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A sequential measurement method is demonstrated for quantifying the variability in exposure concentration during public transportation. This method was applied in Hong Kong by measuring PM2.5 and CO concentrations along a route connecting 13 transportation-related microenvironments within 3-4 h. The study design takes into account ventilation, proximity to local sources, area-wide air quality, and meteorological conditions. Portable instruments were compacted into a backpack to facilitate measurement under crowded transportation conditions and to quantify personal exposure by sampling at nose level. The route included stops next to three roadside monitors to enable comparison of fixed site and exposure concentrations. PM2.5 exposure concentrations were correlated with the roadside monitors, despite differences in averaging time, detection method, and sampling location. Although highly correlated in temporal trend, PM2.5 concentrations varied significantly among microenvironments, with mean concentration ratios versus roadside monitor ranging from 0.5 for MTR train to 1.3 for bus terminal. Measured inter-run variability provides insight regarding the sample size needed to discriminate between microenvironments with increased statistical significance. The study results illustrate the utility of sequential measurement of microenvironments and policy-relevant insights for exposure mitigation and management. [ABSTRACT FROM AUTHOR]

The majority of urban residents live in places with air quality exceeding World Health Organization guidelines. To quantify infiltration of outdoor pollution, and key factors affecting it, common outdoor air pollutants (PM 2.5 , NO 2 , and O 3) were measured at 49 homes in Hong Kong. Infiltration factors (F inf) were derived based on linear regression of simultaneous indoor and outdoor measurements for each pollutant at each home. F inf estimated based on data for home occupancy, during which people were actually exposed, differed by up to 22%, 73% and 63% for PM 2.5 , NO 2 and O 3, respectively, from estimates based on whole monitoring data. This indicates the importance of separating occupancy time to quantify F inf for exposure estimation. The inter-home variability in occupancy F inf ranged from 0.11 to 1.00 (mean: 0.75) for PM 2.5 , 0.14 to 1.00 (mean: 0.53) for NO 2 , and 0.05 to 0.95 (mean: 0.47) for O 3. Ventilation practices (e.g., window opening duration and air-conditioning on/off) explained 48%, 20%, and 10% of the inter-home variations in PM 2.5 , NO 2 and O 3 F inf , respectively. Use of air purifiers explained an additional 8%–9% of variations for PM 2.5 and NO 2. Thus, there is potential to reduce outdoor infiltration by modifying occupant behaviours. Compared to PM 2.5 (R2 = 0.63), the developed models explained less variability in F inf for NO 2 (R2 = 0.40) and O 3 (R2 = 0.10). These two gases are chemically reactive. Further investigation, supported by additional measurements of related chemical species, is needed to improve understanding of infiltration process of reactive gases such as NO 2 and O 3. [Display omitted] • Outdoor infiltration for PM 2.5 , NO 2 and O 3 were quantified in 49 homes. • Separation of occupancy and non-occupancy improved accuracy of infiltration estimation. • Outdoor infiltration varied by a factor of 9, 7 and 19 for PM 2.5 , NO 2 and O 3 among homes. • Ventilation, use of air conditioner and purifier were dominant predictors for infiltration. • Identified predictors may serve as intervention to reduce concentrations at home. [ABSTRACT FROM AUTHOR]

The reduction of NO x emissions in a VOC-limited region can lead to an increase of the local O 3 concentration. An evaluation of the net health effects of such pollutant changes is therefore important to ascertain whether the emission control measures effectively improve the overall protection of public health. In this study, we use a short-term health risk (added health risk or AR) model developed for the multi-pollutant air quality health index (AQHI) in Hong Kong to examine the overall health impacts of these pollutant changes. We first investigate AR changes associated with NO 2 and O 3 changes, followed by those associated with changes in all four AQHI pollutants (NO 2 , O 3 , SO 2 , and particulate matter (PM)). Our results show that for the combined health effects of NO 2 and O 3 changes, there is a significant reduction in AR in urban areas with dense traffic, but no statistically significant changes in other less urbanized areas. The increase in estimated AR for higher O 3 concentrations is offset by a decrease in the estimated AR for lower NO 2 concentrations. In areas with dense traffic, the reduction in AR as a result of decreased NO 2 is substantially larger than the increase in AR associated with increased O 3. When additionally accounting for the change in ambient SO 2 and PM, we found a statistically significant reduction in total AR everywhere in Hong Kong. Our results show that the emission control measures resulting in NO 2 , SO 2 , and PM reductions over the past decade have effectively reduced the AR over Hong Kong, even though these control measures may have partially contributed to an increase in O 3 concentrations. Hence, efforts to reduce NOx, SO 2 , and PM should be continued. Image 1 • Health risk tradeoffs between decreased NO 2 and increased O 3 concentrations. • Lower NO 2 concentrations offset short-term health risk for higher O 3 concentrations. • Short-term health risk for the mixture of NO 2 and O 3 decreased at roadside stations. • Overall health risk related to four AQHI pollutants (NO 2 , O 3 , SO 2 and PM) decreased. [ABSTRACT FROM AUTHOR]

• Developed a personalized real-time air quality forecasting system. • Integrated air quality modelling systems, big data and sensor technologies. • High agreement of predicted and observed air quality. • Interactive mobile application to engage citizens in exposure management. Exposure to air pollutants causes a range of adverse health effects. These harmful effects occur whenever and wherever people come into direct contact with air pollution. Therefore, individual actions that reduce the frequency, duration, and severity of personal contact with air pollution can reduce health risks. We developed a system that empowers the public with personalized information on air quality and exposure health risk. This system, the Personalised Real-Time Air Quality Informatics System for Exposure – Hong Kong (PRAISE-HK, http://praise.ust.hk/), is embodied in an interactive mobile application. PRAISE-HK is based on real-time data on emissions, high resolution urban morphology, meteorology, physical and chemical processes affecting pollutant transport and transformations, extensive measurements of air pollution concentrations in typical locations such as homes, schools, offices, and transportation, and big data integration of sensor monitoring to accurately estimate current and short-term forecasted street-level air quality. The street-level air quality simulation has been validated against reference monitoring data. Ongoing and planned future enhancements to PRAISE-HK include prediction of personal exposure and health response. PRAISE-HK is an example of the use of collective intelligence in a smart city to engage citizens in learning about and managing their own exposure to air pollution. [ABSTRACT FROM AUTHOR]