeling approach. Environ Pollut 2024; 343:123227. [PMID: 38147948 PMCID: PMC10922961 DOI: 10.1016/j.envpol.2023.123227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 12/15/2023] [Accepted: 12/23/2023] [Indexed: 12/28/2023]
Abstract
Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 μg/m3]) compared to the model with all LCM measurements (0.84 [0.9 μg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 μg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 μg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 μg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.
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Affiliation(s)
- Jianzhao Bi
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA.
| | - Dustin Burnham
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Christopher Zuidema
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Cooper Schumacher
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Amanda J Gassett
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, Seattle, USA
| | - Joel D Kaufman
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA; Department of Medicine, University of Washington, Seattle, USA; Department of Epidemiology, University of Washington, USA
| | - Lianne Sheppard
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA; Department of Biostatistics, University of Washington, Seattle, USA
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Ma X, Zou B, Deng J, Gao J, Longley I, Xiao S, Guo B, Wu Y, Xu T, Xu X, Yang X, Wang X, Tan Z, Wang Y, Morawska L, Salmond J. A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023. Environ Int 2024; 183:108430. [PMID: 38219544 DOI: 10.1016/j.envint.2024.108430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/26/2023] [Accepted: 01/04/2024] [Indexed: 01/16/2024]
Abstract
Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.
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Affiliation(s)
- Xuying Ma
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China; College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China.
| | - Jun Deng
- College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; Shaanxi Key Laboratory of Prevention and Control of Coal Fire, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Jay Gao
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
| | - Ian Longley
- National Institute of Water and Atmospheric Research, Auckland 1010, New Zealand
| | - Shun Xiao
- School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yarui Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Tingting Xu
- School of Software Engineering, Chongqing University of Post and Telecommunications, Chongqing 400065, China
| | - Xin Xu
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Xiaosha Yang
- Shandong Nova Fitness Co., Ltd., Baoji, Shaanxi 722404, China
| | - Xiaoqi Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Zelei Tan
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yifan Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Jennifer Salmond
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
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Nikolaou N, Bouwer LM, Dallavalle M, Valizadeh M, Stafoggia M, Peters A, Wolf K, Schneider A. Improved daily estimates of relative humidity at high resolution across Germany: A random forest approach. Environ Res 2023; 238:117173. [PMID: 37734577 DOI: 10.1016/j.envres.2023.117173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 09/01/2023] [Accepted: 09/17/2023] [Indexed: 09/23/2023]
Abstract
The lack of readily available methods for estimating high-resolution near-surface relative humidity (RH) and the incapability of weather stations to fully capture the spatiotemporal variability can lead to exposure misclassification in studies of environmental epidemiology. We therefore aimed to predict German-wide 1 × 1 km daily mean RH during 2000-2021. RH observations, longitude and latitude, modelled air temperature, precipitation and wind speed as well as remote sensing information on topographic elevation, vegetation, and the true color band composite were incorporated in a Random Forest (RF) model, in addition to date for capturing the temporal variations of the response-explanatory variables relationship. The model achieved high accuracy (R2 = 0.83) and low errors (Root Mean Square Error (RMSE) of 5.07%, Mean Absolute Percentage Error (MAPE) of 5.19% and Mean Percentage Error (MPE) of - 0.53%), calculated via ten-fold cross-validation. A comparison of our RH predictions with measurements from a dense monitoring network in the city of Augsburg, South Germany confirmed the good performance (R2 ≥ 0.86, RMSE ≤ 5.45%, MAPE ≤ 5.59%, MPE ≤ 3.11%). The model displayed high German-wide RH (22y-average of 79.00%) and high spatial variability across the country, exceeding 12% on yearly averages. Our findings indicate that the proposed RF model is suitable for estimating RH for a whole country in high-resolution and provide a reliable RH dataset for epidemiological analyses and other environmental research purposes.
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Affiliation(s)
- Nikolaos Nikolaou
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Pettenkofer School of Public Health, Munich, Germany.
| | - Laurens M Bouwer
- Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany.
| | - Marco Dallavalle
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Pettenkofer School of Public Health, Munich, Germany.
| | - Mahyar Valizadeh
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service - ASL Roma 1, Rome, Italy.
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Pettenkofer School of Public Health, Munich, Germany.
| | - Kathrin Wolf
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
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Nikolaou N, Dallavalle M, Stafoggia M, Bouwer LM, Peters A, Chen K, Wolf K, Schneider A. High-resolution spatiotemporal modeling of daily near-surface air temperature in Germany over the period 2000-2020. Environ Res 2023; 219:115062. [PMID: 36535393 DOI: 10.1016/j.envres.2022.115062] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
The commonly used weather stations cannot fully capture the spatiotemporal variability of near-surface air temperature (Tair), leading to exposure misclassification and biased health effect estimates. We aimed to improve the spatiotemporal coverage of Tair data in Germany by using multi-stage modeling to estimate daily 1 × 1 km minimum (Tmin), mean (Tmean), maximum (Tmax) Tair and diurnal Tair range during 2000-2020. We used weather station Tair observations, satellite-based land surface temperature (LST), elevation, vegetation and various land use predictors. In the first stage, we built a linear mixed model with daily random intercepts and slopes for LST adjusted for several spatial predictors to estimate Tair from cells with both Tair and LST available. In the second stage, we used this model to predict Tair for cells with only LST available. In the third stage, we regressed the second stage predictions against interpolated Tair values to obtain Tair countrywide. All models achieved high accuracy (0.91 ≤ R2 ≤ 0.98) and low errors (1.03 °C ≤ Root Mean Square Error (RMSE) ≤ 2.02 °C). Validation with external data confirmed the good performance, locally, i.e., in Augsburg for all models (0.74 ≤ R2 ≤ 0.99, 0.87 °C ≤ RMSE ≤ 2.05 °C) and countrywide, for the Tmean model (0.71 ≤ R2 ≤ 0.99, 0.79 °C ≤ RMSE ≤ 1.19 °C). Annual Tmean averages ranged from 8.56 °C to 10.42 °C with the years beyond 2016 being constantly hotter than the 21-year average. The spatial variability within Germany exceeded 15 °C annually on average following patterns including mountains, rivers and urbanization. Using a case study, we showed that modeling leads to broader Tair variability representation for exposure assessment of participants in health cohorts. Our results indicate the proposed models as suitable for estimating nationwide Tair at high resolution. Our product is critical for temperature-based epidemiological studies and is also available for other research purposes.
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Affiliation(s)
- Nikolaos Nikolaou
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, LMU Munich, Munich, Germany.
| | - Marco Dallavalle
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, LMU Munich, Munich, Germany
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | - Laurens M Bouwer
- Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, LMU Munich, Munich, Germany
| | - Kai Chen
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Kathrin Wolf
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
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Giffin A, Gong W, Majumder S, Rappold AG, Reich BJ, Yang S. Estimating intervention effects on infectious disease control: The effect of community mobility reduction on Coronavirus spread. Spat Stat 2022; 52:100711. [PMID: 36284923 PMCID: PMC9584839 DOI: 10.1016/j.spasta.2022.100711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 01/29/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Understanding the effects of interventions, such as restrictions on community and large group gatherings, is critical to controlling the spread of COVID-19. Susceptible-Infectious-Recovered (SIR) models are traditionally used to forecast the infection rates but do not provide insights into the causal effects of interventions. We propose a spatiotemporal model that estimates the causal effect of changes in community mobility (intervention) on infection rates. Using an approximation to the SIR model and incorporating spatiotemporal dependence, the proposed model estimates a direct and indirect (spillover) effect of intervention. Under an interference and treatment ignorability assumption, this model is able to estimate causal intervention effects, and additionally allows for spatial interference between locations. Reductions in community mobility were measured by cell phone movement data. The results suggest that the reductions in mobility decrease Coronavirus cases 4 to 7 weeks after the intervention.
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Affiliation(s)
- Andrew Giffin
- North Carolina State University, Department of Statistics, 2311 Stinson Drive, Raleigh, NC 27607, United States of America
| | - Wenlong Gong
- North Carolina State University, Department of Statistics, 2311 Stinson Drive, Raleigh, NC 27607, United States of America
| | - Suman Majumder
- Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, United States of America
| | - Ana G Rappold
- Environmental Protection Agency, 104 Mason Farm Road, Chapel Hill, NC 27514, United States of America
| | - Brian J Reich
- North Carolina State University, Department of Statistics, 2311 Stinson Drive, Raleigh, NC 27607, United States of America
| | - Shu Yang
- North Carolina State University, Department of Statistics, 2311 Stinson Drive, Raleigh, NC 27607, United States of America
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Wüstner D. Image segmentation and separation of spectrally similar dyes in fluorescence microscopy by dynamic mode decomposition of photobleaching kinetics. BMC Bioinformatics 2022; 23:334. [PMID: 35962314 PMCID: PMC9373304 DOI: 10.1186/s12859-022-04881-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 08/03/2022] [Indexed: 12/03/2022] Open
Abstract
Background Image segmentation in fluorescence microscopy is often based on spectral separation of fluorescent probes (color-based segmentation) or on significant intensity differences in individual image regions (intensity-based segmentation). These approaches fail, if dye fluorescence shows large spectral overlap with other employed probes or with strong cellular autofluorescence. Results Here, a novel model-free approach is presented which determines bleaching characteristics based on dynamic mode decomposition (DMD) and uses the inferred photobleaching kinetics to distinguish different probes or dye molecules from autofluorescence. DMD is a data-driven computational method for detecting and quantifying dynamic events in complex spatiotemporal data. Here, DMD is first used on synthetic image data and thereafter used to determine photobleaching characteristics of a fluorescent sterol probe, dehydroergosterol (DHE), compared to that of cellular autofluorescence in the nematode Caenorhabditis elegans. It is shown that decomposition of those dynamic modes allows for separating probe from autofluorescence without invoking a particular model for the bleaching process. In a second application, DMD of dye-specific photobleaching is used to separate two green-fluorescent dyes, an NBD-tagged sphingolipid and Alexa488-transferrin, thereby assigning them to different cellular compartments. Conclusions Data-based decomposition of dynamic modes can be employed to analyze spatially varying photobleaching of fluorescent probes in cells and tissues for spatial and temporal image segmentation, discrimination of probe from autofluorescence and image denoising. The new method should find wide application in analysis of dynamic fluorescence imaging data. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04881-x.
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Affiliation(s)
- Daniel Wüstner
- Department of Biochemistry and Molecular Biology and Physics of Life Sciences (PhyLife) Center, University of Southern Denmark, Campusvej 55, DK-5230, Odense, Denmark.
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Bonannella C, Hengl T, Heisig J, Parente L, Wright MN, Herold M, de Bruin S. Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal machine learning. PeerJ 2022; 10:e13728. [PMID: 35910765 PMCID: PMC9332400 DOI: 10.7717/peerj.13728] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/22/2022] [Indexed: 01/17/2023] Open
Abstract
This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species (Abies alba Mill., Castanea sativa Mill., Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H. Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestris L., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for a total of three million of points was used to train different algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 305 coarse and high resolution covariates representing spectral reflectance, different biophysical conditions and biotic competition was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to tune and train an ensemble model based on stacking with a logistic regressor as a meta-learner. An ensemble model was trained for each species: probability and model uncertainty maps of realized distribution were produced for each species using a time window of 4 years for a total of six distribution maps per species, while for potential distributions only one map per species was produced. Results of spatial cross validation show that the ensemble model consistently outperformed or performed as good as the best individual model in both potential and realized distribution tasks, with potential distribution models achieving higher predictive performances (TSS = 0.898, R2 logloss = 0.857) than realized distribution ones on average (TSS = 0.874, R2 logloss = 0.839). Ensemble models for Q. suber achieved the best performances in both potential (TSS = 0.968, R2 logloss = 0.952) and realized (TSS = 0.959, R2 logloss = 0.949) distribution, while P. sylvestris (TSS = 0.731, 0.785, R2 logloss = 0.585, 0.670, respectively, for potential and realized distribution) and P. nigra (TSS = 0.658, 0.686, R2 logloss = 0.623, 0.664) achieved the worst. Importance of predictor variables differed across species and models, with the green band for summer and the Normalized Difference Vegetation Index (NDVI) for fall for realized distribution and the diffuse irradiation and precipitation of the driest quarter (BIO17) being the most frequent and important for potential distribution. On average, fine-resolution models outperformed coarse resolution models (250 m) for realized distribution (TSS = +6.5%, R2 logloss = +7.5%). The framework shows how combining continuous and consistent Earth Observation time series data with state of the art machine learning can be used to derive dynamic distribution maps. The produced predictions can be used to quantify temporal trends of potential forest degradation and species composition change.
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Affiliation(s)
- Carmelo Bonannella
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands
- OpenGeoHub, Wageningen, The Netherlands
| | | | - Johannes Heisig
- Institute for Geoinformatics, University of Münster, Münster, Germany
| | | | - Marvin N. Wright
- Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany
- University of Bremen, Bremen, Germany
| | - Martin Herold
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands
- Section 1.4 Remote Sensing and Geoinformatics, GFZ German Research Centre for Geosciences, Potsdam, Germany
| | - Sytze de Bruin
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands
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Li H, Zhu L, Dai Z, Gong H, Guo T, Guo G, Wang J, Teatini P. Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR. Sci Total Environ 2021; 799:149244. [PMID: 34365261 DOI: 10.1016/j.scitotenv.2021.149244] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 07/17/2021] [Accepted: 07/20/2021] [Indexed: 06/13/2023]
Abstract
The demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and inducing regional land subsidence (LS). This has greatly threatened sustainable development in the long run. LS modeling helps understanding the factors responsible for the ongoing loss of land elevation and hence enhances the development of prevention strategies. Data-driven LS models perform well with fewer variables and faster convergence than physically-based hydrogeological models. However, the former models often cannot simultaneously reflect the temporal nonlinearity and spatial correlation (SC) characteristics of LS under complex variables. We proposed a LS spatiotemporal model which considers both nonlinear and spatial correlations between LS and groundwater level change of exploited aquifers. It is based on deep learning method and LS time series detected by permanent scatterer-interferometric synthetic aperture radar (PS-InSAR). The LS time series and hydrogeological properties are constructed as a spatiotemporal dataset for model training. The spatiotemporal LS model, geographically weighted long short-term memory (GW-LSTM), is constructed by integrating SC with LSTM. This latter is a deep recurrent neural network approach incorporating sequential data. The model is validated by a case study in the Beijing plain. The results show that the accuracy of the proposed model can be greatly improved considering the spatial correlation between LS and influencing factors. Furthermore, the comparison between the LSTM and GW-LSTM models reveals that groundwater level variation is not a unique causation of LS in the study area. The developed model deals with the spatiotemporal characteristics of LS under multiple variables and can be used to predict LS under different scenarios of groundwater level variations for the purpose of monitoring and providing evidence to support the prevention of future LS.
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Affiliation(s)
- Huijun Li
- Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Key Laboratory of 3-Dimensional Information Acquisition and Application, Capital Normal University, Beijing 100048, China
| | - Lin Zhu
- Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Key Laboratory of 3-Dimensional Information Acquisition and Application, Capital Normal University, Beijing 100048, China.
| | - Zhenxue Dai
- College of Construction Engineering, Jilin University, Changchun 130026, China
| | - Huili Gong
- Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Key Laboratory of 3-Dimensional Information Acquisition and Application, Capital Normal University, Beijing 100048, China
| | - Tao Guo
- Institute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China
| | - Gaoxuan Guo
- Beijing Institute of Hydrogeology and Engineering Geology, Beijing 100048, China
| | - Jingbo Wang
- National Computational Infrastructure, Australian National University, Canberra, Australia
| | - Pietro Teatini
- Dept. of Civil, Environmental and Architectural Engineering, University of Padova, Padova 35121, Italy; UNESCO-LaSII (Land Subsidence International Initiative), Querétaro, Mexico
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Ngwira A, Kumwenda F, Munthali ECS, Nkolokosa D. Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi. PeerJ 2021; 9:e11003. [PMID: 33665042 PMCID: PMC7912604 DOI: 10.7717/peerj.11003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 02/02/2021] [Indexed: 12/23/2022] Open
Abstract
Background COVID-19 has been one of the greatest challenges the world has faced since the second world war. This study aimed at investigating the distribution of COVID-19 in both space and time in Malawi. Methods The study used publicly available data of COVID-19 cases for the period from 2 April 2020 to 28 October 2020. Semiparametric spatial temporal models were fitted to the number of monthly confirmed cases as an outcome data, with time and district as independent variables, where district was the spatial unit, while accounting for sociodemographic factors. Results The study found significant effects of location and time, with the two interacting. The spatial distribution of COVID-19 risk showed major cities being at greater risk than rural areas. Over time, the COVID-19 risk was increasing then decreasing in most districts with the rural districts being consistently at lower risk. High proportion of elderly people was positively associated with COVID-19 risk (β = 1.272, 95% CI [0.171, 2.370]) than low proportion of elderly people. There was negative association between poverty incidence and COVID-19 risk (β = −0.100, 95% CI [−0.136, −0.065]). Conclusion Future or present strategies to limit the spread of COVID-19 should target major cities and the focus should be on time periods that had shown high risk. Furthermore, the focus should be on elderly and rich people.
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Affiliation(s)
- Alfred Ngwira
- Basic Sciences Department, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Felix Kumwenda
- Basic Sciences Department, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Eddons C S Munthali
- Basic Sciences Department, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Duncan Nkolokosa
- Basic Sciences Department, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
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11
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Ren X, Mi Z, Georgopoulos PG. Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States. Environ Int 2020; 142:105827. [PMID: 32593834 DOI: 10.1016/j.envint.2020.105827] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 04/29/2020] [Accepted: 05/19/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Spatial linear Land-Use Regression (LUR) is commonly used for long-term modeling of air pollution in support of exposure and epidemiological assessments. Machine Learning (ML) methods in conjunction with spatiotemporal modeling can provide more flexible exposure-relevant metrics and have been studied using different model structures. There is however a lack of comparisons of methods available within these two modeling frameworks, that can guide model/algorithm selection in air quality epidemiology. OBJECTIVE The present study compares thirteen algorithms for spatial/spatiotemporal modeling applied for daily maxima of 8-hour running averages of ambient ozone concentrations at spatial resolutions corresponding to census tracts, to support estimation of annual ozone design values across the contiguous US. These algorithms were selected from nine representative categories and trained using predictors that included chemistry-transport model predictions, meteorological factors, land use and land cover, and stationary and mobile emissions. METHODS To obtain the best predictive performance, model structures were optimized through a repeated coarse/fine grid search with expert knowledge. Six target-oriented validation strategies were used to prevent overfitting and avoid over-optimistic model evaluation results. In order to take full advantage of the power of different algorithms, we introduced tuning sample weights in spatiotemporal modeling to ensure predictive accuracy of peak concentrations, that is crucial for exposure assessments. In spatial modeling, four interpretation and visualization tools were introduced to explain predictions from different algorithms. RESULTS Nonlinear ML methods achieved higher prediction accuracy than linear LUR, and the improvements were more significant for spatiotemporal modeling (nearly 10%-40% decrease of predicted RMSE). By tuning the sample weights, spatiotemporal models can predict concentrations used to calculate ozone design values that are comparable or even better than spatial models (nearly 30% decrease of cross-validated RMSE). We visualized the underlying nonlinear relationships, heterogeneous associations and complex interactions from the two best performing ML algorithms, i.e., Random Forest and Extreme Gradient Boosting, and found that the complex patterns were relatively less significant with respect to model accuracy for spatial modeling. CONCLUSION Machine Learning can provide estimates that are actually more interpretable and practical than linear regression to improve accuracy in modeling human exposures. A careful design of hyperparameter tuning and flexible data splitting and validations is crucial to obtain reliable and stable results. Desirable/successful nonlinear models are expected to capture similar nonlinear patterns and interactions using different ML algorithms.
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Affiliation(s)
- Xiang Ren
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA; Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854, USA
| | - Zhongyuan Mi
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA; Department of Environmental Sciences, Rutgers University, New Brunswick, NJ 08901, USA
| | - Panos G Georgopoulos
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA; Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854, USA; Department of Environmental Sciences, Rutgers University, New Brunswick, NJ 08901, USA; Department of Environmental and Occupational Health, Rutgers School of Public Health, Piscataway, NJ 08854, USA.
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12
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He Q, Gu Y, Zhang M. Spatiotemporal trends of PM 2.5 concentrations in central China from 2003 to 2018 based on MAIAC-derived high-resolution data. Environ Int 2020; 137:105536. [PMID: 32036122 DOI: 10.1016/j.envint.2020.105536] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 01/10/2020] [Accepted: 01/27/2020] [Indexed: 06/10/2023]
Abstract
Long-term PM2.5 levels with high precision at fine spatiotemporal resolution are essential for quantitatively understanding the health risk of exposure to ambient fine particulate matter (PM2.5) and making effective air pollution control policies. The emerging statistically derived PM2.5 estimations from satellite remote sensing observations of aerosol optical depth (AOD) data are an effective alternative to reconstruct global, long-term, high spatiotemporal resolution PM2.5 information. However, studies on PM2.5 estimation and its application to exposure and health-related studies are limited in China due to the lack of historical in-situ measurements before 2013. In this study, we explored the long-term trends of PM2.5 exposure in central China, a hotspot that has recently been experiencing severe particulate pollution, at the local scale. We first developed a spatiotemporal model incorporating periodical characteristics within the data to estimate daily concentrations of historical PM2.5 at a fine scale of 1 km for 2003-2018. The linear effects of predictors including AOD, meteorological and land-use parameters and the non-linear interaction between AOD and meteorological parameters were considered in the modeling process. The most recently released high-resolution satellite aerosol product, Multi-Angle Implementation of Atmospheric Correction (MAIAC) was used to help to represent the fine-scale particle gradients. Our daily estimates correlated well with in-situ observations (cross-validation R2 = 0.59), achieving precision comparable to previous statistical models. Through linking with gridded demographic data, the population-weighted PM2.5 average during 2003 to 2018 was found to be high (62.23 μg/m3 for the whole domain) with obvious spatial variations and seasonality. An inverse U pattern was seen in the time series, with two inflection points around 2008 and 2015. Our model provides reliable particulate information with high spatial resolution and long-term temporal coverage, which can inform local-scale PM2.5-related epidemiological studies and health-risk assessments for central China.
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Affiliation(s)
- Qingqing He
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong.
| | - Yefu Gu
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong
| | - Ming Zhang
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China.
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13
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Muche ME, Hutchinson SL, Hutchinson JMS, Johnston JM. Phenology-adjusted dynamic curve number for improved hydrologic modeling. J Environ Manage 2019; 235:403-413. [PMID: 30708277 PMCID: PMC6747703 DOI: 10.1016/j.jenvman.2018.12.115] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 12/21/2018] [Accepted: 12/30/2018] [Indexed: 06/09/2023]
Abstract
The Soil Conservation Service Curve Number (SCS-CN, or CN) is a widely used method to estimate runoff from rainfall events. It has been adapted to many parts of the world with different land uses, land cover types, and climatic conditions and successfully applied to situations ranging from simple runoff calculations and land use change assessment to comprehensive hydrologic/water quality simulations. However, the CN method lacks the ability to incorporate seasonal variations in vegetated surface conditions, and unnoticed landuse/landcover (LULC) change that shape infiltration and storm runoff. Plant phenology is a main determinant of changes in hydrologic processes and water balances across seasons through its influence on surface roughness and evapotranspiration. This study used regression analysis to develop a dynamic CN (CNNDVI) based on seasonal variations in the remotely-sensed Normalized Difference Vegetation Index (NDVI) to monitor intra-annual plant phenological development. A time series of 16-day MODIS NDVI (MOD13Q1 Collection 5) images were used to monitor vegetation development and provide NDVI data necessary for CNNDVI model calibration and validation. Twelve years of rainfall and runoff data (2001-2012) from four small watersheds located in the Konza Prairie Biological Station, Kansas were used to develop, calibrate, and validate the method. Results showed CNNDVI performed significantly better in predicting runoff with calibrated CNNDVI runoff increasing by approximately 0.74 for every unit increase in observed runoff compared to 0.46 for SCS-CN runoff and was more highly correlated to observed runoff (r = 0.78 vs. r = 0.38). In addition, CNNDVI runoff had better NSE (0.53) and PBIAS (4.22) compared to the SCS-CN runoff (-0.87 and -94.86 respectively). In the validated model, CNNDVI runoff increased by approximately 0.96 for every unit of observed runoff, while SCS-CN runoff increased by 0.49. Validated runoff was also better correlated to observed runoff than SCS-CN runoff (r = 0.52 vs. r = 0.33). These findings suggest that the CNNDVI can yield improved estimates of surface runoff from precipitation events, leading to more informed water and land management decisions.
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Affiliation(s)
- Muluken E Muche
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Athens, GA, USA; Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS, USA.
| | - Stacy L Hutchinson
- Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS, USA
| | | | - John M Johnston
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Athens, GA, USA
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14
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Abstract
Infectious diseases continue to pose a significant public health burden despite the great progress achieved in their prevention and control over the last few decades. Our ability to disentangle the factors and mechanisms driving their propagation in space and time has dramatically advanced in recent years. The current era is rich in mathematical and computational tools and detailed geospatial information, including sociodemographic, geographic, and environmental data, which are essential to elucidate key drivers of infectious disease transmission from epidemiological and genetic data. Indeed, this paradigm shift was driven by dramatic advances in complex systems approaches along with substantial improvements in data availability and computational power. The burgeoning output of infectious disease spatial modeling suggests that we are close to a fully integrated approach for early epidemic detection and intervention. This special collection in BMC Medicine aims to bring together a broad range of quantitative investigations that improve our understanding of the spatiotemporal transmission dynamics of infectious diseases in order to mitigate their impact on the human population.
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Affiliation(s)
- G Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.
| | - R Rothenberg
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
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15
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Ward EJ, Oken KL, Rose KA, Sable S, Watkins K, Holmes EE, Scheuerell MD. Applying spatiotemporal models to monitoring data to quantify fish population responses to the Deepwater Horizon oil spill in the Gulf of Mexico. Environ Monit Assess 2018; 190:530. [PMID: 30121848 DOI: 10.1007/s10661-018-6912-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 08/08/2018] [Indexed: 06/08/2023]
Abstract
Quantifying the impacts of disturbances such as oil spills on marine species can be challenging. Natural environmental variability, human responses to the disturbance (e.g., fisheries closures), the complex life histories of the species being monitored, and limited pre-spill data can make detection of effects of oil spills difficult. Using long-term monitoring data from the state of Louisiana (USA), we applied novel spatiotemporal approaches to identify anomalies in species occurrence and catch rates. We included covariates (salinity, temperature, turbidity) to help isolate unusual events. While some species showed evidence of unlikely temporal anomalies in occurrence or catch rates, we found that the majority of the observed anomalies were also before the Deepwater Horizon event. Several species-gear combinations suggested upticks in the spatial variability immediately following the spill, but most species indicated no trend. Across species-gear combinations, there was no clear evidence for synchronous or asynchronous responses in occurrence or catch rates across sites following the spill. Our results are in general agreement to other analyses of monitoring data that detected small impacts, but in contrast to recent results from ecological modeling that showed much larger effects of the oil spill on fish and shellfish.
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Affiliation(s)
- Eric J Ward
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd E, Seattle, WA, 98112, USA.
| | - Kiva L Oken
- Department of Marine and Coastal Sciences, Rutgers University, 71 Dudley Rd, New Brunswick, NJ, 08901, USA
| | - Kenneth A Rose
- Horn Point Laboratory, University of Maryland Center for Environmental Science, PO Box 775, Cambridge, MD, 21613, USA
| | - Shaye Sable
- Dynamic Solutions, LLC, 450 Laurel Street, Suite 1650, Baton Rouge, LA, 70801, USA
| | - Katherine Watkins
- Dynamic Solutions, LLC, 450 Laurel Street, Suite 1650, Baton Rouge, LA, 70801, USA
| | - Elizabeth E Holmes
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd E, Seattle, WA, 98112, USA
| | - Mark D Scheuerell
- Fish Ecology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd E, Seattle, WA, 98112, USA
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16
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He Q, Huang B. Satellite-based high-resolution PM 2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model. Environ Pollut 2018; 236:1027-1037. [PMID: 29455919 DOI: 10.1016/j.envpol.2018.01.053] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 01/17/2018] [Accepted: 01/17/2018] [Indexed: 05/28/2023]
Abstract
Ground fine particulate matter (PM2.5) concentrations at high spatial resolution are substantially required for determining the population exposure to PM2.5 over densely populated urban areas. However, most studies for China have generated PM2.5 estimations at a coarse resolution (≥10 km) due to the limitation of satellite aerosol optical depth (AOD) product in spatial resolution. In this study, the 3 km AOD data fused using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOD products were employed to estimate the ground PM2.5 concentrations over the Beijing-Tianjin-Hebei (BTH) region of China from January 2013 to December 2015. An improved geographically and temporally weighted regression (iGTWR) model incorporating seasonal characteristics within the data was developed, which achieved comparable performance to the standard GTWR model for the days with paired PM2.5- AOD samples (Cross-validation (CV) R2 = 0.82) and showed better predictive power for the days without PM2.5- AOD pairs (the R2 increased from 0.24 to 0.46 in CV). Both iGTWR and GTWR (CV R2 = 0.84) significantly outperformed the daily geographically weighted regression model (CV R2 = 0.66). Also, the fused 3 km AODs improved data availability and presented more spatial gradients, thereby enhancing model performance compared with the MODIS original 3/10 km AOD product. As a result, ground PM2.5 concentrations at higher resolution were well represented, allowing, e.g., short-term pollution events and long-term PM2.5 trend to be identified, which, in turn, indicated that concerns about air pollution in the BTH region are justified despite its decreasing trend from 2013 to 2015.
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Affiliation(s)
- Qingqing He
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong; Big Data Decision Analytics (BDDA) Research Centre, 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; Big Data Decision Analytics (BDDA) Research Centre, The Chinese University of Hong Kong, Shatin, Hong Kong; Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong.
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17
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Pakzad R, Pakzad I, Safiri S, Shirzadi MR, Mohammadpour M, Behroozi A, Sullman MJM, Janati A. Spatiotemporal analysis of brucellosis incidence in Iran from 2011 to 2014 using GIS. Int J Infect Dis 2017; 67:129-136. [PMID: 29122689 DOI: 10.1016/j.ijid.2017.10.017] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 10/19/2017] [Accepted: 10/23/2017] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVE To investigate the distribution and trends associated with brucellosis incidence rates in Iran from 2011 to 2014. METHODS The reported incidence rates of brucellosis for the years 2011-2014 were collected and entered into GIS 10.1. The Cochran-Armitage test for linear trends, choropleth maps, hot-spot analysis, and high-low clustering analysis were used to investigate patterns of the disease over the study period and by season, and to identify high-risk areas and any clustering of the disease. The significance level was set at p<0.05. RESULTS A total of 68493 cases of brucellosis were reported during the study period, giving an average brucellosis incidence rate for this period of 38.67/100000. In 2011, the highest rate of brucellosis was observed in Koohrang County of Chaharmahal-Bakhtiari Province, with 317/100 000. In the subsequent years, 2012-2014, Charuymaq County of East-Azerbaijan Province had incidence rates of 384, 534, and 583/100000, respectively. However, the incidence rate of the disease did not follow a linear trend (p<0.001). The maximum and minimum incidence rates of the disease occurred in mid-summer and mid-winter, respectively. The results of the hot-spot analysis showed that the distribution of the disease was highest in the mountainous areas of Iran, particularly along the Zagros mountain range and in most cities near the Zagros Mountains (p<0.01). In addition, the cluster analysis showed a clustering pattern in these high incidence areas (p<0.01). CONCLUSIONS There were significant differences in the geographic distribution of brucellosis, with the incidence rates being highest in most of the cities in the west and north-west of the country. The incidence of this disease also increased during the summer. It is important to take these patterns into account when allocating resources to combat this disease and to ensure that health programs and other interventions focus on the areas of greatest need.
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Affiliation(s)
- Reza Pakzad
- Department of Epidemiology, Faculty of Health, Ilam University of Medical Sciences, Ilam, Iran; Student Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
| | - Iraj Pakzad
- Clinical Microbiology Research Center, Ilam University of Medical Sciences, Ilam, Iran
| | - Saeid Safiri
- Middle East Technical University, Northern Cyprus Campus, Guzelyurt, Cyprus
| | - Mohammad Reza Shirzadi
- Communicable Diseases Management Center, Ministry of Health and Medical Education, Tehran, Iran
| | - Marzieh Mohammadpour
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Abbas Behroozi
- Middle East Technical University, Northern Cyprus Campus, Guzelyurt, Cyprus
| | - Mark J M Sullman
- Iranian Center of Excellence in Health Management (IceHM), School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Janati
- Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran.
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18
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Abstract
Fine particulate matter (PM2.5) measured at a given location is a mix of pollution generated locally and pollution traveling long distances in the atmosphere. Therefore, the identification of spatial scales associated with health effects can inform on pollution sources responsible for these effects, resulting in more targeted regulatory policy. Recently, prediction methods that yield high-resolution spatial estimates of PM2.5 exposures allow one to evaluate such scale-specific associations. We propose a two-dimensional wavelet decomposition that alleviates restrictive assumptions required for standard wavelet decompositions. Using this method we decompose daily surfaces of PM2.5 to identify which scales of pollution are most associated with adverse health outcomes. A key feature of the approach is that it can remove the purely temporal component of variability in PM2.5 levels and calculate effect estimates derived solely from spatial contrasts. This eliminates the potential for unmeasured confounding of the exposure - outcome associations by temporal factors, such as season. We apply our method to a study of birth weights in Massachusetts, U.S.A from 2003-2008 and find that both local and urban sources of pollution are strongly negatively associated with birth weight. Results also suggest that failure to eliminate temporal confounding in previous analyses attenuated the overall effect estimate towards zero, with the effect estimate growing in magnitude once this source of variability is removed.
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19
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Soltani M, Sefidgar M, Bazmara H, Casey ME, Subramaniam RM, Wahl RL, Rahmim A. Spatiotemporal distribution modeling of PET tracer uptake in solid tumors. Ann Nucl Med 2017; 31:109-24. [PMID: 27921285 DOI: 10.1007/s12149-016-1141-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Accepted: 10/18/2016] [Indexed: 12/25/2022]
Abstract
OBJECTIVE Distribution of PET tracer uptake is elaborately modeled via a general equation used for solute transport modeling. This model can be used to incorporate various transport parameters of a solid tumor such as hydraulic conductivity of the microvessel wall, transvascular permeability as well as interstitial space parameters. This is especially significant because tracer delivery and drug delivery to solid tumors are determined by similar underlying tumor transport phenomena, and quantifying the former can enable enhanced prediction of the latter. METHODS We focused on the commonly utilized FDG PET tracer. First, based on a mathematical model of angiogenesis, the capillary network of a solid tumor and normal tissues around it were generated. The coupling mathematical method, which simultaneously solves for blood flow in the capillary network as well as fluid flow in the interstitium, is used to calculate pressure and velocity distributions. Subsequently, a comprehensive spatiotemporal distribution model (SDM) is applied to accurately model distribution of PET tracer uptake, specifically FDG in this work, within solid tumors. RESULTS The different transport mechanisms, namely convention and diffusion from vessel to tissue and in tissue, are elaborately calculated across the domain of interest and effect of each parameter on tracer distribution is investigated. The results show the convection terms to have negligible effect on tracer transport and the SDM can be solved after eliminating these terms. CONCLUSION The proposed framework of spatiotemporal modeling for PET tracers can be utilized to comprehensively assess the impact of various parameters on the spatiotemporal distribution of PET tracers.
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20
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Chien LC, Yu HL. Impact of meteorological factors on the spatiotemporal patterns of dengue fever incidence. Environ Int 2014; 73:46-56. [PMID: 25084561 DOI: 10.1016/j.envint.2014.06.018] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2013] [Revised: 06/17/2014] [Accepted: 06/22/2014] [Indexed: 05/05/2023]
Abstract
Dengue fever is one of the most widespread vector-borne diseases and has caused more than 50 million infections annually over the world. For the purposes of disease prevention and climate change health impact assessment, it is crucial to understand the weather-disease associations for dengue fever. This study investigated the nonlinear delayed impact of meteorological conditions on the spatiotemporal variations of dengue fever in southern Taiwan during 1998-2011. We present a novel integration of a distributed lag nonlinear model and Markov random fields to assess the nonlinear lagged effects of weather variables on temporal dynamics of dengue fever and to account for the geographical heterogeneity. This study identified the most significant meteorological measures to dengue fever variations, i.e., weekly minimum temperature, and the weekly maximum 24-hour rainfall, by obtaining the relative risk (RR) with respect to disease counts and a continuous 20-week lagged time. Results show that RR increased as minimum temperature increased, especially for the lagged period 5-18 weeks, and also suggest that the time to high disease risks can be decreased. Once the occurrence of maximum 24-hour rainfall is >50 mm, an associated increased RR lasted for up to 15 weeks. A temporary one-month decrease in the RR of dengue fever is noted following the extreme rain. In addition, the elevated incidence risk is identified in highly populated areas. Our results highlight the high nonlinearity of temporal lagged effects and magnitudes of temperature and rainfall on dengue fever epidemics. The results can be a practical reference for the early warning of dengue fever.
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Affiliation(s)
- Lung-Chang Chien
- Division of Biostatistics, University of Texas School of Public Health at San Antonio Regional Campus, San Antonio, TX 78229, USA; Research to Advance Community Health Center, University of Texas Health Science Center at San Antonio Regional Campus, San Antonio, TX 78229, USA
| | - Hwa-Lung Yu
- Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan.
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Olives C, Sheppard L, Lindström J, Sampson PD, Kaufman JD, Szpiro AA. Reduced-Rank Spatio-Temporal Modeling of Air Pollution Concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution. Ann Appl Stat 2014; 8:2509-2537. [PMID: 27014398 DOI: 10.1214/14-aoas786] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members. In general, spatio-temporal models are limited in their efficacy for large data sets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of oxides of nitrogen (NO x )-a pollutant of primary interest in MESA Air-in the Los Angeles metropolitan area via cross-validated R2. Our findings suggest that use of reduced-rank models can improve computational efficiency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings.
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Abstract
Real-time cardiovascular MRI is a useful and challenging dynamic imaging application. The partial separability (PS) model enables reconstruction of dynamic cardiac images from highly undersampled (k, t)-space data. However, the underlying PS model-based reconstruction problem is ill-conditioned, so regularization is often necessary to stabilize its solution. It has been shown that ℓ1 regularization is useful for finding sparse solutions, and ℓ2 regularization is widely used to incorporate anatomical constraints. An important practical question is which regularization scheme to use for PS model-based cardiovascular imaging. We address this problem by implementing both schemes and evaluating their performances in terms of reconstruction error, image artifacts, image noise, computation time, and performance characterizability. The ℓ1-regularized results exhibit lower reconstruction error, artifact energy, and noise variance, while ℓ2 regularization is much faster and produces predictable reconstruction results. This study indicates that the ℓ1 scheme is preferable when image quality is the main concern.
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Affiliation(s)
- Anthony G Christodoulou
- Department of Electrical and Computer Engineering, and Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign
| | - Bo Zhao
- Department of Electrical and Computer Engineering, and Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign
| | - Zhi-Pei Liang
- Department of Electrical and Computer Engineering, and Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign
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