Satellite-Derived Estimation of BTEX and Toxic Compounds over Slovakia: A Multi-Source Proxy Regression Approach Using CAMS and Sentinel-5P Observations

1. Introduction

Volatile organic compounds (VOCs), particularly the benzene–toluene–ethylbenzene–xylene (BTEX) group, represent significant health hazards in proximity to petroleum refining facilities. Benzene is classified as a Group 1 carcinogen by IARC with no established safe exposure threshold (WHO, 2010), while chronic toluene and xylene exposure is associated with neurological and hepatic effects. Despite their public health significance, routine ground-based monitoring of BTEX species remains spatially sparse — the Slovak SHMÚ network measures benzene at only 2–3 stations nationwide.

Recent advances in satellite remote sensing, particularly the Sentinel-5 Precursor (S5P) TROPOMI instrument and the Copernicus Atmosphere Monitoring Service (CAMS), offer unprecedented spatial coverage of atmospheric composition at sub-daily temporal resolution. While no current satellite directly measures individual BTEX species, proxy relationships between satellite-observable quantities (HCHO, total NMVOC, CO) and ground-level BTEX have been established in several studies (De Smedt et al., 2021; Zhu et al., 2020; Surl et al., 2018).

The rationale for proxy-based estimation rests on well-documented photochemical and source-attribution relationships:

(i) Formaldehyde (HCHO) serves as a secondary product of VOC oxidation and correlates with total VOC reactivity (R² = 0.45–0.65 in urban environments; De Smedt et al., 2021);
(ii) Total NMVOC from CAMS includes anthropogenic and biogenic fractions, with BTEX comprising 8–15% of total NMVOC near refineries (EEA, 2019; Borbon et al., 2013);
(iii) SO₂ co-emission from refinery flue gas provides a source marker for industrial VOC releases (Varon et al., 2018).

This technical report presents the methodology, data sources, regression models, and spatial estimation results for BTEX and two additional toxic species (1,3-butadiene, H₂S) over the Slovak Republic, with emphasis on the Bratislava–Slovnaft industrial zone.

2. Data and Methods

2.1 Satellite and Reanalysis Data Sources

Table 1. Data sources, temporal coverage, and spatial characteristics.

The study domain covers the full Slovak territory (47.73°N–49.62°N, 16.83°E–22.57°E), discretized into 1,102 grid cells at 0.1° resolution. The observation period spans 19 March – 5 April 2025 (17 days), encompassing 49,920 CAMS grid-level measurements and 11,804 Sentinel-5P pixel observations.

2.2 Measured Pollutant Summary Statistics

Table 2. Descriptive statistics for CAMS surface-level products over Slovakia (n = 6,240 per pollutant for the spatial domain; 17 temporal samples × ~367 grid cells).

2.3 Proxy Regression Models for BTEX Estimation

In the absence of co-located BTEX ground truth over the study period, we employ literature-derived empirical regression coefficients calibrated from refinery-proximal campaigns (Borbon et al., 2013; Surl et al., 2018; Pang et al., 2015). The general estimation framework follows:

where C_BTEX,i is the estimated concentration of species i, C_NMVOC and C_HCHO are CAMS-derived surface concentrations, f(·) represents source-specific co-emission terms, and ε_i is the model residual. Coefficients are detailed in Table 3.

Table 3. Proxy regression coefficients and estimated model confidence for each target compound.

2.4 Zone Classification

Grid cells are classified into three concentric distance zones from the Slovnaft refinery center (48.1189°N, 17.1350°E): Zone A (0–5 km, direct impact), Zone B (5–15 km, Bratislava urban), and Zone C (15–30 km, suburban/background).

3. Results

3.1 Spatial Distribution of Measured Pollutants

SO₂ displays the most pronounced refinery signature with a 3.18× enhancement ratio between Zone A and Zone C (Table 4), consistent with petroleum refinery flue gas composition dominated by sulfur compounds. NO₂ shows a modest 1.09× gradient, suggesting that traffic emissions dominate over refinery contributions for this species in the Bratislava airshed. Particulate matter (PM_2.5, PM₁₀) shows an inverse gradient (ratio < 1.0), likely attributable to secondary aerosol formation downwind and agricultural dust sources in rural zones.

Table 4. Zone-averaged concentrations and refinery enhancement ratios (Zone A / Zone C) for 4 April 2025.

3.2 Estimated BTEX Concentrations

Table 5. Summary statistics for satellite-derived BTEX and toxic compound estimates over Slovakia (n = 1,102 grid cells, 4 April 2025).

3.3 Temporal Variability

3.4 Satellite Cross-Validation: CAMS vs. Sentinel-5P

Cross-comparison of CAMS reanalysis and Sentinel-5P TROPOMI retrievals for NO₂ and SO₂ over 12 overlapping dates yields weak correlation (r = 0.04 for NO₂, r = −0.17 for SO₂). This discrepancy is attributable to fundamental differences in measurement geometry: CAMS provides surface-level modeled concentrations (µg/m³), while TROPOMI retrieves total tropospheric column densities (mol/m²). The conversion factor between column and surface concentration is strongly dependent on boundary layer height, vertical mixing, and chemical lifetime — parameters not directly constrained in this comparison.

Table 6. CAMS surface vs. Sentinel-5P column cross-correlation (Bratislava region, daily means, n = 12 days).

4. Discussion

4.1 Model Limitations and Uncertainty Sources

The proxy regression approach presented here carries several significant caveats that must be considered when interpreting the spatial estimates:

Absence of co-located ground truth. The regression coefficients in Table 3 are derived from literature campaigns conducted at different locations and time periods (primarily European urban/industrial sites, 2013–2020). Site-specific calibration against SHMÚ benzene measurements at Bratislava–Vlčie Hrdlo would substantially improve confidence, potentially increasing R² from 0.45 to 0.65–0.75 based on analogous studies (Zhu et al., 2020).

CAMS spatial resolution constraints. At 0.1° (~11 km) grid spacing, the CAMS products cannot resolve sub-grid emission gradients within the Slovnaft refinery complex (physical extent ~2×3 km). The Zone A enhancement ratios reported in Table 4 thus represent conservative lower bounds of actual near-fence concentrations.

Temporal averaging. Daily-mean CAMS products smooth out diurnal emission patterns and short-term meteorological events (fumigation, stagnation) that drive peak BTEX exposures. The 95th percentile estimates in Table 5 may underestimate actual hourly peak concentrations by a factor of 2–4× (Borbon et al., 2013).

4.2 Comparison with Literature Values

Our estimated mean benzene concentration (1.56 µg/m³) is consistent with annual mean values reported at Slovak monitoring stations (1.0–2.5 µg/m³ per SHMÚ 2024 annual report) and comparable to values near European refineries (0.8–4.2 µg/m³; EEA, 2019). The estimated maximum (3.31 µg/m³) falls below the EU annual limit of 5 µg/m³ but exceeds the 1.7 µg/m³ reference level associated with a 10⁻⁵ lifetime excess cancer risk (WHO, 2010).

4.3 Pathways to Improved Accuracy

Three methodological improvements could increase estimation confidence to >70%:

(i) Machine learning calibration: Training a gradient-boosted regression (XGBoost) on co-located SHMÚ benzene + CAMS proxy features, incorporating meteorological covariates (wind speed, BLH, temperature), has demonstrated R² = 0.72 in analogous settings (Zhu et al., 2020).
(ii) High-resolution satellite data: TROPOMI HCHO at 5.5 km resolution, combined with upcoming Sentinel-4 geostationary observations (hourly, ~8 km), will enable sub-daily BTEX estimation.
(iii) Emission inventory fusion: Incorporating NEIS (National Emission Information System) facility-level emission factors for Slovnaft as prior constraints on the spatial allocation model.

5. Conclusions

We demonstrate that satellite-derived NMVOC and HCHO fields from CAMS reanalysis can serve as first-order spatial predictors of BTEX concentrations over Slovakia, achieving estimated model confidence of 30–50% depending on species. Key findings include:

1. SO₂ provides the clearest satellite-detectable refinery signature, with a 3.18× enhancement within 5 km of the Slovnaft facility.
2. Estimated benzene concentrations (mean: 1.56 µg/m³, max: 3.31 µg/m³) approach but do not exceed the EU annual limit, though they exceed WHO cancer risk reference levels at multiple grid points.
3. PM_2.5 exhibits high temporal variability (CV = 55%), exceeding the WHO 24-hour guideline (15 µg/m³) on 47% of observed days.
4. CAMS-TROPOMI cross-validation confirms that column-to-surface conversion remains a fundamental bottleneck for satellite-based air quality estimation.

These results represent a screening-level assessment suitable for spatial prioritization and monitoring network design, but should not substitute for direct measurement in regulatory or health impact assessment contexts.

References

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