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Dr. Shih-Chun Candice Lung
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan

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0 Exposure and Risk Assessment
0 Organic Aerosols
0 Environmental Health Management
0 Health Adaptation
0 Heat Vulnerability Assessment

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Environmental Health Management

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Journal article
Published: 04 July 2021 in Sensors
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Smartwatches are being increasingly used in research to monitor heart rate (HR). However, it is debatable whether the data from smartwatches are of high enough quality to be applied in assessing the health impacts of air pollutants. The objective of this study was to assess whether smartwatches are useful complements to certified medical devices for assessing PM2.5 health impacts. Smartwatches and medical devices were used to measure HR for 7 and 2 days consecutively, respectively, for 49 subjects in 2020 in Taiwan. Their associations with PM2.5 from low-cost sensing devices were assessed. Good correlations in HR were found between smartwatches and certified medical devices (rs > 0.6, except for exercise, commuting, and worshipping). The health damage coefficients obtained from smartwatches (0.282% increase per 10 μg/m3 increase in PM2.5) showed the same direction, with a difference of only 8.74% in magnitude compared to those obtained from certified medical devices. Additionally, with large sample sizes, the health impacts during high-intensity activities were assessed. Our work demonstrates that smartwatches are useful complements to certified medical devices in PM2.5 health assessment, which can be replicated in developing countries.

ACS Style

Ming-Chien Tsou; Shih-Chun Lung; Chih-Hui Cheng. Demonstrating the Applicability of Smartwatches in PM2.5 Health Impact Assessment. Sensors 2021, 21, 4585 .

AMA Style

Ming-Chien Tsou, Shih-Chun Lung, Chih-Hui Cheng. Demonstrating the Applicability of Smartwatches in PM2.5 Health Impact Assessment. Sensors. 2021; 21 (13):4585.

Chicago/Turabian Style

Ming-Chien Tsou; Shih-Chun Lung; Chih-Hui Cheng. 2021. "Demonstrating the Applicability of Smartwatches in PM2.5 Health Impact Assessment." Sensors 21, no. 13: 4585.

Preprint content
Published: 05 April 2021
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Background Climate change has augmented heat-related illnesses and deaths. Selecting proper thresholds in a heat-warning system is critical to reducing health risks. Methods This work evaluates the applicability of a modified generalized additive model (GAM) at different spatial scales, and identifies proper heat-warning thresholds using empirical morbidity and mortality records of 18 years from a population database and taking into account substantial health risk increases of the lag effects of 0–2 days. Reference-adjusted risk ratio (RaRR), i.e., risk ratio of threshold candidates against that of a reference, was respectively evaluated for heat-related emergency and hospital visits and all-cause mortality. The threshold with the highest RaRR among the candidates with infrequent occurrence is potentially the best one. Wet-bulb globe temperature (WBGT) and temperature were both used heat indicator in the model for comparison. Results It was found that WBGT is a more sensitive heat-health indicator than temperature. The highest RaRR with WBGT for the whole Taiwan island was observed to shift from lag 0 in emergency visits (1.44) to lags 0–1 in hospital visits (1.18) and also to lag 1 in all-cause mortality (1.04). For different age groups, children had the highest RaRR with WBGT of emergency visits on lag 0 (1.87) while the elderly had the highest RaRR for all-cause mortality on lag 0 (1.04), for hospital visits on lag 1 (1.23), and for emergency visits on lag 2 (1.38), respectively. Emergency visit is the most sensitive heat-related health record and should thus be employed, if available, to select heat-warning threshold. With the highest RaRR in emergency visits and the occurring frequency considered, the best area-specific thresholds can be chosen for various sizes of population at-risk. The novel RaRR allows comparison of health risks across different categories, providing a solid scientific basis for threshold selection. Conclusions This work demonstrated the feasibility and flexibility of the proposed approach which considers substantial enhancement of health risks on lags 0 to 2, removes the rare-event interference, and accommodates at-risk populations of different sizes. This methodology can be applied by authorities worldwide for selecting proper heat-health thresholds according to their own morbidity/mortality records.

ACS Style

Shih-Chun Candice Lung; Jou-Chen Joy Yeh; Jing-Shiang Hwang. Using GAM regression analysis to identify proper heat-warning thresholds: a population-based observational study. 2021, 1 .

AMA Style

Shih-Chun Candice Lung, Jou-Chen Joy Yeh, Jing-Shiang Hwang. Using GAM regression analysis to identify proper heat-warning thresholds: a population-based observational study. . 2021; ():1.

Chicago/Turabian Style

Shih-Chun Candice Lung; Jou-Chen Joy Yeh; Jing-Shiang Hwang. 2021. "Using GAM regression analysis to identify proper heat-warning thresholds: a population-based observational study." , no. : 1.

Journal article
Published: 23 March 2021 in Science of The Total Environment
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In recent years, many surveillance cameras have been installed in the Greater Taipei Area, Taiwan; traffic data obtained from these surveillance cameras could be useful for the development of roadway-based emissions inventories. In this study, web-based traffic information covering the Greater Taipei Area was obtained using a vision-based traffic analysis system. Web-based traffic data were normalized and added to the Community Multiscale Air Quality (CMAQ) model to study the impact of vehicle emissions on air quality in the Greater Taipei Area. According to an analysis of the obtained traffic data, sedans were the most common vehicles in the Greater Taipei Area, followed by motorcycles. Moderate traffic conditions with an average speed of 30–50 km/h were most prominent during weekdays, whereas traffic flow with an average speed of 50–70 km/h was most common during weekends. The proportion of traffic flows in free-flow conditions (>70 km/h) was higher on weekends than on weekdays. Two peaks of traffic flow were observed during the morning and afternoon peak hours on weekdays. On the weekends, this morning peak was not observed, and the variation in vehicle numbers was lower than on weekdays. The simulation results suggested that the addition of real-time traffic data improved the CMAQ model's performance, especially for the CO and PM2.5 concentrations. According to sensitivity tests for total and vehicle emissions in the Greater Taipei Area, vehicle emissions contributed to >90% of CO, 80% of NOx, and approximately 50% of fine particulate matter in the downtown areas of Taipei. The vehicle emissions contribution was affected by vehicle emissions and meteorological conditions. The connection between the surveillance camera data, vehicle emissions, and regional air quality models in this study can also be used to explore the impact of special events (e.g., long weekends and COVID-19 lockdowns) on air quality.

ACS Style

I-Chun Tsai; Chen-Ying Lee; Shih-Chun Candice Lung; Chih-Wen Su. Characterization of the vehicle emissions in the Greater Taipei Area through vision-based traffic analysis system and its impacts on urban air quality. Science of The Total Environment 2021, 782, 146571 .

AMA Style

I-Chun Tsai, Chen-Ying Lee, Shih-Chun Candice Lung, Chih-Wen Su. Characterization of the vehicle emissions in the Greater Taipei Area through vision-based traffic analysis system and its impacts on urban air quality. Science of The Total Environment. 2021; 782 ():146571.

Chicago/Turabian Style

I-Chun Tsai; Chen-Ying Lee; Shih-Chun Candice Lung; Chih-Wen Su. 2021. "Characterization of the vehicle emissions in the Greater Taipei Area through vision-based traffic analysis system and its impacts on urban air quality." Science of The Total Environment 782, no. : 146571.

Preprint content
Published: 04 March 2021
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Background: Wind power has been applied around the world as a source of clean energy. However, wind turbines generate low-frequency noise (LFN, 20-200 Hz), which poses health risks to nearby residents. This study aimed to assess heart rate variability (HRV) response to LFN exposure and to evaluate the LFN exposure (dB, L Aeq) inside households located near wind turbines. Methods: Thirty subjects living within a 500 m radius of wind turbines were recruited. The field campaigns for LFN (L Aeq) and HRV monitoring were carried out in July and December 2018. A generalized additive mixed model was employed to evaluate the relationship between HRV changes and LFN. Results: The results suggested that the standard deviations of all normal to normal R-R intervals reduced significantly by 3.39% with a 95% CI = (0.15%, 6.52%) per 7.86 dB (L Aeq) of LFN in the exposure range of 38.2-57.1 dB (L Aeq)—i.e., a 0.43% reduction per 1 dB (L Aeq). The results of household monitoring showed that the indoor LFN exposure (L Aeq) ranged between 30.7 and 43.4 dB (L Aeq) at a distance of 124-330 m from wind turbines. The worst case had 99.6%, 89.1%, and 96.8% at daytime, evening, and nighttime, respectively, exceeding the LFN standards of the Taiwan Environmental Protection Administration. Moreover, households built with concrete and equipped with airtight windows showed the highest LFN difference of 13.7 dB between indoors and outdoors. Conclusion: This work is the first study assessing the HRV impacts from turbine LFN in Asia, where wind turbines installed within short distances from residential areas. In view of the adverse health impacts of LFN exposure, there should be regulations on the requisite distances of wind turbines from residential communities for health protection.

ACS Style

Chun-Hsiang Chiu; Shih-Chun Candice Lung; Nathan Chen; Jing-Shiang Hwang. Effects of low-frequency noise from wind turbines on heart rate variability in healthy individuals. 2021, 1 .

AMA Style

Chun-Hsiang Chiu, Shih-Chun Candice Lung, Nathan Chen, Jing-Shiang Hwang. Effects of low-frequency noise from wind turbines on heart rate variability in healthy individuals. . 2021; ():1.

Chicago/Turabian Style

Chun-Hsiang Chiu; Shih-Chun Candice Lung; Nathan Chen; Jing-Shiang Hwang. 2021. "Effects of low-frequency noise from wind turbines on heart rate variability in healthy individuals." , no. : 1.

Journal article
Published: 01 March 2021 in Environmental Pollution
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Ambient fine particulate matter (PM2.5) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM2.5 spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM2.5. Daily average PM2.5 data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM2.5 variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM2.5 exposures.

ACS Style

Pei-Yi Wong; Hsiao-Yun Lee; Yu-Cheng Chen; Yu-Ting Zeng; Yinq-Rong Chern; Nai-Tzu Chen; Shih-Chun Candice Lung; Huey-Jen Su; Chih-Da Wu. Using a land use regression model with machine learning to estimate ground level PM2.5. Environmental Pollution 2021, 277, 116846 .

AMA Style

Pei-Yi Wong, Hsiao-Yun Lee, Yu-Cheng Chen, Yu-Ting Zeng, Yinq-Rong Chern, Nai-Tzu Chen, Shih-Chun Candice Lung, Huey-Jen Su, Chih-Da Wu. Using a land use regression model with machine learning to estimate ground level PM2.5. Environmental Pollution. 2021; 277 ():116846.

Chicago/Turabian Style

Pei-Yi Wong; Hsiao-Yun Lee; Yu-Cheng Chen; Yu-Ting Zeng; Yinq-Rong Chern; Nai-Tzu Chen; Shih-Chun Candice Lung; Huey-Jen Su; Chih-Da Wu. 2021. "Using a land use regression model with machine learning to estimate ground level PM2.5." Environmental Pollution 277, no. : 116846.

Journal article
Published: 17 February 2021 in Atmosphere
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Small low-cost sensing (LCS) devices enable assessment of close-to-reality PM2.5 exposures, though their data quality remains a challenge. This work evaluates the precision, accuracy, wearability and stability of a wearable particle LCS device, Location-Aware Sensing System (LASS, with Plantower PMS3003), which is 104 × 66 × 46 mm3 in size and less than 162 g in weight. Real-time particulate matter (PM) exposures in six major Asian transportation modes were assessed. Side-by-side laboratory evaluation of PM2.5 between a GRIMM aerosol spectrometer and sensors yielded a correlation of 0.98 and a mean absolute error of 0.85 µg/m3. LASS readings collected in the summer of 2016 in Taiwan were converted to GRIMM-comparable values. Mean PM2.5 concentrations obtained from GRIMM and converted LASS values of the six different transportation microenvironments were 16.9 ± 11.7 (n = 1774) and 17.0 ± 9.5 (n = 3399) µg/m3, respectively, showing a correlation of 0.93. The average one-hour PM2.5 exposure increments (concentration increase above ambient levels) from converted LASS values for Mass Rapid Transit (MRT), bus, car, scooter, bike and walk were 15.6, 6.7, −19.2, 8.1, 6.1 and 7.1 µg/m3, respectively, very close to those obtained from GRIMM. This work is one of the earliest studies applying wearable particulate matter (PM) LCS devices in exposure assessment in different transportation modes.

ACS Style

Wen-Cheng Vincent Wang; Shih-Chun Candice Lung; Chun-Hu Liu; Tzu-Yao Julia Wen; Shu-Chuan Hu; Ling-Jyh Chen. Evaluation and Application of a Novel Low-Cost Wearable Sensing Device in Assessing Real-Time PM2.5 Exposure in Major Asian Transportation Modes. Atmosphere 2021, 12, 270 .

AMA Style

Wen-Cheng Vincent Wang, Shih-Chun Candice Lung, Chun-Hu Liu, Tzu-Yao Julia Wen, Shu-Chuan Hu, Ling-Jyh Chen. Evaluation and Application of a Novel Low-Cost Wearable Sensing Device in Assessing Real-Time PM2.5 Exposure in Major Asian Transportation Modes. Atmosphere. 2021; 12 (2):270.

Chicago/Turabian Style

Wen-Cheng Vincent Wang; Shih-Chun Candice Lung; Chun-Hu Liu; Tzu-Yao Julia Wen; Shu-Chuan Hu; Ling-Jyh Chen. 2021. "Evaluation and Application of a Novel Low-Cost Wearable Sensing Device in Assessing Real-Time PM2.5 Exposure in Major Asian Transportation Modes." Atmosphere 12, no. 2: 270.

Journal article
Published: 16 February 2021 in Environmental Pollution
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Few studies have investigated the effect of personal PM2.5 and PM1 exposures on heart rate variability (HRV) for a community-based population, especially in Asia. This study evaluates the effects of personal PM2.5 and PM1 exposure on HRV during two seasons for 35 healthy adults living in an urban community in Taiwan. The low-cost sensing (LCS) devices were used to monitor the PM levels and HRV, respectively, for two consecutive days. The mean PM2.5 and PM1 concentrations were 13.7 ± 11.4 and 12.7 ± 10.5 μg/m3 (mean ± standard deviation), respectively. Incense burning was the source that contributed most to the PM2.5 and PM1 concentrations, around 9.2 μg/m3, while environmental tobacco smoke exposure had the greatest impacts on HRV indices, being associated with the highest decrease of 20.2% for high-frequency power (HF). The results indicate that an increase in PM2.5 concentrations of one interquartile range (8.7 μg/m3) was associated with a change of −1.92% in HF and 1.60% in ratio of LF to HF power (LF/HF). Impacts on HRV for PM1 were similar to those for PM2.5. An increase in PM1 concentrations of one interquartile range (8.7 μg/m3) was associated with a change of −0.645% in SDNN, −1.82% in HF and 1.54% in LF/HF. Stronger immediate and lag effects of PM2.5 exposure on HRV were observed in overweight/obese subjects (body mass index (BMI) ≥24 kg/m2) compared to the normal-weight group (BMI <24 kg/m2). These results indicate that even low-level PM concentrations can still cause changes in HRV, especially for the overweight/obese population.

ACS Style

Ming-Chien Mark Tsou; Shih-Chun Candice Lung; Yu-Sheng Shen; Chun-Hu Liu; Yu-Hui Hsieh; Nathan Chen; Jing-Shiang Hwang. A community-based study on associations between PM2.5 and PM1 exposure and heart rate variability using wearable low-cost sensing devices. Environmental Pollution 2021, 277, 116761 .

AMA Style

Ming-Chien Mark Tsou, Shih-Chun Candice Lung, Yu-Sheng Shen, Chun-Hu Liu, Yu-Hui Hsieh, Nathan Chen, Jing-Shiang Hwang. A community-based study on associations between PM2.5 and PM1 exposure and heart rate variability using wearable low-cost sensing devices. Environmental Pollution. 2021; 277 ():116761.

Chicago/Turabian Style

Ming-Chien Mark Tsou; Shih-Chun Candice Lung; Yu-Sheng Shen; Chun-Hu Liu; Yu-Hui Hsieh; Nathan Chen; Jing-Shiang Hwang. 2021. "A community-based study on associations between PM2.5 and PM1 exposure and heart rate variability using wearable low-cost sensing devices." Environmental Pollution 277, no. : 116761.

Journal article
Published: 29 November 2020 in International Journal of Environmental Research and Public Health
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Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) models, to assess variations in particulate matter (PM10) and nitrogen dioxide (NO2) concentrations in Surabaya City, Indonesia. This is the first study to implement spatiotemporal variability of air pollution concentrations in Surabaya City, Indonesia. To develop the prediction models, air pollution data collected from seven monitoring stations from 2010 to 2018 were used as dependent variables, while land-use/land cover allocations within a 250 m to 5000 m circular buffer range surrounding the monitoring stations were collected as independent variables. A supervised stepwise variable selection procedure was applied to identify the important predictor variables for developing the LUR, GWR, and GTWR models. The developed models of LUR, GWR, and GTWR accounted for 49%, 50%, and 51% of PM10 variations and 46%, 47%, and 48% of NO2 variations, respectively. The GTWR model performed better (R2 = 0.51 for PM10 and 0.48 for NO2) than the other two models (R2 = 0.49–0.50 for PM10 and 0.46–0.47 for NO2), LUR and GWR. In the PM10 model four predictor variables, public facility, industry and warehousing, paddy field, and normalized difference vegetation index (NDVI), were selected during the variable selection procedure. Meanwhile, paddy field, residential area, rainfall, and temperature played important roles in explaining NO2 variations. Because of biomass burning issues in South Asia, the paddy field, which has a positive correlation with PM10 and NO2, was selected as a predictor. By using long-term monitoring data to establish prediction models, this model may better depict PM10 and NO2 concentration variations within areas across Asia.

ACS Style

Liadira Kusuma Widya; Chin-Yu Hsu; Hsiao-Yun Lee; Lalu Muhamad Jaelani; Shih-Chun Candice Lung; Huey-Jen Su; Chih-Da Wu. Comparison of Spatial Modelling Approaches on PM10 and NO2 Concentration Variations: A Case Study in Surabaya City, Indonesia. International Journal of Environmental Research and Public Health 2020, 17, 8883 .

AMA Style

Liadira Kusuma Widya, Chin-Yu Hsu, Hsiao-Yun Lee, Lalu Muhamad Jaelani, Shih-Chun Candice Lung, Huey-Jen Su, Chih-Da Wu. Comparison of Spatial Modelling Approaches on PM10 and NO2 Concentration Variations: A Case Study in Surabaya City, Indonesia. International Journal of Environmental Research and Public Health. 2020; 17 (23):8883.

Chicago/Turabian Style

Liadira Kusuma Widya; Chin-Yu Hsu; Hsiao-Yun Lee; Lalu Muhamad Jaelani; Shih-Chun Candice Lung; Huey-Jen Su; Chih-Da Wu. 2020. "Comparison of Spatial Modelling Approaches on PM10 and NO2 Concentration Variations: A Case Study in Surabaya City, Indonesia." International Journal of Environmental Research and Public Health 17, no. 23: 8883.

Journal article
Published: 23 November 2020 in Environmental Research Letters
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ACS Style

Aji Kusumaning Asri; Chia-Pin Yu; Wen-Chi Pan; Yue Leon Guo; Huey-Jen Su; Shih-Chun Candice Lung; Chih-Da Wu; John D Spengler. Global greenness in relation to reducing the burden of cardiovascular diseases: ischemic heart disease and stroke. Environmental Research Letters 2020, 15, 124003 .

AMA Style

Aji Kusumaning Asri, Chia-Pin Yu, Wen-Chi Pan, Yue Leon Guo, Huey-Jen Su, Shih-Chun Candice Lung, Chih-Da Wu, John D Spengler. Global greenness in relation to reducing the burden of cardiovascular diseases: ischemic heart disease and stroke. Environmental Research Letters. 2020; 15 (12):124003.

Chicago/Turabian Style

Aji Kusumaning Asri; Chia-Pin Yu; Wen-Chi Pan; Yue Leon Guo; Huey-Jen Su; Shih-Chun Candice Lung; Chih-Da Wu; John D Spengler. 2020. "Global greenness in relation to reducing the burden of cardiovascular diseases: ischemic heart disease and stroke." Environmental Research Letters 15, no. 12: 124003.

Journal article
Published: 22 October 2020 in Atmosphere
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Environmental epidemiological studies have consistently reported associations between ambient particulate matter (PM) concentrations and everyday mortality/morbidity. Many urban dwellers in Asia live in high-rise apartment buildings; thus, the pollutant concentrations of their immediate outdoor environments are affected by the vertical distribution of pollutants in the atmosphere. The vertical distributions of pollutants provide unique information about their sources and dynamic transport in urban areas, as well as their relationship to people’s exposure at ground level, while the vertical distributions of pollutants have rarely been considered in exposure assessment. In the current study, PM concentrations (with aerodynamic diameters less than 1.0 μm (PM1), 2.5 μm (PM2.5), and 10 μm (PM10)), nanoparticles, black carbon (BC), and particle-bound polycyclic aromatic hydrocarbons (p-PAHs) were measured at different residential heights—6 m, 16 m, and 27 m—at Feng Chia University near a popular night market in Western Taiwan. PM2.5 data were further adopted for health risk estimations. In winter, the magnitude of PM1, PM2.5, and PM10 concentrations were 16 m > 6 m > 27 m; nanoparticle concentrations were 6 m > 27 m > 16 m; and BC and p-PAHs concentrations were 27 m > 16 m > 6 m. In summer, PM1, PM2.5, and PM10 concentrations ranged from 6 m > 16 m > 27 m; nanoparticle concentrations were 6 m > 16 m; and BC and p-PAHs concentrations were from 27 m > 16 m. PM and constituents concentrations during winter were significantly higher in the nighttime than those in daytime, and levels of PM1, PM2.5, and PM10 increased rapidly on 6 m and 16 m heights (but did not increase at 27 m) after 5 pm, whereas these trends became less significant in summer. Health risk analysis for PM2.5 concentrations showed a decrease in lung cancer mortality rate and an extended lifespan for residents living at 27 m. Overall, the current study investigated the vertical profile of particulate matters and analyzed health impacts of PM2.5 at different residential heights in urban area of Taiwan. As the distributions of PM and the constituents varied at different residential heights, exposure and risk assessment of particle concentrations with multiple sizes and various components at broader vertical heights should be further investigated.

ACS Style

Hsiu-Ling Chen; Chi-Pei Li; Chin-Sheng Tang; Shih-Chun Lung; Hsiao-Chi Chuang; Da-Wei Chou; Li-Te Chang. Risk Assessment for People Exposed to PM2.5 and Constituents at Different Vertical Heights in an Urban Area of Taiwan. Atmosphere 2020, 11, 1145 .

AMA Style

Hsiu-Ling Chen, Chi-Pei Li, Chin-Sheng Tang, Shih-Chun Lung, Hsiao-Chi Chuang, Da-Wei Chou, Li-Te Chang. Risk Assessment for People Exposed to PM2.5 and Constituents at Different Vertical Heights in an Urban Area of Taiwan. Atmosphere. 2020; 11 (11):1145.

Chicago/Turabian Style

Hsiu-Ling Chen; Chi-Pei Li; Chin-Sheng Tang; Shih-Chun Lung; Hsiao-Chi Chuang; Da-Wei Chou; Li-Te Chang. 2020. "Risk Assessment for People Exposed to PM2.5 and Constituents at Different Vertical Heights in an Urban Area of Taiwan." Atmosphere 11, no. 11: 1145.

Original article
Published: 12 October 2020 in Indoor Air
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The intensity, frequency, duration, and contribution of distinct PM2.5 sources in Asian households have seldom been assessed; these are evaluated in this work with concurrent personal, indoor, and outdoor PM2.5 and PM1 monitoring using novel low‐cost sensing (LCS) devices, AS‐LUNG. GRIMM‐comparable observations were acquired by the corrected AS‐LUNG readings, with R2 up to 0.998. Twenty‐six non‐smoking healthy adults were recruited in Taiwan in 2018 for 7‐day personal, home indoor, and home outdoor PM monitoring. The results showed 5‐min PM2.5 and PM1 exposures of 11.2 ± 10.9 and 10.5 ± 9.8 µg/m3, respectively. Cooking occurred most frequently; cooking with and without solid fuel contributed to high PM2.5 increments of 76.5 and 183.8 µg/m3 (1‐min), respectively. Incense burning had the highest mean PM2.5 indoor/outdoor (1.44 ± 1.44) ratios at home and on average the highest 5‐min PM2.5 increments (15.0 µg/m3) to indoor levels, among all single sources. Certain events accounted for 14.0‐39.6% of subjects’ daily exposures. With the high resolution of AS‐LUNG data and detailed time–activity diaries, the impacts of sources and ventilations were assessed in detail.

ACS Style

Shih‐Chun Candice Lung; Ming‐Chien Mark Tsou; Shu‐Chuan Hu; Yu‐Hui Hsieh; Wen‐Cheng Vincent Wang; Chen‐Kai Shui; Chee‐Hong Tan. Concurrent assessment of personal, indoor, and outdoor PM 2.5 and PM 1 levels and source contributions using novel low‐cost sensing devices. Indoor Air 2020, 31, 755 -768.

AMA Style

Shih‐Chun Candice Lung, Ming‐Chien Mark Tsou, Shu‐Chuan Hu, Yu‐Hui Hsieh, Wen‐Cheng Vincent Wang, Chen‐Kai Shui, Chee‐Hong Tan. Concurrent assessment of personal, indoor, and outdoor PM 2.5 and PM 1 levels and source contributions using novel low‐cost sensing devices. Indoor Air. 2020; 31 (3):755-768.

Chicago/Turabian Style

Shih‐Chun Candice Lung; Ming‐Chien Mark Tsou; Shu‐Chuan Hu; Yu‐Hui Hsieh; Wen‐Cheng Vincent Wang; Chen‐Kai Shui; Chee‐Hong Tan. 2020. "Concurrent assessment of personal, indoor, and outdoor PM 2.5 and PM 1 levels and source contributions using novel low‐cost sensing devices." Indoor Air 31, no. 3: 755-768.

Journal article
Published: 23 September 2020 in International Journal of Environmental Research and Public Health
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This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial–temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms—random forest (RF) and extreme gradient boosting (XGBoost)—for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.

ACS Style

Chin-Yu Hsu; Yu-Ting Zeng; Yu-Cheng Chen; Mu-Jean Chen; Shih-Chun Candice Lung; Chih-Da Wu. Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration. International Journal of Environmental Research and Public Health 2020, 17, 6956 .

AMA Style

Chin-Yu Hsu, Yu-Ting Zeng, Yu-Cheng Chen, Mu-Jean Chen, Shih-Chun Candice Lung, Chih-Da Wu. Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration. International Journal of Environmental Research and Public Health. 2020; 17 (19):6956.

Chicago/Turabian Style

Chin-Yu Hsu; Yu-Ting Zeng; Yu-Cheng Chen; Mu-Jean Chen; Shih-Chun Candice Lung; Chih-Da Wu. 2020. "Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration." International Journal of Environmental Research and Public Health 17, no. 19: 6956.

Journal article
Published: 03 September 2020 in Sensors
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Many low-cost sensors (LCSs) are distributed for air monitoring without any rigorous calibrations. This work applies machine learning with PM2.5 from Taiwan monitoring stations to conduct in-field corrections on a network of 39 PM2.5 LCSs from July 2017 to December 2018. Three candidate models were evaluated: Multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). The model-corrected PM2.5 levels were compared with those of GRIMM-calibrated PM2.5. RFR was superior to MLR and SVR in its correction accuracy and computing efficiency. Compared to SVR, the root mean square errors (RMSEs) of RFR were 35% and 85% lower for the training and validation sets, respectively, and the computational speed was 35 times faster. An RFR with 300 decision trees was chosen as the optimal setting considering both the correction performance and the modeling time. An RFR with a nighttime pattern was established as the optimal correction model, and the RMSEs were 5.9 ± 2.0 μg/m3, reduced from 18.4 ± 6.5 μg/m3 before correction. This is the first work to correct LCSs at locations without monitoring stations, validated using laboratory-calibrated data. Similar models could be established in other countries to greatly enhance the usefulness of their PM2.5 sensor networks.

ACS Style

Wen-Cheng Vincent Wang; Shih-Chun Candice Lung; Chun-Hu Liu. Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network. Sensors 2020, 20, 5002 .

AMA Style

Wen-Cheng Vincent Wang, Shih-Chun Candice Lung, Chun-Hu Liu. Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network. Sensors. 2020; 20 (17):5002.

Chicago/Turabian Style

Wen-Cheng Vincent Wang; Shih-Chun Candice Lung; Chun-Hu Liu. 2020. "Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network." Sensors 20, no. 17: 5002.

Journal article
Published: 19 August 2020 in Sensors
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Traffic emission is one of the major contributors to urban PM2.5, an important environmental health hazard. Estimating roadside PM2.5 concentration increments (above background levels) due to vehicles would assist in understanding pedestrians’ actual exposures. This work combines PM2.5 sensing and vehicle detecting to acquire roadside PM2.5 concentration increments due to vehicles. An automatic traffic analysis system (YOLOv3-tiny-3l) was applied to simultaneously detect and track vehicles with deep learning and traditional optical flow techniques, respectively, from governmental cameras that have low resolutions of only 352 × 240 pixels. Evaluation with 20% of the 2439 manually labeled images from 23 cameras showed that this system has 87% and 84% of the precision and recall rates, respectively, for five types of vehicles, namely, sedan, motorcycle, bus, truck, and trailer. By fusing the research-grade observations from PM2.5 sensors installed at two roadside locations with vehicle counts from the nearby governmental cameras analyzed by YOLOv3-tiny-3l, roadside PM2.5 concentration increments due to on-road sedans were estimated to be 0.0027–0.0050 µg/m3. This practical and low-cost method can be further applied in other countries to assess the impacts of vehicles on roadside PM2.5 concentrations.

ACS Style

Wen-Cheng Vincent Wang; Tai-Hung Lin; Chun-Hu Liu; Chih-Wen Su; Shih-Chun Candice Lung. Fusion of Environmental Sensing on PM2.5 and Deep Learning on Vehicle Detecting for Acquiring Roadside PM2.5 Concentration Increments. Sensors 2020, 20, 4679 .

AMA Style

Wen-Cheng Vincent Wang, Tai-Hung Lin, Chun-Hu Liu, Chih-Wen Su, Shih-Chun Candice Lung. Fusion of Environmental Sensing on PM2.5 and Deep Learning on Vehicle Detecting for Acquiring Roadside PM2.5 Concentration Increments. Sensors. 2020; 20 (17):4679.

Chicago/Turabian Style

Wen-Cheng Vincent Wang; Tai-Hung Lin; Chun-Hu Liu; Chih-Wen Su; Shih-Chun Candice Lung. 2020. "Fusion of Environmental Sensing on PM2.5 and Deep Learning on Vehicle Detecting for Acquiring Roadside PM2.5 Concentration Increments." Sensors 20, no. 17: 4679.

Journal article
Published: 04 August 2020 in Journal of Exposure Science & Environmental Epidemiology
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Background/objectiveThis work applied a newly developed low-cost sensing (LCS) device (AS-LUNG-P) and a certified medical LCS device (Rooti RX) to assessing PM2.5 impacts on heart rate variability (HRV) and determining important exposure sources, with less inconvenience to subjects.MethodsObservations using AS-LUNG-P were corrected by side-by-side comparison with GRIMM instruments. Thirty-six nonsmoking healthy subjects aged 20–65 years were wearing AS-LUNG-P and Rooti RX for 2–4 days in both Summer and Winter in Taiwan.ResultsPM2.5 exposures were 12.6 ± 8.9 µg/m3. After adjusting for confounding factors using the general additive mixed model, the standard deviations of all normal to normal intervals reduced by 3.68% (95% confidence level (CI) = 3.06–4.29%) and the ratios of low-frequency power to high-frequency power increased by 3.86% (CI = 2.74–4.99%) for an IQR of 10.7 µg/m3 PM2.5, with impacts lasting for 4.5–5 h. The top three exposure sources were environmental tobacco smoke, incense burning, and cooking, contributing PM2.5 increase of 8.53, 5.85, and 3.52 µg/m3, respectively, during 30-min intervals.SignificanceThis is a pioneer in demonstrating application of novel LCS devices to assessing close-to-reality PM2.5 exposure and exposure–health relationships. Significant HRV changes were observed in healthy adults even at low PM2.5 levels.

ACS Style

Shih-Chun Candice Lung; Nathan Chen; Jing-Shiang Hwang; Shu-Chuan Hu; Wen-Cheng Vincent Wang; Tzu-Yao Julia Wen; Chun-Hu Liu. Panel study using novel sensing devices to assess associations of PM2.5 with heart rate variability and exposure sources. Journal of Exposure Science & Environmental Epidemiology 2020, 30, 937 -948.

AMA Style

Shih-Chun Candice Lung, Nathan Chen, Jing-Shiang Hwang, Shu-Chuan Hu, Wen-Cheng Vincent Wang, Tzu-Yao Julia Wen, Chun-Hu Liu. Panel study using novel sensing devices to assess associations of PM2.5 with heart rate variability and exposure sources. Journal of Exposure Science & Environmental Epidemiology. 2020; 30 (6):937-948.

Chicago/Turabian Style

Shih-Chun Candice Lung; Nathan Chen; Jing-Shiang Hwang; Shu-Chuan Hu; Wen-Cheng Vincent Wang; Tzu-Yao Julia Wen; Chun-Hu Liu. 2020. "Panel study using novel sensing devices to assess associations of PM2.5 with heart rate variability and exposure sources." Journal of Exposure Science & Environmental Epidemiology 30, no. 6: 937-948.

Journal article
Published: 03 August 2020 in Journal of Exposure Science & Environmental Epidemiology
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Daily exposure to PM2.5 in developing countries has not been thoroughly studied partly due to limited resources available. In this research, personal PM2.5 exposures in urban communities in Indonesia were examined using a low-cost sensor, AS-LUNG. Fifty subjects were recruited in both wet and dry seasons. Their personal PM2.5 concentrations, environmental temperature, and relative humidity were measured using corrected AS-LUNG Portable worn or placed in their vicinity. Details on their activities and locations, air quality (air pollution sources), and weather conditions during monitoring were recorded in time-activity diaries completed at 30 min intervals. Results revealed mosquito coil burning as the source of highest exposure, reaching 241.5 μg/m3 but with significant difference between wet and dry seasons. With ambient PM2.5 and relative humidity controlled for, mosquito coil burning contributed 12.02 μg/m3 and 4.84 μg/m3 of personal PM2.5 exposure in wet and dry season, respectively, which was several times higher than the contribution from vehicle emission. The second most contributive source was factory smoke, which increased 4.99 μg/m3 and 3.17 μg/m3 of exposure in wet and dry season, respectively. Findings on contributive factors of high daily personal exposures can serve as useful references for formulating policies and recommendations on exposure reduction and health protection.

ACS Style

Delvina Sinaga; Wiwiek Setyawati; Fang Yi Cheng; Shih-Chun Candice Lung. Investigation on daily exposure to PM2.5 in Bandung city, Indonesia using low-cost sensor. Journal of Exposure Science & Environmental Epidemiology 2020, 30, 1001 -1012.

AMA Style

Delvina Sinaga, Wiwiek Setyawati, Fang Yi Cheng, Shih-Chun Candice Lung. Investigation on daily exposure to PM2.5 in Bandung city, Indonesia using low-cost sensor. Journal of Exposure Science & Environmental Epidemiology. 2020; 30 (6):1001-1012.

Chicago/Turabian Style

Delvina Sinaga; Wiwiek Setyawati; Fang Yi Cheng; Shih-Chun Candice Lung. 2020. "Investigation on daily exposure to PM2.5 in Bandung city, Indonesia using low-cost sensor." Journal of Exposure Science & Environmental Epidemiology 30, no. 6: 1001-1012.

Journal article
Published: 10 July 2020 in Atmospheric Pollution Research
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Identifying realistic pollution source profiles and quantifying the contributions of atmospheric particulate matter are crucial for the development of pollution mitigation strategies to protect public health. In this paper, we proposed a multivariate source apportionment model by using a Bayesian framework for latent source profiles to incorporate expert knowledge regarding emissions that can facilitate source profile estimation, and atmospheric effects, such as meteorological conditions, can improve source concentration estimations. This approach can maintain positivity and summation constraints for source contributions and profiles. Furthermore, available expert knowledge regarding source profiles is incorporated as prior knowledge to avoid restrictive assumptions regarding the presence or absence of chemical constituent tracers in source profile modeling. We used long-term PM2.5 measurements collected from two locations with different environmental characteristics in northern Taiwan to demonstrate the feasibility of the proposed model and evaluated its performance by using simulated data.

ACS Style

Jia-Hong Tang; Shih-Chun Candice Lung; Jing-Shiang Hwang. Source apportionment of PM2.5 concentrations with a Bayesian hierarchical model on latent source profiles. Atmospheric Pollution Research 2020, 11, 1715 -1727.

AMA Style

Jia-Hong Tang, Shih-Chun Candice Lung, Jing-Shiang Hwang. Source apportionment of PM2.5 concentrations with a Bayesian hierarchical model on latent source profiles. Atmospheric Pollution Research. 2020; 11 (10):1715-1727.

Chicago/Turabian Style

Jia-Hong Tang; Shih-Chun Candice Lung; Jing-Shiang Hwang. 2020. "Source apportionment of PM2.5 concentrations with a Bayesian hierarchical model on latent source profiles." Atmospheric Pollution Research 11, no. 10: 1715-1727.

Journal article
Published: 30 June 2020 in Sensors
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To tackle the challenge of the data accuracy issues of low-cost sensors (LCSs), the objective of this work was to obtain robust correction equations to convert LCS signals into data comparable to that of research-grade instruments using side-by-side comparisons. Limited sets of seed LCS devices, after laboratory evaluations, can be installed strategically in areas of interest without official monitoring stations to enable reading adjustments of other uncalibrated LCS devices to enhance the data quality of sensor networks. The robustness of these equations for LCS devices (AS-LUNG with PMS3003 sensor) under a hood and a chamber with two different burnt materials and before and after 1.5 years of field campaigns were evaluated. Correction equations with incense or mosquito coils burning inside a chamber with segmented regressions had a high R2 of 0.999, less than 6.0% variability in the slopes, and a mean RMSE of 1.18 µg/m3 for 0.1–200 µg/m3 of PM2.5, with a slightly higher RMSE for 0.1–400 µg/m3 compared to EDM-180. Similar results were obtained for PM1, with an upper limit of 200 µg/m3. Sensor signals drifted 19–24% after 1.5 years in the field. Practical recommendations are given to obtain equations for Federal-Equivalent-Method-comparable measurements considering variability and cost.

ACS Style

Wen-Cheng Vincent Wang; Shih-Chun Candice Lung; Chun Hu Liu; Chen-Kai Shui. Laboratory Evaluations of Correction Equations with Multiple Choices for Seed Low-Cost Particle Sensing Devices in Sensor Networks. Sensors 2020, 20, 3661 .

AMA Style

Wen-Cheng Vincent Wang, Shih-Chun Candice Lung, Chun Hu Liu, Chen-Kai Shui. Laboratory Evaluations of Correction Equations with Multiple Choices for Seed Low-Cost Particle Sensing Devices in Sensor Networks. Sensors. 2020; 20 (13):3661.

Chicago/Turabian Style

Wen-Cheng Vincent Wang; Shih-Chun Candice Lung; Chun Hu Liu; Chen-Kai Shui. 2020. "Laboratory Evaluations of Correction Equations with Multiple Choices for Seed Low-Cost Particle Sensing Devices in Sensor Networks." Sensors 20, no. 13: 3661.

Journal article
Published: 29 June 2020 in Environmental Pollution
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Conducting studies on sharp particulate matter (PM) gradients in Asian residential communities is difficult due to their complex building arrangements and various emission sources, particularly road traffic. In this study, a synthetic methodology, combining numerical simulations and minor field observations, was set up to investigate the dispersion of traffic-related PM in a typical Asian residential community and its contribution to PM exposure. A Lagrangian particle model (GRAL) was applied to estimate the spatiotemporal variation of the traffic-related PM increments within the community. A detailed topography dataset with 5 m horizontal resolution was used to simulate a micro-scale flow field. The model performance was comprehensively validated using both in-situ and mobile observations. The coefficient of determination (R2) of the simulated vs. observed PM2.5 reached 0.81 by an artery road, and 0.85 in alleys without significant road traffic. The maximum increments of kerbside PM exposure concentration contributed by road traffic during rush hour were found to be 38% (PM10) and 40% (PM2.5). This synthetic method was used to assess the impact of synoptic wind and canyon orientation on residents’ PM2.5 exposure related to traffic exhaust. Perfect exponential decay curves of traffic-related PM2.5 were found within canyons. The decrease of road-traffic PM2.5 on five different floor levels, compared with that on kerbside levels, ranged between 42% and 100%. The results demonstrated that in complex Asian communities, Lagrangian particle models such as GRAL can simulate the spatial distribution of PM10 and PM2.5 and assess the residents’ outdoor exposure.

ACS Style

Hong Ling; Shih-Chun Candice Lung; Ulrich Uhrner. Micro-scale particle simulation and traffic-related particle exposure assessment in an Asian residential community. Environmental Pollution 2020, 266, 115046 .

AMA Style

Hong Ling, Shih-Chun Candice Lung, Ulrich Uhrner. Micro-scale particle simulation and traffic-related particle exposure assessment in an Asian residential community. Environmental Pollution. 2020; 266 ():115046.

Chicago/Turabian Style

Hong Ling; Shih-Chun Candice Lung; Ulrich Uhrner. 2020. "Micro-scale particle simulation and traffic-related particle exposure assessment in an Asian residential community." Environmental Pollution 266, no. : 115046.

Journal article
Published: 23 June 2020 in International Journal of Environmental Research and Public Health
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Exposure to surrounding greenness is associated with reduced mortality in Caucasian populations. Little is known however about the relationship between green vegetation and the risk of death in Asian populations. Therefore, we opted to evaluate the association of greenness with mortality in Taiwan. Death information was retrieved from the Taiwan Death Certificate database between 2006 to 2014 (3287 days). Exposure to green vegetation was based on the normalized difference vegetation index (NDVI) collected by the Moderate Resolution Imagine Spectroradiometer (MODIS). A generalized additive mixed model was utilized to assess the association between NDVI exposure and mortality. A total of 1,173,773 deaths were identified from 2006 to 2014. We found one unit increment on NDVI was associated with a reduced mortality due to all-cause (risk ratio [RR] = 0.901; 95% confidence interval = 0.862–0.941), cardiovascular diseases (RR = 0.892; 95% CI = 0.817–0.975), respiratory diseases (RR = 0.721; 95% CI = 0.632–0.824), and lung cancer (RR = 0.871; 95% CI = 0.735–1.032). Using the green land cover as the alternative green index showed the protective relationship on all-cause mortality. Exposure to surrounding greenness was negatively associated with mortality in Taiwan. Further research is needed to uncover the underlying mechanism.

ACS Style

Hsiao-Yun Lee; Chih-Da Wu; Yi-Tsai Chang; Yinq-Rong Chern; Shih-Chun Candice Lung; Huey-Jen Su; Wen-Chi Pan. Association between Surrounding Greenness and Mortality: An Ecological Study in Taiwan. International Journal of Environmental Research and Public Health 2020, 17, 4525 .

AMA Style

Hsiao-Yun Lee, Chih-Da Wu, Yi-Tsai Chang, Yinq-Rong Chern, Shih-Chun Candice Lung, Huey-Jen Su, Wen-Chi Pan. Association between Surrounding Greenness and Mortality: An Ecological Study in Taiwan. International Journal of Environmental Research and Public Health. 2020; 17 (12):4525.

Chicago/Turabian Style

Hsiao-Yun Lee; Chih-Da Wu; Yi-Tsai Chang; Yinq-Rong Chern; Shih-Chun Candice Lung; Huey-Jen Su; Wen-Chi Pan. 2020. "Association between Surrounding Greenness and Mortality: An Ecological Study in Taiwan." International Journal of Environmental Research and Public Health 17, no. 12: 4525.