Trends in ambient air pollution levels and PM_2.5 chemical compositions in four Chinese cities from 1995 to 2017

Introduction

The associations have been well established between ambient air pollutants, such as sulfur dioxide (SO₂), nitrogen oxides (NOx), aerosol particulate matter (PM), ozone (O₃), and human health (1-4). With rapid economic growth, China has suffered from substantial air pollution, resulting in many studies evaluating the impacts of air pollution on human health (5-8). For example, exposure to NO₂ or SO₂ has been associated with bronchitis, asthma, and emphysema, all of which were more pronounced in children and asthmatic patients (9). Numerous studies have reported that PM_2.5 and its specific chemical constituents were linked to the incidence of respiratory diseases and mortality as well as lung function (10-12).

In the period of 1993–1996, a cross-sectional study of children’s respiratory health in relation to ambient air pollution was conducted based on a gradient in pollutant concentrations across the four Chinese cities of Lanzhou (LZ), Wuhan (WH), Chongqing (CQ), and Guangzhou (GZ) (13-17). The study found that higher air pollution levels were significantly associated with a greater risk for developing symptoms, respiratory disease, and reduced lung function in children. Parents had a greater risk of respiratory diseases. More than 20 years later, with significant changes in many aspects of the society and the population, it is important to understand the extent to which air pollution changes contributed to changes in respiratory health in children in these cities.

In general, heavy air pollution events were highly concentrated in four regions: North China Plain, Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (18,19). Three of the four cities (WH, GZ, and CQ), located in YRD and PRD, have often been the sites for studying various characteristics of air pollution (20-24). Taking PM pollution as an example, improvement in the PRD region has been substantial in the past decade (making O₃ often become the primary pollutant in recent years) (25), while PM_2.5 remained the primary pollutant in the YRD region and the Sichuan Basin (26,27). Many studies have also been launched in LZ because of its unique meteorological and geographical conditions (near the desert with four distinct seasons) (28,29). The published reports provided valuable information to understand longitudinal changes in ambient air quality in the four cities.

In the present review, based on the background information of socio-economic development, we aim to systematically examine the changes in air pollution levels and PM_2.5 chemical compositions from the 1990s to 2017. Data for air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, and O₃) in the four cities were collected from 1995 to 2017 to study the evolution of the air pollution in each city. In addition, spanning more than a decade we analyzed data on the chemical compositions of PM_2.5 to identify the temporal and spatial changes in the source apportionments of PM_2.5. We anticipate that the findings of this review can provide insights to help understand potential changes in health outcomes attributable to changes in air quality from the time of the original health study to the time of the current health study in the same cities. We also expect that the findings can provide historical perspectives on air quality evolutions to inform new control policies.

Data and methods

Study site description

Based on the studies conducted more than 20 years ago, our current analysis included four Chinese cities of LZ, WH, CQ, and GZ (see Figure 1). Located in the northeastern side of the Qinghai-Tibet Plateau, LZ is situated in a semi-enclosed Yellow River valley basin that narrows in the north and south and extends in the east and west direction. LZ is characterized by windy and arid springs, which is a high season of sand and dust weather, where the climate has clear vertical variation spectrum and transitional characteristics (30-32). WH is geographically situated at the confluence of the Yangtze and Han Rivers and lies in central China, where the north subtropical monsoon climate offers sufficient light and heat and abundant rainfall (33). Moreover, WH is the core city of the Yangtze River Economic Belt, which has developed as an important industrial base and comprehensive transportation hub in China. CQ is characterized as a mountainous city located in the southwestern part of China and the upper reaches of the Yangtze River. With the annual humidity upwards of 70%–80%, CQ is nicknamed the City of Fog (34). As the core city of the Pearl River Delta metropolitan area, GZ is located in the south-central part of Guangdong Province, which is the largest city in South China. GZ belongs to the subtropical monsoon climate zone with an average annual temperature of 20–22 °C.

Figure 1 Map showing the four cities included in this study, including Lanzhou (LZ), Wuhan (WH), Chongqing (CQ), and Guangzhou (GZ).

Data collection

Data on socioeconomic indicators

It is known that air quality is generally associated with economic development stages. To help understand the long-term trend of ambient air pollution, we collected auxiliary data pertaining to the socioeconomic indicators from the Statistical Bulletin on National Economic and Social Development (35-38) in four cities. Annual values of four indicators between 1995 to 2017 included Gross Domestic Product (GDP), resident population, gross industrial output, and domestic car ownership. Measurement units for the indicator values were 100 billion yuan for GDP and gross industrial output, million people for the resident population, and ten thousand vehicles for domestic car ownership.

Data on air pollutants

The official data of air pollutants were annually averaged and extracted from the Report on the State of Environment (25,39-41) for the four cities during 1995 and 2017. Although we collected a significant amount of data, reports documenting annual concentrations of SO₂ and NO₂ in 1997 and 1998 were not available in WH and GZ (data was missing for CQ in 1997), while data from 1997 to 2001 were not available in LZ. Based on the Chinese Ambient Air Quality Standards formulated in 1996 (GB3095-1996) (42), the state requirement to monitor PM₁₀ data began in 2000, replacing total suspended particulate (TSP). Thus, the PM₁₀ data were first added into the report from 2000 in CQ, and other three cities began keeping records in 2001. Similarly, the PM_2.5 monitoring data were first added to the reports in 2012 based on the Chinese Ambient Air Quality Standards revised in 2012 (GB3095-2012) (43). We chose the daily averaged values in maximum 8-hour O₃ concentrations from 2014 on the website (44) where the data were collected from China National Environmental Monitoring Centre, due to the inconsistencies in the reports (such as the different year when data were first added and different type of monitoring O₃ including daily averaged in maximum 8-hour and one-hour averaged).

Data sources of chemical components and source apportionments of PM_2.5

We reviewed articles regarding chemical composition and source apportionment of PM_2.5 to summarize spatial and temporal evolution of source emissions in the four cities. We integrated the absolute values and relative proportions of chemical compositions from PM_2.5 across different years (from 2000), including major water-soluble ions [sulfate (SO₄²⁻), nitrate (NO₃⁻), ammonium (NH₄⁺), potassium (K⁺), and chloride (Cl⁻)], carbonaceous components [elemental carbon (EC), organic carbon (OC)], main toxic and source-characteristic metals [zinc (Zn), manganese (Mn), lead (Pb), copper (Cu), and chromium (Cr)] and others (relative proportion not shown). All relevant studies discussing individual cities are summarized in Table 1. Strict screening throughout the entire reviewed processing for considering the variable methods of data presentation in each document was reflected in the selection of long-term research data for one subject group in a given city. We found that the data for EC and OC were not available before 2013 in LZ based on the above screening principles. Additionally, normalization methods were used to eliminate gaps between the data with a wide range of sources. The model equation can be written as follows:

$$y{\%}_{ij}={\displaystyle {\sum }_{k=1}^n{(x/P{M}_{2.5})}_{ijk}/n}$$[1]

Table 1 Published studies reporting chemical compositions of PM_2.5 in the four cities during 2000 and 2017
Full table

where y%_ij is the normalized proportion of one component in PM_2.5 of city j in year i; x is the absolute concentrations of component, while n represents the article counts of city j in year i.

To better describe the local pollution characteristics during the examined period, we also reviewed the annual environmental protection regulations and specific implementation methods from the State and Urban Report on the State of Environment in the Environmental Protection Bureau’s official website (25,39-41).

An overview of social and economic development in four cities

Since entering into the 21^st century, China has experienced rapid urbanization and an exponential economic growth (77). Based on the available accounting, the four cities presented an obvious gap in the urban development for diverse socioeconomic indicators summarized in Table 2.

Table 2 Comparison of social and economic development for four cities between 1995 and 2017
Full table

By 2017, GZ was recognized as one of the first-tier cities with the largest regional GDP (2,150 billion yuan) and gross industrial output (2,269 billion yuan) compared to other cities. Even in 1996, the number of domestic vehicles in GZ reached 871 thousand vehicles, resulting in the lower growth rate compared to other cities. The resident population has increased by 2.51 million people, and the growth rate since 1995 was only second to WH (where the growth rate was up by 53.4%, from 7.10–10.89 million people). The available data suggest that WH experienced very significant development owing to the highest growth rates of GDP by 21 times and resident population growth by 53.4%. Although GZ and CQ were growing at the same rate in GDP (a multiple of 16) from 1995 to 2017, the development strength of CQ ranked number 5 among all large Chinese cities, reflected in approximately a 9.5% increase in GDP growth rate in 2017 compared to 2016 (78), reaching almost 2 trillion yuan, significantly more than GDP growth rate observed for GZ. In contrast, the slowest development was observed for LZ which had the lowest values for all indicators in 2017 compared to the Yangtze River Delta and PRD cities (40).

Long-term variations in major air pollutants

Using available research data on the effects of air pollution on human respiratory health across 20 years (79,80), we analyzed the long-term trends of air pollutants including SO₂, NO₂, PM₁₀ [1995–2017], PM_2.5 and PM_2.5/PM₁₀ [2012–2017] and O₃ [2014-2017]. The results are summarized in Figure 2.

Figure 2 Temporal variations in five major air pollutants in four cities: (A) SO₂, (B) NO₂, (D) PM₁₀ for 1995–2017, (C) O₃, (E) PM_2.5, (F) PM_2.5/PM₁₀ for 2012–2017. The black dotted line indicates the second-level concentration standards of pollutants according to the government GB3095-2012 (43) standard. SO₂, sulfur; O₃, ozone; PM, particulate matter.

SO₂

Data presented in Figure 2A indicate a significant downward trend for SO₂ annual concentrations among the four cities from 1995 to 2017. In 2017, SO₂ levels in all the cities dipped below 20 µg/m³, which were approximately one fourth of the values reported in the late 1990s. The SO₂ levels in CQ showed the greatest improvement over the years (81), with a 96.4% decrease from 338 µg/m³ in 1995 to 12 µg/m³ in 2017. A similar change was observed in LZ, where the declining rate of SO₂ was 80.4% (from 102 µg/m³ in 1995 to 20 µg/m³ in 2017). The trends in SO₂ levels were drastically different for GZ and WH which showed gradual rise prior to 2004 and 2008, respectively. Then we saw the values in all four cities descending below the second-level concentrations (60 µg/m³) after 2008. It was related to the rigorous investigation of reducing pollutant emissions in the Eleventh Five-Year Plan (25,39-41). Despite air quality standards revision in 2012, SO₂ was still considered as one of the six major pollutants, resulting in sustained efforts put into monitoring of SO₂ performed across the country (43). The type of pollution has shifted from soot-pollution (PM₁₀ associated with SO₂) to single type of PM (PM_2.5 and PM₁₀ pollution) in some cities (e.g., the primary pollutant in LZ was PM₁₀ and PM_2.5, while in CQ and WH was PM_2.5), which resulted from a series of measures to reduce air pollution, such as the implementation of coal-to-gas projects and clean coal-fired technologies (82).

NO₂

Based on the data in Figure 2B, the NO₂ levels have not really changed between 1995 and 2017, showing a relatively flat trend compared with SO₂. Given the NO₂ concentration values from 1995, the reductions of 91, 23, and 47 µg/m³ were seen in GZ, CQ and LZ, respectively, while there was a rise of 7 µg/m³ in WH. However, in recent years, the level of NO₂ pollution has far exceeded the second-level concentration (standard) after 2014 in all four cities. The main anthropogenic source of nitrogen oxides in cities is the burning of fossil fuels, two-thirds of which were emitted from mobile sources such as motor vehicles, and one-third from fixed sources such as factories and power plants (83). The available data indicate that the popularity of clean fuels has increased over the years, and exhaust emissions of motor vehicles have been continuously reduced (84). Nevertheless, the rapid urbanization has led to a sustained increase in the number of motor vehicles, dramatically increasing the emission of NO₂ to some extent (49,64). The evolution of NO₂ pollution and factors associated with NO₂ emissions remain complicated.

O₃

Ozone, with annual mean concentrations between 64 to 102 µg/m³ now, has become the main risk restricting the optimization of urban air quality after PM_2.5 level declined in many cities in recent years. Based on the result in Figure 2C, the daily average in maximum 8-hour O₃ concentrations showed upward trend since 2015 in GZ, CQ, and LZ. Especially, LZ has experienced a remarkable increase leading to the highest values (101.3 µg/m³) in 2017. It is tangible that volatile organic compounds (VOCs) and NOx provide important precursors for ozone formation (33) largely deriving from process of petroleum refining and vehicle exhaust emissions (85). The rising trend of ozone was also highly consistent with the rise of NO₂ (Figure 2B) in the same period, which have been reported in previous studies (86,87). In general, longer daylight hours and stronger solar radiation contribute to ozone levels (22). The PRD region has subtropical climate that favors ozone formation when precursor pollutants are present. This made O₃ pollution receiving particular attention in GZ and Shenzhen (88). Although O₃ concentration in WH appeared to be on a decreasing trend, it was in a high concentration range (between 80 and 100 µg/m³).

PM₁₀

A clear reduction in PM₁₀ levels has been found between 1995 and 2017 in the four cities (see Figure 2D). Compared to 1995, the levels of PM₁₀ in 2017 were 78.2%, 60.7%, 59.3%, and 33.8% lower in GZ, CQ, LZ, and WH, respectively. However, the pollution level in 2017 was still higher than the second-level concentration (70 µg/m³) in CQ, LZ, and WH, while the value in GZ dropped below the standard for the first time in 2014. These reductions are likely a reflection of the active rectification of PM₁₀ carried out at the nationwide level which has achieved good results (44). The exception occurred in 2013, when the concentration of PM₁₀ was significantly increased in all four cities corresponding to the worst smog in China that occurred the same year, which spread to 25 provinces and affected more than 100 different cities (44). Interestingly, LZ was heavily polluted with PM₁₀, which was mainly from the natural and meteorological conditions (89-91). Additionally, LZ suffered from a very dusty weather all year with frequent dust storms in the city resulting in high PM₁₀ concentrations (92). In general, the technological transformation, tightening of environmental management policies and increased funds (25,39-41) for controlling dust and coal emission were efficiently utilized in all four cities.

PM_2.5

Monitoring of PM_2.5 levels began in 2012 and showed very comparable trends to PM₁₀ pollution. We also calculated the ratio of PM_2.5/PM₁₀ that represents the composition of particulate pollution. The larger the ratio, the higher the mass concentration of fine particles (respirable particles) in total inhalable particles (all particles with an aerodynamic diameter ≤10 µm). For the same inhaled mass of PM₁₀, a higher PM_2.5/PM₁₀ ratio means that more fine particles can reach and deposit in the deep lung and cause more health damages (93). Specifically, there was a clear downward trend in PM_2.5 levels from 2013 in GZ, CQ and WH, with decreases of 18 µg/m³, 25 µg/m³, and 42 µg/m³ respectively (concentrations dropped 18 µg/m³ since 2012 in LZ). Unlike with PM₁₀, the most serious pollution with PM_2.5 was observed in WH. Data suggest that the transport of local air masses from the northeast of WH may have contributed the most to PM_2.5 pollution, which originated from the cluster of steel plants located in the northeast region of WH (94). As shown in Figure 2F, we observed continuously decreasing trends of PM_2.5/PM₁₀ ratio from 2012 in both GZ and WH, while LZ had the lowest value because of serious PM₁₀ pollution (shown in Figure 2D). Although the pollution level was overall reduced, the other three cities did not meet the second-level standard (35 µg/m³) formulated in GB3095-2012 (43), while GZ has reached the value of 35 µg/m³ in 2017. The strict prevention and control during the multi-sport Asian Games played an important role, leading to reductions in PM levels in the southern China compared to other regions of the country (95).

Spatiotemporal variation of chemical composition and sources of PM_2.5 in four cities during 2000 and 2017

Based on the available literature, the chemical characteristics of PM_2.5 in China are considered to be a mixture of organic and inorganic matter including water-soluble ions, elemental carbon, crustal material, and hydrocarbons (63,96-98), mainly originating from meteorological evolutions and potential human activities, such as transportation, household activities, vehicular movement and industrial sector (99). Due to the regional economic development, changes in industrial and energy structures, and an increasing number of vehicles, the composition ratios of PM_2.5 vary with location and time (100). Based on the policy—Air Pollution Prevention and Control Action Plan, introduced by the Chinese government in 2013 (101), we compiled the available data representing the average PM_2.5 concentrations and the relative composition of PM_2.5 during two periods (2000–2013 and 2014–2017) in the four cities (Figures 3 and 4).

Figure 3 Spatiotemporal variation of mass fractions in major chemical composition of PM_2.5 in the four cities prior to and after 2013. Digital label for every sector indicates the corresponding component proportion, and the size of each fan corresponds to the absolute concentration of PM_2.5. PM, particulate matter.

Figure 4 Variations in the absolute concentrations (μg/m³) of examined PM_2.5 and seven components before and after 2013 in LZ, WH, CQ, and GZ. Solid column represents the first period [2010–2013] while the dotted line column indicates the second period [2014–2017] in each component across four cities. PM, particulate matter; LZ, Lanzhou; WH, Wuhan; CQ, Chongqing; GZ, Guangzhou.

Review of PM_2.5 concentrations

The averaged PM_2.5 concentrations after 2013 were 34.5, 34.4, 19.1 and 2.3 µg/m³ lower than previous values at first periods [2000–2013] in GZ, CQ, WH and LZ, respectively (Figure 3). The data were in agreement with the results presented in Figure 2E. A significant decrease in the average values of PM_2.5 occurred geographically from north to south after the overall rectification in 2013, ranging from 39 to 102 µg/m³.

SO₄²⁻, NO₃⁻, and NH₄⁺

The proportions of three ions, crucial elements of the secondary inorganic component of air pollution, including SO₄²⁻ (8.4–57%), NO₃⁻ (4.2–21%) and NH₄⁺ (2.3–19%), showed different changes across different dimensions. Considered as the first major anthropogenic source, SO₄²⁻ contributed the most. The data indicate that LZ had the largest absolute levels of sulfates (28±18 µg/m³) prior to 2013, followed by CQ, where the absolute concentration was upwards of 27±15 µg/m³. Given high levels of SO₄²⁻, there was a possibility that the sulfides from the coal burning had undergone a secondary conversion, indicating that CQ and LZ experienced more serious coal combustion emission prior to mandatory rectification in 2013. The long-term use of coal-fired heating in LZ and clusters of factories in CQ contributed significantly to high SO₄²⁻ levels (32,102). Importantly, recent concentrations of SO₄²⁻ in GZ, CQ and LZ decreased sharply, with average values of 6.5±2.2, 10±3.2 and 11±3.7 µg/m³, respectively.

Based on the literature evidence suggesting that approximately 50% of nitrate mass can be attributed to coal combustion (103), NOx, as the precursor of nitrate, is mainly derived from urban anthropogenic activities such as traffic and factory emissions. The continuously upward trend occurred in WH during the two periods (average values 11–20 µg/m³). A slightly upward trend in the levels of NO₃⁻ also occurred in CQ mainly due to the surge in the number of motor vehicles (Table 2), while LZ and GZ showed considerably lower levels of NOx emissions.

The trends of NH₄⁺ in four cities were somewhat comparable to trends seen for SO₄²⁻ and NO₃⁻. Specifically, we observed that the secondary inorganic pollution sources dominated the composition of PM_2.5 in WH. Cheng et al. (104) found stronger oxidation process of SO₂ and NO₂ in the atmosphere from research between 2016–2017. Due to the massive burning of fossil fuels, coal and biomass, the secondary inorganic aerosols accounted for a large proportion in the industrial area of WH, while the soil source dominated the large traffic volume and frequent urban construction throughout the year (29). Notably, in LZ, only 1.3±0.8 µg/m³ of concentration value was recently normalized and similar results were found for ammonium levels which were lower than 3% in Northwest China during 2006–2013 based on a previous study (105). Overall, the data suggest that the secondary source of PM_2.5 in the northwest region (like LZ) was relatively small due to the yearly dry climate, which is not conducive to the occurrence of secondary reactions (48).

EC and OC

The levels of OC ranked first or second among the constituents shown in Figure 3 in four cities, accounting for approximately 7–30% in terms of fraction. The proportions of OC were relatively reduced after 2013 in all regions, especially in CQ (from 37±21 µg/m³ in first period to 13±2.2 µg/m³ in second period). Other studies suggested that OC can be used to estimate the organic matters (OM), which is typically obtained by multiplying OC by a specific coefficient (63). Additional indirect data suggest that VOCs, the precursor of organic matter, were recently increasing. Taken together, CQ had the highest organic pollution compared to other cities prior to 2013 and the most effective control over VOC emissions. As a primary burning indicator, EC accounted for approximately 5–9% of emissions in the four cities. There were clear downward trends in WH, CQ and GZ, presenting the absolute concentration changes of 12±7.1 to 6.1 µg/m³, 7.6±3.3 to 4.0±1.3 µg/m³ and 5.4±2.9 to 2.3±0.4 µg/m³, respectively. Geographically, values of EC decreased from north to south. The EC had a similar source like particulate organic carbon (POC), and was not a major chemical fraction in aerosol particles found in China (105).

K⁺ and Cl⁻

The highest average concentrations of Cl⁻ were seen in LZ (6.4±1.9 µg/m³ and 3.3±0.6 µg/m³ before and after 2013, respectively) while the trends for other cities were relatively flat. Combined with the highest proportion of Cl⁻ (3–7%) in LZ, the data further suggested that coal burning was the main source of aerosol pollution in this local area. Primary component K⁺, as a biomass indicator, showed significant decreases in average concentrations across all cities (ranging from 0.5 to 2.1 µg/m³), suggesting that the overall control of biomass burning has achieved very good results.

Trace metal elements

Data summarized in Figure 5 show the comparison of trace metal concentrations in 1996 and in recent years (from 2010 to 2017) in four cities, including Zn, Mn, Pb, Cu, Cr, that are either specific source indicators or trace elements with serious impact on human health (106-109).

Figure 5 Comparisons of concentrations of five major metal elements during two periods [1996 and 2010–2017]. Solid column represents the first period [1996] while the dotted line column indicates the second period [2010–2017] for each component across four cities.

The concentrations of metal elements were generally low, ranging from 0.003 to 2.8 µg/m³ more than 20 years ago, compared to recent values in the range of 0.012−1.7 µg/m³. Data suggested that Mn, Zn, Pb were mainly derived from metal smelting (110) or combustion processes (111). Additionally, we observed the highest concentration values of Zn in all cities due to background content in soil and supernumerary content of vehicle exhaust emissions and tire wear (112). A small increase in the concentration of Zn and Mn was found in CQ and WH, while LZ and GZ showed an obvious decrease. The levels of Cu in CQ and LZ showed significant rise, while they were relatively flat in WH and GZ. Two toxic metals, Pb and Cr (113), showed variable trend concentrations across the four cities. At present, coal burning and industrial production (such as smelting and sintering process) have become the main sources of Pb due to mandatory use of unleaded gasoline since late 1990s (114). During the 12^th Five-Year Plan period for controlling heavy metal pollution, Pb was included as one of the key target pollutants (115). Emission of Pb from factories seem to be effectively controlled in recent years (114). However, emissions of Cr from coal burning and industrial production appear to have increased significantly in WH, possibly due to the specificity of the study site (e.g., near the factory) in the reviewed literatures.

Conclusions and perspectives

Inter-annual variations in five major pollutants across two decades and chemical components of PM_2.5 in the recent years in the four cities were comprehensively summarized. It helped to provide a better understanding of the evolution of pollution sources for studying changes in health outcomes related to changes in air quality in populations with similar ages living in the same cities. The findings can be summarized as follows:

• All four cities experienced rapid growth over the last two decades; however, there were obvious geographical differences. For example, GZ, as one of the first-tier cities, was ranked first in GDP among all cities, while LZ showed slow development. Of note, the growth rate of CQ was the highest as of 2017 from the prior year.
• The evolution of five conventional pollutants in GZ, LZ, WH and CQ varied with space and over time. Clear downward trends occurred in SO₂ and PM (including PM₁₀ and PM_2.5) levels. SO₂ concentrations in all four cities were below the second-level concentration (standard) of 60 µg/m³ after 2008, while PM_2.5 and PM₁₀ were still not up to the standard, except for PM₁₀ in Guangzhou. In particular, the greatest improvement of SO₂ pollution occurred in CQ (a decline of 96.4%), and GZ showed the best results for reduction in particulate pollution. The levels of NO₂ showed relatively flat trends compared to other pollutants. Importantly, O₃ concentrations have been on rise, which should be considered in examining the effects of ambient air pollution on human respiratory health.
• Among the chemical components of PM_2.5, organic carbon and SO₄²⁻ dominated PM_2.5 mass concentration in all cities. The overall levels of pollutants showed decreases after 2013 in LZ, CQ and GZ, but the trend was the opposite in WH showing an upward trend in SO₄²⁻, NO₃⁻, NH₄⁺, and Cl⁻ between 2014 and 2017. Moreover, WH had the highest mass fraction of SO₄²⁻, NO₃⁻ and NH₄⁺, indicating that the control of coal combustion and vehicle emissions should be stricter in this city. Finally, LZ, among the four cities, had the lowest proportion of secondary components and highest levels of EC and Cl^- mainly emitted from perennial coal-fired emissions.
• The concentrations of different metals showed different long-term trends, ranging from 0.003 to 2.8 µg/m³ more than 20 years ago, compared to recent values in the range of 0.012−1.7 µg/m³.

Acknowledgments

Funding: This work was supported by the Ministry of Science and Technology of China, China (No. 2017YFC0210004), and National Natural Science Foundation of China (91744202).

Footnote

Provenance and Peer Review: This article was commissioned by the Guest Editor (Junfengf Zhang, Howard Kipen and Haidong Kan) for the focused issue “Children’s Respiratory Health and Air Quality” published in Journal of Thoracic Disease. The article was sent for peer review organized by the Guest Editors and the editorial office.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at doi: http://dx.doi.org/10.21037/jtd-19-crh-aq-004). The issue “Children’s Respiratory Health and Air Quality” was commissioned by the editorial office without any funding or sponsorship. JJZ served as the unpaid Guest Editor of the issue. JJZ also serves as an unpaid editorial board member of Journal of Thoracic Disease. The other author has no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Tao J, Zhang L, Cao J, et al. Source apportionment of PM2.5 at urban and suburban areas of the Pearl River Delta region, south China-With emphasis on ship emissions. Sci Total Environ 2017;574:1559-70. [Crossref] [PubMed]
2. Kinney PL. Interactions of Climate Change, Air Pollution, and Human Health. Current Environmental Health Reports 2018.
3. Mannucci PM, Franchini M. Health Effects of Ambient Air Pollution in Developing Countries. Int J Environ Res Public Health 2017.14. [Crossref] [PubMed]
4. Schwarze PE, Øvrevik J, Låg M, et al. Particulate matter properties and health effects: consistency of epidemiological and toxicological studies. Hum Exp Toxicol 2006;25:559-79. [Crossref] [PubMed]
5. Zhang Y, Ning GC, Kang YZ, et al. Time-series analysis of the association between outdoor air pollution and emergency room admissions for respiratory diseases in Beijing City. Journal of Lanzhou University 2015;51:87-92. (Natural Sciences).
6. Wu YZ, Zhang JL, Zhao XG, et al. Relationship between ambient air pollution and children’s respiratory health in China. J Environ Health 2009;26:471-7.
7. Xie P, Liu XY, Liu ZR, et al. Impact of exposure to air pollutants on human health effects in Pearl River Delta. Environ Sci-China 2010;30:997-1003.
8. Song GX, Jiang LL, Chen GH, et al. A time-series study on the relationship between gaseous air pollutants and daily mortality in Shanghai. J. Environ Health 2006;23:390-3.
9. Nascimento-Carvalho CM, Cardoso MR, Barral A, et al. Seasonal patterns of viral and bacterial infections among children hospitalized with community-acquired pneumonia in a tropical region. Scand J Infect Dis 2010;42:839-44. [Crossref] [PubMed]
10. Wang X, Deng FR, Wu SW, et al. Short-time effects of inhalable particles and fine particles on children’s lung function in a district in Beijing. Beijing Da Xue Xue Bao Yi Xue Ban 2010;42:340-4. [PubMed]
11. Hu J, Huang L, Chen M, et al. Premature Mortality Attributable to Particulate Matter in China: Source Contributions and Responses to Reductions. Environ Sci Technol 2017;51:9950-9. [Crossref] [PubMed]
12. Huang T, Chen J, Zhao W, et al. Seasonal Variations and Correlation Analysis of Water-Soluble Inorganic Ions in PM2.5 in Wuhan, 2013. Atmosphere 2016.7. [Crossref]
13. Wei FS, Teng EJ, Wu GP, et al. Concentrations and elemental components of PM2.5, PM10 in ambient air in four large Chinese cities. Environmental Monitoring in China 2001;17:1-6.
14. Wei FS, Hu W, Teng EJ, et al. Relation analysis of air pollution and children’s respiratory system disease prevalence. J Environ Sci-China 2000;20:220-4.
15. Teng EJ, Hu W, Wu GP, et al. The effect of indoor coal heating and smoke degree on respiratory health. Environmental Monitoring in China 2001;17:28-32.
16. Hu W, Wei FS, Teng EJ, et al. The impact of air pollution on respiratory health of children and their parents. J Environ Sci-China 2000;20:425-8.
17. Hu W, Wei FS, Wu GP, et al. The analysis of confound for effect on children’s respiratory illness or symptom. Environmental Monitoring in China 2001;17:23-7.
18. Zhang XY, Sun JY, Wang YQ, et al. Factors contributing to haze and fog in China. Chinese Sci Bull 2013;13:1178-87.
19. Xu X, Zhao T, Liu F, et al. Climate modulation of the Tibetan Plateau on haze in China. Atmos. Chem. Phys 2016;16:1365-75. [Crossref]
20. Hao H, Guo Q. Spatial and temporal characteristics of PM2.5 and source apportionment in Wuhan. IOP Conference Series: Earth Env Sci 2018.121. [Crossref]
21. Zhang F, Wang ZW, Cheng HR, et al. Seasonal variations and chemical characteristics of PM2.5 in Wuhan, central China. Sci Total Environ 2015;518-519:97-105. [Crossref] [PubMed]
22. Cao JJ, Shen ZX, Chow JC, et al. Winter and Summer PM2.5 Chemical Compositions in Fourteen Chinese Cities. J Air Waste Manag Assoc 2012;62:1214-26. [Crossref] [PubMed]
23. Lv W, Wang Y, Querol X, et al. Geochemical and statistical analysis of trace metals in atmospheric particulates in Wuhan, central China. Environ Geol 2006;51:121-32. [Crossref]
24. Yang F, Tan J, Zhao Q, et al. Characteristics of PM2.5 speciation in representative megacities and across China. Atmos Chem Phys 2011;11:5207-19. [Crossref]
25. Environmental Protection Bureau, Guangzhou. The State of Environment in Guangzhou, Annual report 1995-2017 [document on the Internet]. no date [cited 2018 Oct]. Available online: http://www.gzepb.gov.cn/gzepb/hjgb/tyglwzlm3_2014.shtml
26. Mi KN, Zhuang RL, Liang LW, et al. Spatio-temporal evolution and characteristics of PM2.5 in the Yangtze River Delta based on real-time monitoring data during 2013-2016. Geogr Res 2018;37:1641-54.
27. Li Y, Chen QL, Zhao HJ, et al. Variations in PM10, PM2.5 and PM1.0 in an urban area of the Sichuan Basin and their relation to meteorological factors. Atmosphere 2015;6:150-63. [Crossref]
28. Wang DL, Zhang YX, Niu WJ. Source analysis of trace metal elements and inorganic water-soluble ions in atmospheric fine particles in Lanzhou during winter. J Environ Eng 2015;9:3944-54.
29. Wang YN. Chemical characterization and source apportionment of PM2.5 in Lanzhou, China. Lanzhou University, Master Degree. Lanzhou 2017.
30. Zhu Y, Huang L, Li J, et al. Sources of particulate matter in China: Insights from source apportionment studies published in 1987-2017. Environ Int 2018;115:343-57. [Crossref] [PubMed]
31. Cheng HB. Pollution Characteristics in particulate matters and health risks of main city area in main area of Lanzhou City. Lanzhou University, Master Degree. Lanzhou 2016.
32. Guo YT, She F, Wang S, et al. Assessment on air quality in Lanzhou and its relation with meteorological conditions. Journal of Arid Land Resources and Environment 2011;25:100-5.
33. Wang WW. Spatial-temporal characteristics and influence factors of air pollutants in Hubei. Central China Normal University, Master Degree. Wuhan 2018.
34. Liang D. Analysis on spatial and temporal distribution and influencing factors of PM2.5 pollution in Chongqing. Beijing Forestry University, Master Degree. Beijing 2016.
35. Chongqing Statistical Bureau. Statistical Bulletin on National Economic and Social Development in Chongqing [online]. Available online: http://www.cqtj.gov.cn/tjsj/shuju/tjgb/
36. Guangzhou Statistical Bureau. Statistical Bulletin on National Economic and Social Development in Guangzhou [online]. Available online: http://www.gzstats.gov.cn/gzstats/
37. Lanzhou Statistical Bureau. Statistical Bulletin on National Economic and Social Development in Lanzhou [online]. Available online: http://tjj.lanzhou.gov.cn/
38. Wuhan Statistical Bureau. Statistical Bulletin on National Economic and Social Development in Wuhan [online]. Available online: http://tjj.wuhan.gov.cn/
39. Environmental Protection Bureau, Chongqing. The State of Environment in Chongqing. Annual report 1995-2017 [document on the Internet]. no date [cited 2018 Oct]. Available online: http://www.cepb.gov.cn/xxgk/hjzl/hjzkgb/index.shtml
40. Environmental Protection Bureau, Lanzhou. The State of Environment in Lanzhou. Annual report 1995-2017 [document on the Internet]. no date [cited 2018 Oct]. Available online: http://hbj.lanzhou.gov.cn
41. Environmental Protection Bureau, Wuhan. The State of Environment in Wuhan. Annual report 1995-2017 [document on the Internet]. no date [cited 2018 Oct]. Available online: http://hbj.wuhan.gov.cn/hbHjzkgb/index.jhtml
42. Ministry of Ecology and Environment of the People’s Republic of China. Ambient Air Quality Standard [standard online]. C1996 [cited 2018 Oct]; GB 3095-1996. Available online: http://www.mee.gov.cn/
43. Ministry of Ecology and Environment of the People’s Republic of China. Ambient Air Quality Standard [standard online]. c2012 [cited 2018 Oct]; GB 3095-2012. Available online: http://www.mee.gov.cn/
44. Wang J [homepage on the Internet]. c2013 [cited 2018 Oct]. Available online: https://www.aqistudy.cn/historydata/
45. Tao Y. Physical and chemical characteristics of atmospheric particulate matter and their impact on human health in Lanzhou. Lanzhou University, Doctoral Degree. Lanzhou 2009.
46. Li M, Lian XW, Wang J, et al. Pollution Characteristics of Metal Components in PM2.5 in Two Districts of Guangzhou. J. Environ Occup Med 2016;33:650-6.
47. Shen YJ, Jia CH, Guo Q, et al. Characteristics and seasonal variations of water-soluble inorganic ions in PM2.5 in Lanzhou City, northwest China. Journal of Lanzhou University 2016;52:364-70. (Natural Sciences).
48. Wei QZ, Wang YH, Li S, et al. The Source Apportionment of PM2.5 Based on PCA-MLR Model in Lanzhou City. J Environ Hyg 2017;7:267-73.
49. Yang DR. The characteristics of PM2.5 and impact on human health in Lanzhou City. Lanzhou University, Master Degree. Lanzhou 2013.
50. Li Y H, Duan JC, Zheng NJ, et al. Characteristics of organic and element carbon in fine particles in Lanzhou. J of University of Chinese Academy of Sciences 2015;32:490-7.
51. Cheng HR, Wang ZW, Feng JL, et al. Carbonaceous species composition and source apportionment of PM2.5 in urban atmosphere of Wuhan. Ecol Environ Sci 2012;21:1574-9.
52. Zhang QY. The variation characteristics of fine articles PM2.5 and its organic carbon and elemental carbon in Guangzhou. Northwest A & F University, Master Degree. Yangling 2012.
53. Qiu T. Characteristics and source apportionment of water-soluble ions in atmospheric PM2.5 of typical regions in Wuhan. Wuhan University of Technology, Master Degree. Wuhan 2014.
54. Zhang F. Chemical characteristics of PM2.5 and haze events in Wuhan, China. Wuhan University, Doctoral Degree. Wuhan 2014.
55. Cao XY. Characteristics of PM2.5 and chemical compositions in Chongqing. University of Chinese Academy of Sciences, Master Degree. Chongqing 2017.
56. Li AH, Jiang JT, Liu QY. Pollution Characteristics of PM2.5 and Its Components in Winter Atmosphere in Wuhan Economic and Technological Development Zone. J Jiang Univ 2017;45:10-6. (Nat Sci Ed).
57. Zhang XY. Study on chemical characteristics and source apportionments of fine particulates in the typical cities over the central and eastern China. Nanjing University, Master Degree. Nanjing 2017.
58. Zhang D. The source apportionment of PM10 in Chongqing City. Southwest University, Master Degree. Chongqing 2007.
59. Zhang D, Zhou ZE, Zhang C, et al. Different Diameter Particle Pollution Character of Chongqing Down City in Spring and Summer. Urban Environment & Urban Ecology 2011;24:1-4.
60. Li L, Yu JY. Concentration monitoring and analysis of air-borne organic and elemental carbons in Chongqing urban area. Environ Ecol in the Three Gorges 2012;34:41-3.
61. Yu JY, Liu RL, Zhai CZ, et al. Concentration and Source Analysis of Metals in PM2.5 in Chongqing Urban. Environmental Monitoring in China 2014;30:37-42.
62. Jiao J, Ji YQ, Bai ZP, et al. Element distribution characteristics and source apportionment in PM10, PM2.5 in Chongqing. Acta Scientiarum Naturalium Universitatis Nankaiensis 2013;46:8-13.
63. Chen Y, Xie SD, Luo B, et al. Particulate pollution in urban Chongqing of southwest China: Historical trends of variation, chemical characteristics and source apportionment. Sci Total Environ 2017;584-585:523-34. [Crossref] [PubMed]
64. Li HL, Tian M, Yang FM, et al. Heavy metal pollution characteristics of PM2.5 in Wanzhou, Chongqing in summer and winter. Environ Impact Asses. 2014;4:47-51.
65. Huang Y, Liu Y, Zhang L, et al. Characteristics of Carbonaceous Aerosol in PM2.5 at Wanzhou in the Southwest of China. Atmosphere 2018.9. [Crossref]
66. Lan JT. Pollution Characteristics of Metals in PM2.5 in University Town of Chongqing in Spring. Environ Impact Asses 2018;40:90-6.
67. Lai SC, Zou SC, Cao JJ, et al. Characterizing ionic species in PM2.5 and PM10 in four Pearl River Delta cities, South China. J Environ Sci (China) 2007;19:939-47. [Crossref] [PubMed]
68. Cao JJ, Lee SC, Ho KF, et al. Spatial and seasonal variations of atmospheric organic carbon and elemental carbon in Pearl River Delta Region, China. Atmos Environ 2004;38:4447-56. [Crossref]
69. Wang X, Bi X, Sheng G, et al. Chemical composition and sources of PM10 and PM2.5 aerosols in Guangzhou, China. Environ Monit Assess 2006;119:425-39. [Crossref] [PubMed]
70. Feng QD, Dang Z, Lv XW, et al. Chemical speciation distribution of PM2.5 - bound heavy metals in the air. Ecol Environ Sci 2011;20:1048-52.
71. Ma Y. Comparative analysis of water-soluble components of aerosol in Central cities and coastal areas of Peral River Delta. Jinan University, Master Degree. Guangzhou 2017.
72. Lai S, Zhao Y, Ding A, et al. Characterization of PM2.5 and the major chemical components during a 1-year campaign in rural Guangzhou, Southern China. Atmos Res 2016;167:208-15. [Crossref]
73. Xiao YH, Liu SR, Tong FC, et al. Characteristics and Sources of Metals in TSP and PM2.5 in an Urban Forest Park at Guangzhou. Atmosphere 2014;5:775-87. [Crossref]
74. Liu YX, Chen WH, Wang XH, et al. Chemical composition of PM2.5 and its relations with meteorological factors in Guangzhou. Acta Scientiae Circumstantiae 2018:1-7.
75. Zhao Y. Characterization of PM2.5 and the Major Chemical Components in Guangzhou. Guangdong Chemistry 2018;45:85-6.
76. Tao J, Zhang L, Ho K, et al. Impact of PM2.5 chemical compositions on aerosol light scattering in Guangzhou-the largest megacity in South China. Atmos Res 2014;135:48-58. [Crossref]
77. Zhou XL. A study on the measure and strategy & governance of China’s society and economy unbalanced development. The journal of Quantitative & Technical Economics 2018;11:21-38.
78. National Bureau of Statistical. China Statistical Yearbook [online]. c2017 [cited 2018 Oct]. Available online: http://www.stats.gov.cn/tjsj/ndsj/2017/indexch.htm.
79. Wu GP, Hu W, Teng EJ, et al. PM2.5 and PM10 pollution level in the four cities in China. J Environ Sci-China 1999;19:133-7.
80. Teng EJ, Hu W, Wu GP, et al. The composing characteristics of elements in coarse and fine particle in air of the four cities in China. J Environ Sci-China 1999;19:238-42.
81. Liu YQ, Li DP, NI CJ. The analysis of atmospheric pollution feature and affecting factors in Chongqing. Sichuan Environ 2009;28:28-32.
82. Xu G, Jiao LM, Zhao SL, et al. Spatial and temporal variability of air quality in Wuhan city from 1990 to 2014. Environ Eng 2016;34:80-5.
83. Ma N. The study of pollution state of vehicle and partake rate of Chongqing. Chongqing University, Master Degree. Chongqing 2008.
84. Xu P, Hao QJ, Ji DS, et al. Characteristics of atmospheric PM2.5, NOx, SO2 and O3 in Beibei district, Chongqing. Acta Scientiae Circumstantiae 2016;36:1539-47.
85. Shang LP, Wang P. Ozone pollution situation and countermeasures of its prevention and control in Lanzhou. Environ Dev 2016;6:64-7.
86. David LM, Nair PR. Diurnal and seasonal variability of surface ozone and NOx at a tropical coastal site: association with mesoscale and synoptic meteorological conditions. J Geophys Res 2011;116:2-3. [Crossref]
87. Sun YC, Yan XY, Gou XH, et al. Characteristics and correlation of ozone and its precursors in typical cities in China. Res Environ Scien 2020;33:44-53.
88. Environmental Protection Bureau, Shenzhen. State of Environment in Shenzhen. Annual report 2017 [document on the Internet]. no data [cited 2018 Oct]. Available online: http://www.sz.gov.cn/szsrjhjw/.
89. Zhu HY, Yang TB, Jin QS. Analysis of current air pollution and corresponding prevention measures in Lanzhou urban area. Environ Sci Surv 2011;30:48-52.
90. Chu R, Zhang GZ, Xie HG. Analysis of the Cause of Lanzhou Air Pollution. J Lanzhou Jiaotong University 2006;25:59-62. (Natural Sciences).
91. Zhi YH, Fu C, Shao ZH. The dust weather and its influence on air quality in Lanzhou City. Journal of Catastrophology 2007;22:77-81.
92. Qi B, Wang JF, Wang H, et al. Analysis of Characteristics and Main Causes of Air Pollution in Lanzhou City. Journal of Shaanxi Meteorology 2001;6:18-20.
93. Li M, He HD, Hao YY. Spatial and temporal variability of PM2.5/PM10 ratio in Shanghai. J Yunnan Univ: Natural Science Edition 2019;41:323-32.
94. Yuan C, Zhou JB, Xiong Y. Chemical compositions and long-range transport of PM2.5 in downtown area of Wuhan. Environ Sci Technol 2018;41:79-86.
95. Yu WH, Lai LF, Shen J. Trends of PM2.5 Pollution in Beijing, Shanghai and Guangzhou during the 12th Five-year-plan Period. Guangdong Chemistry 2017;44:111-2.
96. Zheng M, Salmon LG, Schauer JJ, et al. Seasonal trends in PM2.5 source contributions in Beijing, China. Atmos Environ 2005;39:3967-76. [Crossref]
97. Zhao M, Huang Z, Qiao T, et al. Chemical characterization, the transport pathways and potential sources of PM2.5 in Shanghai: Seasonal variations. Atmos Res 2015;158-159:66-78. [Crossref]
98. Liu B, Song N, Dai Q, et al. Chemical composition and source apportionment of ambient PM2.5 during the non-heating period in Taian, China. Atmos Res 2016;170:23-33. [Crossref]
99. Liu G, Li J, Wu D, et al. Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China. Particuology 2015;18:135-43. [Crossref]
100. Cao WX, Chen N, Tian YP, et al. Characteristic analysis of water-soluble ions during clean and heavy pollution processes in autumn and winter in Wuhan. Acta Scientiae Circumstantiae 2016;37:82-8.
101. The State Council of the People’s Republic of China. The action plan for the control of air pollution. Beijing: The State Council of the People’s Republic of China; 2013. Available online: http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm
102. Ren LH, Zhou ZE, Zhao XY, et al. Source Apportionment of PM10 and PM2.5 in Urban Areas of Chongqing. Res Environ Sci 2014;27:1387-94.
103. Cao GL, An XQ, Zhou CH, et al. Emission inventory of air pollutants in China. Environ Sci-China 2010;30:900-6.
104. Cheng Y, Wu JH, Bi XH, et al. Characteristics and source apportionment of water-soluble ions in ambient PM2.5 in Wuhan, China. Acta Scientiae Circumstantiae 2019;39:189-96.
105. Zhang XY, Wang JZ, Wang YQ, et al. Changes in chemical components of aerosol particles in different haze regions in China from 2006 to 2013 and contribution of meteorological factors. Atmos Chem Phys 2015;15:12935-52. [Crossref]
106. Crilley LR, Ayoko GA, Stelcer E, et al. Elemental Composition of Ambient Fine Particles in Urban Schools: Sources of Children’s Exposure. Aerosol Air Qual Res 2014;14:1906-16. [Crossref]
107. Cyrys J, Stölzel M, Heinrich J, et al. Elemental composition and sources of fine and ultrafine ambient particles in Erfurt, Germany. Sci Total Environ 2003;305:143-56. [Crossref] [PubMed]
108. Mohanraj R, Azeez PA, Priscilla T. Heavy metals in airborne particulate matter of urban Coimbatore. Arch Environ Contam Toxicol 2004;47:162-7. [Crossref] [PubMed]
109. Moreno T, Querol X, Alastuey A, et al. Variations in atmospheric PM trace metal content in Spanish towns: Illustrating the chemical complexity of the inorganic urban aerosol cocktail. Atmos Environ 2006;40:6791-803. [Crossref]
110. Viana M, Kuhlbusch TAJ, Querol X, et al. Source apportionment of particulate matter in Europe: A review of methods and results. J Aerosol Sci 2008;39:827-49. [Crossref]
111. Cruz Minguillon M, Querol X, Alastuey A, et al. PM sources in a highly industrialised area in the process of implementing PM abatement technology. Quantification and evolution. J Environ Monit 2007;9:1071-81. [Crossref] [PubMed]
112. Lu XH, Wu LJ, Ren L, et al. Pollution characteristics analysis and health risk assessment of heavy metals in PM2.5 in Nanjing. Sichuan Environ 2016;35:115-9.
113. Dai S, Ren D, Chou CL, et al. Geochemistry of trace elements in Chinese coals: A review of abundances, genetic types, impacts on human health, and industrial utilization. Int J Coal Geol 2012;94:3-21. [Crossref]
114. Cheng K, Wang Y, Tian HZ, et al. Atmospheric emission characteristics and control policies of five precedent-controlled toxic heavy metals from anthropogenic sources in China. Environ Sci Technol 2015;49:1206-14. [Crossref] [PubMed]
115. Wang Y, Cheng K, Yi P, et al. Characteristics of atmospheric emission of lead from industrial process in China. Acta Scientiae Circumstantiae 2016;36:1589-94.