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\title{Urban Air Quality Responses to Lockdowns in China: Insights from
Additive Model and Transfer Learning}



\author[1]{Yuchen Ji}%
\author[1]{Xiaonan Zhang}%
\author[1]{Yueqian Cao}%
\affil[1]{School of Transportation and Civil Engineering, Nantong University}%


\vspace{-1em}



  
  \date{April 28, 2026}


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\selectlanguage{english}
\begin{abstract}
The impact of the COVID-19 lockdown on air quality in seven major
Chinese cities was investigated by utilizing long-term datasets of air
pollutants and meteorological conditions from 2016 to 2021. Generalized
additive model (GAM) was developed to predict air quality during the
lockdown period. The model accounting for weather conditions
demonstrated high accuracy with predictions compared against
measurements during the lockdown. Significant reductions in NO2, \selectlanguage{english}CO, and
PM10 \selectlanguage{english}concentrations were observed primarily due to decreased vehicular
traffic and industrial activities. Notable reductions were particularly
evident in cities with high traffic volumes and industrial emissions
prior to the lockdown. The study also employed transfer learning to
enhance the accuracy of lockdown model with limited data. Despite
occasional anomalies caused by specific events like fireworks and
agricultural burning, the findings suggest that extended training
periods and advanced modeling techniques can significantly improve air
quality predictions. This research highlights the potential long-term
benefits of sustained reductions in human activities and provides
valuable insights for future air quality management and policy-making.%
\end{abstract}%



\sloppy


\par\null

\section*{1. Introduction}\label{introduction}

~Air quality is crucial for human health, e.g., PM\textsubscript{10}
(particulate matter smaller than 10 microns) can penetrate deep into the
lungs, causing respiratory issues, blood disorders, and
neurodevelopmental problems such as autism, attention deficit disorders,
and cognitive delays (Neogi, 2023). Besides, air pollution negatively
impacts cognitive function in the elderly and is linked to higher
mortality rates (Lin et al., 2024). The economic costs of air pollution
are substantial, underscoring the importance of reducing pollution (Lu
et al., 2016), especially in densely populated urban areas. During the
COVID-19 pandemic, long-term exposure to PM\textsubscript{10} and
nitrogen dioxide (NO\textsubscript{2}) significantly increased human's
susceptibility to the virus (Copat et al., 2020) and led to higher
fatality rates among infected individuals (Zhang et al., 2023).
Additionally, emerging evidence suggests that the virus can be detected
in outdoor particulate matter, raising concerns about the transmission
routes (Comunian et al., 2020). While further investigations are
necessary to fully understand these dynamics, it is increasingly clear
that air pollution played a crucial role in both the transmission and
severity of COVID-19.

~ During the winter and spring of 2020, most cities in China implemented
lockdown measures of various durations and strictness levels to combat
the spread of the virus, which has been proved effective in
significantly slowing the transmission of COVID-19 (Ahmed et al., 2024;
Murphy et al., 2023). Concurrently, the widespread adoption of reduced
mobility prompted researchers to examine the potential benefits of
pollution reduction, which is particularly pertinent in the context of
global warming, as these shifts in behavior and operations might offer a
blueprint for reducing pollution while maintaining the essential
functions of cities and nations. The pandemic inadvertently provided a
unique opportunity to rethink and reimagine urban planning and
environmental policies for a more sustainable future (Afrin et al.,
2021; Sharifi and Khavarian-Garmsir, 2020).

~ Despite multiple studies have estimated pollution reductions during
lockdowns across different countries (Briz-Red\selectlanguage{ngerman}ón et al., 2021; Fu et
al., 2020; Ghahremanloo et al., 2021; Venter et al., 2021, 2020), these
results often aggregate differences to various baselines without
modeling the relationships between air pollution, local weather
conditions, time variations, and land-use patterns (Tsilimigkas et al.,
2016). On the other hand, the short duration of lockdowns resulted in a
scarcity of representative data, complicating detailed analysis.
Moreover, the strict initial lockdown measures in many countries were
only implemented for a few weeks, making it difficult to establish
effective models for the lockdown period. Addressing those challenge
will help accurately measure the reduction in local pollution during the
lockdown and understand its spatiotemporal variations, as well as
predicting how pollution patterns might change if lockdowns occur in
different seasons or are extended in duration.

~ In this paper, a long-term predictive model for the lockdown period
(LD model) was developed to address the challenges associated with
analyzing air quality during the lockdown period. Historical weather
data preceding the lockdown were utilized to train interpretable pre-LD
model based upon the generalized additive model (GAM), even with limited
records available during the lockdown. Model predictions were then
compared with actual measurements during the lockdown. Adjustments for
environmental factors were made to enhance accuracy, and transfer
learning was employed to refine parameters related to land use and
daytime activities. The research approach and findings offer valuable
insights into the potential impacts of lockdown measures on air quality
in China.

\section*{\texorpdfstring{{2. Study area and
datasets}}{2. Study area and datasets}}\label{study-area-and-datasets}

\subsection*{2.1. Study area}\label{study-area}

The study area encompasses seven Chinese cities: Beijing, Changchun,
Chongqing, Guangzhou, Hangzhou, Wuhan, and Xiamen. Boasting substantial
populations, high levels of urbanization and robust economic prowess,
these metropolises represent major urban centers across different
geographical regions and experience significant daily traffic volumes
leading to motor vehicle emissions that impact local air quality.
Notably, Changchun is a prominent industrial hub in China where
industrial emissions significantly contribute to urban air pollution.
The onset of stringent lockdown measures in early 2020 resulted in a
marked reduction in traffic flow and industrial emissions across various
cities in China --- providing an opportunity for our research endeavors.
Consequently, these seven cities were strategically selected as
representatives for studying the epidemic-induced lockdowns' influences
on Chinese urban air quality.

\subsection*{2.2. Datasets}\label{datasets}

Training pre-LD and LD models utilized hourly datasets of air pollutants
and meteorology conditions from January 1, 2016 to December 31, 2021
across the aforementioned seven Chinese cities with one site designated
per city. The lockdown period is from January 23 to April 7, 2020. Air
pollutant data, encompassing NO\textsubscript{2} concentration
(\selectlanguage{english}?\selectlanguage{english}?/\selectlanguage{english}?\textsuperscript{3}), CO concentration (\selectlanguage{english}?\selectlanguage{english}?/\selectlanguage{english}?\textsuperscript{3}),
and PM\textsubscript{10} concentration (\selectlanguage{english}?\selectlanguage{english}?/\selectlanguage{english}?\textsuperscript{3}), as
well as meteorological records, including temperature (\selectlanguage{english}), pressure
(hPa), dew point (\selectlanguage{english}), wind direction (deg), and wind speed (m/s), were
both sourced from the China Meteorological Data Center.

\section*{3. Methods}\label{methods}

To estimate air pollution levels in Chinese urbans unaffected by
pandemic-related lockdown measures, the pre-LD and LD models should be
with high accuracy and robust predictive power for data forecasting
during the lockdown period, while also maintaining a high level of
interpretability. A more interpretable model facilitates better
comprehension of the impact of traffic patterns on air quality, enabling
effective measures to mitigate air pollution through adjustments in
relevant traffic flows. Previous studies (Dominici, 2002; John et al.,
2011; Khaiwal et al., 2019) have demonstrated the success of {GAM} in
predicting air pollution. Therefore, as illustrated by \textbf{Fig. 1},
this work adopted the GAM as the pre-lockdown module. Subsequently, a
transfer learning mechanism was employed for training the LD model.
Given variations in pollutant concentrations across different regions,
certain parameters were adjusted and optimized to enhance prediction
accuracy.

\subsection*{3.1. Pre-LD Model}\label{pre-ld-model}

Hastie and Tibshirani (1986) expanded the application of additive models
(Stone, 1985) to include the Generalized Additive Model (GAM) as a
versatile and flexible statistical tool for identifying non-linear
regression effects:

where \emph{} ~and ~represents a non-parametric smooth function such as
a smooth spline function, kernel function, or local regression smooth
function. The distribution of air pollutant concentration closely
follows a lognormal distribution (Limpert et al., 2001). The
non-parametric nature of GAM provides significant flexibility to the
model and facilitates the elucidation of nonlinear effects arising from
derived variables.

In the context of model selection, this study employs the forward
selection method, which has been utilized in related fields of
environmental science with promising outcomes (Ghasemi and Amanollahi,
2019). The model incorporates two key indicators: the Akaike Information
Criterion (AIC) (Bozdogan, 1987) and the Variance Inflation Factor (VIF)
(Akaike, 1974).

AIC serves as a standard for assessing the adequacy of statistical model
fitting, which is defined as follows:

where ~is the number of model parameters, and ~denotes the maximum value
of the model likelihood. A small ~indicates a parsimonious model,
whereas a large ~signifies a precise model. AIC emphasizes the
significance of data fitting while endeavoring to mitigate overfitting.
Consequently, the preferred model for consideration is the one with the
lowest AIC value.

VIF is a number that characterizes the degree of complex collinearity
between observations of the independent variable as follows:

where ~marks the coefficient of determination for the regression
analysis of the \selectlanguage{english}?-th variable with all other explanatory variables.
Multicollinearity is a linear or approximate linear relationship between
regression variables. The general criteria of VIF are: 0 \textless{} VIF
\selectlanguage{english}[?] 5, no multicollinearity; 5 \textless{} VIF \selectlanguage{english}[?] 10, weak
multicollinearity; 10 \textless{} VIF \selectlanguage{english}[?] 100, moderate or strong
multicollinearity; VIF \textgreater{} 100, severe multicollinearity. The
VIF threshold is typically set at 2.5 for processing meteorological data
(Senaviratna and A. Cooray, 2019). Thus, this study excluded variables
with VIF \textgreater{} 2.5.

For each explanatory variable, a GAM containing only one variable is
fitted, and the model with the lowest AIC was chosen. Subsequently, an
iterative process was employed to identify the next optimal variable to
be added to the existing model. In order to accommodate the weekly
variations in pollutants resulting from the pandemic lockdown, the
explanatory variable `weekday' was artificially incorporated into cities
where it had not been selected.

\textbf{3.2 LD Model}

The pre-LD model (GAM) was trained through the pre-LD data. Tied to the
limited duration of the lockdown, it is unfeasible to employ the same
GAM for training during this period; hence, transfer learning (Pan and
Yang, 2010) was employed. Given the consistent relationship between
weather conditions and air pollution, insights gained from studying how
weather impacts air pollution prior to the lockdown can be leveraged
during this period. In conducting experiments, it becomes imperative to
adjust model variables, particularly with regard to the `weekday'
variable that signifies fluctuations in traffic intensity --- a pivotal
factor influencing air pollution.

\section*{4. Results and analysis}\label{results-and-analysis}

\textbf{4.1 The impact of lockdown on air pollutants and weather}

As depicted by \textbf{Figs. 2-4}, the concentrations of
NO\textsubscript{2}, CO, and PM\textsubscript{10} in the seven cities
during the 2020 lockdown were consistently lower than those recorded
during the same period in 2019 (Kumari and Toshniwal, 2020; He et al.,
2020). This reduction highlights the significant impact of decreased
human activities, such as reduced vehicular traffic and industrial
operations, on mitigating air pollution in Chinese urban areas.
Particularly, there was a significant reduction in NO\textsubscript{2}
levels in Beijing, Changchun, and Wuhan during the lockdown
(\textbf{Fig. 3}). NO\textsubscript{2} primarily originates from
automobile exhaust (Wang et al., 2022) and industrial emissions, and
Beijing experienced prolonged and widespread traffic congestion (Bian et
al., 2016). The substantial decrease in traffic volume during the
lockdown resulted in a marked decline of NO\textsubscript{2} levels.
Changchun's extensive industrial emissions were notably reduced tied to
the lockdown measures, leading to a direct decrease in nitrogen dioxide
emissions. In comparison to other cities, Wuhan's stringent lockdown
measures significantly lowered its pollutant concentration. However,
despite the overall decline in pollutant levels, there were notable
exceptions. For instance, a marked increase in pollutant concentrations
was observed in Beijing in 2020 mid-February. This anomaly can be
attributed to the extensive use of fireworks during the Spring Festival,
which significantly contributed to the surge in air pollutants (Zhang et
al., 2017). Furthermore, low wind speeds during this period exacerbated
the situation by trapping pollutants close to the ground, further
intensifying air quality issues. Similarly, a significant rise in
pollutant levels occurred in Changchun in 2020 early April was
attributed to the burning of agricultural straw in rural areas
surrounding the cities (Liu et al., 2020), which released substantial
quantities of particulate matter and other pollutants into the
atmosphere. The reduction in pollution levels during the lockdown
provides a compelling argument for the potential long-term benefits of
sustained reductions in human activities. Nevertheless, the occasional
spikes in pollutant concentrations during specific events highlight the
need for targeted air quality management strategies that address both
routine and exceptional situations of pollution.

The comparison in \textbf{Fig. 5} illustrates the variation in weather
elements between the pandemic lockdown period in 2020 and the
corresponding period in 2019. Overall, a significant majority of cities
experienced cooler temperatures during the lockdown in 2020 compared to
the same period in 2019, attributed to a notable reduction in human
activities that weakened the urban heat island effect (Liu et al.,
2022). However, it is worth noting that these lower temperatures, slower
wind speeds, and higher air pressure observed in 2020 are not conducive
to pollutant diffusion.

\textbf{4.2 Model predictions}

The pre-LD model's predictions utilized data from training periods of 3,
6, 9, 12, 18, and 24 months prior to the lockdown, with subsequent
testing against the next month's data. As shown by \textbf{Fig. 6}, the
model's performance improves with longer training durations, achieving
optimal results when trained on 24 months of data. This indicates that a
longer training period allows the model to better capture complex
temporal patterns, enhancing predictive accuracy. However, the increased
RMSE of PM\textsubscript{10} in Beijing, despite extended training, can
be attributed to the frequent occurrence of sand and haze weather,
leading to extreme PM\textsubscript{10} values prior to the lockdown
(Liu et al., 2023). The inclusion of these extreme values during the
extended training period influenced the model's accuracy. Overall,
training on 24 months of data generally provided better outcomes, as it
offered a richer context for predicting future conditions. This is
consistent with the statement that extended training periods promote
model performance by incorporating more comprehensive historical data
(Eklund and Kapetanios, 2008).

Due to the scarcity of data during the lockdown period, only three
consecutive days of data were used as the test set in the experiment,
and the remaining data during the lockdown were used to train the LD
model. \textbf{Table 1} provides a summarized performance of pre-LD and
LD models in predicting air pollutant concentrations
(NO\textsubscript{2}, CO, and PM\textsubscript{10}) across seven cities.
Among the seven cities, Xiamen's coastal location, high temperatures,
strong wind speeds, and open terrain promote the dispersion of air
pollutants. The city also experiences low industrial emissions and
maintains smooth traffic year-round. Consequently, Xiamen's air quality
remains generally high on non-lockdown days (Lin et al., 2023). As a
result, the level of air pollution blocked by the epidemic decreased
less than usual, and the predictions of the pre-LD and LD models are
more accurate than that of the other six cities. Generally, the RMSE of
CO exhibits a significantly lower value against the ones of other two
pollutants and there was an average reduction of 14\% for CO, 34\% for
NO\textsubscript{2}, and 28\% for PM\textsubscript{10} across the seven
cities, after comparing observations from the 2020 lockdown with those
during the corresponding period in 2019. In contrast to the pre-LD
model, the LD one estimated a reduction of 44\%, 32\%, and 30\% in the
average RMSE for NO\textsubscript{2}, CO, and PM\textsubscript{10}
predictions in the seven cities, respectively.

Similarly, \textbf{Figs. 7-9} support this behavior, proving that the LD
model predicted more accurately than the pre-LD model did. The
discrepancy between predicted and actual values is notably reduced with
the LD model, indicating enhanced predictive accuracy. This improved
performance can be attributed to the transfer learning that is crucial
for enhancing prediction accuracy. However, \textbf{Fig. 7} reveals a
significant increase in CO concentrations during the 2020 winter in
Beijing and Changchun, leading to considerable deviations between the LD
model's predictions and actual observed values. This discrepancy is
likely because of higher CO emissions resulted from increased heating
activities in northern cities during the winter (Fan et al., 2020; Liu
et al., 2022). Moreover, pollution levels were generally higher in 2019
than 2020, which can be explained by the higher emissions of air
pollutants resulted from much larger traffic volumes prior to the
lockdown in the seven cities. In contrast, the lockdown period, with its
restrictions on movement and reduced industrial activity, contributed to
a significant promotion in overall air quality.

\textbf{Table 2} presents the agreement between the LD model-predicted
NO\textsubscript{2} reduction and the measurements from satellite
images. The model's predictions fall within a reasonable range, with
those for Beijing and Wuhan aligning closely with observations from both
satellites. Discrepancies in observations explain Chongqing and
Guangzhou predictions being consistent with one of the satellites, which
is understandable. This comparison highlights the LD model's predictive
accuracy.

\section*{5. Conclusions}\label{conclusions}

~~~~~~~~~~~ This study investigated the impact of the COVID-19 lockdown
on air quality in seven major Chinese cities representing diverse
geographic regions with high urbanization and economic activities to
understand the broader effects of reduced human activity on air
pollution levels. Using long-term datasets of air pollutants and
meteorological conditions from January 1, 2016 to December 31, 2021,
generalized additive models (GAM) was employed to develop predictive
models for the pre-lockdown (pre-LD) and lockdown (LD) periods. The GAM
showed high accuracy in predicting air pollutant levels via
incorporating weather conditions and capturing complex temporal
patterns. The pre-LD model was trained by extensive historical data,
while the LD models utilized transfer learning to adjust for the limited
data during the lockdown.

The results showed significant reductions in NO\selectlanguage{english}2, CO, and PM10
\selectlanguage{english}concentrations during the lockdown compared to the same period in 2019.
This reduction was primarily attributed to decreased vehicular traffic
and industrial activities (e.g., Beijing and Changchun). In contrast,
Xiamen, with its coastal location and lower industrial emissions, showed
less pronounced improvements, indicating the diverse impact of lockdown
measures across different urban settings.

The study also highlighted the importance of extended training periods
in enhancing model performance. Models trained on 24 months of data
generally provided better predictive accuracy by offering a richer
context for understanding future conditions. Specific anomalies (e.g.,
high pollution levels in 2020 caused by extensive use of fireworks and
agricultural straw burning in Beijing and Changchun, respectively) were
observed, which underscores the need for targeted air quality management
strategies addressing both routine and exceptional pollution sources.
The application of transfer learning was crucial in refining the LD
model since allowing them to leverage pre-lockdown insights despite the
limited lockdown data. This approach significantly enhanced the model's
predictive accuracy, as evidenced by the reduced discrepancies between
predicted and actual values. The LD model was more accurate than the
pre-LD one, which validates the efficacy of transfer learning in
adapting to new, constrained datasets.

The COVID-19 lockdown provided a unique natural experiment to explore
the impact of reduced human activity on urban air quality. The findings
underscore the potential long-term benefits of sustained reductions in
human activities for air quality promotion. Additionally, the study
demonstrates the feasibility of combining model interpretability with
advanced techniques like transfer learning to enhance the reliability
and accuracy of air quality predictions. These insights can inform
future policy-making and air quality management strategies to mitigate
urban pollution more effectively.

\section*{CRediT authorship contribution
statement}\label{credit-authorship-contribution-statement}

~~~~~~~~Yuchen Ji: Software, Data Curation, Writing - original draft.
Xiaonan Zhang: Software, Visualization, Writing - original draft.
Yueqian Cao: Conceptualization, Methodology, Writing -- review \&
editing, Funding acquisition.

\section*{Acknowledgments}\label{acknowledgments}

This research was jointly supported by the National Natural Science
Foundation of China (Grant No. 42301428). The authors appreciate the
China Meteorological Data Center for providing the meteorological data
and air pollutants records.

\section*{Declaration of Competing
Interest}\label{declaration-of-competing-interest}

The authors declare no conflict of interest.

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