Influencing factors of PM2.5 and O3 from 2016 to 2020 based on DLNM and WRF-CMAQ

Duan, Wenjiao; Wang, Xiaoqi; Cheng, Shuiyuan; Wang, Ruipeng; Zhu, Jiaxian

doi:10.1016/j.envpol.2021.117512

Cited by 49 publications

(25 citation statements)

References 54 publications

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“…To broadly explore the spatial-temporal evolution of air pollution, the perspectives include emission inventories of air pollutants [12], source analysis of particulate matter [5], and spatialtemporal evolution and prediction of air pollution from differentiated data sources [11,13]. The selected research objects include single air pollutants, such as SO 2 [14,15], NO 2 [16,17], O 3 [18,19], PM 10 [20,21], and PM 2.5 [22,23], and composite indices, such as API [24] and AQI [25]. The spatial scales of the studies are mainly focused on the national scale [3], provincial scale [26], urban clusters [11], and urban scale [27].…”

Section: Introductionmentioning

confidence: 99%

“…Existing studies have shown that air pollution is formed by a combination of natural environmental factors and socioeconomic factors, whose formation, and transmission mechanisms are complex [29,31]. Natural environmental factors mainly include precipitation [23], wind speed [19], and vegetation [32], directly affecting the transport and degradation of air pollutants. However, socioeconomic factors are the fundamental contributors to air pollution.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, existing studies mostly focus on single air pollutants or the spatialtemporal characteristics of multiple air pollutants in a single year, and there are few comprehensive studies that cover multiple air pollutants over a long period [19,39]. In recent years, most of the relevant studies have focused on PM 2.5 , with few providing a comparative analysis of the spatial-temporal patterns of multiple air pollutants and their factors [40,41].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy

Wang

et al. 2022

Sustainability

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Air pollution has seriously hindered China’s sustainable development. The impact mechanism of industrial upgrading on air pollution is still unclear, given the rapid digital economy. It is necessary to analyze the impact of industrial structure upgrading on air pollution through the digital economy. To investigate the impact of industrial upgrading and the digital economy on air pollution, this paper selected the industrial advanced index and the digital economy index to construct a panel regression model to explore the improvement effect of industrial upgrading on air pollution and selected China’s three typical areas to construct a zonal regression model. The concentrations of air pollutants showed a downward trend during 2013–2020. Among them, the SO2 concentration decreased by 63%, which is lower than the PM2.5 and NO2 concentrations. The spatial pattern of air pollutants is heavier in the north than in the south and heavier in the east than in the west, with the North China Plain being the center of gravity. These air pollutants have significant spatial spillover effects, while local spatial correlation is dominated by high-high and low-low clustering. Industrial upgrading has a stronger suppressive effect on the PM2.5 concentration than the suppressive effect on the SO2 and NO2 concentrations, while the digital economy has a stronger improvement effect on the SO2 concentration than its improvement effect on the PM2.5 and NO2 concentrations. Industrial upgrading has a stronger improvement effect on air pollution in the Yangtze River Delta urban agglomeration than in Beijing–Tianjin–Hebei and its surrounding areas, while the improvement in air pollution attributable to the digital economy in Beijing–Tianjin–Hebei and its surrounding areas is stronger than in the Yangtze River Delta urban agglomeration. There are significant differences in the effects of industrial upgrading and the digital economy on the various types of air pollutants.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy

Wang

et al. 2022

Sustainability

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“…Moreover, no collinearity was detected, because the

of all independent variables was lower than 2.0 μg/m 3 . Details on DLNMs are provided in an earlier study ( Duan et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…The advantage of these methods is that the data source can represent the real atmospheric conditions better, providing a more objective conclusion. In addition, according to the research needs, various observational parameters can be selected for analysis; among these, the distributed lag nonlinear models (DLNMs) can represent the nonlinear effects of various factors on the concentrations of pollutants, including lag effects, pollution levels, and other dimensions ( Duan et al, 2021 ; Yang et al, 2021 ). Thus, DLNMs are a reliable method for analyzing the nonlinear relationship between various factors and pollution formation.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear influence of winter meteorology and precursor on PM2.5 based on mathematical and numerical models: A COVID-19 and Winter Olympics case study

Wang

Duan

Zhu

et al. 2022

Atmospheric Environment

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Prediction of PM2.5 time series by seasonal trend decomposition-based dendritic neuron model

Yuan

Gao

Wang

et al. 2023

Neural Comput & Applic

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The rapid industrial development in the human society has brought about the air pollution, which seriously affects human health. PM2.5 concentration is one of the main factors causing the air pollution. To accurately predict PM2.5 microns, we propose a dendritic neuron model (DNM) trained by an improved state-of-matter heuristic algorithm (DSMS) based on STL-LOESS, namely DS-DNM. Firstly, DS-DNM adopts STL-LOESS for the data preprocessing to obtain three characteristic quantities from original data: seasonal, trend, and residual components. Then, DNM trained by DSMS predicts the residual values. Finally, three sets of feature quantities are summed to obtain the predicted values. In the performance test experiments, five real-world PM2.5 concentration data are used to test DS-DNM. On the other hand, four training algorithms and seven prediction models were selected for comparison to verify the rationality of the training algorithms and the accuracy of the prediction models, respectively. The experimental results show that DS-DNM has the more competitive performance in PM2.5 concentration prediction problem.

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Influencing factors of PM2.5 and O3 from 2016 to 2020 based on DLNM and WRF-CMAQ

Cited by 49 publications

References 54 publications

Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy

Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy

Nonlinear influence of winter meteorology and precursor on PM2.5 based on mathematical and numerical models: A COVID-19 and Winter Olympics case study

Prediction of PM2.5 time series by seasonal trend decomposition-based dendritic neuron model

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