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Developing Street-level PM and PM Land Use Regression Models in High-Density Hong Kong with Urban Morphological Factors Yuan Shi, Kevin Ka-Lun Lau, and Edward Ng Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b01807 • Publication Date (Web): 06 Jul 2016 Downloaded from http://pubs.acs.org on July 6, 2016
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Developing Street-level PM2.5 and PM10 Land Use
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Regression Models in High-Density Hong Kong
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with Urban Morphological Factors
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Yuan Shi*,† Kevin Ka-Lun Lau†, ‡ and Edward Ng,†, ‡
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†School
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China
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‡The
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Hong Kong, Shatin, NT, Hong Kong SAR, China
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*The Corresponding Author:
[email protected] of Architecture, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR,
Institute of Environment, Energy and Sustainability (IEES), The Chinese University of
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ABSTRACT. Monitoring street-level particulates is essential to air quality management but
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challenging in high-density Hong Kong due to limitations in local monitoring network and the
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complexities of street environment. By employing vehicle-based mobile measurements, Land
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Use Regression (LUR) models are developed to estimate the spatial variation of PM2.5 and PM10
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in the downtown area of Hong Kong. Sampling runs were conducted along routes measuring a
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total of 30km during selected measurement period of total 14 days. In total, 321 independent
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variables were examined to develop LUR models by using stepwise regression with PM2.5 and
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PM10 as dependent variables. Approximately, 10% increases in the model adjusted R2 were
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achieved by integrating urban/building morphology as independent variables into the LUR
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models. Resultant LUR models show that the most decisive factors on street-level air quality in
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Hong Kong are frontal area index, an urban/building morphological parameter, and road network
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line density and traffic volume, two parameters of road traffic. The adjusted R2 of the final LUR
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models of PM2.5 and PM10 are 0.633 and 0.707 respectively. These results indicate that urban
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morphology is more decisive to the street-level air quality in high-density cities than other cities.
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Air pollution hotspots are also identified based on the LUR mapping.
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TOC ART
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1. INTRODUCTION
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Many epidemiological investigations proved that particulate matters (PM) were associated with
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adverse health outcomes. Particulate air pollution leads to higher health risks of cardiovascular
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and respiratory diseases 1. These health impacts and risks are further accentuated in high-density
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urban environment. In cities with compact urban development, the dispersion of street-level
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particulates is impeded by their high-density urban morphology because densely-constructed
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buildings block air ventilation and consequently retard the dispersion 2. Several retrospective
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epidemiological studies in Hong Kong showed that health risks (measured as the hospitalization
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and mortality) connected to cardiovascular and respiratory diseases were significantly associated
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with both long-term and short-term exposure to PM2.5 and PM10 3–8. Therefore, monitoring street-
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level air pollution in high-density urban environment is essential to prevent or mitigate the health
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risks. Several studies have been conducted to observe the temporal and spatial variation of street-
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level particulate air pollution in Hong Kong by using either historical monitoring data from the
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existing Air Quality Monitoring Network (AQMN) of Hong Kong Environmental Protection
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Department (HKEPD) 9,10 or stationary measurements at sampling sites 11–14. However, the
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coverage of such monitoring network is very limited. Among the 15 AQMN stations, only three
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roadside stations monitor street-level air quality within the urban environment (Figure S-4, SI),
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while the number of roadside sampling locations in other studies are also limited (not more than
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three in general). The complex urban morphology and compact urban traffic network in Hong
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Kong make the conditions of air quality vary significantly from site to site. However, the street-
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level air quality of many high-density sites and those with heavy traffic in downtown Hong Kong
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is not monitored by AQMN. Thus, quantifying the street-level air pollution and identifying
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hotspots of human exposure are difficult by using AQMN, because it provides insufficient
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information about the spatial variation of pollutants.
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Land Use Regression (LUR) has become a popular method to explore the spatial variation of
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outdoor air pollution in environmental studies and to assess the health risks of human exposure
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to pollutants in epidemiological and public health studies 15 in Europe 16–23 and North America
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24–28.
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extensive applications of LUR models is mainly because it can be used to evaluate human
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exposure to air pollution in unmonitored areas and to identify urban air pollution hotspots which
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are vital to epidemiological and environmental studies 32.
The application of LUR is also increasing in other regions 29–31. The reason for such
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The aim of this study is to develop LUR models for a sub-tropical high-density urban
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environment by focusing on the unique urban scenario of Hong Kong in order to supplement the
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inadequacy of the local monitoring network and provide a better understanding of the spatial
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variation of street-level air pollution. The compact high-density urban development of Hong
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Kong forms much higher buildings and very deep street canyons with intensive traffic and
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pedestrian activities, which makes it almost impossible to use conventional fixed monitoring
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locations to represent the conditions of street-level human exposure (Section 1, SI). In this
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study, under the special circumstance of Hong Kong’s street environment, vehicle-based mobile
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measurements of particulate air pollution are employed as the approach to conduct the sampling
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of the dataset for LUR development. Mobile measurements are conducted in designated periods
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and routes in order to minimize the impact of temporal variability and extreme weather
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conditions on LUR model development. Correlation analysis between outdoor air pollution and
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building morphology is performed by integrating urban/building morphological parameters into
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the LUR model. As such, the study results can be not only used for the purpose of air quality
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management and human exposure evaluation but also directly as a reference in the optimization
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of urban development strategies and decision-making in urban planning on the basis of air
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quality considerations.
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2. MATERIALS AND METHODS
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Previous LUR studies typically set up 20-100 fixed sampling locations within the study area 15.
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However, compact urban development, crowded space and bustling activities occurred within
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street canyons in the downtown area of Hong Kong has made it almost impossible to set up
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sufficient fixed long-term street-level sampling locations without random interference. In this
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study, the sampling of the concentration of street-level particulate air pollution is conducted in
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the downtown area of Hong Kong by using mobile measurements which were tested to be
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feasible and provide valid data for such a purpose 33,34. LUR models of street-level PM2.5 and
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PM10 were then developed.
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2.1 THE MOBILE MEASUREMENTS. Mobile measurements have been increasingly used to
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monitor the air pollution in the last decade 35–43, especially for the development of LUR models
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33,34,44–46.
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variability of air pollutants concentration at a much higher spatial resolution and locate the place
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where its concentration culminates high level (“air pollution hotspots”) that may not be possible
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to be identified by using a limited number of fixed monitoring locations, especially in cases like
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Hong Kong (Section 1, SI).
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2.1.1 THE MOBILE MEASUREMENT PLATFORM. A Toyota HiAce vehicle with necessary
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particulate matter monitor and meteorological sensors on board served as the mobile
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measurement platform that is used to measure the concentration of air particulates and
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meteorological variables. The concentration levels of PM2.5 and PM10 (the concentration level of
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particles |t|) and variance inflation factor (VIF) of each independent variables
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were checked to confirm the variables significance level and ensure that there is no collinearity
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issues in resultant regression models (Section 3.2.3, SI). The adjusted R2 values of each model
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were examined to evaluate the model performance. Both the root-mean-square error (RMSE)
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from leave-one-out cross validation (LOOCV) and the adjusted R2 of 10-fold cross validation
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were used to validate the LUR models (shown in Table 3, details in Section 3.2.4, SI). The final
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models also show reasonably good performance (0.582 and 0.611 for PM2.5 and PM10
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respectively) in an external validation using a separately sampled mobile measurement dataset
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(Section 3.2.5, SI).
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3. RESULTS
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3.1 THE FINAL LUR MODELS OF PM2.5 AND PM10. Using spatially aggregated PM2.5 and
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PM10 as the dependent variables, final LUR models were established. The adjusted R2 values of
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final LUR models for the 300m-spatially aggregated concentration of both PM2.5 and PM10 are
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0.633 and 0.707 respectively (300m-aggregated dependent variables provide the best model
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performance, which is consistent with the semivariogram modeling results in Section 2.2. Other
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models using different dependent variables aggregation resolution are shown in Table S-12, SI
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for comparison). As indicated by the final models of PM2.5 and PM10 (Table 3 and Figure S-20,
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SI), the most essential determinants of the concentration of street-level particulate air pollution in
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the downtown area of Hong Kong are building morphology and urban road traffic. These results
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indicate that, beside the commonly applied LUR independent variables such as land use and
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traffic, building morphology is also one of the determinants of the street-level particulate air
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pollution concentration in the Hong Kong’s high-density urban environment. To quantify the
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model performance improvement introduced by adding urban morphology as independent
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variables, models completely excluding urban morphology were also established for comparison,
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which showed an adjusted R2 increase of 0.111 and 0.150 on the model performance of PM2.5
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and PM10 respectively when building morphological variables were used in modeling (Section
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3.3, SI). The concentration of street-level air pollutants is largely determined by both emission
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and dispersion of pollutants. Road traffic measured as the road line density and traffic volume
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represents the distribution of pollution sources and emission intensity. Building morphology
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quantified by FAI directly affects air ventilation in urban areas and the dispersion capacity of air
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pollution, especially in extremely compact urban environment. It should be noted that, as a
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measure of evaluating urban air ventilation, FAI represents the horizontal permeability of an
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urban area to prevailing wind. It implies that enhancing urban ventilation by optimizing the
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building morphology is more important to high-density cities like Hong Kong than other low-
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density or mid-density cities when dealing with street-level air pollution.
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Table 3. Summary of the final resultant LUR regression models of PM2.5 and PM10. SUMMARY OF FIT OF PM2.5 LUR MODEL Spatially averaged PM2.5 data using spatial resolution of 300m Dependent Variable 2 0.646 R 2 0.633 Adjusted R 6.516 RMSE 51.759 Mean of Response |t| VIF Intercept 27.363 2.458 11.13
