Effect of Climate Forcing Parameters on Coastal Regions Due to Maritime Aerosols

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This study examines marine aerosol emissions and their radiative impacts along India’s coastline for the decade 2015–2024. Emission inventories from the Directorate General of Shipping (DGS), observations from Continuous Ambient Air Quality Monitoring Stations (CAAQMS), and short-term ship-based sampling near Chennai Port were analyzed. To assess radiative impacts, data were modeled using the OPAC–SBDART framework, and simulations were run to capture spatial and temporal variability in aerosol concentrations and radiative forcing. The results show a steady rise in aerosol levels along the eastern seaboard, with Chennai, Paradip, and Kolkata recording annual averages up to 68 µg/m³ by 2024. Modeled radiative forcing intensified to –1.6 W/m², pointing to stronger atmospheric cooling. Collectively, thirteen major ports accounted for 73.2% of emissions, averaging 168.4 µg/m³, compared with 26.8% from non-port coastal areas, which averaged 61.7 µg/m³. A temporary decline during the COVID-19 lockdown (2020–2021) demonstrated the mitigation potential of reduced shipping activity. Correlations between particulate levels, wind speed, and vessel orientation reaffirmed the localized influence of port operations. These findings underscore the urgency of port-specific mitigation strategies, including cleaner fuels, stricter emission controls, and expanded monitoring networks. The study highlights how shipping and port-based activities, when analyzed through modeled datasets, are reshaping aerosol dynamics and radiative forcing along India’s coast, with significant implications for air quality and climate policy. Maritime aerosols Radiative forcing Indian ports Ship emission Coastal air quality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Coastal zones play a pivotal role in sustaining ecological balance, enabling international trade, and supporting dense human populations. These regions, however, are becoming increasingly susceptible to atmospheric disturbances, particularly from aerosols originating through both natural oceanic processes and anthropogenic maritime activities. Maritime aerosols, comprising sea-salt, organic compounds, and combustion-related particulates, significantly alter the regional radiation budget and cloud microphysical properties, which in turn affect precipitation dynamics and boundary layer stability (Gantt et al., 2011; Pandis et al., 2016). Aerosols associated with maritime operations exert both cooling and warming influences. While sea-salt particles typically act as cloud condensation nuclei (CCN), increasing albedo and exerting a net cooling effect, aerosols derived from fuel combustion—such as black carbon and sulfates—tend to absorb radiation and contribute to atmospheric warming (Capaldo et al., 1999; Anand et al., 2016). These competing effects complicate efforts to quantify aerosol-induced radiative forcing (RF), especially in regions like the Bay of Bengal and Arabian Sea, where shipping traffic is dense and seasonal meteorology further modulates aerosol dispersion and transformation (IPCC, 2007; Boucher, 2015). Evidence from satellite remote sensing and ground-based monitoring consistently highlights elevated aerosol optical depth (AOD) and PM concentrations over urban coastal zones. Indian ports such as Chennai, Visakhapatnam, and Kolkata frequently report seasonal peaks in PM₂.₅ and PM₁₀ due to shipping activities, cargo operations, and nearby urban emissions. These aerosol burdens influence cloud formation, atmospheric thermodynamics, and surface radiation fluxes (Liu et al., 2022; UNEP, 2023). Moreover, decreasing single scattering albedo (SSA) observed over the years suggests an increasing presence of absorbing aerosols, which diminishes the net cooling potential and exacerbates regional climate variability. Despite growing recognition of the environmental and health risks posed by maritime emissions, recent studies indicate that Indian ports have seen limited progress in reducing particulate pollution from ship activities. Investigations at Chennai port show consistently high PM₂.₅ and PM₁₀ concentrations in adjacent urban zones, attributed to unregulated fuel usage, poor cargo handling practices, and insufficient emission control policies (Nandhakumar et al., 2016). Similarly, ship-based measurements along the South Indian coast highlight persistent aerosol loading and a lack of sustained decline in radiative forcing over time, underscoring the absence of effective port-level mitigation despite international frameworks like MARPOL Annex VI (Nandhakumar et al., 2020). This study addresses this gap by presenting a comprehensive decadal analysis (2015–2024) of marine aerosol emissions and associated radiative forcing across thirteen major Indian ports. Utilizing multi-source data—ranging from in-situ PM measurements to OPAC–SBDART simulations—we quantify the spatiotemporal variability of aerosol-driven RF and its dependence on port-specific emissions. This port-level resolution not only enhances understanding of aerosol–climate linkages in the Indian context but also supports policy formulation for sustainable maritime development (Andreae & Rosenfeld, 2008; Seinfeld & Pandis, 2016). 2. Materials and Methods This study utilized a multi-tiered observational and modeling approach to evaluate marine aerosol concentrations and radiative forcing across India's coastal regions from 2015 to 2024. The methodological framework comprised (1) emission inventory compilation, (2) air quality data acquisition from national monitoring systems, (3) focused ship-based sampling, and (4) modeling using OPAC and SBDART for radiative forcing estimation. Table 1 Description of Air Quality Monitoring equipment Parameter PM 10 PM 2.5 PM 10 & PM 2.5 (Combined) Equipment Respirable Dust Sampler (Envirotech APM 460 NL) Fine Particulate Sampler (Envirotech APM 550) Dust Monitor Model 1.108 Sampling Period 24 hours 24 hours — Measuring Principle Filtration with aerodynamic size cut via impaction Filtration with aerodynamic size cut via impaction Light scattering principle (± 2% accuracy) Flow Rate 0.9–1.15 m³/min 0.9–1.15 m³/min — Analysis Method Gravimetric Gravimetric Light Scattering Minimum Detection Limit 1 µg/m³ 1 µg/m³ 0.1 µg/m³ Table 1 Sampling methods and equipment specifications for PM₁₀ and PM₂.₅ measurements used in the study. The instrumentation includes gravimetric and light scattering-based methods, with specified flow rates and detection limits. 2.1 Emission Inventories and Source Data Marine aerosol emissions were quantified using port-level activity data provided by the Directorate General of Shipping (DGS, 2022; 2024). Key variables included vessel traffic frequency, fuel type and consumption rates, operational duration, and cargo tonnage. These data were combined with standardized emission factors (per IPCC and IMO guidelines) to compute annual port-level emission loads. 2.2 CAAQMS Coastal Monitoring and Instrumentation Continuous Ambient Air Quality Monitoring Stations (CAAQMS), maintained by CPCB, provided high-resolution pollutant measurements including PM₂.₅, PM₁₀, SO₂, and NOₓ for major Indian coastal cities. Instrumentation included Respirable Dust Samplers (Envirotech APM 460 NL) for PM₁₀, Fine Particulate Samplers (Envirotech APM 550) for PM₂.₅, and real-time Dust Monitors (Model 1.108) for concurrent data collection. These instruments were deployed at predefined geotagged sites near high-traffic ports (see Fig. 2 ), with a sampling interval of 24 hours and detection limits down to 0.1 µg/m³. 2.3 Ship-Based Observations and Localized Emission Tracking Ship-based measurements were conducted aboard the Research Vessel Sagar Manjusha off the Chennai coast during targeted campaigns in 2016 and 2018. Onboard instrumentation included portable optical particle counters, filter-based gravimetric samplers, and wind-speed sensors. The emission behavior was correlated with vessel maneuvers, anchorage patterns, and fuel-switching events using Automatic Identification System (AIS) data. (Nandhakumar et al., 2016; 2020) 2.4 Modeling: OPAC and SBDART Integration To quantify the radiative forcing (RF) of maritime aerosols, we integrated the Optical Properties of Aerosols and Clouds (OPAC) model with the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) code. The following assumptions, input parameters, and settings were applied: Assumptions Maritime aerosol composition dominated by sea-salt and combustion-related particulates (sulfates, black carbon, organics). Aerosols assumed externally mixed (sea-salt + combustion products), consistent with prior Indian coastal studies. Simulations under clear-sky conditions (no cloud interference). Coastal surface albedo fixed at 0.12, based on regional radiometric observations. Input Parameters Aerosol Optical Properties from OPAC: Aerosol Optical Depth (AOD): from CAAQMS and MODIS (550 nm). Single Scattering Albedo (SSA): 0.85–0.92 across ports. Asymmetry Factor (g): 0.65–0.72, representing forward scattering. Wavelength range: 0.25–4.0 µm (shortwave). Meteorological inputs: boundary layer height, relative humidity (70% for OPAC hygroscopic growth). Model Settings OPAC run with maritime and continental modes, size-resolved outputs. Outputs coupled with SBDART (DISORT solver, 8-stream approximation). Solar zenith angles: 30°, 45°, 60°. Radiative fluxes computed at surface and TOA; RF defined as: RF=(F with aerosol –F without aerosol ) where F is the net shortwave radiative flux (downwelling – upwelling). Uncertainty Analysis AOD uncertainties (±0.05) and SSA errors (±0.03) propagated via Monte Carlo. RF uncertainty: ±15–20%, consistent with South Asian aerosol studies. SSA variability largest contributor (±0.2 W/m²); meteorological inputs minor (<±0.05 W/m²) Aerosol Optical Properties were computed using the OPAC (Optical Properties of Aerosols and Clouds) model based on port-specific emission compositions. Derived optical parameters such as aerosol optical depth (AOD), single scattering albedo (SSA), and asymmetry factor (g) were then input into the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) model. RF values were calculated under clear-sky conditions for each region. 2.5 Data Integration and Analytical Workflow All datasets were spatially and temporally aligned using GIS tools and validated against meteorological parameters from the India Meteorological Department (IMD). The final output included year-wise RF values for each port, PM emission maps, AOD profiles, and statistical correlation matrices for inter-port comparison. A methodological flowchart summarizing this pipeline is provided. 3. Results and Discussion The decadal analysis (2015–2024) of marine aerosol emissions across India’s 13 major ports highlights key spatial and temporal patterns in both pollutant concentrations and climate-forcing metrics. The eastern coast—including Chennai, Kolkata, Paradip, and Visakhapatnam—exhibited a stronger upward trend in PM₂.₅ and PM₁₀ concentrations compared to the western coast. Average marine aerosol concentrations in eastern ports rose from ~45 µg/m³ in 2015 to ~68 µg/m³ by 2024, while western ports such as Mumbai and Kandla increased more modestly from ~35 µg/m³ to ~47 µg/m³. Additional correlations between PM concentrations and meteorological parameters were examined. June 2016 data show an inverse relationship between wind speed and PM levels at Chennai Port—higher wind speeds corresponded with lower PM₂.₅ and PM₁₀ values. This supports prior observations that maritime wind disperses pollutants seaward, reducing urban aerosol loading (Nandhakumar et al., 2016; Capaldo et al., 1999). Similarly, ship orientation analysis found that emissions were elevated when vessels were aligned closer to shore, due to increased exhaust activity during maneuvering and docking phases. The impact of COVID-19-related reductions in maritime traffic is also evident. From 2019 to 2021, ports including Chennai, JNPT, and Kolkata showed declines in PM emissions of 6–10%, illustrating the potential benefits of emission control and regulation. While emissions rebounded post-lockdown, the temporary improvements provide a policy benchmark for future interventions. Comparable emission reductions were reported in European and North American ports (UNEP, 2023; Smith et al., 2018), reinforcing that India’s experience fits within a global context of reduced emissions under operational restrictions. Between 2015 and 2024, negative radiative forcing (RF) representing the cooling effect caused by aerosols like PM₂.₅ intensified along India's coasts, reflecting increased aerosol loading from anthropogenic sources. Specifically, on the eastern coast, negative forcing increased from approximately –1.2 W/m² in 2015 to –1.6 W/m² in 2024, indicating a stronger cooling impact due to growing industrial and urban emissions in cities such as Kolkata and Chennai. On the western coast, a moderate rise was observed from around –0.9 W/m² to –1.2 W/m², consistent with increased aerosol emissions linked to urban expansion and industrial activity in Mumbai and adjacent port areas. Nationally, the overall negative forcing intensified from nearly –1.05 W/m² in 2015 to –1.4 W/m² in 2024, highlighting sustained high aerosol burdens contributing to atmospheric cooling but also posing significant health risks. These patterns are consistent with global findings: shipping-dense regions such as the South China Sea and Mediterranean have reported similarly strong cooling radiative forcing (–1.2 to –1.5 W/m²), reinforcing that port emissions in emerging economies like India represent a growing climate challenge (Capaldo et al., 1999; Liu et al., 2022; Boucher, 2015). The enhanced contribution of Indian eastern ports mirrors East Asian megahubs such as Shanghai and Busan, where maritime activity dominates regional aerosol optical depth (Corbett & Fischbeck, 2001; Eyring et al., 2005). Radiative forcing, calculated using the OPAC–SBDART model, exhibited a strong correlation with marine aerosol concentration and optical properties. High-emission ports such as Mumbai, Kolkata, and Paradip displayed RF values ranging from –1.38 W/m² to –1.14 W/m², signifying strong shortwave cooling effects. A declining Single Scattering Albedo (SSA) trend—from 0.92 in 2015 to 0.85 in 2024—was observed at most ports, indicating a shift toward more absorbing aerosol mixtures and reduced net cooling. This aligns with recent reports from North India and East Asia, where an increasing fraction of absorbing aerosols such as black carbon has intensified atmospheric heating despite surface cooling (Mandal et al., 2024; Zhou et al., 2024). Thus, while negative forcing dominates at the surface, the warming potential of absorbing aerosols in the atmosphere may complicate regional climate dynamics. Notably, Tuticorin, Visakhapatnam, and New Mangalore reflected intermediate emission trends and moderate RF values (–1.00 to –1.22 W/m²), suggesting that secondary aerosol formation, wind transport, and port-specific emission controls all contribute to spatiotemporal variability. This reinforces the need for region-specific maritime emission mitigation strategies. The results align with prior global estimates on shipping-induced climate forcing (Corbett & Fischbeck, 2001; IPCC, 2007), and underscore the urgency of adopting uniform low-sulfur fuel standards and emission monitoring systems at Indian ports. Port aerosol The decadal assessment of marine aerosol emissions across 13 major Indian ports reveals a consistent downward trend in high-traffic ports such as JNPT, Mumbai, and Chennai. JNPT reported the highest emission levels, exceeding 5400 tonnes in early years, with a gradual reduction towards 4900 tonnes by 2024. This is attributed to evolving maritime policies and partial enforcement of fuel standards. Conversely, Port Blair and Kochi maintained relatively low emission levels (<2500 tonnes), reflecting their lower industrial throughput. Similar imbalances in emission shares have been reported internationally, where major hubs account for the majority of marine aerosol forcing (Seinfeld & Pandis, 2016; Gantt et al., 2011). The strong inter-port correlations observed among eastern ports (r > 0.9) suggest shared atmospheric transport conditions and similar shipping intensities. This indicates that coordinated regional policies—such as joint fuel-switching regulations or synchronized monitoring networks—could provide greater effectiveness than isolated port-level interventions. Such cluster-based mitigation has been successful in the Baltic and Mediterranean regions (Eyring et al., 2005), and a similar approach may benefit India’s Bay of Bengal corridor. From a policy standpoint, the higher contribution of major ports (73.2% of total emissions) compared to other coastal regions (26.8%) suggests that emission reductions at a few high-traffic hubs can deliver disproportionately large climate and health co-benefits. This aligns with India’s commitments under IMO 2020 sulfur caps and CPCB air quality management frameworks. Strengthening coastal monitoring networks and integrating AIS-linked ship tracking with emission inventories will further enable real-time compliance and climate resilience. Overall, the results confirm that maritime aerosols are not just local air quality hazards but also significant modulators of India’s coastal climate system, influencing radiative balance, atmospheric stability, and precipitation dynamics. By situating these findings within global literature, this study highlights that India’s maritime sector is undergoing changes similar to other high-density shipping regions, and that timely interventions can reduce both health and climate burdens. To better understand inter-port similarities, a Pearson correlation matrix was developed using the decadal emission data from the 13 major ports. High positive correlations were found between ports such as JNPT, Chennai, Kolkata, and Paradip (r ≥ 0.95), suggesting shared emission characteristics driven by similar operational scales, cargo profiles, and regional trade demands. These strong correlations reflect common patterns in maritime traffic density, fuel usage, and port infrastructure. In contrast, ports like Mumbai and Port Blair exhibited lower or even negative correlations with other major ports (e.g., r = –0.49 between Mumbai and Chennai), indicating distinct emission dynamics potentially shaped by localized policies, coastal meteorology, and hinterland connectivity. The disparity underlines that one-size-fits-all mitigation strategies may be ineffective, and that port-specific action plans are necessary. Table 2: Contribution of Major Ports and Other Coastal Regions to Marine Aerosol Emissions Group Total Emission (µg/m³, sum) Mean Annual Concentration (µg/m³) Percent of Total (%) Major Ports (n=13) 168,400 168.4 73.2 Other Coastal 61,700 61.7 26.8 All Regions 230,100 — 100.0 Table 2 Summary of total emissions, mean annual concentrations, and percent contributions of marine aerosol from major ports (n = 13) and other coastal regions in India during 2015–2024. Values are derived from annual data as shown in Figures 1–4 These insights are consistent with earlier findings by Nandhakumar et al. (2016) and reaffirmed in the South Indian coastal study by Nandhakumar et al. (2020), both of which emphasized the persistent aerosol contribution of maritime operations and the underutilization of available emission reduction frameworks in India. Implications and Recommendations The observed aerosol dynamics underline the critical role maritime activities play in regional atmospheric pollution. Reductions observed during COVID-19 provide an empirical benchmark for potential emission reductions achievable through regulated activities and technological interventions. To mitigate future marine aerosol emissions, it is essential to implement strategic measures, including: Adoption of cleaner fuels and improved emission control technologies. Regular monitoring and stringent regulatory compliance for major ports. Enhanced inter-port collaboration for sharing best practices in environmental management. This comprehensive assessment of aerosol emissions serves as a valuable reference for policymakers and port authorities aiming for sustainable maritime operations and effective climate-change mitigation strategies. 4. Conclusion This study presents a decade-long evaluation of marine aerosol emissions and their radiative impacts across India’s major port cities. The findings demonstrate that maritime operations are significant contributors to coastal aerosol loading and negative radiative forcing, particularly in eastern port clusters. High concentrations of PM2.5 and intensified cooling effects (up to − 1.6 W/m²) were observed in Kolkata, Paradip, and Chennai, highlighting urgent needs for emission control. Major Ports vs. Other Coastal Regions Major Ports (13 ports) contribute 73.2% of total emissions with an average concentration of 168.4 µg/m³.Other Coastal Regions contribute only 26.8%, averaging 61.7 µg/m³. In contrast, other coastal regions showed relatively lower emissions and milder climate impacts. Variations in wind patterns, port activity, and policy implementation contributed to spatial and seasonal differences. A decline in Single Scattering Albedo over time suggests a shift toward more absorbing aerosols, reducing the cooling effect. The findings underscore the need for cleaner fuel use, better emission controls at ports, and targeted mitigation strategies. Strengthening air quality monitoring and adopting port-specific climate policies will be essential for sustainable maritime operations and coastal climate resilience. Policy implications from this study stress the adoption of cleaner marine fuels, emission standards enforcement, and expanded coastal air monitoring. Integration of AIS-linked ship tracking with real-time air quality surveillance can further strengthen regulatory frameworks. Ultimately, this study underscores the dual importance of maritime sector sustainability and coastal climate resilience in India’s air quality management agenda. Declarations Ethics Approval and Consent to Participate Not applicable. This study did not involve human participants, animals, or clinical data. Consent for Publication Not applicable. Author Contributions Nandhakumar S K: Conceptualization, Methodology, Data curation, Formal analysis, Writing—original draft. M. Muruganandam: Supervision, Review & Editing. Chaitali Thali: Data acquisition and statistical analysis. A. Sankaran: Provided research guidance and critical revisions. All authors have read and approved the final manuscript and agree to be accountable for all aspects of the work. Funding No specific funding was received for this study. Data Availability Public datasets used in this study include Directorate General of Shipping (DGS) emission inventories, Central Pollution Control Board (CPCB–CAAQMS) monitoring data, India Meteorological Department (IMD) records, Ship-based measurements (2016, 2018) and OPAC–SBDART model outputs are available from the corresponding author upon reasonable request. Competing Interests The authors declare no competing interests. Acknowledgements The authors thank the Directorate General of Shipping (DGS), the Central Pollution Control Board (CPCB), the India Meteorological Department (IMD), and the crew of ORV Sagar Manjusha for their support in data access and field measurements. References Andreae, M. O., & Rosenfeld, D. (2008). Aerosol-cloud-precipitation interactions. Part 1. The nature and sources of cloud-active aerosols. Earth-Science Reviews, 89(1–2), 13–41. Anand, S., et al. (2016). Environmental Science & Technology , 50(7), 3868–3876. Bhaduri, A., et al. (2023). Surface radiative forcing as a climate-change indicator in North India. Atmosphere. Boucher, O. (2015). Atmospheric Aerosols: Properties and Climate Impacts. Springer. Capaldo, K., Corbett, J. J., Kasibhatla, P., Fischbeck, P., & Pandis, S. N. (1999). Effects of ship emissions on sulphur cycling and radiative climate forcing over the ocean. Nature, 400(6746), 743–746. Central Pollution Control Board (CPCB). (2024). 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Trends and patterns in the contributions to cumulative radiative forcing. PNAS. Tripathi, S. N., et al. (2022). New estimates of aerosol radiative effects over India from surface and satellite observations. Science of the Total Environment. UNEP. (2023). Air Pollution in Asia and the Pacific: Science-Based Solutions for Clean Air . United Nations Environment Programme, Nairobi. Zhou, M., et al. (2024). Impacts of current and climate-induced changes on PM2.5 pollution in India and implications for air quality policies. Nature Communications. Additional Declarations No competing interests reported. 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. 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1","display":"","copyAsset":false,"role":"figure","size":252338,"visible":true,"origin":"","legend":"\u003cp\u003ePort-Driven Marine Aerosol Emissions and Climate Impacts\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7265006/v1/e15a197b5481629c65d1c81b.png"},{"id":92711643,"identity":"05e43381-52ae-44bf-9a70-7cf24a60c3a2","added_by":"auto","created_at":"2025-10-03 11:22:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126877,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMethodological framework of marine aerosol forcing\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7265006/v1/78540011e173d4dc0596477e.png"},{"id":92712888,"identity":"78c815b6-2727-490f-ad8e-7add396b9cc0","added_by":"auto","created_at":"2025-10-03 11:38:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":228232,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends, regional heatmap, and correlation matrix of marine aerosol concentration and radiative forcing across four Indian coastal regions (2015–2024)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7265006/v1/2811ddc977b881648f9b8de7.png"},{"id":92713573,"identity":"abae3f0a-58f6-4aea-9246-ad1c83f167f0","added_by":"auto","created_at":"2025-10-03 11:46:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":162832,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in Annual Marine Aerosol Concentrations across India's Maritime Regions (2015–2024).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7265006/v1/f92615cbe18c33b21e623919.png"},{"id":92711644,"identity":"c50d3a9b-039f-4315-8df7-9a3cb7a26bcc","added_by":"auto","created_at":"2025-10-03 11:22:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":123977,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Distribution of Aerosol Concentrations at Key Coastal Monitoring Stations (2015–2024).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7265006/v1/c7415339d4bd7ee0ec248771.png"},{"id":92712354,"identity":"4f8757c8-a82a-4901-9548-b85759683903","added_by":"auto","created_at":"2025-10-03 11:30:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":393305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnnual marine aerosol concentration and radiative forcing trends (2015–2024) for four Indian coastal regions, with (e) heatmap showing regional variation and (f) correlation matrix highlighting inter-port relationships.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7265006/v1/e2e088567edebc052ccbf186.png"},{"id":92711651,"identity":"0c360e56-a4d6-4a7b-b766-ae4d1ac40d0d","added_by":"auto","created_at":"2025-10-03 11:22:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":230798,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap depicting the annual marine aerosol emissions (in 1,000 tonnes) across 13 major Indian ports from 2015 to 2024. Higher emissions are concentrated in port-dense regions such as Chennai, JNPT, and Kolkata, with noticeable temporal variation\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7265006/v1/e4d5c798a17ff91219802daf.png"},{"id":92712355,"identity":"7897dabf-1159-47ec-8a35-79281422bbdc","added_by":"auto","created_at":"2025-10-03 11:30:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":93023,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003epercentage of total marine aerosol\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7265006/v1/977c05df89509469d9983539.png"},{"id":98622772,"identity":"c50cac41-5d19-4a03-9f7a-7401e7246705","added_by":"auto","created_at":"2025-12-19 17:02:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2053388,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7265006/v1/4188ab55-10df-496c-bba5-7bac3e58bf8d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of Climate Forcing Parameters on Coastal Regions Due to Maritime Aerosols","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoastal zones play a pivotal role in sustaining ecological balance, enabling international trade, and supporting dense human populations. These regions, however, are becoming increasingly susceptible to atmospheric disturbances, particularly from aerosols originating through both natural oceanic processes and anthropogenic maritime activities. Maritime aerosols, comprising sea-salt, organic compounds, and combustion-related particulates, significantly alter the regional radiation budget and cloud microphysical properties, which in turn affect precipitation dynamics and boundary layer stability (Gantt et al., 2011; Pandis et al., 2016).\u003c/p\u003e\u003cp\u003eAerosols associated with maritime operations exert both cooling and warming influences. While sea-salt particles typically act as cloud condensation nuclei (CCN), increasing albedo and exerting a net cooling effect, aerosols derived from fuel combustion\u0026mdash;such as black carbon and sulfates\u0026mdash;tend to absorb radiation and contribute to atmospheric warming (Capaldo et al., 1999; Anand et al., 2016). These competing effects complicate efforts to quantify aerosol-induced radiative forcing (RF), especially in regions like the Bay of Bengal and Arabian Sea, where shipping traffic is dense and seasonal meteorology further modulates aerosol dispersion and transformation (IPCC, 2007; Boucher, 2015).\u003c/p\u003e\u003cp\u003eEvidence from satellite remote sensing and ground-based monitoring consistently highlights elevated aerosol optical depth (AOD) and PM concentrations over urban coastal zones. Indian ports such as Chennai, Visakhapatnam, and Kolkata frequently report seasonal peaks in PM₂.₅ and PM₁₀ due to shipping activities, cargo operations, and nearby urban emissions. These aerosol burdens influence cloud formation, atmospheric thermodynamics, and surface radiation fluxes (Liu et al., 2022; UNEP, 2023). Moreover, decreasing single scattering albedo (SSA) observed over the years suggests an increasing presence of absorbing aerosols, which diminishes the net cooling potential and exacerbates regional climate variability.\u003c/p\u003e\u003cp\u003eDespite growing recognition of the environmental and health risks posed by maritime emissions, recent studies indicate that Indian ports have seen limited progress in reducing particulate pollution from ship activities. Investigations at Chennai port show consistently high PM₂.₅ and PM₁₀ concentrations in adjacent urban zones, attributed to unregulated fuel usage, poor cargo handling practices, and insufficient emission control policies (Nandhakumar et al., 2016). Similarly, ship-based measurements along the South Indian coast highlight persistent aerosol loading and a lack of sustained decline in radiative forcing over time, underscoring the absence of effective port-level mitigation despite international frameworks like MARPOL Annex VI (Nandhakumar et al., 2020).\u003c/p\u003e\u003cp\u003eThis study addresses this gap by presenting a comprehensive decadal analysis (2015\u0026ndash;2024) of marine aerosol emissions and associated radiative forcing across thirteen major Indian ports. Utilizing multi-source data\u0026mdash;ranging from in-situ PM measurements to OPAC\u0026ndash;SBDART simulations\u0026mdash;we quantify the spatiotemporal variability of aerosol-driven RF and its dependence on port-specific emissions. This port-level resolution not only enhances understanding of aerosol\u0026ndash;climate linkages in the Indian context but also supports policy formulation for sustainable maritime development (Andreae \u0026amp; Rosenfeld, 2008; Seinfeld \u0026amp; Pandis, 2016).\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis study utilized a multi-tiered observational and modeling approach to evaluate marine aerosol concentrations and radiative forcing across India\u0026apos;s coastal regions from 2015 to 2024.\u003c/p\u003e\n\u003cp\u003eThe methodological framework comprised (1) emission inventory compilation, (2) air quality data acquisition from national monitoring systems, (3) focused ship-based sampling, and (4) modeling using OPAC and SBDART for radiative forcing estimation.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of Air Quality Monitoring equipment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e \u0026amp; PM\u003csub\u003e2.5\u003c/sub\u003e (Combined)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEquipment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespirable Dust Sampler (Envirotech APM 460 NL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFine Particulate Sampler (Envirotech APM 550)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDust Monitor Model 1.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSampling Period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasuring Principle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFiltration with aerodynamic size cut via impaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFiltration with aerodynamic size cut via impaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLight scattering principle (\u0026plusmn;\u0026thinsp;2% accuracy)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlow Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u0026ndash;1.15 m\u0026sup3;/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u0026ndash;1.15 m\u0026sup3;/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis Method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGravimetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGravimetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLight Scattering\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum Detection Limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 \u0026micro;g/m\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 \u0026micro;g/m\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1 \u0026micro;g/m\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e Sampling methods and equipment specifications for PM₁₀ and PM₂.₅ measurements used in the study. The instrumentation includes gravimetric and light scattering-based methods, with specified flow rates and detection limits.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Emission Inventories and Source Data\u003c/h2\u003e\n \u003cp\u003eMarine aerosol emissions were quantified using port-level activity data provided by the Directorate General of Shipping (DGS, 2022; 2024). Key variables included vessel traffic frequency, fuel type and consumption rates, operational duration, and cargo tonnage. These data were combined with standardized emission factors (per IPCC and IMO guidelines) to compute annual port-level emission loads.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 CAAQMS Coastal Monitoring and Instrumentation\u003c/h2\u003e\n \u003cp\u003eContinuous Ambient Air Quality Monitoring Stations (CAAQMS), maintained by CPCB, provided high-resolution pollutant measurements including PM₂.₅, PM₁₀, SO₂, and NOₓ for major Indian coastal cities. Instrumentation included Respirable Dust Samplers (Envirotech APM 460 NL) for PM₁₀, Fine Particulate Samplers (Envirotech APM 550) for PM₂.₅, and real-time Dust Monitors (Model 1.108) for concurrent data collection. These instruments were deployed at predefined geotagged sites near high-traffic ports (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), with a sampling interval of 24 hours and detection limits down to 0.1 \u0026micro;g/m\u0026sup3;.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Ship-Based Observations and Localized Emission Tracking\u003c/h2\u003e\n \u003cp\u003eShip-based measurements were conducted aboard the Research Vessel Sagar Manjusha off the Chennai coast during targeted campaigns in 2016 and 2018. Onboard instrumentation included portable optical particle counters, filter-based gravimetric samplers, and wind-speed sensors. The emission behavior was correlated with vessel maneuvers, anchorage patterns, and fuel-switching events using Automatic Identification System (AIS) data. (Nandhakumar et al., 2016; 2020)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Modeling: OPAC and SBDART Integration\u003c/h2\u003e\n \u003cp\u003eTo quantify the radiative forcing (RF) of maritime aerosols, we integrated the Optical Properties of Aerosols and Clouds (OPAC) model with the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) code. The following assumptions, input parameters, and settings were applied:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAssumptions\u003c/strong\u003e\u003c/p\u003e\n \u003cul type=\"disc\"\u003e\n \u003cli\u003eMaritime aerosol composition dominated by sea-salt and combustion-related particulates (sulfates, black carbon, organics).\u003c/li\u003e\n \u003cli\u003eAerosols assumed externally mixed (sea-salt + combustion products), consistent with prior Indian coastal studies.\u003c/li\u003e\n \u003cli\u003eSimulations under clear-sky conditions (no cloud interference).\u003c/li\u003e\n \u003cli\u003eCoastal surface albedo fixed at 0.12, based on regional radiometric observations.\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cstrong\u003eInput Parameters\u003c/strong\u003e\u003c/p\u003e\n \u003cul type=\"disc\"\u003e\n \u003cli\u003eAerosol Optical Properties from OPAC:\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\u003cstrong\u003eAerosol Optical Depth (AOD):\u003c/strong\u003e from CAAQMS and MODIS (550 nm).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSingle Scattering Albedo (SSA):\u003c/strong\u003e 0.85\u0026ndash;0.92 across ports.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAsymmetry Factor (g):\u003c/strong\u003e 0.65\u0026ndash;0.72, representing forward scattering.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n \u003cli\u003eWavelength range: 0.25\u0026ndash;4.0 \u0026micro;m (shortwave).\u003c/li\u003e\n \u003cli\u003eMeteorological inputs: boundary layer height, relative humidity (70% for OPAC hygroscopic growth).\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cstrong\u003eModel Settings\u003c/strong\u003e\u003c/p\u003e\n \u003cul type=\"disc\"\u003e\n \u003cli\u003eOPAC run with maritime and continental modes, size-resolved outputs.\u003c/li\u003e\n \u003cli\u003eOutputs coupled with SBDART (DISORT solver, 8-stream approximation).\u003c/li\u003e\n \u003cli\u003eSolar zenith angles: 30\u0026deg;, 45\u0026deg;, 60\u0026deg;.\u003c/li\u003e\n \u003cli\u003eRadiative fluxes computed at surface and TOA; RF defined as:\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;RF=(F\u003csub\u003ewith\u0026nbsp;aerosol\u003c/sub\u003e\u0026ndash;F\u003csub\u003ewithout\u0026nbsp;aerosol\u003c/sub\u003e)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; where\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;F is the net shortwave radiative flux (downwelling \u0026ndash; upwelling).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eUncertainty Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cul type=\"disc\"\u003e\n \u003cli\u003eAOD uncertainties (\u0026plusmn;0.05) and SSA errors (\u0026plusmn;0.03) propagated via Monte Carlo.\u003c/li\u003e\n \u003cli\u003eRF uncertainty: \u0026plusmn;15\u0026ndash;20%, consistent with South Asian aerosol studies.\u003c/li\u003e\n \u003cli\u003eSSA variability largest contributor (\u0026plusmn;0.2 W/m\u0026sup2;); meteorological inputs minor (\u0026lt;\u0026plusmn;0.05 W/m\u0026sup2;)\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eAerosol Optical Properties were computed using the OPAC (Optical Properties of Aerosols and Clouds) model based on port-specific emission compositions. Derived optical parameters such as aerosol optical depth (AOD), single scattering albedo (SSA), and asymmetry factor (g) were then input into the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) model. RF values were calculated under clear-sky conditions for each region.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.5 Data Integration and Analytical Workflow\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAll datasets were spatially and temporally aligned using GIS tools and validated against meteorological parameters from the India Meteorological Department (IMD). The final output included year-wise RF values for each port, PM emission maps, AOD profiles, and statistical correlation matrices for inter-port comparison. A methodological flowchart summarizing this pipeline is provided.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003e\u003cem\u003eThe decadal analysis (2015\u0026ndash;2024) of marine aerosol emissions across India\u0026rsquo;s 13 major ports highlights key spatial and temporal patterns in both pollutant concentrations and climate-forcing metrics. The eastern coast\u0026mdash;including Chennai, Kolkata, Paradip, and Visakhapatnam\u0026mdash;exhibited a stronger upward trend in PM₂.₅ and PM₁₀ concentrations compared to the western coast. Average marine aerosol concentrations in eastern ports rose from ~45 \u0026micro;g/m\u0026sup3; in 2015 to ~68 \u0026micro;g/m\u0026sup3; by 2024, while western ports such as Mumbai and Kandla increased more modestly from ~35 \u0026micro;g/m\u0026sup3; to ~47 \u0026micro;g/m\u0026sup3;.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdditional correlations between PM concentrations and meteorological parameters were examined. June 2016 data show an inverse relationship between wind speed and PM levels at Chennai Port\u0026mdash;higher wind speeds corresponded with lower PM₂.₅ and PM₁₀ values. This supports prior observations that maritime wind disperses pollutants seaward, reducing urban aerosol loading (Nandhakumar et al., 2016; Capaldo et al., 1999). Similarly, ship orientation analysis found that emissions were elevated when vessels were aligned closer to shore, due to increased exhaust activity during maneuvering and docking phases.\u003c/p\u003e\n\u003cp\u003eThe impact of COVID-19-related reductions in maritime traffic is also evident. From 2019 to 2021, ports including Chennai, JNPT, and Kolkata showed declines in PM emissions of 6\u0026ndash;10%, illustrating the potential benefits of emission control and regulation. While emissions rebounded post-lockdown, the temporary improvements provide a policy benchmark for future interventions. Comparable emission reductions were reported in European and North American ports (UNEP, 2023; Smith et al., 2018), reinforcing that India\u0026rsquo;s experience fits within a global context of reduced emissions under operational restrictions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBetween 2015 and 2024, negative radiative forcing (RF) representing the cooling effect caused by aerosols like PM₂.₅ intensified along India\u0026apos;s coasts, reflecting increased aerosol loading from anthropogenic sources. Specifically, on the eastern coast, negative forcing increased from approximately \u0026ndash;1.2 W/m\u0026sup2; in 2015 to \u0026ndash;1.6 W/m\u0026sup2; in 2024, indicating a stronger cooling impact due to growing industrial and urban emissions in cities such as Kolkata and Chennai. On the western coast, a moderate rise was observed from around \u0026ndash;0.9 W/m\u0026sup2; to \u0026ndash;1.2 W/m\u0026sup2;, consistent with increased aerosol emissions linked to urban expansion and industrial activity in Mumbai and adjacent port areas. Nationally, the overall negative forcing intensified from nearly \u0026ndash;1.05 W/m\u0026sup2; in 2015 to \u0026ndash;1.4 W/m\u0026sup2; in 2024, highlighting sustained high aerosol burdens contributing to atmospheric cooling but also posing significant health risks.\u003c/p\u003e\n\u003cp\u003eThese patterns are consistent with global findings: shipping-dense regions such as the South China Sea and Mediterranean have reported similarly strong cooling radiative forcing (\u0026ndash;1.2 to \u0026ndash;1.5 W/m\u0026sup2;), reinforcing that port emissions in emerging economies like India represent a growing climate challenge (Capaldo et al., 1999; Liu et al., 2022; Boucher, 2015). The enhanced contribution of Indian eastern ports mirrors East Asian megahubs such as Shanghai and Busan, where maritime activity dominates regional aerosol optical depth (Corbett \u0026amp; Fischbeck, 2001; Eyring et al., 2005).\u003c/p\u003e\n\u003cp\u003eRadiative forcing, calculated using the OPAC\u0026ndash;SBDART model, exhibited a strong correlation with marine aerosol concentration and optical properties. High-emission ports such as Mumbai, Kolkata, and Paradip displayed RF values ranging from \u0026ndash;1.38 W/m\u0026sup2; to \u0026ndash;1.14 W/m\u0026sup2;, signifying strong shortwave cooling effects. A declining Single Scattering Albedo (SSA) trend\u0026mdash;from 0.92 in 2015 to 0.85 in 2024\u0026mdash;was observed at most ports, indicating a shift toward more absorbing aerosol mixtures and reduced net cooling. This aligns with recent reports from North India and East Asia, where an increasing fraction of absorbing aerosols such as black carbon has intensified atmospheric heating despite surface cooling (Mandal et al., 2024; Zhou et al., 2024). Thus, while negative forcing dominates at the surface, the warming potential of absorbing aerosols in the atmosphere may complicate regional climate dynamics.\u003c/p\u003e\n\u003cp\u003eNotably, Tuticorin, Visakhapatnam, and New Mangalore reflected intermediate emission trends and moderate RF values (\u0026ndash;1.00 to \u0026ndash;1.22 W/m\u0026sup2;), suggesting that secondary aerosol formation, wind transport, and port-specific emission controls all contribute to spatiotemporal variability. This reinforces the need for region-specific maritime emission mitigation strategies. The results align with prior global estimates on shipping-induced climate forcing (Corbett \u0026amp; Fischbeck, 2001; IPCC, 2007), and underscore the urgency of adopting uniform low-sulfur fuel standards and emission monitoring systems at Indian ports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePort aerosol\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe decadal assessment of marine aerosol emissions across 13 major Indian ports reveals a consistent downward trend in high-traffic ports such as JNPT, Mumbai, and Chennai. JNPT reported the highest emission levels, exceeding 5400 tonnes in early years, with a gradual reduction towards 4900 tonnes by 2024. This is attributed to evolving maritime policies and partial enforcement of fuel standards. Conversely, Port Blair and Kochi maintained relatively low emission levels (\u0026lt;2500 tonnes), reflecting their lower industrial throughput. Similar imbalances in emission shares have been reported internationally, where major hubs account for the majority of marine aerosol forcing (Seinfeld \u0026amp; Pandis, 2016; Gantt et al., 2011).\u003c/p\u003e\n\u003cp\u003eThe strong inter-port correlations observed among eastern ports (r \u0026gt; 0.9) suggest shared atmospheric transport conditions and similar shipping intensities. This indicates that coordinated regional policies\u0026mdash;such as joint fuel-switching regulations or synchronized monitoring networks\u0026mdash;could provide greater effectiveness than isolated port-level interventions. Such \u003cstrong\u003ecluster-based mitigation\u003c/strong\u003e has been successful in the Baltic and Mediterranean regions (Eyring et al., 2005), and a similar approach may benefit India\u0026rsquo;s Bay of Bengal corridor.\u003c/p\u003e\n\u003cp\u003eFrom a policy standpoint, the higher contribution of major ports (73.2% of total emissions) compared to other coastal regions (26.8%) suggests that emission reductions at a few high-traffic hubs can deliver disproportionately large climate and health co-benefits. This aligns with India\u0026rsquo;s commitments under \u003cstrong\u003eIMO 2020 sulfur caps\u003c/strong\u003e and CPCB air quality management frameworks. Strengthening coastal monitoring networks and integrating AIS-linked ship tracking with emission inventories will further enable real-time compliance and climate resilience.\u003c/p\u003e\n\u003cp\u003eOverall, the results confirm that maritime aerosols are not just local air quality hazards but also significant modulators of India\u0026rsquo;s coastal climate system, influencing radiative balance, atmospheric stability, and precipitation dynamics. By situating these findings within global literature, this study highlights that India\u0026rsquo;s maritime sector is undergoing changes similar to other high-density shipping regions, and that timely interventions can reduce both health and climate burdens.\u003c/p\u003e\n\u003cp\u003eTo better understand inter-port similarities, a Pearson correlation matrix was developed using the decadal emission data from the 13 major ports. High positive correlations were found between ports such as JNPT, Chennai, Kolkata, and Paradip (r \u0026ge; 0.95), suggesting shared emission characteristics driven by similar operational scales, cargo profiles, and regional trade demands. These strong correlations reflect common patterns in maritime traffic density, fuel usage, and port infrastructure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, ports like Mumbai and Port Blair exhibited lower or even negative correlations with other major ports (e.g., r = \u0026ndash;0.49 between Mumbai and Chennai), indicating distinct emission dynamics potentially shaped by localized policies, coastal meteorology, and hinterland connectivity. The disparity underlines that one-size-fits-all mitigation strategies may be ineffective, and that port-specific action plans are necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Contribution of Major Ports and Other Coastal Regions to Marine Aerosol Emissions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Emission (\u0026micro;g/m\u0026sup3;, sum)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Annual Concentration (\u0026micro;g/m\u0026sup3;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent of Total (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMajor Ports (n=13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e168,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e168.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e73.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eOther Coastal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e61,700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eAll Regions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e230,100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 Summary of total emissions, mean annual concentrations, and percent contributions of marine aerosol from major ports (n = 13) and other coastal regions in India during 2015\u0026ndash;2024. Values are derived from annual data as shown in Figures 1\u0026ndash;4\u003c/p\u003e\n\u003cp\u003eThese insights are consistent with earlier findings by Nandhakumar et al. (2016) and reaffirmed in the South Indian coastal study by Nandhakumar et al. (2020), both of which emphasized the persistent aerosol contribution of maritime operations and the underutilization of available emission reduction frameworks in India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications and Recommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe observed aerosol dynamics underline the critical role maritime activities play in regional atmospheric pollution. Reductions observed during COVID-19 provide an empirical benchmark for potential emission reductions achievable through regulated activities and technological interventions. To mitigate future marine aerosol emissions, it is essential to implement strategic measures, including:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAdoption of cleaner fuels and improved emission control technologies.\u003c/li\u003e\n \u003cli\u003eRegular monitoring and stringent regulatory compliance for major ports.\u003c/li\u003e\n \u003cli\u003eEnhanced inter-port collaboration for sharing best practices in environmental management.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis comprehensive assessment of aerosol emissions serves as a valuable reference for policymakers and port authorities aiming for sustainable maritime operations and effective climate-change mitigation strategies.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study presents a decade-long evaluation of marine aerosol emissions and their radiative impacts across India\u0026rsquo;s major port cities. The findings demonstrate that maritime operations are significant contributors to coastal aerosol loading and negative radiative forcing, particularly in eastern port clusters. High concentrations of PM2.5 and intensified cooling effects (up to \u0026minus;\u0026thinsp;1.6 W/m\u0026sup2;) were observed in Kolkata, Paradip, and Chennai, highlighting urgent needs for emission control. Major Ports vs. Other Coastal Regions Major Ports (13 ports) contribute 73.2% of total emissions with an average concentration of 168.4 \u0026micro;g/m\u0026sup3;.Other Coastal Regions contribute only 26.8%, averaging 61.7 \u0026micro;g/m\u0026sup3;.\u003c/p\u003e\u003cp\u003eIn contrast, other coastal regions showed relatively lower emissions and milder climate impacts. Variations in wind patterns, port activity, and policy implementation contributed to spatial and seasonal differences. A decline in Single Scattering Albedo over time suggests a shift toward more absorbing aerosols, reducing the cooling effect.\u003c/p\u003e\u003cp\u003eThe findings underscore the need for cleaner fuel use, better emission controls at ports, and targeted mitigation strategies. Strengthening air quality monitoring and adopting port-specific climate policies will be essential for sustainable maritime operations and coastal climate resilience.\u003c/p\u003e\u003cp\u003ePolicy implications from this study stress the adoption of cleaner marine fuels, emission standards enforcement, and expanded coastal air monitoring. Integration of AIS-linked ship tracking with real-time air quality surveillance can further strengthen regulatory frameworks. Ultimately, this study underscores the dual importance of maritime sector sustainability and coastal climate resilience in India\u0026rsquo;s air quality management agenda.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable. This study did not involve human participants, animals, or clinical data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNandhakumar S K: Conceptualization, Methodology, Data curation, Formal analysis, Writing\u0026mdash;original draft.\u003cbr\u003eM. Muruganandam: Supervision, Review \u0026amp; Editing.\u003cbr\u003eChaitali Thali: Data acquisition and statistical analysis.\u003cbr\u003eA. Sankaran: Provided research guidance and critical revisions.\u003cbr\u003eAll authors have read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublic datasets used in this study include Directorate General of Shipping (DGS) emission inventories, Central Pollution Control Board (CPCB\u0026ndash;CAAQMS) monitoring data, India Meteorological Department (IMD) records, Ship-based measurements (2016, 2018) and OPAC\u0026ndash;SBDART model outputs are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Directorate General of Shipping (DGS), the Central Pollution Control Board (CPCB), the India Meteorological Department (IMD), and the crew of ORV Sagar Manjusha for their support in data access and field measurements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndreae, M. O., \u0026amp; Rosenfeld, D. (2008). Aerosol-cloud-precipitation interactions. Part 1. The nature and sources of cloud-active aerosols. Earth-Science Reviews, 89(1\u0026ndash;2), 13\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003eAnand, S., et al. (2016). \u003cem\u003eEnvironmental Science \u0026amp; Technology\u003c/em\u003e, 50(7), 3868\u0026ndash;3876.\u003c/li\u003e\n\u003cli\u003eBhaduri, A., et al. (2023). Surface radiative forcing as a climate-change indicator in North India. Atmosphere.\u003c/li\u003e\n\u003cli\u003eBoucher, O. (2015). Atmospheric Aerosols: Properties and Climate Impacts. Springer.\u003c/li\u003e\n\u003cli\u003eCapaldo, K., Corbett, J. J., Kasibhatla, P., Fischbeck, P., \u0026amp; Pandis, S. N. (1999). Effects of ship emissions on sulphur cycling and radiative climate forcing over the ocean. Nature, 400(6746), 743\u0026ndash;746.\u003c/li\u003e\n\u003cli\u003eCentral Pollution Control Board (CPCB). (2024). Coastal Air Quality Data Reports. Ministry of Environment, Forest and Climate Change, Government of India.\u003c/li\u003e\n\u003cli\u003eCentre for Science and Environment (CSE). (2025). Annual PM2.5 levels rose in 2024 for the second consecutive year.\u003c/li\u003e\n\u003cli\u003eCorbett, J. J., \u0026amp; Fischbeck, P. (2001). Emissions from ships. Science, 278(5339), 823\u0026ndash;824.\u003c/li\u003e\n\u003cli\u003eDirectorate General of Shipping (DGS). (2022). Marine Environmental Management Report 2022. Ministry of Shipping, Government of India.\u003c/li\u003e\n\u003cli\u003eDirectorate General of Shipping (DGS). (2024). Emission Inventory Report. Ministry of Shipping, Government of India.\u003c/li\u003e\n\u003cli\u003eEyring, V., et al. (2005). Emissions from international shipping: 1. The last 50 years. Journal of Geophysical Research: Atmospheres, 110(D17).\u003c/li\u003e\n\u003cli\u003eGantt, B., Meskhidze, N., \u0026amp; Nenes, A. (2011). The role of sea spray in marine cloud condensation nuclei. Atmospheric Chemistry and Physics, 11(16), 8777\u0026ndash;8790.\u003c/li\u003e\n\u003cli\u003eIPCC. (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report. Cambridge University Press.\u003c/li\u003e\n\u003cli\u003eLiu, Y., Zhang, Y., Wu, Y., \u0026amp; Wang, Y. (2022). Long-term changes of aerosol properties in East Asia. Atmospheric Environment, 277, 119062.\u003c/li\u003e\n\u003cli\u003eMandal, S., et al. (2024). Nationwide estimation of daily ambient PM2.5 from 2008 to 2020: High-resolution spatiotemporal modeling across India. PNAS Nexus.\u003c/li\u003e\n\u003cli\u003eNandhakumar, S. K., Aram, A. I., \u0026amp; Sivasami, K. (2016). Effects of particulate pollutants from ship emissions in Chennai Port. International Journal of Scientific Engineering and Applied Science (IJSEAS), 2(6), 95\u0026ndash;100.\u003c/li\u003e\n\u003cli\u003eNandhakumar, S. K., Arul Aram, I., \u0026amp; Sivasami, K. (2020). Climate forcing due to aerosol and air pollution over South Indian coast. In Proceedings of the National Conference on Energy, Environment and Sustainable Shipping, Indian Maritime University, Chennai.\u003c/li\u003e\n\u003cli\u003eSeinfeld, J. H., \u0026amp; Pandis, S. N. (2016). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change (3rd ed.). Wiley.\u003c/li\u003e\n\u003cli\u003eSmith, S. J., et al. (2018). Trends and patterns in the contributions to cumulative radiative forcing. PNAS.\u003c/li\u003e\n\u003cli\u003eTripathi, S. N., et al. (2022). New estimates of aerosol radiative effects over India from surface and satellite observations. Science of the Total Environment.\u003c/li\u003e\n\u003cli\u003eUNEP. (2023). \u003cem\u003eAir Pollution in Asia and the Pacific: Science-Based Solutions for Clean Air\u003c/em\u003e. United Nations Environment Programme, Nairobi.\u003c/li\u003e\n\u003cli\u003eZhou, M., et al. (2024). Impacts of current and climate-induced changes on PM2.5 pollution in India and implications for air quality policies. Nature Communications.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Maritime aerosols, Radiative forcing, Indian ports, Ship emission, Coastal air quality","lastPublishedDoi":"10.21203/rs.3.rs-7265006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7265006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMaritime activities, particularly shipping and port-based activities, are major sources of fine aerosols that shape air quality and regional climate in densely populated coastal zones. This study examines marine aerosol emissions and their radiative impacts along India’s coastline for the decade 2015–2024. Emission inventories from the Directorate General of Shipping (DGS), observations from Continuous Ambient Air Quality Monitoring Stations (CAAQMS), and short-term ship-based sampling near Chennai Port were analyzed. To assess radiative impacts, data were modeled using the OPAC–SBDART framework, and simulations were run to capture spatial and temporal variability in aerosol concentrations and radiative forcing.\u003c/p\u003e\n\u003cp\u003eThe results show a steady rise in aerosol levels along the eastern seaboard, with Chennai, Paradip, and Kolkata recording annual averages up to 68 µg/m³ by 2024. Modeled radiative forcing intensified to –1.6 W/m², pointing to stronger atmospheric cooling. Collectively, thirteen major ports accounted for 73.2% of emissions, averaging 168.4 µg/m³, compared with 26.8% from non-port coastal areas, which averaged 61.7 µg/m³. A temporary decline during the COVID-19 lockdown (2020–2021) demonstrated the mitigation potential of reduced shipping activity. Correlations between particulate levels, wind speed, and vessel orientation reaffirmed the localized influence of port operations.\u003c/p\u003e\n\u003cp\u003eThese findings underscore the urgency of port-specific mitigation strategies, including cleaner fuels, stricter emission controls, and expanded monitoring networks. The study highlights how shipping and port-based activities, when analyzed through modeled datasets, are reshaping aerosol dynamics and radiative forcing along India’s coast, with significant implications for air quality and climate policy.\u003c/p\u003e","manuscriptTitle":"Effect of Climate Forcing Parameters on Coastal Regions Due to Maritime Aerosols","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 11:22:12","doi":"10.21203/rs.3.rs-7265006/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":"bbf89166-8c82-4a30-9ba4-e768371c59f6","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-14T12:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 11:22:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7265006","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7265006","identity":"rs-7265006","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}