Retrospective prediction of intraurban spatiotemporal distribution of PM2.5 in Taipei

Yu, Hwa‐Lung; Wang, Chih-Hsin

doi:10.1016/j.atmosenv.2010.04.030

Cited by 45 publications

(11 citation statements)

References 28 publications

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“…PM2.5 during 2000–2005 was estimated by geographic information system (GIS) with the Bayesian Maximum Entropy (BME) method. The GIS prediction with PM2.5/PM10 and PM2.5/TSP ratios has shown to provide good estimations of the PM2.5 exposure levels [ 40 ]. Third, the average measures of air pollution instead of a weighted time average may underestimate the effect of air pollution on RA.…”

Section: Discussionmentioning

confidence: 99%

Association between Exposure to Ambient Air Pollution and the Risk of Rheumatoid Arthritis in Taiwan: A Population-Based Retrospective Cohort Study

Chou

Wang

et al. 2022

IJERPH

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Background: The association between ambient air pollution (AAP) and the risk of Rheumatoid arthritis (RA) remains debatable. We conducted a population-based cohort study to investigate the association between exposure to AAP and the risk of RA in Taiwan. Methods: We analyzed and combined the longitudinal Health Insurance Database (LHID) and the Taiwan Air Quality-Monitoring Database (TAQMD), which were in line with the residential areas. We calculated the RA incidence rates per 10,000 person-years exposed to each quartile of PM2.5 or PM10 concentrations or RH. Hazards regression was conducted to analyze the associations between exposure to each quartile of PM2.5 and PM10 concentrations and the risk of developing RA. The hazard ratios of RA were analyzed between participants exposed to annual average concentrations of PM2.5 and PM10. All the hazard ratios of RA were stratified by gender and adjusted for age and relative humidity (RH). A p-value < 0.05 was considered statistically significant. Results: Among 722,885 subjects, 9338 RA cases were observed. The analyses adjusted for age, gender, and humidity suggested an increased risk of developing RA in the exposure to PM2.5 in the last quartile (Q4) with the adjusted hazard ratio (aHR) was 1.053 (95%CI: 1.043 to 1.063). Conclusion: Our study suggests that exposure to PM2.5 is associated with an increased risk of RA. The finding has implications for policymaking to develop coping strategies to confront AAP as a risk factor for RA.

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Section: Discussionmentioning

confidence: 99%

Association between Exposure to Ambient Air Pollution and the Risk of Rheumatoid Arthritis in Taiwan: A Population-Based Retrospective Cohort Study

Chou

Wang

et al. 2022

IJERPH

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“…Socioeconomic factors are related to economic and social development and include industrial agglomeration (Ia), The proportion of tertiary industry (Iha) [36], technology investment (Ti), degree of openness (Fo), population density (Pd), urbanization rate (UR), and per capital GDP (PGDP). Ecological factors include ecological safety (ES) [37], ecological cleanliness (Er) [38], and ecological damage (ER).…”

Section: Plos Onementioning

confidence: 99%

“…Past studies have primarily been based on two types of research data: remote sensing image retrieval of atmospheric aerosol thickness (AOD) and real-time monitoring point data. Existing studies mainly used the energy-dispersive X-ray spectroscopy model [ 9 ] Backward air parcel trajectories method [ 10 ], land to use regression [ 11 ], mixed regression [ 12 ], and spatial metrology models [ 13 ] to evaluate the chemical characteristics [ 14 ], spatial heterogeneity [ 15 ], pollution sources [ 16 ], human health risks [ 17 ], and influencing factors [ 18 ] of PM 2.5 pollution. Numerous studies have shown that meteorological elements play a key role in the air pollution PM 2.5 concentration [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

The pattern and mechanism of air pollution in developed coastal areas of China: From the perspective of urban agglomeration

Shan

Wang

et al. 2020

PLoS ONE

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The green development of coastal urban agglomerations, which are strategic core areas of national economic growth in China, has become a major focus of both academics and government agencies. In this paper, China's coastal urban agglomeration is taken as the research area, aiming at the serious air pollution problem of coastal urban agglomeration, geographic information system (ArcGIS10.2) spatial analysis and the spatial Dubin model were applied to National Aeronautics and Space Administration atmospheric remote sensing image inversion fine particulate matter (PM 2.5) data from 2010-2016 to reveal the temporal and spatial evolution characteristics and Influence mechanism of PM 2.5 in China's coastal urban agglomerations, with a view to providing a reference value for coordinating air pollution in the coastal cities of the world. From 2010-2016, the PM 2.5 concentration in China's coastal urban agglomerations decreased as a whole, and large spatial differences in PM 2.5 concentration were observed in China's coastal urban agglomerations; the core highpollution areas were the Beijing-Tianjin-Hebei, Shandong Peninsula, and Yangtze River Delta urban agglomerations. Large spatial differences in PM 2.5 concentration were also observed within individual urban agglomerations, with higher PM 2.5 concentrations found in the northern parts of the urban agglomerations. Significant spatial autocorrelation and spatial heterogeneity were observed among PM 2.5-polluted cities in China's coastal urban agglomerations. The northern coastal urban agglomerations formed a relatively stable and continuous high-pollution zone. The spatial Dubin model was used to analyze the driving factors of PM 2.5 pollution in coastal urban agglomerations. Together, meteorological, socioeconomic, pollution source, and ecological factors affected the spatial characteristics of PM 2.5 pollution during the study period, and the overall effect was a mixed effect with significant spatial variation. Among them, meteorological factors were the greatest driver of PM 2.5 pollution. In the short term, the rapid increase in population density, industrial emissions, industrial energy consumption, and total traffic emissions were the important driving factors of PM 2.5 pollution in the coastal urban agglomerations of China.

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“…There are two main products when using SOM: (i) a low-dimensional model of the high-dimensional input space, the SOM itself; and (ii) a low-dimensional representation of high-dimensional vectors after they are mapped onto the trained SOM (17, pp. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The former is often useful in explaining patterns observed in the latter, once both are given visual form, as demonstrated in section 4.2.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…BME is a well-established spatiotemporal statistics and geostatistics methodology (8) for spatiotemporal prediction of environmental attributes. With its advanced features (e.g., lack of restrictive assumptions, assimilating input from monitor measurements, a variety of certain and uncertain observations, and physical laws), BME has been employed in many atmospheric studies and has provided accurate prediction of air pollutants across space and time (e.g., [10][11][12][13][14][15]. Meanwhile, spatialization is an approach to making complex high-dimensional data accessible to the human perceptual and cognitive system through computational and visual means (9).…”

Section: Introductionmentioning

confidence: 99%

Multi-Perspective Analysis and Spatiotemporal Mapping of Air Pollution Monitoring Data

Kolovos

Skupin

Jerrett

et al. 2010

Environ. Sci. Technol.

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Space-time data analysis and assimilation techniques in atmospheric sciences typically consider input from monitoring measurements. The input is often processed in a manner that acknowledges characteristics of the measurements (e.g., underlying patterns, fluctuation features) under conditions of uncertainty, leads to the derivation of secondary information that serves study-oriented goals, and provides input to space-time prediction techniques. We present a novel approach that blends a rigorous space-time prediction model (Bayesian Maximum Entropy, BME) with a cognitively informed visualization of high-dimensional data (spatialization). The combined BME and spatialization approach (BME-S) is used to study monthly-averaged NO 2 and mean annual SO 4 measurements in California over the 15-year period 1988-2002. Using the original scattered measurements of these two pollutants BME generates spatiotemporal predictions on a regular grid across the state. Subsequently, the prediction network undergoes the spatialization transformation into a lower-dimensional geometric representation, aimed at revealing patterns and relationships that exist within the input data. The proposed BME-S provides a powerful spatiotemporal framework to study a variety of air pollution data sources.

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Retrospective prediction of intraurban spatiotemporal distribution of PM2.5 in Taipei

Cited by 45 publications

References 28 publications

Association between Exposure to Ambient Air Pollution and the Risk of Rheumatoid Arthritis in Taiwan: A Population-Based Retrospective Cohort Study

Association between Exposure to Ambient Air Pollution and the Risk of Rheumatoid Arthritis in Taiwan: A Population-Based Retrospective Cohort Study

The pattern and mechanism of air pollution in developed coastal areas of China: From the perspective of urban agglomeration

Multi-Perspective Analysis and Spatiotemporal Mapping of Air Pollution Monitoring Data

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