Abstract
INTRODUCTION
High-density high-rise cities have become a more prominent feature globally. Air quality is a significant public health risk in many of these cities. There is a need to better understand the extent to which vertical variation in air pollution and population mobility in such cities affect exposure and exposure-response relationships in epidemiological studies.
METHODS
We used a novel strategy to execute a staged model development that incorporated horizontal and vertical pollutant dispersion, building infiltration, and population mobility patterns in estimating traffic-related air pollution (TRAP) exposures in the Hong Kong Special Administrative Region (HK SAR).
Two street-level spatial monitoring campaigns were undertaken to facilitate the creation of a two-dimensional land-use regression (LUR) model. A network of approximately 100 passive nitric oxide-nitrogen dioxide (NO-NO2) monitors was deployed for two-week periods during the cool and warm seasons. Sampling locations were selected based on population and road network density with a range of physical and geographical characteristics represented. Eight sets of portable monitors for black carbon (BC) and particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5) were rotated so as to be deployed at 80 locations for a 24-hour period. Land-use, geographical, and emissions layers were combined with the spatial monitoring campaign results to create spatiotemporal exposure models.
Vertical air pollution monitoring was carried out at six strategic locations for two weeks in the warm season and two weeks in the cool season. Continuous measurements were carried out at four different heights of a residential building and on both sides of a street canyon. The heights ranged from as close to street level as practically possible up to a maximum of 50 meters (i.e., below the 20th floor). Paired indoor monitoring was included to allow the calculation of infiltration coefficients to feed into the dynamic component of the exposure model.
The final phase of model development addressed population mobility. A population-representative travel behavior survey (n = 89,358) was used to produce the dynamic component of the model, with time-weighted exposure estimates split between home and work or school. Transport microenvironment exposures were taken from published literature. Time-activity exposure estimates were split by age, sex, and employment status.
Development of the exposure model in distinct packages allowed the application of a staged approach to an existing cohort data set. Mortality risk estimates for an elderly cohort of 66,000 Hong Kong residents were calculated using increasing exposure model complexity.
RESULTS
The street-level (2-dimensional [2D]) LUR modeling captured important spatial parameters and represented spatial patterns of air quality in Hong Kong that were consistent with the literature. Higher concentrations of gaseous pollutants were centered in Kowloon and the northern region of Hong Kong Island. PM2.5 and BC predictions exhibited a north-south/west-east gradient, with higher concentrations in the northwest due to regional transport of particulate pollutants from Mainland China. While the degree of explained variance of the models was in line with other LUR modeling efforts in Asia, R2 values ranged from 0.46 (NO2) to 0.59 (PM2.5).
Exponential decay rates (k) were calculated at each monitoring location. While it was clear that k values were higher during the warm season than the cool season, no robust patterns were identified relating to the canyon physical parameters. Therefore, a single decay rate was used for each pollutant across the whole region for derivation of the 3-dimensional (3D) exposure layer (k = 0.004 and 0.012 for PM2.5 and BC, respectively). An alternative decay profile that capped decay at 20 meters above street level was proposed and evaluated. The electrochemical sensors deployed during the canyon campaigns did not exhibit the degree of interunit precision necessary to detect vertical variations in gaseous pollutants, and these results were excluded from the study.
We found that values of the median infiltration efficiencies (Finf) for both BC and PM2.5 were especially high during the cool season (91%). Finf values were somewhat lower during the warm season (81% and 88% for PM2.5 and BC, respectively), and we found a significant negative correlation between air conditioning use and Finf. The Finf for a mechanically ventilated office building was 45% and 40% during the cool and warm seasons, respectively.
Dynamic exposure estimates were compared against home outdoor estimates. As expected, the addition of an indoor component decreased time-weighted exposure estimates, which were balanced out to some extent by the inclusion of transport microenvironments. Overall, mean time-weighted exposures for the full dynamic model were around 20% lower than home outdoor estimates.
Higher levels of exposures were found with working adults and students than for those neither in work nor study. This was due to the increased mobility of people going to work or school. The exposures to PM2.5, BC, and NO2 were, respectively, 13%, 39%, and 14% higher for people who were under age 18, compared with people who were 65 or older. Exposure estimates for the female population were approximately 4% lower.
The availability of an existing cohort data set of elderly Hong Kong residents (n = 66,820) facilitated the calculation and comparison of mortality risk estimates for the different exposure models.
Overall, results indicated that the application of exposure estimates that incorporated infiltration, vertical, and to a lesser extent, dynamic components resulted in higher hazard ratios (HRs) than the standard street-level model and increased the number of significant associations with all-natural-cause, cardiovascular, and respiratory mortality outcomes.
CONCLUSIONS
The results from the study provided the first evidence that considering air pollution exposure in a dynamic 3D landscape would benefit epidemiological studies. Higher HRs and a greater number of significant associations were found between mortality and pollutant exposures that would not have been found had standard 2D exposure models been used. Dynamic models can also identify differential exposures between population subtypes (e.g., students and working adults; those neither in work nor study).
Improved urban building design appears to be stimulating the dispersion of local TRAP in street canyons. Conversely, Finf values found in naturally ventilated buildings were high, and residences provided little protection from ambient air pollution.
We have demonstrated that the creation of effective advanced exposure models is possible in Asian cities without an undue burden on resources. We recommend that vertical exposure patterns be incorporated in future epidemiological studies in high-rise cities where the floor of residence is recorded in health record data.
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