ABSTRACT
Objectives: This study aimed to set up the prediction model of COVID-19 hotspot areas by using the census data and human mobility from telecommunication data in Taipei and New Taipei City. The comparison between their accuracy and limitations can provide the relevant insights for future epidemic control. Methods: The spatio-temporal resolution is fixed at the village level in two cities in May 2021. The static and dynamic data are used to construct the mobility network. The former applies gravity model to mimic human flow, and the latter uses telecommunication data as the measure of mobility. We propose the footprints similarity by structural equivalence of spatial networks and integrate it with the number of confirmed cases for computing the risk level of the villages. The performance of the models is evaluated using ROC curves and logistic regression under different thresholds for the confirmed cases. Results: The mobility derived from the telecommunication data provided better prediction performance than that from the census data;they have an average AUC of 0.75 and 0.69, respectively. Besides, the telecommunication data had a tendency to identify a further village as high-risk zone compared to the gravity model. According to the results of logistic regression, the odds ratio (OR) of exceeding the cases' threshold estimated from the telecommunication data is 1.45 on average, while the one estimated from the census data is 1.10. Conclusions: Telecommunication data can be beneficial in identifying the potential high-risk areas and enhancing situational awareness in advance. © 2022, Taiwan Public Health Association. All rights reserved.
ABSTRACT
To evaluate the difference in hazardous air pollutants in PM2.5 between reference method (National Institute of Environmental Analysis;NIEAA205) and high-volume air sampler (European standard:EN14907 and Japan method), we set up a sampling station on the campus of National Yang-Ming Chiao Tung University, northern Taiwan, during 2014-2015. Both vapor and solid phases of dioxins were collected using high-volume samplers, according to EN14907 and Japan method. The flow rate was set at 500 L min(-1) and 1000 L min(-1), respectively. To compare the difference with the high-volume air sampler, we simultaneously used the reference air sampler based on Taiwan NIEA A205.11C, at the flow rate of 16.7 L min(-1) (BGI PQ200-FRM). The mass concentrations of PM2.5 measured with NIEA A205, EN14907, and Japan method were 20.2 +/- 8.79, 25.4 +/- 10.5 and 28.6 +/- 13.9 mu g m(-3), respectively. The difference of the mass concentration of PM2.5 obtained from two different methods was lower than 3.9%. Moreover, the concentrations of PCDD/F between solid and vapor phases were 56.9-1,090 and 38.6-67.1 fg m(-3) via EN14907 and 51.1-1,150 and 18.4-81.8 fg m(-3) via Japan method, respectively. Obviously, there is no significant difference between these two samplers. Compared to the method of NIEA, high volume air sampling method not only provided equivalently good quality data but offer a higher sample quantity for analyzing the trace level chemical component of hazardous air pollutants and the toxicity in different areas.
ABSTRACT
Long-range pollution transport (LRT) events have a wide impact across East Asia, but are often difficult to track due to imprecise emission inventories and changing domain scales as the plume moves from source to receptor locations. This study adjusts a bottom-up emission inventory based on changes in remotely sensed NO2 column densities for a source region of East Asia, then with CMAQv5.2.1 simulates transport of LRT plumes to Taiwan. Adjustment of an emissions inventory based on satellite measurements during the COVID-19 lockdown in China led to a -59% reduction in emissions over the relevant source area in China compared to base emissions. As a result, PM2.5 mass concentrations were reproduced to match observations (mean fractional bias, MFB of -13.9% and 18.5% at a remote and urban station) as the plume passed through northern Taiwan. Furthermore, the OMI-adjusted emissions simulation brought all of the major PM2.5 components to within -50% of the measured values. Another LRT event from 2018 with more subtle OMI-adjustments to the emissions was also simulated and with improved overall PM2.5 mass concentration at the northern tip of Taiwan (MFB: -91.5%) compared to the base model (MFB: -102.1%), and an acceptable index of agreement (0.78). For the 2018 event, non sea-salt sulfate concentrations were consistently underpredicted (0.2-0.4), while nitrate concentrations were overpredicted by up to factor of 11. Copernicus Atmosphere Monitoring Service (CAMS) reanalysis of the PM(2.)5 concentrations shows high sulfate concentrations in eastern China in the areas associated with 72-h back-trajectories from northern Taiwan during both events, lending support for future model investigations of sulfate source area production and transport to Taiwan. In order to better track these LRT events out of East Asia and optimize OMI-adjustment methodology, it is recommended to explore other satellite-based products to map unaccounted for SO2 sources upstream of Taiwan.
ABSTRACT
The two main approaches that countries are using to ease the strain on healthcare infrastructure is building temporary hospitals that are specialized in treating COVID-19 patients and promoting preventive measures. As such, the selection of the optimal location for a temporary hospital and the calculation of the prioritization of preventive measures are two of the most critical decisions during the pandemic, especially in densely populated areas where the risk of transmission of the virus is highest. If the location selection process or the prioritization of measures is poor, healthcare workers and patients can be harmed, and unnecessary costs may come into play. In this study, a decision support framework using a fuzzy analytic hierarchy process (FAHP) and a weighted aggregated sum product assessment model are proposed for selecting the location of a temporary hospital, and a FAHP model is proposed for calculating the prioritization of preventive measures against COVID-19. A case study is performed for Ho Chi Minh City using the proposed decision-making framework. The contribution of this work is to propose a multiple criteria decision-making model in a fuzzy environment for ranking potential locations for building temporary hospitals during the COVID-19 pandemic. The results of the study can be used to assist decision-makers, such as government authorities and infectious disease experts, in dealing with the current pandemic as well as other diseases in the future. With the entire world facing the global pandemic of COVID-19, many scientists have applied research achievements in practice to help decision-makers make accurate decisions to prevent the pandemic. As the number of cases increases exponentially, it is crucial that government authorities and infectious disease experts make optimal decisions while considering multiple quantitative and qualitative criteria. As such, the proposed approach can also be applied to support complex decision-making processes in a fuzzy environment in different countries. © 2021 Tech Science Press. All rights reserved.