Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model.

Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.

Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan.

PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 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 PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 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, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.

Quantifying source contributions to ambient NH3 using Geo-AI with time lag and parcel tracking functions.

Ambient ammonia (NH3) plays an important compound in forming particulate matters (PMs), and therefore, it is crucial to comprehend NH3's properties in order to better reduce PMs. However, it is not easy to achieve this goal due to the limited range/real-time NH3 data monitored by the air quality stations. While there were other studies to predict NH3 and its source apportionment, this manuscript provides a novel method (i.e., GEO-AI)) to look into NH3 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 NH3 concentrations, marking the first instance of such a comprehensive methodology. The Shapley additive explanation (SHAP) was used to further analyze source contribution of NH3 with domain knowledge. From 2016 to 2018, Taichung's hourly average NH3 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 NH3 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.

Non-linear association between long-term air pollution exposure and risk of metabolic dysfunction-associated steatotic liver disease.

BACKGROUND: Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD) has become a global epidemic, and air pollution has been identified as a potential risk factor. This study aims to investigate the non-linear relationship between ambient air pollution and MASLD prevalence. METHOD: In this cross-sectional study, participants undergoing health checkups were assessed for three-year average air pollution exposure. MASLD diagnosis required hepatic steatosis with at least 1 out of 5 cardiometabolic criteria. A stepwise approach combining data visualization and regression modeling was used to determine the most appropriate link function between each of the six air pollutants and MASLD. A covariate-adjusted six-pollutant model was constructed accordingly. RESULTS: A total of 131,592 participants were included, with 40.6% met the criteria of MASLD. "Threshold link function," "interaction link function," and "restricted cubic spline (RCS) link functions" best-fitted associations between MASLD and PM2.5, PM10/CO, and O3 /SO2/NO2, respectively. In the six-pollutant model, significant positive associations were observed when pollutant concentrations were over: 34.64 µg/m3 for PM2.5, 57.93 µg/m3 for PM10, 56 µg/m3 for O3, below 643.6 µg/m3 for CO, and within 33 and 48 µg/m3 for NO2. The six-pollutant model using these best-fitted link functions demonstrated superior model fitting compared to exposure-categorized model or linear link function model assuming proportionality of odds. CONCLUSION: Non-linear associations were found between air pollutants and MASLD prevalence. PM2.5, PM10, O3, CO, and NO2 exhibited positive associations with MASLD in specific concentration ranges, highlighting the need to consider non-linear relationships in assessing the impact of air pollution on MASLD.

Air pollution as a potential risk factor for hepatocellular carcinoma in Taiwanese patients after adjusting for chronic viral hepatitis.

BACKGROUND: Air pollution is a risk factor for hepatocellular carcinoma (HCC). However, the effect of air pollution on HCC risk in patients with hepatitis remains unclear. METHODS: This cross-sectional study recruited 348 patients with chronic hepatitis who were tested for serum hepatitis B surface antigen (HBsAg) and for antibodies against hepatitis B core antigen (HBcIgG) and hepatitis C virus (anti-HCV) in 2022. The diagnosis of HCC was based on the International Classification of Diseases, 10th revision (ICD-10). Daily estimates of air pollutants were aggregated into mean estimates for the previous year based on the date of recruitment or HCC diagnosis. RESULTS: Out of 348 patients, 12 had HCC (3.4%). Patients with HCC were older (71.7 vs 50.9 years; p = 0.004), had higher proportion of HBsAg seropositivity (41.7% vs 5.1%; p < 0.001), and substantially higher levels of particulate matter 2.5 (PM 2.5 ) (21.5 vs 18.2 µg/m 3 ; p = 0.05). Logistic regression analysis revealed that the factors associated with HCC were age (odds ratio [OR]: 1.10; CI, 1.03-1.17; p = 0.01), PM 2.5 level (OR: 1.51; CI, 1.02-2.23; p = 0.04), and HBsAg seropositivity (OR: 6.60; CI, 1.51-28.85; p = 0.01) ( Table 3 ). There was a combined effect of PM 2.5 and HBsAg seropositivity on the risk of HCC development (OR: 22.17; CI, 3.33-147.45; p = 0.001). CONCLUSION: In this study, we demonstrated that PM 2.5 and HBsAg seropositivity were associated with HCC occurrence and had synergistic effects after adjusting for confounding factors.

A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan.

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health.

Exposure to PM2.5 Metal Constituents and Liver Cancer Risk in REVEAL-HBV.

BACKGROUND: Ambient particulate matter is classified as a human Class 1 carcinogen, and recent studies found a positive relationship between fine particulate matter (PM2.5) and liver cancer. Nevertheless, little is known about which specific metal constituent contributes to the development of liver cancer. OBJECTIVE: To evaluate the association of long-term exposure to metal constituents in PM2.5 with the risk of liver cancer using a Taiwanese cohort study. METHODS: A total of 13,511 Taiwanese participants were recruited from the REVEAL-HBV in 1991-1992. Participants' long-term exposure to eight metal constituents (Ba, Cu, Mn, Sb, Zn, Pb, Ni, and Cd) in PM2.5 was based on ambient measurement in 2002-2006 followed by a land-use regression model for spatial interpolation. We ascertained newly developed liver cancer (ie, hepatocellular carcinoma [HCC]) through data linkage with the Taiwan Cancer Registry and national health death certification in 1991-2014. A Cox proportional hazards model was utilized to assess the association between exposure to PM2.5 metal component and HCC. RESULTS: We identified 322 newly developed HCC with a median follow-up of 23.1 years. Long-term exposure to PM2.5 Cu was positively associated with a risk of liver cancer. The adjusted hazard ratio (HR) was 1.13 (95% confidence interval [CI], 1.02-1.25; P = 0.023) with one unit increment on Cu normalized by PM2.5 mass concentration in the logarithmic scale. The PM2.5 Cu-HCC association remained statistically significant with adjustment for co-exposures to other metal constituents in PM2.5. CONCLUSION: Our findings suggest PM2.5 containing Cu may attribute to the association of PM2.5 exposure with liver cancer.

Estimating morning and evening commute period O3 concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology.

Elevated levels of ground-level ozone (O3) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R2 of 0.91. Internal and external validation procedures and verification of O3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies.

Air pollution associate with advanced hepatic fibrosis among patients with chronic liver disease.

We aimed to investigate the association between air pollution and advanced fibrosis among patients with metabolic associated fatty liver disease (MAFLD) and chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections. A total of 1376 participants who were seropositive for HBV surface antigen (HBsAg) or antibodies to HCV (anti-HCV) or had abnormal liver function in a community screening program from 2019 to 2021 were enrolled for the assessment of liver fibrosis using transient elastography. Daily estimates of air pollutants (particulate matter ≤2.5 µm in diameter [PM2.5 ], nitrogen dioxide [NO2 ], ozone [O3 ] and benzene) were aggregated into mean estimates for the previous year based on the date of enrolment. Of the 1376 participants, 767 (52.8%) and 187 (13.6) had MAFLD and advanced fibrosis, respectively. A logistic regression analysis revealed that the factors associated with advanced liver fibrosis were HCV viremia (odds ratio [OR], 3.13; 95% confidence interval [CI], 2.05-4.77; p < 0.001), smoking (OR, 1.79; 95% CI, 1.16-2.74; p = 0.01), age (OR, 1.04; 95% CI, 1.02-1.05; p < 0.001) and PM2.5 (OR, 1.10; 95% CI, 1.05-1.16; p < 0.001). Linear regression analysis revealed that LSM was independently correlated with PM2.5 (ß: 0.134; 95% CI: 0.025, 0.243; p = 0.02). There was a dose-dependent relationship between different fibrotic stages and the PM2.5 level (the PM2.5 level in patients with fibrotic stages 0, 1-2 and 3-4: 27.9, 28.4, and 29.3 µg/m3 , respectively; trend p < 0.001). Exposure to PM2.5 , as well as HBV and HCV infections, is associated with advanced liver fibrosis in patients with MAFLD. There was a dose-dependent correlation between PM2.5 levels and the severity of hepatic fibrosis.

Evaluating long-term and high spatiotemporal resolution of wet-bulb globe temperature through land-use based machine learning model.

BACKGROUND: The increase in global temperature and urban warming has led to the exacerbation of heatwaves, which negatively affect human health and cause long-term loss of work productivity. Therefore, a global assessment in temperature variation is essential. OBJECTIVE: This paper is the first of its kind to propose land-use based spatial machine learning (LBSM) models for predicting highly spatial-temporal variations of wet-bulb globe temperature (WBGT), which is a heat stress indicator used to assess thermal comfort in indoor and outdoor environments, specifically for the main island of Taiwan. METHODS: To develop spatiotemporal prediction models for both the working period and noon period, we calculated the WBGT of each weather station from 2001 to 2019 using temperature, humidity, and solar radiation data. These WBGT estimations were then used as the dependent variable for developing the spatiotemporal prediction models. To enhance model performance, we used innovative approaches that combined SHapley Additive exPlanations (SHAP) values for the selection of non-linear variables, along with machine learning algorithms for model development. RESULTS: When incorporating temperature along with other land-use/land cover predictor variables, the performance of LBSM models was excellent, with an R2 value of up to 0.99. The LBSM models explained 98% and 99% of the spatial-temporal variations in WBGT for the working and noon periods, respectively, within the complete models. In the temperature-excluded models, the explained variances were 94% and 96% for the working and noon periods, respectively. IMPACT: WBGT is a common method used by many organizations to access the impact of heat stress on human beings. However, limited studies have mentioned the association between WBGT and health impacts due to the absence of spatiotemporal databases. This study develops a new approach using land-use-based spatial machine learning (LBSM) models to better predict the fine spatial-temporal WBGT levels, with a 50-m × 50-m grid resolution for both working time and noontime. Our proposed methodology could be used in future studies aimed at evaluating the potential long-term loss of work productivity due to the effects of global warming or urban heat island.

Air pollution impede ALT normalization in chronic hepatitis B patients treated with nucleotide/nucleoside analogues.

Biochemical response is an important prognostic indicator in chronic hepatitis B (CHB) patients receiving nucleotide/nucleoside analogues (NAs). However, the effects of air pollution in alanine aminotransferase (ALT) normalization remain elusive. This longitudinal study recruited 80 hepatitis B e antigen-negative CHB patients who received NAs. ALT levels were measured during the first year of anti-hepatitis B virus therapy. Normal ALT levels were defined as <19 U/L for females and <30 U/L for males, and the risk factors associated with ALT abnormalities were analyzed. The daily estimations of air pollutants (particulate matter ≤2.5 µm in diameter (PM2.5), nitrogen dioxide, ozone (O3), and benzene) were aggregated into the mean estimation for the previous month based on the date of recruitment (baseline) and 1 year later. Sixteen patients (20.0%) had a baseline ALT > 40 U/L; overall, 41 (51.6%) had an abnormal ALT (≥19 U/L for females and ≥ 30 U/L for males). After 1 year of NA therapy, 75 patients (93.8%) had undetectable hepatitis B virus DNA levels. Mean post-treatment ALT levels were significantly lower than mean pretreatment levels (21.3 vs 30.0 U/L, respectively; P < .001). The proportion of patients with a normal ALT was also significantly higher after versus before treatment (71.2% vs 51.2%, respectively; P = .001). The strongest factors associated with ALT abnormality after 1 year of NA treatment were body mass index (odds ratio [OR], 1.28; 95% confidence interval [CI], 1.05-1.54; P = .01) and ozone level (OR, 1.11; 95% CI, 1.02-1.22; P = .02). Among hepatitis B e antigen-negative CHB patients with relatively low viral loads, 1 year of NA treatment improved ALT levels after the adjustment for confounding factors and increased the proportion of patients with normal ALT levels. Air pollution affects the efficacy of ALT normalization.

Determinants of residential greenness and its association with prostate cancer risk: A case-control study in Singapore.

BACKGROUND: Exposure to greenness has been shown to be beneficial to health, but few studies have examined the association between residential greenness and prostate cancer (PCa) risk. Our main objectives were to identify the determinants of residential greenness, and to investigate if residential greenness was associated with PCa risk in Singapore. METHODS: The hospital-based case-control study was conducted between April 2007 and May 2009. The Singapore Prostate Cancer Study (SPCS) comprised 240 prostate cancer cases and 268 controls, whose demographics and residential address were collected using questionnaires. Residential greenness was measured by normalized difference vegetation index (NDVI) around the participants' homes using a buffer size of 1 km. Determinants of NDVI were identified using a multivariable linear regression model. Logistic regression models were used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) of associations between NDVI and PCa risk, adjusting for potential confounders. RESULTS: Having a BMI within the second quartile, as compared to the lowest quartile, was associated with higher levels of NDVI (ß-coefficient = 0.263; 95% CI = 0.040-0.485) after adjusting for covariates. Additionally, being widowed or separated, as compared to being married, was associated with lower levels of NDVI (ß-coefficient = -0.393; 95% CI = -0.723, -0.063). An interquartile range (IQR) increase in NDVI was positively associated with prostate cancer risk OR = 1.45; 95% CI = 1.02-2.07). Stratified analysis by tumour grade and stage showed that higher NDVI was associated with higher risk of low grade PCa. CONCLUSION: Our findings suggested that residential greenness was associated with higher risk of PCa in Singapore. Future studies on the quality and type of green spaces, as well as other factors of residential greenness, in association with PCa risk should be conducted to better understand this relationship.

Greenspace related to bipolar disorder in Taiwan: Quantitative benefits of saving DALY loss and increasing income.

Scientific evidence reported that surrounding greenspace could promote better mental health. Considering bipolar disorder as the health outcome, this study aimed to investigate the association between greenspace and bipolar disorder in Taiwan and quantified the benefits of greenspace on bipolar disorder adjusted for the international greenspace availability standard. By examining datasets across 348 townships, two quantitative measures (i.e., disability-adjusted life year loss and income) were used to represent the benefits. The incidence rate of bipolar disorder was obtained from Taiwan's National Health Insurance Research Database. Normalized different vegetation index (NDVI) was measured as a proxy for the greenspace availability. A generalized additive mixed model coupled with a sensitivity test were applied to evaluate the statistical association. The prevented fraction for the population (PFP) was then applied to develop a scenario for quantifying benefit. The result showed a significant negative association between greenspace and bipolar disorder in Taiwan. Compared to low greenspace, areas with medium and high greenspace may reduce the bipolar risk by 21% (RR = 0.79; 95% CI = 0.76-0.83) and 51% (RR = 0.49; 95% CI = 0.45-0.53). Calculating benefits, we found that the development of a scenario by increasing greenspace adjusted for availability indicator in township categorized as low greenspace could save in DALY loss due to bipolar disorder up to10.97% and increase in income up to 11.04% from the current situation. Lastly, this was the first study in Asia-Pacific to apply a customized greenspace increment scenario to quantify the benefits to a particular health burden such as bipolar disorder.

Effects of indoor air quality and home environmental characteristics on allergic diseases among preschool children in the Greater Taipei Area.

Indoor air quality and home environmental characteristics are potential factors associated with the onset and exacerbation of allergic diseases. Our study examined the effects of these factors on allergic diseases (i.e., asthma, allergic rhinitis, allergic conjunctivitis, and atopic dermatitis) among preschool children. We recruited a total of 120 preschool children from an ongoing birth cohort study in the Greater Taipei Area. A comprehensive environmental evaluation was conducted at each participant's residence and included measurements of indoor and outdoor air pollutants, fungal spores, endotoxins, and house dust mite allergens. A structured questionnaire was used to collect information on the allergic diseases and home environments of participants. Land-use characteristics and points of interest in the surrounding area of each home were analyzed. Other covariates were obtained from the cohort data. Multiple logistic regressions were used to examine the relationships between allergic diseases and covariates. We observed that all mean indoor air pollutant levels were below Taiwan's indoor air quality standards. After adjustment for covariates, the total number of fungal spores and the ozone, Der f 1, and endotoxin levels were significantly associated with increased risks of allergic diseases. Biological contaminants more significantly affected allergic diseases than other pollutants. Moreover, home environmental characteristics (e.g., living near power facilities and gas stations) were associated with an increased risk of allergic diseases. Regular and proper home sanitation is recommended to prevent the accumulation of indoor pollutants, especially biological contaminants. Living away from potential sources of pollution is also crucial for protecting the health of children.

Estimating the daily average concentration variations of PCDD/Fs in Taiwan using a novel Geo-AI based ensemble mixed spatial model.

It is generally established that PCDD/Fs is harmful to human health and therefore extensive field research is necessary. This study is the first to use a novel geospatial-artificial intelligence (Geo-AI) based ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and geographic predictor variables selected using SHapley Additive exPlanations (SHAP) values to predict spatial-temporal fluctuations in PCDD/Fs concentrations across the entire island of Taiwan. Daily PCDD/F I-TEQ levels from 2006 to 2016 were used for model construction, while external data was used for validating model dependability. We utilized Geo-AI, incorporating kriging, five machine learning, and ensemble methods (combinations of the aforementioned five models) to develop EMSMs. The EMSMs were used to estimate long-term spatiotemporal variations in PCDD/F I-TEQ levels, considering in-situ measurements, meteorological factors, geospatial predictors, social and seasonal influences over a 10-year period. The findings demonstrated that the EMSM was superior to all other models, with an increase in explanatory power reaching 87 %. The results of spatial-temporal resolution show that the temporal fluctuation of PCDD/F concentrations can be a result of weather circumstances, while geographical variance can be the result of urbanization and industrialization. These results provide accurate estimates that support pollution control measures and epidemiological studies.

Higher Particulate Matter Deposition in Alveolar Region Could Accelerate Body Fat Accumulation in Obstructive Sleep Apnea.

We conducted a cross-sectional study to investigate associations of particulate matter (PM) of less than 2.5 µm in aerodynamic diameter (PM2.5) and PM deposition with nocturnal changes in body composition in obstructive sleep apnea (OSA) patients. A bioelectric impedance analysis was used to measure the pre- and postsleep body composition of 185 OSA patients. Annual exposure to PM2.5 was estimated by the hybrid kriging/land-use regression model. A multiple-path particle dosimetry model was employed to estimate PM deposition in lung regions. We observed that an increase in the interquartile range (IQR) (1 µg/m3) of PM2.5 was associated with a 20.1% increase in right arm fat percentage and a 0.012 kg increase in right arm fat mass in OSA (p < 0.05). We observed that a 1 µg/m3 increase in PM deposition in lung regions (i.e., total lung region, head and nasal region, tracheobronchial region, and alveolar region) was associated with increases in changes of fat percentage and fat mass of the right arm (ß coefficient) (p < 0.05). The ß coefficients decreased as follows: alveolar region > head and nasal region > tracheobronchial region > total lung region (p < 0.05). Our findings demonstrated that an increase in PM deposition in lung regions, especially in the alveolar region, could be associated with nocturnal changes in the fat percentage and fat mass of the right arm. PM deposition in the alveolar region could accelerate the body fat accumulation in OSA.

Long-Term Exposure to Air Pollution and the Occurrence of Metabolic Syndrome and Its Components in Taiwan.

BACKGROUND: Metabolic syndrome (MetS), a major contributor to cardiovascular and metabolic diseases, has been linked with exposure to air pollution. However, the relationship between air pollutants and the five components of MetS [abdominal obesity, elevated triglyceride, decreased high-density lipoprotein cholesterol (HDL-C), elevated blood pressure, and elevated fasting blood glucose levels], has not been clearly described. OBJECTIVE: We examined the association between long-term exposure to air pollutants and the occurrence of MetS and its components by using a longitudinal cohort in Taiwan. METHODS: The MJ Health Research Foundation is a medical institute that conducts regular physical examinations. The development of MetS, based on a health examination and the medical history of an MJ cohort of 93,771 participants who were enrolled between 2006 and 2016 and had two or more examinations, was compared with estimated exposure to air pollutants in the year prior to health examination. The exposure levels to fine particulate matter [PM with an aerodynamic diameter of ≤2.5µm (PM2.5)] and nitrogen dioxide (NO2) in the participants' residential areas were estimated using a hybrid Kriging/land-use regression (LUR) model executed using the XGBoost algorithm and a hybrid Kriging/LUR model, respectively. Cox regression with time-dependent covariates was conducted to estimate the effects of annual air pollutant exposure on the risk of MetS and its components. RESULTS: During the average follow-up period of 3.4 y, the incidence of MetS was 38.1/1,000 person-years. After mutual adjustment and adjustments for potential covariates, the results indicated that every 10-µg/m3 increase in annual PM2.5 concentration was associated with an increased risk of abdominal obesity [adjusted hazard ratio (aHR)=1.07; 95% confidence interval (CI): 1.01, 1.14], hypertriglyceridemia (aHR=1.17; 95% CI: 1.11, 1.23), low HDL-C (aHR=1.09; 95% CI: 1.02, 1.17), hypertension (aHR=1.15; 95% CI: 1.09, 1.21), and elevated fasting blood glucose (aHR=1.15; 95% CI: 1.10, 1.20). Furthermore, PM2.5 and NO2 may increase the risk of developing MetS among people who already "have" some components of MetS. DISCUSSION: Our findings suggest that in apparently healthy adults undergoing physical examination, exposure to PM2.5 and NO2 might be associated with the occurrence of MetS and its components. https://doi.org/10.1289/EHP10611.

A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan.

High levels of ground level ozone (O3) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.

An ensemble mixed spatial model in estimating long-term and diurnal variations of PM2.5 in Taiwan.

Meteorology, human activities, and other emission sources drive diurnal cyclic patterns of air pollution. Previous studies mainly focused on the variation of PM2.5 concentrations during daytime rather than nighttime. In addition, assessing the spatial variations of PM2.5 in large areas is a critical issue for environmental epidemiological studies to clarify the health effects from PM2.5 exposures. In terms of air pollution spatial modelling, using only a single model might lose information in capturing spatial and temporal correlation between predictors and pollutant levels. Hence, this study aimed to propose an ensemble mixed spatial model that incorporated Kriging interpolation, land-use regression (LUR), machine learning, and stacking ensemble approach to estimate long-term PM2.5 variations for nearly three decades in daytime and nighttime. Three steps of model development were applied: 1) linear based LUR and Hybrid Kriging-LUR were used to determine influential predictors; 2) machine learning algorithms were used to enhance model prediction accuracy; 3) predictions from the selected machine learning models were fitted and evaluated again to build the final ensemble mixed spatial model. The results showed that prediction performance increased from 0.514 to 0.895 for daily, 0.478 to 0.879 for daytime, and 0.523 to 0.878 for nighttime when applying the proposed ensemble mixed spatial model compared with LUR. Results of overfitting test and extrapolation ability test confirmed the robustness and reliability of the developed models. The distance to the nearest thermal power plant, density of soil and pebbles fields, and funeral facilities might affect the variation of PM2.5 levels between daytime and nighttime. The PM2.5 level was higher in daytime compared with nighttime with little difference, revealing the importance of estimating nighttime PM2.5 variations. Our findings also clarified the emission sources in daytime and nighttime, which serve as valuable information for air pollution control strategies establishment.

Determination of potential sources of heavy metals in patients with urothelial carcinoma in central Taiwan: a biomonitoring case-control study.

The clarification of possible exposure sources of multiple metals to identify associations between metal doses and urothelial carcinoma (UC) risk is currently limited in the literature. We sought to identify the exposure sources of 10 metals (Vanadium, chromium, manganese, cobalt, nickel, copper, zinc, arsenic, cadmium, and lead) using principal component analysis (PCA) and then linked various principal component (PC) scores with environmental characteristics, including smoking-related indices, PM2.5, and distance to the nearest bus station. In addition, urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) and DNA hypomethylation markers (5-methyl-2'-deoxycytidine levels; %5-MedC) were investigated in combination with UC risks. We conducted this hospital-based case control study in 359 UC patients with histologically confirmed disease and 718 controls. All data were collected from face-to-face interviews and medical records. Approximately 6 mL blood was collected from participants for analysis of multiple heavy metal and DNA methylation in leukocyte DNA. Further, a 20 mL urine sample was collected to measure urinary cotinine and 8-OHdG levels. In addition, average values for PM2.5 for individual resident were calculated using the hybrid kriging/land-use regression model. In UC patients, significantly higher cobalt, nickel, copper, arsenic, and cadmium (µg/L) levels were observed in blood when compared with controls. Three PCs with eigenvalues > 1 accounted for 24.3, 15.8, and 10.7% of UC patients, and 26.9, 16.7, and 11.1% of controls, respectively. Environmental metal sources in major clusters were potentially associated with industrial activities and traffic emissions (PC1), smoking (PC2), and food consumption, including vitamin supplements (PC3). Multiple metal doses were linked with incremental urinary 8-OHdG and DNA hypomethylation biomarkers. For individuals with high PC1 and PC2 scores, both displayed an approximate 1.2-fold risk for UC with DNA hypomethylation.In conclusion, we provide a foundation for health education and risk communication strategies to limit metal exposure in environment, so that UC risks can be improved potentially.