Quantitative assessment of environmental exposure of delivery men in Wuhan

An exploration of urban air health navigation system based on dynamic exposure risk forecast of ambient PM2.5

Under international advocacy for a low-carbon and healthy lifestyle, ambient PM2.5 pollution poses a dilemma for urban residents who wish to engage in outdoor exercise and adopt active low-carbon commuting. In this study, an Urban Air Health Navigation System (UAHNS) was designed and proposed to assist users by recommending routes with the least PM2.5 exposure and dynamically issuing early risk warnings based on topologized digital maps, an application programming interface (API), an eXtreme Gradient Boosting (XGBoost) model, and two-step spatial interpolation. A test of the UAHNS's functions and applications was carried out in Wuhan city. The results showed that, compared with trained random forest (RF), LightGBM, Adaboost models, etc., the XGBoost model performed better, with an R2 exceeding 0.90 and an RMSE of approximately 15.74 μg/m3, based on data from national air and meteorological monitoring stations. Further, the two-step spatial interpolation model was adopted to dynamically generate pollution distribution at a spatial resolution of 300 m*300 m. Then, an exposure comparison was performed under randomly selected commuting routes and times in Wuhan, showing the recommended routes for lower PM2.5 exposure made effectively help. And the route difference ratios of about 14.9 % and 16.9 % for riding and walking, respectively. Finally, the UAHNS platform was integrally realized for Wuhan, consisting of a real-time PM2.5 query, a one-hour PM2.5 prediction function at any location, health navigation on city map, and a personalized health information query.

Polycyclic aromatic hydrocarbons exposure and arterial stiffness-related plasma miRNAs: A panel study

The underlying mechanisms between polycyclic aromatic hydrocarbons (PAHs) exposure and arterial stiffness are poorly understood. We carried out a panel study involving three repeated surveys to examine the associations of individual and mixture of PAHs exposure with arterial stiffness-related miRNAs among 123 community adults. In linear mixed-effect (LME) models, we found that urinary 9-hydroxyfluorene (9-OHFlu), 2-hydroxyphenanthrene (2-OHPh), 9-hydroxyphenanthrene (9-OHPh) at lag 0 day were positively linked to miR-146a and/or miR-222. The Bayesian kernel machine regression (BKMR) analyses revealed positive overall associations of PAHs mixture at lag 0 day with miR-146a and miR-222, and urinary 9-OHFlu contributed the most. In addition, an inter-quartile range (IQR) increase in urinary 9-OHFlu at lag 0 day was associated with elevated miR-146a and miR-222 by 0.16 (95% CI: 0.02, 0.30) to 0.34 (95% CI: 0.13, 0.54). Accordingly, exposure to PAHs, especially 9-OHFlu at lag 0 day, was related to elevated arterial stiffness-related plasma miRNAs.

A Vector Field Approach to Estimating Environmental Exposure Using Human Activity Data

Environmental exposure of people plays an important role in assessing the quality of human life. The most existing methods that estimate the environmental exposure either focus on the individual level or do not consider human mobility. This paper adopts a vector field generated from the observed locations of human activities to model the environmental exposure at the population level. An improved vector-field-generation method was developed by considering people’s decision-making factors, and we proposed two indicators, i.e., the total exposure indicator (TEI) and the average exposure indicator (AEI), to assess various social groups’ environmental exposure. A case study about the risky environmental exposure of coronavirus disease 2019 (COVID-19) was conducted in Guangzhou, China. Over 900 participants with various socioeconomic backgrounds were involved in the questionnaire, and the survey-based activity locations were extracted to generate the vector field using the improved method. COVID-19 pandemic exposure (or risk) was estimated for different social groups. The findings show that people in the low-income group have an 8% to 10% higher risk than those in the high-income group. This new method of vector field may benefit geographers and urban researchers, as it provides opportunities to integrate human activities into the metrics of pandemic risk, spatial justice, and other environmental exposures.