The impact of wind and non-wind factors on PM2.5 levels

Xu, Bing; Lin, Weiran; Taqi, Syed Ali

doi:10.1016/j.techfore.2020.119960

Cited by 21 publications

(5 citation statements)

References 24 publications

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“…As the research scale increased, the direct benefits of wind speed in the BTH were shown to be not significant, while the spillover benefits of wind speed always showed a strong positive correlation. The impact of increasing wind speed in different directions on PM 2.5 was also different [29]; it can be inferred that the low-wind-speed conditions in the BTH do not support the diffusion of PM 2.5 [30]. A poor correlation between the PM 2.5 concentration and wind speed was found in the PRD and YRD.…”

Section: Wind Speedmentioning

confidence: 87%

Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation

Zhou,

Dai,

et al. 2024

Atmosphere

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PM2.5 particles with an aerodynamic diameter of less than 2.5 μm are receiving increasing attention in China. Understanding how complex factors affect PM2.5 particles is crucial for the prevention of air pollution. This study investigated the influence of meteorological factors and land use on the dynamics of PM2.5 concentrations in four urban agglomerations of China at different scales from 2010 to 2020, using the Durbin spatial domain model (SDM) at five different grid scales. The results showed that the average annual PM2.5 concentration in four core urban agglomerations in China generally had a downward trend, and the meteorological factors and land use types were closely related to the PM2.5 concentration. The impact of temperature on PM2.5 changed significantly with an increase in grid scale, while other factors did not lead to obvious changes. The direct and spillover effects of different factors on PM2.5 in inland and coastal urban agglomerations were not entirely consistent. The influence of wind speed on coastal urban clusters (the Pearl River urban agglomeration (PRD) and Yangtze River urban agglomeration (YRD)) was not significant among the meteorological factors, but it had a significant impact on inland urban clusters (the Beijing–Tianjin–Hebei urban agglomeration (BTH) and Chengdu–Chongqing urban agglomeration (CC)). The direct effect of land use type factors showed an obvious U-shaped change with an increase in the research scale in the YRD, and the direct effect of land use type factors was almost twice as large as the spillover effect. Among land use type factors, human factors (impermeable surfaces) were found to have a greater impact in inland urban agglomerations, while natural factors (forests) had a greater impact in coastal urban agglomerations. Therefore, targeted policies to alleviate PM2.5 should be formulated in inland and coastal urban agglomerations, combined with local climate measures such as artificial precipitation, and urban land planning should be carried out under the consideration of known impacts.

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Section: Wind Speedmentioning

confidence: 87%

Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation

Zhou,

Dai,

et al. 2024

Atmosphere

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“…Previous studies have shown that meteorological parameters and geographic information have an impact on pollutant transmission and accumulation of pollutants (Fu et al., 2008; Gao et al., 2016; Sun et al., 2016). Meteorological parameters include boundary layer height (BLH, m; Han et al., 2018), 2 m air temperature (TM, K; Ma et al., 2021), relative humidity (RH, %; F. Liu et al., 2019), u and v components of 10 m wind ( U 10, V 10, m/s; B. Xu et al., 2020), and surface air pressure (SP, Pa; G. Xu et al., 2020). These data were obtained from ERA‐5 ECMWF reanalysis data (https://cds.climate.copernicus.eu/cdsapp).…”

Section: Methodsmentioning

confidence: 99%

Estimation of Atmospheric PM₁₀ Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

et al. 2022

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“…On the other hand, air pollution also indirectly affects people’s happiness feelings through microscopic mechanisms. Objective air pollution increases the incidence of cardiovascular and respiratory diseases [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ], which affects happiness by affecting residents’ health. Air pollution also reduces residents’ happiness by eroding transportation facilities and causing inconvenience to their lives [ 4 , 49 ].…”

Section: Theoretical Background and Hypothesesmentioning

confidence: 99%

The Impact of Air Pollution on Residents’ Happiness: A Study on the Moderating Effect Based on Pollution Sensitivity

Tian

Zhang

Xu³

2022

IJERPH

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Promoting people’s happiness is a vital goal of public policy, and air pollution, as the focus of public opinion, is an important influencing factor of residents’ happiness. Although previous literature has explored the relationship between air pollution and happiness, the impact of pollution sensitivity on the relationship has so far received little attention. This paper uses the 2016 China Labor-force Dynamics Survey database (CLDS) to study the impact of air pollution on personal happiness and dissects the moderating effect of air pollution sensitivity from the stock and incremental perspectives. The results found that (1) there is an inverted U-shaped relationship between air pollution and residents’ happiness, such that happiness increases and then decreases with increasing air pollution. The PM10 concentration at the turning point is 119.69 μg/m3, which exceeds the national secondary standard limit (70 μg/m3) by 70.99% and is at the intermediate stage of mild pollution, exceeding the WHO recommended standard (20 μg/m3) by 498.45%, far higher than the international standard recommended level; (2) both air pollution stock sensitivity and incremental sensitivity have a significant positive moderating effect on the relationship between air pollution and happiness, and pollution sensitivity exacerbates the negative effect of air pollution on residents’ happiness by shifting the curve turning point to the left and steepening the curve shape; (3) in addition, the effect of air pollution on different groups is significantly heterogeneous, with lower-age and male groups more likely to have lower happiness due to air pollution; the positive moderating effect of pollution sensitivity is more significant in lower-age, female, and higher-income groups. Therefore, in order to enhance residents’ happiness, the government should not only improve air quality, but also focus on helping residents establish an appropriate subjective perception of air quality.

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The impact of wind and non-wind factors on PM2.5 levels

Cited by 21 publications

References 24 publications

Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation

Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation

Estimation of Atmospheric PM₁₀ Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

The Impact of Air Pollution on Residents’ Happiness: A Study on the Moderating Effect Based on Pollution Sensitivity

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The impact of wind and non-wind factors on PM2.5 levels

Cited by 21 publications

References 24 publications

Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation

Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation

Estimation of Atmospheric PM10 Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

The Impact of Air Pollution on Residents’ Happiness: A Study on the Moderating Effect Based on Pollution Sensitivity

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Estimation of Atmospheric PM₁₀ Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A