Simeng Ma1, Zhimei Xiao 2, Yufen Zhang1, Litao Wang3, Min Shao4

State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
Tianjin Eco-Environm Monitoring Ctr, Tianjin 300071, China
Department of Environmental Engineering, Hebei University of Engineering, Handan, Hebei 056038, China
School of the Environment, Nanjing Normal University, Nanjing 210023, China

Received: August 15, 2019
Revised: October 1, 2019
Accepted: November 27, 2019
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Cite this article:

Ma, S., Xiao, Z., Zhang, Y., Wang, L. and Shao, M. (2020). Assessment of Meteorological Impact and Emergency Plan for a Heavy Haze Pollution Episode in a Core City of the North China Plain. Aerosol Air Qual. Res. 20: 26-42.


  • The effect of mitigation measures during severe pollution episodes are evaluated.
  • Unfavorable meteorological conditions contribute ~34% of PM2.5 concentrations.
  • Different mitigation measures should be taken in different districts.
  • Fossil fuel combustion and dust play a significant role in most districts.
  • Industrial processes contribute more in adjoining districts and Binhai District.


The Beijing-Tianjin-Hebei region, characterized by frequent episodes of severe haze pollution during winter, is recognized as one of the key regions requiring air pollution control. To reduce the effects of severe pollution, early warning and emission reduction measures should be executed prior to these haze episodes. In this study, the efficacy of emission reduction procedures during severe pollution episodes was evaluated using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). To provide feedback and optimize emergency emission reduction plans, a pollution episode that occurred during the period of December 20–26, 2015, which was characterized by a high warning level, long warning period, and integrated pollution process, was selected as a case study to determine the influence of meteorological conditions and the effects of mitigation measures on heavy haze pollution episodes. Adverse meteorological conditions were found to increase PM2.5 concentrations by approximately 34% during the pollution episode. Moreover, the largest contributor to the episode was fossil fuel combustion, followed by dust emission and industrial processes; the first two factors play a significant role in most districts in Tianjin, whereas the third more strongly affects the adjoining districts and Binhai District. Emission reduction for industrial sources and domestic combustion more obviously decreases PM2.5 concentrations during the pollution dissipation stage than the pollution accumulation stage. Thus, different mitigation measures should be adopted in different districts and during different pollution stages. An approximate decrease of 18.9% in the PM2.5 concentration can be achieved when an emergency plan is implemented during the red alert period for heavy haze pollution episodes.

Keywords: WRF-Chem; Mitigation measures; PM2.5; Early warning.


PM2.5, also known as fine particulate matter, has an aerodynamic diameter of less than 2.5 µm and plays an important role in atmospheric visibility, human health, and climate change (Lim et al., 2012). Regional haze with extremely high PM2.5 concentrations has become the primary air quality problem in China, especially in the Beijing-Tianjin-Hebei (BTH) region. Severe haze pollution episodes have been frequently observed over the BTH region, especially during winter (Wang et al., 2014c; Han et al., 2015). 

The Ministry of Environmental Protection of China reported that six of the ten most polluted cities from January to December 2017 were located in the BTH region (EEPRC, 2018). The severe air pollution in the BTH region requires urgent attention. The variations and uncertainties in the emissions, species compositions, spatial and temporal resolution, and distribution among various emission sectors in the BTH region cause complexity in the regional formation and distribution of PM2.5.

Tianjin is a crucial city in the BTH region, which is the most severely polluted region in China. Tianjin, which is located at the west of the Bohai Sea and enclosed by Beijing and Hebei, is the largest coastal city and one of the most polluted cities in northern China. Tianjin is a typical metropolis in northern China with a population of 15.47 million, a land area of approximately 11,946 km2, and more than 2.85 million vehicles as of 2015. Moreover, Tianjin is also a large industrial city whose gross domestic product (GDP) was 1653.8 billion RMB in 2015. It ranked third in China according to GDP (CSP, 2016; CSPT, 2016; TJSP, 2016). The major industries in Tianjin include the port, petrochemical, iron and steel, and chemical industries. Moreover, the major energy resources consumed in Tianjin include coal, coke, crude oil, fuel oil, gasoline, kerosene, diesel oil, natural gas, and electric power. According to the Natural Bureau of Statistics (CSP, 2018), approximately 47.3 million tons of coal and 15.44 million tons of crude oil were consumed in Tianjin in 2017, which resulted in large amounts of gaseous pollutant emissions.

However, previous studies have revealed that the causes and characteristics of air pollution in Beijing, Tianjin, and Hebei differ (Gu et al., 2011; Wang et al., 2013, 2014b; Chen et al., 2017; Liu et al., 2018). Huang et al. (2017) determined that secondary inorganic aerosol was the largest source of PM2.5 in the BTH region (29.2–40.5%). Moreover, motor vehicle exhaust was the second largest source of PM2.5, especially in Beijing (24.9%). Furthermore, coal combustion was also a large source of PM2.5 in Tianjin (12.4%) and Shijiazhuang (15.5%), with particular dominance during winter. Han et al. (2018) indicated that Hebei and Shandong were the dominant sources for PM2.5 over the North China Plain (NCP). However, Beijing and Tianjin are usually affected by local emissions. Wang et al. (2017) applied Particulate Matter Source Apportionment Technology (PSAT) in the Comprehensive Air Quality Model with Extensions (CAMx). They determined that local emissions contributed 83.6% of the total PM2.5 concentration in the urban center of Beijing and that the local contribution can more easily cause a sharp increase or reduction in the PM2.5 concentration in central Beijing than in other locations. Peng et al. (2019) identified coal combustion, biomass burning, and vehicle emission and determined the presence of sea salt, nitrate sources, sulfate sources, and crustal dust sources as major sources of PM2.5 in Tianjin by using three different methods. Liu et al. (2018) indicated that crustal dust, secondary sources, vehicle emissions, coal combustion, and industrial emissions were the primary sources of PM2.5 in Shijiazhuang. Therefore, air pollution prevention and control should be adapted to local conditions. Because of the various emission features and urban constructions in different cities, the “One City, One Policy” project was proposed to establish the targeted and possible measures for reducing and limiting emissions in each city. Due to the requirement of urgent air quality control, the effectiveness of air quality control managements and heavy pollution emergency plans should be dynamically evaluated. Moreover, feedbacks should be provided and final decisions should be optimized.

Severe pollution episodes in Tianjin mainly occur during winter. Thus, we selected December as an example for this study. Fig. 1 presents the air quality index (AQI) (MEP, 2012) and daily primary pollutants in December 2015. The moderately to seriously polluted days account for 64.5% of the entire month, and the days with PM2.5 as the primary pollutant account for 82.1% of the entire month. Thus, air pollution is extremely severe and characterized by fine particles in Tianjin during winter. 

Fig. 1. AQI and daily primary pollutants in December in Tianjin. I, II, III, IV, V, VI represent different air quality index, where I represents well, II represents fine, III represents mild pollution, IV represents moderate pollution, V represents severe pollution and VI represents serious pollution (MEP, 2012).

Currently, studies on the emission reduction effect are mainly based on two methods—observation and air quality model methods. The observation method is widely used (Liu et al., 2013; Wang et al., 2014a). However, the use of the observation method alone has some limitations, such as the inability to forecast and assess measures that have not been implemented or are unfinished and the inability to rule out the effects of meteorological conditions and background concentrations. The air quality model method has the advantages of scenario analysis and has a good ability to predict the temporal-spatial distribution of pollutants (Wang, 2014; Li et al., 2015; Han et al., 2016). Han et al. (2016) used the Regional Atmospheric Modeling System model and Models-3 Community Multiscale Air Quality (CMAQ) model (RAMS–CMAQ) with a zero-out sensitivity test for conducting source sensitivity approaches for PM2.5. They concluded that residential and industrial sources were the largest contributors to PM2.5 in the NCP regions. Li et al. (2015) used the Weather Research and Forecasting (WRF) model and CMAQ model (WRF-CMAQ) to simulate and action plan (2013–2017). The study concluded that under weak, medium, and strong emission reduction scenarios, the evaluate the effect of air quality improvement in the Yangtze River Delta region after the implementation of a clean air average annual concentration of PM2.5 in the Yangtze River Delta region decreased by 8.7%, 15.9%, and 24.3%, respectively. However, the aforementioned studies mainly focused on the overall effect of emission reduction and paid less attention to the effect of early warning and emission reduction measures of detailed sectors on the temporal-spatial distribution during severe haze episodes.

In this study, the WRF model coupled with chemistry (WRF-Chem) was applied to evaluate the effect of emission reduction measures during severe pollution episodes. The influence of different factors on the air quality, feedback, and optimized emission reduction plans was analyzed in this study. The roles of meteorological conditions and different emission categories, such as fossil fuel combustion, industrial processes, transport, solvent use, dust, agriculture, biomass burning, storage, and waste disposal, were investigated by conducting sensitivity experiments in terms of the PM2.5 mass burden. The effects of different emission reduction scenarios were evaluated under different pollution levels.

Model description, input datasets, and protocols

Configurations and Domain of the WRF-Chem Model

WRF-Chem is an online-coupled three-dimensional (3D) air quality model that has been widely used in recent years (Fast et al., 2006; Han et al., 2008; Misenis and Zhang, 2010; Yan, 2013; An et al., 2013; Wu et al., 2013; Zhang et al., 2013). The model considers the feedback between the meteorology of a region and aerosols and thus can simulate the emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology (Grell et al., 2005). In this study, WRF-Chem (version 3.7) was used for simulating the distribution of gaseous pollutants (e.g., SO2, NOx, CO, and O3), PM2.5, PM10, and their major components in December 2015. A typical severe haze episode from December 20–26, 2015, was selected to assess the effects of major emission reduction measures in Tianjin.

As displayed in Fig. 2, the model domain includes a horizontal grid spacing of 36 km that covers most of the area of China (Domain 1) and a nested-domain horizontal grid spacing of 12 km that covers northeastern China, encompassing Beijing, Tianjin, Hebei, Henan, Shandong, and Shanxi (Domain 2). Twenty-three vertical levels were set up from the surface to the model lid (~100 mb) for the WRF-Chem model. The corresponding sigma levels were 1.000, 0.995, 0.988, 0.980, 0.970, 0.956, 0.938, 0.916, 0.893, 0.868, 0.839, 0.808, 0.777, 0.744, 0.702, 0.648, 0.582, 0.500, 0.400, 0.300, 0.200, 0.120, 0.052, and 0.000. 

Fig. 2. WRF-Chem modeling domains at a horizontal grid resolution of 36 km for East Asia (Domain 1 with 164 × 97 cells) and at 12 km for an area in northern China (Domain 2 with 126 × 144 cells) and the location of the 14 sites used for evaluating in Tianjin.Fig. 2. WRF-Chem modeling domains at a horizontal grid resolution of 36 km for East Asia (Domain 1 with 164 × 97 cells) and at 12 km for an area in northern China (Domain 2 with 126 × 144 cells) and the location of the 14 sites used for evaluating in Tianjin.

A summary of the major chemical and physical parameters in this study is presented in Table 1. The gas-phase chemistry and aerosol modules include the Carbon-Bond Mechanism version Z (CBMZ) (Zaveri and Peters, 1999) and 8-bin MOSAIC aerosol, which include some aqueous reactions (Zaveri et al., 2008). The major physical parameterization options used are the Goddard shortwave radiation scheme (Chou et al., 1998), the Rapid Radiative Transfer Model (RRTM) longwave radiation scheme (Mlawer et al., 1997), the Yonsei University planetary boundary layer (PBL) scheme (YSU) (Hong et al., 2006), and the Grell 3D ensemble cumulus parameterization scheme (Grell and Devenyi, 2002). The biogenic emissions employed the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2 (Guenther et al., 2006). 

For the meteorology field simulation, the National Center for Environmental Prediction (NCEP) Final Operational Global Analysis datasets were used as the initial and boundary conditions and the NCEP Automated Data Processing surface and upper air datasets were used as four-dimensional data assimilation for WRF-Chem model.

The initial chemicals for Domain 1 include the default chemical profiles in WRF-Chem with 7 days (November 24–30, 2015) of pre-simulation to minimize the influence of the initial conditions. The model’s default profile was used as the chemical boundary condition.

mission Inventory

In this study, a combination of the “bottom-up” and “top-down” methods was used to establish the anthropogenic emissions of eight species, namely sulfur dioxide (SO2), nitrogen oxide (NOx), carbon monoxide (CO), nonmethane volatile organic compounds (NMVOCs), ammonia (NH3), carbon dioxide (CO2), coarse particulate matter (PM10), and PM2.5. The 2013 local emission inventory for Tianjin pertaining to 10 sectors was provided by Tianjin Eco-Environmental Monitoring Center. These sectors are fossil fuel combustion (three subsectors: domestic combustion, industrial boiler combustion, and power plant combustion), industrial processes, transport, solvent use, dust, agriculture, biomass burning, storage and transport, waste disposal, and other sectors. The Multi-resolution Emission Inventory for China (MEIC) for 2012 was used to determine the anthropogenic emissions in other parts of China except in Tianjin. The data for five sectors, namely industry, power plant, transport, residential, and agriculture, were obtained.

Fig. 3 displays the emission inventory distribution of SO2, NOx, PM2.5, and CO in Tianjin. The PM2.5 emission intensity was the highest in central districts, followed by adjoining districts such as Jizhou District, Wuqing District, and Ninghe District. The locations of different districts are illustrated in Fig. 2. The spatial distributions of SO2 and CO emissions were similar to that of PM2.5 emission, which is mainly consistent with distributions such as the residential coal combustion, steel, and cement industrial distributions. Industrial processes were the major sources of SO2 and NOx emissions in Dongli District and Ninghe District and accounted for more than 90% of the total SO2 and NOx emissions. Residential coal combustion emissions accounted for more than 50% of the total SO2 and CO emissions in Wuqing District and Jizhou District. The NOx emission intensity was higher in central districts and Binhai District than in adjoining districts, followed by outer suburban districts. This finding indicated a higher amount of vehicle emissions in the central districts and Binhai District than in other districts. The Port of Tianjin has the fifth highest throughput in the world, which leads to high emissions of diesel vehicles in Binhai District. Maritime emissions accounted for 76.8% of the total NOx emissions in Binhai District. Moreover, in central districts, heavy-duty diesel vehicles were forbidden and nonlocal vehicles were restricted during the peak time. Thus, these types of vehicles can only be driven in the adjoining districts. The emissions due to transportation were the major emissions in central districts and accounted for 99.6% of the total NOx and CO emissions. 

Fig. 3. Emission intensity of (a) SO2, (b) NOx, (c) PM2.5, and (d) CO in Tianjin (unit: t a−1).
Fig. 3. Emission intensity of (a) SO2, (b) NOx, (c) PM2.5, and (d) CO in Tianjin (unit: t a−1).

Evaluation of the model performance

The observational datasets used for model evaluation in this study are presented in Table 2. The temperature at 2 m (T2), water vapor mixing ratio at 2 m (Q2), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and daily cumulative precipitation data for December 2015 from the National Climate Data Center (NCDC) integrated surface database ( for 371 sites were used to evaluate the meteorological simulations.

PM (PM2.5 and PM10) and trace gases (SO2, NO2, and CO) were evaluated using the observational datasets presented in Table 2. The average hourly concentrations of PM2.5, PM10, SO2, NO2, and CO at 14 monitoring sites in Tianjin were obtained from the China National Environmental Monitoring Center (CNEMC) dataset (four sites in the central districts, four sites in the adjoining districts, five sites in suburban districts, and one site in Binhai District). The locations and descriptions of these sites are presented in Table 3 and Fig. 2

Table 4 presents the overall statistics of the meteorological simulations for Domain 1 at a 36-km resolution and for Domain 2 at a 12-km resolution according to comparisons with the NCDC dataset. The mean bias (MB), root mean square error (RMSE), normalized mean bias (NMB), and normalized mean error (NME) were defined in a study by Zhang et al. (2006). T2 agrees well with the observations in which MBs range from 0 to 0.2°C and NMBs range from 0.6% to –11.4% for both domains. Although T2 is marginally underestimated in Domain 2, T2 is within the criteria of MB at ≤ ±0.5 K (Tesche et al., 2001). Q2 is overesitimated for the MBs of 0.0004 kg kg–1 for Domain 1 and 0.0000 kg kg–1 for Domain 2. Both values are within the criteria of MB at ≤ ±1 g kg–1. Wind speed is overesitimated for MBs at 0.6 m s1 for Domain 1 and 0.1 m s–1 for Domain 2. The predictions for Domain 1 are beyond the criteria for MB at ≤ ±0.5 m s–1 (Emery et al., 2001). This result was obtained possibly because the finite-difference model is one of the most accepted medium-range weather forecasting models but it cannot suitably describe atmospheric motions smaller than the grid size (Wang et al., 2011). However, the predictions for Domain 2 meet the criteria for MB at ≤ ±0.5 m s–1. The model predictions for wind directions are overesitimated with MBs of 2.6° for Domain 1 at a 36-km resolution and underestimated with MBs of –0.9° for Domain 2 at a 12-km resolution. These results agree well with the observations, and the predictions at both resolutions are within the criteria of MB of ≤ ±10° (Emery et al., 2001). The daily cumulative precipitation was significantly underestimated for both domains, with MBs of –10.6 mm and –24.0 mm for Domain 1 at a 36-km resolution and Domain 2 at a 12-km resolution, respectively. 

The performance statistics based on comparisons with the CNEMC dataset, including the NMB, NME, mean fractional bias (MFB), and mean fractional error (MFE), for PM2.5, PM10, SO2, NO2, and CO simulations at 496 national monitoring stations in 74 major cities in China for Domain 1 at a 36-km resolution and Domain 2 at a 12-km resolution are presented in Table 5. The MFB value is within ±60% and the MFE value is within 75% in this study. These values are considered to represent a satisfactory performance according to the guidelines (Boylan, 2005). As presented in Table 5, the simulated PM2.5 concentrations were marginally higher than the observed concentrations, with MBs and NMBs of 26.7 µg m–3 and 33.7% for Domain 1 and 33.6 µg m–3 and 31.4% for Domain 2, respectively. However, the predictions for Domain 1 and Domain 2 meet the criteria of MFB at ≤ ±60% and MFE at ≤ ±75%. PM10 data were underestimated, with the MBs of –9.4 and –18.4 µg m–3 for Domain 1 at a 36-km resolution and Domain 2 at a 12-km resolution, respectively. Both values are within the criteria of MFB (–6.2% for Domain 1 and –12.4% for Domain 2) and MFE (64.2% for Domain 1 and 66.3% for Domain 2). As for trace gases, SO2 was significantly overesitimated for both domains, with MBs of 23.2 and 93.6 µg m–3 for Domains 1 and 2, respectively. Both values are within the criteria for MFB at ≤ ±60% (MFE is 13.7% for Domain 1 and 30.8% for Domain 2) and exceed the criteria for MFE at ≤ ±75% (MFE is 92.2% for Domain 1 and 101.8% for Domain 2). The results are similar to those of some previous studies (Wang et al., 2014b; Wang et al., 2016). The results imply that large uncertainties are caused in spatial allocations of the emissions into a grid with a low resolution. These uncertainties may result in overestimation of the urban SO2 emissions if the population density and GDP are used to spatially allocate the industrial and fossil fuel combustion emissions (Wang et al., 2014b). Moreover, uncertainties exist in the model’s description of the conversion process from SO2 to sulfate (Wang et al., 2016). NO2 was also overesitimated in both domains; however, this overprediction was not as significant as that of SO2, with NMBs of 32.2% for Domain 1 and 50.3% for Domain 2. The MFBs and MFEs of NO2 were within the criteria (MFB ≤ ±60% and MFE ≤ ±75%). The underprediction of CO (with an MB of –0.1 mg m–3 and NMB of –3.5%) for Domain 1 and overprediction (with an MB of 0.5 mg m–3 and NMB of 25.6%) for Domain 2 meet the criteria over the two domains. 

To evaluate the performance statistics of the PM2.5 concentrations between the simulations and observations, the observed average hourly PM2.5 concentrations at 14 monitoring sites in different districts in Tianjin were compared with the base simulations for December 2015. The overall simulated (Domain 2) and observed hourly concentrations of PM2.5 for the 14 sites are depicted in Fig. 4. The model appropriately reproduced the PM2.5 concentrations for the suburban districts and Binhai District, including Donghuan Road, Hanbei Road, Baobai Road, and Yongyang West Road sites. The PM2.5 concentrations for the Huaihe Road sites in Beichen District were significantly overesitimated, especially during the peak that occurred on December 8–12 and 20–26, 2015. The simulations for the central districts and most of the adjoining districts generally agreed with the observations of PM2.5 during most days of the month. However, a relative overestimation occurred during the heavy pollution episode. One reason for the overestimation may be that the mitigation measures adopted in the industry after 2013 were not considered. Another reason is that the spatial allocation of emissions included large uncertainties. In recent years, most industrial plants have been removed from urban Tianjin. Thus, space allocation based on the population density for some sectors results in inconsistencies between real and estimated emissions.

Fig. 4. Comparison between simulated and observed hourly PM2.5 concentration at the 14 sites in Tianjin.
Fig. 4. Comparison between simulated and observed hourly PM2.5 concentration at the 14 sites in Tianjin.Fig. 4. Comparison between simulated and observed hourly PM2.5 concentration at the 14 sites in Tianjin.

Evolution of the effect of meteorological conditions

Adverse meteorological conditions are crucial external factors that lead to severe pollution episodes. The same emissions as a base case as well as different meteorological conditions a week before the base case (BW) and a week after the base case (AW) were used to simulate the contribution of adverse meteorological conditions during severe pollution episodes.

Fig. 5 presents the temporal variation in the meteorological parameters, such as WD10, WS10, T2, Q2, the planetary boundary layer height (PBLH), surface pressure (PSFC), and PM2.5 concentrations for December 2015. The results in Fig. 5 indicate that the combined influence of a relatively high water vapor mixing ratio, low wind speed, low temperature, low PBLH, and low pressure caused a 33.5% increase in the PM2.5 concentration during the severe pollution episode of December 20–26, 2015. The wind speed of the base case decreased by 0.91 and 0.12 m s–1  compared with the BW and AW cases, respectively. The temperature for the base case exhibited a 0.91°C decrease and 0.75°C increase compared with the values for the BW and AW cases, respectively. For the base case, the water vapor mixing ratio increased by 0.0006 and 0.0005 kg kg–1 over the BW and AW cases, respectively. Then, the PBLH decreased by 249.5 and 39.1 m for the base case compared with the PBLH for the BW and AW cases, respectively. The surface pressure decreased the least among the meteorological parameters. The base case was 3.9 and 6.8 kPa lower than the BW and AW cases, respectively. 

Fig. 5. Temporal variation of meteorological parameters and PM2.5 concentrations.Fig. 5. Temporal variation of meteorological parameters and PM2.5 concentrations.

Fig. 6 displays the variation in the PM2.5 concentrations due to different meteorological conditions. The contributions from meteorology were estimated by calculating the differences between the base case and the other cases. The PM2.5 concentration was in the range of 58.2–483.3 µg m–3 during the BW case that occurred from December 13 to 19, which was approximately 17.9–78.3% of the baseline PM2.5 concentrations. The contribution of the baseline PM2.5 concentration was in the range of 8.2–73.2% for the AW case that occurred from December 27 to 31. In general, compared with the BW and AW cases, adverse meteorological conditions caused a 33.5% (the averages are 41.0% and 25.9% for the BW and AW cases, respectively) increase of the PM2.5 concentrations during December 20–26. Therefore, the considerable contribution by adverse meteorological conditions should not be neglected.

Overall, the unfavorable meteorological conditions caused a 101.3 µg m–3 increase in the PM2.5 concentration during the severe pollution episode. 

Fig. 6. The variation of daily PM2.5 concentrations due to different meteorological conditions (BW: meteorological conditions from December 13 to 19; AW: meteorological conditions from December 27 to 31).Fig. 6. The variation of daily PM2.5 concentrations due to different meteorological conditions (BW: meteorological conditions from December 13 to 19; AW: meteorological conditions from December 27 to 31).

Simulation scenario design and assessment of emission reduction

Simulation Scenario Design

The emergency plan for heavy pollution episodes in Tianjin includes three mitigation measure classes (TJMPG, 2016). These classes are 40%, 30%, and 20% reductions in the fossil fuel combustion, industrial processes, transport, solvent use, and dust during red, orange, and yellow alerts, respectively. Twelve experiments with reduction in each emission category (nine major sectors and three subsectors) for the red alert scenario were conducted for Domain 2 to evaluate the influence of the major emission reduction measures on air quality (Table 6). 

Source Assessment

The effect of each emission reduction scenario on the ambient PM2.5 concentrations during a typical severe pollution episode was simulated using the WRF-Chem model. The contributions (Eq. (1)) and change ratio (Eq. (2)) resulting from 40% emission reductions are illustrated in Figs. 7 and 8

where Cs,i is the contribution of source i to PM2.5, Cb is the baseline PM2.5 concentration, Ci is the prediction made in the emission reduction scenario of source i, and CRs,i represents the change ratio of source i to the total reduction of PM2.5.

An 18.9% decrease in the total PM2.5 concentration was observed due to the summation of the emission reduction in the nine major sectors during the pollution episode. Among the nine sectors, fossil fuel combustion contributed the most to PM2.5 and accounted for 8.5% of the total PM2.5 concentrations. Decreases of 3.4% and 5% were observed in the total PM2.5 concentrations due to reductions in the industrial boiler combustion and domestic combustion, respectively. Decreases of 5.3% and 4% were observed in the total PM2.5 concentrations due to the emission reduction pertaining to dust and industrial processes, respectively. The contribution of transport was 1% of the total simulated PM2.5 concentration. Moreover, the contributions of other sectors were all at low levels. In addition, it is worth noting that the contribution from meteorology was up to 41.0% for the total PM2.5 concentrations during the pollution episode, which indicates the importance of favorable meteorological conditions for improving the ambient air quality.

Fig. 7. Simulated PM2.5 concentrations for the base case and other cases.
Fig. 7. Simulated PM2.5 concentrations for the base case and other cases. 

Fig. 8. Change ratio of PM2.5 concentration owing to 40% emissions reductions.
Fig. 8. Change ratio of PM2.5 concentration owing to 40% emissions reductions.

To understand the contribution variations in sector emission reduction under different pollution levels, the baseline PM2.5 concentrations and change ratio of PM2.5 concentrations caused by the emission reductions of nine major sectors are presented in Fig. 9. The change ratios of fossil fuel combustion and dust were relatively high when the PM2.5 concentrations were above 500 µg m–3, and the average change ratios were 12.2% and 7.0%, respectively. The change ratio caused by industrial processes retains a relatively high value as the PM2.5 concentrations were in the range of 300–400 µg m–3, and the average change ratio was 7.8%. The change ratio pertaining to transport was 1.2% and increasing marginally as the PM2.5 concentrations increased above 400 µg m–3. The change ratio pertaining to agriculture remained stable, which may be because NH3 was underestimated in the emission inventory for all sectors except agriculture. The change ratio pertaining to industrial boiler and domestic combustions had peak values when the PM2.5 concentrations were high. Moreover, the contributions from domestic combustion were considerably higher than those from industrial boiler combustion when the PM2.5 concentrations were higher than 500 µg m–3. This result occurred due to the large increase in energy consumption caused by winter heating systems. The remaining sectors maintained relatively low contributions regardless of whether the PM2.5 concentrations were low or high. 

Fig. 9. Scatterplot of the baseline PM2.5 concentrations and change ratio of PM2.5 concentrations owing to the reductions in the nine major sector emissions in Tianjin.Fig. 9. Scatterplot of the baseline PM2.5 concentrations and change ratio of PM2.5 concentrations owing to the reductions in the nine major sector emissions in Tianjin.

Two stages were selected to further explore the underlying cause of the aforementioned phenomenon—the pollution accumulation stage (S1) and pollution dissipation stage (S2). The ventilation, a combined response of both the maximum mixing layer depth and transport wind speed (Huang et al., 2018), and the PM2.5 concentration during the episode are presented in Fig. 10. The average ventilation was 21.8 m2 s–1 during S1 and up to 1497.5 m2 s–1 during S2. The dust emission reduction effect did not vary with the pollution stage, which may be because most of the dust was primary coarse particulate matter. The effect of emission reduction for industrial sources (including industrial processes and industrial boiler combustion) and domestic combustion during the pollutant dissipation stage was superior to the effect during the pollutant accumulation stage. There may exist two reasons for this phenomenon. First, the diffusion condition becomes favorable with an increase in ventilation, thus directly causing a decrease in the PM2.5 and gaseous precursor (SO2, NOx, and NH3) concentrations. Second, secondary reactions occur with more difficulty during the dissipation stage than during the accumulation stage due to an improvement in the meteorological conditions (e.g., low relative humidity and high PBLH) during the dissipation stage. 

Fig. 10. Influence of meteorological conditions on percentage of PM2.5 concentration decrease caused by different source emissions reductions.Fig. 10. Influence of meteorological conditions on percentage of PM2.5 concentration decrease caused by different source emissions reductions.

Spatial Variation of the Emission Reduction Effect

Fig. 11 displays the relative contributions caused by different mitigation measures in the different districts of Tianjin. Beichen District exhibited the largest decrease in the PM2.5 concentrations, followed by Dongli District and central districts. Fossil fuel combustion contributed to most of the nine major sectors in most districts of Tianjin except for Ninghe District and Binhai District. The decrease in fossil fuel combustion accounted for 39.3–68.3% of the total decrease in the PM2.5 concentrations. The PM2.5 concentrations decreased the most in Jizhou District and Wuqing District due to decrease in fossil fuel combustion, which accounted for 68.3% and 66.6% of the total decrease in PM2.5 concentrations, respectively (compared with the results in Figs. 3(a) and 3(d)). The contribution of fossil fuel combustion reduction was followed by dust reduction, which accounted for 15.4–34.5% of the total decrease in PM2.5 concentrations. The largest contribution of dust was observed in Baodi District, whereas the least contribution was observed in Ninghe District. Decreases in industrial process emissions had the highest contribution in Ninghe District and Binhai District, accounting for 58.2% and 50.7%, respectively, of the total decrease in the PM2.5 concentrations, which play a dominant role in the two districts. This result is consistent with the high industrial emissions due to petrochemical, iron and steel, and chemical processes in Binhai District and due to the iron and steel industrial emissions in Ninghe District (compared with the results shown in Figs. 3(c) and 3(d)). Moreover, 33.8% of the total decrease in the PM2.5 concentrations in Jinnan District is also prominent. The remaining regions in Tianjin contributed to 5.6–18.7% of the total decrease in the PM2.5 concentrations. Moreover, the relatively obvious contribution of transport occurred in Beichen District and Dongli District, followed by the central districts. Transport contributed to 5.7–8.5% of the PM2.5 in the aforementioned three regions, which may be due to the high heavy-duty diesel vehicle emissions in the two adjoining districts and the emissions from a high number of private vehicles in the central districts (compared with the results presented in Fig. 3(b)). Agriculture plays a significant role in Jinghai District and Jinnan District and contributed to 6% and 3.7% of the total decrease in PM2.5 concentrations, respectively. In the other districts, agriculture contributed to between –1.1% and 1.6% of the decrease in PM2.5. The decrease in PM2.5 caused by the reduction of emissions from solvent use, biomass burning, storage and transport, and waste disposal were minimal in all districts of Tianjin. 

Fig. 11. Regional difference caused by different sectors in Tianjin.Fig. 11. Regional difference caused by different sectors in Tianjin.

The relative contributions of the three subsectors of fossil fuel combustion—industrial boiler combustion, domestic combustion, and power plant combustion—to the PM2.5 concentrations for different districts in Tianjin are illustrated in Fig. 11. Significant regional differences were observed at different sites. For Jizhou District, Baodi District, and Wuqing District, domestic combustion was the major contributor to the total fossil fuel combustion and accounted for 81.4–92.5% of the total fossil fuel combustion contribution. Domestic combustion accounted for 75.0% and 63.3% of the total fossil fuel combustion contribution in Jinghai District and Xiqing District, respectively. Industrial boiler combustion plays a significant role in Binhai District and Beichen District and accounted for 57.2% and 54.3% of the three subsectors, respectively. In the rest of Tianjin, the contributions from domestic combustion were similar to the contribution from industrial boiler combustion. However, the contributions from domestic combustion were marginally higher than those from industrial boiler combustion. The emission reduction of power plant combustion was not significant for all sites in Tianjin.

In summary, mitigation measures for dust should be adopted in all districts of Tianjin. Moreover, fossil fuel combustion should be preferentially reduced in most districts of Tianjin, especially in Jizhou District and Wuqing District. However, the emissions due to industrial processes should be controlled on a priority basis in Ninghe District and Binhai District, followed by Jinnan District. The reduction measures for transport emission were found to be the most influential in Beichen District, Dongli District, and the central districts and should be a priority in these districts.


In this study, two nested domains, with a horizontal resolution of 12 km for the inner domain, were set up with the WRF-Chem model to simulate a severe pollution episode that occurred in northeastern China during December 2015. The model’s performance was then evaluated using NCDC meteorological surface observations and the air quality monitoring database released by CNEMC. Twelve emission reduction scenarios involving nine major sectors and three subsectors were simulated for Domain 2 to evaluate the effects of emission reduction measures on air quality. Based on our results, we have provided feedback and optimized emergency emission reduction plans.

Overall, the meteorological simulations by the WRF-Chem model are acceptable; however, an overestimation of the wind speed and an underestimation of the precipitation occurred for December 2015. All of the predicted PM2.5 concentrations were also acceptable, agreeing well with the observations in the suburban districts and Binhai District, although they were relatively overesitimated in the central districts and most sites in the adjoining districts, except for Huaihe Road in Beichen District.

Adverse meteorological conditions increased the PM2.5 concentrations by approximately 34% during the heavy haze pollution episode. Thus, the considerable influence of adverse meteorological conditions should not be neglected.

An approximate decrease of 18.9% was observed in the total PM2.5 concentration when an emergency plan during the red alert condition was implemented during the pollution episode. The largest contributor to the PM2.5 was fossil fuel combustion, followed by dust emission and industrial processes. Due to the decrease in gaseous precursors and the adverse secondary reactions during the pollution dissipation stage, mainly, emission reductions for industrial sources (including industrial processes and industrial boiler combustion), domestic combustion, and agriculture produced greater effects during this stage than the accumulation stage. According to the difference assessments, dust emission and fossil fuel combustion play major roles in the PM2.5 concentrations in most districts of Tianjin, but industrial processes contribute a higher proportion in the adjoining districts and Binhai District. Moreover, controlling agricultural emission is crucial in the southwestern suburban districts of Tianjin.

Several suggestions are offered in this study. First, different mitigation measures should be adopted in different districts, e.g., dust emission and industrial processes should be controlled on a priority basis in the northeastern suburban districts, and the adjoining districts and Binhai District, respectively. Second, different mitigation measures should be adopted during different pollution stages, e.g., industrial sources should be controlled on a priority basis during the dissipation stage. The present study provides preliminary results, but additional experiments with different scenarios containing heavy haze episodes are required for further analysis.


This study is supported by the National Key Research and Development Program of China (2016YFC0208500 and 2016YFC0208502), Tianjin Science and Technology Foundation (16YFZCSF00260), Hebei Science Fund of Distinguished Young Scholars (No. D2017402086), and the Hebei Support Program of Hundred Outstanding Innovative Talents from Universities (SLRC2017025).


  1. An, J., Li, Y., Chen, Y., Li, J., Qu, Y. and Tang, Y. (2013). Enhancements of major aerosol components due to additional HONO sources in the North China Plain and implications for visibility and haze. Adv. Atmos. Sci. 30: 57–66. [Publisher Site]

  2. Boylan, J.W. (2005). PM model performance goal and criteria. Presented at the National Regional Planning Organizations Modeling Meeting, Denver, CO, USA, 9–10 June, 2005.

  3. Chen, F., Zhang, X., Yu, H., Zhang. H. and Gao. J. (2017). Characteristics, causes and potential source regions of PM2.5 Pollution in winter in Shijiazhuang, China. J. Ecol. Rural Environ. 33: 975–982.

  4. Chen, S.H. and Sun, W.Y. (2002). A one dimensional time dependent cloud model. J. Meteorol. Soc. Jpn. 80: 99–118. [Publisher Site]

  5. Chou, M.D., Suarez, M.J., Ho, C.H., Yan, M.M. and Lee, K.T. (1998). Parameterizations for cloud overlapping and shortwave single-scattering properties for use in general circulation and cloud ensemble models. J. Clim. 11: 202–214. [Publisher Site]

  6. CSP (China Statistical Press) (2016). China Statistical Yearbook 2016. Beijing, China.

  7. CSP (China Statistical Press) (2018). Tianjin Statistical Yearbook 2018. Beijing, China.

  8. CSPT (China Statistical Press) (2016). Tianjin Statistical Yearbook 2016. Beijing, China.

  9. EEPRC (Ministry of Ecology and Environment of the People’s Republic of China) (2018). Ecology and environment status bulletin of China in 2017. Beijing, pp. 9–14.

  10. Ek, M.B., Mitchell, K.B., Lin, Y., Rogers, B., Grunmann, P., Koren, V., Gayno, G. and Tarpley, J.D. (2003). Implementation of NOAH land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res. 108: 8851. [Publisher Site]

  11. Emery, C., Tai, E. and Yarwood, G. (2001). Enhanced meteorological modeling and performance evaluation for two Texas ozone episodes (final report). Prepared for Texas Natural Resource Conservation Commission, Austin, TX, by Environ International Corp., Novato, CA.

  12. Fast, J.D., Gustafson Jr, W.I., Easter, R.C., Zaveri, R.A., Barnard, J.C., Chapman, E.G., Grell, G.A. and Peckham, S.E. (2006). Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model. J. Geophys. Res. 111: D21305. [Publisher Site]

  13. Gong, S.L. (2003). A parameterization of seasalt aerosol source function for sub and supermicron particles. Global Biogeochem. Cycles 17: 1097. [Publisher Site]

  14. Grell, G.A. and Devenyi, D. (2002). A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett. 29: 1693. [Publisher Site]

  15. Grell, G.A., Peckham, S.E., Schmitz, R., McKeen, S.A., Frost, G., Skamarock, W.C. and Eder, B. (2005). Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 39: 6957–6975. [Publisher Site]

  16. Gu, J., Bai, Z., Li, W., Wu, L., Liu, A., Dong, H. and Xie, Y. (2011). Chemical composition of PM2.5 during winter in Tianjin, China. Particuology 9: 215–221. [Publisher Site]

  17. Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I. and Geron, C. (2006). Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys. 6: 3181–3210. [Publisher Site]

  18. Han, B., Zhang, R., Yang, W., Bai, Z., Ma, Z. and Zhang, W. (2015). Heavy air pollution episodes in Beijing during January 2013: Inorganic ion chemistry and source analysis using Highly Time-Resolved Measurements in an urban site. Atmos. Chem. Phys. 15: 11111–11141. [Publisher Site]
  19. Han, S.Q., Feng, Y.C., Bian, H., Tie, X.X., Xie, Y.Y., Li, X.J. and Sun, M.L. (2008). Numerical simulation of diurnal variation of major pollutants with WRF-Chem model in Tianjin. China Environ. Sci. 28: 828–832. (in Chinese)

  20. Han, X., Zhang, M., Zhu, L. and Skorokhod, A. (2016). Assessment of the impact of emissions reductions on air quality over North China Plain. Atmos. Pollut. Res. 7: 249–259. [Publisher Site]

  21. Han, X. and Zhang, M.G. (2018). Assessment of the regional source contributions to PM2.5 mass concentration in Beijing. Atmos. Oceanic Sci. Lett. 11: 143–149. [Publisher Site]

  22. Hong, S.Y., Noh, Y. and Dudhia, J. (2006). A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev. 134: 2318–2341. [Publisher Site]

  23. Huang, Q., Cai, X., Wang, J., Song, Y. and Zhu, T. (2018). Climatological study of the boundary-layer air Stagnation Index for China and its relationship with air pollution. Atmos. Chem. Phys. 18: 7573–7593. [Publisher Site]

  24. Huang, X., Liu, Z., Liu, J., Hu, B., Wen, T., Tang, G., Zhang, J., Wu, F., Ji, D., Wang, L. and Wang, Y. (2017). Chemical characterization and source identification of PM2.5 at multiple sites in the Beijing–Tianjin–Hebei region, China. Atmos. Chem. Phys. 17: 12941–12962. [Publisher Site]

  25. Ikeda, R. and Kusaka, H. (2010). Proposing the simplification of the multilayer urban canopy model: Intercomparison study of four models. J. Appl. Meteorol. Clim. 49: 902–919. [Publisher Site]

  26. Janjić, Z.I. (2001). Nonsingular Implementation of the Mellor-Yamada Level 2.5 Scheme in the NCEP Meso Model. NCEP Off. Note 437, 61. Camp Springs: National Center for Environmental Prediction.

  27. Li, L., An, J. and Lu, Q. (2015). Modeling assessment of PM2.5 concentrati on under implementation of Clean Air Action Plan in Yangtze River Delta region. Res. Environ. Sci. 28: 1653–1661. (in Chinese)

  28. Lim, S.S., Vos, T., Flaxman, A.D., Danaei, G., Shibuya, K., Adair-Rohani, H., AlMazroa, M.A., Amann, M., et al. (2012). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 380: 2224–2260. [Publisher Site]

  29. Liu, B., Cheng, Y., Zhou, M., Liang, D., Dai, Q., Wang, L., Jin, W., Zhang, L., Ren, Y., Zhou, J., Dai, C., Xu, J., Wang, J., Feng, Y. and Zhang, Y. (2018). Effectiveness evaluation of temporary emission control action in 2016 in winter in Shijiazhuang, China. Atmos. Chem. Phys. 18: 7019–7039. [Publisher Site]

  30. Liu, H., Wang, X., Zhang, J., He, K., Wu, Y. and Xu, J. (2013). Emission controls and changes in air quality in Guangzhou during the Asian Games. Atmos. Environ. 76: 81–93. [Publisher Site]

  31. Madronich, S. and Flocke, S. (1993). The role of solar radiation in atmospheric chemistry. In Handbook of environmental chemistry, Boule, P. (Ed.), Springer_Verlag, Heidelberg, 126.

  32. MEP (Ministry of Environmental Protection) (2012). Technical regulation on ambient air quality index. Beijing, China.

  33. Misenis, C. and Zhang, Y. (2010). An examination of sensitivity of WRF/Chem predictions to physical parameterizations, horizontal grid spacing, and nesting options. Atmos. Res. 97: 315–334. [Publisher Site]

  34. Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J. and Clough, S.A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated Parameterizations for cloud overlapping and shortwave single scattering properties for use in general circulation and cloud ensemble models correlated-k model for the longwave. J. Geophys. Res. 102: 16663–16682. [Publisher Site]

  35. Peng, X., Liu, X., Shi, X., Shi, G., Li, M., Liu, J., Huangfu, Y., Xu, H., Ma, R., Wang, W. and Feng, Y. (2019). Source apportionment using receptor model based on aerosol mass spectra and 1 h resolution chemical dataset in Tianjin, China. Atmos. Environ. 198: 387–397. [Publisher Site]

  36. Shaw, W.J., Allwine, K.J., Fritz, B.G., Rutz, F.C., Rishel, J.P. and Chapman, E.G. (2008). An evaluation of the wind erosion module in DUSTRAN. Atmos. Environ. 42: 1907–1921. [Publisher Site]

  37. Tesche, T.W., McNally, D.E., Emery, C.A. and Tai, E. (2001). Evaluation of the MM5 Model over the Midwestern U.S. for Three 8-hour Oxidant Episodes. Alpine Geophysics, LLC, Ft. Wright, KY, and ENVIRON International Corp., Novato, CA, USA.

  38. TJMPG (General Office of Tianjin Municipal People's Government) (2016). Tianjin Emergency Plan for Heavy Pollution 2016. Tianjin, China.

  39. TJSP (Tianjin Statistical Press) (2016). Tianjin National Economic and Social Development Statistical Bulletin 2016. Tianjin, China.

  40. Wang, H., Li, Y., Gao, J., Wang, S.L., Chai, F.H. and Wei, J.L. (2014a). Characteristics of air pollution and evaluation of the effects of air quality assurance measures in Shijiazhuang City during the 2014 APEC Conference. Res. Environ. Sci. 26: 13–22. (in Chinese)

  41. Wang, H.C., Long, X. and Yang, Y. (2011). Atmospheric numerical models and simulations, China Meteorological Press, Beijing.

  42. Wang, L., Yang, J., Zhang, P., Zhao, X., Wei, Z., Zhang, F., Su, J. and Meng, C. (2013). A review of air pollution and control in Hebei Province, China. Open J. Air Pollut. 2: 47–55. [Publisher Site]

  43. Wang, L., Zhang, Y., Wang, K., Zheng, B., Zhang, Q. and Wei, W. (2016). Application of Weather Research and Forecasting Model with Chemistry (WRF/Chem) over northern China: Sensitivity study, comparative evaluation, and policy implications. Atmos. Environ. 124: 337–350. [Publisher Site]

  44. Wang, L.T., Wei, Z., Yang, J., Zhang, Y., Zhang, F.F., Su, J., Meng, C.C. and Zhang, Q. (2014b). The 2013 severe haze over the southern Hebei, China: Model evaluation, source apportionment, and policy implications. Atmos. Chem. Phys. 13: 3151–3173. [Publisher Site]

  45. Wang, Q. (2014). Numerical forecast analysis of typical PM2.5 pollution episode over Shanghai in autumn. Environ. Monit. China 30: 7–13. (in Chinese)

  46. Wang, Y., Yao, L., Wang, L., Liu, Z., Ji, D., Tang, G., Zhang, J., Sun, Y., Hu, B. and Xin, J. (2014c). Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Sci. China Earth Sci. 57: 14–25. [Publisher Site]

  47. Wang, Y., Bao, S., Wang, S., Hu, Y., Shi, X., Wang, J., Zhao, B., Jiang, J., Zheng, M., Wu, M., Russell, A.G., Wang, Y. and Hao, J. (2017). Local and regional contributions to fine particulate matter in Beijing during heavy haze episodes. Sci. Total Environ. 580: 283–296. [Publisher Site]

  48. Wu, L., Su, H. and Jiang, J. H. (2013). Regional simulation of aerosol impacts on precipitation during the East Asian summer monsoon. J. Geophys. Res. 118: 6454–6467. [Publisher Site]

  49. Yan, R.S. (2013). Simulation and analysis of wintertime typical particulate matter pollution episode in Beijing. Master thesis. Nanjing University of Information Science and Technology, Nanjing, China.

  50. Zaveri, R.A. and Peters, L.K. (1999). A new lumped structure photochemical mechanism for large‐scale applications. J. Geophys. Res. 104: 30387–30415. [Publisher Site]

  51. Zaveri, R.A., Easter, R.C., Fast, J.D. and Peters, L.K. (2008). Model for simulating aerosol interactions and chemistry (MOSAIC). J. Geophys. Res. 113: D13204. [Publisher Site]

  52. Zhang, Y., Liu, P., Pun, B. and Seigneur, C. (2006). A comprehensive performance evaluation of MM5-CMAQ for the Summer 1999 Southern Oxidants Study episode—Part I: Evaluation protocols, databases, and meteorological predictions. Atmos. Environ. 40: 4825–4838. [Publisher Site]

  53. Zhang, Y., Sartelet, K., Zhu, S., Wang, W., Wu, S.Y., Zhang, X., Wang, K., Tran, P., Seigneur, C. and Wang, Z.F. (2013). Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe-Part 2: Evaluation of chemical concentrations and sensitivity simulations. Atmos. Chem. Phys. 13: 6845–6875. [Publisher Site]

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