Air quality health index (AQHI) based on multiple air pollutants and mortality risks in Taiwan: Construction and validation.

The currently used air quality index (AQI) is not able to capture the additive effects of air pollution on health risks and reflect non-threshold concentration-response relationships, which has been criticized. We proposed the air quality health index (AQHI) based on daily air pollution-mortality associations, and compared its validity in predicting daily mortality and morbidity risks with the existing AQI. We examined the excess risk (ER) of daily elderly (≥65-year-old) mortality associated with 6 air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3) in 72 townships across Taiwan from 2006 to 2014 by performing a time-series analysis using a Poisson regression model. Random effect meta-analysis was used to pool the township-specified ER for each air pollutant in the overall and seasonal scenarios. The integrated ERs for mortality were calculated and used to construct the AQHI. The association of the AQHI with daily mortality and morbidity were compared by calculating the percentage change per interquartile range (IQR) increase in the indices. The magnitude of the ER on the concentration-response curve was used to evaluate the performance of the AQHI and AQI, regarding specific health outcomes. Sensitivity analysis was conducted using coefficients from the single- and two-pollutant models. The coefficients of PM2.5, NO2, SO2, and O3 associated with mortality were included to form the overall and season-specific AQHI. An IQR increase in the overall AQHI at lag 0 was associated with 1.90%, 2.96%, and 2.68% increases in mortality, asthma, and respiratory outpatient visits, respectively. The AQHI had higher ERs for mortality and morbidity on the validity examinations than the current AQI. The AQHI, which captures the combined effects of air pollution, can serve as a health risk communication tool to the public.

Model development and validation of personal exposure to PM2.5 among urban elders.

Indirect measurements through a combination of microenvironment concentrations and personal activity diaries provide a potentially useful alternative for PM2.5 exposure estimates. This study was to optimize a personal exposure model based on spatiotemporal model predictions for PM2.5 exposure in a sub-cohort study. Personal, home indoor, home outdoor, and ambient monitoring data of PM2.5 were conducted for an elderly population in the Taipei city of Taiwan. The proposed microenvironment exposure (ME) models incorporate PM2.5 measurements and individual time-activity information with a generalized estimating equation (GEE) analysis. We evaluated model performance with daily personal PM2.5 exposure based on the coefficient of determination, accuracy, and mean bias error. Ambient and home outdoor measures as exposure surrogates are likely to under- and overestimate personal exposure to PM2.5 in our study population, respectively. Measured and predicted indoor exposures were highly correlated with personal PM2.5 exposure. The awareness of peculiar smells is an important factor that significantly increases personal PM2.5 exposure by 46-70%. The model incorporating home indoor PM2.5 can achieve the highest agreement (R2 = 0.790) with personal exposure and the lowest measurement error. The ME model with the GEE analysis combining home outdoor PM2.5 determined by LUR model with a machine learning technique can improve the prediction (R2 = 0.592) of personal PM2.5 exposure, compared with the prediction of the traditional LUR model (R2 = 0.385).

The impact of heavy precipitation and its impact modifiers on shigellosis occurrence during typhoon season in Taiwan: A case-crossover design.

Because of climate change, heavy precipitation is likely to become frequent and intense, thereby increasing the risk of shigellosis occurrence. However, few studies examined the impact of heavy precipitation on shigellosis and its impact modifiers in developed countries. This study aims to analyze the association between heavy precipitation and shigellosis in Taiwan, and to identify the vulnerable population and impact modifiers. We adopted a case-crossover design, and used conditional logistic regression to estimate odds ratios (ORs) for shigellosis occurrence. Information were collected on the daily shigellosis cases, precipitation, temperature, and typhoons from 1994 to 2015, and yearly data of medical resources and environmental factors were obtained at the city level from 1998 to 2015. Stratification analyses were performed by age, sex, medical resource, and environmental factors. We discovered that heavy precipitation ≥80 mm/day considerably increased the risk of shigellosis occurrence. The ORs of heavy rain (80 to <200 mm/day) were 2.08-2.26 at lags 0-1. The ORs of extremely heavy rain (≥200 mm/day) increased to 2.17-4.73 at lags 5-8. Moreover, the effect of heavy precipitation was greater under high temperature condition (≥23.6 °C). Adults were more susceptible to heavy-precipitation-associated shigellosis, especially the elderly. Males experienced marginally higher effects than females did. Moreover, cities with more medical resources and forest cover and higher percentage of completed storm sewers had lower effects; however, dense population and higher pig density were the risk factors. Although the high water-supply penetration rate did not decrease Shigella infection after heavy precipitation, it did lower the risk of typhoon-related shigellosis. In conclusion, hot temperature could enhance the impact of heavy precipitation on shigellosis. Public health interventions should be introduced according to the lag period after heavy precipitation, particularly in areas with high population density, proportion of elderly people, and pig density. The improvement of medical resources and tree cover as well as the construction of storm sewers and piped water systems might be mitigation measures that can be considered.

Exposure variability and determining factors of urinary metals for schoolchildren in Taiwan.

Urinary heavy metal levels in schoolchildren vary greatly over time, and research on the determinant variables explaining variance components in urinary metal exposure is limited. This study assessed metal concentrations and variability in the urine of schoolchildren and explored their important determining factors. We collected spot urine samples from schoolchildren (n = 321) living in urban, suburban, and rural areas during the warm and cold seasons. The toxic (As, Cd, Ni, Cd, and Pb) and essential (Co, Cu, Mo, and V) elements of urine samples were analyzed by inductively coupled plasma mass spectrometry. We assessed the within- and between-subject variability in urinary metal concentrations, calculated the "probability of overexposure (θ)," compared with the reference values. We evaluated the spatiotemporal and biological factors that determine average exposure by using a mixed-effects model. The within-subject variance accounting for 63.8%-95.4% of the total variance in exposures was predominant for selected urinary metals. Urinary As levels (subject-specific mean = 69.0 µg/g creatinine) in schoolchildren were remarkably high and presented ∼99.9% of θ. After adjusting for the selected variables, we found that urban schoolchildren had significantly higher urinary metal levels (As, Cr, Co, and Ni) than suburban and rural schoolchildren. The urinary levels of most metals (except for Cu and Pb) increased during the cold season. Girls had higher urinary Cr, Co, Cu, Ni, and Pb levels than boys. Body mass index and urinary creatinine could also affect urinary metal levels. Those variables explained 15.8% (Pb), 11.6% (Cr), and 6.5% (Cd) of total variance in urinary concentrations. The repeated measurements of spot urine samples across seasons in individuals for long-term exposure estimates of metals were suggested. This biomonitoring survey of a large number of urine samples provided useful information on toxic metals and on the important determinants of exposure in schoolchildren.

Simple scoring algorithm to identify community-dwelling older adults with limited health literacy: a cross-sectional study in Taiwan.

OBJECTIVE: Health literacy (HL) is the degree of individuals' capacity to access, understand, appraise and apply health information and services required to make appropriate health decisions. This study aimed to establish a predictive algorithm for identifying community-dwelling older adults with a high risk of limited HL. DESIGN: A cross-sectional study. SETTING: Four communities in northern, central and southern Taiwan. PARTICIPANTS: A total of 648 older adults were included. Moreover, 85% of the core data set was used to generate the prediction model for the scoring algorithm, and 15% was used to test the fitness of the model. PRIMARY AND SECONDARY OUTCOME MEASURES: Pearson's χ2 test and multiple logistic regression were used to identify the significant factors associated with the HL level. An optimal cut-off point for the scoring algorithm was identified on the basis of the maximum sensitivity and specificity. RESULTS: A total of 350 (54.6%) patients were classified as having limited HL. We identified 24 variables that could significantly differentiate between sufficient and limited HL. Eight factors that could significantly predict limited HL were identified as follows: a socioenvironmental determinant (ie, dominant spoken dialect), a health service use factor (ie, having family doctors), a health cost factor (ie, self-paid vaccination), a heath behaviour factor (ie, searching online health information), two health outcomes (ie, difficulty in performing activities of daily living and requiring assistance while visiting doctors), a participation factor (ie, attending health classes) and an empowerment factor (ie, self-management during illness). The scoring algorithm yielded an area under the curve of 0.71, and an optimal cut-off value of 5 represented moderate sensitivity (62.0%) and satisfactory specificity (76.2%). CONCLUSION: This simple scoring algorithm can efficiently and effectively identify community-dwelling older adults with a high risk of limited HL.

Emergency room visits for childhood atopic dermatitis are associated with floods?

Floods are known to increase the level of allergens such as molds in the environments. Under climate change, the frequency of floods could be increased, which highlights the importance of understanding the impacts of floods on atopic diseases. However, there was a lack of studies. This study examines whether floods induce attacks of childhood atopic dermatitis (AD). A retrospective population-based study was conducted in Taiwan Island using Taiwan's National Health Insurance Research Database. Emergency room (ER) visits for AD were identified among children aged 0-12 years. Weekly data of flood occurrence, number of flood sites, temperature, and air pollution were obtained for each township of the identified cases. A time-stratified case-crossover design was used. The relationship between ER visits for AD and floods was assessed by conditional logistic regression, adjusting for weekly mean temperature, PM2.5 and NO2. There were a total of 55,488 ER visits due to AD during the study period. Such visits increased when flood occurred, and then declined. The effects of floods at the week of flood remained robust, with OR of 1.14 (95% CI = 1.01-1.28) for flood occurrence and 1.31 (95% CI = 1.10-1.55) for the number of flood sites, after adjusting for covariates. Such effects were slightly higher in boys and children aged 1-12 years. This study demonstrated the impact of floods on flare-up of childhood AD, and the effect was most prominently at the week of flood. Healthcare workers should be alarmed for potential increase of AD flare ups after flood events.

Integrated analysis of source-specific risks for PM2.5-bound metals in urban, suburban, rural, and industrial areas.

The levels and characteristics of atmospheric metals vary in time and location, can result in various health impacts, which increases the challenge of air quality management. We aimed to investigate PM2.5-bound metals in multiple locations and propose a methodology for comparing metal elements across study regions and prioritizing source contributions through integrated health risk assessments. PM2.5-bound metals were collected in the urban, suburban, rural, and industrial regions of Taiwan between 2016 and 2018. We incorporated the positive matrix factorization (PMF) with health risk assessments (considering estimates of the margin of exposure (MOE) and excess cancer risk (ECR)) to prioritize sources for control. We found that the concentrations of Fe, Zn, V, Cu, and Mn (industry-related metals) were higher at the industrial site (Kaohsiung) and Ba, Cr, Ni, Mo, and Co (traffic-related metals) were higher at the urban site (Taipei). The rural site (Hualian) had good air quality, with low PM2.5 and metal concentrations. Most metal concentrations were higher during the cold season for all study sites, except for the rural. Ambient concentrations of Mn, Cr, and Pb obtained from all study sites presents a higher health risk of concern. In Kaohsiung, south Taiwan, PM2.5-bound metals from the iron ore and steel factory is suggested as the first target for control based on the calculated health risks (MOE < 1 and ECR > 10-6). Overall, we proposed an integrated strategy for initiating the source management prioritization of PM2.5-bound metals, which can aid an effort for policymaking.

Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration.

This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial-temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms-random forest (RF) and extreme gradient boosting (XGBoost)-for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.

Impacts of hazardous metals and PAHs in fine and coarse particles with long-range transports in Taipei City.

This study assessed the impact on air quality and health risk by long-range transported (LRT) PM2.5-10- and PM2.5-bound metals and PAHs in Taipei City, Taiwan. Several methods with receptor aerosol measurements were used to quantify the effect of LRT. The hybrid single particle lagrangian integrated trajectory model (HYSPLIT) was used in conjunction with the potential source contribution function (PSCF) to distinguish the LRT aerosols. By using a general linear model (GLM) with a marginal mean and positive matrix fraction (PMF), this study also evaluated the annual increased level of LRT (AIRLRT) for each source contribution to the concentration and the resultant health risk of particle-bound metals and polycyclic aromatic hydrocarbons (PAHs). The LRT influenced fine-sized metals and PAHs rather than coarse-sized ones. We found that the level of PM2.5-bound toxic metals (Pb, Cd, and As) and PAHs (Benzo[a]pyrene and dibenzo[a,e]pyrene) could increase by 90% under the influence of LRT in 2014, while an AIRLRT value of 25% for the PM2.5 mass concentration was observed. Overall, the excess cancer risk (ECR) resulting from PM2.5-bound metal and PAH exposures was 6.40 × 10-5 in relation to coal combustions (20.7%), traffic-related emissions (59.7%) and re-suspended aerosols (19.6%). Among these contributors, LRT-related metals and PAHs in PM2.5 accounted for 51% of the total ECR.

Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration.

This paper developed a land use regression (LUR) model to study the spatial-temporal variability of O3 concentrations in Taiwan, which has typical Asian cultural characteristics with diverse local emission sources. The Environmental Protection Agency's (EPA) data of O3 concentrations from 2000 and 2013 were used to develop this model, while observations from 2014 were used as the external data verification to assess model reliability. The distribution of temples, cemeteries, and crematoriums was included for a potential predictor as an Asian culturally specific source for incense and joss money burning. We used stepwise regression for the LUR model development, and applied 10-fold cross-validation and external data for the verification of model reliability. With the overall model R² of 0.74 and a 10-fold cross-validated R² of 0.70, this model presented a mid-high prediction performance level. Moreover, during the stepwise selection procedures, the number of temples, cemeteries, and crematoriums was selected as an important predictor. By using the long-term monitoring data to establish an LUR model with culture specific predictors, this model can better depict O3 concentration variation in Asian areas.

PM2.5- and PM10-bound polycyclic aromatic hydrocarbons (PAHs) in the residential area near coal-fired power and steelmaking plants of Taichung City, Taiwan: In vitro-based health risk and source identification.

We investigated spatial and season variations in particle-bound PAH concentrations, identified their potential sources and estimated resultant health risk of activate toxicity pathways in a residential area near coal-fired power and steelmaking plants. Both atmospheric PM2.5 and PM10 samples (n = 94) were simultaneously collected for summer and winter in the Wuqi and Shalu districts of Taichung City, central Taiwan. The principal component analysis (PCA) measure was used to evaluate the sources of particle-bound PAHs. The health risk of PAHs-activated toxicity pathways was estimated through a probabilistic model in cooperation with high-throughput screening (HTS) in vitro assays and measurement data for children and adults. No spatial difference, but significant seasonal variation, in PAH concentrations for PM2.5 (summer = 1.7 ng m-3 and winter = 4.7 ng m-3) and PM10 (summer = 2.1 ng m-3 and winter = 4.8 ng m-3) between two sites was observed, where both sites shared the similar PAH patterns in congener concentrations. PAH contents in the fine mode (PM2.5) of ambient particles are predominant while coarse mode (PM2.5-10) PAHs is negligible. Children with particle-bound PAH exposures have a relatively high health risk of aryl hydrocarbon receptor (AhR)-mediated adverse outcomes than adults, in particular in the winter period, while the activations of Nrf2 and p53 pathways are insignificant. Vehicle emission (67.1%), unburned petroleum (15.0%), steel industry and stationary emission (6.1%), and oil combustion + cooking oil fume (5.6%) associated with PM2.5-bound PAHs were apportioned. The emission from the Taichung coal-fired power plant is rarely attributable to particle-bound PAHs of the study area based on results of spatiotemporal variation of PAHs, wind direction, and source apportionment.

Pollen of Broussonetia papyrifera: An emerging aeroallergen associated with allergic illness in Taiwan.

Pollen has long been recognized as a major allergen, having diverse patterns of allergenicity caused by differences in climate, geography, and vegetation. Our research aimed to explore the role of a regionally dominant pollen in Taiwan, Broussonetia papyrifera, on clinical sensitization and daily 5collected and extracted for a skin prick test on 30 volunteers recruited from a medical college. Daily atmospheric pollen levels were measured using a Burkard 7-day volumetric trap. The association between daily atmospheric pollen levels and clinic visits for allergic illness was examined using a generalized additive model with a normal assumption. After excluding four participants with a positive response to a negative control, 10 participants (38.4%) were determined to be sensitive to B. papyrifera pollen extract. The three-day lagged concentration of B. papyrifera pollen exhibited the highest risk of daily asthma visits (relative risk [RR] = 1.166, 95% confidence interval [CI]: 1.014-1.341) and allergic rhinitis visits (RR = 1.119, 95% CI: 0.916-1.367) when the pollen increased equally in magnitude to its mean. Our study is the first to provide evidence indicating that the most dominant airborne pollen in Taiwan, B. papyrifera, plays a major role in sensitization and clinic visits for asthma and allergic rhinitis, thus highlighting the need to integrate aeroallergen monitoring with clinical diagnosis.

Ambient VOCs in residential areas near a large-scale petrochemical complex: Spatiotemporal variation, source apportionment and health risk.

This study investigated ambient volatile organic compounds (VOCs) and assessed excess health risks for child, adult and elderly populations in a residential area near a large-scale petrochemical complex in central Taiwan. A total of 155 daily VOC samples were collected in canisters from nine sites in spring, summer and winter during 2013-2014. We used a positive matrix factorization (PMF) model incorporating a conditional probability function (CPF) to quantify the potential sources of VOCs with the influences of local source directions. We then evaluated the non-cancer and cancer risks of specific VOCs with probabilistic distributions by performing a Monte-Carlo simulation for the child, adult, and elderly populations. Most of the VOCs were higher in summer than in winter or spring for the sampling sites. The presence of vinyl acetate, chloroethene, and 1,2-dichloroethane were significantly high within a 5-km radius of the petrochemical complex. Four potential sources of ambient VOCs, industrial emission (49.2%-63.6%), traffic-related emission (13.9%-19.1%), fuel evaporation (12.3%-16.9%), and aged emission (10.2%-14.8%), were identified. The cancer risk of ambient VOC exposure was mainly attributed to the industrial source in the study area, while the non-cancer risk was of less concern. Benzene associated with fuel evaporation resulted in the highest cancer risk (4.1 × 10-5-5.5 × 10-5) as compared to that of the other toxic VOCs.

Ambient PM2.5 in the residential area near industrial complexes: Spatiotemporal variation, source apportionment, and health impact.

This study systemically investigated the ambient PM2.5 (n=108) with comprehensive analyses of the chemical composition, identification of the potential contributors, and estimation of the resultant respiratory physician visits in the residential regions near energy-consuming and high-polluting industries in central Taiwan. The positive matrix fraction (PMF) model with chemical profiles of trace metals, water-soluble ions, and organic/elemental carbons (OC/EC) was applied to quantify the potential sources of PM2.5. The influences of local sources were also explored using the conditional probability function (CPF). Associations between the daily PM2.5 concentration and the risk of respiratory physician visits for the elderly (≥65years of age) were estimated using time-series analysis. A seasonal variation, with higher concentrations of PM2.5, metals (As, Cd, Sb, and Pb), OC/EC and ions (i.e., NO3-, SO42- and NH4+) in the winter than in the spring and summer, was observed. Overall, an increase of 10µgm-3 in the same-day PM2.5 was associated with an ~2% (95% CI: 1.5%-2.5%) increase in respiratory physician visits. Considering the health benefits of an effective reduction, we suggest that the emission from coal combustion (23.5%), iron ore and steel industry (17.1%), and non-ferrous metallurgy (14.4%), accounting for ~70% of the primary PM2.5 in the winter are prioritized to control.

Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability.

This study utilized a long-term satellite-based vegetation index, and considered culture-specific emission sources (temples and Chinese restaurants) with Land-use Regression (LUR) modelling to estimate the spatial-temporal variability of PM2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average PM2.5 concentrations from 2006 to 2012 of 17 air quality monitoring stations established by Environmental Protection Administration of Taiwan were used for model development. PM2.5 measurements from 2013 were used for external data verification. Monthly Normalized Difference Vegetation Index (NDVI) images coupled with buffer analysis were used to assess the spatial-temporal variations of greenness surrounding the monitoring sites. The distribution of temples and Chinese restaurants were included to represent the emission contributions from incense and joss money burning, and gas cooking, respectively. Spearman correlation coefficient and stepwise regression were used for LUR model development, and 10-fold cross-validation and external data verification were applied to verify the model reliability. The results showed a strongly negative correlation (r: -0.71 to -0.77) between NDVI and PM2.5 while temples (r: 0.52 to 0.66) and Chinese restaurants (r: 0.31 to 0.44) were positively correlated to PM2.5 concentrations. With the adjusted model R2 of 0.89, a cross-validated adj-R2 of 0.90, and external validated R2 of 0.83, the high explanatory power of the resultant model was confirmed. Moreover, the averaged NDVI within a 1750 m circular buffer (p < 0.01), the number of Chinese restaurants within a 1750 m buffer (p < 0.01), and the number of temples within a 750 m buffer (p = 0.06) were selected as important predictors during the stepwise selection procedures. According to the partial R2, NDVI explained 66% of PM2.5 variation and was the dominant variable in the developed model. We suggest future studies consider these three factors when establishing LUR models for estimating PM2.5 in other Asian cities.

Ambient PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in Changhua County, central Taiwan: Seasonal variation, source apportionment and cancer risk assessment.

This study investigates PM2.5-bound PAHs for rural sites (Dacheng and Fangyuan) positioned close to heavy air-polluting industries in Changhua County, central Taiwan. A total of 113 PM2.5 samples with 22 PAHs collected from 2014 to 2015 were analyzed, and Positive Matrix Factorization (PMF) and diagnostic ratios of PAHs were applied to quantify potential PAH sources. The influences of local and regional sources were also explored using the conditional probability function (CPF) and potential source contribution function (PSCF) with PMF-modeled results, respectively. Annual mean concentrations of total PAHs were 2.91 ± 1.34 and 3.04 ± 1.40 ng/m3 for Dacheng and Fangyuan, respectively, and their corresponding BaPeq were measured at 0.534 ± 0.255 and 0.563 ± 0.273 ng/m3 in concentration. Seasonal variations with higher PAHs found for the winter than for the spring and summer were observed for both sites. The lifetime excess cancer risk (ECR) from inhalation exposure to PAHs was recorded as 4.7 × 10-5 overall. Potential sources of PM2.5-bound PAHs include unburned petroleum and traffic emissions (42%), steel industry and coal combustion (31%), and petroleum and oil burning (27%), and unburned petroleum and traffic emission could contribute the highest ECR (2.4 × 10-5). The CPF results show that directional apportionment patterns were consistent with the actual locations of local PAH sources. The PSCF results indicate that mainly northeastern regions of China have contributed elevated PM2.5-bound PAHs from long-range transports.

Fine Particle Pollution, Alanine Transaminase, and Liver Cancer: A Taiwanese Prospective Cohort Study (REVEAL-HBV).

BACKGROUND: Exposure to fine particulate matter (PM2.5) may promote hepatic tumorgenesis through low-grade inflammation. Therefore, we assessed the association of long-term exposure levels of PM2.5 and subsequent risk of hepatocellular carcinoma (HCC) and investigated the mediation effect of inflammation as represented by alanine aminotransferase (ALT) on this association. METHODS: Between 1991 and 1992, we recruited 23 820 participants in Taiwan with no history of HCC. Case patients of HCC were ascertained through computerized data linkage with the National Cancer Registry and death certification systems. Participants' exposures to PM2.5 were based on a four-year average retrieved from stationary monitoring sites. Cox proportional hazards models were used to assess the association between PM2.5 exposure and HCC incidence. Mediation effects of ALT on PM2.5-associated HCC incidence were estimated. RESULTS: A total of 464 HCC cases were newly diagnosed with a median follow-up of 16.9 years. Statistically significantly increasing trends between PM2.5 exposures and ALT were observed on the Main Island and Penghu Islets. The adjusted hazard ratio (HR) for HCC on the Penghu Islets was 1.22 (95% confidence interval [CI] = 1.02 to 1.47) per PM2.5 interquartile range (IQR) increment (0.73 µg/m(3)) exposure. We also found a positive association between PM2.5 exposure (per IQR increment, 13.1 µg/m(3)) and HCC incidence on the Main Island. Furthermore, ALT had a statistically significant mediation effect on PM2.5-associated HCC incidence (HR = 1.17, 95% CI = 1.02 to1.52 on the Main Island; HR = 1.04, 95% CI = 1.03 to 1.07 on the Penghu Islets) per PM2.5 IQR increment. CONCLUSIONS: Long-term PM2.5 exposure increased the risk for liver cancer, and chronic inflammation of the liver may underlie the pathogenesis.

Elemental characterization and source apportionment of PM10 and PM2.5 in the western coastal area of central Taiwan.

This study investigated seasonal variations in PM10 and PM2.5 mass and associated trace metal concentrations in a residential area in proximity to the crude oil refinery plants and industrial parks of central Taiwan. Particle measurements were conducted during winter, spring and summer in 2013 and 2014. Twenty-six trace metals in PM10 and PM2.5 were analyzed using ICP-MS. Multiple approaches of the backward trajectory model, enrichment factor (EF), Lanthanum enrichment and positive matrix fraction (PMF) were used to identify potential sources of particulate metals. Mean concentrations of PM10 in winter, spring and summer were 76.4 ± 22.6, 33.2 ± 9.9 and 37.4 ± 17.0 µg m(-3), respectively, while mean levels of PM2.5 in winter, spring and summer were 47.8 ± 20.0, 23.9 ± 11.2 and 16.3 ± 8.2 µg m(-3), respectively. The concentrations of carcinogenic metals (Ni, As and adjusted Cr(VI)) in PM10 and PM2.5 exceeded the guideline limits published by WHO. The result of EF analysis confirmed that Mo, Sb, Cd, Zn, Mg, Cr, As, Pb, Cu, Ni and V were attributable to anthropogenic emission. PMF analysis demonstrated that trace metals in PM10 and PM2.5 were from the similar sources, such as coal combustion, oil combustion and traffic-related emission, except for soil dust and crustal element emissions only observed in PM10 and secondary aluminum smelter only observed in PM2.5. Considering health-related particulate metals, the traffic-related emission and coal combustion for PM10 and PM2.5, respectively, are important to control for reducing potential carcinogenic risk. The results could aid efforts to clarify the impact of source-specific origins on human health.

Precipitation increases the occurrence of sporadic legionnaires' disease in Taiwan.

Legionnaires' disease (LD) is an acute form of pneumonia, and changing weather is considered a plausible risk factor. Yet, the relationship between weather and LD has rarely been investigated, especially using long-term daily data. In this study, daily data was used to evaluate the impacts of precipitation, temperature, and relative humidity on LD occurrence in Taiwan from 1995-2011. A time-stratified 2:1 matched-period case-crossover design was used to compare each case with self-controlled data using a conditional logistic regression analysis, and odds ratios (ORs) for LD occurrence was estimated. The city, gender and age were defined as a stratum for each matched set to modify the effects. For lag day- 0 to 15, the precipitation at lag day-11 significantly affected LD occurrence (p<0.05), and a 2.5% (95% CIs = 0.3-4.7%) increased risk of LD occurrence was associated with every 5-mm increase in precipitation. In addition, stratified analyses further showed that positive associations of precipitation with LD incidence were only significant in male and elderly groups and during the warm season ORs = 1.023-1.029). However, such an effect was not completely linear. Only precipitations at 21-40 (OR = 1.643 (95% CIs = 1.074-2.513)) and 61-80 mm (OR = 2.572 (1.106-5.978)) significantly increased the risk of LD occurrence. Moreover, a negative correlation between mean temperature at an 11-day lag and LD occurrence was also found (OR = 0.975 (0.953-0.996)). No significant association between relative humidity and LD occurrence was identified (p>0.05). In conclusion, in warm, humid regions, an increase of daily precipitation is likely to be a critical weather factor triggering LD occurrence where the risk is found particularly significant at an 11-day lag. Additionally, precipitation at 21-40 and 61-80 mm might make LD occurrence more likely.

When are we most vulnerable to temperature variations in a day?

Daily temperature measures are commonly used when examining the association between temperature and mortality. In fact, temperature measures are available 24 hours a day and more detailed records may provide a better prediction of mortality compared to daily statistics. In this article, monthly stratified analysis modeling for mortality is conducted for the total population as well as the stratified elderly and younger subgroups. We identified the most significant time during the day that is associated with daily mortality. Surprisingly, the estimates of relative risk and magnitude of associations derived from the hourly temperature measures are similar or even stronger compared to those modeled by the daily statistics. This phenomenon remains true for lagged hourly temperature measures and the changing patterns of associations from January through December are revealed. In summary, people are the most vulnerable to temperature variations in the early morning around 5 am and the night time around 8 pm.