Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Atmos. Chem. Phys., 19, 5791-5803, 2019
https://doi.org/10.5194/acp-19-5791-2019
Atmos. Chem. Phys., 19, 5791-5803, 2019
https://doi.org/10.5194/acp-19-5791-2019

Research article 03 May 2019

Research article | 03 May 2019

# Local and regional contributions to fine particulate matter in the 18 cities of Sichuan Basin, southwestern China

Local and regional contributions to fine particulate matter
Xue Qiao1,2,3, Hao Guo3, Ya Tang4, Pengfei Wang3, Wenye Deng5, Xing Zhao6, Jianlin Hu7, Qi Ying8, and Hongliang Zhang3 Xue Qiao et al.
• 1Institute of New Energy and Low-carbon Technology, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China
• 2Healthy Food Evaluation Research Center, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China
• 3Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
• 4College of Architecture and Environment & Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
• 5Xinjiang Academy of Environmental Protection Science, Urumqi 830011, China
• 6Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, No. 17, Section 3, South Renmin Road, Chengdu, Sichuan 610041, China
• 7Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Nanjing University of Information Science & Technology, Nanjing 210044, China
• 8Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA
Abstract

The Sichuan Basin (SCB) is one of the regions suffering from severe air pollution in China, but fewer studies have been conducted for this region than for the more developed regions in eastern and northern China. In this study, a source-oriented version of the Community Multiscale Air Quality (CMAQ) model was used to quantify contributions from nine regions to PM2.5 (i.e., particulate matter, PM, with an aerodynamic diameter less than 2.5 µm) and its components in the 18 cities within the SCB in the winter (December  2014 to February 2015) and summer (June to August 2015). In the winter, citywide average PM2.5 concentrations are 45–126 µg m−3, with 21 %–51 % and 39 %–66 % being due to local and nonlocal emissions, respectively. In the summer, 15 %–45 % and 25 %–52 % of citywide average PM2.5 (14–31 µg m−3) are due to local and nonlocal emissions, respectively. Compared to primary PM (PPM), the inter-region transport of secondary inorganic aerosols (SIA), including ammonia, nitrate, and sulfate ions (${\mathrm{NH}}_{\mathrm{4}}^{+}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, respectively), and their gas-phase precursors are greater. The region to the east of SCB (R7, including central and eastern China and others) is the largest contributor outside the SCB, and it can contribute approximately 80 % of PM2.5 in the eastern, northeastern, and southeastern rims of the SCB but only 10 % in other SCB regions in both seasons. Under favorable transport conditions, regional transport of air pollutants from R7 could account for up to 35–100 µg m−3 of PM2.5 in each of the SCB cities in the winter. This study demonstrates that it is important to have joint emission control efforts among cities within the SCB and regions to the east in order to reduce PM2.5 concentrations and prevent high PM2.5 days for the entire basin.

1 Introduction

Particulate matter (PM) is one of the major air pollutants in China, including primary and secondary components. Primary PM (PPM) is directly released from emission sources, while secondary PM is formed from their precursors, such as sulfur dioxide (SO2), nitrogen oxide (NOx), and ammonia (NH3). All of them are released from local sources or transported over a long distance (Ying et al., 2014; Zhao et al., 2018). The relative contributions of secondary components to total PM2.5 (PM with an aerodynamic diameter less than 2.5 µm) usually increase as PM2.5 concentration elevates in megacities (Huang et al., 2014; Qiao et al., 2018).

Air pollution in major economic centers in China, including the North China Plain (NCP), Yangtze River Delta (YRD), and Pearl River Delta (PRD), has been extensively studied in recent years. Regional transport of air pollutants has been identified as an important source of PM in the three regions, particularly the precursors of secondary PM (Zhang et al., 2013; X. Zhao et al., 2013; Ying et al., 2014; Jiang et al., 2015; Li et al., 2015; Wang et al., 2015; Zheng et al., 2015; Tang et al., 2016; Yang et al., 2018). Several urbanized areas in western China have also been suffering from air pollution due to rapid industrial and urban development, but fewer studies have been conducted compared to the NCP, YRD, and PRD. One such area is the Sichuan Basin (SCB), which covers an area about 0.22 million km2 and is home to more than 100 million residents in 18 cities, among which Chengdu and Chongqing are the largest two cities in western China (National Bureau of Statistics of China (NBSC), 2015; Table S1 in the Supplement). The SCB is topographically isolated, with mountains or plateaus on all sides. They are the Qinghai–Tibetan Plateau (QTP), Yunnan–Guizhou Plateau (YGP), Wu Mountains (WUM), and Daba Mountains (DBM) to the west, south, east, and north of the SCB, respectively. As a result of the basin topography, emissions released from the SCB tend to accumulate in the basin, causing severe air pollution (Ning et al., 2018a; Zhao et al., 2018). In addition, east and central China, which are to the east of the SCB, have considerable contributions to PM2.5 for the SCB. For example, Ying et al. (2014) predicted that central and east China had a combined contribution of 29.6 % to the total mass of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ for Chongqing in January 2009. Due to high emissions within the basin and deep basin landforms, annual average concentrations of PM2.5 in the SCB were similar to that of NCP and Central China (Fig. S1 in the Supplement). Annual PM2.5 measured in Chengdu and Chongqing in 2015 were 64 and 57 µg m−3, respectively, about 6 times the World Health Organization (WHO) guideline (10 µg m−3) (WHO, 2006; NBSC, 2015).

To design effective PM2.5 control strategies for the SCB, it is necessary to quantify the source contributions and inter- and intra-region transport of PM2.5 and its precursors within the SCB and its surrounding regions. There are many types of source apportionment methods, such as receptor-based models, air parcel trajectory models, remote-sensing, and chemical transport models (CTMs). Receptor-based models, such as the Positive Matrix Factorization model (PMF) (Paatero and Tapper, 1994; Qiu et al., 2019), the Chemical Mass Balance model (CMB) (Watson et al., 1990), and a local contribution model proposed by Zhao et al. (2019), are semiquantitative and cannot quantitatively determine the source contributions from an exact emission sector or a specific location. They also require a large number of monitoring data and can only resolve source contributions at the monitoring sites (Hopke, 2016). Remote-sensing and air parcel trajectory models, such as the Potential Source Contribution Function (PSCF) and the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) can only reflect the atmospheric dynamics, so they are not quantitative for source apportionment of secondary species (Begum et al., 2005; Uno et al., 2009; Stein et al., 2015; Liu et al., 2018; Wu et al., 2018). Compared to above methods, CTMs are more quantitative, as they can track the source contributions to both primary and secondary air pollutants from a specific region or sectorial source for studies at the local, regional, or global scale (Bove et al., 2014; Kim et al., 2015; Lelieveld et al., 2015; Itahashi et al., 2017; Shi et al., 2017). In CTMs, source contributions are quantified through two methods, namely, sensitivity analysis and tagged-tracer methods (Burr and Zhang, 2011). Sensitivity analysis such as the brute-force method is more suitable to estimate air quality changes due to emission perturbations, as emissions from certain sources would be eliminated or reduced in each simulation of sensitivity analysis (Burr and Zhang, 2011; Han et al., 2018; Huang et al., 2018). In the tagged-tracer method, source-tagged species are used to track air pollutants from specific emission regions and sectors, and they go through all of the nonlinear chemical and physical processes in the model, and thus this method is considered to provide more realistic evaluations of the contributions of different source sectors or source regions to the current level of air pollution under the current emission intensity (Wang et al., 2009; Burr and Zhang, 2011; Chen et al., 2017).

Transport of PM2.5 and its precursors has been studied for Chengdu, Chongqing, and the entire SCB region (Zhu et al., 2018). Based on in situ observations and the HYSPLIT model, studies have found that dust storms from northwestern China and biomass burning activities would cause high PM days in the two cities, particularly in spring (Zhao et al., 2010; Tao et al., 2013; Chen and Xie, 2014; Chen et al., 2015). Using the PSCF model, a study reported that the main potential sources of PM2.5 for Chengdu were from southeastern cities and the western margin of the SCB, in addition to local emissions from December 2013 to February 2014 (Liao et al., 2017). Based on the HYSPLIT and PSCF analyses, air pollution was determined mainly from the south during persistent extreme haze days in Chengdu from 6 to 16 January 2015 (Li et al., 2017). However, the aforementioned studies based on the HYSPLIT and PSCF models are not quantitative in terms of emission contributions. Their accuracy is also limited by the meteorological inputs that drive these models, which are often in very coarse resolutions (0.5 to 1.0) that are not enough to accurately predict air pollutant transport within the SCB, as meteorological conditions and pollutant concentrations may vary greatly within short distances (Shi et al., 2018). Also, previous country-level modeling studies did not have sufficient spatial resolution to properly quantify the transport among cities within the SCB.

Since both inter-regional transport within the cities in the basin and from outside the region can greatly affect PM2.5 concentrations in the 18 SCB cities, systematically quantifying contributions from different regions to PM2.5 for all the 18 cities in the SCB is urgently needed as emission controls are further tightened to improve air quality in this region. In this study, an improved source-oriented community multiscale air quality (CMAQ) model was used to quantify the contributions from nine regions (five within the SCB and four outside) to PM2.5 and its components for the 18 SCB cities. The assumption is that the transport of air pollutants is evident among the SCB cities and some cities on the rim of the SCB are greatly affected by emissions outside SCB. Therefore, the objectives of this study are to quantitatively determine (1) the inter-region transport of air pollutants emitted in the SCB and its contributions to PM2.5 in the 18 SCB cities and (2) the contributions of emissions outside the basin to PM2.5 in the SCB. In this study, the percentage contributions and maximum mass contributions from each region to PM2.5 in each city are both presented to better understand the extent of air pollutant transport. We modeled PM2.5 and its source contributions for only two seasons, as PM2.5 concentrations in the SCB are highest and lowest in winter and summer, respectively (Ning et al., 2018a).

2 Methods and materials

## 2.1 Model description

The source-oriented CMAQ model used in this study is based on the CMAQ model version 5.0.1. The gas-phase and aerosol mechanisms are extended from the standard SAPRC-99 photochemical mechanism and aerosol module version 6 (AERO6). This version of the source-oriented CMAQ is capable of simultaneously tracking both primary particulate matter (PPM) and secondary inorganic aerosols (SIA, including ${\mathrm{NH}}_{\mathrm{4}}^{+}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$) from multiple source sectors and regions. It unifies the two previous models individually developed for PPM (Hu et al., 2015) and SIA (Ying et al., 2014; Shi et al., 2017) into a single consistent model framework. For SIA, multiple source-tagged reactive species are introduced in both gas and particle phases to represent the same species originated from different source sectors or regions. The corresponding photochemical mechanisms, aerosol and cloud modules are expanded so that SIA and its precursors from different regions can be tracked separately throughout the model calculations. For example, NO2_S1 and NH3_S2 can be used to represent NO2 from region 1 and NH3 from region 2, respectively. After the photochemical mechanism is expanded, the source-tagged species are allowed to go through all processed to form (NH4_S2) (NO3_S1) based on additional reactions of ${\mathrm{NO}}_{\mathrm{2}}+\mathrm{OH}\to {\mathrm{HNO}}_{\mathrm{3}}$ and ${\mathrm{NH}}_{\mathrm{3}}+{\mathrm{HNO}}_{\mathrm{3}}\to {\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$. Thus, the contributions of region 1 to ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and region 2 to ${\mathrm{NH}}_{\mathrm{4}}^{+}$ are quantified. For PPM, source-tagged nonreactive tracers are added to track the total amount of PPM emitted from different source sectors and regions. SOA is included in the current model but its source contributions are not resolved.

## 2.2 Model application

The source-oriented CMAQ model was applied to quantify nine source region contributions to PM2.5 and its components (PPM and SIA) for the 18 cities in the winter (from December 2014 to February 2015) and summer (June to August 2015) using nested domain settings. The locations of the domains, the nine source regions, and the 18 cities of the SCB are shown in Fig. 1. The horizontal resolutions of the parent and nested domains are 36 and 12 km, respectively. There are 18 vertical layers with an overall height of 20 km and the layer closest to the land surface is up to 35 m. The geographical regions of emissions are classified into nine source regions. As Chengdu and Chongqing are the two largest cities in western China and within the SCB, we classified Chengdu, eastern Chongqing, and western Chongqing into three individual regions (R4, R5, and R1, respectively). Western Chongqing is well-urbanized and eastern Chongqing is mostly rural area. The five cities in the northeastern SCB (Bazhong, Dazhou, Guangyuan, Guang'an, and Nanchong) are grouped into R2, as they have relatively low anthropogenic emission densities compared to most of the other SCB cities and they are located in the upwind areas within the SCB (Qiao et al., 2019). The remaining SCB cities are grouped into R3. Sichuan Province, excluding those cities within the SCB, is R8, most of which is remote rural areas. R6 includes three provinces to the south of the SCB and R7 has the Chinese provinces to the east and northeast of the SCB. R9 includes the other jurisdictions to the west of the SCB, including Xinjiang, Qinghai, Gansu, Tibet, and other countries.

Figure 1(a) Locations of the 12 and 36 km domains (grey rectangles) and the locations of provincial capitals and municipalities (orange circles), (b) terrain within and surrounding the 18 cities of the SCB (black line), and (c) locations of region categories 1–9 and the prefecture-level cities (yellow circles). Regions 1–5 are the cities within the SCB. Regions 1, 4, and 5 are western Chongqing, Chengdu, and eastern Chongqing, respectively. The city center of Chongqing is located in western Chongqing. Region 8 is the area of Sichuan Province, excluding those cities in the SCB. QTP, Qinghai–Tibetan Plateau; YGP, Yunnan–Guizhou Plateau; DBM, Daba Mountains; WUM, Wu Mountains.

Meteorological inputs were generated using the Weather Research and Forecasting (WRF) model version 3.9 based on the 6-hourly FNL (Final) Operational Global Analysis data from the National Center for Atmospheric Research (NCAR) with a spatial resolution of $\mathrm{1.0}{}^{\circ }×\mathrm{1.0}{}^{\circ }$ (https://rda.ucar.edu/datasets/ds083.2/, last access: 27 April 2019). The anthropogenic emission inventory used was the Emission Database for Global Atmospheric Research (EDGAR) version 4.3.1 for the year of 2012 (Crippa et al., 2018). The inventory was directly used for the model year of 2014–2015, as no reliable sources for emission changes in the SCB are available. The monthly EDGAR inventories have a spatial resolution of $\mathrm{0.1}{}^{\circ }×\mathrm{0.1}{}^{\circ }$ (∼10 km × 10 km) and were re-projected to the model domains using the Spatial Allocator (https://www.cmascenter.org/sa-tools/, last access: 27 April 2019). Temporal profiles specific to sources were used to allocate the monthly emission rates to hourly values for CMAQ modeling (Wang et al., 2010). The EDGAR inventory includes carbon monoxide (CO), NOx, SO2, NH3, non-methane volatile organic compounds (NMVOCs), PM2.5, PM10 (PM with an aerodynamic diameter less than 10 µm), elemental carbon (EC), and organic carbon (OC) from various sources. Emission sources are grouped into six categories: energy, industries, residential activities, on-road transportation, off-road transportation, and agriculture. In addition to these six anthropogenic sources, contributions of biogenic sources were also determined using emissions generated by the Emissions of Gases and Aerosols from Nature (MEGAN) model version 2.1 (Guenther et al., 2012). The emissions from open burning were estimated based on the Fire Inventory from the National Center for Atmospheric Research (NCAR FINN) (Wiedinmyer et al., 2011). Contributions of windblown dust and sea salt emissions were determined based on in-line generated emissions during CMAQ simulations. It should be noted that the uncertainties in emission inventories potentially lead to uncertainties in the contributions.

The initial and boundary conditions (ICs and BCs, respectively) for the 36 km domain were based on CMAQ default profiles, and those for the 12 km domain were generated using the CMAQ outputs from the 36 km simulations. More details about the setup and configuration of the WRF/CMAQ modeling system can be found for China in a previous publication (Kang et al., 2016).

3 Results and discussion

## 3.1 Model performance

The model performance on meteorological parameters and 24 h PM2.5 in the 12 km domain for the two seasons has been evaluated in a companion paper (Qiao et al., 2019) and is briefly summarized here (Fig. S2). As the predictions on wind speed (WS) and wind direction (WD) are important in modeling air pollutant transport (Zhao et al., 2009), the WRF model performance for WS, WD, ambient air temperature (T), and relative humidity (RH) were evaluated by using hourly observations at China's national meteorological stations and the observation data that were downloaded from the National Climate Data Center (NCDC; ftp://ftp.ncdc.noaa.gov/pub/data/noaa/, last access: 20 June 2018). The mean biases (MBs) of predicted RH (−10.8 % to −1.1 %) and T (−0.9 to −0.1C) in each month are comparable to other studies in China (Wang et al., 2010, 2014; B. Zhao et al., 2013). The MB of WD in each month (−5 to 6) meet the benchmark of < $±\mathrm{10}{}^{\circ }$ suggested by Emery et al. (2001). Although the MB of WS in each month (0.5 to 1.1 m s−1) does not meet the benchmark of < ±0.5 m s−1, the gross errors (GEs: 1.4–1.9 m s−1) are within the benchmark of 2.0 m s−1. For 24 h PM2.5 concentrations, the statistical metrics of model performance are generally within the criteria recommended by Emery et al. (2017) for regulatory applications, with only a few cities exceeding the normalized mean bias (NMB) criteria of < ±30 % in the winter (Chongqing 42 %, Guangyuan 41 %, Mianyang 37 %, Meishan 31 %, Ziyang 48 %) and in the summer (Dazhou −39 %) (Fig. S2). The 24 h PM2.5 predictions meet the goals of normalized mean error (NME ±35 %), fractional bias (FB ±30 %), and fractional error (FE ±50 %) in all the cities in both seasons, except for the NME of Ziyang (58 %) in the winter. The predictions of major PM2.5 components (including OC, EC, ${\mathrm{NH}}_{\mathrm{4}}^{+}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$) in Chengdu and Chongqing are comparable with observations, and both predictions and observations suggest that OC and SIA are the largest contributors to PM2.5 in summer and winter, with combined contributions of about 70 % (Qiao et al., 2019).

Table 1Predicted source-region contributions to PM2.5 in the 18 SCB city centers in the winter. The data in bold font show the contributions due to local emissions, emissions within SCB, or emissions outside SCB.

a Others includes initial and boundary conditions, windblown dust, and sea salt. b Nonlocal = within SCB + outside SCB – local. c The city center of Chongqing.

## 3.2 Seasonal average contributions

### 3.2.1 Source contributions at the city centers

In each city, there are 4 to 17 national air quality stations (NAQs), and almost all the NAQs are located in the urban areas, where population densities are higher. Thus, coordinates of the NAQs in the urban area of a given city were averaged to define the city center in order to understand PM2.5 concentrations and its sources for the most-populated region of each city. The predicted PM2.5 concentrations and source-region contributions at the 18 SCB city centers are presented in Table 1 for winter and Table S2 for summer. In all the city centers, the predicted PM2.5 concentrations are much higher in the winter (60–191 µg m−3) than in the summer (14–64 µg m−3). The city centers are considerably affected by both local and regional emissions in both seasons. Emissions within the SCB are the major contributor to PM2.5 in Chengdu and Chongqing in both seasons (∼80 %) and emissions outside the SCB contribute approximately 7 %–15 %. Among the regions within the SCB, local emissions (i.e., emissions from the region where the city center is located) are the largest contributor to PM2.5 in Chongqing and Chengdu in both seasons (about 70 % and 58 %, respectively). However, emissions from R3 (i.e., the 11 cities in the northwestern, western, and southwestern SCB) also have considerable contribution in Chengdu (∼20 % and 14 % in the winter and summer, respectively). For the R3 cities, the contributions of emissions within the SCB (64 %–83 %) are also larger than that from outside the SCB (8 %–26 %) in both seasons. Local emissions are the largest contributor for R3 cities (40 %–60 %), except that Suining has only ∼13 % due to its local region. The low local contribution in Suining might be because it is less economically developed compared to other cities, except for Bazhong, Guangyuan, and Ya'an, as suggested by the 2015 gross domestic product (GDP; Table S1). For the five cities in the northern SCB (R2), emissions within the SCB account for 40 %–70 % in both seasons, including 37 %–57 % from local emissions. Emissions outside the SCB also have large contributions to the R2 cities (21 %–36 % and 17 %–28 % in the winter and summer, respectively), as R2 is located in one of the regions where winds from R7 intrude the basin (Fig. 2a). In the winter, contributions from SOA and others (including IC, BC, windblown dust, and sea salt) are less than 8 % each. In the summer, SOA and others each contribute 9 %–28 % and less than 10 %, respectively, but the SOA contributions larger than 15 % are found only in the city centers where summer PM2.5 concentrations are less than 30 µg m−3. In summary, local emissions are the largest contributor for all the city centers in both seasons, except for Suining. The nonlocal contributions for the city centers are in the range of 25 %–52 % in the winter (except for 75 % in Suining) and of 14 %–40 % in the summer (except for 61 % in Suining) and emissions outside the SCB account for 7 %–36 % in both seasons.

Figure 2(a) Spatial distributions of predicted PM2.5 concentrations (µg m−3) and (b–l) source-region contributions to PM2.5 (%) in the winter. Others includes IC, BC, windblown dust, and sea salt. Black arrows in (a) are wind vectors.

### 3.2.2 Spatial variations and citywide area-weighted averages

The spatial variations in source-region contributions to PM2.5 in the winter and summer are presented in Figs. 2 and 3, respectively. In both seasons, local emissions are generally the largest contributor in each city, except that R7 has contributions similar to or larger than those of local emissions for most regions in eastern Chongqing (R5) and R2. Specifically, the contributions from R7 to PM2.5 in R2 and R5 are approximately 20 %–80 % in the winter and 20 %–60 % in the summer. R7 also has contributions larger than 20 % for a few areas in R3. The regions of R6, R8, and R9 outside the SCB each has contributions of < 5 % across the basin, except for some very limited areas in the western and southern rims of the SCB. The contributions from R6, R8, and R9 are low because these areas are less urbanized and industrialized. In addition, the mountains to the west and south of the SCB also prevent the transport of air pollutants from these regions into the SCB (Figs. 1c, 2a, and 3a). In summary, R7 is the sole non-SCB region that can have > 20 % contributions to PM2.5 in the SCB, and its impact decreases from the northeast, east, and southeast of others in the basin.

Table 2Predicted citywide area-weighted average PM2.5 concentrations and source-region contributions in the 18 SCB cities in the winter and summer.

* Nonlocal =100 %-local-SOA-others; Others includes initial and boundary conditions, windblown dust, and sea salt.

As shown in Figs. 2 and 3, PM2.5 concentrations and their source contributions from a given region may vary greatly within a city in both seasons. For example, about 20 %–80 % of PM2.5 across Chengdu (R4) and western Chongqing (R1) are due to local emissions in each season, and higher PM2.5 concentrations are generally related to higher local contributions. For the downwind regions of Chengdu and western Chongqing, they receive considerable contributions from the two megacities. For example, over half of Meishan and Ya'an, which are downwind of Chengdu, have 20 %–40 % and 20 %–60 % of PM2.5 concentrations due to Chengdu in the winter, respectively. In the two seasons, western Chongqing contributes about 10 %–40 % of PM2.5 concentrations in its neighboring cities, except that most of eastern Chongqing (R5) is not affected by emissions from western Chongqing, as R5 is upwind of western Chongqing (Fig. 2a). Because of the large spatial variations of PM2.5 and its source contributions in the basin, we further calculated their citywide area-weighted averages (Table 2). In the winter, the citywide average PM2.5 concentrations in Chengdu and urban Chongqing are 99 and 110 µg m−3, with only 38 % and 47 % due to local emissions, respectively. Nonlocal emissions also have high contributions in other SCB cities, with citywide averages of 39 %–66 % and 25 %–52 % in the winter and summer, respectively. The above suggests the importance of regional emission control for reducing PM2.5 concentrations for the entire basin.

Figure 3(a) Spatial distributions of predicted PM2.5 concentrations (µg m−3) and (b–l) the source-region contributions to PM2.5 (%) in the summer. Others includes IC, BC, windblown dust, and sea salt.

### 3.2.3 Differences in PPM and SIA

The transport distances of PPM, ${\mathrm{NH}}_{\mathrm{4}}^{+}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ might be different because of the differences in chemical and physical processes that affect their concentrations in the atmosphere (Ying et al., 2014; Hu et al., 2015). This leads to significant differences in their regional distributions and thus requires different control strategies. From the source-region contributions to PPM and SIA for each city center shown in Fig. 4, it is obvious that the regional transport of SIA is more significant than that of PPM. In the city centers of Chengdu and Chongqing, 55 %–65 % of PPM and 25 %–45 % of SIA are due to local emissions in the two seasons. In the city centers of R2, PPM is also more from local emissions (65 %–80 %) than SIA is (25 %–45 %) in both seasons. Similarly, local emissions have larger contributions to PPM (50 %–85 %) than to SIA (34 %–50 %) in all the city centers of R3 except for Suining, which is not significantly affected by local emissions. The spatial distributions of source-region contributions to PPM and SIA also indicate more significant transport of SIA (Figs. S3–6) than PPM. For example, R3 contributes to > 20 % of SIA across all of Chengdu, but only half the areas of Chengdu are about equally affected (> 20 %) by R3 for PPM. From the north to south in R2, the contributions from R7 to PPM decrease from ∼55 % to ∼10 %, while the contributions of R7 to SIA decrease from ∼75 % to ∼20 %. The contributions to ${\mathrm{NH}}_{\mathrm{4}}^{+}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ in each city center from local emissions and emissions within and outside SCB are further analyzed (Tables S3 and S4). In each city center, concentrations of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ (3.8–12.6 and 12–41 µg m−3) are much higher than that of ${\mathrm{NH}}_{\mathrm{4}}^{+}$ (1.4–4.0 and 6.0–17.0 µg m−3) and ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (0.3–2.4 and 6–20 µg m−3) in the summer and winter, respectively. Also, the transport of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ and its precursor is greater than the other two ions, as the percentage contributions from emissions outside the SCB to ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ is higher than that to ${\mathrm{NH}}_{\mathrm{4}}^{+}$ and ${\mathrm{NO}}_{\mathrm{3}}^{-}$ in each city center. In both seasons, emissions outside SCB contribute < 25 % of ${\mathrm{NH}}_{\mathrm{4}}^{+}$ in the city centers, except for Chongqing (26 %) in the summer and Bazhong (36 %) and Guangyuan (33 %) in the winter. As for ${\mathrm{NO}}_{\mathrm{3}}^{-}$, emissions outside SCB also contribute < 25 % in the city centers in both seasons, except for Bazhong (49 %), Dazhou (34 %), and Guangyuan (25 %) in the summer and all the cities of R2 (27 %–57 %) in the winter. In the two seasons, emissions outside SCB account for 22 %–33 % of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ in Chengdu and Chongqing, while they contribute 52 %–70 % of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ for the R2 cities. For the R3 cities, emissions outside SCB account for 25 %–53 % of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ in the city centers in both seasons, except for Meishan (21 %) in the winter. All the above suggest that it would be more efficient to control the SIA (particularly ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$) and its precursors than PPM in order to reduce the transport of air pollutants within and into the basin.

Table 3Predicted maximum daily contribution from a given region (MDCs) in the SCB city center and the corresponding PM2.5 concentrations in the city center on the same day. Only winter data are included in this table. The units are µg m−3. The numbers in the bold present the contributions due to local emissions or that from R7.

a Others includes initial and boundary conditions, windblown dust, and sea salt. b Includes the city center of Chongqing.

Figure 4Predicted source-region contributions to SIA (A) and PPM (P) (bars, left y axis) and the predicted proportions of SIA and PPM in PM2.5 (circles, right y axis) for the 18 city centers of the SCB in the summer (S) and winter (W). Others includes IC, BC, windblown dust, and sea salt.

## 3.3 Maximum daily contributions from a given region

The maximum daily contribution from a given region to PM2.5 (MDC, µg m−3) in each city center is shown in Table 3 for winter and Table S5 for summer. The largest MDC for each city center (79–291 and 13–147 µg m−3 in the winter and summer, respectively) are found to be associated with local emissions, except for Guangyuan and Suining. In Guangyuan and Suining, the largest MDCs in the winter are from R7 (62 µg m−3) and R2 (110 µg m−3), both are slightly higher than that from the local region of 60 and 105 µg m−3, respectively. Table 3 also shows that the interbasin transport of air pollutants can have large contributions (> 50 µg m−3) on high PM2.5 days (> 150 µg m−3). For example, R7 contributes 99 µg m−3 to total PM2.5 (200 µg m−3) in Chongqing on a winter day. In Nanchong, the MDC due to western Chongqing (R1) is 58 µg m−3, when daily PM2.5 is 180 µg m−3 on that day. In Chengdu, R3 and R7 can contribute up to 86 and 63 µg m−3 on the days with daily PM2.5 of 267 and 151 µg m−3, respectively. In Deyang and Meishan, the MDCs from Chengdu are 147 and 138 µg m−3 on the days that have daily PM2.5 of 288 and 235 µg m−3, respectively. Table S4 shows that air pollutant regional transport is also significant on certain days in the summer. For example, the highest summer MDC from R7 among the 18 central cities is found for Bazhong (36 µg m−3), when daily PM2.5 is 63 µg m−3. Chengdu contributes about 44, 16, 55, 13, 7, and 21 µg m−3 to Deyang, Leshan, Meishan, Ya'an, and Mianyang on the summer days, when daily PM2.5 are 89, 56, 100, 85, 22, and 54 µg m−3, respectively. All the above suggest that joint effects should be made by neighboring cities and the provinces to the east of the SCB in order to prevent high PM2.5 episodes for the SCB.

## 3.4 Impacts of topography on PM2.5 concentrations

While air pollutant emissions are the root of air pollution, topography and meteorological conditions play a very important role in determining the fate of pollutants including dispersion, accumulation, and transformation (Arya, 1999; Zhang et al., 2015; He et al., 2017). It has been widely noticed that heavy air pollution often occurs in well-urbanized and/or industrialized cities associated with mountains and basins, such as Beijing, Chengdu, Xi'an, and Lanzhou in China (Chambers et al., 2015; Bei et al., 2017, 2018; Ning et al., 2018a, b); Mexico City, Salt Lake City, and Los Angeles in the North America (Langford et al., 2010; Whiteman et al., 2014; Calderón-Garcidueñas et al., 2015); and megacities in the Mediterranean Basin of the Europe (Kanakidou et al., 2011). The SCB is surrounded by the QTP to the west, YGP to the south, WUM to the east, and DBM to the northeast. Mainly affected by the high elevations of the QTP and YGP, near-surface winds mainly intrude the basin from the north, east, and southeast in the summer and winter, as shown in Figs. 2a and 3a. Consequently, R7 is the largest contributor outside the basin, contributing 20 %–60 % of PM2.5 in the eastern, northeastern, and southeastern parts of the SCB (Figs. 2h and 3h), where PM2.5 concentrations are relatively low in the SCB (< 75 and 25 µg m−3 in the winter and summer, respectively). The contributions from R6 (including YGP) and R8 (including QTP) are < 10 % along the western and southern rims of the SCB. Within the basin, near-surface winds travel anticlockwise and form a cyclone near Yibin, Zigong, Neijiang, and Luzhou in the south (Figs. 1b, 2a, and 3a) (Lin, 2015), causing air pollutants transported to be accumulated within the cyclone. PM2.5 concentrations in the cyclone-affected region (mostly 100–150 and 30–50 µg m−3 in the winter and summer, respectively) are generally lower than those of Chengdu and Chongqing but are higher than those of most of the other regions. In Yibin, Zigong, Neijiang, and Luzhou at least 39 %–53 % and 25 %–44 % of citywide average PM2.5 concentrations are not due to their own emissions in the winter and summer, respectively (Tables 2 and S2). R7 only contributes about 10 % to PM2.5 in the cyclone-affected region. In order to reduce seasonal and annual concentrations of PM2.5 within the SCB, the emissions and intercity transport of air pollutants within the basin should be prioritized for being controlled.

4 Conclusions

In this study, a source-oriented CMAQ model was applied to quantify contributions of nine regions to PM2.5 for the 18 cities in the SCB. The simulations were carried out for winter (December 2014 to February 2015) and summer (June to August 2015). Predicted citywide area-weighted average PM2.5 concentrations are much higher in the winter (60–191 µg m−3) than in the summer (14–64 µg m−3). In the winter, the citywide average PM2.5 concentrations in Chengdu and western Chongqing are 99 and 110 µg m−3, with 44 % and 52 % due to nonlocal emissions, respectively. Nonlocal emissions also have high contributions in other SCB cities, with citywide averages of 39 %–66 % and 25 %–52 % in the winter and summer, respectively. Among the four regions outside the SCB, only the one to the northeast, east, and southeast of the SCB (R7) has large contributions to PM2.5 concentrations for the SCB in both seasons (10 %–80 %), and the contributions decrease from the rims of the northeastern, eastern, and southeastern SCB to other regions. However, the MDCs from R7 are large (35–99 µg m−3) for all the city centers in the winter. On high PM2.5 days in the winter, emissions outside SCB can contribute up to 99 µg m−3 in the city center, suggesting the importance of regional emission control in not just reducing averaged PM2.5 but also preventing severe PM pollution events. The transport of SIA and its precursors is greater than that of PPM, suggested by the fact that local emissions have higher contributions to PPM (> 55 %) than to SIA (< 45 %) in the city centers in both seasons. Among the three ions of SIA, the transport of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ and its gas-phase precursor (SO2) is the greatest in general, as > 50 % of it in all the city centers is associated with nonlocal emissions in both seasons, except that the contributions are 37 %–44 % in Chongqing and Chengdu in the summer and Chongqing in the winter. In conclusion, in order to reduce PM2.5 concentrations and prevent high PM2.5 days for the entire SCB, local emissions and the transport of air pollutants within and across SCB should be controlled simultaneously.

Data availability
Data availability.

Hourly modeling results of all the domains are not available online yet because we are using the data for two other studies. We are using hourly modeling data of PM2.5, PM10, SO2, NO2, O3, and CO to characterize air pollution for the entire basin. The results will be published in Qiao et al. (2019). We are using hourly source apportionment data to quantify the risks from different regions and sectors to human health through PM2.5 pollution. All of the modeling results will be available online after we publish the paper.

Supplement
Supplement.

Author contributions
Author contributions.

XQ, YT, and HZ designed research. HG, JH, QY, and HZ contributed to model development and configuration. XQ, HG, PW, WD, and XZ analyzed the data. XQ prepared the manuscript and all coauthors helped improve the manuscript.

Competing interests
Competing interests.

The authors declare that they have no conflict of interest.

Special issue statement
Special issue statement.

Acknowledgements
Acknowledgements.

Portions of this research were conducted with high-performance computing resources provided by Louisiana State University (http://www.hpc.lsu.edu, last access: 28 April 2019). This study is sponsored by the International Collaboration Project of the Science & Technology Department of Sichuan Province [2017HH0048], the National Natural Science Foundation of China [41628102], the Program of Introducing Talents of Discipline to Universities [B08037], PM2.5 monitoring in the campuses of Sichuan University [SCU2015CC0001], and the Chinese Scholarship Council [201706245007].

Review statement
Review statement.

This paper was edited by Yuan Wang and reviewed by three anonymous referees.

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