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. 2023 May 24;331:121886. doi: 10.1016/j.envpol.2023.121886

Response of PM2.5 pollution to meteorological and anthropogenic emissions changes during COVID-19 lockdown in Hunan Province based on WRF-Chem model☆

Simin Dai a,1, Xuwu Chen b,1, Jie Liang a, Xin Li a, Shuai Li a, Gaojie Chen c, Zuo Chen d, Juan Bin a, Yifan Tang a, Xiaodong Li a,∗
PMCID: PMC10206404  PMID: 37236582

Abstract

In December 2019, the New Crown Pneumonia (the COVID-19) outbroke around the globe, and China imposed a nationwide lockdown starting as early as January 23, 2020. This decision has significantly impacted China's air quality, especially the sharp decrease in PM2.5 (aerodynamic equivalent diameter of particulate matter less than or equal to 2.5 μm) pollution. Hunan Province is located in the central and eastern part of China, with a “horseshoe basin” topography. The reduction rate of PM2.5 concentrations in Hunan province during the COVID-19 (24.8%) was significantly higher than the national average (20.3%). Through the analysis of the changing character and pollution sources of haze pollution events in Hunan Province, more scientific countermeasures can be provided for the government. We use the Weather Research and Forecasting with Chemistry (WRF-Chem, V4.0) model to predict and simulate the PM2.5 concentrations under seven scenarios before the lockdown (2020.1.1–2020.1.22) and during the lockdown (2020.1.23–2020.2.14). Then, the PM2.5 concentrations under different conditions is compared to differentiate the contribution of meteorological conditions and local human activities to PM2.5 pollution. The results indicate the most important cause of PM2.5 pollution reduction is anthropogenic emissions from the residential sector, followed by the industrial sector, while the influence of meteorological factors contribute only 0.5% to PM2.5. The explanation is that emission reductions from the residential sector contribute the most to the reduction of seven primary contaminants. Finally, we trace the source and transport path of the air mass in Hunan Province through the Concentration Weight Trajectory Analysis (CWT). We found that the external input of PM2.5 in Hunan Province is mainly from the air mass transported from the northeast, accounting for 28.6%–30.0%. To improve future air quality, there is an urgent need to burn clean energy, improve the industrial structure, rationalize energy use, and strengthen cross-regional air pollution synergy control.

Keywords: PM2.5, COVID-19, Hunan province, Meteorological, Anthropogenic emissions, Regional transportation

Graphical abstract

Image 1

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1. Introduction

In the end of 2019, COVID-19 broke out unexpectedly and spread worldwide in an explosive pace (Chen, Z. L. et al., 2020; Huang et al., 2020; Qi et al., 2020). To ensure the safety of people's lives and disrupt the rapid spread of the virus, several countries began to implement various control measures one after another (Abdullah et al., 2020; Sun et al., 2020; Tobias et al., 2020). Since February 2020, China started a series of outbreak controls, including strict closure of public transport systems, restrictions on daily travel, and avoidance of mass gatherings. As a result, sharply reduced activities in transportation and industrial production brought down the emissions of various types of pollution in numerous Chinese cities and improving the quality of the atmosphere to a large extent (Bao & Zhang, 2020; Pei et al., 2022; Wang, Y. et al., 2020). This natural alteration gave researchers a good time to investigate the effects of reduced human emissions on atmospheric quality. Several reports have shown some reduction in PM2.5 concentrations in several regions of China during the COVID-19 (Bao & Zhang, 2020; Huang et al., 2020; Sokhi et al., 2021; Wang, P. et al., 2020). Compared with the pre-lockdown period, PM2.5 concentrations decreased by 41.2% in Wuhan, the central city of the outbreak, by 50% in Hangzhou, by 37%–55% in four major cities of Yangtze River Delta (YRD) (i.e., Hangzhou, Nanjing, Shanghai, and Hefei), and by 33%–44% in Shanghai, among others (Chen, H. et al., 2020; Liu et al., 2020). Nevertheless, with the COVID-19 on lockdown, heavy PM2.5-dominated haze pollution events continue to occur the east and north of China (Huang et al., 2020). In particular, the Beijing-Tianjin-Hebei (BTH) region in northeast China had exceptionally high concentrations of the atmospheric fine particle. In January and February 2020, the average hourly concentration of PM2.5 in Beijing reached 58.6 and 62.7 μg/m3 respectively, while that in Tianjin reached 102.6 and 61.6 μg/m3 respectively (Nichol et al., 2020). The above results indicate the PM2.5 levels in China have significant spatial and temporal variability under the tight control of COVID-19. In addition, the driving forces for PM2.5 concentrations difference vary by regions. In the BTH during the COVID-19, the increased PM2.5 concentrations could be contributed to the combination effect of reduced anthropogenic emissions, adverse meteorological parameters (e.g., lower speed of wind and higher relative humidity), and the cross-regional migration of pollutants. In contrast, the main factor for the decreased PM2.5 concentrations in Wuhan and the four main cities of the YRD (Hangzhou, Nanjing, Shanghai and Hefei) is the control of anthropogenic emission sources (Huang et al., 2021; Liu et al., 2020). This also indicates that more stringent and targeted measures are advantageous to suppressing emissions and improving air quality in China. Therefore, it is meaningful to explore the spatial distribution patterns of PM2.5, the influencing factors, and the key pollution sources of PM2.5 pollution changes in diverse Chinese cities during the lockdown period for analysis.

Around the world, many researchers have used satellite data, recommendation analysis, machine learning and model simulation to understand and analyze the changes of air pollutants during COVID-19 and their main influencing factors and related mechanisms. In China, scholars made a comparative analysis of the observed data and the simulation results of WRF-Chem model in the YRD and the PRD and found that during COVID-19 lockdown, differences in meteorological conditions and O3–VOCs-nox relationship led to different O3 responses in the two urban agglomerations (Wang, Nan et al., 2021). For Wuhan, Hubei Province, the epicenter of the epidemic, many researchers quantified the contribution of meteorological conditions change and pollution reduction to the improvement of ambient air quality in Hubei province through WRF-Chem model and random forest tree method (Yin et al., 2020; Zheng, Huang et al., 2020). In Southern California, the researchers compared the change of population-weighted PM2.5 concentrations before and after the lockdown, based on the WRF-Chem model and ground-based observations, and found that most of the drop coming from emissions reductions (68%) and the rest from meteorological changes (32%) (Jiang et al., 2021). In the Italian study, they used a high-resolution chemical transport model (CTM) to assess how different restrictions affect air pollutant concentrations and the complex physicochemical mechanisms involved (D'Isidoro et al., 2022).

Hunan Province is an inland region with a developed economy and rapid urban development, which has a special topography of “U" shape surrounding three sides of mountains. At the same time, it is close to Wuhan, the epicenter of the COVID-19. In recent years, the overall PM2.5 pollution level in Hunan Province is relatively serious in the whole country (Dai et al., 2019). However, up to now, the impact of the most stringent lockdown measures and meteorological conditions on PM2.5 levels, chemical composition, and local or regional transport sources in Hunan Province remains unknown. Therefore, this project intends to take Hunan Province as the research object on the basis of the WRF-Chem coupled model, and quantitatively assess the impact of the changing meteorological conditions, local anthropogenic emission reduction and cross-regional transmission on PM2.5 pollution. Finally, this study is helpful for decision makers to formulate more effective and accurate pollutant emission control measures and short- and long-term emission control targets for Hunan Province and other regions with similar topography and pollution characteristics even in the post-COVID-19 era.

2. Methods and materials

2.1. Data sources

The current study obtained hourly ground-level PM2.5 mass concentrations observations from January 1, 2020, to February 14, 2020, from the China National Environmental Monitoring Center (CNEMC, http://www.cnemc.cn/). So far, there are more than 1500 monitoring stations nationwide, and there are 66 monitoring stations in Hunan Province in the study area to monitor data, including PM2.5, PM10, NO2, SO2, O3, and CO, which are six conventional air pollutants and have been well applied in domestic atmospheric research in recent years. All meteorological observations of wind speed (WS), wind direction (WD), surface temperature (T2), relative humidity (RH), etc. For each city are taken from the China National Meteorological Data Center (NOAA, http://www.cma.gov.cn/). In this simulation study, the meteorological conditions during the study period were divided into two groups. One group was the meteorological conditions during 1 January to January 22, 2020 (PRE), and the other group was the meteorological conditions during 23 January to February 22, 2020 (LOCK).

This paper proposed to use the Multiresolution Emission Inventory for China (MEIC, http://www.meicmodel.org) published from Tsinghua University as the emission source data for the WRF-Chem model. The inventory that counts gases and aerosol species from the industrial, power plant, transportation, residential combustion, and agricultural activity sectors (SO2, NOx, NH3, CO, VOCs, BC, OC, PM10, and PM2.5) (Li et al., 2017; Zheng et al., 2018). The MEIC inventories have been applied in many of these air quality investigations (Li et al., 2017; Zheng et al., 2018; Zheng et al., 2021).

In the simulation, the baseline anthropogenic emission inventory was derived from the anthropogenic emission data for January 2020 (M1) and February 2020 (M2) in the MEIC inventory. In order to meet the needs of scenario simulation in the research process, we processed the benchmark lists M1 and M2 to obtain the emission list under the specific scenario as shown in Table 1 . M3 and M4 are obtained by adjusting the anthropogenic emissions of M1 and M2 in Hunan Province to 0. M5 and M6 are obtained by adjusting the anthropogenic emissions of four provinces in the northeast of Hunan province (Hubei, Jiangxi, Anhui and Henan province) in M1 and M2 to 0.

Table 1.

Emission inventory settings in WRF-Chem simulation scenario settings.

Anthropogenic emission inventory (MEIC) 2020.01 (Pre-COVID) 2020.02 (COVID)
Baseline anthropogenic emissions inventory M1 M2
Anthropogenic emissions in Hunan Province are zero M3 M4
Anthropogenic emissions in the four northeastern provinces (Hubei, Jiangxi, Anhui and Henan provinces) of Hunan were zero M5 M6
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2.2. WRF-chem model

Utilizing the regional mesoscale air quality model WRF-Chem version 4.0 (Skamarock et al., 2019), the spatial distribution of PM2.5 pollution events and meteorological parameters in Hunan Province before and during the lockdown of the COVID-19 was simulated. WRF-Chem was jointly developed by National Center for Atmospheric Research (NCAR), Pacific Northwest National Laboratory (PNNL) and National Oceanic and Atmospheric Administration (NOAA). It is commonly used to study atmospheric pollution occurrences and the relationship between clouds and photochemical reactions, and also couples all transport processes of the chemical transport module Chem and the meteorological model WRF online. For instance, it can be used to simulate not only meteorological data in terms of T2, RH, WS, and pressure, but also processes such as emission, transport, mixing, and transformation of each pollutant gas and aerosol in the atmosphere. We utilize the WRF-Chem model to explore how PM2.5 and its fractions change driven by anthropogenic emission controls, regional transport, and changes in meteorological conditions, which will enable us to get a deeper understanding of the intrinsic drivers of PM2.5 and its components.

Regarding the model simulation configuration, Tables S1 and S2 summarize the physical and chemical settings of WRF-Chem, respectively. In this study, the WRF-Chem single-layer model was adopted, and the simulation area covered the eastern part of China (Fig. 1 a). Starting from the central location (34.5°N, 116.5°E), the number of grids was set at 105 and 120 along the east-west and north-south directions, with a grid resolution of 30 × 30 km and 35 vertical layers. The initial and boundary conditions of the meteorological field are sourced from National Center for Environmental Prediction's Final Operational Global Analysis (NCEP/FNL) reanalysis information with a time interval of 6 h and a horizontal grid spacing of 0.25° (https://rda.ucar.edu/datasets/ds083.2/) (NCEP, 2015). In parallel, the simulation also utilizes the MM5 similarity surface layer scheme, the Yonsei University (YSU) boundary layer parameterization scheme (Hong et al., 2006), and the Grell 3D cumulus parameterization scheme (Grell et al., 2011; Grell et al., 2005; Grell GA et al., 2010), etc. The chemical initial fields and boundary conditions were obtained from CESM data with a time resolution of 6 h (Horowitz et al., 2003). Other settings on gas and aerosol parameters include the MOSAIC aerosol activation scheme (Zaveri & Peters, 1999) and the CBM-Z chemical mechanism (Zaveri & Peters., 1999) and, among others. The sources of anthropogenic emissions are from the MEIC released by Tsinghua University (www.meicmodel.org); natural gas and aerosol emission models (MEGAN) are calculated online to simulate biogenic emissions (Guenther et al., 2012).

Fig. 1.

Fig. 1

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(a) WRF-Chem simulation area. Important provinces (including Hunan, Hubei, Henan, Jiangxi and Anhui) and important city clusters (including Yangtze River Delta (YRD), Pearl River Delta (PRD) and Beijing-Tianjin-Hebei (BTH)) are marked on the map; (b) elevation map of Hunan Province and location of air quality stations in Hunan Province.

2.2.1. Scenario settings

To investigate the driving forces of changes in PM2.5 and its components in Hunan Province during the COVID-19 and associated impacts of meteorological changes anthropogenic emissions and regional transport on PM2.5 pollution in Hunan Province, we set up a series of simulation scenarios using the WRF-Chem model (Table 2 ). So C1 and C2 are a set of baseline simulations designed to simulate the true atmospheric environment and pollutant levels before and after COVID-19 lockdown; C2 and C3 are a set of sensitivity simulation scenarios designed to explore meteorological effects; C4 and C5 are a series of sensitive simulation scenarios to investigate the impact of local emissions in Hunan Province; C6 and C7 are a set of sensitivity simulation scenarios to study the impact of regional traffic.

Table 2.

Emission inventory and Meteorological conditions in Seven simulation scenarios.

Case Emission inventory Meteorological conditions
C1 M1 PRE
C2 M2 LOCK
C3 M2 PRE
C4 M3 PRE
C5 M4 LOCK
C6 M5 PRE
C7 M6 LOCK
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The significance of the simulation scenario design for this study is described in Table 3 . According to the backward trajectory model to simulate the transport path of PM2.5 air mass in Hunan Province (see Section 3.3), PM2.5 pollution air mass in Hunan Province mainly comes from four important provinces in the northeast of Hunan Province (Henan, Shandong, Anhui, Jiangsu and Jiangxi Province) (Fig. 7). The emission reduction mentioned in the article refers to the reduction of man-made emissions of various sectors and emissions of various pollutants in the MEIC inventories during the COVID-19 control period compared with the pre-COVID-19 control period. All experimental scenario simulations started running one week earlier than the first day of the month.

Table 3.

Simulation scenarios analysis methods and meaning descriptions.

Analysis methods Descriptions Absolute value
C2–C1 Impact of COVID-19 control measures on PM2.5 concentration in Hunan Province 14.37 μg/m3
C2–C3 Impact of changing meteorological conditions on changes in PM2.5 during COVID-19 0.21 μg/m3
(C2–C3)/C3 Contribution of changing meteorological conditions on changes in PM2.5 during COVID-19 0.5%
C4–C1 Impact of local emissions to PM2.5 concentration in Hunan Province before the COVID-19 31.11 μg/m3
(C4–C1)/C1 Contribution of local emissions to PM2.5 concentration in Hunan Province before the COVID-19 56.9%
C5–C2 Impact of local emissions to PM2.5 concentration in Hunan Province during the COVID-19 20.65 μg/m3
(C5–C2)/C2 Contribution of local emissions to PM2.5 concentration in Hunan Province during the COVID-19 51.2%
C6–C1 Impact of PM2.5 contributed by the main source provinces and regions before the COVID-19 15.64 μg/m3
(C6–C1)/C1 Contribution of PM2.5 contributed by the main source provinces and regions before the COVID-19 28.6%
C7–C2 Impact of PM2.5 contributed by the main source provinces and regions during the COVID-19 12.12 μg/m3
(C7–C2)/C2 Contribution of PM2.5 contributed by the main source provinces and regions during the COVID-19 30.0%
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Fig. 7.

Fig. 7

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Cluster analysis of backward air flow trajectory and potential PM2.5 pollution sources in Hunan Province (a) before and (b) during lockdown.

2.3. Backward trajectory and CWT model

The Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT, http://www.arl.NOAA.gov/hysplit_info.php) is largely credited to the Australian Bureau of Meteorology (BOM) and the National Oceanic Atmospheric Center (NOAA). And it is one of the most popular models to identify air mass trajectories, which are available to assess the inter-regional spread of contaminants (Li et al., 2018; Liu et al., 2016; Lu et al., 2018; Su et al., 2015; Wang, Y. Q. et al., 2009). The HYSPLIT model was used to obtain the inverse trajectories of air masses of this study, and then cluster to determine the potential origins and dominant transport paths of atmospheric pollutants. By inputting the wind field reanalysis meteorological data provided by the Global Data Assimilation System (GDAS) of the National Center for Environmental Prediction (http://www.arl.noaa.gov/), we calculate the inverse trajectory reaching Changsha City (28.12°N, 112.59°E) for a duration of 48 h with a horizontal resolution of 1°, a time step of 1 h, and an initial height of 100 m above the ground.

Concentration Weight Trajectory Analysis (CWT) gets the weighted concentration of a trajectory by calculating the average value of sample concentrations that correspond to each path crossing a single grid at a specific time, thus quantifying the contamination levels of different trajectories. We use CWT to weigh the trajectories associated with PM2.5 in this study to analyze the possible area of PM2.5 pollution sources and to quantify the concentrations contribution levels of external transport. The geographical area is divided into 0.5° × 0.5° grid cells. The CWT is expressed as：

CWTij=∑l=1MCl×τijl∑l=1Mτijl×Wij (1)

where CWTij means the average weight concentrations of the lth backward trajectory in the ijth grid cell, and Cl represents the corresponding PM2.5 concentrations when trajectory l goes through the ijth grid cell. τ ij is the residence time of trajectory l when it is in the ijth grid cell. Wij is the weight factor assigned, which is related to the number of trajectories, the number of endpoints and the number of grids the trajectory passes through. It is used to reduce the uncertainty of the grid cells. The CWT model analysis is also performed through the TrajStat module of HYSPLIT model (Wang, K. et al., 2009).

2.4. Model performance evaluation

In this study, the mean deviation (MB), standardized and mean deviation (NMB), root mean square error (RMSE) and index of agreement（IOA）are selected to evaluate and analyze the model simulated pollutant concentrations results and the measured results (Emery et al., 2017). The equations for these parameters are presented in Table S3.

According to the ground monitoring data from China National Environmental Monitoring Station (CNEMC) and the meteorological data from NOAA (National Climatic Data Center) (https://gis.ncdc.noaa.gov/maps/ncei), the simulated values of PM2.5 in Hunan Province during 2020.1.1–2020.2.14 were compared with the simulated values. PM2.5 observations were compared with the simulated values to verify the simulation accuracy of the WRF-Chem model for the three meteorological factors and PM2.5 concentrations. A comparison of the hour-by-hour observations with the model simulation results is shown in Fig. 2 . The effectiveness of WRF-Chem for meteorological simulations and PM2.5 concentrations is generally reasonable in terms of the evaluation metrics of meteorological simulations and PM2.5 concentrations (e.g., Table 4 ). The NMB metrics indicate that the simulated deviations of the WRF-Chem model for these three meteorological factors and PM2.5 concentrations range from 7% (T2) to 24% (WS), corresponding to IOA of 0.93 (T2), 0.77 (RH),0.46 (WS), and 0.69 (PM2.5), respectively. These data indicate that the WRF-Chem model simulations are able to reproduce the temporal trends of meteorological elements and PM2.5 concentrations, especially temperature.

Fig. 2.

Fig. 2

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Verification of simulation performance of meteorological elements (T2, RH and WS) and PM2.5 concentrations.

Table 4.

The results of model performance related indexes of WRF-Chem model when reflecting meteorological parameters and PM2.5 concentration.

MB NMB RMSE IOA
T2 0.5 7% 2.1 0.93
RH −9.6 −11% 13.5 0.77
WS 0.1 5% 2.1 0.46
PM2.5 2.9 8% 14.6 0.69
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3. Results and discussions

3.1. The change of emissions and observed PM2.5 in hunan during the COVID-19 lockdown

After the Chinese government took a series of closure measures to keep the spread of the COVID-19 under control, the PM2.5 concentrations changed significantly (Chu et al., 2021; Xu, K. J. et al., 2020; Zheng, H., Kong, S. et al., 2020). Fig. 3 a shows the spatial distribution of air pollutants in Hunan Province and surrounding areas before the COVID-19 lockdown. The central provinces of Hubei, Hunan and Anhui are the second most polluted after the northern BTH region and its surrounding cities, while the southeast coastal region and Yunnan Province are relatively less polluted, which is basically in line with the outcomes of previous studies (Li et al., 2016; Wang et al., 2015). In terms of PM2.5 concentrations, the 24 h average PM2.5 concentrations in most of the simulated areas was higher than the WHO standard (＞15 μg/m3). In Hunan Province, the 24 h average PM2.5 concentrations reached 54.72μg/m3, far higher than the WHO standard, and also belonged to the region with serious PM2.5 pollution.

Fig. 3.

Fig. 3

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(a) Spatial distribution of PM2.5 concentrations in Hunan Province and its surrounding areas before the COVID-19 lockdown; (b) Compared with before the COVID-19 lockdown，Spatial distribution of PM2.5 concentration variation in Hunan and surrounding areas during the COVID-19 lockdown.

As can be observed from Fig. 3b, clear spatial differences in PM2.5 concentrations existed during the lockdown period. Especially in China's economically important city clusters, such as the Pearl River Delta (PRD), YRD and BTH regions. The PM2.5 concentrations in the PRD and YRD decreased remarkably 34.1% and 34.2%, respectively, but the average PM2.5 concentrations in the BTH region merely dropped by 7.9% (Fig. 4 ). Previous studies have suggested that the dominant factor in the reduction of PM2.5 concentrations in the YRD is the reduced primary PM (Liu et al., 2020). Similarly, the impact of meteorological conditions is less than that of emission reduction on PM2.5 in most areas of PRD, while regional transport strengthens secondary pollution (Wang, N. et al., 2021). However, In the BTH, due to the significant reduction of NOx and the accumulation of O3 during the COVID-19, atmospheric oxidation and the generation of secondary particulate matter are promoted. In addition, adverse meteorological factors and the spread of polluted air masses in peripheral areas also offset part of the decrease in PM2.5 concentration (Nichol et al., 2020).

Fig. 4.

Fig. 4

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Change rate of observed PM2.5 concentrations in China, Hunan Province, Yangtze River Delta (YRD), Pearl River Delta (PRD) and Beijing-Tianjin-Hebei (BTH).

According to the data statistics from the MEIC (Table S4), Hunan Province has a large primary pollutant emission. For example, PM2.5, SO2, CO, OC, BC emissions ranked second in the country, NH3, VOCs, NOx emissions compared with other provinces in the forefront. As seen in Table S5, the COVID-19 control measures in Hunan Province have significantly reduced the emissions of primary pollutants. PM2.5, NOx, SO2, CO, BC and OC emissions decreased significantly, by 55.0%, 44.9%, 54.4%, 56.3%, 54.9% and 56.8%, respectively, at which time the rate of change of the average PM2.5 concentrations in Hunan Province was −24.8%. These emission reductions were also reflected in the observed changes in NO2 and SO2 concentrations, which were −54.9% and −30.3%, respectively, in Hunan Province. At a national scale, however, PM2.5, NOx, SO2, CO, BC, and OC emissions decreased by 37.0%, 38.3%, 39.9%, 40.5%, 35.8%, and 34.7%, respectively. The rate of change of the nationwide average PM2.5 concentrations at this time was −20.3%. This points to greater anthropogenic emission reductions and PM2.5 concentrations changes in Hunan Province during the COVID-19 compared to the nationwide average anthropogenic emission reductions and PM2.5 concentrations changes. The specific reasons for the improvement in air quality in Hunan Province are also of interest.

Fig. 5a explores that the PM2.5 emission sources in Hunan Province are mainly contributed by the residential sector and the industrial sector, accounting for 82.5% and 14.9% respectively. During the control period, the emissions of the residential sector and the industrial sector decreased by 57.4% and 45.8% respectively (Table S6). Therefore, one of the reasons for the improvement in PM2.5 pollution in Hunan Province is that large scale restrictions such as catering service industries, industrial enterprises and construction sites are limited to a minimum, while small reductions in household burning and biomass burning. Yet existing studies suggest that emissions are the dominant factor influencing the interannual variability of air pollutants, while meteorological conditions drive their daily variability (Chen et al., 2019; He et al., 2017; Zhai et al., 2019; Zhang et al., 2012; Zhang et al., 2019; Zheng, H., Kong, S. F. et al., 2020). At present, the dominant role of meteorological factors and anthropogenic emissions on PM2.5 concentration changes in Hunan Province during the lockdown period remains to be studied.

Fig. 5.

Fig. 5

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(a) The proportion of PM2.5 emissions from different sources in Hunan Province before and after the COVID-19 lockdown; (b) Changes of PM2.5 fraction in local emissions in Hunan Province before and after COVID-19 lockdown.

Thus, we first quantitatively assessed the contribution and impact of changes in meteorological conditions on PM2.5 pollution applying the WRF-Chem model. Then we further explored the contributing factors of emission reduction from anthropogenic activities and cross-regional transport of pollutants to PM2.5 concentrations changes.

3.2. Influence of meteorological conditions on PM2.5 pollution in Hunan Province

As far as we know, the concentrations levels of contaminants in the atmosphere are influenced by a combination of anthropogenic emissions and meteorological factors (Chen et al., 2018; Dang et al., 2021; Dong et al., 2020; Goldberg et al., 2020; Menut et al., 2020; Sindelarova et al., 2014; Wang et al., 2019; Wang et al., 2016; Zhang, W. J. et al., 2020). Moreover, quantification of meteorological contributions of meteorological effects revealed that PM2.5 pollution cases are more than 50% driven by meteorological factors (Gui et al., 2019; Ma et al., 2020; Yang et al., 2011; Zhang et al., 2018). So quantifying the specific effects of anthropogenic emissions and meteorological factors on PM2.5 will be one of the effective ways to control PM2.5 pollution.

To conduct researches on the sensitivity of PM2.5 pollution to varying meteorological symptoms during the COVID-19 in Hunan Province, based on NCEP/FNL atmospheric reanalysis data, we analyze the spatial distribution and changes of four meteorological elements (T2, RH, WS, and planetary boundary layer height (PBLH)) by comparing scenarios C2 and C3, as shown in Fig. 6 a–d. Then the influence and contribution of meteorological factors to PM2.5 pollution in Hunan Province are discussed.

Fig. 6.

Fig. 6

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(a–d) Changes of meteorological conditions in Hunan Province before and after the COVID-19 lockdown, including surface temperature (°C), relative humidity (%), boundary layer height (m) and wind speed (m/s). (e) Variation of PM2.5 (μg/m3) concentration in Hunan Province.

During the COVID-19 lockdown, the changes of meteorological conditions in Hunan province represent: the average T2 in Hunan province rose by 1.2 °C, RH increased by 0.2%, PBLH increased by 36.65 m, and the WS decreased by 0.11 m/s. Through statistical analysis of the change values of meteorological elements in the same period from 2015 to 2019 (Table 5 ), it is found that before and during COVID-19 lockdown in 2020, meteorological changes are within the normal range of changes in the same period. Only under the above meteorological conditions, the average PM2.5 concentration in Hunan Province changed from 40.3 μg/m3 to 40.6 μg/m3.

Table 5.

Changes of meteorological elements (T2, RH, WS) in the same period before and after covid-19 lockdown from 2015 to 2020.

△T (°C) △RH (%) △WS(m/s)
2015 −2.2 5.3% 0.58
2016 −0.5 −13.2% 0.14
2017 −0.9 −3.9% 0.00
2018 −1.1 −11.9% 0.40
2019 2.5 −9.9% 0.42
2020 1.2 0.2% 0.11
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There are several possible reasons for these occurrences. Firstly, during the control period, the T2 in Hunan Province increased by 14.9%, and the increase in T2 facilitated the multiphase reaction of secondary aerosols, thus aggravating the pollution of the haze. The increase of PM2.5 concentrations further hinders the solar radiation incident to the ground, resulting in a significant weakening of the O3 titration effect and an increase in O3 concentrations instead of a decrease (Le et al., 2020; Sokhi et al., 2021). Furthermore, as pointed out by other authors, the reduction of NOx may be related to the increase of ozone due to the titration effect (Guevara et al., 2021; Sokhi et al., 2021). During COVID-19, the most important process affecting ozone behavior was shown to be the titration effect in studies related to western China, the Yangtze River Delta, the Beijing-Tianjin-Hebei urban agglomeration, Hubei province, and cities in Colombia, Italy, and Rio de Janeiro (Casallas et al., 2023; D'Isidoro et al., 2022; Dantas et al., 2020; Wang, Nan et al., 2021; Xu, K. et al., 2020; Zhang, J. J. et al., 2020; Zhao et al., 2020). In all of these cities, a sharp decrease in NOx concentrations led to a weakening of the titration effect, resulting in an increase in O3. This is one of the reasons why the average concentration of O3 in Hunan Province increased by 45.5% during the lockdown period. Secondly, the average RH in Hunan Province increased from 77.5% to 77.7%. The more significant and stable RH level is suitable for secondary aerosol formation and multiphase reactions (An et al., 2019; Le et al., 2020). Another, the average WS in Hunan Province decreases by 0.11 m/s and remains stable, which is unfavorable to the diffusion of atmospheric pollutants. Meanwhile, Hunan Province is surrounded on three sides by high mountains and ranges between 1000 and 2000m above sea level. In contrast, most of central Hunan consists of basins and hills below 500 m above sea level. Northern Hunan is the lowest and flattest, all of which are below 50 m above sea level, presenting a special topography of horseshoe basin (Fig. 1b), which forms a barrier against the dispersion of air pollutants to the northeast, further aggravating the air pollution in Hunan Province.

Finally, contrary to the increase of RH and decrease in WS, the PBLH in Hunan Province generally increased by 36.72 m (10.5%). Under this combined influence, the contribution of meteorological conditions during COVID-19 was calculated as 0.5% according to the calculation method in Table 3. This indicates that the contribution of meteorological conditions to PM2.5 pollution in Hunan Province is much smaller than that of anthropogenic emissions.

From Fig. 6e, an apparent spatial diversity exists in the influence of meteorological factors on the variation of PM2.5 concentration in Hunan Province, with only a small increase in PM2.5 concentrations in some areas in the northeast direction. This may be due to the fact that this region contains part of the Chang-Zhu-Tan urban agglomeration, where the anthropogenic emissions are relatively large and the background value of air pollutant concentration is higher. In the region with higher air pollution levels, PM2.5 concentrations may decrease substantially when PBLH increases. At the same time, the T2 in the region increases to offset part of the effect of the change in PBLH. Simultaneously, the airflow source of the backward trajectory (Fig. 7) shows that the northeasterly wind is mainly in Hunan Province, while the topographic feature of Hunan Province surrounded by mountains on three sides hinders the outward diffusion of polluted air masses in other directions. Therefore, we speculate that besides meteorological factors, local emissions and regional transport also have a non-negligible impact on air pollution in Hunan Province.

3.3. Different responses of PM2.5 pollution to local emission and regional transport in Hunan Province during the COVID-19

3.3.1. Local emission

To further explore the local emission source drivers of PM2.5 changes, we compare C4 with C1 and C5 with C2 to obtain the local emission sources' contribution to PM2.5 concentrations in Hunan Province before and during the COVID-19 control. Fig. 8 a-b show that PM2.5 concentrations of different areas in Hunan province vary with local anthropogenic emissions during the COVID-19 lockdown. Especially in the central area of economy and urbanization in Hunan Province, the Chang-Zhu-Tan city cluster, the local human-caused sources in Hunan Province contribute much more to PM2.5 in this area than to other cities in Hunan Province. On the one hand, this is due to differences in the magnitude of emission reduction from local sources in different cities. On the other hand, it is also due to different geographical locations and different influences from regional transport. According to the analysis method in Table 3, the contribution rate of local emissions to PM2.5 decreased from 71.4% to 69.9% during the lockdown period.

Fig. 8.

Fig. 8

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(a) PM2.5 concentrations (μg/m3) change in Hunan Province after local emission sources were shut down before the COVID-19 lockdown; (b) Same as (a) except for during the COVID-19 lockdown; (c) PM2.5 concentrations (μg/m3) changes after transported emission sources were shut down before the COVID-19 lockdown; (d) Same as (a) except for during the COVID-19 lockdown.

Table 6 shows the percentage of contribution of different sectors to reducing anthropogenic emissions in Hunan Province. We identified PM2.5, SO2, NH3, CO, VOCs, BC, and OC among the primary pollutants mainly from emission reductions in the residential sector, which contributed 86.1%, 81.8%, 63.4%, 84.0%, 58.4%, 90.9%, and 97.0% to these primary pollutants, respectively. It can be seen from Fig. 5b that OC and BC are essential components of PM2.5, accounting for 37.5% and 19.4%, respectively, during the COVID-19. Evidently, limiting residential sector emissions (e.g., cooking and heating) in Hunan Province can effectively control most of the local primary pollutant emissions and thus improve PM2.5 pollution directly at the source.

Table 6.

Contribution of different resources to the reduction of anthropogenic pollutants in Hunan Province.

Total reduction
 Power
 Industry
 Residential
 Transportation
 Agriculture
(107Kg) Contribution (107Kg) Proportion (%) Contribution (107Kg) Proportion (%) Contribution (107Kg) Proportion (%) Contribution (107Kg) Proportion (%) Contribution (107Kg) Proportion (%)
PM2.5 31,323 125 0.4% 3884 12.4% 26,969 86.1% 345 1.1% 0 0.0%
NOx 24,474 2643 10.8% 9471 38.7% 5140 21.0% 7220 29.5% 0 0.0%
SO2 40,048 761 1.9% 6448 16.1% 32,759 81.8% 80 0.2% 0 0.0%
NH3 2319 0 0.0% 276 11.9% 1470 63.4% 98 4.2% 475 20.5%
CO 694,669 3473 0.5% 70,162 10.1% 583,522 84.0% 37,512 5.4% 0 0.0%
VOCs 52,464 52 0.1% 15,792 30.1% 30,639 58.4% 5981 11.4% 0 0.0%
BC 7044 0 0.0% 465 6.6% 6403 90.9% 176 2.5% 0 0.0%
OC 15,071 0 0.0% 392 2.6% 14,619 97.0% 60 0.4% 0 0.0%
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Additionally, NOx emission reductions were mostly from the industrial sector, followed by the transportation and residential sector. The SO2 and VOCs emission reductions also contributed 16.1% and 30.1%, respectively, from the industrial sector. This is owing to that most of the industries in Hunan province are highly energy-consuming, such as steel, petrochemical, non-ferrous metal smelting and construction industries, these industries are incredibly dependent on coal combustion, thus producing massive SO2, NOx and VOCs (Dai et al., 2019; Xu, W. J. et al., 2020). According to the observed data, the change rates of the average concentrations of NO2 and SO2 during COVID-19 were −54.9% and −30.3%, respectively. Therefore, limiting emissions from the industrial sector can be directly reflected in the observed concentrations of NO2 and SO2.

SO2 forms SO4 2− through a series of atmospheric reactions. During the epidemic lockdown, SO2 emissions decreased by 54.4%, but the overall amount of SO4 2− decreased by only 2.66%. That's caused by the fact that although SO2 emissions were drastically reduced, the total amount of SO2 emissions was still large. At the same time, with the decrease of NOx emission, the average concentration increases from 30.5 to 44.4 μg/m3 due to the accumulation of O3 in the atmosphere, and the atmospheric oxidation is enhanced, so the decrease of SO4 2− is small. As essential precursors of PM2.5, the reduction of NOx and VOCs emissions directly contributed to the reduced PM2.5 concentrations. Consequently, Hunan Province is expected to strictly control industrial production activities, plan pollutant emissions, and even to positively implement state-of-the-art industrial emission reduction technologies to enhance the efficiency of conversion and usage of energy. In this way, we can ultimately achieve the control of SO2, NOx and VOCs emissions and reduce PM2.5 concentration in the air.

The NH4 + component of PM2.5 is converted from NH3. In Table 3, besides the larger contribution from the residential sector, 20.5% of the NH3 reduction comes from agricultural activities. During the COVID-19 lockdown, although NH3 emissions reduced by 7.3%, the total emissions were still up to 29,469 × 107 kg, resulting in a 21.2% increase in NH4 + concentrations contributed by local sources and an increase in the share of PM2.5 fraction from 3.3% to 4.0% (Fig. 5b). These point out that NH3 slight emission reduction on PM2.5 pollution alleviation effect is not obvious in Hunan Province. Moreover, studies have shown that different agricultural discharge processes and methods (e.g., ammonium nitrate instead of urea, mud acidification, etc.) can reduce ammonia emission by 20%–90%. For residential areas, emissions from this source can be reduced by adding acid scrubber, increasing ventilation, and reducing manure storage. For livestock buildings, the installation of air handling systems is the best available method (Wyer et al., 2022). However, it is not suitable for daily use due to high economic cost, high energy consumption, serious pollution and high technical requirements. At the same time, the source contribution of each agricultural activity and the response plan are significantly different due to regional agricultural and meteorological conditions (Balasubramanian et al., 2020; Singles et al., 1998). Therefore, it is worth paying attention to how to control NH3 emission from agricultural sources at the source in different regions in the future. The technological upgrading of agriculture and fertilizer and the control of consumption amount are the breakthrough directions of significant emission reduction.

3.3.2. Regional transport

To track the source and transport path of the air mass in Hunan Province before and during the COVID-19 lockdown, we use concentrations-weighted trajectory analysis (CWT) to draw the PM2.5 reverse trajectory analysis chart and identify four air mass tracks from different transport directions, as shown in Fig. 7. The results show that before the COVID-19 lockdown, Cluster 1 (32.0%) and Cluster 2 (21.8%) originated from the northeast direction, and the transport share of this direction was the largest at 53.8%. Cluster 1 was a long-range remote regional transport. Cluster 3 (24.6%) originated from the eastern regional transport, while Cluster 4 (21.6%) belonged to the local pollutant emission diffusion. During the COVID-19 lockdown, Cluster 3 (55.7%) was from the nearest transport within Hunan Province, accounting for 55.7% of the transport direction. Cluster 1 (13.1%) and Cluster 2 (26.2%) were both from the long-range transport in the northeast direction, while Cluster 4 (5.1%) was from the southwest direction, all of which were long-range regional transports.

In conclusion, the out-of-province sources of PM2.5 in Hunan Province before and during the COVID-19 lockdown are mainly from long-range regional transported air masses in the northeast direction, and the potential contributing regions mainly include Hubei, Jiangxi, Anhui and Henan provinces. As described in 2.2.1, the final results are shown in Fig. 8 by comparing scenarios C6 and C1 before the COVID-19 lockdown and scenarios C7 and C2 during the lockdown. Fig. 8c-d reveal significant regional differences in the contribution of regional transport effects from the four provinces in northeastern Hunan Province to PM2.5 concentrations in Hunan Province. Besides, PM2.5 concentrations in Hunan Province decreased significantly before and during the COVID-19 lockdown. The change in PM2.5 concentrations in northeastern Hunan Province was particularly pronounced because the airflow trajectory of PM2.5 in Hunan Province was mainly from the northeast.

According to the analysis method in Table 3, the contribution rate of regional transport to PM2.5 in major provinces increased from 28.6% to 30.0% during the lockdown period. This indicates that the impact of regional traffic on air pollution in Hunan Province is less than the contribution of local emissions in any period, but its impact on air quality in Hunan Province cannot be ignored. From the research results of regional transport of pollutants, in the future control of PM2.5 in Hunan Province, the influence of regional transport should be gradually brought into focus based on controlling local emissions. At present, many cities already have specific pollution correlations, so it is necessary to develop scientific joint prevention and control measures jointly with several provinces and cities based on scientific research.

4. Conclusions

Data analysis indicates that the PM2.5 concentrations in Hunan Province during the COVID-19 lockdown period in 2020 showed a significant downward trend compared to that in eastern China. Before the follow-up study, we first isolated the response of PM2.5 concentrations to meteorological change based on the simulating scenarios of the WRF-Chem model. The results showed that during the lockdown period, the T2, RH and PBLH increased slightly, the WS decreased somewhat, and the pollutant concentrations fluctuated slightly under this influence. The contribution of meteorological conditions accounted for only 0.5%, so the emission reduction significantly improved PM2.5 pollution. Subsequently, by further exploring the response of PM2.5 concentrations to local anthropogenic emission controls, we find that the residential and industrial sectors are the main sources of emissions. Strictly limiting emissions from the residential sector (e.g., heating and cooking) can maximize direct improvements in PM2.5 pollution at the source. For the industrial sector, in addition to planned pollutant emission control, advanced technologies can be introduced to reduce emissions. At the same time, in the context of the rapid development of transportation, stricter controls and the continuous development of new energy sources are urgently needed. Finally, the efficient control of emissions from the agricultural sector is also a point of concern for the future. Technological upgrading of agricultural activities and consumption control are important directions for effective emission reduction.

Lastly, we used backward trajectory model (HYSPLIT) and concentration-weighted trajectory (CWT) analysis to track the sources and transport paths of air masses in Hunan Province before and during the COVID-19 lockdown. The analysis shows that the exogenous sources of PM2.5 in Hunan Province mainly come from long-range air masses transported from the northeast. Based on this study, we quantitatively analyzed the share of PM2.5 local emission sources and regional transport contributions, and the share of regional transport increased slightly before and during the COVID-19 blockade, 34.3% and 36.7%, respectively. This indicates that the influence of regional transport of pollutants on PM2.5 pollution in Hunan Province should not be underestimated, therefore, it is necessary to jointly develop scientific joint prevention and control measures with several provinces for the prevention and control of PM2.5 pollution in Hunan Province, in addition to controlling local emissions.

Author Contributions Statement

Simin Dai: Conceptualization, Writing – original, draft, Writing – review & editing. Xuwu Chen: Writing – review & editing. Jie Liang: Resources, Software, Validation. Xin Li: Validation, Visualization. Shuai Li: Supervision, Data curation. Gaojie ChenResources, Supervision, Data curation. Zuo Chen: Writing – review & editing. Juan Bin: Data curation. Yifan Tang: Data curation. Xiaodong Li: Writing – review & editing.

Declaration of competing interest

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.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (52170180), the Key Research and Development Program of Hunan Province of China (2022SK 2062), the National Key Research and Development Program of China (2021YFC1910401), the National Natural Science Foundation of China (72088101), and the Research Foundation of Education Bureau of Hunan Province, China (22B0650). We greatly appreciate the team at Tsinghua University for providing the MEIC emission inventory for this study.

Footnotes

☆

This paper has been recommended for acceptance by Da Chen.

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envpol.2023.121886.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (237.1KB, docx)

Data availability

No data was used for the research described in the article.

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