Impact of COVID-19 Restrictions Liberalization on Air Quality: A Case Study of Chongqing, Southwest China

Impact of COVID-19 Restrictions Liberalization on Air Quality: A Case Study of Chongqing, Southwest China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of COVID-19 Restrictions Liberalization on Air Quality: A Case Study of Chongqing, Southwest China Haozheng Wang, Liuyi Zhang, Yuanjun Chen, Guangming Shi, Chentao Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4584877/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Oct, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 9 You are reading this latest preprint version Abstract To mitigate the societal impact of the COVID-19 pandemic, China implemented long-term restrictive measures. The sudden liberalization at the end of 2022 disrupted residents’ daily routines, making it scientifically intriguing to explore its effect on air quality. Taking Chongqing City in Southwest China as an example, we examined the impact of restriction liberalization on air quality, identified potential sources of pollutants, simulated the effects of abrupt anthropogenic control relaxation using a Random Forest Model, and applied an optimized model to predict the post-liberalization pollutant concentrations. The results showed increases in PM 2.5 (72.3%), PM 10 (67.7%), and NO 2 (21.9%) concentrations while O 3 concentration decreased by 20.5%. Although potential pollution source areas contracted, pollution levels intensified with northeastern Sichuan, interior Chongqing, and northern Guizhou being major contributors to pollutant emissions. Anthropogenic emissions accounted for 26.7% ~ 33% changes in PM 2.5 、PM 10 concentrations while meteorological conditions contributed to 40.2% ~ 43.3% variations observed during the period. The optimized model demonstrated correlation between predicted and observed values with R 2 ranging from 0.70 to 0.89, enabling accurate prediction of post-liberalization pollutant concentrations. This study can enhance our understanding regarding the impact of sudden social lockdown relaxation events on air quality while providing support for urban air pollution prevention. Air quality Chongqing city COVID-19 restrictions liberalization Random forest Meteorological normalization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction With the rapid development of industrialization and urbanization, air quality has become increasingly severe worldwide, with escalating levels of air pollutants such as ozone (O 3 ), nitrogen dioxide (NO 2 ), and particulate matter (PM 2.5 and PM 10 ). Currently, there is an increasing emphasis on establishing a healthy atmospheric environment (He et al., 2017 ). In 2013, the Chinese government implemented the “Air Pollution Prevention and Control Action Plan ( 2013 )” (Chinese State Council, 2013), resulting in significantly reduction of air pollution and remarkable achievement. However, due to complex meteorological conditions, air pollution events still occur, particularly in southwestern China where the Chengdu-Chongqing area has emerged as one of the four major hotspots for such incidents in China (Zhao et al., 2018 ). Nonetheless, it remains imperative to improve China's air quality by intensifying the management of particulate matter pollution and promptly curbing the increasing trend of ozone pollution (Lv et al., 2023 ). Over the COVID-19 pandemic from 2020 to 2022 (Zhang et al., 2020 ), the Chinese government implemented stringent measures to combat COVID-19, including travel restrictions, production limitations in factories, and social quarantine measures (Zhang et al., 2021 , Chen et al., 2020 ). Consequently, these measures have significantly reduced air pollutant concentrations by curbing anthropogenic emissions. The interplay between meteorological conditions and anthropogenic emissions largely determines atmospheric pollution processes (von Schneidemesser et al., 2019 ), make the emergence of COVID-19 an exceptional context for comprehending and quantifying the how anthropogenic emissions and meteorological conditions influence air quality (Li et al., 2022 ). In recent years, researchers have conducted analyses on the impact of restrictions during COVID-19 on air quality, while discussing the contributions of anthropogenic emissions and meteorological factors. These studies revealed varying degrees of decrease in PM 2.5 , PM 10 , NO 2 concentrations among different cities globally during periods of restrictive policies. For instance, observed PM 2.5 and PM 10 concentrations decreased by approximately 60.81% and 43.12%, respectively, compared to predicted values during the restriction period in Shanghai; whereas considering only anthropogenic factors without accounting for meteorological influences resulted in decreases of around 50.20% for PM 2.5 and 19.06% for PM 10 (Zhang et al., 2023b ). In Beijing, observed PM 2.5 , PM 10 , and NO 2 concentrations exhibited reductions of 39.4%, 50.1%, and 43.1%, respectively, during the restriction period. However, adverse meteorological conditions resulted in an increase in NO 2 concentrations by 15.2–32.4% (Hu et al., 2021 ). In Campania, Italy, the restriction period positively influenced air quality with a significant decrease in NO 2 levels ranging from 42–48% (Cardito et al., 2023 ). Nevertheless, certain regions and cities experienced an upward trend in O 3 concentrations despite overall improvements in air quality; examples include the eastern part of China (Li et al., 2020 ) and several cities in southern Europe (Donzelli et al., 2020 ). This phenomenon can be attributed primarily to reduced primary emissions such as NOx due to restrictions and a consequent decline in the titration effect of NO on O 3 concentration (Zhang et al., 2018 ). Varying trends of pollutant concentrations were observed across urban areas during the COVID-19 pandemic owing to the diversity in geography, meteorology, and emission sources, which necessitates accurate quantification of the impacts results from restriction measures along with cross-regional collaborative governance considerations for addressing air pollution issues. During the initial phase of the outbreak, numerous reports have extensively documented the impact of restrictive measures on air quality, leading to unequivocal conclusions. However, there is a noticeable absence of studies investigating the changes in air quality subsequent to the relaxation of these measures. In this study, we conducted a comprehensive analysis based on the monitoring data from 36 air quality monitoring stations and 33 meteorological stations in Chongqing. The study period was divided into three distinct phases: the lockdown period during the implementation of restrictive policies(November 2022-December 2022), the buffer period following their liberalization༈December 2022-January 2023༉, and the free period after complete liberalization༈January 1st, 2023 - January 31st, 2023༉. The spatial and temporal variations of air quality in Chongqing during these different periods were comparatively analyzed. The trajectories of air masses and the potential sources of various pollutants during different periods were analyzed. Additionally, the effects of meteorological conditions and anthropogenic activities on air pollutant concentrations were quantified. A random forest model with optimized parameters for ntree and mtry values was employed to predict the post-liberalization air pollutant concentrations accurately. The parimary objective of this study is to comprehend shifts in air quality within a typical mountain city following restriction liberalization while also aiming to develop an appropriate model capable of forecasting changes in pollutant concentration as a response to alterations in restrictive policies. The results of this study can provide theoretical support for similar post-lockdown liberalization. 2. Material and Methods 2.1 Research area Chongqing, located in southwest China, spans longitude 105°11′~ 110°11′E and latitude 28°10′~ 32°13′N, encompassing an area of 82,400 km 2 . It serves as a pivotal nexus connecting the Yangtze River Economic Belt, the Belt and Road Initiative Area, and the Three Gorges Reservoir Area (Yang et al., 2020 ), exhibiting typical mountainous characteristics. In recent years, Chongqing has witnessed a resident population of approximately 32.1243 million individuals along with a vehicle population of around 8.3709 million units, consequently leading to a total energy consumption of about 80.4631 million tons of standard coal. These factors have exerted substantial pressure on the regional atmospheric environment. Furthermore, the terrain in this area is complex, most of which is surrounded by mountains. The closed terrain leads to high humidity, low wind speed, and stable atmospheric boundary layer, which facilitate the formation of temperature inversions and imped atmospheric pollutant dispersion (Chen and Xie, 2017 ). Considering the topography and social-economic factors, we divided the research area into four regions: Urban Function Area (UFA), New Area for Urban Development (NAU), Northeast Chongqing Ecological Conservation Development Area (NCA), and Southeast Chongqing Ecological Protection Area (SCA). Notably, UFA and NAU exhibit higher population and regional GDPs compared to the other two regions. Furthermore, NAU demonstrates the highest total industrial energy consumption while SCA exhibits the lowest levels of population, economic activity, and industrial energy consumption. The distribution of monitoring sites and different functional areas in Chongqing is illustrated in Fig. 1 . Figure 1. (a) Geographical location of Chongqing in China; (b) Distribution of meteorological and air quality monitoring sites in Chongqing (see Table S2 for details); (c) Proportion of basic situation of different functional areas in Chongqing. 2.2 Definition of research period In late October 2022, Chongqing was struck by a mutant strain, leading to the implementation of a series of lockdown measures by municipal government. By December 2022, these restrictions were unexpectedly lifted. The research period for this study spans from November 1st, 2022, to January 31st, 2023. Analysis of the daily air quality index (AQI) fluctuations during this period (refer to Fig. S1 ) reveals significant variations in AQI levels in Chongqing, indicating alternating periods of lockdown and normalized restrictions. However, following the relaxation of restrictive policies, there was an upward trend observed in AQI changes which surpassed both the restrictive policies period and corresponding periods in 2021 and 2022 (see Appendix for detailed descriptions). Therefore, it is imperative to conduct a specific analysis on factors influencing air quality during this period. In order to distinguish the impact of meteorology and human activities on air quality during different periods, the restrictive policies implemented before and after the liberalization is categorized into three phases: pre-liberalization lockdown period (P1: November 2022-December 2022), post-liberalization transitional period (P2: December 2022-January 2023), and post-liberalization unrestricted period (P3: January 1st, 2023 - January 31st, 2023). P1 represents a scenario where industrial enterprises curtail production while people’s mobility is restricted during the lockdown phase. P2 represents a situation where individuals predominantly remain indoors or venture out cautiously due to widespread infection concerns. Lastly, P3 denotes a stage when most individuals have recovered and societal operations have fully returned to normalcy. 2.3 Research data The air pollutant concentrations were obtained from 36 state and provincial control air quality monitoring sites in Chongqing. These data were sourced from the historical records of the online air quality monitoring and analysis platform ( www.aqistudy.cn/historydata/ ), including AQI, PM 2.5 , PM 10 , NO 2 , and O 3 . The daily AQI, which is a dimensionless relative value used to indicate the level of air pollution on a given day (Zhang et al., 2023b ). For PM 2.5 , PM 10 , and NO 2 concentrations, average values from 24-hour monitoring data were employed; while for O 3 levels, daily 8-hour moving averages were used. Meteorological parameters during the same period were collected from hourly observations at 33 meteorological monitoring stations in Chongqing. Conventional near-surface meteorological parameters including atmospheric pressure, maximum wind speed and wind direction, average wind speed, air temperature, maximum temperature, minimum temperature, relative humidity, and precipitation were considered as well. Meteorological data used in backward trajectory modeling was acquired from GDAS dataset provided by National Centers for Environmental Prediction (NCEP) via ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1/ . 2.4 Research Methods 2.4.1 Potential Source Contribution Function The HYSPLIT trajectory model, jointly developed by the National Oceanic and Atmospheric Center (NOAA) and the Australian Bureau of Meteorology (ABM), is widely used as a professional meteorological trajectory model system for simulating the transport and dispersion of diverse atmospheric pollutants (Shan et al., 2023 , Bilal et al., 2022 ). In this study, Chongqing (106.5°E, 29.6°N) was selected as the receptor point for backward trajectory analysis. The Meteoinfo software (Wang et al., 2009 , Wang, 2014 ) along with global assimilated meteorological data were utilized to simulate the backward trajectories at an altitude of 500 m and with a tracking time of 24 hours, covering the period from November 1st, 2022, to Januart 31st, 2023. The Potential Source Contribution Function (PSCF) is a conditional probability statistic that evaluates the potential contribution of pollutants at a study site. It utilizes backward trajectory calculations of air masses to identify the likely emission source locations (Zhou et al., 2017 ). The PSCF method divides the study area into i×j grids and establishes a concentration threshold for pollutants. If the pollutant concentration corresponding to the trajectory exceeds this threshold, then the trajectory is classified as polluted. In this study, trajectories with PM 2.5 concentrations exceeding 50 µg/m³, PM 10 concentrations exceeding 75 µg/m³, and NO 2 and O 3 concentrations exceeding the monthly average are considered polluted trajectories. The PSCF is calculated using Eq. (1): $${PSCF}_{ij}={m}_{ij}/{n}_{ij} \left(1\right)$$ where m ij is the number of polluted trajectories in grid (i, j) and n ij is the number of total trajectories in grid (i, j). The grid spacing was set as 0.2°×0.2° in this study. Due to the conditional probability statistics used in PSCF analyzing, there may be significant uncertainty in the calculation results. To mitigate this, a weighting factor (W ij ) is usually employed to reduce the uncertainty (Xiao et al., 2023 ). The weighted PSCF was calculated as WPSCF = W ij ×PSCF ij , and W ij is defined as Eq. (2). $${W}_{ij}=\left\{ \begin{array}{c}1.00 4{n}_{ave}＜{n}_{ij}\\ 0.70{ n}_{ave}＜{n}_{ij}\le 4{n}_{ave}\\ 0.42 0.5{n}_{ave}＜{n}_{ij}\le {n}_{ave}\\ 0.17{ n}_{ij}\le 0.5{n}_{ave}\end{array}\right. \left(2\right)$$ where n ave is the average number of endpoints per grid track. 2.4.2 Mantel Test Variations in pollutant concentrations are not solely determined by anthropogenic emissions; a multitude of parameters influence these changes, making the process considerably more complex. Usually, we need to consider the effects of meteorological conditions when predicting changes in pollutant concentrations. Therefore, it is important to determine the potential effects of important meteorological parameters on these concentrations and optimize the prediction model accordingly. The Mantel Test offers several advantages such as nonparametric nature, asymmetric consideration, incorporation of spatial structure, and substitution when evaluating the relationship between meteorological conditions and pollutant concentrations. In this study, the Mantel Test was used to analyze the effects of meteorological parameters on pollutant concentrations. The analytical process of the Mantel Test primarily involves calculating the distance matrix using the Euclidean distance formula (Breiding et al., 2021 ) and obtaining compressed distance columns for correlation calculation, enabling a significance test to determine whether there exists a significant correlation between the two matrices (Crabot et al., 2019 ). In this study, we employ Pearson correlation coefficient (Asmel et al., 2022 ) as a Model description. 2.4.3 Model Introduction Random forest (RF) is an ensemble model composing numerous independent decision trees and using the bagging algorithm (Bootstrap aggregation). This model has been widely used in air pollutant prediction due to its advantages of fast training speed and prevention of overfitting (Peng et al., 2022 , Wang et al., 2020 ). In this study, we employ the ten-fold cross-validation method to determine the two most important parameters, ntree and mrty, in the random forest model. By optimizing these parameters, we established two scenarios. Firstly, the RF model is used for predicting air pollutant concentrations after the liberalization. Secondly, meteorological normalization is used to eliminate the influence of meteorological conditions on air pollutant concentrations. 1. Prediction experiment Meteorological and temporal variables were used as input parameters in the model for this study. Meteorological variables encompassed atmospheric pressure (AP), maximum wind speed and direction (Max_WS/WD), wind speed (WS), temperature (T), maximum temperature (Max_T), minimum temperature (Min_T), relative humidity (RH), and rainfall (Rainfall). Temporal variables included Unix timestamp (date_unix), Julian date (date_julian), working date (weekday), and hour value (hour). The air pollutant concentration served as the dependent variable, while the meteorological parameters and time predictors were used as independent predictors. The RF modeling was conducted on a dataset spanning from January 1st, 2021 to October 31st, 2022. The entire dataset was randomly divided into a training set for model constructing and a test set for performance validating, where the training set accounting for 70% of the data and the remaining portion used for the testing purposes. The RF model was applied to predict air pollutant concentrations from November 1st, 2022 to January 31st, 2023. 2. Meteorological Normalization Experiment Changes in atmospheric pollutant concentrations are determined by both meteorological conditions and emissions. To quantify the effects of emissions and meteorological conditions on pollutants, meteorological normalization has been widely used as a technique to decouple the effects of meteorology on air pollutants in a time series. In this study, the ‘rmweather’ R package was used to implement the random forest model for meteorological normalization of pollutants, aiming to eliminate the effect of meteorological factors (Grange and Carslaw, 2019 , Grange et al., 2018 , Lv et al., 2023 ). RF modeling was conducted using the 'Ranger' package in the R language (Wright and Ziegler, 2017 ). To achieve weather normalization, a subset of weather variables for a specific day (excluding the time variable) was randomly selected from historical data and included in the model dataset. The RF model was then applied to forecast the newly generated dataset (Zhang et al., 2023a ). Specifically, the day-specific weather variables for each day in the input new dataset were generated by randomly selecting from observed weather data during a two-week period before and after that particular day. This process was repeated 1000 times, and the average of the these predictions was calculated to obtain final weather-normalized results using Equations (3) and (4), which quantify the contributions of both weather conditions and emissions to changes in pollutant concentrations. The meteorological normalization process is shown in Fig. S2. where E and M represent the emission and meteorological contributions to changes in pollutant concentrations, respectively. \({\text{C}}_{\text{i}}^{\text{d}}\) and \({\text{C}}_{\text{i}}^{\text{o}\text{b}}\) represent the meteorologically normalized pollutant concentration and the actual observed concentration in month i, respectively. \({\text{C}}_{\text{i}+1}^{\text{d}}\) and \({\text{C}}_{\text{i}+1}^{\text{o}\text{b}}\) represent the meteorologically normalized pollutant concentration and the actual observed concentration in month i+1, respectively. 2.4.4 Model Performance and Evaluation In this study, three metrics were used to evaluate the performance of the model prediction on the test dataset, including R 2 , mean absolute error (MAE) and root mean square error (RMSE), which were calculated by Equations (5–7), respectively. $${\text{R}}^{2}=1-\frac{\sum _{\text{i}=1}^{\text{n}}{({\text{y}}_{i}-{\widehat{\text{y}}}_{i})}^{2}}{{\sum }_{\text{i}=1}^{\text{n}}({{\text{y}}_{i}-\stackrel{-}{\text{y}})}^{2}} \left(5\right)$$ $$\text{M}\text{A}\text{E}=\frac{1}{\text{n}} {\sum }_{\text{i}=1}^{\text{n}}\left|({\widehat{\text{y}}}_{\text{i}}-{\text{y}}_{\text{i}})\right| \left(6\right)$$ $$\text{R}\text{M}\text{S}\text{E}=\sqrt{\frac{1}{n}{\sum }_{i=1}^{n}{({\widehat{\text{y}}}_{\text{i}}-{\text{y}}_{\text{i}})}^{2}} \left(7\right)$$ where n is the number of samples in the test dataset, y is the measured value, \(\widehat{\text{y}}\) is the predicted value, and \(\stackrel{-}{y}\) is the mean value. 3. Results and discussion 3.1 Spatial and temporal variations of air pollutants The average hourly concentrations of PM 2.5 , PM 10 , NO 2 , and O 3 at 36 air pollutant monitoring stations were calculated during the study period to obtain the daily variation trend map of pollutant concentration ( Fig. 2 ). The changes in pollutant concentration across different periods within four functional areas are shown in Table 1. Notably, there are evident temporal and spatial similarities in the characteristics of PM 2.5 , PM 10 , and NO 2 . Figure 2. Time changes in PM 2.5 , PM 10, NO 2 , and O 3 concentrations in Chongqing during the study period (shaded areas are P1-P3 for the study period);The red line represents the average pollutant. Table 1 Spatial variations in PM 2.5 , PM 10 , NO 2 , and O 3 concentrations in Chongqing during the study period(unit: µg/m 3 ) Period Area PM 2.5 PM 10 NO 2 O 3 P1 Urban Function Area(UFA) New Area for Urban Development(NAU) Northeast Chongqing Ecological Conservation Development Area (NCA) Southeast Chongqing Ecological Protection Area(SCA) 44.8 48.9 33.3 30.0 65.0 65.1 43.2 40.5 33.9 25.7 23.7 19.2 61.4 63.5 47.4 65.4 P2 UFA NAU NCA SCA 58.1 62.6 63.0 55.0 81.7 83.0 78.1 68.5 41.7 33.9 31.9 25.1 29.2 33.4 39.9 52.1 P3 UFA NAU NCA SCA 72.9 80.3 79.9 62.5 102.3 108.3 99.4 72.7 39.8 32.4 33.2 22.2 46.9 52.6 38.5 55.1 During the P1 period, a series of lockdown measures resulted in average concentrations of PM 2.5, PM 10 , NO 2 , and O 3 at 44.1 µg/m³, 61.0 µg/m³, 29.2 µg/m³, and 60.5 µg/m³, respectively. While Chongqing generally exhibited low levels of PM 2.5 , PM 10 , and NO 2 concentrations. The UFA and NAU regions showed significantly higher levels compared to the NCA and SCA regions due to their higher population density, industrial concentration, and anthropogenic activities. Specifically, concentrations of PM 2.5 were elevated by 11.5–18.9 µg/m³ while those of PM 10 were increased by 11.8–24.6 µg/m³ along with an increase in NO 2 levels ranging from 2.0-14.7 µg/m³. During the P2 period, the average concentrations of PM 2.5, PM 10 , NO 2 , and O 3 were 60.5 µg/m³, 81.3 µg/m³, 37 µg/m³, and 33.1 µg/m³, respectively. compared to the P1 period, there was an increase in concentrations of PM 2.5 by 37.2%, PM 10 by 33.3%, and NO 2 by 26.7%. This may be attributed to a series of anthropogenic activities such as increased motor vehicle usage and industrial production following the liberalization of restrictions. However, O 3 concentrations decreased significantly by 45.3% during this period. Surprisingly, the O 3 concentration of SCA is significantly higher than that of other areas in Chongqing. The elevated O 3 levels observed in SCA may be explained by analyzing its precursors using the EMKA curve (Jin and Holloway, 2015). It is possible that lower emission of NOx weaken the “titration reaction” between O 3 and NOx resulting higher O 3 concentrations being retained rather than removed through chemical reactions facilitated by high levels of NOx. During the P3 period, the average concentrations of PM 2.5 , PM 10 , NO 2 , and O 3 were 76.0 µg/m³, 102.3 µg/m³, 35.6 µg/m³, and 48.1 µg/m³, respectively. The relaxation of restrictive policies during the Spring Festival coincided with increased transportation and human activities which led to rapid surge in emission sources. Compared to the P1 period, there was a significant increase in concentrations of PM 2.5 (72.3%), PM 10 (67.7%), and NO 2 (21.9%). Unlike the relatively low and high levels of NOx during the P1 and P2 period, NOx concentration remained relatively stable during the P3 period, while O 3 decreased compared to the P2 period across all districts of Chongqing. At the same time, varying degrees of increases were observed for PM 2.5 , PM 10 , and NO 2 concentrations. Compared to the SCA, notably higher pollutant concentrations including PM 2.5 (+ 17.4 µg/m³), PM 10 (+ 26.7 µg/m³), and NO 2 (+ 11.0 µg/m³) were found in NCA possibly attributed to higher population and more frequent human activities over recent years. This indicated that policy relaxation resulted in diverse variations characteristics of spatial-temporal distribution of pollutants. Overall findings underscoring detrimental effects on air quality due to liberalization measures, emphasized significant impact exerted by anthropogenic activities on overall air quality. Comparable findings were observed in other regions where lockdown and normalized restrictions were alternated ( Table S3 ). In Jiangsu Province, China, the PM 10 concentration increased by 23.2%, NO 2 concentration increased by 16.6%, and CO concentration increased by 1.4% after entering normalized restriction (Bhatti et al., 2022). Similarly, in Wuhan, NO 2 and PM 10 concentrations increased by 55.5% and 5.9%, respectively (Sulaymon et al., 2021). Pollutant levels in Tianjin, Shijiazhuang and Baoding have returned to pre-lockdown levels (Ren et al., 2023). A study in the southwest coastal region of India found that the changes in PM 2.5 , PM 10 , NO 2 and O 3 concentrations relative to the lockdown period ranged from + 11.5% to + 38.3%, from + 1.3% to + 36.0%, from − 5.1% to + 46.1% and from − 21.2% to + 4.1%, respectively (Thomas et al., 2023). The study conducted in Campania, Italy revealed an increase in pollutant concentrations at all stations following the lifting of lockdown measures with significant changes observed for NO 2 concentrations ranging between + 32% to + 63% (Cardito et al., 2023). Studies have demonstrated a robust association between O 3 and PM 2.5 , exhibiting a negative correlation on an hourly scale but a positive correlation on a daily scale, primarily attributed to variations in concentrations and sensitivities towards NOx and VOC emissions (Huang et al., 2021). Consequently, it is imperative to focus on coordinating the control measures for both pollutants, aligning with the current direction of atmospheric prevention and control in Chongqing. Furthermore, there exists a significant relationship between air pollutants and meteorological conditions such as rainfall, which can directly mitigate atmospheric particulate matter through scavenging processes resulting in concentration reductions (Gao et al., 2019). As shown in Fig. 2, there was a substantial decrease in particulate matter concentrations on December 25 and 27 in the P2 period, mainly due to rainfall in these days, with rainfall amounts of 3.1 mm and 8.2 mm, which led to a reduction of PM 2.5 by 48.6 µg/m³; a similar situation occurred in the P3 period, with 4.93 mm of rainfall on January 14, which led to a reduction of PM 10 by 130.8 µg/m³. Therefore, it is crucial to analyze the impact of meteorological conditions on pollutant concentrations while exploring their correlations and significance (as described in 3.3 below), ultimately facilitate further optimization of input parameters for prediction models. 3.2 Potential source contribution factors of pollutants In order to analyze the potential sources of pollutants in Chongqing, it is necessary to further investigate the characteristics of air mass transport during the study period. The Meteoinfo software was utilized for calculating 24-hour backward trajectories passing through Chongqing City, computed hourly throughout the P1-P3 period, resulting in a cumulative total of 2208 trajectories (P1: 720, P2: 744, P3: 744). Based on the spatial consistency of various air masses, the trajectories of air masses moving in different directions were clustered and the results are shown in Fig. S3 . The length of these trajectories can be used as an indicator for determining their movement speed. Longer trajectories correspond to fast-moving air masses that facilitate pollutant dispersion, while shorter trajectories indicate slow-moving air streams with poor diffusion conditions. During the study period, predominant air masses originated from Sichuan, Chongqing, and Guizhou. Among them, local air masses from Chongqing accounted for the largest proportion ranging from 60.5–75.54% among all the clustered trajectories. However, most of these trajectories were relatively short due to Chongqing’s topographic features characterized by surrounded mountains and closed terrain leading to low wind speeds that are unfavorable for pollutant dispersion. The remaining air masses primarily originated from northeastern Sichuan (12.92%-25.54%), southern Shaanxi (3.33%-6.32%), northeastern Guizhou (6.99%-8.33%) and southern Guizhou (5.69%-7.36%), with their trajectory directions mainly aligned with northwest and northeast direction consistent with the prevailing northerly winds observed in winter. To further analyze the potential pollutant source areas in Chongqing, we calculated the weighted potential pollution source contribution factors (WPSCF) for different pollutant concentrations, as shown in Fig. 3. During P1, the potential source ranges of PM 2.5 , PM 10 , and NO 2 were similar, however, their pollution levels varied with PM 2.5 exhibiting higher pollution than PM 10 . Dazhou and Nanchong in Sichuan province, along with NAU, emerged as the main potential source areas for PM 2.5 . Meanwhile, Dazhou was identified as the primary potential source area for PM 10 while NAU remained significant for NO 2 emissions. O 3 exhibited a wide range of potential sources including Hanzhong in Shaanxi province, Bazhong, Nanchong and Guang'an in Sichuan province, along with NAU being recognized as major contributors. Throughout the P1 period, high-value regions associated with potential pollutant sources were relatively dispersed indicating less influence from regional transmission. During P2, the potential source contribution ranges of PM 2.5 , PM 10 , and NO 2 were more concentrated. Guang'an and UFA were the main potential source areas for PM 2.5 and PM 10 . NCA and UFA were the main potential source areas for NO 2 . Dazhou, Guang'an, NCA, and NAU were the main potential source areas for O 3 , whose WPSCF values were basically less than 0.6 indicating that O 3 was weakly affected by the other areas. During P3, potential sources of pollutants were more polluted. Among them, the high-potential sources of PM 2.5 and PM 10 were concentrated at the junction between Southern Chongqing and Northern Guizhou provinces along with NAU. In comparison to the P2 period, the main potential source region of NO 2 shifted towards south, resulting in a significant increase in the number of high-value areas. The junction between Southern Sichuan, Western Chongqing and NAU, and the junction between Southern Chongqing and Northern Guizhou were the main potential sources of O 3 . In general, the potential sources of PM 2.5 , PM 10 , and NO 2 exhibit an expanding trend and shift from northeast to southeast. However, they are primarily concentrated in the UFA and NAU regions due to their high population density and industrial area distribution. On the other hand, the potential source area for O 3 are more dispersed with a majority located around the UFA rather than its center. This could be attributed to lower NOx emissions in surrounding areas resulting in elevated levels of O 3 pollution. 3.3 Influence of meteorological conditions on pollutants In order to highlight the changes in pollutant concentrations following the liberalization and to accurately predict the predictors of the pollutant model, we selected key meteorological parameters, including atmospheric pressure, maximum wind speed and direction, wind speed, temperature, relative humidity, and rainfall. We computed the correlation between these meteorological parameters as well as their correlation with pollutant concentrations and assessed the significance of these correlations. The heat map of correlation coefficients is presented in Fig. 4. The results showed that all meteorological parameters except for maximum wind direction exhibited p-values less than 0.01, indicating a significant correlation with pollutant concentrations. The positive correlation between O 3 and temperature was particularly significant (Mantel's r ≥ 0.4, p < 0.01), suggesting that higher light or UV intensities can accelerate O 3 precursor reactions and exacerbate O 3 pollution. (Porter and Heald, 2019). Additionally, we observed a significant negative correlation between wind speed and particulate matter (r < 0.2, p < 0.01). Higher wind speeds promote dilution of particulate matter in the air while facilitating diffusion of pollutants (Reiminger et al., 2020). Conversely, weak winds hinder air pollutant dispersion leading to increased particulate matter concentration. Furthermore, the significant positive correlation between PM 2.5 and temperature (r ≥ 0.4, p < 0.01) suggests that elevated temperatures may enhance chemical reactions in the atmosphere resulting in conversion of gaseous pollutants into solid particulate matter thereby increasing PM 2.5 concentration (Le et al., 2023). 3.4 Relative contribution of emissions and meteorology to pollutants Anthropogenic emissions and meteorological conditions are the primary determinants of air quality. Meteorological conditions may mask the true impact of emissions on air quality. To investigate the relative contribution of emissions and meteorology to pollution levels, we use the 'rmweather' package for meteorological normalization. Figure 5 displays the values of the pollutant concentrations after meteorological normalization. The results showed that, except for the relatively large fluctuation in O 3 concentration, the concentrations of other three pollutants remained relatively stable during the lockdown period (P1) under restrictive policies. The stability can be attributed to industrial moratoriums, motor vehicle restrictions, and people staying indoors. However, with the relaxation of these policies (P2) and even during subsequent Chinese holidays (P3), normalized values of these pollutants exhibited varying degrees of increase, suggesting an elevation in anthropogenic emission levels since policy liberalization. Through statistical analysis, we quantified the individual contributions of emissions and meteorology to air quality during different study periods (P1-P3). Our findings reveal that anthropogenic emissions contributed to a 33% increase in PM 2.5 concentration while unfavorable meteorological conditions accounted for a 40.2% increase in PM 2.5 concentration. Similar patterns were observed for PM 10 ; emission-related contribution was estimated at 26.7%, whereas unfavorable meteorological contributions contributed by 43.3%. It is worth noting that although the meteorological conditions had a higher contribution rate than emissions alone, there was a substantial increase in emissions following policy liberalization compared to those recorded during restrictive periods, highlighting adverse effects associated with such policy changes on air quality. 3.5 Air Pollutant Analysis and Forecast To establish an accurate model for predicting air pollutant concentrations following a sudden relaxation of restrictive policies, we employed meteorological and pollutant data in the Random Forest algorithm while continuously optimizing the model parameters. Compared to the initial R 2 values ranging from 0.60 to 0.72, higher R 2 values were achieved in this study ranging from 0.70 to 0.89, indicates strong agreement between the predicted and observed values. In general, an R 2 value above 0.70 along with lower RMSE and MAE suggest improved performance of our optimized model. It is worth noting that the RMSE values for NO 2 , PM 2.5 , and PM 10 models shown in Fig. 6 are lower than those reported by other studies utilizing the same modeling technique (Lv et al., 2023). Furthermore, the RMSE for PM 2.5 is consistently around 13 which outperforms XGBoost model for predicting PM 2.5 (Fan et al., 2020 ; Gui et al., 2020), whereas the RMSE for PM 10 is approximately 16 with MAE around 11.8. Both results surpass those obtained through XGBoost or ANN models as well as other prediction models (Zhang et al., 2023b, Masood and Ahmad, 2021). Therefore, our model demonstrates satisfactory performance in prediction. The comparison between the predicted and measured concentrations of pollutants (Fig. 7) indicates that the differences between the predicted and measured concentrations fall within a controllable range, demonstrating its strong prediction performance. Upon complete lifting of the restrictive policies, the disparity between measured and predicted monthly average concentrations becomes more pronounced. Most measured values for PM 2.5 , PM 10 , and NO 2 exceed their corresponding predicted values. Conversely, the predicted values for all three phases of O 3 are higher than the measured values. The mean differences between the measured and predicted values for these pollutants are calculated to be -3.98 µg/m 3 , -4.87 µg/m 3 , -3.31 µg/m 3 , and 1.3 µg/m 3 , respectively. These discrepancies can primarily be attributed to the assumption that pollutant emissions remain at restricted levels after policy relaxation, a pattern consistent with findings from previous studies on pollutant concentration trends during periods of restriction (Hu et al., 2021, Li et al., 2022). Consequently, our model demonstrates superior capability in predicting air pollutant concentrations following policy liberalization while offering valuable insights into future air pollution control strategies under similar scenarios. Conclusion The air quality in Chongqing has exhibited significant variations across different time periods, with the respective AQI values of 62.0, 82.2, and 102.7 for P1, P2, and P3. In terms of spatial-temporal pollutant concentrations, PM 2.5 , PM 10 , and NO 2 levels were consistently low during the lockdown period but increased after the relaxation period commenced, indicating a substantial anthropogenic influence. Spatial distribution analysis also reveals that areas with concentrated human activities have higher pollution levels compared to regions with fewer anthropogenic activities, futher emphasizing the impact of anthropogenic activities on air quality in Chongqing. Conversely, O 3 demonstrates an inverse trend when compared to the other three pollutants both temporally and spatially. This observation may be attributed to the ‘titration reaction’ involving NOx compounds. Most of the pollutants were from the local air masses during the study period. However potential sources from Guang'an, Dazhou, and Nanchong in northeast of Sichuan province, NAU in Chongqing as well as Zunyi in Guizhou province are identified as major contributors too. Following complete lifting of restrictive policies, the distribution of high-value areas of potential pollution sources demonstrates an expanding trend geographically. The parameter optimization within our random forest model enables accurate prediction of changes in air pollutant concentrations subsequent to policy relaxation. The impact of meteorological factors on pollutants is more pronounced in Chongqing, where anthropogenic emissions contribute to a 33% increase in PM 2.5 concentration, while unfavorable meteorological conditions accounted for a further 40.2% increase. For PM 10 , the emission and meteorological contributions are 26.7% and 43.3%, respectively, exacerbating the challenges faced by air pollution prevention, control, and management in this region. In summary, the abrupt policy liberalization has resulted in elevated concentrations of PM and NO 2 but a decrease in O 3 levels. Therefore, it is imperative to adopt coordinated control measures that consider both the influence of meteorological conditions and the complex terrain characteristics of Chongqing. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The datasets used or analyzed in this study may be obtained from corresponding authors upon reasonable request. Funding This work was funded by the Science and Technology Commission of Chongqing project (No. CSTB2022NSCQ‐MSX0818) and Wanzhou project (wzstc-20220303). Author contribution Haozheng Wang: Writing - original draft, Data Curation. Liuyi Zhang: Writing - review & editing, Review. Yuanjun Chen: Resources. Guangming Shi: Review & Revise, Supervision. Chentao Huang: Validation. Fumo Yang: Review, Supervision, Resources. 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1","display":"","copyAsset":false,"role":"figure","size":432399,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Geographical location of Chongqing in China; (b) Distribution of meteorological and air quality monitoring sites in Chongqing (see \u003cstrong\u003eTable S2\u003c/strong\u003e for details); (c) Proportion of basic situation of different functional areas in Chongqing.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4584877/v1/ea15fba5ee8565a8c6baa34c.png"},{"id":61009626,"identity":"82006718-014c-4a30-a49e-a7e47a4fc04e","added_by":"auto","created_at":"2024-07-24 14:19:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":906873,"visible":true,"origin":"","legend":"\u003cp\u003eTime changes in PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10,\u003c/sub\u003e NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e concentrations in Chongqing during the study period (shaded areas are P1-P3 for the study period)；The red line represents the average pollutant.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4584877/v1/c27b76b6654c4f4a997f028a.png"},{"id":61009629,"identity":"eeae0440-c903-42f6-98fc-568fbb9af72b","added_by":"auto","created_at":"2024-07-24 14:19:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1104586,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of potential source areas of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e in different periods (P1-P3) of Chongqing (a, b, c, d represent PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e, respectively).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4584877/v1/f115d7cac862c27446f4fbe3.png"},{"id":61009628,"identity":"8b98b7dc-bf95-4158-b8a9-39ea707bfca3","added_by":"auto","created_at":"2024-07-24 14:19:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":928787,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between meteorological parameters and pollutant concentration in Chongqing\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4584877/v1/39da8d633a13d97e3cd3e23b.png"},{"id":61009627,"identity":"d9281f1d-03e1-4996-9df8-5bee1260b13f","added_by":"auto","created_at":"2024-07-24 14:19:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":268422,"visible":true,"origin":"","legend":"\u003cp\u003eMeteorological normalization of air pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e) in Chongqing from January 2021 to January 2023 (the shadow part is the study period of P1 to P3)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4584877/v1/81c996cce0d6aa52693aa51d.png"},{"id":61010757,"identity":"bb6a7bf3-5124-48c3-a8ad-5aa1ef2c6959","added_by":"auto","created_at":"2024-07-24 14:27:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":274092,"visible":true,"origin":"","legend":"\u003cp\u003eModel validation for each pollutant test dataset. a, b, c, and d represent NO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e respectively, and count in the legend represents the day of the week.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4584877/v1/778c9bc5674726aa7d00f44c.png"},{"id":61009630,"identity":"0f2c97bb-1b01-42a5-a9f3-f7a3771721f6","added_by":"auto","created_at":"2024-07-24 14:19:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":237068,"visible":true,"origin":"","legend":"\u003cp\u003eMeasured and predicted pollutant values in different periods in Chongqing. a, b, c, and d represent PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4584877/v1/58bf659f18fa7646c1c72b74.png"},{"id":68206509,"identity":"f1a1ce9a-b021-4594-9245-c62dcf1eacf5","added_by":"auto","created_at":"2024-11-04 16:32:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5043560,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4584877/v1/1ed7f07c-d0e6-4513-b773-2a2c8eeddb37.pdf"},{"id":61009631,"identity":"94b6b669-e9d2-48af-9bf0-b86e2837c593","added_by":"auto","created_at":"2024-07-24 14:19:21","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":373122,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4584877/v1/4ad9d35af30635c51fd2cc22.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of COVID-19 Restrictions Liberalization on Air Quality: A Case Study of Chongqing, Southwest China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the rapid development of industrialization and urbanization, air quality has become increasingly severe worldwide, with escalating levels of air pollutants such as ozone (O\u003csub\u003e3\u003c/sub\u003e), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), and particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e). Currently, there is an increasing emphasis on establishing a healthy atmospheric environment (He et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In 2013, the Chinese government implemented the \u0026ldquo;Air Pollution Prevention and Control Action Plan ( 2013 )\u0026rdquo; (Chinese State Council, 2013), resulting in significantly reduction of air pollution and remarkable achievement. However, due to complex meteorological conditions, air pollution events still occur, particularly in southwestern China where the Chengdu-Chongqing area has emerged as one of the four major hotspots for such incidents in China (Zhao et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nonetheless, it remains imperative to improve China's air quality by intensifying the management of particulate matter pollution and promptly curbing the increasing trend of ozone pollution (Lv et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the COVID-19 pandemic from 2020 to 2022 (Zhang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the Chinese government implemented stringent measures to combat COVID-19, including travel restrictions, production limitations in factories, and social quarantine measures (Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, these measures have significantly reduced air pollutant concentrations by curbing anthropogenic emissions. The interplay between meteorological conditions and anthropogenic emissions largely determines atmospheric pollution processes (von Schneidemesser et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), make the emergence of COVID-19 an exceptional context for comprehending and quantifying the how anthropogenic emissions and meteorological conditions influence air quality (Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In recent years, researchers have conducted analyses on the impact of restrictions during COVID-19 on air quality, while discussing the contributions of anthropogenic emissions and meteorological factors. These studies revealed varying degrees of decrease in PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e concentrations among different cities globally during periods of restrictive policies. For instance, observed PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations decreased by approximately 60.81% and 43.12%, respectively, compared to predicted values during the restriction period in Shanghai; whereas considering only anthropogenic factors without accounting for meteorological influences resulted in decreases of around 50.20% for PM\u003csub\u003e2.5\u003c/sub\u003e and 19.06% for PM\u003csub\u003e10\u003c/sub\u003e (Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). In Beijing, observed PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e concentrations exhibited reductions of 39.4%, 50.1%, and 43.1%, respectively, during the restriction period. However, adverse meteorological conditions resulted in an increase in NO\u003csub\u003e2\u003c/sub\u003e concentrations by 15.2\u0026ndash;32.4% (Hu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Campania, Italy, the restriction period positively influenced air quality with a significant decrease in NO\u003csub\u003e2\u003c/sub\u003e levels ranging from 42\u0026ndash;48% (Cardito et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nevertheless, certain regions and cities experienced an upward trend in O\u003csub\u003e3\u003c/sub\u003e concentrations despite overall improvements in air quality; examples include the eastern part of China (Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and several cities in southern Europe (Donzelli et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This phenomenon can be attributed primarily to reduced primary emissions such as NOx due to restrictions and a consequent decline in the titration effect of NO on O\u003csub\u003e3\u003c/sub\u003e concentration (Zhang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Varying trends of pollutant concentrations were observed across urban areas during the COVID-19 pandemic owing to the diversity in geography, meteorology, and emission sources, which necessitates accurate quantification of the impacts results from restriction measures along with cross-regional collaborative governance considerations for addressing air pollution issues.\u003c/p\u003e \u003cp\u003eDuring the initial phase of the outbreak, numerous reports have extensively documented the impact of restrictive measures on air quality, leading to unequivocal conclusions. However, there is a noticeable absence of studies investigating the changes in air quality subsequent to the relaxation of these measures. In this study, we conducted a comprehensive analysis based on the monitoring data from 36 air quality monitoring stations and 33 meteorological stations in Chongqing. The study period was divided into three distinct phases: the lockdown period during the implementation of restrictive policies(November 2022-December 2022), the buffer period following their liberalization༈December 2022-January 2023༉, and the free period after complete liberalization༈January 1st, 2023 - January 31st, 2023༉. The spatial and temporal variations of air quality in Chongqing during these different periods were comparatively analyzed. The trajectories of air masses and the potential sources of various pollutants during different periods were analyzed. Additionally, the effects of meteorological conditions and anthropogenic activities on air pollutant concentrations were quantified. A random forest model with optimized parameters for ntree and mtry values was employed to predict the post-liberalization air pollutant concentrations accurately. The parimary objective of this study is to comprehend shifts in air quality within a typical mountain city following restriction liberalization while also aiming to develop an appropriate model capable of forecasting changes in pollutant concentration as a response to alterations in restrictive policies. The results of this study can provide theoretical support for similar post-lockdown liberalization.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Research area\u003c/h2\u003e\n \u003cp\u003eChongqing, located in southwest China, spans longitude 105\u0026deg;11\u0026prime;~ 110\u0026deg;11\u0026prime;E and latitude 28\u0026deg;10\u0026prime;~ 32\u0026deg;13\u0026prime;N, encompassing an area of 82,400 km\u003csup\u003e2\u003c/sup\u003e. It serves as a pivotal nexus connecting the Yangtze River Economic Belt, the Belt and Road Initiative Area, and the Three Gorges Reservoir Area (Yang et al., \u003cspan\u003e2020\u003c/span\u003e), exhibiting typical mountainous characteristics. In recent years, Chongqing has witnessed a resident population of approximately 32.1243 million individuals along with a vehicle population of around 8.3709 million units, consequently leading to a total energy consumption of about 80.4631 million tons of standard coal. These factors have exerted substantial pressure on the regional atmospheric environment. Furthermore, the terrain in this area is complex, most of which is surrounded by mountains. The closed terrain leads to high humidity, low wind speed, and stable atmospheric boundary layer, which facilitate the formation of temperature inversions and imped atmospheric pollutant dispersion (Chen and Xie, \u003cspan\u003e2017\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eConsidering the topography and social-economic factors, we divided the research area into four regions: Urban Function Area (UFA), New Area for Urban Development (NAU), Northeast Chongqing Ecological Conservation Development Area (NCA), and Southeast Chongqing Ecological Protection Area (SCA). Notably, UFA and NAU exhibit higher population and regional GDPs compared to the other two regions. Furthermore, NAU demonstrates the highest total industrial energy consumption while SCA exhibits the lowest levels of population, economic activity, and industrial energy consumption. The distribution of monitoring sites and different functional areas in Chongqing is illustrated in \u003cstrong\u003eFig.\u0026nbsp;1\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;1.\u003c/strong\u003e (a) Geographical location of Chongqing in China; (b) Distribution of meteorological and air quality monitoring sites in Chongqing (see \u003cstrong\u003eTable S2\u003c/strong\u003e for details); (c) Proportion of basic situation of different functional areas in Chongqing.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Definition of research period\u003c/h2\u003e\n \u003cp\u003eIn late October 2022, Chongqing was struck by a mutant strain, leading to the implementation of a series of lockdown measures by municipal government. By December 2022, these restrictions were unexpectedly lifted. The research period for this study spans from November 1st, 2022, to January 31st, 2023. Analysis of the daily air quality index (AQI) fluctuations during this period (refer to \u003cstrong\u003eFig.\u003cspan\u003eS1\u003c/span\u003e\u003c/strong\u003e) reveals significant variations in AQI levels in Chongqing, indicating alternating periods of lockdown and normalized restrictions. However, following the relaxation of restrictive policies, there was an upward trend observed in AQI changes which surpassed both the restrictive policies period and corresponding periods in 2021 and 2022 (see Appendix for detailed descriptions). Therefore, it is imperative to conduct a specific analysis on factors influencing air quality during this period.\u003c/p\u003e\n \u003cp\u003eIn order to distinguish the impact of meteorology and human activities on air quality during different periods, the restrictive policies implemented before and after the liberalization is categorized into three phases: pre-liberalization lockdown period (P1: November 2022-December 2022), post-liberalization transitional period (P2: December 2022-January 2023), and post-liberalization unrestricted period (P3: January 1st, 2023 - January 31st, 2023). P1 represents a scenario where industrial enterprises curtail production while people\u0026rsquo;s mobility is restricted during the lockdown phase. P2 represents a situation where individuals predominantly remain indoors or venture out cautiously due to widespread infection concerns. Lastly, P3 denotes a stage when most individuals have recovered and societal operations have fully returned to normalcy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Research data\u003c/h2\u003e\n \u003cp\u003eThe air pollutant concentrations were obtained from 36 state and provincial control air quality monitoring sites in Chongqing. These data were sourced from the historical records of the online air quality monitoring and analysis platform (\u003cspan\u003e\u003cspan\u003ewww.aqistudy.cn/historydata/\u003c/span\u003e\u003c/span\u003e), including AQI, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e. The daily AQI, which is a dimensionless relative value used to indicate the level of air pollution on a given day (Zhang et al., \u003cspan\u003e2023b\u003c/span\u003e). For PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e concentrations, average values from 24-hour monitoring data were employed; while for O\u003csub\u003e3\u003c/sub\u003e levels, daily 8-hour moving averages were used. Meteorological parameters during the same period were collected from hourly observations at 33 meteorological monitoring stations in Chongqing. Conventional near-surface meteorological parameters including atmospheric pressure, maximum wind speed and wind direction, average wind speed, air temperature, maximum temperature, minimum temperature, relative humidity, and precipitation were considered as well. Meteorological data used in backward trajectory modeling was acquired from GDAS dataset provided by National Centers for Environmental Prediction (NCEP) via \u003cspan\u003e\u003cspan\u003eftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1/\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Research Methods\u003c/h2\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.4.1 Potential Source Contribution Function\u003c/h2\u003e\n \u003cp\u003eThe HYSPLIT trajectory model, jointly developed by the National Oceanic and Atmospheric Center (NOAA) and the Australian Bureau of Meteorology (ABM), is widely used as a professional meteorological trajectory model system for simulating the transport and dispersion of diverse atmospheric pollutants (Shan et al., \u003cspan\u003e2023\u003c/span\u003e, Bilal et al., \u003cspan\u003e2022\u003c/span\u003e). In this study, Chongqing (106.5\u0026deg;E, 29.6\u0026deg;N) was selected as the receptor point for backward trajectory analysis. The Meteoinfo software (Wang et al., \u003cspan\u003e2009\u003c/span\u003e, Wang, \u003cspan\u003e2014\u003c/span\u003e) along with global assimilated meteorological data were utilized to simulate the backward trajectories at an altitude of 500 m and with a tracking time of 24 hours, covering the period from November 1st, 2022, to Januart 31st, 2023.\u003c/p\u003e\n \u003cp\u003eThe Potential Source Contribution Function (PSCF) is a conditional probability statistic that evaluates the potential contribution of pollutants at a study site. It utilizes backward trajectory calculations of air masses to identify the likely emission source locations (Zhou et al., \u003cspan\u003e2017\u003c/span\u003e). The PSCF method divides the study area into i\u0026times;j grids and establishes a concentration threshold for pollutants. If the pollutant concentration corresponding to the trajectory exceeds this threshold, then the trajectory is classified as polluted. In this study, trajectories with PM\u003csub\u003e2.5\u003c/sub\u003e concentrations exceeding 50 \u0026micro;g/m\u0026sup3;, PM\u003csub\u003e10\u003c/sub\u003e concentrations exceeding 75 \u0026micro;g/m\u0026sup3;, and NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e concentrations exceeding the monthly average are considered polluted trajectories. The PSCF is calculated using Eq. (1):\u003c/p\u003e\n \u003cdiv id=\"Equa\"\u003e\n \u003cdiv id=\"FileID_Equa\" name=\"EquationSource\"\u003e$${PSCF}_{ij}={m}_{ij}/{n}_{ij} \\left(1\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere m\u003csub\u003eij\u003c/sub\u003e is the number of polluted trajectories in grid (i, j) and n\u003csub\u003eij\u003c/sub\u003e is the number of total trajectories in grid (i, j). The grid spacing was set as 0.2\u0026deg;\u0026times;0.2\u0026deg; in this study.\u003c/p\u003e\n \u003cp\u003eDue to the conditional probability statistics used in PSCF analyzing, there may be significant uncertainty in the calculation results. To mitigate this, a weighting factor (W\u003csub\u003eij\u003c/sub\u003e) is usually employed to reduce the uncertainty (Xiao et al., \u003cspan\u003e2023\u003c/span\u003e). The weighted PSCF was calculated as WPSCF\u0026thinsp;=\u0026thinsp;W\u003csub\u003eij\u003c/sub\u003e\u0026times;PSCF\u003csub\u003eij\u003c/sub\u003e, and W\u003csub\u003eij\u003c/sub\u003e is defined as Eq. (2).\u003c/p\u003e\n \u003cdiv id=\"Equb\"\u003e\n \u003cdiv id=\"FileID_Equb\" name=\"EquationSource\"\u003e$${W}_{ij}=\\left\\{ \\begin{array}{c}1.00 4{n}_{ave}＜{n}_{ij}\\\\ 0.70{ n}_{ave}＜{n}_{ij}\\le 4{n}_{ave}\\\\ 0.42 0.5{n}_{ave}＜{n}_{ij}\\le {n}_{ave}\\\\ 0.17{ n}_{ij}\\le 0.5{n}_{ave}\\end{array}\\right. \\left(2\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere n\u003csub\u003eave\u003c/sub\u003e is the average number of endpoints per grid track.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.4.2 Mantel Test\u003c/h2\u003e\n \u003cp\u003eVariations in pollutant concentrations are not solely determined by anthropogenic emissions; a multitude of parameters influence these changes, making the process considerably more complex. Usually, we need to consider the effects of meteorological conditions when predicting changes in pollutant concentrations. Therefore, it is important to determine the potential effects of important meteorological parameters on these concentrations and optimize the prediction model accordingly. The Mantel Test offers several advantages such as nonparametric nature, asymmetric consideration, incorporation of spatial structure, and substitution when evaluating the relationship between meteorological conditions and pollutant concentrations. In this study, the Mantel Test was used to analyze the effects of meteorological parameters on pollutant concentrations.\u003c/p\u003e\n \u003cp\u003eThe analytical process of the Mantel Test primarily involves calculating the distance matrix using the Euclidean distance formula (Breiding et al., \u003cspan\u003e2021\u003c/span\u003e) and obtaining compressed distance columns for correlation calculation, enabling a significance test to determine whether there exists a significant correlation between the two matrices (Crabot et al., \u003cspan\u003e2019\u003c/span\u003e). In this study, we employ Pearson correlation coefficient (Asmel et al., \u003cspan\u003e2022\u003c/span\u003e) as a Model description.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.4.3 Model Introduction\u003c/h2\u003e\n \u003cp\u003eRandom forest (RF) is an ensemble model composing numerous independent decision trees and using the bagging algorithm (Bootstrap aggregation). This model has been widely used in air pollutant prediction due to its advantages of fast training speed and prevention of overfitting (Peng et al., \u003cspan\u003e2022\u003c/span\u003e, Wang et al., \u003cspan\u003e2020\u003c/span\u003e). In this study, we employ the ten-fold cross-validation method to determine the two most important parameters, ntree and mrty, in the random forest model. By optimizing these parameters, we established two scenarios. Firstly, the RF model is used for predicting air pollutant concentrations after the liberalization. Secondly, meteorological normalization is used to eliminate the influence of meteorological conditions on air pollutant concentrations.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e1. Prediction experiment\u003c/h3\u003e\n\u003cp\u003eMeteorological and temporal variables were used as input parameters in the model for this study. Meteorological variables encompassed atmospheric pressure (AP), maximum wind speed and direction (Max_WS/WD), wind speed (WS), temperature (T), maximum temperature (Max_T), minimum temperature (Min_T), relative humidity (RH), and rainfall (Rainfall). Temporal variables included Unix timestamp (date_unix), Julian date (date_julian), working date (weekday), and hour value (hour). The air pollutant concentration served as the dependent variable, while the meteorological parameters and time predictors were used as independent predictors. The RF modeling was conducted on a dataset spanning from January 1st, 2021 to October 31st, 2022. The entire dataset was randomly divided into a training set for model constructing and a test set for performance validating, where the training set accounting for 70% of the data and the remaining portion used for the testing purposes. The RF model was applied to predict air pollutant concentrations from November 1st, 2022 to January 31st, 2023.\u003c/p\u003e\n\u003ch3\u003e2. Meteorological Normalization Experiment\u003c/h3\u003e\n\u003cp\u003eChanges in atmospheric pollutant concentrations are determined by both meteorological conditions and emissions. To quantify the effects of emissions and meteorological conditions on pollutants, meteorological normalization has been widely used as a technique to decouple the effects of meteorology on air pollutants in a time series. In this study, the \u0026lsquo;rmweather\u0026rsquo; R package was used to implement the random forest model for meteorological normalization of pollutants, aiming to eliminate the effect of meteorological factors (Grange and Carslaw, \u003cspan\u003e2019\u003c/span\u003e, Grange et al., \u003cspan\u003e2018\u003c/span\u003e, Lv et al., \u003cspan\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eRF modeling was conducted using the \u0026apos;Ranger\u0026apos; package in the R language (Wright and Ziegler, \u003cspan\u003e2017\u003c/span\u003e). To achieve weather normalization, a subset of weather variables for a specific day (excluding the time variable) was randomly selected from historical data and included in the model dataset. The RF model was then applied to forecast the newly generated dataset (Zhang et al., \u003cspan\u003e2023a\u003c/span\u003e). Specifically, the day-specific weather variables for each day in the input new dataset were generated by randomly selecting from observed weather data during a two-week period before and after that particular day. This process was repeated 1000 times, and the average of the these predictions was calculated to obtain final weather-normalized results using Equations (3) and (4), which quantify the contributions of both weather conditions and emissions to changes in pollutant concentrations. The meteorological normalization process is shown in \u003cstrong\u003eFig. S2.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Equc\"\u003e\n \u003cdiv id=\"FileID_Equc\" name=\"EquationSource\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equd\"\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1721822068.png\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cp\u003ewhere E and M represent the emission and meteorological contributions to changes in pollutant concentrations, respectively. \u003cspan\u003e\u003cspan\u003e\\({\\text{C}}_{\\text{i}}^{\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\({\\text{C}}_{\\text{i}}^{\\text{o}\\text{b}}\\)\u003c/span\u003e\u003c/span\u003erepresent the meteorologically normalized pollutant concentration and the actual observed concentration in month i, respectively. \u003cspan\u003e\u003cspan\u003e\\({\\text{C}}_{\\text{i}+1}^{\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\({\\text{C}}_{\\text{i}+1}^{\\text{o}\\text{b}}\\)\u003c/span\u003e\u003c/span\u003erepresent the meteorologically normalized pollutant concentration and the actual observed concentration in month i+1, respectively.\u003c/p\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e2.4.4 Model Performance and Evaluation\u003c/h2\u003e\n \u003cp\u003eIn this study, three metrics were used to evaluate the performance of the model prediction on the test dataset, including R\u003csup\u003e2\u003c/sup\u003e, mean absolute error (MAE) and root mean square error (RMSE), which were calculated by Equations (5\u0026ndash;7), respectively.\u003c/p\u003e\n \u003cdiv id=\"Eque\"\u003e\n \u003cdiv id=\"FileID_Eque\" name=\"EquationSource\"\u003e$${\\text{R}}^{2}=1-\\frac{\\sum _{\\text{i}=1}^{\\text{n}}{({\\text{y}}_{i}-{\\widehat{\\text{y}}}_{i})}^{2}}{{\\sum }_{\\text{i}=1}^{\\text{n}}({{\\text{y}}_{i}-\\stackrel{-}{\\text{y}})}^{2}} \\left(5\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equf\"\u003e\n \u003cdiv id=\"FileID_Equf\" name=\"EquationSource\"\u003e$$\\text{M}\\text{A}\\text{E}=\\frac{1}{\\text{n}} {\\sum }_{\\text{i}=1}^{\\text{n}}\\left|({\\widehat{\\text{y}}}_{\\text{i}}-{\\text{y}}_{\\text{i}})\\right| \\left(6\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equg\"\u003e\n \u003cdiv id=\"FileID_Equg\" name=\"EquationSource\"\u003e$$\\text{R}\\text{M}\\text{S}\\text{E}=\\sqrt{\\frac{1}{n}{\\sum }_{i=1}^{n}{({\\widehat{\\text{y}}}_{\\text{i}}-{\\text{y}}_{\\text{i}})}^{2}} \\left(7\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere n is the number of samples in the test dataset, y is the measured value, \u003cspan\u003e\u003cspan\u003e\\(\\widehat{\\text{y}}\\)\u003c/span\u003e\u003c/span\u003eis the predicted value, and \u003cspan\u003e\u003cspan\u003e\\(\\stackrel{-}{y}\\)\u003c/span\u003e\u003c/span\u003e is the mean value.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv\u003e\n \u003ch2\u003e3.1 Spatial and temporal variations of air pollutants\u003c/h2\u003e\n \u003cp\u003eThe average hourly concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e at 36 air pollutant monitoring stations were calculated during the study period to obtain the daily variation trend map of pollutant concentration (\u003cstrong\u003eFig.\u0026nbsp;2\u003c/strong\u003e). The changes in pollutant concentration across different periods within four functional areas are shown in Table\u0026nbsp;1. Notably, there are evident temporal and spatial similarities in the characteristics of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;2.\u003c/strong\u003e Time changes in PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10,\u003c/sub\u003e NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e concentrations in Chongqing during the study period (shaded areas are P1-P3 for the study period);The red line represents the average pollutant.\u003c/p\u003e\n \u003cdiv\u003e \u0026nbsp;\u003ctable border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSpatial variations in PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e concentrations in Chongqing during the study period(unit: µg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban Function Area(UFA)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNew Area for Urban Development(NAU)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNortheast Chongqing Ecological Conservation Development Area (NCA)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSoutheast Chongqing Ecological Protection Area(SCA)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003cp\u003e48.9\u003c/p\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e65.0\u003c/p\u003e\n \u003cp\u003e65.1\u003c/p\u003e\n \u003cp\u003e43.2\u003c/p\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e33.9\u003c/p\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003cp\u003e23.7\u003c/p\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e61.4\u003c/p\u003e\n \u003cp\u003e63.5\u003c/p\u003e\n \u003cp\u003e47.4\u003c/p\u003e\n \u003cp\u003e65.4\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUFA\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNAU\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNCA\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e58.1\u003c/p\u003e\n \u003cp\u003e62.6\u003c/p\u003e\n \u003cp\u003e63.0\u003c/p\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e81.7\u003c/p\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003cp\u003e78.1\u003c/p\u003e\n \u003cp\u003e68.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003cp\u003e33.9\u003c/p\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003cp\u003e39.9\u003c/p\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUFA\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNAU\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNCA\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e72.9\u003c/p\u003e\n \u003cp\u003e80.3\u003c/p\u003e\n \u003cp\u003e79.9\u003c/p\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e102.3\u003c/p\u003e\n \u003cp\u003e108.3\u003c/p\u003e\n \u003cp\u003e99.4\u003c/p\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e39.8\u003c/p\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003cp\u003e52.6\u003c/p\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003cp\u003e55.1\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eDuring the P1 period, a series of lockdown measures resulted in average concentrations of PM\u003csub\u003e2.5,\u003c/sub\u003e PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e at 44.1 µg/m³, 61.0 µg/m³, 29.2 µg/m³, and 60.5 µg/m³, respectively. While Chongqing generally exhibited low levels of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e concentrations. The UFA and NAU regions showed significantly higher levels compared to the NCA and SCA regions due to their higher population density, industrial concentration, and anthropogenic activities. Specifically, concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e were elevated by 11.5–18.9 µg/m³ while those of PM\u003csub\u003e10\u003c/sub\u003e were increased by 11.8–24.6 µg/m³ along with an increase in NO\u003csub\u003e2\u003c/sub\u003e levels ranging from 2.0-14.7 µg/m³.\u003c/p\u003e\n \u003cp\u003eDuring the P2 period, the average concentrations of PM\u003csub\u003e2.5,\u003c/sub\u003e PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e were 60.5 µg/m³, 81.3 µg/m³, 37 µg/m³, and 33.1 µg/m³, respectively. compared to the P1 period, there was an increase in concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e by 37.2%, PM\u003csub\u003e10\u003c/sub\u003e by 33.3%, and NO\u003csub\u003e2\u003c/sub\u003e by 26.7%. This may be attributed to a series of anthropogenic activities such as increased motor vehicle usage and industrial production following the liberalization of restrictions. However, O\u003csub\u003e3\u003c/sub\u003e concentrations decreased significantly by 45.3% during this period. Surprisingly, the O\u003csub\u003e3\u003c/sub\u003e concentration of SCA is significantly higher than that of other areas in Chongqing. The elevated O\u003csub\u003e3\u003c/sub\u003e levels observed in SCA may be explained by analyzing its precursors using the EMKA curve (Jin and Holloway, 2015). It is possible that lower emission of NOx weaken the “titration reaction” between O\u003csub\u003e3\u003c/sub\u003e and NOx resulting higher O\u003csub\u003e3\u003c/sub\u003e concentrations being retained rather than removed through chemical reactions facilitated by high levels of NOx.\u003c/p\u003e\n \u003cp\u003eDuring the P3 period, the average concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e were 76.0 µg/m³, 102.3 µg/m³, 35.6 µg/m³, and 48.1 µg/m³, respectively. The relaxation of restrictive policies during the Spring Festival coincided with increased transportation and human activities which led to rapid surge in emission sources. Compared to the P1 period, there was a significant increase in concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e (72.3%), PM\u003csub\u003e10\u003c/sub\u003e (67.7%), and NO\u003csub\u003e2\u003c/sub\u003e (21.9%). Unlike the relatively low and high levels of NOx during the P1 and P2 period, NOx concentration remained relatively stable during the P3 period, while O\u003csub\u003e3\u003c/sub\u003e decreased compared to the P2 period across all districts of Chongqing. At the same time, varying degrees of increases were observed for PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e concentrations. Compared to the SCA, notably higher pollutant concentrations including PM\u003csub\u003e2.5\u003c/sub\u003e (+ 17.4 µg/m³), PM\u003csub\u003e10\u003c/sub\u003e (+ 26.7 µg/m³), and NO\u003csub\u003e2\u003c/sub\u003e (+ 11.0 µg/m³) were found in NCA possibly attributed to higher population and more frequent human activities over recent years. This indicated that policy relaxation resulted in diverse variations characteristics of spatial-temporal distribution of pollutants. Overall findings underscoring detrimental effects on air quality due to liberalization measures, emphasized significant impact exerted by anthropogenic activities on overall air quality.\u003c/p\u003e\n \u003cp\u003eComparable findings were observed in other regions where lockdown and normalized restrictions were alternated (\u003cstrong\u003eTable S3\u003c/strong\u003e). In Jiangsu Province, China, the PM\u003csub\u003e10\u003c/sub\u003e concentration increased by 23.2%, NO\u003csub\u003e2\u003c/sub\u003e concentration increased by 16.6%, and CO concentration increased by 1.4% after entering normalized restriction (Bhatti et al., 2022). Similarly, in Wuhan, NO\u003csub\u003e2\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations increased by 55.5% and 5.9%, respectively (Sulaymon et al., 2021). Pollutant levels in Tianjin, Shijiazhuang and Baoding have returned to pre-lockdown levels (Ren et al., 2023). A study in the southwest coastal region of India found that the changes in PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e concentrations relative to the lockdown period ranged from + 11.5% to + 38.3%, from + 1.3% to + 36.0%, from − 5.1% to + 46.1% and from − 21.2% to + 4.1%, respectively (Thomas et al., 2023). The study conducted in Campania, Italy revealed an increase in pollutant concentrations at all stations following the lifting of lockdown measures with significant changes observed for NO\u003csub\u003e2\u003c/sub\u003e concentrations ranging between + 32% to + 63% (Cardito et al., 2023).\u003c/p\u003e\n \u003cp\u003eStudies have demonstrated a robust association between O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e, exhibiting a negative correlation on an hourly scale but a positive correlation on a daily scale, primarily attributed to variations in concentrations and sensitivities towards NOx and VOC emissions (Huang et al., 2021). Consequently, it is imperative to focus on coordinating the control measures for both pollutants, aligning with the current direction of atmospheric prevention and control in Chongqing. Furthermore, there exists a significant relationship between air pollutants and meteorological conditions such as rainfall, which can directly mitigate atmospheric particulate matter through scavenging processes resulting in concentration reductions (Gao et al., 2019). As shown in Fig.\u0026nbsp;2, there was a substantial decrease in particulate matter concentrations on December 25 and 27 in the P2 period, mainly due to rainfall in these days, with rainfall amounts of 3.1 mm and 8.2 mm, which led to a reduction of PM\u003csub\u003e2.5\u003c/sub\u003e by 48.6 µg/m³; a similar situation occurred in the P3 period, with 4.93 mm of rainfall on January 14, which led to a reduction of PM\u003csub\u003e10\u003c/sub\u003e by 130.8 µg/m³. Therefore, it is crucial to analyze the impact of meteorological conditions on pollutant concentrations while exploring their correlations and significance (as described in 3.3 below), ultimately facilitate further optimization of input parameters for prediction models.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e3.2 Potential source contribution factors of pollutants\u003c/h2\u003e\n \u003cp\u003eIn order to analyze the potential sources of pollutants in Chongqing, it is necessary to further investigate the characteristics of air mass transport during the study period. The Meteoinfo software was utilized for calculating 24-hour backward trajectories passing through Chongqing City, computed hourly throughout the P1-P3 period, resulting in a cumulative total of 2208 trajectories (P1: 720, P2: 744, P3: 744). Based on the spatial consistency of various air masses, the trajectories of air masses moving in different directions were clustered and the results are shown in \u003cstrong\u003eFig. S3\u003c/strong\u003e. The length of these trajectories can be used as an indicator for determining their movement speed. Longer trajectories correspond to fast-moving air masses that facilitate pollutant dispersion, while shorter trajectories indicate slow-moving air streams with poor diffusion conditions. During the study period, predominant air masses originated from Sichuan, Chongqing, and Guizhou. Among them, local air masses from Chongqing accounted for the largest proportion ranging from 60.5–75.54% among all the clustered trajectories. However, most of these trajectories were relatively short due to Chongqing’s topographic features characterized by surrounded mountains and closed terrain leading to low wind speeds that are unfavorable for pollutant dispersion. The remaining air masses primarily originated from northeastern Sichuan (12.92%-25.54%), southern Shaanxi (3.33%-6.32%), northeastern Guizhou (6.99%-8.33%) and southern Guizhou (5.69%-7.36%), with their trajectory directions mainly aligned with northwest and northeast direction consistent with the prevailing northerly winds observed in winter.\u003c/p\u003e\n \u003cp\u003eTo further analyze the potential pollutant source areas in Chongqing, we calculated the weighted potential pollution source contribution factors (WPSCF) for different pollutant concentrations, as shown in Fig.\u0026nbsp;3. During P1, the potential source ranges of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e were similar, however, their pollution levels varied with PM\u003csub\u003e2.5\u003c/sub\u003e exhibiting higher pollution than PM\u003csub\u003e10\u003c/sub\u003e. Dazhou and Nanchong in Sichuan province, along with NAU, emerged as the main potential source areas for PM\u003csub\u003e2.5\u003c/sub\u003e. Meanwhile, Dazhou was identified as the primary potential source area for PM\u003csub\u003e10\u003c/sub\u003e while NAU remained significant for NO\u003csub\u003e2\u003c/sub\u003e emissions. O\u003csub\u003e3\u003c/sub\u003e exhibited a wide range of potential sources including Hanzhong in Shaanxi province, Bazhong, Nanchong and Guang'an in Sichuan province, along with NAU being recognized as major contributors. Throughout the P1 period, high-value regions associated with potential pollutant sources were relatively dispersed indicating less influence from regional transmission. During P2, the potential source contribution ranges of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e were more concentrated. Guang'an and UFA were the main potential source areas for PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e. NCA and UFA were the main potential source areas for NO\u003csub\u003e2\u003c/sub\u003e. Dazhou, Guang'an, NCA, and NAU were the main potential source areas for O\u003csub\u003e3\u003c/sub\u003e, whose WPSCF values were basically less than 0.6 indicating that O\u003csub\u003e3\u003c/sub\u003e was weakly affected by the other areas. During P3, potential sources of pollutants were more polluted. Among them, the high-potential sources of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e were concentrated at the junction between Southern Chongqing and Northern Guizhou provinces along with NAU. In comparison to the P2 period, the main potential source region of NO\u003csub\u003e2\u003c/sub\u003e shifted towards south, resulting in a significant increase in the number of high-value areas. The junction between Southern Sichuan, Western Chongqing and NAU, and the junction between Southern Chongqing and Northern Guizhou were the main potential sources of O\u003csub\u003e3\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003eIn general, the potential sources of PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e exhibit an expanding trend and shift from northeast to southeast. However, they are primarily concentrated in the UFA and NAU regions due to their high population density and industrial area distribution. On the other hand, the potential source area for O\u003csub\u003e3\u003c/sub\u003e are more dispersed with a majority located around the UFA rather than its center. This could be attributed to lower NOx emissions in surrounding areas resulting in elevated levels of O\u003csub\u003e3\u003c/sub\u003e pollution.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e3.3 Influence of meteorological conditions on pollutants\u003c/h2\u003e\n \u003cp\u003eIn order to highlight the changes in pollutant concentrations following the liberalization and to accurately predict the predictors of the pollutant model, we selected key meteorological parameters, including atmospheric pressure, maximum wind speed and direction, wind speed, temperature, relative humidity, and rainfall. We computed the correlation between these meteorological parameters as well as their correlation with pollutant concentrations and assessed the significance of these correlations. The heat map of correlation coefficients is presented in Fig.\u0026nbsp;4. The results showed that all meteorological parameters except for maximum wind direction exhibited p-values less than 0.01, indicating a significant correlation with pollutant concentrations. The positive correlation between O\u003csub\u003e3\u003c/sub\u003e and temperature was particularly significant (Mantel's r ≥ 0.4, p \u0026lt; 0.01), suggesting that higher light or UV intensities can accelerate O\u003csub\u003e3\u003c/sub\u003e precursor reactions and exacerbate O\u003csub\u003e3\u003c/sub\u003e pollution. (Porter and Heald, 2019). Additionally, we observed a significant negative correlation between wind speed and particulate matter (r \u0026lt; 0.2, p \u0026lt; 0.01). Higher wind speeds promote dilution of particulate matter in the air while facilitating diffusion of pollutants (Reiminger et al., 2020). Conversely, weak winds hinder air pollutant dispersion leading to increased particulate matter concentration. Furthermore, the significant positive correlation between PM\u003csub\u003e2.5\u003c/sub\u003e and temperature (r ≥ 0.4, p \u0026lt; 0.01) suggests that elevated temperatures may enhance chemical reactions in the atmosphere resulting in conversion of gaseous pollutants into solid particulate matter thereby increasing PM\u003csub\u003e2.5\u003c/sub\u003e concentration (Le et al., 2023).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e3.4 Relative contribution of emissions and meteorology to pollutants\u003c/h2\u003e\n \u003cp\u003eAnthropogenic emissions and meteorological conditions are the primary determinants of air quality. Meteorological conditions may mask the true impact of emissions on air quality. To investigate the relative contribution of emissions and meteorology to pollution levels, we use the 'rmweather' package for meteorological normalization. Figure\u0026nbsp;5 displays the values of the pollutant concentrations after meteorological normalization. The results showed that, except for the relatively large fluctuation in O\u003csub\u003e3\u003c/sub\u003e concentration, the concentrations of other three pollutants remained relatively stable during the lockdown period (P1) under restrictive policies. The stability can be attributed to industrial moratoriums, motor vehicle restrictions, and people staying indoors. However, with the relaxation of these policies (P2) and even during subsequent Chinese holidays (P3), normalized values of these pollutants exhibited varying degrees of increase, suggesting an elevation in anthropogenic emission levels since policy liberalization. Through statistical analysis, we quantified the individual contributions of emissions and meteorology to air quality during different study periods (P1-P3). Our findings reveal that anthropogenic emissions contributed to a 33% increase in PM\u003csub\u003e2.5\u003c/sub\u003e concentration while unfavorable meteorological conditions accounted for a 40.2% increase in PM\u003csub\u003e2.5\u003c/sub\u003e concentration. Similar patterns were observed for PM\u003csub\u003e10\u003c/sub\u003e; emission-related contribution was estimated at 26.7%, whereas unfavorable meteorological contributions contributed by 43.3%. It is worth noting that although the meteorological conditions had a higher contribution rate than emissions alone, there was a substantial increase in emissions following policy liberalization compared to those recorded during restrictive periods, highlighting adverse effects associated with such policy changes on air quality.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e3.5 Air Pollutant Analysis and Forecast\u003c/h2\u003e\n \u003cp\u003eTo establish an accurate model for predicting air pollutant concentrations following a sudden relaxation of restrictive policies, we employed meteorological and pollutant data in the Random Forest algorithm while continuously optimizing the model parameters. Compared to the initial R\u003csup\u003e2\u003c/sup\u003e values ranging from 0.60 to 0.72, higher R\u003csup\u003e2\u003c/sup\u003e values were achieved in this study ranging from 0.70 to 0.89, indicates strong agreement between the predicted and observed values. In general, an R\u003csup\u003e2\u003c/sup\u003e value above 0.70 along with lower RMSE and MAE suggest improved performance of our optimized model. It is worth noting that the RMSE values for NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e models shown in Fig.\u0026nbsp;6 are lower than those reported by other studies utilizing the same modeling technique (Lv et al., 2023). Furthermore, the RMSE for PM\u003csub\u003e2.5\u003c/sub\u003e is consistently around 13 which outperforms XGBoost model for predicting PM\u003csub\u003e2.5\u003c/sub\u003e (Fan et al., 2020 ; Gui et al., 2020), whereas the RMSE for PM\u003csub\u003e10\u003c/sub\u003e is approximately 16 with MAE around 11.8. Both results surpass those obtained through XGBoost or ANN models as well as other prediction models (Zhang et al., 2023b, Masood and Ahmad, 2021). Therefore, our model demonstrates satisfactory performance in prediction.\u003c/p\u003e\n \u003cp\u003eThe comparison between the predicted and measured concentrations of pollutants (Fig.\u0026nbsp;7) indicates that the differences between the predicted and measured concentrations fall within a controllable range, demonstrating its strong prediction performance. Upon complete lifting of the restrictive policies, the disparity between measured and predicted monthly average concentrations becomes more pronounced. Most measured values for PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e exceed their corresponding predicted values. Conversely, the predicted values for all three phases of O\u003csub\u003e3\u003c/sub\u003e are higher than the measured values. The mean differences between the measured and predicted values for these pollutants are calculated to be -3.98 µg/m\u003csup\u003e3\u003c/sup\u003e, -4.87 µg/m\u003csup\u003e3\u003c/sup\u003e, -3.31 µg/m\u003csup\u003e3\u003c/sup\u003e, and 1.3 µg/m\u003csup\u003e3\u003c/sup\u003e, respectively. These discrepancies can primarily be attributed to the assumption that pollutant emissions remain at restricted levels after policy relaxation, a pattern consistent with findings from previous studies on pollutant concentration trends during periods of restriction (Hu et al., 2021, Li et al., 2022). Consequently, our model demonstrates superior capability in predicting air pollutant concentrations following policy liberalization while offering valuable insights into future air pollution control strategies under similar scenarios.\u003c/p\u003e\n \n \n \n \n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe air quality in Chongqing has exhibited significant variations across different time periods, with the respective AQI values of 62.0, 82.2, and 102.7 for P1, P2, and P3. In terms of spatial-temporal pollutant concentrations, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e levels were consistently low during the lockdown period but increased after the relaxation period commenced, indicating a substantial anthropogenic influence. Spatial distribution analysis also reveals that areas with concentrated human activities have higher pollution levels compared to regions with fewer anthropogenic activities, futher emphasizing the impact of anthropogenic activities on air quality in Chongqing. Conversely, O\u003csub\u003e3\u003c/sub\u003e demonstrates an inverse trend when compared to the other three pollutants both temporally and spatially. This observation may be attributed to the ‘titration reaction’ involving NOx compounds.\u003c/p\u003e\u003cp\u003eMost of the pollutants were from the local air masses during the study period. However potential sources from Guang'an, Dazhou, and Nanchong in northeast of Sichuan province, NAU in Chongqing as well as Zunyi in Guizhou province are identified as major contributors too. Following complete lifting of restrictive policies, the distribution of high-value areas of potential pollution sources demonstrates an expanding trend geographically.\u003c/p\u003e\u003cp\u003eThe parameter optimization within our random forest model enables accurate prediction of changes in air pollutant concentrations subsequent to policy relaxation. The impact of meteorological factors on pollutants is more pronounced in Chongqing, where anthropogenic emissions contribute to a 33% increase in PM\u003csub\u003e2.5\u003c/sub\u003e concentration, while unfavorable meteorological conditions accounted for a further 40.2% increase. For PM\u003csub\u003e10\u003c/sub\u003e, the emission and meteorological contributions are 26.7% and 43.3%, respectively, exacerbating the challenges faced by air pollution prevention, control, and management in this region. In summary, the abrupt policy liberalization has resulted in elevated concentrations of PM and NO\u003csub\u003e2\u003c/sub\u003e but a decrease in O\u003csub\u003e3\u003c/sub\u003e levels. Therefore, it is imperative to adopt coordinated control measures that consider both the influence of meteorological conditions and the complex terrain characteristics of Chongqing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe datasets used or analyzed in this study may be obtained from corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp; \u0026nbsp;\u003c/strong\u003eThis work was funded by the Science and Technology Commission of Chongqing project (No. CSTB2022NSCQ‐MSX0818) and Wanzhou project (wzstc-20220303).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution \u0026nbsp;\u003c/strong\u003e Haozheng Wang: Writing - original draft, Data Curation. Liuyi Zhang: Writing - review \u0026amp; editing, Review. Yuanjun Chen: Resources. Guangming Shi: Review \u0026amp; Revise, Supervision. Chentao Huang: Validation. Fumo Yang: Review, Supervision, Resources. Weihao Li: Investigation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; email address\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\[email protected]；[email protected]；[email protected]；[email protected]；[email protected]；[email protected]；[email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsmel N K, Muhammed F I, Hassan S I, et al. 2022. Assessment of ambient air quality in urban places of Mosul City, Iraq. International Journal of Environmental Science and Technology [J], 20: 3247-3264. https://doi.org/10.1007/s13762-022-04197-6.\u003c/li\u003e\n\u003cli\u003eBhatti U A, Zeeshan Z, Nizamani M M, et al. 2022. 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Acta Scientiae Circumstantiae [J], 37: 1835-1842. https://doi.org/10.13671/j.hjkxxb.2016.0356.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Air quality, Chongqing city, COVID-19 restrictions liberalization, Random forest, Meteorological normalization","lastPublishedDoi":"10.21203/rs.3.rs-4584877/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4584877/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo mitigate the societal impact of the COVID-19 pandemic, China implemented long-term restrictive measures. The sudden liberalization at the end of 2022 disrupted residents\u0026rsquo; daily routines, making it scientifically intriguing to explore its effect on air quality. Taking Chongqing City in Southwest China as an example, we examined the impact of restriction liberalization on air quality, identified potential sources of pollutants, simulated the effects of abrupt anthropogenic control relaxation using a Random Forest Model, and applied an optimized model to predict the post-liberalization pollutant concentrations. The results showed increases in PM\u003csub\u003e2.5\u003c/sub\u003e (72.3%), PM\u003csub\u003e10\u003c/sub\u003e (67.7%), and NO\u003csub\u003e2\u003c/sub\u003e (21.9%) concentrations while O\u003csub\u003e3\u003c/sub\u003e concentration decreased by 20.5%. Although potential pollution source areas contracted, pollution levels intensified with northeastern Sichuan, interior Chongqing, and northern Guizhou being major contributors to pollutant emissions. Anthropogenic emissions accounted for 26.7% ~ 33% changes in PM\u003csub\u003e2.5\u003c/sub\u003e、PM\u003csub\u003e10\u003c/sub\u003e concentrations while meteorological conditions contributed to 40.2% ~ 43.3% variations observed during the period. The optimized model demonstrated correlation between predicted and observed values with R\u003csup\u003e2\u003c/sup\u003e ranging from 0.70 to 0.89, enabling accurate prediction of post-liberalization pollutant concentrations. This study can enhance our understanding regarding the impact of sudden social lockdown relaxation events on air quality while providing support for urban air pollution prevention.\u003c/p\u003e","manuscriptTitle":"Impact of COVID-19 Restrictions Liberalization on Air Quality: A Case Study of Chongqing, Southwest China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 14:19:15","doi":"10.21203/rs.3.rs-4584877/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-11T21:29:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-11T14:13:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-02T08:03:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177012953521054437922385399720536487127","date":"2024-08-02T01:15:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289741221720186222099888496655464339146","date":"2024-07-22T00:36:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-05T12:52:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-02T12:21:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-02T12:20:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-06-15T04:53:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2f425b39-b90d-4a54-b44e-6dde7fa9bf7d","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T16:22:59+00:00","versionOfRecord":{"articleIdentity":"rs-4584877","link":"https://doi.org/10.1007/s10661-024-13213-w","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2024-10-28 16:13:04","publishedOnDateReadable":"October 28th, 2024"},"versionCreatedAt":"2024-07-24 14:19:15","video":"","vorDoi":"10.1007/s10661-024-13213-w","vorDoiUrl":"https://doi.org/10.1007/s10661-024-13213-w","workflowStages":[]},"version":"v1","identity":"rs-4584877","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4584877","identity":"rs-4584877","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}