implementation of the strategy of "One city and nine
towns" in Shanghai, the urbanization area of
Shanghai has expanded by leaps and bounds to the
outer suburbs. The development of the whole city has
also changed from the original single-center structure
centered on the People's Square to the open multi-
center group structure, and Xujiahui is becoming the
new center of their respective regions.
In terms of time and space, it can be seen from
the classification map that the spatial change of land
development and utilization in Shanghai in different
periods is uneven, and the pattern is generally more
in the east and less in the west. With the passage of
time, the trend of suburbanization of land
development and utilization is obvious. In the early
1980s, the land development and utilization change in
Shanghai was mainly concentrated in the central city,
and by 2010, the land development and utilization
change had shifted to the outer suburbs, and even
crossed the near suburbs rapidly in the middle period.
In terms of land development and utilization
types, the overall trend of land development and
utilization change in Shanghai is that high-density
urban areas increase the most area, and transportation
develops significantly during this period. The
emergence and concentration of industrial and
transportation areas reflect the huge growth of
industrial land in Shanghai and the great role of
transportation in the development of Shanghai.
The change of rural construction land is to
continue to expand outward, but with the
development of central cities, the growth rate of rural
construction will slow down to a certain extent, and
rural land may be replaced by high-density buildings
in the future.
REFERENCES
Air Quality and Climate Change Research. (2019, Jun 18).
Retrieved from https://papersowl.com/examples/air-
quality-and-climate-change-research/
Akimoto, H. (2003). Global air quality and pollution.
Science, 302(5651), 1716 – 1719.
https://doi.org/10.1126/science.1092666
Bao, X., Zhou, W., Zheng, Z., Xu, L. (2023). The
interactions and mechanisms between biogenic volatile
organic compounds emissions and ozone
concentrations in urban areas: A review. Acta
Ecologica Sinica, 43(5). https://doi.org/10.5846/
stxb202202240431
Bergmann, S., Li, B., Pilot, E., Chen, R., Wang, B., & Yang,
J. (2020). Effect modification of the short-term effects
of air pollution on morbidity by season: A systematic
review and meta-analysis. Science of the Total
Environment, 716, 136985. https://doi.org/10.1016/
j.scitotenv.2020.136985
Chen, Yaping., Deng, Akot. (2023). Evolution and
Influencing Factors of Urban Built-Up Areas in the
Yangtze River Delta Urban Agglomeration. IEEE
Access. PP. 1-1. 10.1109/ACCESS.2023.3336735.
China Mobile Source Environmental Management Annual
Report (2023)
Fu, M., Zheng, Y., Xu, X., NIU, L. (2011). Advances of
study on monitoring and evaluation of PM2.5.
Meteorology and Disaster Reduction Research,
34(4):1-6. https://cstj.cqvip.com/Qikan/Article/
Detail?id=40851670
Hu, H. (2012). Real-time monitoring of the environment
and gas sampling methods (CHN. Patent No.
CN201110050054.5). China National Intellectual
Property Administration.
Managing Air Quality and Pollution Environmental
Sciences Essay. (2018, Jul 23). Retrieved from
https://phdessay.com/managing-air-quality-and-
pollution-Environmental-sciences-essay/
Wang, X., Chen, R., Kan, H. (2011). Application of Remote
Sensing Technology in Atmospheric Pollutant
Monitoring: a Review of Recent Studies. Journal of
Environment and Health, 28(10),
4.https://www.cnki.com.cn/Article/CJFDTotal-
HJYJ201110028.htm
Wei, J., Li, Z. (2023). ChinaHighO3: High-resolution and
High-quality Ground-level MDA8 O3 Dataset for
China (2000-2022). National Tibetan Plateau / Third
Pole Environment Data Centre.
https://doi.org/10.5281/zenodo.10477125.
Wei, J., Li, Z. (2023). ChinaHighPM10: High-resolution
and High-quality Ground-level PM10 Dataset for China
(2000-2022). National Tibetan Plateau / Third Pole
Environment Data Centre.
https://doi.org/10.5281/zenodo.3752465.
Wei, J., Li, Z. (2023). ChinaHighPM2.5: High-resolution
and High-quality Ground-level PM2.5 Dataset for
China (2000-2022). National Tibetan Plateau / Third
Pole Environment Data Centre.
https://doi.org/10.5281/zenodo.3539349.
Wei, J., Li, Z. (2023). ChinaHighSO2: High-resolution and
High-quality Ground-level SO2 Dataset for China
(2013-2022). National Tibetan Plateau / Third Pole
Environment Data Centre.
https://doi.org/10.5281/zenodo.4641538.
Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L.,
Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H.,
& Song, Y. (2020). Improved 1 km resolution PM2.5
estimates across China using enhanced space-time
extremely randomized trees. Atmospheric Chemistry
and Physics, 20(6), 3273-3289.
https://doi.org/10.5194/acp-20-3273-2020
Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J.,
Liu, X., Sun, L., Xue, W., & Cribb, M. (2022). Full-
coverage mapping and spatiotemporal variations of
ground-level ozone (O3) pollution from 2013 to 2020
across China. Remote Sensing of Environment, 270,
112775. https://doi.org/10.1016/j.rse.2021.112775