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. 2025 Feb 11;18(1):62.
doi: 10.1186/s13104-025-07099-1.

Ecosense: a revolution in urban air quality forecasting for smart cities

Kalyan Chatterjee  1 , Machakanti Navya Thara  2 , Mandadi Sriya Reddy  3 , N Selvamuthukumaran  2 , M Priyadharshini  4 , Tummala Abhinav Vardhan Reddy  2 , Somenath Chakraborty  5 , Saurav Mallik  6   7 , Mohd Asif Shah  8   9   10   11 , Aimin Li  12
Affiliations

Ecosense: a revolution in urban air quality forecasting for smart cities

Kalyan Chatterjee et al. BMC Res Notes. .

Abstract

The Smart City (SC) framework is popular due to its advancement in enhancing lives and public safety. However, these advancements lead to many challenges due to the dependency of Internet of Things (IoT) devices in terms of electronic waste and resource consumption. To address those challenges, the integration of a weather-smart grid (WSG) with SC becomes crucial to safeguard the environment and residents' well-being. Along with these concepts, this study proposes a novel approach, EcoSense: A Revolution in Urban Air Quality Forecasting for Smart Cities, which incorporates Bi-directional Stacked LSTM with a Weather-Smart Grid (BlaSt). BlaSt innovatively integrates several key components: (i) the model captures intricate temporal dependencies and trends in air quality data by incorporating historical air pollutant and meteorological data. (ii) integration of the WSG component enhances the model's capability to incorporate weather data, which is critical for accurate air quality forecasting. (iii) the model computes 12-hour predictions by designing 1-hour prediction models, enabling it to provide timely forecasts with high precision. BlaSt demonstrates significant improvements over existing models, with enhancements of 36%, 26%, 21%, 46%, 14%, 10%, and 6% in accuracy compared to SVR, MLP, RAQP, Vlachogianni, LSTM, BLSTM, and SLSTM models, respectively. It achieves a mean absolute error (MAE) of 0.10 and a mean squared error (MSE) of 0.08. Additionally, BlaSt reduces computational complexity by 25%, making it more efficient in processing large-scale air quality data. The experimental results demonstrate BlaSt's superior accuracy and efficiency, showcasing its potential to advance urban air quality forecasting in SCs.

Keywords: Air Pollutant Concentrations (APCs); Air quality; Internet of Things (IoT); Meteorological Factors (MFs); Smart City (SC); Weather Smart Grid (WSG).

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Conflict of interest statement

Declarations. Informed consent: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Model Architecture of BlaSt. Here, MinMaxScaler scales features to a 0–1 range, aiding LSTM neural networks by ensuring consistent input feature scaling for better convergence and performance. linear interpolation is used to smoothly estimate missing data, preserving time series integrity by considering trends in adjacent points. Also, the choice of 12 LSTM units in the model is based on a balance between complexity and performance. This specific number was determined through empirical experimentation, where we found that 12 units provided sufficient capacity to capture the temporal dependencies and patterns in the air quality data without leading to overfitting. Additionally, 12 units aligns with the 12 time step resolution we are working with, ensuring that the model effectively captures the necessary temporal dynamics for accurate predictions
Algorithm 1
Algorithm 1
Integration Process of the WSG.
Fig. 2
Fig. 2
ROC curve for (a) Experiment 1. (b) Experiment 2. (c) Experiment 3
Fig. 3
Fig. 3
PR curve for (a) Experiment 1. (b) Experiment 2. (c) Experiment 3
Fig. 4
Fig. 4
(a) MSE and (b) MAE of the Proposed BlaSt Model
Fig. 5
Fig. 5
SCs Future Air Quality Forecasting using the BlaSt Model in terms of the Future Concentration of PM2.5, CO, and NO2
See this image and copyright information in PMC

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