
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39300130
72570
10.1038/s41598-024-72570-1
Article
Fine-scale monitoring of catkins reveals an association between catkin concentration and plant community characteristics and microclimate
Mi Xiayuan
Li Yunyuan
Ding Kang
Yu Miao
Wu Zuomin
Chen Ying
Cai Linghao Cailinghao@bjfu.edu.cn

https://ror.org/04xv2pc41 grid.66741.32 0000 0001 1456 856X School of Landscape Architecture, Beijing Forestry University, Beijing, 100083 China
19 9 2024
19 9 2024
2024
14 2184712 1 2024
9 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Catkins, as a significant source of plant-caused pollution, disrupts daily human activities and industrial processes. Despite their impact, catkins have not been included in official environmental quality monitoring indicators, leading to a deficiency in scientifically rigorous collection and monitoring methodologies, as well as a lack of ecological prevention and management strategies. In this study, we introduced a fine-scale monitoring approach for catkins. Qualitative and quantitative relationships between catkin concentrations, plant community characteristics and microclimate factors were elucidated by analyzing on-site catkin concentration data from 33 representative plant communities in Beijing. Furthermore, we summarized the ecological strategies for the prevention and management of these catkins. The results indicated that (1) TS (three-dimensional green volume of trees in the catkin source layer), SB (three-dimensional green volume of shrubs in the catkin barrier layer), GB (three-dimensional green volume of ground cover plants in the catkin barrier layer), T (three-dimensional green volume of trees in the whole plant community), W (three-dimensional green volume of the whole plant community), species diversity, and relative air humidity were key plant community characteristics and microclimate factors influencing catkin concentration. Among these factors, TS, T, W, and relative air humidity showed a significant positive correlation with catkin concentration, while SB, GB, and species diversity exhibited a significant negative correlation with catkin concentration. (2) All seven key factors exhibited nonlinear relationships with catkin concentration. (3) TS served as the primary deciding factor for catkin concentration within the plant community. When TS > 744.0755 m3, the secondary decision factor for catkin concentration was GB. Otherwise, the determinants were SB and species diversity. The results showed that enhancing tree species diversity, enhancing the three-dimensional green volume of shrubs and ground cover plants, and increasing air humidity were practical means to facilitate the sedimentation of catkins. The measures used to obstruct catkins vary depending on the TS. When catkin source plants are abundant within a plant community, it is advisable to prioritize increasing ground cover plants. Conversely, when fewer sources of such plants exist, emphasis can be placed on augmenting mid-layer shrubs and diversifying plant species. These findings provide a scientific foundation for the planting design and stock optimization of communities containing catkin source plants.

Keywords

Catkins
Environmental monitoring and management
Plant community
Plant-caused pollution
Microclimate
Subject terms

Sustainability
Urban ecology
Beijing Key Research and Development Program of ChinaD171100007117003 D171100007117003 D171100007117003 D171100007117003 D171100007117003 D171100007117003 D171100007117003 Mi Xiayuan Li Yunyuan Ding Kang Yu Miao Wu Zuomin Chen Ying Cai Linghao the National Forestry Grassland Landscape Engineering Technology Research Center (2023)PTYX202333 PTYX202333 PTYX202333 PTYX202333 PTYX202333 PTYX202333 PTYX202333 Mi Xiayuan Li Yunyuan Ding Kang Yu Miao Wu Zuomin Chen Ying Cai Linghao issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

The genera Populus L. (poplars) and Salix L. (willows) within the family Salicaceae are important woody trees1 with considerable ecological, economic, and ornamental value2,3. They are widely distributed and cultivated, covering more than 100 million hectares globally4. However, due to their inherent biological traits, female individuals of poplar and willow plants develop catkins upon the maturation of their capsules5. These catkins consist of seeds and accompanying catkin hairs6 (Fig. 1a). The unique structure of catkins greatly enhances the airborne dispersal capability of seeds, thereby augmenting their potential for long-distance dispersal by wind7. Nevertheless, for humans and ecological environments, airborne catkins contribute to various adverse effects, including water and air contamination, fire incidents, the spread of germs and viruses, and allergic reactions8–10, positioning them as significant plant-caused pollutants11 (Fig. 1b).Fig. 1 (a) The structure of catkins in female Salicaceae plants6 (b) Catkin pollution in urban areas.

Many countries and regions face ecological and health threats posed by catkins12–15, particularly in densely populated areas16. As the country with the largest distribution17 and number of 18 of Salicaceae species in the world, China, with its dense population, is deeply troubled by catkin pollution, with the capital city Beijing as a typical example. During the 1960s and 1970s, Beijing initiated large-scale afforestation projects to address the issues of deforestation and rampant sandstorms, and poplar and willow plants were chosen as one of the main tree species due to their rapid growth and good adaptability19. Given the faster growth and better seedling quality of female individuals than of male individuals, the majority of poplar and willow trees planted in Beijing were female20. However, as these trees mature, each female tree can produce approximately 25 kg of catkins annually, leading to severe catkin pollution issues21. Despite Beijing's cessation of planting such trees in 2005, a report released in 2017 revealed that there were still approximately 2,000,000 female willow and poplar trees in the city22, implying an annual production of 50,000,000 kg of catkins in the urban area alone. This challenge was compounded by urban expansion and ground surface hardening, preventing catkins from settling23. Additionally, Beijing's dry, windy climate with low precipitation intensified catkin pollution19.

Reviewing the global air quality guidelines released by the World Health Organization (WHO)24 and the air quality index system established by the United Nations Environment Programme (UNEP)25, as well as official air pollution monitoring systems of multiple countries and regions26–30, we have not found that any international organizations, countries, or regions have designated catkins as an air quality monitoring indicator. Current strategies for managing catkin pollution primarily focus on two aspects: (1) Reducing the source of pollution. These methods include chemical methods such as injecting chemical suppressants31, biological methods such as breeding sterile varieties32, and physical methods such as reducing pollution source plants33 and removing the upper portion of female plants14,34. These methods aim to curtail the reproduction of female plants and thus the spread of catkins. However, they may cause irreversible adverse impacts on urban ecological environments and biodiversity35. (2) Interrupting the transmission pathway. According to phenological research36, physical measures such as spraying tree canopies and washing inflorescences are employed to preemptively curb the dispersal of catkins35. However, these measures require annual individualized interventions for each tree, consuming significant human, material, and financial resources. The large number of trees also presents significant challenges to achieving substantive results. Therefore, it is imperative to explore ecological governance and management measures for catkins. These approaches can not only adhere to the natural growth and development patterns of plants but also offer sustainable, effective methods to reduce the impact of catkins.

The appearance of catkins is fluffy and lightweight, making them highly susceptible to the influence of climatic factors37, bearing some resemblance to airborne pollen, another plant-caused pollutant. Numerous researchers have focused on the risks and hazards caused by airborne pollen38,39and explored the intrinsic correlations between pollen concentration, plant community characteristics and microclimate factors40,41. They have proposed ecological prevention and control measures for pollen pollution42–45, providing valuable insights for exploring ecological governance related to catkins. From this perspective, several researchers have employed ENVI-met software to simulate the impact of microclimatic factors on the reproductive phenology of female poplar and willow trees in a micro-scale green space, suggesting the regulation of microclimates to shorten the duration of catkin dispersal and alleviate catkin pollution36. However, they were unable to collect microclimate data with field measurements, which may affect the robustness and accuracy of the results. Additionally, some scholars have proposed theoretical concepts of utilizing plant communities to obstruct the spread of catkins46,47. However, due to the continuous movement and variability of catkins in the air, there is currently no mature and widely accepted method for collecting and monitoring catkins, leading to a lack of scientific analysis and empirical research support for those theoretical concepts.

Therefore, this study focuses on the scale of plant communities. Taking Beijing as a case study, we employed fine-scale catkin monitoring methods to obtain concentration data and analyzed the relationship between catkin concentration and plant community characteristic factors and microclimate factors, explored the combined effects and mechanisms of multiple factors on catkin concentration, and subsequently proposed ecological prevention and control methods and strategies for catkins. The detailed objectives were: (1) Develop a detailed procedure for capturing catkins and a method for calculating their concentration; (2) Identify key plant community characteristic factors and microclimate factors that influence the internal catkin concentration within plant communities; (3) Analyze the quantitative relationship between the key factors and catkin concentration; (4) Propose planting design and microclimate regulation strategies that aid in either the sedimentation or dispersal of catkins.

Catkin monitoring and concentration calculation

Three conceptual methods for catkin monitoring

Drawing on the literature23 and consulting expert opinions, we proposed three conceptual methods for catkin monitoring:

(1) Photographic Counting Method: Based on Han et al.'s approach48, this method involves positioning a fixed camera set up at a height of 1.5 m (corresponding to the human breathing height) at a designated observation point and capturing images once every hour. The average number of catkins across multiple photographs during the monitoring period is calculated. Combined with the spatial volume of the captured area, this number was then converted to represent the number of catkins per unit area, characterizing the average hourly catkin concentration (pcs/(m3·h)).

(2) Weight Conversion Method: Recognizing that water bodies can absorb and retain catkins46, we implemented a method involving a flat plate filled with water positioned at a height of 1.5 m at a fixed observation point. This method estimates the mass of catkins by measuring the weight difference of the plate before and after the catkins fall into it each hour. Combined with the plate's area, this weight was then converted to represent the catkin weight per unit area, characterizing the average hourly catkin concentration (g/(m2·h)).

(3) Direct Counting Method: Inspired by Hirst's gravitational sedimentation method, which is widely used in airborne pollen researches49, this method involves placing a flat plate (or a rectangular open box) with an adhesive board at a height of 1.5 m at a fixed observation point. The number of catkins on the adhesive board was counted every hour. Combined with the board's area, this number was then converted to represent the number of catkins per unit area, characterizing the average hourly catkin concentration (pcs/(m2·h)).

Prior to the formal experiment, a 5-day preliminary test was conducted to assess the viability of the three monitoring methods. The results indicated: (1) Catkin dispersion was highly susceptible to instantaneous wind speed and air humidity. The photographic counting method captured only the transient state of drifting catkins, posing challenges in accurately assessing daily catkin pollution. (2) Given the extreme lightness of catkins, the weight conversion method faced accuracy issues due to water evaporation from the plate, leading to a significant margin of error in the final calculations. (3) In the direct counting method, the adhesive board on the plate captured many catkins, which tended to cluster together, making counting difficult. Additionally, higher wind speeds during the experiment caused previously adhered catkins to be blown away. In contrast, the rectangular open box method showed fewer pollutants and did not have problems with catkin clustering or dispersal postcapture. However, the number of adhered catkins was relatively low. It became clear that the direct counting method could more accurately represent the average hourly catkin concentration, but further refinements in operational specifics were needed.

CFD model construction

In this study, we utilized the Computational Fluid Dynamics (CFD) simulation software ANSYS Fluent to construct a wind field model, simulating the wind environment around a catkin collector. Our hypothesis was that the accumulation and clustering of catkins on the adhesive board during preliminary testing were primarily due to high wind speeds. Nonetheless, the impact of transient high wind speeds cannot be overlooked. Consequently, we developed two types of catkin collectors—one equipped with a flat plate and the other with a rectangular open box—to simulate their wind environments under four scenarios with initial wind speeds of 1 m/s, 3 m/s, 5 m/s, and 10 m/s.

The results showed that the average wind speed reduction above the catkin collector with a flat plate for all four scenarios was less than 1%, indicating relatively fast airflow (Table 1). While the plate’s efficiency in reducing wind speed increased marginally with increasing initial wind speeds, the rate of improvement was minimal. A higher wind speed means that a larger volume of air passes over the catkin collector per unit of time, leading to a greater number of pollutants and catkins being captured by the adhesive board. This resulted in the clustering phenomenon, which was almost unaffected by the initial wind speed, confirming our hypothesis. In contrast, the interior of the rectangular box maintained a stable, low-wind-speed environment. Although this possibly led to a slight underestimation of the actual catkin concentration due to reduced catkin fall, it effectively mitigated the problems of catkin clustering and dispersal postcapture. Table 1 Comparative analysis of wind speeds across two distinct catkin collectors in four different scenarios.

	A1	B1	A2	B2	A3	B3	A4	B4	
Initial wind speed/(m/s)	1.000	3.000	5.000	10.000	
Mean wind speed/(m/s)	0.994	0.085	2.978	0.262	4.962	0.438	9.923	0.876	
Mean wind speed reduction rate/%	0.647%	91.467%	0.737%	91.266%	0.764%	91.239%	0.769%	91.238%	
'A' represents the fluffy catkin collector equipped with a flat plate, while 'B' represents the fluffy catkin collector fitted with a rectangular open box.

Catkin monitoring procedure

The final procedure of the catkin monitoring method we adopted entails three key steps (Fig. 2):Fig. 2 Catkin monitoring procedure.

(1) Collector Setup: Two adhesive boards, each 20 cm × 25 cm, are joined along their longer sides and positioned at the bottom of a 25 cm × 40 cm rectangular open box. This configuration serves as a simple yet effective catkin collector.

(2) Installation: The collector was positioned at the geometric center of each sample plot using a tripod. Ensure it is elevated 1.5 m above the ground, approximating the height of human respiration.

(3) Monitoring and Maintenance: Every hour during the monitoring period, the number of catkins collected on the adhesive boards was replaced with fresh boards. In counting, clusters of seeds within catkins are considered single entities, ignoring variations in seed numbers per cluster.

Catkin concentration calculation

Given the absence of a standardized definition for catkin concentration and its exclusion from Chinese official government environmental quality monitoring factors50, this study, drawing inspiration from the methods used to measure airborne pollen concentrations45, proposed a formula to calculate the concentration of catkins in the air. The specific equation is:C=∑i=1nxi(n×S)

In this equation, C represents the catkin concentration within the plant community; n represents the number of monitoring hours; Xi represents the number of catkins collected in the collector during the ith hour of the monitoring period; and S represents the base area of the catkin collector.

Materials and methods

Study area

According to a report released in 2017, there were approximately 2,000,000 female poplar and willow trees throughout the entire city of Beijing22. Moreover, according to the latest statistics from the Beijing Municipal Bureau of Gardens and Landscapes in 2018, within the central urban area enclosed by the Fifth Ring Road of Beijing, there were only 284,000 female poplar and willow trees51. This indicates that approximately 85% of the female poplar and willow trees are distributed in the suburbs of Beijing. With an increased emphasis on catkin pollution, central urban districts have progressively implemented various management measures, such as injecting chemical suppressants and washing inflorescences, which significantly reduce the catkin concentration during the peak dispersal periods of catkins52. However, in the more expansive suburban areas of the city, the problem of catkin pollution remains severe due to constraints such as human, material and financial resources. Therefore, based on the distribution map of female poplar and willow trees in Beijing53, this study selected Yanqing, Shunyi, and Tongzhou, three suburban districts severely affected by catkin pollution, which are located in the northeast, northwest, and southeast directions of Beijing, respectively, as the research regions.

Plots selection

Field investigations and preliminary experimental findings suggested that when the source plants of catkins were situated more than 150 m away, their presence was almost negligible. Therefore, by adhering to the principle of selecting plots containing catkin source plants, encompassing various community structure types, and situated at a minimum distance of 150 m from other catkin source plants, we selected 33 sample plots (Table S1). Each plot was defined as an area of 20 m × 20 m54,55. These plots were situated within 11 urban green spaces in the study area. Additionally, to mitigate the potential impact of site specificity, we ensured that the distance from each plot maintained a minimum distance of more than 5 m from paved surfaces and over 20 m from buildings and water bodies.

Vegetation inventory and indicator system construction

Following the classification method of Yang et al.56, on-site inventory records were compiled for three functional life forms of plants: trees, shrubs, and ground cover plants, encompassing 16 basic parameters including quantity, height, canopy width, lower branch height, coverage, crown morphology, etc. Considering the irregular movement of catkins in three-dimensional space, which might be influenced by the structure and green volume of the plant community, this study constructed an evaluation system of plant community characteristic factors from two dimensions: spatial structure and three-dimensional green volume52,57. Additionally, three evaluation factor layers were established: the catkin source layer (including all the female poplar and willow trees within the plant community), the catkin barrier layer (including all the plants within the plant community except for female poplar and willow trees) and the whole plant community (including all the plants within the plant community). We ultimately identified 11 evaluation indicators (Table 2). The spatial structure dimension comprises the species diversity of the whole plant community as the indicator. The three-dimensional green volume dimension included ten indicators categorized into the catkin source layer, the catkin barrier layer, and the whole plant community (Fig. 3). The specific calculation formulas for each indicator can be found in Tables S2 and S3. Table 2 Evaluation system of plant community characteristic factors.

Evaluation dimension	Evaluation factor layer	Evaluation factor	Description of the factor	
Spatial structure	The whole plant community	Species diversity	Species diversity assessment within a plant community, measured by Shannon‒Wiener index	
Three-dimensional green

volume

	Catkin source layer	TS	Three-dimensional green volume of trees in the catkin source layer	
Catkin barrier layer	TB	Three-dimensional green volume of trees in the

catkin barrier layer

	
SB	Three-dimensional green volume of shrubs in the

catkin barrier layer

	
GB	Three-dimensional green volume of ground cover plants in the catkin barrier layer	
CTB	Three-dimensional green volume of coniferous trees in the catkin barrier layer	
BTB	Three-dimensional green volume of broad-leaved trees in the catkin barrier layer	
RCBTB	Ratio of the three-dimensional green volume of coniferous trees to broad-leaved trees in the catkin barrier layer	
TSGB	Total three-dimensional green volume of plants in the catkin barrier layer	
The whole plant community	T	Three-dimensional green volume of trees in the whole plant community	
W	Three-dimensional green volume of the whole plant community	

Fig. 3 Decomposition of the evaluation factors for the three-dimensional green volume dimensions.

Data monitoring and preprocessing

Due to variations in tree species and climatic conditions, Beijing typically experiences three peak dispersal periods for these catkins annually19. Among these, the second peak period is notably the most significant, characterized by its extensive impact, prolonged duration, greater management complexity, and increased public attention, surpassing the other two periods58. Therefore, guided by the catkin peak dispersal predictions issued by the Beijing Meteorological Service Center, we focused our monitoring efforts on the second peak period, from late April to early May 2021. The monitoring was conducted on clear days, specifically between 10:00 and 17:00 daily. To mitigate the potential impact of meteorological factors such as precipitation and strong winds, monitoring was carried out on calm or light winds (wind speed < 5 m/s)59 days without rainfall and irrigation on the green spaces. A total of 11 days of valid monitoring data were obtained.

Simultaneously, we utilized a thermos hygrometer recorder (LM-8010, Shenzhen EnCi Electronics Co., Ltd.) to record the daily relative air humidity, temperature, and wind speed within the plots. The monitoring duration and collection intervals were consistent with those used for catkin monitoring. To ensure accuracy, the average of seven hourly meteorological readings per plot was computed, representing the daily microclimatic conditions for each plot.

Statistical analysis

Data consolidation and integration were performed using Microsoft Excel 2020. We did not identify any outliers by applying the Mahalanobis distance method60. The Shapiro‒Wilk test indicated that the data did not adhere to the assumption of a normal distribution61. Therefore, in this study, we first utilized Spearman correlation analysis to explore the qualitative relationships between catkin concentrations and various plant community characteristics and microclimate factors and identify key factors influencing catkin concentration. Building upon this foundation we conducted regression analyses employing linear, quadratic, and cubic regression equation models to further elucidate the quantitative relationships between individual key factors and catkin concentration, with catkin concentration as the dependent variable and the key factors as independent variables. Finally, to further explore the plant community characteristics corresponding to different catkin concentrations, we applied the Classification and Regression Tree (CART) decision tree model62 to analyze the combined effects of multiple key factors on catkin concentration. As a commonly used nonparametric predictive modeling technique, the CART decision tree model allows for the inclusion of multiple independent variables without making any assumptions about the data distribution and is not sensitive to multicollinearity among variables63,64. By recursively dividing the dataset into smaller subsets, the CART decision tree can provide a simple and intuitive approach to data analysis and model construction63 while effectively identifying the most critical factors in model predictions. Additionally, through a clear and intuitive visualization of the tree-like structure, the CART decision tree can elucidate decision mechanisms based on these critical factors. All analyses mentioned above were conducted in IBM SPSS software (R26.0.0.0, 32-bit).

Results

Correlation between catkin concentration and plant community characteristics and microclimate factors

Correlation between catkin concentration and plant community characteristic factors

The catkin concentration was found to be significantly correlated with 6 of the 11 plant community characteristic factors (Table 3). Specifically, the catkin concentration showed a significant positive correlation with three factors: the three-dimensional green volume of trees in the catkin source layer (TS), the three-dimensional green volume of trees in the whole plant community (T), and the three-dimensional green volume of the whole plant community (W). In contrast, it displayed a significant negative correlation with four factors: the three-dimensional green volume of shrubs in the catkin barrier layer (SB), the three-dimensional green volume of ground cover plants in the catkin barrier layer (GB) and species diversity. This suggested that the increase in TS、T and W contributed to the increase in catkin concentration, whereas with the increase in SB and GB, the catkin concentration gradually decreased. Additionally, plant communities with richer species diversity and more uniform distributions exhibited lower catkin concentrations.Table 3 Spearman correlation between catkin concentration and plant community characteristic factors.

		TS	TB	SB	GB	CTB	BTB	RCBTB	TSGB	T	W	Species diversity	
Catkin concentration (pcs/(m2·h))	Spearman correlation	0.497**	0.131	-0.524**	-0.359*	-0.080	0.168	-0.045	-0.229	0.464*	0.395*	-0.483**	
Sig. (2-tailed)	0.003	0.476	0.002	0.040	0.657	0.350	0.802	0.200	0.006	0.023	0.004	
**Indicates a highly significant correlation (P < 0.01), * indicates a significant correlation (P < 0.05).

Correlation between catkin concentration and microclimate factors

Among the three microclimate factors, only the relative air humidity exhibited a positive correlation with the catkin concentration (Table 4). This suggested that as relative air humidity increased, the number of catkins captured by the collector increased. This correlation may be attributed to the increased mass of catkins as they absorb moisture under high relative air humidity conditions. As a result, the duration and distance of catkins in the air were reduced46, making them more likely to fall directly onto the collector within the plant community. Simultaneously, the catkin concentration in the urban environments probably decreased because the catkins settled closer to the catkin source plants. Table 4 Spearman correlation between catkin concentration and microclimate factors.

		Relative air humidity/%	Temperature/℃	Wind speed/(m/s)	
Catkin concentration (pcs/(m2 h))	Spearman correlation	0.418**	0.246	-0.317	
Sig.(2-tailed)	0.016	0.168	0.072	
**Indicates a highly significant correlation (P < 0.01).

Impacts of key plant community characteristics and microclimate factors on catkin concentration

The results of regression analyses revealed that among all of the seven key factors, TS, GB and T, the cubic regression models exhibited the best fit and accuracy, explaining 32.0%, 28.4%, 27.5% and 43.6% of the variation in catkin concentration, respectively (Fig. 4a,c,d and g). For SB, W and species diversity, the quadratic regression models exhibited the best fit and accuracy, explaining 19.3%, 21.5%, and 21.7% of the variation in catkin concentration, respectively (Fig. 4b,e and f). Although all the models passed the significance test, the respective coefficients of determination were < 0.5, indicating the limited explanatory power of trees with respect to variations in catkin concentration at the plant community scale65.Fig. 4 Regression fitted curves of the effects of seven key plant community characteristics and microclimate factors on catkin concentration. The solid lines represent the fitted curves; the dark and light shadowed areas represent the 95% confidence intervals and prediction intervals, respectively.

Further analysis revealed that two of the seven key plant community characteristic factors, SB and species diversity, exhibited a general decreasing trend in the fitting curves for catkin concentration, indicating that an increase in these factors can lead to a reduction in catkin concentration. As SB increased from 0 to 100 m3, the catkin concentration decreased from 80 to 24 pcs/( m2·h), with a slight subsequent increase thereafter (Fig. 4b). Similarly, as the species diversity increased from 0 to 1, the catkin concentration decreased from 88 to 29 pcs/( m2·h) (Fig. 4f). On the other hand, three out of the seven key plant community characteristic factors (TS, T and W) exhibited an increasing trend in the fitting curves for catkin concentration, indicating that an increase in these factors contributes to an increase in catkin concentration. As TS, T, and W increased from 500 to 1500 m3, the corresponding changes in catkin concentration ranged from 64 to 116 pcs/(m2·h) (Fig. 4a), from 58 to 107 pcs/(m2·h) (Fig. 4d), and from 58 to 99 pcs/(m2·h) (Fig. 4e), respectively. Notably, the impacts of these three factors also exhibited certain thresholds, at approximately 1700 m3 for TS and approximately 2400 m3 for both T and W. For the key microclimatic factor of relative air humidity, when it was below 25%, the catkin concentration within the community remained approximately at 50 pcs/(m2·h). However, when it exceeded 25%, the catkin concentration significantly increased. The findings indicated that a relative air humidity of 25% was the critical threshold for promoting the settlement of catkins. Additionally, GB showed a trend of "initial increase—decrease—subsequent increase" in its impact on catkin concentration, with two threshold values occurring at approximately 20 m3 and 60 m3 for GB. As the GB increased from 20 to 60 m3, the catkin concentration decreased from 94 to 23 pcs/(m2·h) (Fig. 4c).

Qualitative relationship between catkin concentration and key factors based on the CART decision tree model

Construction of the CART decision tree model

To further explore the combined effects of multiple key factors on catkin concentration, we constructed a CART decision tree model with catkin concentration as the dependent variable. Six key plant community characteristic factors (TS, SB, GB, T, W, and species diversity) identified through correlation analysis were considered as independent variables, and one key microclimate factor (relative air humidity) was served as the influencing variable. The Gini coefficient was employed as the splitting criterion for the decision tree construction. We also conducted post-pruning measures to prevent overfitting and enhance the model's generalizability.

CART decision tree pathway

The CART decision tree model we derived consisted of three levels, featuring four decision nodes and five significant terminal nodes (Fig. 5). The decision factors at these four decision nodes were TS, SB, GB, and species diversity. The sequence of their splits within the model elucidated the relative importance of each decision factor. Notably, TS was the primary factor determining catkin concentrations within different plant communities. SB and GB were positioned at the second level, while species diversity was situated at the third level.Fig. 5 CART decision tree model of catkin concentration and key factors.

Plant community configuration patterns of different predicted catkin concentrations

Derived from the decision tree analysis, we identified plant community configurations that corresponded to distinct predicted catkin concentrations. When TS ≤ 744.0755 m3, SB served as the secondary decision factor for catkin concentration, with a critical threshold of 4.7137 m3. The concentrations were projected at 73 pcs/(m2·h) when SB ≤ 4.7137m3 (Fig. 6a). However, when SB > 4.7137 m3, the decision tree bifurcated further, employing species diversity as the decision factor. A catkin concentration of 90 pcs/(m2·h) was projected when the species diversity ≤ 0.1445 (Fig. 6b), whereas a value of 30 pcs/(m2·h) was anticipated otherwise (Fig. 6c). When TS > 744.0755m3, GB emerged as the pivotal secondary decision factor. Specifically, a catkin concentration of 149 pcs/(m2·h) was predicted for GB ≤ 20.000 m3 (Fig. 6d); conversely, a concentration of 81 pcs/(m2·h) was predicted for these scenarios (Fig. 6e).Fig. 6 Plant community configuration patterns of different predicted catkin concentrations. (a) 73 pcs/(m2 h) (b) 90 pcs/(m2 h) (c) 30 pcs/(m2 h) (d) 149 pcs/(m2 h) (e) 81 pcs/(m2 h).

Discussion

Collection and concentration calculation of catkins

Monitoring the catkin concentration is crucial for effectively reducing pollution from these catkins and devising appropriate management strategies. However, more efficacious methodologies are needed in current research. Our study introduced an innovative approach—the direct counting method—which combined field experiments with CFD model simulations to ascertain the specifics of the catkin capture apparatus. We also established a comprehensive process, from capturing catkins to calculating their concentration, thus providing a new perspective in this research field. Our method offered advantages over the photographic counting method proposed by Han et al.48, as it allowed for more prolonged monitoring of catkin concentrations, thereby minimizing data variances due to short-term meteorological fluctuations. Furthermore, the experimental instruments employed by our method were cost-effective and readily available, serving as a valuable reference for subsequent related studies.

Association of catkin concentration with plant community characteristics and microclimate factors

Through correlation analysis, we identified key plant community characteristics and microclimate factors influencing catkin concentration. By employing regression analysis, we also established optimal fitting equations to explore the specific effects of these key factors on catkin concentration. We found that increases in SB and GB within certain ranges (from 0 to 100 m3 for SB and from 20 to 60 m3 for GB) significantly reduced the catkin concentration, even surpassing the reduction achieved by decreasing the TS from 1500 to 500 m3. Moreover, it was possible to effectively reduce catkin concentrations without increasing W by enhancing species diversity through methods such as introducing different types of vegetation. Our analysis suggested that the rational design of plant communities can effectively control catkin concentration, rivaling source control measures. Multiple key factors exhibited thresholds for catkin concentration, possibly due to the interdependence and influence among them. For instance, as TS increased, the production of catkins naturally increased. However, this increase can also result in an increase in relative air humidity within the plant community66, increasing the number of settled catkins. Such interactions may explain the emergence of thresholds.

The CART decision tree model simultaneously considered the joint effects of multiple factors and further refined the key factors identified through correlation analysis, resulting in the identification of four crucial decision factors: TS, SB, GB, and species diversity. Notably, TS emerged as the primary determinant influencing catkin concentration. However, two factors, T and W, did not emerge as decision factors, possibly because the significant positive correlation between them and catkin concentration may be influenced by TS, and the correlation coefficients of T and W are relatively small.

Conversely, both SB and GB were significantly negatively correlated with catkin concentration. This is hypothesized to be due to the propensity of middle and lower strata vegetation to ensnare and anchor catkins, thereby impeding their dispersion. Decision tree analysis indicated that both SB and GB served as secondary decision factors in regulating catkin concentration, yet their modulatory roles were influenced by TS. Specifically, when TS ≤ 744.0755 m3, the secondary decision factor was SB, which was postulated to be due to the intermediary position of the shrub layer between the layers of the trees and ground cover plants, acting as an early interception mechanism. When the TS was low, the number of catkins produced was typically low, and the proportions of catkins caught by shrubs and later deposited into ground cover plants were negligible. As a result, the relationship between GB did not serve as a decision factor under these conditions. However, when TS > 744.0755 m3, the secondary decision factor shifted to GB. A substantial volume of catkins, even after being intercepted by upper strata vegetation, descended to the ground cover plants, accentuating the role of ground vegetation in reducing catkin concentration. Additionally, we discerned that plant communities with higher species diversity tended to have lower catkin concentrations. This can be attributed to the intricate internal structure typical of species-rich and evenly distributed plant communities, facilitating the multitiered interception and ensnarement of catkins.

Similar to other airborne pollutants, such as PM2.5 67 and pollen68, microclimate factors can influence the catkin concentration within plant communities. Our research revealed a positive correlation between relative air humidity and catkin concentration, consistent with previous research46. However, while prior studies have suggested a relationship between wind speed and catkin concentration, our findings did not corroborate this association. This discrepancy might be attributed to the source of the wind speed data. Traditional studies often rely on data from meteorological observation stations69 (e.g., the average wind speed in Beijing in April 2021 was 2.583 m/s, and in May, it was 3.194 m/s). In contrast, our study used on-site measured data (with an average wind speed of 1.395 m/s over a monitoring period of 11 days). Due to the wind-blocking effect of plants, the observed data tended to be lower70, which might have influenced the outcomes of the correlation analysis.

Application scenarios and green space management

The goal in managing catkin pollution is not complete elimination but rather prudent regulation of its prevalence, aimed at reducing its impact on the daily lives of the public. Through CART decision tree analysis, we synthesized plant community configuration patterns corresponding to various predicted catkin concentrations, thereby offering theoretical underpinnings for catkin pollution ecological management. With a thorough understanding of the existing conditions, we can progressively and precisely improve urban green spaces on a selective basis tailored to the unique catkin pollution levels and specific needs of different areas. This approach aims to facilitate either the dispersion or sedimentation of catkins, adapting to the varying requirements of urban greenery. Our research findings suggested that plant communities promoting catkin dispersion typically had a uniform tree species composition, a permeable spatial structure, and a minimal three-dimensional green volume of shrubs and ground cover plants. In contrast, communities that facilitated catkin sedimentation were marked by diverse tree species, complex structures, and substantial three-dimensional green volume in their middle and lower layers. Therefore, in areas with high pedestrian traffic, where it is challenging to reduce the number of catkin source plants or change tree species, strategies such as reducing the coverage of shrubs and ground cover plants and simplifying community structure can be employed to promote catkin dispersion, thereby decreasing catkin concentration in active spaces and enhancing the comfort of space utilization. In less frequented areas, increasing the three-dimensional green volume of shrubs and ground cover plants surrounding catkin source plants, along with diversifying tree species and enriching the structural layers of plant communities, can bolster the ability of green spaces to retain catkins, preventing their dispersion into urban environments and mitigating their adverse impacts on the public. Additionally, from a microclimatic regulation perspective, various measures, such as expanding water body areas and mist spraying, can be adopted to increase relative air humidity, further aiding in catkin sedimentation.

Limitations and future research prospects

Naturally, our empirical approach has limitations. First, although our monitoring approach for catkins effectively mitigated the clustering of catkins, it may also compromise the efficiency of catkin capture. As such, it may not be suitable as a data source for forecasting catkin concentrations. Second, due to research constraints, the primary catkin source plant species in our study plots were Salix matsudana and Populus tomentosa. We did not explore the differences in catkin dispersal timing, distance, or quantity between these species. Other catkin-producing species, such as Salix babylonica, Populus × canadensis, and Populus cathayana, were not included in this study. Third, considering the comprehensiveness and completeness of our description of plant community structures, as well as the unique contributions of various factors, we did not exclude any factors through collinearity tests before performing statistical analyses. Although we ultimately used the CART decision tree analysis, which is insensitive to multicollinearity, to analyze the combined effects of multiple factors on catkin concentration, we must acknowledge that the interdependencies and interactions among multiple factors can indeed influence the study results. Fourth, this research primarily focused on internal plant community characteristics and microclimate factors influencing catkin concentration within the community. However, the influence of factors such as the relative vertical and horizontal positioning of catkin source plants and non-catkin plants within a community, as well as their configuration methods, on catkin dispersion remains an area for further research and discussion. Furthermore, external environmental factors, such as the specific geographical location and the surrounding built environment and water bodies, may also affect the dispersal and deposition of catkins. Although we mitigated this limitation to some extent by controlling the distance from the plot boundaries to hard surfaces and water bodies, numerous other complex external factors might still impact the internal catkin concentration within the plant community.

For future research, it is essential to expand the sample size to gather data on catkin concentrations from a more comprehensive array of representative green spaces, which can facilitate further validation of the universality of the catkin monitoring method across diverse scenarios and subsequent optimization for more accurate catkin concentration monitoring. Concurrently, there is a need for independent monitoring of catkins from different species of catkin source plants and elucidating the disparities among them. Moreover, utilizing advanced technologies such as drone mapping71 and three-dimensional point cloud techniques72 can enhance the efficiency and accuracy of collecting plant community characteristic data. Integrating with suitable software platforms for parametric simulations, these methods allow for the control of single variables, thereby mitigating the impact of multicollinearity among factors. This approach also facilitates a more comprehensive understanding of the spatial and temporal patterns of catkin dispersion.

Conclusions

This study introduced an efficacious direct counting method, meticulously monitoring the catkin concentration across 33 plant communities, identifying key plant community characteristic factors and microclimate factors influencing catkin concentration and elucidating the quantitative relationships between these key factors and catkin concentration. Furthermore, we synthesized ecological mitigation strategies for catkin pollution. Our findings revealed that TS, SB, GB, T, W, species diversity, and relative air humidity were key factors influencing catkin concentration, all of which exhibited nonlinear relationships with catkin concentration. Ecological strategies can effectively manage catkin sedimentation or dispersion. By enhancing species diversity, the three-dimensional green volume of shrubs and ground cover plants, as well as increasing relative air humidity within plant communities, facilitated catkin sedimentation, whereas the opposite promoted dispersion. Catkin retention measures vary depending on the TS. In communities with abundant catkin source plants, emphasis can be placed on augmenting ground cover plants. Conversely, when there are fewer catkin source plants, the focus can shift to enhancing mid-layer shrubs, diversifying plant species, and enriching structural layers. Our research offers novel insights into managing catkin pollution and provides a scientific foundation for the meticulous management and renewal of urban green spaces.

Supplementary Information

Supplementary Tables.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72570-1.

Acknowledgements

Thanks to the fundings by Beijing Key Research and Development Program of China, grant number D171100007117003; Operation Fund Project of the National Forestry Grassland Landscape Engineering Technology Research Center (2023), grant number PTYX202333.

Author contributions

X.M.: Conceptualization, Visualization, Writing – original draft, Methodology, Software and Data curation. K.D.: Methodology, Investigation and Data curation. M.Y.: Methodology, Formal analysis and Writing – review & editing. Z.W.: Software and Investigation. Y.C.: Software. Y.L. and L.C.: Writing – review & editing, Conceptualization, Funding acquisition and Project administration. All authors reviewed the manuscript and approved the submitted version.

Data availability

The data presented in this study are available on request from the corresponding author.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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