# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 3 Results
### The HPC Environment  
The CRS4 HPC is a cutting-edge, in-house high-performance computing environment with over 300 compute nodes and more than 9000 cores. This infrastructure is a cornerstone of the AERQ decision support system, satisfying the computational demands of the AERMOD air quality model and its applications. Due to its complex physics and the need for high spatial and temporal resolutions, AERMOD is computationally intensive, making efficient processing essential.  
The CRS4 HPC provides the required computational power to pre-process and post-process large geospatial datasets and simulate pollutant dispersion at fine scales. This allows us to deliver results with the accuracy, timeliness, and reliability necessary for effective air quality management.  
In the specific use case for the metropolitan area of Cagliari, simulating air quality for a single pollutant over the entire road network (over 1500 sources) and all active receptors (over 2500) would take over 36 h for a seasonal simulation on a modern mid-sized desktop machine. For a full year, accounting for all variables, this would extend to approximately 62 days. Calibrating the AERMOD model for the Cagliari domain required hundreds of these simulations, translating to several years on a single machine.  
Although AERMOD is typically serial, the CRS4 HPC cluster enabled an embarrassingly parallel strategy by running multiple simulations with varying parameters across different scenarios and pollutants. This approach reduced the computation time for a complete simulation involving five pollutants (NO\({}_{2}\), CO, SO\({}_{2}\), PM, and NMHC) to just 15 h using 40 concurrent processes, a 99% reduction compared to modern desktop systems (Table 2). This highlights how the HPC environment transforms computational feasibility and accelerates air quality decision making, enabling large-scale, high-resolution environmental modeling.  
An unconventional hybrid infrastructure of the SQL database and file system has been designed to meet the demands of our large-scale geospatial simulations. In a 5-year simulation scenario, the system manages 315 GB of imagery (43,200 GeoTTFF files) and 575 GB of structured data within the SQL database. The SQL component (PostgreSQL with PostGIS) handles metadata management and complex spatial queries, ensuring data integrity and efficient access to vector data. Meanwhile, the filesystem is optimized for storing and retrieving large raster datasets, providing high-throughput access to high-resolution geospatial imagery. This dual approach combines the structured, query-optimized environment of SQL with the flexibility and speed of the filesystem, delivering the scalability and performance required for diverse, intensive simulation workflows.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Sim** & **Input Data** & **N.** & **Execution** & **N. Images** & **Output Data** \\  & **Length** & **Size** & **Sim** & **Time** & **(Size)** & **Size** \\ \hline Eolo & 1 year & 24 GB & 120 & 33 h & 8640 (63 GB) & 115 GB \\ Seixi & 5 years & 120 GB & 600 & 15 h & 43,200 (315 GB) & 575 GB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenarios are run on the CRS4 HPC environment. Eolo and Seixi clusters are used in the test to run 1-year and 5-year scenarios for 5 different pollutants. The Eolo cluster consists of 32 compute nodes, each equipped with two Intel E5-2680 CPUs @ 2.8 GHz (10 cores), 128 GB of RAM, and a 56 G Infiniband network. The Seixi cluster is composed of 124 compute nodes, each with two Intel Xeon Gold 6226R CPUs @ 2.90 GHz (16 cores), 384 GB of RAM, and a 100 G Infiniband network. A 1.3 PB BeeGFS parallel file system over Infiniband and a 1.5 PB NFS file system are available.