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Design and application of a hybrid assessment of air quality models for the source apportionment of PM2.5
Atmospheric Environment 212 (2019) 116–127
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Design and application of a hybrid assessment of air quality models for the source apportionment of PM2.5
T
Hsin-Chih Laia, Hwong-Wen Mab,∗, Chih-Rung Chenb, Min-Chuan Hsiaoc, Bo-Han Pand a
Department of Occupational Safety and Health, Chang Jung Christian University, Tainan, Taiwan Graduate Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan c Institute of Environmental Engineering and Management, National Taipei University of Technology, Taipei, Taiwan d Environmental Research and Information Center, Chang Jung Christian University, Tainan, Taiwan b
GRAPHICAL ABSTRACT
ARTICLE INFO
ABSTRACT
Keywords: Hybrid air quality model Source apportionment Primary PM2.5 Secondary PM2.5 CMAQ AERMOD
Increasing levels of air pollution greatly affect the environment and human health; air quality control is therefore of particular importance. To improve the efficiency of air pollution control, reliable air quality models for source apportionment are critically in need. The multitude of air quality models to find the sources of air pollution, however, have their own limitations. Hence, this study seeks to integrate different air quality models, including a diffusion model (AERMOD) and a grid model (CMAQ), employing initial meteorological fields provided by the Weather Research and Forecasting (WRF) model. Using the advantages of these models, this study builds a hybrid air quality model, which provides a more effective analysis of the distribution of primary pollutants, secondary pollutants, and other environmental information. Two significant fine particulate matter (PM2.5) events were selected in this study to discuss the influence of the Taichung coal-fired power plant and the Taichung traffic source on the PM2.5 in the Taichung area, as well as to evaluate the performance of the hybrid model. Simulation results for the two cases show that if the coal-fired power loads are reduced by 20% (around 1100 MW), the concentration of PM2.5 in the Taichung area will decrease by 0.5%; such decrease will reach 1.25% when the power load reduction is 40%. If the traffic source is reduced by 20%, the concentration of PM2.5 in the Taichung area will decrease by 4.3%, and by 6.6% with 30% traffic source reduction. The hybrid model shows that the contribution of different pollution sources can be illuminated to support air quality control strategies.
∗
Corresponding author. Graduate Institute of Environmental Engineering, National Taiwan, University, 71 Chou-Shan Rd, Taipei, 106, Taiwan, ROC. E-mail address: [email protected] (H.-W. Ma).
https://doi.org/10.1016/j.atmosenv.2019.05.038 Received 31 January 2019; Received in revised form 29 April 2019; Accepted 18 May 2019 Available online 20 May 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
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1. Introduction
than the monitoring data; that is, there is an underestimation at small scales by CMAQ model simulations. To overcome the resolution restrictions of the model, Stein et al. (2007) combined the CMAQ model and the AMS/EPA Regulatory Model (AERMOD) to calculate high-resolution benzene concentrations at the local scale. The meteorological fields were created using MM5. Nevertheless, this integrated model has not yet been applied to PM2.5. Air quality monitoring data provides information on the PM2.5 pollution situation at neighboring monitoring stations but does not provide further information to identify the secondary formation. In order to implement effective management of PM2.5 pollution, it is urgent to establish a more accurate method to find the impact of primary and secondary aerosols of PM2.5. The objective of this study is to develop a hybrid model that integrates the Weather Research and Forecasting (WRF), the CMAQ model, and the AERMOD model to identify the relationship between atmospheric transport patterns and the primary and secondary PM2.5 concentrations at the urban scale. The WRF model is a numerical weather prediction system designed to serve both atmospheric research and operational forecasting needs. This hybrid model can improve the analytical capabilities of air quality simulations, as well as overcome the difficulty in accounting for the impact of PM2.5, hence facilitating the decision-makers’ ability to interpret air quality conditions. The study applies this air quality hybrid model to a metropolitan area with a mixture of multiple emission sources for the further identification of the contributors to ambient PM2.5. In this study, the Taichung metropolitan area is chosen. Located in central Taiwan, the area is characterized by a complex geographical environment and intricate air pollution in Taiwan. The air pollution derives from the local pollution and also from the northern and southern districts of Taiwan. Particularly, the Taichung metropolitan area is occupied by the world's second largest thermal power plant as well as 2.76 million vehicles; therefore, it is essential to recognize the contributors to the primary and secondary components of PM2.5. This paper is structured as follows. Section 2 describes the conceptual framework of the air quality hybrid model, the connection between the three models, and the emission reduction scenarios. Section 3 presents the simulation results and discusses the implication of the hybrid model in the support of air quality management. Section 4 summarizes the integration of the specific models and provides directions for future research.
Fine particulate matter (PM2.5) is a severe problem for air pollution worldwide; it causes serious impacts on the environment and human health. PM2.5 mainly enters the respiratory organs and accumulates in the body through the blood circulation system and can cause health hazards. PM2.5 can be suspended in air for weeks and can be transported over long distances. It is also small enough to enter the respiratory system through the nose or mouth (Hu et al., 2010; Tsai and Kuo, 2005; Tsai et al., 2015). In addition, studies have also shown that toxic substances in PM2.5 can damage organs and have adverse effects if they are inhaled by humans (Louie et al., 2005; Pope III et al., 2006; Wu et al., 2009). Even if the concentration of PM2.5 is not high, prolonged exposure can induce acute or chronic respiratory diseases (Leiva et al., 2013). This major component of outdoor air pollution was classified as Group 1 carcinogenic to humans (IARC, 2013). In order to solve the PM2.5 pollution problem effectively, the source apportionment technique, to guide air pollution control strategies, is imperative; consequently, the technique becomes one of the core tasks in terms of air quality management. Source apportionment can be performed using emissions inventories, the source-oriented (or dispersion) model, and the receptor model. The apportionment results can quantify the contribution of different sources, and the impact on different regions can be assessed with image data (Aggarwal and Jain, 2015; Chen et al., 2014; Hsu et al., 2016; Taiwo et al., 2014). The emission inventory approach aims to establish pollution source databases through statistics and surveys; nevertheless, this method only considers the relative importance of various types of pollution emissions, which are identified as an auxiliary to the source apportionment method (Zhang et al., 2012, 2013). On the other hand, the receptor model facilitates the determination of the average contribution of specific source's profile abundance, such as the CMB (chemical mass balance) and PMF (positive matrix factorization). Zhou et al. (2017) used the CMB model to estimate the main sources of PM2.5 in four key emission regions in China. Their research clarified that the major primary sources of organic carbon in PM2.5 included vehicle emissions, biomass burning, coal combustion, meat cooking, and natural gas combustion. Mansha et al. (2012) used the PMF model to identify the major emission sources of the ambient PM2.5 in Karachi, indicating five major contributors to PM2.5 in Karachi which were: soil/road dust, industrial emissions, vehicular emissions, sea salt originated from Arabian Sea and secondary aerosols. The existing receptor model illuminates the major contributors to the total PM2.5. However, the assessment of the total PM2.5 may lead to inefficient air quality management due to lack of response to the primary and secondary aerosol formation (Zhang et al., 2015). This bias may result in more emphasis on the regulation of stationary and mobile sources of pollution from nearby pollution receptors, while neglecting the management of long-range transboundary air pollution; this will consequently lead to ineffective outcomes and a great drain on air pollution control budgets. In order to evaluate the impact of primary and secondary components of PM2.5, some literature has applied the Community Multi-Scale Air Quality (CMAQ) dispersion model to analyze the diffusion, transmission, and sedimentation of pollutants in the atmosphere. Numerical methods were used to identify pollution sources using emission inventories and meteorological field studies (Baek et al., 2005; Byun and Schere, 2006; Jeon et al., 2014; Zhang et al., 2014). Wang et al. (2015) adopted the Mesoscale Meteorological ModelCommunity Multi-Scale Air Quality (MM5-CMAQ) model to analyze the source of regional PM2.5; sources included industrial, household, and agricultural pollution in cities. Cheng et al. (2013) used the MM5CMAQ model to express the contribution of mobile sources of PM2.5. Currently, the grid model CMAQ suffers from resolution limitations in its application. For finer scale simulations, insufficient computing capacity exposes the defects of the grid model. Accordingly, the concentration of primary PM2.5 assessed by the CMAQ model is often lower
2. Methods 2.1. Development of the hybrid model The developed hybrid model consists of the diffusion model AERMOD and the grid model CMAQ. It is desirable to understand how each grid point in the simulated range is affected by a single source of pollution, and to clarify the primary and secondary aerosol concentrations. Possible sources of pollution can be identified by the transmission path. The framework for the hybrid model is shown in Fig. 1. Observational data is used to adjust the simulation results. The observation values (Observation PM2.5) and the simulation results of the CMAQ model can be linked by an adjustment ratio (n) as in formula (1). Observation
PM2.5
= n x (CMAQPM2.5
primary
+ CMAQ
PM2.5 Secondary)
(1)
PM2.5 concentration of CMAQ models includes primary (CMAQPM2.5 and secondary (CMAQ PM2.5 Secondary) pollution. The concentration adjustment factor (n) can be solved with the formula. Because CMAQ modeling includes secondary PM2.5, consisting of SO42−(PM2.5SO4), NO3−(PM2.5NO3), NH3 (PM2.5Ammonia), organic carbon (PM2.5OC), and elemental carbon (PM2.5EC), the secondary PM2.5 is calculated as formula (2). The primary PM2.5 of CMAQ is obtained by primary)
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Fig. 1. Hybrid model framework.
Fig. 2. Overview of the sampling sites in the Taichung area. Table 1 Verification of WRF simulation at the Taichung monitoring station.
Table 2 Performance comparison of CMAQ and the hybrid model.
Duration
Temperature
Wind Speed
Wind Direction
MFB
CMAQ
Hybrid Model
MFE
CMAQ
Hybrid Model
2013 January April July October Average Recommended Values
MBE 0.01 1.2 0.5 0.3 0.5 ≦ ± 1.5
MBE 0.6 0.8 0.9 0.7 0.8 ≦ ± 1.5
WNMB 1% −0.3% 0.3% 2% 0.8% ≦ ± 10%
Fengyuan Shalu Xitun Dali Average
−15% −40% −24% −19% −25%
6% −20% −4% 3% −4%
Fengyuan Shalu Xitun Dali Average
15% 40% 24% 21% 25%
10% 20% 7% 10% 12%
MAGE 0.6 1.5 0.8 0.6 0.9 ≦3
RMSE 1.0 1.2 1.5 1.3 1.3 ≦3
WNME 11% 16% 16% 11% 0.1 ≦30%
MAGE: Mean Absolute Gross Error; MBE: Mean Bias Error; WNMB: Wind Normalized Mean Bias; WNME: Wind Normalized Mean Error.
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Fig. 3. Comparison of the WRF simulation with the Taichung monitoring station during the four case days.
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does; it only simulates the concentrations of the primary PM2.5. In the hybrid model, this study assumes the primary PM2.5 simulations from CMAQ and AERMOD models are linearly related. Hence, the formula for the primary PM2.5 linear adjustment is as follows:
Table 3 Verification of WRF simulation at the Taichung monitoring station during case days. Duration
Temperature
Wind Speed
Wind Direction
2013 Case A Case B Recommended Values
MBE 0.5 0.1 ≦ ± 1.5
MBE 1.2 1.9 ≦ ± 1.5
WNMB −1.0% 1.9% ≦ ± 10%
MAGE 1.7 1 ≦3
RMSE 1.7 2.3 ≦3
AERMODPrimary = k x CMAQPrimary
WNME 14.4% 9.0% ≦30%
k: The adjustment ratio between the CMAQ and AERMOD. The aim of hybrid model is the concentration of Models approach the observation data and the components of the primary and secondary PM2.5. The spatial resolution of AERMOD is better than CMAQ. Hence, the formula to calculate the concentration of fine suspended particles is as follows:
MAGE: Mean Absolute Gross Error; MBE: Mean Bias Error; WNMB: Wind Normalized Mean Bias; WNME: Wind Normalized Mean Error. Table 4 Performance comparison of CMAQ and the hybrid model during case days. MFB
CMAQ
Hybrid Model
MFE
CMAQ
Hybrid Model
CASE A CASE B
−9% 1%
5% 0.4%
CASE A CASE B
18% 30%
8% 8%
Observation CMAQ PM2.5
CMAQ
PrimaryPM2.5 =
CMAQ
+
SUM PM2.5
PM2.5NO3
- CMAQ
+
PM2.5Ammonia (2)
Secondary PM2.5
PM2.5
≈Hybrid PM2.5 = n x k x CMAQ
Secondary
PM2.5 Primary
+nx (5)
In the CMAQ model, the components of the primary and secondary PM2.5 are classified according to their compositions. The air quality models used in this study adopt the same emission and meteorological data; the WRF model provides the weather field, and emission data is based on TEDS9.0 (Taiwan emission data system). In this hybrid model, the estimation of the PM2.5 concentration is achieved through the AERMOD and CMAQ models combined with the monitoring data. Since the AERMOD model does not take photochemical reaction mechanisms into account, its simulation results can be treated as the primary pollutant. The CMAQ simulation results can be divided into primary and secondary pollutants, so the simulation results of AERMOD can be compared with the simulation results of the primary pollutants from the CMAQ results to obtain the adjustment ratio for primary pollutants.
subtracting the secondary PM2.5 (CMAQ Secondary PM2.5) from the sum of PM2.5 (CMAQ SUM PM2.5) as in formula (3). CMAQ SecondaryPM2.5 = PM2.5SO4 + PM2.5OC + PM2.5EC
(4)
(3)
In terms of diffusion model, the AERMOD does not consider the mechanism of photochemical reactions as the CMAQ Secondary PM2.5
Fig. 4. Simulations of the Taichung area emission with different resolutions under case A.
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Fig. 5. Comparison of the simulation results for the coal-fired power plant.
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Fig. 6. Comparison of simulation results for the traffic source.
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2.2. Site description and case selection
Table 5 Contribution of the coal-fired power plant to PM2.5 in the Taichung area (μg/ m3). Duration
Fengyuan
Shalu
Xitun
Zhongming
Dali
AVG
Primary Case A Case B
0.15 0.08
0.27 0.21
0.21 0.19
0.15 0.18
0.09 0.24
0.17 0.18
Secondary Case A Case B
3.93 1.82
3.51 3.34
3.90 3.48
3.96 3.50
3.97 4.15
3.85 3.26
This study selected Taichung city as the simulated area. Central Taiwan has one of the most complicated air pollution situations. Major air pollution sources include coal-fired power plants, heavy industries, and traffic emissions. The entire metropolitan area covers 1800 km2 and has a population of approximately 4 million. About 2.76 million vehicles, including automobiles and motorcycles, are registered. Furthermore, the Taichung power plant is the world's second largest coal-fired power plant, and it is the largest power plant in Taiwan. It is necessary to explore the contributions from the multiple sources. The Taichung coal-fired power plant has 10 units, each with a generating capacity of about 550 MW. The scenarios of emission reduction were used to evaluate the impact on the Taichung metropolitan area. Two high pollution cases of PM2.5 were used for the simulations. The design of the Taichung power plant load reduction considers three scenarios: 180 MW, −1100 MW, and −2200 MW. The emission reduction scenarios of the traffic source are −10%, −20%, and −30%, respectively. Emission data used in this study are based on TEDS9.0 (Taiwan Emission Data System with the base year of 2013), a comprehensive emission database of various pollution sources in Taiwan. TEDS covers stationary sources, mobile sources, and natural sources. Each source has its own column name, code, and data type. We selected emission data
Table 6 Contribution of the traffic source to PM2.5 in Taichung area (μg/m3). Duration
Fengyuan
Shalu
Xitun
Zhongming
Dali
AVG
Primary Case A Case B
4.89 4.56
5.61 5.56
5.42 6.13
7.07 8.20
5.93 7.25
5.78 6.34
Secondary Case A Case B
8.82 7.10
7.22 6.13
7.93 6.66
8.33 7.26
8.29 8.69
8.12 7.17
Fig. 7. Comparison of the simulation results for the power plant with load reductions for Case A.
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Fig. 8. Comparison of simulation results for the power plant with load reductions for Case B.
related to Taichung city for the model simulation and the design of various emission reduction scenarios. According to the sum of emissions, the PM2.5 emission rate in the Taichung coal-fired power plant is about 39.5 g/s, equivalent to 3.4 tons per day. The traffic source emission rate is about 72.9 g/s, equivalent to 6.3 tons per day. The traffic source emissions in Taichung are about 1.846 times those of the coal-fired power plant. This study used the year 2013 that is consistent with TEDS9.0 to carry out the model simulation. Case days with an average of more than 54 μg/m3 of PM2.5 were selected, as daily averages of PM2.5 exceeding 54 g/m3 had a higher health risk to most people (Taiwan-EPA, 2018). Case A is March 6th to 8th, 2013 and Case B is November 20th to 22nd, 2013. The sampling sites shown in Fig. 2 (Fengyuan, Shalu, Xitun, Zhongming, and Dali) were used as the benchmark for the comparison between the monitoring data and the model simulation.
Wind Normalized Mean Error (WNME) should be less than 30%. The performance of the WRF model is shown in Table 1. The air quality monitoring stations of the Environmental Protection Administration were used to validate the PM2.5 estimations. According to the specifications for air quality model simulation developed by the Environmental Protection Administration of Taiwan, Mean Fractional Bias (MFB) between observed and simulated values of PM2.5 should be ± 35% and Mean Fractional Error (MFE) should be less than 55%. The air quality in January, April, July and October 2013 was simulated (first month of each quarter), and the simulation performance of CMAQ for the central Taiwan has satisfied the specifications, while the hybrid model simulation shows a better performance with MFB and MFE better than CMAQ model. The comparison between the simulations of CMAQ and the hybrid model is shown in Table 2. For the case study, the WRF model simulations and the observed values were compared for the two case days in 2013. Comparisons of the time sequences are shown in Fig. 3. The simulation results at the sea level pressure and surface temperatures were close to the observed value. The humidity was slightly underestimated and the wind speed was overestimated. Table 3 also shows a comparison of the two case days in terms of the temperature, wind speed, and wind direction. The comparison shows that simulation results conform to the weather model simulations specified by the Taiwan EPA. The normalized mean bias (NMB) of the humidity was close to the recommended level (Emery et al., 2001). Therefore, the WRF model presents acceptable results during these case days. The air quality performance of hybrid model is
2.3. The performance of the hybrid model In this study, the meteorological station of Taiwan Central Weather Bureau was used to validate the temperature, wind speed and wind direction estimations. According to the specifications for weather model simulation developed by the Environmental Protection Administration of Taiwan, Mean Absolute Gross Error (MAGE) between observed and simulated values of temperature and wind speed should be less than 3 and Mean Bias Error (MBE) should be within ± 1.5. For wind direction, Wind Normalized Mean Bias (WNMB) should be less than 10% and 124
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Fig. 9. Comparison of simulation results for the reduction of the traffic source for Case A.
better than the CMAQ model, with the MFB 5% and the MFE 8% under case A. The MFB was 0.4% and the MFE was 8% under case B. The performance comparison of CMAQ and hybrid model is shown in Table 4.
concentration, transmission path, and primary and secondary pollutants. Fig. 5 shows the results of the AERMOD and CMAQ simulations of the Taichung coal-fired power plant emissions under the two cases; the weather patterns of both cases are weak wind environment. Simulations of the primary PM2.5 concentration show that the AERMOD results were higher than those for the CMAQ modeling in both cases. For the simulation of the Taichung coal-fired power plant, the results of the two models differed in quantity but had the similar trend of spatial distribution. Fig. 5c and f shows the secondary formation PM2.5 formed in the areas away from emission source. By following the methodology of developing the hybrid model specified in section 2.1, Fig. 5d and h show the modeling results of the hybrid model, which indicate the significant contribution of secondary PM2.5 to the air quality in the Taichung area from the power plant. In contrast, Fig. 6 shows the simulations of the traffic source in Taichung in the same periods. It is shown that the difference between AERMOD and CMAQ models was not as large as that for the coal-fired power plant. The contributions to the primary and secondary concentrations are relatively similar. Table 5 and Table 6 show the contributions of coal-fired power plant and traffic source to Taichung during the case periods. The average contributions of Taichung coal-fired power plant to the primary and secondary PM2.5 in the study area were 0.18 and 3.56 μg/m3, respectively, while the primary and secondary contributions for traffic source were 6.06 and 7.65 μg/m3, respectively. The primary to secondary ratio for the coal-fired power plant is 1:20; therefore strictly regulating the primary emission of the coal-fired power plant may not
3. Results and discussion 3.1. Effects of resolution Two different resolution simulations were carried out with AERMOD using a finer resolution of 200 m and 3 km as used for CMAQ, respectively. The differences between the two resolutions are discussed. The simulations were compared for the Taichung power plant and the traffic source under Case A, as shown in Fig. 4.a and Fig. 4.c. The simulations show that the concentration for a 200-m resolution was higher than that for the 3-km resolution; but the two resolutions differ little from each other in the simulation of stationary or mobile sources. Considering the time required to calculate the finer resolution model, we determined that such a small resolution is not beneficial, and thus the subsequent model simulations were performed with 3 km resolution that matches CMAQ. 3.2. Feasibility assessment of the hybrid model The primary PM2.5 concentration in the hybrid model is jointly determined by AERMOD and CMAQ. The hybrid model is designed to simultaneously display information including the total PM2.5 125
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Fig. 10. Comparison of simulation results for the traffic source reduction for Case B.
61.7 μg/m3. When the coal-fired power generation is reduced by 180 MW, the average concentration of PM2.5 will be reduced by 0.1 μg/ m3 (about 0.2% of the average concentration). When the power generation is reduced by 1100 MW, the average concentration of PM2.5 will be reduced by 0.3 μg/m3 (about 0.5% of the average concentration). When the reduction is 2200 MW, the average concentration of PM2.5 will be reduced by 0.8 μg/m3 (about 1.3% of the average concentration). Fig. 9 and Fig. 10 show the simulated emission reduction of the traffic source for Case A and Case B. It can be seen that, similar to the stationary source, the concentration decreases gradually with the emission reduction. When the traffic source emission is reduced by 10% for Case A, the average concentration of PM2.5 will be reduced by 1.4 μg/m3 (reduction of 2.2%). When the traffic source emission is reduced by 20%, the average concentration of PM2.5 will be reduced by 2.7 μg/m3 (reduction of 4.2%). When the reduction is 30%, the average concentration of PM2.5 will be reduced by 4.2 μg/m3 (reduction of 6.5%). When the traffic source emission is reduced by 10% for Case B, the average concentration of PM2.5 will be reduced by 1.4 μg/m3 (reduction of 2.3%). When the traffic source emission is reduced by 20%, the average concentration of PM2.5 will be reduced by 2.7 μg/m3 (reduction of 4.4%). When the reduction is 30%, the average concentration of PM2.5 will be reduced by 4.1 μg/m3 (reduction of 6.6%).
have a significant effect on the secondary concentration. Because the traffic source has a greater impact on both of the primary and secondary concentrations; therefore, the control of traffic sources will have has greater effect on the ambient PM2.5. The simulation results from both point and traffic emissions show that the hybrid model can provide the spatial distributions of primary and secondary PM2.5, and can be employed to assess the effectiveness of air quality control strategies as presented in the following section. 3.3. Scenario analysis Fig. 7 and Fig. 8 show the simulated load reductions of the power plant for Case A and Case B. It can be seen from the figures that the concentration decreases gradually with the reduction in emissions. The average concentration of PM2.5 monitored during case A was 64.4 μg/ m3. When the coal-fired power generation is reduced by 180 MW, the average concentration of PM2.5 will be reduced by 0.1 μg/m3 (about 0.2% of the average concentration, Fig. 7.a ∼ Fig. 7c). When the power generation is reduced by 1100 MW, the average concentration of PM2.5 will be reduced by 0.3 μg/m3 (about 0.5% of the average concentration, Fig. 7.d ∼ Fig. 7f). When the reduction is 2200 MW, the average concentration of PM2.5 will be reduced by 0.78 μg/m3 (about 1.2% of the average concentration, Fig. 7.g ∼ Fig. 7i). The average concentration of PM2.5 monitored during case B was
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Conclusion
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The study combines the CMAQ and AERMOD model to develop a hybrid model for estimating ambient PM2.5. The hybrid model has shown a better performance than mere CMAQ modeling compared with the observations from the monitoring stations. It is applied in this study to a metropolitan area in central Taiwan with a comprehensive database that incorporates the complex emission sources. Primary and secondary PM2.5 concentrations are separated and the emission sources can be assessed in terms of their contribution to the ambient concentrations to support development of source control strategies. The simulation results for the two cases show that if the coal-fired power plant loads are reduced by 20% (reduction of 1100 MW), the concentration of PM2.5 in the Taichung area will be reduced by 0.5% on average. The concentration will be reduced by 1.25% if the power plant loads are reduced by 40%. When the traffic source is reduced by 20%, the concentration of PM2.5 in Taichung area will be reduced by 4.3%. The ambient concentration will be reduced by 6.6% when the traffic source is reduced by 30%. Overall, the results of the hybrid model simulation during the case days show that the primary PM2.5 to secondary PM2.5 ratio caused by the coal-fired power plant is 1:20; therefore the regulation that strictly controls the primary emission of the coal-fired power plant may not have a significant effect on the secondary concentration. The reduction of traffic source emissions can achieve a greater reduction on the concentration of PM2.5 in Taichung. Although the hybrid model exhibits better performance, there are some uncertainties on the classification of the primary and secondary pollutants as well as the concentrations modeled in the current hybrid model, which are expected to be resolved in the future in order to enhance the modular approach for modeling. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.05.038. References Aggarwal, P., Jain, S., 2015. Impact of air pollutants from surface transport sources on human health: a modeling and epidemiological approach. Environ. Int. 83, 146–157. Baek, J., Park, S.-K., Hu, Y., Russell, A.G., 2005. In: Source Apportionment of Fine Organic Aerosol Using CMAQ Tracers. Paper Presented at the Models-3 Conference, (Research Triangle Park, NC). Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl. Mech. Rev. 59 (1–6), 51–77. https://doi.org/10. 1115/1.2128636. Retrieved from < Go to ISI > ://WOS:000202993600004. Chen, C.R., You, Y.D., Wang, K.M., 2014. Testing the iscst3 model on air pollution from road vehicles in tao yuan, Taiwan. Int. J. Oral Implant. 6 (4), 217–235. Cheng, S., Lang, J., Zhou, Y., Han, L., Wang, G., Chen, D., 2013. A new monitoringsimulation-source apportionment approach for investigating the vehicular emission contribution to the PM2. 5 pollution in Beijing, China. Atmos. Environ. 79, 308–316. Emery, W.J., Baldwin, D.J., Schlüssel, P., Reynolds, R.W., 2001. Accuracy of in situ sea
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Design and application of a hybrid assessment of air quality models for the source apportionment of PM2.5
T
Hsin-Chih Laia, Hwong-Wen Mab,∗, Chih-Rung Chenb, Min-Chuan Hsiaoc, Bo-Han Pand a
Department of Occupational Safety and Health, Chang Jung Christian University, Tainan, Taiwan Graduate Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan c Institute of Environmental Engineering and Management, National Taipei University of Technology, Taipei, Taiwan d Environmental Research and Information Center, Chang Jung Christian University, Tainan, Taiwan b
GRAPHICAL ABSTRACT
ARTICLE INFO
ABSTRACT
Keywords: Hybrid air quality model Source apportionment Primary PM2.5 Secondary PM2.5 CMAQ AERMOD
Increasing levels of air pollution greatly affect the environment and human health; air quality control is therefore of particular importance. To improve the efficiency of air pollution control, reliable air quality models for source apportionment are critically in need. The multitude of air quality models to find the sources of air pollution, however, have their own limitations. Hence, this study seeks to integrate different air quality models, including a diffusion model (AERMOD) and a grid model (CMAQ), employing initial meteorological fields provided by the Weather Research and Forecasting (WRF) model. Using the advantages of these models, this study builds a hybrid air quality model, which provides a more effective analysis of the distribution of primary pollutants, secondary pollutants, and other environmental information. Two significant fine particulate matter (PM2.5) events were selected in this study to discuss the influence of the Taichung coal-fired power plant and the Taichung traffic source on the PM2.5 in the Taichung area, as well as to evaluate the performance of the hybrid model. Simulation results for the two cases show that if the coal-fired power loads are reduced by 20% (around 1100 MW), the concentration of PM2.5 in the Taichung area will decrease by 0.5%; such decrease will reach 1.25% when the power load reduction is 40%. If the traffic source is reduced by 20%, the concentration of PM2.5 in the Taichung area will decrease by 4.3%, and by 6.6% with 30% traffic source reduction. The hybrid model shows that the contribution of different pollution sources can be illuminated to support air quality control strategies.
∗
Corresponding author. Graduate Institute of Environmental Engineering, National Taiwan, University, 71 Chou-Shan Rd, Taipei, 106, Taiwan, ROC. E-mail address: [email protected] (H.-W. Ma).
https://doi.org/10.1016/j.atmosenv.2019.05.038 Received 31 January 2019; Received in revised form 29 April 2019; Accepted 18 May 2019 Available online 20 May 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
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1. Introduction
than the monitoring data; that is, there is an underestimation at small scales by CMAQ model simulations. To overcome the resolution restrictions of the model, Stein et al. (2007) combined the CMAQ model and the AMS/EPA Regulatory Model (AERMOD) to calculate high-resolution benzene concentrations at the local scale. The meteorological fields were created using MM5. Nevertheless, this integrated model has not yet been applied to PM2.5. Air quality monitoring data provides information on the PM2.5 pollution situation at neighboring monitoring stations but does not provide further information to identify the secondary formation. In order to implement effective management of PM2.5 pollution, it is urgent to establish a more accurate method to find the impact of primary and secondary aerosols of PM2.5. The objective of this study is to develop a hybrid model that integrates the Weather Research and Forecasting (WRF), the CMAQ model, and the AERMOD model to identify the relationship between atmospheric transport patterns and the primary and secondary PM2.5 concentrations at the urban scale. The WRF model is a numerical weather prediction system designed to serve both atmospheric research and operational forecasting needs. This hybrid model can improve the analytical capabilities of air quality simulations, as well as overcome the difficulty in accounting for the impact of PM2.5, hence facilitating the decision-makers’ ability to interpret air quality conditions. The study applies this air quality hybrid model to a metropolitan area with a mixture of multiple emission sources for the further identification of the contributors to ambient PM2.5. In this study, the Taichung metropolitan area is chosen. Located in central Taiwan, the area is characterized by a complex geographical environment and intricate air pollution in Taiwan. The air pollution derives from the local pollution and also from the northern and southern districts of Taiwan. Particularly, the Taichung metropolitan area is occupied by the world's second largest thermal power plant as well as 2.76 million vehicles; therefore, it is essential to recognize the contributors to the primary and secondary components of PM2.5. This paper is structured as follows. Section 2 describes the conceptual framework of the air quality hybrid model, the connection between the three models, and the emission reduction scenarios. Section 3 presents the simulation results and discusses the implication of the hybrid model in the support of air quality management. Section 4 summarizes the integration of the specific models and provides directions for future research.
Fine particulate matter (PM2.5) is a severe problem for air pollution worldwide; it causes serious impacts on the environment and human health. PM2.5 mainly enters the respiratory organs and accumulates in the body through the blood circulation system and can cause health hazards. PM2.5 can be suspended in air for weeks and can be transported over long distances. It is also small enough to enter the respiratory system through the nose or mouth (Hu et al., 2010; Tsai and Kuo, 2005; Tsai et al., 2015). In addition, studies have also shown that toxic substances in PM2.5 can damage organs and have adverse effects if they are inhaled by humans (Louie et al., 2005; Pope III et al., 2006; Wu et al., 2009). Even if the concentration of PM2.5 is not high, prolonged exposure can induce acute or chronic respiratory diseases (Leiva et al., 2013). This major component of outdoor air pollution was classified as Group 1 carcinogenic to humans (IARC, 2013). In order to solve the PM2.5 pollution problem effectively, the source apportionment technique, to guide air pollution control strategies, is imperative; consequently, the technique becomes one of the core tasks in terms of air quality management. Source apportionment can be performed using emissions inventories, the source-oriented (or dispersion) model, and the receptor model. The apportionment results can quantify the contribution of different sources, and the impact on different regions can be assessed with image data (Aggarwal and Jain, 2015; Chen et al., 2014; Hsu et al., 2016; Taiwo et al., 2014). The emission inventory approach aims to establish pollution source databases through statistics and surveys; nevertheless, this method only considers the relative importance of various types of pollution emissions, which are identified as an auxiliary to the source apportionment method (Zhang et al., 2012, 2013). On the other hand, the receptor model facilitates the determination of the average contribution of specific source's profile abundance, such as the CMB (chemical mass balance) and PMF (positive matrix factorization). Zhou et al. (2017) used the CMB model to estimate the main sources of PM2.5 in four key emission regions in China. Their research clarified that the major primary sources of organic carbon in PM2.5 included vehicle emissions, biomass burning, coal combustion, meat cooking, and natural gas combustion. Mansha et al. (2012) used the PMF model to identify the major emission sources of the ambient PM2.5 in Karachi, indicating five major contributors to PM2.5 in Karachi which were: soil/road dust, industrial emissions, vehicular emissions, sea salt originated from Arabian Sea and secondary aerosols. The existing receptor model illuminates the major contributors to the total PM2.5. However, the assessment of the total PM2.5 may lead to inefficient air quality management due to lack of response to the primary and secondary aerosol formation (Zhang et al., 2015). This bias may result in more emphasis on the regulation of stationary and mobile sources of pollution from nearby pollution receptors, while neglecting the management of long-range transboundary air pollution; this will consequently lead to ineffective outcomes and a great drain on air pollution control budgets. In order to evaluate the impact of primary and secondary components of PM2.5, some literature has applied the Community Multi-Scale Air Quality (CMAQ) dispersion model to analyze the diffusion, transmission, and sedimentation of pollutants in the atmosphere. Numerical methods were used to identify pollution sources using emission inventories and meteorological field studies (Baek et al., 2005; Byun and Schere, 2006; Jeon et al., 2014; Zhang et al., 2014). Wang et al. (2015) adopted the Mesoscale Meteorological ModelCommunity Multi-Scale Air Quality (MM5-CMAQ) model to analyze the source of regional PM2.5; sources included industrial, household, and agricultural pollution in cities. Cheng et al. (2013) used the MM5CMAQ model to express the contribution of mobile sources of PM2.5. Currently, the grid model CMAQ suffers from resolution limitations in its application. For finer scale simulations, insufficient computing capacity exposes the defects of the grid model. Accordingly, the concentration of primary PM2.5 assessed by the CMAQ model is often lower
2. Methods 2.1. Development of the hybrid model The developed hybrid model consists of the diffusion model AERMOD and the grid model CMAQ. It is desirable to understand how each grid point in the simulated range is affected by a single source of pollution, and to clarify the primary and secondary aerosol concentrations. Possible sources of pollution can be identified by the transmission path. The framework for the hybrid model is shown in Fig. 1. Observational data is used to adjust the simulation results. The observation values (Observation PM2.5) and the simulation results of the CMAQ model can be linked by an adjustment ratio (n) as in formula (1). Observation
PM2.5
= n x (CMAQPM2.5
primary
+ CMAQ
PM2.5 Secondary)
(1)
PM2.5 concentration of CMAQ models includes primary (CMAQPM2.5 and secondary (CMAQ PM2.5 Secondary) pollution. The concentration adjustment factor (n) can be solved with the formula. Because CMAQ modeling includes secondary PM2.5, consisting of SO42−(PM2.5SO4), NO3−(PM2.5NO3), NH3 (PM2.5Ammonia), organic carbon (PM2.5OC), and elemental carbon (PM2.5EC), the secondary PM2.5 is calculated as formula (2). The primary PM2.5 of CMAQ is obtained by primary)
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Fig. 1. Hybrid model framework.
Fig. 2. Overview of the sampling sites in the Taichung area. Table 1 Verification of WRF simulation at the Taichung monitoring station.
Table 2 Performance comparison of CMAQ and the hybrid model.
Duration
Temperature
Wind Speed
Wind Direction
MFB
CMAQ
Hybrid Model
MFE
CMAQ
Hybrid Model
2013 January April July October Average Recommended Values
MBE 0.01 1.2 0.5 0.3 0.5 ≦ ± 1.5
MBE 0.6 0.8 0.9 0.7 0.8 ≦ ± 1.5
WNMB 1% −0.3% 0.3% 2% 0.8% ≦ ± 10%
Fengyuan Shalu Xitun Dali Average
−15% −40% −24% −19% −25%
6% −20% −4% 3% −4%
Fengyuan Shalu Xitun Dali Average
15% 40% 24% 21% 25%
10% 20% 7% 10% 12%
MAGE 0.6 1.5 0.8 0.6 0.9 ≦3
RMSE 1.0 1.2 1.5 1.3 1.3 ≦3
WNME 11% 16% 16% 11% 0.1 ≦30%
MAGE: Mean Absolute Gross Error; MBE: Mean Bias Error; WNMB: Wind Normalized Mean Bias; WNME: Wind Normalized Mean Error.
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Fig. 3. Comparison of the WRF simulation with the Taichung monitoring station during the four case days.
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does; it only simulates the concentrations of the primary PM2.5. In the hybrid model, this study assumes the primary PM2.5 simulations from CMAQ and AERMOD models are linearly related. Hence, the formula for the primary PM2.5 linear adjustment is as follows:
Table 3 Verification of WRF simulation at the Taichung monitoring station during case days. Duration
Temperature
Wind Speed
Wind Direction
2013 Case A Case B Recommended Values
MBE 0.5 0.1 ≦ ± 1.5
MBE 1.2 1.9 ≦ ± 1.5
WNMB −1.0% 1.9% ≦ ± 10%
MAGE 1.7 1 ≦3
RMSE 1.7 2.3 ≦3
AERMODPrimary = k x CMAQPrimary
WNME 14.4% 9.0% ≦30%
k: The adjustment ratio between the CMAQ and AERMOD. The aim of hybrid model is the concentration of Models approach the observation data and the components of the primary and secondary PM2.5. The spatial resolution of AERMOD is better than CMAQ. Hence, the formula to calculate the concentration of fine suspended particles is as follows:
MAGE: Mean Absolute Gross Error; MBE: Mean Bias Error; WNMB: Wind Normalized Mean Bias; WNME: Wind Normalized Mean Error. Table 4 Performance comparison of CMAQ and the hybrid model during case days. MFB
CMAQ
Hybrid Model
MFE
CMAQ
Hybrid Model
CASE A CASE B
−9% 1%
5% 0.4%
CASE A CASE B
18% 30%
8% 8%
Observation CMAQ PM2.5
CMAQ
PrimaryPM2.5 =
CMAQ
+
SUM PM2.5
PM2.5NO3
- CMAQ
+
PM2.5Ammonia (2)
Secondary PM2.5
PM2.5
≈Hybrid PM2.5 = n x k x CMAQ
Secondary
PM2.5 Primary
+nx (5)
In the CMAQ model, the components of the primary and secondary PM2.5 are classified according to their compositions. The air quality models used in this study adopt the same emission and meteorological data; the WRF model provides the weather field, and emission data is based on TEDS9.0 (Taiwan emission data system). In this hybrid model, the estimation of the PM2.5 concentration is achieved through the AERMOD and CMAQ models combined with the monitoring data. Since the AERMOD model does not take photochemical reaction mechanisms into account, its simulation results can be treated as the primary pollutant. The CMAQ simulation results can be divided into primary and secondary pollutants, so the simulation results of AERMOD can be compared with the simulation results of the primary pollutants from the CMAQ results to obtain the adjustment ratio for primary pollutants.
subtracting the secondary PM2.5 (CMAQ Secondary PM2.5) from the sum of PM2.5 (CMAQ SUM PM2.5) as in formula (3). CMAQ SecondaryPM2.5 = PM2.5SO4 + PM2.5OC + PM2.5EC
(4)
(3)
In terms of diffusion model, the AERMOD does not consider the mechanism of photochemical reactions as the CMAQ Secondary PM2.5
Fig. 4. Simulations of the Taichung area emission with different resolutions under case A.
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Fig. 5. Comparison of the simulation results for the coal-fired power plant.
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Fig. 6. Comparison of simulation results for the traffic source.
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2.2. Site description and case selection
Table 5 Contribution of the coal-fired power plant to PM2.5 in the Taichung area (μg/ m3). Duration
Fengyuan
Shalu
Xitun
Zhongming
Dali
AVG
Primary Case A Case B
0.15 0.08
0.27 0.21
0.21 0.19
0.15 0.18
0.09 0.24
0.17 0.18
Secondary Case A Case B
3.93 1.82
3.51 3.34
3.90 3.48
3.96 3.50
3.97 4.15
3.85 3.26
This study selected Taichung city as the simulated area. Central Taiwan has one of the most complicated air pollution situations. Major air pollution sources include coal-fired power plants, heavy industries, and traffic emissions. The entire metropolitan area covers 1800 km2 and has a population of approximately 4 million. About 2.76 million vehicles, including automobiles and motorcycles, are registered. Furthermore, the Taichung power plant is the world's second largest coal-fired power plant, and it is the largest power plant in Taiwan. It is necessary to explore the contributions from the multiple sources. The Taichung coal-fired power plant has 10 units, each with a generating capacity of about 550 MW. The scenarios of emission reduction were used to evaluate the impact on the Taichung metropolitan area. Two high pollution cases of PM2.5 were used for the simulations. The design of the Taichung power plant load reduction considers three scenarios: 180 MW, −1100 MW, and −2200 MW. The emission reduction scenarios of the traffic source are −10%, −20%, and −30%, respectively. Emission data used in this study are based on TEDS9.0 (Taiwan Emission Data System with the base year of 2013), a comprehensive emission database of various pollution sources in Taiwan. TEDS covers stationary sources, mobile sources, and natural sources. Each source has its own column name, code, and data type. We selected emission data
Table 6 Contribution of the traffic source to PM2.5 in Taichung area (μg/m3). Duration
Fengyuan
Shalu
Xitun
Zhongming
Dali
AVG
Primary Case A Case B
4.89 4.56
5.61 5.56
5.42 6.13
7.07 8.20
5.93 7.25
5.78 6.34
Secondary Case A Case B
8.82 7.10
7.22 6.13
7.93 6.66
8.33 7.26
8.29 8.69
8.12 7.17
Fig. 7. Comparison of the simulation results for the power plant with load reductions for Case A.
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Fig. 8. Comparison of simulation results for the power plant with load reductions for Case B.
related to Taichung city for the model simulation and the design of various emission reduction scenarios. According to the sum of emissions, the PM2.5 emission rate in the Taichung coal-fired power plant is about 39.5 g/s, equivalent to 3.4 tons per day. The traffic source emission rate is about 72.9 g/s, equivalent to 6.3 tons per day. The traffic source emissions in Taichung are about 1.846 times those of the coal-fired power plant. This study used the year 2013 that is consistent with TEDS9.0 to carry out the model simulation. Case days with an average of more than 54 μg/m3 of PM2.5 were selected, as daily averages of PM2.5 exceeding 54 g/m3 had a higher health risk to most people (Taiwan-EPA, 2018). Case A is March 6th to 8th, 2013 and Case B is November 20th to 22nd, 2013. The sampling sites shown in Fig. 2 (Fengyuan, Shalu, Xitun, Zhongming, and Dali) were used as the benchmark for the comparison between the monitoring data and the model simulation.
Wind Normalized Mean Error (WNME) should be less than 30%. The performance of the WRF model is shown in Table 1. The air quality monitoring stations of the Environmental Protection Administration were used to validate the PM2.5 estimations. According to the specifications for air quality model simulation developed by the Environmental Protection Administration of Taiwan, Mean Fractional Bias (MFB) between observed and simulated values of PM2.5 should be ± 35% and Mean Fractional Error (MFE) should be less than 55%. The air quality in January, April, July and October 2013 was simulated (first month of each quarter), and the simulation performance of CMAQ for the central Taiwan has satisfied the specifications, while the hybrid model simulation shows a better performance with MFB and MFE better than CMAQ model. The comparison between the simulations of CMAQ and the hybrid model is shown in Table 2. For the case study, the WRF model simulations and the observed values were compared for the two case days in 2013. Comparisons of the time sequences are shown in Fig. 3. The simulation results at the sea level pressure and surface temperatures were close to the observed value. The humidity was slightly underestimated and the wind speed was overestimated. Table 3 also shows a comparison of the two case days in terms of the temperature, wind speed, and wind direction. The comparison shows that simulation results conform to the weather model simulations specified by the Taiwan EPA. The normalized mean bias (NMB) of the humidity was close to the recommended level (Emery et al., 2001). Therefore, the WRF model presents acceptable results during these case days. The air quality performance of hybrid model is
2.3. The performance of the hybrid model In this study, the meteorological station of Taiwan Central Weather Bureau was used to validate the temperature, wind speed and wind direction estimations. According to the specifications for weather model simulation developed by the Environmental Protection Administration of Taiwan, Mean Absolute Gross Error (MAGE) between observed and simulated values of temperature and wind speed should be less than 3 and Mean Bias Error (MBE) should be within ± 1.5. For wind direction, Wind Normalized Mean Bias (WNMB) should be less than 10% and 124
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Fig. 9. Comparison of simulation results for the reduction of the traffic source for Case A.
better than the CMAQ model, with the MFB 5% and the MFE 8% under case A. The MFB was 0.4% and the MFE was 8% under case B. The performance comparison of CMAQ and hybrid model is shown in Table 4.
concentration, transmission path, and primary and secondary pollutants. Fig. 5 shows the results of the AERMOD and CMAQ simulations of the Taichung coal-fired power plant emissions under the two cases; the weather patterns of both cases are weak wind environment. Simulations of the primary PM2.5 concentration show that the AERMOD results were higher than those for the CMAQ modeling in both cases. For the simulation of the Taichung coal-fired power plant, the results of the two models differed in quantity but had the similar trend of spatial distribution. Fig. 5c and f shows the secondary formation PM2.5 formed in the areas away from emission source. By following the methodology of developing the hybrid model specified in section 2.1, Fig. 5d and h show the modeling results of the hybrid model, which indicate the significant contribution of secondary PM2.5 to the air quality in the Taichung area from the power plant. In contrast, Fig. 6 shows the simulations of the traffic source in Taichung in the same periods. It is shown that the difference between AERMOD and CMAQ models was not as large as that for the coal-fired power plant. The contributions to the primary and secondary concentrations are relatively similar. Table 5 and Table 6 show the contributions of coal-fired power plant and traffic source to Taichung during the case periods. The average contributions of Taichung coal-fired power plant to the primary and secondary PM2.5 in the study area were 0.18 and 3.56 μg/m3, respectively, while the primary and secondary contributions for traffic source were 6.06 and 7.65 μg/m3, respectively. The primary to secondary ratio for the coal-fired power plant is 1:20; therefore strictly regulating the primary emission of the coal-fired power plant may not
3. Results and discussion 3.1. Effects of resolution Two different resolution simulations were carried out with AERMOD using a finer resolution of 200 m and 3 km as used for CMAQ, respectively. The differences between the two resolutions are discussed. The simulations were compared for the Taichung power plant and the traffic source under Case A, as shown in Fig. 4.a and Fig. 4.c. The simulations show that the concentration for a 200-m resolution was higher than that for the 3-km resolution; but the two resolutions differ little from each other in the simulation of stationary or mobile sources. Considering the time required to calculate the finer resolution model, we determined that such a small resolution is not beneficial, and thus the subsequent model simulations were performed with 3 km resolution that matches CMAQ. 3.2. Feasibility assessment of the hybrid model The primary PM2.5 concentration in the hybrid model is jointly determined by AERMOD and CMAQ. The hybrid model is designed to simultaneously display information including the total PM2.5 125
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Fig. 10. Comparison of simulation results for the traffic source reduction for Case B.
61.7 μg/m3. When the coal-fired power generation is reduced by 180 MW, the average concentration of PM2.5 will be reduced by 0.1 μg/ m3 (about 0.2% of the average concentration). When the power generation is reduced by 1100 MW, the average concentration of PM2.5 will be reduced by 0.3 μg/m3 (about 0.5% of the average concentration). When the reduction is 2200 MW, the average concentration of PM2.5 will be reduced by 0.8 μg/m3 (about 1.3% of the average concentration). Fig. 9 and Fig. 10 show the simulated emission reduction of the traffic source for Case A and Case B. It can be seen that, similar to the stationary source, the concentration decreases gradually with the emission reduction. When the traffic source emission is reduced by 10% for Case A, the average concentration of PM2.5 will be reduced by 1.4 μg/m3 (reduction of 2.2%). When the traffic source emission is reduced by 20%, the average concentration of PM2.5 will be reduced by 2.7 μg/m3 (reduction of 4.2%). When the reduction is 30%, the average concentration of PM2.5 will be reduced by 4.2 μg/m3 (reduction of 6.5%). When the traffic source emission is reduced by 10% for Case B, the average concentration of PM2.5 will be reduced by 1.4 μg/m3 (reduction of 2.3%). When the traffic source emission is reduced by 20%, the average concentration of PM2.5 will be reduced by 2.7 μg/m3 (reduction of 4.4%). When the reduction is 30%, the average concentration of PM2.5 will be reduced by 4.1 μg/m3 (reduction of 6.6%).
have a significant effect on the secondary concentration. Because the traffic source has a greater impact on both of the primary and secondary concentrations; therefore, the control of traffic sources will have has greater effect on the ambient PM2.5. The simulation results from both point and traffic emissions show that the hybrid model can provide the spatial distributions of primary and secondary PM2.5, and can be employed to assess the effectiveness of air quality control strategies as presented in the following section. 3.3. Scenario analysis Fig. 7 and Fig. 8 show the simulated load reductions of the power plant for Case A and Case B. It can be seen from the figures that the concentration decreases gradually with the reduction in emissions. The average concentration of PM2.5 monitored during case A was 64.4 μg/ m3. When the coal-fired power generation is reduced by 180 MW, the average concentration of PM2.5 will be reduced by 0.1 μg/m3 (about 0.2% of the average concentration, Fig. 7.a ∼ Fig. 7c). When the power generation is reduced by 1100 MW, the average concentration of PM2.5 will be reduced by 0.3 μg/m3 (about 0.5% of the average concentration, Fig. 7.d ∼ Fig. 7f). When the reduction is 2200 MW, the average concentration of PM2.5 will be reduced by 0.78 μg/m3 (about 1.2% of the average concentration, Fig. 7.g ∼ Fig. 7i). The average concentration of PM2.5 monitored during case B was
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The study combines the CMAQ and AERMOD model to develop a hybrid model for estimating ambient PM2.5. The hybrid model has shown a better performance than mere CMAQ modeling compared with the observations from the monitoring stations. It is applied in this study to a metropolitan area in central Taiwan with a comprehensive database that incorporates the complex emission sources. Primary and secondary PM2.5 concentrations are separated and the emission sources can be assessed in terms of their contribution to the ambient concentrations to support development of source control strategies. The simulation results for the two cases show that if the coal-fired power plant loads are reduced by 20% (reduction of 1100 MW), the concentration of PM2.5 in the Taichung area will be reduced by 0.5% on average. The concentration will be reduced by 1.25% if the power plant loads are reduced by 40%. When the traffic source is reduced by 20%, the concentration of PM2.5 in Taichung area will be reduced by 4.3%. The ambient concentration will be reduced by 6.6% when the traffic source is reduced by 30%. Overall, the results of the hybrid model simulation during the case days show that the primary PM2.5 to secondary PM2.5 ratio caused by the coal-fired power plant is 1:20; therefore the regulation that strictly controls the primary emission of the coal-fired power plant may not have a significant effect on the secondary concentration. The reduction of traffic source emissions can achieve a greater reduction on the concentration of PM2.5 in Taichung. Although the hybrid model exhibits better performance, there are some uncertainties on the classification of the primary and secondary pollutants as well as the concentrations modeled in the current hybrid model, which are expected to be resolved in the future in order to enhance the modular approach for modeling. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.05.038. References Aggarwal, P., Jain, S., 2015. Impact of air pollutants from surface transport sources on human health: a modeling and epidemiological approach. Environ. Int. 83, 146–157. Baek, J., Park, S.-K., Hu, Y., Russell, A.G., 2005. In: Source Apportionment of Fine Organic Aerosol Using CMAQ Tracers. Paper Presented at the Models-3 Conference, (Research Triangle Park, NC). Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl. Mech. Rev. 59 (1–6), 51–77. https://doi.org/10. 1115/1.2128636. Retrieved from < Go to ISI > ://WOS:000202993600004. Chen, C.R., You, Y.D., Wang, K.M., 2014. Testing the iscst3 model on air pollution from road vehicles in tao yuan, Taiwan. Int. J. Oral Implant. 6 (4), 217–235. Cheng, S., Lang, J., Zhou, Y., Han, L., Wang, G., Chen, D., 2013. A new monitoringsimulation-source apportionment approach for investigating the vehicular emission contribution to the PM2. 5 pollution in Beijing, China. Atmos. Environ. 79, 308–316. Emery, W.J., Baldwin, D.J., Schlüssel, P., Reynolds, R.W., 2001. Accuracy of in situ sea
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