This manuscript presents a large-scale cross-sectional ecological study of neighborhood-level PM2.5 exposure across 53,041 neighborhoods in 340 Latin American cities, using the SALURBAL dataset. By linking satellite-derived PM2.5 estimates with socioeconomic and urban characteristics, the authors identify substantial between- and within-city disparities in exposure, with all residents living above the 2021 WHO guideline. The study makes a valuable contribution to the literature on environmental health and inequality in a region where monitoring data are limited. I have some comments as follows:
1. Incorrect Citation/Description: In Section 2.3.2, the authors cite Shen et al. as the source of the PM2.5 dataset. However, the referenced paper describes a deep learning–based approach, whereas the manuscript states that the dataset was generated using geographically weighted regression. This suggests either an incorrect citation or description of the dataset. The authors should clarify the correct source and ensure that the dataset description accurately reflects the methodology used.
2. Urban vs. Suburban Neighborhoods: Given the large population coverage, it is unclear whether all neighborhoods in the dataset can be accurately classified as “urban.” Have the authors considered distinguishing between urban and suburban (or peri-urban) neighborhoods, and analyzing them separately? Such a stratification could provide additional insights into potential differences in PM2.5 exposure patterns across neighborhood types.
Minor Comments
• Some tables and figures (e.g., violin plots in Figure 2) could be better explained in the captions for broader accessibility.
• A brief comparison with other global urban datasets (e.g., similar analyses in Asia or Africa) would situate the findings in a wider context.
Recommendation
Overall, this is a strong manuscript that addresses an important public health and environmental justice issue in Latin America. I recommend minor revisions to strengthen the discussion of limitations, interpretation, and policy implications.