The research area of this paper is the Liaoning region. To minimize the impact of meteorological conditions on changes in air pollutant concentrations, we compared and analyzed air pollutant data from 2017 to 2019 with data from 2020 to 2022, of which the period from 2020 to 2022 due to the implementation of governmental preventive and control measures to form the typical limit of emission reduction scenarios, for the study of emission reduction under the air quality and its impact on the factors to establish the “natural laboratory.” This extreme emission reduction and environmental reduction together achieve “double emission reduction.” Hence, this paper calculates the average concentration of air pollutants in the two time periods and makes comparisons.

The adopted data were all obtained from the air quality monitoring information of 77 state-controlled stations of the Liaoning Provincial Department of Ecology and Environment. Figure 1 displays the distribution locations of state-controlled stations in Liaoning Province. The monitoring period was from 2017 to 2022. The types of atmospheric pollutants studied included particulate matter (PM_2.5, PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and Ozone (O₃).

EMI is a dimensionless indicator that measures the contribution of meteorological conditions in the change of PM_2.5 concentration. The calculation method of Evaluation on the meteorological condition index of PM_2.5 pollution (EMI) used in this paper follows the meteorological industry standard of the People’s Republic of China (QX/T479-2019), which indicates the impact of meteorological elements on the change of air pollutant concentration through diffusion, transport, dry and wet deposition, and chemical transformation in a specific period without considering the evolution of emission sources (China Meteorological Administration, 2019). EMI is an indicator of the change in fine particulate matter (PM_2.5) concentration due to changes in meteorological conditions. The EMI is calculated using a tracer approach, defined here as the dimensionless ratio of the average concentration of the tracer within the air column from the ground to 1,500 m altitude to a reference concentration. According to the PM_2.5 concentration continuity equation, the concentration at time t can be obtained, which is related to the meteorological conditions of emission deposition, transport and diffusion, and the difference of EMI is the change of concentration due to the meteorological conditions under the assumption of constant PM_2.5 emission rate in the same period in different years. The Eqs (1), (2) is as follows: E M I = C C 0 (1) E M I t = E M I t 0 + ∫ t 0 t i E m i s 0 + i d e p + i T r a n + i D i f f · d t (2)

Where C is the average concentration of tracer in the gas column (ground to 1,500 m altitude); C₀ is the upper limit (35 μg/m³) for the optimal level of PM_2.5 concentration by the Ambient Air Quality Index Technical Regulation (HJ633-2012); The four sub-indices of i E m i s 0 , i d e p , i T r a n and i D i f f respectively represent the contribution rate of emission and settlement of surface exchange layer, atmospheric advection transport and atmospheric turbulence diffusion to the change of tracer concentration, and the unit is s⁻¹. A positive value indicates that pollutants have an increasing effect, while a negative value indicates that pollutants have a clearing effect.

When comparing different periods with each other, the difference in EMI is the rate of change of the average aerosol concentration in the boundary layer due to meteorological conditions under constant emission conditions. Let the emissions of “period 1” and “period 0” be S₁ and S₀, respectively, the measured concentrations be O₁ and O₀, and the meteorological indices are EMI₁ and EMI₀, respectively. It is assumed that the contribution of meteorological factors to actual concentrations is linear and that the contribution of emissions to actual concentrations is also linear. That meteorological and emission factors are variably separable. Then, there is a fundamental relationship (3): O 1 O 0 = S 1 S 0 · E M I 1 E M I 0 (3)

Then the rate of change of meteorological conditions (4) is: R A T E W = E M I 1 − E M I 0 E M I 0 × 100 % (4)

The rate of change of pollutant concentration (5) is: R A T E 0 = O 1 − O 0 O 0 × 100 % (5)

The rate of change of emissions is (6): R A T E S = S 1 − S 0 S 0 (6)

Finally (7): R A T E S = O 1 O 0 E M I 1 E M I 0 − 1.0 × 100 % (7)

This paper calculates Liaoning’s average EMI indices for two periods: 2017 to 2019 and 2020 to 2022. The differences in EMI indices between the two periods were compared to obtain the changes in meteorological elements and the contribution of emission reduction measures to the impact on air quality from 2020 to 2022.

Kriging is a regression algorithm for spatial modeling and prediction (interpolation) of stochastic processes based on the covariance function, also known as spatial local interpolation, one of Geostatistics’ main elements (WU et al., 2006). Widely used in spatial statistical analysis, its basic theory is based on the semi-variational function for analysis, optimizing the unbiased optimal estimation of variables in a given space. ArcGIS 8 and above integrates geostatistics as a separate analytic extension module into the framework architecture of the ArcGIS software. In this paper, a comparative study of the concentration of air pollutants in various topographic areas of Liaoning Province uses the Kriging interpolation method in the GA module of ArcGIS software.

As the capital of Liaoning Province and a city of heavy industry, Shenyang is more affected by the emission reduction measures. Therefore, this article explores the impact of emission reduction measures on the city’s AQI. To analyze the AQI more comprehensively and objectively, the annual average data of the AQI from 2017 to 2019 and from 2020 to 2022 are selected here for comparison. The results show that by comparing the AQI of Shenyang City from 2017 to 2019 with that of 2020–2022 (Figure 2), it can be visualized that the AQI fluctuated wildly from 2017 to 2019. From 2020 to 2022, due to the implementation of the “double emission reduction” measures, the change of AQI is more moderate, and the AQI categories are primarily concentrated in excellent and good. This shows that during the “double emission reduction” period, the measures of reducing travel by residents and stopping production in factories have, to a certain extent, increased air quality and made it more stable. On the other hand, due to the increased use of coal and the increase of static weather in winter and spring seasons, and the high wind and sufficient precipitation in summer and fall seasons, the air quality of Shenyang City in different seasons during the two periods of time shows a trend of more severe pollution conditions in winter and spring, and better in summer and fall. In 2017–2019, the frequency of more than mild pollution in winter and spring was 4.84%, while the frequency of weather with more than mild pollution in summer and fall was 1.37%. From 2020 to 2022, the frequency of weather with more than mild pollution is 2.7% in winter and spring and 1.8% in summer and fall. As a result of the implementation of the “double emission reduction” measures, air quality improvement has been accelerated, and air pollution has changed from heavy pollution to light pollution, which will lead to an increase in the frequency of weather with more than light pollution during the period from 2020 to 2022.

A general overview of the atmospheric quality situation through the AQI can visualize the overall changes in air quality in Shenyang City over two periods. In this paper, monitoring data from a total of 77 state-controlled stations in Liaoning Province were selected. The changes in the annual average concentrations of each pollutant in Liaoning from 2017 to 2019 and from 2020 to 2022 were calculated and compared (Figure 3). The average concentrations of air pollutants PM_2.5, PM₁₀, SO₂, NO₂ and O₃ in the period from 2020 to 2022 are 35.59 μg/m³, 58.86 μg/m³, 14.39 μg/m³, 26.13 μg/m³ and 88.85 μg/m³, respectively. The concentration of CO is 0.85 mg/m³. Comparing pollutant concentrations for the period 2020 to 2022 with those for the period 2017 to 2019, the PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ concentrations in 2020–2022 are reduced by about 13.8%, 15.5%, 36.4%, 10.1%, 9.7%, and 4.3%, respectively, compared with the average pollutant concentrations in 2017–2019. It can be seen that the implementation of the “double emission reduction” measures has significantly improved the air quality in Liaoning, which is consistent with the findings of Zhang Chunying and Yu Feng (YU et al., 2021; ZHANG, 2022). The concentrations of particulate pollutants PM_2.5, PM₁₀, and SO₂ were significantly reduced. On the one hand, local particulate matter is generated from residues emitted by combustion during industrial production and automobile exhaust emissions. On the other hand, due to coal combustion for heating in the winter months, many tiny particles are generated, which are the vital source of the particles that constitute PM_2.5 (Xing-liang et al., 2021). The concentration of particulate pollutants declined significantly after implementing of the “double emission reduction” measures in the local area due to factors such as the shutdown of factories, the warming of the weather, the intensification of atmospheric diffusion, and the reduction of coal heating. SO₂ and NO₂ are the primary pollutants originating from coal combustion and industry. SO₂ is primarily generated from the exhaust gases of chemical and sulfuric acid plants, and NO_x is generated from automobile exhaust and nitric acid production plants (SU et al., 2012; Li et al., 2019; WANG et al., 2021). NO₂, as a representative pollutant of traditional photochemical smog, has a critical conversion function in tropospheric atmospheric chemistry, and environmental problems such as fog, haze, and acid rain all originate from NO₂ (Han-li and WANG, 2005; Hong-qun and CUI, 2016; Zhou et al., 2016). During the “double emission reduction” period, due to the reduction of coal-fired operations in heavy industries and vehicle trips, the emission intensity was weakened, which to a certain extent resulted in the reduction of SO₂ and NO₂ concentrations, especially SO₂, which had a clear downward trend after a series of emission reduction measures. O₃ for these types of pollutants in the concentration of the slightest reduction of pollutants, which indicates that O₃ pollution is still the current pollution control difficulty, the concentration of which is affected by various types of meteorological elements conditions (Xian-yu et al., 2020; ZHOU et al., 2023), the subsequent local authorities in the regional pollution prevention and control need to focus on O₃ pollution prevention and control.

Under relatively stable air pollution emission sources, the variation of EMI index and PM_2.5 concentration is mainly determined by meteorological conditions. This paper uses the EMI index in Liaoning explore the role of meteorological conditions in influencing the period from 2020 to 2022. The average EMI index for Liaoning province during the period from 2020 to 2022 is 1.7, which is about 1.7% lower than the average EMI index of 1.73 for the section during the period from 2017 to 2019 (Figure 4), and the atmospheric dispersion conditions are relatively good but flat. This shows that assuming that the air pollution emission sources remain unchanged, the average PM_2.5 concentration in Liaoning during 2020–2022 should theoretically decrease by about 1.7% compared with the average PM_2.5 concentration during 2017–2019, but in fact, the average PM_2.5 concentration in Liaoning during 2020–2022 is only 35.59 μg/m³, a decrease of about 13.8% compared with the average PM_2.5 concentration during 2017–2019, and the difference of 12.1% is the result of the “double emission reduction” of the government’s prevention and control measures and the corresponding air pollution emission reduction measures. Through the estimation, we get that the environmental reduction rate of air pollution is 3.715% during the period from 2017 to 2019, so we can speculate that the ecological reduction rate of air pollution is about 3.715% during the period from 2020 to 2022, and finally we get that the emission reduction rate brought by the governmental preventive and control measures is about 8.385%, which indicates that the air quality of Liaoning region will be significantly improved in 2020–2022 due to the implementation of the “double emission reduction” measures.

Since both meteorological conditions and emission sources influence the concentration of air pollutants, the values of meteorological elements and emission values are different for different periods (WU et al., 2016). This study, selected Shenyang city as a representative city of the Liaoning region to study the daily change pattern of pollutant concentration in Shenyang between 2017 and 2019 and between 2020 and 2022. As can be seen from the annual average hourly changes in Shenyang City’s concentrations of various pollutants from 2017 to 2019 versus 2020 to 2022 (Figure 5). During the period from 2020 to 2022, PM_2.5 and PM₁₀ concentrations show a double-peak and single-valley daily trend, with the peak of the day occurring around 9:00–10:00 a.m., and the valley of the day emerging around 16:00 as the top of the boundary layer lifts, which is mainly because of the large number of nitrogen oxides (NO_x) emitted by motor vehicles in the morning rush hour to the atmosphere as well as the road dust, which makes particulate pollutants produced in large quantities. Whereas the period from 2017 to 2019 was mainly characterized by double peaks and double valleys peaking at around 9:00 to 10:00 a.m., followed by an increase in the intensity of afternoon sunshine and a decrease in relative humidity, the particulate pollutant concentrations reached a low value at around 16:00 to 17:00. At this point, due to the increase in traffic flow during the evening rush hour, coupled with a decrease in the height of the mixing layer, the pollutant accumulation intensified, and at the peak around 23:00 to 24:00 p.m. At night, due to the decrease in human activities, the pollutant concentration decreases slowly and changes slowly between 0:00 and 7:00 a.m. From 2020 to 2022, morning and evening work travel peaks are reduced. Although the lower height of the boundary layer at night is not conducive to the dispersion of air pollutants, fewer emissions from sources still result in lower nighttime concentrations of air pollutants. It can be seen that governmental prevention and control measures, as well as environmental protection and emission reduction measures, can change the daily change patterns of particulate pollutants, which is consistent with the findings of Cui Zhihao and Wang Aiping (CUI et al., 2021; WANG et al., 2021).

The daily variation of CO and SO₂ showed a double-valley, single-peak type and reached the pollution peak around 8:00 a.m. and the pollution valley around 16:00. Due to the implementation of the “double emission reduction” measures, it can be seen that the daily trend of SO₂ concentration in the period from 2020 to 2022 is smoother. The average concentration is lower than that from 2017 to 2019. The sources of SO₂ and CO are mainly from chemical and sulfuric acid industries, and the presence of low-level inversions during the pre-sunrise hours leads to the peak of the day at around 8:00 a.m. (XUE et al., 2006).

The daily variation of NO₂ mainly shows a single valley with double peaks, especially around 8:00 a.m. and 21:00. NO_x production is primarily associated with morning and evening traffic peaks and variations in boundary layer heights. Since NO₂ is a precursor of O₃ (Rui-na et al., 2022), the NO₂ concentration will continue to deplete and decrease with the enhancement of solar radiation, reaching a trough around 15:00 to 16:00. A positive correlation can also be found between NO₂ and PM_2.5 (YIN et al., 2021). Comparing 2020–2022 with 2017–2019, it is evident that the concentration of NO₂ is significantly lower than in the previous 4 years, which shows that the effect of “double emission reduction” measures on NO_x is very obvious.

The daily variation of O₃ mainly showed a monomodal pattern, high during the day and low at night, and peaked around 16:00 O₃ production is closely related to photochemical reactions. In addition to the effects of meteorological conditions, ozone’s main precursors, such as NO_X and VOCs, vary with human activities throughout the day. O₃ concentrations generally reach their lowest values of the day just before sunrise, which is the accumulation phase of O₃. Starting from 8:00 a.m., with the enhancement of solar radiation and temperature increase, the photochemical reaction that generates O₃ is gradually strong, peaks at about 16:00 and then decreases with the reduction of solar radiation, and at night, O₃ is consumed by atmospheric photochemical reaction and accumulation near the surface, so that the concentration is at a low level (SUN et al., 2021; Wei-jie et al., 2022), with minor small fluctuations and a tendency to flatten out. Comparing 2020 to 2022 with 2017–2019, O₃ concentrations during the period from 9:00 a.m. to 12:00 p.m. are essentially similar to those in 2017–2019, but at around 20:00, the concentrations during 2020–2022 are slightly higher than those in 2017–2019, but in general, O₃ concentrations during 2020–2022 are higher than the O₃ concentrations during the 2017–2019 period.

To visualize the spatial changes in the concentrations of each air pollutant in Liaoning, this paper examines the concentrations in different spatial locations using statistical methods. It uses the Kriging interpolation method to compare the distribution of pollutant concentrations during the period of emission reduction in Liaoning Province.