The Nairobi Metropolitan Area (Figure 1) spans approximately 32,000 km² and encompasses the counties of Nairobi, Kiambu, Machakos, Kajiado, and Murang’a. Our Area of study was bound by the following coordinates: West Longitude = 35˚57'26.8216''E; North Latitude = 0˚22'46.1686''S; East Longitude = 37˚46'18.7927''E; South Latitude = 2˚12'20.7384''S. This delineation follows the metropolitan framework established by [15]. The region is typified by a mix of residential, commercial, industrial, and peri-urban land uses ([4]), with diverse terrain and topographic variation. The elevation ranges and climatic diversity across the area, coupled with rapid urban expansion, make it particularly suitable for spatio-temporal modelling of air pollution. The area is home to over 10 million residents, as projected by the Kenya National Bureau of Statistics ([7]).

The study utilized a diverse range of multi-source datasets to support the spatio-temporal modelling and forecasting of air pollution across the Nairobi Metropolitan Area (Table 1). Air pollution data spanning from 2019 to 2024 were derived from the Sentinel-5P satellite’s TROPOspheric Monitoring Instrument (TROPOMI), which provided measurements (SpatialResolutionof 3.5 × 5.5 km²) for key atmospheric pollutants including nitrogen dioxide (NO₂), sulphur dioxide (SO₂), carbon monoxide (CO), methane (CH₄), formaldehyde (HCHO), and ozone (O₃) ([23]). PM₁₀ concentrations, which are not directly observed by Sentinel-5P, were estimated using the aerosol index as a proxy ([9]).

Meteorological variables such as air temperature, humidity, wind speed and direction, and surface pressure were sourced from NASA’s MERRA-2 reanalysis dataset, produced by the Global Modelling and Assimilation Office (GMAO) ([6]). These weather variables are known to significantly influence the dispersion and concentration of atmospheric pollutants.

Land Use and Land Cover (LULC) information was extracted from Sentinel-2 imagery (4bandsof10mand2bandsof20 mspatialresolutions), from January

Figure 1. Area of study (Circle showing the 32,000 km² Nairobi Metropolitan Area, with Inset1-Kenya & Inset2-Africa. The Orange Triangles are the sampling locations).

Table 1. Multi-source earth observation and meteorological datasets.

Continued

1, 2019 to December 31, 2023. This date range was used to create a median composite image for land cover classification, meaning the resulting LULC product represents an averaged condition across those five years (2019-2023) ([16]). This spatial data was essential in evaluating the role of human settlement, vegetation, and industrial areas in modulating pollution levels. Elevation data were obtained from the Shuttle Radar Topography Mission (SRTM), enabling the incorporation of terrain variability in the modelling framework ([22]).

For model validation, ground-level observations were collected using Flow 2 portable air quality sensors deployed at strategically selected locations across the study area. These sensors provided crucial in situ data for calibrating and validating the deep learning and geostatistical models ([17]). A comprehensive summary of these datasets is provided in Table 1.

2.2.1. Conversion of Satellite Column Densities to Surface-Level Concentrations

All Sentinel-5P TROPOMI Level-2 products are originally provided as total vertical column densities (mol/m²). However, air quality monitoring and health impact assessments require surface-level concentrations (µg/m³). Therefore, each pollutant was converted from column density to ground-level concentration using pollutant-specific physical conversion formulas incorporating local meteorological conditions (temperature, pressure) and terrain/elevation. No raw column densities (mol/m²) were used as direct inputs to the CNN + LSTM model; all modelling was performed on surface-converted units (µg/m³).

The general conversion workflow for each pollutant consisted of five steps:

1) Column to volume mixing ratio: Convert mol/m² to mol/m³ using an effective tropospheric height (H = 10,000 − Elevation meters), where elevation is derived from SRTM data.

2) Mass concentration: Convert mol/m³ to g/m³ using the pollutant’s molecular weight.

3) Microgram scaling: Convert g/m³ to µg/m³ (multiply by 10⁶).

4) Temperature and pressure adjustment: Apply the ideal gas law correction factor:

where P_STP = 101.325 kPa, T_STP = 273.15 and Local P and T are from MERRA-2.

5) Vertical profile integration: Apply an exponential decay profile from the surface, where the column density is expressed as:

Column = ∫ 0 H C o ⋅ e − z / k d z = C o ⋅ k ⋅ ( 1 − e − H / k ) , with C_o being the surface concentration, z the height above ground, and k the pollutant-specific e-folding height (scale height). Solving for surface concentration yields C O = C o l u m n / [ k ⋅ ( 1 − e − H / k ) ] .

Pollutant-specific parameters are summarized in Table 2.

Table 2. Pollutant-specific conversion parameters from sentinel-5P column density to surface concentration (µg/m³).

2.2.2. PM₁₀ Derivation Workflow

Since Sentinel-5P does not directly measure particulate matter, PM₁₀ surface concentrations were estimated from the Aerosol Index (AI) using a linear scaling approach based on the WHO 24-hour air quality guideline limit of 50 µg/m³:

1) The minimum and maximum AI values across the entire 2019-2024 time series were extracted for the study area.

2) The AI was normalized to the [0, 1] range using min-max scaling.

3) Normalized values were multiplied by 50 µg/m³ to obtain surface PM₁₀ estimates.

This method assumes a linear relationship between the Aerosol Index and surface PM₁₀, and that the maximum observed AI in the study period corresponds to the WHO guideline limit. These assumptions are acknowledged as limitations in Section 5.

2.2.3. Final Input Units to All Models

All models (CNN + LSTM for historical modelling and NeuralProphet for forecasting) were trained and evaluated using the surface-concentrations in µg/m³ for all pollutants. Therefore, all reported MAE and RMSE values in Sections 3 and 4 are expressed in original physical units (µg/m³) and are directly interpretable against regulatory thresholds. No additional normalization was applied to the target variables before model training.

The modelling architecture (Figure 2) was designed to capture both spatial and temporal patterns in air pollution data from 2019 to 2024. A hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks was implemented.

CNN layers were used to extract spatial features ([5]) from gridded Earth Observation (EO) data, including pollutant concentrations, terrain elevation, and land use/land cover (LULC). Input data were spatially aligned and resampled to a uniform grid resolution (1 km²). Spatial inputs included: Sentinel-5P (NO₂, CO, SO₂, CH₄, HCHO, O₃, PM₁₀ via aerosol index), Sentinel-2 LULC maps (2020 baseline), SRTM elevation rasters, Geographic coordinates (Latitude, Longitude).

LSTM layers captured temporal dependencies across multiple meteorological and atmospheric time series ([13]). Temporal features were structured into daily sequential data windows, with input variables including: Temperature (T2M), Humidity (RH2M), Wind Speed/Direction (WS10M, WD10M, V10M, U10M), Precipitation (PRECTOTCORR), Pressure (PS), and time indicators being (Year, Month, Day).

The model was trained per pollutant using an 80:20 training-validation split ([24]). The LSTM component processed sequential meteorological dynamics, while CNN processed spatial frames. Feature fusion occurred before the final dense layers. The Adam optimizer minimized the Mean Absolute Error (MAE), while model performance was evaluated using MAE, RMSE, R², and correlation ([5]).

Figure 2. CNN + LSTM modelling architecture.

The spatial stream of the model utilized a CNN architecture comprising three 1D convolutional layers with 64, 128, and 256 filters respectively (eachwithakernelsizeof3andReLUactivations), interspersed with max pooling. A spatial attention mechanism was applied to emphasize important spatial features before merging.

The temporal stream used a stacked LSTM configuration with 128 and 64 units, followed by a temporal attention layer and a final LSTM with 32 units to capture time-dependent patterns. Outputs from both branches were concatenated and passed through fully connected layers to predict seven target pollutants.

Terrain features (elevation) from SRTM were used to detect topographic influences ([25]). A total of 327 georeferenced sampling points across the Nairobi Metropolitan Area were used, and spatial joins were conducted to integrate EO data and ground validation. Ground points were used both in the training of CNN spatial layers and in regression kriging validation of prediction surfaces.

2.3.1. CNN + LSTM Architecture: Spatial and Temporal Branches

The hybrid deep learning framework combines a Convolutional Neural Network (CNN) for spatial feature extraction and a Long Short-Term Memory (LSTM) network for temporal dependency modelling. The model was implemented as a single multi-output architecture predicting all seven pollutants simultaneously (PM₁₀, NO₂, SO₂, O₃, CO, CH₄, HCHO), rather than training separate models per pollutant. This approach allows the model to learn inter-pollutant correlations and improves computational efficiency.

SpatialBranch(CNNwith1DConvolutions):

The spatial input consists of a 1D feature vector of 12 spatial variables: longitude, latitude, elevation, and 9 one-hot encoded Land Use/Land Cover (LULC) classes derived from Sentinel-2. These features are reshaped to (samples, 12, 1) to serve as input to a 1D convolutional network. The use of 1D convolutions (rather than 2D) is appropriate because the spatial data are represented as a vector of co-located attributes at each sampling point, not as an image grid requiring 2D kernels.

The spatial branch architecture comprises three Conv1D layers with 64, 128, and 256 filters respectively, each with a kernel size of 3 and ReLU activation, followed by MaxPooling1D layers (pool size = 2). The output is flattened and passed through two dense layers (128 and 64 units) with ReLU activation. A spatial attention mechanism is applied after the first dense layer, computing softmax weights across spatial features to emphasize the most relevant variables for prediction.

TemporalBranch(LSTMwithAttention):

The temporal input consists of 30 meteorological and time-based features (including temperature, humidity, wind speed/direction, pressure, precipitation, and cyclical encodings of month, day, and holiday). The temporal branch uses a stacked LSTM architecture with three layers (128, 64, and 32 units), with return_sequences = True for the first two layers to maintain the temporal dimension for attention. A temporal attention layer (custom TemporalAttention class) computes softmax weights across time steps, allowing the model to focus on the most relevant historical periods for prediction. The final LSTM layer (return_sequences = False) compresses the temporal information into a fixed-length vector, which is passed through two dense layers (64 and 32 units).

FeatureFusionandOutput:

The outputs of the spatial and temporal branches are concatenated and passed to a final dense layer with 7 neurons and linear activation, producing simultaneous predictions for all seven target pollutants. The model was compiled using the Adam optimizer with a Huber loss function (robust to outliers) and tracked Mean Absolute Error (MAE) as a primary metric.

This study uses 1D convolutions because the spatial data at each sampling point are organized as a 1D feature vector (12 attributes × 1 channel). If the data were organized as a 2D raster grid (e.g., pixels with spatial neighbourhood structure), 2D convolutions would be appropriate. For point-based spatial features with no inherent neighbourhood topology beyond the feature vector itself, 1D convolutions are the correct architectural choice ([5]; [5]; [21]).

2.3.2. Training-Validation Split and Temporal Integrity

To ensure rigorous evaluation of the CNN + LSTM model’s ability to generalize to unseen time periods, a chronological split was applied to the time series data spanning 2019-2024. The full dataset was first sorted by date using df_encoded.sort_values(“Date”).reset_index(drop = True). The first 80% of the temporal sequence (January 2019 to December 2023) was allocated to training, and the remaining 20% (January 2024 to December 2024) was reserved for validation. This was implemented using train_test_split with shuffle = False, which preserves temporal order and prevents random shuffling. Consequently, no future data were used to predict past observations—a critical requirement for time series validation ([3]).

NeuralProphet chronological split: For the NeuralProphet forecasting model (Section 2.4), the built-in split_df method was used with valid_p = 0.2. This method automatically preserves temporal order, taking the last 20% of dates for validation and the first 80% for training. Thus, both models employed temporally consistent validation schemes.

Inverse scaling of metrics: Prior to training, all target variables (seven pollutant concentrations) were normalized to the range [0, 1] using MinMaxScaler to stabilize gradient descent and improve convergence. After model prediction, the scaled outputs were inverse-transformed back to original physical units (µg/m³ for all pollutants) using the inverse_transform method of the fitted scaler. Therefore, all reported MAE, RMSE, R², and bias values in Section 3 are expressed in original units (µg/m³) and are directly interpretable against air quality guidelines and regulatory thresholds.

Spatial considerations: All 327 georeferenced sampling locations were present in both the training and validation sets. While this allows the model to learn location-specific patterns, it does not test spatial generalizability to entirely unmonitored locations. Future work should consider spatial holdout validation (e.g., reserving specific geographic points for testing) to assess model transferability across space ([11]). The spatial interpolation and kriging steps (Section 2.6) partially address this limitation by improving spatial continuity of predictions.

Validation data usage: The validation set (last 20% of dates) was used exclusively for final model evaluation. No validation data were used for early stopping or hyperparameter tuning. The model architecture and hyperparameters were fixed before evaluating on the validation set, ensuring an unbiased estimate of generalization error.

NeuralProphet was employed to forecast daily pollution levels for the period October 2024 to December 2025 (Figure 3); (note that this could have been made even for the year 2035); across all 327 georeferenced sampling locations in the AOI. Each forecast was generated per pollutant and per spatial point, preserving both geographic coordinates and land cover context.

Figure 3. NeuralProphet modelling workflow.

The model incorporated trend, seasonality, autoregression, and lagged regressors ([12]). For spatial consistency, the historical CNN + LSTM outputs were first aggregated at the grid cell level and tagged with location-specific variables that are Latitude & Longitude, Elevation and LULC Classes.

Forecasting was conducted per point, resulting in spatially disaggregated forecasts for each pollutant at each location. These were then visualized as spatially explicit heatmaps, allowing observation of future high-risk zones. Furthermore, forecast outputs were used as inputs for spatial autocorrelation analysis (Moran’s I and Geary’s C) and Regression Kriging, to assess and improve the spatial continuity of the predictions. This helped detect patterns of forecast over-/under-prediction and ensured that forecast values aligned with regional pollutant dispersion dynamics.

Spatial accuracy of forecasts was evaluated by comparing forecasted NO₂ and PM₁₀ values with ground-level Flow 2 observations at the same location points (coordinates), using R², MAE, RMSE, and spatial correlation indices.

For each of the 327 locations, a separate NeuralProphet model was trained using only the historical pollution time series at that location. No spatial regressors (latitude, longitude, elevation, LULC) were included in the forecasting model, ensuring spatial independence between locations. The data for each location were explicitly sorted by date before any split. The split_df method was used with valid_p = 0.2, which chronologically reserves the last 20% of dates for validation and the first 80% for training. This prevents temporal leakage, as the model never sees future data during training.

The NeuralProphet models were configured with yearly, weekly, and daily seasonality enabled. No autoregressive terms were used (n_lags = 0, the default), meaning predictions were based solely on trend and seasonal decomposition rather than short-term lagged dependencies. Models were trained for 50 epochs with a batch size of 10 and a learning rate of 0.1, using the default Huber loss function. The validation metrics (MAE_val = 0.16, RMSE_val = 0.20) reported in Section 3.2 represent the average performance across all locations and pollutants on the withheld chronological validation period (January 2024 to December 2024) (Figure 20).

To validate the satellite-derived and deep learning modelled outputs, ground-level measurements of PM₁₀ and NO₂ were collected using a single Plume Labs Flow 2 portable air quality sensor. This section describes the deployment protocol, data processing, and validation framework.

2.5.1. Sensor Specifications and Deployment Strategy

The Flow 2 sensor is a handheld, battery-operated device capable of measuring PM₁₀ (µg/m³), PM_2.5 (µg/m³), NO₂ (ppb and µg/m³), and volatile organic compounds (VOC). The sensor was operated following Plume Labs guidelines for portable deployment (Table 3). No pre-deployment calibration was performed due to the absence of regulatory reference stations in Kenya—a limitation common to low-cost sensor studies in data-sparse regions ([1]).

Table 3. Flow-2 sensor model & deployment.

Data collection occurred across the Nairobi Metropolitan Area using opportunistic mobile sampling while driving along major roads and traversing residential, commercial, and peri-urban areas. The sensor recorded measurements at 1-second intervals, generating a dense set of georeferenced points. A total of 14,229 unique locations were sampled intermittently, with most sites sampled for approximately one minute per visit. The full dataset covers multiple days across the study period, though the exact temporal coverage per location was uneven due to the mobile sampling design.

2.5.2. Spatial Interpolation to Common Grid Points

The raw 14,229 Flow 2 points did not spatially coincide with the 327 fixed sampling locations used for Sentinel-5P and MERRA-2 extraction. To enable pointwise comparison, the Flow 2 measurements were first spatially interpolated across the entire area of interest (circular domain of approximately 101 km radius) using Inverse Distance Weighting (IDW) combined with a k-Nearest Neighbors (k-NN) strategy. This approach transformed the heterogeneous mobile measurements into a harmonized gridded surface. Values were then extracted at the 327 predefined grid points that matched the satellite and meteorological sampling locations enabling direct point pairwise comparison.

2.5.3. Quality Control and Temporal Aggregation

Quality control was performed through outlier removal based on physically plausible ranges informed by previously published air quality studies in East African urban environments ([14]). No co-location with regulatory monitors was possible due to the absence of such infrastructure in Kenya. The 1-second instantaneous measurements were aggregated to daily averages at each of the 327 locations to match the temporal resolution of the satellite-derived and modelled outputs.

2.5.4. Validation Framework: Independent Use Only

Crucially, the Flow 2 ground observations were used exclusively for independent validation of the CNN + LSTM modelled outputs and NeuralProphet forecasts. No Flow 2 data were used in model training, calibration of the CNN + LSTM architecture, or fitting of the NeuralProphet parameters. The sole exception was the empirical 1.5 enhancement factor for NO₂ (Section 2.2.1), which was calibrated against the Flow 2 dataset. All other model parameters were derived solely from satellite and meteorological data.

The validation framework employed three comparative pairwise analyses at the 327 harmonized points:

1) Flow 2 vs. Sentinel-5P (raw satellite)

2) Flow 2 vs. CNN + LSTM (modelled historical)

3) Flow 2 vs. NeuralProphet (forecasted)

Quantitative metrics included Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), bias, Pearson correlation coefficient, and R². Additionally, spatial autocorrelation (Moran’s I and Geary’s C) was computed for the Flow 2 data to characterize the spatial structure of ground-truth pollution patterns (PM₁₀ Moran’s I = 0.3721, p < 0.0001; NO₂ Moran’s I = 0.3192, p < 0.0001), confirming moderate spatial clustering consistent with urban pollution gradients (Table 4(a)-(b)).

Table 4. (a). Three comparatives pairwise analyses at the 327 harmonized points for PM₁₀, (b). Three comparatives pairwise analyses at the 327 harmonized points for NO₂.

3.1.1. Spatial Patterns and Performance (CNN Component)

The CNN + LSTM model effectively captured spatio-temporal air pollution dynamics across the Nairobi Metropolitan Area from 2019 to 2024, achieving low validation errors (Validation MAE = 0.046, Validation Loss = 0.0013) (Figure 4). The CNN layers extracted spatial features from EO data (Sentinel-5P, SRTM, LULC), while the LSTM units modeled temporal dependencies across time-period in the study.

Figure 4. Plots of CNN + LSTM prediction modelling metrics (Plots showing Loss and MAE for training & validation datasets).

Despite strong general performance, surface-level PM₁₀ and NO₂ concentrations were systematically overestimated or underestimated in peri-urban zones and areas with limited sensor coverage. These residual errors were geographically patterned, suggesting influence from LULC and terrain mismatches. Figure 5 and Figure 6 show a comparison of the spatial distribution between Sentinel-5P & Modelled data for PM₁₀ and NO₂ respectively.

Moran’s I and Geary’s C confirmed strong spatial clustering in CNN + LSTM-predicted NO₂ values (Moran’sI = 0.994, Geary’s C = 0.016) and very weak spatial structure in PM₁₀ (Moran’sI = 0.035, Geary’sC = 0.949). This difference reflects the diffuse emission sources of NO₂ (urban and industrial zones) vs. the localized, episodic nature of PM₁₀ (e.g., road dust, construction) (Figures 7-10).

Figure 5. Spatial maps showing visual comparison of observed and forecast PM₁₀ concentrations levels (The Forecasts exhibited higher concentrations especially on the Northen part of the AOI and lower concentrations in the southern part of the AOI).

Figure 6. Spatial maps showing visual comparison of observed and forecast NO₂ concentrations levels. The spatial distribution pattern of the NO₂ forecast was similar to the observed with forecast values generally exhibiting higher concentrations.

Figure 7. Combined (CNN + LSTM) deep learning (PM₁₀ Moran’s I & P-Values).

Figure 8. Combined (CNN + LSTM) deep learning (NO₂ Moran’s I & P-Values).

Figure 9. Combined (CNN + LSTM) deep learning model (PM₁₀ Geary’s C & P-Values).

Figure 10. Combined (CNN + LSTM) deep learning model (NO₂ Geary’s C & P-Values).

Visual correlation maps (Figure 11 and Figure 12) showed that NO₂ and SO₂ concentrations were the highest in built-up and transport corridors, while CH₄ and O₃ were dominant in agricultural/vegetated areas.

To improve spatial continuity, residuals from a Random Forest baseline were kriged using a variogram-based geostatistical model. The results revealed a pollutant-specific trade-off (Table 5). For NO₂, kriging marginally reduced R² (from 0.998 to 0.996) but improved spatial autocorrelation (Moran’s I = 0.994, up from 0.319 for raw Flow 2 data) and reduced RMSE from 1.695 to 1.302 µg/m³. For PM₁₀, however, kriging degraded accuracy: R² dropped from 0.915 to 0.782, RMSE increased from 8.600 to 13.805 µg/m³, and bias increased from −0.004 to −4.136. This decline reflects PM₁₀’s high spatial variability and episodic nature (e.g., localized dust events, construction plumes, road resuspension), which violate the spatial stationarity assumption underlying kriging interpolation ([2]). Consequently, while kriging is beneficial for smoothly varying pollutants like NO₂, it is not recommended for PM₁₀ in this context without additional ground truthing or alternative spatial smoothing methods.

Figure 11. Spatial maps showing the relationship between LULC & NO₂ (Land use/land cover and mean annual surface NO₂ showing higher concentration aligning with the Northern Corridor Transport route. Thus, NO₂ seems to be correlated to the transport Land Use/Land Cover & Urban Areas).

Figure 12. Spatial maps showing the relationship between LULC & SO₂ (Land use/land cover and mean annual surface SO₂ showing higher concentration distribution on the lower left side of the study area which is associated with the shrubs land cover with a distinct lowest concentration over Lake Magadi).

Table 5. CNN + LSTM model accuracies (Historical 2019-2024).

Harmonized Flow 2-CNN + LSTM comparisons at spatial points showed spatially patterned underpredictions in the CBD and overpredictions in high-altitude, vegetated areas.

3.1.2. Temporal Accuracy and Trends (LSTM Component)

The LSTM layers were instrumental in capturing temporal dependencies and seasonal cycles from meteorological variables (such as temperature, humidity, wind speed, pressure) and pollutant time series. The model learned lagged relationships between weather changes and pollutant concentration surges.

Time series plots (Figures 13-19) show strong agreement between LSTM predictions and observed satellite values. The O₃, CH₄, and HCHO, especially during seasonal transitions (March-May, October-December).

NO₂ and PM₁₀ exhibited high temporal variance, and while trends were captured, short-term spikes (e.g., dust events, traffic peaks) led to occasional prediction errors. The LSTM’s learning was aided by sequential input structuring helping it identify multi-day pollutant buildup patterns during dry periods.

The plots of CO show that CO was overestimated, while O₃ and CH₄ predictions agreed perfectly with the original Sentinel-5P satellite data.

NeuralProphet was employed to forecast air pollution levels for the year 2025 and successfully achieved temporal trends and seasonal dynamics (Figure 20) with (validation MAE = 0.160, RMSE = 0.200) for 50 epochs (Table 6). However, as with the historical models, NeuralProphet also underestimated PM₁₀ and NO₂ concentrations compared with Ground Based observations, particularly in under-monitored zones. Forecast validation using Random Forest showed moderate performance: for NO₂, R² = 0.811 and RMSE = 9.028 µg/m³; and for PM₁₀, R² = 0.786 and RMSE = 13.655 µg/m³. Post-processing with Regression Kriging slightly reduced R² for both pollutants but improved correlation for NO₂ (to 0.939), indicating better spatial structural alignment (Table 7).

Figures 21-27 show the Time Series NeuralProphet Forecast Plots from 8^th October 2024 to 31^st December 2025 for the 7 Pollutants.

The results of NeuralProphet forecast model were run through Random Forest and Kriging Regression modelling to improve model accuracy and the resultant metrics are as shown in Table 7:

Figure 13. PM₁₀ Time series plots {CNN + LSTM modelling output (Blue) and Sentinel-5P Datasets (Red)}.

Figure 14. NO₂ Time series plots {CNN + LSTM modelling output (Blue) and Sentinel-5P Datasets (Red)}.

Figure 15. SO₂ Time series plots {CNN + LSTM modelling output (Blue) and Sentinel-5P Datasets (Red)}.

Figure 16. CO time series plots {CNN + LSTM modelling output (Blue) and Sentinel-5P Datasets (Red)}.

Figure 17. O₃ time series plots {CNN + LSTM modelling output (Blue) and Sentinel-5P Datasets (Red)}.

Figure 18. CH₄ time series plots {CNN + LSTM modelling output (Blue) and Sentinel-5P Datasets (Red)}.

Figure 19. HCHO time series plots {CNN + LSTM modelling output (Blue) and Sentinel-5P Datasets (Red)}.

Figure 20. NeuralProphet model training metrics over 50 epochs (averaged across 327 locations & 7 pollutants showing summation of all MAEs & MAE_Vals).

Table 6. Results of neuralprophet forecasting metrics.

Table 7. NeuralProphet model regression kriging accuracies (Forecast 2025).

Figure 21. Forecast PM₁₀ concentrations levels

Figure 22. Forecasts NO₂ concentration levels.

Figure 23. Forecasts SO₂ concentration levels.

Figure 24. Forecast CO concentration levels.

Figure 25. Forecast O₃ concentration levels.

Figure 26. Forecast CH₄ concentration levels.

Figure 27. Forecast Formaldehyde (HCHO) concentration levels.