Classification of air quality zones and fine particulate matter sensitive areas by risk assessment approach.

Many studies have shown that fine particulate matter can cause health problems. Thus, effectively controlling fine particulate matter concentration is an important issue around the world. The Taiwan Environmental Protection Administration (TWEPA) divides Taiwan into seven air quality zones based on counties and cities for managing air quality and analyzing pollution transmission. However, this artificial division by administrative areas relatively poorly match natural conditions and topographical and geographic factors and hence poorly represent air quality characteristics. This study proposes an air quality sensitive map analysis framework, which uses hierarchical agglomerative clustering with empirical orthogonal function and analysis of variance methods, to provide more detailed, reasonable, and township-level air quality zones incorporating the different spatial-temporal characteristics over the region. The risk concept is introduced to evaluate PM2.5 risk sensitivity for each administrative district, combining three aspects: hazard (PM2.5 exceedance probability), exposure (population density of sensitive groups), and vulnerability (average wind speed). Considering air quality spatial-temporal characteristics, Taiwan can be optimally divided into 14 air quality zones. PM2.5 risk is highest for western inland towns than western coastal towns, with eastern regions exhibiting least risk. Adopting the proposed air quality zones and clarifying high risk areas allows PM2.5 causes to be identified for different air quality zones. This allows a targeted control strategy for high risk areas to effectively improve domestic air quality. The proposed model also provides powerful reference for environmental management and environmental impact assessment for future construction and development.

Long-term nonlinear relationship between PM2.5 and ten leading causes of death.

Air pollution has become a major concern worldwide. Many epidemiological studies have proved relationships between fine particulate matter (PM2.5) and various diseases, but most studies only use short-term and models for specific groups to derive relationships with acute diseases. This makes it difficult to understand long-term exposure, nonlinear relationships, and spatial-temporal health risks regarding chronic diseases. Therefore, this study proposed to analyze and map PM2.5 exceedance probability from long-term spatial-temporal monitoring data using radial basis function estimation. We then constructed and compared multiple linear regression and generalized additive models to investigate linear and nonlinear relationships between long-term average PM2.5 concentration, PM2.5 potential probability for exceeding the standard, and standardized mortality for the top ten causes of death in all towns and villages in Taiwan nationally from 2010 to 2017. Linear models indicate that increasing PM2.5 concentration increased malignant neoplasm, pneumonia, and chronic lower respiratory disease mortalities; chronic liver diseases; and cirrhosis; whereas heart diseases and esophagus cancer mortality decreased. For the nonlinear model results, it can be found that there were also significant nonlinear relationships between PM2.5 concentration and malignant mortalities for neoplasm, heart disease, diabetes; and trachea, bronchus, lung, liver, intrahepatic bile duct, and esophagus cancer. Thus, long-term exposure to PM2.5 may be a significant risk factor for multiple acute and chronic diseases. Results from this study can be directly applied worldwide to provide air quality and health management references for governments, and important information on long-term health risks for local residents in the study area.

A Longitudinal Study on the Association of Interrelated Factors Among Frailty Dimensions, Cognitive Domains, Cognitive Frailty, and All-Cause Mortality.

BACKGROUND: Cognitive frailty integrating impaired cognitive domains and frailty dimensions has not been explored. OBJECTIVE: This study aimed to explore 1) associations among frailty dimensions and cognitive domains over time and 2) the extended definitions of cognitive frailty for predicting all-cause mortality. METHODS: This four-year cohort study recruited 521 older adults at baseline (2011-2013). We utilized 1) generalized linear mixed models exploring associations of frailty dimensions (physical dimension: modified from Fried et al.; psychosocial dimension: integrating self-rated health, mood, and social relationship and support; global frailty: combining physical and psychosocial frailty) with cognition (global and domain-specific) over time and 2) time-dependent Cox proportional hazard models assessing associations between extended definitions of cognitive frailty (cognitive domains-frailty dimensions) and all-cause mortality. RESULTS: At baseline, the prevalence was 3.0% for physical frailty and 37.6% for psychosocial frailty. Greater physical frailty was associated with poor global cognition (adjusted odds ratio = 1.43-3.29, ß: -1.07), logical memory (ß: -0.14 to -0.10), and executive function (ß: -0.51 to -0.12). Greater psychosocial frailty was associated with poor global cognition (ß: -0.44) and attention (ß: -0.15 to -0.13). Three newly proposed definitions of cognitive frailty, "mild cognitive impairment (MCI)-psychosocial frailty," "MCI-global frailty," and "impaired verbal fluency-global frailty," outperformed traditional cognitive frailty for predicting all-cause mortality (adjusted hazard ratio = 3.49, 6.83, 3.29 versus 4.87; AIC = 224.3, 221.8, 226.1 versus 228.1). CONCLUSION: Notably, extended definitions of cognitive frailty proposed by this study better predict all-cause mortality in older adults than the traditional definition of cognitive frailty, highlighting the importance of psychosocial frailty to reduce mortality in older adults.

Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan.

In the past few years, human health risks caused by fine particulate matters (PM2.5) and other air pollutants have gradually received attention. According to the Disaster Prevention and Protection Act of Taiwan's Government enforced in 2017, "suspended particulate matter" has officially been acknowledged as a disaster-causing hazard. The long-term exposure to high concentrations of air pollutants negatively affects the health of citizens. Therefore, the precise determination of the spatial long-term distribution of hazardous high-level air pollutants can help protect the health and safety of residents. The analysis of spatial information of disaster potentials is an important measure for assessing the risks of possible hazards. However, the spatial disaster-potential characteristics of air pollution have not been comprehensively studied. In addition, the development of air pollution potential maps of various regions would provide valuable information. In this study, Hsinchu County was chosen as an example. In the spatial data analysis, historical PM2.5 concentration data from the Taiwan Environmental Protection Administration (TWEPA) were used to analyze and estimate spatially the air pollution risk potential of PM2.5 in Hsinchu based on a geographic information system (GIS)-based radial basis function (RBF) spatial interpolation method. The probability that PM2.5 concentrations exceed a standard value was analyzed with the exceedance probability method; in addition, the air pollution risk levels of tourist attractions in Hsinchu County were determined. The results show that the air pollution risk levels of the different seasons are quite different. The most severe air pollution levels usually occur in spring and winter, whereas summer exhibits the best air quality. Xinfeng and Hukou Townships have the highest potential for air pollution episodes in Hsinchu County (approximately 18%). Hukou Old Street, which is one of the most important tourist attractions, has a relatively high air pollution risk. The analysis results of this study can be directly applied to other countries worldwide to provide references for tourists, tourism resource management, and air quality management; in addition, the results provide important information on the long-term health risks for local residents in the study area.