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Traditional air quality data have a spatial resolution of 1 km or above, making it challenging to resolve detailed air pollution exposure in complex urban areas. Combining urban morphology, dynamic traffic emission, regional and local meteorology, physicochemical transformations in air quality models using big data fusion technology, an ultra-fine resolution modeling system was developed to provide air quality data down to street level. Based on one-year ultra-fine resolution data, this study investigated the effects of pollution heterogeneity on the individual and population exposure to particulate matter (PM 2.5 and PM 10), nitrogen dioxide (NO 2), and ozone (O 3) in Hong Kong, one of the most densely populated and urbanized cities. Sharp fine-scale variabilities in air pollution were revealed within individual city blocks. Using traditional 1 km average to represent individual exposure resulted in a positively skewed deviation of up to 200% for high-end exposure individuals. Citizens were disproportionally affected by air pollution, with annual pollutant concentrations varied by factors of 2 to 5 among 452 District Council Constituency Areas (DCCAs) in Hong Kong, indicating great environmental inequities among the population. Unfavorable city planning resulted in a positive spatial coincidence between pollution and population, which increased public exposure to air pollutants by as large as 46% among districts in Hong Kong. Our results highlight the importance of ultra-fine pollutant data in quantifying the heterogeneity in pollution exposure in the dense urban area and the critical role of smart urban planning in reducing exposure inequities. [Display omitted] [ABSTRACT FROM AUTHOR]

City air quality monitoring (AQM) network are typically sparsely distributed due to high operation costs. It is of the question of how well it can reflect public health risks to air pollution given the diversity and heterogeneity in pollution, and spatial variations in population density. Combing high-resolution air quality model, spatial population distribution and health risk factors, we proposed a population-health based metric for AQM representativeness. This metric was demonstrated in Hong Kong using hourly modelling data of PM10, PM2.5, NO2 and O3 in 2019 with grid cells of 45m * 48m. Individual and total hospital admission risks (%AR) of these pollutants were calculated for each cell, and compared with those calculated at 16 monitoring sites using the similarity frequency (SF) method. AQM Representativeness was evaluated by SF and a population-health based network representation index (PHNI), which is population-weighted SF over the study-domain. The representativeness varies substantially among sites as well as between population- and area-based evaluation methods, reflecting heterogeneity in pollution and population. The current AQM network reflects population health risks well for PM10 (PHNI = 0.87) and PM2.5 (PHNI = 0.82), but is less able to represent risks for NO2 (PHNI = 0.59) and O3 (PHNI = 0.78). Strong seasonal variability in PHNI was found for PM, increasing by >11% during autumn and winter compared to summer due to regional transport. NO2 is better represented in urban than rural, reflecting the heterogeneity of urban traffic pollution. Combined health risk (%ARtotal) is well represented by the current AQM network (PHNI = 1), which is more homogenous due to the dominance and anti-correlation of NO2 and O3 related %AR. The proposed PHNI metric is useful to compare the health risk representativeness of AQM for individual and multiple pollutants and can be used to compare the effectiveness of AQM across cities. [ABSTRACT FROM AUTHOR]

School‐age children are particularly susceptible to exposure to air pollutants. To quantify factors affecting children's exposure at school, indoor and outdoor microenvironmental air pollutant concentrations were measured at 32 selected primary and secondary schools in Hong Kong. Real‐time PM10, PM2.5, NO2, and O3 concentrations were measured in 76 classrooms and 23 non‐classrooms. Potential explanatory factors related to building characteristics, ventilation practice, and occupant activities were measured or recorded. Their relationship with indoor measured concentrations was examined using mixed linear regression models. Ten factors were significantly associated with indoor microenvironmental concentrations, together accounting for 74%, 61%, 46%, and 38% of variations observed for PM2.5, PM10, O3, and NO2 microenvironmental concentrations, respectively. Outdoor concentration is the single largest predictor for indoor concentrations. Infiltrated outdoor air pollution contributes to 90%, 70%, 75%, and 50% of PM2.5, PM10, O3, and NO2 microenvironmental concentrations, respectively, in classrooms during school hours. Interventions to reduce indoor microenvironmental concentrations can be prioritized in reducing ambient air pollution and infiltration of outdoor pollution. Infiltration factors derived from linear regression models provide useful information on outdoor infiltration and help address the gap in generalizable parameter values that can be used to predict school microenvironmental concentrations. [ABSTRACT FROM AUTHOR]

Commuters are often exposed to relatively high air pollutant concentrations in public transport microenvironments (TMEs) because of their proximity to emission sources. Previous studies have mainly focused on assessing the concentrations of air pollutants in TMEs, but few studies have distinguished between the contributions of ambient air and internal sources to the exposure of commuters to air pollutants. The main objective of this study was to quantify the contributions of ambient air and internal sources to the measured particulate matter and gaseous pollutant concentrations in selected TMEs in Hong Kong, a high-rise, high-density city in Asia. A sampling campaign was conducted to measure air pollutant concentrations in TMEs in Hong Kong in July and November 2018 using portable air quality monitors. We measured the concentrations of each pollutant in different TMEs and quantified the infiltration of particulate matter into these TMEs. The double-decker bus had the lowest particulate matter concentrations (mean PM 1 , PM 2.5 , and PM 10 concentrations of 5.1, 9.5, and 13 μg/m3, respectively), but higher concentrations of CO (0.9 ppm), NO (422 ppb), and NO 2 (100 ppb). For all the TMEs, about half of the PM 2.5 were PM 1 particles. The Mass Transit Railway (MTR) subway system had a PM 2.5 /PM 10 ratio of about 0.90, whereas the PM 2.5 /PM 10 ratio was about 0.60–0.70 for the other TMEs. The MTR had infiltration factor estimates <0.4 for particulate matter, lower than those of the double-decker bus and minibus. The MTR had the highest contribution from internal sources (mean PM 1 , PM 2.5 , and PM 10 concentrations of 4.6, 13.4, and 15.8 μg/m3, respectively). This study will help citizens to plan commuting routes to reduce their exposure to air pollution and help policy-makers to prioritize effective exposure reduction strategies. [Display omitted] • The PM 2.5 /PM 10 ratio ranged from 0.60 to 0.90 for the studied transportation microenvironments. • The filtering systems of the transportation modes removed PM 1-10 effectively. • The PM infiltration factor was lower for the subway than other modes. • The PM infiltration factor of the subway system was lower than 0.4. • Internal sources contributed more than 60% of the subway PM 1 concentrations. [ABSTRACT FROM AUTHOR]

[Display omitted] For the monitoring of urban air pollution, smart sensors are often seen as a welcome addition to fixed-site monitoring (FSM) networks. Due to price and simple installation, increases in spatial representation are thought to be achieved by large numbers of these sensors, however, a number of sensor errors have been identified. Based on a high-resolution modelling system, up to 400 pseudo smart sensors were perturbated with the aim of simulating common sensor errors and added to the existing FSM network in Hong Kong, resulting in 1200 pseudo networks for PM 2.5 and 1040 pseudo networks for NO 2. For each pseudo network, population-weighted area representativeness (PWAR) was calculated based on similarity frequency. For PM 2.5 , improvements (up to 16%) to the high baseline representativeness (PWAR = 0.74) were achievable only by the addition of high-quality sensors and favourable environmental conditions. The baseline FSM network represents NO 2 less well (PWAR = 0.52), as local emissions in the study domain resulted in high spatial pollution variation. Due to higher levels of pollution (population-weighted average 37.3 ppb) in comparison to sensor error ranges, smart sensors of a wider quality range were able to improve network representativeness (up to 42%). Marginal representativeness increases were found to exponentially decrease with existing sensor number. The quality and maintenance of added sensors had a stronger effect on overall network representativeness than the number of sensors added. Often, a small number of added sensors of a higher quality class led to larger improvements than hundreds of lower-class sensors. Whereas smart sensor performance and maintenance are important prerequisites particularly for developed cities where pollutant concentration is low and there is an existing FSM network, our study shows that for places with high pollutant variability and concentration such as encountered in some developing countries, smart sensors will provide benefits for understanding population exposure. [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]

Sensor technology has enabled the development of portable low-cost monitoring kits that might supplement many applications in conventional monitoring stations. Despite the sensitivity of electrochemical gas sensors to environmental change, they are increasingly important in monitoring polluted microenvironments. The performance of a compact diffusion-based Personal Exposure Kit (PEK) was assessed for real-time gaseous pollutant measurement (CO, O3, and NO2) under typical environmental conditions encountered in the subtropical city of Hong Kong. A dynamic baseline tracking method and a range of calibration protocols to address system performance were explored under practical scenarios to assess the performance of the PEK in reducing the impact of rapid changes in the ambient environment in personal exposure assessment applications. The results show that the accuracy and stability of the ppb level gas measurement is enhanced even in heterogeneous environments, thus avoiding the need for data post-processing with mathematical algorithms, such as multi-linear regression. This establishes the potential for use in personal exposure monitoring, which has been difficult in the past, and for reporting more accurate and reliable data in real-time to support personal exposure assessment and portable air quality monitoring applications. [ABSTRACT FROM AUTHOR]

Hong Kong is an area of complex topography, with mixtures of urban and greenbelt spaces. Local bioaerosol concentrations are multifaceted, depending on seasonal variations of meteorological conditions and emission sources. This study is the first known attempt at both quantitatively measuring and identifying airborne bioaerosol contributions, by utilising multiple single particle ultraviolet light-induced fluorescence spectrometers. Based in the Hong Kong University of Science and Technology's super-site, a WIBS-NEO and PLAIR Rapid-E were operated from June to November, 2018. The purpose of this long-term campaign was to observe the shift in wind patterns and meteorological conditions as the seasons changed, and to investigate how, or if, this impacted on the dispersion and concentrations of bioaerosols in the area. Bioaerosol concentrations based on the particle auto-fluorescence spectra remained low through the summer and autumn months, averaging 4.2 L−1 between June and October. Concentrations were greatest in October, peaking up to 23 L−1. We argued that these concentrations were dominated by dry-weather fungal spores, as evidenced by their spectral profile and relationship with meteorological variables. We discuss potential bioaerosol source regions based on wind-sector cluster analysis and believe that this study paints a picture of bioaerosol emissions in an important region of the world. [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]