Environmental Protection Administration Research Articles

Incorporating source apportionment and health risk assessment of heavy metals from indoor dust of an industrial area in Dhaka, Bangladesh

A total of 45 deposited dust samples were collected from the preselected 15 households near an industrial area at Savar, Bangladesh. The concentrations of metals were measured by EDXRF spectroscopy and was found at the following trend: Ca > Fe > K > Ti > Mn > Zr > Sr > Rb > Zn > Cu > Pb > Co. The average concentrations of Cu, Zn, Zr, and Pb were found to be higher than that of the soil background values set by Chinese Environmental Protection Administration (CEPA) and the upper continental crust (UCC), while the average concentrations of K, Ti, Fe, and Rb were found to be higher than that of the background values and lower than that of UCC. Alternately, %RSD for some toxic metals (i.e., Fe, Co, Zn and Pb) in the dust samples were ranged broadly (%RSD: 33.54–86.88 %), which indicates that these trace metals might be present in indoor dust samples due to anthropogenic sources, which is consistent with the ANOVA test. In this study, the contamination levels of metals were assessed based on the enrichment factor (EF), pollution load index (PLI), and geo-accumulation index (Igeo). The two ways ANOVA (Fcal = 3.86 >Fcritic = 1.78, df = 14) for EF data had revealed the heterogeneity of metal enrichment in the study area, whereas PLI values were close to 1.0 or > 1, which indicated that the indoor dust samples in the study area might be deteriorated of site quality by the studied metals. The analysis of Igeo also revealed that these dust samples are slightly to extremely polluted. Conversely Pearson correlation analysis, principal component analysis, and cluster analysis were employed to identify the possible sources of such heavy metals, which suggested that the anthropogenic sources are the main sources of the examined heavy elements. Subsequently, the human health risk assessment strategies were applied to identify the routes of exposures based on the US EPA health risk models. For non-cancer effect, ingestion of dust particles is the main exposure route to both the children and adults. The total hazard index (HI) values indicate that both children and adults are vulnerable to non-carcinogenic effect, but children are more vulnerable than adults (tstat = 1.97 >tcritic = 2.039, p = 0.029) at a 95 % confidence level. Nevertheless, no significant carcinogenic health risk due to the presence of Co was found for both children and adults in the study area. According to sensitivity analysis, the ingestion rate (IR) and the concentration of Co posed the most significant impact (> 79.9 %) on cancer risk estimation.

Long-term exposure to air pollution and the risks of venous thromboembolism: a nationwide population-based retrospective cohort study.

To delineate the effects of exposure to air pollution on the risk of venous thromboembolism (VTE). The association between air pollution and arterial occlusive diseases has been well reported in the literature. VTE is the third most common acute cardiovascular syndrome; however, its relationship with exposure to air pollution has been controversial. This study linked data from the Taiwan National Health Insurance Research Database with that from the Taiwan Environmental Protection Administration. Patients who were first admitted for VTE between January 1, 2001, and December 31, 2013, were analyzed. A time-stratified, case-crossover design was employed. Three different exposure periods were defined: exposure for 1month, one quarter, and 1year. Four control periods were designated for each exposure period. The association between exposure to air pollutants and the risk of VTE was tested using logistic regression analysis. Subgroup analyses were also performed, stratified by age, sex, type of VTE, the use of hormone therapy, and level of urbanization at the site of residence. Exposures to particulate matter (PM) smaller than 2.5µm (PM2.5) and those smaller than 10µm (PM10) were associated with higher risks of VTE, with longer exposures associated with higher risk. The concentration of PM2.5 exposure for 1month was linearly associated with a greater risk of VTE up to 28.0µg/m3, beyond which there was no association. PM2.5 exposure for one quarter or 1year remained significantly associated with higher risks of VTE at higher concentrations. The increased risk in VTE associated with exposure to PM2.5 was more prominent in older patients and in patients not under hormone therapy. Similar results were observed for PM10 exposures. Exposure to PM, particularly PM2.5, leads to an increased risk of VTE, with possible accumulative effects. With increased PM production in industrializing countries, the effects of PM on VTE occurrence warrant further attention.

Association Between Exposure to Particulate Matter and the Incidence of Parkinson's Disease: A Nationwide Cohort Study in Taiwan.

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. We utilized data from the Taiwan National Health Insurance Research Database, which is 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 1, 2005) until the occurrence of PD or the end of the study period (December 31, 2017). Participants who were diagnosed with PD before the index date were excluded. To evaluate the association between exposure to PM2.5 and incident PD risk, we employed Cox regression to estimate the hazard ratio and 95% confidence interval (CI). A total of 454,583 participants were included, with a mean (standard deviation) 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. We provide further evidence of an association between PM2.5 exposure and the 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.

Cigarette smoking and PM2.5 might jointly exacerbate the risk of metabolic syndrome.

Cigarette smoking and particulate matter (PM) with aerodynamic diameter < 2.5 μm (PM2.5) are major preventable cardiovascular mortality and morbidity promoters. Their joint role in metabolic syndrome (MS) pathogenesis is unknown. We determined the risk of MS based on PM2.5 and cigarette smoking in Taiwanese adults. The study included 126,366 Taiwanese between 30 and 70 years old with no personal history of cancer. The Taiwan Biobank (TWB) contained information on MS, cigarette smoking, and covariates, while the Environmental Protection Administration (EPA), Taiwan, contained the PM2.5 information. Individuals were categorized as current, former, and nonsmokers. PM2.5 levels were categorized into quartiles: PM2.5 ≤ Q1, Q1 < PM2.5 ≤ Q2, Q2 < PM2.5 ≤ Q3, and PM2.5 > Q3, corresponding to PM2.5 ≤ 27.137, 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3. The prevalence of MS was significantly different according to PM2.5 exposure (p-value = 0.0280) and cigarette smoking (p-value < 0.0001). Higher PM2.5 levels were significantly associated with a higher risk of MS: odds ratio (OR); 95% confidence interval (CI) = 1.058; 1.014-1.104, 1.185; 1.134-1.238, and 1.149; 1.101-1.200 for 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. The risk of MS was significantly higher among former and current smokers with OR; 95% CI = 1.062; 1.008-1.118 and 1.531; 1.450-1.616, respectively, and a dose-dependent p-value < 0.0001. The interaction between both exposures regarding MS was significant (p-value = 0.0157). Stratification by cigarette smoking revealed a significant risk of MS due to PM2.5 exposure among nonsmokers: OR (95% CI) = 1.074 (1.022-1.128), 1.226 (1.166-1.290), and 1.187 (1.129-1.247) for 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. According to PM2.5 quartiles, current smokers had a higher risk of MS, regardless of PM2.5 levels (OR); 95% CI = 1.605; 1.444-1.785, 1.561; 1.409-1.728, 1.359; 1.211-1.524, and 1.585; 1.418-1.772 for PM2.5 ≤ 27.137, 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. After combining both exposures, the group, current smokers; PM2.5 > 38.205 μg/m3 had the highest odds (1.801; 95% CI =1.625-1.995). PM2.5 and cigarette smoking were independently and jointly associated with a higher risk of MS. Stratified analyses revealed that cigarette smoking might have a much higher effect on MS than PM2.5. Nonetheless, exposure to both PM2.5 and cigarette smoking could compound the risk of MS.

Deep learning-based air pollution analysis on carbon monoxide in Taiwan

Global air pollution poses a threat to humanity. Specifically, CO directly affects cardiovascular and other organ tissues and leads to numerous chronic diseases and major public health problems. The effective implementation of a deep learning model for predicting variations in CO levels would enable the early formulation of policies for controlling air pollution. In this study, a seasonal gated recurrent unit (SGRU) model, which is a deep learning time-series prediction model, was developed to predict the levels of CO in Taiwan. Atmospheric CO measurements from 2005 to 2021 were collected from the Environmental Protection Administration of Taiwan and preprocessed using the Kalman filter to achieve accurate forecasting. The performance of the proposed SGRU model was compared with that of the autoregressive integrated moving average (ARIMA), seasonal ARIMA, exponential smoothing (ETS), Holt–Winters ETS, support vector regression, and seasonal long short-term memory models in terms of mean absolute percentage error (MAPE) and root mean square error. The SGRU model achieved the lowest MAPE value of 0.94, which demonstrated its superior performance. The construction of an accurate air pollution prediction model can assist government entities in formulating health and social care strategies and in planning future air pollution control measures.

High-spatiotemporal-resolution PM2.5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data

High-resolution real-time air quality forecasting can alert decision-makers and residents about forthcoming air pollution events and refine air quality management. The Environmental Protection Administration in Taiwan has deployed numerous low-cost air quality microsensors near industrial zones lately to facilitate local air quality monitoring. Nevertheless, the frequent occurrence of missing sensor data due to problems of mobile transmission, frontend/backend device malfunction, or other unforeseen issues would raise difficulty in making quick responses to air pollution incidents. This study proposed a hybrid deep learning model (AE-CNN-BP) collaborating an Autoencoder (AE), a Convolutional Neural Network (CNN), and a Back Propagation Neural Network (BPNN) to effectively extract crucial features from big data for making successive high-spatiotemporal-resolution forecasts of PM2.5 concentrations 4 h ahead. The proposed model was trained and tested in three industrial zones densely installed with microsensors in Kaohsiung City of Taiwan. A high pollution incident was selected to evaluate model performance. The results show that the proposed model could reliably produce nice high-spatiotemporal-resolution forecasts for 12 air quality monitoring stations and 485 microsensors, with Coefficient of Determination (R2) values and Root Mean Squared Error (RMSE) of 0.82 (0.76) and 11.05 (12.75) μg/m3 in the training (testing) stage, respectively. For the selected incident, the Mean Absolute Percentage Error (MAPE) values of the proposed model were 22.3% and 27.1% at T+1 and T+4, respectively. This study demonstrates that the proposed deep learning model based on ensemble datasets of sparsely distributed monitoring stations and densely deployed microsensors can offer reliable high-spatiotemporal-resolution air quality forecasts, benefiting environmental studies and informed policymaking by accounting for local-scale variations in PM2.5 concentrations.

The effect of environmental centralisation on productivity: Evidence from an administrative reform in China

AbstractThis paper adopts a novel institutional perspective—environmental centralisation—to examine its impact on firm productivity in developing countries. In 2008, China's State Environmental Protection Administration was upgraded to the Ministry of Environmental Protection (MEP). This administrative reform strengthened the central government's ability to protect the environment, enabling it to address more environmental affairs and concentrate on environmental rights. Based on the quasi‐natural experiment of the establishment of the MEP, this study takes a new perspective on the impact of environmental centralisation on productivity. The difference‐in‐difference‐in‐difference approach is used for the first time to estimate the impact of environmental centralisation on productivity. We find that environmental centralisation caused by the establishment of the MEP significantly increased the productivity of heavily polluting companies, and a series of robustness tests confirm that the results are credible. We identify three possible mechanisms, namely, facilitating innovation, curbing rent‐seeking behaviour and inhibiting over‐investments. The policy implication of this study is that the effectiveness of environmental policy depends not only on the policy itself but also on the allocation of environmental rights among different levels of government.

An unmanned aerial vehicle for search and rescue applications

Unmanned aerial vehicles (UAVs) have emerged as potential tools for search and rescue. This study evaluates the feasibility of developing UAVs for search and rescue from multiple perspectives, including positioning and environmental monitoring considerations. The system architecture incorporates a UAV equipped with sensors for particulate matter, temperature/humidity, and carbon monoxide (CO) detection. Data collected by these sensors were compared with official data from the Environmental Protection Administration (EPA) in Taiwan to assess the reliability of the obtained results. The findings indicate that the data collected by the UAV closely align with the official data, suggesting their reliability and values as references. Furthermore, the study simulates the use of the UAV for automated flight route planning during disaster scenarios. The UAV efficiently navigates predetermined paths around disaster sites while monitoring environmental factors. The results highlight the importance of developing UAVs for search and rescue, as they integrate Internet of things (IoT) technology and provide geolocation and environmental information. The developed UAV enables the rapid acquisition of critical data, supporting decision-making and execution during rescue operations. This research provides a substantial demonstration of the UAV for search and rescue applications.

The effect of various air pollution and participants' age on semen quality in southern Taiwan

ObjectiveThis study aimed to investigate the association between semen quality and air pollution in southern Taiwan. Materials and methodsIn this retrospective study, 4338 males aged 21–70 years were recruited between 2001 and 2018 from a reproductive medical center. Semen quality was assessed according to standardized methods outlined in the World Health Organization (WHO) Laboratory Manual 1999 and 2010, including total sperm count, progressive sperm motility (%), rapid progressive sperm motility (%), and sperm with normal morphology (%). All designated national air quality automatic continuous monitoring stations measured the levels of air pollution [particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3)], and was documented by Environmental Protection Administration in Taiwan. We collected data on the levels of air pollution based on the participants' residential addresses. ResultsIn our study, we found that progressive and rapid progressive sperm motility significantly decreased annually (p < 0.05). In addition, increasing age influenced total sperm count, progressive sperm motility, rapid progressive sperm motility, and sperm with normal morphology (p < 0.05). Among different air pollution, we observed SO2 was associated with lower rapid progressive sperm motility and lower sperm with normal morphology (β = −0.103, p = 0.043; β = 0.118, p = 0.001, respectively). However, NO2 was associated with higher rapid progressive sperm motility and a high number of sperm with normal morphology (β = 0.129, p = 0.002; β = 0.127, p < 0.001, respectively). ConclusionsThe semen quality in southern Taiwan appears to have declined in recent years. The participant's age for semen analysis was most strongly associated with semen parameters, Moreover, a significant association between SO2 and NO2 levels and semen motility was observed, even after adjusting for multiple comparisons. Further study is required to analyze the dose-dependent effect of SO2 and NO2 on semen parameters.

Effect of Meteorological Factors and Air Pollutants on Daily Hospital Admissions for Ischemic Heart Disease in Lanzhou, China

Introduction: Meteorological factors and air pollutants are believed to be associated with cardiovascular disease. Ischemic heart disease (IHD) is a major public health issue worldwide. Few studies have investigated the associations among meteorological factors, air pollutants, and IHD daily hospital admissions in Lanzhou, China. Methods: We conducted a distributed lag nonlinear model on the basis of 5-year data, aiming at disentangling the impact of meteorological factors and air pollutants on IHD hospital admissions. All IHD daily hospital admissions recorded from January 1, 2015, and December 31, 2019, were obtained from three hospitals in Lanzhou, China. Daily air pollutant concentrations and meteorological data were synchronously collected from Gansu Meteorological Administration and Lanzhou Environmental Protection Administration. Stratified analyses were performed by sex and two age groups. Results: A total of 23,555 IHD hospital admissions were recorded, of which 10,477 admissions were for coronary artery disease (CAD) and 13,078 admissions were for acute coronary syndrome. Our results showed that there was a nonlinear (J-shaped) relationship between temperature and IHD hospital admissions. The number of IHD hospital admissions was positively correlated with NO2, O3, humidity, and pressure, indicating an increased risk of hospital admissions for IHD under NO2, O3, humidity, and pressure exposure. Meanwhile, both extremely low (−12°C) and high (30°C) temperatures reduced IHD hospital admissions, but the harmful effect increased with the lag time in Lanzhou, China, while the cold effect was more pronounced and long-lasting than the heat effect. Subgroup analysis demonstrated that the risk on CAD hospital admissions increased significantly in females and <65 years of age at −12°C. Conclusion: Our findings added to the growing evidence regarding the potential impact of meteorological factors and air pollutants on policymaking from the perspective of hospital management efficiency.

Critical air pollutant assessments and health effects attributed to PM2.5 during and after COVID-19 lockdowns in Iran: application of AirQ+ models.

The aim of this study was to evaluate changes in air quality index (AQI) values before, during, and after lockdown, as well as to evaluate the number of hospitalizations due to respiratory and cardiovascular diseases attributed to atmospheric PM2.5 pollution in Semnan, Iran in the period from 2019 to 2021 during the COVID-19 pandemic. Daily air quality records were obtained from the global air quality index project and the US Environmental Protection Administration (EPA). In this research, the AirQ+ model was used to quantify health consequences attributed to particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5). The results of this study showed positive correlations between air pollution levels and reductions in pollutant levels during and after the lockdown. PM2.5 was the critical pollutant for most days of the year, as its AQI was the highest among the four investigated pollutants on most days. Mortality rates from chronic obstructive pulmonary disease (COPD) attributed to PM2.5 in 2019-2021 were 25.18% in 2019, 22.55% in 2020, and 22.12% in 2021. Mortality rates and hospital admissions due to cardiovascular and respiratory diseases decreased during the lockdown. The results showed a significant decrease in the percentage of days with unhealthy air quality in short-term lockdowns in Semnan, Iran with moderate air pollution. Natural mortality (due to all-natural causes) and other mortalities related to COPD, ischemic heart disease (IHD), lung cancer (LC), and stroke attributed to PM2.5 in 2019-2021 decreased. Our results support the general finding that anthropogenic activities cause significant health threats, which were paradoxically revealed during a global health crisis/challenge.

PO-03-151 TEN-YEAR EFFECTS OF AMBIENT PM2.5 EXPOSURE SIGNIFICANTLY INCREASE SUDDEN CARDIAC DEATH, CARDIOVASCULAR AND ALL-CAUSE MORTALITIES: A NATIONWIDE COHORT STUDY.

Cardiovascular mortality and morbidity have been associated with the air pollution, but the chronic effects and its relation between pollutants and sudden cardiac death remain unclear. The study aimed to investigate the associations between sudden cardiac death, mortality, MACE and air pollutant in a 10-year follow-up nationwide cohort. This study used the National Health Insurance Research Database and comprised 16,778,374 participants over 20 years old staying in the same districts during 10-year follow-ups between 2008 to 2017 in Taiwan. Incidences of sudden cardiac death, all-cause and cardiovascular mortalities, heart failure, myocardial infarction and MACE were compared among air pollutant exposures (PM2.5, PM10, O3, NOx, NO, NO2, CO and SO2) to different quintiles. Mean 10-year exposures of all pollutants at different districts were obtained from Taiwan Environmental Protection Administration Database. The incidences of sudden cardiac death, all-cause and cardiovascular mortalities, myocardial infarction, stroke and MACE increased after mean PM2.5 exposure >23.8μg/m3 and were highest at the upper quantile (>33.0μg/m3) of PM 2.5 exposure, when compared to the other quantiles (p<0.001). All those incidences remained the same when mean PM2.5 exposure was below 23.7μg/m3(lowest & 2nd quantiles). These findings were not found in PM10, O3, NOx, NO, NO2, CO and SO2 pollutants among different quantiles, respectively. Details are show in Fig. A to H. Very long-term (10-year) exposure to ambient PM2.5 significantly increased the risk of sudden cardiac death cardiac and all-cause mortality. A positive correlated increase of those morbidities and mortalities happened after a mean PM2.5 cut-off value of 23.7μg/m3 .

Air Conditioning Operation Strategies for Comfort and Indoor Air Quality in Taiwan’s Elementary Schools

The Executive Yuan in Taiwan plans to install air-conditioning (A/C) in all elementary schools within two years. However, besides the associated energy consumption and environmental issues, the use of A/C will inevitably result in the doors and windows of the classroom being closed, which will increase the accumulation of carbon dioxide (CO2) within the classroom. An excessive indoor CO2 concentration can result in reduced cognitive performance and an impaired learning efficiency. Therefore, the moderate introduction of external air into the classroom is essential to increase the air exchange rate (AER) and reduce the CO2 concentration level. Accordingly, the present study conducts a numerical investigation into the effects of various A/C operation strategies on the CO2 concentration within the classroom given different proportions of students remaining in the classroom during the recess. Overall, the results indicate that the optimal usage strategy is to operate the A/C over the full school day (08:00~15:50 p.m.) in conjunction with a mechanical ventilation system providing a fresh air exchange rate of 5 l/s for every person in the room. However, the use of a mechanical ventilation system inevitably incurs an additional hardware and energy consumption. Thus, an alternative recommendation is also proposed, in which the windows are opened and the air conditioner is turned off at every recess and during the lunchtime period. It is shown that the resulting CO2 concentration in the classroom is still consistent with the Taiwan Environmental Protection Administration (EPA) regulations and the thermal comfort of the students is achieved for more than three-quarters of the school day.

Human Health and Ecological Risks Associated with Total and Bioaccessible Concentrations of Cadmium and Lead in Urban Park Soils.

This study investigated the total and bioaccessible concentrations of cadmium (Cd) and lead (Pb) in urban soils and their associated human health and ecological risk. Total and bioaccessible metal concentrations were found within the safe limits except for Cd, surpassing the State Environmental Protection Administration (SEPA) China limit in 9.5% of parks. Bioaccessible concentrations were higher in the gastric (G) phase than the intestinal (I) phase, while Cd showed more bioaccessibility compared to Pb. Bioaccessible concentrations reduced Hazard Quotient (HQing) values by 2-22 times and 0-2 times for children and adults, respectively, while hazard index (HI) declined by 1.7 times, and the mean total bioaccessible risk of Pb decreased by 20.8 times. Further, the study revealed a low level of contamination factor (CF < 1) and a low degree of contamination (CD < 6), and Potential Ecological Risk Index (PERI) values for all the cities were less than 150, indicating low ecological risk.

Bioaccumulation and Health Risk Assessment of Metals in Small-Sized Fish (Rhodeus sinensis, Ctenogobius giurinus) and Mussel (Cristaria plicata) from a River Reservoir, Southwest China.

In order to assess the bioaccumulation and health risk of metals in a river reservoir, concentrations of copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg) in the water, sediments, two small-sized fish, and a freshwater mussel from the Zhoubai reservoir were examined. The results indicated that all of these metals conform with class one of environmental quality standards for surface water (State Environmental Protection Administration of China, GB 3838-2002). There were no significant differences for total metal concentrations in sediment between the three sampling sites (p > 0.05), but the bioavailable concentrations in S3 were the lowest. The Cd was dominated with exchangeable fraction and showed considerable risk. All metal concentrations except for Pb in Rhodeus sinensis were significantly higher than those in Ctenogobius giurinus (p < 0.05). The metal concentrations in Cristaria plicata showed a similar pattern of bioavailable metals in sediment, indicating that the metal concentrations in aquatic organisms were determined by the bioavailable forms of metals. Negative correlations were observed between the size of fish and concentrations of Cu, Zn, Pb, Cd, and As. However, significant positive correlations were found between the size of mussel and concentrations of Cd (p < 0.01), As (p < 0.05), and Hg (p < 0.01). Zn had the highest BCF values in fish and mussel. The aquatic organisms showed lower ability of metal bioaccumulation from the sediment. Low values of target hazard quotient (THQ), hazard index (HI), and carcinogenic risk (CR) indicated that these metals do not pose a health risk to public through fish and mussel consumption in this study area.

Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions.

Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have been identified as risk factors for chronic diseases such as ischemic heart disease. However, the likelihood evaluation of the disease occurrence probability due to environmental factors is missing. We defined people aged 51-90 years who were free from ischemic heart disease (ICD9: 410-414) in 1996-2002 as the susceptible group. A Bayesian conditional logistic regression model based on a case-crossover design was utilized to construct a risk information system and applied to data from three databases in Taiwan: air quality variables from the Environmental Protection Administration (EPA), meteorological parameters from the Central Weather Bureau (CWB), and subject information from the National Health Insurance Research Database (NHIRD). People living in different geographic regions in Taiwan were found to have different risk factors; thus, disease risk alert intervals varied in the three regions. Disease risk alert intervals can be a reference for weather bureaus to issue health warnings. With early warnings, susceptible groups can take measures to avoid exacerbation of disease when meteorological conditions and air pollution become hazardous to their health.

Spatially adaptive calibrations of airbox PM2.5 data.

The Taiwan air quality monitoring network (TAQMN) and the AirBox network both monitor PM2.5 in Taiwan. The TAQMN, managed by Taiwan's Environmental Protection Administration (EPA), provides high-quality PM2.5 measurements at 77 monitoring stations. The AirBox network launched more recently consists of low-cost, small internet-of-things (IoT) microsensors (i.e., AirBoxes) at thousands of locations. While the AirBox network provides broad spatial coverage, its measurements are unreliable and require calibrations. However, applying a universal calibration procedure to all AirBoxes does not work well because the calibration line varies with local factors, including the chemical compositions of PM2.5 , which are not homogeneous in space. Therefore, different calibrations are needed at different locations to adapt to their local environments. Unfortunately, AirBoxes and EPA locations are misaligned, challenging the calibration task. In this paper, we propose a spatial model with spatially varying coefficients to account for the heterogeneity in the data. Our method gives spatially adaptive calibrations of AirBoxes and produces accurate PM2.5 concentration estimates with their error bars at any location, incorporating two types of measurements. In addition, the proposed method is robust to outliers, requires no colocated data, and provides calibration formulas for new AirBoxes once they are added to the network. We illustrate our approach using hourly PM2.5 data in 2020. After the calibration, the results show that the PM2.5 prediction improves by about 38%-68% in root-mean-squared prediction error. Once the calibration formulas are established, we can obtain reliable PM2.5 values even if we ignore EPAdata.

Environmental laws and ecological restoration projects enhancing ecosystem services in China: A meta-analysis

In recent decades, China has implemented ecological restoration projects (ERPs) to improve biodiversity and ecosystem services (ESs), accordingly, a series of environmental laws were issued to guide ecological restoration. However, quantitative evaluation of the effectiveness of ERPs remains ambiguous. To respond to the UN Decade on Ecosystem Restoration (UNDER), we conducted a meta-analysis of 85 peer-reviewed publications and an interdisciplinary evaluation framework based on China's environmental protection and land administration laws (EPLALs) were established to assess the effectiveness of ERPs. We found that ERPs enhanced ESs by 15–58%. Specifically, ERPs implemented in industrial/mining, and wetland regions significantly increased regulating and cultural services, and in arid and semi-arid regions mainly enhance provisioning services (72.98%). Climate factors were found to be crucial for ecological restoration effectiveness (temperature: r = −0.582, significance <0.05; precipitation: r = 0.635, significance <0.05). China's environmental laws emphasized management and investment in ecological restoration. However, the disclosure, public participation and real-time monitoring of ecological conditions need to be improved urgently. We therefore developed ERP-related policy recommendations and global lessons to help improve the effectiveness of ecological restoration.

Association between Exposure to Particulate Matter Air Pollution during Early Childhood and Risk of Attention-Deficit/Hyperactivity Disorder in Taiwan

(1) Background: Recently, a growing number of studies have provided evidence to suggest a strong correlation between air pollution exposure and attention-deficit/hyperactivity disorder (ADHD). In this study, we assessed the relationship between early-life exposure to particulate matter (PM)10, PM2.5, and ADHD; (2) Methods: The National Health Insurance Research Database (NHIRD) contains the medical records, drug information, inspection data, etc., of the people of Taiwan, and, thus, could serve as an important research resource. Air pollution data were based on daily data from the Environmental Protection Administration Executive Yuan, R.O.C. (Taiwan). These included particulate matter (PM2.5 and PM10). The two databases were merged according to the living area of the insured and the location of the air quality monitoring station; (3) Results: The highest levels of air pollutants, including PM2.5 (adjusted hazard ratio (aHR) = 1.79; 95% confidence interval (CI) = 1.58-2.02) and PM10 (aHR = 1.53; 95% CI = 1.37-1.70), had a significantly higher risk of ADHD; (4) Conclusions: As such, measures for air quality control that meet the WHO air quality guidelines should be strictly and uniformly implemented by Taiwanese government authorities.

Association of ambient air pollutants and cognitive impairment in a community‐dwelling elderly population: A four‐year cohort study

AbstractBackgroundLong‐term ambient air pollution has known to affect cognitive function. However, limited studies investigated the relationship between co‐exposure to ambient air pollutants and cognitive functions in non‐demented older adults. Therefore, this study aimed to explore this association in a community‐based prospective cohort study.MethodThis four‐year prospective cohort study is part of the ongoing Taiwan Initiative Geriatric Epidemiological Research (TIGER, 2011‐present) and included 286 non‐demented older adults between 2015‐2019. Ambient air pollution data were collected from 29 monitoring stations from Taiwan Environmental Protection Administration (TEPA) between 1994 and 2019. Global and domain‐specific (memory, attention, executive function, and language) cognitive functions were assessed using the Taiwanese version of Montreal Cognitive Assessment (MoCA‐T) and a battery of neuropsychological tests. The generalized linear mixed models were used to evaluate the association of co‐exposure to air pollutants with cognitive function adjusting for important covariates. Furthermore, we conducted stratified analyses by olfaction, known as an early marker of dementia.ResultExcept for PM2.5, the annual average exposure of the other five pollutants was below the TEPA standards. A 25‐year high‐level exposure of NO2 (≥ 27.7 ppb) was associated with good performance executive function (β = 0.25) over four years. For joint effect, high‐level (the highest tertile) PM2.5 with low to medium concentration level of NO2, CO, or SO2 were significantly associated with the poor performance of memory (β = ‐0.39 to ‐0.35). Similarly, high‐level PM2.5 with low to medium concentration level of NO2, PM10 with O3 were significantly associated with poor executive function (β = ‐0.28 to ‐0.19). In participants with normal olfaction, high co‐exposure level to PM2.5 and PM10, with CO, SO2, O3, and NO2 was significantly associated with poor memory and executive function (β = ‐0.55 to ‐0.30). No significant associations were found in participants with abnormal olfaction.ConclusionLong‐term low to medium‐level co‐exposure to NO2, CO, or SO2 and high‐level PM2.5 was associated with poor memory; co‐exposure to O3 with PM10 and co‐exposure to NO2 with PM2.5 was associated with poor executive function. A revision on exposure standards shall be considered to protect older adults at the preclinical phase of dementia.