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Frévent C, Ahmed MS, Dabo-Niang S, Genin M. A Shared-Frailty Spatial Scan Statistic Model for Time-to-Event Data. Biom J 2024; 66:e202300200. [PMID: 38988210 DOI: 10.1002/bimj.202300200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 01/24/2024] [Accepted: 05/04/2024] [Indexed: 07/12/2024]
Abstract
Spatial scan statistics are well-known methods widely used to detect spatial clusters of events. Furthermore, several spatial scan statistics models have been applied to the spatial analysis of time-to-event data. However, these models do not take account of potential correlations between the observations of individuals within the same spatial unit or potential spatial dependence between spatial units. To overcome this problem, we have developed a scan statistic based on a Cox model with shared frailty and that takes account of the spatial dependence between spatial units. In simulation studies, we found that (i) conventional models of spatial scan statistics for time-to-event data fail to maintain the type I error in the presence of a correlation between the observations of individuals within the same spatial unit and (ii) our model performed well in the presence of such correlation and spatial dependence. We have applied our method to epidemiological data and the detection of spatial clusters of mortality in patients with end-stage renal disease in northern France.
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Affiliation(s)
- Camille Frévent
- Université de Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, Université de Lille, Lille, France
| | - Mohamed-Salem Ahmed
- Université de Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, Université de Lille, Lille, France
- Alicante SARL, Lesquin, France
| | - Sophie Dabo-Niang
- CNRS, UMR 8524 - Laboratoire Paul Painlevé, Université de Lille, Lille, France
- MODAL team, INRIA Lille-Nord Europe, Villeneuve-d'Ascq, France
| | - Michaël Genin
- Université de Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, Université de Lille, Lille, France
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Li C, Wang J, Zhang H, Diner DJ, Hasheminassab S, Janechek N. Improvement of Surface PM 2.5 Diurnal Variation Simulations in East Africa for the MAIA Satellite Mission. ACS ES&T AIR 2024; 1:223-233. [PMID: 38633207 PMCID: PMC11019548 DOI: 10.1021/acsestair.3c00008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 04/19/2024]
Abstract
The Multi-Angle Imager for Aerosols (MAIA), supported by NASA and the Italian Space Agency, is planned for launch into space in 2025. As part of its mission goal, outputs from a chemical transport model, the Unified Inputs for Weather Research and Forecasting Model coupled with Chemistry (UI-WRF-Chem), will be used together with satellite data and surface observations for estimating surface PM2.5. Here, we develop a method to improve UI-WRF-Chem with surface observations at the U.S. embassy in Ethiopia, one of MAIA's primary target areas in east Africa. The method inversely models the diurnal profile and amount of anthropogenic aerosol and trace gas emissions. Low-cost PurpleAir sensor data are used for validation after applying calibration functions obtained from the collocated data at the embassy. With the emission updates in UI-WRF-Chem, independent validation for February 2022 at several different PurpleAir sites shows an increase in the linear correlation coefficients from 0.1-0.7 to 0.6-0.9 between observations and simulations of the diurnal variation of surface PM2.5. Furthermore, even by using the emissions optimized for February 2021, the UI-WRF-Chem forecast for March 2022 is also improved. Annual update of monthly emissions via inverse modeling has the potential and is needed to improve MAIA's estimate of surface PM2.5.
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Affiliation(s)
- Chengzhe Li
- Department
of Chemical and Biochemical Engineering, Center for Global & Regional
Environmental Research, and Iowa Technology Institute, The University of Iowa, Iowa City, Iowa 52240, United States
| | - Jun Wang
- Department
of Chemical and Biochemical Engineering, Center for Global & Regional
Environmental Research, and Iowa Technology Institute, The University of Iowa, Iowa City, Iowa 52240, United States
| | - Huanxin Zhang
- Department
of Chemical and Biochemical Engineering, Center for Global & Regional
Environmental Research, and Iowa Technology Institute, The University of Iowa, Iowa City, Iowa 52240, United States
| | - David J. Diner
- Jet
Propulsion Laboratory, California Institute
of Technology, Pasadena, California 91109, United States
| | - Sina Hasheminassab
- Jet
Propulsion Laboratory, California Institute
of Technology, Pasadena, California 91109, United States
| | - Nathan Janechek
- Department
of Chemical and Biochemical Engineering, Center for Global & Regional
Environmental Research, and Iowa Technology Institute, The University of Iowa, Iowa City, Iowa 52240, United States
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Hael MA. Modeling spatial-temporal variability of PM2.5 concentrations in Belt and Road Initiative (BRI) region via functional adaptive density approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:110931-110955. [PMID: 37798523 DOI: 10.1007/s11356-023-30048-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 09/19/2023] [Indexed: 10/07/2023]
Abstract
The rapid development of the Belt and Road Initiative (BRI) has led to severe air pollution dominated by PM2.5 concentrations which can cause a profound negative impact on human health and economic activity. This problem poses a critical environmental challenge to efficiently handling large-scale spatial-temporal PM2.5 data in this extended region. Functional data analysis (FDA) technique offers powerful tools that have the potential to enhance the analysis of spatial distributions and temporal dynamic changes in high-dimensional pollution data. However, modeling the spatial-temporal variability of PM2.5 concentrations by FDA remains unrevealed in the BRI region. To address this research gap, our study aimed to achieve two main objectives: first, to model the spatial-temporal dynamic variability of PM2.5 in 125 BRI nations (1998-2021), and second, to identify the underlying clusters behind the variations. We employed the recently developed functional adaptive density peak (FADP) clustering approach to solve the current problem. The proposed method is based on the joint use of functional principal components (FPCs) and functional cluster analyses. The main results are as follows: (i) The first three FPCs almost captured 99% of the total variations involving all valuable information on PM2.5 concentrations. (ii) PM2.5 pollution was highly concentrated in the developing countries (Pakistan, Bangladesh, and Nigeria) and the developed countries (Arabian Gulf countries: Qatar, United Arab Emirates, Bahrain, Saudi Arabia, Oman), and the least developed countries (Yemen and Chad). (iii) Three optimal clusters were identified and thus classified the PM2.5 into three distinct degrees of pollution: severe, moderate, and light. (iv) Cluster 1 had a severe pollution effect degree with a high rate of change, and it covered the Arabian Peninsula countries, African countries (Cameroon, Egypt, Gambia, Mali, Mauritania, Nigeria, Sudan, Senegal, Chad), Bangladesh, and Pakistan. (v) About 62 BRI countries belonged to cluster 2 showing a light pollution degree with annul average of less than 20 [Formula: see text]; this pointed out that the PM2.5 concentration remains stable in the cluster 2-related countries. The findings of this research would benefit governments and policymakers in preventing and controlling PM2.5 pollution exposure in BRI. Furthermore, this research could pay attention to sustainable development goals and the vision of the Green BRI policy.
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Affiliation(s)
- Mohanned Abduljabbar Hael
- School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, 330013, China.
- Department of Data Science and Information Technology, Taiz University, 9674, Taiz, Yemen.
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Hael MA. Unveiling air pollution patterns in Yemen: a spatial-temporal functional data analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:50067-50095. [PMID: 36790700 PMCID: PMC9930045 DOI: 10.1007/s11356-023-25790-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 02/03/2023] [Indexed: 04/16/2023]
Abstract
The application of spatiotemporal functional analysis techniques in environmental pollution research remains limited. As a result, this paper suggests spatiotemporal functional data clustering and visualization tools for identifying temporal dynamic patterns and spatial dependence of multiple air pollutants. The study uses concentrations of four major pollutants, named particulate matter (PM2.5), ground-level ozone (O3), carbon monoxide (CO), and sulfur oxides (SO2), measured over 37 cities in Yemen from 1980 to 2022. The proposed tools include Fourier transformation, B-spline functions, and generalized-cross validation for data smoothing, as well as static and dynamic visualization methods. Innovatively, a functional mixture model was used to capture/identify the underlying/hidden dynamic patterns of spatiotemporal air pollutants concentration. According to the results, CO levels increased 25% from 1990 to 1996, peaking in the cities of Taiz, Sana'a, and Ibb before decreasing. Also, PM2.5 pollution reached a peak in 2018, increasing 30% with severe concentrations in Hodeidah, Marib, and Mocha. Moreover, O3 pollution fluctuated with peaks in 2014-2015, 2% increase and pollution rate of 265 Dobson. Besides, SO2 pollution rose from 1997 to 2010, reaching a peak before stabilizing. Thus, these findings provide insights into the structure of the spatiotemporal air pollutants cycle and can assist policymakers in identifying sources and suggesting measures to reduce them. As a result, the study's findings are promising and may guide future research on predicting multivariate air pollution statistics over the analyzed area.
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Affiliation(s)
- Mohanned Abduljabbar Hael
- School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, China.
- Department of Data Science and Information Technology, Taiz University, 9674, Taiz, Yemen.
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She Y, Chen Q, Ye S, Wang P, Wu B, Zhang S. Spatial-temporal heterogeneity and driving factors of PM 2.5 in China: A natural and socioeconomic perspective. Front Public Health 2022; 10:1051116. [PMID: 36466497 PMCID: PMC9713317 DOI: 10.3389/fpubh.2022.1051116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/04/2022] [Indexed: 11/18/2022] Open
Abstract
Background Fine particulate matter (PM2.5), one of the major atmospheric pollutants, has a significant impact on human health. However, the determinant power of natural and socioeconomic factors on the spatial-temporal variation of PM2.5 pollution is controversial in China. Methods In this study, we explored spatial-temporal characteristics and driving factors of PM2.5 through 252 prefecture-level cities in China from 2015 to 2019, based on the spatial autocorrelation and geographically and temporally weighted regression model (GTWR). Results PM2.5 concentrations showed a significant downward trend, with a decline rate of 3.58 μg m-3 a-1, and a 26.49% decrease in 2019 compared to 2015, Eastern and Central China were the two regions with the highest PM2.5 concentrations. The driving force of socioeconomic factors on PM2.5 concentrations was slightly higher than that of natural factors. Population density had a positive significant driving effect on PM2.5 concentrations, and precipitation was the negative main driving factor. The two main driving factors (population density and precipitation) showed that the driving capability in northern region was stronger than that in southern China. North China and Central China were the regions of largest decline, and the reason for the PM2.5 decline might be the transition from a high environmental pollution-based industrial economy to a resource-clean high-tech economy since the implementation the Air Pollution Prevention and Control Action Plan in 2013. Conclusion We need to fully consider the coordinated development of population size and local environmental carrying capacity in terms of control of PM2.5 concentrations in the future. This research is helpful for policy-makers to understand the distribution characteristics of PM2.5 emission and put forward effective policy to alleviate haze pollution.
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Affiliation(s)
- Yuanyang She
- School of Geography and Environment, Jiangxi Normal University, Nanchang, China,Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, China
| | - Qingyan Chen
- Science and Technology College, Jiangxi Normal University, Jiujiang, China
| | - Shen Ye
- School of Geography and Environment, Jiangxi Normal University, Nanchang, China,Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, China
| | - Peng Wang
- School of Geography and Environment, Jiangxi Normal University, Nanchang, China,Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, China,*Correspondence: Peng Wang
| | - Bobo Wu
- School of Geography and Environment, Jiangxi Normal University, Nanchang, China,Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, China
| | - Shaoyu Zhang
- School of Geography and Environment, Jiangxi Normal University, Nanchang, China,Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, China
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