Effects of Green space landscape patterns on particulate matter in Zhejiang Province, China
Effects of Green space landscape patterns on particulate matter in Zhejiang Province, China
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中国浙江省绿地景观格局对颗粒物的影响
DOI：
10.1016/j.apr.2018.03.004
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发表时间：
2018-09
影响因子：
4.5
通讯作者：
Xintao Lin
中科院分区：
文献类型：
--
作者：
Haitian Wu;Chang Yang;Jian Chen;Shan Yang;Ting Lu;Xintao Lin
颗粒物（PM）污染及其对健康的影响越来越受到关注。绿色空间可以提供关键的生态系统服务，增加绿色空间的供应可以减少PM污染，但绿色空间在不同尺度上对PM的影响尚不清楚。以50个监测站点的交通和气象因子为数据源，采用主成分聚类分析和层次聚类分析方法进行分析。2015年2月1日至2017年2月28日，对浙江省50个监测点的PM（PM10和PM2. 5）浓度进行了逐日监测，以定量分析PM浓度的时空变化及其与绿色空间和景观结构的经验关系。结果表明：（1）在5 km及以下尺度上，绿色空间与PM2.5的相关性强于PM10：（2）在2 km及以下尺度上，总边长对PMs的影响大于绿色覆盖面积，而在3-5 km尺度上，绿色覆盖对PMs的影响更大;（3）在浙江省以气象因子为主的山区和丘陵区，可以通过3-4 km尺度的绿色空间建立PM2.5预报模型，而在盆地或低地，利用1-2 km尺度的绿色空间可以建立PM2.5预测模型。研究结果对城市绿色空间规划，特别是绿色空间的规模和形态的规划具有重要意义。此外，该研究还为LUR模型在浙江地区的应用提供了指导。
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 (PM10and 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 PM2.5is 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 PM2.5forecast model by 3–4-km scale green spaces, while in those basins or low lands, we can build the PM2.5forecast 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.
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影响因子：
37.8
作者：
Pope, CA;Burnett, RT;Godleski, JJ
通讯作者：
Godleski, JJ
影响因子：
13.6
作者：
V. Shridhar;P. Khillare;Tripti Agarwal;S. Ray
通讯作者：
V. Shridhar;P. Khillare;Tripti Agarwal;S. Ray
影响因子：
4.8
作者：
Chen J;Zhu L;Fan P;Tian L;Lafortezza R
通讯作者：
Lafortezza R
DOI：
--
发表时间：
2013
期刊：
--
影响因子：
--
作者：
Li Hong-bo
通讯作者：
Li Hong-bo
影响因子：
3.7
作者：
Wu J;Xie W;Li W;Li J
通讯作者：
Li J