Temporal variations in ambient air quality indicators in Shanghai municipality, China

Abstract

Official data on daily PM_2.5, PM₁₀, SO₂, NO₂, CO, and maximum 8-h average O₃ (O₃_8h) concentrations from January 2015 to December 2018 were evaluated and air pollution status and dynamics in Shanghai municipality were examined. Factors affecting air quality, including meteorological factors and socio-economic indicators, were analyzed. The main findings were that: (1) Overall air quality status in Shanghai municipality has improved and number of days meeting ‘Chinese ambient air quality standards’ (CAAQS) Grade II has increased. (2) The most frequent major pollutant in Shanghai municipality is O₃ (which exceeded the standard on 110 days in 2015, 84 days in 2016, 126 days in 2017, 113 days in 2018), followed by PM_2.5 (120days in 2015, 104 days in 2016, 67 days in 2017, 61 days in 2018) and NO₂ (50 days in 2015, 67 days in 2016, 79 days in 2017, 63 days in 2018). (3) PM_2.5 pollution in winter and O₃ pollution in summer are the main air quality challenges in Shanghai municipality. (4) Statistical analysis suggested that PM_2.5, PM₁₀, SO₂ and NO₂ concentrations were significantly negatively associated with precipitation (Prec) and atmosphere temperature (T) (p < 0.05), while the O₃ concentration was significantly positively associated with Prec and T (p < 0.05). Lower accumulation of PM, SO₂, NO₂, and CO and more serious O₃ pollution were revealed during months with higher temperature and more precipitation in Shanghai. The correlation between the socio-economic factors and the air pollutants suggest that further rigorous measures are needed to control PM_2.5 and that further studies are needed to identify O₃ formation mechanisms and control strategies. The results provide scientific insights into meteorological factors and socio-economic indicators influencing air pollution in Shanghai.

Introduction

China’s reforms and opening-up policies since 1970s have contributed to rapid economic growth, industrialization, and urbanization^1,2, as evidenced by increased gross domestic product (GDP), urban population, and energy consumption^1,3,4. However, this has resulted in high levels of environmental degradation^1,5,6 and associated health effects^2,6. Air pollution in China is mainly caused by coal combustion, motor vehicles, industrial dust, chemical conversion in the atmosphere in urban centers, and unfavorable meteorological conditions, all of which are linked to rapid socioeconomic development^1,3,7,8. With an increasing number of Chinese cities suffering from serious air pollution problems in recent decades^1,2,9, air pollution has become one of the top environmental concerns in China^1,6,9–13. Serious air pollution hinders economic development¹⁴ and deteriorates people’s quality of life, with increasing reports of negative health risks^6,15. Many epidemiological studies have shown that air pollution has strong associations with impaired human health¹⁶ and mortality^14,16,17. A recent study found that a 10 μg m⁻³ increase in particulate matter (PM₁₀) reduced life expectancy in China by 0.64 years¹⁸. Other studies in China have estimated that a 10 μg m⁻³ increase in PM₁₀ led to a 0.44% increase in daily number of deaths¹⁹, that PM_2.5 accounted for 15.5% (1.7 million) of all-cause deaths in China in 2015²⁰, and that 2.19 million (2013), 1.94 million (2014), and 1.65 million (2015) premature deaths could be attributed to long-term exposure to PM_2.5²¹. However, a recent study estimated that the number of premature deaths in China attributable to PM_2.5 has decreased by 12.6%, from 1.20 million in 2013 to 1.05 million in 2017²².

With the growing need for improving air quality across cities, municipalities, and provinces in China, a series of laws, regulations, standards and control measures have been formulated and promulgated^1,2,4,8,23. The ‘Air Pollution Prevention Action Plan’ was enacted on September 10, 2013, and the most stringent environmental protection law to date was implemented on January 1, 2015⁸. Significant measures have also been taken to mitigate the adverse effects of air pollution²⁴. Air quality monitoring systems have been established in more than 330 cities¹⁶ and at 1,300 national air quality monitoring sites²⁴. Daily data on air quality index (AQI) and air quality indicators are released publicly on local government websites, providing an important foundation for air quality research and policy. In the past three decades, knowledge on air pollution has improved considerably with the growing number of publications on air pollution in megacities^{2,4,8,14,16,22,24,25}. Many studies have reported spatio-temporal variations in particulate matter (PM_2.5 and PM₁₀) and gaseous (SO₂, NO₂, CO, and O₃) pollutants in Chinese cities^4,8,16,24,26, and associated health and socioeconomic costs^{3,6,14,21,22,27–29}. Between 2013 and 2018, China’s rigorous air pollution control greatly reduced the annual mean level of PM_2.5 in the atmosphere of 74 large cities³⁰.

Shanghai is an important political, economic, and cultural center of China. With the acceleration of urbanization and industrial processes, Shanghai’s environmental problems have become increasingly prominent, with air quality being one of the most serious issues. As a pioneer city in construction of ecological civilization, Shanghai’s air quality has received much attention. In this study, official data on daily concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and maximum 8-h average concentration of O₃ (O₃_8h) in the air in Shanghai municipality from January 2015 to December 2018 were used to examine air pollution status and dynamics in the municipality. The following aspects are addressed in this paper: (1) Temporal variations in average daily concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃_8h in the air in Shanghai municipality during 2015–2018; (2) annual and seasonal variations in major pollutants and number of days when concentrations exceeded the air quality standard; and (3) the main meteorological factors and socio-economic indicators affecting air pollution in Shanghai. The results were used to identify air quality management gaps in the municipality.

Results and discussion

Overview of air pollutants in Shanghai during 2015–2018

The average mass concentrations of the target pollutants during 2015–2018 were analyzed. We used the cumulative distribution of daily average values of PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃_8h to determine the number of days during which Shanghai municipality was exposed to air pollution (Fig. 1)²⁴. For at least some half-days in 2015 (2016, 2017, 2018), Shanghai municipality was exposed to average values higher than 59 (50, 45, 40) μg m⁻³ for PM_2.5, 52 (48, 47, 40) μg m⁻³ for PM₁₀, 45 (43, 47, 44) μg m⁻³ for O₃_8h, 48 (45, 47, 44) μg m⁻³ for NO₂, 13 (12, 9, 8) μg m⁻³ for SO₂, and 18 (18, 18, 15) mg m⁻³ for CO. This indicates a decrease in the number of days per year in which Shanghai residents were exposed to high concentrations of PM_2.5, PM₁₀, NO₂, SO₂, and CO.

Figure 1.

(a–f) Cumulative distribution of daily average mean concentrations of air pollutants in Shanghai municipality.

Temporal variations in air pollutants

Following implementation of the six-round, 3-year environmental protection action plan, ambient air quality in Shanghai municipality has improved slightly. In 2018, the average annual concentration of SO₂ and PM₁₀ in Shanghai municipality was 10 μg m⁻³ and 51 μg m⁻³ respectively, the 90th percentile of O₃_8h concentration was 160 μg m⁻³, and daily CO concentration was within the range 0.4–2.0 mg m⁻³. All these concentrations met the national Level I or Level II for annual mean ambient air quality. However, the average annual concentration of NO₂ and PM_2.5 in the city in 2018 was 42 μg m⁻³ and 36 μg m⁻³, respectively, which did not meet the Level II annual mean level air quality standard. Moreover, monitoring data for the past 4 years show that the annual mean concentrations of NO₂ and PM_2.5 in Shanghai are generally declining, but they still exceed the national Level II air quality standards. The daily maximum 8-h average, 24-h average, and annual mean concentrations of six air pollutants in Shanghai municipality during 2015–2018 are summarized in Fig. 2. Compared with 2015, the average concentration in 2018 decreased by 32.08%, 26.09%, 0.62%, 41.18%, 8.70%, and 22.09% for PM_2.5, PM₁₀, O₃_8h, SO₂, NO₂, and CO, respectively. The large decrease in SO₂ in the air Shanghai municipality was consistent with the overall trend in annual mean concentration of SO₂ in China⁸. This indicates effective control of combustion emissions and implementation of desulfurization systems^8,31. Our results also indicated that more than 70% of the total mass of PM₁₀ was composed of PM_2.5, which is close to the ratio reported in previous studies^8,24. The decreases in CO and NO₂ concentrations were mainly attributable to effective regulation of coal combustion emissions and traffic-related emissions^8,31–33. The reductions amplitudes were lower for CO and NO₂ compared with PM_2.5, PM₁₀, and SO₂, which may be related to the rapid increase in vehicles in Chinese cities⁸. No clear decrease was observed for the 90th percentile of O₃_8h concentration in this study. Air pollution has gradually changed from the conventional coal combustion type to mixed coal combustion/motor vehicle emission type³, reflecting the rapid increase in the number of motor vehicles in Shanghai municipality³⁴. This poses enormous challenges for air pollution control and environmental management.

Figure 2.

Temporal variations in 24-h average concentrations and annual mean concentrations of air pollutants in Shanghai municipality, 2015–2018.

Major pollutants and non-attainment days

The number of days meeting the mean concentration limits of ‘Chinese ambient air quality standards’ (CAAQS) in Shanghai municipality during 2015–2018 was examined (Fig. 3). In 2015 (2016, 2017, 2018), 18.6% (27.5%, 33.6%, 41.5%), 77.9% (85.3%, 92.6%, 91.5%), 35.8% (40.1%, 35.2%, 41.0%), 99.5% (100%, 100%,100%), 99.7 (100%, 100%, 100%), and 58.4% (67.2%, 57.0%, 60.8%) of days met the concentration limit in CAAQS Grade II for 24-h average PM_2.5, PM₁₀, NO₂, SO₂, CO, and maximum 8-h average O₃. Compared with 2015, the number of days in 2018 that met the level in CAAQS Grade II increased by 124.3%, 17.5%, 4.1%, 14.5%, 0.5%, and 0.3% for PM_2.5, PM₁₀, O₃_8h, SO₂, NO₂, and CO, respectively. The number of days with excellent air quality increased from 55 in 2015 to 93 in 2018, while the number of days with ‘good’ air quality remained consistent at 203 days between 2015 and 2018.

Figure 3.

Number of days per year on which each pollutant was designated a “major pollutant” (different shapes) and air quality level (different colors) in Shanghai municipality.

The most frequent “major pollutant” in Shanghai municipality was O₃, followed by PM_2.5 and then NO₂ and PM₁₀. In comparison, SO₂ and CO were the “major pollutant” considerably less frequently. The number of days on which PM_2.5, O₃, NO₂, and PM₁₀ was designated the “major pollutant” was 120 (104, 67, 61), 110 (84, 126, 113), 50 (67, 79, 63) and 16 (13, 13, 14) in 2015 (2016, 2017, 2018), respectively. The low incidence of SO₂ as a “major pollutant” again indicated effective control of coal combustion and implementation of desulphurization systems^8,31. Compared with 2015, the incidence of O₃ as a major pollutant in Shanghai increased to reach its highest value in 2017. This is consistent with the 90th percentile of O₃_8h concentration, which also peaked in 2017. Previous studies have suggested that O₃ is a complex secondary pollutant related to solar radiation, NO_x, volatile organic compounds (VOC), and vertical transport in the boundary layer⁸, factors that are difficult to control effectively^35,36. While the number of polluted days with PM_2.5 concentrations over 75 μg m⁻³ decreased from 2015 to 2018, the complex mixture of PM_2.5 and O₃ in the air is still a challenge to continuous improvement of air quality in Shanghai municipality^8,24.

There were seasonal variations in the concentrations of each pollutant (Fig. 4a), and thus the days on which the air quality standard was exceeded (non-attainment days) were not equally distributed throughout the year (Fig. 4b), which is consistent with findings in previous studies^24,37. November, December, January, February, and March were the dominant months with non-attainment days for PM_2.5 in Shanghai municipality, while April, May, June, July, August, and September were the dominant months with non-attainment days for O₃_8h. Overall, winter months had the largest number of polluted days and highest mean concentration of PM_2.5, followed by spring, autumn, and summer, which is consistent with previous findings¹⁶. This trend has been mainly attributed to coal-fired heating of buildings^16,38–40. Summertime O₃ pollution in Shanghai was much more severe than in the other seasons (Fig. 4b), and the probability of O₃_8h exceeding the CAAQS Grade II value was highest in July (11.25 ± 5.85 day), followed by August (6.25 ± 4.65 day), May (5.75 ± 3.2 day), and June (5.5 ± 1.29 day). This is consistent with findings in previous studies that summer is the O₃ episode season in Chinese megacity clusters^41,42. Polluted days with NO₂ > 80 μg m⁻³ were mainly observed during winter and spring. The low probability of SO₂ exceeding the CAAQS Grade II value reflected the stringent SO₂ emission regulations in Shanghai municipality³¹.

Figure 4.

(a) Average concentration of the pollutants PM_2.5, PM₁₀, SO₂, and NO₂ and (b) percentage of non-attainment days and major pollutant on polluted days in each month during 2015–2018.

Correlations between air pollutants

Different air pollutants were significantly correlated (p < 0.01) with each other, except for SO₂ and O₃ (Table 1). There were significant positive correlations between PM_2.5, PM₁₀, CO, SO₂, and NO₂, suggesting that these pollutants originated from the same sources (e.g., vehicle and coal emissions) or were impacted by the same drivers²⁴. Therefore controlling traffic and coal combustion emissions might be a way of simultaneously decreasing the concentrations of these pollutants. O₃ was significantly positively correlated with PM, and negatively correlated with NO₂ and CO (p < 0.01). The correlation coefficients were weaker, however, which can mainly be attributed to the complex, nonlinear, and temperature-dependent chemistry of O₃ concentration^20,43. This indicates difficulty in controlling O₃ concentration and merits further investigations on O₃ formation and control strategies in Shanghai municipality.

Table 1.

Correlations between pollutants based on daily data for Shanghai during 2015–2018 (**p < 0.01; *p < 0.05).

	PM10	O3	SO2	NO2	CO
PM2.5	0.879**	0.093**	0.708**	0.693**	0.817**
PM10		0.172**	0.739**	0.632**	0.686**
O3			− 0.026	− 0.206**	− 0.128**
SO2				0.602**	0.633**
NO2					0.706**

Correlations between air pollutants and meteorological factors

Correlations between the six main pollutants and meteorological factors are shown in Table 2. The results suggested that temperature (T) significantly impacted accumulation of all six pollutants in Shanghai municipality, while precipitation (Prec) and relative air humidity (RH) may have affected accumulation of some pollutants. Of all the meteorological factors that significantly impacted pollutant concentrations, the correlations between meteorological factors and PM_2.5, PM₁₀, CO, SO₂, and NO₂ were negative, while the correlations between meteorological factors and O₃ were positive.

Table 2.

Correlations between air pollutants and meteorological factors based on the monthly data for Shanghai during 2015–2018.

	W	T	RH	PM2.5	PM10	O3	SO2	NO2	CO
Prec	− 0.093	0.532**	0.765**	− 0.353*	− 0.435**	0.342*	− 0.459**	− 0.429**	− 0.289*
W		− 0.205	− 0.222	− 0.056	0.033	− 0.072	0.125	− 0.154	− 0.212
T			0.416**	− 0.77**	− 0.674**	0.735**	− 0.703**	− 0.839**	− 0.67**
RH			1	− 0.252	− 0.472**	− 0.015	− 0.403**	− 0.293*	− 0.185

Prec: precipitation; W: wind speed in two minutes; T: temperature; RH: relative air humidity.

**p < 0.01; *p < 0.05.

The concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO displayed a significantly negative relationship with Prec (p < 0.05 or p < 0.01), suggesting that the wet deposition could mitigate air pollution by the scavenge and wash-out process^16,44,45. Relative humidity was strongly positively correlated with Prec, leading consistently to significantly negative correlations between PM₁₀, SO₂ and NO₂ and RH. The consistency in correlations between the pollutants and T, and that between the pollutants and Prec, was partly explained by the significantly positive correlation between Prec and T. This also explains why the average concentration of the pollutants PM_2.5, PM₁₀, SO₂, and NO₂ during June–September was lower than in other months^46,47. Wind speed (W) did not show any marked relationship with the air pollutants studied, indicating that W did not enhance air ventilation and turbulence and thus improve air quality.

Correlations between air pollutants and socio-economic indicators

Shanghai is undergoing strong socioeconomic development, with the permanent resident population (PRP) increasing from 14.14 million in 1995 to 24.18 million in 2017, and the GDP of Shanghai municipality increasing from 251.8 billion RMB in 1995 to 3,063.2 billion RMB in 2017³⁴ (Fig. 5). In the same period, Shanghai municipality continuously increased its environmental protection and construction efforts, with rolling implementation of the six-round, 3-year environmental protection action plan. Green space area (GE) has increased, from 6,561 hm² in 1995 to 136,327 hm² in 2017, environmental investment (EI) has also increased, from 4.65 billion RMB in 1995 to 92.35 billion RMB in 2017, and total amount of smoke emissions (SE) and total exhaust sulfur dioxide emissions (SDE) has decreased from 207.8 thousand tons and 534.1 thousand tons, respectively, in 1995 to 47 thousand tons and 18.5 thousand tons, respectively, in 2017³⁴ (Fig. 5). However, energy consumption (EC) has increased, from 4,392.48 × 10⁴ tons of standard coal in 1995 to 11,858.96 × 10⁴ tons of standard coal in 2017, the number of motor vehicles (MV) has increased, from 1.39 million in 2002 to 3.92 million in 2017³⁴ (Fig. 5), and the volume of total industrial exhaust emissions (IEE) has increased, from 4,625 billion standard m³ in 1995 to 13,867 billion standard m³ in 2017³⁴ (Fig. 5). Although ambient air quality in Shanghai municipality has improved slightly in recent decades as a result of its environmental regulations (Fig. 5), Shanghai is still one of the cities with the highest levels of air pollutants worldwide⁴⁸.

Figure 5.

Annual change in average concentrations of three pollutants (PM₁₀, SO₂, NO₂) relative to (a) permanent resident population, (b) gross domestic product (GDP), (c) energy combustion, (d) number of motor vehicles, (e) total industrial exhaust emissions, (f) total amount of smoke emissions and exhaust sulfur dioxide emissions, (g) green space area, and (h) environmental investment in Shanghai during 1995–2017.

The correlations between GS, IEE, SE, SDE, PRP, GDP, EC, MV, EI, and air concentrations of PM₁₀, SO₂ and NO₂ are shown in Table 3. Although there have been large increases in PRP, GDP, EC, MV, and IEE in Shanghai in recent years, the increase in EI and the decrease in SE and SDE have compensated for the negative effects of the other factors, leading to positive effects in decreasing the concentrations of PM₁₀, SO₂, and NO₂. The results revealed that investments in environmental protection and pollution control strategies were the main factors affecting accumulation of PM₁₀, SO₂, and NO₂, indicating that such strategies are effective in reducing air pollution. The control in SE and SDE, and increase in EI and GS may be masking the increase in EC, MV, and IEE, leading to significant decrease in PM₁₀, and slight decrease in NO₂ and SO₂. The increased vehicle emissions and main energy would also help explain the relative stability NO₂ and SO₂ levels. As a pioneering city in the construction of ecological civilization, Shanghai has implemented several master plans to optimize GS in integration with an environmental sustainability agenda⁴⁹. The implementation of ecological redline policy in Shanghai municipality could guarantee that GS be increased systematically or stabilized at this level⁵⁰ toward increasing the air quality. However, due to the lack in more detailed emission data per activity sector for all the pollutants, it is difficult to provide more concrete and quantitative evidence of the reasons that are driving the changes in the air quality, and explain if changes in air quality are really happening or if industrial sources are just getting better at not emitting the pollutants being monitored. Further studies are needed to reveal the percentage contribution of emission sources and atmospheric processes to the emissions of the pollutants.

Table 3.

Correlations between pollutants and socio-economic indicators based on yearly data for the period 1995–2017.

	PM10	SO2	NO2
GS	− 0.984**	− 0.410	− 0.153
IEE	− 0.940**	− 0.328	− 0.080
SE	0.842**	0.144	− 0.051
SDE	0.699**	0.707**	0.491*
PRP	− 0.979**	− 0.401	− 0.159
GDP	− 0.837**	− 0.428*	− 0.153
EC	− 0.901**	− 0.192	− 0.145
MV	− 0.942**	− 0.602*	− 0.705**
EI	− 0.849**	− 0.417*	− 0.153

GS: green space area; IEE: total industrial exhaust emissions; SE: total amount of smoke emissions; SDE: total amount of exhaust sulfur dioxide emissions; PRP: permanent resident population; GDP: gross domestic product; EC: energy combustion; MV: number of motor vehicles; EI: environmental investment.

**p < 0.01; *p < 0.05.

Conclusions

This study analyzed temporal variations in the concentrations of air pollutants (PM_2.5, PM₁₀, O₃, SO₂, NO₂, and CO), the major pollutant on polluted days, and the number of non-attainment days in Shanghai municipality from January 2015 to December 2018. Based on 4-year data from the Shanghai Environmental Monitoring Center, the overall status of air quality in Shanghai has improved. The number of days that met CAAQS Grade II standards increased from 258 in 2015 to 296 in 2018.

We found that SO₂ was rarely the “major pollutant”, indicating effective control of coal combustion and implementation of desulphurization system in Shanghai municipality. However, PM_2.5 pollution in wintertime and O₃ pollution in summertime are still major challenges to air quality improvement in Shanghai municipality. Our findings suggest that the most frequent major pollutant in Shanghai municipality is O₃ (110 days in 2015, 84 days in 2016, 126 days in 2017, 113 days in 2018), followed by PM_2.5 (120 days in 2015, 104 days in 2016, 67 days in 2017, 61 days in 2018) and NO₂ (50 days in 2015, 67 days in 2016, 79 days in 2017, 63 days in 2018). O₃ is a complex secondary pollutant that is difficult to control effectively. The non-clear decrease in O₃_8h concentration from 2015 to 2018 and a peak in O₃_8h concentration in 2017 indicate a need for further studies on O₃ formation and control strategies.

Statistical analysis suggested that different air pollutants were significantly correlated with each other, apart from SO₂ and O₃. Significantly positive correlations between PM_2.5, PM₁₀, CO, SO₂, and NO₂ were observed, suggesting that these pollutants may have originated from the same sources (e.g., vehicle and coal combustion emissions) or were impacted by the same drivers. The correlation results suggested that temperature (T) significantly impacted accumulation of all six pollutants in Shanghai municipality, while precipitation (Prec) and relative air humidity (RH) affected accumulation of some pollutants. Lower accumulation of PM, SO₂, NO₂, CO and more serious O₃ pollution in Shanghai were revealed in months with higher temperature and more precipitation. The correlation between the socio-economic factors and the air pollutants suggest that further rigorous measures are needed to control air pollution in the city. Investments in environmental protection and pollution control strategies were the main factors reducing accumulation of PM₁₀, SO₂, and NO₂, indicating that these strategies are effective in reducing air pollution. Overall, this study provided scientific insights into impacts of meteorological factors and socio-economic indicators on air pollution in Shanghai.

Methods

The most recent CAAQS were published in 2012^8,51, when PM_2.5 and O₃_8h were added for the first time²⁴. These latest CAAQS set annual, 24-h average, and 1-h average concentration limits for SO₂ and NO₂, annual and 24-h average concentration limits for PM_2.5 and PM₁₀, 24-h average and 1-h average concentration limits for CO, and maximum 8-h average and 1-h average concentration limits for O₃. In the same year, a ‘Technical Regulation on Ambient Air Quality Index (on trial)’ (HJ 633–2012) released by the Chinese Ministry of Environmental Protection (MEP)⁵² replaced air pollution index (API) with AQI and divided air quality into six classes: 0–50 (Level I, excellent), 51–100 (Level II, good), 101–150 (Level III, lightly polluted), 151–200 (Level IV, moderately polluted), 201–00 (Level V, heavily polluted), and above 300 (Level VI, severely polluted)^8,28. Daily individual AQI (IAQI) is calculated from the concentrations of individual pollutants, and the AQI value is determined to be the maximum IAQI of the six pollutants. When daily AQI is greater than 50, the pollutant that has the highest IAQI index is referred to as the daily ‘major pollutant’ contributing most to the air quality deterioration^{8,24, 28}. When daily IAQI is greater than 100, air quality does not meet the CAAQS-Grade II level for 24-h average PM_2.5, PM₁₀, SO₂, NO₂, CO, or maximum 8-h average O₃, and such days are considered ‘non-attainment days’²⁴. The corresponding concentration limits of PM_2.5, PM₁₀, SO₂, NO₂, 24-h average CO, and O₃_8h when IAQI equals 50 or 100 are shown in Table 4.

$$AQI=max\left\{IAQ{I}_1,IAQ{I}_2,IAQ{I}_3,\dots ,IAQ{I}_p\right\}$$

where IAQI is individual air quality index and p is pollutant; and

$$IAQ{I}_p=\frac{IAQ{I}_{\mathit{Hi}}-IAQ{I}_{\mathit{Lo}}}{B{P}_{\mathit{Hi}}-B{P}_{\mathit{Lo}}}\left(C_p-B{P}_{\mathit{Lo}}\right)+IAQ{I}_{\mathit{Lo}}$$

where IAQI_p is individual air quality index of pollutant p, C_p is concentration of pollutant p, BP_Hi is high-value pollutant concentration limit when close to C_p (in Table 4), BP_LO is low-value pollutant concentration limit when close to C_p (in Table 4), IAQI_Hi is the individual air quality index corresponding to BP_Hi, and IAQI_LO is the individual air quality index corresponding to BP_LO.

Table 4.

Individual air quality index (IAQI) and corresponding pollutant concentration limit⁵².

IAQI	Pollutant concentration limit (μg m−3)
	SO2		NO2		PM10	CO (mg m−3)		O3		PM2.5
	24-h average	1-h averagea	24-h average	1-h averagea	24-h average	24-h average	1-h averagea	1-h average	8-h average	24-h average
0	0	0	0	0	0	0	0	0	0	0
50	50	150	40	100	50	2	5	160	100	35
100	150	500	80	200	150	4	10	200	160	75
150	475	650	180	700	250	14	35	300	215	115
200	800	800	280	1200	350	24	60	400	265	150
300	1600	b	565	2340	420	36	90	800	800	250
400	2100	b	750	3090	500	48	120	1000	c	350
500	2620	b	940	3840	600	60	150	1200	c	500

^a1-h average concentration limits of SO₂, NO₂, and CO are only used in real-time reporting, and the 24-h average concentration limits of SO₂, NO₂, and CO are used in daily reporting.

^bWhen 1-h average concentration limit of SO₂ is higher than 800 μg m⁻³, the individual air quality index of SO₂ is not reported and the reported individual air quality index of SO₂ is calculated by 24-h average concentration limits.

^cWhen 8-h average concentration limit of O₃ is higher than 800 μg m⁻³, the individual air quality index of 8-h average concentration of SO₂ is not reported and the reported individual air quality index of SO₂ is calculated by 1-h average concentration limit.

Data on the real-time daily average concentrations of PM_2.5, PM₁₀, CO, NO₂, and SO₂ and the maximum 8-h average concentration of O₃ at nine national air quality monitoring stations (Fig. 6) were obtained from the Shanghai Environmental Monitoring Center. Data on different air quality levels were obtained from Shanghai Environmental Bulletin (2015–2017) and Shanghai Ecological Environmental Bulletin (2018), which is open-access (https://sthj.sh.gov.cn/hb/fa/cms/shhj/list_login.jsp?channelId=2144). Monthly meteorological data (Prec, W, T, and RH) from two ground-level monitoring sites were downloaded from the China Meteorological Data Sharing Service System (https://data.cma.cn/).

Figure 6.

Location of national air quality monitoring stations in Shanghai municipality.

CO is measured using the non-dispersive infrared absorption method^8,51, PM_2.5 and PM₁₀ are measured using the micro-oscillating balance method and the β absorption method^8,51, and SO₂, NO₂, and O₃ are measured by the fluorescence method, the chemiluminescence method, and the UV-spectrophotometry method, respectively^8,51. Correlation analysis (using SPSS 16.0) was applied to determine the relevance of the six pollutants, meteorological factors, and socio-economic indicators. Independence and normality tests were performed before the correlation analysis. Pearson correlation analysis was performed when the data were normally distributed, otherwise Spearman correlation analysis was applied.

Author contributions

Y.C. and B.J. designed the study, performed the data analysis, and wrote the manuscript. Y.B. and H.L. participated in data analysis. Y.B., H.L. and J.M.A. reviewed and approved the manuscript.

Data availability

All relevant data are available upon request from the authors.

Competing interests

The authors declare no competing interests.