The regional impact of the COVID-19 lockdown on the air quality in Ji'nan, China

Overall characteristics of air pollutants

The results of previous studies indicated that local pollution is highly important in determining the emissions of air pollutant. Therefore, in this study, we estimated the changes in pollution and the AQI between the pre-COVID and COVID lockdown periods and among the different regions in Ji’nan. A comparison of the different pollutant concentrations analysed in this study shows that the concentrations of almost all pollutants decreased during the COVID lockdown period; only the concentration of O₃ increased continuously as the COVID lockdown period progressed (Fig. 1).

During the observation period, the daily average mass concentrations of PM₁₀, PM_2.5, SO₂, NO₂, CO, and O₃ in Ji’nan were 137.09 µg/m³, 101.35 µg/m³, 22.70 µg/m³, 39.77 µg/m³, 1.28 mg/m³, and 71.84 µg/m³, respectively (Fig. 2). The mass concentrations of PM₁₀ and PM_2.5 exceeded the daily average Grade I values (50 µg/m³ and 35 µg/m³) of the Ambient Air Quality Standard of China (CAAQS, GB 3095-2012) during the whole observation period. In contrast, the mass concentrations of NO₂, SO₂, CO and O₃ were substantially lower than the daily average Grade I values (80 µg/m³, 50 µg/m³, 4 mg/m³ and 100 µg/m³, respectively) of the CAAQS each day. During the pre-COVID period, the daily average mass concentrations of PM₁₀, PM_2.5, SO₂, NO₂, CO, and O₃ in Ji’nan were 177.03 µg/m³, 125.94 µg/m³, 26.39 µg/m³, 54.52 µg/m³, 1.59 mg/m³, and 60.72 µg/m³, respectively. The mass concentrations of all these pollutants, except NO₂, CO and O₃, exceeded the daily average Grade I values of the CAAQS. The mass concentration trends during the COVID lockdown period were consistent with those during the pre-COVID period, but there were significant differences in the concentrations between the periods. In summary, the air quality in Ji’nan was generally good from January 24 to February 7, 2020, mainly due to the strict prevention and control measures for COVID-19.

Effects of regional differences and lockdown on air pollutants

Our results reveal that the PM₁₀, PM_2.5, NO₂, SO₂, CO and O₃ concentrations in the urban, suburban and urban-industrial regions differed significantly between the COVID lockdown and pre-COVID periods (Figs. 3, 4).

NOx, one of the most important pollutants and a major health hazard, was studied in different countries across the world during COVID-19-related lockdowns. In all three regions studied herein, the highest rate of reduction in NO₂ concentrations was observed during the COVID lockdown period (Fig. 4), with the NO₂ levels in the COVID lockdown period being 54.02% on average lower than those during the pre-COVID period (53.07% in urban area, 48.31% in the suburban areas and 55.74% in the urban-industrial area) (Fig. 4); this reduction is greater than that reported at other sites by 26–42%¹¹ and 14–38%¹⁸ but lower than that (50–62%) in Barcelona and Madrid in Spain³³. As shown in Fig. 3E, the NO₂ concentrations in the urban, suburban and urban-industrial areas were significantly higher in the pre-COVID period than in the COVID lockdown period, with the pre-COVID the NO₂ levels in the urban area being 13.46% and 27.63% higher than those in the suburban and urban-industrial areas, respectively. During the COVID-19 lockdown period, the NO₂ levels in urban areas were 4.69% and 31.75% higher than those in the suburban and urban-industrial areas, respectively. Blocking and controlling the air pollution associated with COVID-19 has helped reduce ground NO₂ levels³⁴ and this effect might be correlated with the tropospheric NO₂ column density²⁷. Among all sources of NO₂, automobile emissions and power generation are the most important⁵. A systematic review confirmed that a short-term increase in the NO₂ concentration in urban areas correlates to an increase in the number of pneumonia hospitalizations^5,35.

The trends in the CO concentration were similar to those in the NO₂ level. During the COVID-lockdown period, the average CO mass concentrations in the urban, suburban and urban industrial areas were 1.08 mg/m³, 1.16 mg/m³ and 1.14 mg/m³, respectively, which decreased by 27.78%, 29.46% and 36.61%, respectively, compared with those during the pre-COVID period. The highest levels of PM₁₀ were also observed during the pre-COVID period in the urban, suburban, and urban-industrial areas in Ji’nan (Fig. 4). The reductions in PM_2.5 and CO emissions in urban and urban-industrial areas are generally higher than those in suburban areas²⁵, supporting our findings. Notably, PM_2.5 and CO are generated mainly by construction activities and from road dust, natural soil dust and dust from urban-industrial activities³⁶. In contrast, the differences in the PM₁₀ concentrations among the three regions were not significant during either the pre-COVID period or the COVID-lockdown period (Fig. 3A), which suggests that particles in Ji’nan are strongly diffused. However, the COVID lockdown period had a significant effect on the PM₁₀ concentrations, with 42.86%, 44.26% and 50.60% differences in the PM₁₀ concentration between the pre-COVID and COVID lockdown periods in the urban, suburban and urban-industrial areas, respectively (average of 44.92%, Fig. 4). The main reasons for the decreases in the concentration of PM were the severe restrictions on vehicle traffic, the cessation of industrial activities, and the stopping of construction projects, which are important sources of floating dust in the urban air³⁷. Despite the overall consistency among the observed changes in all regions for the different air pollutants (except O₃), at the regional level, some differences were statistically significant, while others were not due to the variability among stations, with the differences being more pronounced at the urban, suburban and urban-industrial stations.

O₃ is a secondary pollutant involved in different atmospheric reaction mechanisms and acts as both a source and sink. Generally, the impact of lockdowns on O₃ was mixed, with its levels generally falling within ± 20%³⁸, but total O₃ levels remained relatively stable¹⁸. In this study, by comparing the regional mean concentrations throughout the COVID-19 period, we found that O₃ concentrations were higher during the COVID lockdown period than during the pre-COVID period, especially in the urban regions (Fig. 3). Furthermore, the mean O₃ concentration at all stations during the COVID lockdown period was 37.42% higher than that during the pre-COVID period (46.84% in the urban areas, 18.27% in the suburban area, and 19.84% in the urban-industrial areas) (Fig. 4); this finding is consistent with the outcomes of other studies, which reported that O₃ concentrations increased by (on average) 20% during lockdowns³⁹, potentially due, in part, to atmospheric reactivity³⁷. The higher lockdown O₃ concentrations can be attributed to the following three reasons: (1) low PM concentrations can result in more sunlight passing through the atmosphere, encouraging increased photochemical activities and thus higher O₃ production⁴⁰; (2) a reduction in NOx emissions increases O₃ formation⁴¹; and (3) lower PM_2.5 concentrations means their role as a sink for hydroperoxy radicals (HO₂) is less effective, which would increase peroxy radical-mediated O₃ production⁴². During the pre-COVID period, the O₃ levels were not significantly different among the region, and the same results were observed during the COVID lockdown period. However, in the urban and urban-industrial areas, the O₃ levels during the COVID lockdown period were significantly higher than those in the pre-COVID period (p < 0.001 and p < 0.01, respectively) (Fig. 3F). Such increases in O₃ are in line with a 17.56% O₃ increase in industrial locations in Delhi, India, with minor O₃ increases of up to 0.78% during the lockdown period⁴³.

Relationships between different pollutants and region in different research stages

As particles scatter and absorb sunlight, air pollution reduces the amount of sunlight reaching the Earth. It has been confirmed that reducing PM emissions had the greatest influence on the improvement of air quality during COVID-related lockdowns, but secondary formation was enhanced after these lockdowns ended¹³. These changes affect the distributions of other pollutants. Specifically, PM_2.5 is best explained by emissions from the transportation and industrial sectors⁴⁴. Some studies have confirmed that PM_2.5 could trigger COVID-19 spread and increase lethality^45,46. Therefore, we estimated the relationships of PM_2.5 with the AQI and the PM₁₀, NO₂, SO₂, CO and O₃ concentrations in the air of Ji’nan during the pre-COVID and COVID lockdown periods (Fig. 5). We found that the lockdown affected the relationship between PM_2.5 and NO₂, which were relatively weakly or not correlated (r = − 0.15, urban; r = 0.052, urban–industrial; r = − 0.35, suburban) in the pre-COVID period but significantly correlated (r = 0.60, urban; r = 0.75, urban–industrial; r = 0.70, suburban) during the COVID lockdown period. This finding is consistent with the results of Mahato et al.⁴³. This clearly means that greater control of regional transport activity is a key factor in reducing pollutant levels as regional transport is completely restricted during the lockdown^43,47, and there were restrictions on human activities, transportation, and factories during the lockdown period. The increase in the PM_2.5 concentration may also be due to the incineration of straw and the use of coal-fired power plants in the neighbouring upwind states, leading to the transport of pollutants to urban areas³⁷, and may be linked to the distance from coast⁴⁸. Moreover, air temperature is an important parameter that affects the dispersion of air pollutants. An increase in air temperature due to a rise in solar radiation also allows more sunlight to pass through the atmosphere, encouraging increased photochemical activities and thus enhancing O₃ production⁴⁰. In this study, we found that the average temperatures during the pre-COVID period and COVID lockdown were 2.75 °C and 2.91 °C, respectively. This also indirectly confirms the influence of meteorological conditions on air quality during the lockdown period.

COVID-19-related lockdowns can improve the air quality index

Figure 6 shows the change in the AQI and the corresponding dominant distribution characteristics during the analysis period. Nations have implemented various containment measures during the COVID-19 pandemic that have resulted in both positive and negative environmental impacts⁴⁹. Nevertheless, a significant improvement in the AQI was observed in the COVID lockdown period in this study. This improvement was caused by a reduction in emissions from the transportation and industry sectors^44,50. During the pre-COVID period, the AQI was generally poor and moderate in all regions, while during the COVID lockdown period, the AQI was generally good, ranging from satisfactory to moderate (Figs. 6A, 7B). Although there was still considerable room for improvement during the COVID lockdown period, the urban-industrial regions had the best air quality in Ji’nan; indeed, only urban-industrial regions obtained an AQI lower than 100 (Fig. 6C). In areas affected by the transportation and industrial sectors, air quality improved by nearly 60%⁴³, especially up to 75% in Tehran⁵¹. In this study, a 37.33% reduction in the AQI was observed during the COVID lockdown period compared to the pre-lockdown period (Fig. 6D). Approximate reductions of 35.48%, 37.01% and 43.43% were observed in the urban, suburban and urban-industrial regions, respectively, and there was a significant difference in the AQI between the COVID lockdown and pre-COVID periods (Fig. 6C). Thus, the high AQIs at the different sites during the implementation of government intervention measures may have been influenced primarily by heavy pollution from industrial sources⁵². Therefore, the AQI showed remarkable regional differences due to industrial emissions in Ji’nan, where the COVID-19-related lockdown prevented these emissions.

Principal component analysis (PCA)

To identify the effects of the different regions and lockdown measures on the air pollutant concentrations, an ordination diagram of air pollutants based on PCA was created using Canoco 5.0, as shown in Fig. 7. In this figure, features with narrow included angles are positively correlated, features at wide angles are negatively correlated, and features at a right angle to one another are not correlated; additionally, the neighbourhood distance between factors is similarly used to calculate the corresponding area and congestion period. Figure 7A illustrates two main patterns of the influence of the COVID lockdown on air pollutants. The first two axes explained approximately 80.6% of the total variance (63.6% by PC1 and 17.0% by PC2) in the pollutant concentrations during the COVID lockdown period (Fig. 7A). The lockdown of the city and the shutdown of industrial regions prevented the spread and emission of pollution. At the regional scale (Fig. 7B), the first two axes explain 73.7% of the total variance (55.7% by PC1 and 18.0% by PC2) in the COVID lockdown period; these results indicated that regions changed the distance pattern and accelerated the movement of pollutant patterns. In urban regions, SO₂ and NO₂ were the main pollutants, while in suburban and urban-industrial regions, PM_2.5 was the main pollutant. Previous studies have confirmed that the primary anthropogenic sources of PM, CO, NO₂, and SO₂ are the burning of fossil fuels in vehicles and the regional transport of these pollutants due to stubble burning in upwind areas; a combination of both of these sources can also occur^37,53. In this study, the air pollutants exhibited clear patterns during the pre-COVID and COVID lockdown periods in Fig. 7B. The monitoring stations can be clearly separated during the pre-COVID and COVID lockdown periods, but during the COVID lockdown period, stations cannot be distinctly separated at the regional scale. PM_2.5 and SO₂ were the main urban-industrial and urban pollutants, respectively. Overall, the concentrations of PM_2.5, PM10, CO, NO₂, SO₂ and AQI were positively related to the COVID lockdown period, whereas the O₃ concentration showed a negative association with the COVID lockdown period. Numerous studies suggest that the lockdown measures related to COVID-19 pandemic caused significant decreases in the concentrations of PM_2.5, NO₂, PM₁₀, SO₂ and CO globally while O₃ concentration increased^54,55. In our study, the results are quite in accord with these conclusions.

Source: Ecology - nature.com