Study on the negative air ion concentration and influencing factors of urban forest in different geographical spaces
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摘要:
目的 开展不同地理空间城市森林的空气负离子浓度（NAIC）研究，为空气负离子的理论研究和森林康养基地的选址依据提供科学支撑。 方法 在2019年9月—2020年1月，2020年5月—8月，选取广东省广州市的3个不同地理位置、森林面积和周边环境梯度的森林环境：远郊、近郊和城区城市森林，同时设置1个城区对照组进行NAIC差异比较，并运用Pearson相关性分析和多元逐步回归分析探讨NAIC与空气含氧量、温度、相对湿度和风速的相关关系。 结果 （1）天然林的NAIC显著高于人工林；在人工林中，高郁闭度林的NAIC显著高于中郁闭度林。（2）不同地理空间城市森林NAIC差异显著，其中远郊城市森林 > 近郊城市森林 > 城区城市森林 > 城区对照组。（3）森林环境产生NAIC的能力显著优于城市环境（无林地区）。（4）远郊和近郊城市森林的NAIC季节变化为：夏季 > 春季 > 秋季 > 冬季，而城区城市森林和城区对照组为：秋季 > 冬季 > 春季 > 夏季。（5）远郊和近郊城市森林的NAIC与空气含氧量、温度和相对湿度均呈极显著正相关，而城区城市森林和城区对照组的NAIC与空气含氧量和温度均呈极显著负相关关系；近郊城市森林的NAIC与风速呈极显著负相关，而远郊城市森林呈极显著正相关。（6）远郊城市森林的NAIC主要与相对湿度和温度密切相关；近郊城市森林的NAIC主要受空气含氧量、风速和温度影响；城区城市森林和城市对照组的NAIC受温度的影响最大。 结论 地理空间对NAIC有显著影响，即距离城市中心较远、森林面积达到一定规模且集中连片对NAIC有正向影响，相关结果为森林康养基地的选址提供了理论依据。不同地理空间城市森林NAIC的环境影响因子各不相同，建议今后的研究综合考虑气象、林分、地形、周边环境等多个方面不同因素的协同影响，深入探讨NAIC与各环境因子的影响机制。 Abstract:Objective The research on the negative air ion concentration(NAIC) of urban forest in different geographical spaces, so as to provide scientific support for the theoretical study of negative air ions and the site selection basis of forest therapy base. Method From September 2019 to January 2020, and from May to August 2020, three forest environments with different geographical locations, forest area and surrounding environment gradients in Guangzhou, Guangdong Province: exurban forest, suburban forest, and downtown forest were selected, and an urban control group was set up to compare NAIC differences. Pearson correlation analysis and multiple stepwise regression analysis were used to explore the correlation between NAIC and air oxygen content, temperature, relative humidity and wind speed. Result (1) NAIC of natural forests is distinctly higher than that of planted forests; In planted forests, NAIC of high canopy density forests is significantly higher than that of middle canopy density forests. (2) The NAIC of urban forest in different geographical spaces is prominently different, including exurban forest > suburban forest > downtown forest > urban control group. (3) The ability of NAIC generation in forest environment is evidently better than that in urban environment. (4) The seasonal variation of NAIC in exurban forest and suburban forest is summer > spring > autumn > winter, while that in downtown forest and urban control group is autumn > winter > spring > summer. (5) NAIC of exurban forest and suburban forest is remarkably positively correlated with air oxygen content, temperature and relative humidity, while the NAIC of downtown forest and urban control group is noticeably negatively correlated with air oxygen content and temperature; NAIC of suburban forest is significantly negatively correlated with wind speed, while that of exurban forest is notably positively correlated. (6) The NAIC of exurban forest is closely related to relative humidity and temperature; The NAIC of suburban forest is mainly affected by air oxygen content, wind speed and temperature; The NAIC of downtown forest and urban control group is most affected by temperature. Conclusion The geographical space has a significant impact on NAIC. Distance from the city center and the forest area reaching a certain scale and concentrated contiguous has a positive impact on NAIC. The relevant results provide a theoretical basis for the location of forest therapy bases. The environmental impact factors of NAIC of urban forest in different geographical spaces are different. It is suggested that future research should comprehensively consider the synergistic effects of different factors in many aspects, such as meteorology, stand, terrain, and surrounding environment, and further explore the impact mechanism of NAIC and various environmental factors. -
Key words:
- urban forest /
- negative air ion concentration /
- air oxygen content /
- temperature /
- relative humidity /
- wind speed
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图 1 不同林分条件NAIC的差异比较
*. p < 0.05；**. p < 0.01；***. p < 0.001； ns. 没有显著性差异。ns, not significant。
Figure 1. Comparison of NAIC in different stand conditions
图 2 不同地理空间城市森林NAIC的季节比较
EF. 远郊城市森林；SF. 近郊城市森林；DF. 城区城市森林；UCG. 城区对照组。不同小写字母表示同一地理空间内不同季节间差异显著。下同。EF, exurban forest; SF, suburban forest; DF, downtown forestc UCG, urban control group. Different lowercase letters indicate significant differences between seasons in the same geographical space. The same below.
Figure 2. Comparison NAIC of urban forest in different geographical spaces
图 3 不同季节城市森林NAIC的地理空间比较
不同小写字母表示同一季节内不同地理空间差异显著。Different lowercase letters indicate significant differences between geographical spaces in the same season.
Figure 3. Geospatial comparison of NAIC of urban forest in different seasons
表 1 不同地理空间城市森林的研究样地概况
Table 1. Overview of study sites of urban forest in different geographical spaces
城市森林类型
Urban forest type研究地
Study sites所选样地的林分类型
The stand type of the sample plot海拔
Altitude/m面积/hm2
Area/ha郁闭度
Canopy
density林龄/a
Stand
age/year样点数量
Sample
quantity远郊城市森林
Exurban forest石门国家森林公园
Shimen National Forest Park阔叶混交林
Broad-leaved mixed forest820 65 0.9 ≥ 100 4 毛竹林
Phyllostachys edulis forest810 19.3 0.8 ≥ 30 4 枫香林
Liquidambar formosana forest450 15.6 0.6 ≥ 20 3 杉木林
Cunninghamia lanceolata forest350 17.9 0.7 ≥ 30 3 枫香杉木混交林
Liquidambar formosana and
Cunninghamia lanceolata mixed forest290 25.6 0.5 ≥ 20 3 近郊城市森林
Suburban forest广东省龙眼洞林场
Guangdong Longyandong Forest Farm红锥林（a）
Castanopsis hystrix forest (a)259 35.6 0.8 ≥ 30 3 红锥林（b）
Castanopsis hystrix forest (b)286 27.8 0.6 ≥ 20 3 木荷林（a）
Schima superba forest (a)320 14.3 0.7 ≥ 20 3 木荷林（b）
Schima superba forest (b)415 15.1 0.6 ≥ 20 3 城区城市森林
Downtown forest长岗山自然保护区
Changgangshan Nature Reserve针阔混交林
Coniferous and broad-leaved mixed forest40 ~ 50 15.0 0.7 ≥ 25 3 城区对照组
Urban control group五山街道
Wushan Street40 5 ~ 6 3 
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表 2 不同林分类型的划分
Table 2. Division of different stand types
林分类型
Stand types天然林
Natural forests人工林 Planted forests 混交林
Mix
forests纯林
Pure
forests成熟林
Mature
forests中龄林
Middle-aged
forests高郁闭度林
High canopy
density forest中郁闭度林
Middle canopy
density forest阔叶混交林 Broad-leaved mixed forest √ 毛竹林 Phyllostachys edulis forest √ √ √ 枫香林 Liquidambar formosana forest √ √ √ 杉木林 Cunninghamia lanceolata forest √ √ √ 枫香杉木混交林
Liquidambar formosana and Cunninghamia lanceolata mixed forest√ √ √ 红锥林（a） Castanopsis hystrix forest (a) √ √ √ 红锥林（b） Castanopsis hystrix forest (b) √ √ √ 木荷林（a） Schima superba forest (a) √ √ √ 木荷林（b） Schima superba forest (b) √ √ √ 针阔混交林 Coniferous and broad-leaved mixed forest √ √ √
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表 3 不同地理空间城市森林的环境因子
Table 3. Environmental factors of urban forest in different geographical spaces
环境因子
Environmental factors远郊城市森林
Exurban forest近郊城市森林
Suburban forest城区城市森林
Downtown forest城区对照组
Urban control group空气含氧量 Air oxygen content 21.17 ± 0.38 a 21.13 ± 0.30 ab 21.10 ± 0.16 b 20.98 ± 0.12 c 温度 Temperature 23.41 ± 4.41 c 24.20 ± 5.12 c 27.51 ± 6.21 b 28.76 ± 6.54 a 相对湿度 Relative humidity 79.72 ± 12.03 a 77.06 ± 11.26 b 72.42 ± 11.99 c 64.51 ± 12.82 d 风速 Wind speed 0.93 ± 0.77 c 2.05 ± 1.64 a 0.83 ± 0.72 c 1.22 ± 0.66 b 不同小写字母表示不同地理空间城市森林的环境因子差异显著。Different lowercase letters indicate significant differences between environmental factors of urban forest in different geographical spaces。
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表 4 不同地理空间城市森林NAIC与环境因子的相关性分析
Table 4. Correlation analysis of NAIC and environmental factors of urban forest in different geographical spaces
相关关系 Correlation 分析 Analysis 远郊城市森林
Exurban
forest近郊城市森林
Suburban
forest城区城市森林
Downtown
forest城区对照组
Urban control
groupNAIC与空气含氧量 NAIC and air oxygen content Pearson相关性 Pearson correlation 0.147** 0.687** −0.333** −0.466** 显著性 Significance 0.000 0.000 0.000 0.000 样本量 Sample capacity 567 378 198 198 NAIC与温度 NAIC and temperature Pearson相关性 Pearson correlation 0.155** 0.633** −0.413** −0.671** 显著性 Significance 0.000 0.000 0.000 0.000 样本量 Sample capacity 567 378 198 198 NAIC与相对湿度 NAIC and relative humidity Pearson相关性 Pearson correlation 0.352** 0.314** 0.089 0.075 显著性 Significance 0.000 0.000 0.214 0.295 样本量 Sample capacity 567 378 198 198 NAIC与风速 NAIC and wind speed Pearson相关性 Pearson correlation 0.130** −0.247** 0.000 0.081 显著性 Significance 0.002 0.000 0.998 0.256 样本量Sample capacity 567 378 198 198
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表 5 不同地理空间城市森林NAIC与环境因子的多元逐步回归分析
Table 5. Multiple stepwise regression analysis of NAIC and environmental factors of urban forest in different geographical spaces
城市森林类型
Urban forest type模型 Model B SE β t Sig. R2 远郊城市森林
Exurban forest1 常量 Constant −952.071 415.033 −2.294 0.022 0.124 相对湿度 Relative humidity 45.973 5.148 0.352 8.930 0.000 2 常量 Constant −2275.262 524.169 −4.341 0.000 0.148 相对湿度 Relative humidity 46.119 5.080 0.353 9.079 0.000 温度 Temperature 56.038 13.857 0.157 4.044 0.000 近郊城市森林
Suburban forest1 常量 Constant −42884.544 2455.112 −17.467 0.000 0.472 空气含氧量 Air oxygen content 2131.428 116.194 0.687 18.344 0.000 2 常量 Constant −41317.185 2428.410 −17.014 0.000 0.497 空气含氧量 Air oxygen content 2065.834 114.645 0.666 18.019 0.000 风速 Wind speed −88.698 20.747 −0.158 −4.275 0.000 3 常量 Constant −31785.841 3790.366 −8.386 0.000 0.511 空气含氧量 Air oxygen content 1572.754 189.409 0.507 8.303 0.000 风速 Wind speed −81.148 20.619 −0.145 −3.936 0.000 温度 Temperature 35.981 11.081 0.200 3.247 0.001 城区城市森林
Downtown forest1 常量 Constant 1597.024 75.315 21.205 0.000 0.171 温度 Temperature −16.978 2.671 −0.413 −6.357 0.000 城区对照组
Urban control group1 常量 Constant 1009.273 38.705 26.076 0.000 0.450 温度 Temperature −16.620 1.313 −0.671 −12.662 0.000
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