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Article

Impact of Precipitation with Different Intensity on PM2.5 over Typical Regions of China

by
Xin Zhao
1,2,
Yue Sun
1,
Chuanfeng Zhao
1,* and
Huifei Jiang
2
1
State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
2
College of Resource and Environment, China Agricultural University, Beijing 100193, China
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(9), 906; https://doi.org/10.3390/atmos11090906
Submission received: 25 July 2020 / Revised: 15 August 2020 / Accepted: 22 August 2020 / Published: 26 August 2020
(This article belongs to the Special Issue Interactions of Aerosols, Clouds, Radiation, Precipitation, and Climate on Regional Scale)
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Abstract

:
Atmospheric aerosol pollution has significant impacts on human health and economic society. One of the most efficient way to remove the pollutants from the atmosphere is wet deposition. This study selected three typical atmospheric pollution regions in China, the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD) regions, as research areas, and used the hourly precipitation and PM2.5 mass concentration data from 2015 to 2017 to investigate the removal impacts of precipitation on PM2.5. The PM2.5 mass concentration difference before and after the hourly precipitation events was used to denote as the impacts of precipitation. Hourly precipitation event was selected so that the time difference between two PM2.5 observations was short enough to limit the PM2.5 change caused by other factors. This study focused on the differences in the removal effect of precipitation on PM2.5 under different precipitation intensities and pollution levels. The results show that both precipitation intensity and aerosol amount affected the removal effect. A negative removal effect existed for both light precipitation and low PM2.5 mass concentration conditions. In contrast, a positive removal effect occurred for both high precipitation and high PM2.5 mass concentration conditions. The removal effect increased with increasing precipitation intensity and PM2.5 mass concentration before precipitation and was consistent with the change trend of wind speed at a height of 100 m. The findings of this study can help understand the mechanism of wet scavenging on air pollution, providing support for air pollution control in future.

1. Introduction

With the rapid development of the economy and urbanization progress in China, more industrial waste gases and particulate matter (PM) have been released to the atmosphere, which threaten the human health and life to a certain extent [1] and have also brought serious ecological and environmental problems [2]. Among various air pollution components, PM with aerodynamic diameters less than 2.5 µm (PM2.5) is often the major contributing factor to air pollution [3,4], which has drawn broad attention worldwide.
Aerosol particles can affect the weather and climate by serving as cloud condensation nuclei (CCN) or changing the radiation balance [5,6,7,8,9,10,11]. By serving as CCN, aerosol particles can increase cloud droplet number concentration and thus enhance the cloud reflection to solar radiation, which is referred to as the cloud albedo effect [8]. For longwave radiation, by serving as CCN, aerosol particles can increase thin cloud thermal emissivity and trap more longwave radiation within the atmosphere, which is referred to as the cloud thermal emissivity effect [6,11]. These two effects are the shortwave and longwave radiative effects from aerosol-cloud interactions. By reducing cloud droplet effective radius (re), aerosol particles could also reduce the efficiency of precipitation and prolong the cloud lifetime, which is referred to as the cloud lifetime effect [5]. In contrast, aerosol can invigorate the convective clouds by releasing more latent heat when freezing, which is referred to as the invigoration effect [12]. These two effects are both the impacts of aerosols on precipitation from the aerosol-cloud interaction. Absorbing aerosol can also burn out the clouds when absorbing solar radiation within clouds, which is referred to as the aerosol semi-direct effect [13].
On the other hand, aerosols can also be affected by weather and climate, such as dispersion by winds and wet scavenging by precipitation. Compared to the studies regarding aerosol effects on weather and climate, the impacts of weather and climate on aerosols are relatively less studied. From the climatological view, aerosols can be influenced by the global warming and large-scale circulation variations, such as the Arctic warming [14], the El Niño event [15], and so on. From the weather view, aerosols can be influenced by various meteorological factors [16]. Solar radiation and water vapor can help the conversion of gases to particles and the growth of fine aerosol particles, and thus increase aerosol concentration [17]. The winds can help disperse and transport aerosol particles to other locations so that the air quality is improved. However, they are still kept within the atmosphere. The mechanism in which the aerosols are really removed from the atmosphere is the dry and wet deposition [18,19,20,21]. The dry deposition is the process of direct deposition to the surface by the gravity of the particulate matters or by the absorption of other materials [22,23]. Wet deposition is the process of removing the PM in the air to the surface through precipitation, which plays a significant role in reducing the PM mass concentration in the air [19,24]. The wet deposition process can be divided into intracloud removal and undercloud removal [25]. Undercloud removal, which scavenges fine PM particles under the cloud, is also an important process for the formation of acid rain [26], so it has received widespread attention by the society. Many studies have confirmed the removal effect of precipitation on PM2.5 by different methods. For example, Tai et al. [27] established a multivariate linear model to investigate the precipitation scavenging effect on PM2.5. Garrett et al. [19] estimated the contribution of precipitation scavenging effect on aerosol amount based on the seasonal variation analysis. Gong et al. [14] applied a generalized additive model to analyze the precipitation scavenging effect on PM2.5.
There are various influential factors to the precipitation scavenging effect on PM2.5, which have been investigated by recent studies. From the perspective of precipitation, Chate [28] found that the PM2.5 mass concentration decreased with the increase of precipitation intensity. Mircea et al. [29] found a linear correlation between the change rate of PM2.5 mass concentration and precipitation intensity. Li et al. [30] found that the PM2.5 mass concentration and precipitation showed a significant negative correlation. Huang et al. [31] found that the PM2.5 mass concentration increased with the increase of precipitation duration, and the correlation was strong. Pranesha et al. [32] found that the change in PM2.5 mass concentration is related to the size of raindrops, as raindrops with a diameter greater than 1 mm play a major role in removing PM2.5 with a diameter of 1–2 microns. Bae et al. [33] pointed out that the distribution of raindrop size is an important factor for efficiency of undercloud aerosol removal. There are also studies that investigated the precipitation scavenging effect from the perspective of PM2.5. Tai et al. [27] found that precipitation removes different types of PM2.5 with different efficiencies. Taylor et al. [34] and Andronache et al. [35] found that the removal effect of precipitation on PM2.5 is related to the size distribution of PM2.5. Sun et al. [21] found that when the PM2.5 mass concentration before precipitation is large, the removal effect of precipitation is more obvious.
In recent years, additional meteorological factors (such as winds) have been considered in the analysis about the removal effect of precipitation on PM2.5. As known, winds can disperse and transport the aerosol particles to other locations. Gong et al. [14] found that the wind speed is linearly related to the PM2.5 mass concentration after logarithm conversion based on a quantitative analysis. Yu et al. [36] indicated that it is easy for winds to blow up the particles attached to the object when the wind speed increases to a certain level, thereby causing the increasing of PM2.5 mass concentration [37]. Li et al. [30] found that the wind direction will affect the PM2.5 mass concentration by affecting the PM2.5 diffusion direction. Chen et al. [16] figured out that the PM2.5 mass concentration is affected by various meteorological factors including air pressure, temperature, winds, relative humidity, and so on. However, we should note that most existing studies have taken a single city or site as the research area, which generally has problems in representing a large domain. Moreover, uncertainty remains regarding the impacts of precipitation on PM2.5 in China, particularly over the well-known pollution regions.
This study took three typical regions in China as the research areas to investigate the precipitation impacts on concentration of PM2.5, and the effects from winds during precipitation period were also briefly discussed. By limiting the particular precipitation events, we intended to identify quantitatively the variation of precipitation impacts on PM2.5 with both precipitation intensity and PM2.5 amount. The paper is organized as follows. Section 2 provides the study area, data, and method. The analysis and results are presented in Section 3. Section 4 summarizes the findings and gives a discussion.

2. Data and Method

2.1. Region of Interest

This study focused on three typical pollution regions in China, which are the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) regions, as shown in Figure 1. The BTH region lies in Northern China, and belongs to the semihumid area, which is located on the ‘leeward slope’ on the east side of Taihang Mountains and the south side of Yanshan Mountains, forming a semi-closed terrain. The PRD region, closed to the South China Sea, lies in the southeast of China and has abundant precipitation throughout the year, making the climate warm and humid in this region. Similar to the PRD region, the YRD region also lies in southern China but in northern part of PRD region, generally with less water supply and precipitation.

2.2. Data

Due to the site difference, we selected the observations of precipitation and PM2.5 at the same site or closest sites at the same time to form a pair of time series to make further investigation. To avoid the impacts from temporal variation of PM2.5 due to either emission or planetary boundary layer variations, we only investigated the hourly precipitation events. For winds near the site, this study analyzed the changes of wind speed over a region around the precipitation site. Note that hourly observation data were adopted in this study.
The precipitation data used in this study was the integration product of ground observation and Climate Prediction Morphing (CMORPH) hourly precipitation products in China. The CMORPH data is a fusion precipitation product with a resolution of 0.1° × 0.1° generated by the National Oceanic and Atmospheric Administration (NOAA) and the Climate Prediction Center (CPC) of the United States Atmospheric and Oceanic Administration. The integration product is generated using a PDF (probability density function matching method) + OI (optimum interpolation method) two-step fusion method based on the observation of precipitation at national automatic sites and the precipitation data retrieved by the CMORPH satellite [38].
The hourly PM2.5 mass concentration data used in this study is provided by China National Environmental Monitoring Station, a national air quality real-time publishing platform [39]. PM2.5 is measured by the β-ray absorption method and the micro-oscillation balance method, and the reference method is used for testing. The PM2.5 data have hourly time resolution and have been qualified officially, and only the data with quality assurance were used in this study.
The hourly reanalysis wind data at 100 m above the ground level from the European Center for Medium-Term Weather Forecast (ECMWF) was used in this study [40]. This data is obtained by applying the laws of physics to combine model data with observational data from all over the world into a global complete and consistent data set, with a spatial resolution of 0.25° × 0.25°. The hourly wind data at the grid that the precipitation site lies in is used to represent the large-scale wind condition at the ground site.

2.3. Method

As indicated earlier, in order to reduce the impact of daily changes in PM2.5, this study selected one-hour precipitation events within a relatively stable period (15:00–17:00 Beijing time) of daily PM2.5 mass concentration as the research objects. We screened out 1083 cases from 78 sites in the BTH, 5341 cases from 56 sites in the YRD, 2491 cases, from 191 sites in the PRD region for analysis. By doing these data selections, our method was more reliable and meaningful to investigate the precipitation impacts on aerosols, because this processing method limited the natural change of PM2.5 due to both planetary boundary layer (PBL) variation or emission variation. Under the premise of clarifying the annual (daily) changes in regional precipitation and PM2.5, this study focused on the differences in the removal effect of precipitation on PM2.5 under different precipitation intensities and different pollution levels.
According to the studies of Olszowski [41] and Wang and Feng [24], the removal effect (ΔC) of precipitation on PM2.5 can be used to represent the change of PM2.5 mass concentration after precipitation, which is defined as
Δ C = C b − C p C b × 100 %
where Cb is the PM2.5 mass concentration value before precipitation, and Cp is the PM2.5 mass concentration value after precipitation.
When ΔC is larger than 0, the PM2.5 mass concentration in the atmosphere decreases with precipitation, which is a positive removal process. In contrast, when it is smaller than 0, the PM2.5 mass concentration in the atmosphere increases with precipitation, which is a negative removal process. When it is equal to 0, the mass concentration of PM2.5 in the atmosphere has no change, which implies that precipitation plays no role to the PM2.5 removal process.
This study focused on the relationship between the removal effect and precipitation intensities, along with the relationship between the removal effect and pollution levels. Based on the number of samples, the precipitation events were divided into three categories according to the precipitation intensity and the PM2.5 mass concentration before precipitation to explore their potential differences in the removal effect of the precipitation on the PM2.5. In addition, the effect of winds on the PM2.5 mass concentration were also investigated and discussed based on hourly winds from ECMWF by selecting typical cases of precipitation events.

3. Analysis and Result

3.1. Temporal Variation of Precipitation and PM2.5

Figure 2 shows the monthly variation of precipitation (bar plots) and PM2.5 (lines) at three study regions, the BTH, YRD, and PRD regions. For all three study regions, there were clear seasonal variations of precipitation, with maximum values in summer (June, July, and August) and minimum values in winter (December and the following January and February). This should be associated with the Asian monsoon system. In summer, the southeast monsoon prevails and carries abundant water vapor from the Pacific Ocean, which enriches summer precipitation. In contrast, wintertime northwest monsoon from the Eurasian continent causes low water vapor content and high-pressure systems, resulting in much less precipitation.
Statistically, the annual total precipitation amounts in the BTH, YRD, and PRD regions are about 550 mm, 1300 mm, and 1800 mm, respectively. The total annual precipitation in the PRD region is the largest, and in the BTH region is the least. The regional difference is highly related to their relative location in China. As known, the PRD is located in the southeast, closed to the South China Sea, and has sufficient water vapor to provide the necessary support for precipitation. Furthermore, it is located in the subtropical region, with strong solar radiation, which can promote the development of convection and support the formation of precipitation. With lower solar radiation than that in the PRD region, the precipitation in YRD region is slightly less than in the PRD region. It is worth mentioning that due to the frequent occurrence of typhoons in recent years, the YRD and the PRD regions have been seriously affected. Particularly, large amounts of water vapor and then precipitation have been brought to these regions. Differently, the BTH region lies in northern China with insufficient water supply and less precipitation amount.
For the study period, the annual precipitation amount varied with time, with the largest value in 2016 and the lowest value in 2017, and the monthly averaged precipitation amount showed clear temporal variation, with the largest values in July 2016 over BTH region, in June 2015 over the YRD region, and in May 2015 over PRD region. In addition to the Asian monsoon system, the temporal variation of precipitation could be also associated with the El Niño event from 2014 to 2016. For example, due to the influence of El Niño, there was a trend of flooding in the southern China and drought in the northern China in 2015. As a result, April was the month with the highest precipitation in 2015 in BTH region, and the precipitation in June, July, and August significantly decreased compared with the same period in previous years.
Figure 2 also shows the monthly variations of PM2.5 mass concentration in BTH, YRD, and PRD regions. Different from precipitation, the monthly variations of PM2.5 mass concentration demonstrate the lowest values from June to September and maximum values in December or January. It was found that the PM2.5 mass concentration in the BTH region was between 37 to 147 μg·m−3 in the past three years. According to the PM2.5 mass concentration level, there were 25 months with good air quality, 8 months light pollution, and 3 months moderate pollution. The PM2.5 mass concentration in the YRD region was between 24 μg·m−3 to 87 μg·m−3, which was excellent in air quality for 12 months, good for 22 months, and lightly polluted for 2 months. The PM2.5 mass concentration in the PRD region was between 15 μg·m−3 to 57 μg·m−3, which was excellent in air quality for 23 months and good for 13 months. Therefore, the air quality in the PRD region was the best in the three years from 2015 to 2017, followed by the YRD, and the worst in BTH region.
The air quality status and temporal variation of PM2.5 is the combination resultant of the PM2.5 emissions, topography, and meteorological factors. The BTH region is located on the ‘leeward slope’ on the east side of Taihang Mountains and the south side of Yanshan Mountains, forming a semiclosed terrain. Therefore, the role of the northwest monsoon in autumn and winter seasons is greatly weakened. PM2.5 is poorly diffused due to unfavorable meteorological conditions and is easily accumulated. In winter, various human activities, such as coal burning and heating, emit a large amount of PM2.5 into the atmosphere and deteriorate the air quality. In contrast, the planetary boundary layer is high and precipitation is strong in summer (as shown in Figure 2), making the PM2.5 in BTH region low. Note that the air quality in the BTH area has gradually improved from 2015 to 2017. The PM2.5 in the YRD and PRD regions has a poor diffusivity under the influence of long-term ‘calm weather’ and ‘radiation inversion’ in winter. Moreover, the relatively high humidity helps the hygroscopic growth of PM2.5 particulates, further causing pollution. Similarly, the planetary boundary layer is high and precipitation is strong in summer (as shown in Figure 2), making the PM2.5 in YRD and PRD region low. Actually, a good negative relationship between precipitation and PM2.5 in seasonal variation has been shown in Figure 2. In next section, we investigate the scavenging effect of precipitation on PM2.5.

3.2. Removal Effect of Precipitation on PM2.5

In order to clearly show the removal effect of precipitation on PM2.5, we first examined the diurnal variation of PM2.5 mass concentration for cases with and without precipitation over the BTH, YRD, and PRD regions from 2015 to 2017, which is shown in Figure 3. Compared with the averages for cases with precipitation, the averaged mass concentration of PM2.5 for cases without precipitation was much larger, along with much more significant diurnal variations. This result implies that significant scavenging effect of precipitation on PM2.5 could exist while other influential factors, such as winds and planetary boundary layer, could also play roles. We next investigate the removal effect of precipitation on PM2.5 for different precipitation intensities and pollution levels.
(a) Removal effect of precipitation on PM2.5 under different precipitation intensities
As shown in Figure 3, there are clear diurnal variation of PM2.5 mass concentration, mainly due to the diurnal variation of planetary boundary layer (PBL). Several studies have shown that the PBL varies significantly diurnally and seasonally [42,43,44]. For diurnal variations in the three study regions, it is generally 200–300 m at early morning, increases to about 1000 m around noon and varies little between noon and 17:00 Beijing time, and decreases after 17:00 Beijing time [42]. To isolate the impacts from PBL variation, we investigated the removal effect of precipitation on PM2.5 for a period when PBL varies little, which is 15:00–17:00 Beijing time. Considering this time period is short, we only focused on short-term convective precipitation events with duration time no more than one hour. By binning the hourly precipitation with an interval of 0.02 mm/h, Figure 4 shows the variation of precipitation removal effect with precipitation intensity over the BTH, YRD, and PRD regions. Note that three ranges of precipitation intensities have been considered. For all three regions, the removal effects of precipitation on PM2.5 had clear variation with precipitation intensity, which changed from negative values to positive values with the increase of precipitation intensity. That is to say, with the increase of precipitation intensity, the removal effect changed from the negative removal process to the positive removal process. This finding is consistent with that found by Sun et al. [21] When the precipitation intensity is low, the negative value of removal effects indicates that PM2.5 mass concentration increases while the increased amount is generally less than 20%. A likely explanation is as follows. When the precipitation is weak, the scavenging effect due to the collision of precipitation droplets and PM2.5 particles is generally low, but the hygroscopic growth of aerosol particles associated with the high humidity becomes strong, which results in an increasing combination effect on PM2.5 mass concentration. With the increase of precipitation intensity, the collision efficiency becomes larger and so as the precipitation scavenging effects, which will result in a more positive removal effect of precipitation on PM2.5. At the same time, heavy precipitation is often accompanied by strong winds, which further improves the PM2.5 diffusion capacity, reducing more the PM2.5 mass concentration.
The removal effects over the three areas all showed the same change law with precipitation intensity, but there were also differences. In BTH region, the negative removal process was dominant when the precipitation intensity was less than 0.28 mm/h, and the positive removal process was dominant when the precipitation intensity was more than 2 mm/h. For precipitation intensity between 0.28 and 2 mm/h, the removal efficiency slightly increased with intensity (from negative to positive) in tendency, but values fluctuated around the 0 value. In other words, there were three stages of removal effects (negative, roughly neutral, and positive) with precipitation intensities. The removal effect also showed the same three stages in the YRD and PRD regions, but with different threshold values of precipitation intensities. The three stages were for precipitation intensities less than 0.6 mm/h, 0.6 to 4.5 mm/h, and more than 4.5 mm/h in the YRD region, and for precipitation intensities less than 0.25 mm/h, 0.25 to 5 mm/h, and more than 5 mm/h in the PRD region.
Shortly, for precipitation intensities less than 0.28 mm/h, 0.6 mm/h, and 0.25 mm/h, the PM2.5 mass concentration increased with precipitation amount in the BTH, YRD and PRD regions, respectively. The threshold values for the negative removal effects were small and close to each other in values among the three regions. In contrast, for precipitation intensities larger than 2 mm/h, 4.5 mm/h, and 5 mm/h, the PM2.5 mass concentration decreased with precipitation amount in the BTH, YRD, and PRD regions, respectively. This result is similar to the findings of Kluska et al. [45]. They took Rzeszów (SE Poland) as the research area and found that aerosol concentrations (pollen) decreased only for precipitation intensities less than approximately 5 mm/h, which indicates that precipitation only decreases the aerosol concentration when the precipitation intensity is greater than a certain value. Combined with the findings from Kluska et al. [45], the critical value of precipitation intensity below which the aerosol concentration increases with precipitation is different among different regions. Since the removal efficiency is dependent on both the hygroscopic growth of aerosols and the collision-coalescence efficiency of precipitation droplets, the threshold values of precipitation intensity for both negative and positive removal efficiencies should be related to both PM2.5 and precipitation properties, along with the potential impacts from other meteorological components such as winds. In next section, the change of removal efficiency to PM2.5 mass concentration is further investigated.
To confirm the findings in this section, we conducted further analysis by dividing the precipitation into three groups with equal sample size according to the precipitation intensity. Correspondingly, the threshold values of precipitation intensities for the classifications were 0.16 mm/h and 0.40 mm/h over BTH region, 0.16 mm/h and 0.36 mm/h over YRD region, and 0.20 mm/h and 0.56 mm/h over PRD region. With this classification, both positive and negative removal effects were found at different precipitation intensity ranges. We then counted the percentages of samples with positive or negative removal effects, which are shown in Table 1 for BTH, YRD, and PRD regions. As can be seen from Table 1, when the precipitation intensity increased, the sample percentage with positive removal effect gradually increased, and the sample percentage with negative removal effect gradually decreased. However, the decreasing (increasing) trend of positive (negative) removal effect with precipitation amount was as clear as that shown in Figure 4. A potential reason is that the threshold values for equal sample size analysis were too close to each other due to the large amount of weak precipitation, which made the differences among the three ranges small. Actually, there were even abnormal trends in the YRD (the sample percentage with positive removal efficiency increased first and then decreased with the intensity of precipitation) and PRD regions (the sample percentage with negative removal efficiency decreased first and then increased with precipitation intensity). More potential reasons are further discussed with case analysis in next section.
(b) Removal effect of precipitation on PM2.5 under different pollution levels
We classified the PM2.5 into three groups with the same sample volume based on PM2.5 mass concentration before precipitation, and then further analyzed the change of precipitation removal effect on PM2.5. The threshold values of PM2.5 for the three-group classification were 25 μg·m−3 and 55 μg·m−3 over TBH, 19 μg·m−3 and 37 μg·m−3 over YRD, and 14 μg·m−3 and 26 μg·m−3 over PRD.
Table 2 shows the sample frequencies with positive, negative, and neutral removal effects over the three study regions. For all three study regions, it is clear that the negative removal effect of precipitation dominated at low PM2.5 mass concentration condition, and the sample frequency with negative removal effect of precipitation decreased with PM2.5 mass concentration. In contrast, the sample frequency with positive removal effect of precipitation increased with PM2.5 mass concentration, and the positive removal effect played a dominant role under heavy pollution condition. A likely explanation is that the collision-coalescence effect increased with PM2.5 mass concentration due to the increased particle density, which made the precipitation scavenging effect more prominent at heavy pollution condition. Differently, when the PM2.5 mass concentration was low, the collision-coalescence effect was weak, and the hygroscopic growth of aerosol particles could be prominent instead, making the negative removal cases more frequent.
Considering that the removal effect of precipitation on PM2.5 depends on both precipitation intensity and PM2.5 amount, Figure 5 shows the change of removal effect of precipitation for three equal-sample bins of precipitation intensities under three different PM2.5 mass concentration conditions over the BTH, YRD, and PRD regions. Consistent with the findings from Table 1 and Table 2 for similar precipitation intensity, the removal effect increased with the increase of PM2.5 mass concentration, that is, the precipitation scavenging was more effective when PM2.5 mass concentration before precipitation was higher. For a similar PM2.5 mass concentration before precipitation, the removal effect roughly increased with the increase of precipitation intensity. Note that the negative removal effect values under low PM2.5 mass concentration conditions were clearly larger than the positive removal effect values under high PM2.5 mass concentration conditions. A likely reason is that the PM2.5 mass concentration before precipitation was much smaller at low PM2.5 mass concentration conditions, making the removal efficiency (relative change of PM2.5 mass concentration) much higher than that for heavy pollution conditions.
(c) Case analysis
Figure 5 shows that under low PM2.5 mass concentration conditions, the negative removal effect decreased first and then increased with precipitation intensity over BTH, and increased first and then decreased with precipitation intensity over YRD. Under moderate PM2.5 mass concentration conditions, the removal effect increased first and then decreased with precipitation intensity over PRD. These results imply that other influential factor than precipitation, such as winds, could also affect the PM2.5 mass concentration simultaneously. We here give a brief discussion about the winds impact on the precipitation removal effect to PM2.5 using case analysis.
The average wind speed, at a height of 100 m within a domain of 0.25° × 0.25° where the study site is located, was explored for three selected cases in the three study regions. The three selected precipitation event cases are those which occurred at the site of Tangshan Ceramic Company (118.22° E, 39.67° N) in the BTH region at 17:00 on 12 March 2016, at the site of the Zhenjiang Vocational Education Center (119.49° E, 32.22° N) in the YRD region at 16:00 on 26 June 2016, and at the Nanyou Station in Shenzhen City (113.92° E, 22.52° N) in the PRD region at 16:00 on 5 September 2017. Note that for these three cases, the precipitation intensity and PM2.5 mass concentrations were all different. For the BTH region case, the precipitation intensity was 3.25 mm/h and the PM2.5 mass concentration before precipitation was 90 μg/m3. For the YRD region case, the precipitation intensity was 6.76 mm/h and the PM2.5 mass concentration before precipitation was 51 μg/m3. For the PRD region case, the precipitation intensity was 5.70 mm/h and the PM2.5 mass concentration before precipitation was 18 μg/m3.
Figure 6 shows the hourly variation of wind speed (at a height of 100 m above surface) and removal effect of precipitation on PM2.5 from 15:00 to 23:00 on the study day over the BTH, YRD, and PRD regions. Note that the precipitation event was still an event with a duration time of no more than one hour, while the PM2.5 mass concentration at different hours after precipitation was used for the calculation of removal effect at that time in Figure 6. Roughly, the precipitation removal effect and the wind speed had a similar hourly variation trend. This could be easily understood since the winds can enhance the diffusivity of PM2.5 and reduce its mass concentration. Figure 6 shows that the wind speeds at the hour when precipitation event occurred in BTH (17:00), YRD (16:00) and PRD (16:00) regions were about 4 m/s, 2 m/s, and 3 m/s, and the removal efficiencies at those hours were all negative. This suggests that even with considerable wind speeds, the PM2.5 still likely increases with precipitation for particular cases due to the aerosol’s hygroscopic growth. However, when the winds are large enough, such as more than 4.7 m/s, 3.1 m/s, and 6.6 m/s over BTH, YRD and PRD, the impacts of winds could cause the removal effect change from negative values to positive values. Of course, when the precipitation is large, the positive removal effect of precipitation on PM2.5 could be further enhanced by the accompanied winds. Gong et al. [14] also found the significant removing effects of winds on PM2.5 particles, which indicated that nearly 60% of PM2.5 mass concentrations could be reduced when the wind speed was up to 6 m/s. Thus, the combination of precipitation and winds could enhance the removal effects of precipitation on PM2.5, which is definitely worthy of further investigation in future.

4. Conclusions

Seasonal variations of precipitation and PM2.5 mass concentration were investigated first. The precipitation intensity showed clear seasonal variation, with maximum values in summer and minimum values in winter for all three study regions of the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) regions. This is highly associated with the Asian monsoon system, including both the southeast and southwest summer Monsoon and northwest winter monsoon. Due to these monsoon system and water supply, the precipitation amount was the largest in the PRD region and the smallest in BTH region. Abnormal changes in the seasonal variations were also affected by the El Niño events. Similar seasonal variations have been found for PM2.5 mass concentration, implying the potential removal effects of precipitation on PM2.5, while other meteorological components, such as winds and planetary boundary layer heights, can also affect the PM2.5 mass concentration. In all three study regions, the PM2.5 mass concentration under clear sky was much larger than that under precipitation conditions, also implying the potential removal effect of precipitation on PM2.5.
The removal effect of precipitation on PM2.5 was then investigated. The study shows that the removal effect of precipitation on PM2.5 is dependent on both the precipitation intensity and PM2.5 mass concentration, along with other coexisting meteorological components. We found that the negative removal effect dominated when the intensity of precipitation was weak. With the increase of precipitation intensity, the removal effect of precipitation on PM2.5 changed from negative to positive values gradually. When the precipitation was heavy, the positive removal effect dominated and reduced the PM2.5 mass concentration clearly. A likely mechanism has been proposed: The hygroscopic growth of aerosols plays a more (less) important effect than the collision-coalescence between precipitation droplets and aerosols when the precipitation is weak (strong), making PM2.5 mass concentration increase (decrease). Further analysis using the equal-sample bins of precipitation data confirmed these results. By binning the PM2.5 mass concentration into three groups with the same sample size, this study found that the negative removal effect dominated for low PM2.5 mass concentration condition, and positive removal effect dominated for high PM2.5 mass concentration condition. This was likely due to the increasing collision-coalescence efficiency of precipitation with aerosol particles when aerosol density (so PM2.5) increased. Shortly, the removal effect was higher when the pollution was heavier and precipitation was stronger.
The impacts of wind speed on the removal effect of precipitation were finally discussed using case studies based on wind speed at 100 m above the surface. As expected, a positive relationship was found between removal effect and wind speed. However, negative removal effects were still found for three cases when the wind speed was high, which indicates that the hygroscopic growth effect was prominent for particular cases. We can also expect that the positive removal effect by precipitation when precipitation or pollution is heavy could be enhanced by the accompanied winds.

Author Contributions

Methodology, X.Z.; Investigation, H.J.; writing—original draft preparation, X.Z.; writing—review and editing, C.Z., Y.S. All authors have read and agree to the published version of the manuscript.

Funding

This work was funded by the National Natural Science Foundation of China (NSFC) (41925022), the Ministry of Science and Technology of China National Key R&D Program (2019YFA0606803, 2017YFC1501403), the Hebei Province Key Research and Development project (20375402D), the Beijing Municipal Commission of science and technology (grant D171100007917001), and the State Key Laboratory of Earth Surface Processes and Resource Ecology.

Acknowledgments

Data used in this study are available online from http://cdc.cma.gov.cn/sksj.do?method=ssrjscprh, http://beijingair.sinaapp.com and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5.

Conflicts of Interest

The authors declare no conflict of interest.

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Atmosphere 11 00906 g001 550
Figure 1. The China map with three typical pollution regions investigated in this study, which are the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) regions.
Figure 1. The China map with three typical pollution regions investigated in this study, which are the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) regions.
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Figure 2. The monthly variations of precipitation and PM2.5 mass concentration in the three research areas of (a) BTH, (b) YRD, and (c) PRD.
Figure 2. The monthly variations of precipitation and PM2.5 mass concentration in the three research areas of (a) BTH, (b) YRD, and (c) PRD.
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Figure 3. Diurnal variation of PM2.5 mass concentration averaged for cases with and without precipitation during the period 2015–2017 over (a) BTH, (b) YRD, and (c) PRD regions. Note the time is Beijing time (BJT).
Figure 3. Diurnal variation of PM2.5 mass concentration averaged for cases with and without precipitation during the period 2015–2017 over (a) BTH, (b) YRD, and (c) PRD regions. Note the time is Beijing time (BJT).
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Figure 4. Scatterplot of removal effect versus precipitation intensity for three different ranges of precipitation intensities over (a) BTH, (b) YRD, and (c) PRD regions. Note that the three different ranges of precipitation intensities are classified for negative, neutral, and positive dominant precipitation removal effects, respectively.
Figure 4. Scatterplot of removal effect versus precipitation intensity for three different ranges of precipitation intensities over (a) BTH, (b) YRD, and (c) PRD regions. Note that the three different ranges of precipitation intensities are classified for negative, neutral, and positive dominant precipitation removal effects, respectively.
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Figure 5. Differences in removal effect of precipitation under different precipitation intensity (unit mm/hour) and PM2.5 mass concentration (before precipitation) (unit μg/m3) conditions over (a), BTH, (b) YRD, and (c) PRD regions.
Figure 5. Differences in removal effect of precipitation under different precipitation intensity (unit mm/hour) and PM2.5 mass concentration (before precipitation) (unit μg/m3) conditions over (a), BTH, (b) YRD, and (c) PRD regions.
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Figure 6. Hourly variation of removal effect of precipitation and wind speed at a height of 100 m above the ground for selected precipitation event cases at the site of Tangshan Ceramic Company (118.22° E, 39.67° N) in the BTH region at 17:00 on 12 March 2016 (a), at the site of the Zhenjiang Vocational Education Center (119.49° E, 32.22° N) in the YRD region at 16:00 on 26 June 2016 (b), and at the Nanyou Station in Shenzhen City (113.92° E, 22.52° N) in the PRD region at 16:00 on 5 September 2017 (c). “BJT” represents Beijing time.
Figure 6. Hourly variation of removal effect of precipitation and wind speed at a height of 100 m above the ground for selected precipitation event cases at the site of Tangshan Ceramic Company (118.22° E, 39.67° N) in the BTH region at 17:00 on 12 March 2016 (a), at the site of the Zhenjiang Vocational Education Center (119.49° E, 32.22° N) in the YRD region at 16:00 on 26 June 2016 (b), and at the Nanyou Station in Shenzhen City (113.92° E, 22.52° N) in the PRD region at 16:00 on 5 September 2017 (c). “BJT” represents Beijing time.
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Table
Table 1. Sample percentage with positive, neutral, and negative removal effect for three equal-sample bins of precipitation intensity in the BTH, YRD, and PRD regions.
Table 1. Sample percentage with positive, neutral, and negative removal effect for three equal-sample bins of precipitation intensity in the BTH, YRD, and PRD regions.
BTHYRDPRD
Precipitation (mm/h)<0.16[0.16,0.40)≥0.40≤0.16(0.16,0.36]>0.36<0.20[0.20,0.56)≥0.56
Sample percentage with positive removal effect (%)41.2845.3149.5843.8644.7644.1736.3337.5942.79
Sample percentage with neutral removal effect (%)6.104.437.3210.4410.9311.2317.1615.8516.11
Sample percentage with negative removal effect (%)52.6250.2643.1045.7044.3144.6046.5146.5641.10
Table
Table 2. Sample percentage with positive, neutral, and negative removal effect for three equal-sample bins of pollution levels in the BTH, YRD, and PRD regions.
Table 2. Sample percentage with positive, neutral, and negative removal effect for three equal-sample bins of pollution levels in the BTH, YRD, and PRD regions.
BTHYRDPRD
Pollution (μg/m3)≤25(25,55]>55≤19(19,37]>37≤14(14,26]>26
Sample percentage with positive removal effect (%)32.8749.4453.9933.4145.3854.1725.4240.9150.94
Sample percentage with neutral removal effect (%)8.016.703.0317.189.635.7425.5415.277.93
Sample percentage with negative removal effect (%)59.1243.8642.9849.4144.9940.0949.0443.8241.13

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Zhao, X.; Sun, Y.; Zhao, C.; Jiang, H. Impact of Precipitation with Different Intensity on PM2.5 over Typical Regions of China. Atmosphere 2020, 11, 906. https://doi.org/10.3390/atmos11090906

AMA Style

Zhao X, Sun Y, Zhao C, Jiang H. Impact of Precipitation with Different Intensity on PM2.5 over Typical Regions of China. Atmosphere. 2020; 11(9):906. https://doi.org/10.3390/atmos11090906

Chicago/Turabian Style

Zhao, Xin, Yue Sun, Chuanfeng Zhao, and Huifei Jiang. 2020. "Impact of Precipitation with Different Intensity on PM2.5 over Typical Regions of China" Atmosphere 11, no. 9: 906. https://doi.org/10.3390/atmos11090906

APA Style

Zhao, X., Sun, Y., Zhao, C., & Jiang, H. (2020). Impact of Precipitation with Different Intensity on PM2.5 over Typical Regions of China. Atmosphere, 11(9), 906. https://doi.org/10.3390/atmos11090906

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