In general, using datasets with different spatial resolutions is not necessarily an issue if the pollutant distributions and variability are studied separately. However, if your study aims to analyze interactions between these pollutants, or if you plan to conduct an integrated air quality assessment, having a consistent spatial resolution is essential for a fair comparison and to ensure the reliability of your results.

Considering the differences in spatial resolution of your data:

• PM2.5 datasets have higher resolution than NO2, Ozone, and SO2 datasets.
• CO2 data has the highest resolution of all.

It's important to consider the spatial variability of the air pollutants you're studying. For instance, PM2.5 and CO2 might have high spatial variability, while the other pollutants might not vary as much over the same area. In such cases, downscaling might lead to an overestimation of the variability of pollutants with originally lower-resolution data.

Generally, upscaling (or spatial aggregation) is preferred to downscaling, because downscaling requires making assumptions about the spatial distribution of data within each coarser pixel, which could introduce errors. In contrast, upscaling involves aggregating finer-resolution data to a coarser scale, which retains more of the original information.

To do this, you could use the maximum, minimum, mean, or median of the finer-resolution pixels that fall within a coarser pixel. The choice depends on the nature of the data and the research questions. You would probably want to aggregate the PM2.5 and CO2 data to match the 0.25 x 0.25 degrees resolution of the other datasets.

There are various software and tools available for spatial resampling, including:

• ArcGIS: A popular geographic information system software, has tools for resampling raster data.
• Python with libraries like Rasterio or GDAL: Both have powerful tools for spatial data processing, including resampling.
• R with the raster package: This package provides a number of functions for handling raster data, including resampling.

Resampling methods in these tools include nearest neighbor, bilinear interpolation, and cubic convolution for upscaling and majority, minimum, maximum, mean, and median for downscaling.

Remember to consider the nature of your data when choosing the method: for example, if your data is not continuous (like certain types of categorical data), some methods like bilinear interpolation would not be appropriate.

In conclusion, whether you should adjust your spatial and temporal resolution depends on the specific requirements of your study. The most important consideration is to ensure that your methods are appropriate for your data and research questions. I hope this helps you.