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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 (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3) 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.

Exposure surrogates, such as air quality measured at a fixed-site monitor (FSM) or residence, are typically used for health estimates. However, people spend various amounts of time in different microenvironments, including the home, office, outdoors and in transit, where they are exposed to different magnitudes of particle and gaseous air pollutants. Health risks caused by air pollution exposure differ among individuals due to differences in activity, microenvironmental concentration, as well as the toxicity of pollutants. We evaluated individual and combined added health risks (AR) of exposure to PM2.5, NO2, and O3 for 21 participants in their daily life based on real-world personal exposure measurements. Exposure errors from using surrogates were quantified. Inter-and intra-individual variability in health risks and key contributors in variations were investigated using linear mixed-effects models and correlation analysis, respectively. Substantial errors were found between personal exposure concentrations and ambient concentrations when using air quality measurements at either FSM or the residence location. The mean exposure errors based on the measurements taken at either the FSM or residence as exposure surrogates was higher for NO2 than PM2.5, because of the larger spatial variability in NO2 concentrations in urban areas. The daily time-integrated AR for the combined PM2.5, NO2, and O3 (TIARcombine) ranged by a factor of 2.5 among participants and by a factor up to 2.5 for a given person across measured days. Inter-and intra-individual variability in TIARcombine is almost equally important. Several factors were identified to be significantly correlated with daily TIARcombine, with the top five factors, including PM2.5, NO2 and O3 concentrations at ‘home indoor’, O3 concentrations at ‘office indoor’ and ambient PM2.5 concentrations. The results on the contributors of variability in the daily TIARcombine could help in targeting interventions to reduce daily heal

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 PM2.5 and 1040 pseudo networks for NO2. For each pseudo network, population-weighted area representativeness (PWAR) was calculated based on similarity frequency. For PM2.5, 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 NO2 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

Exposure to air pollutants causes adverse human health effects. Indoor air pollution is caused by infiltration of ambient air pollution indoors and indoor generated sources (e.g., cooking). Infiltration of ambient air pollution into homes depends on type and age of home, pollutant, and type of ventilation. Stochastic exposure models quantify variability in air pollutant exposures for a microenvironment based on probabilistic inputs of pollutant infiltration factors. This work develops a procedure to quantify inter-home variability in air pollution infiltration factors. The procedure was developed and demonstrated for a selected pollutant and selected homes in a selected city, with a focus on PM2.5 and selected ventilation conditions. Infiltration factors were estimated based on simultaneous indoor and outdoor concentrations with different averaging times. Three scenarios were implemented: (1) linear regression for all homes combined; (2) linear regression for each home individually; and (3) use of a linear mixed effects model (LMM). The procedure developed in this work will help in quantification of air pollution infiltration factor distributions for indoor microenvironments for use in stochastic exposure models. © 2022 Air and Waste Management Association. All rights reserved.

Shopping malls in Hong Kong are usually located near major roads. Indoor air quality (IAQ) in these buildings is subject to infiltration of outdoor traffic-related pollutants, such as PM10, PM2.5, CO, and NO2. Furthermore, the existence of indoor sources and building geometry added to the complexity of variations in IAQ. To understand outdoor infiltration and spatial heterogeneity of these pollutants, we conducted fixed and cruise indoor sampling, together with simultaneous outdoor measurements, in a typical mall in Hong Kong. The cruise sampling was conducted indoors on a predesigned route and repeated 15 times. Outdoor infiltration was quantified based on regression analysis between indoor and outdoor sampling. Results showed that 75% of PM2.5, 53% of PM10, and 59% of NO2 were infiltrated into the mall during opening hours. Elevated PM2.5 and CO were observed during the dinner period, suggesting an impact from cooking. Substantial spatial variations were observed for PM10, PM2.5, and NO2, particularly at locations near entrances and restaurants. Measures are needed to reduce pollution intrusion from building openings and cook-ing-related sources to improve air quality in the selected mall. Fixed and cruise sampling methods used in this study provide insights on sensor deployment for future air quality monitoring in build-ings. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Lack of characterization of indoor pollutant concentrations has been identified as a key barrier for exposure and health estimates. In this study, a field campaign was conducted to measure indoor concentrations of PM1, PM2.5, PM10, CO, and NO2 in a mechanically ventilated building. Statistical method using multivariate linear regression (MLR) and machine learning using random forest (RF) were used and compared to quantify variations in observed concentrations and were then used to predict indoor concentrations for selected pollutants. The two methods were consistent in identifying major predictors for each pollutant. Outdoor particles were the single largest predictors found for PM1 and PM2.5, while indoor environment and occupant-related variables were dominant predictors for PM10, CO, and NO2 in the selected mall. Based on MLR models, outdoor PM accounted for 91%, 64%, and 25% of variations in indoor PM1, PM2.5, and PM10 during opening hours. More than 30% of indoor CO variations were related to time-dependent activities. Nearly 50% of the indoor NO2 variations were explained by temperature and relative humidity. Both models are useful in predicting indoor concentrations. In the tenfold cross validation, RF models showed high prediction capability for PM1 (R2 > 0.9) and moderate (R2: 0.5 ~ 0.7) for the other four pollutants in both periods except for PM10 during non-opening hours (R2 = 0.3). MLR models exhibited comparable prediction power for PM1 and PM2.5, but generally lower for PM10 and gases. Availability of parameter information in modern cities facilitates the application of such models on large scale, based on proper validation, for better characterizing of indoor air quality. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.

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.

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.

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. © 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd

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.

Portable light-scattering aerosol monitors (PLSAMs) can supplement existing air quality monitoring networks through measuring air pollutant exposure concentrations at high spatiotemporal resolution. However, data collected by PLSAMs are often subject to the simplicity of measurement principle which may lead to errors compared to the regulatory data observed at fixed-site air quality monitoring stations. The main objective of this study was to develop a feasible experimental framework to assess the influence of key factors (e.g., relative humidity (RH)) on the performance of PLSAMs in the real-world conditions. Following the proposed framework, the accuracy and precision of the TSI DustTrak aerosol monitor were evaluated through side-by-side comparison with the stationary reference instruments (SRIs) while taking characteristics of particles, RH, and the concentration range into consideration. DustTrak generally demonstrated low accuracy but high precision in measuring PM2.5 concentrations at the two selected stations. Three calibration models between DustTrak and the SRIs were used to bias correct the DustTrak PM2.5 measurements. The RH-adjusted linear regression calibration method led to better calibration results than the simple linear regression method and the RH-adjusted empirical method, with CV R2 values higher than 0.97, root mean square error less than 1.0 μg/m3, and accuracy values at 3% for two DustTraks. The proposed experimental framework can be extended to field calibration of various types of PLSAMs, and the obtained calibration results can promote a more accurate investigation of particle air pollution using these PLSAMs. © 2019 Elsevier Ltd

Retrofit on existing buildings for better energy performance is widely desired due to large share of building energy consumption. Meanwhile, the importance of thermal comfort and air pollution exposure has attracted increasing attention for occupant health, productivity and sustainable development. Heat, ventilation and air conditioning (HVAC) systems are responsible for a substantial proportion of energy use in buildings and are closely related to indoor environment quality. This study examined the energy consumption and indoor environment in a commercial office building with a retrofitted HVAC system. The retrofitting measures included a sensor-based building management system, dehumidification of outdoor air, and a two-stage particle filtration system. Energy data were collected before and after the retrofit. Field measurements were conducted in both winter and summer to evaluate the thermal comfort and indoor exposure to air pollutants in the retrofitted area. An experiment was designed to assess the benefits of upgraded filters on exposure to ambient particles during summer. By combining all of these measures, the retrofitted HVAC system was able to reduce energy use by 50% while maintaining generally acceptable indoor thermal comfort. Most of the time, the indoor particle levels complied with the World Health Organization's guidelines. The upgraded filtration system with a pleated filter reduced outdoor PM ingress by 30% to 60% more than the aluminum filter used before the retrofit. Co-benefit assessment provides insights into sustainability in building development during a retrofit by holistically examining energy use and the environment. © 2019

Recently, portable monitors have been increasingly used to quantify air pollutant concentrations at high spatiotemporal resolution. A sampling campaign was conducted to measure the fine particulate matter (PM2.5) and carbon monoxide (CO) exposure concentrations in transport microenvironments (TMEs) in Hong Kong in January and June 2015 using TSI DustTrak and Q-Trak portable monitors. The objectives were to: (1) calibrate DustTrak and Q-Trak; (2) evaluate variability between seasons and microenvironments; (3) estimate indoor/outdoor relationships; and (4) determine minimum sample size. Calibration equations, obtained through side-by-side measurement against stationary reference methods in winter and summer, were applied to correct the measured PM2.5 data set. In general, PM2.5 concentrations in all TMEs were significantly higher in winter than in summer. The mean PM2.5 concentration in winter was lower for underground sections of the Mass Transit Railway (MTR) metro system (31 μg/m3) than for other TMEs, whereas in summer TMEs had mean PM2.5 concentrations in the range of 10–15 μg/m3, with above-ground MTR train as an exception, at 23 μg/m3. PM2.5 concentrations measured in TMEs were strongly correlated with nearby air quality monitoring stations (AQMSs) measurements in winter, but in summer there was little correlation. The minimum sample size estimates varied more among TMEs in summer versus winter because of the differences in PM2.5 concentration distributions related to changes in ambient PM2.5 concentrations and ventilation practices. This study provides a feasible protocol on the calibration and application of portable monitors in TME air quality measurement and develops a method for estimating minimum sample size. © 2017

An estimated 54% of the world population resides in cities. In 2012, an estimated 6.5 million deaths (11.6% of all global deaths) were associated with air pollution, which is identified as the biggest environmental risk to health by the World Health Organization (WHO). The 194 WHO Member States developed a road map for “an enhanced global response to the adverse health effects of air pollution” in 2016. One of the main elements of the road map is the estimation of human exposure to pollutants. Exposure depends on the air quality in various microenvironments (e.g. school, offices) where people spend most of their daily time. However, such information is not well characterized, especially in urban areas. Over the past three years, we have carried out a range of exposure measurements in Hong Kong. Concentrations of fine particulate matter (PM2.5) and carbon monoxide (CO) were measured using portable air quality monitors in several microenvironments including schools, offices, homes, shopping malls and popular transportation modes, such as the Mass Transit Railway (MTR) subway system, transit buses, trams, and minibuses. Field data collection was designed based on factors that may affect variability in pollutant concentrations in these microenvironments. A rigorous protocol was developed to enable accurate, reliable and valid measurement using portable monitors. The results demonstrate substantial variations in exposure concentration among microenvironments, affected by the differences in ventilation systems and ventilation practices, diversity in local emissions, and heterogeneity in outdoor concentrations caused by complex urban morphology, such as street canyons and high-rise buildings. Analysis results indicate that outdoor concentration can be a useful indicator for indoor exposure under certain conditions. The collected data will aid in developing estimates of human exposure in various microenvironments, and designing air pollution exposure management strategies.

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.

The magnitude of children's exposure to ambient O3 and PM2.5 and the inter-individual variability in children's exposure were studied. The magnitude and variability in children's exposure were closely associated with children's daily time patterns, varying among individuals by a factor of 7 and 11 over a 95% frequency range for PM2.5 and O3, respectively. The four most important microenvironments to children's daily exposure were home, school, outdoor, and vehicle microenvironments. High end PM2.5 exposures to school age children in Hong Kong were quite high for the October 2011 case study relative to levels that may be of health concern. The estimated high end O3 exposure concentrations were not particularly high. This is an abstract of a paper presented at the 108th AWMA Annual Conference and Exhibition (Raleigh, NC 6/22-25/2015).

School age children are particularly susceptible to exposure to ambient fine particle (PM2.5). To provide insight into factors affecting variability in ambient PM2.5 exposure, distributions of daily PM2.5 exposures for school age children are estimated for four seasons in three climatic zones of the United States using a stochastic microenvironmental exposure model, based on ambient concentration, air exchange rate, penetration factor, deposition rate, census data, meteorological data, and time pattern data. Estimated daily individual exposure varies largely among seasons, regions, and individuals. The mean ratio of ambient exposure to ambient concentration (E-a/C-a) ranges from 0.46 to 0.61 among selected regions and seasons, resulting from differences in air exchange rate. The individual E-a/C-a varies by a factor of 2 to 3 over a 95% frequency range among simulated children, resulting from variability in children's time patterns. These patterns are similar among age groups, but vary with the day of the week and outdoor temperature. Variability in exposure is larger between individuals than between groups. The high end ratio of the E-a/C-a, at the 95th percentile of inter-individual variability, is 30% to 50% higher than the mean E-a/C-a ratio. Results can be used to intepret and adjust exposure errors in epidemiology and to assist in development of exposure mitigation strategies.

Population and diary sampling methods are employed in exposure models to sample simulated individuals and their daily activity on each simulation day. Different sampling methods may lead to variations in estimated human exposure. In this study, two population sampling methods (stratified-random and random-random) and three diary sampling methods (random resampling, diversity and autocorrelation, and Markov-chain cluster [MCC]) are evaluated. Their impacts on estimated children's exposure to ambient fine particulate matter (PM2.5) are quantified via case studies for children in Wake County, NC for July 2002. The estimated mean daily average exposure is 12.9 g/m(3) for simulated children using the stratified population sampling method, and 12.2 g/m(3) using the random sampling method. These minor differences are caused by the random sampling among ages within census tracts. Among the three diary sampling methods, there are differences in the estimated number of individuals with multiple days of exposures exceeding a benchmark of concern of 25g/m(3) due to differences in how multiday longitudinal diaries are estimated. The MCC method is relatively more conservative. In case studies evaluated here, the MCC method led to 10% higher estimation of the number of individuals with repeated exposures exceeding the benchmark. The comparisons help to identify and contrast the capabilities of each method and to offer insight regarding implications of method choice. Exposure simulation results are robust to the two population sampling methods evaluated, and are sensitive to the choice of method for simulating longitudinal diaries, particularly when analyzing results for specific microenvironments or for exposures exceeding a benchmark of concern.