Treated drinking water is delivered to customers through drinking water distribution systems (DWDSs). Although studies have focused on exploring the microbial ecology of DWDSs, knowledge about the effects of different water treatments on the bacterial community of biofilm and loose deposits in DWDS is limited. This study assessed the effects of additional treatments on the bacterial communities developed in 10 months’ old pilot DWDSs. The results showed a similar bacterial community in the pipe-wall biofilm, which was dominated by Novosphingobium spp. (20–82 %) and Sphingomonas spp. (11–53 %), regardless of the treatment applied. The bacterial communities that were retained in the distribution systems (including pipe-wall biofilm and loose deposits) were similar to the particle-associated bacteria (PAB) in the corresponding supply water. The additional treatments showed clear effects of the removal and/or introduction of particles. The genera Aeromonas spp., Clostridium spp., Legionella spp., and Pseudomonas spp., which contain opportunistic pathogenic species, were only detected among the PAB in ion exchange system. Our study demonstrated that the biofilm community is consistent across treatments, and the contribution from bacteria in loose deposits is important but can be controlled by removing particles. These findings offer more insight into the origin and development of microbial ecology in DWDSs and suggest paths for further research on the possibility of managing the microbial ecology in distribution systems.

Evaluating the sustainable development level and obstacle factors of small towns is an important guarantee for implementing China's new-type urbanization and rural revitalization strategies, and is also a key path to promoting the United Nations Sustainable Development Goal 11 (SDG11). Traditional evaluation methods (such as Analytic Hierarchy Process, AHP, and Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) mainly calculate the comprehensive score of each indicator through weighting. These methods have limitations in handling multidimensional data and system nonlinearity, and they cannot fully reveal the complex relationships and interactions within the sustainability systems of small towns. In contrast, the evaluation model combining Principal Component Analysis (PCA) and Catastrophe Progression Method (CPM) used in this study can better handle multidimensional data and system nonlinear relationships, reducing subjectivity in evaluation and improving the accuracy and reliability of the assessment results. The specific research process is as follows: First, based on the United Nations SDG11 framework, using multi-source big data, a theoretical framework and evaluation index system for the sustainable development of small towns suitable for the Chinese context were established. The impact of county-level factors on the sustainable development of small towns was also considered, and an entropy weight-grey correlation model was used to measure these impacts, resulting in a town-level dataset incorporating county-level influences. Secondly, the sustainability levels of 782 top small towns in China were evaluated using the comprehensive evaluation model based on PCA-CPM Model. Finally, an improved diagnostic model was used to identify obstacles influencing the sustainable development of small towns. The main findings include: 52.69% of the small towns have a sustainable development score exceeding 0.7255, indicating that the overall performance of small towns is at a medium to high development level. The development of small towns exhibits significant differences across regions and types, which are closely linked to county-level effects. Economic and social factors are the main obstacles to the sustainable development of small towns, and the impact of these obstacles intensifies from the eastern to the central, western, and northeastern regions. This study provides valuable insights for policymakers and scholars, promoting a deeper understanding of the sustainable development of small towns.