Taiwan Environmental Protection Agency Research Articles

Exploring the association between knee osteoarthritis outpatient visits and Asian dust storms: a time-series analysis

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases in Taiwan, posing a significant public health challenge. In recent years, outdoor air pollution has become an increasingly critical global health issue. Asian Dust Storms (ADS) are known to exacerbate various health conditions due to elevated levels of particulate matter and other pollutants. However, the relationship between ADS and knee OA remains insufficiently explored. This study investigates the association between ADS occurrences and knee OA outpatient visits from January 2006 to December 2012, aiming to understand the potential health impacts of dust storms on OA patients. Using data from the National Health Insurance Research Database (NHIRD), the Taiwan Environmental Protection Agency (TEPA), and the Taiwan Central Weather Bureau, we conducted a time-series analysis employing the autoregressive moving average with exogenous variables (ARMAX) model. This approach accounted for daily outpatient visits related to knee OA, ADS events, and various environmental and meteorological factors. The results revealed a significant increase in knee OA outpatient visits on days immediately following ADS events, with peaks observed one to two days after the event. This increase was most pronounced among females, individuals aged 61 and above, and residents in the western regions. The study demonstrates an association between ADS and increased knee OA outpatient visits, highlighting the need for public health strategies to mitigate the health impacts of dust storms.

Application of fluidized-bed homogeneous crystallization technology to carbon sequestration and recovery from flue gas

The Earth’s energy balance produced by human activity is the main factor in the complex relationship between greenhouse gases and global warming. The Taiwan Environmental Protection Agency reports that carbon dioxide makes up more than 95% of Taiwan’s most recent greenhouse gas emissions. This study used fluidized-bed homogeneous crystallization (FBHC) technology to recover carbonate in a simulated CO2-enriched flue gas. It was specifically designed to determine how carbonate removal and crystallization efficiency were affected by carbonate surface loading, the influence of the source of calcium ions, and interfering substances. Results revealed that the best surface loading at 55 kg m− 2 h− 1 achieved 93% removal and 84% crystallization efficiency. At 50 mg L− 1 of sulfate ions, the presence of more interfering compounds tends to reduce carbonate removal to 97% and 91% crystallization. Regarding X-ray diffraction data, the recovered carbonate crystals resembled calcium carbonate crystals. It has been demonstrated that carbonate can be recovered using FBHC technology as a method of CO2 capture and storage.

Long-term exposure to air pollution and risk of Sarcopenia in adult residents of Taiwan: a nationwide retrospective cohort study

BackgroundSarcopenia is an age-related, multifactorial syndrome. Previous studies have shown that air pollutants are associated with inflammation and oxidative stress. However, the association between long-term exposure to air pollution and sarcopenia is not completely understood.MethodsThe Taiwan National Health Research Database (NHIRD) contains medical records of almost all Taiwanese residents. Daily air pollution data collected by the Taiwan Environmental Protection Agency was used to analyze concentrations of sulfur oxide (SO2), carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide (NO2), and particulate matter (PM2.5, PM10). The databases were merged according to the insurants’ living area and the location of the air quality monitoring station. We categorized the pollutants into quartiles (Q1, Q2, Q3, and Q4).ResultsOur study population consisted of 286,044 patients, among whom 54.9% were female and 45.1% were male. Compared to Q1 levels of pollutants, Q4 levels of SO2 (adjusted hazard ratio [aHR] = 8.43; 95% confidence interval [CI] = 7.84, 9.07); CO (aHR = 3.03; 95%CI = 2.83, 3.25); NO (aHR = 3.47; 95%CI = 3.23, 3.73); NO2 (aHR = 3.72; 95%CI = 3.48, 3.98); PM2.5 (aHR = 21.9; 95% CI = 19.7, 24.5) and PM10 (aHR = 15.6; 95%CI = 14.1, 17.4) increased risk of sarcopenia.ConclusionsOur findings indicated a significantly increased risk of sarcopenia in both male and female residents exposed to high levels of air pollutants.

Sustainable Use of Sludge from Industrial Park Wastewater Treatment Plants in Manufacturing Lightweight Aggregates.

The aim of this study was to investigate the development of a process for manufacturing lightweight aggregates (LWAs) by incorporating sludge from wastewater treatment plants in industrial parks with reservoir sediments. The research was divided into two stages: laboratory-scale firing and large-scale firing. In the laboratory-scale stage, a high-temperature furnace was used for trial firing. In the large-scale stage, a commercial rotary kiln was used for trial firing for mass production. The test results showed that the water absorption, dry loose bulk density, and crushing strength of the sintered LWAs were 14.2–26.9%, 634–753 kg/m3, and 1.29–2.90 MPa, respectively. Moreover, the water absorption of the sintered LWAs increased as the percentage of added sludge increased. In addition, the dry loose bulk density of the sintered LWAs gradually decreased as the percentage of added sludge increased. Moreover, the results of the heavy metal toxicity characteristic leaching procedure (TCLP) dissolution test for the LWAs produced by blending 30–50% sludge were all lower than the standard value required by the Taiwan Environmental Protection Agency for general industrial waste. The strength grade of the sintered LWAs was 20 MPa. From this point of view, the sintered LWAs that were studied under the test conditions could be used as aggregates for lightweight concrete and would allow it to have a reasonable strength of greater than 20 MPa.

An integrated strategy by using long-term monitoring data to identify volatile organic compounds of high concern near petrochemical industrial parks

Exposure to ambient volatile organic compounds (VOCs) is associated with a risk of cancer in the residents living near petrochemical facilities. However, research on the contribution of different VOCs to the lifetime cancer risk remains inconclusive. The variability in source emissions, geographical locations, seasons, and meteorological conditions can be assessed through long-term measurement of ambient VOCs with a wide spatial distribution, thus reducing the uncertainty of health risk assessment from source emissions. This study analyzed comprehensive measurement data of 109 VOCs at 17 monitoring stations around petrochemical industrial parks, collected once every six days during 2015–2018 by the Taiwan Environmental Protection Agency. We calculated the annual mean concentration of selected VOCs and then integrated the probability risk assessment (PRA) and positive matrix factorization (PMF) models to identify the sources of VOCs of high concern. First, we prioritized 12 out of 23 carcinogenic VOCs based on the PRA results. Further, the results obtained from the PMF model revealed that petrochemical industrial parks contributed to more than 50% of the emissions of six VOCs, namely 1,3-butadiene, benzene, 1,2-dichloroethane, chloroform, vinyl chloride, and acrylonitrile, measured at a few monitoring stations. This integrated approach can help regulatory agencies to efficiently propose control strategies on the emissions of VOCs of high concern, thereby reducing the population's health risk.

Increased Risk of Gastric Cancer in Asbestos-Exposed Workers: A Retrospective Cohort Study Based on Taiwan Cancer Registry 1980–2015

Asbestos has been recognized as a human carcinogen associated with malignant mesothelioma, cancers of lung, larynx, and ovary. However, a putative association between gastric cancer and asbestos exposure remains controversial. In this study, we aimed to explore gastric cancer risk of workers potentially exposed to asbestos in Taiwan. The asbestos occupational cohort was established from 1950 to 2015 based on the Taiwan Labor Insurance Database, and Taiwan Environmental Protection Agency regulatory datasets, followed by the Taiwan Cancer Registry for the period 1980–2015. Standardized incidence ratios (SIRs) for cancer were computed for the whole cohort using reference rates of the general population, and also reference labor population. Compared with the general population, SIR of the asbestos occupational cohort for the gastric cancer increased both in males (1.05, 95% confidence interval (CI): 1.02–1.09) and females (1.10, 95% CI: 1.01–1.18). A total of 123 worksites were identified to have cases of malignant mesothelioma, where increased risk for gastric cancer was found with a relative risk of 1.76 (95% CI: 1.63–1.90). This 35-year retrospective cohort study of asbestos-exposed workers in Taiwan may provide support for an association between occupational exposure to asbestos and gastric cancer.

Activity-determined Steps on the Attenuation of Atmospheric PM2.5 Concentration Environmental Kuznets Curve (EKC) at the Country Level: Empirical Evidence in Taiwan

Finding ways to minimize the atmospheric concentration of PM2.5 is an emergency issue throughout the world. This research has found a method to calculate historical PM2.5 data and to define the PM2.5 environmental Kuznets curve (EKC) pattern in Taiwan. From the analysis of the Taiwan Environmental Protection Agency’s public emission data, results showed that the continuous decay of PM2.5 is mostly contributed by the energy policy, while several short-term regular stepwise fluctuations in PM2.5 concentrations are caused by natural activity. Our study also proved that the linear decay with stepwise fluctuations also exhibits an EKC pattern.
 The results herein mean that the continuous decay of PM2.5 is caused by the energy policy, while the regular fluctuation of PM2.5 concentrations is caused by natural activity, like the La Nino phenomena. Based on the viewpoint of this paper, the oil consumption percentage for electricity generation should be first reduced to zero. However, oil-based power plants are an important emergency source of electricity supply. If Taiwan wants to continue the reduction of PM2.5 in the future, then a big capacity electric storage facility should be built as an emergency source of electricity supply. The energy policy in Taiwan at 2025 will be 50% natural gas, 30% coal, and 20% green energy. As oil fuel will reach 0%, PM2.5 will be affected by the energy intensity contributed from coal and natural gas, meaning that PM2.5 will maintain a long-term attenuation trend until 2035.

Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging

This paper proposes a method to detect air pollution by applying a hyperspectral imaging algorithm for visible light, near infrared, and far infrared. By assigning hyperspectral information to images from monocular, near infrared, and thermal imaging, principal component analysis is performed on hyperspectral images taken at different times to obtain the solar radiation intensity. The Beer–Lambert law and multivariate regression analysis are used to calculate the PM2.5 and PM10 concentrations during the period, which are compared with the corresponding PM2.5 and PM10 concentrations from the Taiwan Environmental Protection Agency to evaluate the accuracy of this method. This study reveals that the accuracy in the visible light band is higher than the near-infrared and far-infrared bands, and it is also the most convenient band for data acquisition. Therefore, in the future, mobile phone cameras will be able to analyze the PM2.5 and PM10 concentrations at any given time using this algorithm by capturing images to increase the convenience and immediacy of detection.

PM2.5 Forecasting Model Using a Combination of Deep Learning and Statistical Feature Selection

This paper proposed a PM 2.5 forecasting model using Long Short-Term Model (LSTM) sequence to sequence combined with the statistical method. Correlation Analysis, XGBoost, and Chemical processed are used as the methods to select the essential features. The air pollution data is extracted from Taiwan Environmental Protection Agency (EPA) for the Taichung City dataset in 2014–2018. The study points out that chemical processed model of particulate matter 10 micrometers or less in diameter (PM10), Sulfur Dioxide (SO2), and Nitrogen Dioxide (NO2) have the highest accuracy or lowest Root Mean Square Error (RMSE) and more short training and testing time among the other models. The chemical processed model of PM10, SO2, and NO2 (model B) has the highest accuracy (lowest RMSE), approximately 1 point lower RMSE values, and the shortest training and testing period among the other models. Furthermore, RMSE calculations based on the stations reveal that training with the entire station dataset has a 3 point higher RMSE value than training with each station dataset.

Air pollutants and development of interstitial lung disease in patients with connective tissue disease: a population-based case–control study in Taiwan

ObjectiveThe aim of this study was to assess the association between air pollutant exposure and interstitial lung disease (ILD) in patients with connective tissue diseases (CTDs).SettingA nationwide, population-based, matched case–control...

Outdoor air pollutants exposure associated with pulmonary function and EBC pH value in atopic asthmatic and non-asthmatic children

Objective Air pollution is associated with the prevalence of respiratory diseases. This study aimed to evaluate the impacts of outdoor air pollutants and indoor Dermatophagoides pteronyssinus 1 (Der p 1) exposure on levels of fractional exhaled nitric oxide (FeNO), exhaled breath condensate (EBC) pH, and pulmonary function in atopic children. Methods This study recruited 59 atopic mild-to-moderate asthmatic children and 23 atopic non-asthmatic children. Data on personal characteristics, FeNO, EBC pH, and pulmonary function were collected. Group 1 allergens of Der p 1 were measured on the tops of mattresses and on bedroom floors in the children’s homes, and outdoor air pollutant concentrations were estimated from air quality monitoring stations, using the ordinary kriging method. Results Exposure levels of outdoor air pollutants, except for particulate matter (PM)2.5, for the recruited children met outdoor air quality standards set by the Taiwan Environmental Protection Agency. The lag effect of outdoor PM10 exposure was negatively associated with the forced expiratory volume in one second (FEV1) [(Lag 1: β=–0.771, p = 0.028), and O3 (Lag 1–7: β=–2.02, p = 0.04, Lag 1–28: β=–3.213, p = 0.029)]. Median pulmonary function parameters differed significantly in forced vital capacity (FVC) (p = 0.004) and FEV1 (p = 0.024) values between atopic asthmatic and non-asthmatic children. No association was found between the FeNO/EBC pH level and exposure to Der p 1 allergen and air pollutants in the recruited children. Conclusions Outdoor PM10 and O3 exposure was associated with reduction in FEV1 in atopic asthmatic and non-asthmatic children.

An LSTM-based aggregated model for air pollution forecasting

During the past few years, severe air-pollution problem has garnered worldwide attention due to its effect on health and wellbeing of individuals. As a result, the analysis and prediction of air pollution has attracted a good deal of interest among researchers. The research areas include traditional machine learning, neural networks and deep learning. How to effectively and accurately predict air pollution becomes an important issue. In this paper, we propose an Aggregated LSTM (Long Short-Term Memory) model (ALSTM) based on the LSTM deep learning method. In this new model, we combine local air quality monitoring station, the station in nearby industrial areas, and the stations for external pollution sources. To improve prediction accuracy, we aggregate three LSTM models into a predictive model for early predictions based on external sources of pollution and information from nearby industrial air quality stations. We exploited the data with 17 attributes collected by Taiwan Environmental Protection Agency from 2012 to 2017 as the training data to build the ALSTM forecasting model, and we tested the model using the data collected in 2018. We conducted some experiments to compare our new ALSTM model with SVR (Support Vector Machine based Regression), GBTR (Gradient Boosted Tree Regression), LSTM, etc., in the prediction of PM2.5 for 1–8 h, and evaluated them using various assessment techniques, such as MAE, RMSE, and MAPE. The results reveal that the proposed aggregated model can effectively improve the accuracy of prediction.

Quantifying source apportionment for ambient haze: An image haze extraction approach with air quality monitoring data

The annual average concentration of fine particles (PM2.5) in Taiwan has been decreasing yearly. However, ambient haze has not abated and visibility is still poor. This study proposes a method for quantifying the source apportionment of ambient haze from various constituents, namely measured PM2.5, moisture, and secondary organic aerosols (SOA, represented by the sum of measured O3 and NO2 levels). This study's model-based demisting technology integrated image haze extracting technology with air quality monitoring data. The images for haze extraction were from the instant cameras of the Taiwan Environmental Protection Agency's air quality monitoring stations (AQMSs). Results indicate that haze constituents with the product of light extinction and scene depth have very high correlation (R2 ≥ 0.8452), which demonstrate that this quantification approach was successful. The contributions of ambient haze from various constituents had quarterly and regional variations in the eastern, northern, central, and southern regions of Taiwan, which are slightly, lowly, moderately, and heavily contaminated areas, respectively. Each area was assigned a selected representative AQMS to represent its ambient haze characteristics. Cases where the dominant contributors to haze were PM2.5, moisture, and SOA were studied, in addition to the event days of PM2.5 and ozone. We detail the limits of this approach, especially with regard to the use of images. This approach can help administrators understand the composition of ambient haze to generate effective haze strategies and policies. Residents can use this method to determine the health effects of daily haze.

Nonconformance probability of an air quality index

Air pollution is one of the most important global environmental issues. Taiwan Environmental Protection Agency (EPA) is currently using an Air Quality Index (AQI) to measure and monitor its national air quality. The main objective of this study is to assess the air quality per hour every month in Taichung City of Taiwan from 2014 to 2016 based on the nonconformance probability of the AQI index. The nonconformance probability is defined as the probability that a characteristic of interest falls outside of an acceptance region. A lower confidence bound for the nonconformance probability is applied to test whether the AQI index value exceeds a warning threshold, and then the government could issue warnings according to the decision made by such statistical inference. An unbalanced two-way random effects model is presented for fitting the AQI index values. We evaluate three different lower confidence bound construction methods, including a t-based, an adjusted t-based and a generalized pivotal quantity (GPQ) based methods, through a detailed simulation study. Finally, a hybrid method of the t-based and the adjusted t-based estimators is recommended for practical use.

Ambient Air Pollution and Hospital Admissions for Peptic Ulcers in Taipei: A Time-Stratified Case-Crossover Study.

Very few studies have been performed to determine whether there is a relationship between air pollution and increases in hospitalizations for peptic ulcer, and for those that have occurred, their results may not be completely relevant to Taiwan, where the mixture of ambient air pollutants differ. We performed a time-stratified case-crossover study to investigate the possible association between air pollutant levels and hospital admissions for peptic ulcer in Taipei, Taiwan. To do this, we collected air pollution data from Taiwan's Environmental Protection Agency and hospital admissions for peptic ulcer data for the years 2009–2013 from Taiwan's National Health Insurance's research database. We used conditional logistic regression to analyze the possible association between the two, taking temperature and relative humidity into account. Risk was expressed as odds ratios and significance was expressed with 95% confidence intervals. In our single pollutant model, peptic ulcer admissions were significantly associated with all pollutants (PM10, PM2.5, SO2, NO2, CO, and O3) on warm days (>23 °C). On cool days (<23 °C), peptic ulcer admissions were significantly associated with PM10, NO2, and O3. In our two-pollutant models, peptic ulcer admissions were significantly associated NO2 and O3 when combined with each of the other pollutants on warm days, and with PM10, NO2, and O3 on cool days. It was concluded that the likelihood of peptic ulcer hospitalizations in Taipei rose significantly with increases in air pollutants during the study period.

Assessing the seasonality of occupancy number-associated CO2 level in a Taiwan hospital.

This study enabled the assessment of indoor CO2 levels and evaluated the relationship between occupancy numbers with CO2 levels in a Taiwan hospital. The measurements were conducted over four seasons for five working days (Monday to Friday), with sampling conducted simultaneously from 09:00 am to 5:00 pm and across six locations (for spatial variability): hall (H), registration and cashier (RC), waiting area (WA), occupational therapy room (OT), physical therapy room (PT), and outdoors (O). Based on the analysis, three of the five indoor sampling sites showed significant differences in seasonal CO2 concentrations (p < 0.0001). Based on our result, the physical therapy room had the highest level of CO2 concentration that exceeded the IAQ standard in Taiwan Environmental Protection Agency (EPA) in all seasons, in that the number of occupants contributing to nearly 40% of the variation in CO2 measured. Our results also showed that the indoor/outdoor (I/O) ratios of CO2 concentration for all locations and seasons exceeded 1 in ~ 100% of those locations. The median I/O ratio at sites WA and OT was 2.37 and 2.08 during four seasons, respectively. The highest median I/O ratio was found at site PT, with a calculated range of 2.69 in spring to 3.90 in fall. The highest correlation of occupancy number and CO2 concentration also occurred in PT which correlation coefficients were estimated at 0.47, 0.65, 0.63, and 0.40 in spring, summer, fall, and winter. The findings of the present study can be used to understand occupancy number and its effect on CO2 levels in a hospital environment, as well as the effect of time of day (Monday to Friday) on the number of patients admitted.

Asian dust storms and diabetes hospitalization: a nationwide population-based study

This study analyzed the association between Asian dust storms events and diabetes hospital admissions during 2000–2009 in Taiwan using time-series autoregressive model with explanatory variables. Data came from National Health Insurance Research Database, Taiwan Environmental Protection Agency, and Central Weather Bureau. There were 1,283,509 diabetes hospital admissions and 55 ADS events. Our study showed that Asian dust storms were positively associated with diabetes hospital admissions for women, but the connection was delayed rather than immediate. The females who were aged above 74 and who were active in the labor market were more vulnerable to Asian dust storms.

High total volatile organic compounds pollution in a hospital dental department

Bioaerosols produced by dental procedures may affect indoor air quality and cause infections in dental healthcare workers. To provide air quality data that can be used to protect dental healthcare workers, this study evaluated the air quality and its influencing factors in the dental department of the Chang Gung Memorial Hospital in Taiwan. The study was a cross-sectional study design. Indoor air quality (IAQ) evaluations were conducted in six locations: pediatric dentistry, craniofacial orthodontic dentistry, periodontal dentistry, and general practice dentistry, instrument washing room, and patient waiting area. The measured air quality parameters included temperature, relative humidity, and concentrations of CO2, total volatile organic compounds (TVOCs), suspended particulate matter (PM), and bacteria. TVOCs concentrations at all six sampling stations were found to exceed the indoor air quality standards prescribed by the Taiwan Environmental Protection Agency. The highest concentrations of atmospheric PM10, PM2.5, and PM1 were found in the periodontal dentistry department, while the lowest concentrations occurred in the patient waiting area. The detection rate for Gram-positive bacteria was highest in the pediatric department (25%) and lowest in the instrument washing room (9%). Micrococcus luteus and Bacillus cereus were the primary pathogens detected. The dental departments of the hospital had a serious TVOCs pollution. The air quality of dental departments deserves long-term surveillance and attention.

Cellular Mutagenicity and Heavy Metal Concentrations of Leachates Extracted from the Fly and Bottom Ash Derived from Municipal Solid Waste Incineration.

Two incinerators in Taiwan have recently attempted to reuse the fly and bottom ash that they produce, but the mutagenicity of these types of ash has not yet been assessed. Therefore, we evaluated the mutagenicity of the ash with the Ames mutagenicity assay using the TA98, TA100, and TA1535 bacterial strains. We obtained three leachates from three leachants of varying pH values using the toxicity characteristic leaching procedure test recommended by the Taiwan Environmental Protection Agency (Taiwan EPA). We then performed the Ames assay on the harvested leachates. To evaluate the possible relationship between the presence of heavy metals and mutagenicity, the concentrations of five heavy metals (Cd, Cr, Cu, Pb, and Zn) in the leachates were also determined. The concentrations of Cd and Cr in the most acidic leachate from the precipitator fly ash and the Cd concentration in the most acidic leachate from the boiler fly ash exceeded the recommended limits. Notably, none of the nine leachates extracted from the boiler, precipitator, or bottom ashes displayed mutagenic activity. This data partially affirms the safety of the fly and bottom ash produced by certain incinerators. Therefore, the biotoxicity of leachates from recycled ash should be routinely monitored before reusing the ash.

Air pollution exposure increases the risk of rheumatoid arthritis: A longitudinal and nationwide study

ObjectiveRheumatoid arthritis (RA) has been associated with inhaled pollutants in several studies, and it is a disease of chronic inflammation. The association between air pollution and the risk of RA remains unclear. Therefore, we conducted this nationwide, retrospective, sex-stratification study to evaluate this association. MethodsWe collected data from the Longitudinal Health Insurance Database (LHID), maintained by the Taiwan Bureau of National Health Insurance, and the Taiwan Air Quality-Monitoring Database (TAQMD), released by the Taiwan Environmental Protection Agency. The TAQMD provides the daily concentrations of particulate matter with the aerodynamic diameter <2.5μm (PM2.5) and nitrogen dioxide (NO2) from 74 ambient air quality-monitoring stations distributed all over Taiwan during 1998–2010. The LHID and TAQMD were linked according to the residential areas of insurants and the areas where the air quality-monitoring stations were located. A residential area was defined according to the location of the clinic and hospital that treated acute upper respiratory tract infections. The yearly average air pollutant concentrations were categorized into 4 levels based on quartiles. We evaluated the risk of RA in residents exposed to 4 levels of PM2.5 and NO2 concentrations. ResultsWe detected an increased risk of RA in participants exposed to PM2.5 and NO2. Among four quartiles of NO2 concentration, namely Q1, Q2, Q3, and Q4, the adjusted hazard ratios (aHRs) in Q2, Q3, and Q4 compared with that in Q1 were 1.07 (95% confidence interval [CI]=0.76–1.50), 1.63 (95% CI=1.16–2.31),and 1.49 (95% CI=1.05–2.12), respectively. Regarding the PM2.5 concentrations, the aHRs after exposure to the Q2, Q3, and Q4 levels were 1.22 (95% CI=0.85–1.74), 1.15 (95% CI=0.82–1.62), and 0.79 (95% CI=0.53–1.16), respectively. ConclusionThe results of this nationwide study suggest an increased risk of RA in residents exposed to NO2.