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Objective: Limited research has explored the long-term effect of reduced PM 2.5 exposure on cognitive function. This study aimed to investigate the effects of time-dependent PM 2.5 exposure and the interactions of PM 2.5 and aging on declines in Mini-Mental State Examination (MMSE) scores, in carriers and non-carriers of the APOE-ε4 allele., Methods: Participants aged over 60 were recruited for this cohort study, undergoing MMSE tests twice from the Taiwan Biobank Program from 2008 to 2020. Participants with dementia or baseline MMSE scores <24 were excluded. Annual PM 2.5 levels were estimated using a hybrid kriging/land use regression model with extreme gradient boosting, treated as a time-dependent variable. Generalized estimating equations were used to assess the impacts of repeated PM 2.5 on MMSE decline, further stratified by the presence of APOE-ε4 alleles., Results: After follow-up, 290 participants out of the overall 7,000 community residents in the Biobank dataset demonstrated incidences of MMSE declines (<24), with an average MMSE score decline of 1.11 per year. Participants with ε4/ε4 alleles in the APOE gene had significantly 3.68-fold risks of MMSE decline. High levels of PM 2.5 across all visits were significantly associated with worsening of scores on the overall MMSE. As annual levels of PM 2.5 decreased over time, the impact of PM 2.5 on MMSE decline also slowly diminished., Conclusion: Long-term PM 2.5 exposure may be associated with increased risk of MMSE decline, despite improvements in ambient PM 2.5 levels over time. Validation of these results necessitates a large-scale prospective cohort study with more concise cognitive screening tools., Competing Interests: DISCLOSURES This study was supported by grants from theMinistry of Science and Technology(MOST 108-2221-E-039-009-MY3) andTaichung Veterans General Hospital(TCVGH-1123601B). The funders did not participate in the study design, data collection and analysis, publication decision, or manuscript preparation. The authors report no conflicts with any product mentioned or concept discussed in this article., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Air pollution is recognized as a modifiable risk factor for dementia, and recent evidence suggests that improving air quality could attenuate cognitive decline and reduce dementia risk. However, studies have yet to explore the effects of improved air quality on brain structures. This study aims to investigate the impact of air pollution reduction on cognitive functions and structural brain differences among cognitively normal older adults., Methods: Four hundred and thirty-one cognitively normal older adults were from the Epidemiology of Mild Cognitive Impairment study in Taiwan (EMCIT), a community-based cohort of adults aged 60 and older, between year 2017- 2021. Annual concentrations of PM 2.5 , NO 2 , O 3 , and PM 10 at participants' residential addresses during the 10 years before enrollment were estimated using ensemble mixed spatial models. The yearly rate of change (slope) in air pollutants was estimated for each participant. Cognitive functions and structural brain images were collected during enrollment. The relationships between the rate of air pollution change and cognitive functions were examined using linear regression models. For air pollutants with significant findings in relation to cognitive function, we further explored the association with brain structure., Results: Overall, all pollutant concentrations, except O 3 , decreased over the 10-year period. The yearly rates of change (slopes) in PM 2.5 and NO 2 were correlated with better attention (PM 2.5 : r = -0.1, p = 0.047; NO 2 : r = -0.1, p = 0.03) and higher white matter integrity in several brain regions. These regions included anterior thalamic radiation, superior longitudinal fasciculus, inferior longitudinal fasciculus, corticospinal tract, and inferior fronto-occipital fasciculus., Conclusions: Greater rate of reduction in air pollution was associated with better attention and attention-related white matter integrity. These results provide insight into the mechanism underlying the relationship between air pollution, brain health, and cognitive aging among older adults., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

PM 2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM 2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM 2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM 2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM 2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM 2.5 values, SO 2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM 2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The influence of long-range transport (LRT) of air pollutants on neighboring regions and countries has been documented. The magnitude of LRT aerosols and related constituents can misdirect control strategies for local air quality management. In this study, we aimed to quantify PM 2.5 (diameter less than 2.5 μm, PM 2.5 ) and associated metals derived from local sources and LRT in different geographic locations in Taiwan using advanced receptor models. We collected daily PM 2.5 samples (n = ∼1000) and analyzed 28 metals every three days from 2016 to 2018 in the northern, central-south, eastern, and southern areas of Taiwan. We first used a machine learning technique with a cluster algorithm coupled with a backward trajectory to classify local, regional, and LRT-related aerosols. We then quantified the source contributions with a positive matrix factorization (PMF) model for Taiwan weighted by region-specific populations. The northern and eastern regions were found to be more vulnerable to LRT-related PM 2.5 and metals than the central-south and southern regions in Taiwan. The LRT increased Pb and As concentrations by 90-200% and ∼40% in the northern and central-south regions. Ambient PM 2.5 -metals mainly originated from local traffic-related emissions in the northern, central-south, and southern regions, whereas oil combustion was the primary source of PM 2.5 -metals in the eastern region. By subtracting the influence from the LRT, the contributions of domestic emission sources to ambient PM 2.5 metals in Taiwan were 35% from traffic-related emission, 17% from non-ferrous metallurgy, 13% from iron ore and steel factories, 12% from coal combustion, 12% from oil combustion, 10% from incinerator emissions, and <1% from cement manufacturing emissions. This study proposed an advanced method for refining local source contributions to ambient PM 2.5 metals in Taiwan, which provides useful information on regional control strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Previous studies have shown inconsistent results regarding the impact of traffic pollution on the prevalence of chronic obstructive pulmonary disease (COPD). Therefore, using frequency matching and propensity scores, we explored the association between traffic pollution and COPD in a cohort of 8284 residents in a major agricultural county in Taiwan., Methods: All subjects completed a structured questionnaire interview and health checkups. Subjects with COPD were identified using Taiwan National Health Insurance Research Databases. A hybrid kriging/LUR model was used to identify levels of traffic-related air pollutants (PM 2.5 and O 3 ). Multiple logistic regression models were used to calculate the prevalence ratios (PRs) of COPD and evaluate the role played by traffic-related indices between air pollutants and COPD. The distributed lag nonlinear model was applied in the analysis; we excluded current or ever smokers to perform the sensitivity analysis., Results: Increased PRs of COPD per SD increment of PM 2.5 were 1.10 (95% CI 1.05-1.15) and 1.25 (95% CI 1.13-1.40) in the population with age and sex matching as well as propensity-score matching, respectively. The results of the sensitivity analysis were similar between the single and two pollutant models. PM 2.5 concentrations were significantly associated with traffic flow including sedans, buses, and trucks (p < 0.01). The higher road area and the higher PM 2.5 concentrations near the subject's residence correlated with a greater risk of developing COPD (p for interaction < 0.01)., Conclusions: Our results suggest that long-term exposure to traffic-related air pollution may be positively associated with the prevalence of COPD., (© 2021. The Author(s).)

This study aimed to identify the spatial patterns of lower respiratory tract infections (LRIs) and their association with fine particulate matter (PM 2.5 ). The disability-adjusted life year (DALY) database was used to represent the burden each country experiences as a result of LRIs. PM 2.5 data obtained from the Atmosphere Composition Analysis Group was assessed as the source for main exposure. Global Moran's I and Getis-Ord Gi* were applied to identify the spatial patterns and for hotspots analysis of LRIs. A generalized linear mixed model was coupled with a sensitivity test after controlling for covariates to estimate the association between LRIs and PM 2.5 . Subgroup analyses were performed to determine whether LRIs and PM 2.5 are correlated for various ages and geographic regions. A significant spatial auto-correlated pattern was identified for global LRIs with Moran's Index 0.79, and the hotspots of LRIs were clustered in 35 African and 4 Eastern Mediterranean countries. A consistent significant positive association between LRIs and PM 2.5 with a coefficient of 0.21 (95% CI 0.06-0.36) was identified. Furthermore, subgroup analysis revealed a significant effect of PM 2.5 on LRI for children (0-14 years) and the elderly (≥ 70 years), and this effect was confirmed to be significant in all regions except for those comprised of Eastern Mediterranean countries.

Exposure to indoor particulate matter less than 2.5 µm in diameter (PM 2.5 ) is a critical health risk factor. Therefore, measuring indoor PM 2.5 concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM 2.5 data is difficult and expensive. Therefore, several indoor PM 2.5 concentration prediction models have been developed. However, these prediction models only assess the daily average PM 2.5 concentrations in cold or temperate regions. The factors that influence PM 2.5 concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM 2.5 concentrations in Taiwan (tropical and subtropical region) by using a multiple linear regression model and investigated the impact factor. The sample comprised 93 study cases (1979 measurements) and 25 potential predictor variables. Cross-validation was performed to assess performance. The prediction model explained 74% of the variation, and outdoor PM 2.5 concentrations, the difference between indoor and outdoor CO 2 levels, building type, building floor level, bed sheet cleaning, bed sheet replacement, and mosquito coil burning were included in the prediction model. Cross-validation explained 75% of variation on average. The results also confirm that the prediction model can be used to estimate indoor PM 2.5 concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM 2.5 concentrations and suggested that outdoor PM 2.5 concentrations, ventilation, building characteristics, and human activities should be considered. Moreover, it is important to consider outdoor air quality while occupants open or close windows or doors for regulating ventilation rate and human activities changing also can reduce indoor PM 2.5 concentrations.

Exposure to surrounding greenness is associated with reduced mortality in Caucasian populations. Little is known however about the relationship between green vegetation and the risk of death in Asian populations. Therefore, we opted to evaluate the association of greenness with mortality in Taiwan. Death information was retrieved from the Taiwan Death Certificate database between 2006 to 2014 (3287 days). Exposure to green vegetation was based on the normalized difference vegetation index (NDVI) collected by the Moderate Resolution Imagine Spectroradiometer (MODIS). A generalized additive mixed model was utilized to assess the association between NDVI exposure and mortality. A total of 1,173,773 deaths were identified from 2006 to 2014. We found one unit increment on NDVI was associated with a reduced mortality due to all-cause (risk ratio [RR] = 0.901; 95% confidence interval = 0.862-0.941), cardiovascular diseases (RR = 0.892; 95% CI = 0.817-0.975), respiratory diseases (RR = 0.721; 95% CI = 0.632-0.824), and lung cancer (RR = 0.871; 95% CI = 0.735-1.032). Using the green land cover as the alternative green index showed the protective relationship on all-cause mortality. Exposure to surrounding greenness was negatively associated with mortality in Taiwan. Further research is needed to uncover the underlying mechanism.

Impaired lung function is associated with morbidity and mortality in the elderly. However, there is a paucity of data regarding the long-term effects of particulate matter (PM) on lung function among the elderly. This study evaluated the exposure-response relationship between ambient PM and different lung function indices among the elderly in Taiwan. A cross-sectional survey of individuals aged ≥65 years was conducted in Taiwan from October 2015 to September 2016. Those who attended the annual health examination for the elderly in five hospitals of varying background PM concentrations were enrolled. The long-term (2015 annual mean concentration) exposure to air pollution was estimated by the Kriging method at the residence of each subject. The association between ambient PM exposure and lung function was evaluated by linear regression modeling, with adjustments for age, sex, height, weight, educational attainment, presence of asthma or chronic obstructive pulmonary disease, smoking status, season, and co-pollutants. There were 1241 subjects (mean age, 70.5 years). The mean residential PM 2.5 and PM 2.5-10 in 2015 was 26.02 and 18.01 μg/m 3 , respectively. After adjustments for confounders and co-pollutants, the FVC decrease was best associated with fine particles (PM 2.5 ), whereas the FEV 1 , FEF 25-75% , FEF 25% and FEF 50% decreases were best associated with coarse particles (PM 2.5-10 ). An IQR (10 μg/m 3 ) increase in PM 2.5 decreased FVC by 106.38 ml (4.47%), while an IQR (7.29 μg/m 3 ) increase in PM 2.5-10 decreased FEV 1 and FEF 25-75% by 91.23 ml (4.85%) and 104.44 ml/s (5.58%), respectively. Among the Taiwanese elderly, long-term PM 2.5 exposure mainly decreases the vital capacity of lung function. Moreover, PM 2.5-10 has a stronger negative effect on the function of conductive airways than PM 2.5 .

Loss of skeletal muscle mass is common with aging and can cause morbidity and mortality in the elderly. The effects of particulate air pollution on skeletal muscle mass is not known. The study aims to assess the chronic effects of ambient fine particulates (PM 2.5 ) on the body composition of the elderly. From October 2015 to November 2016, a cross-sectional survey on 530 elderly (age > = 65 years) was conducted in the Taipei Basin, Taiwan. The body composition was measured by bioelectrical impedance analysis (InBody 120). One year exposure to air pollution was estimated using the Kriging method at the participant's residence. Multiple linear regression analysis, after adjustments for demographics and co-pollutants, was used to examine the effects of PM 2.5 on body composition indices and force of handgrip. Changes in body composition for an interquartile (1.4 μm/m 3 ) increase in PM 2.5 concentration included a 0.4 kg (95% confidence interval (CI): -0.31, -0.58; p < 0.0001) decrease in skeletal muscle mass (2.0%) and a 0.7 kg (95% CI: 0.47, 0.91; p < 0.0001) increase in body fat mass (3.6%). While PM 2.5 reduced fat free mass in the upper extremities and trunk, but not in the lower extremities, it increased body fat mass in the three parts. There was no significant effect of PM 2.5 on handgrip force. Higher physical activity (versus lower than median) was associated with less detrimental effect of PM 2.5 on skeletal muscle mass and body fat mass (p values for interaction term: 0.009 and 0.013, respectively). Long-term PM 2.5 exposure is associated with decreased skeletal muscle mass and increased body fat mass in the elderly, which can be ameliorated by physical activity.

Background: Long-term exposure to fine particulate matter (PM with an aerodynamic diameter ≤2.5 µm; PM2.5) contributes to an elevated incidence of type 2 diabetes (T2D) in North America and Europe, but there is limited empirical evidence for Asian countries. This study determined the association between and the exposure-response relationship for PM2.5 and the incidence of T2D in Taiwan., Methods: This retrospective cohort study was conducted for the years 2001-2012. Health information, including age, sex, health insurance premium, type of occupation, medication, and disease status, was retrieved from the Longitudinal Health Insurance Database 2000. Monitoring data for PM2.5 came from the Environmental Protection Administration of Taiwan, and Land-use Regression modeling was used to approximate participants' long-term exposure to PM2.5. Cox proportional hazards models with a generalized estimating equation to account for the correlation within the locations of the medical facilities were used to estimate the association between exposure to PM2.5 and the incidence of T2D, adjusting for the potential confounders. We also examined effect modification of sex, age, hyperlipidemia, and National Health Insurance premium for the association., Results: Forty-eight thousand six hundred eleven new cases of diabetes were identified among 505,151 eligible participants, with the median follow-up of 12 years. Positive associations were identified between long-term exposure to PM2.5 exposure and the incidence of T2D. An increase of 10 μg/m PM2.5 was associated with an 11.0% increase in the risk of contracting diabetes (95% confidence interval = 8.0%, 13.0%). The results show that there is an almost linear relationship between exposure to PM2.5 and the incidence of T2D. Sex, age, hyperlipidemia, and National Health Insurance premium acted as effect modifiers of the association between diabetes incidence and levels of PM2.5 exposure in Taiwan., Conclusions: In the population in Taiwan, long-term exposure to PM2.5 increases the risk of incidence of T2D by 11%. This effect is more pronounced in elderly male patients who exhibit hyperlipidemia and in individuals who have a lower insurance health insurance premium.

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.

Background: The link between PM 2.5 exposure and adverse health outcomes is well documented from studies across the world. However, the reported effect estimates vary across studies, locations and constituents. We aimed to conduct a meta-analysis on associations between short-term exposure to PM 2.5 constituents and mortality using city-specific estimates, and explore factors that may explain some of the observed heterogeneity., Methods: We systematically reviewed epidemiological studies on particle constituents and mortality using PubMed and Web of Science databases up to July 2015.We included studies that examined the association between short-term exposure to PM 2.5 constituents and all-cause, cardiovascular, and respiratory mortality, in the general adult population. Each study was summarized based on pre-specified study key parameters (e.g., location, time period, population, diagnostic classification standard), and we evaluated the risk of bias using the Office of Health Assessment and Translation (OHAT) Method for each included study. We extracted city-specific mortality risk estimates for each constituent and cause of mortality. For multi-city studies, we requested the city-specific risk estimates from the authors unless reported in the article. We performed random effects meta-analyses using city-specific estimates, and examined whether the effects vary across regions and city characteristics (PM 2.5 concentration levels, air temperature, elevation, vegetation, size of elderly population, population density, and baseline mortality)., Results: We found a 0.89% (95% CI: 0.68, 1.10%) increase in all-cause, a 0.80% (95% CI: 0.41, 1.20%) increase in cardiovascular, and a 1.10% (95% CI: 0.59, 1.62%) increase in respiratory mortality per 10μg/m 3 increase in PM 2.5 . Accounting for the downward bias induced by studies of single days, the all-cause mortality estimate increased to 1.01% (95% CI: 0.81, 1.20%). We found significant associations between mortality and several PM 2.5 constituents. The most consistent and stronger associations were observed for elemental carbon (EC) and potassium (K). For most of the constituents, we observed high variability of effect estimates across cities., Conclusions: Our meta-analysis suggests that (a) combustion elements such as EC and K have a stronger association with mortality, (b) single lag studies underestimate effects, and (c) estimates of PM 2.5 and constituents differ across regions. Accounting for PM mass in constituent's health models may lead to more stable and comparable effect estimates across different studies., Systematic Review Registration: PROSPERO: CRD42017055765., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

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 PM 2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average PM 2.5 concentrations from 2006 to 2012 of 17 air quality monitoring stations established by Environmental Protection Administration of Taiwan were used for model development. PM 2.5 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 PM 2.5 while temples (r: 0.52 to 0.66) and Chinese restaurants (r: 0.31 to 0.44) were positively correlated to PM 2.5 concentrations. With the adjusted model R 2 of 0.89, a cross-validated adj-R 2 of 0.90, and external validated R 2 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 R 2 , NDVI explained 66% of PM 2.5 variation and was the dominant variable in the developed model. We suggest future studies consider these three factors when establishing LUR models for estimating PM 2.5 in other Asian cities., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

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., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Owing to data collection challenges, the vertical variation in population in cities and particulate air pollution are typically not accounted for in exposure assessments, which may lead to misclassification of exposures based on elevation of residency. To better assess this misclassification, the vertical distribution of the potentially highly exposed population (PHEP), defined as all residents within the 100-m buffer zone of above-ground highways or the 200-m buffer zone of a highway-tunnel exit, was estimated for four floor categories in Boston's Chinatown (MA, USA) using the three-dimensional digital geography methodology. Vertical profiles of particle number concentration (7-3000 nm; PNC) and particulate matter (PM2.5) mass concentration were measured by hoisting instruments up the vertical face of an 11-story (35-m) building near the study area throughout the day on multiple days. The concentrations from all the profiles (n=23) were averaged together for each floor category. As measurement elevation increased from 0 to 35 m PNC decreased by 7.7%, compared with 3.6% for PM2.5. PHEP was multiplied by the average PNC for each floor category to assess exposures for near-highway populations. The results show that adding temporally-averaged vertical air pollution data had a small effect on residential ambient exposures for our study population; however, greater effects were observed when individual days were considered (e.g., winds were off the highways).

Background and objective: The prevalence of smoking among women in Taiwan is <5%, but the incidence of lung cancer remains high. This study determined the association between PM2.5 (fine particulate matter with an aerodynamic diameter of ≤2.5 μm) exposure and lung cancer among women in Taiwan. Methods: In total, 21,301 female lung cancer cases nationwide were newly diagnosed between 2012 and 2017. Each case was age‐, sex‐ and calendar year‐matched with four controls randomly selected from the general population. Allowing a latent period of 5 years, we estimated the PM2.5 and nitrogen dioxide (NO2) exposures for each individual according to the residential changes from 2000. We adopted self‐reported smoking statuses for the cases, while those of controls were estimated using annual surveys in each residential county. We performed multiple logistic regression analyses to examine the associations between PM2.5 and NO2 exposures and incident lung cancer cases. Results: The ORs of lung adenocarcinoma for the third (30.5–35.1 μg/m3), fourth (35.1–39.3 μg/m3) and fifth PM2.5 exposure quintiles (39.3–48.1 μg/m3) relative to the first quintile were 1.10 (95% CI: 1.04–1.16), 1.12 (95% CI: 1.06–1.19) and 1.10 (95% CI: 1.04–1.16), respectively, after adjusting for smoking, residence and comorbidities. A dose–response relationship (p = 0.004) was found. The associations persisted with a 10‐year latency and were not detected for small‐cell and squamous cell carcinoma after control for smoking. We did not observe a similar effect for NO2 exposure. Conclusion: Residential PM2.5 exposure higher than 30 μg/m3 was associated with an increased risk of lung adenocarcinoma in women of Taiwan. [ABSTRACT FROM AUTHOR]

Eczema was defined as having physician-diagnosed eczema and presence of eczema in the last 12 months through a modified International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire provided by parents/guardians of the study participants.7 We applied a distributed lag nonlinear model to examine the exposure-lag-response association of eczema with mean weekly PM SB 2.5 sb estimates using a highly spatial-temporal resolution hybrid kriging/land-use regression model. This study has identified, for the first time, a sensitive window of exposure to PM SB 2.5 sb at gestational weeks 7 to 17 on the risk of developing childhood eczema, which may provide insight into underlying mechanisms. In gestational week 12, the concentration-response relationship indicated that the AOR of childhood eczema was significantly higher than 1.0 at PM SB 2.5 sb concentrations greater than 21.2 g/m SP 3 sp (Figure S3). Atopic eczema (or atopic dermatitis) is a chronic relapsing inflammatory skin disease affecting 15-30% of children worldwide.1 Although previous studies have attempted to link higher prenatal exposure to particulate matter with childhood eczema,2-6 most studies examined particulate matter with an aerodynamic diameter of 10 m or less (PM SB 10 sb ), but not particulate matter with an aerodynamic diameter of 2.5 m or less (PM SB 2.5 sb ), and yielded inconsistent results. [Extracted from the article]

PM 2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM 2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM 2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM 2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.–9 a.m.) and dusk (4 p.m.–6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM 2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM 2.5 values, SO 2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM 2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations. [Display omitted] • Rush-hour exposure to PM 2.5 is high, but the spatiality is less understood. • PM 2.5 variations during morning and dusk rush hours are captured by Geo-AI models. • The Geo-AI model performance for morning/dusk is 0.95 and 0.93, respectively. • PM 2.5 is higher in southern Taiwan and urban areas in the morning. • Low-exposure commuting routes could be identified by the rush-hour Geo-AI models. [ABSTRACT FROM AUTHOR]

• AutoML, time lag and parcel tracking functions were used to develop model. • The SHAP function was used to analyze feature importance. • Developed model has the potential to account for up to 96% of the total variance. • Waterbody, traffic and agriculture were improtant factors for NH 3 concentrations. Ambient ammonia (NH 3) plays an important compound in forming particulate matters (PMs), and therefore, it is crucial to comprehend NH 3 ′s properties in order to better reduce PMs. However, it is not easy to achieve this goal due to the limited range/real-time NH 3 data monitored by the air quality stations. While there were other studies to predict NH 3 and its source apportionment, this manuscript provides a novel method (i.e., GEO-AI)) to look into NH 3 predictions and their contribution sources. This study represents a pioneering effort in the application of a novel geospatial-artificial intelligence (Geo-AI) base model with parcel tracking functions. This innovative approach seamlessly integrates various machine learning algorithms and geographic predictor variables to estimate NH 3 concentrations, marking the first instance of such a comprehensive methodology. The Shapley additive explanation (SHAP) was used to further analyze source contribution of NH 3 with domain knowledge. From 2016 to 2018, Taichung's hourly average NH 3 values were predicted with total variance up to 96%. SHAP values revealed that waterbody, traffic and agriculture emissions were the most significant factors to affect NH 3 concentrations in Taichung among all the characteristics. Our methodology is a vital first step for shaping future policies and regulations and is adaptable to regions with limited monitoring sites. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4) is a type II acute-phase protein; however, the role of pulmonary ITIH4 after exposure to air pollution remains unclear. In this study, we investigated the role of ITIH4 in the lungs in response to air pollution. ITIH4 expression in bronchoalveolar lavage fluid (BAL) of 47 healthy human subjects and of Sprague-Dawley rats whole-body exposed to air pollution was determined, and the underlying antiapoptotic and matrix-stabilizing pathways in alveolar epithelial A549 cells induced by diesel exhaust particles (DEPs) as well as ITIH4-knockdown were investigated. We found that an interquartile range (IQR) increase in PM 2.5 was associated with a decrease of 2.673 ng/mL in ITIH4, an increase of 1.104 pg/mL of 8-isoprostane, and an increase of 6.918 pg/mL of interleukin (IL)-6 in human BAL. In rats, increases in 8-isoprostane, IL-6, and p53 and a decrease in sirtuin-1 (Sirt1) in the lungs and decreases in ITIH4 in the BAL, lungs, and serum were observed after PM 2.5 and gaseous exposure. ITIH4 levels in lung lysates were correlated with levels in BAL samples (r = 0.377, p < 0.01), whereas ITIH4 levels in BAL were correlated with IL-6 levels (r = −0.420, p < 0.01). ITIH4 expression was significantly reduced in alveolar epithelial A549 cells by DEP in a dose-dependent manner. A decrease in Sirt1 and increases in phosphorylated extracellular signal-regulated kinase (p-ERK) and caspase-3 were observed after DEP exposure and ITIH4-knockdown. In conclusion, air pollution decreased ITIH4 expression in the lungs, which was associated with alveolar epithelial cell senescence and apoptosis. ITIH4 could be a vital protein in regulating alveolar cell destruction and its inhibition after exposure to air pollution. [Display omitted] • PM 2.5 was associated with an ITIH4 deficiency in the lungs. • Decrease of ITIH4 by PM 2.5 presented in alveolar epithelial cells of rats. • Loss of ITIH4 activated apoptosis and senescence in alveolar epithelial cells. • ITIH4 may be a specific protein in response to PM 2.5 in alveolar epithelium. Capsule of main finding : The significance and novelty of this study are that ITIH4 regulates alveolar cell destruction by air pollution and thus could be an indicator of lung injury due to air pollution. [ABSTRACT FROM AUTHOR]

East Asia dust storms originated in China, bringing abundant suspended particulate matters (PMs), are investigated to examine the association between their arrivals and clinical visits for respiratory diseases (RCV – Respiratory disease Clinical Visit). Taiwan, located in the downwind with a well-documented health database from the National Health Insurance, is selected for the study. Children under the age of 17 years, because of their regular daily activities and outdoor exposures, who are diagnosed with respiratory disease, are our targeted population. We conducted a case-crossover design and used the conditional regression analysis to explore the relationship between the dust storms and RCV, for cases between 2000 and 2012, when severe dust storms were mostly recorded. In total, 2,253,092 patients were recruited. The analysis shows that the coming of dust storms significantly affects the condition of RCV in children. After adjusting for temperature, relative humidity, and air pollutant concentrations, dust storm in springtime is associated with an increase in RCV. In contrast, dust storm impact in wintertime is associated with a decrease in RCV. In addition, PM 2.5 concentrations associated positively with RCV among children, especially in the southern region of Taiwan in springtime and in the central and southern regions of Taiwan in wintertime. In short, the results showed that the occurrence of dust storms affects the RCV in children. PM 2.5 exposure is also responsible for the increase in children's RCV. • We assessed the association between Asian dust storms and RCV in more 2 million children. • Dust storm is associated with the increase of RCV in springtime and with the decrease of RCV in wintertime. • PM 2.5 concentration was associated with the increased risk of RCV in children. [ABSTRACT FROM AUTHOR]

Loss of skeletal muscle mass is common with aging and can cause morbidity and mortality in the elderly. The effects of particulate air pollution on skeletal muscle mass is not known. The study aims to assess the chronic effects of ambient fine particulates (PM2.5) on the body composition of the elderly. From October 2015 to November 2016, a cross-sectional survey on 530 elderly (age > = 65 years) was conducted in the Taipei Basin, Taiwan. The body composition was measured by bioelectrical impedance analysis (InBody 120). One year exposure to air pollution was estimated using the Kriging method at the participant's residence. Multiple linear regression analysis, after adjustments for demographics and co-pollutants, was used to examine the effects of PM2.5 on body composition indices and force of handgrip. Changes in body composition for an interquartile (1.4 μm/m3) increase in PM2.5 concentration included a 0.4 kg (95% confidence interval (CI): −0.31, −0.58; p < 0.0001) decrease in skeletal muscle mass (2.0%) and a 0.7 kg (95% CI: 0.47, 0.91; p < 0.0001) increase in body fat mass (3.6%). While PM2.5 reduced fat free mass in the upper extremities and trunk, but not in the lower extremities, it increased body fat mass in the three parts. There was no significant effect of PM2.5 on handgrip force. Higher physical activity (versus lower than median) was associated with less detrimental effect of PM2.5 on skeletal muscle mass and body fat mass (p values for interaction term: 0.009 and 0.013, respectively). Long-term PM2.5 exposure is associated with decreased skeletal muscle mass and increased body fat mass in the elderly, which can be ameliorated by physical activity. [ABSTRACT FROM AUTHOR]

Epidemiology estimates how exposure to pollutants may impact human health. It often needs detailed determination of ambient concentrations to avoid exposure misclassification. However, it is unrealistic to collect pollutant data from each and every subject. Land-use regression (LUR) models have thus been used frequently to estimate individual levels of exposures to ambient air pollution. This paper used remote sensing and geographical information system (GIS) tools to develop ten regression models for PM2.5-bound compound concentration based on measurements of a six-year period including NH 4 + ,   SO 4 2 − ,   NO 3 − , OC, EC, Ba, Mn, Cu, Zn, and Sb. The explained variance (R2) of these LUR models ranging from 0.60 to 0.92 confirms that this study successfully estimated the fine spatial variability of PM2.5-bound compound concentrations in Taiwan where the distribution of traffic, industrial area, greenness, and culture-specific PM2.5 sources like temples collected from GIS and remote sensing data were main variables. In particular, while they were much less used, this study showcased the necessity of remote sensing data of greenness in future LUR studies for reducing the exposure bias. In terms of local residents' health outcome or health effect indicators, this study further offers much-needed support for future air epidemiological studies. The results provide important insights into expanding the application of GIS and remote sensing on exposure assessment for PM2.5-bound compounds. [ABSTRACT FROM AUTHOR]

Estimating short-term exposure to PM 2.5 has been achieved for population health studies using the land use regression with machine learning (LUR_ML) and microenvironmental exposure (ME) models. However, there is a lack of clarity regarding the performance of these models in predicting PM 2.5 exposure for individuals residing in diverse environments, and the factors influencing the variations in accuracy between these models. This study performed the LUR_ML and ME models to estimate daily exposure concentrations of PM 2.5 for elders residing in urban, suburban, rural, and industrial regions in Taiwan. The accuracy of the model predictions was assessed by comparing them with personal PM 2.5 monitoring for both overall and regional assessments. The LUR_ML model demonstrated reasonably moderate agreement (R2 = 0.516) overall with personal exposure to PM 2.5 , while the ME models exhibited relatively higher predictions (R2 = 0.535–0.575) and lower biases. The agreement of PM 2.5 predictions varies across regions, particularly in areas with higher exposure contrast. The ME model 1, utilizing region-specific microenvironmental measurements rather than generic data, highlights the potential for accurate prediction of personal PM 2.5 exposure. This study contributed to the understanding of variations in prediction accuracy across different regions and support the need for improved exposure models of air pollution. [Display omitted] • The land use regression with machine learning moderately agreed (R2 = 0.516) with PM 2.5 exposure. • Microenvironmental exposure models had a higher prediction (R2 = 0.535–0.575) and lower bias. • Exposure predictions of daily PM 2.5 concentrations vary by regions. • Indoor exposure is a stronger predictor of personal PM 2.5 exposure than outdoor exposure. • Higher PM 2.5 exposure agreement is likely due to greater exposure contrast in the population. [ABSTRACT FROM AUTHOR]