Deposition Velocity of PM2.5 in the Winter and Spring above Deciduous and Coniferous Forests in Beijing, China

Sun, Fengxin; Yin, Zhe; Lun, Xiaoxiu; Zhao, Yang; Li, Renna; Shi, Fangtian; Yu, Xinxiao

doi:10.1371/journal.pone.0097723

Cited by 42 publications

(32 citation statements)

References 24 publications

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“…According to previous measurements at the site, the leaf area index (LAI) at the Jiufeng National Forest Park site was 3.8 in spring, and was 1.8 at the Olympic Forest Park site [18].…”

Section: Experimental Descriptionmentioning

confidence: 84%

“…According to previous measurements at the site, the leaf area index (LAI) at the Jiufeng National Forest Park site was 3.8 in spring, and was 1.8 at the Olympic Forest Park site [18]. in forest systems have been mainly conducted in Europe and the United States [8][9][10][11][12][13][14], as well as some Asian countries other than China [15][16][17].…”

Section: Experimental Descriptionmentioning

confidence: 97%

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Deposition of PM2.5 Sulfate in the Spring on Urban Forests in Beijing, China

Zhao

Lun

et al. 2017

Atmosphere

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Water-soluble ions such as sulfate may have great impacts on atmospheric pollution. To determine the deposition effect of PM 2.5 sulfates, the major PM 2.5 constituents, in the forest canopy in Beijing, China, an investigation was carried out in an urban forest, Olympic Forest Park, and a suburban forest, Jiufeng National Forest Park. We measured the vertical distributions of sulfate by using monitoring towers in the two forests at the same times in March and May 2013. Sulfate (SO 4 2− )was predominant in all PM 2.5 water-soluble inorganic ions, with a proportion >40%. The vertical concentration of SO 4 2− increased with height, and the average sulfate concentration in Olympic ForestPark was higher than that in Jiufeng National Forest Park. Results also revealed that meteorological conditions had significant impacts on both the sulfate concentration and the deposition velocity. In winter, the deposition of SO 4 2− through the canopy was not obvious in the two forest parks, which was quite the opposite in spring. The deposition velocity in the daytime was higher than that in the nighttime in both Olympic Forest Park (0.9 ± 1.0 cm/s vs. 0.4 ± 0.3 cm/s) and Jiufeng National Forest Park (1.3 ± 1.2 cm/s vs. 0.8 ± 0.8 cm/s). While Jiufeng National Forest Park turned out to have a higher average sulfate deposition velocity than Olympic Forest Park.

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“…According to previous measurements at the site, the leaf area index (LAI) at the Jiufeng National Forest Park site was 3.8 in spring, and was 1.8 at the Olympic Forest Park site [18].…”

Section: Experimental Descriptionmentioning

confidence: 84%

Section: Experimental Descriptionmentioning

confidence: 97%

Deposition of PM2.5 Sulfate in the Spring on Urban Forests in Beijing, China

Zhao

Lun

et al. 2017

Atmosphere

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“…The studies showed that for Populus, when the wind speed is 3 m·s . Due to this study taking place in the same experimental area as that of Sun et al [57], we regard the deposition velocity of PM2.5 in this study as 0.9 ± 0.8 cm·s [55,58]; here we also adopted this value. With an increase in the particle size range of 0.1-10 μm, aerosol deposition velocities are greater [58].…”

Section: Urban Forest Effects Model: Air Particulate Removed By Treesmentioning

confidence: 99%

The Concentrations and Reduction of Airborne Particulate Matter (PM10, PM2.5, PM1) at Shelterbelt Site in Beijing

Chen

Sun

et al. 2015

Atmosphere

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Particulate matter is a serious source of air pollution in urban areas, where it exerts adverse effects on human health. This article focuses on the study of subduction of shelterbelts for atmospheric particulates. The results suggest that (1) the PM mass concentration is higher in the morning or both morning and noon inside the shelterbelts and lower mass concentrations at other times; (2) the particle mass concentration inside shelterbelt is higher than outside; (3) the particle interception efficiency of the two forest belts over the three months in descending order was PM10 > PM1 > PM2.5; and (4) the two shelterbelts captured air pollutants at rates of 1496.285 and 909.075 kg/month and the major atmospheric pollutant in Beijing city is PM10. Future research directions are to study PM mass concentration variation of shelterbelt with different tree species and different configuration.

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“…In Jing-Jin-Ji and Chengdu-Chongqing regions, however, the urbanization is characterized by few highly-developed metropolitans, resulting in relatively centralized pollution sources [61,62]. Under this condition, the heterogeneous landscape might be helpful for the PM 2.5 dispersion over the regions [26,27,63]. The AI also positively correlates to the PM 2.5 concentration in most regions, except for the Yangtze River Delta and Chang-Zhu-Tan urban agglomerations.…”

Section: Regional Scalementioning

confidence: 99%

Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning

2017

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Under rapid urbanization, many cities in China suffer from serious fine particulate matter (PM 2.5 ) pollution. As the emission sources or adsorption sinks, land use and the corresponding landscape pattern unavoidably affect the concentration. However, the correlation varies with different regions and scales, leaving a significant gap for urban planning. This study clarifies the correlation with the aid of in situ and satellite-based spatial datasets over six urban agglomerations in China. Two coverage and four landscape indices are adopted to represent land use and landscape pattern. Specifically, the coverage indices include the area ratios of forest (F_PLAND) and built-up areas (C_PLAND). The landscape indices refer to the perimeter-area fractal dimension index (PAFRAC), interspersion and juxtaposition index (IJI), aggregation index (AI), Shannon's diversity index (SHDI). Then, the correlation between PM 2.5 concentration with the selected indices are evaluated from supporting the potential urban planning. Results show that the correlations are weak with the in situ PM 2.5 concentration, which are significant with the regional value. It means that land use coverage and landscape pattern affect PM 2.5 at a relatively large scale. Furthermore, regional PM 2.5 concentration negatively correlate to F_PLAND and positively to C_PLAND (significance at p < 0.05), indicating that forest helps to improve air quality, while built-up areas worsen the pollution. Finally, the heterogeneous landscape presents positive correlation to the regional PM 2.5 concentration in most regions, except for the urban agglomeration with highly-developed urban (i.e., the Jing-Jin-Ji and Chengdu-Chongqing urban agglomerations). It suggests that centralized urbanization would be helpful for PM 2.5 pollution controlling by reducing the emission sources in most regions. Based on the results, the potential urban planning is proposed for controlling PM 2.5 pollution for each urban agglomeration.

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Deposition Velocity of PM2.5 in the Winter and Spring above Deciduous and Coniferous Forests in Beijing, China

Cited by 42 publications

References 24 publications

Deposition of PM2.5 Sulfate in the Spring on Urban Forests in Beijing, China

Deposition of PM2.5 Sulfate in the Spring on Urban Forests in Beijing, China

The Concentrations and Reduction of Airborne Particulate Matter (PM10, PM2.5, PM1) at Shelterbelt Site in Beijing

Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning

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