The Impact of Typical Plant Community Characteristics in the Langya Mountain Urban Forest Park on Climate Effects

doi:10.21203/rs.3.rs-3233909/v1

Download PDF

Article

The Impact of Typical Plant Community Characteristics in the Langya Mountain Urban Forest Park on Climate Effects

https://doi.org/10.21203/rs.3.rs-3233909/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

With the rapid development of urbanization, the urban heat island effect poses a huge threat to the urban climate environment, which objectively reflects the microclimate effect of urban forest parks on urban climate. The paper investigates the characteristics of typical plant communities and the relationship between plant communities and microclimate regulation in Langya mountain urban forest park, and explores the transformation of plant communities on their occupied environment. By monitoring the impact of typical characteristics of different plant communities on the microclimate of forest parks, the microclimate effect of plant communities of different forest types was studied, and the regulation of plant communities in urban forest parks on local Microclimate was analyzed. Our findings indicate that the community forest density and plant coverage at sampling point C are relatively high, with a canopy density close to 0.85, which has no significant impact on refrigeration and humidification, but can effectively increase the concentration of negative ions in the air. Moreover, sampling site C belongs to arbor, bush, and shrub layer structure with high green content, which has a good effect on dust reduction and retention.

Earth and environmental sciences/Environmental social sciences

Earth and environmental sciences/Environmental social sciences/Sustainability

typical plant community

Langya mountain

urban forest park

microclimate

Ecological civilization construction has become an effective approach for regulating and balancing the ecological climate of urbanization in the context of rapid socio-economic growth [1–3]. From a micro-scale perspective, stabilizing the ecological systems of urban green spaces enhances urban air quality, improves microclimatic conditions, and optimizes the urban living environment[4, 5]. Creating important urban ecosystem such as parks, green space, and urban forests can improve the resilience of cities by forming an organic communication space between cities and the natural systema [6–8]. Dobbs et al. compared the three main ecosystem services of urban green spaces, including carbon storage, air quality improvement, and water cycle restoration, and provided more refined indicators for biodiversity and other services[9]. Compared with other ecosystems, attention to urban forests is still insufficient, especially considering that urban areas will accommodate 68% of the world's population by 2050 [10]. Moreover, with the enhancement of people's awareness of ecological civilization, the ecological role of urban forest parks has become more prominent, and the impact of forest resources on microclimate environment has gradually received attention [11]. Therefore, through research and analysis of typical plant communities in forest parks, enriching the study of typical plant community characteristics and microclimate effects, and exploring plant communities with comprehensive climate effects can provide a reference for optimizing the design of plant communities in urban forest parks and environmental design for urban microclimates.

Microclimate refers to the climate within a smaller range than the mesoclimate, especially the climate near the earth's surface (near-surface climate) [12]. Exploring the environmental benefits of green roofs on the microclimate, it was found that the average air temperature and CO₂ concentration above the green roof were 3.7°C and 20.71 ppm lower compared to the control[13]. Besides, trees in real street canyons decrease particulate matter (PM)2.5 concentrations by 7.0-7.5% in spring, autumn, and summer [14]. Street trees effectively alleviate the pollution of coarse PM4, PM10, and total suspended particulate matter (TSP) at tree-stand location[15]. Shi et al reported that the random forest model (R² = 0.0931) has high prediction accuracy, indicating that particulate matter, soil moisture and relative humidity have a great impact on negative air ions[16]. As a result, by studying the relationship between different plant communities and microclimate regulation mechanisms, key influencing factors for the formation and regulation of local microclimates as important components of urban green spaces are examined. Applying the results of this research to the design of plant landscape spatial structures and climate-adaptive design can improve thermal comfort for outdoor activities and enhance the urban living environment, making it relevant in practice for urban ecological construction.

Plant communities have a certain effect on their surrounding environment, such as regulating climate factors, reducing the harm of sandstorms and pollutants, etc[17, 18]. Different plant communities create different community environments, and their structural characteristics have regulatory effects on microclimate factors within the community[19, 20]. Taking Langya Mountain National Forest Park as an example, this study selects the typical seasons of spring and summer as the measurement periods and chooses four representative sites as measurement points: the starting point of the health trail, the vicinity of the entrance plaza, the vicinity of the wildflower garden, and the vicinity of Lotus Pond. The study utilizes instruments such as the WST-1501 negative air ion detector, LD-5 particulate matter detector, and handheld meteorological instruments to explore the regulatory effects of typical community structures on temperature, humidity, PM, and negative air ion. By studying the characteristics of different plant community structures and their impacts on local microclimate changes within the park, the study analyzes the microclimate effects of different mature forest types. This research provides a reference for greening arrangements in recreational environments surrounding cities, vegetation optimization and transformation in Langya Mountain Forest Park, as well as the selection of greenery and roadside tree species.

2.1. Study Area and Measurement Sites

Langya mountain urban forest park is located in the southwestern part of Chuzhou City, Anhui Province, between the Yangtze River and the Huai River, with coordinates ranging from 118°11′ to 118°21′E and 32°14′ to 32°20′N. Langya mountain urban forest park is an important urban recreation place of Chuzhou city, rich in animal and plant resources, forest coverage rate of more than 85%. Langya mountain belongs to the low mountain and hilly area that extends eastward from the Huaiyang Mountains, with elevations ranging from 100 to 320 meters.

2.2. Research Method

The plot survey method was employed to select observation plots within the Langya mountain forest park for field measurements of different plant communities in various regions. Representative plant communities were selected the beginning section of the mountain health trail in the scenic area, the entrance plaza, the wildflower garden at major nodes, and the vicinity of the lotus pond along the recreational routes in Langya mountain urban forest park (Fig. 1 and Table S1). The vegetation survey was conducted to collect data on plant species, individual counts, as well as factors such as tree height, diameter at breast height, height of the lower branches, canopy cover, and growth vigor within each sample plot. The plot boundaries were marked using ropes or tape measures, and within each plot, a rectangular sample plot measuring 20 m × 20 m or another regular shape with an area of 400 square meters was selected. The basic information about the forest stands is summarized in Table S2.

2.3. Microclimate Measurements

The study was conducted during the spring and summer seasons, selecting clear weather conditions with stable climate and no clouds or wind to avoid errors caused by environmental factors. The measurements were carried out continuously for three consecutive days from April 10, 2023, to April 12, 2023, and from June 7, 2023, to June 9, 2023. Handheld meteorological instruments were used to observe the temperature and humidity of predetermined four sample plots.

The observation time was from 10:00 to 14:00, and measurements were taken three times daily for three consecutive days from April 10, 2023, to April 12, 2023. The plum blossom five-point sampling method was used to set measurement points. The relative humidity and temperature at each measurement point within each sample plot were observed in a fixed manner using digital anemometer with temperature and humidity (PM6612, Shenzhen Xinhuayi Instrument Co., Ltd). The measurement points within each sample plot were located 1.5 meters above the ground and kept shaded to avoid direct sunlight that could affect the measurement data[19]. Air negative ion (ANI) concentration was observed using air negative ion detection instrument (WST-1501, Beijing Wositong Technology Co., Ltd). The observation time was from 8:00 to 18:00, with measurements taken every 2 hours for three consecutive days. Five measurement points were set within each sample plot, and five data points were collected for each measurement point. The handheld laser dust analyzer (LD-5/J, Nanjing Telinyal Environmental Protection Technology Co., Ltd) dust particle detector was used for observation the air particle concentration of sample plots. The observation time was from 8:00 to 18:00, with measurements taken every 2 hours for three consecutive days. Five measurement points in different directions were measured within each sample plot, measuring the values of PM2.5, PM10, and TSP.

2.4. Data Analysis

For each monitoring point, the mean value of round-trip mobile data at the same time represented its observational value at this time. The averages of the five rounds of data monitoring were used to represent daily averages of humidity, temperature, ANI, PM2.5, PM10, and TSP. One-way analysis of variance (ANOVA) and Duncan test were carried out to determine the significant difference among treatments (p < 0.05). All data analyses were conducted using SPSS Statistics 26.0 (IBM, USA).

3.1 Plant Community Characteristics

From the community structure investigation of four sample plots, 57 species of Vascular bundle plants were found, belonging to 39 families and 51 genera, including 2 families, 3 genera, and 3 species of Gymnosperm, and 37 families, 48 genera, and 54 species of Flowering plant (Table S3). Among these, Rosaceae has the largest number of genera and species, with 6 genera and 6 species, followed by 2 genera and 3 species of Liliaceae, Euphorbiaceae and Oleaceae, 1 genus and 2 species of Fagaceae, Violaceae and Aceraceae, and 1 genus and 1 species of other plants. The plant composition of the observed sample plots is all composed of an arbor-shrub-herb structure community, with the highest frequency of occurrence being Lindera glauca. Lindera glauca has the characteristics of strong vitality and easy survival, which is suitable for growing in the low altitude hillside, forest edge, and slightly acidic sandy soil [21, 22], so it is widely distributed in the natural secondary forest of Langya mountain. The main herbaceous types in the natural vegetation of Langya mountain are rattan rattan, radix ophiopogonis, chickweed, and radix japonicum, which have strong reproductive ability and good coverage. In the sample plot, there are abundant species of herbaceous plants in the moso bamboo-maple poplar-cork oak communities, while the species of shrub plants are scarce[23]. Additionally, there are more dominant tree species in the broussonetia papyrifera-quercus acutissima-lindera glauca community.

As shown in Table 1, the community of moso bamboo-maple poplar-cork oak showed the largest species richness. Compared with other tall broad-leaved trees, the branches and leaves of P. pubescens forest were sparse, and the space under the forest was larger, which was more conducive to the growth of herbaceous plants and the formation of more plant species. However, the community on broussonetia papyrifera-quercus acutissima-lindera glauca community showed the smallest species richness, which was affected by certain human factors. The community of metasequoia is located at the entrance square of the scenic spot and the boundary of the stream. It is a plant community that combines artificial and natural communities. It is subject to direct sunlight, has sufficient water source, and has good habitat to meet the growth needs of herbaceous plants. Among different forest community types, the herbaceous layer has the most abundant species, the tree layer is basically the same, and the shrub layer has a smaller richness (Table 1). Among the four communities, simpson index and shannon wiener index of two communities are the largest in the herb layer, and the largest in the arbor layer of two communities. The largest in the herb layer indicates that the community is still in the initial stage of succession, and herbs have a greater contribution to the species diversity of the community.

Table 1

Indices of composition and structure of plant communities in different places
Sample Number	Living layer	Species richness	Simpson index	Shannon-wiener index	Pielou index
A	Arbor layer	8	0.766	1.674	0.805
	Shrub layer	3	0.221	0.438	0.399
	Herb layer	8	0.823	1.833	0.882
	Plant community	19	0.850	2.073	0.704
B	Arbor layer	7	0.798	1.716	0.882
	Shrub layer	6	0.614	1.287	0.718
	Herb layer	7	0.717	1.426	0.733
	Plant community	20	0.738	1.618	0.540
C	Arbor layer	11	0.774	1.935	0.807
	Shrub layer	4	0.624	1.159	0.836
	Herb layer	8	0.684	1.466	0.705
	Plant community	23	0.746	1.882	0.600
D	Arbor layer	11	0.529	1.351	0.564
	Shrub layer	3	0.604	1.008	0.917
	Herb layer	14	0.856	2.265	0.858
	Plant community	28	0.878	2.523	0.757

3.2 Air Temperature and Relative Humidity

The average temperature and humidity changes of different plant community plots are shown in Fig. 2a, and the cooling and humidifying effects of each plot have certain differences. In spring and summer, the relatively lowest temperature in the B plot community indicates that the cooling effect is most significantly attributed to the cooling effect of the water source[24, 25], with temperatures of 25.96 ℃ and 29.08 ℃, respectively (Fig. 2a). However, the cooling effect of the C and D plots is the worst. The average humidity (spring and summer) of different community plots is shown in Fig. 2b, and the best humidifying effect is the A plot community, with an average humidity of 31.88% and 78.71%. But, the less significant humidifying effects were observed in the C and D plots. There is no significant correlation between species richness and temperature within a plant community (R² in the range of 0.019–0.059, Figure S1), but there is a good correlation between humidity and species richness (R² in the range of 0.605–0.668), demonstrating that species richness has no effect on temperature. In addition, shrubs and herbs do not have a very significant effect on cooling and humidifying, while arbors show a correlation between temperature and humidity, indicating the cooling and humidifying effect.

3.3 Concentration of air particulate matter

The impact of diverse plant communities on PM2.5, PM10, and TSP content in spring and summer at the sampling site is shown in Fig. 3. From the concentration curve of air PM, it can be seen that the variation trends of PM2.5, PM10, and TSP concentrations in different plant communities in spring and summer are consistent. The communities with the highest concentrations of PM2.5, PM10, and TSP were all communities in sampling site A, with concentrations of 38.70–46.80, 43.50-49.18, and 66.50-82.37 µg/m³, respectively, indicating a high concentration of particulate matter and poor air quality in sampling site A. However, the particle concentration in sample plot C is the smallest and the air quality is good. The arbor, bush, and shrub structure are the most complete and uniform in sample plot C, stating clearly that the mixed plant community structure is more conducive to dust reduction and retention[26], which is consistent with the conclusion of Wang et al.[27]. The community has the highest canopy density, manifesting that the plant branches and leaves within the community are lush, and the leaves are more conducive to dust retention. Hence, the concentration of PM in the air of the mountain pepper and photinia palm fragrance community is low, and the air quality is good. The correlation analysis between the concentration of PM and TSP and the plant community at the sampling site are shown in Figure S5-8. Bush and herbal had no effect on the concentrations of PM2.5, PM10, and TSP, but species composition and arbor affected it significantly.

3.4 Air negative ions concentration

From the air negative ion concentration curve in Fig. 4, it can be seen that the air negative ion concentration in the sample C community is the highest, at 839.78-864.92/cm³, while the air negative ion concentration in the other three plant communities is relatively low. The community with the highest canopy density in sampling site C also has a higher ground cover, higher green content, and the formation of more negative ions [28, 29]. At the same time, the community has the lowest concentration of air particulate matter and weak adsorption of negative ions, resulting in the highest concentration of air negative ions. However, the forest canopy density and ground cover plant coverage in the compound structure green space of arbor, bush, and shrub in the sampling site A community are also high and have a large amount of greenery, resulting in the lowest concentration of air negative ions and the highest concentration of particulate matter. This may be due to poor plant density, crowd flow, and ventilation conditions of arbor, bush, and shrub in the sample plot. In addition, as shown in Figure S9-12, arbor have a strong impact on the concentration of air negative ions.

Langya Mountain belongs to the transitional zone of vegetation types in the north-south region, with secondary deciduous broad-leaved forests as the main natural vegetation. The community structure is relatively simple, and the diversity of tree species is not high, with the main tree species being the main tree species. In comparison, the forest density and plant coverage of the community in sampling site C are high, belonging to a relatively complete layered green space. The canopy density of the green space in Sample C is close to 0.85, which does not have a significant effect on cooling and humidifying, but can effectively increase the concentration of negative ions in the air. Moreover, sampling site C belongs to arbor, bush, and shrub layer structure with high green content, which has a good effect on dust reduction and retention. Meanwhile, tall arbor can also block and filter external dust, while low shrubs and bush can effectively reduce ground dust. The climate effect of plant communities is influenced by various factors, such as regional natural conditions, altitude, light intensity, green patch shape, and plant planting forms. Consequently, the mutual influence of multiple factors should be fully considered to further optimize research methods in future research.

Funding

The authors gratefully acknowledge the Key Research Project of Natural Science in Colleges and Universities of Anhui Province (2022AH051096, KJ2021A1082).

Acknowledgments

This research was funded by the Key Research Project of Natural Science in Colleges and Universities of Anhui Province (2022AH051096, KJ2021A1082). We would like to thank Rui Hua and Yinuo Li for their support in the field work and data processing. We sincerely thank the anonymous reviewers for their valuable comments.

Author contributions

Conceptualization, L.R., S.G.; funding acquisition, L.R., S.G., L.G.; project administration, L.R.; data acquisition, R.H.; formal analysis, R.H., Y.L.; drafting the manuscript, L.R., S.G.; revising the manuscript critically for important intellectual content, S.G., L.G. All authors have read and agreed to the published version of the manuscript.

Data availability

All data relevant to the study are included in the article or uploaded as supplementary information. In addition, the datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Jamean, E.S.; Abas, A. Valuation of visitor perception of urban forest ecosystem services in kuala lumpur. Land 2023, 12, 572.
Peng, J.; Zhao, H.;Liu, Y. Urban ecological corridors construction: A review. Acta Ecol. Sin. 2017, 37, 23–30.
Hu, X.; Dong, C.; Wang, Y. Coupled and coordinated analysis of urban green development and ecological civilization construction in the yangtze river delta region. Sustainability 2023, 15, 5955.
Jana, A.; Sarkar, A.; Bardhan, R. Analysing outdoor airflow and pollution as a parameter to assess the compatibility of mass-scale low-cost residential development. Land Use Policy 2020, 99, 105052.
Tsoka, S.; Tsikaloudaki, K.; Theodosiou, T.; Bikas, D. Urban warming and cities’ microclimates: Investigation methods and mitigation strategies—a review. Energies 2020, 13, 1414.
Huff, E.; Johnson, M.; Roman, L.; Sonti, N.; Pregitzer, C.; Campbell, L.; McMillen, H. A literature review of resilience in urban forestry. Arboriculture & Urban Forestry 2020, 46, 185–196.
Hale, J.D.; Pugh, T.A.M.; Sadler, J.P.; Boyko, C.T.; Brown, J.; Caputo, S.; Caserio, M.; Coles, R.; Farmani, R.; Hales, C.; Horsey, R.; Hunt, D.V.; Leach, J.M.; Rogers, C.D.F.; Mackenzie, A.R. Delivering a multi-functional and resilient urban forest. Sustainability 2015, 7, 4600–4624.
Mahmoudi, B.; Sorouri, Z.; Zenner, E.K.; Mafi-Gholami, D. Development of a new social resilience assessment model for urban forest parks. Environ. Dev. 2022, 43, 100724.
Dobbs, C.; Nitschke, C.R.; Kendal, D. Global drivers and tradeoffs of three urban vegetation ecosystem services. PLoS One 2014, 9, 113000.
Roeland, S.; Moretti, M.; Amorim, J.H.; Branquinho, C.; Fares, S.; Morelli, F.; Niinemets, Ü.; Paoletti, E.; Pinho, P.; Sgrigna, G.; Stojanovski, V.; Tiwary, A.; Sicard,P.; Calfapietra, C. Towards an integrative approach to evaluate the environmental ecosystem services provided by urban forest. J. For. Res. 2019, 30, 1981–1996.
Sun, L.; Chen, J.; Li, Q.; Huang, D. Dramatic uneven urbanization of large cities throughout the world in recent decades. Nat. Commun. 2020, 11, 5366.
Bramer, I.; Anderson, B.J.; Bennie, J.; Bladon, A.J.; De Frenne, P.; Hemming, D.; Hill, R.A.; Kearney, M.R.; Körner, C.; Korstjens, A.H.; Lenoir, J.; Maclean, I.M.D.; Marsh, C.D.; Morecroft, M.D.; Ohlemüller, R.; Slater, H.D.; Suggitt, A.J.; Zellweger, F.; Gillingham, P.K. Chapter three-Advances in monitoring and modelling climate at ecologically relevant scales. Adv. Ecol. Res. 2018, 58, 101–161.
Moghbel, M.; Erfanian Salim, R. Environmental benefits of green roofs on microclimate of Tehran with specific focus on air temperature, humidity and CO2 content. Urban Clim. 2017, 20, 46–58.
Jeanjean, A.P.R.; Buccolieri, R.; Eddy, J.; Monks, P.S.; Leigh, R.J. Air quality affected by trees in real street canyons: The case of marylebone neighbourhood in central London. Urban for Urban Gree 2017, 22, 41–53.
Miao,C.; Yu,S.; Hu,Y.; Liu,M.; Yao,J.; Zhang,Y.; He,X.; Chen,W. Seasonal effects of street trees on particulate matter concentration in an urban street canyon. Sustain. Cities. Soc. 2021, 73, 103095.
Shi, G.Y.; Zhou, Y.; Sang, Y.-Q.; Huang, H.; Zhang, J.S.; Meng, P.; Cai, L.-L. Modeling the response of negative air ions to environmental factors using multiple linear regression and random forest. Ecol. Inform. 2021, 66, 101464.
González-M., R. ; Norden, N.; Posada, J.M.; Pizano, C. ; García, H.; Idárraga-Piedrahita, Á.; López-Camacho, R.; Nieto, J.; Rodríguez-M, G.M.; Torres, A.M.; Castaño-Naranjo, A.; Jurado, R.; Franke-Ante, R.; Galindo-T, R.; Hernández R., E.; Barbosa, A.; Salgado-Negret, B. Climate severity and land-cover transformation determine plant community attributes in Colombian dry forests. Ecology Letters 2019, 51,826–837.
Mandal, M.; Das, S.; Roy, A.; Rakwal, R. Jones, O.A.H.; Popek, R.; Agrawal, G.K.; Sarkar, A. Interactive relations between plants, the phyllosphere microbial community, and particulate matter pollution, Sci. Total Environ. 2023, 890, 164352.
Fan, S.; Zhang, M.; Li, Y.; Li, K.; Dong, L. Impacts of composition and canopy characteristics of plant communities on microclimate and airborne particles in Beijing, China. Sustainability 2021, 13, 4791.
Zhu, C.; Przybysz, A.; Chen, Y.; Guo, H.; Chen, Y.; Zeng,Y. Effect of spatial heterogeneity of plant communities on air PM10 and PM2.5 in an urban forest park in Wuhan, China. Urban Forestry & Urban Greening 2019, 46, 126487.
Niu, J.; Chen, Y.; An, J.; Hou, X.; Cai, J.; Wang, J.; Zhang, Z.; Lin, S. Integrated transcriptome sequencing and dynamic analysis reveal carbon source partitioning between terpenoid and oil accumulation in developing Lindera glauca fruits. Sci. Rep. 2015, 5, 15017.
Huh, G.; Park, J.; Shrestha, S.; Lee, Y.; Ahn, E.; Kang, H.; Kim, Y. ;Baek, N. New diarylpropanoids from Lindera glauca Bl. Heartwood. Holzforschung 2012, 66(5), 585–590.
Spicer, M.E.; Radhamoni, H.V.N.; Duguid, M.C.; Queenborough, S.A.; Comita, L.S. Herbaceous plant diversity in forest ecosystems: patterns, mechanisms, and threats. Plant Ecol. 2022, 223, 117–129.
Cai, Z.; Guldmann, J.-M.; Tang, Y.; Han, G. Does city-water layout matter comparing the cooling effects of water bodies across 34 Chinese megacities. J. Environ. Manage. 2022, 324, 116263.
Sun, X.; Tan, X.; Chen, K.; Song, S.; Zhu, X.; Hou, D. Quantifying landscape-metrics impacts on urban green-spaces and water-bodies cooling effect: The study of Nanjing, China. Urban Forestry & Urban Greening 2020, 55, 126838.
Yin, Z.; Zhang, Y.; Ma, K. Evaluation of PM2.5 retention capacity and structural optimization of urban park green spaces in Beijing. Forests 2022, 13, 415.
Wang, A.; Guo, Y.; Fang, Y.; Lu, K. Research on the horizontal reduction effect of urban roadside green belt on atmospheric particulate matter in a semi-arid area. Urban Forestry & Urban Greening 2022, 68, 127449.
Chen, Q.; Wang, R.; Zhang, X.; Liu, J.; Wang, D. Effects of different site conditions on the concentration of negative air ions in mountain forest based on an orthogonal experimental study.Sustainability 2021, 13, 12012.
Zhu, C.Y.; Ji, P.; Li, S.H. Effects of the different structure of urban green belts on the air quality. J. Nanjing For. Univ. 2013, 56,18–24.

No competing interests reported.

SI.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

The Impact of Typical Plant Community Characteristics in the Langya Mountain Urban Forest Park on Climate Effects

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Study Area and Measurement Sites

2.2. Research Method

2.3. Microclimate Measurements

2.4. Data Analysis

3. Results and Discussion

3.1 Plant Community Characteristics

3.2 Air Temperature and Relative Humidity

3.3 Concentration of air particulate matter

3.4 Air negative ions concentration

4. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1