Taiwan Environmental Protection Administration Research Articles

Associations of ambient air pollution with overnight changes in body composition and sleep-related parameters

This study aims to investigate the association of air pollution with overnight change in 4body composition and sleep-related parameters. Body composition of 197 subjects in New Taipei city was measured before and after sleep by bioelectric impedance analysis. Air pollutant data were collected from Taiwan Environmental Protection Administration. Sleep parameters were examined by polysomnography. We observed fine particulate matter (PM2.5) decreased arterial oxygen saturation (SaO2) and increased apnea-hypopnea index (AHI); NO2 increased arousal, AHI, and decreased mean SaO2; and O3 inmcreased mean SaO2. We observed 0.99-μg/m3 increase in PM2.5 was associated with 18.8% increase in changes of right arm fat percentage (95% confidence interval (CI): 0.004, 0.375) and 0.011-kg increase in changes of right arm fat mass (95% CI: 0.000, 0.021). 2.45-ppb increase in NO2 was associated with 0.181-kg decrease in changes of muscle mass (95% CI: −0.147, −0.001), 0.192-kg decrease in changes of fat free mass (95% CI: −0.155, −0.001), 21.1% increase in changes of right leg fat percentage (95% CI: 0.012, 0.160), and 21.3% increase in changes of left leg fat percentage (95% CI: 0.006, 0.168). 1.56-ppb increase in O3 was associated with 29.3% decrease in changes of right leg fat percentage (95% CI: −0.363, −0.013), 0.058-kg increase in changes of right leg fat free mass (95% CI: 0.008, 0.066), and 0.059-kg increase in changes of right leg muscle mass (95% CI: 0.010, 0.066). We observed AHI was associated with overnight changes in fat percentage, total fat mass, muscle mass, bone mass, fat free mass, extracellular water, basal metabolic rate, leg fat percentage, leg fat mass, and trunk fat percentage (p < 0.05). In conclusion, exposure to air pollutants was associated with overnight body composition changes and sleep-related parameters. Nocturnal changes in total muscle mass and leg fat percentage likely contribute to the relationship between air pollution and obstructive sleep apnea.

Using High-Frequency Information and RH to Estimate AQI Based on SVR.

The Environmental Protection Administration of Taiwan’s Executive Yuan has set up many air quality monitoring stations to monitor air pollution in the environment. The current weather forecast also includes information used to predict air pollution. Since air quality indicators have a considerable impact on people, the development of a simple, fast, and low-cost method to measure the AQI value is a worthy topic of research. In this study, a method was proposed to estimate AQI. Visibility had a clear positive relationship with AQI. When images and AQI were compared, it was easy to see that visibility decreased with the AQI value increase. Distance is the main factor affecting visibility, so measuring visibility with images has also become a research topic. Images with high and low PM2.5 concentrations were used to obtain regions of interest (RoI). The pixels in the RoI were calculated to obtain high-frequency information. The high-frequency information of RoI, RH, and true AQI was used for training via SVR, which was used to generate the model for AQI estimation. One year of experimental samples was collected for the experiment. Two indices were used to evaluate the performance of the proposed method. The results showed that the proposed method could be used to estimate AQI with acceptable performance in a simple, fast, and low-cost way.

Air Pollution Is Associated with Cognitive Deterioration of Alzheimer’s Disease

Introduction: Dementia is one of the major causes of disability and dependency among older people worldwide. Alz­heimer’s disease (AD), the most common cause of dementia among the elderly, has great impact on the health-care system of developed nations. Several risk factors are suggestive of an increased risk of AD, including APOE-ε4, male, age, diabetes mellitus, hypertension, and low social engagement. However, data on risk factors of AD progression are limited. Air pollution is revealed to be associated with increasing dementia incidence, but the relationship between air pollution and clinical AD cognitive deterioration is unclear. Methods: We conducted a case-control and city-to-city study to compare the progression of AD patients in different level of air-polluted cities. Clinical data of a total of 704 AD patients were retrospectively collected, 584 residences in Kaohsiung and 120 residences in Pingtung between 2002 and 2018. An annual interview was performed with each patient, and the Clinical Dementia Rating score (0 [normal] to 3 [severe stage]) was used to evaluate their cognitive deterioration. Air pollution data of Kaohsiung and Pingtung city for 2002–2018 were retrieved from Taiwan Environmental Protection Administration. Annual Pollutant Standards Index (PSI) and concentrations of particulate matter (PM₁₀), sulfur dioxide (SO₂), ozone (O₃), nitrogen dioxide (NO₂), and carbon monoxide (CO) were obtained. Results: The PSI was higher in Kaohsiung and compared with Pingtung patients, Kaohsiung patients were exposed to higher average annual concentrations of CO, NO₂, PM₁₀, and SO₂. AD patients living in Kaohsiung suffered from faster cognitive deterioration in comparison with Pingtung patients (log-rank test: p = 0.016). When using multivariate Cox proportional hazards regression analysis, higher levels of CO, NO₂, PM₁₀, and SO₂ exposure were associated with increased risk of AD cognitive deterioration. Among all these air pollutants, high SO₂ exposure has the greatest impact while O₃ has a neutral effect on AD cognitive deterioration. Conclusions: Air pollution is an environment-related risk factor that can be controlled and is associated with cognitive deterioration of AD. This finding could contribute to the implementation of public intervention strategies of AD.

Comprehending the Causes of Presence of Copper and Common Heavy Metals in Sediments of Irrigation Canals in Taiwan

In 2019, Taiwan completed its first thorough heavy metal investigation of irrigation canal sediments by this study with the support of Taiwan Environmental Protection Administration. Box-and-whisker plots were used to analyze the sediment distribution and to define metal concentrations. Possible metal pollution sources, the polluted agricultural land, irrigation area, and water sources were also evaluated using spatial analysis to understand the possible causes of sediment pollution. Results showed that the main heavy metal in agricultural land was Cu, found in 77% of contaminated agricultural land sites. Most sites with Cu pollution in sediments were in Taoyuan City and Changhua County. The heavy metals present in the sediment pollution sites in Taoyuan were consistent with those of possible pollution sources upstream, namely, Cr, Cu, and Pb. The main heavy metals in sediment pollution sites in Changhua were Cr, Cu, Ni, Pb, and Zn, whereas those for the polluted agricultural land sites were Cr, Cu, Ni, and Zn, without Pb. The main irrigation water sources in Changhua include drainages and rivers, with some receiving most wastewater pollution mass of release of Changhua, and functions as an irrigation water source with a high release mass in Cr, Cu, Ni and Zn. These findings indicate that the sites of sediment pollution, sites of polluted agricultural land, and the sources of pollution share corresponding heavy metal characteristics. Therefore, in Changhua, the sediments were polluted mainly because (1) the irrigation canals received the highest masses of pollutant releases into drainage wastewater of the county; and (2) the return flow from irrigation and the illegal discharge of wastewater. The preliminary assessment results for sediment pollution in Taoyuan also suggest that the main causes may be irrigation by polluted rivers or drainages and return flows.

Effectiveness of SOx, NOx, and Primary Particulate Matter Control Strategies in the Improvement of Ambient PM Concentration in Taiwan

The Community Multiscale Air Quality (CMAQ) measurement was employed for evaluating the effectiveness of fine particulate matter control strategies in Taiwan. There are three scenarios as follows: (I) the 2014 baseline year emission, (II) 2020 emissions reduced via the Clean Air Act (CAA), and (III) other emissions reduced stringently via the Clean Air Act. Based on the Taiwan Emission Data System (TEDs) 8.1, established in 2014, the emission of particulate matter 2.5 (PM2.5) was 73.5 thousand tons y−1, that of SOx was 121.3 thousand tons y−1, and that of NOx was 404.4 thousand tons y−1 in Taiwan. The CMAQ model simulation indicated that the PM2.5 concentration was 21.9 μg m−3. This could be underestimated by 24% in comparison with data from the ambient air quality monitoring stations of the Taiwan Environmental Protection Administration (TEPA). The results of the simulation of the PM2.5 concentration showed high PM2.5 concentrations in central and southwestern Taiwan, especially in Taichung and Kaohsiung. Compared to scenario I, the average annual concentrations of PM2.5 for scenario II and scenario III showed reductions of 20.1% and 28.8%, respectively. From the results derived from the simulation, it can be seen that control of NOx emissions may improve daily airborne PM2.5 concentrations in Taiwan significantly and control of directly emitted PM2.5 emissions may improve airborne PM2.5 concentrations each month. Nevertheless, the results reveal that the preliminary control plan could not achievethe air quality standard. Therefore, the efficacy and effectiveness of the control measures must be considered to better reduce emissions in the future.

Analysis of plastic waste reduction and recycling in Taiwan.

With the mass production and consumption of single-use plastics and other plastic products, plastic waste management and its resulting microplastic pollution have become the most noteworthy environmental issues. The main objectives of this paper were to review the regulatory measures for plastic waste reduction and recycling, analyse the trend changes on the recycled amounts of regulated plastic containers and also address the discussions on the impacts of COVID-19 on the expected increase in plastic waste. In the past two decades, the central competent authority (i.e. Environmental Protection Administration (EPA)) in Taiwan has promulgated some regulations governing plastic waste reduction and recycling, especially plastic containers. Based on the official statistics, it showed that total certified amounts of regulated plastic containers have increased from 45,552 tonnes in 1998 to 194,133 tonnes in 2019. This shift was in line with circular economy policies by the Zero Waste Program and Resource Recycling and Reuse Plan launched by the Taiwan EPA during this period. On the other hand, the COVID-19 pandemic will impact on the regulatory policies for plastic waste management because the excess use of regulated plastic products will be temporarily permitted due to the epidemic prevention and control measures. Although the increased proportion of plastic waste in municipal solid waste (MSW) will be beneficial for the energy efficiencies of waste-to-power systems in MSW incineration plants, the recycling and reduction rates of regulated plastic containers or products could decline during the COVID-19 breakout.

Association Between PM2.5 Exposure Level and Primary Open-Angle Glaucoma in Taiwanese Adults: A Nested Case-Control Study.

Primary open-angle glaucoma (POAG) is the most common type of glaucoma. However, little is known about POAG in adults and exposure to air pollution. The current study aims to investigate whether exposure to particulate matter with a mass median aerodynamic diameter of ≤2.5 μm (PM2.5) is associated with POAG diagnosis. Patient data were obtained from the Longitudinal Health Insurance Database 2010 (LHID2010) of Taiwan for the 2008–2013 period. PM2.5 concentration data, collected from the Ambient Air Quality Monitoring Network established by the Environmental Protection Administration of Taiwan, were categorized into four groups according to World Health Organization (WHO) exposure standards for PM2.5. We estimated the odds ratios (ORs) and 95% CIs for risk factors for POAG with logistic regression. The OR of per WHO standard level increase was 1.193 (95% CI 1.050–1.356). Compared with the normal level, the OR of WHO 2.0 level was 1.668 (95% CI 1.045–2.663, P < 0.05). After excluding confounding risk factors for POAG in this study, we determined that increased PM2.5 exposure is related to POAG risk (ORs > 1, P < 0.05). In this study, PM2.5 was an independent factor associated with open-angle glaucoma. Further research is required to better understand the mechanisms connecting PM2.5 and open-angle glaucoma.

Long-term exposure to ozone and sulfur dioxide increases the incidence of type 2 diabetes mellitus among aged 30 to 50 adult population

Aims/hypothesisWorldwide, the information regarding the associations between long-term exposure to ozone (O3) and sulfur dioxide (SO2) and the development of type 2 diabetes remains scarce, especially in Asia. This study aimed to investigate the long-term effects of exposure to ambient O3 and SO2 on the incidence of type 2 diabetes with consideration of other air pollutants in Taiwanese adults aged 30 to 50 years. MethodsA total of 6,426,802 non-diabetic participants aged between 30 and 50 years old were obtained from the National Health Insurance Research Database between 2005 and 2016. Incident type 2 diabetes was the main diagnosis at medical visits. Air quality data were provided by the Taiwan Environmental Protection Administration. The air pollutant concentrations for each participant were estimated using the ordinary kriging method to interpolate daily concentrations of O3, SO2, carbon monoxide (CO), nitrogen dioxide (NO2), suspended fine particles (with an aerodynamic diameter less than 2.5 μm; PM2.5), and suspended particles (with an aerodynamic diameter less than 10 μm; PM10) in residential districts across Taiwan. Six-year average concentrations of pollutants were calculated from January 1, 2005 to December 31, 2010, and data were categorized into quartiles. We performed Cox regression models to analyze the long-term effects of exposure to O3 and SO2 on the incidence of type 2 diabetes. ResultsThe hazard ratio (HR) for the incidence of diabetes per each interquartile range (IQR) increase in ozone exposure (3.30 ppb) was 1.058 (95% confidence interval (CI): 1.053, 1.064) and 1.011 (95% CI: 1.007, 1.015) for SO2 exposure (1.77 ppb) after adjusting for age, sex, socioeconomic status, urbanization level, temperature, humidity, and chronic comorbidities (Model 3). Furthermore, for every 3.30 ppb increase of O3, the HR for incident type 2 diabetes was 1.093 (95% CI: 1.087, 1.100) after controlling factors shown in Model 3 plus SO2 and PM2.5. On the other hand, for every 1.77 ppb increase of SO2, the HR for incident type 2 diabetes was 1.073 (95% CI: 1.068, 1.079) after controlling factors shown in Model 3 plus NO2 and PM2.5. ConclusionsLong-term exposure to ambient O3 and SO2 was associated with a higher risk of developing type 2 diabetes for Taiwanese population. Exposure to O3 and SO2 may play a role in the adult early-onset type 2 diabetes.

Hybrid Time-Series Framework for Daily-Based PM2.5 Forecasting

The impact of fine particulate matter on health has captured attention worldwide. Many studies have proven that fine particulate matter harms the respiratory system and the cardiovascular system. To prevent people from being harmed, many scientific research studies on PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> prediction have been conducted in recent years. Accurate PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> forecasting can not only alert people to stay away from concentrated areas but also provide the government with environmental policies in the future. In this paper, we propose a hybrid time-series prediction framework for daily-based PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> forecasting. The proposed framework consists of three components: the autoencoder, the dilated convolutional neural network, and the gated recurrent unit. The experimental dataset with 76 monitoring stations from the Taiwan Environmental Protection Administration is applied for comparison of the baseline and the proposed models. The proposed model is not only for the specified city-/county-wide region but also for the particular monitoring station/site to predict PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration. By considering air quality data, meteorological data, and geographical data simultaneously, the proposed model can increase the accuracy of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> prediction. In addition, the proposed PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> forecasting model can learn the location-centric spatial features and the daily-based temporal features simultaneously. The experimental results show that the prediction accuracy of the proposed model is superior to those of the baseline models.

Application of Factor Analysis for Characterizing the Relationships between Groundwater Quality and Land Use in Taiwan’s Pingtung Plain

Although the average municipal water coverage in Taiwan is quite high, at 93.91%, only around half of the residents in the Pingtung Plain use tap water originating from the Taiwan Water Corporation to meet their needs. This means the exploitation of a substantial amount of groundwater as a source of water to meet drinking, agriculture, aquaculture, and industry requirements. Long-term groundwater quality surveys in Taiwan have revealed obvious contamination of the groundwater in several locations in the Pingtung Plain, with measured concentration levels of some groundwater quality parameters in excess of the permissible levels specified by the Taiwan Environmental Protection Administration. Clearly, establishing a sound plan for groundwater quality protection in this area is imperative for maximizing the protection of human health. The inappropriate use of hazardous chemicals and poor management of land use have allowed pollutants to permeate through unsaturated soil and ultimately reach the underlying shallow unconfined groundwater system. Thus, the quality of the water stored in shallow aquifers has been significantly affected by land use. This study is designed to characterize the relationship between groundwater quality and land use in the Pingtung Plain. This goal is achieved by the application of factor analysis to characterize the measured concentrations of 14 groundwater quality parameters sampled from 46 observation wells, the area percentages for nine land use categories in the neighborhood of these 46 observation wells, and the thicknesses of four unsaturated types of soil based on core samples obtained during the establishment of 46 observation wells. The results show that a four-factor model can explain 56% of the total variance. Factor 1 (seawater salinization), which includes the groundwater quality parameters of EC, SO42−, Cl−, Ca2+, Mg2+, Na+, and K+, shows a moderate correlation to land used for water conservation. Factor 2 (nitrate pollution), which includes the groundwater quality parameters of NO3−-N and HCO3−, shows a strong correlation to land used for fruit farming and a moderate correlation to the thickness of the gravel comprising unsaturated soil. Factor 3 (arsenic pollution), which is composed of groundwater quality parameters of total organic carbon (TOC) and As, is very weakly affected by land use. Factor 4 (reductive dissolution of Fe3+ and Mn2+), which involves Mn2+ and Fe3+, is weakly impacted by land use. Based on a geographic visualization of the scores for the four different factors and the patterns for land use, we can demarcate the areas where the groundwater in shallow unconfined aquifers is more vulnerable to being polluted by specific contaminants. We can then prioritize the areas where more intensive monitoring might be required, evaluate current land use practices, and adopt new measures to better prevent or control groundwater pollution.

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

Previous studies have assessed limited cognitive domains with relatively short exposure to air pollutants, and studies in Asia are limited. This study aims to explore the association between long-term exposure to air pollutants and cognition in community-dwelling older adults. 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. Exposure to high-level PM2.5 (>29.98μg/m3) was associated with an increased risk of global cognitive impairment (adjusted odds ratio = 4.56; β= -0.60). High-level PMcoarse exposure (>26.50μg/m3) was associated with poor verbal fluency (β= -0.19). High-level PM10 exposure (>51.20μg/m3) was associated with poor executive function (β= -0.24). Medium-level NO2 exposure (>28.62 ppb) was associated with better verbal fluency (β= 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: β= -0.17; PMcoarse and NO2: β= -0.23; PM10 and NO2: β= -0.21) and poor executive function (PM10 and NO2: β= -0.16). These associations became more evident in women, apolipoprotein ɛ4 non-carriers, and those with education > 12 years. 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.

Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan.

In the past few years, human health risks caused by fine particulate matters (PM2.5) and other air pollutants have gradually received attention. According to the Disaster Prevention and Protection Act of Taiwan’s Government enforced in 2017, “suspended particulate matter” has officially been acknowledged as a disaster-causing hazard. The long-term exposure to high concentrations of air pollutants negatively affects the health of citizens. Therefore, the precise determination of the spatial long-term distribution of hazardous high-level air pollutants can help protect the health and safety of residents. The analysis of spatial information of disaster potentials is an important measure for assessing the risks of possible hazards. However, the spatial disaster-potential characteristics of air pollution have not been comprehensively studied. In addition, the development of air pollution potential maps of various regions would provide valuable information. In this study, Hsinchu County was chosen as an example. In the spatial data analysis, historical PM2.5 concentration data from the Taiwan Environmental Protection Administration (TWEPA) were used to analyze and estimate spatially the air pollution risk potential of PM2.5 in Hsinchu based on a geographic information system (GIS)-based radial basis function (RBF) spatial interpolation method. The probability that PM2.5 concentrations exceed a standard value was analyzed with the exceedance probability method; in addition, the air pollution risk levels of tourist attractions in Hsinchu County were determined. The results show that the air pollution risk levels of the different seasons are quite different. The most severe air pollution levels usually occur in spring and winter, whereas summer exhibits the best air quality. Xinfeng and Hukou Townships have the highest potential for air pollution episodes in Hsinchu County (approximately 18%). Hukou Old Street, which is one of the most important tourist attractions, has a relatively high air pollution risk. The analysis results of this study can be directly applied to other countries worldwide to provide references for tourists, tourism resource management, and air quality management; in addition, the results provide important information on the long-term health risks for local residents in the study area.

The Tendency of Eutrophication Level Prediction in Chengchinghu Reservoir, Kaohsiung City, Taiwan

Introduction: Reservoir management problems are increasing, and tools are needed to categorize and predict their eutrophication status in order to provide technical support for the government’s decision to protect drinking water resource. Thus, this study aims to predict and classify the tendency of eutrophication level in Chengchinghu Reservoir, Kaohsiung City, Taiwan as one of major water sources for industrial and domestical needs by supplying 109,170,00 m3 for Southern Taiwan. Method: The CTSI (Carlson’s Trophic States Index, which calculated from Chl-a, TP, and transparency) datasets in winter (December-February), spring (March-May), summer (June-August), and fall (September-November) from 2000 to 2017 was collected from Taiwan Environmental Protection Administration (EPA). This study used the Classification and Regresiion Tree (CART) model provides the explicit categorical rules for Chengchinghu Reservoir. Results and Discussion: The CART results for Chengchinghu Reservoir showed the good performance of prediction since the accuracy of the CART training process value reached 61.89%. According to the CART results, the eutrophic state condition is most probably occur in Chengchinghu Reservoir when the TP concentration is greater than 22.86 mg/L or Chl-a concentration is greater than 5.2 μg/L or SD is less than 1.1 m. Conclusion: The CART result may helps the local governments to understand the pollution conditions in Chengchinghu Reservoir and take responsibility for reservoir water management and conservation. Therefore, they could make policies to treat and manage water pollution sources in Chengchinghu Reservoir.

Effects of surrounding environment on incidence of end stage renal disease

The rising prevalence and incidence of end-stage renal disease (ESRD) have been noted around the world. However, no study has been conducted to examine the effect of surrounding environment on incidence of ESRD. This study assessed the associations of exposure to PM2.5 level and surrounding green spaces, separately, with incidence of ESRD in Taiwan. Demographic and clinical data used in this study was retrieved from the National Health Insurance Research Database from 2003 to 2012.PM2.5 data collected from the Environmental Protection Administration of Taiwan and a hybrid land-use regression model was used to approximate long-term exposure to PM2.5. Percentage of exposure to surrounding green spaces was used to determine individual exposure level. Cox proportional hazards models with a generalized estimating equation were applied to investigate the effect of surrounding environment on incidence of ESRD.The results showed significant positive association between exposure to PM2.5 level and incidence of ESRD; but inverse association between exposure to surrounding green spaces and incidence of ESRD (adjusted hazard ratio (AHR) = 1.08, 95% CI: 1.00–1.15 for exposure to PM2.5 level; AHR = 0.90, 95%CI: 0.84–0.98 for surrounding green spaces). Together, the findings from this study have added suggestive evidence on the adverse effect of exposure to PM2.5 level and the beneficial effect of exposure to surrounding green spaces on the incidence of ESRD in a general population in Taiwan.

Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources

The influenza problem has always been an important global issue. It not only affects people’s health problems but is also an essential topic of governments and health care facilities. Early prediction and response is the most effective control method for flu epidemics. It can effectively predict the influenza-like illness morbidity, and provide reliable information to the relevant facilities. For social facilities, it is possible to strengthen epidemic prevention and care for highly sick groups. It can also be used as a reminder for the public. This study collects information on the influenza-like illness emergency department visits to the Taiwan Centers for Disease Control, and the PM2.5 open-source data from the Taiwan Environmental Protection Administration's air quality monitoring network. By using deep learning techniques, the relevance of short-term estimates and the outbreak calculation method can be determined. The techniques are published by the WHO to determine whether the influenza-like illness situation is still in a stage of reasonable control. Finally, historical data and future forecasted data are integrated on the web page for visual presentation, to show the actual regional air quality situation and influenza-like illness data and to predict whether there is an outbreak of influenza in the region.

Spatiotemporal assessment of mortality attributable to ambient PM2.5 exposure in Taiwan during 2008–2015

In this study, we quantified the spatiotemporal burden of mortality attributable to ambient fine particulate matter (PM2.5, which is defined as a mass of particles with an aerodynamic dry diameter of ≤ 2.5 μm) in Taiwan during 2008–2015. Hourly concentrations of PM2.5 were obtained from the Taiwan Air Quality Monitoring Network. According to geographical and climatic characteristics, the Taiwan Environmental Protection Administration has divided Taiwan into seven air quality regions (AQRs): the North (four districts), Chu-Miao (two districts), Central (three districts), Yun-Chia-Nan (three districts), Kao-Ping (two districts), Yilan (one district), and Hua-Tung (two districts) regions. Demographic information and mortality data were acquired from the Department of Household Registration and National Death Registry of Taiwan, respectively. The World Health Organization’s (WHO) AirQ+ software was used to estimate the mortality attributable to exposure to PM2.5 for specific causes of deaths, specifically from ischemic heart disease (IHD), cerebrovascular disease (stroke), lung cancer (LC), and chronic obstructive pulmonary disease (COPD). The results indicated that nationally, the annual mean concentration of PM2.5 decreased from 2008 (30.75 μg m−3) to 2015 (20.07 μg m−3). Moreover, the population-attributable fraction (PAF) of cause-specific mortality due to PM2.5 exposure for IHD, stroke, LC, and COPD decreased from 21.72%, 30.31%, 15.97%, and 13.04%, respectively, in 2008 to 17.40%, 18.18%, 10.77%, and 9.09%, respectively, in 2015. Geographic variation was observed in the PM2.5 concentration and PAF. High PM2.5 levels and PAF of mortality due to PM2.5 were observed in southwestern and central Taiwan. Moreover, cardiovascular and cerebrovascular disease (IHD and stroke) accounted for the majority of the premature deaths related to PM2.5. Stroke was the largest contributor in PM2.5-related premature mortality; however, IHD instead of stroke became the largest contributor in the Yilan AQR in 2010, 2012, and 2014–2015 and in the Hua-Tung AQR in 2012–2015 with relatively low PM2.5 levels (less than 19 μg m−3). In Taiwan, ambient PM2.5 contributed substantially to the burden of disease. Our results could aid authorities in determining the effectiveness of current air pollution control strategies and plans.

Dynamic System Approach for Improved PM2.5 Prediction in Taiwan

This study presents two dynamic models, namely recurrent neural network and long short-term memory (LSTM) models, for predicting PM2.5 concentrations in Taiwan by using PM2.5 time series obtained at air quality monitoring stations and weather information obtained at neighboring weather stations. The proposed models can efficiently predict PM2.5 by incorporating a learned memory structure with a forgetting gate. To evaluate the predictive performance of the proposed models, large-scale databases established by Taiwan's Environmental Protection Administration, and Central Weather Bureau were used; these databases include hourly data from 77 air quality monitoring stations and 580 weather stations over a 1-year period. The results demonstrated that the proposed models outperformed three traditional machine learning methods (gradient boosting, support vector machine, and classification and regression tree models) by 27.12% and 33.69% on average in terms of the coefficient of determination and root mean square error, respectively. A geographical divergence analysis was conducted to compare predictive performance in different regions. The results revealed that the most significant improvement in predictive performance was achieved in central Taiwan. The seasonal and pollution effect on predictive performance were reduced by the LSTM and the source distribution of PM2.5 emission in Taiwan was also analyzed.

Simulating the spatiotemporal distribution of BTEX with an hourly grid-scale model.

Modeling approaches have been utilized to simulate ambient pollutant concentrations, but very limited efforts have been made to estimate volatile organic compounds in the atmosphere. For this reason, an hourly grid-scale simulation model was developed to determine ambient air concentrations of benzene, toluene, ethylbenzene, and xylene (BTEX). BTEX data were collected over a one-year time frame from the database of the Taiwan Environmental Protection Administration's photochemical assessment monitoring stations. Multivariate linear regression models were used along with correlation analysis to simulate hourly grid-scale BTEX concentrations, using criteria pollutants and selected meteorological variables as predictors. The simulation model was validated in the southern Taiwan area via a portable micro gas chromatography system (n=121) with significant correlation (r=0.566**, ** indicated p<0.01). Moreover, the grid-scale model was applied to areas covering about 72% of the population in Taiwan. A geographic information system (GIS) was used to visualize the spatial distribution of BTEX concentrations from the modeling results. This new grid-scale modeling strategy, which incorporated the GIS output of the simulated data, provides a useful alternative tool for personal exposure analysis and health risk assessment of ambient air BTEX.

Analysis of air quality and health co-benefits regarding electric vehicle promotion coupled with power plant emissions

Abstract Purpose of this study is to discuss the electric vehicle policy’s effects on air pollution reduction in Taiwan. Since PM2.5 is one of Taiwan’s major air-pollution issues, Environmental Protection Administration of Taiwan (TEPA) promoted a policy that the sale of cars powered by fossil fuels would be banned in 2040, which means all the pollutants emitted by petrol-engine vehicles will be reduced. But at the same time the electric vehicles require additional power consumption, therefore, it is important to investigate the effect of air quality and health benefit when mobile emission reduces but power plant emission increases. To evaluate this clean air policy, Weather Research and Forecasting model (WRF) - Community Multi-scale Air Quality model (CMAQ) and Benefits Mapping and Analysis Program (BenMAP) were applied in scenarios discussion. The location of power plants to generate additional electric power and the seasonal variation were adapted in scenarios for considering the atmospheric transportation effects. The results showed if additional power supply was generated in northern, central, or southern Taiwan, the average annual PM2.5 concentration would be reduced by 2.88, 2.90, and 2.92 μg/m3, respectively. The associated health benefits would be 43.35 billion, 43.40 billion, and 43.54 billion USD. This evaluation presents adopting electric vehicles would improve the air quality of Taiwan significantly. The analysis of seasonal scenarios also indicates the location to generate additional electric power is important when adopting electric vehicles policy. The prevailing wind of different season will transport the air pollutant to diverse downwind area. The additional electricity demand generated by northern power plants in summer and autumn but switched to southern power plants in spring and winter would reduce 2.95 μg/m3 PM2.5 and lead to the best air quality and health benefits across Taiwan among the considered options.

Application of Graphene Oxide Aerogel to the Adsorption of Polycyclic Aromatic Hydrocarbons Emitted from the Diesel Vehicular Exhaust

For reducing the carcinogenic BaP and polycyclic aromatic hydrocarbons (PAHs) from the moving and stationary pollution source in the atmosphere, this study demonstrated useful adsorbent of graphene oxide aerogel (GOA). The application of GOA to the adsorption of polycyclic aromatic hydrocarbons (PAHs) emitted from diesel vehicular exhaust via the dynamometer was investigated. The surface morphology and microstructure of GOA were characterized by SEM, TEM, elemental analysis, Raman, and TGA. According to the result of XRD, the grain size of GOA after 5 hours of oxidation time increased to 28.0 nm with 38 layers estimated. It demonstrated that more oxidation time greatly enhanced the crystalline structure and reformation between the GOA sheets. The GOA sheets possessed the molecular sieving property for the filtration and adsorption of carcinogenic PAH molecules due to the strong hydrogen bonding interaction along the edges and the Van der Waal's force interactions. Naphthalene, acenaphthene, benzo[a]pyrene (BaP), and dibenz[a,h]anthracene, were chosen to carry out the pre-test adsorption experiments. Especially, the BaP compound was known to be carcinogens and mutagens for humans because of their carcinogenicity and genotoxicity. From the result of pre-test adsorption of PAHs, the BAP adsorption capacity of the GOA adsorbent (310 ng BaP / 1 g GOA) in 1 hour was much better than that of XAD16 resin (0.060 ng / 1 g XAD16), which is the standard adsorbents of PAHs announced by the Environmental Protection Administration of Taiwan. Furthermore, the sample collection of PAHs emitted from the diesel exhaust was performed by using the vehicle dynamometer. At the idling speed, the best adsorption capacity of total PAHs per gram GOA was 12.1 μg g−1, and better than that of XAD16 (1.03 μg g−1). Moreover, the adsorption sleeve and adsorbent of GOA material could be recycled and reused repeatedly. The structure of the GOA adsorbent has maintained after three runs of the Soxhlet extraction and drying. Nevertheless, the structure of XAD16 resin is decomposed obviously and decreased after the Soxhlet extraction. Owing to the superior adsorption capacity of PAHs and low synthesis cost, the GOA material is possible to be one of the excellent environmental and air pollution adsorbent material in the future.