\begin{thebibliography}{24}
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\providecommand{\url}[1]{\texttt{#1}}
\expandafter\ifx\csname urlstyle\endcsname\relax
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\newblock Hvaq: A high-resolution vision-based air quality dataset.
\newblock \emph{arXiv preprint arXiv:2102.09332}, 2021.
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\newblock Estimates and 25-year trends of the global burden of disease
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\newblock \emph{The Lancet}, 389\penalty0 (10082):\penalty0 1907--1918, 2017.

\bibitem[Ganin and Lempitsky(2016)]{Ganin2016DANN}
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\newblock Domain-adversarial training of neural networks.
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Chuan Guo, Geoff Pleiss, Yu~Sun, and Kilian~Q Weinberger.
\newblock On calibration of modern neural networks.
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\newblock Single image haze removal using dark channel prior.
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\newblock Searching for mobilenetv3.
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\newblock Using haze level estimation in data cleaning for single-image
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\newblock \emph{Electronics}, 12\penalty0 (16):\penalty0 3485, 2023.
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\newblock Deep haze estimation with convolutional neural network for air
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\newblock Real-time image-based air quality estimation by deep learning neural
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Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and
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\newblock Benchmarking single-image dehazing and beyond.
\newblock \emph{arXiv preprint arXiv:1712.04143}, 2017.
\newblock URL \url{https://arxiv.org/abs/1712.04143}.

\bibitem[Li et~al.(2021)Li, Zhang, and Sun]{Li2021}
Chen Li, Wei Zhang, and Jian Sun.
\newblock Smog level prediction from webcam images using deep learning.
\newblock \emph{IEEE Transactions on Intelligent Transportation Systems},
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\bibitem[Liang et~al.(2020)Liang, Hu, and Feng]{Liang2020SHOT}
Jian Liang, Dapeng Hu, and Jiashi Feng.
\newblock Do we really need to access the source data? source hypothesis
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\bibitem[Narasimhan and Nayar(2002)]{Narasimhan2002}
Srinivasa~G. Narasimhan and Shree~K. Nayar.
\newblock Vision and the atmosphere.
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World~Health Organization.
\newblock Air pollution.
\newblock \emph{WHO Fact Sheet}, 2020.
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\newblock \emph{Dataset Shift in Machine Learning}.
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Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh
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\newblock Mobilenetv2: Inverted residuals and linear bottlenecks.
\newblock In \emph{Proceedings of the IEEE/CVF Conference on Computer Vision
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\newblock Grad-cam: Visual explanations from deep networks via gradient-based
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\newblock In \emph{2017 IEEE International Conference on Computer Vision
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\bibitem[Sun and Saenko(2016)]{Sun2016DeepCORAL}
Baochen Sun and Kate Saenko.
\newblock Deep coral: Correlation alignment for deep domain adaptation.
\newblock In \emph{European Conference on Computer Vision (ECCV) Workshops},
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Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell.
\newblock Tent: Fully test-time adaptation by entropy minimization.
\newblock In \emph{International Conference on Learning Representations
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\bibitem[Wang and Deng(2018)]{Wang2018SurveyUDA}
Mei Wang and Weihong Deng.
\newblock Deep visual domain adaptation: A survey.
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\bibitem[Wang et~al.(2023)Wang, Wang, Liu, Mao, and
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Xiaochu Wang, Meizhen Wang, Xuejun Liu, Ying Mao, and Yang Chen.
\newblock Surveillance-image-based outdoor air quality monitoring.
\newblock \emph{Environmental Science and Ecotechnology}, 18:\penalty0 100319,
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\bibitem[Wilson and Cook(2020)]{Wilson2020SurveyUDA}
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\newblock A survey of unsupervised domain adaptation for visual recognition.
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\bibitem[Zhu(2024)]{SAPID2024}
Ruikuan Zhu.
\newblock Smartphone-based air pollution image dataset (sapid).
\newblock Mendeley Data, Version 1, 2024.
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\end{thebibliography}
