Effect of indoor air quality on the association of long-term exposure to low-level air pollutants with cognition in older adults.

BACKGROUND: How indoor air quality affects the temporal associations of long-term exposure to low-level air pollutants with cognition remains unclear. METHODS: This cohort study (2011-2019) included 517 non-demented older adults at baseline with four repeated cognitive assessments. The time-varying exposure to PM2.5, PM10, NO2, SO2, CO, and O3 was estimated for each participant from 1994 to 2019. Indoor air quality was determined by ventilation status and daily indoor time. Generalized linear mixed models were used to analyze the association of air pollutants, indoor air quality, and cognition adjusting for important covariates. RESULTS: Over time, per 2.97 µg/m3 (i.e., an interquartile range) increment of PM2.5 was associated with the poor performance of memory (Z score of a cognitive test, ߈:-0.14), attention (߈:-0.13), and executive function (߈:-0.20). Similarly, per 2.05 µg/m3 increase in PM2.5-10 was associated with poor global cognition [adjusted odds ratio (aOR): 1.48, ߈:-0.28], attention (߈:-0.07), and verbal fluency (߈:-0.09); per 4.94 µg/m3 increase in PM10 was associated with poor global cognition (aOR: 1.78; ߈:-0.37). In contrast, per 2.74 ppb increase in O3 was associated with better global cognition (߈:0.36 to 0.47). These associations became more evident in participants with poor ventilation or short daily indoor time (<12.5 h/day). For global cognition, the exposure to a 10-µg/m3 increment in PM2.5, PM2.5-10, and PM10 corresponded to 1.4, 5.8, and 2.8 years of aging, respectively. CONCLUSION: This study demonstrated how indoor air quality in areas using clean fuels differentially affected the associations of long-term exposure to low-level air pollutants with cognition. Tightening air quality standards may help prevent dementia.

A temporally relaxed theory of physically or chemically non-equilibrium solute transport in heterogeneous porous media.

Groundwater constitutes a critical component in providing fresh water for various human endeavors. Never-theless, its susceptibility to contamination by pollutants represents a significant challenge. A comprehensive understanding of the dynamics of solute transport in groundwater and soils is essential for predicting the spatial and temporal distribution of these contaminants. Presently, conventional models such as the mobile-immobile (MIM) model and the rate-limited sorption (RLS) model are widely employed to describe the non-Fickian behavior of solute transport. In this research, we present a novel approach to solute transport that is founded on the temporally relaxed theory of Fick's Law. Our methodology introduces two relaxation times to account for solute particle collisions and attachment, leading to the derivation of a new advection-dispersion equation. Our findings indicate that the relaxation times possess similar properties to the transport parameters in the MIM and RLS models, and our solution can be applied to accurately predict transport parameters from soil column experiments. Additionally, we discovered that the relaxation times are proportional to the magnitude of Peclet number. This innovative approach provides a deeper insight into solute transport and its impact on groundwater contamination.

An online spatiotemporal prediction model for dengue fever epidemic in Kaohsiung (Taiwan).

The emergence and re-emergence of disease epidemics is a complex question that may be influenced by diverse factors, including the space-time dynamics of human populations, environmental conditions, and associated uncertainties. This study proposes a stochastic framework to integrate space-time dynamics in the form of a Susceptible-Infected-Recovered (SIR) model, together with uncertain disease observations, into a Bayesian maximum entropy (BME) framework. The resulting model (BME-SIR) can be used to predict space-time disease spread. Specifically, it was applied to obtain a space-time prediction of the dengue fever (DF) epidemic that took place in Kaohsiung City (Taiwan) during 2002. In implementing the model, the SIR parameters were continually updated and information on new cases of infection was incorporated. The results obtained show that the proposed model is rigorous to user-specified initial values of unknown model parameters, that is, transmission and recovery rates. In general, this model provides a good characterization of the spatial diffusion of the DF epidemic, especially in the city districts proximal to the location of the outbreak. Prediction performance may be affected by various factors, such as virus serotypes and human intervention, which can change the space-time dynamics of disease diffusion. The proposed BME-SIR disease prediction model can provide government agencies with a valuable reference for the timely identification, control, and prevention of DF spread in space and time.

Association between exposure to particulate matter and incidence of Parkinson's disease: A nationwide cohort study in Taiwan.

Objective: Emerging evidence suggests that air pollution exposure may increase the risk of Parkinson's disease (PD). We aimed to investigate the association between exposure to fine particulate matter (PM2.5) and the risk of incident PD nationwide. Methods: We utilized data from the Taiwan National Health Insurance Research Database, which was spatiotemporally linked with air quality data from the Taiwan Environmental Protection Administration website. The study population consisted of participants who were followed from the index date (January 1st, 2005) until the occurrence of PD or the end of the study period (December 31st, 2017). Participants who had a prior diagnosis of PD before the index date were excluded. To evaluate the association between exposure to PM2.5 and incident PD, we employed a Cox regression to estimate the hazard ratio with a 95% confidence interval (CI). Results: A total of 454,583 participants were included, with a mean (SD) age of 63.1 (9.9) years and a male proportion of 50%. Over a mean follow-up period of 11.1 (3.6) years, 4% of the participants (n = 18,862) developed PD. We observed a significant positive association between PM2.5 exposure and the risk of PD, with a hazard ratio of 1.22 (95% CI, 1.20-1.23) per interquartile range increase in exposure (10.17 µg/m3) when adjusting for both SO2 and NO2. Conclusion: We provide further evidence of an association between PM2.5 exposure and risk of PD. These findings underscore the urgent need for public health policies aimed at reducing ambient air pollution and its potential impact on PD.

Quantile-based Bayesian maximum entropy approach for spatiotemporal modeling of ambient air quality levels.

Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants.

Recharge signal identification based on groundwater level observations.

This study applied a method of the rotated empirical orthogonal functions to directly decompose the space-time groundwater level variations and determine the potential recharge zones by investigating the correlation between the identified groundwater signals and the observed local rainfall records. The approach is used to analyze the spatiotemporal process of piezometric heads estimated by Bayesian maximum entropy method from monthly observations of 45 wells in 1999-2007 located in the Pingtung Plain of Taiwan. From the results, the primary potential recharge area is located at the proximal fan areas where the recharge process accounts for 88% of the spatiotemporal variations of piezometric heads in the study area. The decomposition of groundwater levels associated with rainfall can provide information on the recharge process since rainfall is an important contributor to groundwater recharge in semi-arid regions. Correlation analysis shows that the identified recharge closely associates with the temporal variation of the local precipitation with a delay of 1-2 months in the study area.

Association between Exposure to Ambient Air Pollution and the Risk of Rheumatoid Arthritis in Taiwan: A Population-Based Retrospective Cohort Study.

Background: The association between ambient air pollution (AAP) and the risk of Rheumatoid arthritis (RA) remains debatable. We conducted a population-based cohort study to investigate the association between exposure to AAP and the risk of RA in Taiwan. Methods: We analyzed and combined the longitudinal Health Insurance Database (LHID) and the Taiwan Air Quality-Monitoring Database (TAQMD), which were in line with the residential areas. We calculated the RA incidence rates per 10,000 person-years exposed to each quartile of PM2.5 or PM10 concentrations or RH. Hazards regression was conducted to analyze the associations between exposure to each quartile of PM2.5 and PM10 concentrations and the risk of developing RA. The hazard ratios of RA were analyzed between participants exposed to annual average concentrations of PM2.5 and PM10. All the hazard ratios of RA were stratified by gender and adjusted for age and relative humidity (RH). A p-value < 0.05 was considered statistically significant. Results: Among 722,885 subjects, 9338 RA cases were observed. The analyses adjusted for age, gender, and humidity suggested an increased risk of developing RA in the exposure to PM2.5 in the last quartile (Q4) with the adjusted hazard ratio (aHR) was 1.053 (95%CI: 1.043 to 1.063). Conclusion: Our study suggests that exposure to PM2.5 is associated with an increased risk of RA. The finding has implications for policymaking to develop coping strategies to confront AAP as a risk factor for RA.

Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods.

Air pollution exposure has been linked to various diseases, including dementia. However, a novel method for investigating the associations between air pollution exposure and disease is lacking. The objective of this study was to investigate whether long-term exposure to ambient particulate air pollution increases dementia risk using both the traditional Cox model approach and a novel machine learning (ML) with random forest (RF) method. We used health data from a national population-based cohort in Taiwan from 2000 to 2017. We collected the following ambient air pollution data from the Taiwan Environmental Protection Administration (EPA): fine particulate matter (PM2.5) and gaseous pollutants, including sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), nitrogen oxide (NOx), nitric oxide (NO), and nitrogen dioxide (NO2). Spatiotemporal-estimated air quality data calculated based on a geostatistical approach, namely, the Bayesian maximum entropy method, were collected. Each subject's residential county and township were reviewed monthly and linked to air quality data based on the corresponding township and month of the year for each subject. The Cox model approach and the ML with RF method were used. Increasing the concentration of PM2.5 by one interquartile range (IQR) increased the risk of dementia by approximately 5% (HR = 1.05 with 95% CI = 1.04-1.05). The comparison of the performance of the extended Cox model approach with the RF method showed that the prediction accuracy was approximately 0.7 by the RF method, but the AUC was lower than that of the Cox model approach. This national cohort study over an 18-year period provides supporting evidence that long-term particulate air pollution exposure is associated with increased dementia risk in Taiwan. The ML with RF method appears to be an acceptable approach for exploring associations between air pollutant exposure and disease.

Short-, Mid-, and Long-Term Associations Between PM2.5 and Stroke Incidence in Taiwan.

OBJECTIVE: To investigate the association between the risk of stroke and exposure to particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) over various exposure periods. METHODS: This was a nationwide population-based case-control study in which 10,035 incident patients with a primary diagnosis of ischemic stroke each were matched with two randomly selected controls for sex, age, Charlson Comorbidity Index, year of stroke diagnosis, and level of urbanization. Multiple logistic models adjusted for potential confounders were used to assess the association of PM2.5 with ischemic stroke incidence. RESULTS: There were significant short-term, medium-term, and long-term relationships between PM2.5 exposure and ischemic stroke incidence. CONCLUSIONS: This study supports existing evidence that PM2.5 should be considered a risk factor for ischemic stroke.

Long-Term Exposure to Air Pollutants and Cognitive Function in Taiwanese Community-Dwelling Older Adults: A Four-Year Cohort Study.

BACKGROUND: Previous studies have assessed limited cognitive domains with relatively short exposure to air pollutants, and studies in Asia are limited. OBJECTIVE: This study aims to explore the association between long-term exposure to air pollutants and cognition in community-dwelling older adults. METHODS: This four-year prospective cohort study recruited 605 older adults at baseline (2011-2013) and 360 participants remained at four-year follow-up. Global and domain-specific cognition were assessed biennially. Data on PM2.5 (particulate matter≤2.5µm diameter, 2005-2015), PM10 (1993-2015), and nitrogen dioxide (NO2, 1993-2015) were obtained from Taiwan Environmental Protection Administration (TEPA). Bayesian Maximum Entropy was utilized to estimate the spatiotemporal distribution of levels of these pollutants. RESULTS: Exposure to high-level PM2.5 (>29.98µg/m3) was associated with an increased risk of global cognitive impairment (adjusted odds ratio = 4.56; ß= -0.60). High-level PMcoarse exposure (>26.50µg/m3) was associated with poor verbal fluency (ß= -0.19). High-level PM10 exposure (>51.20µg/m3) was associated with poor executive function (ß= -0.24). Medium-level NO2 exposure (>28.62 ppb) was associated with better verbal fluency (ß= 0.12). Co-exposure to high concentrations of PM2.5, PMcoarse or PM10 and high concentration of NO2 were associated with poor verbal fluency (PM2.5 and NO2: ß= -0.17; PMcoarse and NO2: ß= -0.23; PM10 and NO2: ß= -0.21) and poor executive function (PM10 and NO2: ß= -0.16). These associations became more evident in women, apolipoprotein ɛ4 non-carriers, and those with education > 12 years. CONCLUSION: Long-term exposure to PM2.5 (higher than TEPA guidelines), PM10 (lower than TEPA guidelines) or co-exposure to PMx and NO2 were associated with poor global, verbal fluency, and executive function over 4 years.

An efficient spatiotemporal data calibration approach for the low-cost PM2.5 sensing network: A case study in Taiwan.

The rapid growth of Internet of Things has provided a new aspect to air quality monitoring system. In Taiwan, over 5000 PM2.5 sensors have been installed in the last two years. The greatest asset of low-cost sensors is possibly mapping spatiotemporal air pollution with finer resolution. But the data quality of low-cost sensors is the most common question that how to take proper interpretation of the measurements. This study proposes an efficient calibration approach based on generalized additive model which is further applied to a particular low-cost PM2.5 sensor in Taiwan. The study carried out a field calibration that collecting both measurements of low-cost sensors and the regulatory stations, and investigated the space/time bias between the low-cost sensors and regulatory stations. Results show that the proposed approach can explain the variability of the biases from the low-cost sensors with R-square of 0.76. In addition, the present calibration model can quantify the uncertainty of the low-cost sensors observations and the average standard deviation is about 13.85% with respect to its adjusted levels. This operational spatiotemporal data calibration approach provides an useful information for local communities and governmental agency to face the new era of IoT sensor for air quality monitoring.

Lagged Influence of Fine Particulate Matter and Geographic Disparities on Clinic Visits for Children's Asthma in Taiwan.

Recent studies have revealed the influence of fine particulate matter (PM2.5) on increased medication use, hospital admission, and emergency room visits for asthma attack in children, but the lagged influence of PM2.5 on children’s asthma and geographic disparities of children’s asthma have rarely been discussed simultaneously. This study investigated the documented diagnosis of children’s asthma in clinic visits for children aged less than 15 years old that were associated with PM2.5 in two counties located in west-central Taiwan during 2005⁻2010. The result shows that PM2.5 had a significant lagged effect on children’s asthma for up to 6 days. A significantly higher relative risk for children’s asthma was more likely to happen at 2-day lag compared to the present day when PM2.5 increased from 36.17 μg/m³ to 81.26 μg/m³. Considering all lagged effects, the highest relative risk for children’s asthma was 1.08 (95% CI = 1.05, 1.11) as PM2.5 increased as high as 64.66 μg/m³. In addition, geographic disparities of children’s asthma were significant, and 47.83% of areas were identified to have children vulnerable to asthma. To sum up, our findings can serve as a valuable reference for the implementation of an early warning to governmental agencies about a susceptible population of children.

Considering spatial heterogeneity in the distributed lag non-linear model when analyzing spatiotemporal data.

The distributed lag non-linear (DLNM) model has been frequently used in time series environmental health research. However, its functionality for assessing spatial heterogeneity is still restricted, especially in analyzing spatiotemporal data. This study proposed a solution to take a spatial function into account in the DLNM, and compared the influence with and without considering spatial heterogeneity in a case study. This research applied the DLNM to investigate non-linear lag effect up to 7 days in a case study about the spatiotemporal impact of fine particulate matter (PM2.5) on preschool children's acute respiratory infection in 41 districts of northern Taiwan during 2005 to 2007. We applied two spatiotemporal methods to impute missing air pollutant data, and included the Markov random fields to analyze district boundary data in the DLNM. When analyzing the original data without a spatial function, the overall PM2.5 effect accumulated from all lag-specific effects had a slight variation at smaller PM2.5 measurements, but eventually decreased to relative risk significantly <1 when PM2.5 increased. While analyzing spatiotemporal imputed data without a spatial function, the overall PM2.5 effect did not decrease but increased in monotone as PM2.5 increased over 20 µg/m3. After adding a spatial function in the DLNM, spatiotemporal imputed data conducted similar results compared with the overall effect from the original data. Moreover, the spatial function showed a clear and uneven pattern in Taipei, revealing that preschool children living in 31 districts of Taipei were vulnerable to acute respiratory infection. Our findings suggest the necessity of including a spatial function in the DLNM to make a spatiotemporal analysis available and to conduct more reliable and explainable research. This study also revealed the analytical impact if spatial heterogeneity is ignored.

Spatiotemporal modelling and mapping of the bubonic plague epidemic in India.

BACKGROUND: This work studies the spatiotemporal evolution of bubonic plague in India during 1896-1906 using stochastic concepts and geographical information science techniques. In the past, most investigations focused on selected cities to conduct different kinds of studies, such as the ecology of rats. No detailed maps existed incorporating the space-time dependence structure and uncertainty sources of the epidemic system and providing a composite space-time picture of the disease propagation characteristics. RESULTS: Informative spatiotemporal maps were generated that represented mortality rates and geographical spread of the disease, and epidemic indicator plots were derived that offered meaningful characterizations of the spatiotemporal disease distribution. The bubonic plague in India exhibited strong seasonal and geographical features. During its entire duration, the plague continued to invade new geographical areas, while it followed a re-emergence pattern at many localities; its rate changed significantly during each year and the mortality distribution exhibited space-time heterogeneous patterns; prevalence usually occurred in the autumn and spring, whereas the plague stopped moving towards new locations during the summers. CONCLUSION: Modern stochastic modelling and geographical information science provide powerful means to study the spatiotemporal distribution of the bubonic plague epidemic under conditions of uncertainty and multi-sourced databases; to account for various forms of interdisciplinary knowledge; and to generate informative space-time maps of mortality rates and propagation patterns. To the best of our knowledge, this kind of plague maps and plots become available for the first time, thus providing novel perspectives concerning the distribution and space-time propagation of the deadly epidemic. Furthermore, systematic maps and indicator plots make possible the comparison of the spatial-temporal propagation patterns of different diseases.

Short-term population-based non-linear concentration-response associations between fine particulate matter and respiratory diseases in Taipei (Taiwan): a spatiotemporal analysis.

Fine particulate matter <2.5 µm (PM2.5) has been associated with human health issues; however, findings regarding the influence of PM2.5 on respiratory disease remain inconsistent. The short-term, population-based association between the respiratory clinic visits of children and PM2.5 exposure levels were investigated by considering both the spatiotemporal distributions of ambient pollution and clinic visit data. We applied a spatiotemporal structured additive regression model to examine the concentration-response (C-R) association between children's respiratory clinic visits and PM2.5 concentrations. This analysis was separately performed on three respiratory disease categories that were selected from the Taiwanese National Health Insurance database, which includes 41 districts in the Taipei area of Taiwan from 2005 to 2007. The findings reveal a non-linear C-R pattern of PM2.5, particularly in acute respiratory infections. However, a PM2.5 increase at relatively lower levels can elevate the same-day respiratory health risks of both preschool children (<6 years old) and schoolchildren (6-14 years old). In preschool children, same-day health risks rise when concentrations increase from 0.76 to 7.44 µg/m(3), and in schoolchildren, same-day health risks rise when concentrations increase from 0.76 to 7.52 µg/m(3). Changes in PM2.5 levels generally exhibited no significant association with same-day respiratory risks, except in instances where PM2.5 levels are extremely high, and these occurrences do exhibit a significant positive influence on respiratory health that is especially notable in schoolchildren. A significant high relative rate of respiratory clinic visits are concentrated in highly populated areas. We highlight the non-linearity of the respiratory health effects of PM2.5 on children to investigate this population-based association. The C-R relationship in this study can provide a highly valuable alternative for assessing the effects of ambient air pollution on human health.

Factors Controlling Changes in Epilithic Algal Biomass in the Mountain Streams of Subtropical Taiwan.

In upstream reaches, epilithic algae are one of the major primary producers and their biomass may alter the energy flow of food webs in stream ecosystems. However, the overgrowth of epilithic algae may deteriorate water quality. In this study, the effects of environmental variables on epilithic algal biomass were examined at 5 monitoring sites in mountain streams of the Wuling basin of subtropical Taiwan over a 5-year period (2006-2011) by using a generalized additive model (GAM). Epilithic algal biomass and some variables observed at pristine sites obviously differed from those at the channelized stream with intensive agricultural activity. The results of the optimal GAM showed that water temperature, turbidity, current velocity, dissolved oxygen (DO), pH, and ammonium-N (NH4-N) were the main factors explaining seasonal variations of epilithic algal biomass in the streams. The change points of smoothing curves for velocity, DO, NH4-N, pH, turbidity, and water temperature were approximately 0.40 m s-1, 8.0 mg L-1, 0.01 mg L-1, 8.5, 0.60 NTU, and 15°C, respectively. When aforementioned variables were greater than relevant change points, epilithic algal biomass was increased with pH and water temperature, and decreased with water velocity, DO, turbidity, and NH4-N. These change points may serve as a framework for managing the growth of epilithic algae. Understanding the relationship between environmental variables and epilithic algal biomass can provide a useful approach for maintaining the functioning in stream ecosystems.

Traffic-related air pollution increased the risk of Parkinson's disease in Taiwan: A nationwide study.

BACKGROUND: Ambient air pollution has been associated with many health conditions, but little is known about its effects on neurodegenerative diseases, such as Parkinson's disease (PD). In this study, we investigated the influence of ambient air pollution on PD in a nationwide population-based case-control study in Taiwan. METHODS: We identified 11,117 incident PD patients between 2007 and 2009 from the Taiwanese National Health Insurance Research Database and selected 44,468 age- and gender-matched population controls from the longitudinal health insurance database. The average ambient pollutant exposure concentrations from 1998 through the onset of PD were estimated using quantile-based Bayesian Maximum Entropy models. Basing from logistic regression models, we estimated the odds ratios (ORs) and 95% confidence intervals (CIs) of ambient pollutant exposures and PD risk. RESULTS: We observed positive associations between NOx, CO exposures, and PD. In multi-pollutant models, for NOx and CO above the 75th percentile exposure compared with the lowest percentile, the ORs of PD were 1.37 (95% CI=1.23-1.52) and 1.17 (95% CI=1.07-1.27), respectively. CONCLUSIONS: This study suggests that ambient air pollution exposure, especially from traffic-related pollutants such as NOx and CO, increases PD risk in the Taiwanese population.

Spatial vulnerability of fine particulate matter relative to the prevalence of diabetes in the United States.

Recent research supports a link between diabetes and fine particulate matter (≤ 2.5µg in diameter; PM2.5) in both laboratory and epidemiology studies. However, research investigating the potential relationship of the spatial vulnerability of diabetes to concomitant PM2.5 levels is still sparse, and the level of diabetes geographic disparities attributed to PM2.5 levels has yet to be evaluated. We conducted a Bayesian structured additive regression modeling approach to determine whether long-term exposure to PM2.5 is spatially associated with diabetes prevalence after adjusting for the socioeconomic status of county residents. This study utilizes the following data sources from 2004 to 2010: the Behavioral Risk Factor Surveillance System, the American Community Survey, and the Environmental Protection Agency. We also conducted spatial comparisons with low, median-low, median-high, and high levels of PM2.5 concentrations. When PM2.5 concentrations increased 1 µg/m(3), the increase in the relative risk percentage for diabetes ranged from -5.47% (95% credible interval = -6.14, -4.77) to 2.34% (95% CI = 2.01, 2.70), where 1323 of 3109 counties (42.55%) displayed diabetes vulnerability with significantly positive relative risk percentages. These vulnerable counties are more likely located in the Southeast, Central, and South Regions of the U.S. A similar spatial vulnerability pattern for concentrations of low PM2.5 levels was also present in these same three regions. A clear cluster of vulnerable counties at median-high PM2.5 level was found in Michigan. This study identifies the spatial vulnerability of diabetes prevalence associated with PM2.5, and thereby provides the evidence needed to prompt and establish enhanced surveillance that can monitor diabetes vulnerability in areas with low PM2.5 pollution.

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

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

Association between air pollutants and dementia risk in the elderly.

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