Distinguishing the roles of meteorology, emission control measures, regional transport, and co-benefits of reduced aerosol feedbacks in “APEC Blue”

Accepted Manuscript Distinguishing the roles of meteorology, emission control measures, regional transport, and co-benefits of reduced aerosol feedbacks in “APEC Blue” Meng Gao, Zirui Liu, Yuesi Wang, Xiao Lu, Dongsheng Ji, Lili Wang, Meng Li, Zifa Wang, Qiang Zhang, Gregory R. Carmichael PII:

S1352-2310(17)30567-8

DOI:

10.1016/j.atmosenv.2017.08.054

Reference:

AEA 15524

To appear in:

Atmospheric Environment

Received Date: 3 June 2017 Revised Date:

6 August 2017

Accepted Date: 22 August 2017

Please cite this article as: Gao, M., Liu, Z., Wang, Y., Lu, X., Ji, D., Wang, L., Li, M., Wang, Z., Zhang, Q., Carmichael, G.R., Distinguishing the roles of meteorology, emission control measures, regional transport, and co-benefits of reduced aerosol feedbacks in “APEC Blue”, Atmospheric Environment (2017), doi: 10.1016/j.atmosenv.2017.08.054. 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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Distinguishing the roles of meteorology, emission control measures, regional

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transport, and co-benefits of reduced aerosol feedbacks in “APEC Blue”

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Meng Gaoa,*, Zirui Liub, Yuesi Wangb, Xiao Luc, Dongsheng Jib, Lili Wangb, Meng Lid, Zifa

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Wangb, Qiang Zhangd, Gregory R. Carmichaela

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a Center for Global and Regional Environmental Research, the University of Iowa, Iowa City, IA

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52242, USA

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b State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry

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(LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,

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China

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c Laboratory for Climate and Ocean–Atmosphere Sciences, Department of Atmospheric and

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Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China

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d Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth

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System Science, Tsinghua University, Beijing 100084, China

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* now at: John A. Paulson School of Engineering and Applied Sciences, Harvard University,

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Cambridge, MA 02138, United States

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Correspondence to: M. Gao ([email protected]) and Y. Wang ([email protected]) and G.

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Carmichael ([email protected])

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Abstract

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Air quality are strongly influenced by both emissions and meteorological conditions. During the

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Asia Pacific Economic Cooperation (APEC) week (November 5-11, 2014), the Chinese

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government implemented unprecedented strict emission control measures in Beijing and

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surrounding provinces, and then a phenomenon referred to as “APEC Blue” (rare blue sky)

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occurred. It is challenging to quantify the effectiveness of the implemented strict control

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measures solely based on observations. In this study, we use the WRF-Chem model to

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distinguish the roles of meteorology, emission control measures, regional transport, and co-

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benefits of reduced aerosol feedbacks during APEC week. In general, meteorological variables,

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PM2.5 concentrations and PM2.5 chemical compositions are well reproduced in Beijing. Positive

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weather conditions (lower temperature, lower relative humidity, higher wind speeds and

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enhanced boundary layer heights) play important roles in “APEC Blue”. Applying strict

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emission control measures in Beijing and five surrounding provinces can only explain an average

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decrease of 17.7µg/m3 (-21.8%) decreases in PM2.5 concentrations, roughly more than half of

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which is caused by emission controls that implemented in the five surrounding provinces

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(12.5µg/m3). During the APEC week, non-local emissions contributed to 41.3% to PM2.5

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concentrations in Beijing, and the effectiveness of implementing emission control measures

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hinges on dominant pathways and transport speeds. Besides, we also quantified the contribution

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of reduced aerosol feedbacks due to strict emission control measures in this study. During

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daytime, co-benefits of reduced aerosol feedbacks account for about 10.9% of the total decreases

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in PM2.5 concentrations in urban Beijing. The separation of contributions from aerosol absorption

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and scattering restates the importance of controlling BC to accelerate the effectiveness of aerosol

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pollution control.

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Keywords: APEC Blue, Emission Controls, Meteorology conditions, Aerosol feedbacks, BC

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absorption

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1 Introduction

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In the past decade, severe haze pollution events, characterized by high PM2.5 (aerosols with

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diameter less than or equal to 2.5 micrometers) concentrations, have been frequently reported in

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city clusters (e.g., Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River

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Delta (PRD) clusters) in China. These haze events have become critical concerns due to their

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adverse influences on visibility, public health, and climate. Haze in Beijing and surrounding

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North China Plain (NCP) cities have attracted more attention because of unprecedentedly high

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PM2.5 concentrations during events, especially in winter (Cheng et al., 2016; Gao et al., 2016a).

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Beijing is located at the northern tip of the NCP, with Taihang mountains range to the west,

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Yanshan mountains range to the north, and plains to the east and south. Such topography is not

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favorable for diffusion of air pollutants associated with southerly and/or easterly winds.

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The contribution of regional transport to PM2.5 concentrations in Beijing has been a controversial

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topic (Guo et al., 2014; Li et al., 2015) and has been studied using multiple observations from

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different platforms and various chemical transport models (Gao et al., 2016a; Hua et al., 2016; Z.

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Wang et al. 2014). Guo et al. (2014) concluded that local aerosol nucleation and growth

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dominates over the insignificant role of regional transport, while other studies (Gao et al., 2016a;

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Hua et al., 2016; Li et al., 2015; Z. Wang et al. 2014) emphasized that regional transport made a

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significant contribution to PM2.5 concentrations in Beijing. These discussions are of great

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importance for implementation of air quality control measures during special events in Beijing,

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such as the 2014 Asia-Pacific Economic Cooperation (APEC) Economic Leaders’ Week and the

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2015 China Victory Day Parade.

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On November 5-11, Beijing, China hosted the 2014 APEC meeting. To ensure good air quality

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during the APEC week, Beijing and surrounding regions including Hebei Province, Tianjin City,

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Shanxi Province, Shandong Province and Inner Mongolia Province cooperated to take strict air

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pollution control measures. The last strict air quality control applied in Beijing before APEC was

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during the 2008 Summer Olympic Games. Post analysis of the air quality suggested that the

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mean PM2.5 concentrations during the Olympic period was 31% lower compared to the non-

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Olympic period and favorable meteorology played a large role in reducing PM concentrations

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(Wang et al., 2009). Summer is generally more favorable for good air quality with more

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precipitation and more unstable weather conditions. However, the APEC month, November, is

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the transition month from fall to winter. And stagnant weather conditions (weaker wind speeds

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and less precipitation) usually happen more frequently than during spring and summer. In

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addition, people in North China start to use residential heating in November, which produces

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large amounts of air pollutants. These combined adverse factors exert enormous pressure upon

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Beijing government. Thus, unprecedented efforts were made to guarantee good air quality during

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APEC in Beijing. The air control measures taken in Beijing included odd-even plate number rule

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for local vehicles, a ban on driving for non-local cars, and suspended or reduced operations of

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construction sites, power plants, and factories (Li et al., 2015).

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According to the report from Beijing Municipal Environmental Protection Bureau, sulfur dioxide

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(SO2), nitrogen oxides (NOx), PM10, PM2.5 and volatile organic compounds (VOCs) emissions

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were reduced by 39.2%, 49.6%, 66.6%, 61.6% and 33.6% respectively (BMEPB, 2014). During

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the APEC week, air quality in Beijing was pleasant with frequently seen blue skies, and Chinese

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created a new word, “APEC Blue”, to describe it.

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A number of studies have used multi-platform based measurements to characterize ambient

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VOCs, ozone, and aerosols during APEC, and the influences of emission controls (C. Chen et al.,

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2015; Z. Chen et al., 2015; Huang et al., 2015; Li et al., 2015; Sun et al., 2015; Tang et al., 2015;

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Wen et al., 2016; Xu et al., 2015; J. Zhang et al., 2016). Most studies have concluded that aerosol

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concentrations significantly decreased, when compared to non-emission control period in Beijing

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(Huang et al., 2015; Wen et al., 2016). However, different meteorological conditions during

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emission control and non-emission control periods could have played important roles in the

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pleasant air quality. Huang et al. (2015) compared temperature, wind speeds, Relative Humidity

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(RH) during the whole study period (October 15-November 30) in each year from 2011 to 2014,

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and concluded no significant differences of meteorological conditions and attributed changes of

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air pollutants mainly to emission changes. It is not persuasive enough to draw conclusions based

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on limited meteorological variables since the evolution of the crucial factor planetary boundary

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layer height (PBLH) is not just simply determined by temperature, but other factors, such as

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sinking motion, as well (Gao et al., 2016a; Wu et al., 2017). In addition, the roles of aerosol

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feedbacks during extreme haze events, and enhanced magnitudes of aerosol feedbacks due to

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emission changes from 1960 to 2010 have been highlighted in previous studies (Gao et al., 2016a,

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2016b; Hong et al., 2017; Wang et al., 2015; Xing et al., 2015, 2016). These studies suggest that

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reducing emissions of air pollutants may have co-benefits of reducing the roles of aerosol

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feedbacks to accelerate the improvements of air quality. Therefore, the roles of meteorology,

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emission control measures, and co-benefits of reduced aerosol effects need to be better

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understood.

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It is challenging to distinguish the roles of these factors solely based on measurements, and air

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quality modeling elaborately fill in the gaps of measurements and provide supplemental answers

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to these questions. In this study, we distinguish the roles of meteorology, emission control

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measures, and co-benefits of reduced aerosol effects during APEC week using a fully online

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coupled meteorology-chemistry model: Weather Research Forecasting coupled with Chemistry

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(WRF-Chem). We also calculate the contribution of regional transport to PM2.5 concentrations in

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Beijing to provide additional information on the controversy on regional transport contributions.

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In Sect. 2, we describe the WRF-Chem model configurations, used measurements, and design of

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experiment simulations. The results are presented in Sect. 3, and conclusions are provided in

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Sect. 4.

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2. Methodology

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2.1 WRF-Chem model

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We used the WRF-Chem model (Grell et al., 2005) version 3.6.1 to simulate meteorology,

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gaseous and aerosol concentrations in this study, which has been shown to be capable of

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reproducing air quality in China in previous studies (Gao et al., 2015; Gao et al., 2016a, 2016b,

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2016c, 2017). WRF-Chem allows the selections of flexible combinations of gas phase chemistry

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and aerosol modules. We used the Carbon Bond Mechanism version Z (CBMZ) gas phase

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chemistry (Zaveri and Peters, 1999) coupled with the Model for Simulating Aerosol Interactions

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and Chemistry (MOSAIC) (Zaveri et al., 2008) aerosol module. CBMZ was extended from

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CBM-IV (Gery et al., 1989) to function properly at larger and longer timescales, which considers

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52 total species and 132 chemical reactions (Zaveri and Peters, 1999). The augmented version

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coupled with MOSAIC consists of 67 total species and 164 reactions (Zaveri et al., 2008).

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MOSAIC treats all important aerosol species, and size distribution was implemented with

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discrete size bins. We used 8 bins version in this study, and the range of particle diameters for

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each size bin is listed in Table 1. The Multicomponent Taylor Expansion Method (MTEM)

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method, instead of the widely used ISORROPIA, was used in MOSAIC to calculate

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thermodynamics and phase equilibrium (Zaveri et al., 2008). Secondary organic aerosol (SOA)

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formation has been added into MOSAIC using the volatility basis set (VBS) (Shrivastava et al.,

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2011). In the simulations, both aerosol direct and indirect effects were included. In the WRF-

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Chem model, aerosol optical properties are determined based on aerosol chemical properties and

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sizes using Fast et al. (2006) method, and then the effect of aerosols on incoming solar radiation

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is determined by transferring related parameters to radiation scheme (Chapman et al., 2009). For

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aerosol indirect effects, aerosols are activated from interstitial aerosols to generate cloud-borne

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“attachment state” based on a maximum supersaturation (Chapman et al., 2009).

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Two nested domains with horizontal resolutions of 81km and 27km (Figure 1) and 27 vertical

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layers up to a pressure level of 100hPa were configured to cover most areas of East Asia and

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focus on the NCP (Figure 1). Selected physical parameterizations include the Lin microphysics

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scheme (Lin et al., 1983), the Yonsei University (YSU) PBL scheme (Hong et al., 2006), the

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rapid radiative transfer model (RRTM) longwave radiation (Mlawer et al., 1997), and the

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Goddard shortwave radiation (Chou et al., 1998). Other chosen physical and chemistry

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parameterization modules are listed in Table 1. The NCEP 1°×1° degree final reanalysis dataset

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(FNL) was used to provide meteorological initial and boundary conditions. To reflect the aerosol

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effects on meteorological variables, observation nudging and reanalysis nudging were not used.

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Chemical initial and boundary conditions were obtained from the MOZART global chemistry

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simulations (Emmons et al., 2010). The simulation period is from October 16 to November 14,

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and the first three days were discarded as spin-up to overcome the influences of initial conditions.

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The model was re-initialized every five days, and chemical predictions from previous simulation

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cycle were used as chemical initial conditions for each re-initialization.

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2.2 Emissions

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The anthropogenic emissions were taken from the MIX Asian emission inventory developed for

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MICS-Asia and HTAP, which combines five emission inventories, including REAS inventory

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version 2.1, the Multi-resolution Emission Inventory for China (MEIC) developed by Tsinghua

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University, a high NH3 emission inventory by Peking University, an Indian emission inventory

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developed by Argonne National Laboratory, and the official Korean emission inventory from the

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Clean Air Policy Support System (CAPSS) (Li et al., 2017). The MIX inventory provides

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monthly emissions of SO2, NOx, CO, NMVOC, NH3, PM10, PM2.5, BC, OC, and CO2 at

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0.25◦×0.25◦ from power, industry, residential, transportation, and agriculture sectors for year

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2008 and 2010 (Li et al., 2017). We replaced the emissions in China with MEIC emission

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inventory for year 2012 to reflect the recent levels of emissions, and considered the diurnal

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variation and vertical distributions of them based on different sources.

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The MEGAN model version 2.1 was used to calculate online emissions of gases and particles

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from terrestrial ecosystems (Guenther et al., 2006, 2012). This model is driven by multiple

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variables, including leaf area index (LAI), meteorological conditions (solar radiation,

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temperature and moisture), CO2 concentration, etc. (Guenther et al., 2012). For emissions from

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biomass burning, we used the Global Fire Emissions Database version 4 (GFEDv4) (Randerson

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et al., 2015).

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2.3 Observations

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The hourly surface concentrations of PM2.5 and daily PM2.5 chemical compositions were provided

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by Dr. Yuesi Wang’s group (Ji et al., 2014; Liu et al., 2015, 2017; Xin et al., 2015; Y. Wang et

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al., 2014), which were measured at the Institute of Atmospheric Physics (IAP), Chinese

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Academy of Sciences (CAS) site. The meteorological measurements were downloaded from the

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National Centers for Environmental Information website

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(https://gis.ncdc.noaa.gov/maps/ncei#app=cdo), which includes near surface temperature,

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relative humidity (RH), wind speed, and wind direction.

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2.4 Numerical experiments and data analysis method

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To investigate the influences of local emission control, surrounding emission control, regional

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transport, and reduced aerosol effects on PM2.5 concentration in Beijing during APEC, we

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performed the following six WRF-Chem simulations.

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NOCTL: Simulations were performed for the period from October 16 to November 14 with

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normal emission settings and the first three days were discarded as spin-up time to overcome the

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influences of initial conditions. Aerosol feedbacks were turned on in this simulation.

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CTL: Same as NOCTL except that emissions of SO2, NOx, PM10, PM2.5, VOCs, and other

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species in Beijing were reduced by 39.2%, 49.6%, 66.6%, 61.6%, 33.6%, and 50% percent,

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respectively. Emissions in the five surrounding provinces, namely Inner Mongolia, Shanxi,

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Hebei, Tianjin, and Shandong were reduced by 35%. These reductions are based on BMEPB

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reports (BMEPB, 2014). The locations of these provinces are marked in Figure 1.

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OnlyBJ: Same as CTL except that only emissions in Beijing were reduced.

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SUR5: Same as CTL except that only emissions in the five surrounding provinces were reduced.

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BJ0: Same as CTL except that emissions in Beijing were reduced to zero.

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NOCTL-NF: Same as NOCTL except that aerosol feedbacks were turned off.

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CTL-NF: Same as CTL except that aerosol feedbacks were turned off.

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CTL-NBCA: Same as CTL except that BC absorption was excluded in the model.

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We used multiple model performance metrics to assess our model, including the correlation

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coefficient (r), the root mean square error (RMSE), the mean bias (MB), the mean fractional bias

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(MFB), the mean fractional error (MFE). The calculation equations of these metrics are

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documented in Boylan and Russell (2006).

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3. Results and Discussions

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3.1 Model Performance

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Although our WRF-Chem configuration has been shown to reliably reproduce evolutions of PM

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during winter episodes in previous studies (Gao, 2015; Gao et al., 2015, 2016a, 2016b, 2016c,

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2017), its performance might exhibit different features during fall, when APEC took place. Thus,

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we used surface meteorological and PM measurements to evaluate how our model performs

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before interpreting its results. The model value for the grid cell containing the monitor was used

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to compare against measurements. Two meteorological sites in Beijing were averaged to conduct

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meteorological comparisons. Figure 2 shows the time series of simulated and observed daily

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mean temperature, RH, and hourly wind vectors in Beijing. Simulated temperature, RH and wind

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vectors generally capture the features of observations. From before the APEC to the APEC

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period, temperature and RH became relatively lower, and there were more winds from northern

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areas, which is more favorable for pleasant air quality. As mentioned in Gao et al. (2016a), from

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clean to haze days, temperature and RH are generally higher, which is related to warm and moist

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air parcels from southern regions, and higher RH conditions favoring and conducive to aerosol

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growth. The higher temperature and RH before APEC is consistent with frequent southerly

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winds shown in Figure 2. The calculated model performance metrics MB, RMSE, and r for

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temperature, RH and wind speeds are summarized in Table 2. The r values are 0.95 (p<0.01),

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0.95 (p<0.01), and 0.84 (p<0.01), respectively. It was proposed that good model performance

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should have temperature bias smaller than 0.5◦ and wind speed RMSE smaller than 2m/s (Emery

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et al., 2001). Our calculated statistics meet this standard.

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Figure 3(a) shows the simulated and observed hourly PM2.5 concentration at the IAP/CAS site.

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Before the APEC, observed high PM2.5 concentration is well reproduced by our model. However,

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during APEC, our model overestimates PM2.5 concentration in the NOCTL case, because the

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implemented strict air quality control measures were not reflected in the emissions. After

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including this (the CTL case), model shows better agreements with measurements, with

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enhanced r value from 0.78 to 0.79. Besides, MB decreases from 38.0 to 28.4µg/m3, and RMSE

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decreases from 68.1 to 62.7µg/m3 (Table 3). It is also found that MFB reduces from 54.7% to

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44.8%, and MFE reduces from 60.5% to 55.0% (Table 3). According to the proposed good

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performance of PM2.5 with MFB within ±60% and MFE below 75% (Boylan and Russell, 2006),

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our model is reasonably satisfactory even before applying reductions in emissions.

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The accuracy of simulated PM2.5 major chemical compositions was validated in previous winter

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haze studies (Gao et al., 2016a, 2016c). However, due to the different characteristics of aerosol

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formation in different seasons, we use extra chemical composition measurements in fall season

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to verify our model here. As shown in Figure 3(a-f), the high nitrate and ammonium

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concentrations during high PM episode in Beijing are captured well by our model, but measured

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peak sulfate concentration is underestimated by our model, probably due to some missing sulfate

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formation pathways in the model (Cheng et al., 2016; Gao et al., 2016c). Simulated BC

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concentration is generally consistent with observations, but OC is underestimated due to large

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uncertainties in current status of SOA modeling (Figure 3). Generally, the variations and

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magnitudes of these compositions are well represented in our model.

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All the comparisons shown above suggest that the model is capable of simulating the major

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meteorological and chemical evolution during fall season, providing solid basis for further model

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analysis in the following sections.

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3.2 Relatively more favorable meteorological conditions during APEC

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During November, Beijing is usually characterized with low wind winds, less precipitation, and

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frequent stagnant weather conditions, which is not favorable for diffusions of air pollutants

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(Huang et al., 2016). For example, during the APEC (November 5-11), accumulated

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precipitation in North China is negligible (Figure S1). However, the diffusion conditions during

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APEC are actually better, compared with the conditions before APEC. As mentioned above,

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from before the APEC to the APEC period, temperature decreases by 34.5% (from12.5 ◦C to 8.2

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◦

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1.8m/s to 2.8m/s). The hygroscopic growth of aerosol, aerosol size distribution and chemical

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processes are largely determined by RH (Sun et al., 2006; Wang et al., 2017). Thus, the relatively

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C), RH decreases by 24.3% (from 66.1% to 50%), and wind speeds increased by 60.2% (from

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lower RH during APEC (50%) is not favorable for aerosol chemistry and growth, and relatively

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lower temperature could have reduced chemical reaction rate (Gao et al., 2016b). The relatively

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higher wind speeds and more northerly wind direction during APEC was associated with two

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cold surges that resulted from the southeastward expansion of the Siberian high (L. Zhang et al.,

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2016).

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In addition to wind speed, PBLH is another essential factor in pollution formation (Gao et al.,

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2016a). Figure 4 illustrates the time series of PBLH derived from the European Center for

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Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis data, the NCEP FNL

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reanalysis data, and WRF model simulations, before the APEC and during the APEC. From

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before the APEC to the APEC period, PBLH increased by 59.7%. Because of the inverse

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relationship between PBLH and PM2.5, the relatively higher PBLH during APEC would lead to

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lower PM2.5 concentrations, even if strict emission control measures had not been applied.

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From above, relatively lower RH and temperature, higher wind speeds and enhanced PBLH

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might have played remarkable role in the good air quality during APEC. However, some

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previous observational studies simply compared concentrations of air pollutants during APEC to

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the concentrations before the APEC, and then concludes the influences of emission control,

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which fails to fully explain the good air quality during APEC. In the next section, we distinguish

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the role of emission control measures from the role of meteorology using modeling tool, which

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can provide supplementary information to observational information.

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3.3 Separate influences of emission control measures implemented in Beijing and

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surrounding provinces

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Since two days before the APEC week, strict emission control measures were implemented not

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just in Beijing, but in the five surrounding provinces as well. Four simulations, namely the CTL

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case, the NOCTL case, the OnlyBJ, and the SUR5 case, are used to reflect the separate

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influences of emission implemented in Beijing and surrounding provinces. The difference

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between PM2.5 concentrations in the CTL case and in the NOCTL case represents the influences

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of emission control applied in both Beijing and surrounding provinces, the difference between

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PM2.5 concentrations in the OnlyBJ case and in the NOCTL case denotes the influence of only

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implementing emission control in Beijing, and the differences between PM2.5 concentrations in

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the SUR5 case and in the NOCTL case represents the influence of only implementing emission

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control in the five surrounding provinces

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As shown in Figure 5 (the concentrations were averaged over Beijing areas that shown with

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green box in Figure S2), the influences of emission control applied in both Beijing and

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surrounding provinces (CTL-NOCTL) show large variations with time. For example, the

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influences reach over 30µg/m3 on November 8, but were almost negligible on November 6.

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During the APEC week, implementing emission control in Beijing and five surrounding

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provinces led to an averaged decrease of 17.7µg/m3 (-21.8%). Figure 6 shows the spatial

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distributions of changes in PM2.5 and PM2.5 individual species due to emission control measures.

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In the Beijing region, the decreases in nitrate, BC and OC mass concentrations are higher than

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sulfate and ammonium. Given the relatively better performance of model in reproducing nitrate

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and BC during APEC week, it is less uncertain that the roles of decreased nitrate and BC were

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important. However, the estimates in the roles of reduced OC have larger uncertainty considering

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the poor performance of model in simulating OC. If control measures were only applied in

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Beijing, PM2.5 concentrations in Beijing decrease by only about half of the total influences,

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which is 8.4µg/m3 (-11.8%). Only implementing emission control measures in the five

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surrounding provinces could have led to 12.5µg/m3 (SUR5-NOCTL) decrease in PM2.5

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concentrations. Controlling emissions in the five surrounding provinces are equally important as

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take strict emission control emissions in Beijing, particularly during days like November 8

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(Figure 5). The effectiveness of implementing emission control hinges on the characteristics of

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transport pathways and transport speed, which is discussed in Sect. 3.4.

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3.4 Characteristics of regional transport and implications for emission control

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When the Beijing local emissions are not included in simulations (the BJ0 case), Beijing still

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experiences notable PM2.5 pollution. On some days, averaged PM2.5 pollution in Beijing can

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reach over 50µg/m3. Compared with the CTL case, the average PM2.5 contribution from non-

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local sources is 41.3%. The average contribution was calculated using grid cells that shown with

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green box in Figure S2, and during the November 5-11 period. This is lower than the

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contribution of 64.5% that calculated during the January 2010 severe haze episode (Gao et al.,

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2016a), and slightly lower than 51.6% that calculated during January 2013 (Z. Wang et al., 2014)

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and 61.5% that calculated during summer time (J. Wu et al., 2017). During haze episodes, there

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were continuously southerly winds brought air pollutants northwards to Beijing, while the

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transport pathways during APEC greatly differ.

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We use the FLEXible PARTicle dispersion (FLEXPART) (Stohl et al., 1998) model driven by

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hourly WRF meteorological simulations to produce 36 hour backward dispersions from urban

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Beijing region. We released 50000 particles from a 1° × 1° × 500m box, and initiated at three

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moments, namely November 5 00:00, November 6 00:00, and November 8 12:00. These three

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moments were selected with three different characteristics: PM2.5 concentrations reduced but still

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at high level, PM2.5 concentrations not reduced, and PM2.5 concentrations reduced to low level.

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The spatial distributions of released particles at 12h and 6h before the released moments are

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shown in Figure 7. As shown in the first column of Figure 7, from 12 hours before to 6 hours

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before, the sources from south Hebei are dominant. A large number of coal mines and high

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emission industry are located in South Hebei, which emit huge amounts of air pollutants (Gao et

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al., 2016a). Thus, although emission control measures are implemented, PM2.5 concentrations

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averaged over Beijing still reach about 150µg/m3 on November 5 00:00. As shown in the second

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column, clean air masses from northwestern regions are quickly moved after 6 hours, indicating

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high transport speed and wind speeds. During this period, implementing emission control

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measures shows no effective impacts on PM2.5 concentrations. PM2.5 concentrations in the

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NOCTL, CTL, OnlyBJ and SUR5 cases all drop to a level below 50µg/m3. The last column

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shows that sources are mostly from westerly and southerly directions, and particles do not move

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far away after 6 hours. Under this circumstance, strict emission control is more effective.

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The transport pathways and transport speed are meaningful for designing emission control

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strategies. If fast transport from clean northwestern regions, reducing emissions from that

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upwind region is not necessary. Under other circumstances, surrounding regions are as important

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as Beijing local emissions. More effectively way is to refer to reliable air quality forecasts, and

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design near real-time air quality control strategies based on the predicted transport characteristics.

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3.5 Co-benefits of reducing emissions

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It was pointed out in previous studies that both reduced radiation at the surface due to aerosol

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scattering and absorption, and heated upper PBL due to BC absorption can increase atmospheric

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stability and exacerbate PM2.5 pollution (Gao et al., 2016a). The magnitudes of this effects also

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increased from 1960 to 2010 due to enhanced emissions and aggravated aerosol pollution in

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China (Gao et al., 2016b). Thus, reducing emissions can also have co-benefits of reducing

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aerosol effects, enhancing PBLHs and further reducing PM2.5 concentrations.

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Due to emission control measures during APEC, daytime averaged PM2.5 concentrations over

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APEC week in Beijing decrease by about 10~24µg/m3 (Figure S3). It is interesting to know how

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much of these total changes are resulted from reduced aerosol effects. To answer question, we

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simulated another two cases: CTL-NF and NOCTL-NF. Figure 8(a, d) shows the daytime

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averaged changes in PBLH and PM2.5 concentration due to aerosol feedbacks, with implemented

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emission control during APEC. Consistent with previous studies, PBLHs are suppressed

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(decrease by about 20m~70m in the NCP) because aerosol scattering and absorption. As a result,

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PM2.5 concentrations in the NCP increase by about 0-5µg/m3. The plots in the second column

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(Figure 8(b, e)) are the same as Figure (a, d) except that emission reductions are not applied, so

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higher aerosol loadings exhibit enhanced effects on PBLH and PM2.5 concentrations. In the NCP,

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PBLHs decrease by about 20m~100m, and PM2.5 concentrations increase by 1~8µg/m3. The

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differences between plots in first column and second column are the consequences of reduced

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aerosol effects resulted from emission control measures during APEC on PBLHs and PM2.5

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concentrations. As shown in Figure 8 (c, f), PBLHs increase by about 0m~40m, and PM2.5

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concentrations decrease by about 1~5µg/m3. In south Beijing areas, PM2.5 concentrations

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decreases by 1~2µg/m3. During daytime, PM2.5 concentrations in urban Beijing region decrease

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by about 17.5µg/m3 (Figure S3), and the co-benefits of reduced aerosol feedbacks lead to about

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1.9µg/m3 decreases (Figure 7(f)), accounting for 10.9%.

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Figure 9 shows the BC absorption contribution to PBL changes and PM2.5 changes during

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daytime. On average, about 34.3% of PBL decreases and 21.2% of PM2.5 increases are resulted

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from BC absorption, and the remaining are caused by aerosol scattering. Although BC

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contributes a small amount to total aerosol mass concentration, its contribution to aerosol

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feedbacks are appreciable, which is consistent with results in Gao et al. (2016a). Thus, giving

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enough priority to BC for emission control will effectively accelerate cleaning the air.

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4 Summary

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In this present study, we have presented detailed modeling study to distinguish the roles of

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meteorology, emission control measures, regional transport, and the co-benefits of reduced

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aerosol effects during APEC using WRF-Chem model, which can provide more sophisticated

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interpretations of the observed good air quality during APEC. This study is unique from several

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perspectives. Firstly, the influences of implementing local emission control and regional

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emission control are separated; secondly, most previous studies of regional transport contribution

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are conducted during winter (Gao et al., 2016a; Z. Wang et al., 2014) and summer (J. Wu et al.,

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2017) episode, and this study can provide supplementary information for fall season; thirdly, the

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reducing emissions also have co-benefits of lowering aerosol feedbacks, which has been shown

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in several studies (Xing et al., 2016), but still lacked for APEC study

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In general, the predicted temporal variations of the magnitudes of temperature, RH, wind, and

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PM2.5 agree well with observations in Beijing. Model biases still exist, particularly in wind

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vectors, which is likely caused by errors in model land use data and used coarse horizontal and

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vertical model resolutions (Gao et al., 2016a). The comparisons of PM2.5 chemical compositions

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in October show that the model performs reasonably well in capturing the variations of aerosol

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evolutions. However, peak values of sulfate and OC are underestimated by our model, which is

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due to the incomplete understanding and parameterizations of sulfate and SOA formation in

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current models.

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The analyses of meteorological conditions in Beijing suggest that good air quality during APEC

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are largely resulted from positive weather conditions (lower temperature, lower RH, higher wind

405

speeds and enhanced PBLHs). Applying strict emission control measures in Beijing and the five

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surrounding provinces can only explain averaged 17.7µg/m3 (-21.8%) decreases in PM2.5

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concentrations, among which emission reductions in surrounding provinces contribute to about

408

half (9.3µg/m3, 52.5%).

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The effectiveness of implementing emission control measures hinges on dominant pathways and

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transport speeds. When transport from highly polluted regions (south Hebei) are dominant, strict

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emission control could not reduce to low level. When transport pathway from north region is

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dominant, PM2.5 concentrations are quickly dispersed even if no emission reduction is applied.

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During the APEC week, non-local emissions contributed to 41.3% to PM2.5 concentrations in

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Beijing, emphasizing the correctness of synergic strict control between local Beijing and

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surrounding provinces.

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In addition, reduced aerosol feedbacks caused by strict emission control measures are studied in

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this study. Without emission control, PBLHs decrease by about 20m~100m, and PM2.5

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concentrations increase by 1~8µg/m3. These two number change to 20m~70m and 0~5µg/m3

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when strict emission reductions are applied. During daytime, PM2.5 concentrations in urban

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Beijing region decrease by about 17.5µg/m3, and the co-benefits of reduced aerosol feedbacks

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lead to about 1.9µg/m3 decreases, accounting for 10.9%. The separation of contributions from

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aerosol absorption and scattering reiterates the importance of controlling BC (Gao et al., 2016a)

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to accelerate the effectiveness of aerosol pollution control.

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5 Data availability

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Contact Meng Gao ([email protected]) or Gregory R. Carmichael

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([email protected]) for modeling data requests, and Yuesi Wang

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([email protected]) for measurements data requests.

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Acknowledgments

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This work was partially supported by grants from NASA Applied Science (NNX11AI52G) and

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EPA STAR (RD-83503701) programs. We acknowledge all developers for contributing to the

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development of the WRF-Chem model, and thank use of the WRF-Chem preprocessor tool

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mozbc and fire_emiss provided by the Atmospheric Chemistry Observations and Modeling Lab

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(ACOM) of NCAR.

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Xing, J., Mathur, R., Pleim, J., Hogrefe, C., Gan, C.M., Wong, D.C., Wei, C. and Wang, J., 2015. Air pollution and climate response to aerosol direct radiative effects: A modeling study of

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decadal trends across the northern hemisphere. Journal of Geophysical Research:

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Atmospheres, 120(23).

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Xing, J., Wang, J., Mathur, R., Pleim, J., Wang, S., Hogrefe, C., Gan, C.M., Wong, D.C. and Hao, J., 2016. Unexpected benefits of reducing aerosol cooling effects. Environmental

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science & technology, 50(14), pp.7527-7534.

Xu, W. Q., Sun, Y. L., Chen, C., Du, W., Han, T. T., Wang, Q. Q., Fu, P. Q., Wang, Z. F., Zhao,

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oxidation properties, and sources in Beijing: Results from the 2014 Asia-Pacific

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Economic Cooperation summit study, Atmos. Chem. Phys., 15(23), 13681–13698,

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doi:10.5194/acp-15-13681-2015, 2015.

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Zaveri, R. a., Easter, R. C., Fast, J. D. and Peters, L. K.: Model for Simulating Aerosol Interactions and Chemistry (MOSAIC), J. Geophys. Res., 113(D13), D13204,

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doi:10.1029/2007JD008782, 2008.

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Zaveri, R. a. and Peters, L. K.: A new lumped structure photochemical mechanism for large-

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scale applications, J. Geophys. Res. Atmos., 104(D23), 30387–30415, doi:10.1029/1999JD900876, 1999.

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Zhang, J. K., Wang, L. L., Wang, Y. H. and Wang, Y. S.: Submicron aerosols during the Beijing

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Asia-Pacific Economic Cooperation conference in 2014, Atmos. Environ., 124, 224–231,

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doi:10.1016/j.atmosenv.2015.06.049, 2016.

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Zhang, L., Shao, J., Lu, X., Zhao, Y., Hu, Y., Henze, D. K., Liao, H., Gong, S. and Zhang, Q.: Sources and Processes Affecting Fine Particulate Matter Pollution over North China: An

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Adjoint Analysis of the Beijing APEC Period, Environ. Sci. Technol., 50(16), 8731–8740,

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doi:10.1021/acs.est.6b03010, 2016.

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Figure 1. Model domain settings and marked emission control regions

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Figure 2. Simulated and observed daily mean near surface temperature and RH, and hourly wind

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vectors (Before APEC: October 19-31, APEC: November 5-11)

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Figure 3. Simulated and observed hourly mean PM2.5 concentrations and daily mean PM2.5

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chemical compositions

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Figure 4. Boundary layer heights from model and reanalysis data

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Figure 5. Time series of simulated PM2.5 concentrations in the NOCTL, OnlyBJ, CTL, BJ0 and

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SUR5 case

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Figure 6. The spatial distributions of changes in sulfate (a), nitrate (b), ammonium (c), BC (d),

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and OC (e) and PM2.5 (f) due to emission controls (CTL-NOCTL) (µg/m3)

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Figure 7. Transport pathways for three moments: November 5 00:00, November 6 00:00, and

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November 8 12:00

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Figure 8. Aerosol feedbacks induced PBL and PM2.5 changes without emission control (a, d),

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with emission control (b, e) and their differences (c, f)

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Figure 9. BC absorption contribution to PBL changes (a) and PM2.5 changes (b) during daytime

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Table 1 Model Configuration Options

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Descriptions

Vertical layers

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Cloud Microphysics

Lin scheme

Longwave Radiation

Rapid Radiative Transfer Model (RRTM)

Shortwave Radiation

Goddard shortwave

Land Surface Model

Noah

Planetary Boundary Layer

Yonsei University

Gas phase chemistry

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Configurations

MOSAIC Aerosol Bins

0.039–0.078µm, 0.078-0.156µm, 0.156–0.312µm,

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0.312–0.625µm, 0.625–1.25µm, 1.25–2.5µm, 2.5– 5.0µm, 5.0–10µm

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Table 2 Model Evaluation Statistics for Meteorological Variables

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r

Wind speed 677

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0.95 (p<0.01)

0.3%

6.2%

0.84 (p<0.01)

-0.6m/s

1.2m/s

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Table 3 Model Evaluation Statistics for PM2.5

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-0.4°C

0.95 (p<0.01)

RH

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RMSE

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Temperature

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MB

RMSE

MFB

MFE

NOCTL

0.78

38.0µg/m3

68.1µg/m3

54.7%

60.5%

CTL

0.79

28.4µg/m3

62.7µg/m3

44.8%

55.0%

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Supplementary Information

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Figure S1. Accumulated precipitation from November 3 to November 12

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Figure S2. Averaged grid cells over Beijing shown with green box

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Figure S3. Averaged reductions of PM2.5 concentrations during daytime

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1. Meteorological and air quality variables are reproduced well by our WRF-Chem simulations during APEC. 2. Meteorological conditions were relatively favorable for pleasant air quality during air quality, and synectic control in both Beijing and surrounding regions were also important. 3. Co-benefits of reduced aerosol feedbacks account for about 10.9% of the total decreases

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in PM2.5 concentrations in urban Beijing.