Xue Qiao1,2,3, Hao Guo3, Pengfei Wang3, Ya Tang4, Qi Ying5, Xing Zhao6, Wenye Deng7, Hongliang Zhang 3,8

Institute of New Energy and Low-Carbon Technology and Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China
Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
College of Architecture and Environment and Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA
Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu 610041, China
Xinjiang Academy of Environmental Protection Science, Urumqi 830011, China
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China


Received: May 6, 2019
Revised: July 30, 2019
Accepted: August 1, 2019

Download Citation: ||https://doi.org/10.4209/aaqr.2019.05.0235  


Cite this article:

Qiao, X., Guo, H., Wang, P., Tang, Y., Ying, Q., Zhao, X., Deng, W. and Zhang, H. (2019). Fine Particulate Matter and Ozone Pollution in the 18 Cities of the Sichuan Basin in Southwestern China: Model Performance and Characteristics. Aerosol Air Qual. Res. 19: 2308-2319. https://doi.org/10.4209/aaqr.2019.05.0235


Highlights

  • Hotspots of winter PM2.5 occur near the urban centers of Chengdu and Chongqing.
  • Summer O3 concentrations are higher in some rural areas than in urban areas.
  • Time-series patterns of 24-h PM2.5 and 8-h O3 are similar among the 18 cities.
  • Primary organic aerosols and SO42− are the top contributors to PM2.5 in winter.
  • Spatial changes of O3 and PM2.5 reflect trans-boundary transport of air pollutants.
 

ABSTRACT


The Sichuan Basin (SCB) is located in southwestern China and has a total population of 108.1 million across 18 cities, including the 2 largest in western China (Chengdu and Chongqing). As most air quality monitoring stations are located in urban areas, we simulated the PM2.5 (i.e., particulate matter with an aerodynamic diameter < 2.5 µm) and ozone (O3) in the entire SCB during winter (December 2014–February 2015) and summer (June–August 2015) by using the Weather Research and Forecasting (WRF) and the Community Multi-scale Air Quality (CMAQ) models. The simulated concentrations of 24-h PM2.5 and its major components generally agree with observations during both seasons, but the simulated 1-h and 8-h O3 are acceptable only for summer. Increasing in severity from the rim of the SCB to its inner areas, the PM2.5, as well as its major components, exhibits hotspots near the central urban areas of Chongqing and Chengdu, with concentrations of 150–200 µg m–3 and 40–60 µg m–3 during winter and summer, respectively. The 1-h and 8-h O3 exhibit no hotspots in the urban centers of Chongqing and Chengdu but show elevated levels in some rural and suburban areas (55–70 ppb and 65–80 ppb, respectively), including those on the western and southwestern rim of the SCB, and downwind of the urban center of Chongqing. Despite the great spatial variations in the PM2.5 and O3 concentrations, the vast majority of the basin fails to meet the WHO guidelines for 24-h PM2.5 (25 µg m–3) and 8-h O3 (~47 ppb) on > 70% of the days during winter and > 40% of the days during summer, respectively. Based on the aforementioned spatial patterns of the PM2.5 and O3 concentrations, and the wind directions within the basin, strictly controlling emissions originating in the SCB may greatly reduce PM2.5 and O3 concentrations within the basin.


Keywords: Chengdu; Chongqing; Spatio-temporal variations; Air pollution


INTRODUCTION


Air pollution is one of the most severe environmental problems in China. While haze is still a severe problem in China (Wang and Hao, 2012; Zhang et al., 2012a; Zhang et al., 2012b; Aunan et al., 2018), ozone (O3) has become anincreasingly important pollutant in recent years (Verstraeten et al., 2015; Anger et al., 2016; Ma et al., 2016; Sun et al., 2016; Wang et al., 2017; Shen et al., 2019a), as a result of increased emissions of volatile organic compounds (VOCs) in the VOC-limited regions (Shen et al., 2019b), decreased nitrogen oxides (NOx) in some VOC-limited regions (Li et al., 2019), and slowed aerosol sink of hydroperoxy radicals that stimulates O3 production (Li et al., 2019). Air pollution has caused significant adverse effects on human health. It was estimated that more than 1 million premature deaths each year in China were attributable to outdoor air pollution (Lelieveld et al., 2015). In order to mitigate air pollution, the Ministry of Environmental Protection of the People’s Republic of China (MEP) has grouped the areas that are most affected by air pollution in the country into 3 regions and 10 city clusters (MEP, 2013; Fig. S1), including (1) Beijing-Tianjin-Hebei (BTH), (2) Yangtze River Delta (YRD), (3) Pearl River Delta (PRD), (4) central regions of Liaoning Province, (5) Shandong Province, (6) Wuhan and its peripheral localities, (7) Changsha, Zhuzhou, and Xiangtan in Hunan Province, (8) the Chengdu-Chongqing city cluster in the Sichuan Basin (SCB), (9) western coast of the Taiwan Strait, (10) central and northern regions of Shansi Province, (11) Guanzhong Plain in Shanxi Province, (12) parts of Gansu and Ningxia Province, and (13) Urumqi in Xinjiang Province. In total, these areas cover 1.32 million km2 and have a population of 675 million, accounting for 14% of China’s land, 48% of the country’s population, 71% of national gross domestic product (GDP), 52% of national coal consumption, and 50% of the national emission of sulfur dioxide (SO2), NOx, particulate matter (PM), and volatile organic compounds (VOCs) (MEP, 2013).

Among the 3 regions and 10 city clusters, the Chengdu-Chongqing city cluster in the SCB has the largest area (0.22 million km2) and the second largest population (108.1 million) (MEP, 2013). Within the basin, there are 16 cities belonging to the Chengdu-Chongqing city cluster and the other 2 cities are located in the north of the SCB (Fig. 1). The information of area, population, GDP, and air pollutant concentrations in each city are provided in Table S1. Due to the basin landform and high anthropogenic emissions within the basin, air pollution is severe and the major air pollutants are O3 and PM2.5 (i.e., PM with an aerodynamic diameter < 2.5 µm) (Ning et al., 2018; Zhao et al., 2018a, b; Zhang et al., 2019). In 2015, annual PM2.5 concentrations measured in the 18 cities were in the range of 21–74 µg m–3 (Table S1), about 2–7 times of the World Health Organization (WHO) guideline of 10 µg m–3 (WHO, 2006). Annual 90th percentiles of 8-h O3 in the 18 cities were measured in the ranges of 32–86 ppb in 2015, with 5 cities having values over 70 ppb. According to the measurements at the national air quality stations (NAQs; Fig. 1), PM2.5 concentrations were highest in winter but lowest in summer, and non-attainment days due to PM2.5 pollution were more frequent in winter (Ning et al., 2018; Zhao et al., 2018b). In contrast, O3 was the major pollutant in summer (Ning et al., 2018; Zhao et al., 2018b). However, as almost all the NAQs are located in the urban and suburban areas, the characteristics of air pollution have not been investigated in detail for the entire basin, particularly for the vast rural areas.


Fig. 1. (a) Locations of simulation domains and meteorological stations and (b) locations of the 18 cities in the SCB and national air quality stations (NAQs). Almost all the NAQs are located in the 18 SCB urban centers. The upwind and downwind regions are defined based on wind directions within the SCB, as shown in Figs. 5(a) and S4(a). Upwind cities: 1) Bazhong, 2) Dazhou, 3) Guangyuan, 4) Mianyang, and 5) Nanchong. Downwind cities: 6) Chengdu, 7) Deyang, 8) Guang’an, 9) Leshan, 10) Luzhou, 11) Meishan, 12) Neijiang, 13) Suining, 14) Yibin, 15) Zigong, and 16) Ziyang. Other cities: 17) Ya’an and 18) Chongqing.Fig. 1. (a) Locations of simulation domains and meteorological stations and (b) locations of the 18 cities in the SCB and national air quality stations (NAQs). Almost all the NAQs are located in the 18 SCB urban centers. The upwind and downwind regions are defined based on wind directions within the SCB, as shown in Figs. 5(a) and S4(a). Upwind cities: 1) Bazhong, 2) Dazhou, 3) Guangyuan, 4) Mianyang, and 5) Nanchong. Downwind cities: 6) Chengdu, 7) Deyang, 8) Guang’an, 9) Leshan, 10) Luzhou, 11) Meishan, 12) Neijiang, 13) Suining, 14) Yibin, 15) Zigong, and 16) Ziyang. Other cities: 17) Ya’an and 18) Chongqing.

Chemical transport models (CTMs) have been applied to study air pollution for China as a whole (Hu et al., 2016; Li et al., 2016; Zhu and Liao, 2016) and for several regions of China, such as the YRD (Liao et al., 2015), NCP (Wang et al., 2012), and PRD (Fan et al., 2014). In previous nationwide CTM simulations, model performance on the SCB region was evaluated only for Chengdu and Chongqing, as observations in most of the cities in the basin were not available before 2015. For example, Hu et al. (2016) applied the Community Multi-scale Air Quality (CMAQ) (Byun and Schere, 2006) and the Weather Research Forecasting (WRF) (University Corporation for Atmospheric Research (UCAR), 2017) modeling system to predict air pollutant concentrations for the whole of China. The results show that 1-h and 8-h O3 and 24-h PM2.5 from March to December 2013 were over-predicted but PM10 (i.e., PM with an aerodynamic diameter < 10 µm) was under-predicted in Chengdu and Chongqing. In addition to the limitations of atmospheric models and emission inventories, the biases in air pollutant predictions might be associated with the coarse horizontal resolution (36 km × 36 km) (Hu et al., 2016).

The SCB is located in the transition region from central China to the Qinghai-Tibetan Plateau (QTP) with great elevation changes; thus, the landform is complex and using coarse horizontal resolutions may not accurately predict wind speed (WS), wind direction (WD), and other meteorological parameters. Given the potential high risk from air pollution to human health, there is a need to use a higher horizontal resolution to systematically model the characteristics, sources, and health impacts of air pollution for the SCB.

In this study, a modified WRF/CMAQ modeling system was used to simulate PM2.5 and O3 in winter (December 2014–February 2015) and summer (June–August 2015) for the entire SCB. The model performance is evaluated, the spatio-temporal variations of the 2 pollutants and PM2.5 components are analyzed, the non-attainment conditions are determined, and air pollution is compared among the 18 SCB cities. This is the first study that comprehensively investigates the air quality status as well as variations of criteria air pollutants and PM2.5 components for all the SCB cities. This study provides the basis for our other studies on the source apportionment (Qiao et al., 2019), health effects, and control strategies of air pollutants in the SCB.


METHODS AND MATERIALS



Model Description and Application

The WRF/CMAQ modeling system was applied for the SCB using nested domains. The 36-km domain (197 × 129 grid cells) covers all of China and surrounding countries in East Asia, while the 12-km domain (118 × 118 grid cells) covers the entire SCB and its adjacent regions (Fig. 1). Meteorological inputs were generated using the WRF model version 3.9 with initial and boundary conditions (IC and BC) from the U.S. National Centers for Environmental Prediction (NCEP) Final (FNL) Operational Model Global Tropospheric Analyses dataset (1° × 1° resolution; available at http://dss.ucar.edu/datazone/dsszone/ds083.2/). Detailed WRF model configurations are listed in Table S2. The CMAQ model version 5.0.1 was used to model air pollutant concentrations and modifications were made to improve the prediction of secondary inorganic and organic aerosols, including (1) providing more detailed treatment of isoprene oxidation chemistry SARPC-11 gas-phase photochemical mechanism (Ying et al., 2015); (2) allowing surface-controlled reactive uptake of dicarbonyls, isoprene epoxydiol (IEPOX), and methacrylic acid epoxide (Crippa et al., 2018) for secondary organic aerosol (SOA) formation (Li et al., 2015; Ying et al., 2015); (3) updating SOA yields based on vapor wall-loss correction (Zhang et al., 2014); and (4) improving nitrate and sulfate formation through heterogeneous reactions of NO2 and SO2 (Wang et al., 2014). More details of these modifications can be found in the cited references and the references therein. The model has been used in some studies for Asian countries (Hu et al., 2016; Kota et al., 2018).

Other input files used to run the CMAQ model include the IC and BC of chemicals and the anthropogenic and natural emissions. The default CMAQ IC and BC profiles were used for the 36-km simulation and the outputs of the 36-km simulation were used to generate the chemical IC and BC files for the 12-km simulations. Anthropogenic emissions files were generated based on the Emission Database for Global Atmospheric Research (EDGAR) version 4.3, which provided the datasets of carbon monoxide (CO), NOx, SO2, ammonia (NH3), VOCs, PM2.5, PM10, elemental carbon (EC), and organic carbon (OC) (Crippa et al., 2018). As the EDGAR database provides only monthly total emissions of air pollutants, an in-house preprocessor was used to generate hourly emissions based on monthly, weekly, and temporal allocation profiles as mentioned in Wang et al. (2014) and references within. Biogenic emission files were simulated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.1 (Guenther et al., 2012). Open burning emission files were generated by using the Fire Inventory from the National Center for Atmospheric Research (NCAR) (FINN) version 1.5 (Wiedinmyer et al., 2011). Dust and sea salt emission files were generated in-line, and the dust emission module was updated to be compatible with the 20-category Moderate Resolution Imaging Spectroradiometer (MODIS) land use data (Hu et al., 2016). The re-gridded emissions of individual species were mapped to model species needed by the SAPRC photochemical mechanism (Carter, 2010) and the AERO6 aerosol module. As shown in Fig. S2, anthropogenic emissions are much higher inside the SCB than in its neighboring regions. Seasonally, PM2.5, SO2, and NOx emissions are higher in winter than in summer, but NH3 emissions are generally similar between the two seasons. 


Model Validation

Hourly observations of meteorological parameters and air pollutants were used to evaluate the model performance. The observations of wind speed (WS) and wind direction (WD) at 10 m above ground level (a.g.l.), ambient air temperature at 2 m a.g.l. (T2), and relative humidity (RH) were obtained from the National Climate Data Center (NCDC; ftp://ftp.ncdc.noaa.gov/pub/data/noaa/, last accessed on June 20, 2018). The meteorological data were available at 1,434 and 101 stations in the 36-km and 12-km domains, respectively (Fig. 1). PM2.5 and O3 measurements were derived from the Air Quality Data Distribution Platform of China (http://106.37.208.233:20035/, last accessed on July 13, 2018), which provided data at 94 NAQs in the SCB (Fig. 1). Almost all the NAQs are located in the urban and suburban areas. Quality control measures were taken to remove possible problematic data points with observed hourly O3 and PM2.5 greater than 250 ppb and 1500 µg m–3, respectively. The data points with a standard deviation of less than 5 ppb for O3 and 5 µg m–3 for PM2.5 within 24 hours were also removed. To calculate 24-h PM2.5 for a given day in a city, citywide hourly PM2.5 data should be available for at least 20 hours (MEP, 2012a). To calculate 1-h and 8-h O3 for a given day in a city, citywide hourly values should be available for at least 14 hours between 08:00 and 24:00 (MEP, 2012b). If hourly O3 concentrations are available for < 14 hours but the calculated 1-h O3 values are greater than the Chinese National Ambient Air Quality Standards (CNAAQS), the 1-h O3 values are still valid (MEP, 2012b).


RESULTS AND DISCUSSION



Model Validation


Meteorology

In order to assess the WRF model performance, the statistical metrics, including mean bias (MB), root mean square error (RMSE), and gross error (GE), were calculated based on the equations in Table S3 and then compared with the benchmarks suggested by Emery et al. (2001). When using the benchmarks to assess model performance, it should be noted that the benchmarks are considered as quantitative measures to compare with previous studies and model performance is affected by model parameterizations, landforms, meteorological reanalysis data, etc. As shown in Table S4, RH and T2 are both under-predicted (MBs < 0), and the under-predictions are greater in winter (MBs for RH and T2: –10.8 to –6.5% and –0.9 to –0.5 K, respectively) than in summer (MBs for RH and T2: –2.9 to –1.1% and –0.3 to –0.1 K, respectively). Compared to the benchmarks, the statistics of T2 are within the MB benchmark (< ±0.5 K) only in summer and there are no benchmarks for RH. In the two seasons, the MBs and RMSEs of WS (0.5–1.1 m s–1 and 1.8–2.3) are slightly larger than the benchmarks (< ±0.5 m s–1 and < 2.0, respectively), but the GEs of WS meet the benchmark value (< 2.0 m s–1) in both seasons. The MBs of WD (–5° to 6°) achieve the benchmark (< ±10°), but its GEs (54–61°) are larger than the benchmark (< 30°). Overall, the WRF model performance is acceptable and comparable to previous studies in China (Hu et al., 2016; Hu et al., 2017a). The biases in the WRF simulations can be partially explained by the complex landforms in the 12-km domain, which is located in the transition region from central China (mostly < 1000 m above sea level [a.s.l.]) to the QTP (> 3000 m a.s.l.) and the spatial resolution of the modeling.


PM2.5 and its Components

Fig. 2 shows the time series of simulated and observed 24-h PM2.5 in the urban centers of the 18 cities (i.e., the center of the NAQs in the urban area of a city) and Table S5 shows relevant evaluation statistics, including normalized mean biases (NMBs), normalized mean errors (NMEs), fractional biases (FBs), and fractional errors (FEs). In general, both the simulated and observed 24-h PM2.5 are much higher in winter than in summer in the urban centers, and the simulations capture the peaks of 24-h PM2.5 most of the time (Fig. 2). The NMBs of PM2.5 meet the criteria (< ±30%; Emery et al., 2017) in all the urban centers in both seasons, except for that of Guanyuan (41%), Mianyang (37%), Meishan (31%), Ziyang (48%), and Chongqing (42%) in winter and the NMB of Dazhou (–39%) in summer (Table S4). The 24-h PM2.5 simulations meet the goals of NME (< 35%), FB (< ±30%), and FE (< 50%) in all the cities in both seasons, except for the NME of Ziyang (58%) in winter (Table S4). The possible causes leading to the uncertainties in PM2.5 simulations include but are not limited to the bias in the emission inventories, uncertainties in the meteorological modeling, complex landforms, and spatial resolutions of the modeling. However, the time series in Fig. 2 and the statistics in Table S5 give confidence in the predictions of 24-h PM2.5 in both seasons for the SCB.


Fig. 2. Simulated and observed 24-h PM2.5 concentrations in the 18 SCB urban centers in summer (June–August 2015) and winter (December 2014–February 2015). “Obs.” shows observations. “Pred.” indicates the simulations at the grid cells covering the urban centers, while “Best” indicates the simulated values closest to the observations within 3 × 3 grid cell regions that surround the urban center.Fig. 2. Simulated and observed 24-h PM2.5 concentrations in the 18 SCB urban centers in summer (June–August 2015) and winter (December 2014–February 2015). “Obs.” shows observations. “Pred.” indicates the simulations at the grid cells covering the urban centers, while “Best” indicates the simulated values closest to the observations within 3 × 3 grid cell regions that surround the urban center.

The model performance on PM2.5 components (including sulfate, nitrate, and ammonia ions (SO42–, NO3, and NH4+, respectively), OC; EC; and others) is assessed for the urban centers of Chengdu and Chongqing (Fig. 3), as observations are available from literature for the 2 cities. In Chengdu, the two largest contributors to PM2.5 are simulated to be secondary inorganic aerosols (SIA; including SO42–, NO3, and NH4+ here; ~40% in total) and OC (28–34%), while the simulated EC fractions are ~5% in both seasons. These simulations are similar to the observations from Chen et al. (2016), which reported that SIA, OC, and EC accounted for 45%, 23–30%, and 5% of PM2.5 from 2012 to 2013, respectively. Within SIA, the simulated contributions from SO42–, NO3, and NH4+ to PM2.5 are 21%/30%, 12%/2%, and 10%/8% in Chengdu in winter/summer, and these values also agree with the observations of 20%/25%, 15%/8%, and ~10% (Chen et al., 2016), respectively. Shi et al. (2017) reported SO42–, NO3, OC, and EC contributions (25%, 8%, 13%, and 7% in winter and 23%, 4%, 12%, and 9% in summer, respectively) similar to our simulations. In Chongqing, SIA and OC are also simulated to be the largest contributors to PM2.5. The simulated SIA fractions in Chongqing (35% and 30% in winter and summer, respectively) are lower than the observations (45% and 50% in winter and summer, respectively) from Chen et al. (2017) but are higher than the annual mean fraction of 29% reported by Yang et al. (2011). The simulated OC fractions in Chongqing (38% and 33% in winter and summer, respectively) are higher than the observations (33% and 25% in winter and summer, respectively) from Chen et al. (2017) and are close to the annual mean of 33% reported by Yang et al. (2011). The EC fractions in Chongqing are similar between simulations (~12%) and observations (~10%; Chen et al., 2012) in the two seasons. The discrepancies between the simulations and observations on PM2.5 components might be associated with the following: (1) The periods of observations and simulations are different; (2) emission inventories, meteorological modeling, etc. lead to the uncertainties of simulations on PM2.5 components; (3) locations of PM2.5 component measurements are not in the urban centers; and (4) concentrations of PM2.5 components may vary greatly within a urban center or a modeling grid cell (12 km × 12 km).


Fig. 3. Comparison of PM2.5 components in Chengdu and Chongqing between the observations and simulations. The simulations of this study are the averages in the winter (December 2014–February 2015) and in the summer (June–August 2015).Fig. 3. Comparison of PM2.5 components in Chengdu and Chongqing between the observations and simulations. The simulations of this study are the averages in the winter (December 2014–February 2015) and in the summer (June–August 2015).
 


1-h and 8-h O3

The time series of 8-h O3 in the 18 urban centers are presented in Fig. 4, and the evaluation statistics of 8-h and 1-h O3 are presented in Tables S6 and S7, respectively. Only 1-h and 8-h O3 concentrations larger than 30 ppb are used to calculate NMB and NME, as a cutoff of 30–60 ppb is suggested by the U.S. Environmental Protection Agency (U.S. EPA, 2005), Hu et al. (2016), and Emery et al. (2017). The 8-h O3 concentrations above 30 ppb in summer are mostly captured in the cities, except for Ya’an (Fig. 4). Also, the simulated diurnal patterns in summer well replicate the time and concentrations of O3 peaks in 11 cities in summer (Fig. S3). The NMBs and NMEs of 1-h and 8-h O3 for summer in all the 18 cities achieve the criteria (NMB < ±15% and NME < 25%) recommended by Emery et al. (2017), except for the NMBs and NMEs of 1-h and 8-h O3 in Ya’an and the NMBs of 8-h O3 in Zigong and Suining (Tables S6 and S7). However, 1-h and 8-h O3 are both greatly over-predicted in winter, with NMBs and NMEs of 20–79%, almost all of which do not meet the corresponding criteria (±15% and 25%, respectively). Thus, the model performance on 1-h and 8-h O3 is acceptable for summer, when the photochemical production of O3 due to anthropogenic emissions should be strongest in the basin.


Fig. 4. Simulated and observed 8-h O3 concentrations in the 18 SCB urban centers in summer (June–August 2015) and winter (December 2014–February 2015). “Obs.” shows observations. “Pred.” indicates the simulations at the grid cells covering the urban centers, while “Best” indicates the simulated values closest to the observations within 3 × 3 grid cell regions that surround the urban center.
Fig. 4. Simulated and observed 8-h O3 concentrations in the 18 SCB urban centers in summer (June–August 2015) and winter (December 2014–February 2015). “Obs.” shows observations. “Pred.” indicates the simulations at the grid cells covering the urban centers, while “Best” indicates the simulated values closest to the observations within 3 × 3 grid cell regions that surround the urban center.
 


Spatial Variations: Air Pollutant Concentrations and Non-attainment Days


PM2.5 and its Compon
ents

Spatial distributions of PM2.5, PM2.5 components, and wind vectors are presented in Fig. 5 for winter and Fig. S4 for summer. The entire SCB experiences much higher PM2.5 concentrations in winter (~50–200 µg m–3) than in summer (~5–60 µg m–3). The higher PM2.5 concentrations in winter are partially due to the lower amount and frequency of precipitation and lower wind speed in the season according to observations (Ning et al., 2018; Zhao et al., 2018b). In general, PM2.5 concentrations are much lower in the rims of the SCB (< 75 µg m–3 and < 20 µg m–3 in winter and summer, respectively) than in the inner SCB (100–200 µg m–3 and 20–60 µg m–3 in winter and summer, respectively). Two hotspots of PM2.5 concentrations are found and they cover the urban centers of Chengdu and Chongqing and their adjacent regions in both seasons and have PM2.5 concentrations of 150–200 µg m–3 in winter and 40–60 µg m–3 in summer. SO42–, NO3, NH4+, primary organic aerosol (POA), and EC present spatial variations similar to that of PM2.5 in both seasons. In contrast, no hotspots are found for SOA. In winter, SOA concentrations are also lower in the rims of the SCB (< 4 µg m–3) than in the inner areas (4–10 µg m–3), except that the southwestern rim of the SCB also has higher values (6–10 µg m–3). In summer, higher SOA concentrations (4–8 µg m–3) are found in the inner and southwestern SCB, as well as some regions in the north and east of the SCB. In summary, PM2.5 and most of its major components have concentrations increasing from the rims to the inner areas of the SCB, have two hotspots near the urban centers of Chengdu and Chongqing, and present decreasing gradients from the hotspots of Chengdu and Chongqing to their downwind areas. The above spatial variations may reflect not just the emissions and accumulation of air pollutants within the SCB but also the trans-boundary transport of air pollutants. In a companion paper, we have revealed that 25–52% and 39–66% of PM2.5 in each urban center are due to non-local emissions in summer and winter, respectively, and emissions outside the SCB can contribute up to 70% of PM2.5 in winter for the eastern SCB rims where PM2.5 concentrations are relatively low compared to that of other SCB regions (Qiao et al., 2019).


Fig. 5. Spatial variations of simulated wind vectors, PM2.5 (µg m−3), and PM2.5 components (µg m−3) in winter (December 2014–February 2015) over the SCB. The left and right crosses show the urban centers of Chengdu and Chongqing, respectively.Fig. 5. Spatial variations of simulated wind vectors, PM2.5 (µg m−3), and PM2.5 components (µg m−3) in winter (December 2014–February 2015) over the SCB. The left and right crosses show the urban centers of Chengdu and Chongqing, respectively.

The percentage of non-attainment days (i.e., the days that do not meet the CNAAQS of 75 µg m–3 or the WHO guideline of 25 µg m–3) is shown in Fig. 6. In summer, only the PM2.5 hotspots located near the central cities of Chengdu and Chongqing have non-attainment days > 10% based on the CNAAQS. However, over half the area of the basin has PM2.5 exceeding the WHO guideline for > 20% of summer days, and > 80% of summer days near the PM2.5 hotspots do not meet the WHO guideline. In winter, over half the area of the basin has PM2.5 exceeding the CNAAQS for > 40% days and almost the entire basin has PM2.5 larger than the WHO guideline for > 70% of winter days. All of the above suggests that the entire basin has PM2.5 pollution based on the WHO guideline in winter.


Fig. 6. The percentage of the days that do not meet the CNAAQS (75 µg m−3) and WHO guideline (25 µg m−3) for 24-h PM2.5 concentrations in summer (June–August 2015) and winter (December 2014–February 2015). The black lines show the SCB city boundaries. The left and right crosses show the urban centers of Chengdu and Chongqing, respectively.Fig. 6. The percentage of the days that do not meet the CNAAQS (75 µg m−3) and WHO guideline (25 µg m−3) for 24-h PM2.5 concentrations in summer (June–August 2015) and winter (December 2014–February 2015). The black lines show the SCB city boundaries. The left and right crosses show the urban centers of Chengdu and Chongqing, respectively.


1-h and 8-h O3

Spatial variations of O3 concentrations and the percentages of non-attainment days in summer are presented in Fig. 7. 1-h and 8-h O3 concentrations are higher in the western and southwestern SCB and downwind from the urban center of Chongqing, with average concentrations of 65–80 ppb and 55–70 ppb, respectively. Only in some of these higher-O3 areas do 8-h and 1-h O3 exceed the corresponding CNAAQS for 10–40% and > 5% of days in summer, respectively. However, almost the entire basin and over half of the basin have 8-h O3 concentrations larger than the WHO guideline (~47 ppb) for > 40% and > 60% of summer days, respectively. Although the trans-boundary transport of O3 and its precursors has not been quantified yet, we have found that over 30% of the O3 concentration in the forested areas of the western SCB from 09:00 to 15:00 in August 2013 is associated with anthropogenic NOx emissions (Qiao et al., 2019b), and this reflects the transport of O3 precursors from urban to rural areas.


Fig. 7. (a) 8-h O3 concentrations and wind vectors, (b) 1-h O3 concentrations, (c–d) percentages of the days that do not meet the CNAAQS and WHO guideline for 8-h O3, and (e) percentage of the days that do not meet the CNAAQS for 1-h O3 in summer 2015. The concentrations and percentages are in µg m−3 and %, respectively. The black lines show the SCB city boundaries. The left and right crosses show the urban centers of Chengdu and Chongqing, respectively.Fig. 7. (a) 8-h O3 concentrations and wind vectors, (b) 1-h O3 concentrations, (c–d) percentages of the days that do not meet the CNAAQS and WHO guideline for 8-h O3, and (e) percentage of the days that do not meet the CNAAQS for 1-h O3 in summer 2015. The concentrations and percentages are in µg m−3 and %, respectively. The black lines show the SCB city boundaries. The left and right crosses show the urban centers of Chengdu and Chongqing, respectively.
 


Comparison of the 18 Cities

In both summer and winter, near-surface winds intrude on the SCB from the north, east, and southeast and then travel anti-clockwise within the basin (Figs. 5 and S4). Based on the wind vectors, we approximately categorized the 18 cities into 3 groups (Fig. 1), including upwind cities (Bazhong, Dazhou, Guangyuan, and Mianyang), downwind cities (Chengdu, Deyang, Guangan, Leshan, Luzhou, Meishan, Nanchong, Neijiang, Suining, Yibin, Zigong, and Ziyang), and others (Ya’an and Chongqing).

The citywide average concentrations and attainments of PM2.5 are compared among the 18 cities in Fig. 8. Citywide average PM2.5 concentrations are generally higher in the downwind cities (106.5 ± 13.6 µg m–3 and 24.3 ± 4.9 µg m–3) than in the upwind cities (62.6 ± 15.9 µg m–3 and 15.6 ± 2.7 µg m–3) and the other cities (16.0 ± 10.5 µg m–3 and 66.0 ± 28.9 µg m–3) in winter and summer, respectively. In summer, 24-h PM2.5 concentrations in the 18 cities exceed the CNAAQS for < 5% days but are higher than the WHO guideline for 12–55% days (except Ya’an). In winter, the percentages of non-attainment days are also much lower based on the CNAAQS (18–80%) than based on the WHO guideline (65–100%). Although PM2.5 concentration and its attainment both vary greatly among the cities, POA and SO42– are the largest contributors to PM2.5 in the 18 cities in winter, accounting for 26–35% and 19–30% of PM2.5, respectively (Fig. 9). In summer, SO42 is still one of the most important contributors with contributions of 23–30% in the urban centers. Although the contributions from SOA (20–33%) in summer are similar to or as important as that from POA (19–24%) in the urban centers of Bazhong, Dazhou, Guangyuan, Leshan, Suining, Yibin, and Ya’an, these cities have average PM2.5 concentrations lower than 30 µg m–3 in the season. In contrast to that of SOA, the contributions from NO3 decease from 6–15% in winter to 1–8% in the 18 cities in summer, as NO3 would be easily transformed into NOx when the air temperature is over 30°C (Chen et al., 2003). Therefore, in order to reduce PM2.5 concentrations in the basin, we suggest that POA and SO42– should receive priority for being controlled in both seasons.


Fig. 8. The citywide average concentrations and non-attainment days of (a) summer 24-h PM2.5, (b) winter 24-h PM2.5, (c) summer 8-h O3, and (d) summer 1-h O3 in the 18 SCB cities.Fig. 8. The citywide average concentrations and non-attainment days of (a) summer 24-h PM2.5, (b) winter 24-h PM2.5, (c) summer 8-h O3, and (d) summer 1-h O3 in the 18 SCB cities.


Fig. 9. Percentage contributions of different components to the total PM2.5 concentrations in the 18 SCB urban centers in summer (June–August 2015) and winter (December 2014–February 2015).Fig. 9. Percentage contributions of different components to the total PM2.5 concentrations in the 18 SCB urban centers in summer (June–August 2015) and winter (December 2014–February 2015).

Citywide concentrations of 1-h and 8-h O3 are slightly higher in the downwind cities (58.9 ± 2.8 ppb and 54.7 ± 2.2 ppb) than in the upwind (53.5 ± 2.7 ppb and 50.6 ± 2.3 ppb) and other (55.0 ± 3.5 ppb and 52.0 ± 3.1 ppb) cities in summer, respectively (Fig. 8). 8-h O3 concentrations in the cities do not meet the WHO guideline for 54–88% of summer days and are higher than the CNAAQS for 0–12% of summer days. 1-h O3 does not meet the CNAAQS for < 5% of summer days in the 18 cities and there is no WHO guideline for 1-h O3. As citywide 8-h O3 concentrations in the 18 cities exceed the WHO guideline for 50% of summer days, there is a need to develop relevant control measures for the basin. 


CONCLUSIONS


In this study, the WRF/CMAQ modeling system was used to simulate the PM2.5 and O3 during winter andsummer in the SCB, which contains the 2 largest cities in western China (namely, Chengdu and Chongqing). The PM2.5 increases in concentration from the rim of the basin (25–100 µg m–3 and 5–20 µg m–3 during winter and summer, respectively) to the inner areas (100–200 µg m–3 and 20–60 µg m–3 during winter and summer, respectively) and exhibits 2 hotspots (150–200 µg m–3 and 40–60 µg m–3 during winter and summer, respectively) near the urban centers of Chengdu and Chongqing. Decreasing gradients for the PM2.5 concentration are found in the regions downwind of these urban centers, reflecting the transport of air pollutants within the basin. POA and SO42– are the largest contributors to the PM2.5 during both winter and summer, with SOA being a third significant factor during the latter. Although noticeable hotspots are absent, the overall 1-h and 8-h O3 concentrations (55–70 ppb and 65–80 ppb, respectively) are higher in some rural and suburban areas, including those located on the western and southwestern rim of the SCB and in regions downwind of the urban center of Chongqing. In spite of the large spatial variations in the PM2.5 and O3 concentrations, the vast majority of the basin exceeds the WHO guidelines for 24-h PM2.5 and 8-h O3 on > 70% of winter days and > 40% of summer days, respectively. Although the basin landform favors the accumulation of air pollutants within the SCB, it inhibits the transportion of pollution from outside. Thus, strictly controlling emissions originating inside the basin may greatly reduce PM2.5 and O3 in this region. 


ACKNOWLEDGMENTS


Portions of this research were conducted with high-performance computing resources provided by Louisiana State University (http://www.hpc.lsu.edu). 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).



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