Advances in Meteorology

Advances in Meteorology / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 2398146 | https://doi.org/10.1155/2020/2398146

Yue Zhou, Yanyu Yue, Yongqing Bai, Liwen Zhang, "Effects of Rainfall on PM2.5 and PM10 in the Middle Reaches of the Yangtze River", Advances in Meteorology, vol. 2020, Article ID 2398146, 10 pages, 2020. https://doi.org/10.1155/2020/2398146

Effects of Rainfall on PM2.5 and PM10 in the Middle Reaches of the Yangtze River

Academic Editor: Giulia Pavese
Received07 Nov 2019
Revised26 Jan 2020
Accepted04 Jul 2020
Published21 Jul 2020

Abstract

Based on the PM2.5 and PM10 mass concentration data obtained from 51 national air quality monitoring stations and the corresponding rainfall intensity data in automatic meteorological stations in Hubei Province from 1 January 2015 to 31 December 2017, the impact of rainfall intensity on PM mass concentrations under relatively different humidity conditions was analyzed. The results showed that light rain occurred most frequently in the pollution process, with Xiangyang being affected for up to 587 h. PM concentration would not change drastically under the effect of precipitation. Mean rainfall intensity responsible for wet growth of PM10 and PM2.5 was mainly <0.5 mm/h, while that responsible for wet removal of PM2.5 was significantly higher (>1.4 mm/h) than that of PM10 (>1.0 mm/h). Precipitation was more likely to produce a wet removal effect for a greater initial value of PM mass concentration, and on the contrary, a wet growth effect was more likely, with the threshold of PM10 mass concentration being 150 μg/m3 and that of PM2.5 mass concentration being 95 μg/m3. Wet removal played a leading role in lower humidity (∼60%) and greater rainfall intensity, but wet growth played a leading role in higher humidity (∼90%) and lower rainfall intensity. As the precipitation level increased (rainfall ≥1.5 mm·h−1), the wet removal to PM10 mass concentration was enhanced more obviously. The variations of PM2.5 had similar distributions to those of PM10 under the effect of precipitation, but the wet removal effect of precipitation was weakened and the wet growth effect was enhanced.

1. Introduction

Atmospheric aerosols, the suspended systems of liquid or solid particles in the air, constitute an important part of the atmosphere [13] and are a key factor affecting environmental and climate change. Atmospheric pollution is a weather phenomenon caused by accumulation of atmospheric aerosols through local emissions and regional transmission [47]. The emission source intensity of atmospheric pollutants determines the atmospheric pollution degree, while meteorological conditions determine the outbreak, persistence, and elimination of pollution weather.

As a key meteorological element affecting pollutant concentration, precipitation dominates the process of wet removal in the atmosphere as one of the most important mechanisms in self-purification of the atmosphere [8]. Wet removal of aerosol particles is the process in which particles eventually fall to the ground after being removed by atmospheric coagulants, mainly caused by Brownian diffusion and inertial collision [9]. However, the humidification effect of precipitation on the near ground significantly enhances the influence of hygroscopic growth on aerosols and accelerates formation of secondary aerosols, resulting in increased mass concentration [10]. The impact of precipitation on aerosols involves a quite complicated mechanism, which closely concerns not only the characteristics of precipitation intensity, rainfall, raindrop spectrum, and final velocity of raindrops [11, 12], but also the aerosol particle size, spectral distribution, and chemical composition [13, 14].

Research on wet removal of precipitation has mainly considered efficiency of precipitation in removing aerosol particles of different particle sizes in observational experiments. Related studies have pointed out that precipitation mainly removes aerosol particles of size <1 μm and 2.5–10 μm [15, 16], while those of 0.2–2 μm are difficult to remove [1719]. Precipitation intensity directly affects aerosol removal efficiency. Short-term heavy precipitation helps remove particles of diameter <2.2 μm, but long-term weak precipitation facilitates removal of particles >2.2 μm [20]. In general, increased precipitation intensity will enhance its wet removal ability. Yao et al. [21] and Zhao and Zheng [22] further established a model on correlation between precipitation intensity and aerosol removal coefficient. At the same time, wet removal effect of precipitation also concerns initial concentration of aerosol before precipitation. For a higher initial concentration, the wet removal effect is more significant in the same context. Xu et al. [23] pointed out that, under 70 μg/m3 in winter and 45 μg/m3 in other seasons, the PM2.5 mass concentration decreased in more than 80% of precipitation processes. Related research also shows that PM2.5 mass concentration does not decrease but increases after precipitation in actual observations [24]. Yue et al. [25] demonstrated that processes with daily precipitation <10 mm produce a humidification effect significantly stronger than its wet removal effect on PM2.5. The wet removal effect of precipitation is closely related to ambient humidity. The scouring coefficient of hygroscopic particles constitutes a function of relative humidity. To achieve the same removal effect, the precipitation intensity required for 50% ambient humidity is twice the intensity required for 95% ambient humidity [26, 27]. The rapid formation of secondary aerosols in high humidity and low wind-speed environments is the main reason for the increase of PM2.5 mass concentration following precipitation [28, 29].

The Twain-Hu Basin (THB) with a special geographical location connecting the major haze pollution areas, represented by Hubei Province, is a hub of regional transport of atmospheric pollutants in China. As the development center of the Yangtze River Economic Belt and a city cluster in the middle reaches of the Yangtze River, the THB area has special “subbasin” topography, which promotes accumulation of atmospheric pollutants. In addition, there are quite developed water systems in the THB [30, 31]. In recent years, the level of atmospheric aerosols in this area has shown an upward trend [32]. The high humidity weather and high concentration pollution have significantly worsened regional visibility [33, 34], leading to gradual formation of a new core region of pollution.

The difference in rainfall intensity, variable humidity, and different pollution characteristics result in many uncertainties in the impact of precipitation on PM (PM2.5 and PM10), which is the main reason for errors in judgment of pollution levels with the occurrence of precipitation. Due to the lack of analysis on the correlation between large-area, long-term sequence precipitation and PM concentration, there is currently no quantitative understanding of the general laws and characteristics of precipitation effects on PM2.5 and PM10 mass concentrations. In the middle reaches of the Yangtze River, some weather conditions during heavy pollution, such as low pressure with an inverted trough and a cold front, are often accompanied by precipitation [35]. Precipitation can occur during 70–80% of pollution events under the effects of cold fronts [36]. Therefore, it is necessary to analyze the variations of long-sequence precipitation and PM in combination with observation data, explore the distributions of rainfall intensity under different pollution conditions, summarize the variation of PM concentrations at the beginning and ending of precipitation hours under different humidity conditions, and determine the different effects of wet removal and wet growth on PM in the presence of precipitation. This will effectively improve the accuracy of aerosol concentration prediction during precipitation and enhance understanding of the variation mechanisms in pollution processes under high-impact weather.

2. Data and Methods

The PM2.5 and PM10 mass concentration data were obtained from 51 national air quality monitoring stations (Figure 1) managed by Hubei Environmental Monitoring Central Station in Enshi (ES), Shiyan (SY), Xiangyang (XY), Jingmen (JM), Jingzhou (JZ), Yichang (YC), Suizhou (SZ), Xiaogan (XG), Wuhan (WH), Huanggang (HG), Ezhou (EZ), Huangshi (HS), and Xianning (XN) in Hubei Province. The data length was from 1 January 2015 to 31 December 2017. The time resolution is 1 h. The mass concentrations of PM2.5 and PM10 were measured using β absorption and micro-oscillating balance methods. The observation equipment was regularly cleaned and calibrated according to the operational specifications, and observation data were subject to quality control to ensure accuracy and validity. Observational data were taken into account when effective data of PM2.5 and PM10 exceeded 20 h in one day. At the same time, according to the standard of the Ministry of Environmental Protection (GB 3095-2012) [37], pollution weather was divided into light (LP), moderate (MP), severe (SP), and terrible pollution (TP), as shown in Table 1.


LevelLPMPSPTP

Concentration (μg·m−3)PM10150–250250–350350–420>420
PM2575–115115–150150–250>250

The collected data included precipitation, relative humidity, and wind speed. In order to match the precipitation and pollutant data more accurately, 2539 automatic meteorological stations in Hubei Province were used, which were maintained by Hubei Meteorological Information and Technology Support Center.

For the summer half-year, there is more local precipitation; for the winter half-year, regional precipitation is the main phenomenon. In order to ensure that when the meteorological automatic station observed precipitation, there was also occurrence of precipitation in the adjacent national air quality monitoring station, we adopted the following main principles: (1) priority selection of the automatic weather station closest to the national air quality monitoring station; (2) using the observation data of the next closest automatic weather station when the observations of the closest automatic weather station were missing. The time-space consistency could be guaranteed in meteorological and pollutant concentration data.

Because we mainly used hourly precipitation, rainfall intensity was classified into light rain (LR), moderate rain (MR), heavy rain (HR), torrential rain (TR), and downpour (SR) according to the local standard of neighboring province (DB 34/T 1592-2012) [38], as shown in Table 2.


LevelLRMRHRTRSR

Rainfall (mm·h−1)0 < R < 1.51.5 ≤ R < 3.53.5 ≤ R < 8.08.0 ≤ R < 2020 ≤ R < 50

We selected time periods with PM10 concentration >150 μg/m3 or PM2.5 concentration >75 μg/m3. Some parameters were determined to explore the impact of precipitation on the change in PM concentration. VPM2.5, VPM10, and CPM2.5/PM10 were determined according to (1)–(3), respectively:where and are the PM2.5 and PM10 mass concentrations at the end moment of the precipitation hour, respectively. and are the PM2.5 and PM10 mass concentrations at the beginning moment of the precipitation hour, respectively. Some researchers term these the initial concentrations [16, 18]. Precipitation data when wind speed exceeded 3 m/s were eliminated to ensure a low wind-speed environment condition [23].

3. Results and Discussion

3.1. Distribution of Pollution Weather at Different Levels

Figure 2 shows the distribution of different-level pollution days in 13 regions of Hubei Province during 2015–2017. Judging from the total days with pollution weather, we found that the pollution weather was most frequent in XY and YC, reaching 335.3 and 304.1 d, respectively. Pollution weather occurs in one-third of days throughout the year in these two cities. These two cities are also typical representative areas affected by the transmission of an external pollution source and accumulation of a local pollution source. The heavy pollution weather in XY is often accompanied by a strong northerly wind and a weak inversion layer, whereas low wind speed and a multilayer inversion often occur in YC during heavy pollution weather [39, 40]. Pollution weather for ES, HG, SY, and XN occurred in less than 160 d, mainly located in the mountainous areas of Hubei Province, where pollution days were significantly lower than other areas.

LP days accounted for >60% of total pollution days, especially in areas with less pollution such as XN and HG where LP days represented >80% of the total pollution days, reaching >100 d. MP days mainly accounted for 18–23%, with differences of less than 30 d among regions. SP days were significantly fewer, representing about half of MP days. However, it is worth noting that XY and YC showed no obvious reduction in SP days. In particular, SP days in XY were similar to MP days, both about 80 d, which was about 2–10 times those in other areas.

3.2. Characteristics of Precipitation during Pollution Weather Days

The distributions of precipitation hours during pollution days in Hubei Province from 2015 to 2017 (Figure 3) revealed that the mean value of LR during pollution weather in each area was 298 h. LR was the most frequent in XY, reaching 587 h, with a mean value of 497 h. Occurrence of MR was lower than LR by an order of magnitude, with a mean value of 21 h. XY, YC, and WH subject to heavy pollution processes were less affected by MR which lasted for only about 10 h. The occurrence of HR was consistent in each region, mainly in the range of 5–12 h. National air quality monitoring stations of WH and EZ located in Wuhan City Circle showed significant differences in occurrence of HR, with values exceeding 10 h. Regions where TR occurred during pollution weather were mainly located in Wuhan City Circle, all within 7 h. The SR occurred rarely, with only 1 h in the four cities.

Precipitation hours of the same level differed for the different national air quality monitoring stations in the same city. As heavy precipitation was usually concentrated in local areas, average deviation of occurrence in LR, MR, and HR gradually increased, with 14.75%, 26.42%, and 35.01%, respectively. The greater the rainfall intensity was, the larger the difference in occurrence of precipitation was at national air quality monitoring stations in the same area, showing that the research on the separate occurrences of precipitation in different national air quality monitoring stations was reasonable.

Strong-thick inversion layers and low altitude mixed layers that often occur at night or in the early morning are typical conditions that aggravate pollution weather [4143]. Their strong inhibition of pollutant diffusion means that the peak concentration of pollutants often occurs at night. Bai et al. [44] found that daily variation of PM2.5 concentrations in Hubei Province showed a bimodal distribution in winter, with peak values during 11:00–14:00 caused by transportation and peak values during 21:00–24:00 caused by accumulation. The frequency of precipitation during different pollution periods can indirectly reflect the impact degree of precipitation on pollution weather. Figure 4 shows diurnal variation of the occurrence of different levels of precipitation during pollution weather in Hubei Province from 2015 to 2017. The daily variations of precipitation showed a unimodal distribution regardless of precipitation level during pollution weather, with peak values during 20:00–23:00. Over time, the ratio of LR to MR and HR at each time was reduced from 17 and 110 during 00:00–03:00 to 6 and 30 during 19:00–23:00, respectively; HR was concentrated during 17:00–23:00. These moments were during the period when PM2.5 mass concentration reached its peak value. With increased rainfall intensity, precipitation was more concentrated at night, indicating that occurrence of precipitation during the pollution process may have had a great impact on the change of PM mass concentration.

3.3. Variation Characteristics of PM Mass Concentration before and after Precipitation during Pollution Process

The value of PM mass concentration is, on the one hand, affected by wet removal caused by inertia coagulation of the precipitation particles during their falling and, on the other hand, affected by aerosol hygroscopic growth and secondary aerosol formation caused by the humidification effect of precipitation. At the same time, the variation law in background concentration of PM under the joint action of local-source emission, external-source transportation, and air mass diffusion also affects the value of PM concentration. Therefore, this section statistically analyzes PM2.5, PM10, and their ratios before and after precipitation to reveal distribution laws governing PM concentration under the influence of precipitation. Table 3 shows the distributions of VPM2.5 and VPM10 under different CPM2.5/PM10 intervals during rainfall hours. With the ending of precipitation, PM10 and PM2.5 concentrations decreased by 61.5% and 52.3% in hours, respectively. Absolute values of the decrease ratios of PM10 and PM2.5 were larger than those of the increase ratios of PM10 and PM2.5. The effect of precipitation on wet removal was significant. Moreover, PM10 was more affected by wet removal of precipitation, which was relatively consistent with the results of Xu et al. on wet removal of precipitation in Shanghai [23]. They showed that precipitation in Shanghai mainly removed atmospheric aerosol particles of diameters <1 μm and 2.5–10 μm, while PM10 contained aerosol particles of both these particle size ranges. At the same time, the formation of secondary aerosols under the influence of precipitation also made PM2.5 concentration subject to a weaker wet removal effect than PM10 concentration.


CPM2.5/PM10VPM10 ≥ 0VPM10 < 0VPM2.5 ≥ 0VPM2.5 < 0
HoursAverage valueHoursAverage valueHoursAverage valueHoursAverage value

−0.6∼−0.480.103−0.0500.0011−0.58
−0.4∼−0.2760.2921−0.16190.0678−0.23
−0.2∼−0.12120.17270−0.111120.07370−0.14
−0.1∼022180.072323−0.0716940.062847−0.08
0∼0.116740.063500−0.0928970.072277−0.07
0.1∼0.2930.07602−0.164860.12209−0.09
0.2∼0.4180.06146−0.251160.2548−0.09
0.4∼0.500.0011−0.4380.253−0.22
Ratio (%)38.5061.5047.7052.30
Average values of VPM0.10-0.160.11−0.19

The distributions of VPM2.5 and VPM10 were further compared under different intervals of CPM2.5/PM10. Figure 5 shows the relationships between VPM2.5 and VPM10 for different values of CPM2.5/PM10. There was a good positive correlation between VPM2.5 and VPM10 in different intervals, with correlation coefficients >0.7, but there were differences in correlations between them for different CPM2.5/PM10 intervals. The values of CPM2.5/PM10 were mainly concentrated within the range of −0.6 to 0.5. When the values of CPM2.5/PM10 were negative (−0.6 to −0.1), the fitted line had a slope greater than 1 in each ratio interval, showing that the values of VPM10 exceeded those of VPM2.5. When CPM2.5/PM10 increased (0.1–0.5), the fitted line had a slope less than 1 in each ratio range, and the values of VPM2.5 exceeded those of VPM10. When CPM2.5/PM10 approached 0 (−0.1 to 0.1), the fitted line had a slope close to 1, and VPM2.5 and VPM10 demonstrated relatively consistent variability.

3.4. Effect of Rainfall Intensity on the PM Mass Concentration

The analysis in the above two sections provided a preliminary understanding of the distributions of precipitation and PM concentration in the pollution process. Therefore, we then combined the two to investigate the law governing the effect of precipitation on variations of PM concentration. Studies have shown that aerosol concentrations before precipitation are closely related to the wet removal effect of precipitation [16, 18], so this section concentrates on analyzing the relationships between VPM2.5 and VPM10, rainfall intensity, and values of and . Figure 6 shows the distributions of average and , average rainfall intensity, and mean values of VPM2.5 and VPM10 with intervals of 0.05. When the average rainfall intensity was <0.5 mm/h, the mean values of VPM10 were in the interval 0.4–0.8, and average also had a low value (<140 μg/cm3; Figure 6(a)). This indicated that precipitation resulted in aerosol hygroscopic growth and that formation of secondary aerosol was stronger than its wet removal, which thereby led to the increase in PM10 mass concentration. When the average rainfall intensity was >1.0 mm/h, the mean values of VPM10 were negative (i.e., −0.66 to −0.20), showing the wet removal effect caused by precipitation. It is noteworthy that when average rainfall intensity was 0.5–1.0 mm/h and average was 110–180 μg/m3, there were both positive and negative values of VPM10, with absolute values lower than those for rainfall intensities of <0.5 and >1.0 mm/h.

The effect of precipitation on PM2.5 mass concentration (Figure 6(b)) was similar to that of PM10. The range of mean rainfall intensity responsible for wet growth was mainly <0.5 mm/h, but the range of mean rainfall intensity responsible for wet removal of PM2.5 was significantly higher than that of PM10, with a value >1.4 mm/h. Particles with smaller size need greater rainfall intensity to be significantly cleared by precipitation. At the same time, the initial value of PM2.5 or PM10 mass concentration was the dominant factor under the rainfall intensity range in which wet growth and wet removal had similar effects. Precipitation was more likely to have a wet removal effect for a greater initial value of mass concentration, and, on the contrary, wet growth effect was more likely. The threshold of PM10 mass concentration was 150 μg/m3, and that of PM2.5 mass concentration was 95 μg/m3, relatively consistent with the results of Xu et al. [23] on initial concentration threshold of PM2.5 corresponding to wet removal in Shanghai.

3.5. Effect of Relative Humidity on the Change of PM Mass Concentration during Precipitation

Aerosol hygroscopic growth and secondary aerosol formation play a key role in formation of heavy pollution. Sun [29] clarified the important role of rapid growth of secondary aerosols in heavy pollution formation in the North China Plain, pointing out that secondary aerosol accounts for 70% of heavy pollution, while high humidity and low wind speed are important environmental factors for aerosol hygroscopic growth and secondary aerosol formation [45, 46]. Precipitation significantly increases the relative humidity of the environment, and the elimination of data when the wind speed reaches 3 m/s or more will maintain the low wind-speed environmental conditions. Therefore, precipitation would also affect the formation of secondary aerosols. Figure 7 shows the distributions of VPM10 in each relative humidity range (0–100% with 1% intervals). Different panels of Figure 7 correspond to different levels of precipitation. The relative humidity was <40% only during LR, with the values of VPM10 ranging from −0.32 to −0.2, showing that precipitation played a significant role in wet removal of PM10 in a dry environment. As the relative humidity increased, wet growth gradually played a more significant role. The PM10 mass concentration in the relative humidity range of 40–65% was subject to the combined action of wet removal and wet growth. When relative humidity was 65–90%, PM10 mass concentration increased under the impact of aerosol hygroscopic growth and secondary aerosol formation, while in the near-saturated relative humidity range (90–100%), PM10 mass concentration was slightly affected by wet removal, with values of VPM10 concentrated within −0.05 to 0. As the precipitation level increased (MR, HR, and TR), the wet removal to PM10 mass concentration was enhanced more obviously, especially in the near-saturated relative humidity range (90–100%). For the relative humidity range (40–90%) under the combined action of wet removal and wet growth, wet removal played a leading role in lower humidity (about 60%) and greater rainfall intensity, but wet growth played a leading role in higher humidity (∼90%) and lower rainfall intensity.

The variations of VPM2.5 had distributions similar to those of PM10 (figure omitted), but the wet removal effect of precipitation was weakened in the near-saturated relative humidity range (90–100%), and the wet growth effect was enhanced under the higher humidity (∼90%) and lower rainfall intensity.

4. Conclusions

In this study, the characteristics of precipitation, distributions of PM concentrations, RH conditions, and evolution of PM under the effect of precipitation during pollution days in Hubei from 2015 to 2017 were comprehensively analyzed. Due to wet removal and wet growth, the PM concentrations varied widely under different rainfall intensities and relative humidity environments.(1)The LR occurred most frequently in the pollution process, with XY being the most affected, up to 587 h. Occurrence of MR was lower than LR by an order of magnitude, with a mean value of 21 h. The occurrence of HR was consistent in each region, mainly in the range of 5–12 h. SR was rare. The greater the rainfall intensity was, the larger the difference in occurrence of precipitation was at national air quality monitoring stations in the same area. Rainfall was more concentrated during 19:00–23:00.(2)With the ending of precipitation, PM10 and PM2.5 concentrations decreased by 61.5% and 52.3% of hours, respectively, showing that PM10 was more affected by wet removal of precipitation. When the values of CPM2.5/PM10 were negative (−0.6 to −0.1), the values of VPM10 exceeded those of VPM2.5. When CPM2.5/PM10 increased (0.1–0.5), the values of VPM2.5 exceeded those of VPM10. When CPM2.5/PM10 approached 0 (−0.1 to 0.1), VPM2.5 and VPM10 demonstrated relatively consistent variability.(3)Mean rainfall intensity responsible for wet growth of PM10 and PM2.5 was mainly <0.5 mm/h, but the ranges responsible for wet removal of PM2.5 (>1.4 mm/h) and for wet removal of PM10 (>1.0 mm/h) were significantly higher. In the rainfall intensity range for which wet growth and wet removal had similar effects, precipitation was more likely to produce a wet removal effect for greater initial values of mass concentration, and, on the contrary, a wet growth effect was more likely. The threshold of PM10 mass concentration was 150 μg/m3 and that of PM2.5 mass concentration was 95 μg/m3.(4)Precipitation played a significant role in wet removal of PM10 in a dry environment (relative humidity <40%), with a weaker wet removal effect in the near-saturated relative humidity range (90–100%). When relative humidity was 40–90%, the effects of wet removal and wet growth were similar. As the precipitation level increased (MR, HR, and TR), the wet removal of PM10 mass concentration was enhanced more obviously. Wet removal played a leading role in lower humidity (∼60%) and greater rainfall intensity, but wet growth played a leading role in higher humidity (∼90%) and lower rainfall intensity. The effect of precipitation on PM2.5 was similar to that on PM10, but the wet removal effect of precipitation was weakened in the near-saturated relative humidity range (90–100%), and the wet growth effect was enhanced under higher humidity (∼90%) and lower rainfall intensity.

Data Availability

The PM2.5 and PM10 mass concentration, precipitation, relative humidity, and wind-speed data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research was funded by the National Natural Science Foundation of China (41830965 and 41875170), Natural Science Foundation of Hubei Province (2016CFB347), and Science and Technology Development Fund Project of Hubei Meteorological Bureau (2019YJ06 and 2018Z07).

References

  1. A. D. Kappos, P. Bruckmann, T. Eikmann et al., “Health effects of particles in ambient air,” International Journal of Hygiene and Environmental Health, vol. 207, no. 4, pp. 399–407, 2004. View at: Publisher Site | Google Scholar
  2. H. Kan, R. Chen, and S. Tong, “Ambient air pollution, climate change, and population health in China,” Environment International, vol. 42, pp. 10–19, 2012. View at: Publisher Site | Google Scholar
  3. X. J. Zhao, P. S. Zhao, J. Xu et al., “Analysis of a winter regional haze event and its formation mechanism in the North China Plain,” Atmospheric Chemistry and Physics, vol. 13, no. 11, pp. 5685–5696, 2013. View at: Publisher Site | Google Scholar
  4. X. Y. Zhang, J. Y. Sun, Y. Q. Wang et al., “Factors contributing to haze and fog in China,” Chinese Science Bulletin, vol. 58, no. 13, pp. 1178–1187, 2013, in Chinese. View at: Google Scholar
  5. J. Li, H. Y. Du, Z. F. Wang et al., “Rapid formation of asevere regional winter haze episode over a mega-city cluster on the North China Plain,” Environmental Pollution, vol. 223, pp. 605–615, 2017. View at: Publisher Site | Google Scholar
  6. A. P. K. Tai, L. J. Mickley, and D. J. Jacob, “Impact of 2000–2050 climate change on fine particulate matter (PM2.5) air quality inferred from a multi-model analysis of meteorological modes,” Atmospheric Chemistry and Physics, vol. 12, no. 23, pp. 11329–11337, 2012. View at: Publisher Site | Google Scholar
  7. J. L. Zhu, H. Liao, and J. P. Li, “Increases in aerosol concentrations over eastern China due to the decadal-scale weakening of the East Asian summer monsoon,” Geophysical Research Letters, vol. 39, no. 9, p. L09809, 2012. View at: Publisher Site | Google Scholar
  8. Y. Qin and C. S. Zhao, Fundamentals of Atmospheric Chemistry, China Meteorological Press, Beijing, China, 2003, in Chinese.
  9. C. C. Zhang and W. X. Zhou, Atmospheric Aerosol Particle Tutorial, China Meteorological Press, Beijing, China, 1995, in Chinese.
  10. X. P. Li, N. F. Bei, and L. N. Zhao, “Influence of meteorological conditions on the wintertime air quality in the Guangzhong basin during 2013 to 2015,” Journal of Earth Environment, vol. 8, pp. 516–523, 2017, in Chinese. View at: Google Scholar
  11. F. Herbert and K. D. Behang, “Scavenging of airborne particles by collision with water drops-model studies on the combined effect of essential micro dynamic mechanisms,” Meteorology and Atmospheric Physics, vol. 35, no. 4, pp. 201–211, 1986. View at: Publisher Site | Google Scholar
  12. S. Y. Bae, R. J. Park, Y. P. Kim, and J.-H. Woo, “Effects of below-cloud scavenging on the regional aerosol budget in East Asia,” Atmospheric Environment, vol. 58, pp. 14–22, 2012. View at: Publisher Site | Google Scholar
  13. M. Mircea, S. Stefan, and S. Fuzzi, “Precipitation scavenging coefficient: influence of measured aerosol and raindrop size distributions,” Atmospheric Environment, vol. 34, no. 29-30, pp. 5169–5174, 2000. View at: Publisher Site | Google Scholar
  14. D. M. Chate, P. Murugavel, K. Ali, S. Tiwari, and G. Beig, “Below-cloud rain scavenging of atmospheric aerosols for aerosol deposition models,” Atmospheric Research, vol. 99, no. 3-4, pp. 528–536, 2011. View at: Publisher Site | Google Scholar
  15. C. Andronache, “Estimated variability of below-cloud aerosol removal by rainfall for observed aerosol size distributions,” Atmospheric Chemistry and Physics, vol. 3, no. 1, pp. 131–143, 2003. View at: Publisher Site | Google Scholar
  16. H. Q. Kang, B. Zhu, and S. X. Fan, “Size distributions and wet scavenging properties of winter aerosol particles in north suburb of Nanjing,” Climatic and Environmental Research, vol. 14, pp. 523–530, 2009, in Chinese. View at: Google Scholar
  17. S. M. Greenfield, “Rain scavenging of radioactive particulate matter from the atmosphere,” Journal of Meteorology, vol. 14, no. 2, pp. 115–125, 1957. View at: Publisher Site | Google Scholar
  18. Y. Wang, B. Zhu, H. Q. Kang, J. W. Gao, Q. Jiang, and X. H. Liu, “Theoretical and observational study on below-cloud rain scavenging of aerosol particles,” Journal of University of Chinese Academy of Sciences, vol. 31, pp. 306–313, 2014, in Chinese. View at: Google Scholar
  19. Q. Dong, P. S. Zhao, and Y. N. Chen, “Impact of collision removal of rainfall on aerosol particles of different sizes,” Environmental Science, vol. 37, no. 10, pp. 3686–3692, 2016, in Chinese. View at: Google Scholar
  20. T. S. Pranesha and A. K. Kamra, “Scavenging of aerosol particles by large water drops: 3. Washout coefficients, half-lives, and rainfall depths,” Journal of Geophysical Research: Atmospheres, vol. 102, no. D20, pp. 23947–23953, 1997. View at: Publisher Site | Google Scholar
  21. K. Y. Yao, J. Guo, Y. F. Fu, and Y. Liu, “Rain scavenging of aerosol particles,” Climatic and Environmental Resarch, vol. 4, pp. 297–302, 1999, in Chinese. View at: Google Scholar
  22. H. B. Zhao and C. G. Zheng, “Numerical simulation of wet removal of aerosols when raindrop falling,” Acta Scientiae Circumstantiae, vol. 25, pp. 1590–1596, 2005, in Chinese. View at: Google Scholar
  23. J. M. Xu, W. Gao, and Y. H. Qu, “Observation of the wet scavenge effect of rainfall on PM2.5 in Shanghai,” Acta Scientiae Circumstantiae, vol. 37, pp. 3271–3279, 2017, in Chinese. View at: Google Scholar
  24. L. M. Zhang, D. V. Michelangeli, and P. A. Taylor, “Numerical studies of aerosol scavenging by low-level, warm stratiform clouds and precipitation,” Atmospheric Environment, vol. 38, no. 28, pp. 4653–4665, 2004. View at: Publisher Site | Google Scholar
  25. Y. Y. Yue, X. L. Wang, M. X. Zhang et al., “Air quality condition in Wuhan and its relationship to meteorological factors,” Torrential Rain and Disasters, vol. 35, no. 3, pp. 271–278, 2016, in Chinese. View at: Google Scholar
  26. D. M. Chate and T. S. Pranesha, “Field studies of scavenging of aerosols by rain events,” Journal of Aerosol Science, vol. 35, no. 6, pp. 695–706, 2004. View at: Publisher Site | Google Scholar
  27. D. M. Chate, P. S. P. Rao, M. S. Naik, G. A. Momin, P. D. Safai, and K. Ali, “Scavenging of aerosols and their chemical species by rain,” Atmospheric Environment, vol. 37, no. 18, pp. 2477–2484, 2003. View at: Publisher Site | Google Scholar
  28. Y. L. Sun, C. Chen, Y. J. Zhang et al., “Rapid formation and evolution of an extreme haze episode in Northern China during winter 2015,” Scientific Reports, vol. 6, no. 1, p. 27151, 2016. View at: Publisher Site | Google Scholar
  29. Y. L. Sun, “Vertical structures of physical and chemical properties of urban boundary layer and formation mechanisms of atmospheric pollution,” Chinese Science Bulletin, vol. 63, no. 14, pp. 1375–1389, 2018, in Chinese. View at: Publisher Site | Google Scholar
  30. D. S. Hu, H. J. Zhang, B. Xu et al., “On the environment evolvement of jingjiang river valley and the forming processes of twain-hu plain basin in the middle reaches of the Yangtze River,” Engineering Science, vol. 12, no. 1, pp. 36–42, 2010, in Chinese. View at: Google Scholar
  31. S. C. Lai, Y. Zhao, A. J. Ding et al., “Characterization of PM2.5, and the major chemical components during a 1-year campaign in rural Guangzhou, Southern China,” Atmospheric Research, vol. 167, pp. 208–215, 2016. View at: Publisher Site | Google Scholar
  32. C. H. Tan, T. L. Zhao, C. G. Cui, B. L. Luo, L. Zhang, and Y. Q. Bai, “Characterization of haze pollution over central China during the past 50 years,” China Environmental Science, vol. 35, no. 8, pp. 2272–2280, 2015, in Chinese. View at: Google Scholar
  33. Y. H. Ding and Y. J. Liu, “Analysis of long-term variations of fog and haze in China in recent 50 years and their relations with atmospheric humidity,” Science China Earth Sciences, vol. 57, no. 1, pp. 36–46, 2014, in Chinese. View at: Publisher Site | Google Scholar
  34. Y. Q. Bai, H. X. Qi, L. Liu, C. G. Cui, C. Z. Lin, and C. H. Tan, “Development and preliminary application of environmental meteorology numerical model system in Central China,” Plateau Meteorology, vol. 35, no. 6, pp. 1671–1682, 2016, in Chinese. View at: Google Scholar
  35. J. M. Xu, L. Y. Chang, J. H. Ma, Z. C. Mao, L. Chen, and Y. Cao, “Objective synoptic weather classification on PM2.5 pollution during autumn and winter seasons in Shanghai,” Acta Scientiae Circumstantiae, vol. 36, pp. 4303–4314, 2016, in Chinese. View at: Google Scholar
  36. Y. Y. Yue, Y. Zhou, X. L. Wang, and B. Zhu, “Analysis on transport/purification effect by cold front and precipitation during haze day in Wuhan,” Acta Scientiae Circumstantiae, vol. 38, pp. 4612–4619, 2018, in Chinese. View at: Google Scholar
  37. Ministry of Environmental Protection, Ambient Air Quality Standard: GB 3095-2012, China Environmental Science Press, Beijing, China, 2016, in Chinese.
  38. Anhui Provincial Bureau of Quality and Technical Supervision, Grade Classification of Nowcasting Rainfall Intensity: DB34/T1592-2012, Anhui Provincial Local Standards, Hefei, China, 2012, in Chinese.
  39. Y. Y. Fan, J. Z. Min, and J. Q. Luo, “Analysis of the reason for a persistent pollution episode of Yichang,” Torrential Rain and Disasters, vol. 35, pp. 76–83, 2016, in Chinese. View at: Google Scholar
  40. X. L. Wang, Y. Y. Yue, S. N. Chen, Y. Zhu, and N. Chen, “Characteristics of AQI and relationship with meteorological factors in three terrains over Hubei,” Meteorological Science Technology, vol. 46, no. 5, pp. 1012–1019, 2018, in Chinese. View at: Google Scholar
  41. Z. Y. Chen, X. M. Xie, J. Cai et al., “Understanding meteorological influences on PM2.5 concentrations across China: a temporal and spatial perspective,” Atmospheric Chemistry and Physics, vol. 18, no. 8, pp. 5343–5358, 2018. View at: Publisher Site | Google Scholar
  42. J. P. Guo, M. J. Deng, J. W. Fan et al., “Precipitation and air pollution at mountain and plain stations in Northern China: insights gained from observations and modeling,” Journal of Geophysical Research: Atmospheres, vol. 119, no. 8, pp. 4793–4807, 2014. View at: Publisher Site | Google Scholar
  43. R. H. Zhang, Z. Q. Li, and R. Zhang, “Meteorological conditions for the persistent severe fog and haze event over eastern China in January 2013,” Science China Earth Sciences, vol. 57, no. 1, pp. 26–35, 2014, in Chinese. View at: Publisher Site | Google Scholar
  44. Y. Q. Bai, H. X. Qi, T. L. Zhao, H. Yang, L. Liu, and C. G. Cui, “Analysis of meteorological conditions and diurnal variation characteristics of PM2.5 heavy pollution episodes in the winter of 2015 in Hubei province,” Acta Meteorologica Sinica, vol. 76, pp. 803–815, 2018, in Chinese. View at: Google Scholar
  45. Y. L. Sun, Q. Jiang, Z. F. Wang et al., “Investigation of the sources and evolution processes of severe haze pollution in Beijing in January 2013,” Journal of Geophysical Research: Atmospheres, vol. 119, no. 7, pp. 4380–4398, 2014. View at: Publisher Site | Google Scholar
  46. G. Wang, R. Zhang, M. E. Gomez et al., “Persistent sulfate formation from London Fog to Chinese haze,” Proceedings of the National Academy of Sciences, vol. 113, no. 48, pp. 13630–13635, 2016. View at: Publisher Site | Google Scholar

Copyright © 2020 Yue Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views1280
Downloads461
Citations

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.