Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring
Abstract
:1. Introduction
2. Airborne Particulate Matter Sensing
2.1. Traditional Methods of Measuring Airborne PM and Their Limitations and Challenges
2.1.1. Gravimetric Methods
2.1.2. Continuous Monitoring Methods
2.1.3. Optical Methods
2.1.4. Electrical Methods
2.1.5. Microscopy Methods
2.1.6. Chemical Analysis Methods
2.1.7. Remote Sensing Methods
2.1.8. Emerging Low-Cost Sensor Technologies
2.1.9. Hybrid and Multi-Sensor Systems
2.2. Significance of Accurate PM Sensing for Air Quality Assessment
3. Deep Learning in Environmental Sensing
3.1. Data Processing and Feature Extraction
3.2. Temporal and Sequential Analysis
3.3. Advanced Learning Paradigms
3.4. Spatial and Relational Analysis
Technique | Application | Key Parameters | Results | Reference |
---|---|---|---|---|
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network | Predicting PM2.5 concentrations 72 h across China | Directed graph with cities as nodes and edges based on distance, terrain, and wind. Node features: meteorological data, time of day, day of the week. Edge features: wind speed, direction, distance. | PM2.5-GNN outperforms baseline models like MLP, LSTM, GRU, GC-LSTM, and nodes FC-GRU on various metrics, including test loss, RMSE, MAE, CSI, and POD, across multiple datasets. Removing PBL height reduced performance. | [139] |
GARNN (Graph Attention Recurrent Neural Network) | Predicting PM2.5 in 308 Chinese cities | Directed graph with nodes (cities) and edges (PM2.5 transport pathways). Node features: 7 pollutants, 8 meteorological variables. Edge features: city distance, connection angle. | GARNN outperforms baseline models such as MLP, LSTM, GRU, GC-LSTM, nodes FC-GRU, and PM2.5-GNN in terms of RMSE, MAE, CSI, POD, and FAR, showing that attention mechanisms improve PM2.5 prediction. | [140] |
DST_GNN (Dynamic Spatiotemporal Graph Neural Network) | Predicting PM2.5 48 h ahead in Jinan and Yangtze River Delta | Dynamic graph with nodes (stations) and edges from HYSPLIT model. Features: meteorology, time, terrain. | Outperformed GC-LSTM, Hybrid Method, STGNN, achieving lower MAE and RMSE. Showed importance of dynamic spatial relationships. | [141] |
Hybrid Deep Learning Framework (LSTM, Bi-LSTM) with Spatial Autocorrelation | Predicting AQI 7 days ahead during COVID-19 lockdowns (Wuhan, Shanghai) | SAC variables, KNN-MI for feature selection, QBSO for optimization. Lockdown effects incorporated | Bi-LSTM (Wuhan) and LSTM (Shanghai) improved RMSE, MAE, and MAPE by 47.2–70.4%. Highlights spatial factors and external events in AQI forecasting. | [142] |
Spatiotemporal Graph Convolutional Recurrent Neural Network (Spatiotemporal GCRNN) | Predicting PM2.5 and PM10 (1–72 h ahead) in Seoul, South Korea | Graph with nodes (stations or 32 × 32 grid cells), edges weighted by distance. Features: air pollution, meteorology, traffic, China air pollution data. | Outperforms ConvLSTM in RMSE for short-term (1–12 h) and long-term (up to 72 h) forecasting, with a 55× smaller model size. Also better than a hybrid GCN-LSTM model. | [143] |
DSGNN (Dual-View Supergrid-Aware Graph Neural Network) | Estimating PM2.5 in areas without stations (YRD-AOD and BTH-AOD datasets) | Supergrid-based modeling using AOD and meteorology. Graph-based correlations, 6 h historical window. | Improved MAE by 19.64%, highlighting the need for dual-view spatial relationships. | [144] |
3.5. Ensemble and Multi-Source Integration
Technique | Application | Architecture | Key Parameters | Performance Metrics | Reference |
---|---|---|---|---|---|
Hybrid | PM2.5 forecasting | CNN-LSTM | 3 CNN layers, 2 LSTM layers | RMSE: 7.5 μg/m3, R2: 0.92 | [147] |
Hybrid | PM2.5 prediction | OR-ELM-AR model | Online recurrent extreme learning machine, autoregressive model | R2: 0.85, RMSE: 12.3 μg/m3 | [148] |
DL | PM2.5 prediction | CNN-LSTM-MLP hybrid model | Weighted LSTM extended model (WLSTME) | RMSE: 8.216 μg/m3, R2: 0.91 | [149] |
Hybrid | PM2.5 prediction | Graph Convolutional Network with LSTM | 3 graph conv layers, 1 LSTM layer, 64 hidden units | MAE: 8.7 μg/m3, R2: 0.86 | [104] |
Hybrid Ensemble | PM2.5 and PM10 forecasting | CNN with spatial-temporal attention and residual learning | Multi-step ahead forecasting system | R2 > 0.90 for both PM2.5 and PM10 | [150] |
Deep Fusion | Air quality prediction | Deep distributed fusion network | Spatial transformation component, neural distributed architecture | Improved accuracy over 10 baseline methods | [145] |
Hybrid | Air pollution prediction | Spatiotemporal convolutional LSTM | 2 LSTM layers, 128 hidden units, spatiotemporal conv layers | MAPE: 11.93%, R2: 0.86 | [124] |
Siamese Network | Land cover change detection | Siamese CNN | Shared weights across twin networks | Overall Accuracy: 92% | [146] |
4. Integration of DL with Airborne PM Sensing
DL Approach | Integration Method | Key Findings | Accuracy Improvement | Reference |
---|---|---|---|---|
Customized neural network + Random Forest | Combined image analysis and machine learning for PM2.5 estimation | Improved PM2.5 prediction using traffic camera images | RMSE of 0.76 μg/m3, R2 of 0.98 | [152] |
Hybrid Graph Neural Network (GNN_LSTM) | Captured spatiotemporal correlations among monitoring sites | Enhanced 72 h PM2.5 forecasting; better pollutant transport representation | R2 increased from 0.6 to 0.79; highest accuracy for long-term prediction | [153] |
EntityDenseNet | Developed interpretable DL model for satellite-based PM2.5 monitoring | Enhanced real-time PM2.5 estimation from satellite data; revealed pollution patterns | Hourly RMSE: 26.85 μg/m3, daily RMSE: 25.3 μg/m3, monthly RMSE: 15.34 μg/m3; outperformed BPNN, XGBoost, LightGBM, and RF in correlation (R2) and RMSE | [151] |
3D-CNN + GRU with Attention Mechanism | Integrated 3D-CNN for spatial and GRU for temporal features with attention mechanism | Superior PM2.5 forecasting; effective multi-horizon predictions | MAPE of 15.6%, MASE of 21.57; superior to 1D CNN, 3D CNN, and CNN-LSTM models | [154] |
Deep LSTM | LSTM-based system for daily PM10 and PM2.5 predictions; used ground-based observations over 2.3 years for training | LSTM superior to CMAQ-based predictions; suitable for operational forecasts | IOA improved from 0.36–0.78 (CMAQ) to 0.62–0.79 (LSTM); LSTM accuracies were 1.01–1.72 times higher than CMAQ-based predictions | [155] |
Deep Neural Network (DNN) | DNN for 3-day forecasting of PM2.5 concentrations using observation and forecast data | Outperformed CMAQ in forecasting; reduced overprediction of high concentrations. | RMSE reduced by 4.1, 2.2, and 3.0 μg/m3 for 3 consecutive days compared to CMAQ; reduced false-alarm rate | [156] |
CNN-LSTM | Combined CNN for feature extraction and LSTM for PM2.5 forecasting | APNet showed highest forecasting accuracy among methods | Lowest MAE and RMSE compared to SVM, RD, DT, MLP, CNN, and LSTM models | [147] |
Spatial-Temporal Attention Residual CNN (STA-ResCNN) | Combined spatial-temporal attention mechanism with residual CNN for multi-step PM2.5 and PM10 forecasting | Improved multi-step forecasting of PM2.5 and PM10 concentrations; outperformed baseline models in accuracy and stability | RMSE reduced by 5.6–15.2% for PM2.5, 6.8–16.9% for PM10 in 1–4 h predictions. | [150] |
CNN-LSTM | Combined CNN and LSTM for PM2.5 prediction | Improved PM2.5 prediction; outperformed BP, RNN, CNN, and LSTM models | Best RMSE (14.3) and correlation (0.92) among compared models | [157] |
Deep Q-Network (DQN) combined with LSTM | DQN-based UAV pollution tracking for air pollution monitoring | Outperformed spiral search method in finding polluted areas | Reduced total search time by 28%, flying distance and sensing time reduced | [128] |
Transformer and CNN-LSTM-attention | Compared Transformer and CNN-LSTM-Attention for hourly PM2.5 prediction | Transformer outperformed CNN-LSTM-Attention in capturing short- and long-term trends | Improved EVS by 12%, MAE by 9%, MSE by 6%, and R2 by 30% compared to CNN-LSTM-Attention; Transformer achieved R2 of 94.4% vs. 83.6% | [158] |
5. Data Acquisition and Use of Synthetic Data for Air Quality Monitoring
5.1. Challenges in Acquiring and Preprocessing Data from Airborne PM Sensors
5.2. Techniques for Handling Noisy and Large Datasets
5.2.1. Noise Reduction Algorithms
5.2.2. Dimensionality Reduction
5.2.3. Data Augmentation
5.2.4. Transfer Learning
5.2.5. Ensemble Methods
5.2.6. Adaptive Sampling Techniques
5.2.7. Distributed Computing
5.3. Importance of Data Quality for Effective DL Models
5.3.1. Model Performance
5.3.2. Generalizability
5.3.3. Interpretability
5.3.4. Robustness to Outliers
5.3.5. Temporal Consistency
5.3.6. Spatial Representativeness and Multi-Source Data Integration
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Technique | Application | Key Parameters | Results | Reference |
---|---|---|---|---|
DBN | Estimating ground-level PM2.5 from satellite TOA reflectance | TOA reflectance | Achieved finer resolution and larger spatial coverage than AOD-derived PM2.5, with state-of-the-art performance over Wuhan. | [103] |
GC-LSTM, LSTM | Hourly PM2.5 forecasting | Air quality, meteorological, spatial, and temporal data | GC-LSTM (R2 = 0.72) outperformed MLR (R2 = 0.13), highlighting spatial-temporal dependencies. Accurately predicted over-standard PM2.5 levels. | [104] |
CNN | Multi-hour, multi-site AQI forecasting in Beijing | Hourly pollutant and meteorological data from 12 stations | CNN-LSTM had the best RMSE (35.57) and IA (0.95). CNN excelled in spatial clustering-based forecasting. | [105] |
Convolutional Denoising Autoencoder | Imputing missing air quality data | Hourly pollutant data from multiple stations | Achieved R2 ≥ 0.6, even with complete data loss at target stations. Improved RMSE up to 65% over univariate and 20–40% over multivariate methods. | [106] |
DRNN | PM2.5 prediction in Japan | Hourly PM2.5, meteorological data from target city and nearby capitals (48 h past data) | Outperformed autoencoders and VENUS (F-measure: 0.615 vs. 0.567). Elastic net removed ~2.1 sensors per city without accuracy loss. | [107] |
CNNs + Transfer Learning, CGANs | Air pollution prediction from camera images | Images, weather data, PM2.5-labeled AQI categories | Custom inception model with CGAN achieved 0.896 training and 0.763 test accuracy (binary classification). | [108] |
Conditional U-Net | Emulating CMAQ simulations for PM2.5 prediction in South Korea | Precursor emissions, boundary conditions | Achieved high accuracy (MAE = 0.221 µg/m3, NMAE = 1.762%, R2 = 0.996) with 1 ms computation time vs. 24 h for CMAQ. | [109] |
LSTM | Air pollutant prediction | Hourly SO2, PM2.5, PM10, NO2, CO, O3, AQI, and meteorological data | Predicted PM2.5 (1 h ahead) with RMSE = 1.11 and MAE = 0.66. | [110] |
Technique | Application | Key Parameters | Results | Reference |
---|---|---|---|---|
Self-tuning Spatiotemporal Neural Network (ST2NN) | AQI prediction | Hourly pollutant and meteorological data from 12 stations in Beijing (2013–2017). | Outperformed models, improved prediction accuracy by 0.51–10.18% (measured using R2) with R2: 0.985, MSE: 2.357, RMSE: 1.467, MAE: 1.132. | [115] |
Gated Recurrent Unit (GRU) | Hourly PM2.5 prediction in Shenyang | Hourly PM2.5, pollutants, meteorology, and factory emissions data (winter 2015–2017). | Best model with MAE: 4.61, MSE: 15.78, MAPE: 6.29%. Dynamic time panel (T = 3) improved accuracy. | [116] |
Graph Long Short-Term Memory with Multi-Head Attention (GLSTMMA) | Hourly air pollutant prediction | Hourly pollutants, meteorology, and POI data from 8 stations in Qinghai (2019–2021). | Outperformed models for 3 to 24 h forecasts. For PM2.5 (3 h), MAE: 4.48, RMSE: 7.51, MAPE: 34.44%. | [117] |
Hybrid CNN-LSTM | Daily PM2.5 prediction in Beijing | Hourly PM2.5, meteorology, and wind data from US Embassy & Beijing Airport (43,800 records). | Best model among CNN-LSTM, LSTM, and univariate models. MAE: 13.97, RMSE: 17.93, faster training (50–60 s/epoch). | [118] |
LSTM-Attention, n-Step Attention-Based Air Quality Predictor (n-step AAQP) | Hourly PM2.5 prediction in Beijing | Hourly pollutants, meteorology (April 2017–Feb 2018 training, March 2018 testing). | 12-step AAQP (LSTM) at Olympic Center: MAE: 14.96, R2: 0.85. GRU at Dongsi: MAE: 26.49, R2: 0.63. | [119] |
VMD-GAT-BiLSTM | Hourly PM2.5 prediction in Beijing | Hourly air quality and meteorology data (PM2.5, PM10, CO, O3, 0 and NO2, temperature, dew point, air pressure, precipitation, wind speed) from 30 stations (2017–2020). | Outperformed models for 1–48 h forecasts. VMD improved accuracy vs. EMD or no decomposition. | [120] |
Technique | Application | Key Parameters | Results | Reference |
---|---|---|---|---|
Transfer Learning | Land cover classification from satellite imagery | ResNet50 pre-trained on ImageNet; fine-tuned last 2 layers | Accuracy improved by 15% compared to training from scratch | [125] |
Domain Adaptation | Cross-city air quality prediction | DANN architecture; gradient reversal layer | Reduced prediction error by 20% in target cities | [104] |
Deep Reinforcement Learning | Optimal placement of air quality sensors | DQN; State: pollution levels, Action: sensor locations | Improved pollution detection coverage and reduced deployment costs | [128] |
GAN | Synthetic PM2.5 image generation | DCGAN; Generator: 4 transpose conv layers; Discriminator: 4 conv layers | Generated 10,000 diverse PM2.5 images for data augmentation | [129] |
Transfer Learning | Land cover classification from satellite imagery | ResNet50 pre-trained on ImageNet; fine-tuned last layers | Improved classification accuracy | [130] |
Domain Adaptation | Cross-sensor PM detection | CycleGAN for style transfer; UNet for segmentation | Achieved 90% accuracy across different sensor types | [131] |
Deep Reinforcement Learning | Adaptive sampling in mobile air quality sensing | DDPG; State: current AQI, location; Action: next sampling location | Reduced sensing time by 40% while maintaining accuracy | [132] |
GAN | Anomaly detection in air quality data | WGAN-GP; 5-layer generator and critic networks | F1-score of 0.92 in detecting unusual pollution events | [133] |
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Chauhan, B.V.S.; Verma, S.; Rahman, B.M.A.; Wyche, K.P. Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring. Atmosphere 2025, 16, 359. https://doi.org/10.3390/atmos16040359
Chauhan BVS, Verma S, Rahman BMA, Wyche KP. Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring. Atmosphere. 2025; 16(4):359. https://doi.org/10.3390/atmos16040359
Chicago/Turabian StyleChauhan, Balendra V. S., Sneha Verma, B. M. Azizur Rahman, and Kevin P. Wyche. 2025. "Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring" Atmosphere 16, no. 4: 359. https://doi.org/10.3390/atmos16040359
APA StyleChauhan, B. V. S., Verma, S., Rahman, B. M. A., & Wyche, K. P. (2025). Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring. Atmosphere, 16(4), 359. https://doi.org/10.3390/atmos16040359