## 5 Conclusions  
This paper presents a forecasting framework for predicting localized air quality indices. The framework utilized deep learning NN models and Recurrent Neural Network models to provide predictions using, as inputs, particle matter measurements from particle matter IoT devices and environmental conditions acquired by meteorological sensory measurements of temperature, humidity, wind speed, and wind direction. The framework differentiates between cloud-based and device-level predictions. This differentiation necessitates the application of different types of models to edge devices, given their limited computational capabilities and memory sizes. To support their framework, the authors implemented two different deep learning models that accept the same types of data inputs and provide similar AQI forecasting outputs as the framework proposes. Upon data partitioning and transformations on the different types of measurements used as inputs, the two implemented models are a neural network model handling multiple strands, called slideNN, and a variable-timestep length GRU model. Both models were investigated for edge device implementations across four distinct time steps and various forecasting output configurations. From the experimental results, the GRU models outperformed the slideNN models by at least 25-80% in less loss, following an increasing performance curve as the number of timesteps increased. Taking as input the better performance of the edge GRU models, the authors transformed their model implementation to implement a variable GRU model, where the number of cells per layer is substantially higher, followed by several NN layers that are posed automatically based on the number of GRU cells on the last layer. This cloud-based hybrid variable GRU-NN model was investigated in terms of loss over timesteps and cross-compared with the losses achieved by smaller edge computing GRU models. From the experimental results, the GRU-NN cloud model achieved its highest relative performance gain in the 128 \(\times\) 4 configuration, where it reduced the RMSE by approximately 34.1% compared to the corresponding edge GRU model (0.334 vs. 0.507). In this respect, all cloud-based GRU models outperformed their corresponding edge-computing counterparts with a mean value of 25.6%. The authors set, as a limitation, the fact that a bigger dataset may also contribute to further accuracy gains, significantly reducing RMSE losses. The authors believe this would favor cloud-computing models; nevertheless, they consider future work to be thoroughly examined. Furthermore, the authors propose a fine-grained examination of their framework as future work, utilizing particulate matter IoT devices and microclimate monitoring meteorological stations to form kilometer-level grids. Conceptualization, S.K. ; methodology, M.X.P. ; software, S.K. and M.X.P. ; validation, M.X.P. and S.K. ; formal analysis, S.K. and C.J.L. ; investigation, M.X.P. ; resources, M.X.P. ; data curation, M.X.P. ; writing--original draft preparation, M.X.P. ; writing--review and editing, C.P., C.J.L., and N.H.; visualization, M.X.P., C.J.L., and N.H.; supervision, S.K. ; project administration, C.J.L. and N.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study, owned by the Region of Epirus and operated by the Epirus Region and the Laboratory of Meteorology and Climatology of the University of Ioannina, are available upon request from [PERSON] and [PERSON]. The authors declare no conflicts of interest.