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Article ; Online: Geospatial techniques for monitoring and mitigating climate change and its effects on human health

Maged N. Kamel Boulos / John P. Wilson

International Journal of Health Geographics, Vol 22, Iss 1, Pp 1-

2023 Volume 7

Abstract: Abstract This article begins by briefly examining the multitude of ways in which climate and climate change affect human health and wellbeing. It then proceeds to present a quick overview of how geospatial data, methods and tools are playing key roles in ...

Abstract	Abstract This article begins by briefly examining the multitude of ways in which climate and climate change affect human health and wellbeing. It then proceeds to present a quick overview of how geospatial data, methods and tools are playing key roles in the measurement, analysis and modelling of climate change and its effects on human health. Geospatial techniques are proving indispensable for making more accurate assessments and estimates, predicting future trends more reliably, and devising more optimised climate change adaptation and mitigation plans.
Keywords	Climate change ; Human-induced warming ; Geospatial data ; Geographic information systems (GIS) ; Remote sensing ; Human health ; Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comparative cranial osteology of subadult eucentrosauran ceratopsid dinosaurs from the Two Medicine Formation, Montana, indicates sequence of ornamentation development and complex supraorbital ontogenetic change

JOHN P. WILSON / JOHN B. SCANNELLA

Acta Palaeontologica Polonica, Vol 66, Iss 4, Pp 797-

2021 Volume 814

Abstract: The eucentrosauran centrosaurines Einiosaurus procurvicornis and Achelousaurus horneri are the two most commonly recovered ceratopsids from the Campanian Two Medicine Formation of northwestern Montana, USA. Einiosaurus procurvicornis is known from at ... ...

Abstract	The eucentrosauran centrosaurines Einiosaurus procurvicornis and Achelousaurus horneri are the two most commonly recovered ceratopsids from the Campanian Two Medicine Formation of northwestern Montana, USA. Einiosaurus procurvicornis is known from at least 15 individuals recovered from two monospecific bonebeds, while Achelousaurus horneri is primarily known from one articulated adult cranium as well as two isolated subadult individuals previously referred to the taxon. Previous assessments of ontogeny in these taxa, alongside closely related centrosaurines, focused primarily on crania of mature individuals and disarticulated elements of immature individuals. Here we describe an articulated subadult Einiosaurus procurvicornis skull (MOR 456 8-8-87-1) from the Einiosaurus procurvicornis type locality bonebed and compare its cranial ornamental development with the only identically sized articulated subadult eucentrosauran skull from the Two Medicine Formation, MOR 591. These individuals represent the only known articulated subadult skulls from the hypothesized eucentrosauran lineage in the Two Medicine Formation, thereby enabling comparison of early ontogenetic developmental sequence and timing of all three primary cranial ornaments (nasal, supraorbital, and parietosquamosal frill). Comparison indicates that parietosquamosal frill and supraorbital ornamentation development may have preceded nasal horncore development in these taxa. MOR 456 8-8-87-1 fills a gap between the plesiomorphic morphology of juvenile Einiosaurus procurvicornis supraorbital horncores and the rounded, spheroid mass of bone which characterizes adults. The complete left squamosal of MOR 456 8-8-87-1 is of adult size, in contrast to its shorter face and immature facial ornamentation, which suggests that in Einiosaurus procurvicornis, the face and facial ornamentation development occurred after the parietosquamosal frill had reached adult size.
Keywords	dinosauria ; ceratopsia ; centrosaurine ; ontogeny ; cretaceous ; two medicine formation ; usa ; Fossil man. Human paleontology ; GN282-286.7 ; Paleontology ; QE701-760
Subject code	590
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	Institute of Paleobiology PAS
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Changes in Long-Term PM 2.5 Pollution in the Urban and Suburban Areas of China’s Three Largest Urban Agglomerations from 2000 to 2020

Lili Zhang / Na Zhao / Wenhao Zhang / John P. Wilson

Remote Sensing, Vol 14, Iss 1716, p

2022 Volume 1716

Abstract: Particulate matter (PM 2.5 ) is a significant public health concern in China, and the Chinese government has implemented a series of laws, policies, regulations, and standards to improve air quality. This study documents the changes in PM 2.5 and ... ...

Abstract	Particulate matter (PM 2.5 ) is a significant public health concern in China, and the Chinese government has implemented a series of laws, policies, regulations, and standards to improve air quality. This study documents the changes in PM 2.5 and evaluates the effects of industrial transformation and clean air policies on PM 2.5 levels in urban and suburban areas of China’s three largest urban agglomerations, Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) based on a new degree of urbanization classification method. We used high-resolution PM 2.5 concentration and population datasets to quantify the differences in PM 2.5 concentrations in urban and suburban areas of these three urban agglomerations. From 2000 to 2020, the urban areas have expanded while the suburban areas have shrunk. PM 2.5 concentrations in urban areas were approximately 32, 10, and 7 μg/m 3 higher than those in suburban areas from 2000 to 2020 in BTH, YRD, and PRD, respectively. Since 2013, the PM 2.5 concentrations in the urban regions of BTH, YRD, and PRD have declined at average annual rates of 7.30, 5.50, and 5.03 μg/m 3 /year, respectively, while PM 2.5 concentrations in suburban areas have declined at average annual rates of 3.11, 4.23 and 4.69 μg/m 3 /year, respectively. By 2018, all of the urban and suburban areas of BTH, YRD, and PRD satisfied their specific targets in the Air Pollution and Control Action Plan. By 2020, the PM 2.5 declines of BTH, YRD, and PRD exceeded the targets by two, three, and four times, respectively. However, the PM 2.5 exposure risks in urban areas are 10–20 times higher than those in suburban areas. China will need to implement more robust air pollution mitigation policies to achieve the World Health Organization’s Air Quality Guideline (WHO-AQG) and reduce long-term PM 2.5 exposure health risks.
Keywords	particulate matter (PM 2.5 ) ; clean air policies ; urban agglomerations ; Beijing–Tianjin–Hebei (BTH) ; Yangtze River Delta (YRD) ; Pearl River Delta (PRD) ; Science ; Q
Subject code	333
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A multi-terrain feature-based deep convolutional neural network for constructing super-resolution DEMs

Annan Zhou / Yumin Chen / John P. Wilson / Guodong Chen / Wankun Min / Rui Xu

International Journal of Applied Earth Observations and Geoinformation, Vol 120, Iss , Pp 103338- (2023)

2023

Abstract: Scale conversion between DEMs is an important issue in geomorphometry. There are many mature studies on the generation of low-resolution(LR) DEMs from high-resolution(HR) DEMs. However, as an important and convenient means of obtaining HR DEMs, ... ...

Abstract	Scale conversion between DEMs is an important issue in geomorphometry. There are many mature studies on the generation of low-resolution(LR) DEMs from high-resolution(HR) DEMs. However, as an important and convenient means of obtaining HR DEMs, traditional super resolution (SR) methods have shown insufficient consideration of the terrain features embedded in DEMs. Therefore, this article investigates the combination of terrain features and the use of convolutional neural networks (CNN) to reconstruct HR DEMs, and proposes a multi-terrain feature-based deep CNN for super-resolution(SR) DEMs (MTF-SR). In the experiments, from the perspective of vector and raster terrain features, we fuse raster terrain features in the input and loss functions, and fuse vector terrain features in the optimization of the output of the model. The results show that the MTF-SR model has a 30–50 % reduction in mean absolute error (MAE) compared with interpolation methods, has the lowest slope and aspect error and has a 10 to 30 % improvement in streamline matching rate (SMR). These results point to the advantages of the method in overall elevation accuracy and the preservation of terrain features.
Keywords	Terrain features ; Convolutional neural networks ; Digital elevation models ; Super-resolution ; Physical geography ; GB3-5030 ; Environmental sciences ; GE1-350
Subject code	006
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A simple serum depletion method for proteomics analysis

Alexandre Zougman / John P Wilson / Rosamonde E Banks

BioTechniques, Vol 69, Iss 2, Pp 148-

2020 Volume 151

Abstract: Serum is the body fluid most often used in biomarker discovery. Albumin, the most abundant serum protein, contributes approximately 50% of the serum protein content, with an additional dozen abundant proteins dominating the rest of the serum proteome. To ...

Abstract	Serum is the body fluid most often used in biomarker discovery. Albumin, the most abundant serum protein, contributes approximately 50% of the serum protein content, with an additional dozen abundant proteins dominating the rest of the serum proteome. To profile this challenging protein mixture by proteomics, the abundant proteins must be depleted to allow for detection of the low-abundant proteins, the primary biomarker targets. Current serum depletion approaches for proteomics are costly and relatively complex to couple with protein digestion. We demonstrate a simple, affordable serum depletion methodology that, within a few minutes of processing, results in two captured serum fractions – albumin-depleted and albumin-rich – which are digested in situ. We believe our method is a useful addition to the biomarker sample preparation toolbox.
Keywords	albumin depletion ; serum protein fractionation ; serum proteomics ; SiTrap ; STrap ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	Future Science Ltd
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

Meijie Chen / Yumin Chen / John P. Wilson / Huangyuan Tan / Tianyou Chu

ISPRS International Journal of Geo-Information, Vol 11, Iss 67, p

2022 Volume 67

Abstract: The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately ... ...

Abstract	The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R 2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental ...
Keywords	COVID-19 ; spatial heterogeneity ; eigenvector spatial filtering ; spatially varying coefficients ; Geography (General) ; G1-922
Subject code	910
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A new, transitional centrosaurine ceratopsid from the Upper Cretaceous Two Medicine Formation of Montana and the evolution of the ‘Styracosaurus-line' dinosaurs

John P. Wilson / Michael J. Ryan / David C. Evans

Royal Society Open Science, Vol 7, Iss

2020 Volume 4

Abstract: Ceratopsids are among the most ubiquitous dinosaur taxa from the Late Cretaceous terrestrial formations of the Western Interior of North America, comprising two subfamilies, Chasmosaurinae and Centrosaurinae. The Two Medicine Formation of northwestern ... ...

Abstract	Ceratopsids are among the most ubiquitous dinosaur taxa from the Late Cretaceous terrestrial formations of the Western Interior of North America, comprising two subfamilies, Chasmosaurinae and Centrosaurinae. The Two Medicine Formation of northwestern Montana has produced numerous remains of centrosaurine dinosaurs, which represent three taxa previously considered valid: Rubeosaurus ovatus, Einiosaurus procurvicornis and Achelousaurus horneri. Here, we reassess the previous referral of specimens to Rubeousaurus ovatus and demonstrate that this taxon is represented solely by its holotype specimen, which was first diagnosed as Styracosaurus ovatus. One of the specimens previously referred to ‘Rubeosaurus’ ovatus instead represents a new eucentrosauran centrosaurine taxon diagnosed here, Stellasaurus ancellae gen. et sp. nov. Stellasaurus expresses a unique combination of eucentrosauran centrosaurine characters, including an elongate nasal horncore, diminutive supraorbital horncores, and a parietal bearing straight, elongate P3 processes, semi-elongate P4 processes and non-elongate P5, P6 and P7 processes. Within the stratigraphic succession of Eucentrosaura, Stellasaurus occurs intermediate to Styracosaurus albertensis and Einiosaurus, and likewise reflects intermediate morphology. Assessed within the stratigraphic, geographical, taphonomic, ontogenetic and phylogenetic framework of Unified Frames of Reference, we fail to reject the hypothesis that Stellasaurus ancellae represents a transitional taxon within an anagenetic lineage of eucentrosauran centrosaurines.
Keywords	dinosaur ; ceratopsid ; centrosaurine ; evolution ; anagenesis ; cretaceous ; Science ; Q
Subject code	590
Language	English
Publishing date	2020-04-01T00:00:00Z
Publisher	The Royal Society
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs

Annan Zhou / Yumin Chen / John P. Wilson / Heng Su / Zhexin Xiong / Qishan Cheng

Remote Sensing, Vol 13, Iss 3089, p

2021 Volume 3089

Abstract: High-resolution DEMs are important spatial data, and are used in a wide range of analyses and applications. However, the high cost to obtain high-resolution DEM data over a large area through sensors with higher precision poses a challenge for many ... ...

Abstract	High-resolution DEMs are important spatial data, and are used in a wide range of analyses and applications. However, the high cost to obtain high-resolution DEM data over a large area through sensors with higher precision poses a challenge for many geographic analysis applications. Inspired by the convolution neural network (CNN) excellent performance in super-resolution (SR) image analysis, this paper investigates the use of deep residual neural networks and low-resolution DEMs to generate high-resolution DEMs. An enhanced double-filter deep residual neural network (EDEM-SR) method is proposed, which uses filters with different receptive field sizes to fuse and extract features and reconstruct a more realistic high-resolution DEM. The results were compared with those generated with the bicubic, bilinear, and EDSR methods. The numerical accuracy and terrain feature preserving effects of the EDEM-SR method can generate reconstructed DEMs that better match the original DEMs, show lower MAE and RMSE, and improve the accuracy of the terrain parameters. MAE is reduced by about 30 to 50% compared with traditional interpolation methods. The results show how the EDEM-SR method can generate high-resolution DEMs using low-resolution DEMs.
Keywords	convolutional neural networks ; DEMs ; super-resolution ; deep learning ; Science ; Q
Subject code	006
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The changing PM2.5 dynamics of global megacities based on long-term remotely sensed observations

Lili Zhang / John P. Wilson / Beau MacDonald / Wenhao Zhang / Tao Yu

Environment International, Vol 142, Iss , Pp 105862- (2020)

2020

Abstract: Satellite observations show that the rapid urbanization and emergence of megacities with 10 million or more residents have raised PM2.5 concentrations across the globe during the past few decades. This study examines PM2.5 dynamics for the 33 cities ... ...

Abstract	Satellite observations show that the rapid urbanization and emergence of megacities with 10 million or more residents have raised PM2.5 concentrations across the globe during the past few decades. This study examines PM2.5 dynamics for the 33 cities included on the UN list of megacities published in 2018. These megacities were classified into densely (>1500 residents per km2), moderately (300–1500 residents per km2) and sparsely (<300 residents per km2) populated areas to examine the effect of human population density on PM2.5 concentrations in these areas during the period 1998–2016. We found that: (1) the higher population density areas experienced higher PM2.5 concentrations; and (2) the megacities with high PM2.5 concentrations in these areas had higher concentrations than those in the moderately and sparsely populated areas of other megacities as well. The numbers of residents experiencing poor air quality is substantial: approximately 452 and 163 million experienced average annual PM2.5 levels exceeding 10 and 35 μg/m3, respectively in 2016. We also examined PM2.5 trends during the past 18 years and predict that high PM2.5 levels will likely continue in many of these megacities in the future without substantial changes in their economies and/or pollution abatement practices. There will be more megacities in the highest PM2.5 pollution class and the number of megacities in the lowest PM2.5 pollution class will likely not change. Finally, we analyzed how the PM2.5 pollution burden varies geographically and ranked the 33 megacities in terms of PM2.5 pollution in 2016. The most polluted regions are China, India, and South Asia and the least polluted regions are Europe and Japan. None of the 33 megacities currently fall in the WHO’s PM2.5 attainment class (<10 μg/m3) while 9 megacities fall into the PM2.5 non-attainment class (>35 μg/m3). In 2016, the least polluted megacity was New York and most polluted megacity was Delhi whose average annual PM2.5 concentration of 110 μg/m3 is nearly three times the WHO’s non-attainment threshold.
Keywords	Megacities ; Gridded population counts ; Remotely sensed PM2.5 concentrations ; Pollution burden ; Environmental sciences ; GE1-350
Subject code	333
Language	English
Publishing date	2020-09-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

Jiping Cao / Yumin Chen / John P. Wilson / Huangyuan Tan / Jiaxin Yang / Zhiqiang Xu

Remote Sensing, Vol 12, Iss 5, p

2020 Volume 839

Abstract: Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to ... ...

Abstract	Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R 2 = 0.98). The ordinary least squares (OLS) (mean R 2 = 0.86) and spatial error model (SEM) (mean pseudo R 2 = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China’s prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing.
Keywords	gross domestic product (gdp) ; prefecture level ; eigenvector spatial filtering regression ; spatial autocorrelation ; nighttime light ; Science ; Q
Subject code	333
Language	English
Publishing date	2020-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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