SavedAdd your journal

Journals / Atmospheric Pollution Research / 2018 / Cilt: 9 - Sayı: 5

Effects of Green space landscape patterns on particulate matter in Zhejiang Province, China

Haitian WU
Chang YANG
Jian CHEN
Shan YANG
Ting LU
Xintao LİN
Pages
923–933
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In addition, it can provide guidance to the future application of LUR model in Zhejiang area.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Particulate matter (PM) pollution and its health effects are receiving more attention.
📊
Key Findings & Data:Based on the traffic and meteorological factors from 50 monitoring stations, principal composition cluster analysis (PCA) and hierarchical cluster analysis (HCA) were implemented.
💡
Conclusion & Impact:In addition, it can provide guidance to the future application of LUR model in Zhejiang area.
🔬Design:Modeling & Simulation
📍Scope:2015 Verileri
⚙️Variables:Domain Parameters

Özet

Particulate matter (PM) pollution and its health effects are receiving more attention. Green space can provide critical ecosystem services, and increasing the supply of green space can reduce PM pollution, but effects of green spaces on PM at different scales are not clear. Based on the traffic and meteorological factors from 50 monitoring stations, principal composition cluster analysis (PCA) and hierarchical cluster analysis (HCA) were implemented. Daily PMs (PM10 and PM2.5) concentrations were measured at 50 monitoring stations in Zhejiang (1 Feb 2015 to 28 Feb 2017) to quantify the spatiotemporal change of PM concentration and its empirical relationship with green spaces and landscape structure. The result shows: (1) At 5-km, or smaller, scale, the correlation between green space and PM2.5 is stronger than PM10; (2) at 2-km scale or less, the total edge length has more impact on PMs than green cover area, while at 3–5-km scale, the influence of green cover is more dominated; (3) In the mountain and hilly area in Zhejiang that are dominated by meteorological factors, we can establish the PM2.5 forecast model by 3–4-km scale green spaces, while in those basins or low lands, we can build the PM2.5 forecast model by using 1–2-km scale green spaces. The results are of great importance for urban green space planning, especially when it comes to the size and shape of the green space. In addition, it can provide guidance to the future application of LUR model in Zhejiang area.

🔖

Cite This Article

WU, H., YANG, C., CHEN, J., YANG, S., LU, T., LİN, X. (2018). Effects of Green space landscape patterns on particulate matter in Zhejiang Province, China. Atmospheric Pollution Research, Cilt: 9 - Sayı: 5, 923–933. https://ajindex.com/articles/209833

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (8)

  1. Aničić, M., Spasić, T., Tomašević, M., Rajšić, S., Tasić, M., 2011. Trace elements accumulation and temporal trends in leaves of urban deciduous trees (Aesculus hippocastanum and Tilia spp.). Ecol. Indic. 11 (3), 824–830.
  2. Bao, Z., Feng, Y.G., Jiao, L., Hong, S.M., Liu, W.G., 2010. Characterization and source apportionment of PM2.5 and PM10 in Hangzhou. Environ. Monit. China 26 (2), 44–48.
  3. Bao, C., Chai, P., Lin, H., Zhang, Z., Ye, Z., Gu, M., et al., 2016. Association of PM2.5 pollution with the pattern of human activity: a case study of a developed city in Eastern China. J. Air & Waste Manag. Assoc. 66 (12), 1202–1213.
  4. Cao, W., Luo, F., Han, J., Caiyan, W.U., Xiang, W., 2014. The impact of road development on landscape pattern change in rapidly urbanizing area. J. Geo-Information Sci. 16 (6), 898–906.
  5. Chen, J., Jiang, H., Wang, B., Xiao, Z.Y., Jiang, Z.S., Zhou, G.M., Yu, S.Q., 2012. Aerosol optical properties from Sun-photometric measurements in Hangzhou, China. Int. J. Remote Sens. 33 (8), 2451–2461.
  6. Chen, T., He, J., Lu, X., She, J., Guan, Z., 2016a. Spatial and temporal variations of PM2.5 and its relation to meteorological factors in the urban area of Nanjing, China. Int. J. Environ. Res. Public Health 13 (9). Chen, J., Zhu, L., Fan, P., Tian, L., Lafortezza, R., 2016b. Do green spaces affect the spatiotemporal changes of PM 2.5 in Nanjing? Ecol. Process 5 (1), 7. Chen, Z., Cai, J., Gao, B., Xu, B., Dai, S., He, B., et al., 2017. Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region. Sci. Rep-Uk 7, 40735. Cheng, Z., Wang, S., Jiang, J., Fu, Q., Chen, C., Xu, B., et al., 2013. Long-term trend of haze pollution and impact of particulate matter in the Yangtze River Delta, China. Environ. Pollut. 182 (6), 101. Choi, Y., Zhang, N., Chen, S.-C., Luo, C.C., 2013. Quantitative ecological risk analysis by evaluating China's eco-efficiency and its determinants. Hum. Ecol. Risk Assess. Int. J. 19 (5), 1324–1337.
  7. Chu, N., Kadane, J.B., Davidson, C.I., 2009. Identifying likely PM2.5 sources on days of elevated concentration: a simple statistical approach. Environ. Sci. Technol. 43 (7), 2407–2411.
  8. Dockery, D.W., Rd, P.C., Xu, X., Spengler, J.D., Ware, J.H., Fay, M.E., et al., 1993. An association between air pollution and mortality in six U.S. cities. N. Engl. J. Med. 329 (24), 1753. Gao, L., Jia, G., Zhang, R., Che, H., Fu, C., Wang, T., et al., 2011. Visual range trends in the Yangtze River