Search Results (206)

Understanding the spatiotemporal patterns of cropland carbon and carbon water use efficiency (CWUE) and its driving factors is essential for sustainable agricultural development. Based on a multi-source remote sensing dataset, this study applies a trend analysis (Sen + Mann–Kendall), a dual-type randomized extraction algorithm, and an optimized XGBoost model to examine the spatiotemporal variations in cropland CWUE, including the water use efficiency of net primary production (WUE_NPP), water use efficiency of gross primary production (WUE_GPP), and carbon use efficiency (CUE) in Henan Province from 2001 to 2019. This study further quantifies the impact of irrigation on the cropland CWUE and explores the synergistic effects of its driving factors in irrigated areas. Results reveal significant regional differences in cropland CWUE across Henan Province. Higher multi-year average values of CUE and WUE_NPP were observed in the western region, while the WUE_GPP was more prominent in the south-central region. Over 76% of cropland areas showed a general downward trend in three indicators, with significant interannual declines. Non-irrigated cropland exhibited higher CWUE values than irrigated ones. The average values over multiple years of the WUE_GPP, WUE_NPP, and CUE of irrigated cropland were 2.51 ${\mathrm{g} \mathrm{C} \mathrm{m}}^{-2}{ \mathrm{mm}}^{-1}$, 1.08 ${\mathrm{g} \mathrm{C} \mathrm{m}}^{-2}{ \mathrm{mm}}^{-1}$, and 0.43, respectively. Sunlight was the dominant factor influencing the WUE_GPP in irrigated areas, while precipitation primarily regulated the WUE_NPP and CUE. The influence of the gross domestic product (GDP) was found to be minimal. Notably, both the leaf area index (LAI) and precipitation exhibited a shift from a positive to negative influence on CUE once their values exceeded optimal thresholds, indicating that resource overabundance can lead to physiological limitations. This study offers valuable insights into how irrigated cropland responds to the combined effects of multiple environmental and socio-economic drivers. Full article

A detailed characterization of evapotranspiration (ET) patterns is of paramount importance for optimizing irrigation scheduling and enhancing water-use efficiency in the North China Plain. To delve into this, a two-season study was conducted at the National Experimental Station for Precise Agriculture in Beijing. Using 12 weighing lysimeters, the study compared two summer maize varieties with contrasting canopy sizes: Jingke 968 (JK), characterized by a large canopy, and CF 1002 (CF), with a small canopy. The comprehensive analysis yielded the following significant findings: (1) The daily average ET rates exhibited consistent trends across cultivars, yet with notable disparities in magnitude. JK consistently demonstrated higher water consumption throughout the growth seasons. In the first season, at the V13–R1 stage, the peak daily ET of JK and CF reached 5.91 mm/day and 5.52 mm/day, respectively. In the second season, during the R1–R3 stage, these values were 5.21 mm/day for JK and 5.22 mm/day for CF, highlighting the nuanced differences in water use between the varieties under varying growth conditions. (2) Regardless of canopy size, the hourly ET fluctuations across different growth stages followed similar temporal patterns. However, the most striking inter-varietal differences in ET emerged during the R1–R3 reproductive stages, when both cultivars had achieved peak canopy development (leaf area index, LAI > 4.5). Notably, the ET differences between JK and CF adhered to a characteristic diurnal “increase–decrease” pattern. These differences peaked during mid-morning (09:00–11:00) and early afternoon (13:00–15:00), while minimal divergence was observed at solar noon. This pattern suggests complex interactions between canopy structure, microclimate, and plant physiological processes that govern water loss over the course of a day. (3) Analysis of the pooled data pinpointed two critical time periods that significantly contributed to the cumulative ET differences between the varieties. The first period was from 12:00–17:00 during the R1–R3 (anthesis) stage, and the second was from 08:00–16:00 during the R3–R5 (grain filling) stage. JK maintained significantly higher transpiration rates (Tr) compared to CF, especially during the morning hours (09:00–12:00). On average, the Tr of JK exceeded that of CF by 5.3% during the pre-anthesis stage and by 16.0% during the post-anthesis stage. These observed Tr differentials strongly indicate that canopy architecture plays a pivotal role in modulating stomatal regulation patterns. Maize varieties with large canopies, such as JK, demonstrated enhanced morning photosynthetic activity, which likely contributed to increased transpiration. At the same time, both varieties seemed to employ similar midday water conservation strategies, possibly as an adaptive response to environmental stress. In summary, this study has comprehensively elucidated the intricate relationship between the leaf area index and the evapotranspiration of summer maize across multiple timescales, encompassing periodic, daily, and hourly variations. The findings provide invaluable data-driven insights that can underpin the development of precise and quantitative irrigation strategies, ultimately promoting sustainable and efficient maize production in the North China Plain. Full article

Atmospheric pollution due to an increased particulate matter (PM) concentration imposes a threat for human health. This is particularly true for regions with intensive industrial activity and nature-based solutions, such as tree plantations, are adopted to mitigate the phenomenon. Here, we report on the case of the lignite complex of western Macedonia (LCWM), the largest in Greece, where extensive Robinia pseudoacacia L. plantations have been established during the last 40 years for post-mining reclamation, but their PM retention capacity and the controlling parameters have not been assessed to date. Thus, during the 2021 growth season (May to October), we determined the PM10 capture by leaves sampled twice per month, across four 10-m long transects, each consisting of five trees, and at three different heights along the tree canopy. During the same period, we also measured the leaf area index (LAI) of the plantations and collected climatic data, as well as data on PM10 production by the belt conveyors system, the main polluting source at the site. We estimated that the plantations’ foliage captures on average c. 42.85 μg cm⁻² PM10 and we developed a robust linear model that describes PM10 retention on a leaf area basis, as a function of PM10 production, LAI (a proxy of seasonal changes in leaf area), distance from the emitting source, and wind speed and foliage height within the crown. The accuracy of the estimates and the performance of the model were tested with the bootstrap cross-validate resampling technique. PM10 retention increased in spring and early summer following the increase in LAI, but its peak in August and October was controlled by the highest PM10 production, due to elevated energy demands. Moreover, PM10 retention was facilitated by wind speed, and it was higher at the lower part of the trees’ canopy. On the contrary, the PM10 load on the trees’ foliage decreased with an increasing distance from the conveyor belt system and the frontline of the plantations. Our findings support the positive role of R. pseudoacacia plantations for PM10 retention at heavily polluted areas, such as the lignite mines in Greece, and provide a model for the estimation of PM10 retention by their foliage based on basic environmental drivers and characteristics of the plantations, which could be helpful for planning their future management. Full article

Within a dam reservoir landscape in the Western Black Sea Region of Türkiye, a dense young-mature stand composed diversely of oriental beeches, European hornbeams, sessile oaks, and silver lindens was chosen as a study field to analyze canopy parameters and to determine phenological patterns along the altitudinal gradients. Referring to the air-soil temperature and precipitation data, intra-annual eco-physiological characteristics of that stand tree canopies, were aimed to be determined regarding those altitudinal gradients. For each of the 10 altitudinal gradients, the mixed deciduous stand canopy physiological characteristics were analyzed by hemispherical photographing. Canopy parameters were acquired from those digital hemispherical photographs, which were confirmed with secondary LAI data from the LAI-2200C. Leaf Area Index, Light Transmission, Canopy Openness, and Gap Fraction were obtained during a total of 21 study field visits throughout the monitoring year. Beginning from a theoretical leafless stage with 0.51 m² m⁻², average LAI increased to 0.89 m² m⁻² during budburst stage, and then gradually up to 3.60 m² m⁻² during climax leaf period, and then to 1.38 m² m⁻² during senescence period, and gradually down to 0.50 m² m⁻² during the next theoretical leafless stage. However, average LT (64%, 61%, 9%, 36%, 74%), CO (65%, 62%, 9%, 37%, 75%), and GF (18%, 14%, 1%, 8%, 14%) followed opposite patterns. Though no apparent trend was valid for those canopy parameters from the lowest to the highest altitudinal gradient, their obvious intra-annual patterns emerged as compatible with the annual air-soil temperature data course. Full article

Rosemary (Salvia rosmarinus L.) is a versatile and resilient plant with significant culinary, medicinal, and ecological value. This study evaluates the impact of four nitrogen (N) fertilization levels (0, 50, 100, and 150 kg N ha⁻¹) on the morphological, physiological, and agronomic traits, as well as vegetative indices, of rosemary over two growing seasons (2022 and 2023). The results indicate that plant height and leaf area index (LAI) increased with N application. Additionally, physiological characteristics such as chlorophyll content, photosynthetic efficiency, and assimilation rates (A) increased by an average of 32%, 17%, and 55%, respectively, compared to the control. Biomass production also improved with N fertilization, with yields rising by 32% in 2022 and 58% in 2023. Furthermore, both essential oil concentration and essential oil yield were enhanced by N application. Radiation use efficiency (RUE), water use efficiency (WUE), agronomic efficiency (AE), and partial factor productivity (PFP) also increased, indicating more efficient utilization of environmental resources. Moreover, higher N rates consistently enhanced vegetation indices, reflecting improved plant health, greenness, biomass, photosynthetic activity, and energy utilization. Therefore, this study highlights that the optimal N range appears to balance biomass yield and essential oil yield while maximizing the efficiency of environmental resource use. Full article

Drip irrigation technology, known for its advantages in high water use efficiency and yield increase, has been a focal point of research regarding its combined effects with the plastic film-mulching technique on field water consumption and crop growth. To accurately quantify the water-saving effect of plastic film-mulching techniques and investigate the mechanisms of mulching on evaporation (E) and transpiration (T), this study was conducted on soybean using the Bowen ratio–energy balance system and micro-lysimeters as the observation means and the MSW model as the data partitioning tool, during 2019–2021 in arid northwest China. We compared evapotranspiration (ET) under the film-mulched drip irrigation (FM) and non-mulched drip irrigation (NM) treatments. The results show that ET, E, and T under FM were reduced by 32.6 mm, 76.1 mm, and −43.5 mm, respectively. Moreover, mulching increased the leaf area index (LAI) by 20.7%, soybean yield from 2727.0 kg ha⁻¹ to 3250.5 kg ha⁻¹, and WUE from 0.64 kg m⁻³ to 0.83 kg m⁻³ on average, which means mulching reduced water consumption in the field by decreasing soil evaporation and improved water use efficiency by promoting crop growth. Further analysis indicated that mulching has strengthened the connection between soil temperature and humidity and weakened the effect of soil temperature on soybean leaf growth. Soil water content (SWC) and LAI had a direct negative effect on E, with LAI causing a stronger effect on E under the FM treatment. Mulching has weakened the direct effect of SWC on T, so that only LAI and soil temperature had a significant direct positive effect on T. Following the rapid growth of soybean LAI, the isolating effect of the mulch was gradually replaced by the dense leaf canopy. The results provide a reference for further exploring the water-saving and yield-increasing benefits of plastic film-mulching techniques, and to facilitate wider promotion of the plastic film-mulching techniques and the water–fertilizer integration technology in arid regions. Full article

The integration of crop models and remote sensing data has become a useful method for monitoring crop growth status and crop yield based on data assimilation. The objective of this study was to use leaf area index (LAI) values and plant nitrogen accumulation (PNA) values generated from spectral indices to calibrate the Decision Support System for Agrotechnology Transfer (DSSAT) model using the Monte Carlo Markov Chain (MCMC) technique. The initial management parameters, including sowing date, sowing rate, and nitrogen rate, are recalibrated based on the relationship between the remote sensing state variables and the simulated state variables. This integrated technique was tested on independent datasets acquired from three rice field tests at the experimental site in Deqing, China. The results showed that the data assimilation method achieved the most accurate LAI (R² = 0.939 and RMSE = 0.74) and PNA (R² = 0.926 and RMSE = 7.3 kg/ha) estimations compared with the spectral index method. Average differences (RE, %) between the inverted initialized parameters and the original input parameters for sowing date, seeding rate, and nitrogen amount were 1.33%, 4.75%, and 8.16%, respectively. The estimated yield was in good agreement with the measured yield (R² = 0.79 and RMSE = 661 kg/ha). The average root mean square deviation (RMSD) for the simulated values of yield was 745 kg/ha. Yield uncertainty from data assimilation between crop models and remote sensing was quantified. This study found that data assimilation of crop models and remote sensing data using the MCMC technique could improve the estimation of rice leaf area index (LAI), plant nitrogen accumulation (PNA), and yield. Data assimilation using the MCMC technique improves the prediction of LAI, PNA, and yield by solving the saturation effect of the normalized difference vegetation index (NDVI). This method proposed in this study can provide precise decision-making support for field management and anticipate regional yield fluctuations in advance. Full article

Wheat is a critical economic and food crop in global agricultural production, with changes in wheat cultivation directly impacting the stability of the global food market. Therefore, developing a method capable of accurately estimating carbon flux in wheat is of significant importance for early warning agricultural production risks and guiding farming practices. This study constructs a multimodal model framework to estimate wheat carbon flux using MODIS data products, including the Leaf Area Index (LAI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and meteorological data products. The results demonstrate that the constructed carbon flux detection model effectively estimates carbon flux across different growth stages of wheat. Evaluation of the model, using comprehensive accuracy metrics, shows an average adjusted R² of 0.88, an RMSE of 5.31 gC·m⁻²·8d⁻¹, and nRMSE of 0.05 across four growth stages, indicating high accuracy with minimal error. Notably, the model performs more accurately at the green-up stage compared to other stages. Interpretability analysis further reveals key features influencing model estimations, with the top five ranked features being (1) LAI, (2) NDVI, (3) EVI, (4) vapor pressure (Vap), and (5) the Palmer Drought Severity Index (PDSI). Remote sensing indices exhibit a greater influence on carbon flux estimation throughout the whole growth stages compared to meteorological indices. Under water-limiting conditions, the importance of evapotranspiration, precipitation, and drought-related factors fluctuates significantly. This study not only provides an important reference for monitoring wheat carbon flux, but also offers novel insights into the crop carbon cycling mechanisms within agroecosystems under the current environmental context. Full article

The Gross Primary Productivity (GPP) of Mediterranean forest is expected to change over the 21st century due to the warmer and drier conditions. In this study, we present a process-based forest carbon-flux model, where stand structure and soil heterotrophic respiration have been parameterized with long-term monitoring data in a Mediterranean Pinus brutia. Ten. forest. The developed model was validated using an independent annual tree-ring increment dataset from the 1980–2020 period (baseline climate) across a post-fire gradient (four plots) and an elevation gradient (five plots). Additionally, the model was forced with two downscaled climate change scenarios (RCP4.5 and RCP8.5) for the 2020–2100 period. Average GPP, Net Primary Productivity (NPP), ecosystem Respiration (R_eco) and Net Ecosystem Productivity (NEP) were calculated for two future time periods (2051–2060 and 2091–2100) under the two climate change scenarios and compared along the two gradients. Under baseline climate conditions, our simulations suggest a temperature sensitivity of GPP and R_eco, as expressed along the elevation gradient. However, the effect of stand structure (represented through the site-specific leaf area index (LAI)) was more prominent, both along the elevation gradient and the post-fire chronosequence. Under the two climate change scenarios, a reduced GPP and an increased R_eco lead to reduced NEP compared to baseline climate conditions across all study plots. Full article

Nitrogen (N), as a macro-element, plays a vital role in plant growth and development. N deficiency affects plant productivity by decreasing the photosynthesis, leaf area, and longevity of green leaf. The experimental design was a randomized complete block design with four replicates: N0 (0 kg N ha⁻¹), N90 (90 kg N ha⁻¹), N180 (180 kg N ha⁻¹), and N210 (210 kg N ha⁻¹), respectively, i.e., the effects of different N application levels on photosynthetic physiology, leaf characteristics, yield, and production. The findings of the present study underscore the importance of optimizing nitrogen application to maximize light capture, photosynthetic efficiency, and crop productivity. Under N-treated groups (N90, N180, and N210), the average photosynthetically active radiation (PAR) of panicle leaves at all levels, N210, was determined to be higher than that of other treated groups, as well as the N0 level and the upper, middle, and lower regions of N0, N90, and N180 plants under the same leaf area index (LAI), and it was noted to be higher under N210, respectively. Dry matter accumulation under N180, and N210 increased, respectively, and under N210, the dry matter accumulation of the population was significantly higher than that under N180, respectively. The nitrogen use efficiency (NUE), nitrogen recovery efficiency (NRE), nitrogen internal efficiency (NIE), and partial factor productivity of nitrogen (PFPN) under different nitrogen (N) application rates were significantly higher than N0, where the NIE of N180 was significantly higher than that of N210, the NUE and NRE of N180 and N210 were higher than those of N0, and the difference from PFPN was not significant, respectively. Full article

Accurate, high-throughput canopy phenotyping using UAV-based multispectral remote sensing is critically important for optimizing the management and breeding of foxtail millet in rainfed regions. This study integrated multi-temporal field measurements of leaf water content, SPAD-derived chlorophyll, and leaf area index (LAI) with UAV imagery (red, green, red-edge, and near-infrared bands) across two sites and two consecutive years (2023 and 2024) in Shanxi Province, China. Various modeling approaches, including Random Forest, Gradient Boosting, and regularized regressions (e.g., Ridge and Lasso), were evaluated for cross-regional and cross-year extrapolation. The results showed that single-site modeling achieved coefficients of determination (R²) of up to 0.95, with mean relative errors of 10–15% in independent validations. When models were transferred between sites, R² generally remained between 0.50 and 0.70, although SPAD estimates exhibited larger deviations under high-nitrogen conditions. Even under severe drought in 2024, cross-year predictions still attained R² values near 0.60. Among these methods, tree-based models demonstrated a strong capability for capturing nonlinear canopy trait dynamics, whereas regularized regressions offered simplicity and interpretability. Incorporating multi-site and multi-year data further enhanced model robustness, increasing R² above 0.80 and markedly reducing average prediction errors. These findings demonstrate that rigorous radiometric calibration and appropriate vegetation index selection enable reliable UAV-based phenotyping for foxtail millet in diverse environments and time frames. Thus, the proposed approach provides strong technical support for precision management and cultivar selection in semi-arid foxtail millet production systems. Full article

Young forest stands from natural regeneration are characterized by high competitive pressure and dynamic changes over time, especially in the initial growth stages. Despite their increasing area in the temperate zone, they have received significantly less scientific attention than old forest stands. Therefore, our research was conducted on young, over-dense European beech (Fagus sylvatica L.) forest originating from natural regeneration, grown in central Slovakia, Western Carpathians. Repeated measurements of tree height and stem diameter measured on the base within a beech stand revealed significant temporal changes in their relationship. Over 16 years, height increased more than stem diameter. Both Lorey’s height and mean diameter d₀ showed continuous growth, with Lorey’s height increasing 3.5-fold and mean diameter increasing 2.8-fold. The height-to-diameter ratio increased until stand age 15, then briefly declined before rising again. Stand density decreased over time, with the sharpest decline occurring between ages 15 and 16 (dropping from 843 to 599 trees per 100 m²). Mortality rates peaked at age 16, with an average annual rate of 9.4% over the entire observation period (2008–2023). Specific leaf area (SLA) was negatively related to tree size, and its value was smaller in 10- than in 20-year-old stands. The increase in SLA was driven by greater leaf area relative to leaf weight. Additionally, allometric relationships showed that branch and leaf contributions to aboveground biomass decreased with tree size within the stand but were greater in the older stand than in the younger growth stage. Estimated aboveground biomass was 667 ± 175 kg per 100 m² in the 10-year-old stand and 1574 ± 382 kg per 100 m² in the 20-year-old stand, with stems contributing the majority of biomass. Leaf Area Index (LAI) remained similar across both stand ages, while the Leaf Area Ratio (LAR) was nearly twice as high in the younger stand. These findings highlight dynamic shifts in beech stand structure, biomass allocation, and leaf traits over time, reflecting growth patterns and competition effects. The outputs indicate that competition in young forest stands is a dominant force in tree mortality. Understanding key interactions in young stands is crucial for sustainable forest management, as these interactions influence long-term stand stability and ecosystem functions. Full article

Agricultural production faces critical challenges in arid regions due to global climate change and water scarcity. Exploring optimal water and nitrogen irrigation combinations is essential to enhancing water use efficiency and crop yields. This study employs the logistic growth model to analyze the impact of varying water and nitrogen treatments on summer maize growth in southern Xinjiang. The goal is to identify an optimal irrigation strategy to enhance maize productivity, optimize water use, and ensure precise crop management. Field experiments included three irrigation levels (W1: 80% ETc, W2: 100% ETc, W3: 120% ETc) and four nitrogen rates (N0: 0 kg/ha, N1: 168 kg/ha, N2: 306.5 kg/ha, N3: 444.5 kg/ha). A logistic growth model, incorporating effective accumulated temperature, plant height, and leaf area index (LAI), quantified growth dynamics. Maximum (v_max) and average (v_avg) growth rates were derived, followed by regression analysis to estimate theoretical maxima and corresponding irrigation–nitrogen requirements. The logistic model provided a good approximation of maize growth dynamics. Maximum growth rates for plant height occurred at 106% ETc and 340 kg/hm² nitrogen, with an effective accumulated temperature of 319.30 °C. LAI growth rates peaked at 105% ETc and 334 kg/hm² nitrogen, with 239.75 °C during rapid growth. Optimal water–nitrogen combinations were identified, highlighting a threshold beyond which excess application becomes counterproductive. The W2N2 combination was identified as optimal, achieving a water use efficiency of 3.04 kg/m³. These findings offer practical guidance for optimizing agricultural practices in arid regions. Full article

In order to explore the effects of different irrigation methods on the physiological characteristics of mulch-free cotton in southern Xinjiang, the following experiments were carried out: (1) Different irrigation amount test: 300, 375, 450, 525, and 600 mm (represented by W1, W2, W3, W4, and W5) and a control (450 mm for film-covered cotton, represented by WCK) were set. (2) Drip irrigation frequency test: drip irrigation 12, 10, 8, and 6 times during the growth period (expressed by P12, P10, P8, and P6). Soil water dynamics, root distribution dynamics, chlorophyll fluorescence, leaf area index (LAI), SPAD (chlorophyll density), stress enzyme activities, and MDA (malondialdehyde) content were observed. The results showed that the average maximum change range of soil water content in the cotton field without film mulching was ±17.7%, which was 1.35 times higher than that in the cotton field with film mulching. Compared with cotton with film mulching, the root distribution characteristics of mulch-free cotton in the surface soil (0–20 cm) and the periphery (30 cm from the main root) decreased by 33.55–74.48% and 14.07–102.18%, respectively, while the root distribution characteristics in the deep layer (40–60 cm) increased by 49.62–242.67%, its average leaf green fluorescence parameters decreased by 9.03–50.44%, the activities of protective enzymes (SOD: superoxide dismutase, POD: peroxidase) decreased by 3.36–3.58%, the SPAD value decreased by 5.55%, and the MDA content increased by 3.17%, indicating that mulch-free cotton reduced the physiological function of cotton leaves, and the yield decreased by 42.07%. In the mulch-free treatments, the average root growth indexes were W2 > W3 > W4 > W5 > W1 and P12 > P10 > P8 > P6, and there was little difference between W2 and W3 and P12 and P10. With the increase in irrigation water and irrigation frequency, the initial fluorescence (F₀) of leaves in each period of mulch-free cotton showed a downward trend, and the maximum fluorescence (F_m), variable fluorescence (F_V), maximum photochemical efficiency (F_V/F_m), potential photochemical activity of PS II (F_V/F₀), electron transfer of PS II (F_m/F₀), and photosynthetic performance index (PIABS) showed an upward trend. In all water treatments, W3 and P12 had the highest SPAD value, protective enzyme activity, and the lowest MDA content, which was significantly different from other treatments except W4 and P10. The yield order of different treatments was W3 > W4 > W5 > W2 > W1, and the difference between W3 and W4 was not significant, but significant with W2 and W1. The irrigation frequency test was P12 > P10 > P8 > P6, and there was no significant difference between P12 and P10. We find that in the mulch-free treatment, all indicators of W3, W2, P12, and P10 were relatively high. It can be concluded that no mulching has a certain impact on cotton root distribution and leaf physiological function. When the irrigation amount is 450–525 mm and irrigation times is 10–12, it is beneficial for promoting root growth and plays a role in leaf physiological function, and the water use efficiency (WUE) is high, which can provide reference for the scientific water management of mulch-free cotton in production practice. Full article

PM_2.5 pollution has intensified with rapid urbanization and industrialization, raising concerns about its health and environmental impacts. Both meteorological factors and urban forests play crucial roles in influencing PM_2.5 concentrations. However, limited attention has been given to the direct impact of canopy structure on PM_2.5 levels at a larger scale. This study analyzes the temporal variation of PM_2.5, including seasonal and diurnal patterns, across different functional zones (park, traffic, and residential zones) in a subtropical region. It also investigates the seasonal responses of PM_2.5 to meteorological factors (temperature, humidity, and precipitation) and canopy structure characteristics, including canopy diameter (CD), canopy thickness (CT), canopy area (CA), canopy volume (CV), canopy height ratio (CH), leaf area index (LAI), and tree canopy cover (CO). The results show that among different functional zones, PM_2.5 concentrations were the highest in park zones, followed by traffic zones. Seasonal variations in PM_2.5 concentrations were the highest in winter (84.00 ± 45.97 μg/m³), with greater fluctuations, and the lowest in summer (36.85 ± 17.63 μg/m³ µg/m³), with smaller fluctuations. Diurnal variation followed an “N”-shaped curve in spring, summer, and autumn, while a “W”-shaped curve was observed in winter. Correlation analysis indicated significant negative correlations between PM_2.5 and humidity, temperature, and rainfall, while CD, CA, and CV showed positive correlations with PM_2.5. Notably, PM_2.5 exhibited greater sensitivity to changes in canopy structure in winter, followed by autumn. Despite these findings, the influence of canopy structure on PM_2.5 concentrations was considerably smaller compared to meteorological factors. In particular, every 1 m² increase in canopy area could raise PM_2.5 levels by 0.864 μg/m³, whereas an average increase of 1 mm in rainfall could raise PM_2.5 by 13.665 μg/m³. These findings provide valuable guidance for implementing protective measures, improving air quality, optimizing urban greening strategies, and enhancing public health outcomes. Full article