Rationale: The biological mechanisms linking air pollution to cardiovascular events still remain largerly unclear. Objectives: To investigate whether biological mechanisms linking air pollution to cardiovascular events occurred concurrently in human subjects exposed to urban air pollutants. Methods: We recruited a panel of 76 young, healthy students from a university in Taipei. Between April and June of 2004 or 2005, three measurements were made in each participant of high-sensitivity C-reactive protein (hs-CRP), 8-hydroxy-2′-deoxyguanosine (8-OHdG), plasminogen activator fibrinogen inhibitor-1 (PAI-1), tissue-type plasminogen activator (tPA) in plasma, and heart rate variability (HRV). Gaseous air pollutants were measured at one air-monitoring station inside their campus, and particulate air pollutants were measured at one particulate matter supersite monitoring station 1 km from their campus. We used linear mixed-effects models to associate biological endpoints with individual air pollutants averaged over 1- to 3-day periods before measurements were performed. Measurements and Main Results: We found that increases in hs-CRP, 8-OHdG, fibrinogen, and PAI-1, and decreases in HRV indices were associated with increases in levels of particles with aerodynamic diameters less than 10 μm and 2.5 μm, sulfate, nitrate, and ozone (O3) in single-pollutant models. The increase in 8-OHdG, fibrinogen, and PAI-1, and the reduction in HRV remained significantly associated with 3-day averaged sulfate and O3 levels in two-pollutant models. There were moderate correlations (r = -0.3) between blood markers of hs-CRP, fibrinogen, PAI-1, and HRV indices. Conclusions: Urban air pollution is associated with inflammation, oxidative stress, blood coagulation and autonomic dysfunction simultaneously in healthy young humans, with sulfate and O3 as two major traffic-related pollutants contributing to such effects.

A peer-reviewed open access journal published by the National Institute of Environmental Health Sciences

Background: Low infection and case-fatality rates have been thus far observed in Taiwan. One of the reasons for this major success is better use of big data analytics in efficient contact tracing and management and surveillance of those who require quarantine and isolation. Objective: We present here a unique application of big data analytics among Taiwanese people who had contact with more than 3000 passengers that disembarked at Keelung harbor in Taiwan for a 1-day tour on January 31, 2020, 5 days before the outbreak of coronavirus disease (COVID-19) on the Diamond Princess cruise ship on February 5, 2020, after an index case was identified on January 20, 2020. Methods: The smart contact tracing–based mobile sensor data, cross-validated by other big sensor surveillance data, were analyzed by the mobile geopositioning method and rapid analysis to identify 627,386 potential contact-persons. Information on self-monitoring and self-quarantine was provided via SMS, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests were offered for symptomatic contacts. National Health Insurance claims big data were linked, to follow-up on the outcome related to COVID-19 among those who were hospitalized due to pneumonia and advised to undergo screening for SARS-CoV-2. Results: As of February 29, a total of 67 contacts who were tested by reverse transcription–polymerase chain reaction were all negative and no confirmed COVID-19 cases were found. Less cases of respiratory syndrome and pneumonia were found after the follow-up of the contact population compared with the general population until March 10, 2020. Conclusions: Big data analytics with smart contact tracing, automated alert messaging for self-restriction, and follow-up of the outcome related to COVID-19 using health insurance data could curtail the resources required for conventional epidemiological contact tracing.