ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-23-3311-2023Global impact of the COVID-19 lockdown on surface concentration and health risk
of atmospheric benzeneAmbient benzene variation during the COVID-19 periodLingChaohaoCuiLuluLiRuirli@geo.ecnu.edu.cnSchool of History and Geography, Minnan Normal University, Zhangzhou, 363000, ChinaState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, ChinaKey Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai, 200241, ChinaInstitute of Eco-Chongming (IEC), 20 Cuiniao Road, Chenjia Town, Chongming District, Shanghai, 202162, China
To curb the spread of the COVID-19 pandemic, many countries around the world
imposed an unprecedented lockdown, producing reductions in pollutant
emissions. Unfortunately, the lockdown-driven global ambient benzene changes
still remain unknown. An ensemble machine-learning model coupled with chemical transport models (CTMs) was applied to estimate global
high-resolution ambient benzene levels. Afterwards, the extreme gradient boosting (XGBoost) algorithm
was employed to decouple the contributions of meteorology and emission
reduction to ambient benzene. The change ratio (Pdew) of the deweathered
benzene concentration from the pre-lockdown to lockdown period was in the order
of India (-23.6 %) > Europe (-21.9 %) > the United
States (-16.2 %) > China (-15.6 %). The detrended change (P∗)
of the deweathered benzene level (change ratio in 2020 - change ratio in 2019)
followed the order of India (P*=-16.2 %) > Europe (P∗=-13.9 %) > China (P∗=-13.3 %) > the United States
(P∗=-6.00 %). Emission reductions derived from industrial activities
and transportation were major drivers for the benzene decrease during the
lockdown period. The highest decreasing ratio of ambient benzene in India
might be associated with local serious benzene pollution during the
business-as-usual period and restricted transportation after lockdown.
Substantial decreases in atmospheric benzene levels had significant health
benefits. The global average lifetime carcinogenic risk (LCR) and hazard
index (HI) decreased from 4.89×10-7 and 5.90×10-3 to 4.51×10-7 and 5.40×10-3,
respectively. China and India showed higher health benefits due to
benzene pollution mitigation compared with other countries, highlighting the
importance of benzene emission reduction.
National Natural Science Foundation of China42107113Natural Science Foundation of Fujian Province2022J05178Natural Science Foundation of Hunan Province2021JJ30555Introduction
Volatile organic compounds (VOCs) are an important class of organic
pollutants in the urban air and have aroused great attention (Kamal et al.,
2016; Koppmann, 2008; Mozaffar and Zhang, 2020). As one of the typical toxic
VOC species, benzene has a variety of negative impacts on human health
including respiratory irritation, asthma, and allergies (Cui et al., 2019;
Kim et al., 2013; Tang et al., 2007). Moreover, benzene has high chemical
reactivity and could participate in photochemical reactions in the
atmosphere, thereby leading to the formation of secondary organic aerosols
(SOAs) and ozone (Dumanoglu et al., 2014; Hsu et al., 2018; Li et al., 2019).
Given its high toxicity to human health and tremendous harm to air quality
(Dumanoglu et al., 2014; Lu et al., 2020), it is highly imperative to
decrease ambient benzene concentration. It has been well documented that
ambient benzene mainly originates from anthropogenic emissions (Mozaffar and
Zhang, 2020; Pakkattil et al., 2021). Therefore, understanding the response
of ambient benzene to anthropogenic emissions is favourable to evaluating the
effectiveness of abatement strategies and informing policy decisions.
Recently, the ongoing global COVID-19 outbreak has resulted in paroxysmal
public health responses including travel restrictions, lockdown, curfews,
and quarantines around the world. These drastic lockdown measures have inevitably
triggered sweeping disruptions of social and economic activities and have
further affected the emissions and concentrations of some air pollutants
(Bauwens et al., 2020; Berg et al., 2021; Doumbia et al., 2021; P. Zheng et
al., 2021). The unexpected public health emergency provided us with an
unprecedented chance to assess the response of air pollutants to emission
reduction. Bauwens et al. (2020) observed that the average NO2
column in China during January–April 2020 decreased by about 40 % relative
to the same period in 2019 due to dramatic decreases in NOx
emissions. Later on, Keller et al. (2021a) analysed the impact of the
COVID-19 lockdown on global NO2 concentrations and found that the
surface NO2 concentrations were 18 % lower than the business-as-usual scenario
from February 2020 onward. In addition, Hammer et al. (2021) estimated that
population-weighted mean PM2.5 concentrations in China, Europe, and
North America experienced changes of -11 to -15, -2 to 1, and -2 to 1 µgm-3, respectively, during the COVID-19 lockdown period. Compared with
NO2 and PM2.5, ambient SO2 levels in China (-4.6 %) and
India (-14 %) did not experience marked variations after lockdown (Zhang
et al., 2021; Zhao et al., 2020). It should be noted that the global O3
concentration even increased by up to 50 % during this period (Keller et al.,
2021a). To date, most of the current studies have focused on regional or global
criteria pollutant (e.g. PM2.5, NO2, and O3) concentration
changes after the COVID-19 outbreak, while few studies have assessed the impact
of the COVID-19 lockdown on ambient benzene levels.
Mor et al. (2021) observed that the atmospheric
benzene level in Chandigarh, India, decreased by 27 % during the COVID-19
period. Afterwards, Pakkattil et al. (2021) demonstrated that the ambient
benzene levels in Delhi (-93 %) and Mumbai (-72 %) have undergone
drastic decreases after the COVID-19 lockdown. In China, Pei et al. (2022)
revealed that the VOC concentration in the Pearl River Delta (PRD) decreased by
19 % and the decrease rate of ambient benzene reached ∼ 40 % after lockdown. In Europe, Cai et al. (2022) revealed that the
ambient benzene level in Orléans even slightly increased after lockdown,
which might be associated with unfavourable meteorological conditions.
Although ground-level measurement could reflect regional ambient
benzene changes during the COVID-19 lockdown period to some extent, few
regions, especially in developing countries, have collected sufficient
observations for ambient benzene exposure assessment (Geddes et al., 2016;
Van Donkelaar et al., 2015). Moreover, the limited monitoring sites around
the world cannot accurately reflect the global benzene pollution because of
large spatial gaps and restricted spatial representativeness of these
ground-based sites (Shi et al., 2018). The health effect assessment based on
these scarce sites alone inevitably increases the probability of exposure
misclassification (Ling and Li, 2021). Fortunately, chemical transport
models (CTMs) give us an unparalleled chance to capture the full-coverage
ambient benzene level at the global scale. Although CTMs generally show
various biases owing to high uncertainties in initial conditions, input
variables, and parameterizations (Ivatt and Evans, 2020), the
machine-learning bias-correction method can significantly reduce bias in
air quality models (Bocquet et al., 2015). To date, some studies have
developed multiple machine-learning models to estimate the concentrations of
PM2.5 (Wei et al., 2021a, b), NO2 (Wei et al.,
2023), and O3 (Wei et al., 2022) around the world. Unfortunately, no
study has employed the ensemble technique to analyse the change in global
ambient benzene after the COVID-19 outbreak. Besides, nearly all of the current
studies have only used original observation data to assess the impact of the COVID-19
lockdown on the ambient benzene level (Pakkattil et al., 2021). Actually, the
concentrations of air pollutants are not only controlled by emissions but
also modulated by complex meteorological conditions (Hammer et al., 2021).
For instance, some pioneering studies have revealed that several severe haze
episodes still occurred even with the strict restrictions put in place in
China (Chang et al., 2020; Huang et al., 2021). Hence, it is necessary to
remove the effects of meteorological parameters and then to further quantify
the isolated contribution of emission reduction to the global ambient benzene
level and health risks during the COVID-19 lockdown period.
In our study, a machine-learning model coupled with CTMs was applied to
estimate the global ambient benzene concentrations from 23 January to 30 June in 2019 and 2020. At first, the CTMs' output, emission inventory,
meteorological parameters, and many other geographical covariates were
integrated into the ensemble decision tree model to obtain global
full-coverage benzene concentrations in the atmosphere. Then, we also
examined the synergetic impacts from the anthropogenic emissions and
meteorological factors during the pre-lockdown and lockdown periods.
Finally, we estimated the emission-induced benzene concentrations before and
after the COVID-19 lockdown and quantified the benzene-related health benefits
due to the COVID-19 lockdown in major regions around the world. This study shows
important implications for developing control strategies to alleviate global
atmospheric benzene pollution.
Data and methodsData preparationGround-level benzene observation
Our analysis was performed based on the recent development of unprecedented
public access to ground-level air quality observations. In our study, we
collected an air quality dataset of hourly surface benzene observations at
669 sites at the global scale during 23 January–30 June in 2019 and 2020
(Fig. S1). The start date of the COVID-19 lockdown in China was 23 January,
and the national lockdown lasted for about 1 month. The
lockdown end date in Wuhan was 8 April. The start and end dates of lockdown
in India were 25 March and 25 April, respectively. The lockdown
in the United States occurred firstly in California from 19 March, and then
the lockdown lasted for 1–2 months. The lockdown dates in most European
countries lasted from March to May. A detailed spatial distribution of
the monitoring sites in India, Europe, and the United States is depicted in Fig. S1. The surface benzene dataset in India was downloaded from the Central
Pollution Control Board (CPCB) online database, which has been widely
utilized in previous studies (Mahato et al., 2020; Mor et al., 2021; Sharma
et al., 2020). The CPCB database provides data quality assurance (QA) or
quality control (QC) programmes by developing strict procedures for sampling,
analysis, and calibration (Gurjar et al., 2016). The ground-level benzene
observations in Europe and the United States were collected from the air quality
data portal of the European Environment Agency (EEA) and United States
Environmental Protection Agency (EPA), respectively. Only days with more
than 12 h of available data are included in the analysis. All of the hourly
data were average to the daily scale.
Independent variables
The daily benzene concentrations at a global scale were simulated using
the GEOS-Chem (https://geos-chem.seas.harvard.edu/, last access: 10 March 2023) model (v12-01), which included the full gaseous
HOx–Ox–NOx–CO–non-methane VOC (NMVOC) chemistry and online aerosol
calculations. The simulation used assimilated meteorological observations
(GEOS MERRA-2) at 2∘× 2.5∘ horizontal resolution with
72 vertical levels for the years 2019 and 2020. The anthropogenic emission
inventory in 2019 was collected from the Community Emissions Data System (CEDS).
Then, the emission inventory in 2020 was calculated based on that in 2019
and on an updated adjustment factor proposed by Doumbia et al. (2021).
The meteorological parameters were obtained from the NASA Goddard Earth
Observing System Composition Forecast (GEOS-CF) model (Keller et al.,
2021b). GEOS-CF integrates the GEOS-Chem atmospheric chemistry model into
the GEOS Earth system model (Hu et al., 2018; Long et al., 2015) and
provides global hourly analyses of meteorological variables at
0.25∘ spatial resolution (Keller et al., 2021b). Meteorological
parameters including surface pressure (Ps), relative humidity (RH), 2 m air
temperature (T2m), total precipitation (TPREC), the 10 m latitudinal wind
component (U10m), the 10 m longitudinal wind component (V10m), and boundary
layer height (BLH) obtained from GEOS-CF were used to develop the model
(Fig. S2). In addition, cropland, forest, grassland, shrubland, and barren
land have also been integrated into the final model (Liu et al., 2020).
All of the independent variables collected from multiple sources were
regridded to 0.25∘ grids using spatial-interpolation algorithms.
During the process of model development, the most important procedure was to
remove some redundant explanatory variables and then to determine the
optimal variable group. The basic principle of the variable selection was to
eliminate the less important predictors. These variables generally suggested
that the R2 value of the submodel did not experience a significant
decrease or that it even experienced a slight increase when these redundant variables
were removed from the model. Finally, a total of 64 001 samples and 7
variables were utilized to predict the ambient benzene concentrations at the
global scale.
Model developmentThe ensemble model development for atmospheric benzene estimates
In pioneering studies, random forest (RF), extreme gradient boosting
(XGBoost), and light gradient boosting machine (LightGBM) have exhibited good estimation accuracy (Li et al., 2021; Wei et al., 2021b). The RF model contains a great number of
decision trees; each one experiences an independent sampling procedure,
and all of these trees show the same distributions (Breiman, 2001). The RF model often
displays excellent prediction performance owing to the injected randomness.
The model accuracy is strongly dependent on the number of trees, splitting
features, and variable group. The detailed procedures are summarized as
follows:
1f(x)=∑z=1ZczR(x∈Qz),2czΔ=average(yi|xi∈Qz),3BR1(p,q)={X|Xj≤q}&BR2(p,q)={X|Xj>q},4minp,qmin∑M1(p,q)(y-c1)2+min∑M2(p,q)(y-c2)2,5c1Δ=average(yi|xi∈Q1(pq))andc2Δ=average(yi|xi∈Q2(pq)),
where (xi, yi) is the sample for i=1, 2, …, N in
Q regions (Q1, Q2, …, Qz); R denotes the weight
of each branch; BR represents the decision tree branch; czΔ represents the optimal value; p
is the feature variable; c1 is the average of the left branch; c2 is
the average of the right branch; and q represents the split point.
The XGBoost model is an improved algorithm of gradient boosting decision tree
(GBDT) models, and loss functions have been extended to the second-order
function. The detailed XGBoost algorithm is shown as the following formula
(Zhai and Chen, 2018):
Y(t)=∑i=1n[l(yi,yΛ(t-1))+∂y(t-1)l(yi,yΛ(t-1))ft(xi)+12∂y(t-1)2l(yi,yΛ(t-1))ft2(xi)]+ε(ft),
where Y(t) is the cost function at the tth period, ∂ represents the derivative of the original function, ∂y(t-1)2 is the second derivative of the original function, l is the
differentiable convex loss function that reflects the difference of the predicted
value (yΛ) of the ith instance at the tth period and
the target value (yi), ft(x) represents the increment, and
ε(ft) is the regularizer.
The LightGBM model is an updated version of the XGBoost method and significantly
improves the running speed of the modelling process. Moreover, this method could
decrease the cache miss by a large margin and further improve the
predictive accuracy. The detailed algorithms are as follows (Sun et al.,
2020):
7f^=argminfL(y,f(x)),8fT(X)=∑t=1Tft(X),9Γt=∑i=1n(gift(xi)+12hift2(xi)),
where f^ is the lowest value of the cost function; L(y,f(x))
is the cost function; fT(X) denotes the total regression trees;
ft(X) represents each regression tree; and gi and hi represent
the first- and second-order gradient statistics of the cost function,
respectively.
The workflow of global atmospheric benzene modelling. CTM output
represents the simulated benzene concentration based on the GEOS-Chem model.
Meteo denotes the meteorological parameters derived from GEOS-CF reanalysis.
Emission represents the daily emissions of benzene. MOY and DOY are the month
of year and day of year, respectively. Simulated benzene represents the
predicted benzene concentrations based on the ensemble model. Deweathered
benzene denotes the benzene concentration after removing meteorological
effects.
Although all of these models showed good performance in predicting air
pollutants, nearly all of the submodels still suffered from some
weaknesses in their prediction accuracy. Hence, it was necessary to collocate
these models using a back-propagation neutral network (BPNN) to further
simulate daily ambient benzene concentrations at the global scale. As
depicted in Fig. 1, three submodels including RF, XGBoost, and LightGBM
were stacked through the BPNN model to simulate the daily atmospheric benzene
levels at the global scale. Firstly, a 5-fold cross-validation method was
utilized to train each submodel to determine the optimal hyperparameter.
Then, the BPNN method was employed to further train the estimated
concentrations of three submodels against the observations (Fig. 1).
Lastly, the global ambient benzene concentrations were predicted on the
basis of the ensemble model.
The meteorology-normalized benzene estimates
The ambient benzene concentration was influenced by both meteorological
parameters and emissions. To isolate the contribution of emissions, the
impacts of meteorological conditions should be removed. In our study, the
XGBoost approach was utilized to eliminate the impacts of meteorological
conditions. The simulated benzene concentration in each grid
(0.25∘) based on the method in Sect. 2.2.1 was treated as the
dependent variable. The daily benzene emissions, meteorological factors,
month of year (MOY), and day of year (DOY) in each grid were regarded as the
explanatory variables. The raw dataset was randomly classified into a
training dataset (90 % of input dataset) for developing the XGBoost model,
and the remaining samples were regarded as the test dataset. After the
development of the XGBoost model, the weather-normalized technique was
employed to predict the ambient benzene concentration at a specific time
point. The detailed deweathered algorithms were first introduced by Grange and
Carslaw (2019). The meteorology-normalized benzene level served as
the concentrations contributed by emissions alone. The differences in total
and deweathered benzene concentrations were regarded as the concentration
contributed by meteorology. In addition, the cross-validation (CV) R2 value of the model used
for the separation of meteorology and emissions should also be higher than
0.50; otherwise the model could be considered unreliable.
Health effect assessment
In our study, the carcinogenic and non-carcinogenic risks of ambient benzene
were assessed based on the standard methodology of the United States
Environmental Protection Agency (EPA). The carcinogenic and
non-carcinogenic risks induced by benzene exposure were evaluated based
on the lifetime carcinogenic risk (LCR) and hazard index (HI). The formulas
for calculating benzene intake (BI), LCR, and HI are as follows (Table S1):
10BI=(C×ET×EF×ED)/(365×24×AT),11HI=BI/RfC,12LCR=BI×IUR,
where C (µgm-3) denotes the concentration of the corresponding
ambient benzene; ET is the exposure time; EF represents the annual exposure
frequency (d a-1); ED is the exposure duration (a); ATnca and
ATca denote the average exposure time for carcinogenic and
non-carcinogenic risks (a), respectively; BI means the benzene intake; RfC
represents the reference dose (µgm-3); and IUR is the inhalation risk
(1/µgm-3). The non-carcinogenic risk of the ambient benzene is
considered high when HI is above 1.0, whereas the health risk is not
obvious when HI is below 1.0. The carcinogenic risk was regarded as definite
risk when LCR was higher than 1×10-4, while it was treated
as possible risk when this indicator was located between 1×10-6 and 1×10-4. The risk was treated as negligible
when the indicator was lower than 1×10-6 (Dumanoglu et
al., 2014; Li et al., 2017).
Results and discussionThe model fitting and validation
The ensemble model was utilized to estimate the ambient benzene
concentrations at the global scale during 23 January–30 June in 2019 and
2020. The CV R2 value of the ensemble model (R2=0.60) was significantly higher than that of RF (0.52), XGBoost (0.53),
and LightGBM (0.55) (Fig. S3). Nevertheless, both the root-mean-square
error (RMSE) (1.18 µgm-3) and the mean absolute error (MAE) (0.59 µgm-3) of the ensemble model were significantly lower than those
of RF (RMSE and MAE: 1.41 and 0.72 µgm-3), XGBoost (RMSE and MAE:
1.37 and 0.70 µgm-3), and LightGBM (RMSE and MAE: 1.34 and 0.69 µgm-3). The higher R2 value and the lower RMSE and MAE
suggested the higher accuracy of the ensemble model in air quality
simulation. In pioneering studies, Wolpert (1992) confirmed that the
joint use of multiple statistical models could decrease the probability of
overfitting and strengthen the predictive accuracy and transferability of
final models. Besides, our previous studies also demonstrated that the
stacking of various decision tree models could significantly outperform
an individual model because each decision tree model could suffer from some
weaknesses (Li et al., 2021). For instance, the dataset in the RF model
appeared to be overfitted when much noise existed in the training data of
regression problems (Breiman, 2001). Besides, the RF model might
underestimate/overestimate the extreme values of ambient benzene (Xue et
al., 2019), which could be neutralized by the XGBoost algorithm through the
boosting method (Li et al., 2020). For XGBoost algorithm, excessive leaf
nodes often showed low splitting gain, while the LightGBM model could make
up for this defect (Nemeth et al., 2019). Overall, the combination of these
decision tree models could overcome the weaknesses of the individual
models and enhance the robustness of the final model.
Although 10-fold CV has verified that the modelling performance of the ensemble
model was superior to that of the individual models, this method cannot examine the
spatial transferability of this model. In our study, many regions (except
India, Europe, and the United States) lacked monitoring sites for
ambient benzene. Fortunately, the CTMs' output provided a strong proxy to
predict the daily ambient benzene concentrations before and after the COVID-19
outbreak. In order to examine the spatial transferability of the
ensemble model, cross-validation was performed. In each round,
two-thirds of the benzene dataset in India, Europe, and the United States
was applied to train the model, and the remaining third was utilized to examine
the model (e.g. India + Europe for training and the United States for
testing). After three rounds, all of the simulated benzene concentrations
were compared with the corresponding observed values. As shown in Fig. S4,
the out-of-bag R2 value reached 0.58, which was slightly lower than the
R2 value (0.60) of the training model. In addition, the RMSE and MAE of the
fitting equation for the out-of-bag data were 1.18 and 0.62, respectively.
The results were in good agreement with those based on the CV database, indicating
the ensemble model showed satisfactory spatial generalization.
The ensemble model can capture the spatiotemporal variation in ambient
benzene during the COVID-19 lockdown period, while the impact of the COVID-19
lockdown cannot be quantified because the contribution of meteorological
parameters cannot be removed based on this model alone. Therefore, it is
proposed to employ the XGBoost algorithm to isolate the contribution of
emission reduction to global atmospheric benzene. As depicted in Fig. S5,
the CV R2 value and slope of the fitting curve reached 0.65 and 0.62,
respectively. The results suggest that the meteorology-normalized model was
robust because the CV R2 value was much higher than 0.50.
The impact of the COVID-19 lockdown on the global atmospheric benzene level
The ensemble model was developed to expand the ground-observed benzene
measurement to the global scale and capture the global spatial variability
in ambient benzene. As shown in Fig. S6, the global simulated (total)
benzene concentration during 23 January–30 June in 2019 and 2020 ranged from
0.52 to 6.36 µgm-3, with an average value of 0.92±0.23µgm-3. At the regional scale, the benzene concentration displayed
significant spatial variability. The benzene concentration followed the
order of India (1.44±0.14µgm-3) > China (1.17±0.13µgm-3) > Europe (1.02±0.08µgm-3) > the United States (0.96±0.09µgm-3)
during 23 January–30 June in 2019 and 2020. Besides, the global simulated mean
benzene level underwent a slight decrease from 0.93±0.06 in 2020
to 0.90±0.06 in 2019. However, the inter-annual variation in ambient
benzene exhibited a remarkable spatial discrepancy at the global scale. As
depicted in Fig. S7, the change ratio of the simulated (total) benzene level
during the COVID-19 lockdown period (the difference in the benzene level
before the COVID-19 lockdown and that during the COVID-19 lockdown period) in 2020
was in the order of India (-18.5 %) > Europe (-16.7 %) > China (-11.7 %) > the United States (-11.5 %).
Compared with 2020, the change ratio of the benzene level during the same period
in 2019 followed the order of India (-16.3 %) > Europe
(-6.62 %) > the United States (-6.46 %) > China
(-4.18 %). It should be noted that the simulated ambient benzene
concentration had a higher decreasing ratio in 2020 compared
with the same period in 2019 in nearly all of the major countries and regions around the
world, which might be associated with the local COVID-19 lockdown measures
in 2020.
The global average deweathered benzene concentrations in 2019 (23 January–30 June) (a) and 2020 (23 January–30 June) (b). Panel (c) represents the
difference in deweathered benzene concentrations in 2020 and 2019 (unit:
µgm-3).
The weekly variations in atmospheric benzene concentrations (µgm-3) in some major regions around the world during 23 January–30 June.
The red line and background denote mean values and standard deviation of
deweathered weekly benzene concentrations in 2020. The cyan line and
background denote mean values and standard deviation of deweathered weekly
benzene levels in 2019. The dashed vertical red line indicates COVID-19
restriction dates, and the dashed vertical black line indicates the beginning of easing
measures.
The change ratio (%) of deweathered (Pdew) and detrended
(P∗) benzene concentrations in major regions around the world.
Change ratioChinaIndiaEuropeUnited StatesPdew in 2020-15.6-23.6-21.9-16.2Pdew in 2019-2.31-7.40-8.04-10.2P∗-13.3-16.2-13.9-6.00
Due to the interference of meteorological conditions, we cannot quantify the
direct impact of the COVID-19 lockdown on ambient benzene based on the
comparison of simulated (total) benzene levels. Thus, the
meteorology-normalized method was employed to decouple the separated
contributions of emission reduction and meteorology to ambient benzene. In
our study, both the change ratio and the detrended change ratio were applied
to evaluate the impact of the COVID-19 lockdown on the global ambient benzene level.
The change ratio represents the variation in the ambient benzene level during the
lockdown period in 2020 compared with the pre-lockdown period in 2020. However,
the detrended change ratio reflects the difference in the change ratio in
2020 and the change ratio during the same period in 2019, which could avoid
the inter-annual system error and contingency of a single year. As
summarized in Figs. 2 and 3, the change ratio of the deweathered benzene
concentration from the pre-lockdown to lockdown period in 2020 was in the order
of India (-23.6 %) > Europe (-21.9 %) > the United
States (-16.2 %) > China (-15.6 %). Meanwhile, the change
ratio of the deweathered benzene concentration during the same time in 2019
followed the order of Europe (-10.2 %) > the United States
(-8.04 %) > India (-7.40 %) > China (-2.31 %).
The large gap in the change ratio of the deweathered benzene level between 2019
and 2020 confirmed that the drastic and consequential quarantine measures
significantly decreased the ambient benzene concentrations in nearly all of
the regions with lockdown measures. Among all of the major countries and regions, India
experienced the most dramatic benzene decrease during 24 March–24 April 2020 (-23.6 %) compared with the same period in 2019 (-7.4 %).
During this period, the prohibition of industrial activities and mass
transportation was proposed to curb the spread of the COVID-19 pandemic, leading
to a tremendous reduction in anthropogenic benzene emissions (Pathakoti et
al., 2021; Zhang et al., 2021). Sahu et al. (2022) revealed that the
substantial increase in OH radicals during the COVID-19 period also facilitated ambient benzene removal due to the photooxidation reaction. The decreasing
ratio of the deweathered benzene level in India was close to that of PM2.5
(-26 %), while it is was markedly lower than that of NO2 (-50 %)
(Zhang et al., 2021). Although both Europe and the United States also
performed stringent lockdown restrictions in some regions such as Italy,
Spain, and California (Guevara et al., 2021a; Keller et al., 2021a), the detrended change (P∗: change ratio in 2020 - change ratio in 2019) for
deweathered benzene in Europe (P∗=-13.9 %) and the United States (P∗=-6 %) between 2020 and 2019 was still lower than that of India (P∗=-16.2 %) (Table 1). It was assumed that the absolute concentrations of
ambient benzene in Europe and the United States were much lower than that in
India. It should be noted that China displayed a relatively gently
decreasing ratio (-15.6 %) after the COVID-19 outbreak, which was even lower
than the ratio in the United States. As the first epidemic epicentre
country, the Chinese government imposed a rapid lockdown measure in Wuhan and
other cities across China in an effort to prevent the spread of the COVID-19
pandemic (Wu et al., 2020). These restrictions interrupted a wide array of
economic activities and reduced primary air pollutant emissions and thus
resulted in remarkable decreases in deweathered NO2 (-43.6 %) and
PM2.5 (-22 %) (Dai et al., 2021). The gently decreasing ratio of
ambient benzene compared with other pollutants might be linked with the
source apportionment of atmospheric benzene. It was well known that
industrial sources (e.g. the chemical industry and solvent use) comprised the major
emission sector of benzene (Li et al., 2019). Although the contribution from
solvent use exhibited substantial decreases in some cities (Qi et al., 2021;
Wang et al., 2021), the chemical industry was not entirely interrupted even
during the COVID-19 lockdown period (Dai et al., 2021). B. Zheng et al. (2021)
also demonstrated that reduction in NMVOC emissions from the industrial sector was much less than for other
pollutants.
The concentration difference for deweathered benzene between the
COVID-19 period in 2020 and the same period in 2019 in (a) East Asia, (b) South
Asia, (c) Europe, and (d) North America (difference = deweathered benzene
concentration in 2020 - deweathered benzene concentration in 2019) (unit: µgm-3).
Although the deweathered benzene concentrations in nearly all of the major
countries underwent obvious decreases during the COVID-19 lockdown period, the
change ratios of deweathered benzene in different regions of these countries
still showed large spatial variability. In China, most of the cities in eastern
China such as Beijing (-30.6 %), Shanghai (-6.25 %), and Wuhan
(-45.3 %) experienced dramatic decreases in deweathered benzene levels
(Fig. S8), which was mainly contributed by the simultaneous emission
reduction of the industrial and transportation sectors. Besides, enhanced
atmospheric oxidation capacity could have accelerated the benzene removal due to
the unbalanced decreases in VOC and NOx emissions (Jensen et al.,
2021). However, the deweathered benzene concentrations in northeastern China
and Yunnan Province even exhibited slight increases after the COVID-19 outbreak
(Fig. 4). Dai et al. (2021) also found that the deweathered PM2.5
concentration in Kunming increased ∼20 % after the COVID-19
outbreak. At first, the contribution of residential combustion sources
(62.1 %) to atmospheric benzene in Yunnan Province was higher than that of other
sectors (Guevara et al., 2021b; Kuenen et al., 2022). Moreover, the increase
in domestic emissions due to home quarantine regulations further increased the
ambient benzene concentration (10 %) in this province, which has been
demonstrated by the updated emission inventory in 2020 (Doumbia et al.,
2021). The slight increases in deweathered benzene levels in northeastern China
after the COVID-19 outbreak could be linked with earlier work
resumption (https://baijiahao.baidu.com/s?id=1658138056285012986&wfr=spider&for=pc, last access: 15 December 2022).
Based on the simulation result, the deweathered ambient benzene level in
northeastern China rebounded sharply after the third week and then returned
back to normal in late February. In India, the decreasing ratios of
deweathered benzene in Delhi, Mumbai, Kolkata, Bengaluru, Hyderabad,
Chennai, Ahmedabad, and Lucknow reached 21.6 %, 20.9 %, 73.7 %,
26.9 %, 38.0 %, 33.7 %, 25.1 %, and 33.3 %, respectively, during the COVID-19
lockdown period (Fig. S9). Among all of the major cities in
India, the ambient benzene level in Kolkata underwent the most drastic
decrease. It was assumed that Kolkata is a dusty city and filled
with vehicle emissions. Fortunately, the city experienced a complete stop of
vehicle movement and of burning of biomass and dust particles from construction works, which had been important sources for ambient benzene (Bera
et al., 2021; Kumar and Singh, 2003). In Europe, the deweathered benzene
levels in nearly all of the cities displayed marked decreases because most
European countries imposed lockdowns to combat the spread of the
COVID-19 pandemic (Guevara et al., 2021a). For example, private cars
and heavy goods vehicles (HGVs) on the road in London reduced by 80 % and
30 %–40 %, respectively (Shi et al., 2021). The drastic decrease in
transportation emissions triggered the P∗ value in London between 2020 and
2019 reaching -43.6 %. In the United States, the decreasing ratios of
deweathered benzene levels in the cities of the eastern United States and
California were generally higher than those in the central United States, which
was in good agreement with the spatial variability in the PM2.5 decrease
(Hammer et al., 2021). It was closely associated with the length of the lockdown
period (https://en.wikipedia.org/wiki/COVID-19_lockdowns, last access: 14 January 2023).
In addition, ambient benzene levels were also strongly affected by
meteorological conditions that alter photochemical production, advection,
and depositional loss. Hence, we examined how meteorological parameters
influenced the temporal variability in ambient benzene during the COVID-19
lockdown period. In 2020, most of the major countries and regions including China
(3.9 %), Europe (5.2 %), and the United States (4.7 %) experienced
slightly unfavourable meteorological conditions, which is in good agreement
with the impact of meteorological conditions on ambient NO2
concentrations (Shi et al., 2021). Among all the meteorological parameters,
air temperature was the most important factor for ambient benzene in nearly
all of the regions around the world during the study period. Compared with
2019, the air temperatures in China, India, Europe, and the United States
increased by 0.4, 0.9, 0.4, and 0.2 ∘C during the same period in
2020, respectively. Jia and Xu (2014) demonstrated that the increased air
temperature generally suppressed benzene photooxidation and secondary
organic aerosol (SOA) formation. Thus, the increased air temperature was not
beneficial to the further reduction in ambient benzene. Apart from air
temperature, some other factors such as RH, rainfall amount, and wind speed
might have affected the ambient benzene level. For instance, the increased RH could have
been favourable to benzene oxidation and the higher rainfall amount could have
promoted benzene removal. However, in the machine-learning model, the
importance values of these variables were much lower than that of air
temperature. Overall, the results suggest that the unfavourable
meteorological conditions (air temperature) weakened the health benefits of reduced
ambient benzene due to drastic lockdown measures around the world.
The carcinogenic risk differences (unit: 10-7) for atmospheric
benzene between the COVID-19 period in 2020 and the same period in 2019 in (a) East
Asia, (b) South Asia, (c) Europe, and (d) North America (difference = benzene
concentration in 2020 - benzene concentration in 2019).
The non-carcinogenic risk differences (unit: 10-3) for
atmospheric benzene between the COVID-19 period in 2020 and the same period in
2019 in (a) East
Asia, (b) South Asia, (c) Europe, and (d) North America (difference = benzene concentration in 2020 - benzene concentration in 2019).
The effect of the COVID-19 lockdown on global health risks
The global average LCR during 23 January–30 June in 2019 and 2020 was 4.89×10-7 and 4.51×10-7, respectively, after removing the
contributions of meteorological conditions (Fig. S10).
Although the COVID-19 lockdown decreased the LCR value slightly, both of the
LCR values during the two periods were lower than the threshold level of
10-6, suggesting that dwellings in most regions could avoid
carcinogenic risk through inhalation exposure to benzene (Li et al., 2017).
However, the LCR values showed significant spatial difference in different
regions. For instance, northern China often suffered from relatively high
benzene pollution, and the LCR value in this region decreased from 1.03×10-6 (possible risk) during the study period in 2019 (the
same period to 2020) to 7.37×10-7 during the COVID-19 lockdown
period. The result verified that the stringent emission control measures
significantly decreased the health risk due to benzene exposure. The LCR
value across India only decreased from 6.55×10-7 to 6.42×10-7 during the study period, whereas the value in the northern part of
India, including for example Bihar, decreased from 1.14×10-6 to 1.09×10-6 due to the impact of the COVID-19 lockdown (Fig. 5). As
the most populous state of India, Bihar had more than 124 million
people at the time (http://kolkata.china-consulate.org/chn/lqgk/t1331638.htm, last access: 31 January 2023). The
results suggest that the COVID-19 lockdown certainly obtained remarkable
short-term health benefits through decreasing ambient benzene exposure.
The LCR values in Europe and the United States decreased from 4.99×10-7 and 4.77×10-7 to 4.57×10-7 and 4.63×10-7, respectively. Compared with China
and India, Europe and the United States faced relatively low
carcinogenic risk of benzene exposure even before the COVID-19 lockdown.
Although the COVID-19 lockdown further decreased the LCR values in these
regions, the overall carcinogenic risk was negligible.
Additionally, the non-carcinogenic risk around the world during the period
was also assessed based on HI. The average HI of ambient benzene exposure in
China, India, Europe, and the United States reduced from 8.92×10-3, 7.45×10-3, 6.32×10-3, and 5.76×10-3 in 2019 to 8.53×10-3, 7.13×10-3, 5.81×10-3, and 5.59×10-3, respectively, during the COVID-19 lockdown period in 2020 (Fig. 6). Although the
HI value in some regions including Bihar (1.52×10-2 to
1.41×10-2) and Uttar Pradesh (1.04×10-2 to
1.03×10-2) in India and Beijing–Tianjin–Hebei (BTH) (1.25×10-2 to 1.14×10-2) in China still
experienced decreases during the COVID-19 lockdown period, the HI values in
these regions were still significantly lower than the risk threshold (HI = 1). Therefore, the impact of the COVID-19 lockdown on the non-carcinogenic risk of
benzene exposure was insignificant.
Conclusions and limitations
The drastic lockdown measures largely reduced the air pollutant emissions.
The meteorology-normalized ambient benzene concentrations in China
(-15.6 %), India (-23.6 %), Europe (-21.9 %), and the United States
(-16.2 %) experienced dramatic decreases after the COVID-19 outbreak.
Furthermore, the decreasing ratios in these major regions during the COVID-19
lockdown period were much higher than in the same period in 2019, indicating
the aggressive emission control measures efficiently decreased ambient
benzene concentrations. Emission reductions from industrial activities and
transportation were major drivers of the decrease in the ambient benzene
level during the lockdown period, while the relatively stable solvent use
emissions could have restricted a further decrease in benzene pollution. Besides,
the slight increase in domestic emissions during this period might be an
important reason for the benzene increase in some regions (e.g. Yunnan
Province). There is also an urgent need to control the household combustion
and solvent use emissions in addition to the emissions from the industrial and
transportation sectors.
Besides, substantial decreases in atmospheric benzene levels could have
sufficient health benefits. Dramatic decreases in benzene emissions in
Europe and the United States could not have brought about effective health benefits because
the ambient benzene levels in both of these regions during the business-as-usual
scenario were already significantly lower than the risk threshold. However, the
benzene decreases in the North China Plain (NCP), China, and Bihar, India, could
have had abundant health benefits because these regions often suffered from
severe atmospheric benzene pollution during the business-as-usual scenario.
Thus, more targeted abatement measures are needed to reduce the benzene
emissions in these areas. For instance, stricter industrial and vehicle
emission standards for VOC control should be implemented in China and India.
Moreover, some measures including limiting the number of coal-fired power
plants, adding environmentally friendly cars and clean fuels for vehicles
and vessels, and strengthening the labelling system for vehicles in use
should be pursued.
It should be noted that our study still suffered from some limitations.
First of all, the monitoring sites were not evenly distributed around the
world, and thus the simulation results might show higher uncertainty in
the regions lacking monitoring sites. Besides, the GEOS-Chem model still
suffered from some uncertainties due to an imperfect chemical mechanism and
inaccurate emission inventory. In future work, the model should be
further improved.
Data availability
The CEDS emission inventory is available on the website of 10.5281/zenodo.3754964 (McDuffie et al., 2020). The global meteorological parameters (reanalysis dataset) can be obtained from the website of are available here: 10.5067/QBZ6MG944HW0 (Global Modeling and Assimilation Office, 2015).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-3311-2023-supplement.
Author contributions
CL and RL wrote the manuscript. RL and LC contributed to the
conceptualization of the study. CL and RL conducted the research and
visualized the results. LC revised the manuscript.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work was supported by the National Natural Science Foundation of China
(grant no. 42107113).
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 42107113), the Natural Science Foundation of Fujian Province (grant no. 2022J05178) and the Natural Science Foundation of Hunan Province (grant no. 2021JJ30555).
Review statement
This paper was edited by Zhanqing Li and reviewed by two anonymous referees.
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