key: cord-0788644-junibzr4 authors: Luo, Keyu; Wang, Zhenyu; Wu, Jiansheng title: Association of population migration with air quality: Role of city attributes in China during COVID-19 pandemic (2019–2021) date: 2022-04-18 journal: Atmos Pollut Res DOI: 10.1016/j.apr.2022.101419 sha: 294d315629a3e763173e386f82609f2b094dea72 doc_id: 788644 cord_uid: junibzr4 Atmospheric pollution studies have linked diminished human activity during the COVID-19 pandemic to improve air quality. This study was conducted during January to March (2019–2021) in 332 cities in China to examine the association between population migration and air quality, and examined the role of three city attributes (pollution level, city scale, and lockdown status) in this effect. This study assessed six air pollutants, namely CO, NO(2), O(3), PM(10), PM(2.5), and SO(2), and measured meteorological data, with-in city migration (WCM) index, and inter-city migration (ICM) index. A linear mixed-effects model with an autoregressive distributed lag model was fitted to estimate the effect of the percent change in migration on air pollution, adjusting for potential confounding factors. In summary, lower migration was associated with decreased air pollution (other than O(3)). Pollution change in susceptibility is more likely to occur in NO(2) decrease and O(3) increase, but unsusceptibility is more likely to occur in CO and SO(2), to city attributes from low migration. Cities that are less air polluted and population-dense may benefit more from decreasing PM(10) and PM(2.5). The associations between population migration and air pollution were stronger in cities with stringent traffic restrictions than in cities with no lockdowns. Based on city attributes, an insignificant difference was observed between the effects of ICM and WCM on air pollution. Findings from this study may gain knowledge about the potential interaction between migration and city attributes, which may help decision-makers adopt air-quality policies with city-specific targets and paths to pursue similar air quality improvements for public health but at a much lower economic cost than lockdowns. associated with decreased air pollution (other than O3). Pollution change in 22 susceptibility is more likely to occur in NO2 decrease and O3 increase, but 23 unsusceptibility is more likely to occur in CO and SO2, to city attributes from low 24 migration. Cities that are less air polluted and population-dense may benefit more from 25 decreasing PM10 and PM2.5. The associations between population migration and air 26 pollution were stronger in cities with stringent traffic restrictions than in cities with no 27 lockdowns. Based on city attributes, an insignificant difference was observed between 28 the effects of ICM and WCM on air pollution. Findings from this study may gain 29 knowledge about the potential interaction between migration and city attributes, which 30 may help decision-makers adopt air-quality policies with city-specific targets and paths 31 to pursue similar air quality improvements for public health but at a much lower 32 economic cost than lockdowns. . Indeed, the inter-city and intra-city traffic recorded a 37.8% and 14.0% 76 drop, respectively, during the lockdown in Wuhan (Xiong et al., 2020) . Consequently, 77 the emission from traffic sources was reduced. Although the shutdown of certain 78 industries (e.g. manufacturing and catering industries) contributed to the improved air 79 quality (e.g., PM2.5), almost no change in SO2 concentration was observed in multiple 80 cities because the production of certain steel, coking, gas, water, and power factories 81 was not interrupted due to production needs (He et al., 2021b) . In addition, pollution 82 discharge from residential emissions (e.g., due to coal heating activities) and essential 83 industry remained steady or not significantly declined (Faridi et al., 2021) . Therefore, 84 J o u r n a l P r e -p r o o f the pollution levels in cities with emissions dominated by coal power generation and 85 residential sources were not significantly reduced (Kerimray et al., 2020) . 86 Because there are obvious differences in climate, population distribution, 87 economic structure, and urbanization process in Chinese regions with a vast territory 88 (H. Wang et al., 2021) , air pollution variation during the lockdown also has regional 89 characteristics. As shown in Table S1 also matters for detecting associations (Chen et al., 2018) . To further empirically 105 examine the intermediary modifier role of city attributes on air pollution decrease due 106 J o u r n a l P r e -p r o o f to migration, to our knowledge, is a novel contribution. 107 Our study aimed to elucidate the modification effects of city attributes (i.e., 108 pollution level, city scale, and response status) on the link between migration level and 109 air quality. For example, the association between population migration and air pollution 110 was expected to be stronger in cities that were polluted (versus clean), were large 111 (versus small), or cities that adopted traffic restrictions (cities with versus no attributes, which may support city-specific targets and paths for air pollution control. 124 The modification effect of city attributes matters for employing initiatives by city 125 managers and residents to reduce emissions, even at a relatively low level for public 126 health. Future environmental policies should pursue similar air quality improvements 127 likewise traffic restrictions but at a much lower economic cost. values that represent the spatial trajectory and characteristics of the population 146 migration, and it does not distinguish between transport types (Lin and Peng, 2020). 147 The dataset has been widely used in geo-economics, demography, and epidemiology, 148 which has also served as an important data source for COVID-19-related research (H. 149 Other data were also used in this study. Ground-observation air pollution data were The total air pollution concentrations changed dramatically in percentage during 328 the three-year study period (Fig. 3d) . The AQI in 2020 decreased by 7.1% from that in Table 2 and Table 5 Table 3 and Table 5 Table 4 and Table 5 present the results of effect modification by response status. 446 The response status modification effect strengthened the associations between WCM-447 NO2 and ICM-NO2, but weakened the associations between ICM-PM2.5. The lockdown was associated with air quality improvement. However, stricter 459 lockdowns had a greater impact on air pollution reduction than district lockdowns. 460 Taking NO2 and PM2.5 as an example, ln(ICM) exerted the largest impact on inter-city 461 lockdown cities, followed by complete lockdown, and within-city lockdown cities. 462 Similarly, ln(WCM) had the largest impact on within-city lockdown cities, followed by 463 the complete lockdown and inter-city lockdown cities. both between and within groups or stratifications of cities; therefore, a linear mixed 571 model with a random subclass-specific intercept was applied in this study. Therefore, 572 the risk of bias due to confounding factors should be dismissed more than those 573 observed in a cross-sectional study design, although bias due to unmeasured or residual 574 J o u r n a l P r e -p r o o f confounding can never be eliminated (Regencia et al., 2020) . The large-sample analysis 575 further helps to address challenges from city-specific time-invariant characteristics and 576 plausibly estimate the average effect in each subclass of city attributes (He et al., 2020) . 577 The novelty of this study is that it identifies the modifier role of city scale, pollution 578 level, and response status in the relationship between migration and air pollution. The 579 evidence from this study may support city-specific air pollution control. Our findings 580 also suggest that initiatives by city managers and residents to reduce emissions can 581 effectively reduce air pollution, even at a relatively low level. Short-term effects of Ambient Ozone, PM2.5, and 629 meteorological factors on COVID-19 confirmed cases and deaths in Queens Introduction: Air Pollution in China Does lockdown reduce air pollution? Evidence from 44 635 cities in northern China Correlation between climate indicators and COVID-19 pandemic in New York Valuation of air pollution externalities: comparative assessment of 643 economic damage and emission reduction under COVID-19 lockdown Correlation 650 between the migration scale index and the number of new confirmed coronavirus 651 disease 2019 cases in China Two-way effect 656 modifications of air pollution and air temperature on total natural and 657 cardiovascular mortality in eight European urban areas Spatiotemporal 660 mapping and multiple driving forces identifying of PM2.5 variation and its joint 661 management strategies across The promise of Beijing: Evaluating the 664 impact of the 2008 Olympic Games on air quality COVID-19 induced lockdown measures on maritime settings of a coastal region The distribution and drivers of PM2.5 in a rapidly urbanizing region: The 671 Belt and Road Initiative in focus The effect of COVID-19 pandemic on human mobility and ambient 675 air quality around the world: A systematic review. Urban Clim. 38, 100888 Spatial-temporal variations in 678 atmospheric factors contribute to SARS-CoV-2 outbreak COVID-19: air pollution remains low as people stay at home. Air 681 Changing travel patterns in China during the early stages of the 691 COVID-19 pandemic Do socioeconomic 694 factors modify the effects of PM1 and SO2 on lung cancer incidence in China Global population exposed to fine 697 particulate pollution by population increase and pollution expansion Global, continental, and national 701 variation in PM2.5, O3, and NO2 concentrations during the early 2020 COVID-702 19 lockdown COVID-19 event on the NOx emissions of key polluting enterprises in China The short-term impacts of COVID-19 lockdown on 708 urban air pollution in China Air pollution and 711 critical air pollutant assessment during and after COVID-19 lockdowns: 712 Evidence from pandemic hotspots in China, the Republic of Korea Road traffic and air pollution: Evidence from a 715 nationwide traffic control during coronavirus disease 2019 outbreak COVID-19's impact on the atmospheric environment in the Southeast Asia 719 region Assessing air quality changes in large cities 723 during COVID-19 lockdowns: The impacts of traffic-free urban conditions in 724 Sky Over Delhi Due to Coronavirus Disease (COVID-19) Lockdown Drop in urban air 731 pollution from COVID-19 pandemic: Policy implications for the megacity of São 732 The positive impact of lockdown in Wuhan on 735 containing the COVID-19 outbreak in China Electrostatic charged nanofiber filter for filtering 738 airborne novel coronavirus (COVID-19) and nano-aerosols COVID-19-Induced Lockdowns Indicate the Short-Term Control Effect Air Pollutant Emission in 174 COVID-19 pandemic persuaded lockdown effects on 745 environment over stone quarrying and crushing areas Global 749 assessment of tropospheric and ground air pollutants and its correlation with Airborne heavy metals and blood pressure: Modification by sex 754 and obesity in the MMDA traffic enforcers' health study Impact of COVID-19 outbreak measures of lockdown on 757 the Italian Carbon Footprint Effect of 760 restricted emissions during COVID-19 on air quality in India Impact of urbanization on air quality in the Yangtze River Delta during the 764 COVID-19 lockdown in China Amplified ozone pollution in cities during the 768 COVID-19 lockdown Lockdown in India Dramatically Reduced Air Pollution Indices in Lucknow and 772 Characteristics of air quality in different climatic zones of China during the 776 COVID-19 lockdown APEC blue"-The effects and implications 779 of joint pollution prevention and control program COVID-19 lockdown measures on air quality in Northern China A preliminary assessment of the impact of COVID-19 on 785 environment -A case study of China Changes in air 788 quality related to the control of coronavirus in China: Implications for traffic and 789 industrial emissions Spatio-temporal evolution of ozone 792 pollution and its influencing factors in the Beijing-Tianjin-Hebei Urban 793 Agglomeration Spatial statistics and influencing 796 factors of the COVID-19 epidemic at both prefecture and county levels in Hubei 797 Spatial-temporal variability of PM2.5 air quality in Beijing, 800 China during 2013-2018 The impacts of human migration and city lockdowns on 803 specific air pollutants during the COVID-19 outbreak: A spatial perspective Drivers of improved PM2.5 air quality in China from Evaluating the contributions of changed meteorological conditions and emission 814 to substantial reductions of PM2 Comparative 818 analysis of the impact of weather conditions and human activities on air quality 819 in the Dongting and