Particulate matter deposition and its impact on tuberculosis severity: A cross-sectional study in Taipei.

The objective of this study was to examine the association between the lung lobe-deposited dose of inhaled fine particulate matter (PM2.5) and chest X-ray abnormalities in different lung lobes of pulmonary tuberculosis (TB), multidrug-resistant tuberculosis (MDR-TB), and non-tuberculosis mycobacteria infections (NTM). A cross-sectional study was conducted between 2014 and 2022, comprising 1073 patients who were recruited from chest department clinic in a tertial refer hospital in Taipei City, Taiwan. Ambient 1-, 7-, and 30-day PM2.5 exposure and the deposition of PM2.5 in different lung lobes were estimated in each subject. The ß coefficient for PM2.5 and deposited PM2.5 in lungs with the outcome variables (pulmonary TB, MDR-TB, and NTM infection) was derived through regression analysis and adjusted for age, gender, BMI, smoking status, and family income. We observed that a 1 µg/m3 increase in ambient PM2.5 was associated with an increase of MDR-TB infections of 0.004 times (95%CI: 0.001-0.007). A 1 µg/m3 increase in 1-day and 7-day PM2.5 deposition in left upper lobe and left lower lobe was associated with an increase in chest X-ray abnormalities of 9.19 % and 1.18 % (95%CI: 0.87-17.51 and 95%CI: 0.08-2.28), and 4.52 % and 5.20 % (95%CI: 0.66-8.38 and 95%CI: 0.51-9.89) in left lung of TB patients, respectively. A 1 µg/m3 increase in 30-day PM2.5 deposition in alveolar region was associated with an increase in percent abnormality of 2.50 % (95%CI: 0.65-4.35) in left upper lobe and 3.33 % (95%CI: 0.65-6.01) in right middle lobe, while in total lung was 0.63 % (95%CI: 0.01-1.27) in right upper lobe and 0.37 % (95%CI, 0.06-0.81) in right lung of MDR-TB patients. Inhaled PM2.5 deposition in lungs was associated with an exacerbation of the radiographic severity of pulmonary TB, particularly in pulmonary MDR-TB patients in upper and middle lobes. Particulate air pollution may potentially exacerbate the radiographic severity and treatment resistance in individuals with pulmonary TB.

Short-term mediating effects of PM2.5 on climate-associated COPD severity.

The impact of short-term exposure to environmental factors such as temperature, relative humidity (RH), and fine particulate matter (PM2.5) on chronic obstructive pulmonary disease (COPD) remains unclear. The objective of this study is to investigate PM2.5 as a mediator in the relationship between short-term variations in RH and temperature and COPD severity. A cross-sectional study was conducted on 930 COPD patients in Taiwan from 2017 to 2022. Lung function, COPD Assessment Test (CAT) score, and modified Medical Research Council (mMRC) dyspnea scale were assessed. The mean and differences in 1-day, 7-day, and 30-day individual-level exposure to ambient RH, temperature, and PM2.5 were estimated. The associations between these factors and clinical outcomes were analyzed using linear regression models and generalized additive mixed models, adjusting for age, sex, smoking, and body mass index. In the total season, increases in RH difference were associated with increases in forced expiratory volume in 1 s (FEV1) / forced vital capacity (FVC), while increases in temperature difference were associated with decreases in FEV1 and FEV1/FVC. Increases in PM2.5 mean were associated with declines in FEV1. In the cold season, increases in temperature mean were associated with decreases in CAT and mMRC scores, while increases in PM2.5 mean were associated with declines in FEV1, FVC, and FEV1/FVC. In the warm season, increases in temperature difference were associated with decreases in FEV1 and FEV1/FVC, while increases in RH difference and PM2.5 mean were associated with decreases in CAT score. PM2.5 fully mediated the associations of temperature mean with FEV1/FVC in the cold season. In conclusion, PM2.5 mediates the effects of temperature and RH on clinical outcomes. Monitoring patients during low RH, extreme temperature, and high PM2.5 levels is crucial. Capsule of findings The significance of this study is that an increase in ambient RH and temperature, as well as PM2.5 exposure, were significantly associated with changes in lung function, and clinical symptoms in these patients. The novelty of this study is that PM2.5 plays a mediating role in the association of RH and temperature with COPD clinical outcomes in the short term.

Investigating the nonlinear and non-stationary relationship between PM2.5 and air pollutants by wavelet signal analysis in central Taiwan.

In recent years, PM2.5 has become a critical factor as an environmental indicator, causing severe air pollution that has negatively impacted nature and human health. This study used hourly data gathered in central Taiwan from 2015 to 2019 and applied spatiotemporal data analysis and wavelet analysis methods to investigate the cross-correlation between PM2.5 and other air pollutants. Furthermore, it explored the correlation differences between adjacent stations after excluding major environmental factors such as climate and terrain. Wavelet coherence shows that PM2.5 and air pollutants mostly have a significant correlation at the half-day and one-day frequencies, while the differences between PM2.5 and PM10 are only particle size; hence, not only is the correlation the most consistent among all air pollutants but also the lag time is the most negligible. Carbon monoxide (CO) is the primary source pollutant of PM2.5 as it is also significantly correlated with PM2.5 at most timescales. Sulfur dioxide (SO2) and nitrogen oxide (NOx) are related to the generation of secondary aerosols, which are important components of PM2.5; therefore, the consistency of significant correlations improves as the timescale increases and the lag time becomes amplified. The pollution source mechanism of ozone (O3) and PM2.5 is not identical, so the correlation is lower than for other air pollutants; the lag time is also obviously influenced by the season changes that have significant fluctuations. At stations near the ocean such as Xianxi station and Shulu station, PM2.5 and PM10 have a higher correlation in the 24-h frequency, while the SO2 and PM2.5 at Sanyi station and Fengyuan station, which are close to industrial areas, have significant correlations in the 24-h frequency. This study hopes to help better understand the impact mechanisms behind different pollutants, and thus construct a better reference for establishing a complete air pollution prediction model in the future.

Ambient relative humidity-dependent obstructive sleep apnea severity in cold season: A case-control study.

BACKGROUND: The objective of this study was to examine associations of daily averages and daily variations in ambient relative humidity (RH), temperature, and PM2.5 on the obstructive sleep apnea (OSA) severity. METHODS: A case-control study was conducted to retrospectively recruit 8628 subjects in a sleep center between January 2015 and December 2021, including 1307 control (apnea-hypopnea index (AHI) < 5 events/h), 3661 mild-to-moderate OSA (AHI of 5-30 events/h), and 3597 severe OSA subjects (AHI > 30 events/h). A logistic regression was used to examine the odds ratio (OR) of outcome variables (daily mean or difference in RH, temperature, and PM2.5 for 1, 7, and 30 days) with OSA severity (by the groups). Two-factor logistic regression models were conducted to examine the OR of RH with the daily mean or difference in temperature or PM2.5 with OSA severity. An exposure-response relationship analysis was conducted to examine the outcome variables with OSA severity in all, cold and warm seasons. RESULTS: We observed associations of mean PM2.5 and RH with respective increases of 0.04-0.08 and 0.01-0.03 events/h for the AHI in OSA patients. An increase in the daily difference of 1 % RH increased the AHI by 0.02-0.03 events/h in OSA patients. A daily PM2.5 decrease of 1 µg/m3 reduced the AHI by 0.03 events/h, whereas a daily decrease in the RH of 1 % reduced the AHI by 0.03-0.04 events/h. The two-factor model confirmed the most robust associations of ambient RH with AHI in OSA patients. The exposure-response relationship in temperature and RH showed obviously seasonal patterns with OSA severity. CONCLUSION: Short-term ambient variations in RH and PM2.5 were associated with changes in the AHI in OSA patients, especially RH in cold season. Reducing exposure to high ambient RH and PM2.5 levels may have protective effects on the AHI in OSA patients.

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.

Air pollution diffusion simulation and seasonal spatial risk analysis for industrial areas.

The petrochemical industry produces many air pollutants during production, such as airborne particulate matters (PM10 and PM2.5), sulfur oxides, nitrogen oxides, volatile organic compounds, carbon oxides, etc. Petrochemical industrial accidents are more likely to cause major air pollution hazards in a short period. Therefore this study simulated diffusion and performed air pollution spatial risk analysis for potential air pollutants generated by the petrochemical industry using meteorological observation data from 2017 to 2019. The study targets were No. 6 Naphtha Cracker Complex Petrochemical Industrial Park (6NCC) of Formosa Petrochemical Corporation and Taichung Thermal Power Plant (TTPP) in central Taiwan. We used the industrial source complex model short term (ISCST3) air simulation model developed by the US Environmental Protection Agency to simulate pollutant diffusion under different weather conditions and seasons. Air pollution spatial risk was investigated for neighboring hospitals and schools for pollutant emission and diffusion to provide feedback to petrochemical related industry's risk management. Emission areas (6NCC and TTPP) were all in the southwest since the main air pollution accumulation and diffusion is to the northeast during monsoon season (October through March). Air pollution April through September each year is more evenly distributed, with pollutant concentrations low in all directions, approximately half the concentration in winter. Simulated air pollutant concentrations often overlapped with high risk population clusters (schools and hospitals). 6NCC posed little impact on nearby schools throughout the year; whereas TTPP posed relatively low risks to nearby schools and hospitals in summer, with slightly higher risk for Shenren Elementary School in Shengang township, Changhua County in winter. Overall 6NCC posed higher risk for Mailiao and Taixi townships in Yunlin County; whereas the TTPP posed higher risk on Longjing District of Taichung City, Shengang and Xianxi townships in Changhua County, particularly during winter. The results of this study will help the petrochemical industry and public health authority to wider manage air pollution risks.

The spatiotemporal estimation of the risk and the international transmission of COVID-19: a global perspective.

An ongoing novel coronavirus outbreak (COVID-19) started in Wuhan, China, in December 2019. Currently, the spatiotemporal epidemic transmission, prediction, and risk are insufficient for COVID-19 but we urgently need relevant information globally. We have developed a novel two-stage simulation model to simulate the spatiotemporal changes in the number of cases and estimate the future worldwide risk. Simulation results show that if there is no specific medicine for it, it will form a global pandemic. Taiwan, South Korea, Hong Kong, Japan, Thailand, and the United States are the most vulnerable. The relationship between each country's vulnerability and days before the first imported case occurred shows an exponential decrease. We successfully predicted the outbreak of South Korea, Japan, and Italy in the early stages of the global pandemic based on the information before February 12, 2020. The development of the epidemic is now earlier than we expected. However, the trend of spread is similar to our estimation.

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.

Zingiber chengii (Zingiberaceae), a new species from Taiwan.

In this article, we describe a new species, Zingiber chengii Y.H. Tseng, C.M. Wang & Y.C. Lin, discovered on a rock cliff of Youluo riverside in northern Taiwan. This species is easily distinguished from other known congeners by its grass-like leaves, spikes composed of a few sterile bracts, and seeds one-third enveloped by the aril. Color illustrations, line drawings, and a key to species of Zingiber in Taiwan are provided as well as comparative morphology in relation to its allied species, geographical distribution, and conservation status.

The improvement of spatial-temporal resolution of PM2.5 estimation based on micro-air quality sensors by using data fusion technique.

With the rapid development of the Internet of things (IoTs) and modern industrial society, forecasting air pollution concentration, e.g., the concentration of PM2.5, is of great significance to protect human health and the environment. Accurate prediction of PM2.5 concentrations is limited by the number and the data quality of air quality monitoring stations. In Taiwan, the spatial and temporal data of PM2.5 concentrations are measured by 77 national air quality monitoring stations (built by Taiwan EPA). However, the national stations are costly and scarce because of the highly precise instrument and their size. Therefore, many places are still out of coverage of the monitoring network. Recently, under the framework of IoTs, there are hundreds of portable air quality sensors called "AirBox" developed jointly by the Taiwan local government and a private company. By virtue of its low price and portability, the AirBox can provide a higher resolution of space-time PM2.5 measurement. However, the spatiotemporal distribution is different between AirBox and EPA stations, and data quality and accuracy of AirBox is poorer than national air quality monitoring stations. Thus, to integrate the heterogeneous PM2.5 data, the data fusion technique should be used before further analysis. In this study, we propose a new data fusion method called multi-sensor space-time data fusion framework. It is based on the Optimum Linear Data Fusion theory and integrating with a multi-time step Kriging method for spatial-temporal estimation. The method is used to do heterogeneous data fusion from different sources and data qualities. It is able to improve the estimation of PM2.5 concentration in space and time. Results have shown that by combining PM2.5 concentration data from 1176 low-cost AirBoxes as additional information in our model, the estimation of spatial-temporal PM2.5 concentration becomes better and more reasonable. The r2 of the validation regression model is 0.89. Under the approach proposed in this study, we made the information of the micro-sensors more reliable and improved the higher spatial-temporal resolution of air quality monitoring. It could provide very useful information for better spatial-temporal data analysis and further environmental management, such as air pollution source localization, health risk assessment, and micro-scale air pollution analysis.

Differential genetic responses to the stress revealed the mutation-order adaptive divergence between two sympatric ginger species.

BACKGROUND: Divergent genetic responses to the same environmental pressures may lead sympatric ecological speciation possible. Such speciation process possibly explains rapid sympatric speciation of island species. Two island endemic ginger species Zingiber kawagoii and Z. shuanglongensis was suggested to be independently originated from inland ancestors, but their island endemism and similar morphologies and habitats lead another hypothesis of in situ ecological speciation. For understanding when and how these two species diverged, intraspecific variation was estimated from three chloroplast DNA fragments (cpDNA) and interspecific genome-wide SNPs and expression differences after saline treatment were examined by transcriptomic analyses. RESULTS: Extremely low intraspecific genetic variation was estimated by cpDNA sequences in both species: nucleotide diversity π = 0.00002 in Z. kawagoii and no nucleotide substitution but only indels found in Z. shuanglongensis. Nonsignificant inter-population genetic differentiation suggests homogenized genetic variation within species. Based on 53,683 SNPs from 13,842 polymorphic transcripts, in which 10,693 SNPs are fixed between species, Z. kawagoii and Z. shuanglongensis were estimated to be diverged since 218~ 238 thousand generations ago (complete divergence since 41.5~ 43.5 thousand generations ago). This time is more recent than the time of Taiwan Island formation. In addition, high proportion of differential expression genes (DEGs) is non-polymorphic or non-positively selected, suggesting key roles of plastic genetic divergence in broaden the selectability in incipient speciation. While some positive selected DEGs were mainly the biotic and abiotic stress-resistance genes, emphasizing the importance of adaptive divergence of stress-related genes in sympatric ecological speciation. Furthermore, the higher proportional expression of functional classes in Z. kawagoii than in Z. shuanglongensis explains the more widespread distribution of Z. kawagoii in Taiwan. CONCLUSIONS: Our results contradict the previous hypothesis of independent origination of these two island endemic ginger species from SE China and SW China. Adaptive divergent responses to the stress explain how these gingers maintain genetic differentiation in sympatry. However, the recent speciation and rapid expansion make extremely low intraspecific genetic variation in these two species. This study arise a more probable speciation hypothesis of sympatric speciation within an island via the mutation-order mechanism underlying the same environmental pressure.

High expression of phospho-H2AX predicts a poor prognosis in colorectal cancer.

BACKGROUND/AIM: Colorectal cancer (CRC) is one of the most common causes of cancer-related deaths worldwide. DNA double-strand breaks (DSBs) are deleterious lesions that can lead to chromosomal anomalies, genomic instability and cancer. The histone H2AX plays an important role in response to DNA damage and phosphorylation of H2AX (p-H2AX) is evidence of DSBs. The aim of this study was to evaluate the clinical significance of p-H2AX expression in CRC. PATIENTS AND METHODS: p-H2AX expression in CRC tissues was analyzed by immunohistochemistry and correlated with clinicopathological variables using the chi-square test. The prognostic value of p-H2AX for distant metastasis-free survival (DMFS) and overall survival (OS) was evaluated by Kaplan-Meier estimates and the individual prognostic components were analyzed with Cox regression analysis. RESULTS: A high p-H2AX expression in CRC tissues was associated with tumor stage and perineurial invasion. Furthermore, a high p-H2AX expression was associated with poor DMFS and OS. Cox regression analysis also revealed that p-H2AX was an independent predictor of DMFS and OS. CONCLUSION: A high p-H2AX expression in CRC tissues is associated with a more malignant cancer behavior, as well as poor patient survival. p-H2AX may, therefore, be an independent prognostic predictor for CRC, as well as a potential therapeutic target.

A time series analysis of multiple ambient pollutants to investigate the underlying air pollution dynamics and interactions.

Understanding the temporal dynamics and interactions of particulate matter (PM) concentration and composition is important for air quality control. This paper applied a dynamic factor analysis method (DFA) to reveal the underlying mechanisms of nonstationary variations in twelve ambient concentrations of aerosols and gaseous pollutants, and the associations with meteorological factors. This approach can consider the uncertainties and temporal dependences of time series data. The common trends of the yearlong and three selected diurnal variations were obtained to characterize the dominant processes occurring in general and specific scenarios in Taipei during 2009 (i.e., during Asian dust storm (ADS) events, rainfall, and under normal conditions). The results revealed the two distinct yearlong NOx transformation processes, and demonstrated that traffic emissions and photochemical reactions both critically influence diurnal variation, depending upon meteorological conditions. During an ADS event, transboundary transport and distinct weather conditions both influenced the temporal pattern of identified common trends. This study shows the DFA method can effectively extract meaningful latent processes of time series data and provide insights of the dominant associations and interactions in the complex air pollution processes.

Association between air pollutants and dementia risk in the elderly.

BACKGROUND: The aging rate in Taiwan is the second highest in the world. As the population ages quickly, the prevalence of dementia increases rapidly. There are some studies that have explored the association between air pollution and cognitive decline, but the association between air pollution and dementia has not been directly evaluated. METHODS: This was a case-control study comprising 249 Alzheimer's disease (AD) patients, 125 vascular dementia (VaD) patients, and 497 controls from three teaching hospitals in northern Taiwan from 2007 to 2010. Data of particulate matter <10 µm in diameter (PM10) and ozone were obtained from the Taiwan Environmental Protection Administration for 12 and 14 years, respectively. Blood samples were collected to determine the apolipoprotein E (APOE) ɛ4 haplotype. Bayesian maximum entropy was used to estimate the individual exposure level of air pollutants, which was then tertiled for analysis. Conditional logistic regression models were used to estimate adjusted odds ratios (AORs) and 95% confidence intervals between the association of PM10 and ozone exposure with AD and VaD risk. RESULTS: The highest tertile of PM10 (≥49.23 µg/m(3)) or ozone (≥21.56 ppb) exposure was associated with increased AD risk (highest vs. lowest tertile of PM10: AOR = 4.17; highest vs. lowest tertile of ozone: AOR = 2.00). Similar finding was observed for VaD. The association with AD and VaD risk remained for the highest tertile PM10 exposure after stratification by APOE ɛ4 status and gender. CONCLUSIONS: Long-term exposure to the highest tertile of PM10 or ozone was significantly associated with an increased risk of AD and VaD.