In this study, to comprehensively investigate the impact of garden plants on air quality, we measured the leaves of 18 common garden plants in three different areas, namely, Suzhou industrial parks (clean air area (CAA)), Xiangcheng district parks (lightly polluted area (LPA)), and Huqiu district parks (highly polluted area (HPA)). We also measured the leaf functional traits of different life-types of plants. To explore the trade-off strategies of the leaf traits of common garden plants in response to air pollution and to assess the adaptive capacity of different life types of plants to air pollution. The results show that plants in the polluted area had higher leaf dry matter content (LDMC) and leaf nitrogen content per unit mass (N_mass), and a lower specific leaf area (SLA), maximum net photosynthetic rate per unit area (A_area), transpiration rate (Tr), stomatal conductance (Gs), and chlorophyll value (SPAD). Pearson correlation analysis showed that SLA was significantly positively correlated with N_mass, Tr, photosynthetic use efficiency (PNUE), and SPAD, and significantly negatively correlated with LDMC, while A_area was significantly positively correlated with chlorophyll value. Redundancy analysis revealed that the correlation between each leaf functional trait and atmospheric pollution factors was as follows: LDMC > N_mass > SLA > LA > A_area > Tr > PNUE > SPAD. The results suggest that different plant types have varying levels of adaptability to environmental conditions. Trees were found to be the most adaptable, followed by shrubs, herbs, and lianas. Additionally, under the stress of air pollution, herbs and lianas exhibited characteristics of “fast investment-return” on the leaf economic spectrum, meaning they were able to quickly allocate resources to maximize their return. However, trees and shrubs displayed traits of “slow investment-return”, indicating a more conservative approach to resource allocation. These results provide valuable insights into the leaf trade-off strategies of plants in Suzhou Park under air pollution stress and can guide the selection of suitable plant species in similar environments.

The Cross-Regional Cultural Heritage (CRCH) is a continuous, large-spanned, and comprehensive system with a large number and diversity of components. To support the integrated conservation of CRCH, an analytical approach with simple structure and easy operation is needed. Therefore, this paper aims to conduct risk maps to interpret, understand, and manage the slow degradation of built heritage in the daily environment. The hazard factors of slow degradation in the environment are extracted and translated into meteorological data for mapping with the Geographic Information Systems (GIS). A risk map to evaluate the risk level of the heritage is obtained by overlapping the vulnerability and hazard factors. With the case study of the Chinese Eastern Railway (CER), the risk maps revealed the risk condition and spatial distribution pattern of CRCH, based on which a network-connected working platform is developed, and suggestions for solutions are proposed. In terms of the operational plan for conservation works, the graded architectural risk sections are delineated. The priority and extent of intervention are proposed according to the degree of urgency and preventive conservation measures can be implemented in advance by using weather forecast data. In terms of social management strategy, through a level-by-level penetration education model, the social awareness of heritage conservation can be cultivated, the technical methods for restoration and maintenance will be trained, and the operating system for monitoring and inspection can be established. Relevant stakeholders, such as government officials, experts, users, and visitors can participate together in the preventive conservation process of the heritage.