The application of land use regression model to investigate spatiotemporal variations of PM2.5 in Guangzhou, China: Implications for the public health benefits of PM2.5 reduction

Mo, Yangzhi; Booker, Douglas; Zhao, Shizhen; Tang, Junyan; Jiang, Hongxing; Shen, Jin; Chen, Duohong; Li, Jun; Jones, Kevin C.; Zhang, Gan

doi:10.1016/j.scitotenv.2021.146305

Cited by 34 publications

(14 citation statements)

References 76 publications

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“…Particle reduction has high health and economic benefits [49,50]. The synergistic effects between anthropogenic emissions and atmospheric processes must be considered to reduce PM 2.5 and PM 10 concentrations.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of PM2.5 and PM10 Spatio-Temporal Distribution and Influencing Meteorological Conditions in Beijing

Xing

Sun

2022

Atmosphere

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PM2.5 and PM10 in the atmosphere seriously affect human health and air quality, a situation which has aroused widespread concern. In this paper, we analyze the temporal and spatial distribution of PM2.5 and PM10 concentrations from 2016 to 2021 based on real-time monitoring data. In addition, we also explore the influence of meteorological conditions on pollutants. The results show that PM2.5 and PM10 concentrations are similarly distribution in temporal and spatial from 2016 to 2021, and the average concentrations of both show a decreasing trend. The ratio of PM2.5 to PM10 is decreasing, indicating that the proportion of fine particles is declining. PM2.5 and PM10 concentrations are higher in spring and winter, but lower in summer. Spatially, it shows a gradual shift from the characteristic of “high in the south and low in the north” to a uniform homogenization across districts. The spatial distribution of PM2.5 and PM10 mass concentrations is synchronous by applying empirical orthogonal functions (EOF). The first EOF pattern exhibits a consistent characteristic of high in the southeast and low in the northwest. The second pattern EOF reflects the effect of impairing PM2.5 concentrations in the southeast during the winter of 2016–2018. The PM2.5 and PM10 concentrations are significantly negatively correlated with wind speed and precipitation in both spring and winter. On the other hand, from the perspective of the circulation situation, the southeasterly and weak westerly wind in spring produce convergence resulting in higher particulate matter concentrations in the south than in the north in Beijing. The westerly wind is flatter at 700 hPa geopotential height, which is conducive to the formation of stationary weather. The vertical direction of airflow in spring and winter is dominated by convergence and sinking, indicating the weak dispersion ability of the atmosphere. The reason for the accumulation of particulate matter at the surface is investigated, which is beneficial to provide the theoretical basis for air quality management and pollution control in Beijing.

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Section: Discussionmentioning

confidence: 99%

Characteristics of PM2.5 and PM10 Spatio-Temporal Distribution and Influencing Meteorological Conditions in Beijing

Xing

Sun

2022

Atmosphere

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“…However, socioeconomic factors are the fundamental contributors to air pollution. Economic development [10], urbanization [11], population density [33], FDI [7], energy structure [34], and land use [35] have been investigated in examining air pollution in current studies. Relevant research methods include geographic probes [28], spatial econometric models [3,36], gray correlation models [37], machine learning [13,38], and geographically weighted regression models [30].…”

Section: Introductionmentioning

confidence: 99%

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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“…In the face of air pollution caused by enterprises, it is necessary to carefully approve the site selection of the enterprise, especially the location upwind of the city, and to increase the cost of exhaust emissions through environmental protection tax, etc., and encourage enterprises to eliminate outdated processes and equipment and use more clean energy such as wind energy and solar energy. It is also crucial for the government to continuously strengthen green management in construction as well as vehicle transport management ( 70 , 71 ). In addition, the government should call on people to prepare necessary protective equipment outdoors, such as masks, to reduce the harm of some inhalable particles.…”

Section: Discussionmentioning

confidence: 99%

Government drivers of breast cancer prevention: A spatiotemporal analysis based on the association between breast cancer and macro factors

Bai

Shi

Geng

et al. 2022

Front. Public Health

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BackgroundCurrently, breast cancer (BC) is ranked among the top malignant tumors in the world, and has attracted widespread attention. Compared with the traditional analysis on biological determinants of BC, this study focused on macro factors, including light at night (LAN), PM2.5, per capita consumption expenditure, economic density, population density, and number of medical beds, to provide targets for the government to implement BC interventions.MethodsA total of 182 prefecture-level cities in China from 2013 to 2016 were selected as the sample of the study. The geographically and temporally weighted regression (GTWR) model was adopted to describe the spatiotemporal correlation between the scale of BC and macro factors.ResultsThe results showed that the GTWR model can better reveal the spatiotemporal variation. In the temporal dimension, the fluctuations of the regression coefficients of each variable were significant. In the spatial dimension, the positive impacts of LAN, per capita consumption expenditure, population density and number of medical beds gradually increased from west to east, and the positive coefficient of PM2.5 gradually increased from north to south. The negative impact of economic density gradually increased from west to east.ConclusionThe fact that the degree of effect of each variable fluctuates over time reminds the government to pay continuous attention to BC prevention. The spatial heterogeneity features also urge the government to focus on different macro indicators in eastern and western China or southern and northern China. In other words, our research helps drive the government to center on key regions and take targeted measures to curb the rapid growth of BC.

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The application of land use regression model to investigate spatiotemporal variations of PM2.5 in Guangzhou, China: Implications for the public health benefits of PM2.5 reduction

Cited by 34 publications

References 76 publications

Characteristics of PM2.5 and PM10 Spatio-Temporal Distribution and Influencing Meteorological Conditions in Beijing

Characteristics of PM2.5 and PM10 Spatio-Temporal Distribution and Influencing Meteorological Conditions in Beijing

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

Government drivers of breast cancer prevention: A spatiotemporal analysis based on the association between breast cancer and macro factors

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