AHP-Weighted Multi-Pollutant Composite with Getis–Ord Gi* Hotspot Analysis from Sentinel-5P over Rajasthan, India

AHP-Weighted Multi-Pollutant Composite with Getis–Ord Gi* Hotspot Analysis from Sentinel-5P over Rajasthan, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AHP-Weighted Multi-Pollutant Composite with Getis–Ord Gi* Hotspot Analysis from Sentinel-5P over Rajasthan, India Saurabh Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8004346/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Satellite observations from Sentinel-5P/TROPOMI provide consistent, statewide trace-gas monitoring suited to capture these patterns. This study establishes a reproducible satellite-based approach that quantifies statewide trends and identifies statistically significant pollution hotspots for action. Pollutant layers were polarity-aligned and normalized to a common 0–1 scale. An Analytic Hierarchy Process (AHP) produced pollutant weights after checking the standard consistency criterion (CR ≤ 0.10), and a Composite Burden (CB) surface was formed as the weighted sum of normalized layers. Hot- and cold-spot clusters were then mapped using the Getis–Ord Gi*. Values were sampled to a regular point grid in a projected CRS, neighborhood scale was set using Incremental Spatial Autocorrelation, Row standardization and false-discovery-rate correction were applied, and significance was reported at conventional statistical levels. O₃ and CH₄ exhibit the clearest statewide increases across 2018–2024 and are significant in non-parametric tests; SO₂ shows a small decline; NO₂, CO, and HCHO display modest, often non-significant drifts. The CB and Getis–Ord Gi*outputs reveal three integrated hotspot belts: (1) the northern canal fringe (Sri Ganganagar–Hanumangarh–Churu), (2) a north-eastern industrial–transport arc from Khairthal–Tijara/Bhiwadi–Neemrana–Kotputli–Behror through Alwar–Bharatpur–Dholpur, and (3) a south-central/south-eastern power–cement corridor around Chittorgarh and Kota–Baran–Jhalawar. Representative districts show broadly uniform O₃ and CH₄ rises, for example Alwar (O₃ +11.2%, CH₄ +3.3%), Jaipur (+ 11.3%, + 3.6%), Kota (+ 10.9%, + 3.2%), and Chittorgarh (+ 11.0%, + 3.6%); Bikaner also follows this pattern (+ 11.4%, + 2.9%). AHP-weighted fusion of Sentinel-5P species combined with inferential Getis–Ord Gi* clustering yields a transparent, state-scale picture of Rajasthan’s multi-pollutant burden, supporting differentiated actions on NOₓ/VOC control along corridors, SO₂ abatement at point sources, and CH₄ mitigation in irrigated and waste-intensive districts. Geographic Information Systems Environmental Engineering Sentinel-5P TROPOMI Analytic Hierarchy Process (AHP) Composite Burden (CB) Getis–Ord Gi* hotspot analysis spatial statistics multi-pollutant Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Air pollution remains one of the world’s leading health risks, implicated in ~ 8.1 million deaths in 2021 and driven largely by exposure to PM₂.₅, NO₂, and O₃[ 1 ]. WHO’s 2021 Air Quality Guidelines (AQGs) further tightened recommended limits for these pollutants in light of stronger epidemiological evidence. India bears a substantial share of the global burden, with persistent exceedances in many urban agglomerations considered districts[ 2 ]. Rajasthan—characterized by arid climate, mineral industries, and rapid infrastructure build-out along the Delhi–Mumbai Industrial Corridor (DMIC) faces complex, seasonally modulated pollution dynamics that interact with monsoon circulation and cross-boundary transport within the broader Indo-Gangetic airshed[ 3 ][ 4 ][ 5 ]. Satellite remote sensing now enables consistent, regional-scale tracking of multiple gaseous pollutants. The Sentinel-5 Precursor (S5P) mission carrying TROPOMI provides daily, high-resolution global observations of NO₂, SO₂, CO, O₃, CH₄ and HCHO used widely for air-quality assessment and policy support; products and algorithms have undergone extensive development and validation. Combined with cloud-based processing in Google Earth Engine (GEE), these datasets enable reproducible spatiotemporal analyses at state and district scales. Bauwens et al. (2020) analyzed global NO₂ responses to COVID-19 restrictions using TROPOMI, reporting ~ 33–38% column decreases relative to 2019 and demonstrating the sensitivity of satellite data to rapid emission changes; our study leverages this sensitivity for multi-pollutant mapping over Rajasthan[ 6 ]. Cooper et al. (2022) mapped fine-scale global changes in ambient NO₂ and confirmed large, heterogeneous reductions during 2020, underscoring the value of high-resolution satellite products for city-corridor diagnostics; we extend this logic to a six-gas composite[ 7 ]. Fisher et al. (2024) revised global NO₂ decline estimates from TROPOMI, emphasizing methodological choices (baselines, sampling) that affect inferred magnitudes; we adopt a transparent, reproducible workflow (monthly OFFL L3 in GEE) to minimize such sensitivities[ 8 ]. Graham et al. (2024) combined TROPOMI with trajectory modeling to partition Delhi NOₓ into ~ 70% local and ~ 30% non-local contributions in the post-monsoon, reinforcing the need to consider transport—addressed here by coupling trend signals with spatial clustering[ 9 ]. Suthar et al. (2024) analyzed Sentinel-5P CH₄ over five Indian cities (including Jaipur), demonstrating urban CH₄ variability and the utility of satellite CH₄ for Indian contexts; we include CH₄ in a multi-gas composite to reflect broader atmospheric stress[ 9 ]. Rathore et al. (2024) evaluated spatio-temporal air quality in Rajsamand (Rajasthan) and documented significant variability across 2015–2019, motivating a state-scale satellite synthesis like ours for 2018–2024 [ 10 ]. Douros et al. (2023) compared TROPOMI NO₂ with CAMS analyses/forecasts, supporting the use of satellite columns for evaluating modeled pollution at sub-regional scales; we rely on monthly OFFL L3 to ensure stability for trend and hotspot statistics[ 11 ]. Ruidas and Pal (2022) used the Getis–Ord Gi* statistic to map PM₂.₅ hotspots in Maharashtra, showing the value of inferential clustering for Indian air-quality applications; we adopt Getis–Ord Gi* for multi-pollutant rasters and classify at 90/95/99% significance[ 12 ]. Recto et al. (2023) demonstrated optimized hotspot analysis (Getis–Ord Gi*) of remote-sensing AOD over Metro Manila, illustrating robust parameterization strategies that we adapt for Rajasthan’s grid and scale [ 13 ]. Chen et al. (2023) developed a multi-pollutant Air Quality Health Index (AQHI) with stronger mortality prediction than the conventional AQI, arguing for health-aligned composites; our AHP-weighted CAQI follows this multi-pollutant, decision-oriented design [ 14 ]. Ouyang et al. (2022) discussed implementing the WHO 2021 Air Quality Guidelines, underscoring the policy need for integrated indicators and local evidence; our Rajasthan-focused composite and hotspots address this implementation gap [ 15 ]. Ishizaka and Labib (2011) reviewed AHP methodological advances (judgment scales, consistency, sensitivity), providing the rationale for transparent weighting; we apply AHP with CR < 0.1 and test weight sensitivity[ 16 ]. Li et al. (2024) proposed an integrated framework for multi-air-pollutant exposure in dense urban areas, reinforcing that multi-pollutant assessment is more policy-relevant than single-species views—precisely the shift our study operationalizes for Rajasthan[ 17 ]. Satellite air-quality studies still lack a reproducible multi-gas framework (NO₂, SO₂, CO, O₃, CH₄, HCHO) that harmonizes Sentinel-5P/TROPOMI OFFL-L3 with transparent QA and version-aware trend estimation and that applies inferential spatial clustering to delineate statistically significant hot/cold corridors across regions and seasons. Reported magnitudes are often sensitive to retrieval upgrades e.g., NO₂ v2.2 increases tropospheric columns by ~ 10–40% in polluted scenes complicating cross-study comparability; moreover, Getis–Ord Gi* based clustering is rarely integrated with multi-pollutant analyses for state- or country-scale inference. This motivates a generalizable pipeline anchored in TROPOMI’s instrument capabilities and formal hot-spot statistics. The objective of this study to establish a compact multi-gas satellite analysis for 2018–2024 that harmonizes Sentinel-5P retrievals for NO₂, SO₂, CO, O₃, CH₄, and HCHO under transparent QA; estimates pixel-level linear trends via version-aware OLS with 95% confidence intervals; synthesizes species using the Analytic Hierarchy Process with pairwise comparisons satisfying CR ≤ 0.10; and delineates statistically significant hot- and cold-spot corridors using the Getis–Ord Gi* statistic at 90/95/99% confidence. 2. Methodology 2.1 Overview This study integrates Sentinel-5P/TROPOMI observations, multi-criteria weighting (AHP), and spatial clustering (Getis–Ord Gi*) to quantify and map multi-pollutant dynamics across Rajasthan during 2018–2024). The workflow comprises four stages (Fig. 1 ). 1. Data acquisition & preprocessing Sentinel-5P OFFL Level-3 column products for SO₂, NO₂, CO, O₃, CH₄, and HCHO were retrieved, quality-filtered using product QA flags, and harmonized to a common grid and coordinate reference system. 2. Temporal aggregation & trend analysis Pixelwise monthly means (2018–2024) were computed. Linear trends were estimated using ordinary least squares (OLS) with 95% confidence intervals, with summaries reported at state and district levels via zonal statistics. Robust, non-parametric checks (Sen’s slope; Mann–Kendall) were applied where indicated. 3. Multi-pollutant composite (AHP) Monthly pollutant maps were normalized with aligned polarity (higher = worse) using a robust P5–P95 min–max scaling to [0,1]. A 6×6 AHP pairwise comparison produced criterion weights (Σw = 1) after consistency verification (CR ≤ 0.10), yielding a Composite Burden map as CB = Σ(w·Z). 4. Spatial clustering (Getis–Ord Gi*) CB (and, optionally, single-species layers) was sampled to a regular point grid in a projected CRS. Neighborhoods were defined via Incremental Spatial Autocorrelation (first peak) or k-nearest neighbors, with Row standardization and FDR multiple-testing correction. Gi* Z-scores and p-values delineated hot- and cold-spots at 90/95/99% confidence for spatial prioritization. 2.2 Sentinel-5P Data Processing Pollutant concentration data were derived from the Copernicus Sentinel-5P/TROPOMI OFFL L3 datasets available on GEE. The pollutants and their parameters are summarized in Table 1 . Data were filtered for the study area (Rajasthan shapefile) and temporally aggregated to monthly means for the 2018–2024 period. Table 1 Sentinel-5P pollutant datasets used in the analysis Pollutant Product ID (GEE) Unit NO₂ COPERNICUS/S5P/OFFL/L3_NO2 Mol m⁻² SO₂ COPERNICUS/S5P/OFFL/L3_SO2 Mol m⁻² CO COPERNICUS/S5P/OFFL/L3_CO Mol m⁻² O₃ COPERNICUS/S5P/OFFL/L3_O3 DU CH₄ COPERNICUS/S5P/OFFL/L3_CH4 ppb HCHO COPERNICUS/S5P/OFFL/L3_HCHO Mol m⁻² 2.3 Temporal Trend Analysis To assess long-term variations, monthly mean pollutant values were fitted with a linear regression model: \(\:{Y}_{t}=\alpha\:+\beta\:t+{\epsilon\:}_{t}\) where \(\:{Y}_{t}\) is the pollutant concentration at time \(\:t\) , \(\:\alpha\:\) is the intercept, and \(\:\beta\:\) represents the temporal trend (positive = increasing, negative = decreasing). The 95% confidence interval of \(\:\beta\:\) was computed to evaluate the significance of changes. Seasonal variability (June–August monsoon) was highlighted for contextual interpretation. 2.4 AHP-Based Multi-Pollutant Weighting Each pollutant raster was min–max normalized over the pooled 2018–2024 distribution: \(\:\begin{array}{cccc}&\:{X}_{i}=\frac{{P}_{i}-{P}_{i,\text{m}\text{i}\text{n}}}{{P}_{i,\text{m}\text{a}\text{x}}-{P}_{i,\text{m}\text{i}\text{n}}},&\:&\:\text{(2)}\end{array}\) where \(\:{P}_{i}\) is the pollutant field and \(\:{X}_{i}\in\:\left[\text{0,1}\right]\) . The AHP was employed to assign relative importance to each pollutant based on environmental and health impact literature (Table 2 ). Pairwise comparison matrices were developed, and consistency ratios (CR < 0.1) were verified. The normalized weights ( \(\:{W}_{i}\) ) were applied to derive a Composite Air Quality Index (CAQI) as: \(\:CAQI=\sum\:_{i=1}^{n}\:{W}_{i}\times\:{X}_{i}\) where \(\:{X}_{i}\) is the normalized value of pollutant i , and \(\:{W}_{i}\) is its corresponding weight. Table 2 AHP-derived weights for six Sentinel-5P pollutants Pollutant Weight (%) Fraction \(\:{\varvec{W}}_{\varvec{i}}\) Explanation Reference for AHP Approach NO₂ 34% 0.34 Most critical pollutant for respiratory and cardiovascular health, frequently assigned the highest weight in published AHP studies. Based on AHP in respiratory AQHI by Zeng et al. (2024)[ 18 ]. O₃ 20% 0.20 Second most influential oxidant, strongly linked to respiratory inflammation and hospital admissions. Zeng et al. (2024) methodology of multi-pollutant weighting[ 18 ]. SO₂ 14% 0.14 Second most influential oxidant, strongly linked to respiratory inflammation and hospital admissions. Zeng et al. (2024) used AHP weighting for SO₂ in AQHI)[ 18 ]. CO 12% 0.12 Moderate influence; affects oxygen transport and cardiovascular systems. Zeng et al. (2024) included CO in the AHP formulation[ 18 ]. HCHO 14% 0.14 Recognized carcinogen (IARC Group 1); weight reflects its chronic exposure risks. Extended by expert judgment in composite index (no direct literature for HCHO) CH₄ 6% 0.06 Minor direct toxicity but contributes to tropospheric O₃ formation and climate forcing. Assigned minimal weight due to low direct toxicity in ambient conditions 3.5 Hotspot and Cold-Spot Detection Spatial clustering of pollutant concentrations was performed using the Getis–Ord Gi* statistic implemented in ArcGIS Pro. Statistics identifies statistically significant clusters of high (hotspot) and low (cold spot) values relative to a random distribution. \(\:{G}_{i}^{*}=\frac{\sum\:_{j}\:{w}_{i,j}{x}_{j}-\stackrel{\prime }{X}\sum\:_{j}\:{w}_{i,j}}{S\sqrt{\frac{n\sum\:_{j}\:{w}_{i,j}^{2}-(\sum\:_{j}\:{w}_{i,j}{)}^{2}}{n-1}}}\) where \(\:{x}_{j}\) is the pollutant value at location j , \(\:{w}_{i,j}\) is the spatial weight, \(\:\stackrel{\prime }{X}\) is the mean, \(\:S\) is the standard deviation, and \(\:n\) is the number of features. Positive Getis–Ord Gi* z-scores indicate hotspots (high-value clusters), while negative values denote cold spots (low-value clusters). Significance levels of 90%, 95%, and 99% were used to classify spatial patterns (Fig. 8 ). 3.6 Validation and Integration To verify the robustness of hotspot results, Getis–Ord Gi* z-scores were cross-validated with the AHP-weighted CAQI raster and annual mean pollutant maps. The integrated interpretation combined temporal trends with spatial clustering, enabling identification of persistent pollution corridors (e.g., Jaipur–Alwar–Bharatpur industrial belt and northern trans-boundary zones). 3. Result The assessment of six pollutants using monthly Sentinel-5P (TROPOMI) column data for Rajasthan from January 2018 to December 2024. Multi-pollutant information is synthesized through an AHP weighted overlay to generate a composite burden surface, and Getis–Ord Gi* statistics are applied to map statistically significant hot- and cold-spot clusters. The approach provides a consistent, space-borne baseline for trend detection and spatial prioritization across the state. 3.1 Spatial hot- and cold-spot clustering of pollutants (Gi), Rajasthan, 2024* 3.1.1 NO₂ hot- and cold-spot patterns from Getis–Ord Gi* clustering (2024) Getis–Ord Gi* resolves three principal NO₂ hotspot corridors in 2024. A persistent belt occupies the northern fringe around Sri Ganganagar–Hanumangarh, consistent with heavy freight interchange with Punjab–Haryana and episodic enhancement during the post-monsoon residue-burning season documented for the Delhi airshed. A second, fragmented but intense arc extends across the Khairthal–Tijara / Bhiwadi–Neemrana / Kotputli–Behror industrial tract through Alwar–Deeg–Bharatpur–Dholpur (Fig. 2 ). This arc overlaps large RIICO estates and the Delhi–Mumbai/Delhi–Jaipur freight spine (NH-48/DMIC), a nationally recognized heavy-haul corridor. A third cluster spans the south-central to south-eastern belt, with maxima near Chittorgarh (cement/mineral processing) and Kota–Baran–Jhalawar (power and allied industry), matching source profiles in which stationary combustion and process stacks dominate local NOₓ. Cold-spot clusters dominate the western Thar districts (Jaisalmer, Barmer, Jalore) where sparse sources and open terrain enhance ventilation. Overall, the pattern aligns with evidence that transport, industry, and episodic biomass burning are the main NOₓ drivers in northern India. 3.1.2 SO₂ hot- and cold-spot patterns from Getis–Ord Gi* clustering (2024) SO₂ hotspots form a continuous north-to-north-east corridor from Sri Ganganagar–Hanumangarh through Churu to the Alwar industrial arc (Khairthal–Tijara–Kotputli–Behror–Deeg–Bharatpur–Dholpur), and a second complex over south-central and south-eastern Rajasthan with peaks at Chittorgarh and the Kota–Baran–Jhalawar power belt (Fig. 3 ). These clusters mirror the geography of coal-fired power and cement manufacturing the dominant SO₂ sources in India and are consistent with national reporting on delayed roll-out of flue-gas desulfurization at coal plants. Extensive cold spots persist across the western desert districts, reflecting low point-source density and stronger ventilation. 3.1.3 CO hot- and cold-spot patterns from Getis–Ord Gi* clustering (2024) CO hotspots trace three belts: the northern fringe (Sri Ganganagar–Hanumangarh–Churu), the north-eastern industrial/transport arc (Khairthal–Tijara–Bhiwadi–Neemrana–Kotputli–Behror to Deeg–Bharatpur–Dholpur), and the south-eastern industrial–power belt centred on Kota–Bundi–Baran–Jhalawar, with an additional cell near Banswara and a smaller hotspot around Beawar–Ajmer along NH-48(Fig. 4 ). This configuration is consistent with CO’s dominant sources vehicle exhaust and other incomplete-combustion processes in transport, small industry, brick kilns and power/industrial complexes while broad cold spots over Jaisalmer–Barmer reflect sparse sources and open desert ventilation. 3.1.4 O₃ hot- and cold-spot patterns from Getis–Ord Gi* clustering (2024) A strongly bifurcated O₃ geography emerges. High-value clusters line the northern fringe (Sri Ganganagar–Hanumangarh–Churu), compatible with downwind photochemical production from NOₓ/VOC precursors under strong insolation and limited near-source titration by NO (Fig. 5 ). Cold-spot clusters dominate the southern hill–forest belt (Udaipur–Banswara–Dungarpur), where biogenic uptake and dry deposition to vegetation are efficient sinks. 3.1.5 CH₄ hot- and cold-spot patterns from Getis–Ord Gi* clustering (2024) CH₄ hotspots follow the irrigated canal belt across the northern fringe (Sri Ganganagar–Hanumangarh, extending eastward toward Bharatpur–Dholpur) and smaller urban-industrial nodes, consistent with sectoral sources from livestock/manure, flooded rice, waste management and fossil-fuel activities. Cold-spot clusters dominate the south and south-west (Udaipur–Dungarpur–Banswara) where emission densities are lower (Fig. 6 ). Globally and in India, agriculture and waste are the largest, near-term-mitigable sources of CH₄, while the primary atmospheric sink is oxidation by the hydroxyl radical context that supports the spatial CH₄ pattern detected by Getis–Ord Gi*. 3.1.6 HCHO hot- and cold-spot patterns from Getis–Ord Gi* clustering (2024) Formaldehyde hotspots align with the north-eastern industrial belt (Khairthal–Tijara to Dholpur), the Kota–Baran–Jhalawar power–industrial corridor, a compact Banswara–Dungarpur cell, and a smaller cluster along the northern fringe(Fig. 7 ). These zones coincide with intense anthropogenic VOC activity (solvents, transport, industry) and, in the south, warm vegetated uplands where biogenic VOCs (e.g., isoprene) elevate HCHO. Cold-spots dominate the arid west (Jaisalmer–Barmer and adjoining interior tracts). Using HCHO as a short-lived proxy for reactive VOC emissions and, together with NO₂, as a diagnostic of O₃ -formation sensitivity is well grounded in recent reviews, supporting interpretation of these clusters as VOC-rich environments. 3.2 Statewide annual trends and significance testing (2018–2024) Figure 8 synthesizes statewide pollutant behavior for 2018–2024 using monthly series with a fitted linear trend and its confidence envelope. Interpretation here focuses on year-to-year tendencies at the Rajasthan scale. O₃ and CH₄ present the clearest statewide rises. The annual means toward the end of the record are consistently above the early-period baseline and the fitted envelopes incline upward across the window, indicating persistent growth in the oxidant and CH₄ burdens. NO₂, carbon monoxide and formaldehyde exhibit only gentle positive drifts when viewed annually. Interannual variability remains large relative to the fitted trends, so any statewide increase over 2018–2024 is gradual rather than decisive. SO₂ is the only species with a weak decline. The regression envelope tilts slightly downward, suggesting easing of the statewide SO₂ burden, though year-to-year fluctuations are substantial. Table 3 shows the Mann–Kendall (MK) and Sen’s slope statistics for Rajasthan’s statewide the non-parametric tests corroborate the visual interpretation. O₃ and CH₄ register statistically significant statewide increases over 2018–2024. NO₂, CO, and HCHO retain positive Sen slopes but do not meet the Mann–Kendall significance threshold at the annual scale, indicating incremental rather than robust change. SO₂ carries a negative Sen slope, yet the Mann–Kendall result remains inconclusive, consistent with a small, uneven decline. The analysis confirms that the clearest statewide trends during 2018–2024 are the increases in O₃ and CH₄, while other pollutants exhibit weak or statistically non-significant annual tendencies. Table 3 Mann–Kendall (MK) and Sen’s slope statistics for Rajasthan’s statewide Pollutant Sen slope (per year) % per year Kendall’s τ Z p (two-sided) NO₂ 5.22×10⁻⁷ Mol m⁻²·yr⁻¹ + 0.74%/yr 0.137 1.786 0.074 SO₂ −3.87×10⁻⁶ Mol m⁻²·yr⁻¹ −4.53%/yr −0.0537 −0.672 0.502 CO 1.87×10⁻⁴ Mol m⁻²·yr⁻¹ + 0.53%/yr 0.0909 1.174 0.241 O₃ 9.62×10⁻⁴ Mol m⁻²·yr⁻¹ + 0.78%/yr 0.218 2.785 0.00535 CH₄ 9.52 ppb·yr⁻¹ + 0.50%/yr 0.553 6.972 3.12×10⁻¹² HCHO 1.11×10⁻⁶ Mol m⁻²·yr⁻¹ + 0.77%/yr 0.0678 0.849 0.396 3.3 Major District-wise change (2018–2024) and the composite hotspot field (2024) To place the statewide trends in a policy frame, we (i) compare major district-level percentage change between 2018 and 2024 for each pollutant (Table 3 ) and (ii) examine the integrated hotspot map produced from the AHP-weighted multi-pollutant surface and Getis–Ord Gi* clustering (Fig. 9 ). Together, these outputs show where the burden has risen or eased and where multiple pollutants co-locate in statistically significant clusters. Table 3 . Major District-wise percentage change Across the 13 major districts shown, SO₂ exhibits a consistent decline, with the steepest reductions in Alwar (− 60.2%) and Sikar (− 52.4%), and the smallest decline in Udaipur (− 33.2%). NO₂ changes are positive but modest for most districts, ranging from ≈ + 2% (Bhilwara, Kota) to a higher increase in Udaipur (+ 15.8%) and Bikaner (+ 12.3%). Carbon monoxide rises slightly everywhere ( ≈ + 9–11%), while O₃ shows a uniform increase of about + 11–12% across districts, indicating a broad strengthening of the oxidant background. CH₄ increases are small but pervasive ( ≈ + 3–4%). Formaldehyde records the sharpest relative growth among the reactive gases, with the largest increases in Kota (+ 34.6%) and Jhalawar/Baran ( ≈ + 30%+), compared with smaller rises in Ajmer (+ 17.7%) and Alwar (+ 15.8%). In short, 2018→2024 brings a widespread sulfur decline, weak-to-moderate gains in primary combustion tracers (NO₂, CO) and pronounced rises in secondary/precursor species (O₃, HCHO) almost everywhere. The AHP-weighted overlay followed by Getis–Ord Gi* clustering resolves three integrated hotspot belts. A continuous northern corridor runs from Sri Ganganagar–Hanumangarh toward Churu, a second high-intensity arc occupies the north-east from Khairthal–Tijara / Bhiwadi–Neemrana / Kotputli–Behror through Alwar–Bharatpur–Dholpur, and a third cluster spans the south-central to south-eastern industrial–power belt around Chittorgarh and the Kota–Baran–Jhalawar region. Secondary cells occur near Beawar–Ajmer and locally in the far south. In contrast, the cold-spot domain covers the western Thar districts—Jaisalmer, Barmer and Jalore—and extends along the Aravalli–Mewar hill belt toward Dungarpur–Banswara, reflecting sparse emission densities and better ventilation. Methodologically, Getis–Ord Gi* identifies locations where values are significantly higher or lower than their neighborhood, while the AHP overlay ensures that clusters reflect agreement across gases rather than a single-species anomaly. 4. Discussion The workflow time-series trend analysis followed by Getis–Ord Gi* clustering yields a coherent picture of Rajasthan’s air-quality burden and its spatial organization. Getis–Ord Gi* is specifically suited to detect statistically significant local clusters (high–high or low–low) relative to the statewide field, making it appropriate for hotspot diagnostics and policy targeting[ 19 ]. At the statewide scale (2018–2024), O₃ and CH₄ exhibit the most persistent annual increases, while NO₂, CO, and HCHO show only modest drifts and SO₂ declines slightly. The hotspot maps help interpret these tendencies. NO₂ and CO hotspots align with high-traffic freight spines and industrial belts most prominently the Delhi–Jaipur–Kota corridors and the Alwar–Bharatpur–Dholpur arc consistent with independent evidence that transport and industrial combustion dominate urban NOₓ and CO loads in Northern India[ 20 ]. SO₂ hotspots, in contrast, concentrate around coal-based power and cement clusters (e.g., Kota–Baran–Jhalawar; Chittorgarh; northern fringe near Suratgarh), a pattern long documented in satellite-based catalogues of large SO₂ point sources and in inventories linking SO₂ primarily to coal combustion and kiln processes[ 21 ]. The O₃ map shows a north–south contrast: enhanced values along the northern fringe and coherent cold spots over the vegetated hill–forest belt in the south. This distribution is consistent with established O₃ chemistry production downwind of NOₓ/VOC source regions under strong radiation, suppression near intense NO by titration, and efficient removal via dry deposition to vegetation[ 22 ]. CH₄ clustering highlights the irrigated canal command areas and selected eastern districts, reflecting the spatial concentration of agricultural and waste sources; this aligns with global assessments that identify agriculture, waste, and fossil energy as the dominant, near-term sectors for CH₄ mitigation[ 23 ]. The Getis–Ord Gi* results corroborate the AHP overlay by showing that multi-pollutant burdens are not randomly distributed but are anchored to specific emission geographies: (i) freight corridors and mixed manufacturing belts (NO₂/CO/HCHO), (ii) coal and cement hubs (SO₂), (iii) photochemical and deposition regimes (O₃), and (iv) irrigated agri-systems and waste footprints (CH₄). This evidence base supports differentiated actions: clean-freight measures and tighter vehicle/solvent controls along the Delhi–Jaipur–Kota axis; compliance and desulfurization at coal and cement point sources; precursor management to limit O₃ formation in the north; and CH₄ interventions focused on livestock/manure handling, flooded-rice practices, and solid-waste systems in hotspot districts[ 24 ][ 25 ]. 5. Conclusions State-scale analysis for 2018–2024 indicates a coherent multi-pollutant picture for Rajasthan: O₃ and CH₄ rise steadily, SO₂ eases modestly, and NO₂, carbon monoxide, and formaldehyde show only small drifts when aggregated annually. The O₃ increase and its north–south contrast (higher along the irrigated/industrial north; lower over the vegetated hill–forest belt) are chemically consistent with stronger photochemical production downwind of NOx–VOC corridors and enhanced dry deposition over forests and are broadly in line with recent India-focused O₃ syntheses and seasonal exceedances reported for NCR[ 26 ][ 27 ]. Getis–Ord Gi* clustering validates that burdens are not random: high-significance hot spots organize along the Delhi–Jaipur–Kota freight/manufacturing axis and other industrial belts (NO₂/CO/HCHO), coal-power and cement hubs (SO₂), and irrigated canal command and waste-rich districts (CH₄), while cold spots dominate sparsely populated desert tracts and the southern forested belt. This use of Getis–Ord Gi* to detect statistically significant high–high and low–low clusters relative to local neighborhoods provides a rigorous spatial diagnostic beyond visual mapping[ 28 ]. External validation supports these patterns. Satellite catalogs consistently resolve India’s largest SO₂ plumes over coal power and cement corridors coincident with the Kota–Baran–Jhalawar and Chittorgarh clusters while newer TROPOMI inventories confirm source localization at plant scale. Lockdown studies in 2020 showed concurrent NO₂ drops in TROPOMI and CPCB monitors, underpinning confidence in statewide NO₂ structures derived from space. Sectoral syntheses attribute most anthropogenic CH₄ to agriculture, waste, and fossil energy, aligning with the irrigated-belt and urban-waste hot spots[ 29 ][ 30 ][ 31 ]. Policy implications are therefore spatially explicit: (i) clean-freight and solvent/vehicle controls along the Delhi–Jaipur–Kota corridor to address NO₂/CO/HCHO; (ii) compliance and desulfurization at coal and cement point sources for SO₂; (iii) precursor management to curb O₃ in the northern plume corridor; and (iv) CH₄ mitigation focused on livestock/manure handling, flooded-rice practices, and solid-waste systems in hot-spot districts. Together, the AHP overlay and Getis–Ord Gi* maps supply a literature-consistent, decision-ready basis for prioritizing monitoring and controls where multi-pollutant benefits are most likely[ 32 ]. References ‘Air pollution accounted for 8.1 million deaths globally in 2021, becoming the second leading risk factor for death, including for children under five years’. Accessed: Oct. 15, 2025. [Online]. 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H. T, and, Puja J ‘What Is Polluting Delhis Air? A Review from 1990 to 2022’, Sustainability (Web) , vol. 15, no. 5, p. 4209, 2023, Accessed: Nov. 01, 2025. [Online]. Available: https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202402288758340440 Faridatul MI, Bari MN (2021) ‘Understanding the Long-Term Changes in Groundwater Level—A Tale of Highly Urbanized City’, Journal of Geographic Information System , vol. 13, no. 4, pp. 466–484, Aug. 10.4236/jgis.2021.134026 Getis A, Ord JK (1992) ‘The Analysis of Spatial Association by Use of Distance Statistics’, Geographical Analysis , vol. 24, no. 3, pp. 189–206. 10.1111/j.1538-4632.1992.tb00261.x Kondo K (2016) ‘Hot and Cold Spot Analysis Using Stata’, The Stata Journal: Promoting communications on statistics and Stata , vol. 16, no. 3, pp. 613–631, Sept. 10.1177/1536867X1601600304 Faridatul MI, Bari MN (2021) ‘Understanding the Long-Term Changes in Groundwater Level—A Tale of Highly Urbanized City’, Journal of Geographic Information System , vol. 13, no. 4, pp. 466–484, Aug. 10.4236/jgis.2021.134026 K GS, Krishna DS, Gautam P, Bhargav K, J. H. T, and, Puja J ‘What Is Polluting Delhis Air? A Review from 1990 to 2022’, Sustainability (Web) , vol. 15, no. 5, p. 4209, 2023, Accessed: Nov. 01, 2025. [Online]. Available: https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202402288758340440 Keerthi Lakshmi KA, Nishanth T, Satheesh Kumar MK, Valsaraj KT (2024) ‘A Comprehensive Review of Surface O₃ Variations in Several Indian Hotspots’, Atmosphere , vol. 15, no. 7, p. 852, July 10.3390/atmos15070852 ‘CPCB reports NCR Mumbai Lead in O₃ pollution levels | Delhi News - The Times of India’. Accessed: Nov. 01, 2025. [Online]. Available: https://timesofindia.indiatimes.com/city/delhi/cpcb-reports-ncr-mumbai-lead-in-O₃ -pollution-levels/articleshow/124198589.cms?utm_source = chatgpt.com Kumar P, Kumar M, Anand S, Tripathi DK, Verma NK (2025) Humanities and Sustainability from Glocal Perspectives Towards Future Earth: Proceedings of IGU Thematic Conference 2022, India . Springer Nature Biswal A et al (2021) ‘COVID-19 lockdown-induced changes in NO 2 levels across India observed by multi-satellite and surface observations’, Atmospheric Chemistry and Physics , vol. 21, no. 6, pp. 5235–5251, Apr. 10.5194/acp-21-5235-2021 Fioletov V et al (May 2020) Anthropogenic and volcanic point source SO 2 emissions derived from TROPOMI on board Sentinel-5 Precursor: first results. Atmos Chem Phys 20(9):5591–5607. 10.5194/acp-20-5591-2020 Gopikrishnan GS, Kuttippurath J, Raj S, Singh A, Abbhishek K (May 2022) Air Quality during the COVID–19 Lockdown and Unlock Periods in India Analyzed Using Satellite and Ground-based Measurements. Environ Process 9(2):28. 10.1007/s40710-022-00585-9 ‘Global Assessment Urgent steps must be taken to reduce CH₄ emissions this decade’. Accessed: Nov. 01, 2025. [Online]. Available: https://www.unep.org/news-and-stories/press-release/global-assessment-urgent-steps-must-be-taken-reduce-CH₄ Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":133199,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of data processing and spatial analysis steps\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8004346/v1/07606af0dcf7bdd9e8c1d226.png"},{"id":95088409,"identity":"d46a7703-d5d7-412c-bb1f-4bfd0dc58d57","added_by":"auto","created_at":"2025-11-04 07:51:12","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2314277,"visible":true,"origin":"","legend":"\u003cp\u003eNO₂ concentration hot- and cold-spot map for Rajasthan (2024) derived from Getis–Ord Gi* analysis.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8004346/v1/468028110b0740b41599916e.jpeg"},{"id":95088415,"identity":"44d98c41-c16f-49fb-9073-11f8c0e5443b","added_by":"auto","created_at":"2025-11-04 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concentration hot- and cold-spot map for Rajasthan (2024) derived from Getis–Ord Gi* analysis.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8004346/v1/866d8f90e52639ab6556ec62.jpeg"},{"id":95223412,"identity":"b7c138cd-de10-4cf1-8bc1-f6ed80de4aa9","added_by":"auto","created_at":"2025-11-05 16:22:12","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1968931,"visible":true,"origin":"","legend":"\u003cp\u003eHCHO concentration hot- and cold-spot map for Rajasthan (2024) derived from Getis–Ord Gi* analysis.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8004346/v1/278f5043d60e85d69f263a70.jpeg"},{"id":95088418,"identity":"c56d20be-2dd1-464e-9882-eb3ee6ec675e","added_by":"auto","created_at":"2025-11-04 07:51:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":884870,"visible":true,"origin":"","legend":"\u003cp\u003eRajasthan-wide monthly pollutant series with linear trend and 95% confidence envelopes, aggregated and interpreted for annual dynamics (2018–2024).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8004346/v1/30473b577bd2952acc5a18bd.png"},{"id":95088417,"identity":"d34dd1df-eadd-4631-8cf5-ff8ba1bf01c0","added_by":"auto","created_at":"2025-11-04 07:51:12","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2160512,"visible":true,"origin":"","legend":"\u003cp\u003eComposite AHP-weighted multi-pollutant surface for 2024 with Getis–Ord Gi* hot and cold clusters (90–99% confidence)\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8004346/v1/7fb18465714702721a351d1a.jpeg"},{"id":95230372,"identity":"86d469f4-10fa-44b3-a01c-43d57d1770c5","added_by":"auto","created_at":"2025-11-05 16:37:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16393591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8004346/v1/0aa9fda5-d91a-4761-abaa-775fdb56abe6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAHP-Weighted Multi-Pollutant Composite with Getis–Ord Gi* Hotspot Analysis from Sentinel-5P over Rajasthan, India\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAir pollution remains one of the world\u0026rsquo;s leading health risks, implicated in ~\u0026thinsp;8.1\u0026nbsp;million deaths in 2021 and driven largely by exposure to PM₂.₅, NO₂, and O₃[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. WHO\u0026rsquo;s 2021 Air Quality Guidelines (AQGs) further tightened recommended limits for these pollutants in light of stronger epidemiological evidence. India bears a substantial share of the global burden, with persistent exceedances in many urban agglomerations considered districts[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Rajasthan\u0026mdash;characterized by arid climate, mineral industries, and rapid infrastructure build-out along the Delhi\u0026ndash;Mumbai Industrial Corridor (DMIC) faces complex, seasonally modulated pollution dynamics that interact with monsoon circulation and cross-boundary transport within the broader Indo-Gangetic airshed[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Satellite remote sensing now enables consistent, regional-scale tracking of multiple gaseous pollutants. The Sentinel-5 Precursor (S5P) mission carrying TROPOMI provides daily, high-resolution global observations of NO₂, SO₂, CO, O₃, CH₄ and HCHO used widely for air-quality assessment and policy support; products and algorithms have undergone extensive development and validation. Combined with cloud-based processing in Google Earth Engine (GEE), these datasets enable reproducible spatiotemporal analyses at state and district scales. Bauwens et al. (2020) analyzed global NO₂ responses to COVID-19 restrictions using TROPOMI, reporting\u0026thinsp;~\u0026thinsp;33\u0026ndash;38% column decreases relative to 2019 and demonstrating the sensitivity of satellite data to rapid emission changes; our study leverages this sensitivity for multi-pollutant mapping over Rajasthan[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Cooper et al. (2022) mapped fine-scale global changes in ambient NO₂ and confirmed large, heterogeneous reductions during 2020, underscoring the value of high-resolution satellite products for city-corridor diagnostics; we extend this logic to a six-gas composite[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Fisher et al. (2024) revised global NO₂ decline estimates from TROPOMI, emphasizing methodological choices (baselines, sampling) that affect inferred magnitudes; we adopt a transparent, reproducible workflow (monthly OFFL L3 in GEE) to minimize such sensitivities[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Graham et al. (2024) combined TROPOMI with trajectory modeling to partition Delhi NOₓ into ~\u0026thinsp;70% local and ~\u0026thinsp;30% non-local contributions in the post-monsoon, reinforcing the need to consider transport\u0026mdash;addressed here by coupling trend signals with spatial clustering[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Suthar et al. (2024) analyzed Sentinel-5P CH₄ over five Indian cities (including Jaipur), demonstrating urban CH₄ variability and the utility of satellite CH₄ for Indian contexts; we include CH₄ in a multi-gas composite to reflect broader atmospheric stress[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Rathore et al. (2024) evaluated spatio-temporal air quality in Rajsamand (Rajasthan) and documented significant variability across 2015\u0026ndash;2019, motivating a state-scale satellite synthesis like ours for 2018\u0026ndash;2024 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Douros et al. (2023) compared TROPOMI NO₂ with CAMS analyses/forecasts, supporting the use of satellite columns for evaluating modeled pollution at sub-regional scales; we rely on monthly OFFL L3 to ensure stability for trend and hotspot statistics[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Ruidas and Pal (2022) used the Getis\u0026ndash;Ord Gi* statistic to map PM₂.₅ hotspots in Maharashtra, showing the value of inferential clustering for Indian air-quality applications; we adopt Getis\u0026ndash;Ord Gi* for multi-pollutant rasters and classify at 90/95/99% significance[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recto et al. (2023) demonstrated optimized hotspot analysis (Getis\u0026ndash;Ord Gi*) of remote-sensing AOD over Metro Manila, illustrating robust parameterization strategies that we adapt for Rajasthan\u0026rsquo;s grid and scale [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Chen et al. (2023) developed a multi-pollutant Air Quality Health Index (AQHI) with stronger mortality prediction than the conventional AQI, arguing for health-aligned composites; our AHP-weighted CAQI follows this multi-pollutant, decision-oriented design [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Ouyang et al. (2022) discussed implementing the WHO 2021 Air Quality Guidelines, underscoring the policy need for integrated indicators and local evidence; our Rajasthan-focused composite and hotspots address this implementation gap [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Ishizaka and Labib (2011) reviewed AHP methodological advances (judgment scales, consistency, sensitivity), providing the rationale for transparent weighting; we apply AHP with CR\u0026thinsp;\u0026lt;\u0026thinsp;0.1 and test weight sensitivity[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Li et al. (2024) proposed an integrated framework for multi-air-pollutant exposure in dense urban areas, reinforcing that multi-pollutant assessment is more policy-relevant than single-species views\u0026mdash;precisely the shift our study operationalizes for Rajasthan[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSatellite air-quality studies still lack a reproducible multi-gas framework (NO₂, SO₂, CO, O₃, CH₄, HCHO) that harmonizes Sentinel-5P/TROPOMI OFFL-L3 with transparent QA and version-aware trend estimation and that applies inferential spatial clustering to delineate statistically significant hot/cold corridors across regions and seasons. Reported magnitudes are often sensitive to retrieval upgrades e.g., NO₂ v2.2 increases tropospheric columns by ~\u0026thinsp;10\u0026ndash;40% in polluted scenes complicating cross-study comparability; moreover, Getis\u0026ndash;Ord Gi* based clustering is rarely integrated with multi-pollutant analyses for state- or country-scale inference. This motivates a generalizable pipeline anchored in TROPOMI\u0026rsquo;s instrument capabilities and formal hot-spot statistics.\u003c/p\u003e\u003cp\u003eThe objective of this study to establish a compact multi-gas satellite analysis for 2018\u0026ndash;2024 that harmonizes Sentinel-5P retrievals for NO₂, SO₂, CO, O₃, CH₄, and HCHO under transparent QA; estimates pixel-level linear trends via version-aware OLS with 95% confidence intervals; synthesizes species using the Analytic Hierarchy Process with pairwise comparisons satisfying CR\u0026thinsp;\u0026le;\u0026thinsp;0.10; and delineates statistically significant hot- and cold-spot corridors using the Getis\u0026ndash;Ord Gi* statistic at 90/95/99% confidence.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Overview\u003c/h2\u003e\u003cp\u003eThis study integrates Sentinel-5P/TROPOMI observations, multi-criteria weighting (AHP), and spatial clustering (Getis\u0026ndash;Ord Gi*) to quantify and map multi-pollutant dynamics across Rajasthan during 2018\u0026ndash;2024). The workflow comprises four stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e1. Data acquisition \u0026 preprocessing\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSentinel-5P OFFL Level-3 column products for SO₂, NO₂, CO, O₃, CH₄, and HCHO were retrieved, quality-filtered using product QA flags, and harmonized to a common grid and coordinate reference system.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e2. Temporal aggregation \u0026 trend analysis\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePixelwise monthly means (2018\u0026ndash;2024) were computed. Linear trends were estimated using ordinary least squares (OLS) with 95% confidence intervals, with summaries reported at state and district levels via zonal statistics. Robust, non-parametric checks (Sen\u0026rsquo;s slope; Mann\u0026ndash;Kendall) were applied where indicated.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e3. Multi-pollutant composite (AHP)\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eMonthly pollutant maps were normalized with aligned polarity (higher\u0026thinsp;=\u0026thinsp;worse) using a robust P5\u0026ndash;P95 min\u0026ndash;max scaling to [0,1]. A 6\u0026times;6 AHP pairwise comparison produced criterion weights (Σw\u0026thinsp;=\u0026thinsp;1) after consistency verification (CR\u0026thinsp;\u0026le;\u0026thinsp;0.10), yielding a Composite Burden map as CB\u0026thinsp;=\u0026thinsp;Σ(w\u0026middot;Z).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e4. Spatial clustering (Getis–Ord Gi*)\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCB (and, optionally, single-species layers) was sampled to a regular point grid in a projected CRS. Neighborhoods were defined via Incremental Spatial Autocorrelation (first peak) or k-nearest neighbors, with Row standardization and FDR multiple-testing correction. Gi* Z-scores and p-values delineated hot- and cold-spots at 90/95/99% confidence for spatial prioritization.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Sentinel-5P Data Processing\u003c/h2\u003e\u003cp\u003ePollutant concentration data were derived from the Copernicus Sentinel-5P/TROPOMI OFFL L3 datasets available on GEE. The pollutants and their parameters are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Data were filtered for the study area (Rajasthan shapefile) and temporally aggregated to monthly means for the 2018\u0026ndash;2024 period.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSentinel-5P pollutant datasets used in the analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePollutant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProduct ID (GEE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO₂\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPERNICUS/S5P/OFFL/L3_NO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMol m⁻\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO₂\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPERNICUS/S5P/OFFL/L3_SO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMol m⁻\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPERNICUS/S5P/OFFL/L3_CO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMol m⁻\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eO₃\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPERNICUS/S5P/OFFL/L3_O3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDU\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCH₄\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPERNICUS/S5P/OFFL/L3_CH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eppb\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCHO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPERNICUS/S5P/OFFL/L3_HCHO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMol m⁻\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Temporal Trend Analysis\u003c/h2\u003e\u003cp\u003eTo assess long-term variations, monthly mean pollutant values were fitted with a linear regression model:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{t}=\\alpha\\:+\\beta\\:t+{\\epsilon\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{t}\\)\u003c/span\u003e\u003c/span\u003eis the pollutant concentration at time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003eis the intercept, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the temporal trend (positive\u0026thinsp;=\u0026thinsp;increasing, negative\u0026thinsp;=\u0026thinsp;decreasing). The 95% confidence interval of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003ewas computed to evaluate the significance of changes. Seasonal variability (June\u0026ndash;August monsoon) was highlighted for contextual interpretation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.4 AHP-Based Multi-Pollutant Weighting\u003c/h2\u003e\u003cp\u003eEach pollutant raster was min\u0026ndash;max normalized over the pooled 2018\u0026ndash;2024 distribution:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\begin{array}{cccc}\u0026amp;\\:{X}_{i}=\\frac{{P}_{i}-{P}_{i,\\text{m}\\text{i}\\text{n}}}{{P}_{i,\\text{m}\\text{a}\\text{x}}-{P}_{i,\\text{m}\\text{i}\\text{n}}},\u0026amp;\\:\u0026amp;\\:\\text{(2)}\\end{array}\\)\u003c/span\u003e\u003c/span\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{i}\\)\u003c/span\u003e\u003c/span\u003eis the pollutant field and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\in\\:\\left[\\text{0,1}\\right]\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe AHP was employed to assign relative importance to each pollutant based on environmental and health impact literature (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Pairwise comparison matrices were developed, and consistency ratios (CR\u0026thinsp;\u0026lt;\u0026thinsp;0.1) were verified. The normalized weights (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{i}\\)\u003c/span\u003e\u003c/span\u003e) were applied to derive a Composite Air Quality Index (CAQI) as:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CAQI=\\sum\\:_{i=1}^{n}\\:{W}_{i}\\times\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003eis the normalized value of pollutant \u003cem\u003ei\u003c/em\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{i}\\)\u003c/span\u003e\u003c/span\u003eis its corresponding weight.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAHP-derived weights for six Sentinel-5P pollutants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePollutant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFraction \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{W}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExplanation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference for AHP Approach\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO₂\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMost critical pollutant for respiratory and cardiovascular health, frequently assigned the highest weight in published AHP studies.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBased on AHP in respiratory AQHI by Zeng et al. (2024)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eO₃\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSecond most influential oxidant, strongly linked to respiratory inflammation and hospital admissions.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZeng et al. (2024) methodology of multi-pollutant weighting[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO₂\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSecond most influential oxidant, strongly linked to respiratory inflammation and hospital admissions.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZeng et al. (2024) used AHP weighting for SO₂ in AQHI)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate influence; affects oxygen transport and cardiovascular systems.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZeng et al. (2024) included CO in the AHP formulation[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCHO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRecognized carcinogen (IARC Group 1); weight reflects its chronic exposure risks.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExtended by expert judgment in composite index (no direct literature for HCHO)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCH₄\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMinor direct toxicity but contributes to tropospheric O₃ formation and climate forcing.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAssigned minimal weight due to low direct toxicity in ambient conditions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Hotspot and Cold-Spot Detection\u003c/h2\u003e\u003cp\u003eSpatial clustering of pollutant concentrations was performed using the Getis\u0026ndash;Ord Gi* statistic implemented in ArcGIS Pro. Statistics identifies statistically significant clusters of high (hotspot) and low (cold spot) values relative to a random distribution.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{i}^{*}=\\frac{\\sum\\:_{j}\\:{w}_{i,j}{x}_{j}-\\stackrel{\\prime }{X}\\sum\\:_{j}\\:{w}_{i,j}}{S\\sqrt{\\frac{n\\sum\\:_{j}\\:{w}_{i,j}^{2}-(\\sum\\:_{j}\\:{w}_{i,j}{)}^{2}}{n-1}}}\\)\u003c/span\u003e\u003c/span\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{j}\\)\u003c/span\u003e\u003c/span\u003eis the pollutant value at location \u003cem\u003ej\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{i,j}\\)\u003c/span\u003e\u003c/span\u003eis the spatial weight, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\prime }{X}\\)\u003c/span\u003e\u003c/span\u003eis the mean, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S\\)\u003c/span\u003e\u003c/span\u003eis the standard deviation, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003eis the number of features.\u003c/p\u003e\u003cp\u003ePositive Getis\u0026ndash;Ord Gi* z-scores indicate hotspots (high-value clusters), while negative values denote cold spots (low-value clusters). Significance levels of 90%, 95%, and 99% were used to classify spatial patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Validation and Integration\u003c/h2\u003e\u003cp\u003eTo verify the robustness of hotspot results, Getis\u0026ndash;Ord Gi* z-scores were cross-validated with the AHP-weighted CAQI raster and annual mean pollutant maps. The integrated interpretation combined temporal trends with spatial clustering, enabling identification of persistent pollution corridors (e.g., Jaipur\u0026ndash;Alwar\u0026ndash;Bharatpur industrial belt and northern trans-boundary zones).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cp\u003eThe assessment of six pollutants using monthly Sentinel-5P (TROPOMI) column data for Rajasthan from January 2018 to December 2024. Multi-pollutant information is synthesized through an AHP weighted overlay to generate a composite burden surface, and Getis\u0026ndash;Ord Gi* statistics are applied to map statistically significant hot- and cold-spot clusters. The approach provides a consistent, space-borne baseline for trend detection and spatial prioritization across the state.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Spatial hot- and cold-spot clustering of pollutants (Gi), Rajasthan, 2024*\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 NO₂ hot- and cold-spot patterns from Getis\u0026ndash;Ord Gi* clustering (2024)\u003c/h2\u003e\u003cp\u003eGetis\u0026ndash;Ord Gi* resolves three principal NO₂ hotspot corridors in 2024. A persistent belt occupies the northern fringe around Sri Ganganagar\u0026ndash;Hanumangarh, consistent with heavy freight interchange with Punjab\u0026ndash;Haryana and episodic enhancement during the post-monsoon residue-burning season documented for the Delhi airshed. A second, fragmented but intense arc extends across the Khairthal\u0026ndash;Tijara / Bhiwadi\u0026ndash;Neemrana / Kotputli\u0026ndash;Behror industrial tract through Alwar\u0026ndash;Deeg\u0026ndash;Bharatpur\u0026ndash;Dholpur (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This arc overlaps large RIICO estates and the Delhi\u0026ndash;Mumbai/Delhi\u0026ndash;Jaipur freight spine (NH-48/DMIC), a nationally recognized heavy-haul corridor. A third cluster spans the south-central to south-eastern belt, with maxima near Chittorgarh (cement/mineral processing) and Kota\u0026ndash;Baran\u0026ndash;Jhalawar (power and allied industry), matching source profiles in which stationary combustion and process stacks dominate local NOₓ. Cold-spot clusters dominate the western Thar districts (Jaisalmer, Barmer, Jalore) where sparse sources and open terrain enhance ventilation. Overall, the pattern aligns with evidence that transport, industry, and episodic biomass burning are the main NOₓ drivers in northern India.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 SO₂ hot- and cold-spot patterns from Getis\u0026ndash;Ord Gi* clustering (2024)\u003c/h2\u003e\u003cp\u003eSO₂ hotspots form a continuous north-to-north-east corridor from Sri Ganganagar\u0026ndash;Hanumangarh through Churu to the Alwar industrial arc (Khairthal\u0026ndash;Tijara\u0026ndash;Kotputli\u0026ndash;Behror\u0026ndash;Deeg\u0026ndash;Bharatpur\u0026ndash;Dholpur), and a second complex over south-central and south-eastern Rajasthan with peaks at Chittorgarh and the Kota\u0026ndash;Baran\u0026ndash;Jhalawar power belt (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These clusters mirror the geography of coal-fired power and cement manufacturing the dominant SO₂ sources in India and are consistent with national reporting on delayed roll-out of flue-gas desulfurization at coal plants. Extensive cold spots persist across the western desert districts, reflecting low point-source density and stronger ventilation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 CO hot- and cold-spot patterns from Getis\u0026ndash;Ord Gi* clustering (2024)\u003c/h2\u003e\u003cp\u003eCO hotspots trace three belts: the northern fringe (Sri Ganganagar\u0026ndash;Hanumangarh\u0026ndash;Churu), the north-eastern industrial/transport arc (Khairthal\u0026ndash;Tijara\u0026ndash;Bhiwadi\u0026ndash;Neemrana\u0026ndash;Kotputli\u0026ndash;Behror to Deeg\u0026ndash;Bharatpur\u0026ndash;Dholpur), and the south-eastern industrial\u0026ndash;power belt centred on Kota\u0026ndash;Bundi\u0026ndash;Baran\u0026ndash;Jhalawar, with an additional cell near Banswara and a smaller hotspot around Beawar\u0026ndash;Ajmer along NH-48(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This configuration is consistent with CO\u0026rsquo;s dominant sources vehicle exhaust and other incomplete-combustion processes in transport, small industry, brick kilns and power/industrial complexes while broad cold spots over Jaisalmer\u0026ndash;Barmer reflect sparse sources and open desert ventilation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.1.4 O₃ hot- and cold-spot patterns from Getis\u0026ndash;Ord Gi* clustering (2024)\u003c/h2\u003e\u003cp\u003eA strongly bifurcated O₃ geography emerges. High-value clusters line the northern fringe (Sri Ganganagar\u0026ndash;Hanumangarh\u0026ndash;Churu), compatible with downwind photochemical production from NOₓ/VOC precursors under strong insolation and limited near-source titration by NO (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Cold-spot clusters dominate the southern hill\u0026ndash;forest belt (Udaipur\u0026ndash;Banswara\u0026ndash;Dungarpur), where biogenic uptake and dry deposition to vegetation are efficient sinks.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.1.5 CH₄ hot- and cold-spot patterns from Getis\u0026ndash;Ord Gi* clustering (2024)\u003c/h2\u003e\u003cp\u003eCH₄ hotspots follow the irrigated canal belt across the northern fringe (Sri Ganganagar\u0026ndash;Hanumangarh, extending eastward toward Bharatpur\u0026ndash;Dholpur) and smaller urban-industrial nodes, consistent with sectoral sources from livestock/manure, flooded rice, waste management and fossil-fuel activities. Cold-spot clusters dominate the south and south-west (Udaipur\u0026ndash;Dungarpur\u0026ndash;Banswara) where emission densities are lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Globally and in India, agriculture and waste are the largest, near-term-mitigable sources of CH₄, while the primary atmospheric sink is oxidation by the hydroxyl radical context that supports the spatial CH₄ pattern detected by Getis\u0026ndash;Ord Gi*.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.1.6 HCHO hot- and cold-spot patterns from Getis\u0026ndash;Ord Gi* clustering (2024)\u003c/h2\u003e\u003cp\u003eFormaldehyde hotspots align with the north-eastern industrial belt (Khairthal\u0026ndash;Tijara to Dholpur), the Kota\u0026ndash;Baran\u0026ndash;Jhalawar power\u0026ndash;industrial corridor, a compact Banswara\u0026ndash;Dungarpur cell, and a smaller cluster along the northern fringe(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These zones coincide with intense anthropogenic VOC activity (solvents, transport, industry) and, in the south, warm vegetated uplands where biogenic VOCs (e.g., isoprene) elevate HCHO. Cold-spots dominate the arid west (Jaisalmer\u0026ndash;Barmer and adjoining interior tracts). Using HCHO as a short-lived proxy for reactive VOC emissions and, together with NO₂, as a diagnostic of O₃ -formation sensitivity is well grounded in recent reviews, supporting interpretation of these clusters as VOC-rich environments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Statewide annual trends and significance testing (2018\u0026ndash;2024)\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e synthesizes statewide pollutant behavior for 2018\u0026ndash;2024 using monthly series with a fitted linear trend and its confidence envelope. Interpretation here focuses on year-to-year tendencies at the Rajasthan scale. O₃ and CH₄ present the clearest statewide rises. The annual means toward the end of the record are consistently above the early-period baseline and the fitted envelopes incline upward across the window, indicating persistent growth in the oxidant and CH₄ burdens.\u003c/p\u003e\u003cp\u003eNO₂, carbon monoxide and formaldehyde exhibit only gentle positive drifts when viewed annually. Interannual variability remains large relative to the fitted trends, so any statewide increase over 2018\u0026ndash;2024 is gradual rather than decisive. SO₂ is the only species with a weak decline. The regression envelope tilts slightly downward, suggesting easing of the statewide SO₂ burden, though year-to-year fluctuations are substantial.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the Mann\u0026ndash;Kendall (MK) and Sen\u0026rsquo;s slope statistics for Rajasthan\u0026rsquo;s statewide the non-parametric tests corroborate the visual interpretation. O₃ and CH₄ register statistically significant statewide increases over 2018\u0026ndash;2024. NO₂, CO, and HCHO retain positive Sen slopes but do not meet the Mann\u0026ndash;Kendall significance threshold at the annual scale, indicating incremental rather than robust change. SO₂ carries a negative Sen slope, yet the Mann\u0026ndash;Kendall result remains inconclusive, consistent with a small, uneven decline. The analysis confirms that the clearest statewide trends during 2018\u0026ndash;2024 are the increases in O₃ and CH₄, while other pollutants exhibit weak or statistically non-significant annual tendencies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMann\u0026ndash;Kendall (MK) and Sen\u0026rsquo;s slope statistics for Rajasthan\u0026rsquo;s statewide\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePollutant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSen slope (per year)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% per year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKendall\u0026rsquo;s τ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep (two-sided)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO₂\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.22\u0026times;10⁻⁷ Mol m⁻\u0026sup2;\u0026middot;yr⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;0.74%/yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO₂\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;3.87\u0026times;10⁻⁶ Mol m⁻\u0026sup2;\u0026middot;yr⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;4.53%/yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.0537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.87\u0026times;10⁻⁴ Mol m⁻\u0026sup2;\u0026middot;yr⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;0.53%/yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eO₃\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.62\u0026times;10⁻⁴ Mol m⁻\u0026sup2;\u0026middot;yr⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;0.78%/yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00535\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCH₄\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.52 ppb\u0026middot;yr⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;0.50%/yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.12\u0026times;10⁻\u0026sup1;\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCHO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11\u0026times;10⁻⁶ Mol m⁻\u0026sup2;\u0026middot;yr⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;0.77%/yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Major District-wise change (2018\u0026ndash;2024) and the composite hotspot field (2024)\u003c/h2\u003e\u003cp\u003eTo place the statewide trends in a policy frame, we (i) compare major district-level percentage change between 2018 and 2024 for each pollutant (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and (ii) examine the integrated hotspot map produced from the AHP-weighted multi-pollutant surface and Getis\u0026ndash;Ord Gi* clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Together, these outputs show where the burden has risen or eased and where multiple pollutants co-locate in statistically significant clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Major District-wise percentage change\u003c/p\u003e\n\u003cp\u003e\u003cimg 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’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\" width=\"724\" height=\"418\"\u003e\u003c/p\u003e\n\u003cp\u003eAcross the 13 major districts shown, SO₂ exhibits a consistent decline, with the steepest reductions in Alwar (\u0026minus;\u0026thinsp;60.2%) and Sikar (\u0026minus;\u0026thinsp;52.4%), and the smallest decline in Udaipur (\u0026minus;\u0026thinsp;33.2%). NO₂ changes are positive but modest for most districts, ranging from \u0026asymp;\u0026thinsp;+\u0026thinsp;2% (Bhilwara, Kota) to a higher increase in Udaipur (+\u0026thinsp;15.8%) and Bikaner (+\u0026thinsp;12.3%). Carbon monoxide rises slightly everywhere (\u0026thinsp;\u0026asymp;\u0026thinsp;+\u0026thinsp;9\u0026ndash;11%), while O₃ shows a uniform increase of about\u0026thinsp;+\u0026thinsp;11\u0026ndash;12% across districts, indicating a broad strengthening of the oxidant background. CH₄ increases are small but pervasive (\u0026thinsp;\u0026asymp;\u0026thinsp;+\u0026thinsp;3\u0026ndash;4%). Formaldehyde records the sharpest relative growth among the reactive gases, with the largest increases in Kota (+\u0026thinsp;34.6%) and Jhalawar/Baran (\u0026thinsp;\u0026asymp;\u0026thinsp;+\u0026thinsp;30%+), compared with smaller rises in Ajmer (+\u0026thinsp;17.7%) and Alwar (+\u0026thinsp;15.8%). In short, 2018\u0026rarr;2024 brings a widespread sulfur decline, weak-to-moderate gains in primary combustion tracers (NO₂, CO) and pronounced rises in secondary/precursor species (O₃, HCHO) almost everywhere.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe AHP-weighted overlay followed by Getis\u0026ndash;Ord Gi* clustering resolves three integrated hotspot belts. A continuous northern corridor runs from Sri Ganganagar\u0026ndash;Hanumangarh toward Churu, a second high-intensity arc occupies the north-east from Khairthal\u0026ndash;Tijara / Bhiwadi\u0026ndash;Neemrana / Kotputli\u0026ndash;Behror through Alwar\u0026ndash;Bharatpur\u0026ndash;Dholpur, and a third cluster spans the south-central to south-eastern industrial\u0026ndash;power belt around Chittorgarh and the Kota\u0026ndash;Baran\u0026ndash;Jhalawar region. Secondary cells occur near Beawar\u0026ndash;Ajmer and locally in the far south. In contrast, the cold-spot domain covers the western Thar districts\u0026mdash;Jaisalmer, Barmer and Jalore\u0026mdash;and extends along the Aravalli\u0026ndash;Mewar hill belt toward Dungarpur\u0026ndash;Banswara, reflecting sparse emission densities and better ventilation. Methodologically, Getis\u0026ndash;Ord Gi* identifies locations where values are significantly higher or lower than their neighborhood, while the AHP overlay ensures that clusters reflect agreement across gases rather than a single-species anomaly.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe workflow time-series trend analysis followed by Getis\u0026ndash;Ord Gi* clustering yields a coherent picture of Rajasthan\u0026rsquo;s air-quality burden and its spatial organization. Getis\u0026ndash;Ord Gi* is specifically suited to detect statistically significant local clusters (high\u0026ndash;high or low\u0026ndash;low) relative to the statewide field, making it appropriate for hotspot diagnostics and policy targeting[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt the statewide scale (2018\u0026ndash;2024), O₃ and CH₄ exhibit the most persistent annual increases, while NO₂, CO, and HCHO show only modest drifts and SO₂ declines slightly. The hotspot maps help interpret these tendencies. NO₂ and CO hotspots align with high-traffic freight spines and industrial belts most prominently the Delhi\u0026ndash;Jaipur\u0026ndash;Kota corridors and the Alwar\u0026ndash;Bharatpur\u0026ndash;Dholpur arc consistent with independent evidence that transport and industrial combustion dominate urban NOₓ and CO loads in Northern India[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. SO₂ hotspots, in contrast, concentrate around coal-based power and cement clusters (e.g., Kota\u0026ndash;Baran\u0026ndash;Jhalawar; Chittorgarh; northern fringe near Suratgarh), a pattern long documented in satellite-based catalogues of large SO₂ point sources and in inventories linking SO₂ primarily to coal combustion and kiln processes[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe O₃ map shows a north\u0026ndash;south contrast: enhanced values along the northern fringe and coherent cold spots over the vegetated hill\u0026ndash;forest belt in the south. This distribution is consistent with established O₃ chemistry production downwind of NOₓ/VOC source regions under strong radiation, suppression near intense NO by titration, and efficient removal via dry deposition to vegetation[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. CH₄ clustering highlights the irrigated canal command areas and selected eastern districts, reflecting the spatial concentration of agricultural and waste sources; this aligns with global assessments that identify agriculture, waste, and fossil energy as the dominant, near-term sectors for CH₄ mitigation[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Getis\u0026ndash;Ord Gi* results corroborate the AHP overlay by showing that multi-pollutant burdens are not randomly distributed but are anchored to specific emission geographies: (i) freight corridors and mixed manufacturing belts (NO₂/CO/HCHO), (ii) coal and cement hubs (SO₂), (iii) photochemical and deposition regimes (O₃), and (iv) irrigated agri-systems and waste footprints (CH₄). This evidence base supports differentiated actions: clean-freight measures and tighter vehicle/solvent controls along the Delhi\u0026ndash;Jaipur\u0026ndash;Kota axis; compliance and desulfurization at coal and cement point sources; precursor management to limit O₃ formation in the north; and CH₄ interventions focused on livestock/manure handling, flooded-rice practices, and solid-waste systems in hotspot districts[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eState-scale analysis for 2018\u0026ndash;2024 indicates a coherent multi-pollutant picture for Rajasthan: O₃ and CH₄ rise steadily, SO₂ eases modestly, and NO₂, carbon monoxide, and formaldehyde show only small drifts when aggregated annually. The O₃ increase and its north\u0026ndash;south contrast (higher along the irrigated/industrial north; lower over the vegetated hill\u0026ndash;forest belt) are chemically consistent with stronger photochemical production downwind of NOx\u0026ndash;VOC corridors and enhanced dry deposition over forests and are broadly in line with recent India-focused O₃ syntheses and seasonal exceedances reported for NCR[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Getis\u0026ndash;Ord Gi* clustering validates that burdens are not random: high-significance hot spots organize along the Delhi\u0026ndash;Jaipur\u0026ndash;Kota freight/manufacturing axis and other industrial belts (NO₂/CO/HCHO), coal-power and cement hubs (SO₂), and irrigated canal command and waste-rich districts (CH₄), while cold spots dominate sparsely populated desert tracts and the southern forested belt. This use of Getis\u0026ndash;Ord Gi* to detect statistically significant high\u0026ndash;high and low\u0026ndash;low clusters relative to local neighborhoods provides a rigorous spatial diagnostic beyond visual mapping[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eExternal validation supports these patterns. Satellite catalogs consistently resolve India\u0026rsquo;s largest SO₂ plumes over coal power and cement corridors coincident with the Kota\u0026ndash;Baran\u0026ndash;Jhalawar and Chittorgarh clusters while newer TROPOMI inventories confirm source localization at plant scale. Lockdown studies in 2020 showed concurrent NO₂ drops in TROPOMI and CPCB monitors, underpinning confidence in statewide NO₂ structures derived from space. Sectoral syntheses attribute most anthropogenic CH₄ to agriculture, waste, and fossil energy, aligning with the irrigated-belt and urban-waste hot spots[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e][\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePolicy implications are therefore spatially explicit: (i) clean-freight and solvent/vehicle controls along the Delhi\u0026ndash;Jaipur\u0026ndash;Kota corridor to address NO₂/CO/HCHO; (ii) compliance and desulfurization at coal and cement point sources for SO₂; (iii) precursor management to curb O₃ in the northern plume corridor; and (iv) CH₄ mitigation focused on livestock/manure handling, flooded-rice practices, and solid-waste systems in hot-spot districts. Together, the AHP overlay and Getis\u0026ndash;Ord Gi* maps supply a literature-consistent, decision-ready basis for prioritizing monitoring and controls where multi-pollutant benefits are most likely[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e\u0026lsquo;Air pollution accounted for 8.1 million deaths globally in 2021, becoming the second leading risk factor for death, including for children under five years\u0026rsquo;. Accessed: Oct. 15, 2025. [Online]. 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Accessed: Nov. 01, 2025. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unep.org/news-and-stories/press-release/global-assessment-urgent-steps-must-be-taken-reduce-CH₄\u003c/span\u003e\u003cspan address=\"https://www.unep.org/news-and-stories/press-release/global-assessment-urgent-steps-must-be-taken-reduce-CH₄\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sentinel-5P, TROPOMI, Analytic Hierarchy Process (AHP), Composite Burden (CB), Getis–Ord Gi*, hotspot analysis, spatial statistics, multi-pollutant","lastPublishedDoi":"10.21203/rs.3.rs-8004346/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8004346/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSatellite observations from Sentinel-5P/TROPOMI provide consistent, statewide trace-gas monitoring suited to capture these patterns. This study establishes a reproducible satellite-based approach that quantifies statewide trends and identifies statistically significant pollution hotspots for action. Pollutant layers were polarity-aligned and normalized to a common 0\u0026ndash;1 scale. An Analytic Hierarchy Process (AHP) produced pollutant weights after checking the standard consistency criterion (CR\u0026thinsp;\u0026le;\u0026thinsp;0.10), and a Composite Burden (CB) surface was formed as the weighted sum of normalized layers. Hot- and cold-spot clusters were then mapped using the Getis\u0026ndash;Ord Gi*. Values were sampled to a regular point grid in a projected CRS, neighborhood scale was set using Incremental Spatial Autocorrelation, Row standardization and false-discovery-rate correction were applied, and significance was reported at conventional statistical levels. O₃ and CH₄ exhibit the clearest statewide increases across 2018\u0026ndash;2024 and are significant in non-parametric tests; SO₂ shows a small decline; NO₂, CO, and HCHO display modest, often non-significant drifts. The CB and Getis\u0026ndash;Ord Gi*outputs reveal three integrated hotspot belts: (1) the northern canal fringe (Sri Ganganagar\u0026ndash;Hanumangarh\u0026ndash;Churu), (2) a north-eastern industrial\u0026ndash;transport arc from Khairthal\u0026ndash;Tijara/Bhiwadi\u0026ndash;Neemrana\u0026ndash;Kotputli\u0026ndash;Behror through Alwar\u0026ndash;Bharatpur\u0026ndash;Dholpur, and (3) a south-central/south-eastern power\u0026ndash;cement corridor around Chittorgarh and Kota\u0026ndash;Baran\u0026ndash;Jhalawar. Representative districts show broadly uniform O₃ and CH₄ rises, for example Alwar (O₃ +11.2%, CH₄ +3.3%), Jaipur (+\u0026thinsp;11.3%, +\u0026thinsp;3.6%), Kota (+\u0026thinsp;10.9%, +\u0026thinsp;3.2%), and Chittorgarh (+\u0026thinsp;11.0%, +\u0026thinsp;3.6%); Bikaner also follows this pattern (+\u0026thinsp;11.4%, +\u0026thinsp;2.9%). AHP-weighted fusion of Sentinel-5P species combined with inferential Getis\u0026ndash;Ord Gi* clustering yields a transparent, state-scale picture of Rajasthan\u0026rsquo;s multi-pollutant burden, supporting differentiated actions on NOₓ/VOC control along corridors, SO₂ abatement at point sources, and CH₄ mitigation in irrigated and waste-intensive districts.\u003c/p\u003e","manuscriptTitle":"AHP-Weighted Multi-Pollutant Composite with Getis–Ord Gi* Hotspot Analysis from Sentinel-5P over Rajasthan, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 07:51:07","doi":"10.21203/rs.3.rs-8004346/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4404818f-30d9-4c62-86a8-9d294dd381f3","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57265832,"name":"Geographic Information Systems"},{"id":57265833,"name":"Environmental Engineering"}],"tags":[],"updatedAt":"2025-11-04T07:51:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 07:51:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8004346","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8004346","identity":"rs-8004346","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}