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Figure 2 illustrates the differences in the average concentrations of NO2 in 2020 and the same period in the years 2015–19 in the large cities of the three regions (BTH, the YRD, and the PRD). The NO2 levels of all cities decreased significantly during the community lockdown in 2020 compared with previous years. Wenzhou had the highest decline at 77.89%, while the lowest decline of 30.53% was observed in Tianjin. There is a close relationship between urban NO2 concentration and traffic volume (Anttila et al., 2011). Large traffic volumes will cause traffic congestion, and NO2 concentrations are positively correlated with traffic congestion in the form of a power function (Shi et al., 2018). The characteristics of the NO2 changes in large cities in 2020 are the likely result of significantly reduced traffic volumes.
Figure 2. Average concentrations of NO2 (μg m–3) in major cities from 24 January to 23 February 2020 (blue bar) and the corresponding period in 2015–19 [red bar, the results are shown as the mean (±SE)] .
The sharp decline in road passenger and cargo transportation volume also supports that the traffic dropped significantly (Table 1). For all regions, compared with the last year, the transportation volume of road passengers decreased by more than 70%. Among them, the YRD had the largest decrease of 88.63%. The transportation volume of road cargo also dropped significantly, noting that the PRD experienced the largest decrease of 64.30%.
Area Transportation volume of
road passengers (104)Change from last year Transportation volume of
road cargo (107 kg)Change from last year BTH Beijing 818 –76.25% 726 –29.72% Tianjin 192 –79.11% 1253 –45.31% Hebei 65 –97.61% 5130 –37.74% YRD Shanghai 2 –99.32% 2200 –21.09% Jiangsu 1181 –84.72% 3869 –47.77% Zhejiang 397 –94.06% 3511 –38.04% Anhui 602 –86.57% 6981 –36.52% PRD Guangdong 2010 –78.60% 7098 –64.30% Table 1. Changes in road-passenger traffic and cargo traffic in February 2020 compared with the same period of 2019.
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Changes in the PM2.5 concentrations of large cities with similar population sizes were not consistent, but large cities in the same region showed similar changes. Figures 3–5 show the differences in average PM2.5 concentrations between 2020 and the same period in 2015–19 in large cities for the three regions. Overall, the air quality of the PRD in all years was significantly better than that of BTH and the YRD, and the air quality of BTH was the worst of the three. The PM2.5 concentrations in 13 major cities in the YRD and PRD urban agglomerations decreased significantly in 2020 compared with the previous years (all passed the significance test of 0.05). The decrease in PM2.5 concentrations of the 13 large cities in the YRD and PRD varied from 27.52% to 51.34%. Among these, PM2.5 concentrations in the 11 cities declined by more than 30% compared with previous years. The cities of Hangzhou and Wenzhou experienced the most significant declines in PM2.5 concentrations. However, for the large cities in BTH, there was no significant decrease in PM2.5 concentrations compared with previous years, despite significant reductions in traffic emissions. The range in PM2.5 concentration changes was –23.82% to 22.46%. The PM2.5 concentrations in Beijing, Tangshan, and Tianjin increased compared to the previous year by 22.46%, 5.50%, and 13.45%, respectively.
Figure 3. Populations of the BTH urban areas (filled) and PM2.5 concentrations (blue bars) during the lockdown of residential areas in large cities in 2020, compared with the same period in previous years (2015–19) (red bars) (* denotes having passed a 0.05 significance test).
The PM2.5 concentrations of large cities in different regions vary when residential areas are closed, and traffic is restricted. Many scholars have reached similar conclusions. During the lockdown of the communities in Shanghai, nitrate and primary aerosol concentrations decreased significantly, as did PM2.5 concentrations (Chen et al., 2020). The increase in PM2.5 concentrations in Beijing during this period indicates that reductions in vehicle emissions alone cannot prevent this type of pollution and that comprehensive controls are still required to improve air quality (Pei et al., 2020). During the APEC meeting held in Beijing in 2014, traffic, industry, and heating simultaneously decreased, and the concentrations of PM2.5 in Beijing dropped significantly (Xu et al., 2019; Wang et al., 2015). Therefore, it is impossible to explain the difference in air quality changes in large cities when only traffic emissions are considered. It follows that the production of PM2.5 in BTH is affected by other factors in addition to traffic.
A significant decrease in hourly changes in PM2.5 concentrations compared with previous years was observed during the community lockdown in the YRD and PRD in 2020 (Fig. 6). However, this was not observed in large cities of BTH. From 1300 to 1900 LST, the average PM2.5 concentration of large cities in BTH in 2020 was higher than in previous years. For the three major regions, there were obvious peaks (local maximums) and valleys (local minimums) in the daily time series of PM2.5 concentrations. Peaks tended to occur after morning rush hour, between 800 and 1100 LST, and valleys were common in the afternoon, between 1300 and 1900 LST. Concentrations were generally higher at night than during the day and reached their daily minimum values in the afternoon. This result is consistent with the study of Wang et al. (2019). The hourly changes in PM2.5 concentrations in Chinese cities show a bimodal distribution. The increased traffic volume combined with the shallow inversion layer contributes to the first-morning peak. A second peak occurs late at night into early morning. One of the reasons for this is that low electricity prices at night lead to more emissions from industrial activities. The strongest reduction occurs in the afternoon when declining traffic volume and enhanced atmospheric convective motion favor lower PM2.5 concentrations (Wang et al., 2019).
Figure 6. Comparison of hourly-averaged PM2.5 concentrations during the lockdown of residential areas in major cities in 2020 and for the same period in previous years (2015–19). The BTH, YRD, and PRD regions are represented by solid lines, dashed lines, and short dotted lines, respectively. Horizontal dashed lines mark the peak and valley for each area. The differences between the peaks and valleys (range of values) are also marked on the left side of the figure. Blue and red distinguish 2020 from the previous years (2015–19), respectively.
In 2020, the peak and valley values in the YRD and PRD were all significantly lower than those in previous years. The peak values decreased by 28.26 μg m–3 and 19.04 μg m–3, respectively, and the valley values decreased by 23.57 μg m–3 and 14.25 μg m–3. Resmi et al. (2020) studied the daily changes in PM2.5 levels in Kannur and Kerala, India, during the lockdown of those cities. They observed similar afternoon declines, which were also attributed to reduced vehicle emissions.
There are some differences between the hourly changes of PM2.5 concentrations in 2020 and those observed in previous years. Compared with previous years, the peak and valley values of PM2.5 in the three regions in 2020 were closer; consequently, the ranges of values is lower. In particular, the drop in the peak is less than the drop in the valley. Again, this observation is consistent with a sharp drop in traffic volume (Table 1) and a consequent reduction in vehicle emissions during the morning and evening rush hours. Furthermore, in BTH, from 1300 to 1900 LST, the valley values of PM2.5 concentrations in 2020 were higher than in previous years. This may be related to a shallower boundary layer and stable atmospheric stratification (Wang et al., 2020) in BTH. In 2020, the nighttime PM2.5 concentration of BTH decreased only slightly, likely related to human activities that did not stop completely (Cui et al., 2020).
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During the lockdown of communities in 2020, the degree of industrial activities (Fig. 7a) changed. These changes were compared with the same one-month period in 2019 (Fig. 7b). The cumulative FRP value during this period in 2020 reveals that more industrial thermal anomalies and overall higher values were observed in northern China compared to southern China. In contrast to southern China, industrial operations in northern China remained active during the city lockdown, and they were even more active than in previous years. Compared with 2019, the number of fire spots in BTH increased by 66.10% in 2020, and the total FRP value increased by 52.73%. This is likely attributed to a greater demand for heating requirements and a higher concentration of heavy industry in northern China. During this period, industrial activities in BTH were mainly concentrated in Tangshan, with Handan being most active in southern Hebei Province. Industries in Shanxi and Shandong around the BTH region remained very active, and areas of large FRP values were found in both provinces. Central Shanxi and Tangshan in Hebei Province are major coal mining regions, where the more intensive industrial activities are associated with energy production. In contrast, the intensity of industrial activities in the YRD was relatively weak and limited to the industrial cities in Xuzhou city, southern Anhui Province, and southern Jiangsu Province. The PRD had the lowest level of industrial activity among the three major regions.
Figure 7. Industrial activity in major urban agglomerations in China during the lockdown of residential areas in 2020. (a) The cumulative FRP values of China's major urban agglomerations (units: MW) from 24 January 2020 to 23 February 2020; (b) The difference, for the years 2020 and 2019, between the cumulative FRP values of China's major urban agglomerations for the same period 24 January to 23 February (2020–19) (units: MW). The areas filled with gray are the BTH, YRD, and PRD. The point represents the sum of the FRP value of the fire point in the area of 0.1° × 0.1°.
The intensity of industrial activities in most parts of China decreased significantly in 2020 compared with the same period in 2019 (Fig. 7b); however, the industrial activities in Shanxi, most parts of Shandong, southern Hebei, and some parts of Tianjin were more intense in 2020 than in 2019. Overall, the change in industrial activities for the three regions in 2020 was essentially the same as the change in PM2.5 concentrations in each large city. Considering that traffic emissions generally decreased, this finding suggests that the altered industrial emissions were the primary reason for the differences in the PM2.5 concentration changes for the different regions. Zhang et al. (2019) analyzed satellite-observed data of China's industrial heat sources and found that the industrial heat radiation flux density was highly correlated to PM2.5 concentrations, indicating that the intensity of industrial activities affected the air quality of many cities in China. Research based on the meteorology-chemistry model (Gao et al., 2018) also confirmed that China's power and industry sectors are the main sources of aerosol emissions. Changes in the intensity of industrial activities in the BTH, YRD, and PRD are closely related to PM2.5 concentrations.
In addition to the possible contribution of local heating and industrial emissions (Nichol et al., 2020), the PM2.5 concentration in BTH is also affected by the transmission of pollutants across regions. Zhao et al. (2020) analyzed the sources of two pollution incidents in BTH during a city blockade: local emissions and short-distance transmission of surrounding air masses (Zhao et al., 2020). The FRP value supports this analysis (Fig. 7). In 2020, there were more intense industrial activities in central Shandong, central Henan, northern Shaanxi, and Shanxi provinces than in 2019, which provides opportunities for the transmission of pollutants into BTH. This supports the premise that in the context of partial emission reduction, coordinated emission reduction among various regions is important for preventing and controlling pollution.
Figure 8a illustrates the profit per unit area of mining and manufacturing in the three regions. The mining profit per unit area in BTH was more than three times higher than that of the YRD and the PRD. The manufacturing profit per unit area in BTH was considerably lower than that of the other two regions, about half of that of either the YRD or PRD. These differences may be related to the different geographical locations and historical development of the three major regions. While residential areas were in lockdown, some manufacturing industries shut down. However, owing to heating and power requirements, the energy supply could not be interrupted, so the mining industry remained operational. Consequently, although the industrial activities were weakened, they were still ongoing in BTH and other surrounding cities in northern China, resulting in the persistent industrial emissions of PM2.5. The thermal power anomaly illustrated in Fig. 7 provides further evidence for this conclusion.
Figure 8. Industrial structure and energy output and supply of the three major urban agglomerations (representing the PRD in Guangdong Province), (a) Mining (dark gray) and manufacturing (light gray) profits (industrial sales output value of the year) per unit area cluster of the three major urban agglomerations; (b) energy output per unit area of the three major urban agglomerations; (c) a ternary phase diagram of the proportion of the power generation composition of the provinces in the three major urban agglomerations.
Further energy production per unit area and composition are shown in Fig. 8b. The output of total energy, coke, and raw coal per unit area in the BTH was significantly higher than that of the YRD and the PRD. Chinese coal mines are mainly concentrated in the north. The raw coal production in the BTH and the YRD accounts for most of the energy sources extracted in these regions, while few raw coal mining activities took place in the PRD. The lower energy extraction level in the PRD was conducive to reduced PM2.5 emissions. Regardless, emissions from coal-fired heating have exacerbated pollution, most especially in northern China (Xiao et al., 2015; Si et al., 2019).
Thermal power is also an important source of urban pollution (Tan et al., 2020). The three regions are all dominated by thermal power generation, accounting for 73.94%–98.76% of the total power generation (Fig. 8c). Clean energy (nuclear power, wind power, and solar power) generation accounts for the smallest proportion of total power generation. The thermal power generation of the three regions accounts for more than 85.79% of the total power generation. Guangdong Province has the lowest proportion of thermal power generation and the highest proportion of clean energy generation of all provinces in the three regions. The electric power sector is among the sectors that emit the most pollutants (Jorgenson et al., 2016; Tong et al., 2018), and the burning of fossil fuels for thermal power generation is one of the main causes of pollution. The increased use of clean power generation favors a reduction in PM2.5 emissions. Conversely, a larger proportion of thermal power generation in BTH regions is conducive to increased PM2.5 emissions.
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Meteorological conditions are also an important factor affecting PM2.5 concentrations in urban agglomerations. The bubble chart in Fig. 9 shows the distribution of the ground temperature, humidity, and wind speed of large cities in the three regions during the city lockdown in 2020 and for the same one-month period in previous years (2015–19). As Fig. 9a illustrates, the distribution of meteorological elements in the BTH urban agglomeration was relatively concentrated in previous years. The temperatures and relative humidity were mostly near 0°C and below 50%, respectively, while wind speeds were between 1–3 m s–1. In contrast, the temperature and humidity distribution in BTH was more dispersed during the 31-day community lockdown in 2020, and the near-surface wind speeds were significantly lower than in previous years. The average wind speed over the 31 days in 2020 was 1.90 m s–1, while the average wind speed in previous years was 2.24 m s–1. In comparison, the distributions of meteorological conditions in large cities in the YRD and the PRD in 2020 were remarkably similar to those in previous years. Backward trajectories also show that the transmission and diffusion of air masses reaching the BTH was weaker than that of the YRD and the PRD [Fig. S1 in the electronic supplementary material (ESM)].
Figure 9. Bubble charts of meteorological elements (temperature, relative humidity, and surface wind speed) of the large cities of the three major urban agglomerations during the 2020 city lockdown (blue bubbles) and the same period in previous years (2015–19) (red bubbles) (a) BTH; (b) YRD; (c) PRD.
In general, unfavorable meteorological conditions aggravated the impact of industrial emissions in the BTH region and its surrounding cities, resulting in higher PM2.5 concentrations in the large cities. The research of Wang et al. (2020) and Sulaymon et al. (2021) also proves this. Lower wind speeds in BTH make it more difficult for air pollutants to spread. Higher relative humidity and warmer temperatures accelerate chemical reactivity, thereby accelerating the formation of secondary particles. At the same time, the lower planetary boundary layer height also inhibits pollutant diffusion. The lack of precipitation makes it impossible for PM2.5 to be eliminated by wet deposition (Sulaymon et al., 2021). For the YRD and the PRD, there were no obvious unfavorable weather conditions. The meteorological conditions of the YRD and PRD had a limited impact on the changes in pollutant concentrations (Liu et al., 2021; Wen et al., 2022). Average weather conditions coupled with a reduction of industrial activities and traffic volume in 2020 caused a significant reduction in PM2.5 concentration compared with previous years. It follows that meteorological conditions can potentially exacerbate PM2.5 concentrations, as they did in BTH in 2020. However, the meteorology may not be the primary reason for the relatively high PM2.5 concentration. Even under unfavorable weather conditions, strict control of pollutant emissions can still be expected to reduce PM2.5 pollution levels (Mahato et al., 2020).
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During the city lockdown, CO2 emissions fell sharply, with a decrease of nearly 20% across the country (Fig. 10a1). Among these, the CO2 emissions from transportation have fallen the most, with a 40% reduction in 2020 compared to the same period in the last year (Figs. 10a3, a6). The reduction rate of industrial CO2 emissions ranked second (Figs. 10a4), and the CO2 emission reduction of residential consumption and power was the smallest (Figs. 10a5, a2). These results show that during the city lockdown, the basic livelihood of citizens was guaranteed with minimal impact. From the perspective of regional reductions in the emission of CO2, emissions attributed to the thermal power of the YRD and the industrial activity of the PRD experienced the largest declines among the three regions, both exceeding 20% (Figs. 10b1, b2). It also proves that the intensity of industrial activity declined most significantly during the lockdown of the PRD.
Figure 10. CO2 emissions and changes in environmental concentration (a1–a6, CO2 emissions in general and by sector in China from 24 January to 23 February 2019 and 2020; b1–b2, CO2 emissions from thermal power and industrial activities in BTH, YRD, and PRD (representing the PRD in Guangdong Province) in January and February 2019 and 2020; (c) the average CO2 concentration observed from 24 January to 23 February 2019 and 2020; (d) CO2 emissions and GDP changes from 2008 to 2018. The correlation coefficient R between the two is marked; ** indicates that the correlation has passed the 0.01 test.
However, a substantial reduction of CO2 has not prevented an upward trend of CO2 concentration in the environment. During the lockdown, the average CO2 concentration in China was 411.85 ppmv, which was still higher than the 409.89 ppmv in the same period last year, and the CO2 concentration of the three regions also rose slightly (Fig. 10c). Unlike particulate matter, which has a short lifetime and often has a timescale of “days” (Wang et al., 2013), CO2 has a perturbation lifetime of hundreds of years or even thousands of years (Montenegro et al., 2007). A sizeable part of the anthropogenic CO2 emissions remains in the atmosphere, waiting for the weathering process or the deposition of CaCO3 to return it to the solid earth, and these processes are quite slow (Archer et al., 2009). Therefore, the consequences of CO2 emissions will continue for many years, and their concentration is difficult to reduce through short-term emission reductions. China's GDP and CO2 emissions have a strong correlation, which indicates that economic growth still relies heavily on the burning of fossil fuels (Fig. 10d). With the city lockdown lifted and the restoration of production, CO2 emissions will rebound (Zheng et al., 2009), which shows that reducing production activities is not an effective long-term strategy for reducing CO2 emissions. However, significantly improving energy efficiency, using clean energy, and developing high-tech industries as the main direction of economic growth do represent effective measures to reduce CO2 emissions.
Area | Transportation volume of road passengers (104) | Change from last year | Transportation volume of road cargo (107 kg) | Change from last year | |
BTH | Beijing | 818 | –76.25% | 726 | –29.72% |
Tianjin | 192 | –79.11% | 1253 | –45.31% | |
Hebei | 65 | –97.61% | 5130 | –37.74% | |
YRD | Shanghai | 2 | –99.32% | 2200 | –21.09% |
Jiangsu | 1181 | –84.72% | 3869 | –47.77% | |
Zhejiang | 397 | –94.06% | 3511 | –38.04% | |
Anhui | 602 | –86.57% | 6981 | –36.52% | |
PRD | Guangdong | 2010 | –78.60% | 7098 | –64.30% |