Analysis of spatiotemporal variation of PM2.5 and its relationship to land use in China

Xu, Weihua; Jin, Xiaobin; Liu, Miaomiao; Ma, Zongwei; QianWang,; Zhou, Yongqiang

doi:10.1016/j.apr.2021.101151

Cited by 23 publications

(7 citation statements)

References 38 publications

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“…This is consistent with the research views and conclusions of Li et al [ 62 ], who determined that reducing agricultural NH 3 emissions can effectively reduce PM 2.5 pollution [ 63 ]. Xu et al [ 64 ] believed that PM 2.5 was significantly positively correlated with cultivated land area, and the fragmentation of cultivated land was beneficial to the decrease of PM 2.5 . However, the correlation coefficient between the land transfer rate and PM 2.5 emissions was negative at the 1% significance level, indicating that an increase in land transfer rate would reduce PM 2.5 emissions.…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Assessing the Relationship among Land Transfer, Fertilizer Usage, and PM2.5 Pollution: Evidence from Rural China

Guo

Song

Tang

et al. 2022

IJERPH

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Concern for environmental issues is a crucial component in achieving the goal of sustainable development of humankind. Different countries face various challenges and difficulties in this process, which require unique solutions. This study investigated the relationship between land transfer, fertilizer usage, and PM2.5 pollution in rural China from 2000 to 2019, considering their essential roles in agricultural development and overall national welfare. A cross section dependence test, unit root test, and cointegration test, among other methods, were used to test the panel data. A Granger causality test was used to determine the causal relationship between variables, and an empirical analysis of the impulse response and variance decomposition was carried out. The results show that the use of chemical fertilizers had a significant positive impact on PM2.5 pollution, but the impact of land transfer on PM2.5 pollution was negative. In addition, land transfer can reduce the use of chemical fertilizers through economies of scale, thus reducing air pollution. More specifically, for every 1% increase in fertilizer usage, PM2.5 increased by 0.17%, and for every 1% increase in land transfer rate, PM2.5 decreased by about 0.07%. The study on the causal relationship between land transfer, fertilizer usage, and PM2.5 pollution in this paper is helpful for exploring environmental change—they are supplements and innovations which are based on previous studies and provide policy-makers with a basis and inspiration for decision-making.

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Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Assessing the Relationship among Land Transfer, Fertilizer Usage, and PM2.5 Pollution: Evidence from Rural China

Guo

Song

Tang

et al. 2022

IJERPH

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“…Spatial distributions of PM 2.5 are closely associated with land use, such as built-up areas, forest, and rivers [57,58]. As such, land use has been an effective variable for the prediction distributions of PM 2.5 [59,60].…”

Section: Land Usementioning

confidence: 99%

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

Song

2022

Remote Sensing

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Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations.

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“…The accelerated development of urbanization and industrialization has created multiple serious air pollution accidents in some cities in China, which have greatly endangered people's daily life and health ( Zhou et al, 2016 ; Jin et al, 2017 ; Chen et al, 2018 ; Xiao et al, 2020 ). Among all types of air pollution, PM 2.5 pollution is the most severe ( Xu et al, 2021 ). Compared with other air pollutants, PM 2.5 has a small particle size, large specific surface area, and strong activity ( Aggarwal and Jain, 2017 ; Sawlani et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal characteristics and socioeconomic factors of PM2.5 heterogeneity in mainland China during the COVID-19 epidemic

et al. 2023

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Analysis of spatiotemporal variation of PM2.5 and its relationship to land use in China

Cited by 23 publications

References 38 publications

Assessing the Relationship among Land Transfer, Fertilizer Usage, and PM2.5 Pollution: Evidence from Rural China

Assessing the Relationship among Land Transfer, Fertilizer Usage, and PM2.5 Pollution: Evidence from Rural China

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

Spatiotemporal characteristics and socioeconomic factors of PM2.5 heterogeneity in mainland China during the COVID-19 epidemic

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