研究报告
苏维,刘苑秋,赖胜男,古新仁,刘青,龚鹏.林冠特征对城市森林斑块消减空气PM2.5质量浓度的影响[J].环境科学学报,2018,38(7):2902-2908
林冠特征对城市森林斑块消减空气PM2.5质量浓度的影响
- Subduction of PM2.5 mass concentration by canopy characteristics of urban forest patches
- 基金项目：江西省重大自然科学基金项目（No.20152ACB20006）
- 作者
- 单位
- 苏维
- 河南科技大学林学院, 洛阳 471023
- 刘苑秋
- 江西农业大学林学院, 南昌 330045
- 赖胜男
- 江西农业大学林学院, 南昌 330045
- 古新仁
- 江西农业大学林学院, 南昌 330045
- 刘青
- 江西农业大学林学院, 南昌 330045
- 龚鹏
- 江西农业大学林学院, 南昌 330045
- 摘要：研究城市森林斑块消减空气PM2.5质量浓度的能力，以及这种能力的主要影响因素，对改善城市空气环境具有重要的意义.本研究测定了南昌市典型城市森林斑块消减PM2.5质量浓度的能力（PM2.5AC），并分析了PM2.5AC对城市森林斑块林分组成、冠层特征的响应.研究表明：南昌市城市森林斑块具有显著消减空气PM2.5质量浓度的能力，其中针叶林斑块PM2.5AC最高，达到9.15%，阔叶纯林最低3.82%；整体上春季PM2.5AC最高，达到7.34%，冬季最低3.76%.城市森林斑块PM2.5AC与林冠特征中的LAI（叶面积指数）显著正相关，与DIFN（天空开度）显著负相关，与斑块特征中的斑块面积、林分结构、斑块总叶面积显著正相关.通过综合分析，城市森林斑块总叶面积是主导PM2.5AC的主要影响因素.
- Abstract：Of interest to researchers and urban planners is the effect of urban forests on concentrations of ambient air pollution. To what extent urban forest patches can improve or subduct air PM2.5 mass concentration is an important question.This study measured the variations of PM2.5 mass concentration (PM2.5AC) under some typical urban forest patches in Nanchang, and analyzed the different responses of PM2.5AC to stand composition and forest canopy characteristic.The results showed that urban forest patches had significant subduction effect on PM2.5AC. Among different forest types, the reduction effect by coniferous forest patches was the highest, which reached to 9.15%, while the reduction by pure broadleaf forest was the lowest with only 3.82%.In temporal pattern, subduction of urban forest patch on PM2.5AC had best effect in spring by 7.34%, but minus effect in winter by 3.76%.In index of canopies, PM2.5AC was shown to depend positively on leaf area index (LAI) and negatively on diffuse none-interceptance (DIFN). In index of patch characteristics, PM2.5AC was positively correlated with patch area, stand structure, and total leaf area of patch. Through the comprehensive analysis of urban forest patches, total leaf area of patch is considered as the most important influence factor for PM2.5AC.
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