Hing Cho Cheung This email address is being protected from spambots. You need JavaScript enabled to view it.1, Chengyu Nie2, Mintao Huang2, Tingting Yang2, Hao Wang4, Celine Siu Lan Lee5, Chenglei Pei6,7,8, Jun Zhao2,3, Baoling Liang2 

1 Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
2 School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou 510275, China
3 Guangdong Provincial Field Observation and Research Station for Climate Environment and Air Quality Change in the Peral River Estuary, Guangdong 510275, China
4 Institute for Environmental and Climate Research, Jinan University, Guangdong 510632, China
5 Department of Civil Engineering, Chu Hai College of Higher Education, Tuen Mun, Hong Kong SAR, China
6 State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangdong Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
7 University of Chinese Academy of Sciences, Beijing 100049, China
8 Guangzhou Ecological and Environmental Monitoring Center of Guangdong Province, Guangzhou 510060, China

Received: February 25, 2022
Revised: May 19, 2022
Accepted: June 7, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.220097  

Cite this article:

Cheung, H.C., Nie, C., Huang, M., Yang, T., Wang, H., Lee, C.S.L., Pei, C., Zhao, J., Liang, B. (2022). Influence of Regional Pollution Outflow on Particle Number Concentration and Particle Size in Airshed of Guangzhou, South China. Aerosol Air Qual. Res. 22, 220097. https://doi.org/10.4209/aaqr.220097


  • Notably high ratios of monsoon versus land–sea breeze particle concentration (0.93–1.18).
  • Significant Aitken mode particles (up to 51.2%).
  • High correlation between particle concentrations (r = 0.60–0.70) of the two sites.
  • Higher averaged growth rates (14 nm h–1) among major urban sites in China.
  • Significant new particle formation under high H2SO4 proxy with low wind speed.


A measurement campaign of particle number concentration and size distribution was conducted at urban (SYSU) and suburban (Panyu) areas of Guangzhou, South China, during 16 January to 3 February 2020 before and during the Chinese New Year (CNY) holiday. Average particle number concentration (PNC) was 6.3 × 103 cm3 and 9.7 × 103 cm3, respectively, at urban and suburban sites, indicating the severe particulate matter (PM) pollution. The PNC in the region was influenced by monsoon and the land–sea breeze systems. During monsoon seasons, PM pollution occurred at a regional scale affecting the urban and suburban areas as indicated by the high PNC correlation (r value = 0.70), but the PNCs were lower than that during land-sea breeze period (with lower PNCM1/PNCLSB1 and PNCM2/PNCLSB2 ratios) due to the higher atmospheric dispersion. There is a strong local emission (mainly vehicular emissions) in both urban and suburban areas which was significantly lowered during the CNY period due to reduced human activities. The PM pollution was found to be significantly influenced by local emissions (dominated by Aitken mode particles) and new particle formation (NPF) process (dominated by nucleation mode particles). NPF event was found to be associated with a higher N10-25/H2SO4 proxy ratio during the low wind speed condition.

Keywords: Particle number concentration, Size distribution, Megacity, Regional pollution, New particle formation


Atmospheric particulate matter (PM) has severe adverse impacts on human health and climate change (Charlson et al., 1992; Donaldson et al., 1998, Myhre, 2009) which is of great concern to the public. Fine particulate matter is known to be associated with respiratory and cardiovascular diseases (Nel, 2005), and ultrafine particles (UFPs, d ≤ 100 nm) may cross the blood-brain and alveolar-capillary barriers and enter the central nervous system (Oberdörster and Utell, 2002).

Furthermore, PM can alter the climate forcing by scattering and absorbing solar radiation through aerosol-radiation interactions (ARI), and also uptake water vapor to act as cloud condensation nuclei (CCN) which then alters cloud albedo and precipitation through aerosol-cloud interactions (ACI) (Boucher et al., 2013). Conventionally, in the context of developing air quality guidelines, mass concentrations of PM such as PM2.5 and PM10 were used as indexes for pollution levels (e.g., WHO, 2006). Numerous studies of PM2.5 and PM10 were conducted in past decades to investigate their physicochemical properties, sources, health effects and pollution control in technical and policy regulation aspect (Chan and Yao, 2008). In recent years, it has been found that there is a strong association between elevated particle number concentrations (PNCs) and vehicle emission influenced environments such as roadside, on-road, and tunnels (Morawska et al., 2008). As UFPs attribute to more than 80% of total particle number concentration (Morawska et al., 2008; Cheung et al., 2016), there is an increasing concern about UFPs in recent years due to its adverse impacts on human health, and this leads to the introduction of vehicle emission standards for PNCs, such as European VI (ICCT, 2016) and China 6 (MEP, 2016) emission standards.

Due to the rapid economic and industrial developments, China has gained enormous economic growth over the past decades, which has resulted in a large increase in emissions of air pollutants, contributing to deteriorating regional air quality in China (He et al., 2002; Cheung et al., 2005; Chan and Yao, 2008; Fu et al., 2016). The Pearl River Delta (PRD) region, including megacities such as Guangzhou, Shenzhen and Hong Kong, are important economic hubs in China which contributed to about 9% of the total gross domestic product (GDP) of China in 2019 (GPBS, 2019; NBSC, 2019). Guangzhou, the capital city of Guangdong Province, situated in the Pearl River Delta (PRD) Region, South China (see Fig. 1), has over 15 million population and is the most densely populated megacity (GPBS, 2019) in southern China. Many previous studies have been conducted to characterize the physicochemical properties of PM2.5 and PM10, and its source apportionment in PRD region which provide scientific information for local government to formulate an effective regulation for air quality improvement (e.g., Fu et al., 2014, 2016; Xie et al., 2020). The air quality in the PRD region was significantly affected by the meteorological conditions, for example, continental outflow pollution was dominated under the influence of winter monsoon, and low wind speed associated with the land-sea breeze circulation may lead to poor air quality (Ding et al., 2004; Fan et al., 2008). Only a few studies were available on the influence of synoptic circulation on PNC (e.g., Cheung et al., 2016; Sebastian et al., 2022). The influence of meteorological conditions on the air masses and atmospheric dilution was found to be highly varied on each study sites (Bousiotis et al., 2021). Notably, the PNCs in urban areas in the PRD region was found to be mainly affected by the intense traffic emissions (Yue et al., 2013). New particle formation has significant contribution to the PNCs (Liu et al., 2008). However, previous PM studies conducted in the PRD region mainly focused on the characterization of fine particulate matter, such as their physiochemical properties, formation process, source apportionment, and their implications on visibility (Wang, 2003; Hagler et al., 2006; Wu et al., 2013). Hussein et al. (2009) investigated the regional new particle formation (NPF) events in Finland and Southern Sweden, and the results showed that the regional NPF can significantly affect the accumulation mode particle concentration by the particle growth of newly formed particles. In addition, Cheung et al. (2012) reported that the influence of regional NPF events which occurred at upwind areas can significantly influence the PNC at downwind suburban area. These studies both demonstrated that the PNC not only affected by local emissions but also by regional sources. However, studies on UFPs in the PRD region, especially on the spatial variations of PNCs among different locations, are still limited (Liu et al., 2008; Yue et al., 2013). To further investigate the implication of transport of the local and regional pollution of UFPs and the associated new particle formation (NPF) process in the megacity, field measurements of particle number concentration and size distribution were conducted at two locations (approximately 30 km apart) which represent upwind urban region and downwind semi-urban region in winter season during 16 January to 3 February 2020. As part of the control measures of COVID-19 pandemic, China imposed nationwide restrictions on travel and required its people to stay home (lockdown) and shutting down commercial activities beginning in late January 2020 (Huang et al., 2021; Wang et al., 2020). Chinese New Year (CNY) holiday commenced on 25 January 2020 and were extended until 9 February in Shanghai, Suzhou, Guangdong Province, Zhejiang Province, Jiangsu Province, and Yunnan Province to prevent mass movement of citizens (China Briefing, 2020). There were minimum human activities in these areas in Guangzhou and its surrounding areas during this period. This study investigated the temporal variation of PNC and particle size distribution in Guangzhou under the influence of winter monsoon circulation, as well as the changes of PNC due to a significant suppression of economic activities during the lockdown period. The impacts of NPF on the PNCs at the study region were also evaluated. We firstly present the overall PNC and size distribution and compare our data to those obtained from other urban regions around the world. We then examine the temporal variations of PNC in megacity of Guangzhou, the influence of environmental parameters (i.e., meteorological conditions and existence of other pollutants) on PNC and the correlation between two sites in a mesoscale airshed. Finally, we examine the influence of NPF on the PNC in an urban megacity, and the influence of sulfuric acid using H2SO4 proxy and wind speed on NPF events.


2.1 Site Description

Field measurements of particles and gaseous pollutants were conducted at two locations in Guangzhou during 16 January to 3 February 2020, to represent the urban and semi-urban environments. Fig. 1 illustrates the sampling location of urban site at rooftop of the eight-floor campus building at Sun Yat-sen University located in Haizhu District of Guangzhou (SYSU, 23.10°N, 113.30°E), and semi-urban site at rooftop of the building of Bureau of Ecology and Environmental Protection at Panyu, approximately 25 km southeast from the central city area of Guangzhou (PY, 22.94°N, 113.37°E). The SYSU site is situated in the center of Guangzhou which is primarily affected by vehicle emissions as Guangzhou was known to have one of the highest traffic in China (16.5 million daily traffic) (Zhao and Moh, 2005), and the PY site is situated in the suburban areas of Panyu, dominated by local light industrial and domestic emissions. Panyu is one of urban districts of the prefecture-level city of Guangzhou. It was a separate county-level city before its incorporation into modern Guangzhou in 2000.

Fig. 1. Locations of sampling sites: urban monitoring site at Sun Yat-sen University (SYSU), Guangzhou (circle); suburban monitoring site at Panyu (PY), Panyu (triangle).Fig. 1. Locations of sampling sites: urban monitoring site at Sun Yat-sen University (SYSU), Guangzhou (circle); suburban monitoring site at Panyu (PY), Panyu (triangle).

2.2 Particle Size Distribution

Particle size ranging from 10 to 480 nm was measured by a scanning mobility particle sizer (SMPS) system, which consisted of two parts: i) an electrostatic classifier (TSI 3082, TSI Inc.) for particle sizing, and ii) a butanol-based Ultrafine Condensation Particle Counter (CPC) (TSI 3756, TSI Inc.) was used to measure particle concentration at SYSU site. The manufacturer-stated particle size measurement range of the TSI 3756 CPC is 2.5 nm to > 3000 nm, with ± 10% accuracy and a response time of < 1 s for 95% response (TSI, 2018). Air sample was drawn from the rooftop through 4.5 m long stainless-steel tube (1 inch OD) with 16.7 L min–1 flowrate to indoor laboratory, and the air stream subsequently passed through a 1.5 m conductive tube (3/8 inch ID) with 3.1 L min–1 flowrate connected with a Nafion™ diffusional dryer prior to a flow splitter at 0.3 lpm for PSD measurement by the SMPS (1 m long conductive tube, 1/4 inch ID) and a bypass air flow of 2.8 L min–1 connected to other equipment for a collaborating project which is outside the scope of this study. The SMPS system was operated with the sheath and aerosol flow of 5 L min–1 and 0.3 L min–1, respectively. The system flow rates were checked before and after the sampling period. Multiple charge and diffusion loss corrections (inside SMPS instrument) were applied to the PSD measurements using the internal algorithm from the Aerosol Instrument Manager Software. Furthermore, diffusion loss in sampling tube was corrected according to the algorithm proposed by Holman (1972). The time resolution for the PSD data was 5 mins, and the total PNC (N10-480) was calculated by integration of particle number concentration for each size bin from SMPS data for SYSU site. Particle number concentration at Panyu site (PY) was measured by a water-based Condensation Particle Counter (MAGIC 200, Aerosol Devices Inc.) which measures particles with diameter from 5 to 2500 nm. Since the PNCs for two sites were obtained from different instruments with different size ranges, caution should be taken when comparing the PNC between these two sites. Nevertheless, the temporal variations and correlations between the PNCs of SYSU and PY sites are still representative for Guangzhou regional air quality.

2.3 Gaseous Pollutants and Meteorological Parameters

Complementary gaseous pollutants such as O3, SO2, CO and NOx concentration data were obtained from the monitoring stations of Bureau of Ecology and Environmental Protection at Haizhu Park station (HZP, 23.08°N, 113.33°E), approximately 4 km to SYSU site, and Daishi station (DS, 23.02°N, 113.30°E) 13km to PY site. The synoptic meteorological conditions and gaseous data at HZP and DS were representative of those at SYSU and PY site, respectively. It was noted that since no solar radiation data is available at HZP, a Reanalysis Shortwave Solar Radiation (SSR) data of ERA5 dataset from European Centre for Medium-Range Weather Forecasts (ECMWF) was used to represent the solar radiation at SYSU site for the estimation of particle production by photochemical reaction.

During the measurement period, two synoptic wind circulations were affecting the study area. One is the winter monsoon which associated with the northerly winds along with high barometric pressure and decrease of ambient temperature, another is the land-sea breeze (LSB) circulation associated with the southeasterly winds from coastal region due to colder air masses at land during the nighttime and early morning which change to southeasterly winds when the air masses on land become warmer during daytime than that in the Pearl River Estuary (Fan et al., 2008). The Monsoon period is classified as the period under the continuous influence of northerly winds with gradually increase of barometric pressure and decrease of temperature until the changing of above conditions. The LSB period is defined as the time when wind direction changes diurnally during daytime and nighttime. Thus, two winter monsoon periods (M) were classified (i.e., M1: 16 January 12:00 LT–20 January 23:00 LT, and M2: 25 January 11:00 LT–31 January 12:00 LT) and two land-sea breeze (LSB) periods (i.e., LSB1: 21 January 00:00 LT–25 January 10:00 LT (LSB1), and LSB 2: 31 January 13:00 LT–3 February 11:00 LT) were identified in this study. The classification of M1, M2, LSB1 and LSB2 are based on the wind directions as illustrated in Fig. 2. The SYSU site is located in the upwind area (see Fig. 1), which is affected by the strong northerly winds (as represented by HZP station) whereas at the downwind PY site, the north westerly winds were dominant (as represented by DS station), during the winter monsoon periods (i.e., M1 and M2). The synoptic wind patterns of HZP site were influenced by land-sea breeze circulation with the easterly/southeasterly winds dominated in morning and changed to northeasterly wind in the evening (see Fig. 3). The LSB circulation observed at DS site was less significant. Nevertheless, during the non-monsoon periods, the wind speeds were significantly lower than those during monsoon periods and a distinct difference can be observed. The variations of pressure and temperature provide additional information on the influence of winter monsoon to southern China during the period of M1 and M2 (see Fig. 1).

Fig. 2. Upper panel: wind direction and speed, and lower panel: pressure (dashed line) and temperature (solid line) measured at HZP (red color) and DS (blue color) stations during the measurement period. The periods under the influence of monsoon circulation were highlighted in blue color (i.e., M1 and M2). The periods of LSB1 and LSB2 were under the influence of land-sea breeze.Fig. 2. Upper panel: wind direction and speed, and lower panel: pressure (dashed line) and temperature (solid line) measured at HZP (red color) and DS (blue color) stations during the measurement period. The periods under the influence of monsoon circulation were highlighted in blue color (i.e., M1 and M2). The periods of LSB1 and LSB2 were under the influence of land-sea breeze.

Fig. 3. Time series of PNC, PM2.5 and gaseous pollutant concentrations of O3, SO2, CO and NOx, measured during the measurement period. Color code: HZP (red line) and DS (blue line) (Chinese New Year holiday commenced on 25 January and extended to 9 February 2020).Fig. 3. Time series of PNC, PM2.5 and gaseous pollutant concentrations of O3, SO2, CO and NOx, measured during the measurement period. Color code: HZP (red line) and DS (blue line) (Chinese New Year holiday commenced on 25 January and extended to 9 February 2020).

2.4 Data Processing and Analysis

Particle number concentrations for different size ranges were calculated by the PSD from SMPS measurement. The particle number concentrations were classified into 10 ≤ d ≤ 480 nm (N10-480), 100 < d ≤ 480 nm (N100-480), 25 < d ≤ 100 nm (N25-100), and 10 ≤ d ≤ 25 nm (N10-25), for total, accumulation mode, Aitken mode and nucleation mode, respectively. For graphical representation, time resolution of 5 min was used for particle data, whereas the data for trace gases and meteorological parameters were hourly based. Thus, the 5-min PNC and PSD data were then calculated into hourly averages for data comparison and analysis purposes.


3.1 Overall PNC at Guangzhou and its Temporal Variations

3.1.1 General statistics of PNC over Guangzhou and comparison with other major cities

The average PNC concentrations measured in this study (6.3 × 103 cm–3 at SYSU; 9.7 × 103 cm–3 at PY) were comparable to that measured in urban and suburban areas Guangzhou city which has indicated that traffic as the major PM pollution source (16.0–29.0 × 103 cm–3) (see Table 1) and that measured in other major cities in China, including Shanghai (17.6 × 103 cm–3) and Nanjing (19.5 × 103 cm–3), and Milan (18.7 × 103 cm–3) in Italy. Nevertheless, the PNC measured at the two sites in the present study were comparable to that in urban cities, such as Los Angeles (8.3 × 103 cm–3) in the U.S. and London (8.5 × 103 cm–3) in the U.K, but higher than that in Brisbane (8.0 × 103 cm–3) in Australia. The PNC levels at urban and suburban Guangzhou were shown comparable to other urban cities in the world which are susceptible to PM pollution.

Table 1. Particle number concentration measured at different locations in China and other major cities in the world.

3.1.2 Temporal variations of meteorological, gaseous pollutants and particulate matter

During the measurement period, air pollutants such as O3, SO2, CO, NOx and PM2.5 in Guangzhou were significantly affected by meteorological conditions as shown in Fig. 3. The data of gaseous pollutants and PM2.5 at Haizhu Park (HZP, 23.08°N, 113.33°E) station (approximately 4 km from SYSU site) and Dasha (DS, 23.02°N, 113.30°E) station (approximately 10 km from PY site) were used to represent the ambient concentrations at urban and suburban Guangzhou, respectively. Both stations are operated under the air monitoring quality network of Bureau of Ecology and Environmental Protection, Guangdong Province. The concentrations of primary pollutants NOx and SO2 were significantly lower during the two monsoon periods (i.e., M1 and M2) compared to the LSB periods (i.e., LSB1 and LSB2) at HZP and DS, while M2 (from 25 to 31 January), which occurs during the CNY period, is seen to have lower NOx and SO2 than M1 (from 16 to 21 January) (see Table 2). During the monsoon, the averaged concentrations for NOx and SO2 were 34.3 and 3.5 µg m–3, respectively in M1 and 11.3 and 2.3 µg m–3 , respectively in M2 at HZP; and were 26.5 and 6.5 µg m–3 , respectively in M1 and 9.6 and 4.9 µg m–3, respectively in M2 at DS which showed a substantial decrease of these primary pollutants (by 67.1 and 34.3%, for NOx and SO2 respectively, at HZP and by 63.8 and 24.6%, for NOx and SO2 respectively, at DS) during the CNY period. It was reported that a sharp reduction of NOx was observed (> 60%) for China after the COVID-19 lockdown commenced (Huang et al., 2021). The limited human activities, such as shutting down of factories and restriction on travel has resulted in a general decrease in gaseous emissions. Similarly, there were a decrease of 32.9% in the averaged concentrations of NOx and a slight increase of 2.4% of SO2 at HZP in LSB2 during the CNY period, and a corresponding decrease of 36.2 and 20.3% at DS. It can be seen that there was generally a sharp decrease in primary emissions. Although primary emissions have been reduced, secondary emissions of pollutants such as O3 and PM2.5 in M2 increased during the CNY period. The reduction of NOx and increase in O3 observed in this study coincided with the results reported in Huang et al. (2021). In the cold season where the incident solar radiation is weak, NOx concentration decreases significantly influenced O3 concentrations by suppressing the scavenging of O3 through NOx titration (Jhun et al., 2015). This has resulted in the increase of O3 which eventually enhanced the atmospheric oxidation capacity and production of secondary PM (Huang et al., 2021). In addition, NOx and SO2 during M2 period (during the CNY period) were lower than those in LSB2 (at both HZP and DS), indicating that in addition to the dispersion effect from strong monsoon in M2, significantly low concentration of PM precursors in the period also attributed to lower PNC in urban and suburban Guangzhou during the CNY period. This will be discussed in detail in later section.

Table 2. Average gaseous concentration (C) measured at HZP and DS during different measurement periods; and the ratios of CM2/CM1, CM1/CLSB1, and CLSB2/CLSB1, CM2/CLSB2.

Fig. 4 and Table 3 show the distinct variations in PNCs during four periods. For the whole sampling period, the average PNCs measured at SYSU and PY were 6.3 × 103 cm–3 and 9.7 × 103 cm–3, respectively. During the measurement campaign, the monsoon and land sea breeze circulation systems both affected urban Guangzhou and Panyu before and during the holiday. This study hence provides an insight into the estimation of the relative influence of regional pollution transport associated with winter monsoon vs local pollution, and also the reduction of primary emission of PM around Guangzhou due to the reduced human activities. The PNCs measured at four different periods were represented by the terms PNCx where x was named by M1, M2, LSB1 and LSB2, respectively. The average PNCs were the highest during LSB1 at SYSU (8.9 × 103 cm–3) and M1 at PY (12.2 × 103 cm–3), indicating that the PNCs before the CNY holiday were generally higher. By comparing the PNCs measured during two monsoon periods (i.e., M1 and M2) and LSB periods (i.e., LSB1 and LSB2), the influences of regional pollution transported from upwind region of Guangzhou on PNCs compared to that by the influence of local pollution can be estimated. Notably, the ratios of PNCM2/PNCLSB2 were 0.40 and 0.53 at SYSU and PY, respectively, indicating the influence of regional pollution on PNCs is relatively less during the CNY period. The ratios of PNCM2/PNCM1 and PNCLSB2/PNCLSB1 at SYSU were 0.33 and 0.76 (reduced by 66.7% and 24.1%), indicating a substantial reduction in PNCs during the holiday, especially under the strong dispersion conditions during the monsoon period. On the contrary, a ratio of PNCM2/PNCM1 of 0.52 (reduced by 48.3%) at PY site also indicated the substantial reduction of PNCs during the holiday in suburban Guangzhou under the impact of monsoon. On the other hand, a ratio of PNCLSB2/PNCLSB1 of 1.16 (increased by 15.7%) at PY indicated the PNC obtained at downwind suburban region of Guangzhou was higher during the normal LSB circulation before the holiday. PM pollution was generally found to be significantly reduced during the CNY holiday with less impact from regional pollution. This indicates that both the reduction in local emissions from Guangzhou (primarily from vehicular emissions) and as well as those from the surrounding cities have impacted the PM level. Furthermore, it was noted that the relatively higher PNC during LSB2 (during the CNY holiday) at PY was due to an elevated PNC (a maxima value 3.9 × 104 cm–3 of PNC in this study) observed on 31 January. We found that there is an NPF event occurred on that day. Hence, the higher average PNC observed at PY is related to the NPF event and will be discussed in the next section.

Fig. 4. Time series of PNC measured at SYSU (black color) and PY (green color), and wind direction and speed measured at HZP during the measurement period. PNC at SYSU was measured by SMPS for particle size from 10 to 480 nm, and PNC at PY was measured by CPC for particle size from 5 to 2500 nm.Fig. 4. Time series of PNC measured at SYSU (black color) and PY (green color), and wind direction and speed measured at HZP during the measurement period. PNC at SYSU was measured by SMPS for particle size from 10 to 480 nm, and PNC at PY was measured by CPC for particle size from 5 to 2500 nm.

Table 3. Average PNC measured at SYSU and PY during different measurement periods; and the ratios of PNCM2/PNCM1, PNCM1/PNCLSB1, and PNCLSB2/PNCLSB1, PNCM2/PNCLSB2.

3.1.3 Diurnal variation of PNC

As shown in Fig. 5(a), the diurnal variations of PNC at SYSU site and PY site were consistent, in which a bimodal distribution pattern was depicted with a smaller peak occurred at noon (12:00 LT) and a larger peak occurred in the evening (18:00–19:00LT). In addition, a small peak of PNC was observed in the morning (07:00–08:00 LT) which coincided with the peak of NOx indicating the influence of vehicular exhaust emissions during the rush hours (Morawska et al., 2008). The noon peak of PNC resembled that of SSR, indicating that from 10:00 LT to noon, as the SSR increased, photochemical rate increased, leading to the increase of the formation rate of new particles (Kulmala, 2003), until the noon peak was reached at 12:00 LT. The concentration of NOx rose from the lowest value in the afternoon (i.e., 15:00 LT) to a daily maximum at 23:00 LT. However, the peak of PNC at around 19:00 LT did not correspond to that of NOx. This will be further discussed in detail below.

Fig. 5. Diurnal variations of (a) total PNCs (at SYSU and Panyu), NOx (HZP) and shortwave solar radiation (SSR); and (b) N10-25, N25-100, N100-480 at SYSU, and PM2.5 and ambient temperature at HZP.Fig. 5. Diurnal variations of (a) total PNCs (at SYSU and Panyu), NOx (HZP) and shortwave solar radiation (SSR); and (b) N10-25, N25-100, N100-480 at SYSU, and PM2.5 and ambient temperature at HZP.

Using the particle size distribution data at the SYSU site, the PNC was divided into three groups according to the particle size, namely N10-25 (nucleation mode), N25-100 (Aitken mode) and N100-480 (accumulation mode). From the diurnal variations of these three groups of PNCs in Fig. 5(b), it can be seen that N25-100 was the highest among the three types of particles for most of the day, indicating that the study area is affected by as severe ultrafine pollution dominated by Aitken mode particles. This showed that the study area is affected by as severe ultrafine pollution dominated by Aitken mode particles. Although N10-25 was lower than other larger particles during the entire measurement period, its increasing rate during 05:00–10:00 LT was higher than that of N25-100 and N100-480 (see Fig. 5(b)), inferring that more nucleation mode particles (N10-25) were produced. This phenomenon has also been found in previous studies where an NPF event started before noontime and in turn enhanced the formation of nucleation mode particles (Cheung et al., 2011; Liu et al., 2008). The impact of NPF events on the PSD at SYSU will be discussed in Section 3.2.3. The maxima of PNCs for different particle sizes occurred in different time periods: the peak of N10-25 appeared at 18:00 LT, and the peaks of N25-100 and N100-480 appeared at 19:00 LT. After the peak at 19:00 LT, N100-480 subsequently plateaued (see Fig. 5(b)). Note that the particle size distribution in the atmosphere changed significantly after the evening period at 19:00 LT, and N25-100, which originally accounted for the largest proportion in PNC dropped sharply. This can be explained by the particle growth processes and the condensation of condensable gas precursors in the atmosphere (Kerminen et al., 2018). Smaller particles in the atmosphere tend to coagulate with larger particles and produce larger particles which leads to significant decrease of concentrations of smaller particles (N10-25 and N25-100) as shown in the diurnal variation of PM2.5 with a peak at about 21:00 LT (Fig. 5(b)) and a flat downward trend of N100-480. The large number of N25-100 dominates PNC, which caused PNC to drop rapidly after its peak in the evening, despite of the increase in NOx (from vehicular exhaust) as observed earlier. In addition to the above reasons, the night peak of PNC may be related to both the mixing layer height and evening traffic peak hours. In addition, lower temperature leads to a lower mixing layer, resulting in weaker dispersion of ground pollutants (Tang et al., 2016). According to the diurnal temperature variation (Fig. 5(b)), the temperature in Guangzhou dropped after 16:00 LT. As a result, the mixing layer height also dropped, causing the PNC to rise until it peaked at 19:00 LT.

3.2 Association of Synoptic Wind Patterns with PNC and NPF

3.2.1 Correlation among PNC within urban airshed under monsoon and land-sea circulations

In this section, correlation analysis was conducted for the hourly averaged PNC between SYSU and PY sites. We first compare the correlation of PNCs of the two datasets during the entire period. Their correlation before and during the CNY period will also be assessed. Finally, we select the period during which Guangzhou area is affected by the monsoon (M1 and M2) to compare the correlation of PNCs between the two sites under the land-sea breeze circulations (LSB1 and LSB2).

Fig. 6(a) depicts the scatterplot of PNCs between SYSU and PY, with the Pearson coefficient of 0.60 (p < 0.001), showing a moderate correlation of PNCs between these two sites. This result indicated that the ambient PNC in Guangzhou region was a regional pollution issue for Guangzhou despite the large distance of approximately 30 km. This is also supported by similar r values in the plots between the PNCs at the two sites for the period before (0.57, p < 0.001) and during (0.50, p < 0.001) the CNY holiday (Fig. 5(b)). During the CNY holiday (beginning at 00:00 LT on 25 January), a sharp reduction of primary emissions occurred in Guangzhou as indicated by significant low concentrations of NOx and SO2 which has been discussed in Section 3.1.2. We further examined the correlation of PNCs between SYSU and PY before the CNY (16 January–24 January) and after the holiday (25 January–3 February). Despite the similar correlation between the two sites during the two periods, a significant reduction of PNC was also observed for urban Guangzhou (SYSU) (as indicated by smaller slope value of 0.30 during the CNY holiday compared to 0.49 before the holiday in the regression line Fig. 6(b)) revealed that there is a greater reduction of primary emissions (e.g., vehicular emissions) in urban Guangzhou during the CNY holiday than that in suburban areas.

Fig. 6. Scatterplots between the PNCs of SYSU and PY for (a) whole measurement period, (b) before and during COVID lockdown periods, (c) monsoon period and (d) land-sea breeze periods. Data on 31 January is excluded in (d). Color marker indicated the corresponding date for the data points.Fig. 6. Scatterplots between the PNCs of SYSU and PY for (a) whole measurement period, (b) before and during COVID lockdown periods, (c) monsoon period and (d) land-sea breeze periods. Data on 31 January is excluded in (d). Color marker indicated the corresponding date for the data points.

To further investigate the influences of monsoon and land-sea breeze circulations on regional PNCs of Guangzhou, Pearson correlation coefficients (r) between the scatterplots of PNCs at SYSU and PY sites were calculated for monsoon period (combined M1 and M2), and LSB period (combined LSB1 and LSB2) (Figs. 6(c) and 6(d)). For monsoon periods, the r value is 0.70 (p < 0.001) a slightly higher than that for whole period (r = 0.60) (Figs. 6(a) and 6(c)) which implied the stronger influence of particle emission sources from upwind urban site (i.e., SYSU) to downwind suburban site (i.e., Panyu) under the northerly winds during the monsoon periods. In contrast, the r value is weaker (0.30, p < 0.001) during land–sea breeze periods (Fig. 6(d)) which implied that the PNCs at both sites were primarily attributed to local emissions. Due to the occurrence of NPF on January 31, there was a high PNC concentration measured at PY. If this is treated as an outliner, the Pearson coefficients become 0.58 (p < 0.001) by excluding the data point on that day (Fig. 6(d)).

3.2.2 Correlation among PNC and other environmental parameters

In this section, the effect of other environmental parameters on the PNC observed at urban (SYSU) and suburban (PY) areas is studied. Table 4 summarized of the PNC under different wind speed and direction. Obviously, the lower wind speed was found to be associated with the higher PNCs at both at SYSU and PY sites which suggested the effect of weak atmospheric dilution. The wind speed for different wind directions were hence calculated (see Table 4). For SYSU site, the higher wind speed was observed in northwesterly-west (270–315 degree) and northeasterly (0–45 degree). In contrast, lower wind speed was observed in the northerly wind (0–135 degree). This result showed the stronger influence of winter monsoon at the upwind urban site (SYSU) compared with that at downwind suburban site (PY). We further investigate the PNC under different wind direction for two sites. Highest PNC (i.e., 12.6 × 103 cm–3) was obtained under the wind direction (225–270 degree) in urban site, while highest PNC (i.e., 10.0 × 103 cm–3) was obtained under the wind direction (0–45 degree) in suburban site. Similar observations for NOx were obtained where higher NOx observed under southerly wind in urban, and higher NOx observed under northerly wind in suburban, respectively. These finding showed that the different variation patterns of PNC at SYSU and PY sites were obtained under the influence of the winter monsoon.

 Table 4. Averaged PNC observed at SYSU and PY associated with different wind direction. The NOx and wind speed data for SYSY and PY were measured at HZP and DS stations.

3.2.3 Characteristics of NPF events in urban Guangzhou

In urban environment, atmospheric particles were dominated by Aitken mode particles associated with vehicle emissions (Morawska et al., 2008). Elevated PNC was well observed in urban environment when NPF occurred which caused an increase in PNC of nucleation mode particles to several times higher than that influences by vehicle emission sources, and the nucleation mode particles that can further grew to larger sizes than that from vehicle emissions (Cheung et al., 2013). An NPF event is defined as the substantial increase of the number concentration of nucleation mode particles, which can further grow to Aitken and/or accumulation mode size range (≥ 25 nm) and last for a few hours until they disappear into the atmosphere by condensation/coagulation sinks (Dal Maso et al., 2005). To investigate the influence of primary and secondary sources on particle number concentration in urban Guangzhou, we analyzed the temporal variations of PNC and size distribution measured at SYSU. A total of six NPF events were identified during the measurement periods, and the particle formation rate (J10-25) and growth rate (GR) for the NPF events on 18 January, 20 January and 31 January were calculated (Table 5), while the J10-25 and GR were unclear for other NPF events (i.e., 28 January, 29 January and 30 January). Following the method by Dal Maso et al. (2005), the particle growth rate (GR) was calculated for NPF days by fitting the geometric mean diameter (GMD) starting from the initial stage of NPF event until the particle size grows beyond the size of 25 nm and the particle formation rate (J10-25) was calculated as the sum of the apparent formation rate (dN10-25/dt) and the coagulation loss rate during the NPF event. The averaged GRs were found to be 14 nm h–1 (7.0–21.0 nm h–1), comparable to those reported in previous studies in the PRD region (2.2–19.8 nm h–1), but relatively higher than those in urban China (median: 6.2 nm h–1, Kerminen et al., 2018). However, the particle formation rate obtained in this study (0.12 ± 0.03, in a range of 0.1–0.16 cm–3 s–1) is lower than that reported in previous studies in the PRD region (J3: 0.5–5.2 cm–3 s–1, for 3–30 nm particles), likely due to the difference in particle size range among measurements. In this study, the particle size can be measured down to 10 nm, while a lower limit of 3 nm was employed in previous studies in the PRD region, which theoretically should have higher formation rate under similar atmospheric conditions. Furthermore, similar results have been observed in urban Taiwan which was 11.9 nm h–1 on average and ranged from 4.4–38.7 nm h–1 (Cheung et al., 2013), and other urban areas with median GR of 5.9 nm h–1 (Kerminen et al., 2018). Sulfuric acid is usually considered as a precursor of new particles, especially in urban environments (Weber et al., 2001). To evaluate the contribution of sulfuric acid to the particle production, a H2SO4 proxy was used as an indicator of nucleation mode particle formation via photochemical production of ambient H2SO4, which was defined by the product of shortwave solar radiation (SSR) and SO2 divided by the condensation sink (CS) of pre-existing particles (SSR × SO2/CS). Fig. 7 illustrates the temporal variations of H2SO4 proxy, particle condensation sink and size distribution during the measurement period. In general, the higher H2SO4 proxy value the higher number concentration of nucleation mode particles measured (Cheung et al., 2013). However, it was noted that the particle number concentration of nucleation mode particles (i.e., N10-25) not solely associated with H2SO4 proxy in this study. The averaged H2SO4 proxies during LSB1 and LSB2 were 11.3 × 103 Wm–2 ppb s and 39.0 × 103 Wm2 ppb s, respectively, while average N10-25 for these periods were 1390 cm–3 and 490 cm–3, respectively. Relatively high H2SO4 proxies observed in the M2 period (during the lockdown) in which only three weak NPF events occurred (i.e., 28 January, 29 January, 30 January), demonstrating that occurrence of NPF in urban Guangzhou is not solely governed by precursors such as sulfuric acid. In addition, we observed that a relatively stronger wind speed was found during the weak NPF event days (i.e., 28 January, 29 January, 30 January, averaged wind speed: 2.2 ± 0.7 m s–1) compared to that for strong NPF event days (i.e., 18 January, 20 January and 31 January, averaged wind speed: 1.3 ± 0.7 m s–1) (see Fig. 8). In this study, the condensation sink (CS) for different periods were 0.0363 s–1 and 0.0143 s–1 for M1 and M2, and 0.0416 s–1 and 0.0357 s–1 for LSB1 and LSB2, respectively. In general, higher condensation sink (CS) was observed during the period before the CNY holiday (i.e., M1 and LSB1) and also higher for Land and Sea Breeze periods than that of Monsoon periods, as more pre-existing particle existed due to more local emissions, which impede the NPF processes. Bousiotis et al. (2021) showed that the wind speed has positive and negative effects on the occurrence of NPF event which varied depending on the air masses and local conditions. High wind speed could enhance the occurrence of NPF event by increased mixing of condensable compounds which eventually lower the condensation sink. On the other hand, the high wind speed may impede NPF due to increased atmospheric dilution (Bousiotis et al., 2021). Our results therefore provide evidence that mix of conditions were affecting the NPF process, where NPF events occurred when the CS level and wind speed are low. This indicated that local conditions need to assess to better understand the NPF process.

Table 5. Particle growth (GR) and formation (J10-25) rates during new particle formation events.

Fig. 7. From bottom to top panel. Temporal profiles of (a) Number concentrations of particles ranging from 10–25 nm diameter (N10-25), and 25–480 nm diameter (N25-480); (b) particle size distribution, geometric mean diameter (GMD); (c) particle condensation sink and H2SO4 proxy with wind speed on color scale. NPF events were highlighted by red rectangular, and weak NPF events were highlighted by blue rectangular.Fig. 7. From bottom to top panel. Temporal profiles of (a) Number concentrations of particles ranging from 10–25 nm diameter (N10-25), and 25–480 nm diameter (N25-480); (b) particle size distribution, geometric mean diameter (GMD); (c) particle condensation sink and H2SO4 proxy with wind speed on color scale. NPF events were highlighted by red rectangular, and weak NPF events were highlighted by blue rectangular.

Fig. 8. Scatterplot between nucleation mode particles (N10-25) and H2SO4 proxy during new particle formation events (NPF) and weak NPF events (wind speed is indicated by the color scale).Fig. 8. Scatterplot between nucleation mode particles (N10-25) and H2SO4 proxy during new particle formation events (NPF) and weak NPF events (wind speed is indicated by the color scale).

To investigate the effects of particle size on NPF, the median particle size distributions for NPF event days and non-NPF event days is plotted (see Fig. 9). The PSD data were fitted by multiple log-normal distribution algorithms by DO-FIT model (Hussein et al., 2005). Three modes represented for nucleation, Aitken and accumulation mode particles were identified for NPF and non-NPF days. In general, comparable median total number concentrations were found for NPF (5.5 × 103 cm–3) and non-NPF (7.1 × 103 cm–3) event days, respectively. The median PNCs for nucleation, Aitken and accumulation modes were 3.2 × 103, 1.7 × 103 and 0.6 × 103 cm–3 for NPF event days, and 2.1 × 103, 3.0 × 103 and 0.7 × 103 cm–3 for non-NPF event days, respectively. The higher Aitken mode particles were observed during non-NPF event days showing particle during this period was emitted from diesel and petrol engine emissions produce particles in the size range of about 20–130 nm and 20–60 nm, respectively (Morawska et al., 2008). The higher nucleation mode PNCs observed during NPF event days were similar to those reported in other studies (e.g., Cheung et al., 2016) suggesting that new particle formation significantly attributed to the PNC which was dominated by nucleation mode particles.

 Fig. 9. Size distribution of particle number concentrations for a) NPF event days and b) non-NPF event days at urban Guangzhou (SYSU site). Solid line: Median particle size distribution by observations; Dashed line: fitted with three modes.Fig. 9. Size distribution of particle number concentrations for a) NPF event days and b) non-NPF event days at urban Guangzhou (SYSU site). Solid line: Median particle size distribution by observations; Dashed line: fitted with three modes.

In addition, a gradually increase of PM2.5 was observed followed by the NPF events (see Fig. S1). The peaks of PM2.5 were observed about 8–13 hr after the maxima PNCs observed during the NPF events. Kulmala et al. (2021) reported that about 65% of haze particles resulted from NPF, and there is about 1–3 days delay to build up the haze episodes in Beijing, China. The moderate PM2.5 levels, ranged from 41–48 µg m–3, were observed after the NPF events due to the subsequent growth of newly formed particles in the study region.


A continuous measurement of particle number concentrations and size distribution was conducted at urban (SYSU) and suburban (Panyu) areas of Guangzhou, South China during 16 January to 3 February 2020 before and during the CNY holiday. The average PNCs were 6.3 × 103 cm–3 and 9.7 × 103 cm–3, respectively for urban and suburban sites. The PNC levels of Guangzhou were significantly influenced by the meteorological conditions and emission activities as exemplified by the lower PNCs during the CNY period which is influenced by the higher atmospheric dispersion due to monsoon circulation, and lower primary emissions during the holiday. Diurnal variations of particle size distribution and PM2.5 data obtained in urban Guangzhou explicating the PNCs variations were mostly affected by vehicular emissions in morning and afternoon peak hours, as well as photochemical production at noon time. Smaller particles in the atmosphere tend to coagulate with larger particles which leads to significant decrease of concentrations of smaller particles (N10-25 and N25-100). A moderate correlation (r = 0.60) obtained between the PNCs of at both the urban and suburban sites which implied that PM pollution was a regional pollution issue for Guangzhou, especially under the influence of monsoon which a significantly higher correlation coefficient (r = 0.70) compared to that during land-sea breeze period (r = 0.30). The high PNC correlation found in the megacity of Guangzhou evidenced that the particulate matter (PM) pollution occurred at a regional scale, which is impacted significantly by local emission sources (Aitken mode particles) and the NPF process (nucleation mode particles), under the influences of monsoon and land-sea breeze. This indicated that the region is affected by a severe ultrafine particulate pollution. A higher N10-25/H2SO4 proxy ratio was obtained under lower wind speed condition during NPF events in an urban environment. The significantly higher PNC of nucleation mode particles during NPF events were found to be associated with lower wind speed condition.


This research was supported by the research startup fund of Sun Yat-sen University (Project number: 74110-18841227) and the Ministry of Science and Technology, Guangdong Province (Project number: 2020A1515011138). The authors thank the Guangzhou Environmental Monitoring Center of Guangzhou, China for providing the air quality and meteorological data and the facilities of Panyu monitoring station. Tareq Hussein is gratefully acknowledged for providing us with the code of DO-FIT.


  1. Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminnen, V.M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S.K., Sherwood, S., Stevens, B., Zhang, X.Y. (2013). Clouds and Aerosols. in: Stocker T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.)], Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 571–657.

  2. Bousiotis, D., Brean, J., Pope, F.D., Dall’Osto, M., Querol, X., Alastuey, A., Perez, N., Petäjä, T., Massling, A., Nøjgaard, J.K., Nordstrøm, C., Kouvarakis, G., Vratolis, S., Eleftheriadis, K., Niemi, J.V., Portin, H., Wiedensohler, A., Weinhold, K., Merkel, M., Tuch, T., Harrison, R.M. (2021). The effect of meteorological conditions and atmospheric composition in the occurrence and development of new particle formation (NPF) events in Europe. Atmos. Chem. Phys. 21, 3345–3370. https://doi.org/10.5194/acp-21-3345-2021

  3. Chan, C.K., Yao, X. (2008). Air Pollution in mega cities in China – A review. Atmos. Environ. 42, 1–42, https://doi.org/10.1016/j.atmosenv.2007.09.003

  4. Charlson, R.J., Schwartz, S.E., Hales, J.M., Cess, R.D., Coakley, Jr., J.A., Hansen, J.E., Hofmann D.J. (1992). Climate forcing by anthropogenic aerosols. Science 255, 423–430. https://doi.org/​10.1126/science.255.5043.423

  5. Cheung, H.C., Wang, T., Baumann, K., Guo, H. (2005). Influence of regional pollution outflow on the concentrations of fine particulate matter and visibility in the coastal area of southern China. Atmos. Environ. 39, 6463–6474. https://doi.org/10.1016/j.atmosenv.2005.07.033

  6. Cheung, H.C., Morawska, L., Ristovski, Z.D. (2011). Observation of new particle formation in subtropical urban environment. Atmos. Chem. Phys. 11, 3823–3833. https://doi.org/10.5194/​acp-11-3823-2011

  7. Cheung, H.C., Morawska, L., Ristovski, Z.D., Wainwright, D. (2012). Influence of medium range transport of particles from nucleation burst on particle number concentration within the urban airshed. Atmos. Chem. Phys. 12, 4951–4962. https://doi.org/10.5194/acp-12-4951-2012

  8. Cheung, H.C., Chou, C.C.K., Huang, W.R., Tsai, C.Y. (2013). Characterization of ultrafine particle number concentration and new particle formation in an urban environment of Taipei, Taiwan. Atmos. Chem. Phys. 13, 8935–8646. https://doi.org/10.5194/acp-13-8935-2013

  9. Cheung, H.C., Chou, C.C.K., Chen, M.J., Huang, W.R., Huang, S.H., Tsai, C.Y., Lee, C.S.L. (2016). Seasonal variations of ultra-fine and submicron aerosols in Taipei, Taiwan: Implications for particle formation processes in a subtropical urban area. Atmos. Chem. Phys. 16, 1317–1330. https://doi.org/10.5194/acp-16-1317-2016

  10. China Briefing (2020). Managing Your China Business During the Coronavirus Outbreak. (accessed January 2022).

  11. Dal Maso, M., Kulmala, M. Riipinen, I., Wagner, R., Hussein, T., Aalto, P.P., Lehtinen, K.E.J. (2005). Formation and growth of fresh atmospheric aerosols: Eight years of aerosol size distribution data from SMEAR II, Hyytiälä, Finland. Boreal Environ. Res. 10, 323–336.

  12. de Jesus, A.L., Rahman, M.M., Mazaheri, M., Thompson, H., Knibbs, L.D., Jeong, C., Evans, G., Nei, W., Ding, A., Qiao, L., Li, L., Portin, H., Niemi, J.V., Timonen, H., Luoma, K., Petäjä, T., Kulmala, M., Kowalski, M., Peters, A., Cyrys, J., et al. (2019). Ultrafine particles and PM2.5 in the air of cities around the world: Are they representative of each other? Environ. Int. 129, 118–135. https://doi.org/10.1016/j.envint.2019.05.021

  13. Ding, A., Wang, T., Zhan, M., Wang, T.J., Li, Z. (2004). Simulation of sea-land breezes and a discussion of their implications on the transport of air pollution during a multi-day ozone episode in the Pearl River Delta of China. Atmos. Environ. 38, 6737–6750. https://doi.org/​10.1016/j.atmosenv.2004.09.017

  14. Donaldson, K., Li, X.Y., MacNee, W. (1998). Ultrafine (nanometer) particle mediated lung injury. J. Aerosol Sci. 29, 553–560. https://doi.org/10.1016/S0021-8502(97)00464-3

  15. Fan, S., Wang, B., Tesche, M., Engelmann, R., Althausen, A., Liu, J., Zhu, W., Li, M., Song, L., Leong, K. (2008). Meteorological structures of atmospheric boundary layer in October 2004 over Pearl River Delta area. Atmos. Environ. 42, 6174–6186. https://doi.org/10.1016/j.atmosenv.2008.​01.067

  16. Fu, X., Wang, X., Guo, H., Cheung, K., Ding, X., Zhao, X., He, Q., Gao, B., Zhang, Z., Liu, T., Zhang, Y. (2014). Trends of ambient fine particles and major chemical components in the Pearl River Delta region: Observation at a regional background site in fall and winter. Sci. Total Environ. 497–498, 274–281. https://doi.org/10.1016/j.scitotenv.2014.08.008

  17. Fu, X., Wang, X., Hu, Q, Li, G., Ding, X., Zhang, Y., He, Q., Liu, T., Zhang, Z., Yu, Q., Shen, R., Bi, X. (2016). Changes in visibility with PM2.5 composition and relative humidity at a background site in the Pearl River Delta region. J. Environ. Sci., 40, 10–19, https://doi.org/10.1016/j.jes.2015.​12.001

  18. Guangdong Provincial Bureau of Statistic (GPBS) (2019). Guangdong Statistical Yearbook 2019, Beijing, China Statistic Press, China

  19. Hagler, G.S.W., Bergin, M.H., Salmon, L.G., Yu, J.Z., Wan, E.C.H., Zheng, M., Zeng, L.M., Kiang, C.S., Zhang, Y.H., Lau, A.K.H., Schauer, J.J. (2006). Source areas and chemical composition of fine particulate matter in the Pearl River Delta region of China. Atmos. Environ. 40, 3802–3815. https://doi.org/10.1016/j.atmosenv.2006.02.032

  20. He, K., Huo, H., Zhang, Q. (2002). Urban air pollution in China: Current status, characteristics, and progress. Annu. Rev. Energy. Environ. 27, 397–431. https://doi.org/10.1146/annurev.energy.​27.122001.083421

  21. Holman, J.P. (1972). Heat Transfer. McGraw-Hill, New York.

  22. Huang, X., Ding, A., Gao, J., Zheng, B., Zhou, D., Qi, X., Tang, R., Wang, J., Ren, C., Nie, W., Chi, X., Xu, Z., Chen, L., Li, Y., Che, F., Pang, N., Wang, H., Tong, D., Qin, W., Cheng, W., et al. (2021). Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. Natl. Sci. Rev. 8, nwaa137. https://doi.org/10.1093/nsr/nwaa137

  23. Hussein, T., Dal Maso, M., Petäjä, T., Koponen, I.K., Paatero, P., Aalto, P.P., Hämeri, K., Kulmala, M. (2005). Evaluation of an automatic algorithm for fitting the particle number size distributions. Boreal Environ. Res. 10, 337–355.

  24. Hussein, T., Junninen, H., Tunved, P., Kristensson, A., Dal Maso, M., Riipinen, I., Aalto, P.P., Hansson, H.C., Swietlicki, E., Kulmala, M. (2009). Time span and spatial scale of regional new particle formation events over Finland and Southern Sweden. Atmos. Chem. Phys. 9, 4699–4716. https://doi.org/10.5194/acp-9-4699-2009

  25. International Council on Clean Transportation (ICCT) (2016). A technical summary of Euro 6/VI vehicle emission standards. The International Council on Clean Transportation.

  26. Jhun, I., Coull, B.A., Zanobetti, A., Koutrakis, P. (2015). The impact of nitrogen oxides concentration decreases on ozone trends in the USA. Air Qual Atmos Health 8, 283–292. https://doi.org/​10.1007/s11869-014-0279-2

  27. Kerminen, V.M., Chen, X., Vakkari, V., Petäjä, T., Kulmala, M., Bianchi, F. (2018). Atmospheric new particle formation and growth: Review of field observations. Environ. Res. Lett. 13, 103003. https://doi.org/10.1088/1748-9326/aadf3c

  28. Kulmala, M. (2003). How particles nucleate and grow. Science 302, 1000–1001. https://10.1126/​science.1090848

  29. Kulmala, M., Dada, L., Daellenbach, K.R., Yan, C., Stolzenburg, D., Kontkanen, J., Ezhova, E., Hakala, S., Tuovinen, S., Kokkonen, T.V., Kurppa, M., Cai, R., Zhou, Y., Yin, R., Baalbaki, R., Chan, T., Chu, B., Deng, C., Fu, Y., Ge, M., et al. (2021). Is reducing new particle formation a plausible solution to mitigate particulate air pollution in Beijing and other Chinese megacities? Faraday Discuss. 226, 334–347. https://doi.org/10.1039/D0FD00078G

  30. Liu, S., Hu, M., Wu, Z., Wehner, B., Wiedensohler, A., Cheng, Y. (2008). Aerosol number size distribution and new particle formation at a rural/ coastal site in Pearl River Delta (PRD) of China. Atmos. Environ. 42, 6275–6283. https://doi.org/10.1016/j.atmosenv.2008.01.063

  31. Ministry of Environmental Protection (MEP) (2016). Limits and measurement methods for emissions form light-duty vehicles (CHINA 6) (GB18352.6-2016). Ministry of Environmental Protection, the People’s Republic of China.

  32. Morawska, L., Ristovski, Z., Jayarathne, E.R., Keogh, D.U., Ling, X. (2008). Ambient nano and ultrafine particles from motor vehicle emissions: Characteristics, ambient processing and implications on human exposure. Atmos. Environ. 42, 8113–8138. https://doi.org/10.1016/j.​atmosenv.2008.07.050

  33. Myhre, G. (2009). Consistency between satellite-derived and modeled estimates of the direct aerosol effect. Science 35, 187–190. https://doi.org/10.1126/science.11774461

  34. National Bureau of Statistics of China (NBSC) (2019). China Statistical Yearbook 2019, Beijing, China Statistics Press, China.

  35. Nel, A. (2005). Air pollution-related illness: Effects of particle. Science 308, 804–806. https://doi.org/10.1126/science.1108752

  36. Oberdörster, G., Utell, M.J. (2002). Ultrafine particles in the urban Air: To the respiratory tract--and beyond? Environ. Health Perspect. 110, A440–A441. https://doi.org/10.1289/ehp.110-1240959

  37. Sebastian, M., Kompalli, S.K., Kumar, V.A., Jose, S., Babu, S.S., Pandithurai, G., Singh, S., Hooda, R.K., Soni, V.K., Pierce, J.R., Vakkari, V., Asmi, E., Westervelt, D.M., Hyvärinen, A.P., Kanawade, V.P. (2022). Observations of particle number size distribution and new particle formation in six Indian locations. Atmos. Chem. Phys. 22, 4491–4508. https://doi.org/10.5194/acp-22-4491-2022

  38. Tang, G., Zhang, J., Zhu, X., Song, T., Münkel, C., Hu, B., Schäfer, K., Liu, Z., Zhang, J., Wang, L., Xin, J., Suppan, P., Wang, Y. (2016). Mixing layer height and its implications for air pollution over Beijing, China. Atmos. Chem. Phys. 16, 2459–2475. https://doi.org/10.5194/acp-16-2459-2016

  39. Wang, C., Horby, P.W., Hayden, F.G., Goa, G.F. (2020). A novel coronavirus outbreak of global health concern. Lancet 395, 470–473. https://doi.org/10.1016/S0140-6736(20)30185-9

  40. Wang, T. (2003). Study of visibility and its causes in Hong Kong. Final Report. Air Services Group, The Environmental Protection Department of HKSAR. Ref. AS01-286.

  41. Weber, R.J., Chen, G., Davis, D.D., Mauldin III, R.L., Tanner, D.J., Eisele, F.L., Clarke, A.D., Thornton, D.C., Bandy, A.R. (2001). Measurements of enhanced H2SO4 and 3–4 nm particles near a frontal cloud during the First Aerosol Characterization Experiment (ACE 1). J. Geophys. Res. 106, 24107–24117. https://doi.org/10.1029/2000JD000109

  42. World Health Organization (WHO) (2006). Air Quality Guidelines Global Update 2005. Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Geneva, WHO Press.

  43. Wu, D., Fung, J.C.H., Yao, T., Lau, A.K.H. (2013). A study of control policy in the Pearl River Delta region by using the particulate matter source apportionment method. Atmos. Environ. 76, 147–161. https://doi.org/10.1016/j.atmosenv.2012.11.069

  44. Xie, J., Jin, L., Cui, J., Luo, X., Li, J., Zhang, G., Li, X. (2020). Health risk-oriented source apportionment of PM2.5-associated trace metals. Environ. Pollut. 262, 114655. https://doi.org/10.1016/j.​envpol.2020.114655

  45. Yue, D.L., Hu, M., Wang, Z.B., Wen, M.T., Guo, S., Zhong, L.J., Wiedensohler, A., Zhang, Y.H. (2013). Comparison of particle number size distributions and new particle formation between the urban and rural sites in the PRD region, China. Atmos. Environ. 76, 181–188. https://doi.org/10.1016/j.atmosenv.2012.11.018

  46. Zhao, R.J., Moh, W.H. (2015). Development of new modality municipal public transportation for Guangzhou – Group rapid transit system as supplementary linkage from Guangzhou city centre to its eastern Tourism zone. Front. Eng. Manage. 2, 378–390. 

Share this article with your colleagues 


Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

77st percentile
Powered by
   SCImago Journal & Country Rank

2021 Impact Factor: 4.53
5-Year Impact Factor: 3.668

Aerosol and Air Quality Research partners with Publons

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.