Assessing the Effect of the Long-Term Variations in Aerosol

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Characterization of Natural and Affected Environments

Assessing effect of the long-term variations in aerosol characteristics on satellite remote sensing of PM2.5 using an observation-based model Changqing Lin, Alexis K. H. Lau, Jimmy C.H. Fung, Xiang Qian Lao, Ying Li, and Chengcai Li Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06358 • Publication Date (Web): 28 Feb 2019 Downloaded from http://pubs.acs.org on March 2, 2019

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Environmental Science & Technology

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Assessing effect of the long-term variations in aerosol

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characteristics on satellite remote sensing of PM2.5 using an

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observation-based model

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Changqing Lin,†,‡,* Alexis K.H. Lau,†,‡,* Jimmy C.H. Fung,‡,§ Xiang Qian Lao,∥ Ying Li,⊥

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Chengcai Li#

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†Department of Civil and Environmental Engineering, the Hong Kong University of Science and

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Technology, Hong Kong, China

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‡Division of Environment and Sustainability, the Hong Kong University of Science and

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Technology, Hong Kong, China

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§Department of Mathematics, the Hong Kong University of Science and Technology, Hong Kong,

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China

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∥Jockey Club School of Public Health and Primary Care, the Chinese University of Hong Kong,

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Hong Kong, China

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⊥Department of Ocean Science and Engineering, Southern University of Science and Technology,

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Shenzhen, China

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#Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University,

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Beijing, China

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AUTHOR INFORMATION

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Corresponding Author:

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*(C.Q. Lin) Tel.: (852) 23586676. E-mail: [email protected].

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*(A.K.H. Lau) Tel.: (852) 23586944. E-mail: [email protected].

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Abstract: Variations in aerosol characteristics play an essential role in satellite remote sensing

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of PM2.5 concentrations. The lack of measurement of aerosol characteristics, however, limits the

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assessment of their effects. This study presented an observation-based model that directly

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considered the effects of aerosol characteristics. In this model, we used an integrated humidity

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coefficient (γ’) and an integrated reference value (K) to delineate the effects of aerosol

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characteristics. We then investigated the effects of the long-term variations in aerosol

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characteristics on satellite remote sensing of PM2.5 concentration in Hong Kong from 2004 to

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2012. The results show that the γ’ value peaked in 2009 because the percentages of highly

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hygroscopic components (e.g., sulfate and nitrate) in aerosols reached their peaks. The K value

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increased from 2004 to 2011 because of the increasing percentages of strong light-extinction

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components (e.g., organic matter) and the decreasing fine mode fraction in aerosols. The

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accuracy of PM2.5 retrieval improved greatly after accounting for the long-term variations in

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aerosol characteristics (e.g., correlation coefficient increased from 0.56 to 0.80). The results

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underscore the need to incorporate the variations in aerosol characteristics in the PM2.5

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estimation models.

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TOC Art

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Introduction

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Traditional studies have relied on ground monitoring to identify the levels of PM2.5

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concentration (particulate matter with an aerodynamic diameter below 2.5 μm).1 Spatial coverage

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of such monitoring, however, is inherently limited by the resource. Satellite remote sensing

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techniques provide an alternative method to delineate PM2.5 distribution with a large spatial

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coverage.2–7 These techniques retrieve the ground-level PM2.5 concentration from aerosol optical

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depth (AOD), which denotes a vertical integration of light extinction from atmospheric aerosols.

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Aerosol characteristics play an essential role in the AOD-PM2.5 relationship through their

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impacts on aerosol extinction efficiency and hygroscopic growth ability.8,9 Typical components

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of particulate matter such as sulfate, nitrate, elemental carbon (EC), organic matter (OM), and

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dust show distinctive light-extinction abilities.10,11 Light-extinction efficiency for OM is typically

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higher than for sulfate and nitrate.12,13 Coarse particles, such as dust, scatter light less effectively

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than fine particles.10 In addition, sulfate and nitrate are considered to be more hygroscopic than

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OM and dust aerosols.14 Therefore, the effects of variations in aerosol characteristics should be

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taken into account when developing a robust AOD-PM2.5 model.

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During the past decade, various simulation-based and observation-based AOD-PM2.5 models

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have been adopted to identify ground-level PM2.5 concentrations around the world.15–21 In the

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simulation-based AOD-PM2.5 models, information on the aerosol characteristics are drawn from

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Chemical Transport Models (CTMs).15–17 Observation-based AOD-PM2.5 models train various

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empirical relationships between the AOD and PM2.5 concentration using ground PM2.5

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measurements.18–21 Most of these empirical models adopt various meteorological and land-use

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parameters as covariates to indicate the variation in aerosol characteristics.9 It is important to

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develop observation-based model that directly addresses the effect of aerosol characteristics.


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Governments around the world have implemented various control measures to reduce the

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levels of PM2.5 concentrations.22,23 These control efforts have reduced anthropogenic emissions

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of primary particles and precursor gases such as sulfur dioxide and nitrogen oxides. As a result,

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aerosol characteristics have changed considerably over a long period.24 In addition, different

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seasons feature distinctive emission sources, meteorological conditions, and evolution processes

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of pollutions.25 Therefore, aerosols tend to show distinctive characteristics among the four

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seasons.26 These long-term variations in aerosol characteristics can pose a significant impact on

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the AOD-PM2.5 relationship. The lack of measurement of aerosol characteristics, however, limits

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the assessment of their effects.

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Hong Kong locates in southeast of the Pearl River Delta (PRD) region of China. It is one of

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the most developed and densely populated cities in the world. Compared with most cities in

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mainland China, Hong Kong operates a much more comprehensive system for monitoring air

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quality over a long period.1,27 In this study, we took advantage of long-term measurements of

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mass concentrations and chemical compositions of particulate matter over Hong Kong from 2004

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to 2012. Inter-annual and seasonal variations in aerosol characteristics were studied. We

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presented an observation-based AOD-PM2.5 model that directly considered the effects of aerosol

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characteristics. The effects of the long-term variations in aerosol characteristics on satellite

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remote sensing of PM2.5 concentration were then investigated over Hong Kong during the study

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period.

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Data

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From Moderate Resolution Imaging Spectroradiometer (MODIS) observations, we retrieved

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AOD at a resolution of 1 km over Hong Kong from 2004 to 2012.28 The AOD dataset was built

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using the dark-target land algorithm at 0.55 μm and our own look-up table. Verifications of the



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satellite-retrieved AOD showed a good agreement with ground observations.28,29 We obtained

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hourly surface meteorological data at Tung Chung station (113.94°E 22.29°N, as shown in

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Figure S1) from 2004 to 2012. The meteorological data included visibility-derived aerosol

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extinction coefficient (σa,0) and relative humidity (RH).

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We also obtained hourly PM10 (particulate matter with an aerodynamic diameter of below 10

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μm) and PM2.5 data from air quality monitoring network over Hong Kong during the study

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period. The air quality monitoring network included eleven general stations. Temporal coverage

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of the PM2.5 concentration data was different at each station. The PM2.5 data with a nine-year

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coverage were available at four stations, including Tung Chung [the other three stations were

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Yuen Long (114.02°E 22.45°N), Tsuen Wan (114.11°E 22.37°N), and Tap Mun (114.36°E

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22.47°N)]. PM2.5 concentrations were available at the other stations since some time in 2010.

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The monthly averages of PM2.5 concentrations were estimated when >80% of the hourly PM2.5

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data within that month were available. The annual averages of PM2.5 concentrations were

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estimated when >80% of the hourly PM2.5 data within that year were available.

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In addition, we obtained daily averages of PM10 chemical speciation data at an interval of six

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days from the PM10 chemical speciation network over Hong Kong from 2004 to 2012. The PM10

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chemical speciation data include collections of mass concentrations of nitrate, sulfate,

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ammonium, EC, and OM in PM10 every six days. We normalized these mass concentrations by

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the PM10 concentration and obtained the percentages of different chemical compositions in PM10.

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Simultaneous observations of meteorological values, mass concentrations and chemical

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compositions of particulate matter were available at Tung Chung station. These observations

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were used to develop the AOD-PM2.5 model. The results of PM2.5 retrieval were evaluated using


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the observations in other stations. More information on these datasets can be found in the

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supplementary material.

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Methodology

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The relationship between AOD and ground-level PM2.5 concentration is affected by aerosol

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vertical structure, hygroscopic growth factor, aerosol extinction efficiency, and fine mode

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fraction.8,17,20 Based on this physical understanding, Lin et al. (2015)8 developed an

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observational-data driven algorithm to retrieve PM2.5 concentration from the AOD. We

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summarize and extend this algorithm here.

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To address the effect of the aerosol vertical structure, we introduced aerosol scale height (H).

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The scale height H at Tung Chung station can be estimated from the ratio of the satellite-based

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AOD over the station and the visibility-derived σa,0. By assuming a uniform H within Hong

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Kong (an area of approximately 30 × 40 km2), spatial distribution of surface σa,0 over Hong

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Kong can be derived by:

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𝜎𝑎,0 =

𝐴𝑂𝐷

(1)

𝐻

Relationship between σa,0 and PM10 concentration can be expressed as: 𝑃𝑀10 =

𝜎𝑎,0 𝛼𝑒𝑥𝑡,10 ∙

(

(2)

1 ― 𝑅𝐻 ―𝛾 1 ― 𝑅𝐻0

)

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where γ represents hygroscopic growth coefficient, which is dependent on aerosol characteristics;

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RH0 = 40% is reference RH in dry conditions; 𝑓(𝑅𝐻) =

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factor; and αext,10 represents aerosol mass extinction efficiency (MEE).

(

1 ― 𝑅𝐻

)

1 ― 𝑅𝐻0

―𝛾

is hygroscopic growth

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Because aerosol characteristics change under different meteorological conditions, the MEE is

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likely to be dependent on RH. We therefore merge the humidity dependence from the MEE with



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hygroscopic growth. The relationship between σa,0 and the PM10 concentration can then be

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expressed as:

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𝑃𝑀10 =

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𝜎𝑎,0

(

𝛼′𝑒𝑥𝑡,10 ∙

(3)

1 ― 𝑅𝐻 ― 𝛾0 1 ― 𝑅𝐻0

)

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where γ0 is defined as the integrated humidity coefficient from the MEE and hygroscopic growth

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in this study; α’ext,10 is the MEE under dry conditions. Both the γ0 and α’ext,10 values are associated

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with the aerosol characteristics. The γ0 and α’ext,10 values at Tung Chung can be fitted using

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simultaneous measurements of the visibility-derived σa,0, RH, and PM10 concentration.

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To estimate PM2.5 concentration, we need to address the effect of fine mode fraction (FMF,

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percentage of PM2.5 mass in PM10 mass). Relationship between σa,0 and PM2.5 concentration can

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be expressed as:

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𝜎𝑎,0

𝑃𝑀2.5 = 𝛼

𝑒𝑥𝑡,10 𝐹

∙

(

)

1 ― 𝑅𝐻 1 ― 𝑅𝐻0

(4)

―𝛾

where F represents the FMF, which is an indicator of aerosol size distribution. Because the FMF is likely to be dependent on RH, we merge the humidity dependence from

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the MEE and FMF with hygroscopic growth. The relationship between σa,0 and PM2.5

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concentration can then be expressed as:

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𝑃𝑀2.5 = 𝛼′

𝜎𝑎,0

𝑒𝑥𝑡,10 𝐹′

∙

(

)

1 ― 𝑅𝐻 1 ― 𝑅𝐻0

= ― 𝛾′

𝜎𝑎,0 𝐾∙

(

1 ― 𝑅𝐻 ― 𝛾′ 1 ― 𝑅𝐻0

)

(5)

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where γ’ is integrated humidity coefficient from the MEE, FMF, and hygroscopic growth; F’ is

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the FMF under dry conditions; and K = α’ext,10/F’ is integrated reference value under dry

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conditions. The γ’ and K values at Tung Chung can be fitted using simultaneous measurements of

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the visibility-derived σa,0, RH, and PM2.5 concentration.


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By introducing the γ0 value, we can separate the humidity dependence from the MEE and

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hygroscopic growth with that from the FMF. Therefore, the γ’ value is associated with γ0 in

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conjunction with the humidity dependence from the FMF. In this study, we estimate the γ0, γ’,

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α’ext,10, F’, and K values on a monthly basis at Tung Chung. We then investigate the associations

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between the long-term variations in the γ’/K values and aerosol characteristics. By assuming that

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the parameters (e.g., γ’, K, and RH) derived at Tung Chung represent the conditions over all of

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Hong Kong, spatial distribution of PM2.5 concentration can be derived by:

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𝐴𝑂𝐷 𝐻

𝑃𝑀2.5 = 𝐾∙

(

1 ― 𝑅𝐻 ― 𝛾′ 1 ― 𝑅𝐻0

)

(6)

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To assess the effect of the long-term variations in aerosol characteristics, we retrieve PM2.5

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concentrations using two AOD-PM2.5 models. First, we retrieve PM2.5 concentration using a static

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AOD-PM2.5 model, which assumes that the γ’ and K values remained unchanged during the study

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period. Second, we retrieve PM2.5 concentration using a dynamic AOD-PM2.5 model, which takes

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into account the long-term variations of γ’ and K. We then compare the performance of these two

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models.

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Results

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Variation in PM10 chemical compositions. Figure 1 shows long-term variations in the

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percentages of different chemical compositions in PM10 at Tung Chung from 2004 to 2012.

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Nine-year averages of the percentages of nitrate, sulfate, ammonium, OM, and EC in PM10 were

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1.4%, 21.8%, 4.4%, 18.9%, and 5.2%, respectively. Sulfate and OM showed the largest

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contributions to PM10 concentration. Remaining components in the PM10 mass were associated

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with other species, such as crustal material, sea salt, non-crustal trace elements, and unidentified

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species.30 It is noted that annual averages of the percentages of nitrate and sulfate reached their



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peaks in 2009 with maximums of 1.9% and 23.0%, respectively. In addition, annual averages of

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the percentages of ammonium and OM showed an ascending trend from 2004 to 2011 with

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maximal levels of 5.4% and 21.7%, respectively. Annual average of the percentage of EC

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showed a general descending trend.

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(a)

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(b)

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(c)

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(d)

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Figure 1. Monthly (solid lines) and yearly (dashed lines) variations in the percentages of

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different chemical components [including (a) nitrate, (b) sulfate, (c) ammonium, (d) OM, and (e)

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EC] in PM10 at Tung Chung from 2004 to 2012.

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The monthly variations in the PM10 chemical components showed significant cyclic

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variations. Figure S2 shows seasonal averages of the percentages of different chemical

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components in PM10 at Tung Chung throughout the study period. In general, nitrate, ammonium,

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and OM showed the highest percentages in PM10 (1.9%, 5.6%, and 21.8%, respectively) during

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winter. Continental outflows pass through vast polluted regions in mainland China and then

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boost the percentages of these secondary pollutants in Hong Kong during winter.31–33

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Temperature is considered another factor that affects the concentration of ammonium nitrate.

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Lower temperatures during the winter favor the formation of particulate ammonium nitrate from

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its precursor gases.34 The highest percentages of sulfate were seen during spring and autumn

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(both were about 23.4%), and the lowest percentage was seen during summer (19.5%). The high

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percentages of sulfate during spring and autumn resulted from a regional accumulation of

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secondary sulfate around the coastal regions in southeastern China.31,35 The transport of marine

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vessels emissions from south contributed to the high percentage of EC during summer.33

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Variation in γ0 and α’ext,10. Monthly γ0 and α’ext,10 values were fitted using simultaneous

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measurements of the visibility-derived σa,0, RH, and PM10 concentration at Tung Chung. Figure 2

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shows long-term variations in (a) γ0 and (b) α’ext,10 at Tung Chung from 2004 to 2012. The nine-

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year averages of γ0 and α’ext,10 were estimated to be 0.89 and 3.34 m2/g, respectively. The γ0 value

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reached its peak in 2009 with a maximal level of 1.06. This long-term pattern is consistent with

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the percentages of highly hygroscopic components (e.g., sulfate and nitrate) in PM10. The α’ext,10

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value experienced an ascending trend from 2004 to 2011 with a maximal level of 4.06 m2/g. This



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long-term pattern is consistent with the percentages of strong light-extinction components (e.g.,

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OM and ammonium) in PM10.

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(a)

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(b)

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Figure 2. Monthly (solid lines) and yearly (dashed lines) variations in (a) γ0 and (b) α’ext,10 at

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Tung Chung from 2004 to 2012.

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Figure S3 shows seasonal averages of the γ0 and α’ext,10 values at Tung Chung throughout the

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study period. The γ0 value showed the highest levels in spring and winter (0.95 and 0.96,

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respectively), when the highly hygroscopic components (e.g., sulfate, nitrate, and ammonium)

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were abundant in PM10. In contrast, the γ0 value reached its lowest level of 0.78 in summer. The

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α’ext,10 value reached its highest level of 3.81 m2/g in winter, which is associated with an

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abundance of the strong light-extinction components (e.g., OM).

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Variation in FMF. Hourly FMF at Tung Chung can be derived using simultaneous

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observations of PM2.5 and PM10 concentrations. We then investigate the characteristics of the

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variations in the FMF. Figure 3(a) shows long-term variation in correlation coefficient between

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the FMF and RH at Tung Chung from 2004 to 2012. The correlation coefficients were positive in

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most months and were as high as 0.6. These correlations were statistically significant at a 99% 12 ACS Paragon Plus Environment

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confidence level for most months (79 out of 108). These phenomena (i.e., positive humidity

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dependence of the FMF, especially in winter) were similar to those observed in northern China,

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showing that dry weathers were associated with more coarse particles.8

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(a)

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(b)

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Figure 3. (a) Monthly (solid lines) and yearly (dashed lines) variations in correlation coefficient

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between the FMF and RH at Tung Chung from 2004 to 2012. (b) Monthly (solid lines) and

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yearly (dashed lines) variations in the average dry FMF (RH < 60%) at Tung Chung from 2004

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to 2012.

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Because of the moist climate, typical RH is high in Hong Kong. We characterized the dry

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FMF by averaging the FMF under a condition of RH < 60%. Figure 3(b) shows long-term

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variation in the average dry FMF (RH < 60%) at Tung Chung from 2004 to 2012. Annual

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average of the dry FMF decreased from 0.74 to 0.60 during the study period. This phenomenon

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(i.e., descending FMF) was observed at all stations over Hong Kong (as shown in Figure S4).

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The descending FMF value corresponds to restrictions on vehicle exhaust and coal/biomass

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burning in Hong Kong.36



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Figure S5 shows seasonal averages of the correlation coefficient between FMF and RH (blue

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bars) and the dry FMF (red bars) at Tung Chung throughout the study period. The humidity

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dependence of the FMF was more significant in spring and winter, with the average correlation

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coefficients of 0.27 and 0.31, respectively. In contrast, the lowest humidity dependence of the

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FMF was observed in summer with a weak correlation coefficient of -0.04. The dry FMF values

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in autumn and winter (0.69 and 0.68, respectively) were higher than in spring and summer (both

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were about 0.64). More fine particles in cold seasons suggest the effect of the long-range

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transports of secondary aerosols from inland.

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Variation in γ’ and K. Monthly γ’ and K values were fitted using simultaneous measurements

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of the visibility-derived σa,0, RH, and PM2.5 concentration at Tung Chung. Figure 4 shows long-

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term variations in the γ’ and K values at Tung Chung from 2004 to 2012. Because of the positive

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humidity dependence of FMF in most months, the γ’ values were generally lower than γ0. Nine-

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year average of γ’ was estimated to be 0.82 with a peak of 0.93 in 2009. The K value steadily

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increased from 4.18 m2/g in 2004 to 6.17 m2/g in 2011. The increasing K was determined by the

275

combined effect of the increase in the dry MEE (α’ext,10) and the decrease in the dry FMF (F’).

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(a)

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Figure 4. Monthly (solid lines) and yearly (dashed lines) variations in the (a) γ’ and (b) K values

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at Tung Chung from 2004 to 2012.

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Figure 5 shows seasonal averages of the γ’ and K values at Tung Chung throughout the study

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period. Similar to the seasonal pattern of γ0, the γ’ values were highest in spring and winter (0.83

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and 0.85 respectively). In contrast, the γ’ value’s lowest level of 0.79 was seen in summer. It is

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noted that the γ’ value was slightly higher than the γ0 value in summer because of its weak-

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negative humidity dependence on the FMF. Similar to the seasonal pattern of the dry MEE, the K

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value’s highest level of 5.77 m2/g occurred in winter.

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Figure 5. Seasonal averages of the γ’ (blue bars) and K (red bars) values at Tung Chung

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throughout the study period.

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Effect of variation in aerosol characteristics. To assess the effect of the long-term

290

variations in aerosol characteristics, we first retrieve PM2.5 concentration using the static AOD-

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PM2.5 model, which assumes that the γ’ and K values remained unchanged (represented by the

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nine-year averages) during the study period. Second, we retrieve PM2.5 concentration using the

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dynamic AOD-PM2.5 model, which takes into account the monthly variations of γ’ and K. Figure

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S6 shows an evaluation of hourly PM2.5 concentrations derived from the (a) static and (b) 15 ACS Paragon Plus Environment


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dynamic AOD-PM2.5 models against the ground observations at all stations during the study

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period. The statistical metrics improved after consideration of the long-term variations of γ’ and

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K. Correlation coefficient increased from 0.64 to 0.68 (N = 8444). Root mean square error

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(RMSE) decreased from 23.6 µg/m3 to 22.0 µg/m3. The evaluations were also performed after

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the training datasets at Tung Chung station were removed. The results show that correlation

300

coefficient increased from 0.60 to 0.63 (N = 7000), and RMSE decreased from 24.7 µg/m3 to

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23.1 µg/m3.

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Figure S7 shows an evaluation of monthly averages of the PM2.5 concentrations derived from

303

the (a) static and (b) dynamic AOD-PM2.5 models against the ground observations at all stations

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during the study period. All statistical metrics improved after consideration of the long-term

305

variations of γ’ and K. Correlation coefficient increased from 0.78 to 0.82 (N = 636). RMSE

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reduced from 11.2 µg/m3 to 9.7 µg/m3, and mean absolute percentage error reduced from 26.4%

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to 22.9%. The evaluations were also performed after the training datasets at Tung Chung station

308

were removed. The results show that correlation coefficient increased from 0.77 to 0.80 (N =

309

528). RMSE reduced from 11.5 µg/m3 to 10.1 µg/m3, and mean absolute percentage error

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reduced from 26.7% to 23.7%.

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Because the nine-year ground observations of PM2.5 concentrations were available at four

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specific stations (e.g., Tung Chung, Yuen Long, Tsuen Wan, and Tap Mun), we further evaluate

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time series of the satellite-derived PM2.5 concentrations at these stations. The solid red lines in

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Figure S8 represent monthly variations of the average satellite-derived PM2.5 concentrations at

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the four stations from 2004 to 2012 using the (a) static and (b) dynamic AOD-PM2.5 models. The

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dashed blue lines represent the corresponding ground observations. Although the static model

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captured the monthly variation of PM2.5 concentration to a certain extent, correlation coefficient


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further increased from 0.85 to 0.90 (N = 108) after accounting for the long-term variations in

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aerosol characteristics. In addition, the RMSE reduced from 9.6 µg/m3 to 7.2 µg/m3, and the mean

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absolute percentage error reduced from 22.9% to 17.6%. Annual averages of the satellite-retrieved PM2.5 concentrations were derived by averaging the

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monthly-mean PM2.5 concentrations. Figure 6 shows an evaluation of annual averages of the

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PM2.5 concentrations derived from the (a) static and (b) dynamic AOD-PM2.5 models against

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ground observations at all stations during the study period. After consideration of the long-term

325

variations in aerosol characteristics, the correlation coefficient greatly increased from 0.56 to

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0.80 (N = 51). This city-scale correlation is comparable to those from other studies on national

327

scales.16,17,37,38 The RMSE decreased from 5.4 µg/m3 to 4.0 µg/m3, and the mean absolute

328

percentage error decreased from 12.3% to 9.5%. The evaluations were also performed after the

329

training datasets at Tung Chung station were removed. The results show that correlation

330

coefficient increased from 0.58 to 0.78 (N = 42). The RMSE decreased from 5.5 µg/m3 to 4.3

331

µg/m3, and the mean absolute percentage error decreased from 12.6% to 10.6%.

332

(a)

333

Figure 6. Evaluation of annual averages of the PM2.5 concentrations derived from the (a) static

334

and (b) dynamic AOD-PM2.5 models against ground observations at all stations during the study

(b)



335

period. Statistical metrics include correlation coefficient (R), root mean square error (RMSE),

336

mean error (E), mean absolute error (|E|), mean percentage error (PE), and mean absolute

337

percentage error (|PE|). Figure 7 shows spatial distributions of the annual satellite-derived PM2.5 concentrations over

338 339

Hong Kong from 2004 to 2012 using the (a) static and (b) dynamic AOD-PM2.5 models. The

340

points represent the available ground observations. Central and northwestern areas of Hong

341

Kong experienced the highest level of PM2.5 concentration. Using the static AOD-PM2.5 model,

342

the PM2.5 concentrations were underestimated during the early years, whereas they were

343

overestimated during the recent years. After accounting for the long-term variations in aerosol

344

characteristics, both the satellite-derived and ground-observed PM2.5 concentrations show a

345

significant decreasing trend during the study period.

346

(a)

347

(b)


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348

Figure 7. Spatial distributions of the annual satellite-derived PM2.5 concentrations over Hong

349

Kong from 2004 to 2012 using the (a) static and (b) dynamic AOD-PM2.5 models. The points

350

represent the available ground observations. The solid red lines in Figure 8 represent inter-annual variations of the average satellite-

351 352

derived PM2.5 concentrations at the four specific stations (i.e., Tung Chung, Yuen Long, Tsuen

353

Wan, and Tap Mun) from 2004 to 2012 using the (a) static and (b) dynamic AOD-PM2.5 models.

354

The dashed blue lines represent the corresponding ground observations. After consideration of

355

the long-term variations in aerosol characteristics, the correlation coefficient greatly increased

356

from 0.64 to 0.94 (N = 9). In addition, the RMSE decreased from 4.3 µg/m3 to 2.0 µg/m3, and the

357

mean absolute percentage error decreased from 10.2% to 4.7%.

358

(a)

359

Figure 8. The solid red lines represent inter-annual variations of the average satellite-derived

360

PM2.5 concentrations at the four specific stations from 2004 to 2012 using the (a) static and (b)

361

dynamic AOD-PM2.5 models. The dashed blue lines represent the corresponding ground

362

observations. Statistical metrics include correlation coefficient (R), root mean square error

363

(RMSE), mean error (E), mean absolute error (|E|), mean percentage error (PE), and mean

364

absolute percentage error (|PE|).

(b)

365

Both the model parameters (e.g., γ’ and K) are associated with aerosol characteristics. We

366

further develop the relationships between the long-term variations in the γ’/K values and aerosol

367

characteristics. The γ’ value reached its peak in 2009, which was consistent with the variations of 19 ACS Paragon Plus Environment


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368

the highly hygroscopic components (e.g., sulfate and nitrate). The K value increased from 2004

369

to 2011, which was determined by the increasing abundance of the strong light-extinction

370

components (e.g., OM and ammonium) and decreasing dry FMF. A multilinear regression using

371

the inter-annual variations of the γ’ and K values, and the aerosol physical and chemical

372

characteristics is performed to empirically establish their associations:

373

(7.1)

𝛾′ = 𝛽1·[𝑆𝑢𝑙𝑓𝑎𝑡𝑒] + 𝛽2·[𝑁𝑖𝑡𝑟𝑎𝑡𝑒] + 𝛽0 1

(7.2)

374

𝐾 = 𝜆1·[𝑂𝑀] + 𝜆2·[𝐴𝑚𝑚𝑜𝑛𝑖𝑢𝑚] + 𝜆3·𝐹′ + 𝜆0

375

where [Sulfate], [Nitrate], [OM], and [Ammonium] represent the percentages of different

376

chemical compositions in PM10; β1, β2, λ1, λ2, and λ3 represent the slopes; β0 and λ0 denote the

377

intercepts. The β1, β2, and β0 values were estimated to be 1.75, 23.16, and 0.11, respectively. The

378

λ1, λ2, λ3, and λ0 values were estimated to be 25.42 m2/g, 7.73 m2/g, 3.36 m2/g, and -5.01 m2/g,

379

respectively. Using these empirical relationships, the γ’ and K values are predicted from the

380

chemical composition data. Figure S9 shows the inter-annual variations of the predicted γ’ and K

381

values using the chemical composition data at Tung Chung station. Because the chemical

382

composition data at Tung Chung were not available for 2007, the predicted γ’ and K values in

383

2007 were filled by the averages of the values in 2006 and 2008. The prediction results are

384

reasonable, with the γ’ value reached its peak in 2009 and the K value increased from 2004 to

385

2011.

386

We then retrieve PM2.5 concentration using the AOD-PM2.5 model that takes the predicted γ’

387

and K values as inputs. Figure S10 shows an evaluation of annual averages of the retrieved PM2.5

388

concentrations against ground observations at all stations during the study period. The

389

correlation coefficient, RMSE, and mean absolute percentage error were estimated to be 0.81 (N

390

= 51), 3.9 µg/m3, and 8.7%, respectively. The evaluations were also performed after the training 20 ACS Paragon Plus Environment

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391

datasets at Tung Chung station were removed. The results show that correlation coefficient,

392

RMSE, and mean absolute percentage error were 0.80 (N = 42), 4.2 µg/m3, and 9.8%,

393

respectively. These statistical metrics are similar to those obtained from the dynamic AOD-PM2.5

394

model that takes the raw γ’ and K values as inputs. These results underscore the importance of

395

incorporation of the long-term variations in aerosol characteristics in the PM2.5 estimation model.

396

Discussion

397

Traditional MODIS AOD products have a spatial resolution of 10 km (recently upgraded to 3

398

km). Hong Kong is a compact city with an area of about 30 × 40 km2. To better delineate the

399

spatial distribution of PM2.5 concentration over Hong Kong, we constructed the AOD data at a

400

high resolution of 1 km. The verifications of the satellite-retrieved AOD against the ground-

401

based observations showed a good agreement.28,29,39 The MODIS dataset is affected by missing

402

data (e.g., resulting from the bright surface and cloud effects) and cannot provide information on

403

diurnal variations in PM2.5 concentration. Both effects introduced an error of within 10%.40 To

404

compensate for the data loss, future studies can obtain other AOD datasets derived from the

405

Deep-Blue algorithm or geostationary satellites.41,42

406

Various models have been developed to retrieve ground-level PM2.5 concentration using

407

satellite remote sensing techniques. The statistical models (e.g., land-use regression models)

408

input as many data as possible. These statistical models therefore tend to have lower prediction

409

errors. Looking from a scientific point of view is important because it can provide supporting

410

information for researches in other regions. Aerosol characteristics play an essential role in the

411

AOD-PM2.5 relationship. A common way to retrieve PM2.5 concentration based on science is

412

relying on the model outputs from the CTMs. It is important to improve our scientific

413

understanding of the relationship between AOD and PM2.5 concentration. We studied the role of



414

the aerosol characteristics in the AOD-PM2.5 relationship using an observation-based model. The

415

relationship between the model parameters and aerosol chemical compositions developed in this

416

study has good applicability. In addition, our model do not need to input extensive ground PM2.5

417

measurements, promoting the applicability of the model.

418

The K value is associated with the dry mass extinction efficiency of PM10 particles (α’ext,10)

419

and dry fine mode fraction (F’). The γ’ value is associated with hygroscopic growth coefficient of

420

PM10 particles (γ0), humidity dependence of mass extinction efficiency of PM10 particles, and

421

humidity dependence of fine mode fraction. The α’ext,10 and γ0 values are associated with

422

chemical compositions of PM10 particles. The effect of size distribution of particles is taken into

423

account by using the fine mode fraction.

424

In this study, we assumed that the model parameters (e.g., γ’ and K) derived at Tung Chung

425

station represented the conditions over all of Hong Kong. Uncertainty caused by this assumption

426

was estimated by assessing the spatial variability of different chemical compositions in PM10

427

over Hong Kong. The long-term measurements of PM10 chemical compositions were available at

428

six general stations (e.g., Tung Chung, Yuen Long, Tsuen Wan, Central Western, Kwun Tong,

429

and Sham Shui Po) over Hong Kong.27 Figure S11 shows the spatial variability of the nine-year

430

average of the percentages of different chemical compositions in PM10 at these six stations. The

431

mean and standard deviation of the nine-year average of the percentages of nitrate, ammonium,

432

EC, OM, and sulfate in PM10 were estimated to be 1.66 ± 0.15%, 4.68 ± 0.13%, 6.39 ± 0.35%,

433

20.73 ± 1.16%, and 21.12 ± 0.91%, respectively. The relative standard deviations (a ratio of

434

standard deviation and mean) for all chemical compositions were within 10%. Future studies can

435

introduce the effect of spatial variation of aerosol characteristics in the model when simultaneous


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436

observations of meteorological values, mass concentrations and chemical compositions of

437

particulate matter are available at more stations over Hong Kong.

438

This study focused on an investigation of the effect of the long-term variations in aerosol

439

characteristics on satellite remote sensing of PM2.5 concentrations. The annual-average K value

440

changed from 4.18 m2/g in 2004 to 5.34 m2/g in 2012. During this nine-year period, the annual

441

average of K experienced a change rate of around 20%. The K value also showed distinctive

442

levels among the four seasons. It changed from 4.92 m2/g in spring to 5.77 m2/g in winter, with a

443

change rate of around 15%. According to Eq. (6), the variations in the K value can affect the

444

retrieved PM2.5 concentration by the same rate, suggesting a need to incorporate variations in

445

aerosol characteristics in the PM2.5 estimation models.

446

By using the γ0 value, we separated the combined humidity dependence from the MEE and

447

hygroscopic growth with that from the FMF. Because of the humidity dependence from the

448

FMF, the γ’ value was lower than γ0 by about 0.07 (about 8%). It suggests that the MEE and

449

hygroscopic growth dominated the humidity dependence. One limitation of this method is that

450

the separation of the humidity dependence from the MEE and hygroscopic growth is difficult.

451

For a city (e.g., Hong Kong) with abundant highly hygroscopic components in aerosols, the

452

hygroscopic growth effect should occupy a substantial part in the humidity dependence.

453

The lack of measurement of aerosol characteristics limits the assessment of their effects.

454

Compared with most cities in mainland China, Hong Kong operates a much more comprehensive

455

system for monitoring air quality over a long period. Therefore, we used the long-term

456

measurements of mass concentrations and chemical compositions of particulate matter over

457

Hong Kong. Given the vast territory of China, aerosol characteristics are likely to show a

458

significant regional disparity. Studies in several cities in China demonstrated that the percentages



459

of sulfate, nitrate, and organic carbon in particulate matter varied from 25% to 54%, from 4% to

460

27%, and from 20% to 48%, respectively.24 In addition, regional disparities were found in their

461

long-term trends. These results suggest that the variations in aerosol characteristics in different

462

regions of China should pose different impacts on PM2.5 retrievals. In addition to this regional

463

disparity, their effects on the AOD-PM2.5 relationship should show some similar features.

464

Variations in components such as sulfate and nitrate should pose impacts on the PM2.5 retrievals

465

through the aerosol hygroscopic-growth ability. Variations in components such as OM should

466

pose impacts on the PM2.5 retrievals through the aerosol light-extinction ability. Therefore, it is

467

of great value for future studies to examine the similarity and disparity of their impacts for

468

different regions.

469

High levels of PM2.5 concentration can cause severe visibility impairments.43 As a result of

470

control efforts, both the PM2.5 concentration and visibility-derived σa,0 showed a descending

471

trend in Hong Kong during the study period. Figure S12 shows the long-term variation in the

472

ratio between the visibility-derived σa,0 and PM2.5 concentration at Tung Chung from 2004 to

473

2012. An ascending trend in the ratio was observed, suggesting that the reduction of σa,0 was

474

slower than the reduction of PM2.5 concentration. In terms of improving visibility, the benefit of

475

the reduction in PM2.5 concentration was offset by other factors, such as the changes in aerosol

476

chemical compositions. To better improve visibility in Hong Kong, it is suggested that more

477

control efforts focus on the reduction of strong light-extinction components, such as OM.

478

Associated content

479

Supporting Information

480

Additional data information and figures (Figure S1-S12).

481

Acknowledgments


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482

We thank the Hong Kong Environmental Protection Department for providing air-quality

483

monitoring data. This work was supported by the National Natural Science Foundation of China

484

(Grant No. 41575106), the Science and Technology Plan Project of Guangdong Province of China

485

(Grant No. 2015A020215020 and 2017A050506003), NSFC/RGC (Grant N_HKUST631/05), and

486

the Fok Ying Tung Graduate School (NRC06/07.SC01). The authors declare they have no actual

487

or potential competing financial interests.

488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504



505

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