Spatial and temporal changes analysis of air quality before and after the COVID-19 in Shandong Province, China

Xing, Huaqiao; Zhu, Lin; Chen, Bingyao; Niu, Jingge; Li, Xuehan; Feng, Yongyu; Fang, Wenbo

doi:10.1007/s12145-021-00739-7

Cited by 11 publications

(5 citation statements)

References 44 publications

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“…Google Earth Engine, a cloud computing platform, which is a modern powerful open tool for solving environmental monitoring problems, was used as an environment for data preparation and analysis [7,8,9]. This platform has been successfully used in various scale research [10,11,12,13,14] to analyze changes in atmospheric air quality during the lockdown due to the COVID pandemic in 2020-2021.…”

Section: Methodsmentioning

confidence: 99%

Air quality impacts of war detected from the Sentinel-5P satellite over Ukraine

Davybida

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The general aim of this research is to analyze spatial-temporal changes of air quality, which are the consequences of the military invasion of the Russian on the territory of Ukraine based on open remote sensing data, geoinformation and cloud technologies. The density of NO2, SO2, CO and HCHO as well as the density of O3 and the absorbing aerosol index (AAI) extracted from the Sentinel-5P satellite using Google Earth Engine (GEE) showed reduced emission of primary air pollutants and a high level of atmospheric restoration compared to 2021, the year before the war. At the same time values of the AAI increased significantly, indicating the presence of smoke and dust in the air. Obtained results showed one more aspect of the drastic effect of the war on the economic and social processes and environmental conditions. The consequences of this ecological effect can have a long-term negative impact on the health of populations.

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Section: Methodsmentioning

confidence: 99%

Air quality impacts of war detected from the Sentinel-5P satellite over Ukraine

Davybida

2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Correlation analysis involves the breakdown of the association among two or more variables, usually to analyze whether the trends in two or more sets of data are consistent [25][26][27]. SPSS25 software was used to explore which factors were associated with the NPP value.…”

Section: Correlation Analysismentioning

confidence: 99%

“…2023, 13, 6457 2 of 14 and explored the effects of climate, altitude and human activity NPP [11]. Li et al (2022) explored the spatial and temporal variability and future trends of NPP in the Yangtze Delta region as well as the response mechanisms of NPP to various driving factors [12]. Shi et al (2023) explored the spatial and temporal variability of NPP, the climate and anthropogenic interactions on NPP impacts and the optimal characteristics of the driving factors in China from 2000 to 2020 [13].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Distribution of Carbon Sink Indicators—NPP and Its Driving Analysis in Ordos City, China

Zhu

Sun

et al. 2023

Applied Sciences

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Ordos City is an important energy supply city for Chinese provinces and cities, providing a secure energy supply for China while also generating corresponding environmental pollution. Examining the spatiotemporal patterns of net primary productivity (NPP) in Ordos City and its driving factors is relevant to the realization of the carbon emission policy in Inner Mongolia. This study was undertaken to analyze NPP and its driving factors in Ordos City from 2000 to 2019 using NPP data, CO2 spatial grid data, meteorological data and statistical yearbook data accordingly. The NPP in Ordos City increased significantly from 2000 to 2019, mainly showing low values of NPP in the northwest and high values in the southeast. The usable grassland area and annual mean precipitation had a significant positive correlation with NPP, whereas the other factors had a more significant negative correlation. The usable grassland area had the largest influence on NPP, and fixed asset investment had the smallest influence on NPP. The total NPP–anthropogenic factor regression model and the mean NPP–natural factor regression model constructed allow for the prediction of NPP. Anthropogenic carbon emissions, population growth and usable grassland area were the main causes of NPP changes. Planting and protecting green plants and scientific and effective energy extraction plans are measures that enhance the degree of carbon sequestration in Ordos City.

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“…Spatial correlation analysis was carried out between NPP and land cover to calculate correlation coefficients and clarify their association. The Pearson correlation coefficient was used to measure whether the two datasets lie on top of a line and to measure the linear relationship between the fixed distance variables [41][42][43]. Sig is an abbreviation for statistical significance [44].…”

Section: Spatial Correlation Analysismentioning

confidence: 99%

Analysis of Changes in Vegetation Carbon Storage and Net Primary Productivity as Influenced by Land-Cover Change in Inner Mongolia, China

et al. 2023

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Exploring the influence of vegetation carbon stocks over land-cover change and the relationship between land-cover change and net primary production (NPP) is of great practical importance for achieving China’s carbon neutrality target. Therefore, this paper analyses the spatio-temporal distribution of land-cover change and NPP change in Inner Mongolia from 2000 to 2020 and explores the vegetation carbon stock change caused by land-cover change, the relationship between land cover and NPP, and the relationship between land-cover change and NPP change. The results show that the main land-cover change in Inner Mongolia during 2000–2020 was the conversion from other land-cover types to grassland, with the conversion of bare land to grassland from 2000 to 2010 covering an area of 20,634.87 km2. During the period of 2000–2020, the high values of NPP were mainly located in northeastern Inner Mongolia, and the low values were mainly distributed in southwestern Inner Mongolia. The total carbon storage changes in vegetation affected by land-cover change during 2000–2010 and 2010–2020 were 10,736,986.11 t and 3,894,272.41 t. The mean values of NPP for different land covers were, in descending order, forest > cultivated land > wetland > grassland > artificial surfaces > shrubland > water bodies > bare land. Between 2000 and 2010, the change in land-cover type to water bodies was the main cause of the decrease in NPP, and the increase in NPP was mainly due to the conversion of other land-cover types to cultivated land, forest, and grassland. The implementation of appropriate conservation and management measures (e.g., the construction of forest and grass ecological protection), planting, and the protection of high-vegetation carbon storage plants and other means can improve the level of vegetation carbon sequestration and protect the ecological environment of Inner Mongolia.

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Spatial and temporal changes analysis of air quality before and after the COVID-19 in Shandong Province, China

Cited by 11 publications

References 44 publications

Air quality impacts of war detected from the Sentinel-5P satellite over Ukraine

Air quality impacts of war detected from the Sentinel-5P satellite over Ukraine

Spatiotemporal Distribution of Carbon Sink Indicators—NPP and Its Driving Analysis in Ordos City, China

Analysis of Changes in Vegetation Carbon Storage and Net Primary Productivity as Influenced by Land-Cover Change in Inner Mongolia, China

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