Zhou Ya'nan, Feng Li, Zhang Xin, Wang Yan, Wang Shunying, Wu Tianjun
College of Hydrology and Water Resources, Hohai University, Address: No. 1, Xikang Road, Nanjing 210010, China.
Aerospace information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Sustain Cities Soc. 2021 Dec;75:103388. doi: 10.1016/j.scs.2021.103388. Epub 2021 Sep 25.
Understanding the spatiotemporal patterns of the COVID-19 impact on industrial production could improve the estimation of the economic loss and sustainable work resumption policies in cities. In this study, assuming and checking a correlation between the land surface temperature (LST) and industrial production, we applied the BFAST algorithm and linear regression models on multi-temporal MODIS data to derive monthly time-series deviation of LST with a spatial resolution of 1 × 1 km, to quantificationally explore the fine-scale spatiotemporal patterns of the COVID-19 control measures impact on industrial production, within Wuhan city. The results demonstrate that (1) the trend of time-series LST could partly reflect the impact of the COVID-19 pandemic on industrial production, and the year-around industrial production was less than expectations, with a fall of 14.30%; (2) the most serious COVID-19 impact on industrial production appeared in Mar. and Apr., then, after the lifting of lockdown, some regions (approximate 4.90%) firstly returned to expected levels in Jun, and almost all regions (98.49%) have completed the resumption of work and production before Nov.; (3) the southwest and south-central had more serious impact of the COVID-19 pandemic, approximate twice as much as that in the north and suburban, in Wuhan. The results and findings elaborated the spatiotemporal distribution and their changes during 2020 within Wuhan, which could provide a beneficial support for assessment of the COVID-19 pandemic and implementation of resumption plans for sustainable development.
了解新冠疫情对工业生产的时空影响模式，有助于提高城市经济损失估计以及可持续复工政策的制定。在本研究中，通过假设并检验地表温度（LST）与工业生产之间的相关性，我们将BFAST算法和线性回归模型应用于多时相MODIS数据，以获取空间分辨率为1×1千米的LST月度时间序列偏差，从而定量探究武汉市新冠疫情防控措施对工业生产影响的精细尺度时空模式。结果表明：（1）LST时间序列趋势能部分反映新冠疫情对工业生产的影响，全年工业生产未达预期，下降了14.30%；（2）新冠疫情对工业生产的最严重影响出现在3月和4月，解封后，部分地区（约4.90%）于6月率先恢复至预期水平，几乎所有地区（98.49%）在11月前完成复工复产；（3）武汉市西南部和中南部受新冠疫情影响更为严重，约为北部和郊区的两倍。研究结果阐述了20年期间武汉市的时空分布及其变化，可为评估新冠疫情及实施可持续发展复工计划提供有益支持。
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