With the implementation of national policies like the 'Belt and Road Initiative', major infrastructure projects are rapidly advancing into the western plateau regions. These regions, nestled deep inland, exhibit typical plateau environmental characteristics, including frequent temperature fluctuations, low precipitation, strong winds with high velocities, low atmospheric pressure, limited oxygen levels, and widespread saline soil/salt lakes, which pose serious challenges to the long-term serviceability of major infrastructure. Determining the service environment is a prerequisite for conducting durability analysis and design of concrete structures. However, compared to the increasingly mature research on extreme loads such as earthquakes and typhoons, research on the long-term environmental actions suffered by concrete is still in its infancy. Most existing studies analyzed environmental factors from the 'environmental' rather than the 'engineering' perspective, lacking suitable environmental analysis tools for actual engineering applications. Furthermore, although extensive experimental research has been conducted on concrete durability, the inconsistency between the actual service environment and laboratory accelerated tests can lead to significant discrepancies in concrete deterioration rates and damage mechanisms. Using performance indicators obtained under laboratory accelerated conditions to design the expected service life of concrete in actual engineering projects will result in significant errors. The absence of a long-term environmental action model for concrete is a key challenge to the quantitative design of concrete durability.

Based on 21 years of meteorological data from 687 weather stations and chemical composition data from 396 salt lakes, using ordinary kriging (OK) interpolation method, with the geographic coordinate system as World Geodetic System 1984 and the projected coordinate system as Albers, regional distribution maps of key environmental parameters were created. Python's distfit library was used to fit the best probability density function (PDF) of environmental data. The Kolmogorov-Smirnov (K-S) test, a non-parametric statistical method, was employed to determine if the sample data sets conformed to the theoretical PDF. Environmental data were categorized using the unsupervised learning method K-means clustering. An intelligent and efficient WeChat mini program was developed to provide technical support for durability analysis and longevity design of engineering structures under special environmental action in the Chinese plateau regions.

The action levels of temperature differences in China were divided into six zones, closely related to the terrain. The temperature differences in the western plateau regions were significantly greater than that in the eastern plain regions. In cities such as Xizang, Xinjiang, Sichuan, Qinghai, and Heilongjiang, the maximum cumulative monthly average temperature difference reached 23 ℃. The cumulative monthly average relative humidity gradually decreased from the southeast coast to the northwest inland, with most Chinese plateau regions having an average relative humidity of less than 40%. The frequency of freeze-thaw cycles differed significantly between the Chinese plateau and plain regions, with Xizang experiencing the highest annual average of up to 188 times. Taking freeze-thaw (F-T) temperature amplitude, the lowest freezing temperature and the annual freeze-thaw cycles as indicators, the F-T action levels in China were divided into six zones, nonfrozen zone, slight F-T zone, light F-T zone, moderate F-T zone, severe F-T zone, and extreme F-T zone, each showing significant differences in annual average freeze-thaw cycles. The variability of annual average atmospheric pressure was relatively small across regions, exhibiting a step-like pattern similar to China's terrain.

For regions with large temperature differences and strong aridity, the month with the most severe environmental action was considered the primary month, and representative values such as frequent occurrence, moderate occurrence, and rare occurrence were provided. For high-frequency freeze-thaw cycles, basic environmental parameters and key design parameters for different regions were provided. Given the relatively small variability of annual average atmospheric pressure across different regions, the mean value of each region was recommended. For hypersaline attack, representative values such as the 95th percentile, third quartile, median, and mean were provided, and it was suggested to set concentration gradients according to representative values.

Conclusions The main conclusions of this paper are summarized as follows. Special service environments affecting concrete durability, such as large temperature fluctuation, strong dryness, high-frequency freeze-thaw cycles, and low atmospheric pressure, have the most severe impact in the Qinghai-Xizang Plateau. There are significant differences in environmental actions across different regions of China, and even within the same province, it is not appropriate to summarize different cities in one group. Indicators characterizing the action level of freeze-thaw cycles include freeze-thaw temperature amplitude, lowest freezing temperature, and annual freeze-thaw cycles. There are distinct differences in freeze-thaw action levels in different regions of China, especially evident in the clear gradient differences in annual average freeze-thaw cycles. The formation and distribution of saline-alkali soils and salt lakes are closely related. A comprehensive analysis of aggressive ion concentrations in different salt lakes reveals strong variability, suggesting the setting of concentration gradients based on statistical results. A rapid query mini program for special environments has been developed on the WeChat platform, integrating meteorological data query, freeze-thaw action analysis, and environmental parameter zonation, offering technical support for improving the efficiency of obtaining real-time information on the special environment near the project location.