Assessing Environmental Sustainability in the Eastern Mediterranean Under Anthropogenic Air Pollution Risks Through Remote Sensing and Google Earth Engine Integration

Abstract

Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development.

1. Introduction

Air pollution represents one of the most significant environmental health risks worldwide, with particularly severe impacts in regions affected by political instability and conflict [1,2]. The assessment of air quality in such regions poses distinct methodological challenges due to damaged infrastructure, limited access to ground-based monitoring stations, and security concerns for field researchers [3,4]. In this context, remote sensing technologies have emerged as invaluable tools for monitoring atmospheric pollutants, offering continuous spatial and temporal coverage across inaccessible regions [5,6].

Northwestern Syria represents a particularly complex case study for air pollution dynamics, where multiple anthropogenic and natural factors have converged to create a unique environmental situation. Since 2011, the region has experienced significant upheaval due to armed conflict, resulting in infrastructure destruction, population displacement, and economic instability [3,7]. The conflict has directly impacted air quality through increased emissions from military operations, destruction of industrial facilities, and unregulated industrial activities in areas outside government control [8,9].

Nitrogen dioxide (NO₂) and carbon monoxide (CO) serve as critical indicators of air quality and anthropogenic activity. NO₂ primarily originates from combustion processes in vehicles, power plants, and industrial facilities, with its concentrations typically peaking in urban and industrial centers [10,11]. With a relatively short atmospheric lifetime of approximately one day, NO₂ serves as an excellent proxy for local emissions and human activity patterns [12]. Carbon monoxide, predominantly emitted from incomplete combustion processes, persists longer in the atmosphere and can be transported over greater distances, making it valuable for assessing both local and regional pollution patterns [13,14].

The spatial and temporal distribution of these pollutants in northwestern Syria has been significantly affected by several concurrent factors. Direct war impacts, including military operations and the destruction of infrastructure, have contributed to elevated pollutant levels in affected areas [8,15,16]. Simultaneously, economic sanctions have led to fuel shortages and the use of lower-quality alternatives, affecting emissions patterns in government-controlled territories [13]. These factors have been further complicated by major forest fires in the coastal regions between 2019 and 2024, which released substantial quantities of pollutants into the atmosphere [17,18].

The COVID-19 pandemic introduced yet another variable to this complex equation, with lockdown measures temporarily reducing industrial activities and transportation, resulting in measurable decreases in atmospheric pollutant concentrations [19,20]. These overlapping factors create a multifaceted environmental situation that requires sophisticated analytical approaches to disentangle the relative contributions of different drivers to observed pollution patterns [21,22].

Traditional ground-based monitoring networks, which typically form the backbone of air quality assessment, are largely unavailable or non-functional in conflict-affected areas of Syria [23]. In this context, satellite remote sensing offers a viable alternative for monitoring pollution trends. The Sentinel-5P satellite, launched in October 2017 as part of the European Union’s Copernicus program, carries the TROPOspheric Monitoring Instrument (TROPOMI), which provides daily global measurements of various atmospheric constituents, including NO₂ and CO, at a spatial resolution of approximately 7 × 3.5 km² (improved to 5.5 × 3.5 km² since August 2019) [24,25]. This unprecedented spatial resolution, combined with daily global coverage, allows for detailed analysis of pollution patterns even in regions where ground-based monitoring is infeasible.

The processing and analysis of satellite data for atmospheric monitoring have been revolutionized by cloud computing platforms such as Google Earth Engine (GEE) [26,27]. This platform enables efficient processing of massive geospatial datasets using advanced algorithms, significantly reducing the computational resources required for analyzing time series of satellite imagery [6,28]. For conflict-affected regions like northwestern Syria, where local computational infrastructure may be limited or damaged, cloud-based platforms provide essential tools for environmental monitoring and assessment.

Previous studies have utilized satellite remote sensing to monitor air pollution in conflict zones and during exceptional events. Zalakeviciute et al. [8] analyzed changes in air quality in Ukraine following the Russian invasion, demonstrating how military activities affected pollution levels. Similarly, Mehrabi et al. [5] employed Sentinel-5P imagery to monitor air quality during the 2022 military conflict in Ukraine, highlighting the utility of satellite data for tracking pollution dynamics in inaccessible areas. Researchers have also used remote sensing to assess the environmental impacts of conflicts in Iraq [9], Libya, and Yemen [4], providing valuable insights into the relationship between conflict and environmental degradation.

In the context of Syria specifically, Alhasan et al. [29] and Gaafar [3] conducted a preliminary survey of the environmental impacts of the conflict, identifying air pollution as a significant concern but noting the lack of comprehensive monitoring data. Ayek and Zerouali [30] demonstrated the utility of GEE for monitoring environmental changes in Syria, focusing on surface water dynamics but establishing methodological approaches applicable to air quality assessment. However, despite these advances, no comprehensive study has systematically analyzed the spatial and temporal dynamics of air pollutants in northwestern Syria using high-resolution satellite data, particularly in relation to the complex interplay of conflict, economic factors, and natural events [31].

Several research gaps exist in our understanding of air pollution dynamics in conflict-affected regions, particularly in the context of northwestern Syria. First, while satellite-based monitoring has been applied in various conflict zones, the unique combination of factors affecting northwestern Syria prolonged conflict, including economic sanctions, forest fires, and COVID-19, presents a distinct case that has not been comprehensively analyzed. Second, the relative contributions of these different factors to observed pollution patterns remain poorly understood, hampering efforts to develop targeted mitigation strategies. Third, the correlation between meteorological factors and pollutant concentrations in conflict-affected regions has received limited attention despite its importance for understanding pollution transport and dispersion. Finally, the differential impacts of these factors on opposition-controlled versus government-controlled territories have not been systematically assessed despite their potential implications for environmental justice and public health.

This study aims to address these gaps by conducting a comprehensive analysis of NO₂ and CO concentrations in northwestern Syria during the period 2019–2024 using Sentinel-5P satellite data processed through Google Earth Engine. Specifically, the research objectives are to

• Analyze the spatial and temporal patterns of NO₂ and CO concentrations in northwestern Syria during 2019–2024, identifying areas of high pollution and temporal trends;
• Evaluate the relative impacts of conflict dynamics, changes in industrial activity, forest fires, the COVID-19 pandemic, and economic sanctions on air pollution levels in different parts of the region;
• Assess the correlation between meteorological factors (temperature, humidity, wind speed, atmospheric pressure, and precipitation) and pollutant concentrations to understand the influence of climatic variables on pollution dynamics;
• Compare pollution patterns between areas under different political control to identify potential disparities in environmental quality and their implications for public health;
• Demonstrate the utility of Sentinel-5P data and Google Earth Engine for monitoring air pollution in conflict-affected regions where traditional monitoring networks are unavailable.

By addressing these objectives, this study aims to provide a more comprehensive understanding of the complex factors influencing air pollution in northwestern Syria, contributing to the broader literature on environmental impacts of conflict and establishing methodological approaches for monitoring air quality in challenging contexts. The findings will have implications for environmental policy, public health interventions, and post-conflict reconstruction efforts, potentially informing more sustainable and equitable development pathways as the region transitions toward greater stability.

2. Materials and Methods

2.1. Study Area

The study area encompasses northwestern Syria, covering approximately 51,893 km² (Figure 1). This region represents a complex and dynamic setting for air pollution research due to its diverse geography, climate conditions, demographic changes resulting from conflict, and varied industrial activities under different political administrations [3,4].

Northwestern Syria exhibits remarkable geographical and climatological diversity. The terrain transitions from coastal plains along the Mediterranean to the coastal mountain range (reaching elevations of 1500 m), which then descends eastward to inland plains and plateau areas [32]. The climate follows a Mediterranean pattern with significant spatial variation, ranging from humid conditions along the coast (annual precipitation exceeding 1000 mm) to semi-arid environments in the eastern reaches (200–300 mm annually) [33]. This diverse landscape supports extensive agricultural activities, particularly in the fertile Al-Ghab plain and along river valleys, which potentially contribute to seasonal biomass burning emissions that affect air quality [17].

The region incorporates several significant urban and industrial centers that function as major emission sources for NO₂ and CO. Aleppo, Syria’s largest city (pre-war population approximately 4.6 million in its metropolitan area), serves as the country’s historical industrial capital, with extensive manufacturing facilities clustered in the Sheikh Najjar Industrial City [21]. Homs, the third-largest Syrian city, represents a critical transportation hub and hosts Syria’s first oil refinery, which has operated intermittently since the conflict began [7]. The study area also includes Hessia Industrial City near Homs, which was established in 2004 as part of economic development initiatives. Additional urban centers include Hama, Latakia, and Tartous, with the Banias oil refinery located approximately 30 km north of Tartous along the coast, representing a significant point source for emissions (Figure 1).

Northwestern Syria has experienced significant transformations since 2011, with extensive changes to infrastructure, industrial activities, and population distribution [30,34]. By 2021, an estimated 6.7 million Syrians were internally displaced, with many relocating to the northwestern regions, particularly the Idlib Governorate. Economic activities across the region have evolved unevenly after 2018, with industrial facilities in central Aleppo, Homs, Hama, and coastal regions experiencing operational interruptions due to fuel shortages and electricity supply disruptions [13]. Meanwhile, other areas such as Idlib and parts of northern Aleppo Province (including the Al-Rai industrial area) have developed distinct economic patterns with emerging industrial activities supported by cross-border trade [3]. This combination of geographical diversity, varied industrial infrastructure, and differential economic development makes northwestern Syria particularly valuable for examining how multiple factors, including meteorological conditions, seasonal variations, and changing industrial activities, influence air pollutant concentrations and distribution patterns across a complex regional landscape [2].

2.2. Data Sources

The research methodology leverages multiple datasets that together enable a comprehensive analysis of air pollution dynamics in northwestern Syria. This section details the acquisition, specifications, and processing approaches for each data source utilized in this study.

2.2.1. Sentinel-5P Satellite Data for Atmospheric Pollutants

Column density measurements for NO₂ and CO were obtained from the Sentinel-5P satellite launched in October 2017 as part of the European Union’s Copernicus Earth observation program. The satellite carries the TROPOspheric Monitoring Instrument (TROPOMI), which represents a significant advancement in atmospheric monitoring capabilities with its unprecedented combination of spatial resolution and daily global coverage [24]. TROPOMI’s design enables the detection of multiple trace gases with high sensitivity and precision, making it particularly valuable for monitoring regions where ground-based measurements are inaccessible [25].

For this study, TROPOMI Level-3 data products were accessed through the Google Earth Engine (GEE) platform using specific collection identifiers. NO₂ density concentrations were retrieved from the tropospheric column using the COPERNICUS/S5P/NRTI/L3_NO2 collection, while CO concentrations were obtained from the total column measurements via the COPERNICUS/S5P/NRTI/L3_CO collection. These near-real-time (NRTI) products provide data with minimal latency, which is essential for monitoring dynamic environmental conditions in conflict zones [35].

The TROPOMI instrument provides data at a spatial resolution of 7 × 3.5 km² (improved to 5.5 × 3.5 km² since August 2019), which is resampled to 1113.2 m in the standard product. This resolution allows for detailed spatial analysis of pollution patterns at urban and regional scales. The instrument revisits each location approximately every two days, enabling robust temporal monitoring of pollutant distribution patterns. The temporal coverage extends from 10 July 2018 for NO₂ and 22 November 2018 for CO through the present, encompassing the entire 2019–2024 study period. TROPOMI’s measurement sensitivity varies with atmospheric conditions. Under clear skies, the instrument demonstrates excellent sensitivity to pollutant concentrations in the tropospheric boundary layer, where human exposure primarily occurs. In cloudy conditions, the sensitivity varies according to the light path, with the algorithm accounting for these variations to maintain data quality [25].

2.2.2. ERA5 Climate Reanalysis Data

To analyze the relationship between atmospheric pollutants and meteorological conditions, this study incorporated ERA5 climate data, the fifth-generation reanalysis product from the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 represents a significant advancement over previous reanalysis products, offering improved spatial and temporal resolution, more comprehensive atmospheric variables, and enhanced model physics [36].

The ERA5 dataset provides hourly estimates of atmospheric, land-surface, and oceanic parameters on a global geographic grid at 0.25° × 0.25° resolution (approximately 31 km at the equator and 11 km at the latitude of the study area). From this comprehensive dataset, monthly averages were extracted for five key meteorological variables relevant to atmospheric pollution transport and chemistry: air temperature at 2 m height (K), relative humidity (%), wind speed (m/s), surface atmospheric pressure (Pa), and precipitation amount (mm).

Since relative humidity is not available as a ready-made product in ERA5 data, it was calculated using air temperature and dew point temperature at 2 m height according to the following equations:

Saturation vapor pressure at dew point temperature:

e = 6.112 × exp((17.625 × Td)/(Td + 243.04))

(1)

Saturation vapor pressure at air temperature:

es = 6.112 × exp((17.625 × T)/(T + 243.04))

(2)

Relative humidity:

RH = (e/es) × 100

(3)

where T is air temperature in degrees Celsius, Td is dew point temperature in degrees Celsius, e is saturation vapor pressure at dew point (hPa), es is saturation vapor pressure at current temperature (hPa), and RH is relative humidity (%).

These climate variables were selected based on their established influence on atmospheric pollutant concentrations. Temperature affects reaction rates and gas-particle partitioning; humidity influences secondary particle formation and deposition processes; wind determines pollutant transport and dilution; atmospheric pressure affects boundary layer dynamics; and precipitation is a primary mechanism for wet deposition of pollutants [35].

2.2.3. Data Processing Platform

Both the satellite and climate datasets were processed using the Google Earth Engine (GEE) platform, a cloud-based computing environment specifically designed for geospatial analysis at scale. GEE provides access to a multi-petabyte catalog of satellite imagery and geospatial datasets, along with high-performance computing resources and algorithms for processing these data [36]. This platform was instrumental for this research, as it enabled efficient processing of the massive datasets required for multi-year analysis of atmospheric conditions across northwestern Syria.

The platform’s JavaScript API facilitated the implementation of complex spatial and temporal analyses, including time series extraction, statistical computations, and visualization of spatial patterns. For more intensive computational tasks and custom algorithm development, Google Colab was employed in conjunction with GEE, leveraging Python version 3.10 libraries for advanced statistical analysis and visualization of the processed data.

Table 1. Summary of data sources utilized in the study of air pollution dynamics in northwestern Syria.

Data Source	Parameter	Spatial Resolution	Temporal Resolution	Coverage Period	Source/Platform	Access Method
Sentinel-5P TROPOMI	NO2 column density	1 × 1 km2	Daily	July 2018–present	ESA/Copernicus	GEE collection: COPERNICUS/S5P/NRTI/L3_NO2
Sentinel-5P TROPOMI	CO column density	1 × 1 km2	Daily	November 2018–present	ESA/Copernicus	GEE collection: COPERNICUS/S5P/NRTI/L3_CO
ERA5	Air temperature (2m)	0.25° (~11 km)	Hourly (aggregated to monthly)	1979–present	ECMWF	GEE collection: ECMWF/ERA5/MONTHLY
ERA5	Relative humidity	0.25° (~11 km)	Hourly (aggregated to monthly)	1979–present	ECMWF	Equations (1)–(3)
ERA5	Wind speed	0.25° (~11 km)	Hourly (aggregated to monthly)	1979–present	ECMWF	GEE collection: ECMWF/ERA5/MONTHLY
ERA5	Surface pressure	0.25° (~11 km)	Hourly (aggregated to monthly)	1979–present	ECMWF	GEE collection: ECMWF/ERA5/MONTHLY
ERA5	Precipitation	0.25° (~11 km)	Hourly (aggregated to monthly)	1979–present	ECMWF	GEE collection: ECMWF/ERA5/MONTHLY

summarizes the key characteristics of the datasets utilized in this study, including their spatial and temporal resolutions, coverage periods, and relevant parameters.

2.3. Methodology

This study employed a comprehensive analytical framework to investigate the spatiotemporal dynamics of air pollution in northwestern Syria, focusing on NO₂ and CO concentrations between 2019 and 2024. The methodology integrated satellite remote sensing, cloud computing, and statistical analysis to overcome the challenges of data collection in conflict-affected regions (Figure 2).

2.3.1. Data Acquisition and Preprocessing

Sentinel-5P TROPOMI Level-3 data for NO₂ and CO column densities were accessed through the Google Earth Engine (GEE) platform. Monthly mean concentration maps were generated for both pollutants across the entire study area (51,893 km²). For consistency in visualization and comparative analysis, a standardized color scale was implemented: 0.2–1.4 mmol/m² for NO₂ and 2.5–4.0 mmol/m² for CO concentrations. To facilitate direct comparison between pollutants with differing concentration ranges, the minimum and maximum values across all temporal observations were assessed and stabilized for each pollutant accordingly.

Data quality assurance included filtering for cloud cover and sensor anomalies using the quality assurance bands provided with the Sentinel-5P products. Following Lorente et al. [25], only measurements with a quality assurance value ≥ 0.75 were retained to ensure data reliability. All data processing operations were executed using Google Colab notebooks, leveraging Python libraries for geospatial analysis (GeoPandas version 0.13.2, Rasterio version 1.3.8) and the GEE Python version 3 API for efficient data retrieval and processing of large datasets.

2.3.2. Spatiotemporal Analysis of Pollutant Concentrations

The spatiotemporal analysis proceeded through two complementary approaches: First, a comprehensive mapping analysis generated a series of 144 monthly average concentration maps (72 for each gas) over the six-year study period. These maps provided visual documentation of the spatial distribution patterns and their evolution over time, particularly highlighting seasonal variations and anomalous events. The mapping analysis paid special attention to urban centers (Aleppo, Hama, Homs, Idlib, and Latakia), industrial zones (Sheikh Najjar, Hessia, and Al-Rai), and surrounding rural areas to identify distinct pollution patterns associated with different land uses and economic activities.

To understand the differential dispersion patterns between NO₂ and CO, a simplified atmospheric dispersion coefficient (D) was calculated using Equation (4), based on the approach described by Zhang et al. [37]:

$$D={\displaystyle \frac{K_H}{M_w\cdot g\cdot H}}$$

(4)

where KH is the horizontal diffusivity coefficient, Mw is the molecular weight of the pollutant (46.01 g/mol for NO₂, 28.01 g/mol for CO), g is gravitational acceleration (9.81 m/s²), and H is the atmospheric boundary layer height (m). This coefficient quantifies the theoretical dispersion capacity of each pollutant based on its physical properties.

Second, time series analysis extracted pollutant concentration data for eight key locations (five urban centers and three industrial areas) to quantify long-term trends and seasonal patterns. For each location, a 3 × 3 km grid cell centered on the site was used to extract monthly mean concentrations. Linear regression analysis was applied to these time series to determine the rate and direction of change in pollutant concentrations over the study period, as expressed in Equation (5):

$$C\left(t\right)={\beta }_0+{\beta }_{1}t+{\epsilon }_t$$

(5)

where C(t) represents the pollutant concentration (NO₂ or CO) at time t, β₀ is the intercept (initial concentration), β1 is the slope coefficient representing the rate of change (mol/m²/month), and ${\epsilon }_t$ is the error term. The statistical significance of the trend was assessed using the t-statistics as follows:

$$t={\displaystyle \frac{{\beta }_1}{SE\left({\beta }_1\right)}}$$

(6)

where SE(β₁) is the standard error of the slope coefficient. Trends were considered statistically significant at p < 0.05. To quantify the seasonal component of pollutant variations, a time series decomposition approach was implemented following the methodology of Filonchyk et al. [14]. An additive decomposition model was applied as shown in Equation (7):

$$Y_t=T_t+S_t+R_t$$

(7)

where Y_t is the observed pollutant concentration at time t, Tt is the trend component, S_t is the seasonal component, and R_t is the remainder or residual component. This decomposition allowed for the systematic evaluation of seasonal patterns independent of long-term trends.

2.3.3. Correlation Analysis with Meteorological Factors

To investigate the influence of meteorological conditions on pollutant concentrations, a correlation analysis was performed between the time series of NO₂ and CO concentrations and five key meteorological variables: air temperature at 2 m height, relative humidity, wind speed, surface atmospheric pressure, and precipitation. Monthly averages of these variables were extracted from the ERA5 reanalysis dataset for the same locations and time periods as the pollutant concentration data.

Spearman’s rank correlation coefficient (ρ) was calculated to quantify the strength and direction of these relationships using Equation (8), as recommended by Seinfeld and Pandis [38] for non-parametric assessment of environmental data:

$$\rho =1-{\displaystyle \frac{6\sum _{i=1}^n{d}_i^2}{n(n^2-1)}}$$

(8)

where ρ is Spearman’s correlation coefficient, di is the difference in ranks between pollutant concentration and meteorological variable for observation i, and n is the number of paired observations. This approach was chosen for its robustness to non-normal distributions and ability to capture monotonic but non-linear relationships that are common in atmospheric processes. Correlation matrices were generated for each location, enabling systematic comparison of meteorological influences on pollution patterns across different urban and industrial settings. Statistical significance was assessed at the α = 0.05 level to identify robust correlations, following the guidelines established by Meng et al. [13].

3. Results

3.1. Spatiotemporal Distribution of NO₂ Concentrations in Northwestern Syria

3.1.1. Seasonal Variation Patterns

Analysis of monthly NO₂ concentration maps derived from Sentinel-5P TROPOMI data (2019–2024) reveals pronounced seasonal cycling across northwestern Syria (Figure 3a–f). The observed pattern demonstrates a consistent annual periodicity characterized by peak concentrations during winter months (December–February), followed by a significant decrease during spring (March–May), a slight elevation during summer (June–August), and a gradual increase through autumn (September–November) toward winter maxima. This seasonal pattern aligns with findings from similar studies in Mediterranean and Middle Eastern regions [12,14], where winter maxima are typically attributed to increased fuel combustion for heating, reduced photochemical degradation, and lower planetary boundary layer heights that concentrate pollutants near the surface.

3.1.2. Spatial Distribution Characteristics

The spatial distribution of NO₂ across northwestern Syria exhibits a distinctive pattern characterized by concentration gradients and localized hotspots (Figure 3). A general east-to-west gradient is evident throughout the study period, with concentrations progressively decreasing eastward from the Mediterranean coast toward inland regions. This pattern is partially modified along the Syrian coast, where a north-to-south gradient emerges, with concentrations decreasing northward except in the vicinity of urban and industrial centers. This spatial arrangement reflects the combined influence of emission sources, topography, and prevailing meteorological conditions. Contrary to expectations, the influence of topography on NO₂ concentrations, specifically the anticipated decrease with elevation, is not prominently visible throughout most of the study area. This topographical effect is only clearly discernible in the coastal mountain range, where measurably lower NO₂ levels correspond to higher elevations. The limited visibility of this effect elsewhere may be attributed to the overwhelming influence of anthropogenic emission sources and regional atmospheric transport patterns that mask elevation-related concentration gradients.

3.1.3. Urban and Industrial Emission Hotspots

The most striking feature of NO₂ spatial distribution is the presence of distinct hotspots centered on major urban and industrial areas (Figure 3a–f). These high-concentration zones appear as well-defined, localized formations that expand and contract seasonally while maintaining their spatial coherence throughout the study period. The persistence of these patterns confirms that human and industrial activities represent the dominant source of NO₂ emissions in the region. Furthermore, the relatively limited dispersion of these hotspots, which maintain their discrete spatial definitions despite seasonal variations, can be attributed to the molecular properties of NO₂, specifically its relatively high molecular weight compared to air, which constrains its horizontal transport and favors vertical mixing near emission sources.

The most prominent hotspots identified in the analysis include the following:

Aleppo metropolitan area and Sheikh Najjar Industrial City complex: This represents the most consistent and intense NO₂ emission center throughout the study period, reflecting Aleppo’s status as Syria’s largest city and principal economic hub.

Homs urban-industrial cluster: A significant concentration zone encompasses Syria’s third-largest city, its associated oil refinery, and Hessia Industrial City. This hotspot exhibits notable temporal variability, occasionally contracting to the immediate vicinity of Hessia’s industrial boundaries.

Hama metropolitan area: A persistent high-concentration zone is evident across all temporal frames, indicating stable emission sources throughout the study period.

Manbij area: A smaller but nearly permanent hotspot emerges in the northeastern section of the study area, becoming particularly pronounced from 2022 onward, suggesting the development or intensification of emission sources in this region.

Notably, the coastal urban centers of Tartous and Latakia display moderate to low NO₂ concentrations relative to inland cities of comparable size. This pattern likely reflects the influence of active westerly winds and elevated humidity levels along the Mediterranean coast, which enhance dispersion and deposition processes, effectively diluting NO₂ concentrations. The Banias area, despite hosting a major oil refinery, shows only intermittent concentration elevations, further supporting the significant role of coastal meteorological conditions in moderating pollution levels.

3.1.4. Areas with Moderate Concentration Patterns

An intriguing aspect of the spatial analysis is the absence of pronounced hotspots around Idlib and the Al-Rai industrial area despite sustained human activity levels. Instead, these regions exhibit generally stable, moderate concentrations that follow seasonal variations consistent with the broader regional pattern. This moderate concentration profile likely stems from two key factors: the relatively recent establishment of the Al-Rai industrial complex, which has not yet reached the emission intensity of more established industrial zones, and the dispersed settlement pattern of Idlib and its surrounding countryside, where significant populations of internally displaced persons have spread, creating a more diffuse emission profile compared to densely urbanized areas.

3.1.5. Temporal Anomalies and Event-Specific Patterns

Several notable temporal anomalies in NO₂ concentrations were identified throughout 2019–2024 (Figure 3a–f), revealing the influence of environmental and socioeconomic factors on regional air quality. Weather-driven anomalies appeared as unusually high concentrations in coastal Tartous during January 2019 and 2022, coinciding with frost events that prompted increased fuel combustion for crop protection, while a region-wide increase occurred in November 2021 during an early cold snap. Conversely, warmer-than-usual temperatures in January 2022/2023 corresponded with reduced NO₂ levels. Forest fires north of Latakia in July 2023 triggered a dramatic concentration spike, while industrial activity fluctuations manifested as concentration decreases during COVID-19 lockdowns (March–May 2020), fuel supply shortages (2021), reduced operations (autumn 2022), and sustained challenges (March–October 2023–2024). These patterns highlight the utility of satellite monitoring for tracking socioeconomic dynamics in data-poor regions. Despite economic challenges and conflict-related disruptions, urban and industrial hotspots persisted throughout the study period, underscoring the significant impact of anthropogenic activities on regional air quality and suggesting that targeted emission controls in these source regions could substantially improve air quality.

3.2. Spatiotemporal Distribution of CO Concentrations in Northwestern Syria

3.2.1. Seasonal Variation Patterns

Analysis of the monthly CO concentration maps derived from Sentinel-5P TROPOMI data (2019–2024) reveals distinct spatiotemporal patterns that differ significantly from those observed for NO₂ (Figure 4a–f). CO exhibits a characteristic seasonal cycle, with concentrations elevated during winter months, peaking in spring (particularly April), and then declining through summer before reaching annual minima in autumn (especially October). This spring maximum coincides with the widespread agricultural practice of pre-planting field burning, a significant source of CO emissions in rural areas throughout the region.

3.2.2. Spatial Distribution Characteristics

Unlike NO₂, which forms concentrated hotspots around urban and industrial centers, CO displays a more diffuse spatial distribution across northwestern Syria. This broader dispersion pattern can be attributed to CO’s molecular weight (28.01 g/mol) being similar to that of air (approximately 28.97 g/mol), facilitating its atmospheric transport and creating more uniform concentration gradients. This physical property enables CO to function as an effective tracer for regional-scale atmospheric transport processes rather than a precise indicator of local emission sources.

The spatial distribution of CO demonstrates a clear topographical influence, with concentrations progressively decreasing toward the southeastern portions of the study area, which correspond to higher-elevation regions. This altitude-dependent pattern is particularly evident over the coastal mountain range, where CO levels diminish markedly compared to adjacent lowland areas such as the Syrian coast and the Al-Ghab plain. This topographical sensitivity reflects the fundamental dynamics of atmospheric mixing and vertical stratification, with pollutants typically more concentrated in low-lying areas where they can accumulate beneath temperature inversions.

3.2.3. Urban and Industrial Emission Hotspots

Several significant spatial anomalies emerged during the study period, disrupting the general distribution patterns. Most notably, extraordinarily high CO concentrations were detected north of Latakia, northwest of Hama, and east of Tartous in September–October 2020, corresponding to documented large-scale forest fires that affected thousands of hectares. Similarly, a pronounced concentration spike occurred north of Latakia in July 2023, coinciding with extensive forest fires that consumed substantial woodland areas. These observations align with established research demonstrating the significant contribution of biomass burning to atmospheric CO loads [17].

3.2.4. Temporal Anomalies and Event-Specific Patterns

Temporal anomalies were also evident, particularly during autumn 2022, when CO concentrations decreased precipitously across the study area, most dramatically in October. This unprecedented reduction, while consistent with the typical seasonal minimum, was intensified by atmospheric instability characterized by turbulent wind patterns and regional temperature drops. Conversely, August 2021 and 2024 exhibited sharp, atypical concentration increases spanning the entire study area, contradicting the expected seasonal pattern. These anomalies corresponded to documented regional-scale increases in CO concentrations across the eastern Mediterranean basin, amplified by synoptic wind patterns that facilitated widespread dispersal.

The combined analysis of spatial and temporal CO patterns demonstrates this pollutant’s utility as an indicator of regional-scale atmospheric processes rather than local emission sources. While NO₂ effectively delineates urban and industrial activity centers through its distinct hotspots, CO provides complementary information on broader atmospheric transport mechanisms, topographical influences, and large-scale combustion events such as forest fires. This differential behavior highlights the importance of multi-pollutant monitoring approaches for comprehensive air quality assessment in complex environments like northwestern Syria, where anthropogenic emissions interact with natural processes and challenging meteorological conditions.

3.3. Time Series and General Trends of NO₂ and CO Concentrations in Major Cities and Industrial Areas

The spatiotemporal analysis of NO₂ and CO concentrations in northwestern Syria reveals compelling patterns that reflect the complex interplay between political governance, economic conditions, and environmental factors. The data presents a natural experiment where different regions under varying political control exhibit distinct pollution trajectories.

Analysis of monthly NO₂ averages (Figure 5) reveals a clear political-economic signature in the data. Areas under opposition control demonstrate stable or increasing NO₂ concentrations, while government-controlled territories show consistent declines. Al-Rai Industrial City exemplifies this pattern with a robust upward trend (6.18 × 10⁻⁸ mol/m²) and average concentration of 0.000031 mol/m², reflecting uninterrupted industrial activities facilitated by stable fuel and raw material supplies. Similarly, Idlib shows a moderate upward trend (1.50 × 10⁻⁸ mol/m²) with average concentrations of 0.000032 mol/m², indicating sustained economic activities despite regional instability.

Conversely, government-controlled territories demonstrate pronounced downward trends, with Hessia Industrial City exhibiting the most dramatic decline (−2.6 × 10⁻⁷ mol/m²) despite its high average concentration (0.000058 mol/m²). This pattern extends to Homs, Hama, Latakia, and Tartous, where average concentrations of 0.000049, 0.000049, 0.000033, and 0.000039 mol/m², respectively, coexist with consistent downward trends. The stark contrast between these regions validates the hypothesis that economic sanctions have significantly constrained industrial activities in government-controlled areas. Interestingly, Aleppo and Sheikh Najjar Industrial City occupy an intermediate position, with high average concentrations (0.000065 and 0.000043 mol/m², respectively) but minimal upward trends (1.57 × 10⁻⁸ and 1.67 × 10⁻⁸ mol/m²). This suggests that despite their historical industrial significance, these areas maintain reduced operations under economic constraints while avoiding complete industrial shutdown.

CO concentrations present a different pattern, with uniform downward trends across all locations regardless of political control (Figure 6). The steepest declines appear in Aleppo (−2.53 × 10⁻⁵ mol/m²), Tartous (−2.23 × 10⁻⁵ mol/m²), and Sheikh Najjar (−1.96 × 10⁻⁵ mol/m²), while Idlib and Hama show more modest reductions (−1.00 × 10⁻⁶ and −3.96 × 10⁻⁶ mol/m² respectively). This universal pattern suggests region-wide improvements in combustion technologies and possibly changes in transportation patterns transcending political boundaries. The coastal cities of Latakia and Tartous recorded the highest average CO concentrations (0.033049 and 0.032499 mol/m²) with the largest fluctuations (standard deviations of 0.002298 and 0.002207 mol/m²), likely reflecting their maritime activities and coastal meteorological conditions. Conversely, Hessia Industrial City maintained the lowest average CO concentration (0.029690 mol/m²), consistent with reduced industrial activity resulting from economic sanctions.

Both pollutants exhibit pronounced seasonal variations, with winter peaks and summer troughs evident across all locations. This cyclical pattern is particularly apparent for NO₂, where winter concentrations exceed summer levels by factors of 2–3 across all sites. The persistence of these seasonal patterns across politically divided regions underscores the fundamental role of meteorological conditions in pollutant accumulation, as reduced boundary layer heights and increased heating demands amplify winter concentrations.

The statistical distribution of the data reveals critical information about emission stability. The standard deviation for NO₂ in Aleppo (0.000018 mol/m²) significantly exceeds that of other cities, indicating greater variability in industrial operations. This suggests sporadic activity patterns possibly tied to fluctuating resource availability under sanctions, contrasting with the more consistent operations in opposition-controlled territories.

These findings demonstrate the unprecedented utility of satellite remote sensing for monitoring socioeconomic dynamics in conflict zones through their environmental signatures. The data reveal how different governance regimes create distinct pollution trajectories within a single geographic region, with unintended environmental benefits emerging from economic constraints rather than deliberate policy interventions. The inverted relationship between economic prosperity and environmental quality in this conflict zone challenges conventional environmental Kuznets curve assumptions, suggesting that any future economic recovery in government-controlled territories may bring increased pollution unless accompanied by deliberate environmental safeguards. This underscores the importance of incorporating environmental considerations into post-conflict reconstruction planning for northwestern Syria.

3.4. Correlation of Climatic Factors with Changes in CO and NO₂ Concentrations

The correlation analysis between climatic factors and air pollutant concentrations reveals distinct patterns that provide valuable insights into the complex dynamics governing pollution distribution in northwestern Syria. The correlation matrices shown in Figure 7 demonstrate clear differences in how NO₂ and CO interact with meteorological conditions, highlighting the influence of both pollutant properties and local geographical contexts.

For NO₂, the correlation matrices reveal a striking dichotomy between industrial and residential areas. Industrial cities consistently demonstrate weaker correlations with climatic variables compared to residential areas, validating the theoretical premise that continuous emission sources dilute climate-driven variability. In Sheikh Najjar Industrial City, correlations with atmospheric pressure (0.49), relative humidity (0.33), and precipitation (0.28) remain moderate to weak, while negative correlations with temperature (−0.39) and wind speed (−0.34) reflect limited meteorological influence on emissions. Hessia Industrial City follows a similar pattern, with marginally weaker correlations with wind speed and precipitation, attributable to its topographical position in the wind shadow of Lebanon’s eastern mountains, which modifies westerly air flows responsible for regional precipitation patterns.

Al-Rai Industrial City presents an extreme case where climate factors have negligible influence on NO₂ concentrations, with even the strongest correlation factor (relative humidity) not exceeding 0.20. This exceptional decoupling from meteorological influence suggests highly consistent industrial operations regardless of seasonal or weather conditions, aligning with the economic stability previously observed in opposition-controlled territories.

Conversely, residential cities demonstrate robust correlations between NO₂ and climate variables. Atmospheric pressure exhibits particularly strong positive correlations (0.62–0.70), as does precipitation (0.40–0.61), while temperature shows pronounced negative correlations (−0.55 to −0.76). Wind speed similarly demonstrates strong negative correlations (−0.53 to −0.73) in most locations, with Latakia being the notable exception (−0.39). These patterns reflect the fundamental influence of meteorological conditions on pollutant dispersion in areas where emissions fluctuate with daily and seasonal human activities, particularly heating demand. The negative temperature correlations align with findings from urban studies in temperate regions [38], where winter conditions enhance NO₂ concentrations through increased heating emissions, lower mixing heights, and reduced photochemical degradation.

Relative humidity exhibits moderate to strong positive correlations (0.43–0.64) with NO₂ across most residential cities, consistent with the literature suggesting that humid conditions can enhance nitrogen oxide chemical transformations [37]. Interestingly, Latakia deviates from this pattern, showing a negative correlation (−0.34), likely due to its consistently high coastal humidity regardless of season, which disrupts the typical humidity-pollution relationship observed inland.

For CO, the correlation patterns differ substantially from NO₂, reflecting fundamental differences in molecular properties. With a molecular weight (28.01 g/mol) slightly less than air (28.97 g/mol), CO’s atmospheric behavior demonstrates greater horizontal transport and vertical mixing, preventing strong localized correlations with climate variables. Consequently, even in residential areas with seasonal emission patterns, CO correlations remain predominantly weak.

Hessia Industrial City and Homs represent particularly interesting cases for CO, showing negligible correlations with all meteorological parameters. This pattern likely results from their strategic positions along the Damascus-Homs-Aleppo transportation corridor, where continuous vehicle emissions combine with industrial activities to maintain relatively stable CO concentrations regardless of meteorological conditions. This finding supports previous research indicating that major transportation networks can stabilize CO concentrations through continuous emissions [14].

Across other cities, the only consistent correlations observed for CO were moderate negative relationships with temperature (−0.49 to −0.29, except Homs at −0.20) and weak positive correlations with precipitation (0.30–0.42). These patterns align with fundamental atmospheric chemistry principles, where lower temperatures reduce photochemical degradation and vertical mixing, while precipitation can temporarily reduce particulate matter but has a limited effect on gaseous CO concentrations [13].

These correlation patterns have significant implications for air quality monitoring and management in conflict-affected regions. The strong climate–pollution correlations in residential areas suggest that meteorological data could serve as a proxy indicator for NO₂ pollution when direct monitoring is impossible. Conversely, the weak correlations in industrial zones indicate that remote sensing of pollutant concentrations provides valuable insights into industrial activity patterns that cannot be inferred from meteorological data alone. This differential sensitivity creates complementary monitoring approaches that together provide a more comprehensive understanding of pollution dynamics in complex conflict zones.

3.5. Contribution to Sustainable Development Goals

Spatiotemporal analysis of air pollution in northwestern Syria through remote sensing technologies directly supports multiple Sustainable Development Goals (SDGs) by providing critical environmental data in a conflict-affected region where conventional monitoring is impossible. This section examines how findings from this investigation contribute to specific SDG targets and indicators, quantifying their respective contributions to sustainability objectives and classifying them as direct or indirect contributions.

3.5.1. Environmental Health and Human Wellbeing Dimension

Significant contributions to SDG 3 (Good Health and Well-being) emerge from this investigation, particularly regarding target 3.9: “By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water, and soil pollution and contamination” [34]. Spatiotemporal mapping of NO₂ and CO concentrations across northwestern Syria provides critical baseline data for indicator 3.9.1 (Mortality rate attributed to household and ambient air pollution), which is otherwise unavailable in this conflict zone. This represents a direct contribution to SDG 3.9 as it directly measures key atmospheric pollutants relevant to mortality assessments.

Remote sensing technologies have emerged as invaluable tools for monitoring atmospheric pollutants, offering continuous spatial and temporal coverage across inaccessible regions, which is essential for health risk assessment in areas where ground-based monitoring networks are compromised [5,6]. Findings regarding pronounced seasonal variations in pollutant concentrations, with winter peaks exceeding summer levels by factors of 2–3 across all sites, highlight critical exposure periods that require targeted public health interventions [1].

Furthermore, this study addresses SDG 11 (Sustainable Cities and Communities), specifically target 11.6: “By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management”. Detailed urban pollution hotspot mapping, particularly in Aleppo (average NO₂ concentration of 0.000065 mol/m²) and Homs (0.000049 mol/m²), provides essential data for indicator 11.6.2 (Annual mean levels of fine particulate matter in cities). This constitutes a direct contribution, as the data can be directly applied to urban air quality assessments. By identifying NO₂ as a critical indicator of air quality and anthropogenic activity, primarily originating from combustion processes in vehicles, power plants, and industrial facilities, these findings facilitate urban air quality management and sustainable urban planning in post-conflict reconstruction scenarios [10,11].

3.5.2. Climate Action and Environmental Governance Dimension

Substantial contributions to SDG 13 (Climate Action) are evident in this investigation, particularly target 13.3: “Improve education, awareness-raising and human and institutional capacity on climate change mitigation, adaptation, impact reduction, and early warning”. The development of satellite-based monitoring methodologies using Google Earth Engine demonstrates innovative approaches for indicator 13.3.2 (Number of countries that have communicated the strengthening of institutional, systemic, and individual capacity-building to implement adaptation, mitigation, and technology transfer) [39,40,41]. This represents an indirect contribution as it provides methodological frameworks rather than direct measurements of the indicator.

The demonstrated correlation between meteorological factors and pollutant concentrations—with atmospheric pressure exhibiting strong positive correlations (0.62–0.70) with NO₂ in residential areas—provides critical insights into climate–pollution interactions. Such knowledge is essential for developing integrated climate–air quality management strategies in conflict-affected regions [37]. Observations that CO’s molecular weight (28.01 g/mol), slightly less than ambient air, facilitates greater horizontal transport and vertical mixing, resulting in more diffuse spatial distribution patterns, highlight the importance of regional atmospheric circulation in pollutant dispersion, directly relevant to climate action planning [13,14].

3.5.3. Peace, Justice, and Institutional Dimension

Perhaps most significantly, this analytical work addresses SDG 16 (Peace, Justice, and Strong Institutions), particularly target 16.6: “Develop effective, accountable, and transparent institutions at all levels”. By demonstrating how satellite remote sensing can effectively monitor socioeconomic dynamics through environmental indicators in conflict zones, these findings support the development of evidence-based environmental governance institutions during and after conflict [4]. This represents an indirect contribution to SDG 16.6, as it provides methodological approaches rather than direct institutional measurements.

Results revealed that “areas under opposition control demonstrate stable or increasing NO₂ concentrations, while government-controlled territories show consistent declines”, with Al-Rai Industrial City showing a robust upward trend (6.18 × 10⁻⁸ mol/m²) compared to Hessia Industrial City’s dramatic decline (−2.6 × 10⁻⁷ mol/m²). These differentiated pollution signatures illuminate how varying governance regimes create distinct environmental trajectories within a shared geographic region, underscoring the importance of institutional capacity and governance quality in environmental management [2].

Additionally, contributions to target 16.7, “Ensure responsive, inclusive, participatory, and representative decision making at all levels”, are achieved by providing transparent environmental data that can support inclusive post-conflict environmental planning. The observation that unintended environmental benefits emerged from economic constraints rather than deliberate policy interventions highlights the need for intentional environmental safeguards in future reconstruction efforts [9].

3.5.4. Technological Innovation and Partnership Dimension

The methodological approach exemplifies SDG 17 (Partnerships for the Goals), particularly target 17.6: “Enhance North-South, South-South, and triangular regional and international cooperation on and access to science, technology, and innovation and enhance knowledge-sharing on mutually agreed terms”. The integration of Sentinel-5P satellite data with Google Earth Engine demonstrates innovative technological applications for environmental monitoring in challenging contexts [42,43]. This represents an indirect contribution as it demonstrates technological knowledge sharing rather than directly measuring cooperation agreements.

The processing and analysis of satellite data for atmospheric monitoring have been revolutionized by cloud computing platforms such as Google Earth Engine (GEE). This platform enables efficient processing of massive geospatial datasets using advanced algorithms, significantly expanding access to environmental monitoring capabilities in resource-limited settings [36]. Correlation analysis between air pollutant concentrations and meteorological variables further demonstrates how integrated data approaches can yield valuable insights despite limited ground-based infrastructure.

Further contributions to target 17.9, “Enhance international support for implementing effective and targeted capacity-building in developing countries”, are achieved by developing transferable methodologies for environmental monitoring in conflict-affected regions. Remote sensing and Google Earth Engine integration for monitoring anthropogenic air pollution risks and enhancing environmental sustainability in the eastern Mediterranean represents a capacity-building framework that can be adapted for other conflict zones globally [39].

3.5.5. Integrated Analysis of SDG Contributions

Table 2. Summary of contributions to Sustainable Development Goals and their specific targets.

SDG Dimension	SDG	Specific Targets	Contribution (%)	Relevance to Research	Contribution Type
Environmental Health and Human Wellbeing	SDG 3: Good Health and Well-being	3.9.1: Mortality rate attributed to household and ambient air pollution	25%	Provides critical baseline data on air pollutant concentrations affecting human health in conflict-affected regions	Direct
	SDG 11: Sustainable Cities and Communities	11.6.2: Annual mean levels of fine particulate matter in cities	20%	Maps urban pollution hotspots in major cities (Aleppo: 0.000065 mol/m2; Homs: 0.000049 mol/m2) to support sustainable urban planning	Direct
Climate Action and Environmental Governance	SDG 13: Climate Action	13.3.2: Number of countries with strengthened capacity for climate change mitigation and adaptation	15%	Demonstrates correlations between meteorological factors and pollution (atmospheric pressure: 0.62–0.70 correlation with NO2)	Indirect
Peace, Justice, and Institutional	SDG 16: Peace, Justice, and Strong Institutions	16.6.1: Primary government expenditures as a proportion of original approved budget	25%	Reveals governance impacts on pollution patterns (Al-Rai: +6.18 × 10−8 mol/m2 vs. Hessia: −2.6 × 10−7 mol/m2)	Indirect
		16.7.2: Proportion of population who believe decision making is inclusive and responsive		Provides transparent environmental data to support inclusive post-conflict planning	Indirect
Technological Innovation and Partnership	SDG 17: Partnerships for the Goals	17.6.1: Number of science and/or technology cooperation agreements between countries	15%	Demonstrates successful integration of European satellite technology (Sentinel-5P) with cloud computing (Google Earth Engine)	Indirect
		17.9.1: Dollar value of financial and technical assistance committed to developing countries		Develops transferable methodologies for environmental monitoring in conflict-affected regions	Indirect

summarizes the contributions to specific SDG targets and indicators, indicating the relative contribution percentage for each goal based on direct relevance and potential impact. The health and environmental dimensions (SDGs 3 and 11) receive the highest contribution percentages (25% and 20% respectively) due to the direct relevance of air pollution monitoring to public health and urban sustainability. The peace and governance dimension (SDG 16) follows closely (25%), given the unique insights into environmental governance in conflict zones. Climate action and partnership dimensions (SDGs 13 and 17) receive somewhat lower percentages (15% each) but remain significant contributions, particularly through methodological innovations.

3.5.6. Pathways from Environmental Data to SDG Achievement

The remote sensing data generated in this study affect SDG achievement through specific practical pathways that extend beyond information provision to measurable development outcomes. For SDG 3, the pollution concentration data enable health systems to establish evidence-based air quality standards, implement early warning systems that trigger public health responses during high-pollution periods, and allocate medical resources to areas with elevated health risks. The seasonal pollution patterns identified (winter peaks 2–3 times higher than summer) support the development of seasonal health preparedness programs and targeted medical interventions. For SDG 11, urban planners can utilize the pollution hotspot maps to redesign transportation networks, establish low-emission zones in the most affected areas (such as Aleppo with 0.000065 mol/m² NO₂), and prioritize green infrastructure investments based on pollution reduction potential. The correlation patterns between meteorological conditions and pollutant concentrations inform building codes and urban design standards that measurably improve air quality. For SDG 13, the demonstrated climate–pollution relationships support the development of integrated climate adaptation strategies that simultaneously address air quality and climate resilience. For SDG 16, transparent environmental monitoring provides accountability mechanisms that support evidence-based governance and environmental justice initiatives in post-conflict reconstruction. For SDG 17, the transferable methodologies enable capacity-building programs that expand environmental monitoring capabilities across similar conflict-affected regions, creating measurable improvements in environmental governance capacity.

3.6. Environmental Management Implications and Recommendations

The correlation patterns identified in this study provide essential scientific foundations for developing evidence-based air quality management strategies in conflict-affected regions. The strong positive correlations between atmospheric pressure and NO₂ concentrations (0.62–0.70) in residential areas suggest that early warning systems could be implemented to alert communities during high-pressure meteorological conditions when pollution levels are likely to increase, enabling proactive health protection measures such as reducing outdoor activities and implementing temporary emission controls. The consistent negative correlations between wind speed and pollutant concentrations (−0.54 to −0.74) support urban planning recommendations, including the creation of wind corridors in cities, avoiding construction patterns that obstruct natural airflow, and strategically positioning industrial facilities to utilize prevailing wind patterns for enhanced pollutant dispersion. The pronounced seasonal variations, with winter concentrations exceeding summer levels by factors of 2–3, indicate the need for seasonal pollution control measures such as promoting alternative heating technologies, implementing stricter emission standards during cold months, and issuing public health advisories during high-pollution periods. Furthermore, the distinct pollution patterns between different political territories highlight the importance of incorporating environmental safeguards into post-conflict reconstruction planning from the outset, ensuring that economic recovery does not compromise air quality gains achieved during periods of reduced industrial activity. These findings collectively support the development of integrated air quality management frameworks that combine meteorological monitoring, urban planning considerations, and targeted policy interventions tailored to the unique challenges of conflict-affected regions.

The findings and methodologies presented in this study are directly applicable to multiple stakeholders operating in conflict-affected regions. International organizations, including the United Nations Environment Programme (UNEP), World Health Organization (WHO), and humanitarian agencies, can utilize these satellite-based monitoring approaches for health risk assessments, environmental impact evaluations, and post-conflict reconstruction planning where traditional monitoring infrastructure is unavailable. National environmental agencies, public health departments, and urban planning authorities can implement these methodologies to establish cost-effective air quality monitoring systems that provide continuous spatial and temporal coverage. The identified correlation patterns between meteorological conditions and pollutant concentrations enable meteorological services to develop predictive models for pollution forecasting, while the pollution hotspot mapping provides crucial information for targeted intervention strategies by local authorities and international development organizations. Furthermore, the demonstrated ability to monitor industrial activities through pollution signatures offers valuable insights for economic planning agencies and international sanctions monitoring bodies seeking to assess industrial capacity and compliance in politically complex regions.

4. Conclusions

This study investigated the spatiotemporal dynamics of air pollution in northwestern Syria (2019–2024) by analyzing NO₂ and CO concentrations using Sentinel-5P satellite data processed through Google Earth Engine. The findings reveal distinctive pollution patterns that reflect the complex interplay between political governance, economic conditions, and environmental factors in a conflict-affected region. NO₂ concentrations demonstrated clear political-economic signatures, with opposition-controlled territories (Al-Rai, Idlib) showing stable or increasing trends and weak meteorological correlations (<0.20), indicating consistent industrial operations despite regional instability. Conversely, government-controlled areas (Hessia and Sheikh Najjar) exhibited significant downward trends with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO displayed a fundamentally different spatial pattern with broader geographical distribution, greater sensitivity to topography, and uniform downward trends across all locations regardless of political control, suggesting region-wide improvements in combustion technologies.

Both pollutants exhibited pronounced seasonal variations, with winter concentrations exceeding summer levels by factors of 2–3 across all sites, underscoring the fundamental role of meteorological conditions in pollutant accumulation. Notably, coastal urban centers (Tartous and Latakia) displayed moderate to low NO₂ concentrations relative to inland cities of comparable size, likely reflecting the influence of active westerly winds and elevated humidity levels. The study also identified significant pollution spikes during extraordinary events, particularly large-scale forest fires north of Latakia in July 2023, and concentration decreases during COVID-19 lockdowns (March–May 2020). Statistically, Aleppo showed significantly higher standard deviation for NO₂ (0.000018 mol/m²) compared to other cities, indicating greater variability in industrial operations, possibly tied to fluctuating resource availability under sanctions. This reveals an inverted relationship between economic prosperity and environmental quality in this conflict zone, challenging conventional environmental Kuznets curve assumptions.

This research demonstrates significant contributions to sustainable development goals, including providing critical baseline data for public health assessment (SDG 3), mapping urban pollution hotspots to support sustainable cities (SDG 11), establishing climate–pollution correlations relevant to climate action (SDG 13), revealing governance impacts on environmental quality essential for peace and institutional development (SDG 16), and demonstrating technological partnerships through integrated satellite and cloud computing methodologies (SDG 17). Looking forward, northwestern Syria faces a critical inflection point in environmental management as reconstruction initiatives advance. To ensure sustainable development that balances economic recovery with public health protection, comprehensive air quality governance frameworks must be incorporated into post-conflict reconstruction planning, including integrated monitoring networks, updated regulations, capacity-building programs, and international partnerships.