Ridership exceedance exposure risk: Novel indicators to assess PM2.5 health exposure of bike sharing riders.

Identifying the fine particulate matter (PM2.5) exposure risk for bicycle riders is crucial for promoting the development of theory and technology in transportation-related air pollution assessment as well as urban health planning. Previous studies have employed daily mean PM2.5 concentrations and designed routes to evaluate air pollution exposure risk. However, because the daily mean PM2.5 concentrations cannot fully illustrate the intra-day variations in PM2.5, which are typically higher than daily mean values, the adverse effects of PM2.5 concentrations remain underestimated. Moreover, the quantity and representativeness of monitoring samples make large spatial-scale and multi-temporal-scale analysis challenging. By defining hourly exceedance PM2.5 concentration and sharing bicycle rider data, two novel indicators were proposed in our study: exceedance exposure risk of PM2.5 for sharing bicycle riders (EPSR) and accumulative exceedance exposure risk of PM2.5 for sharing bicycle riders (AEPSR). Standard deviation ellipse analysis was conducted to investigate the multi-temporal variation of ESPR and AEPSR. A geographically weighted regression model was applied to quantify the relationship between city function zones and exceedance PM2.5 exposure risk for sharing bicycle riders. Results revealed that the mean values of EPSR and AEPSR during morning peak periods ranged between 0.109 min µg/m3 and 1.27 min µg/m3 and 6.83 min µg/m3 and 43.41 min µg/m3, respectively, whereas the mean values of EPSR and AEPSR during evening peak periods ranged between 0.19 min µg/m3 and 4.28 min µg/m3 and 14.67 min µg/m3 and 357.66 min µg/m3, respectively. This implied that sharing bicycle riders were exposed to higher PM2.5-related risks during the evening than in the morning. When considering the accumulative effects, the average centers of the AEPSR moved to the north side as compared to the average centers of the EPSR. Expanding areas of EPSR shrunk by 20.25 km2. This indicated that accumulative effects aggregated spatial clusters of exceedance PM2.5 exposure risk for sharing bicycle riders more tightly to the north of the study areas. Spatiotemporal variation of EPSR and AEPSR led us to investigate the mechanism behind this phenomenon. Spatial associations between city function zones and EPSR and AEPSR showed that sharing bicycle riders experienced more severe exceedance PM2.5 exposure risk around financial/corporations and leisure service areas, with R2 values of 0.33 and 0.35, respectively. This spatial association tended to be more significant during the evening peak periods. By developing two novel indicators, the increasing health threats for bicycle riders caused by exceedance PM2.5 were investigated in this study. The mechanism results should be included for developing mitigation strategies to alleviate the adverse effects of air pollution for public rider participators and achieving the goal of eco-health cities.

Explicit Spatializing Heat-Exposure Risk and Local Associated Factors by coupling social media data and automatic meteorological station data.

Extremely high temperatures, a major cause for weather-related public health issues, are projected to intensify and become more frequent. To mitigate the adverse effects, a low-cost and effective risk assessment method should be developed. Therefore, we applied automatic meteorological station data and population mobility data to develop a high spatiotemporal resolution temperature risk assessment method. The population mobility analysis results showed the working/residential complex pattern in Tianhe District, with hotspots of spatial clustering located in the north, southwest, and southeast of the study area. Taking the population mobility patterns into consideration, high-temperature risk assessment results with a resolution of 100 m were obtained. The total mortality cases in 2014 and 2015 were used to validate this result. The validation showed that the total mortality in the high-temperature risk areas accounted for over 36% of that in Tianhe District. Thus, the method introduced in this study is capable of reflecting weather-related risk. Furthermore, the high-temperature risk assessment results showed that most of the risky areas were located in the southwest of the study area. Two peak times of the risk areas were determined, being before dawn and in the evening. Compared with the risk areas during weekdays, those at weekends expanded. In addition, we used the geographically weighted regression model to investigate the potential influencing factors. Individual factor contributed more than 22.4% to the spatial distribution of heat exposure. Catering services, transportation services, and living services were higher than others, with mean R2 values of 0.28, 0.23, and 0.25, respectively. More than 47.9% of spatial distribution of heat exposure was attributed to joint function of influencing factors, with global R2 ranged from 0.23 to 0.34. Our research introduces a spatial-specific method to quantitatively assess high-temperature risk. Moreover, the mechanisms behind the spatial distribution of the high-temperature risk were discussed. The theoretical and management implications can help urban designers and energy governors to develop useful strategies to mitigate weather-related public health risks.

Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data.

The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey-related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the no-sample characteristics of social media data, PM2.5 exposure risk for older adults is absent. We proposed combining social media data and population survey-derived data to map the total PM2.5 exposure risk. Hourly exceedance PM2.5 exposure risk indicators based on population modeling (HEPEpmd) and social media data (HEPEsm) were developed. Daily accumulative HEPEsm and HEPEpsd ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPEsm and HEPEpsd were observed at 13:00, 18:00, and 22:00. The peak value of HEPEsm increased with time, which exhibited a reverse trend to HEPEpsd. The spatial center of HEPEsm moved from the northwest of the study area to the center. The spatial center of HEPEpsd moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPEsm was nearly 1.5 times larger than that of HEPEpsd. The expansion areas of HEPEpsd aggregated in the old downtown, in which the contribution of HEPEpsd was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM2.5 exposure risk. Consequently, exposure risk results provided foundations to develop PM2.5 pollution mitigation strategies as well as scientific supports for sustainability and eco-health achievement.