Analysis of factors influencing air quality in different periods during COVID- 19: A case study of Tangshan, China

doi:10.21203/rs.3.rs-1967047/v1

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Analysis of factors influencing air quality in different periods during COVID- 19: A case study of Tangshan, China

https://doi.org/10.21203/rs.3.rs-1967047/v1

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Since the outbreak of COVID-19, China has experienced the Level I, Level II, and Level III responses to the epidemic, with different measures have an impact on air quality in difference periods. This study aimed to analyze the main factors influencing air quality in three different periods during COVID-19 in Tangshan: the COVID-19 period, the Level I response period, and the Spring Festival period. Here, we used comparative analysis and difference-in-differences (DID) method to explore differences in air quality. During COVID-19, the air quality index (AQI) and the concentrations of six conventional air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃-8h) decreased significantly compared to 2017–2019. Air quality improved may be the result of industry closures not related to livelihoods during COVID-19 in 2020. For the Level I response period, multiple linear regression (MLR) models were used to quantify the impact of COVID-19 control measures on AQI, and it was shown that the measures reduced the average AQI by 29.07%, 31.43%, and 20.04% in February, March, and April 2020, respectively. During the Spring Festival, the concentrations of the above-mentioned six pollutants were significantly higher than those in 2019 and 2021 as there were two heavy pollution episodes in 2020, and the possible pollutant sources were local emissions and short-distance transportation. As for the further improvement of air quality, it is necessary to take strict measures to prevent and control air pollution while paying attention to meteorological factors.

air quality

COVID-19

anthropogenic factors

meteorological factors

difference-in-differences analysis

backward trajectory cluster

In recent years, China has successively issued the "National Air Pollution Prevention and Control Action Plan (2013–2017)" and the "Three-Year Action Plan to Win the Battle for a Blue Sky (2018–2020)", which have substantially reduced the concentration of air pollutants and significantly improved air quality (Li et al. 2022; Wen et al. 2021). Starting from the first day of lockdown declared in Wuhan on January 23, 2020, all the parts of China that reported the corona virus disease 2019 (COVID-19) entered the Level I response period of the epidemic. A series of measures were implemented to limit anthropogenic activities, including the suspension of work and production, the blocking of traffic arteries, isolation, etc. As the outbreak receded, the level of epidemic prevention and control in various places was lowered (Tian et al. 2020). Research on COVID-19, which is a global catastrophe that lasted more than two years, has never stopped and it revealed that the virus had a serious negative impact on human health and economy, but at the same time it brought a significant reduction in air pollution (Muhammad et al. 2020).

Numerous studies have investigated the variation of air quality during COVID-19 (2020), and most focused on the Level I response period and the Spring Festival period. During the Level I response period, the SO₂, CO, and PM₁₀ emissions after the lockdown decreased by 9.3%, 12.7%, and 7.4%, respectively, in the North China Plain (Li et al. 2021b) compared with pre-lockdown levels, and the PM_2.5, NO₂, and SO₂ emissions were reduced by 52%, 67%, and 25%, respectively, in the Pearl River Delta (Wang et al. 2021c). Compared to the same period of 2019, the concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO from February to April 2020 decreased by 29%, 30%, 27%, 22%, and 17%, respectively, while that of O₃ increased by 6% in Hebei Province (Chen et al. 2020); the concentrations of PM_2.5, NO₂ decreased by 31.7%, 51.8%, respectively, while that of O₃ increased by 43.9% in Wuhan (Huang et al. 2021a). The emissions of NO₂, SO₂, and PM_2.5 during the Spring Festival of 2020 were reduced by 51%, 28%, and 63%, respectively, in the North China Plain compared with those recorded during the same period in 2019 (Xing et al. 2020), and the lockdown during the Spring Festival of 2020 resulted in a decrease in PM_2.5 (36.5%) and NO₂ (51.9%) concentrations in Shanghai compared to the values detected 10 days before the festival (Niu et al. 2021). The degree of change in air quality varied from region to region during COVID-19 (Adam et al. 2021; Rana et al. 2021), which greater in northern and more industrialized cities in China (Wang et al. 2021b).

Air quality is affected by both natural and anthropogenic factors, and determining which are the dominant ones depends on the temporal and spatial scope of research studies (Kang et al. 2019). Quantifying the degree of influence of related factors is one of the key objectives of the current air quality research. For example, when investigating anthropogenic factors, the commonly used methodology involves: (i) the subtraction of the impact of natural factors from the final pollutant concentration (Dai, et al. 2021; Rahman et al. 2021); (ii) the determination of the relationship between specific anthropogenic measures and the corresponding pollutant emissions based on the emission inventory (Li et al. 2020; Wang et al. 2020). Machine learning techniques can be used to quantitatively assess the effect of abatement measures on air quality improvement (Lu et al. 2021). Common quantitative analysis methods include regression and classification (Gkatzelis et al. 2021), such as multiple linear regression (MLR), random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM), among others.

In the present study, Tangshan was selected as the study area to analyze the variation of the air quality index (AQI) and six pollutants (i.e., PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) in three different periods during COVID-19. The comparative analysis method, difference-in-differences (DID) model, MLR model, backward trajectory model, cluster analysis method, and potential source contribution function (PSCF) analysis method were used to investigate the main factors affecting air quality.

Study area

Tangshan is located northeast of Hebei Province, China, and belongs to the Beijing-Tianjin-Hebei urban agglomeration (Fig. 1). It is a typical industrial city in northern China, whose multiple industries produce steel, energy, building materials, chemicals, ceramics, and other goods. It is also one of the cities included in the atmospheric pollution transmission channel of the Beijing-Tianjin-Hebei region. Tangshan is dominated by secondary industries, and its GDP accounted for 55.24% of the total GDP in 2021 (http://www.tangshan.gov.cn/zhuzhan/jjsj/). The secondary industry of Tangshan is led by steel production, which accounted for 12.05% of the total steel production in China in 2021 (https://data.stats.gov.cn/). Although a series of air pollution control measures have been implemented in recent years, due to its industrial structure characterized by heavy industry and unfavorable geographical conditions, heavy pollution episodes are still inevitable, especially during the winter and spring seasons, in which buildings require heating (Guo et al. 2020).

Time periods

This study focused on data collected from January 24 to June 30 between 2017 and 2021. The comparative analysis was conducted among these five years, considering 2017–2019 as the years without COVID-19, and 2020 and 2021 as the first and second year of the COVID-19 outbreak, respectively.

The anthropogenic control measures varied widely in different periods of the COVID-19 outbreak. January 24, 2020, was the first day of the Spring Festival and also the day in which the Hebei Provincial Government activated the Level I response to major public health emergencies. At the beginning of the response in Tangshan, public transportation was suspended across the city, fireworks and firecrackers were banned, and all villages, communities, and enterprises were placed under close management. After the response level was adjusted to Level II on April 30, 2020, the epidemic control status changed from wartime to normal, while measures such as access control and strict vehicle control were still in effect. After the response level was adjusted to Level III on June 6, 2020, normal working conditions and daily life gradually resumed, but basic measures like wearing masks, not gathering, and reducing travel were still being implemented. Due to the normalization stage of the epidemic prevention and control, the intensity of control measures in 2021 may have been weaker than that in 2020. Therefore, the analysis conducted in this study was divided into the following three periods based on the occurrence of COVID-19 and the level of response to it (Fig. 2).

COVID-19 period: Jan. 24 to Jun. 30. This period included the Level I, II, and III response periods in 2020, which can roughly reflect the overall air quality status during this time span. The comparison with the same period in 2017–2019 and 2021 aimed to analyze the factors influencing air quality based on a larger time span.

Level I response period: Jan. 24 to Apr. 30. As the COVID-19 control measures during the Level I response were significantly stricter than those during the Level II and III response periods in 2020, this period was selected to investigate the impact of COVID-19 control measures on air quality.

The Spring Festival period. The Spring Festival is a traditional Chinese holiday period that lasts 7 days during which the intensity of anthropogenic activities increases, causing potential impacts on air quality. A separate interannual comparative analysis was conducted for the Spring Festival period from 2019 to 2021 including the following time spans: Feb. 4–10, 2019; Jan. 24–30, 2020; and Feb. 11–17, 2021.

Data sources

The AQI data and concentrations of six conventional atmospheric pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃-8h) for six air quality monitoring stations in the downtown area of Tangshan were obtained from the comprehensive stereo observation data sharing platform for the Beijing-Tianjin-Hebei area and its surrounding regions (http://123.127.175.60:8765/siteui/index).

Among the above-mentioned data, O₃-8h corresponded to the average value of the maximum ozone concentration over an 8-h period each day. The hourly value was the average of hourly observations for the six air quality monitoring stations. The AQI is a dimensionless index that can quantitatively describe air quality, and it is calculated based on the concentrations of the six conventional atmospheric pollutants mentioned above in China (Aragao et al. 2022). The index defines six levels of air quality: excellent (0 < AQI ≤ 50), good (51 < AQI ≤ 100), lightly polluted (101 < AQI ≤ 150), moderately polluted (151 < AQI ≤ 200), heavily polluted (201 < AQI ≤ 300), and severely polluted (AQI > 300). When the AQI is > 50, the pollutant with the largest individual air quality index (IAQI) is considered as the primary pollutant.

The data for near-ground meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, visibility, and precipitation) were obtained from the Huiju Data platform (http://hz.hjhj-e.com/home/), while the economic data were acquired from the Tangshan Bureau of Statistics (http://new.tangshan.gov.cn/).

The data on air mass trajectories were sourced at the National Center for Environmental Prediction (NCEP, http://ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1), and were generated every 6 h.

Difference-in-differences (DID) model

The DID model is a powerful tool in the evaluation of policy effects (Liu et al. 2022). The differences in air quality can be attributed to either response measures or seasonal influences or both. To measure whether the response measures is effective, the DID model was employed. During 2017–2019, no major public health emergency occurred in Tangshan. In addition, air quality in different years in the same time period may have similar trends (Li et al. 2017). Therefore, data from 2017–2019 and 2021 were used as the control group, while data from 2020 were used as the experimental group. The DID model setting was as follows:

$$\begin{array}{c}{Y}_{it}=\alpha {Measure}_{it}\times {Post}_{it}+\beta {Y}_{it-1}+\gamma {X}_{it}+\delta \left({Trend}_{i}\right)+{\epsilon }_{it}\left(1\right)\end{array}$$

where ${Y}_{it}$ is the dependent variable, which represents the air quality of Tangshan on $t$ day in $i$ year, denoted by AQI. $Measure$ is a dummy variable that represents whether Tangshan launched response to the public health emergency. If launched, $Measure=1$; otherwise, $Measure=0$. $Post$ is a dummy variable that reflects whether day $t$ is before or after the day node (January 24), and before the day node, $Post=0$; otherwise, $Post=1$. In addition, considering that the air quality on a given day will be affected by that on the previous day and meteorological factors, the explanatory variables${ Y}_{it-1}$ and ${X}_{it}$ were added to the model. Meteorological factors include temperature, humidity, wind speed, visibility, and precipitation. It is usually that there is a trend of improving air quality year by year (Wu et al. 2021), and we assume it exists in Tangshan. We controlled with a dummy variable $Trend$, which was introduced only when the number of study days per year exceed six months.

Passing the parallel trend test is a necessary condition for the application of the DID model: that the seasonal influences of the experimental and control groups are similar without the measures (Song et al. 2021).

Multiple linear regression (MLR) model

Regression analysis is generally used to statistically describe the relationship between independent and dependent variables, and the value of the latter can be estimated based on that of the former (Schneider et al. 2010). Linear regression is a type of regression analysis that has been widely used in air quality prediction (Abdullah et al. 2019; Westerlund et al. 2014). When independent variables are related to a dependent variable, a predictive MLR equation can be established (Krzywinski and Altman 2015), which is expressed as:

$$\begin{array}{c}Y={\beta }_{0}+{\beta }_{1}{X}_{1}+{\beta }_{2}{X}_{2}+\cdots +{\beta }_{k}{X}_{k}+\epsilon \left(2\right)\end{array}$$

where Y is the dependent variable, ${X}_{1},{X}_{2}\cdots ,{X}_{k}$ are independent variables; ${\beta }_{0},{\beta }_{1}\cdots ,{\beta }_{k}$ are linear regression parameters; and ε is a random error term that follows a normal distribution.

Backward trajectory cluster analysis and PSCF analysis

In this study, backward trajectory cluster analysis and potential source contribution function (PSCF) analysis were conducted using MeteoInfo software (Wang 2014) and TrajStat plug-in (Wang et al. 2009).

The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was employed to obtain the backward trajectories. The HYSPLIT model, which has been jointly developed by the Air Resources Laboratory of the National Oceanic and Atmospheric Administration (NOAA) and the Australian Meteorological Administration to simulate the trajectory of air masses, is widely used in the fields of meteorology and atmospheric sciences. Cluster analysis is used as a method to group air mass trajectories with similar geographical origins, and PSCF analysis is used to qualitatively identify potential pollution sources based on conditional probability functions (Polissar et al. 2001). Based on spatial grid calculations, the research area selected in this study was divided into $i\times$j uniform grids; the ratio of the number of polluted air trajectory endpoints ${m}_{ij}$ through a certain grid ($i,$j) to the total air trajectory endpoint number ${n}_{ij}$ of the grid was defined as the PSCF value (${P}_{ij}$). To eliminate the effect on the results caused by the overestimation of some grids with a very small number of trajectories a weighting factor (${W}_{i}$) was introduced (Polissar et al. 1998):

$$\begin{array}{c}{W}_{i}=\left\{\begin{array}{c}1.00 {n}_{ij}\ge 4{W}_{ij}\\ 0.70 { W}_{ij}\le {n}_{ij}<{4W}_{ij}\\ 0.42 0.5 {W}_{ij}\le {n}_{ij}<{W}_{ij}\\ 0.05 {n}_{ij}<{0.5 W}_{ij}\end{array}\right.\left(3\right)\end{array}$$

where ${N}_{i}$ represents the total residence time of all trajectories in the $i$-th grid, and ${W}_{ij}$ represents the average number of trajectories passed by each grid.

In this study, the Materials Bureau air quality station (E118.17°, N39.63°) was selected as the receiving point and the starting height of the trajectory simulation was set at 500 m. The air mass trajectory was calculated backward for 24 h each time.

Analysis of factors influencing air quality during COVID-19

Interannual differences of air quality

The average AQI value and concentration of the six pollutants observed in different periods are shown in Table S1. From Jan. 24 to Jun. 30, 2020, the average AQI value in Tangshan was 93.20, which corresponded to the second air quality level (good), and the average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃-8h were 50.03 µg/m³, 93.04 µg/m³, 19.86 µg/m³, 41.36 µg/m³, 1.25 mg/m³, and 110.17 µg/m³, respectively, which were all below the Ambient Air Quality Standard Grade II (PM_2.5 75 µg/m³, PM₁₀ 150 µg/m³, SO₂ 150 µg/m³, NO₂ 80 µg/m³, CO 4 mg/m³, O₃-8h 160 µg/m³).

Compared with the data from Jan. 24 to Jun. 30, 2017–2019, the daily average concentration of all pollutants in 2020 decreased to varying degrees between 6.13% and 42.78%, with SO₂ and O₃-8h showing the largest (42.78%) and smallest (6.13%) reductions, respectively. There were significant differences in the concentrations of PM_2.5 at the 95% confidence interval between 2017–2019 and 2020, and significant differences in the concentrations of PM₁₀, SO₂, NO₂, and CO at the 99% confidence interval. No significant decreased O₃ in Tangshan was mainly attributed to the enhanced atmospheric oxidation capacity (Zhu et al., 2021). During the same period in 2021, the concentrations of PM_2.5, PM₁₀, and NO₂ increased slightly compared with those observed in 2020, potentially meaning that the Level I and II response had a greater impact on air quality than III response. While the SO₂ and CO values in 2021 were lower than those in 2020, which were mainly related to the continuous implementation of the “coal to gas” and “coal to electricity” policies (Liu et al. 2020).

The 2019–2021 period was selected as the study scope to analyze the temporal trend of six atmospheric pollutant concentrations (Fig. 3). PM_2.5, PM₁₀, SO₂, NO₂, and CO concentrations showed similar variation patterns, suggesting that they might have common sources and influencing factors. O₃ is a secondary pollutant affected by the combined action of nitrogen oxides (NOx) and volatile organic compounds (VOCs) (Li et al. 2019; Roy et al. 2021), and due to the complexity of its mechanism of generation, it shows a different pattern of variation compared to the other pollutants. Overall, the range of variation in the concentration of each pollutant in 2020 was smaller than that in 2019 and 2021, and the number of clean air days was significantly higher. The proportion of days in which PM_2.5 concentration exceeded the standard (75 µg/m³) in 2019, 2020, and 2021 was 19.2%, 13.29%, and 25.95%, respectively, indicating that the health risks associated with the exposure to particulate matter were clearly lower in 2020 (Shen et al. 2017).

Analysis of significant difference based on DID model

This section explored the long-term effect of COVID-19 control measures on the air quality in 2020. The DID model used the data from 23 days (January 1) before the Level I response and 159 days (June 30) after the response. The descriptive statistical results of the main variables were reported in Table S2A. Table S3A reported the estimation results of model. The coefficient $\alpha$ was found statistically significantly negative at the 1% level, which indicated that air quality improved significantly during COVID-19 in Tangshan. The coefficient $\delta$ significantly negative at the 1% level, which confirmed the previous hypothesis, i.e., Tangshan has a trend of improving air quality year by year.

However, the parallel trend test was conducted, and the result opposed the use of the DID model (Figure S1A). In 2017–2019 and 2021, the coefficients were not significantly different from 2020, which shown that the seasonal influences played an important role in improving air quality. In conclusion, the results of the DID model cannot demonstrate that the control measures have a significant impact on air quality after the beginning of the Level III response for a period of time.

Impact of meteorological factors

Meteorological factors have a strong influence on the variation of air pollution levels (Dantas et al. 2020; Shenfeld 2011). To explore the impact of these factors on air quality in 2020, the Spearman correlation analysis was conducted among AQI, six pollutants, and six meteorological parameters (i.e., temperature, humidity, wind speed, atmospheric pressure, visibility, and precipitation) using SPSS software. The data from Jan. 24 to Jun. 30, 2017–2021 was selected for the analysis because the use of longer time series could reduce the impact of uncertainties. The results were shown in Fig. 4: temperature was strongly positively correlated with O₃ (r_s = 0.842, P < 0.01), and negatively correlated with PM_2.5, PM₁₀, NO₂, and CO; humidity was positively correlated with pollutants other than O₃, and moderately positively correlated with PM_2.5 and CO (r_s = 0.441, P < 0.01; r_s = 0.372, P < 0.01); wind speed was negatively correlated with pollutants other than O₃, as wind could transfer, diffuse, and dilute pollution; air pressure was strongly negatively correlated with O₃ (r_s = − 0.682, P < 0.01), and was positively correlated with PM_2.5, NO₂, SO₂, and CO; visibility was negatively correlated with PM_2.5, PM₁₀, NO₂, and CO, and was more significantly correlated with particulate matter; precipitation was negatively correlated with pollutants other than CO, but the overall correlation was low.

From Jan. 24 to Jun. 30, 2020, the average temperature in Tangshan was 10.56°C, humidity was 54.43%, wind speed was 1.92m/s, atmospheric pressure was 1015 hPa, visibility was 16.09 km, and precipitation was 0.73 mm (Table S4). These were mostly unfavorable meteorological conditions compared to those recorded in 2017–2019 and 2021, indicating that meteorological factors in 2020 had a negative effect on the improvement of air quality. However, overall, the air quality in 2020 was better than that in 2017–2019 and 2021, showing that anthropogenic factors might played a positive role in improving the air quality and masked the impact of meteorological factors.

Impact of COVID-19 control measures

Based on the Air Pollution Source Emission Inventory of Tangshan (2017), it is clear that industrial processes, dust, fossil fuel combustion, and vehicles are the main sources of conventional air pollutants (Figure S2). In particular, the emission contribution rates of industrial processes to PM_2.5, PM₁₀, SO₂, and CO were 53.55%, 38.03%, 78.16%, and 90.68% in 2017, respectively. The measures adopted to control the epidemic had an impact on the emission intensity of the above-mentioned pollution sources. Therefore, the available economic indicators closely related to them were selected as parameters reflecting the impact of the variation of anthropogenic emissions on air quality during the 2019–2021 COVID-19 period (Table 1).

Table 1

Main economic indicators for the city of Tangshan in the first and second quarters of the 2019–2021 period
Economic Indicator	Unit	2019		2020		2021
Economic Indicator	Unit	1st	2ed	1st	2ed	1st	2ed
GDP of the Secondary Industry	Billion Yuan	82.21	188.79	73.69	171.49	91.91	208.74
Total Profit of Manufacturing	Billion Yuan	3.79	16.46	1.57	8.35	/	/
Product Output
Steel	Million Tons	34.12	40.41	34.74	44.46	38.74	37.53
Coal	Million Tons	5.98	5.73	5.60	5.47	5.07	4.80
Cement	Million Tons	3.92	9.05	3.42	10.88	5.64	10.90
Power Generation	Billion kWh	16.6	15.6	16.8	16.9	19.7	16.4
Electricity Consumption
Industry	Billion kWh	16.72	18.08	15.18	18.18	17.26	18.29
Construction	Billion kWh	0.19	0.13	0.16	0.14	0.23	0.17
Urban and Rural Life	Billion kWh	1.36	1.08	1.52	1.14	1.75	1.21
Operating Income of Transportation, Warehousing and Postal Industry	Billion Yuan	8.52	13.22	7.77	15.18	8.51	14.15

The GDP of the secondary industry in the first and second quarters of 2020 was significantly lower than that in 2019 and 2021, and the total profit of manufacturing in 2020 was about 50% of that of 2019, which indicates that COVID-19 had a certain impact on industrial production in 2020. However, the product output related to people's livelihood, such as steel, coal, cement, and power generation, was not significantly different from that reported in 2019–a year not affected by COVID-19–which demonstrates that the drop in air pollutant concentrations was likely due to the closure of industries not related to livelihood (Hu et al. 2021). The secondary industry includes industry and construction. The electricity consumption of industry dropped significantly in the first quarter of 2020 and that of the construction, which is a major source of particulate matter, showed the same trend. As work and production resumed in late February, the levels of power usage gradually returned to normal. The increase in electricity consumption by urban and rural residents and the apparent decline in the level of transportation in the first quarter of 2020 may be due to the lockdowns and travel controls imposed in this year, which led to a reduction of NO₂, SO₂, and CO concentrations. In summary, COVID-19 control measures had a significant impact on air pollutant emissions, which in turn affected pollutant concentrations (Rocha et al. 2022).

Analysis of factors influencing air quality during the Level I response

Interannual differences of air quality

Existing research shows that the COVID-19 outbreak has improved China’s air quality in the short term (Wang and Su 2020), and the impact of related measures on pollutant concentrations was gradually weakened as the prevention and control levels were lowered (Huang et al. 2021b). In this section, we attempted to conduct an in-depth comparative analysis of AQI and pollutant concentrations during the Level I response to examine the impact of COVID-19 control measures on air quality. The average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃-8h from Jan. 24 to Apr. 30, 2020, were 56.22 µg/m³, 96.93 µg/m³, 19.38 µg/ m³, 41.73 µg/m³, 1.32 mg/m³, and 87.80 µg/m³ (Table S1), respectively. Compared with the same period in 2017–2019, the concentrations decreased by 21.26%, 24.58%, 46.02%, 26.08%, 23.70%, and 0.54%, respectively; of these, the variations of PM₁₀, NO₂, and SO₂ were significantly different (P < 0.05). In the same period in 2021, the concentrations of PM_2.5, PM₁₀, and NO₂ increased by 19.57%, 38.86% and 19.39%, respectively, and the differences were significant (P < 0.05).

Analysis of significant difference based on DID model

The DID model was used to study the impact of measures on air quality during Level I response. The model used the data from 23 days before and after the Level I response. The descriptive statistical results of the main variables and the estimation results of model were reported in Table S2B and Table S3B, respectively. Similar to the study results of long-term, the coefficient $\alpha$ was found statistically significantly negative at the 1% level, which indicates that air quality improved significantly during the Level I response.

The parallel trends test was conducted, and Figure S1B presented the estimated coefficients and their 95% confidence intervals. In 2017–2019, the coefficient $\alpha$ was significantly positive, indicating that air quality after January 24 was significantly worse than before. In 2020, the coefficient $\alpha$ changed from significantly positive to significantly negative, which means that 23 days after the Level I response, the air quality suffered an impact. Obviously, this result further supported the use of the DID model.

Additionally, the results of the DID model illustrated that the air quality improved was mainly attribute to anthropogenic factors during the Level I response, while natural factors might have played a counterproductive role. Current environmental protection measures include basic environmental protection measures and indirect COVID-19 control measures, and good air pollution control strategies can provide crucial impacts in reducing air pollution (Othman and Latif 2021).

Impact of COVID-19 control measures based on MLR model

Assuming that basic environmental protection measures remained unchanged from 2017 to 2021, regression models were established to identify the relationship between meteorological parameters and AQI, and to quantify the impact of COVID-19 control measures on air quality. After the comparative analysis of the MLR model and the principal component regression (PCR) model, the former, which included the meteorological parameters as the independent variable and logAQI as the dependent variable, was finally adopted for subsequent simulations (Text S1). In studies simulating air quality using meteorological data, the difference between the simulated and observed values can be considered as the influence of COVID-19 control measures. Data from Feb. 1 to Apr. 30, 2017–2019 were used to build MLR models simulating air quality from Feb. 1 to Apr. 30, 2019–2021.

The model simulation results for February, March, and April 2019–2021 are shown in Fig. 5. In 2019 (the year without COVID-19), the simulated monthly AQI average values for the three months were 88.52 µg/m³, 100.23 µg/m³, and 99.66 µg/m³, respectively, and compared to them, the observed values increased by 12.54% for February, and decreased by 8.83% and 11.73% for March and April, respectively. In 2020, the observed monthly AQI average values in February, March, and April were 96.79 µg/m³, 73.19 µg/m³, and 80.50 µg/m³, respectively, while the simulated values were 136.45 µg/m³, 106.74 µg/m³, and 100.68 µg/m³, respectively. Therefore, the reduction in AQI caused by COVID-19 control measures in February, March, and April during the Level I response in 2020 was estimated at approximately 29.07%, 31.43%, and 20.04%, respectively. While the reduction in February, March, and April during the Level III response in 2021 was estimated at approximately 20.68%, 18.37% and 22.04%, respectively.

The above results showed that COVID-19 control measures played a role in improving air quality. The impact of anthropogenic factors on the AQI in February and March was significantly higher in 2020 than in 2021, which might be attributable to the difference in the Level I and Level III response. This result also shown that, to a certain extent, air pollution could be reduced by strengthening control efforts. However, the AQI in April was slightly lower in 2020 than in 2021, which may be due to the fact that the difference in COVID-19 control measures during the month of April in these two years was not significant enough to influence the air quality (Li et al. 2021a). However, it cannot be ignored that basic environmental protection measures have been continuously upgraded, such as prevention and regulations implemented by environmental protection departments, and the adjustment of the industrial structure and energy sources. Therefore, the effect of COVID-19 control measures on the AQI reduction should be smaller than that estimated by the simulations for each month.

The AQI can only reflect changes in the concentration of primary pollutants. From February to April 2020, the number of days in which PM_2.5 or PM₁₀ was the primary pollutant accounted for 70%, indicating that the AQI decrease was largely related to the decrease in particulate matter (PM) concentrations. In other words, restrictive measures implemented to combat the COVID-19 pandemic had a positive impact on reducing PM concentrations. The changes in the PM concentrations were mainly attributable to reduction in anthropogenic emissions as a result of COVID-19 control measures, although seasonal influences might also have contributed in part (Hu et al. 2015).

Analysis of factors influencing air quality during the Spring Festival

Interannual differences of air quality

Due to the large population movement and fireworks being set off during the Spring Festival period, short-term air pollution episodes are usually inevitable (Pang et al. 2021). The survey results showed that the level of anthropogenic activity during this period in 2020 dropped significantly; the average daily indices of immigrant population and emigrant population decreased by 0.629 and 0.982, respectively, compared with the values observed during the Spring Festival of 2019 (https://qianxi.baidu.com/); and the level of congestion on highways decreased significantly compared with the same period in previous years (https://jiaotong.baidu.com/).

However, during the Spring Festival in 2020, the average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃-8h were 122.43 µg/m³, 158.29 µg/m³, 36.57 µg/m³, 50.29 µg/m³, 2.37 mg/m³, and 69.14 µg/m³, which were significantly higher than those recorded in 2019 and 2021. From the point of view of numerical analysis, the phenomenon that the T-test was not significant with the large difference between the means exists, which was considered to be affected by outliers (Table S1).

The hourly changes in pollutant concentrations and some meteorological elements during the Spring Festival between 2019 and 2021 may provide answers to the above questions (Fig. 6). The results showed that two heavy pollution episodes occurred on Jan. 22–23 and Jan. 25–27, 2020, in Tangshan. As anthropogenic activities were restricted during the Spring Festival of 2020, it is excluded that population movements and firework set off had an impact. Therefore, the heavy pollution episodes might have been caused by relatively unfavorable meteorological conditions (Sulaymon et al. 2021). Compared with the Spring Festival period of 2019 and 2021, the high average humidity and low average wind speed in 2020 (Table S4) promoted the formation and growth of aerosols and the formation of haze (Le et al. 2020; Wang et al. 2021a).

Impact of zone transfers

The possible sources of pollutants causing heavy pollution episodes may be local anthropogenic emissions and transportation from surrounding cities. This section simulated the backward trajectory of air masses from 0:00 on Jan. 24 to 23:00 on Jan. 30, 2020, and identified possible transport routes for pollutants through cluster analysis. Through the total spatial variance analysis, all backward trajectories could be aggregated into six categories (Fig. 7). The trajectory-3 from Qinhuangdao-Tangshan and the trajectory-5 from Beijing-Tianjin corresponded to air masses with a short-distance transport and accounted for 25.60% and 26.79% of the total trajectories, respectively. These air masses move slowly, which is not conducive to the diffusion of pollutants, and they are most likely to carry the pollution to the receiving point. In contrast, the trajectory-1 from the northeastern region and the trajectory-6 from central Inner Mongolia corresponded to air masses with a long-distance transport and accounted for 16.67% and 5.36% of the total trajectories, respectively. Due to their high speed and low possibility of occurrence, this type of air masses may have little effect on pollutant concentration in Tangshan.

Based on the analysis of the transportation path, PSCF analysis was used to determine the main potential areas representing pollution sources (Fig. 8). The results showed that local emissions and short-distance transportation were the main sources of air pollution in Tangshan. The main potential sources of PM_2.5 and PM₁₀ were located in the northern parts of Beijing and Tianjin, and this is specifically related to the increase in particulate matter concentration caused by the frequent occurrence of sandstorms in Beijing in winter and spring (Meo et al. 2021). The main potential sources of SO₂ and CO were located in Tangshan and Qinhuangdao, which were the leading cities in terms of growth rates of the industrial output value in Hebei Province in 2020, and the main sources of the two above-mentioned pollutants were the industries producing steel, energy, glass, and building materials. As an important indicator to evaluate the contribution of mobile sources, the potential sources of NO₂ were mainly distributed between Beijing and Tangshan, suggesting that high-intensity anthropogenic activities may occur in these areas (Aneja et al. 2001). O₃ may be greatly affected by strong sources, mainly through long-distance transportation, and the potential source of this pollutant was located in northeastern China.

In the face of the COVID-19 pandemic, China has always taken a proactive attitude and adjusted its response level to public health emergencies according to the epidemic situation. From January 24th to June 30th, 2020, Tangshan experienced the adjustment of the response level from Level I to Level II, and then to Level III, which corresponding control measures affected air quality. The aim of our study was to identify the dominant factors affecting air quality in different time periods of COVID-19 and to quantify the magnitude of their impact as much as possible. The comparative analysis method and DID model were used to analyze the interannual differences in air quality after the implementation of COVID-19 control measures and the differences before and after COVID-19 in 2020, respectively. The MLR model successfully established a linear relationship between meteorological factors and AQI, which was used to quantitatively assess the contribution of COVID-19 control measures to air quality improvement during the Level I response. The Spring Festival was studied as a special period during the Level I response, and backward trajectory cluster analysis and PSCF analysis were used to analyze the possible sources of pollutants.

During the COVID-19 period in 2020, the average concentration of six conventional air pollutants decreased by 6.13–42.78% compared to 2017–2019, and the concentrations of PM_2.5, PM₁₀, and NO₂ were lower than those recorded in 2021. Compared to the 23 days before the COVID-19 control measures began to be implemented, the significant difference caused by the measures due to seasonal factors cannot be manifested. Spearman correlation analysis showed that meteorological factors had a negative effect on air quality during COVID-19 in 2020, while pollutant concentrations declined might be caused by the closure of industries not related to livelihood.

During the Level I response, air quality was significantly better in 2020 than in 2017–2019 and 2021. The COVID-19 control measures resulted in a significant difference in air quality at 95% confidence intervals for 23 days before and after the response was initiated. The MLR model simulation results showed that in February, March, and April, the observed average AQI in 2020 was 29.07%, 31.43%, and 20.04% lower than the simulated average, respectively, and the value in 2021 was 20.68%, 18.37% and 22.04% lower than the simulated average, respectively. In addition, the stricter the COVID-19 control measures, the more obvious the air quality improvement effect.

The Spring Festival period of 2020 was possibly affected by adverse meteorological conditions. Two heavy pollution episodes occurred, which resulted in the average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ being significantly higher than those recorded in 2019 and 2021. Therefore, meteorological conditions were the dominant factors affecting air quality during this period in 2020, which masked the emission reduction effect of COVID-19 control measures. The analysis of the zone transfer impact showed that local emissions and short-distance transport were the main sources of air pollution during the Spring Festival of 2020 in Tangshan.

In conclusion, strict COVID-19 control measures and meteorological factors have a more significant impact on the air quality over a time span of several months and several days, respectively. As for the continuous improvement of air quality, it is also necessary to adhere to the prevention and control of air pollution on the basis of paying attention to the impact of meteorology.

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Consent to Publish

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Author Contributions

All authors contributed to the study conception and design. Data collection and analysis were performed by Wenlu Wu, Jing Liu, Jinyun Long, and Jinglin Zhao. The first draft of the manuscript was written by Wenlu Wu. The manuscript was critically reviewed and commented by Chunyan Shan, and revised by Wenlu Wu. All authors read and approved the final manuscript.

Funding

This work was supported by the Ministry of Ecology and Environmental of the People’s Republic of China (DQGG0304-04).

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Analysis of factors influencing air quality in different periods during COVID- 19: A case study of Tangshan, China

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