Meteorological characteristics within boundary layer and its influence on PM2.5 pollution in six cities of North China based on WRF-Chem

https://doi.org/10.1016/j.atmosenv.2020.117417Get rights and content

Highlights

  • Conclude the temporal-spatial variation of PBLH, and the change trend of meteorological elements with PBLH change.

  • Find the synergy between WS decreasing and PBLH falling and the synergy between RH increasing and PBLH falling.

  • Identify and quantify the influences of emission and key meteorological factors on the PM2.5.

Abstract

North China is recognized as one of the region with most severe air pollution in China, and the PM2.5 in six major cities (Beijing, Tianjin, Shijiazhuang, Taiyuan, Jinan and Zhengzhou) of this region was measured to be 108.7–186.0 μg m−3 in January 2017 and 39.2–56.3 μg m−3 in July 2017. This study conducts the meteorological and chemical simulations based on WRF-Chem, and explores the impacts of meteorology and emissions on PM2.5 (particulate matter with aerodynamic diameters ≤ 2.5 μm) pollutions. Simulations show a noticeable seasonal variation in planetary boundary layer height (PBLH) and relative humidity (RH), but a similar trend in wind speed (WS). Compared to PM2.5 good condition (≤75 μg m−3), daily PBLH decreased by 7.6–39.6% in January and 9.2–44.1% in July during PM2.5 polluted condition (75~150 μg m−3), and by 22.3–51.2% in January during severely polluted condition (≥150 μg m−3). PBLH is thought of as the key factor for PM2.5. Then, the synergy between PBLH and other meteorological factors are studied. For a 100 m failing in PBLH, the decrease in surface WS reaches 0.2–0.8 m s−1 0.1–0.3 m s−1 in January and July. It means a synergic effect of unfavorable horizontal and vertical dispersions, more adversely in January than in July. While, RH presents an increasing trend with PBLH falling, about +2.1~+3.2% in January and +2.7~+4.6% in July per 100 m falling in PBLH. Considering the improvement of high RH in heterogeneous chemistry to form the secondary PM2.5, we believe that the disadvantaged dispersion condition is always accompanied by enhanced secondary PM2.5 formation chemistry. Then, we summarize the average simulated PM2.5 under various combinations of PBLH, WS and RH, and find that under the same meteorological combination, PM2.5 in January was about 1.7–3.6 times that of July, which could be explained by the more emissions of PM2.5 and its precursors in January. Finally, we determine the unfavorable meteorology condition based on current regional emissions and China's standard (hourly PM2.5<75 μg m−3), low PBLH (<600 m) or middle-high RH (>40%) for January, high RH (>60%) & low-middle WS (<4 m·s-1), or middle RH (40~60%) & low PBLH (<400 m) for July. These unfavorable meteorological conditions accounted for 75.8% in January and 47.8% in July. Therefore, it is still necessary to continuously reduce anthropogenic emissions in this region for attainment of China's PM2.5 standards.

Introduction

China has suffered from increasingly serious air pollution over the past decades, due to the massive anthropogenic emissions caused by rapid industrialization and urbanization (Li et al., 2017; Chen et al., 2019). Fine particulate matter smaller than 2.5 μm in diameter (PM2.5), as a key component of pollution episodes accompanying haze, has also drawn worldwide attentions. Elevated PM2.5 not only has harmful impacts on human health (Wang et al., 2019; Dai et al., 2018), and causes low visibility and traffic issues (Luan et al., 2018), but also affects weather and accelerates the climate change (Zhang et al., 2016a; Shi et al., 2018).

Great efforts have been devoted to explore the cause of China's air pollution issues, most of which focused on the physical, chemical, optical properties, and the transport of aerosol particles and meteorological conditions during severe pollution episodes (Yang et al., 2018; Hu et al., 2016; Zhu et al., 2012). These studies have revealed that apart from anthropogenic emissions and secondary transformation as primary causes of air pollution, meteorological parameters are also of great significance (Hu et al., 2014; Long, 2016), including the dynamic and thermal process and the vertical structure of the planetary boundary layer (PBL) (Wei et al., 2018; Sun et al., 2013).

The vertical mixing of air pollutants is determined by the thermodynamic structure of the PBL. Otherwise, the PBL dynamics also influences wind shear and turbulence. The pollutants by anthropogenic activities mix and transfer within the PBL; strong temperature inversion and weak wind speed resulted in the accumulation of pollutants in the shallow layer, which further caused a high local pollutant concentration. In addition, seasonal changes, such as monsoons can influence the wind direction, temperature and relatively humidity in different seasons. Local meteorological conditions during specific seasons may affect the variations in the air pollution levels (Cai et al., 2017; Zhang et al., 2013). Thus, researches on the meteorological characteristics within PBL during PM2.5 pollution levels is of critical importance with great practical value and significance for air quality management.

The air pollution dominated by PM2.5 mass concentration with surface temperature inversion, low wind speed, high relative humidity and low temperature, and has become a key factor blighting regional economic growth and the urban environment improvement. It has been well established that there exists a “two-way feedback mechanism” between unfavorable meteorological conditions and cumulative PM2.5 when temperature inversions occur and play an impactful role due to the radiative cooling effects of elevated aerosols under slight or calm winds and keep pollutants in the lower PBL (Liu et al., 2019a). Higher aerosol emissions increase low-level stability exacerbating the fog-haze episodes formation (Lolli et al., 2019; Miao et al., 2016). Moreover, comprehensive observational campaigns related to the PBL have been carried out in different cities in China. Han et al. (2018) used vertical profiles of PM2.5 concentrations and meteorological parameters to analyze the effect of PBL structure on a typical haze-fog event in Tianjin, China. Liu et al. (2019b) classified the wintertime synoptic circulation patterns into seven types by Principal Components in T-mode (PCT) method in Shanghai, and analyzed the relationship between PBL structure and PM2.5 under different synoptic patterns. Li et al. (2019) used meteorological sounding data and LIDAR-retrieved profiles of aerosol extinction coefficients to investigate the linkage between the PBL structure and air pollution in four pollution episodes during autumn and winter of 2016 at Shenyang, China. The results showed that wind speed and high relative humidity in the PBL exacerbated air pollution near the surface. Liu et al. (2019c) investigated the characteristics, synoptic condition, PBL structure, and sources of a severe fog-haze episode during the December of 2016 and January of 2017 in Beijing-Tianjin-Hebei (BTH), and the results showed the correlation coefficient between PM2.5 concentration and relative humidity, wind speed were 0.56 and −0.2, respectively. Nevertheless, previous studies regarding the connection of synoptic conditions to the haze pollution are limited to case studies and the investigated areas are mainly located in one city, and the relationship between PM2.5 pollution and the structure of the PBL is still not clear.

North China Plain, is one of the regions with the most severe pollution in the eastern China. In this study, meteorological characteristics within PBL in six cities of North China under different level of PM2.5 pollution during January and July in 2017 are analyzed by the weather Research and Forecasting model with Chemistry (WRF-Chem). Aircraft Meteorological Data Relay (AMDAR) was also used to calculate the hourly planetary boundary layer height (PBLH), to test and verify the model accuracy. The rest parts of this paper are organized as follows. Section 2 introduces the observation data, model configuration and verification. Section 3 describes the characteristics of PM2.5 pollution and PBLH, discusses the change of PBLH, winds, temperature, relatively humidity and pressure system among different PM2.5 pollution levels, and explores the influences of these meteorological factors on PM2.5 pollution. Conclusion are drawn in Section 4.

Section snippets

Observation data

Surface PM2.5 concentration during January and July in 2017 were obtained from National Urban Air Quality Real-time Release Platform (http://106.37.208.233:20035/) with a temporal resolution of 1 h. The PM2.5 concentration of the cities was all average data collected from cities’ observation sites.

Surface meteorological observation data at the 1-h time resolution in six cities were obtained from the website Weather Underground (https://www.wunderground.com), including temperature (T), wind

Characteristics of PM2.5 pollution

According to China's air quality standard, PM2.5 pollution is classified into 3 levels based on observed PM2.5 daily average mass concentration: good condition (PM2.5 ≤75 μg m−3), polluted (PM2.5 = 75–150 μg m−3) and severely polluted (PM2.5≥150 μg m−3). Fig. 2 shows the statistics of daily PM2.5 in six cities during January and July of 2017. In January, the sequence of PM2.5 concentration of the cities was SJZ (186.0 ± 79.3 μg m−3) > ZZ (155.6 ± 84.1 μg m−3) > JN (129.5 ± 72.0 μg m−3) > TY

Conclusion

PM2.5 is the most severe air pollution problem in North China, and presents the significant seasonal difference with monthly concentration of 108.7–186.0 μg m−3 in January 2017 and 39.2–56.3 μg m−3 in July 2017. Facing this important issue, this study uses WRF-Chem model to conduct the meteorological and chemical simulations for this region; and then based on the simulation results in six major cities (BJ, TJ, SJZ, TY, JN and ZZ), concludes synergy between various meteorological factors and

Author contributions section

Zhe Lv: Conceptualization, Software, Investigation, Writing - original draft. Wei Wei: Writing - review & editing, Project administration. Shuiyuan Cheng: Methodology, Supervision. Xiaoyan Han: Validation, Investigation, Data curation. Xiaoqi Wang: Writing - review & editing.

Declaration of competing interest

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.

Acknowledgement

This research was financially supported by the National Key Research and Development Program of China (No. 2018YFC0213206), the National Natural Science Foundation of China (No. 91544232 & 51638001), the Beijing Municipal Commission of Science and Technology (Z181100005418017). Additionally, we are grateful to the anonymous reviewers for their constructive comments.

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