Relationships between Changes in Urban Characteristics and Air

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Relationships between Changes in Urban Characteristics and Air Quality in East Asia from 2000 to 2010 Andrew Larkin, Aaron van Donkelaar, Jeffrey A. Geddes, Randall V Martin, and Perry Hystad Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b02549 • Publication Date (Web): 21 Jul 2016 Downloaded from http://pubs.acs.org on July 21, 2016

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Environmental Science & Technology

Relationships between Changes in Urban Characteristics and Air Quality in East Asia from 2000 to 2010

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Andrew Larkin1*, Aaron van Donkelaar2, Jeffrey A. Geddes2, Randall V. Martin2,3, Perry Hystad1

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College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA, 97331

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Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USA

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*Corresponding email: [email protected]

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*Corresponding telephone: 001-541-737-2743

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ABSTRACT Characteristics of urban areas, such as density and compactness, are associated with local

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air pollution concentrations. The potential for altering air pollution through changing urban

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characteristics, however, is less certain, especially for expanding cities within the developing

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world. We examined changes in urban characteristics from 2000 to 2010 for 830 cities in East

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Asia to evaluate associations with changes in nitrogen dioxide (NO2) and fine particulate matter

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(PM2.5) air pollution. Urban areas were stratified by population size into small (100,000-

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250,000), medium, (250,000-1,000,000) and large (>1,000,000). Multivariate regression models

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including urban baseline characteristics, meteorological variables, and change in urban

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characteristics explained 37%, 49%, and 54% of the change in NO2 and 29%, 34%, and 37% of

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the change in PM2.5 for small, medium and large cities, respectively. Change in lights at night

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strongly predicted change in NO2 and PM2.5, while urban area expansion was strongly associated

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with NO2 but not PM2.5. Important differences between changes in urban characteristics and

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pollutant levels were observed by city size, especially NO2. Overall, changes in urban

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characteristics had a greater impact on NO2 and PM2.5 change than baseline characteristics,

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suggesting urban design and land use policies can have substantial impacts on local air pollution

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levels.

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2 ACS Paragon Plus Environment

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BACKGROUND For the first time in human history more than 50% of the world’s population resides in

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urban areas, with an expected increase to 66% by 2050.1 Much of this growth is from, and will

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continue to occur in, rapidly urbanizing East Asia (China, Japan, Indonesia, Malaysia, Vietnam,

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Thailand, Korea, Philippines, Taiwan, Cambodia, Loa). In China alone the urban population

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grew from 191 million in 1980 to 622 million in 2009.2 Only 36% of East Asia's population

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currently lives in urban areas, suggesting substantial future urban growth in this region.3

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Important public health gains have resulted from urban growth, such as increased sanitation, safe

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drinking water, and access to education and healthcare; however, negative influences have also

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emerged, including increases in outdoor air pollution levels.

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Global burden of disease estimates for 2013 include 2.9 million premature deaths and 70

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million years of healthy life lost from exposure to outdoor fine particulate matter (PM2.5) air

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pollution.4 This places outdoor PM2.5 air pollution ahead of such risk factors as physical

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inactivity, alcohol use, and high body-mass index in terms of disease burden in East Asia. Air

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pollution levels in this part of the world have also deteriorated from 2000-2010,5 potentially due

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to rapid urbanization and accompanying air pollution emissions from energy production,

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industry, home heating and vehicles.

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Planning of urban areas and land use policies can have important implications for local

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air pollution concentrations. A number of studies, primarily in developed countries, have

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documented that urban form characteristics, such as compactness (or alternatively sprawl),6

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green space7, population density8 and road density9 can significantly improve or degrade local air

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pollution levels. For example, in 111 US urban areas, an interquartile range change in population

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density, centrality and transit supply was associated with 4-14% change in PM2.5 air pollution



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concentrations; impacts equivalent in size to those from meteorological conditions.10 However,

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studies examining urban characteristics and air pollution in rapidly developing countries are rare.

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One study conducted using 83 global cities in the World Bank’s Urban Growth Management

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Initiative observed that, on average, cities with highly contiguous urban form had lower nitrogen

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dioxide (NO2) air pollution (a marker for traffic-related air pollution), while urban compactness

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was not associated with NO2 concentrations.8 Importantly, no analyses have been conducted on

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the change in air pollution associated with urban expansion and changing urban characteristics –

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information vital to informing urban planning. Much of the existing literature also focuses on

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large urban areas, despite the fact that approximately 50% of the world’s urban population reside

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in relatively small cities of less than 500,000 inhabitants,1 and that small urban areas may be

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more amenable to policy and design changes that could reduce air pollution.

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The degree to which urban growth increases air pollution is complex and an important

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policy question that has not been fully explored, especially in rapidly developing countries. Here

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we examined changes in NO2 and PM2.5 air pollution from 2000 to 2010 using satellite derived

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estimates for 830 cities in East Asia with 2010 populations’ ≥100,000, and examine associations

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with urban expansion levels and changes in urban characteristics over this ten-year period.

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METHODS

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Study Region and Urban Areas

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The geographical extent of our study region covers all of Eastern Asia, including Oceania

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island nations such as Marshall Islands and Micronesia (Figure 1, created using ArcGIS 10.3.1).

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We used World Bank 2000 and 2010 East Asia urban area classifications3 to define urban areas


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based on the extent of urban development rather than municipal boundaries. City footprints were

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esimated using a slight modification of an equation originally derived by Angel et al.11

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1 if classified as urban by the World Bank Urban Expansion dataset Footprint status = 1 if 20% or more of the area within a 1 km radius is classified as urban 0 otherwise

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Urban area population size was calculated by multiplying footprint size and mean

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population density from Gridded Population of the World population density estimates.12 Urban

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areas with 2010 populations ≥100,000 were selected for analysis, which included 830 urban

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areas (Figure 1). Urban areas were stratified by 2010 population size based on US Census

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Bureau city size (by population) classification due to preliminary observations of differing

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typology-air pollution relationships and covariate correlations between urban areas with small

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(100,000-250,000), medium (250,000-1,000,000) and large (> 1,000,000) populations (Figure

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S1)

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Air Pollution Estimates

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Ground-based air pollution monitoring data are not available for most urban areas in East

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Asia in 2010 and were extremely sparse in 2000. We therefore used satellite-derived estimates of

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NO2 and PM2.5 to determine changes from 2000 to 2010. Satellite data offer global coverage and

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a uniform approach to measuring air pollution concentrations. Estimates of NO2 for 1999-2001

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and 2009-2011 were derived from the GOME and SCIAMACHY satellite instruments, which

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provide measurements of NO2 atmospheric column abundance with global coverage every three

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and six days respectively. Three-year averages were used to reduce the effects of inter-annual

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variability that may in part arise from random error. A global chemical transport model (GEOS-

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Chem) was used to relate the column observations to surface concentrations as described in

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Geddes et al.13 In their approach, GOME observations were downscaled to achieve the finer 5 ACS Paragon Plus Environment


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resolution captured by SCIAMACHY by applying the mean spatial structure observed by

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SCIAMACHY at 0.1° x 0.1° over 2003-2005. There is no evidence of a systematic discontinuity,

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and a high correlation (r=0.89) between the two instruments was observed during a period of

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overlap.13 PM2.5 was estimated from retrievals of aerosol optical depth from three satellite

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instruments (MODIS, MISR, and SeaWIFS) with coincident aerosol vertical profiles from

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GEOS-Chem.14 Three year average surfaces (at a 10x10 km resolution) were also created (1999-

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2001 and 2009-2011). These estimates have shown good agreement with ground-based

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measurements and have an expected 1-sigma uncertainty of 1 µg/m3 +25%.15 Similar satellite-

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derived PM2.5 estimates have been used in recent global epidemiological studies,16,17 and were

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the basis of the 2013 global disease burden estimates.4,5

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Calculations of Change in Air Pollution and Urban Characteristics

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Air pollution, urban expansion, and urban typology variables are listed in Table 1, with

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data sources listed in Table S1. Variables are expressed as absolute rather than relative levels of

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change from 2000-2010. Air pollution variables include change in mean PM2.5 and NO2 from

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2000-2010. Urban expansion variables include change in population and urban footprint area

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from 2000-2010. Urban characteristic variables include changes in compactness index, a

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measure of urban sprawl (Figure S2) and changes in mean and within-city-variation in

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population density, green space (calculated from the normalized difference vegetation index

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(NDVI)), impervious surface area (ISA), and percent lights at night, a satellite-based relative

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measure of global nighttime light emissions often used as an indirect measure of economic

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activity.18 Measures of within-city-variation were calculated in addition to mean levels to

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evaluate the potential influence of spatial heterogeneity in urban typology on air pollution. For


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example, increased variation in population density is an indirect indicator of urban sprawl (low

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density suburban areas contrasting with high density urban centers). Change in mean air pollution and urban typology variables were calculated as the

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difference in means for the corresponding 2000 and 2010 urban footprint: & #'()($'()( − !"#'((($'((( ∆!"#$ = !

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Eq. 1

Where ∆!"#$ denotes the change from 2000-2010 in the variable i mean for urban area j

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!"#'((($'((( denotes the year 2000 variable i mean for the area covered by the year 2000

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footprint of urban area j

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Similarly, changes in within-city-variation were calculated as the difference in standard deviation

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for the corresponding 2000 and 2010 urban footprint: ∆+#$ = +#'()($'()( − +#'((($'(((

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Eq. 2

Where

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∆+#$ denotes the change from 2000-2010 in the standard deviation of variable i

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for urban area j

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+#'((($'((( denotes the year 2000 variable i standard deviation for the area covered by the

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year 2000 footprint of urban area j

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Cities with urban footprints smaller than 10km2 in area were assigned a within-city-variation

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value of zero due to the spatial resolution of several urban characteristic datasets.

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Due to urban spillover there were several regions where a single 2010 urban footprint

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covered multiple distinct year 2000 urban areas. For these instances, changes in air pollution,

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urban expansion, and typology variables were calculated using an area-weighted average of

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corresponding 2000 urban areas. 7 ACS Paragon Plus Environment


& #'()($'()( − ∑19: -8./0001/000∗34567/000 ∆!"#$ = ! ∑ ; ∗? 8

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"

19:

1/000

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Eq. 3

Where

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∆!"#$ denotes the area-weighted change from 2000-2010 in the variable i mean for urban

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area j

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!"#'(((@'((( denotes the year 2000 variable i mean for the area covered by the year 2000

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footprint of urban area k

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Areak2000 denotes total area of year 2000 footprint for urban area k

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And n is the number of year 2000 urban areas covered by the year 2010 urban area j

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Statistical analysis First, we tested for differences between 2000 and 2010 levels of independent variables

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using multiple paired t-tests with Bonferroni-adjusted confidence intervals. Second, linear

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regression models were developed for all univariate combinations of dependent variables

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(change in NO2 and PM2.5 air pollution) and independent variables (urban expansion and change

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in urban typology) stratified by small, medium, and large urban areas. Third, multiple,

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multivariate linear regression models were created with model structures consisting of 1) all of

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the independent variables, or 2) a subset of variables selected via bidirectional stepwise

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regression. Adjustment factors in the multivariate models include baseline (year 2000) urban

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characteristics, annual mean rainfall and temperature, and distance to coast.

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RESULTS

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Locations of the 830 urban areas in East Asia examined in our study are illustrated in

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Figure 1, along with the surface estimates for changes in NO2 and PM2.5 concentrations and an

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example of the urban expansion footprint for Beijing, China. Table 1 summarizes the air

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pollution concentrations, urban expansion levels, changes in urban characteristics (both in terms 8 ACS Paragon Plus Environment

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of average measures and within-area variability) and baseline (year 2000) characteristics by

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urban size category. On average the change in NO2 and PM2.5 levels ranged from 1.21 to 2.25

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ppb and 10.54 to 11.02 µg/m3 based on urban size. The size of urban expansion, in terms of

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geographic area and population, was large for all urban size categories. On average, the area of

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urban expansion for small, medium and large cities was 6.9, 49.3, and 379 km2, while the

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population size expansion was 23,470, 342,400, and 2,178,000, respectively. Generally, urban

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expansion was associated with decreasing average compactness index and increasing population

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density, NDVI and lights at night. In small and medium cities, urban expansion was associated

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with decreases in the percent impervious surface (due to sprawl) but in large cities were

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associated with an increase. Change in variable heterogeneity showed similar patterns across

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urban size categories with the exception of lights at night that showed a decrease in

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heterogeneity with increasing urban size.

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Table 1. Descriptive statistics of change in NO2 and PM2.5, urban expansion and urban characteristic variables from 2000-2010 stratified by urban population. 100,000-250,000 (n=235) Mean Std dev

Change in Air Pollution NO2 (ppb) 1.21 1.75*** PM2.5 (µg/m3) 10.54 10.19*** Urban Expansion Area (sq km) 6.93 7.67*** Population (1,000s) 23.47 32.29 Change in Urban Characteristics Compactness Index (0-1) -1.72x10-2 4.65x10-2*** 2 2 Population Density (pop/km ) 7.72x10 1.12x103*** NDVI (-1-1) 0.35 0.10*** Impervious Surface Area (%) -0.10 10.96*** Lights at Night (%) 12.09 7.83*** Change in Urban Characteristic Heterogeneity Population Density 5.21x102 1.03x103*** -2 NDVI (-1-1) 8.24x10 2.83x102*** Impervious Surface Area (%) -15.96 8.92*** Lights at Night (%) 0.69 2.84** Baseline (2000) Urban Characteristics NO2 (ppb) 1.58 1.27

Urban Size 250,000-1,000,000 (n=340) Mean Std dev

> 1,000,000 (n=255) Mean Std dev

2.07 12.53

2.15*** 10.91***

2.25 11.02

3.03*** 12.42***

49.27 3.42x102

63.86*** 2.25x102***

3.79x102 2.18x103

8.72x102*** 1.96x104***

-2.87x10-2 2.76x103 0.33 -0.13 10.62

8.43x10-2*** 2.94x103*** 0.11*** 11.10*** 6.26***

-2.35x10-2 4.56x104 0.31 10.16 8.79

9.09x10-2*** 2.93x105 0.12*** 11.15*** 7.11***

3.31x103 8.32x102 -16.82 -1.47

2.76x103*** 3.57x10-2*** 7.92*** 3.34

6.33x103 9.05x10-2 -13.43 -3.20

5.13x103*** 3.77x10-2*** 7.13*** 3.81***

1.77

1.28

2.51

1.27



PM2.5 (µg/m3) Area (sq km) Population (1000s) NDVI (-1-1) Impervious Surface Area (%) Lights at Night (%)

28.05 53.54 52.18 1.41x10-2 24.46 35.49

19.15 43.90 73.05 3.06x10-3 9.35 11.00

31.42 1.14x102 1.41x102 1.33x10-2 28.27 37.75

21.07 93.51 2.17x102 2.88x10-3 11.21 11.31

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23.86 5.50x102 1.68x103 1.28x10-2 33.14 11.16

17.81 43.90 7.31 3.06x10-3 9.35 11.00

199 200 201 202 203

* p-value < 0.05, **p-value < 0.01, *** p-value < 0.001. Differences between 2000 and 2010 levels were tested for significance using multiple paired t-tests with Bonferroni-adjusted confidence intervals.

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changes in urban characteristics are shown in Table 2. For all urban areas the geographic size of

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expansion was associated with increased NO2, but only in small urban areas was expansion size

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associated with increased PM2.5. Population growth in small and medium urban areas was

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associated with increases in both NO2 and PM2.5, while no statistically significant association

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was seen for large cities. A number of significant associations were observed between changes in

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air pollution concentrations and urban characteristics that correspond to hypothesized effect

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directions. For example, NO2 increased with increases in population density, % impervious

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surface, and lights at night, while NO2 decreased with increasing compactness and NDVI.

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Table 2. Univariate associations between NO2 and PM2.5 change, urban expansion, and change in urban characteristics.

Univariate associations between changes in NO2 and PM2.5, urban expansion, and

100,000-250,000 Coefficient Std Error NO2 Urban Expansion Area (sq km) 3.82x10-2 9.56x10-3*** -2 Population (1000s) 2.02x10 2.14x10-3* Change in Urban Characteristics Compactness Index (0-1) -2.86 1.60 Population Density 4.08x10-4 6.42x10-5*** NDVI (-1-1) -3.97 0.71*** Impervious Surface Area (%) 2.09x10-2 6.73x10-3** -2 Lights at Night (%) 9.76x10 8.54x10-3*** Change in Urban Characteristic Heterogeneity Population Density 2.64x10-4 7.12x10-5*** -3 NDVI (-1-1) -1.21x10 2.58x10-4*** -2 Impervious Surface Area (%) 3.21x10 8.22x10-3*** -2 Lights at Night (%) -6.25x10 2.61x10-2*

Urban Size 250,000-1,000,000 Coefficient Std Error

> 1,000,000 Coefficient Std Error

9.32x10-3 2.78x10-3

2.37x10-3*** 6.70x10-4*

1.24 x10-3 -2.50x10-7

3.45x10-4*** 1.62x10-7

-4.58 -1.06x10-5 -3.47 2.85x10-2 8.00x10-2

1.84* 5.37x10-5 1.38* 1.41x10-2* 2.45x10-2**

-1.26 -1.91x10-6 -2.74 4.81x10-2 0. 24

3.55 1.08x10-6 2.68 2.85x10-2 3.79x10-2***

2.35x10-4 -1.46x10-3 6.60x10-2 -0.13

5.45x10-5*** 4.29x10-4*** 1.93x10-2*** 4.62x10-2**

2.48x10-4 -2.47x10-3 4.77x10-2 -0.3.1

5.71x10-5*** 8.15x10-4** 4.50x10-2 7.80x10-2***


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PM2.5 Urban Expansion Area (sq km) 0.12 5.64x10-2* Population (1000s) 0.10 1.27x10-2*** Change in Urban Characteristics Compactness Index (0-1) -23.35 9.29* Population Density 3.24x10-3 3.63x10-4*** NDVI (-1-1) -1.25x10-3 4.20x10-4** -2 Impervious Surface Area (%) 2.91x10 3.96x10-2 Lights at Night (%) 0.421 5.25x10-2*** Change in Urban Characteristic Heterogeneity Population Density 1.624x10-3 4.15x10-4*** NDVI (-1-1) Impervious Surface Area (%) Lights at Night (%)

-6.10x10-3 4.61x10-2 0.20

1.52x10-3*** 4.86x10-2 0.15

2.28x10-2 1.53x10-2

1.24x10-2 3.38x10-2***

9.85x10-4 -7.00x10-6

1.51x10-3 6.66x10-6

-8.44 3.53x10-4 -1.43x10-3 7.11x10-2 0.56

9.50 2.72x10-4 7.05x10-4* 7.201x10-2 0.12***

9.78 5.90x10-6 -8.01x10-4 0.13 0.83

14.45 4.44x10-6 1.09x10-3 0.12 0.17***

1.53x10-3

2.68x10-4***

9.77x10-4

2.35x10-4***

-6.65x10-3 0.17 0.52

2.20x10-3** 0.10 0.24*

-1.01x10-2 0.20 -0.78

3.32x10-3** 0.18 0.34*

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* p-value < 0.05, **p-value < 0.01, *** p-value < 0.001.

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Stepwise multivariate models of change in NO2 and PM2.5 are shown in Tables 3 and 4,

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respectively. Saturated multivariate models are shown in Tables S3 and S4. The reduced

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stepwise model variables included urban expansion, change in urban characteristics (mean and

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variability measures), baseline (2000) urban characteristics, and other adjustment factors

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(distance to coast and meteorological variables). For change in NO2, the overall model explained

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37%, 49% and 54% for small, medium and large urban areas. The largest predictors were lights

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at night (R2=0.20) and annual rainfall (R2=0.16) for small urban areas; lights at night (R2=0.13)

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and the annual rainfall (R2=0.13) for medium urban areas; and heterogeneity in population

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density (R2=0.12) and annual rainfall (R2=0.12) for large urban areas.

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Models of PM2.5 change generally had lower performance than NO2 models. The overall

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models explained 29%, 34% and 37% of change in PM2.5 for small, medium and large urban

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areas. The largest predictors were annual rainfall (R2=0.10) and baseline urban area (R2=0.07)

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for small urban areas; lights at night (R2=0.10) and baseline impervious surface area (R2=0.05)

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for medium urban areas; and lights at night (R2=0.08) and population density heterogeneity

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(R2=0.11) for large urban areas. The change in urban characteristics and urban characteristic



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heterogeneity explained 14.7-28.8% and 4.0-20.5% of the decadal change in NO2 and PM2.5,

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respectively, compared to 3-13% and 4-14% explained by baseline urban characteristics.

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Table 3. Multivariate associations between NO2 change and urban expansion and urban characteristics – reduced model. Urban Size 250,000-1,000,000 (n=340)

100,000-250,000 (n=235) Coefficient

Std Error

3.84x10-2

1.23x10-2**

R2

> 1,000,000 (n=255)

Coefficient

Std Error

R2

Coefficient

Std Error

0.02

1.38x10-2

4.72x10-3**

Relationships between Changes in Urban Characteristics and Air

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