## 4 Conclusions  
In this study, the air quality in the year of 2015 and during pollution episodes in Haizhu district were analysed, and the impacts of emission control scenarios by the year of 2020 on air quality were evaluated using the WRF-Chem numerical simulation. For the air quality in Haizhu 2015, the annually-averaged concentrations of PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), and NO\({}_{2}\) were higher than those in the entire Guangzhou city. O\({}_{3}\)-8h was slightly lower than that in Guangzhou. Pollution episodes in Haizhu in 2015 primarily occurred in summer and winter. The typical winter pollution episode (14 to 28 January) was a process of gradual accumulation of pollution and dissipation, with NO\({}_{2}\) or PM\({}_{2.5}\) as the key pollutants, associated with the wind. The heavy pollution episode in the hot season (3 to 8 August) was a process of pollution that occurred and disappeared quickly, with O\({}_{3}\) as the key pollutant, due to suitable local pollution and strong sunshine. The WRF-Chem simulation generally captured the observed chemical characteristics, suggesting that the WRF-Chem model could be used to simulate air quality in the research case. Emission control scenario 2020C (comprehensive measures taken in the whole of Guangzhou city) would improve air quality more significantly than other scenarios (measures taken in Haizhu) under all conditions (heavy pollution conditions and annual level). For urban areas, scenario 2020D (vehicle emission control) would account for more than half of the influence of 2020B (all source emission controls) on air quality. By the end of the 13 th FYP, it is noticeable that O\({}_{3}\) pollution would increase, which indicates that the control ratio of VOCs and NO\({}_{x}\) may be unfavourable and requiresfurther assessment. It would be of interest to perform simulations with the meteorology/chemistry interactions to investigate the influence of policies on O\({}_{3}\) and other air pollutants in future research. Our study suggested that control measures should be strengthened for NO\({}_{2}\), PM\({}_{2.5}\), and PM\({}_{10}\), and control ratio of VOCs and NO\({}_{x}\) should be adjusted for controlling O\({}_{3}\). The urban area should focus on vehicle emission control, strengthen regional cooperation on pollution control, and establish short-term measures for heavy pollution conditions. The following are available online at [[http://www.mdpi.com/2076-3417/10/15/5276/s1](http://www.mdpi.com/2076-3417/10/15/5276/s1)]([http://www.mdpi.com/2076-3417/10/15/5276/s1](http://www.mdpi.com/2076-3417/10/15/5276/s1)), Figure S1: Research framework, Figure S2: WRF-Chem simulation domain, Table S1: Emission sectors, corresponding inventory technical guidelines and special allocation rules, Table S2: Description of emission control scenarios, Table S3: Detailed information for pollution episodes observed in Hazihu monitoring stations, 2015, Table S4: Statistical data for heavy pollution episode in Hazihu district during 14-28 January 2015, Table S5: Statistical data for heavy pollution episode in Hazihu district during 3-8 August 2015, Table S6: Performance statistics for meteorological simulation ( hourly statistical data in October 2015), Table S7: Statistical comparison of model evaluation in this paper and previous study. [PERSON] and [PERSON] designed the study. [PERSON] and [PERSON] collected and analysed data. [PERSON] and [PERSON] did emission data preparation and generated the figures. [PERSON] and [PERSON] did the policy investigation. [PERSON] contributed to literature search, numerical simulation and writing the manuscript. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research Program (No. 2016 YFC0202005), the Natural Science Foundation of China (No. 41975165; No. 11802347), the Fundamental Research Funds for the Central Universities (No. 20 lgpy180), and the social science and technology development (key) project of Dongguan city (No. 2019507101161).