Relative contributions of ambient air and internal sources to multiple air pollutants in public transportation modes.

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 PM1, PM2.5, and PM10 concentrations of 5.1, 9.5, and 13 µg/m3, respectively), but higher concentrations of CO (0.9 ppm), NO (422 ppb), and NO2 (100 ppb). For all the TMEs, about half of the PM2.5 were PM1 particles. The Mass Transit Railway (MTR) subway system had a PM2.5/PM10 ratio of about 0.90, whereas the PM2.5/PM10 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 PM1, PM2.5, and PM10 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.

Impacts of pollution heterogeneity on population exposure in dense urban areas using ultra-fine resolution air quality data.

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.

Assessment of the impact of sensor error on the representativeness of population exposure to urban air pollutants.

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 for understanding population exposure.

Inter- and Intra-Individual Variability of Personal Health Risk of Combined Particle and Gaseous Pollutants across Selected Urban Microenvironments.

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 health damage related to air pollutants.

Reducing the Influence of Environmental Factors on Performance of a Diffusion-Based Personal Exposure Kit.

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.

A proposed population-health based metric for evaluating representativeness of air quality monitoring in cities: Using Hong Kong as a demonstration.

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.

Factors affecting variability in gaseous and particle microenvironmental air pollutant concentrations in Hong Kong primary and secondary schools.

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.

A feasible experimental framework for field calibration of portable light-scattering aerosol monitors: Case of TSI DustTrak.

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.

Indoor Exposure to Ambient Particles and Its Estimation Using Fixed Site Monitors.

Ambient PM2.5 concentrations measured at fixed site monitors (FSM) are often biased with respect to exposure concentrations because of spatial variability and infiltration. Based on comparison of ambient concentrations from 14 FSMs and of exposure concentrations measured indoors and outdoors at two schools in Hong Kong for winter and summer seasons, the magnitude and sources of exposure error based on using FSMs as a surrogate for exposure are quantified. An approach for bias correcting surrogate exposure estimates from FSMs is demonstrated. The approach is based on a proximity factor (PF) that accounts for differences in spatial locations, proximity to emissions and deviation from dominant wind direction, and an infiltration factor (IF) that varies by season. The combination of the PF and IF reduce bias in mean school exposure estimates from ±90% to ±20%. Bias in exposure estimates from using FSMs as surrogates tend to be smaller for which the exposure site and FSM are aligned with wind direction, have similar sampling height, and are in close proximity. The methodology demonstrated to assess concordance between FSMs and exposure measurement sites can be applied more broadly to help reduce exposure error, which may help to interpret seasonal variations in health estimates.

Factors affecting variability in PM2.5 exposure concentrations in a metro system.

The objectives of this study were to: (1) evaluate PM2.5 inflow to metro train cabins when doors open at stations; (2) assess the spatial and temporal variability in PM2.5 exposure concentration; and (3) quantify the relationship between in-cabin concentration versus outdoor and non-ambient PM2.5. We measured in-cabin PM2.5 concentrations using portable monitors at the door-side and center of a train cabin simultaneously on a Hong Kong metro line. In addition, platform and in-cabin pollutant concentrations near a train door were simultaneously measured. Short-term spikes in PM2.5 concentrations typically occur near train doors when doors open, related to inflow of ambient air aboveground and tunnel air underground. In-cabin PM2.5 exposure concentrations are typically lower away from the doors when the doors open. PM2.5 concentrations inside train cabins and on station platform operating above-ground are more influenced, compared to underground, by outdoor PM2.5. Moreover, non-ambient sources contribute approximately 50% of train in-cabin and station platform PM2.5 concentrations during underground operation. The results help more accurately quantify commuting PM2.5 exposure on a metro system, and can be used to improve population-based exposure simulation models.

Characterization of PM2.5 exposure concentration in transport microenvironments using portable monitors.

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.

Speciated VOC emission inventory and spatial patterns of ozone formation potential in the Pearl River Delta, China.

The Pearl River Delta region (PRD) of China has long suffered from severe ground-level ozone pollution. Knowledge of the sources of volatile organic compounds (VOCs) is essential for ozone chemistry. In this work, a speciated VOC emission inventory was established on the basis of updated emissions and local VOC source profiles. The top 10 species, in terms of ozone formation potentials (OFPs), consisted of isoprene, mp-xylene, toluene, ethylene, propene, o-xylene, 1,2,4-trimethylbenzene, 2-methyl-2-butene, 1-butene, and alpha-pinene. These species contributed only 35.9% to VOCs emissions but accounted for 64.1% of the OFP in the region. The spatial patterns of the VOC source inventory agreed well with city-based source apportionment results, especially for vehicle emissions and industry plus VOC product-related emissions. Mapping of the OFPs and measured ozone concentrations indicated that the formation of higher ozone in the south and southeast of the PRD region differed from that in the Conghua area, a remote area in the north of the PRD. We recommend that the priorities for the control of VOC sources include motorcycles, gasoline vehicles, and solvent use because of their larger OFP contributions.

Road-network-Based spatial allocation of on-road mobile source emissions in the Pearl River Delta region, China, and comparisons with population-based approach.

Gridded air pollutant emission inventories are prerequisites for using air quality models to assess air pollution control strategies and predict air quality. A precise gridded emission inventory will help improve the accuracy of air quality simulation. Mobile source emissions are one of the major contributors to volatile organic compound (VOC) and nitrogen oxide (NOx) pollutants, the precursors of ozone formation. However, because of the complexity of road networks and variations in traffic flows at different road types and locations, spatial allocation of emissions from mobile sources into grid cells is challenging. This paper proposes a new methodological framework, named as "the road-network-based approach," for spatially allocating regional mobile source emission inventories. The new approach utilizes the Geographic Information System (GIS)-based road network information and road-types-based traffic flow data to provide spatial surrogates for allocating Pearl River Delta (PRD) regional mobile source emission inventories. The results show that the new approach provides reasonable spatial distributions of mobile source emissions, and the distributions are in good agreement with PRD regional on-road emission line sources. Comparisons between using the population-based and the new road-network-based approaches are made. The air quality modeling results indicate that the new approach can obviously improve model predictions with increasing accuracy in mobile source emission allocations. Means of choosing appropriate approaches for spatially allocating regional mobile source emissions are discussed.