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.


Introduction
Adverse health effects of air pollution are a major public health issue globally. The World Health Organization (WHO) estimated that over 80% of the population living in urban areas breathes where the air quality does not meet the WHO guidelines [1]. Exposure to air pollution is related to numerous types of health damage. Exposure to daily PM 2.5 concentrations has a causal relationship with cardiovascular illnesses and premature mortality [2]. There is a causal relationship between short-term exposure to 1 h NO 2 concentrations and respiratory disease outcomes [3]. Exposure to an 8 h daily maximum ambient O 3 concentration is causally associated with respiratory illness [4].
A number of epidemiological studies have estimated the health risk of air pollution based on fixed-site monitor (FSM) as a surrogate for exposure because of readily available real-time data with high measurement accuracy [5][6][7][8][9]. People typically

Materials and Methods
This section describes: the study design for personal exposure concentration measurements; the instrumentation for air quality measurements; the time-location record of the participants; the exposure error estimation between personal exposure concentrations and ambient concentrations; the calculation of health risk; time-integrated health risk; and interand intra-individual variability in TIAR for PM 2.5 , NO 2 , and O 3 and combined PM 2.5 , NO 2 , and O 3 exposure concentrations.
The supplementary material contains additional details on: (a) sensors specifications that are used to measure personal exposure concentrations; (b) carbon dioxide as an indicator of microenvironments to check the consistency of the recorded time-location patterns of the individual; (c) sensitivity analysis of AR estimation based on 1 min exposure concentrations of the pollutants instead of 3 h moving average concentrations; and (d) quantification of inter-and intra-individual variability in the daily time spent in each microenvironment.

Study Design
To quantify the health risk of combined personal exposure to particle and gaseous pollutants across the selected microenvironments, a longitudinal study was conducted among twenty-one participants. All participants were recruited from faculty, staff, and postgraduate students from the Hong Kong University of Science and Technology, Hong Kong. The participants lived in different districts and areas across Hong Kong, including urban and suburban areas. The air quality of Hong Kong is influenced by local emission sources, including on-road motor vehicles, power plants and marine vessels [33], and regional air pollution from mainland China [34]. Each participant performed the continuous air quality measurement on multiple consecutive days based on their availability, with an average of five days. Data were collected for 106 person-days. During the personal monitoring campaigns, the outdoor air quality of residences was also measured for seven participants. Demographic information (e.g., age, occupation, number of occupants at home, living floor, smoking) and residential characteristics (e.g., type of ventilation status at home, AC type, type of cooking stoves, cooking duration per day and frequency of floor cleaning) were collected using a web-based structured questionnaire.

Instrumentation
The PM 2.5 , NO 2 , and O 3 exposure concentration were measured across the microenvironments that were visited by the participants. As an indicator of indoor and outdoor differences, CO 2 concentrations were also measured. PM 2.5 concentrations were measured using a photometer Aerocet 831 Handheld Particle Counter (Metone, Grants Pass, OR, USA) for personal exposure concentration across the microenvironments and a photometer model 212 Ambient Particulate Profiler (Metone, Grants Pass, OR, USA) for outdoor air quality at residence locations. The concentrations of O 3 and NO 2 were measured using electrochemical sensors that were the model B series from Alphasense, Braintree, UK. The CO 2 concentration was measured using a nondispersive infrared Premier CO 2 sensor from Dynament, Mansfield, UK. The sensors of all pollutants were integrated into a portable system called Portable Air Station (PAS) and an outdoor portable system called a Mini Air Station (MAS) [35,36]. The PAS was used to measure personal exposure concentrations across the microenvironments (Figure 1). The PAS was able to store local data and transmit data to a server in real-time.

Instrumentation
The PM2.5, NO2, and O3 exposure concentration were measured across the microenvironments that were visited by the participants. As an indicator of indoor and outdoor differences, CO2 concentrations were also measured. PM2.5 concentrations were measured using a photometer Aerocet 831 Handheld Particle Counter (Metone, Grants Pass, OR, USA) for personal exposure concentration across the microenvironments and a photometer model 212 Ambient Particulate Profiler (Metone, Grants Pass, OR, USA) for outdoor air quality at residence locations. The concentrations of O3 and NO2 were measured using electrochemical sensors that were the model B series from Alphasense, Braintree, UK. The CO2 concentration was measured using a nondispersive infrared Premier CO2 sensor from Dynament, Mansfield, UK. The sensors of all pollutants were integrated into a portable system called Portable Air Station (PAS) and an outdoor portable system called a Mini Air Station (MAS) [35,36]. The PAS was used to measure personal exposure concentrations across the microenvironments (Figure 1). The PAS was able to store local data and transmit data to a server in real-time. The sensors measured air pollution concentrations at 1 min resolution. The PAS has 12 V Li-ion Battery inside for a standby capacity of more than 10 h. The PAS has rolling wheels that make the 15 kg PAS convenient to move from one place to another with participants. Participants were instructed to always carry the instrument and keep the instrument within 1 m of them in indoor environments (e.g., home and office). Participants were given some flexibility to keep the PAS in a nearby safe place while taking a bath.
Details of the quality assurance (QA) and quality control (QC) for the sensor measurements are provided elsewhere [36][37][38]. The accuracy of the sensors used in PAS and MAS were evaluated based on measurements with standard gas at known concentrations in the laboratory and collocation with reference methods at the government operated Air The sensors measured air pollution concentrations at 1 min resolution. The PAS has 12 V Li-ion Battery inside for a standby capacity of more than 10 h. The PAS has rolling wheels that make the 15 kg PAS convenient to move from one place to another with participants. Participants were instructed to always carry the instrument and keep the instrument within 1 m of them in indoor environments (e.g., home and office). Participants were given some flexibility to keep the PAS in a nearby safe place while taking a bath.
Details of the quality assurance (QA) and quality control (QC) for the sensor measurements are provided elsewhere [36][37][38]. The accuracy of the sensors used in PAS and MAS were evaluated based on measurements with standard gas at known concentrations in the laboratory and collocation with reference methods at the government operated Air Quality Monitoring Station (AQMS) at Mong Kok. The differences in concentration measurements between the reference methods and sensors were, on average, less than 1% for PM 2.5 and NO 2 , and about 4% for O 3 over 350 sampling hours [38]. During air quality measurements and post data analysis, QA and QC were maintained to obtain a high quality of data. Detailed procedures regarding the QA and QC for air quality data are published elsewhere [39].

Time-Location Records
Participants recorded the start and end times of each location that they visited and categorized each location into one of the five predefined microenvironment categories using the aTimeLogger app on their smartphone (Available online: http://atimelogger.com, accessed on 20 December 2021). The five categories of microenvironments were: 'home indoor', 'office indoor', 'others indoor', 'outdoor', and 'transit'. Two participants recorded their time-location information in open text on their smartphones. Participants were asked to record their time-location immediately after changing each microenvironment to avoid misclassification of time-location information. CO 2 is produced by human exhalation as a function of metabolic processes [40]. The outdoor CO 2 concentration level is lower than the CO 2 concentration in indoor environments because of human occupancy [41]. Based on our analysis, CO 2 concentration substantially varied between indoor and outdoor microenvironments and among different indoor microenvironments depending on the occupancy number and ventilation condition, provided in supplementary material. Therefore, we used real-time CO 2 concentration timeseries (1 min resolution) in this study to check the consistency of the recorded time-location patterns of the individual.

Quantification of Exposure Error between Personal Exposure Concentrations and Ambient Concentrations
To compare personal exposure concentrations with the ambient concentration at the residence location, the outdoor air quality was measured at seven participants' residences. The MAS was used to measure residences' outdoor air quality. The MAS was placed on the balcony or another outdoor space of the residence.
Hourly-average ambient concentrations of PM 2.5 , NO 2 and O 3 were retrieved from FSM stations of the Hong Kong Environmental Protection Department (HKEPD). FSM stations were selected based on the nearest distance to the participant's resident address.
The exposure error between personal exposure concentrations and ambient concentrations was estimated as below: where EE p,j,i is the exposure error (%) in daily average concentration between personal exposure concentration and ambient concentration of pollutant p in each person-day j for participant i. PC p,j,i is the daily average personal exposure concentrations of pollutant p in each person-day j for participant i. AC p,j,i,l is the daily average ambient concentrations of pollutant p in each person-day j for participant i at location l (i.e., nearest FSM or residence location).

Calculation of Health Risk
For a mixture of PM 2.5 , NO 2 and O 3 , AR of hospital admission for respiratory diseases (International Classification of Disease codes 460-519) and cardiovascular diseases (International Classification of Disease codes 390-459) for all ages were quantified [31]. Although AR in AQHI was quantified hourly based on a 3 h moving average of ambient concentrations [31,32], we estimated AR based on continuous 1 min personal exposure concentrations. This is because we quantified AR in each microenvironment across the person-days, and participants spent less than 3 h in some of the microenvironments. Thus, 3 h moving average concentrations may cause misrepresentation of those microenvironments by averaging before and after the microenvironmental concentrations. Based on our analysis, the median differences of the daily average AR estimation between 1 min exposure concentrations and 3 h moving average concentrations were less than 2% for each pollutant, regardless of the microenvironments. For each pollutant (AR p ) in the mixture, AR is quantified: where C pt is the 1 min personal exposure concentration (µg/m 3 ) of pollutant p at time t, and β p is a regression coefficient in units of (µg/m 3 ) −1 of pollutant p. Wong et al. [31] reported the values of β p based on time series analysis, including β PM2.5 = 2.1 × 10 −4 per µg/m 3 , β NO2 = 4.5 × 10 −4 per µg/m 3 and β O3 = 5.1 × 10 −4 per µg/m 3 . The AR for a mixture of PM 2.5 , NO 2 , and O 3 is quantified: where AR combine is the sum of the AR for PM 2.5 , NO 2 and O 3 concentrations

Time-Integrated Health Risk
Because the health risk of air pollution depends on the exposure concentration over time in a microenvironment, the contribution of each microenvironment to the daily health risk was estimated based on TIAR. The TIAR for a mixture of PM 2.5 , NO 2 and O 3 across the microenvironments for a participant was estimated as where, TIAR combine,k,i,j is time-integrated AR combine (%h) in microenvironment k in personday j exposed by participant i for the AR of combined PM 2.5 , NO 2 and O 3 ; AR combine, k,i,j is the average AR combine (%) in microenvironment k for participant i in person-day j; t k,i,j is the duration (hour) spent in microenvironment k by participant i in person-day j; and n is the total number of microenvironments. This approach of estimating the TIAR in a given microenvironment is similar to the method for estimating time-integrated exposure in a microenvironment used by Klepeis [42]. We also quantified TIAR across the microenvironments for the individual pollutant, i.e., PM 2.5 , NO 2 and O 3 exposure concentrations: where, TIAR p,k,i,j is time-integrated AR p (%h) in microenvironment k in person-day j exposed by participant i for the pollutant p; AR p, k,i,j is the average AR p (%) in microenvironment k for participant i in person-day j for pollutant p.
To quantify which microenvironments contribute disproportionately, the health risk relative intensity was estimated. Health risk intensity was calculated by dividing the fraction of the health risk for a participant exposed in a microenvironment by the time fraction of the day that the participant spent in that microenvironment. The approach of calculating health risk intensity is similar to the approach for calculating the dose relative intensity estimation used by Mazaheri et al. [43].
where, I k,j,i is the health risk intensity in microenvironment k in person-day j for participant i; TIAR combine,j,i is the total time-integrated AR combine (%h) on a given day of j for participant i; t k,j,i is total time spent (hour) in microenvironment k in person-day j for participant i; and t j,i is total spent on a given day of j for participant i. A relative intensity value greater than one for a microenvironment suggests that the fraction of the TIAR combine was higher than the time spent in that microenvironment. A value smaller than one indicates that the fraction of the TIAR combine was less than the fraction of the time spent in that microenvironment.

Quantifying Inter-and Intra-Individual Variability in Time-Integrated Health Risk
To quantify proportions of inter-and intra-individual variability in the daily total TIAR, variance component analysis was used. Variance component analysis in the daily total TIAR was performed separately for PM 2.5 , NO 2 and O 3 , and combined PM 2.5 , NO 2 and O 3 . To quantify the variance component, a linear mixed-effects model with only a random intercept was developed [17,25]: where Z ij is the log-transformed daily total TIAR for PM 2.5 , NO 2 and O 3 , and combined PM 2.5 , NO 2 and O 3 in person-day j for participant i; µ is the fixed mean (logged) TIAR for all subjects; and ε ij is the error. In linear mixed-effects models, a person-specific random effect b i assumed to be normally distributed with zero mean and variance σ 2 inter (the inter-individual variability). The error ε ijp is assumed to have normal distribution with zero mean and variance σ 2 intra (the intra-variability). The variance components for interand intra-individual were estimated using the method of restricted maximum likelihood (REML) [44].
Intra-class correlation coefficients (ICC), i.e., the proportion of total variability attributable to inter-individual variability, was estimated as [45]: To identify key contributors affecting the inter-and intra-individual variability in daily TIAR combine , we performed correlation analysis between TIAR combine and selected independent variables. The variables were the daily time spent at 'home indoor', 'office indoor', 'others indoor', 'outdoor', and 'transit'; the daily average PM 2.5 concentrations at 'home indoor', 'office indoor', 'others indoor', 'outdoor', and 'transit'; the daily average NO 2 concentrations at 'home indoor', 'office indoor', 'others indoor', 'outdoor', and 'transit'; the daily average O 3 concentrations at 'home indoor', 'office indoor', 'others indoor', 'outdoor', and 'transit'; and the daily average ambient PM 2.5 , NO 2 and O 3 concentrations. The variables were averaged daily and for each participant in order to analyze correlation with the daily TIAR combine and participant-wise average TIAR combine , respectively.

Statistical Analysis
The normality of the distribution of data was checked using the Shapiro-Wilks test. For non-normal distribution, the Mann-Whitney test was used to determine the statistical significance of the difference between two independent groups. The Kruskal-Wallis test was used to compare more than two independent groups.

Results
The results include study participants and the time-location pattern; exposure error between personal exposure concentrations and ambient concentrations; time-integrated health risk across the selected microenvironments; and inter-individual and intra-individual variability in the time-integrated health risk.
The supplementary material contains additional results for (a) general characteristics of the study participants; (b) inter-and intra-individual variability in the daily time spent by participants in each microenvironment; (c) summary statistics of personal exposure concentrations of PM 2.5 , NO 2 and O 3 in each microenvironment across the participants; and (d) the relationship of ambient PM 2.5 , NO 2 and O 3 concentrations between FSM and the residence location.

Study Participants and Time-Location Pattern
General characteristics of the participants are given in the supplementary material. Twenty-one participants aged 21-60 years old participated in the longitudinal measurement of the health risk of personal exposure study. All of the participants are non-smokers and lived in non-smoking homes.
The mean daily percent of time spent by participants across the microenvironments are provided in Table 1. Participants spent, on average, only 2.5% of their daily time in an 'outdoor' environment. Among the five categories of microenvironments, participants spent most of their daily average time in the 'home indoor' (range: 9.3-24.0 h), followed by the 'office indoor' (range: 0-13.9 h), the 'others indoor' (range: 0-7.5 h), in transit (range: 0-5.8 h) and in the 'outdoor' (range: 0-4.6 h) microenvironment. Based on variance component analysis (given in supplementary material), intra-individual variability in daily time spent in all selected microenvironments ranged from 59% to 91%. Inter-individual variability was from 9% to 41% across the microenvironments. This indicates that the day to day variability in the time spent for a given individual in a microenvironment is higher than the differences in the average time spent in that microenvironment between individuals during the whole measurement period.

Exposure Error between Personal Exposure Concentrations and Ambient Concentration
The error percentage between the daily average personal exposure concentrations and ambient concentration for PM 2.5 , NO 2 and O 3 are provided Figure 2, where Figure 2A indicates the ambient concentrations from FSM and Figure 2B indicates the ambient concentrations from the residence location.
When ambient concentrations were based on FSM, the mean error percentage was 39% and 70% for PM 2.5 and NO 2 concentrations, respectively. In comparison, the mean errors were 45% for PM 2.5 and 87% for NO 2 when ambient concentration was based on the residence location. This exposure error is primarily because of the spatial variability of the concentrations due to the daily movement of the participants across the microenvironments. The mean exposure error of NO 2 was higher than PM 2.5 when based on either the measurements at FSM and residence as exposure surrogates because of larger the spatial variability in NO 2 concentrations in urban areas. The coefficient of determination (R 2 ) values between daily average concentrations at FSM and the residence location was lower for NO 2 (0.25) than PM 2.5 (0.87) (this is provided in the supplementary material), indicating the larger spatial variability of NO 2 concentrations. When ambient concentrations were based on FSM, the mean error percentage was 39% and 70% for PM2.5 and NO2 concentrations, respectively. In comparison, the mean errors were 45% for PM2.5 and 87% for NO2 when ambient concentration was based on the residence location. This exposure error is primarily because of the spatial variability of the concentrations due to the daily movement of the participants across the microenvironments. The mean exposure error of NO2 was higher than PM2.5 when based on either the measurements at FSM and residence as exposure surrogates because of larger the spatial variability in NO2 concentrations in urban areas. The coefficient of determination (R 2 ) values between daily average concentrations at FSM and the residence location was lower for NO2 (0.25) than PM2.5 (0.87) (this is provided in the supplementary material), indicating the larger spatial variability of NO2 concentrations.
For O3, the mean exposure error was 104% based on the air quality measurements at FSM and 39% based on the measurements at the residence location. This is because people are exposed to lower levels of personal exposure concentrations in different microenvironments than outdoors. For example, on a given person-day, the average personal O3 exposure concentrations were 27 µg/m 3 in 'home indoor', 37 µg/m 3 in 'office indoor', 25 µg/m 3 in 'others indoor', and 17 µg/m 3 in 'transit', while the daily average ambient O3 concentration on that day was 61 µg/m 3 at FSM. The mean O3 exposure error using FSM was higher than using the residence location. This is because FSM used in our study is general FSM located at the top of the roof and not adjacent to busy roads. Thus, we measured higher O3 concentrations at FSM. In contrast, the participants live in urban areas close For O 3 , the mean exposure error was 104% based on the air quality measurements at FSM and 39% based on the measurements at the residence location. This is because people are exposed to lower levels of personal exposure concentrations in different microenvironments than outdoors. For example, on a given person-day, the average personal O 3 exposure concentrations were 27 µg/m 3 in 'home indoor', 37 µg/m 3 in 'office indoor', 25 µg/m 3 in 'others indoor', and 17 µg/m 3 in 'transit', while the daily average ambient O 3 concentration on that day was 61 µg/m 3 at FSM. The mean O 3 exposure error using FSM was higher than using the residence location. This is because FSM used in our study is general FSM located at the top of the roof and not adjacent to busy roads. Thus, we measured higher O 3 concentrations at FSM. In contrast, the participants live in urban areas close to busy roads, where an abundance of nitric oxide (NO) from on-road vehicles titrate O 3 concentrations [33]. Thus, lower O 3 concentrations were measured at residence locations.
The exposure errors based on FSM or residence locations were consistent for PM 2.5 and lower than 50%, indicating small spatial variability in PM 2.5 concentrations, similar to the previous study [46,47]. For NO 2 , exposure errors were higher (≥70%) because of larger spatial variability and were consistent throughout, based on FSM and residence locations. This indicates that NO 2 exposure concentrations based on a single location either FSM or residence location cannot represent the large spatial variability in personal exposure concentrations across the microenvironments. For O 3 , exposure errors were inconsistent between FSM and residence locations because of O 3 titration by NO. Thus, the results of exposure errors in NO 2 and O 3 concentrations as a surrogate highlights the importance of real world personal exposure concentration measurements with the daily active lifestyle of the individuals.

Contribution of the Selected Microenvironments in Time-Integrated Health Risk
The mean percent of the daily time-integrated health risk for combined PM 2.5 , NO 2 and O 3 exposure concentrations in each selected microenvironment are given in the supplementary material. There were significant differences (p < 0.001) in the median TIAR combine among the microenvironments. The daily highest TIAR combine (mean ± standard deviation, SD) 64% (±19%) was in the 'home indoor' microenvironment, where participants spent an average 66% (±17%) of their total daily time. The daily minimum TIAR combine 5% (±5%) was in the 'outdoor' microenvironment, where participants spent an average of 3% (±3%) of their daily time. Although AR combine in 'home indoor' microenvironments was lower than the 'others indoor', 'outdoor' and 'transit' microenvironments, TIAR combine in 'home indoor' were found to be higher than those microenvironments due to the long dwelling time of the participants in the 'home indoor' microenvironment.
Because TIAR combine is related to the duration spent in each microenvironment, health risk intensities provide a clear picture of which microenvironment contributes disproportionately to daily health risk. Figure 3 presents the average health risk intensities in each microenvironment.
the previous study [46,47]. For NO2, exposure errors were higher (≥70%) because of larger spatial variability and were consistent throughout, based on FSM and residence locations. This indicates that NO2 exposure concentrations based on a single location either FSM or residence location cannot represent the large spatial variability in personal exposure concentrations across the microenvironments. For O3, exposure errors were inconsistent between FSM and residence locations because of O3 titration by NO. Thus, the results of exposure errors in NO2 and O3 concentrations as a surrogate highlights the importance of real world personal exposure concentration measurements with the daily active lifestyle of the individuals.

Contribution of the Selected Microenvironments in Time-Integrated Health Risk
The mean percent of the daily time-integrated health risk for combined PM2.5, NO2 and O3 exposure concentrations in each selected microenvironment are given in the supplementary material. There were significant differences (p < 0.001) in the median TIARcombine among the microenvironments. The daily highest TIARcombine (mean ± standard deviation, SD) 64% (±19%) was in the 'home indoor' microenvironment, where participants spent an average 66% (±17%) of their total daily time. The daily minimum TIARcombine 5% (±5%) was in the 'outdoor' microenvironment, where participants spent an average of 3% (±3%) of their daily time. Although ARcombine in 'home indoor' microenvironments was lower than the 'others indoor', 'outdoor' and 'transit' microenvironments, TIARcombine in 'home indoor' were found to be higher than those microenvironments due to the long dwelling time of the participants in the 'home indoor' microenvironment.
Because TIARcombine is related to the duration spent in each microenvironment, health risk intensities provide a clear picture of which microenvironment contributes disproportionately to daily health risk. Figure 3 presents the average health risk intensities in each microenvironment.  The maximum average health risk intensity 1.64 (±0.52) was found in 'outdoor' followed in order by 'transit', 'others indoor', 'home indoor' and 'office indoor' microenvironments. Among the microenvironments, intensity values exceeded 1 for the 'outdoor', 'transit' and 'others indoor', which indicated the fraction of TIAR combine in those microenvironments was higher than the faction of time spent in the corresponding microenvironments. This means that, although participants spent only a small fraction of their time in these settings, these microenvironments contribute disproportionately to AR combine because of the elevated concentrations of pollutants. Thus, these microenvironments with large health risk intensity could help to prioritize interventions to reduce the daily health risk. Higher health risk intensity for combined PM 2.5 , NO 2 and O 3 exposure in the 'transit' microenvironment than in the 'home indoor' and 'others indoor' was because of the much higher NO 2 concentrations in the vehicle's cabin. Although PM 2.5 and O 3 concentrations in 'transit' were lower than 'home indoor' and 'others indoor' microenvironments, the higher health risk posed by NO 2 offset the lower health risk of PM 2.5 and O 3 concentration. Among the two main microenvironments, 'home indoor' and 'office indoor' where participants spend most of their daily time, health risk intensity in the 'office indoor' microenvironment was much less than in the 'home indoor' microenvironment. This is because the average concentration for PM 2.5 and NO 2 in 'office indoor' microenvironments were always encountered as lower than the 'home indoor' microenvironment. The 'office indoor' was operated with a centralized mechanical ventilation and air-conditioned system with high-efficiency filters for particles (Minimum Efficiency Reporting values of 13, MERV 13), whereas the 'home indoor' microenvironment was commonly ventilated by opening windows which introduce more infiltration of outdoor pollution than the office. In addition, home is intensely influenced by indoor emission sources, including cooking and cleaning inside of the home. For example, Baxter et al. [48] found that cooking for more than an hour per day was significantly associated with a 5.7 µgm −3 increase in indoor PM 2.5 concentrations compared to less than an hour per day.

Inter-Individual and Intra-Individual Variability in Time-Integrated Health Risk
Inter-and intra-individual variability of daily TIAR for PM 2.5 , NO 2 and O 3 and combined PM 2.5 , NO 2 and O 3 exposure concentrations across the microenvironment is given in Figure 4.
The results demonstrated that there was substantial inter-individual variability in mean TIAR by a factor of 3.9 for TIAR PM2.5 , 3.0 for TIAR NO2 , 5.1 for TIAR O3 and 2.5 for TIAR combine across the participants. Intra-individual variability indicates the daily fluctuations of TIAR that vary temporally from day to day. Large intra-individual variability in TIAR PM2.5 , TIAR NO2 , TIAR O3 and TIAR combine was found in many participants. For example, the daily total TIAR combine varied by a factor up to 2.5 across the person-days of a given person. TIAR PM2.5 , TIAR NO2 and TIAR O3 varied by a factor of up to 7.8, 3.1 and 5.6 for a given person across the measured days, respectively. Table 2 shows the variance component for inter-and intra-individual variability in lognormal daily TIAR for each pollutant and for combined PM 2.5 , NO 2 and O 3 . The interand intra-individual variability between particle and gaseous pollutants was substantially different from each other. For TIAR PM2.5 , intra-individual variability (78%) was substantially higher than inter-individual variability (22%). This indicates that the daily personal activities and lifestyle of the participants are more important than where the participants live because of the small spatial variability in PM 2.5 concentration. Thus, an individual has the potential to reduce PM 2.5 -induced health risk by modifying their daily activities and lifestyle. In contrast, for gaseous pollutants, inter-and intra-individual variance in both TIAR NO2 and TIAR O3 were comparable. Because of the larger spatial variability of gaseous pollutants than PM 2.5 , where participants live is important to reduce health risk for NO 2 and O 3 exposure. The larger inter-individual in TIAR NO2 indicates that the health risk and sources of NO 2 concentrations are widely varied across the participants. For TIAR combine , inter-and intra-individual variability is almost equally important. Thus, for combined PM 2.5 , NO 2 and O 3 exposure, both daily personal activities and a participant's location are important to reduce health damages of air pollution exposure. The results demonstrated that there was substantial inter-individual variability in mean TIAR by a factor of 3.9 for TIARPM2.5, 3.0 for TIARNO2, 5.1 for TIARO3 and 2.5 for TIARcombine across the participants. Intra-individual variability indicates the daily fluctuations of TIAR that vary temporally from day to day. Large intra-individual variability in TIARPM2.5, TIARNO2, TIARO3 and TIARcombine was found in many participants. For example, the daily total TIARcombine varied by a factor up to 2.5 across the person-days of a given   53 47 Notes: TIAR = time-integrated health risk; ICC = σ 2 inter /(σ 2 inter + σ 2 intra ), indicates proportion of the total variations attributed to inter-individual variance. a log-transformed to ensure the normal distribution of residuals. b AR combine = AR PM2.5 + AR NO2 + AR O3.
Identified key contributors affecting inter-and intra-individual variability in TIAR combine are provided in Table 3. For inter-individual variability, TIAR combine was significantly (p < 0.05) correlated with PM 2.5 , NO 2 and O 3 exposure concentration at 'home indoor' microenvironments, PM 2.5 and NO 2 at 'others indoor' microenvironments, O 3 at 'office indoor' microenvironments and ambient PM 2.5 concentrations. Based on an interpretation of the correlation coefficient by Schober et al. [49], exposure to O 3 concentration at 'home indoor' microenvironments was strongly correlated (r = 0.74, p < 0.001) with TIAR combine . There was a moderate correlation (i.e., 0.45 to 0.60, p < 0.05) of TIAR combine with PM 2.5 and NO 2 concentration at 'home indoor' microenvironments, PM 2.5 and NO 2 at 'others indoor' microenvironments, O 3 at 'office indoor' microenvironments and ambient PM 2.5 concentrations. For intra-individual variability, TIAR combine was strongly correlated (r = 0.76, p < 0.001) with O 3 concentration at 'home indoor' microenvironments and moderately correlated (i.e., 0.40 to 0.59, p < 0.05) with PM 2.5 and NO 2 concentration at 'home indoor' microenvironments, NO 2 in 'others indoor' and 'transit' microenvironments, O 3 at 'office indoor' microenvironments, and ambient PM 2.5 and O 3 concentration. There was also a weak but statistically significant correlation (i.e., 0.20 to 0.33, p < 0.05) of TIAR combine with time spent at 'home indoor' microenvironments, PM 2.5 and O 3 at 'others indoor' microenvironments, PM 2.5 , and NO 2 and O 3 at 'outdoor' microenvironments. These key contributors of intraindividual variability indicate that individual has great potential to reduce their health damage related to combined exposure to mixed particle and gaseous pollutants.

Discussion
To our best knowledge, this study is new in that is quantifies the health risk for combined personal PM 2.5 , NO 2 and O 3 exposure concentrations across different urban microenvironments, including 'home indoor', 'office indoor', 'others indoor', 'outdoor' and 'transit' microenvironments. Because health risk intensities are greater than 1 in 'others indoor', 'outdoor' and 'transit' microenvironments, these microenvironments contribute disproportionately to the daily health risk for combined personal PM 2.5 , NO 2 and O 3 exposure concentrations. This indicates that there are potentials to reduce individual's daily health damage related to air pollution by minimizing exposure time in those microenvironments. The highest health risk intensity in 'outdoor' microenvironments indicates that sensitive people should avoid outdoor exposure for a longer time during high air pollution episode days. This is because the outdoor air quality in Hong Kong is contributed to by both local emission sources (e.g., on-road vehicles, marine vessels, and power plants) and regional air pollution [33,34] [54] and Plume Labs Air Quality App (Plume Labs, Paris, France) [55]. For example, the PRAISE-HK app offers real-time and forecasted high-resolution (hourly) health risk information that is related to the air pollution at a street-by-street level [50]. However, our recent study demonstrated that the long-term trend in AR for a mixture of outdoor PM 10 , NO 2 , O 3, and SO 2 concentrations from 2000 to 2018 has decreased over Hong Kong because of the various emission control measures implemented by the Hong Kong government [33].
The longitudinal study design of the study provided an opportunity to quantify the inter-and intra-individual variability and the contributors of variability in terms of daily health risk. The results demonstrated that inter-and intra-individual variability in TIAR combine is almost equally important. Among the identified key contributors for intra-individual variability, PM 2.5 , NO 2 and O 3 concentration in 'home indoor', O 3 in 'office indoor' and ambient PM 2.5 concentrations were significantly correlated with TIAR combine by a correlation coefficient, r > 0.50. Thus, by reducing indoor sources and infiltrated ambient concentrations of PM 2.5 , NO 2 and O 3 in the home, O 3 concentration at the office, and exposure to ambient PM 2.5 concentrations, people can reduce health risk in their daily life, such as hospital admissions and emergency room visits due to respiratory and cardiovascular diseases. People can reduce the infiltrated ambient concentration at home by changing ventilation operation (i.e., reducing window opening duration, aircondition on) and using an air purifier (i.e., high-efficiency particulate air filter) [56][57][58][59]. Exposure concentration at home can be reduced from indoor sources by reducing cooking duration, not smoking inside the home, using an exhaust fan during and after cooking and using an electric stove [59][60][61][62]. At the office, people can reduce the indoor emission of O 3 by not using the photocopier or printer and using an air purifier that produces O 3 [63][64][65][66]. Thus, this study argues that current air quality management strategies focusing on single pollution emission sources and ambient air quality are not sufficient for effectively mitigating the public health risk of air pollution. Intra-individual variability in TIAR shows how health risk changes with changes in daily exposure concentrations and an individual's activities. People can use PRAISE-HK app, that provides personalized information on air pollution exposure and health risk, to check their daily health risk and examine the effects of different interventions by modifying their daily activities and lifestyle to reduce their daily health risk [50]. This study provides an insight into the importance of capturing the day-to-day fluctuation in health risk of a mixture of air pollutants within an individual in future epidemiological studies. This study is new in terms of assessing the inter-and intra-individual variability and the key contributors affecting variability in the daily timeintegrated health risk for combined personal exposure to particle and gaseous pollutants.
With the recent technological advancement, we measured high-resolution (1 min) air quality concentrations simultaneously for both particle and gaseous pollutants using an integrated portable sensor across the various microenvironments encountered by the participants. High-resolution personal exposure concentrations and time-location patterns across the various microenvironments provided a better representation of each microenvironment for the health risk of combined personal PM 2.5 , NO 2 and O 3 exposure concentrations [20]. Overall, the method based on an epidemiological AR model has been proved to be effective in quantifying the health risk for combined personal PM 2.5 , NO 2 and O 3 exposure concentrations across various urban microenvironments. This method provides a valuable reference that can be applied in other cities of the world to estimate personal health risk for a mixture of pollutants across the microenvironments.
The study has some limitations. The study subject sample was small because of: (1) the cost of integrated portable sensors; and (2) participant's burdens to carry the 15 kg portable sensors all the time with them during the measurement campaign, which prevented the implementation of a large number of samples. Our study subjects were only limited to those who were affiliated to the university (i.e., students, faculty members, and staff). Hence, the results may not be representative of the people who are housewives, unemployed, retired or employed in refinery, chemical plants, and restaurants.

Conclusions
The exposure error of gaseous pollutants (i.e., NO 2 and O 3 ) based on either a fixed-site monitor or residence location as a surrogate indicates the importance of real world personal exposure concentration measurements with a daily active lifestyle. This study is new in terms of quantifying the health risk for combined personal PM 2.5 , NO 2 and O 3 exposure concentrations based on an epidemiological model for respiratory and cardiovascularrelated hospitalization across five different microenvironments. Despite a small fraction of time spent, 'others indoor', 'outdoor' and 'transit' microenvironments contribute disproportionately to the daily health risk. Thus, there are potential ways to reduce an individual's daily health risk related to air pollution by minimizing their exposure time in these microenvironments. The inter-and intra-individual variability in daily time-integrated health risk for PM 2.5 was substantially different from the gaseous pollutants. The daily time-integrated health risk for combined PM 2.5 , NO 2 , and O 3 (TIAR combine ) varied by a factor up to 2.5 for a given person across measured days. Several factors were identified to be significantly correlated with daily TIAR combine , with the top five factors including the PM 2.5 , NO 2 and O 3 concentrations at 'home indoor' microenvironments, O 3 concentrations at 'office indoor' microenvironments and ambient PM 2.5 concentrations. The results on the key contributors of variability in the daily personal health risk for combined PM 2.5 , NO 2, and O 3 exposure concentrations can be used to guide developing strategies for reducing an individual's health effects of air pollution. This study highlights the importance of individual health risk based on real-world exposure to a mixture of air pollutants that policymakers can prioritize in future policy development. This study provides a reference for the method that can be applied in other cities of the world to quantify and evaluate the health risk for combined personal exposure to a mixture of pollutants.