Sabzchi-Dehkharghani H, Majnooni-Heris A, Fakherifard A, Yegani R
Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, 51666-16471 Iran.
Faculty of Chemical Engineering, Membrane Technology Research Center, Sahand University of Technology, Tabriz, Iran.
Int J Environ Sci Technol (Tehran). 2023;20(3):3161-3176. doi: 10.1007/s13762-023-04761-8. Epub 2023 Jan 24.
A new approach for estimating the household water consumption pattern was developed by taking the impact of the COVID-19 pandemic using geographical data. Water consumption data for two years before and a year after the outbreak of the pandemic were analyzed to recognize the consumption pattern on annual and bi-monthly time scales as well as in different spatial classes. Following the recognition of the pattern, the spatiotemporal distribution of household water consumption was estimated based on the discovered connections between consumption and geographical variables. Once a regression relationship between consumption and population density was observed, an idea was developed to investigate the linear equations and their coefficient of parameters in water consumption groups from very low to very high classes using the training data. The coefficients were then adjusted to account for the pandemic's impact on the consumption pattern. Results showed that the highest increases in consumption were 11% for May-July due to the impact of the pandemic while the impact was from decreasing type during lockdowns. A pandemic-induced decline in the mean of consumption was linked to temporary migration by high-income families, whereas the water consumption of others faced an increase. The impact has also increased the slope of the linear relationship between the annual water consumption and population density increased by 3.5%. The proposed model estimated the annual water consumption with the accuracy of %3.77, %1.82, and %1.85 for two years before, one year before and one year after the pandemic, respectively.
一种利用地理数据考虑新冠疫情影响来估算家庭用水模式的新方法被开发出来。对疫情爆发前两年和爆发后一年的用水数据进行了分析，以识别年度和双月时间尺度以及不同空间类别上的用水模式。在识别出模式之后，基于发现的用水与地理变量之间的联系，估算了家庭用水的时空分布。一旦观察到用水与人口密度之间的回归关系，就提出了一个想法，即使用训练数据研究从极低到极高类别用水组中的线性方程及其参数系数。然后对系数进行调整，以考虑疫情对用水模式的影响。结果表明，由于疫情的影响，5月至7月用水量增幅最高达11%，而在封锁期间影响为下降类型。疫情导致的用水量均值下降与高收入家庭的临时迁移有关，而其他家庭的用水量则有所增加。这种影响还使年度用水量与人口密度之间线性关系的斜率增加了3.5%。所提出的模型在疫情前两年、前一年和后一年分别以3.77%、1.82%和1.85%的准确率估算了年度用水量。
