Investigating the relationship between air pollution variation and urban form

Accepted Manuscript Investigating the relationship between air pollution variation and urban form Chao Li, Zhanyong Wang, Bai Li, Zhong-Ren Peng, Qingyan Fu PII:

S0360-1323(18)30381-0

DOI:

10.1016/j.buildenv.2018.06.038

Reference:

BAE 5540

To appear in:

Building and Environment

Received Date: 10 April 2018 Revised Date:

1 June 2018

Accepted Date: 15 June 2018

Please cite this article as: Li C, Wang Z, Li B, Peng Z-R, Fu Q, Investigating the relationship between air pollution variation and urban form, Building and Environment (2018), doi: 10.1016/ j.buildenv.2018.06.038. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Investigating the relationship between air pollution variation and urban form

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Chao Li a, Zhanyong Wang a, b, *, Bai Li a, Zhong-Ren Peng c, a, d, **, Qingyan Fu e

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Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean & Civil Engineering,

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Shanghai Jiao Tong University, Shanghai 200240, China. b

College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University,

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Fuzhou 350108, China. c

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Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State

China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai 200240, China. d

International Center for Adaptation Planning and Design (iAdapt), School of Landscape

Architecture and Planning, College of Design, Construction, and Planning, University of Florida,

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P.O. Box 115706, Gainesville, FL 32611-5706, USA.

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Shanghai Environmental Monitoring Center, Shanghai 200030, China.

*, ** Corresponding authors: [email protected] (Z. Wang); [email protected] (Z.R. Peng).

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Abstract

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Air quality in megacities and its correlation with urban form have become a priority, since

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numerous cities worldwide are encountering a development bottleneck due to air pollution. This

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paper analyzed the spatial variations in 8 air pollutants among 18 environment monitoring stations

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across Shanghai based on continuously hourly mass concentration data from 28 days in February,

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2017. In each of the 18 stations, we created a buffer zone with a radius of 1 km, and derived 18

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quantified features of the urban form including points of interest and environmental information by

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the geographic information system and the Baidu Map program interface. Then, the 18 stations

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were clustered into 3-level groups by using the k-nearest neighbor method according to every

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pollutant and urban form. Results showed that the spatial variation differed per pollutant, as did

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ACCEPTED MANUSCRIPT urban form. PM2.5 concentrations were high in the western and low in the eastern of Shanghai,

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which can be explained by regional influences and the distance from East China Sea. PM10 showed

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a relatively high level in the developed urban areas, and high buildings with a similar height in these

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areas significantly restrain the dispersion of PM10. The spatial variations of the 6 gaseous pollutants

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mainly depend on local human activities and transport-related emissions. The distance to primary

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road, standard deviation of building floors, and average building floors were the top 3 urban form

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features influencing the spatial variations of all pollutants. Wind ventilation was identified as a

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critical index for air quality-oriented urban design.

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Keywords: Air pollution; Urban form; Spatial variation; Cluster analysis

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1. Introduction

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Numerous epidemiological studies have noted that air pollution strongly correlates with public

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health, and long-term exposure to ambient air pollution can cause respiratory and lung diseases [1–

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4]. These effects are of a global scale, and nowadays become particularly prominent in developing

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countries [5,6]. Research also finds that the rapid industrialization and urbanization in the last half

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century have been worsening the air pollution worldwide, and typical events often occur in

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developed cities, such as Los Angeles and Tokyo in the 1950s and now in Beijing, Shanghai, and

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other fast developing cities in China [7,8]. In recent years, Chinese cities have been experiencing a

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high-speed development with an intensive infrastructure construction. It is unfortunate that the haze

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frequently outbreaks in most cities of China and seriously affects people's life. This unbearable

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problem has recently attracted more and more public attentions, making it a priority of studying

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how to coordinate urban development with air quality in China [2,9–11].

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Typically, many studies focus on the risk assessment of human exposures to ambient air pollutants

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in a specific built environment and then to understand how much the pollution risk can be produced

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in this environment. In the 1990s, one study has ever compared mortality between cities, but the

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authors quantified exposure as an average concentration from some monitoring stations in the city

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[12]. As known, important pollutants, such as particulate matter, black carbon (BC), carbon

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monoxide (CO) and nitrogen dioxide (NO2), have always shown significant variation in a small

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spatial and time scale [13–22]. This is likely due to various urban morphologies leading to a

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changeful dispersion of air pollutants among cities [23–25]. The contrast of intra-city results could

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also be larger than that of between-city results. For example, previous epidemiological studies have

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reported that for particulate matter in air pollutants, intra-city contrasts tend to be larger than

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between-city concentration contrasts [26]. These fine-scale contrasts are thus necessary to be fully

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considered in order to more accurately understand the relationships of urban features with air

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pollutants. However, previous epidemiological studies usually used a handful of sites to

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characterize the long-term average exposure of air pollution [12]. It becomes difficult to well

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correlate the complex urban features with air pollution. Therefore, an appropriate number of

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monitoring stations surrounded by a diversity of urban features is very imperative to study the

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impacts of urban features on air quality.

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In some previous studies, one method called land use regression (LUR) was widely used to

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determine the correlation between land use and air pollution, and it has yielded promising results so

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far [27]. The very first application of the LUR model in mapping intra-city air pollution was in the 3

ACCEPTED MANUSCRIPT Small Area Variation in Air quality and Health (SAVIAH) project [28]. In the SAVIAH project, in

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every season lasting for 4 years, 14-day monitored data in 80 monitoring sites were collected for

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respiratory disease research in Amsterdam, Huddersfield and Prague. Potential air pollution

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indicators, traffic-related parameters, land use types, population density and altitude were involved

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in the modelling. After SAVIAH, the LUR model has also been utilized in Europe, North America,

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and in the metropolitan area of China [27,29–31]. Although the LUR model has been used in many

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studies, its deficiencies are obvious. One important deficiency is that the average concentration of a

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target pollutant or a single value for a constraint is treated as a dependent or independent variable.

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This aspect is not sufficient to describe the status of actual air pollutants. The cluster analysis, a

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simply but practical method, aims to group objects based on the similarity among them. It has

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recently drawn wide attentions, and can intuitively reveal the similar behavior of the spatial

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distribution of different air pollutants or air quality monitoring stations in a city [11,32]. The cluster

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method based on the k-nearest neighbor algorithm has been employed to describe spatial variations

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of air pollutants at an hourly time scale in our study.

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Here, our study aims to describe the air quality through 8 pollutants, each with 26 dimensions

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defined in Section 2. In addition, 18 features derived to quantify the urban form are used to abstract

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the urban area into statistics. The relationships between air pollutants and urban form features are

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finally determined by a cluster method. Generally, the study attempts to identify the role of the

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urban form on air quality based on various pollutant indices, and hopes to shed light on the

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development of effective strategies for urban planning and design from a viewpoint of environment

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sustainability.

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2. Materials and methods

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Shanghai was selected as the study area, because as one of the most international and prosperous

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megacities in China, it suffers from severe air pollution. Shanghai is located in the Yangtze River

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Delta region of China, with a population of 24.2 million in 2016. Its municipal area is 6341 km2,

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consisting of 17 districts. Due to its rapid development, Shanghai has a great diversity in urban

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form, with a vast contrast between the central district and the suburban area.

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2.1. Air pollutant data

Air pollutant data were collected from 18 environment monitoring stations across Shanghai,

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including mass concentrations of particulate matter less than 2.5 µm (PM2.5) and less than 10 µm

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(PM10), sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO), nitrogen oxide (NOx), nitrogen

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monoxide (NO) and nitrogen dioxide (NO2) which are updated with an interval of 1 hour. The data

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from 18 monitoring stations were provided by the Shanghai Environmental Monitoring Center.

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Among these stations, 10 stations represent national stations and are released publicly via the

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official website of the Shanghai Environmental Monitoring Center. In this study, our data also

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covered the other 8 stations which were supported by the Shanghai Environmental Monitoring

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Center on the basis of our project cooperation. As seen from Fig. 1, the 18 stations basically cover

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the built-up area of Shanghai and provide us with a variety of urban form ranging from central

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urban areas to suburban areas.

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Fig. 1 Study area and spatial distribution of 18 monitoring stations across Shanghai

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At the 18 monitoring stations, we obtained the hourly average concentration data of the 8 pollutants

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from February 2017 (samples from 28 days in total). February is on the heel of the Chinese New

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Year vacation and is the most important time for people to go back to their hometown, which means

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that major industry and manufacturing activities stop in megacities such as Shanghai. The whole

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ACCEPTED MANUSCRIPT month of February is basically within the vacation and has obviously less production than other

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months over the year, so the industrial impact on the sample data can be neglected to a great extent.

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The data are thus more favorable to studying the relationship between air pollution variation and

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urban form.

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All the monitoring data were updated hourly every day; thus, in this study, the fine temporal

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variation becomes an important feature to describe air quality around each station. For each of the 8

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pollutants, a common protocol was applied for data processing. Twenty-six elements were

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introduced to describe each pollutant as cluster dimensions, including the concentration value

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corresponding to 24 hourly average values in a day, the daily average value, and the peak index

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value. The peak index is the standard deviation of the peak hours. According to the 5th

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comprehensive traffic survey of Shanghai in 2014, peak hours are from 7 to 10 and 15 to 20 o’clock

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in a day. This peak index describes the traffic impact on variations in the concentrations of air

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pollutants, because traffic is an important element of the urban form.

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2.2. Urban form

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Eighteen potential features of the urban form were extracted around the 18 monitoring stations. To

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be specific, we created a buffer zone with a radius of 1 km for each monitoring station to describe

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the surrounding built environment. This buffer scale mainly refers to the scale of the air quality

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forecast model named Community Multi-scale Air Quality Modelling System (CMAQ). The

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CMAQ sets a gridding resolution of 1 km because of the computing capacity and the physical

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characteristics of air pollutions. The contrast of the buffer zone of each monitoring station provides

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ACCEPTED MANUSCRIPT the divergence of built environments among stations. Within the buffer zone for each station, a

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specific urban form is demonstrated with different features. Fig. 2 shows the satellite images of the

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18 stations with buffer zones, which were arranged in the order of increasing building density.

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Among them, Dianshan Lake station is located in a lake park in west of Shanghai. The stations

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shown in the first row of the figure are all in the suburban area. Compared with other stations,

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Jing’an station is a city center site located in the most flourishing center area of Shanghai. Nanqiao

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station sits in a sub center area with complete infrastructural facilities in south of Shanghai, and

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Shangshida station is located in the old town that used to be the central area of Shanghai in the

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1980s. Each station presents its own unique urban form characteristic.

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Fig. 2 Satellite images of 18 monitoring stations with buffer zones in a radius of 1 km

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ACCEPTED MANUSCRIPT In our study, 18 features were defined and calculated to interpret the urban form. Seen from Table 1,

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all 18 features were divided into 4 categories including points of interest (POI), building density

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(BD), distance-based features, and building floor-based features. POI features include leisure

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facility (LF), transport facility (TF), corporation, hygiene facility (GF), government agency (GA),

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catering facility (CF), road crossing (RC), parking lot (PL), and gas station (GS). The POI features

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are significant in assessing the vitality and convenience of the urban form. BD is a critical index for

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the urban form. The distance-based features are distance from the monitoring station to the nearest

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water body and the primary road, as well as the total length of subway lines, primary roads and

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secondary roads within a buffer zone. This category is mainly related to air pollutant dispersion,

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because roads are stationary emission sources with time variation, and water body helps pollutant

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deposit that implies a role of cleansing and improving air quality. The building floor-based features

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were extracted as an average floor number, a standard deviation of building floors, and a maximum

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building floor. These features were used to abstract and describe the ventilation performance of

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each buffer zone. Buildings can be identified as obstacles in the city, and they coordinating well

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with wind is critical for urban ventilation, which is thus considered to be a very effective way to

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improve air quality.

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Table 1 Description of 18 defined features of the urban form

Features

Leisure facility (LF)

Transport facility (TF)

Description

Unit

Number of leisure facilities, such as KTV, café, public bath and salon within the buffer zone Number of total transport facilities, such as bus stops and metro entrances within the buffer zone

Record count Record count

Corporation (CORP)

Number of corporations within the buffer zone

Record count

Hygiene facility (GF)

Number of hospitals and clinics within the buffer zone

Record count

Government agency (GA)

Number of government agencies within the buffer zone

Record count

Catering facility (CF)

Number of catering service facilities within the buffer zone

Record count

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ACCEPTED MANUSCRIPT Number of road crossings within the buffer zone

Record count

Parking lot (PL)

Number of parking lots within the buffer zone

Record count

Gas station (GS)

Number of gas stations within the buffer zone

Record count

Building density (BD)

Floor area divided by land area within the buffer zone

Percentage

Average building floors (ABF)

The average floors of all buildings within the buffer zone

Record count

Max building floors (MBF)

The maximum floors of all buildings within the buffer zone

Record count

The standard deviation of all building floors within the buffer zone

NA

Standard deviation of building floors (SDBF)

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Road crossing (RC)

Distance from the monitoring station to the nearest open water area

Meter

Distance to primary road (DPR)

Distance from the monitoring station to the nearest primary road

Meter

Subway length (SL)

The total length of metro lines within the buffer zone

Meter

Primary road length (PRL)

The total length of primary roads within the buffer zone

Meter

Secondary road length (SRL)

The total length of secondary roads within the buffer zone

Meter

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Distance to water (DW)

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2.3. Cluster analysis

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Cluster analysis aims to group objects based on the similarity among them [11,32]. The cluster

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method based on the k-nearest neighbor algorithm was employed in this study. In the cluster model,

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18 stations were grouped into 3 levels (i.e., high, medium and low) based on the 8 air pollutants and

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the urban form. For each pollutant, 26 dimensions, as defined before, were considered as inputs for

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grouping. We empirically determined the k value based on the integer value of square root of n,

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where n denotes the 18 stations samples. Additionally, we did a cross validation after the clustering

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to further confirm the classification results. For the urban form, the 18 features mentioned in Table

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1 were introduced to the cluster in order to divide the 18 stations into 3 groups from high to low,

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shown according to the order of building density. In total, 9 clusters were performed including

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PM2.5, PM10, SO2, O3, CO, NOx, NO, NO2, and urban form. Each cluster was given a pseudo

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F-statistic to assess the grouping performance. Then, for each pollutant and the urban form, the R2

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of every dimension was calculated to evaluate the goodness of fit, and their groupings were mapped

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through the ArcGIS 10.2 version software with a built-in Python package. In addition to

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ACCEPTED MANUSCRIPT visualization of the cluster results, descriptive statistics were applied to determine the relationship

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between different pollutants and urban form features. Under three clusters for each pollutant, we

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also calculated the mean value of each urban feature to see what trend the urban feature varies with

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the spatial cluster of the pollutant. In this way, we can further investigate the relationship between

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air pollution variation and urban form from local features.

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3. Results

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Fig. 3 shows statistical distributions of mass concentrations of all the 8 pollutants among the 18

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monitoring stations, which is based on all the hourly samples in February, 2017. Table 2 shows the

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cluster results for the 8 pollutants and urban form, as well as the pseudo F-Statistic and the top 5 R2

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determinants for each pollutant and urban form. The cluster map is shown in Fig. 4 to demonstrate

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the spatial variations of each pollutant and the urban form among 18 monitoring stations.

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Fig. 3 Statistical distribution of hourly average concentrations (mg/m3) for (a) PM2.5, (b) PM10, (c)

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SO2, (d) O3, (e) CO, (f) NOx, (g) NO and (h) NO2 among 18 monitoring stations across Shanghai.

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Note: Median, 25th and 75th quantiles are shown in the box; whiskers indicate the lower and upper

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boundaries; and outliers are shown as points.

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Table 2 Results of the cluster analysis with top 5 determinants for 8 pollutants and urban form

Monitoring station

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Group classification PM2.5

PM10

SO2

O3

CO

NOX

NO

NO2

Urban features

High

Medium

High

Low

High

Medium

Low

Medium

Low

Yingpu

High

Medium

Medium

Medium

High

Medium

Medium

Medium

Low

Songjiang

High

Medium

Medium

Medium

High

Medium

Medium

Medium

Low

Jinshan

Low

Medium

Low

High

Medium

Low

Low

Low

Medium

Yanghang

Medium

High

Medium

Low

High

High

High

High

Medium

Jiading

High

Low

Medium

Low

High

High

High

Medium

Medium

Pudong

Low

Medium

Medium

High

Medium

Medium

Low

High

High

Dianshan Lake

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ACCEPTED MANUSCRIPT Low

Medium

High

Low

Medium

Medium

Medium

High

Medium

Nanqiao

Low

Medium

Medium

High

High

Medium

Medium

Medium

Medium

Zhangjiang

Low

High

High

Medium

High

Medium

Medium

High

Medium

Luwan

Low

Medium

High

Low

High

Medium

Medium

High

High

Shangshida

Low

Medium

High

Low

High

Medium

Low

High

Medium

Chuansha

Low

Medium

Medium

Medium

Medium

Medium

Medium

High

High

Liangcheng

Low

Medium

Medium

Medium

High

Medium

Medium

High

High

Chengqiao

Medium

Low

High

High

Low

Medium

Low

Medium

Medium

Putuo

Low

High

High

Medium

Medium

Medium

Low

Medium

High

Sipiao

Low

High

High

Medium

High

Medium

Medium

High

High

Jing’an

Low

Medium

High

High

Medium

Medium

Low

High

High

Pseudo F-Statistic

9.6037

15.0588

17.9204

20.4718

26.3961

7.1001

12.6316

11.754

7.4666

PM2.5_8,

PM10_AV

SO2_18,

O3_AVG

CO_1,

NOx_19,

NO_23,

NO2_7,

DPR,

0.700

G, 0.834

0.894

, 0.933

0.917

0.734

0.881

0.775

0.674

PM2.5_6,

PM10_5,

SO2_AVG,

O3_23,

CO_0,

NOx_PI,

NO_PI,

NO2_AV

SDBF,

0.700

0.828

0.881

0.879

0.899

0.725

0.880

G, 0.738

0.630

PM2.5_9,

PM10_9,

SO2_20,

0.696

0.770

0.826

PM2.5_15,

PM10_6,

SO2_19,

0.684

0.756

0.825

PM2.5_11,

PM10_10,

SO2_17,

0.681

0.746

0.817

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NO_8,

NO2_5,

ABF,

0.888

0.701

0.877

0.721

0.622

O3_19,

CO_4,

NOx_22,

NO_2,

NO2_8,

CORP,

0.848

0.885

0.658

0.874

0.721

0.622

O3_0,

CO_AV

NOx_2,

NO_AVG,

NO2_6,

BD,

0.843

G, 0.882

0.646

0.869

0.714

0.604

Note: SO2 with the number from 0 to 23 means the SO2 value at some hour of day averaged by 28 samples of the month, SO2_AVG means the whole day average value of SO2, SO2_PI means the peak index of SO2. These annotations are suitable for other pollutants in the table.

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CO_2,

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R)

O3_22,

0.872

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top 5 R , (Factor,

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Fig. 4 Mapping the cluster results of 18 monitoring stations for (a) PM2.5, (b) PM10, (c) SO2, (d) O3,

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(e) CO, (f) NOx, (g) NO, (h) NO2, and (i) Urban features

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As shown in Fig. 3a, the PM2.5 concentration level was nearly equal among the 18 stations. In spite

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of this, Jiading and Chuansha stations had the highest and lowest PM2.5 concentration, respectively.

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Both stations were located in the suburban area, and the difference was small. As seen in Table 2,

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the clustering results show that 4 stations were high, 2 stations were medium, and 12 stations were

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low in PM2.5 level, while the top 5 determinants were hourly PM2.5 values at 8, 6, 9, 15, and 11

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o’clock. It is obvious that the morning peak hours were significantly related to the contrast of the

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PM2.5 concentrations among all stations, which is consistent with previous studies [19-21,33]. Seen

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from Fig. 4a, the spatial variation of the PM2.5 cluster represented the characteristic of high cluster;

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the west part was high and the east part was low, while medium levels were observed in the north.

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Additionally, the PM2.5 concentrations were low in the downtown areas.

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Seen from Fig. 3b, the highest concentration of PM10 was detected at the Sipiao station; Yanghang

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and Yingpu stations showed the largest contrast within the station and were different from the other

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16 stations which had the same pattern within their own station. PM10 concentrations at Yanghang

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and Yingpu stations also reached a maximum concentration twice those observed in the other

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stations. Table 2 shows that 12 stations were classified as medium and 2 stations were low, while 4

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stations were classified as high. The top 5 determinants were the daily average PM2.5 value and

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hourly PM2.5 values at 5, 9, 6, and 10 o’clock. The map in Fig. 4b shows that the PM10

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concentrations were low in the northwest, high in the northeast, and medium in the south of

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Shanghai.

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Fig. 3c shows that the SO2 concentration was lowest in Jinshan station, a southern coastal station,

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and highest in Xinzhuang station, which was located in the joint part between the urban and

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suburban area. Jiading station in the northern suburban area showed the largest contrast in the

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concentration. In general, the SO2 concentration in Shanghai was rather evenly distributed. In Table

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2, the clustering results show that 9 stations were high, 8 were medium, and only 1 was low. The

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top determinants were hourly PM2.5 values at 18 to 20 o’clock (evening peak hours) and the daily

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average PM2.5 value. The spatial distribution of the group classification was basically random (see

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Fig. 4c). However, the Chengqiao and Jinshan stations, which were both located by the coast,

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showed high contrast. The Dianshan Lake station had the lowest building density but had a high

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concentration of SO2. The medium level stations were more likely to be in the suburban or rural

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area.

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O3 and CO

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Fig. 3d shows that the highest concentration of O3 was observed in Nanqiao station, while the

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lowest was observed in Yanghang station. The O3 concentrations were relatively lower in Yanghang,

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Jiading, and Xinzhuang stations than in the other stations, and these three stations were all located

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in the joint urban and suburban areas. Table 2 indicates that 5, 7 and 6 stations were designated as

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high, medium, and low clusters in O3 concentrations, respectively. Meanwhile, top 5 determinants

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of the clustering were the daily average O3 concentration, and the hourly O3 concentration at 19, 22,

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23, and 0 o’clock when the concentration was relatively low in a day. The spatial distribution

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showed that the number of stations classified as high, medium, and low were equal, and these

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stations were randomly located in Shanghai, as shown in Fig. 4d.

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The CO concentration was highest in Liangcheng station and lowest in Chengqiao station (see Fig.

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3e). These two stations also indicate a large contrast in the O3 concentration although they had a

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similar building density. In contrast, the other stations showed small differences in O3

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concentrations. As shown in Table 2, the clustering identified 11 stations as high and only one

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station (Chengqiao) as low in CO levels. Fig. 4e shows that all stations in mainland of Shanghai had

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a higher level than Chengqiao, an island station. Table 2 further reveals that the top 5 determinants

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contributing to the group classification were the hourly CO concentration at 1, 0, 2, 4 o’clock and

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the daily average CO concentration, which occurred during midnight and dawn.

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NOx, NO and NO2

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As described in Fig. 3f, the NOx concentration was highest in Yanghang station and lowest in

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Jinshan station. The average NOx concentration was low and was similar among and within stations,

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but the contrast differed within each station. The maximum NOx concentration in Yanghang was

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nearly 8 times higher than its average of this site and was 4 times higher than that in Jinshan station

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although both sites had a similar building density. From Table 2, we see that 15 stations were

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classified as medium, 2 as high, and 1 as low. Meanwhile, a key point should be noted that the top 5

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determinants were NOx values at 19, 20, 22, and 2 o’clock and the peak index. The peak index

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implies traffic as an important indicator closely related to NOx. As seen in Fig. 4f, the stations with

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medium NOx levels almost covered the whole area, while Jianshan station showed the lowest NOx

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concentration, and only Jiading and Yanghang stations, located in north of Shanghai, had high

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levels of NOx.

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Similar to NOx, as shown in Fig. 3g, the NO concentration in the 18 stations showed a small

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contrast, but the concentration range was quite wide among and within the stations. The stations

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showed a distribution pattern of NO similar to that of NOx. Yanghang, Sipiao, Jiading, and

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Xinzhuang stations had high concentrations of both NO and NOx, and the other stations presented a

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quite similar variation trend in both pollutants. As seen in Table 2, there are 2 stations identified as

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high, 9 as medium, and 5 as low NO classifications. The top 5 determinants were NO values at 23,

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8, and 2 o’clock, the daily average NO value, and the peak index, which are similar to NOx. The

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spatial distribution of NO generally showed a random pattern (see Fig. 4g). Except Dianshan Lake

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and Shangshida, the other stations showed an inverse pattern between O3 and NO. This supports

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that NO, as a reducing agent, often consumes O3 which acts as an oxidizing agent [33].

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From Fig. 3h, NO2 generally showed a large contrast within a specific station. The lowest average

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level was found in Jinshan station, and the highest average level was observed in Luwan station.

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Stations, except Chengqiao and Jinshan with clearly low concentrations, showed a similar pattern of

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NO2 distribution. The group classification resulted in 11 high, 6 medium, and 1 low station, as

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shown in Table 2. The top 5 determinants were NO2 values from 5 to 8 o’clock and the daily

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average value, implying that the cluster depended more on the morning peak hours. On the whole,

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the concentration levels were higher in the east than in the west of the mainland of Shanghai;

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additionally, the medium-level stations were mainly located in suburban and rural areas, as shown

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in Fig. 4h.

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Urban form

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As shown in Table 2, the 18 stations were further clustered according to the 18 quantified features

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of the urban form. Results show that 7 stations were designated as high, 8 as medium, and 3 as low.

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The group classification basically followed the order of the developed degree of each station’s

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buffer zone. The top 5 features contributing to the clustering were DPR, SDBF, ABF, CORP, and

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BD, with R2 ranging from 0.674 to 0.604. From the spatial variation (Fig. 4i), the classification of

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the urban form was not a result of random chance. The stations classified as high level were mainly

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distributed in the central area, and medium classifications were more likely to be in the joint urban

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and suburban areas. The stations with a low level of classification were located at the west side of

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the city, where had a relatively low building density.

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4. Discussion

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Seen from Fig. 3a and 3b, the 18 stations show a quite close statistical distribution for PM2.5 or

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PM10. However, as illustrated in Fig. 4a and 4b, the spatial clustering of each particulate matter is

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quite different. The west of Shanghai, mainly referring to suburban and rural areas, shows high

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PM2.5 levels, and in contrast, the east of Shanghai shows low PM2.5 concentrations. This spatial

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difference of PM2.5 concentrations has also been demonstrated by Liu et al [27]. They concluded

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that the neighboring provinces in west and north of Shanghai had major effects on the spatial

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distribution of PM2.5 in Shanghai. Although the east area of Shanghai has a higher development

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a certain extent [27,34]. Table 3 shows the tendency relationship of each pollutant with top 9 urban

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form features under three clusters from high to low, with a R2 greater than 0.5. Seen from the table,

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the PM2.5 level is negatively related with CORP and TF. This further reveals local intense built

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environments do little contribution to PM2.5 under the decisive influences of regional features

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discussed above. From Fig. 4b, PM10 presents a nearly inverse spatial pattern in contrast to PM2.5,

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and has a relatively high level in the developed urban areas. Table 3 shows that the spatial

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distribution of PM10 has a trend same as ABF but contrary to SDBF. This indicates that PM10 is

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difficult to disperse by wind in a built environment consisting of high buildings with a similar

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height, which seems to be a wooden barrel.

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Table 3 Tendency relationship between 8 pollutants and top 9 urban form features with the

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clustering from high to low

DPR 0.674 784 205 258 137 487 128 331 454 27 209 389 478 497 173 133

SDBF 0.630 1.27 3.93 3.66 2.36 3.36 3.58 3.68 2.67 2.41 3.52 2.58 3.54 2.46 4.13 5.04

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Urban Form Features R2 High PM2.5 Medium Low High PM10 Medium Low High SO2 Medium Low High O3 Medium Low High CO Medium Low

ABF 0.622 1.38 5.78 5.00 4.90 4.26 3.14 5.07 3.52 3.26 4.59 3.56 4.86 3.37 5.81 5.16

CORP 0.622 96 274 299 308 230 268 283 217 240 256 238 263 249 258 242

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BD 0.604 0.019 0.286 0.185 0.239 0.134 0.156 0.194 0.138 0.027 0.165 0.172 0.140 0.143 0.168 0.286

PRL 0.599 959 5913 4129 3934 3472 3907 5345 2138 0 4244 2647 4242 2751 4522 7814

LF 0.579 29 143 113 138 76 145 122 79 33 116 101 79 89 99 182

TF 0.577 23 86 87 89 65 89 100 51 0 66 72 79 63 83 116

GS 0.538 0.75 2.00 0.92 1.25 0.83 1.50 1.00 1.13 0.00 0.80 1.29 0.83 0.91 1.00 2.00

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High 200 2.47 3.76 300 0.156 2006 106 58 NOx Medium 414 3.30 4.42 245 0.169 4080 101 80 Low 27 2.41 3.26 240 0.027 0 33 0 High 200 2.47 3.76 300 0.156 2006 106 58 NO Medium 434 3.03 4.07 264 0.157 3198 89 78 Low 334 3.52 4.70 220 0.163 4631 106 71 High 293 3.79 5.08 320 0.208 3753 113 90 NO2 Medium 526 2.36 3.29 155 0.109 3954 86 59 Low 27 2.41 3.26 240 0.027 0 33 0 Note: The thick underline means a crosscurrent trend, while the thin underline means a synthetic trend.

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1.50 1.00 0.00 1.50 0.67 1.29 1.00 1.14 0.00

Every pollutant has a significant contrast within a single monitoring station. Generally, the

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maximum concentration is 4-6 times higher than the average concentration. Fig. 3 shows that for

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NO and NOx, 75% of each station’s concentrations are lower than their maximum concentration.

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This implies that during most of the time the concentrations of NO and NOx are low and stable, and

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only a small portion contributes to the high concentration. NOx is generally the sum of NO and NO2.

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NO2 concentrations within the stations range from 0 to 0.2 mg/m3, accounting for 75% of the NOx

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concentration. Our results indicate that NO mainly came from tailpipe emissions during peak hours

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and was oxidized to NO2 and NOx by O3 and other oxidizing agents. From Table 3, we find that the

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NO2 distribution follows the same trend as ABF, BD, LF and TF. This relationship is almost similar

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to the NOx clusters. Higher building floors and density, more leisure places and more transport

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facilities represent higher vitality and more activities, which can result in more traffic. NO provides

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new explanations by sharing crosscurrents with SDBF, ABF, BD, and PRL. Considering that BD is

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almost equal at three clusters for NO, high SDBF and ABF imply a rough surface out of flatness to

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the buffer zone of the monitoring station, which is not helpful for wind ventilation. Besides, a high

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level of NO was observed with low density of primary roads. Since NO, NO2 and NOx are all

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traffic-related pollutants, the proper building layout and height in cooperation with lower

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development intensity may help mitigate NOx pollution.

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For SO2, its main sources are industry, thermal power generation, and coal-fired heating. As seen in

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Table 3, the SO2 distribution follows the same trend as SDBF, ABF, BD, PRL, LF and TF. Higher

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building density and more leisure places and transport facilities imply more vitality of the urban

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form, and thus more SO2 sources. Primary roads in Shanghai consist of massively elevated roads.

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Higher SDBF, ABF, and more elevated roads contribute to the surface roughness of the urban form,

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and a rough surface suggests weak wind ventilation and weak pollutant elimination. O3 is a

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secondary pollutant produced from the photochemical reaction among NOx, VOCs (volatile organic

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compounds), and CO. Table 3 shows that O3 represents a relationship inverse to DPR and TF, but

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positive with LF. The higher the O3 level is, the closer the primary road is located to the monitoring

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station, and the fewer transport facilities are in the buffer zone. By comparison, more leisure places

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bring higher O3 concentrations. Primary roads with a mass of traffic emissions can provide

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precursors for O3. After the accumulation of precursors during morning peak hours, O3 is generated

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due to the photochemistry. CO is considered to be directly sourced from tailpipe and fossil fuel

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burning, and it is an important component in the photochemistry of O3. CO is of crosscurrent with

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SDBF, BD, PRL, LF, TF, and GA, but follows the same trend with DPR (see Table 3). This implies

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a low CO concentration with a short distance of the primary road, a rough ground surface against

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the dispersion of air pollution, and a highly developed and intensive land use. This relationship

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between CO and urban features may be attributed to a dynamic balance of the photochemistry

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process among CO, NO, NO2, NOx and O3 as well as the complex urban features. For example,

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ACCEPTED MANUSCRIPT after morning traffic peak hours, the precursors of O3 are accumulated, and when solar radiation is

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enhanced, the photochemistry reaction produces O3 and consumes the precursors. Therefore, traffic,

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building layout, and land development intensity play a significant role in urban air pollution control.

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The analysis of these gaseous pollutants agrees with previous evidences that, in the urban

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microenvironments, the pollution mainly comes from transport-related emissions induced by local

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heavy human activities [35]. Additionally, a smooth surface of building layout may help eliminate

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air pollution via wind ventilation to accelerate dispersion.

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4. Conclusions

This study evaluated the spatial variation of 8 pollutants among the 18 outdoor monitoring stations

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across Shanghai, and discussed the relationships of the quantized features of the urban form with

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each pollutant. Spatial variability was found different in 8 pollutants, and the role of urban form

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features in air pollution variation varied between different pollutants. Specifically, PM2.5

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concentrations were higher in the western part and lower in the eastern part of Shanghai, which

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coincides with the study by Liu et al [27] based on samples in 2014. Regional influences from the

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neighboring provinces in west and north of Shanghai and the distance from East China Sea can

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explain the spatial pattern of PM2.5. PM10 presented a nearly inverse spatial pattern in contrast to

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PM2.5, and had a relatively high level in the developed urban areas, where PM10 becomes not easy

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to disperse under wind due to the built environment consisting of high buildings with a similar

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height. O3, CO, NO, NO2, and NOx were in a dynamic balance with each other under the complex

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process of the meteorological factors such as solar radiation. However, their variations in time and

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space were primarily dependent on local human activities and transport-related emissions. The

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standard deviation of building floors, the average building floors, and the building density played

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significant roles in the dispersion of six pollutants, except O3 and PM2.5. From a sustainable

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perspective, wind ventilation will be a critical index for air quality-oriented urban design.

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This study assessed the spatial variations of multiple air pollutants and their relationships with

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urban form features at a fine time scale (i.e., hourly). The cluster analysis on the basis of the

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similarity of monitoring stations was applied and demonstrated to be feasible with an outcome

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similar to the LUR model used in Shanghai in the past. The results of this study can enrich an

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elaborate urban planning and design with a more specific optimization of air quality. However, the

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study also has limitations. The authors did not obtain synchronous meteorology data, which limited

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the further analysis on the results. Future research is also recommended to examine what

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combination of the key urban form features can more effectively improve the air quality in the

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community.

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Acknowledgements

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This work was partially supported by the National Key R&D Program of China (No.

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2016YFC0200500), the National Natural Science Foundation of China (No. 41701552), the

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National Planning Office of Philosophy and Social Science (No. 16ZDA048), and the Science and

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Technology Project of Guangzhou, China (No. 201803030032).

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Investigating the relationship between air pollution variation and urban form

Highlights Spatial variability is analyzed for 8 air pollutants among 18 monitoring stations. The cluster analysis is performed to correlate air pollutants and urban form.

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Spatial variation of PM2.5 relies critically on regional impacts and distance to East China Sea. High buildings with a similar height significantly restrain the dispersion of PM10.

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Gaseous pollutants strongly correlate to local activities and transport-related emissions.