Anthropogenic emissions drive elevated HCl and particulate chloride in polluted urban air

Anthropogenic emissions drive elevated HCl and particulate chloride in polluted urban air | 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 Article Anthropogenic emissions drive elevated HCl and particulate chloride in polluted urban air Rakesh Maity, Vikram Singh, Dilip Ganguly, Mayank Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9138509/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Particulate chloride (Cl − ) and hydrogen chloride (HCl) strongly influence aerosol hygroscopicity, acidity, and visibility in polluted urban atmospheres, yet they remain poorly represented in chemical transport models. We quantify the role of anthropogenic emissions in driving elevated Cl − and HCl over Delhi by implementing anthropogenic HCl and Cl − emissions in WRF-Chem and simulating post-monsoon, winter, summer seasons. We show that the long-standing underestimation of particulate chloride in previous modeling studies over Asian urban environments arises from inadequate representation of gas-particle partitioning under high aerosol number concentrations. Revising the thermodynamic equilibrium constant using observation-constrained partitioning improves model performance, enabling accurate simulation of the observed magnitude and seasonal variability of particulate chloride. Observed HCl concentrations over northern Indian cities are among the highest reported globally for urban environments. Local anthropogenic emissions of Delhi dominate chloride formation, with open waste burning contributing 34% and 44% during the post-monsoon and winter seasons, respectively. In contrast, regional agricultural residue burning contributes only ~ 14% within Delhi, despite enhancing chloride levels in nearby urban centers. These results establish anthropogenic chlorine emissions as a dominant driver of urban aerosol chemistry and underscore the need to revise chloride representation in chemical transport models for polluted inland cities. Main Text Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences air pollution chloride aerosol atmospheric HCl open waste burning Indo-Gangetic plain Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Particulate chloride (Cl − ) is an important yet understudied constituent of fine particulate matter (PM 2.5 ), exerting a disproportionate influence on aerosol hygroscopicity, acidity, visibility degradation, and heterogeneous chemistry in the troposphere 1 . Owing to its strong affinity for water, chloride enhances aerosol liquid water content, intensifying light scattering and often causing severe visibility degradation, frequently exceeding 50% under humid conditions. In addition, chlorine radicals derived from particulate chloride and its gas-phase precursor hydrogen chloride (HCl) are highly reactive toward a broad range of volatile organic compounds (VOCs), often exceeding hydroxyl radicals in reactivity, thereby accelerating oxidation pathways and promoting secondary organic aerosol (SOA) formation 2 , 3 . Despite these critical roles, chloride chemistry remains poorly constrained in atmospheric models, particularly over polluted continental environments. In marine and coastal regions, particulate chloride is dominated by sea-salt emissions. In contrast, a growing body of observational evidence demonstrates that anthropogenic activities dominate chloride loading in inland urban atmospheres. Major non-marine chlorine sources include open waste incineration, industrial processes, coal combustion, brick kilns, and agricultural residue burning 4 – 6 . Rapid urbanization, expanding waste streams, and increasing fossil-fuel consumption have amplified anthropogenic chlorine emissions across densely populated regions such as East Asia and South Asia. Consequently, understanding particulate chloride formation pathways, gas-particle partitioning behavior, and source contributions has become particularly important in highly polluted continental regions such as East China and the Indo-Gangetic Plain (IGP). The IGP is among the most polluted regions globally, experiencing persistently hazardous air quality throughout the year 7 , 8 . Millions of residents across this densely populated corridor are chronically exposed to PM 2.5 concentrations far exceeding health-based guidelines 9 . Fine particulate matter in this region not only drives the air quality crisis but also strongly modulates aerosol acidity and aerosol water content (AWC), with cascading implications for acid deposition, visibility reduction, heterogeneous chemistry, and regional radiative forcing 10 – 12 . Despite extensive monitoring efforts, the formation mechanisms and source contributions of particulate chloride across the IGP remain poorly constrained, particularly in urban environments such as Delhi, where observed chloride concentrations are consistently high. Previous studies have identified open waste burning, industrial activities, and agricultural residue burning as major chlorine sources over northern India 1 , 13 . However, particulate chloride concentrations exhibit pronounced sensitivity to meteorological conditions, including temperature, boundary-layer dynamics, and wind patterns 14 . Source apportionment remains challenging because particulate chloride, predominantly present as semi-volatile ammonium chloride (NH 4 Cl), is largely secondary in origin 15 . As a result, receptor-based approaches often misclassify chloride sources or fail to distinguish between primary emissions and secondary formation. At the same time chemical transport model simulations that include only waste burning emissions substantially underestimate observed chloride concentrations 13 , 16 . Moreover, most modelling efforts have focused primarily on winter conditions (10–15°C), when low temperatures favor NH 4 Cl stability, while neglecting warmer seasons when thermodynamic partitioning becomes critical. The combined influence of NH 4 Cl temperature sensitivity and additional anthropogenic HCl sources beyond trash burning therefore remains inadequately explored. Agricultural residue burning, another important source of HCl and particulate chloride 17 , produces episodic yet intense pollution events across the IGP 15 . As particulate chloride forms almost entirely through secondary gas-particle partitioning, quantifying contributions from such sources requires thermodynamically consistent chemical transport modeling 18 . Observational studies further demonstrate that effective equilibrium constants governing NH 3 -HCl partitioning under polluted conditions, particularly at elevated temperatures and high aerosol number concentrations, can be an order of magnitude lower than theoretical estimates, leading to systematic underprediction of particulate chloride in models 19 – 21 . These discrepancies highlight fundamental limitations in current thermodynamic parameterizations controlling NH 4 Cl formation under polluted continental conditions. To address these gaps, we use a modified version of the WRF-Chem model that explicitly incorporates anthropogenic HCl emissions, which are absent in standard model configurations, to simulate particulate chloride over northern India. Simulations are conducted for three contrasting periods encompassing a wide range of meteorological and thermodynamic conditions: post-monsoon agricultural biomass burning (November 2020), peak winter (January 2021), and summer agricultural biomass burning (30 April to 11 May 2021). While the modified model reproduces observed chloride concentrations during post-monsoon and winter, it initially predicts negligible levels in summer. Incorporation of an empirically adjusted thermodynamic equilibrium substantially improves the model’s ability to reproduce daytime chloride concentrations during post-monsoon season and yields realistic particulate chloride levels even under warmer summer conditions. Source attribution reveals that local emissions dominate Delhi’s particulate chloride burden, with open waste burning contributing approximately 35% and 44% during post-monsoon and winter, respectively, while agricultural residue burning accounts for only ~ 14%. The model predicted HCl concentrations during summer were exceptionally high over northern India, which ranks among the highest reported globally for urban environments. Collectively, these findings establish anthropogenic emissions, particularly local waste burning, as the dominant driver of chloride formation in urban atmospheres and underscore the need to reassess chloride thermodynamics in chemical transport models to accurately represent polluted continental environments. Results and Discussion Observed Seasonal Characteristics of Particulate Chloride Observed particulate chloride concentrations over Delhi were consistently high during the post-monsoon and winter seasons, highlighting the importance of chloride in the urban aerosol burden. Mean chloride concentrations reached 8.72 µg m − 3 in November 2020 and 9.5 µg m − 3 in January 2021, accounting for approximately 6% of non-refractory PM 2.5 (NR-PM 2.5 ) mass (Fig. 1 a, b). During the post-monsoon season, extensive agricultural residue burning in neighboring states of Haryana, Uttar Pradesh, and Punjab resulted in widespread pollution episodes over the Indo-Gangetic Plain (IGP) 15 . Biomass burning is recognized as a global source of particulate chloride, contributing ~ 16% of global particulate chloride emissions, with a smaller contribution to gaseous HCl 5 . However, the extent to which regional biomass burning controls chloride levels within densely populated urban environments such as Delhi remains uncertain. Meteorological conditions during the post-monsoon season were characterized by daytime temperatures near 25°C and nighttime temperatures around 15°C, with relative humidity (RH) varying from ~ 30% during the day to ~ 70% at night (Fig. 1 d). Mean PM 2.5 concentrations reached 206 µg m − 3 , corresponding to the “very poor” air quality category defined by the Central Pollution Control Board (CPCB) 22 (Supplementary Fig. 1), with episodic peaks exceeding 500 µg m − 3 (severe category). Elevated nighttime RH coinciding with biomass burning underscores the importance of thermodynamic and microphysical controls on particulate chloride formation during this season. Wintertime chloride concentrations were comparable to post-monsoon levels (Fig. 1 b), reflecting thermodynamically favorable conditions for gas-to-particle partitioning of HCl under colder temperatures and abundant ambient NH 3 . January temperatures ranged from 10–18°C (mean 13.6°C), with RH between 50% and 80% (Fig. 1 e). Severe pollution persisted, with mean PM 2.5 concentrations of 282 µg m − 3 (Supplementary Fig. 1). These conditions frequently coincide with pronounced visibility reduction, driven by high particulate chloride concentrations and enhanced aerosol liquid water content (ALWC) 12 . In contrast, summer conditions (late April to early May) were marked by substantially lower background particulate chloride. Between 30 April and 11 May 2021, the observed mean chloride concentration was 1.40 µg m − 3 (Fig. 1 c), although transient peaks exceeding 10 µg m − 3 were observed during episodic biomass-burning events. Mean temperatures reached 31.85°C, while RH averaged ~ 36.46% (Fig. 1 f). High temperatures and low humidity suppressed NH 4 Cl formation, resulting in reduced particulate chloride, even though short-lived enhancements associated with burning events were still detectable. Representation of Chloride in the Model and Seasonal Performance Gas-to-particle partitioning of HCl to particulate chloride is governed by reversible thermodynamic equilibrium, in which gaseous NH 3 reacts with HCl to form NH 4 Cl in the solid or aqueous phase (R1) 18 . In continental environments, anthropogenic HCl emissions often dominate the regional chlorine budget 5 , 23 , while particulate chloride can readily volatilize back to the gas phase under warm or dry conditions 24 . As a result, temperature and RH exert first-order control on chloride partitioning. $$\text{N}{\text{H}}_{4}\text{C}\text{l}(\text{s}/\text{a}\text{q})\rightleftharpoons\text{N}{\text{H}}_{3}\left(\text{g}\right)+\text{H}\text{C}\text{l}\left(\text{g}\right)$$ R1 \(\text{K}\left(\text{T}\right)=\text{K}\left({\text{T}}_{0}\right)\text{exp}\left[\alpha\left(\frac{{T}_{0}}{T}-1\right)+\beta\left(1+{ln}\left(\frac{{T}_{0}}{T}\right)-\left(\frac{{T}_{0}}{T}\right)\right)\right]\) (Equilibrium constant at any temperature (T)), \({T}_{0}=298.15K\) , \(\alpha=-71.00\) , \(\beta=2.40\) The WRF-Chem simulations reproduced key meteorological parameters (Temperature and RH) with high fidelity. During the post-monsoon period, correlation coefficients (R 2 ) for temperature and RH were 0.87 and 0.77, respectively, increasing to 0.91 and 0.70 in winter. Summer performance was similarly strong, with R 2 values of 0.92 for temperature and 0.87 for RH (Supplementary Fig. 2). Simulated PM 2.5 and NO x concentrations also showed good agreement with observations (Supplementary Fig. 1), providing confidence in the modeled chemical and dynamical environment controlling chloride partitioning. Inclusion of anthropogenic chlorine emissions enabled the model to successfully reproduce observed particulate chloride during the post-monsoon and winter seasons (Fig. 1 a, b). During the post-monsoon season, the simulated mean chloride concentration (8.55 µg m - 3 ) closely matched observations (8.72 µg m - 3 ), with a mean bias of -0.24 µg m - 3 , normalized mean bias of -2.7%, and an index of agreement of 0.75. Time-series comparisons further demonstrate strong agreement (Supplementary Fig. 3). However, the diurnal cycle revealed systematic daytime underestimation between 11:00 and 18:00 local time, while the nighttime concentrations were captured well (Fig. 2 b and Supplementary Fig. 4a). This behavior is consistent with temperature-driven suppression of NH 4 Cl stability during the day. Model performance during winter was similarly robust. The observed mean chloride concentration (9.5 µg m - 3 ) was closely reproduced by the model (9.24 µg m - 3 ), with a mean bias of -0.42 µg m - 3 and an index of agreement of 0.70 (Supplementary Fig. 4b). As in the post-monsoon season, minor daytime underestimation persisted, reflecting thermodynamic constraints on gas-to-particle conversion. In sharp contrast, summer simulations substantially underestimated observed particulate chloride. While observations averaged 1.40 µg m - 3 , simulated values were only 0.07 µg m - 3 (Fig. 1 c and Supplementary Fig. 4c), despite accurate representation of RH (model: 35.59%; observed: 36.46%; Supplementary Fig. 2e, f). These results suggest that the equilibrium parameterization governing the thermodynamic partitioning of HCl to particulate chloride under warm and dry conditions may be inaccurately represented and warrants re-evaluation. Influence of Meteorological Controls on Chloride Partitioning Both observations and simulations demonstrate a pronounced dependency of particulate chloride on meteorological conditions, particularly temperature and RH. In ammonia-rich continental urban environments, NH 4 Cl is the dominant particulate chloride species 25 . Both observations and model simulations consistently show higher particulate chloride concentrations at night when temperatures are lower and reduced concentrations during the day, when elevated temperatures favor volatilization to the gas phase. During the post-monsoon season, modelled mean temperatures (~ 21°C) exceed those in winter (~ 15°C), leading to stronger daytime suppression of particulate chloride. The equilibrium constant governing gas-to-particle partitioning is primarily temperature-dependent, while RH determines whether chloride resides in the solid or aqueous phase 26 . Daytime particulate chloride concentrations declined to 4.33 µg m − 3 in observations and 1.68 µg m − 3 in the model, (Fig. 2 c) consistent with reduced partitioning under warmer conditions. During summer, mean temperatures near ~ 31.9°C further inhibited condensation, explaining its severe underestimation. Daytime RH during winter (~ 50%) exceeded that in post-monsoon (~ 35%), while summer RH remained lowest overall. For mixed salts such as (NH 4 ) 2 SO 4 , NH 4 Cl, NH 4 NO 3 , Na 2 SO 4 , MgSO 4 , K 2 SO 4 , the mutual deliquescence relative humidity (MDRH) is ~ 46% 27 favoring solid phase partitioning under summer and daytime post-monsoon conditions. The model reproduced nighttime chloride concentrations well in both post-monsoon and winter, with simulated nighttime means of 12.67 µg m − 3 and 12.70 µg m − 3 closely matching observations (11.22 µg m − 3 and 11.59 µg m − 3 , respectively). This consistency points to thermodynamic rather than source-related limitations. Role of the Equilibrium Constant in Cl Partitioning Gas-to-particle partitioning of NH 4 Cl from gaseous NH 3 and HCl can proceed via two thermodynamic pathways: (i) condensation into the solid phase and (ii) dissolution in the aqueous phase. In this study, aerosol chemistry and thermodynamics were represented using the MOSAIC (Model for Simulating Aerosol Interactions and Chemistry) scheme 26 . In MOSAIC, gas-to-solid partitioning is governed by a single equilibrium reaction (R1, \({K}_{N{H}_{4}Cl}^{gs}\) = 8.43 × 10 − 17 atm 2 , α = -71, β = 2.40) 26 , gas-to-liquid partitioning proceeds through a four-step sequence. First, HCl(g) dissolves into the aqueous phase as H + (aq) and Cl − (aq); second, NH 3 (g) dissolves to form NH 3 (aq); third, NH 3 (aq) hydrolyzes to generate NH 4 + (aq) and OH − , and finally, NH 4 + (aq) combines with Cl − (aq) to form NH 4 Cl (aq). The equilibrium constants for these reactions are adopted from Kim et al. (1993) 28 and the AIM model 29 . Model diagnostics indicate strong seasonal shifts in chloride phase state as particulate chloride resides predominantly in the aqueous phase during winter (~ 90%), transitions to solid-phase dominance during summer (~ 89%) and exhibits mixed partitioning during the post-monsoon season (Supplementary Fig. 5). Despite this thermodynamic treatment, the base model substantially underestimates chloride during warm/dry conditions, particularly during daytime in the post-monsoon and throughout summer. For example, during daytime in November, simulated particulate chloride averaged 1.68 µg m − 3 compared to an observed mean of 4.33 µg m − 3 , while summer simulations predicted near-zero chloride (~ 0.07 µg m − 3 ). The diurnal behavior of the gas-to-solid equilibrium constant and its implications for daytime partitioning are illustrated in Fig. 2 . Figure 2 a shows post-monsoon average of observed particulate chloride, temperature, RH, and the MOSAIC gas-to-solid equilibrium constant. To contextualize phase-state behavior, an MDRH of 40% is assumed for a mixed salts comprising (NH 4 ) 2 SO 4 , NH 4 Cl, NH 4 NO 3 , Na 2 SO 4 , MgSO 4 , and K 2 SO 4 , lower than the value used in ISORROPIA II 27 . Although MOSAIC dynamically computes aerosol liquid water rather than explicitly prescribing MDRH, the overall phase behavior remains qualitatively similar. Between 10:00 and 18:00, when RH remains below 40%, gas-to-solid partitioning is favored, and an overly large equilibrium constant raises the saturation requirement for NH 4 Cl formation. However, since total chloride (HCl + particulate chloride) emissions remain approximately constant, the concurrent daytime boundary-layer growth and enhanced mixing further dilute HCl and NH 3 , limiting partitioning into the particulate phase. Two explanations could, in principle, account for the reduced modeled particulate chloride during summer and post-monsoon daytime conditions: (i) the effective equilibrium constant is too high, or (ii) additional, unrepresented particulate chloride species contribute substantially. The second explanation is less consistent with the strong nighttime agreement, suggesting that thermodynamic partitioning is the dominant issue. Alternative chemical pathways are unlikely to explain this systematic bias. Haslett et al. 30 reported that nitryl chloride (ClNO 2 ) production in Delhi is strongly suppressed by elevated nighttime NO concentrations, which rapidly titrates NO 3 (NO + NO 3 → 2 NO 2 ), thereby limiting NO 3 and N 2 O 5 formation. Because N 2 O 5 is the key precursor for heterogeneous ClNO 2 production (N 2 O 5 + Cl − → ClNO 2 + NO 3 − ), suppression of N 2 O 5 reduces ClNO 2 formation, constraining morning ClNO 2 photolysis and chloride radical production. Instead, multiple studies have attributed similar model underestimation of particulate chloride to equilibrium constants that are effectively too large relative to values inferred from observations 19 , 20 . Allen et al. (1989) 31 also found that measured equilibrium constants for gas-to-solid NH 4 Cl partitioning at higher temperatures are lower than theoretical estimates derived for pure NH 4 Cl systems 21 . This discrepancy is physically plausible because atmospheric aerosols are multicomponent mixtures, in which non-ideal interactions introduce excess Gibbs free energy that lowers the effective equilibrium constant relative to the idealized pure-salt behavior 32 – 34 . While MOSAIC 26 treats multicomponent aqueous thermodynamics using the Multicomponent Equilibrium Solver for Aerosols (MESA) 35 solver, it does not explicitly represent metastable aerosols, unlike ISSOROPIA 27 , and AIM 29 , 36 . In addition, extremely high particle number concentrations typical of Delhi (~ 10 4 – 10 5 cm − 3 ) (Supplementary Fig. 6) and the IGP across seasons can suppress evaporation and enhance condensation, effectively lowering the operational equilibrium constant 37 . Measurements reported in previous studies indicate that effective equilibrium constants under polluted and warm atmospheric conditions can differ by more than an order of magnitude from theoretical estimates 19 , 20 . To evaluate the sensitivity of modeled chloride partitioning to NH 4 Cl volatility, we performed an additional simulation wherein the gas-to-solid equilibrium parameterization was reduced by a factor of 3, 6 and 10 (Fig. 2 b), resulting in substantially higher simulated particulate chloride concentrations during the day. Daytime particulate chloride increased progressively as the equilibrium constant was reduced, with the best agreement obtained when the parameterization was decreased by a factor of 10 (Fig. 2 a, c), consistent with values inferred in previous studies. Daytime post-monsoon particulate chloride increased from 1.68 µg m − 3 to 3.72 µg m − 3 (Fig. 2 c). The largest improvement occurred in summer, where the simulated concentrations rose from ~ 0.07 µg m − 3 to 1.71 µg m − 3 (Supplementary Fig. 7). These results suggest that the effective volatility of NH 4 Cl in ambient multicomponent aerosols may be higher than represented in the default thermodynamic formulation. The simulation using the reduced equilibrium constant improved agreement with observations and reproduced diurnal peaks more realistically. While the reduced equilibrium constant improves agreement with observations, further laboratory and field studies are needed to better constrain NH 4 Cl thermodynamics under complex atmospheric conditions. Nonetheless, even with the reduced equilibrium constant, simulated particulate chloride occasionally drops to zero, whereas observed values rarely fell below 0.1 µg m − 3 (above the detection limit of both instruments), suggesting that additional sources and/or unresolved heterogeneous processes may contribute to sustaining a non-zero chloride background and warrant further investigation. Spatial Distribution of HCl and Particulate Chloride Across all seasons, particulate chloride in Delhi exhibits a pronounced diurnal cycle, with peak concentrations during the early morning hours and minima during the daytime (Fig. 3 ). In contrast, gaseous HCl (mean ~ 0.54 µg m − 3 in post-monsoon and ~ 0.42 µg m − 3 in winter) displays an inverse diurnal behavior during the post-monsoon and winter seasons, characterized by daytime maxima and nighttime minima (Fig. 3 ). Summer conditions differ markedly. Although summer exhibits the highest mean HCl concentrations (~ 2.66 µg m − 3 ), its diurnal pattern is less pronounced than in cooler seasons (Fig. 3 ), reflecting the combined influence of elevated temperatures and a much deeper boundary layer. Total chloride (particulate chloride + HCl × 35.45/36.46) exhibits broadly similar diurnal behavior across all three seasons (Supplementary Fig. 8), indicating this diurnal of HCl is closely correlated with boundary layer height. Daytime dilution associated with enhanced boundary-layer heights suppresses near-surface HCl, while a distinct morning peak around 10:00 AM likely arises from fumigation, whereby pollutants stored aloft overnight are rapidly mixed down to the surface following boundary-layer growth after sunrise (Supplementary Fig. 4). Peak summer HCl concentrations reach ~ 6.87 µg m − 3 , among the highest values reported globally, implying a substantial potential exposure burden. At the regional scale, simulated gaseous HCl concentrations across northern Indian cities within the model domain range from 0.1 to 5 µg m − 3 in all seasons (Fig. 4 a, b, c). Elevated HCl levels during summer are widespread, consistent with higher temperatures suppressing partitioning into the particulate phase. Monthly contours of modelled particulate chloride further illustrate the seasonal behavior (Fig. 4 d, e, f). Seasonal mean particulate chloride concentrations across seven major cities (Supplementary Fig. 9) consistently identify Delhi as the dominant hotspot, reflecting strong local emissions and dense urban activity. In contrast, Karnal and Meerut show pronounced enhancements during post-monsoon and summer biomass-burning periods, highlighting the influence of regional agricultural residue burning. The remaining cities exhibit relatively similar particulate chloride levels between post-monsoon and winter, suggesting comparable meteorological conditions and emission influences during these seasons. Source Attribution of Particulate Chloride Identifying the dominant sources of particulate chloride in Delhi is critical for understanding its formation pathways and for designing effective air quality management strategies. Source attribution in this study is based on the anthropogenic chlorine emissions inventory developed by Zhang et al. (2022), 5 which represents six major continental chlorine source sectors. For clarity and regional relevance, these sources are grouped into four broad categories: open waste burning, agricultural residue burning, energy production, and other sources, including industrial, residential, and open biomass burning activities 13 , 17 , 38 . Model-derived source contributions for Delhi during the post-monsoon and winter seasons are shown in Fig. 5 , with panels (a) and (c) representing post-monsoon conditions and panels (b) and (d) corresponding to winter. Open waste burning emerges as the dominant contributor to particulate chloride in both seasons. These emissions are primarily local in origin. India generates an estimated 70–170 Tg yr − 1 of municipal solid waste, and Delhi alone produces approximately 9500 tons of waste per day, a substantial fraction of which is openly dumped and burned at major landfill sites 39 , 40 . As a result, open waste burning accounts for about 34% of Delhi’s particulate chloride during the post-monsoon season, increasing to 44% in winter, consistent with enhanced waste-burning activity during colder months 41 . The energy sector constitutes the second-largest contributor, responsible for roughly 30% of particulate chloride in both seasons. In contrast, the contribution of agricultural residue burning within Delhi is comparatively modest. Although crop residue burning emits both HCl and particulate chloride, with reported emission factors of 0.18 g kg − 1 and 0.30 g kg − 1 , respectively 5 (0.26 g kg − 1 ; Pandey et al., unpublished data), its contribution to Delhi’s particulate chloride burden is limited to approximately 14% during the post-monsoon period. This finding contrasts with the common perception that agricultural burning is the dominant driver of all post-monsoon pollution in Delhi. Nevertheless, its regional impact is substantial. Cities located closer to active burning regions exhibit much stronger influences from this source (Fig. 4 d). For example, agricultural residue burning accounts for 63% of particulate chloride in Karnal and 38% in Meerut (Fig. 6 ), where mean chloride concentrations are comparable to those observed in Delhi. The diurnal profile of biomass-burning-derived particulate chloride in Delhi during the post-monsoon season (Fig. 5 a, inset) shows relatively weak diurnal variability, with only a modest morning enhancement, consistent with its predominantly regional and advective nature (Fig. 4 d). To further quantify the relative importance of local versus regional sources, an additional sensitivity simulation was performed in which local emissions from Delhi were excluded. The result indicates that local sources account for 63.8% of the total particulate chloride during the post-monsoon season, underscoring the dominant role of local anthropogenic activities, particularly open waste burning, in driving chloride pollution within the city, relative to transboundary or regional contributions. Summary and Conclusions This work quantifies the processes and sources controlling particulate chloride and HCl in the polluted urban atmosphere of northern Indian cities using a combination of high-time-resolution observations and a modified WRF-Chem framework that explicitly represents anthropogenic HCl emissions and chloride-containing particles. Simulations spanning three contrasting seasons, post-monsoon, winter, and summer, demonstrate that chloride in this inland megacity is overwhelmingly governed by anthropogenic emissions rather than marine influence, and that particulate chloride is dominated by semi-volatile ammonium chloride whose phase partitioning is tightly regulated by temperature and relative humidity. A key finding is that commonly used thermodynamic parameterizations systematically overpredict NH 4 Cl volatility under warm and/or dry conditions, leading to persistent model underestimation of daytime chloride in post-monsoon and severe under prediction in summer. Reducing the effective NH 3 -HCl gas-particle equilibrium constant by one order of magnitude substantially improved model performance, enabling realistic reproduction of both the magnitude and diurnal variability of particulate chloride across seasons. The remaining low bias, especially the tendency for modeled chloride to intermittently collapse to near-zero, suggests that additional chloride formation pathways, non-ideal aerosol effects, or heterogeneous processes may help sustain a non-zero chloride background under highly polluted conditions and merit further investigation. Importantly, Delhi exhibits exceptionally high summer HCl, among the highest reported globally for urban environments, underscoring the potential for substantial population exposure. Source attribution indicates that Delhi’s chloride burden is dominated by local anthropogenic activities. Open waste burning is the single largest contributor, accounting for 34% of particulate chloride in the post-monsoon and 44% in winter, followed by the energy sector (~ 30%). In contrast, agricultural residue burning contributes only ~ 14% within Delhi, despite exerting strong influence in nearby cities such as Karnal and Meerut. A sensitivity experiment excluding Delhi emissions shows that local sources explain 63.8% of post-monsoon particulate chloride, confirming that local controls outweigh regional transport for chloride within the city. Overall, these results call for (i) revised thermodynamic treatments of NH 4 Cl in chemical transport models to represent real polluted aerosol mixtures and (ii) targeted mitigation of open waste burning and related local emissions as a priority pathway to reduce chloride-rich PM in Delhi and other rapidly urbanizing Asian regions. Methods Observational Dataset High-time-resolution measurements of speciated PM 2.5 , elemental composition, and trace metals were conducted at the Indian Institute of Technology (IIT) Delhi (28.54° N and 77.19° E). Instruments were installed on the rooftop of a three-story building at a sampling height of approximately 12 m above ground level in a temperature-controlled environment. This site represents one of the state-of-the-art facilities in the region providing continuous, co-located measurements of PM 2.5 chemical speciation and elemental composition. Elemental and metal concentrations were measured using an XACT 625i (Sailbri Cooper Inc., Tigard, OR, USA), which employes reel-to-reel filter tape sampling coupled with continuous, nondestructive energy-dispersive X-ray fluorescence (EDXRF) analysis. Non-refractory submicron aerosol composition was measured using a Quadrupole Aerosol Chemical Speciation Monitor (Q-ACSM, Aerodyne Research Inc., Billerica, MA, USA). The ACSM quantifies sulfate, nitrate, ammonium, chloride, and organic aerosol mass concentrations through a flash vaporization at 600°C followed by electron impact ionization mass spectrometry. Gas-phase pollutant measurements were obtained from monitoring stations operated by the Central Pollution Control Board (CPCB; https://www.cpcb.nic.in/ ). Meteorological variables were derived from the National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration (NOAA), using data from the ground-based station at Indira Gandhi International Airport (IGIA) ( https://www.ncei.noaa.gov/ ). Numerical Simulations Numerical simulations were conducted to examine particulate chloride formation under three contrasting pollution regimes over northern India, each characterized by distinct emission patterns and thermodynamic conditions. The selected periods include (i) November 2020, representing the post-monsoon agricultural biomass-burning period, (ii) January 2021, corresponding to the winter extreme pollution period marked by low temperatures, high humidity, stagnant boundary-layer conditions, and elevated emissions, and (iii) May 2021, coinciding with the summer biomass burning season. These periods were chosen to capture the wide range of meteorological and chemical environments under which particulate chloride exerts a strong influence on regional air quality. The exact simulation windows were 5–30 November 2020, 5 January-5 February 2021, and 30 April-11 May 2021. In addition to baseline simulations, targeted sensitivity experiments were performed for each season to assess the influence of thermodynamic equilibrium parameterization governing the gas-to-particle partitioning of NH 4 Cl. Furthermore, sectoral contribution analyses of anthropogenic HCl and particulate chloride emissions were also conducted for all the simulation periods, following the methodology described in Supplementary Table 1. Model Configuration All simulations were performed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem version 4.2.2 42 ). The model was configured with two one-way nested domains at horizontal resolutions of 27 km and 9 km, respectively, centered over Delhi (28.70° N, 77.10° E) (Supplementary Fig. 10). The outer domain encompasses central and northern India to capture large-scale transport and synoptic influences, while the inner domain resolves Delhi and adjoining northern Indian states, allowing explicit representation of local emissions, boundary-layer processes, and regional-scale interactions. Gas-phase chemistry was represented using the Model for Ozone and Related Chemical Tracers (MOZART) 43 , which includes 157 gas-phase reactions, 85 chemical species, and 12 aerosol components. Aerosol microphysics and chemistry were simulated using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) 26 , which resolves size-segregated aerosol processes including nucleation, condensation, coagulation, and thermodynamic gas-particle partitioning. A complete description of the physical and chemical parameterizations used in the model is provided in Supplementary Text 1. Since the standard WRF-Chem configurations do not explicitly include anthropogenic HCl emissions, these emissions were incorporated in the present study to better represent chloride formation in polluted continental environments. Emission Inventories Anthropogenic emissions were obtained from the Emissions Database for Global Atmospheric Research version 6 (EDGARv6) 44 , provided at a spatial resolution of 0.1° × 0.1° based on 2018 activity data. Biomass burning emissions were taken from the Fire INventory from NCAR (FINN) 45 , while biogenic emissions were simulated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) 46 . Emissions of hydrogen chloride (HCl) and particulate chloride were implemented using the global gridded anthropogenic chlorine emission inventory developed by Zhang et al. (2022) 5 . This inventory, the only comprehensive global dataset representing continental chlorine sources, provides annual emissions at 0.1° × 0.1° resolution for the period 1960–2014. Emissions corresponding to the year 2014 were applied in this study. The inventory includes six major source sectors: energy production, industrial processes, residential households, open waste burning, open biomass burning and agricultural activities, enabling detailed assessment of sector specific contributions to anthropogenic chlorine over Delhi and northern India. Declarations Competing Interest Statement The authors declare no financial and non-financial competing interests. Author Contribution RM, MK, DG and VS designed the research. RM carried out the chemical transport model simulations, reaction equilibrium adjustments. Funding acquisition by MK and VS. RM executed data analysis and formal interpretation. RM, MK, DG and VS led the manuscript writing. Acknowledgement We acknowledge Douglas Worsnop for his valuable suggestions during the preparation of the manuscript. This work is supported by the IRD Grand Challenge Project grant at the Indian Institute of Technology Delhi (Grant No. 428 IITD/IRD/MI01810G), originally funded by the Ministry of Human Resource Development (MHRD), Government of India, for the establishment of a state-of-the-art observational site. The High-Performance Computing Center (HPC) at the Indian Institute of Technology Delhi provided the computational resources for this work. Data Availability Gas phase pollutants and PM 2.5 data are taken from Central Pollution Control Board ( [https://www.cpcb.nic.in/](https:/www.cpcb.nic.in) ). The meteorological data taken from National Centers for Environmental Information ( [https://www.ncei.noaa.gov/](https:/www.ncei.noaa.gov) ). All other data analyzed during the study are included in this article and in the supplementary information files. References Gunthe, S. S. et al. Enhanced aerosol particle growth sustained by high continental chlorine emission in India. Nat. Geosci. 14, 77–84 (2021). Qiu, X. et al. Modeling the impact of heterogeneous reactions of chlorine on summertime nitrate formation in Beijing, China. Atmos. Chem. Phys. 19, 6737–6747 (2019). Choi, M. S. et al. Study of secondary organic aerosol formation from chlorine radical-initiated oxidation of volatile organic compounds in a polluted atmosphere using a 3D chemical transport model. Environ. Sci. Technol. 54, 13409–13418 (2020). Fu, X. et al. Anthropogenic emissions of hydrogen chloride and fine particulate chloride in China. Environ. Sci. Technol. 52, 1644–1654 (2018). Zhang, B. et al. Global emissions of hydrogen chloride and particulate chloride from continental sources. Environ. Sci. Technol. 56, 3894–3904 (2022). Graedel, T. E. & Keene, W. C. Tropospheric budget of reactive chlorine. Global Biogeochem. Cycles 9, 47–77 (1995). Beig, G. et al. 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Chloride (HCl / Cl-) dominates inorganic aerosol formation from ammonia in the Indo-Gangetic Plain during winter: modeling and comparison with observations. Atmos. Chem. Phys. 23, 41–59 (2023). Manchanda, C., Kumar, M. & Singh, V. Meteorology governs the variation of Delhi’s high particulate-bound chloride levels. Chemosphere 291, 132879 (2022). Faisal, M., Ali, U., Kumar, A., Kumar, M. & Singh, V. Unveiling PM2. 5 sources: Double and tracer conjugate PMF approaches for high-resolution organic, BC, and inorganic PM2. 5 data. Atmos. Environ. 343, 121011 (2025). Bharali, C. et al. Role of atmospheric aerosols in severe winter fog over the Indo-Gangetic Plain of India: a case study. Atmos. Chem. Phys. 24, 6635–6662 (2024). Sahoo, P. et al. What drives anthropogenic fine particulate chloride emissions in India?--A quantitative assessment of hotspots. Science of The Total Environment 991, 179949 (2025). Seinfeld, J. H. & Pandis, S. N. Atmospheric chemistry and physics: from air pollution to climate change. Ianniello, A. et al. Chemical characteristics of inorganic ammonium salts in PM_2.5 in the atmosphere of Beijing (China). Atmos. Chem. Phys. 11, 10803–10822 (2011). Behera, S. N., Betha, R. & Balasubramanian, R. Insights into chemical coupling among acidic gases, ammonia and secondary inorganic aerosols. Aerosol Air Qual. Res. 13, 1282–1296 (2013). Pio, C. A. & Harrison, R. M. Vapour pressure of ammonium chloride aerosol: effect of temperature and humidity. Atmospheric Environment ( 1967) 21, 2711–2715 (1987). Central Pollution Control Board. National Air Quality Index (AQI). Wang, X. et al. Effects of anthropogenic chlorine on PM2. 5 and ozone air quality in China. Environ. Sci. Technol. 54, 9908–9916 (2020). Wang, X. et al. The role of chlorine in global tropospheric chemistry. Atmos. Chem. Phys. 19, 3981–4003 (2019). Chen, Y. et al. Ammonium Chloride Associated Aerosol Liquid Water Enhances Haze in Delhi, India. Environ. Sci. Technol. 56, 7163–7173 (2022). Zaveri, R. A., Easter, R. C., Fast, J. D. & Peters, L. K. Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). Journal of Geophysical Research. D. (Atmospheres), 113(D13):Art. No. D13204 113, (2008). Fountoukis, C. & Nenes, A. ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+--Ca 2+--Mg 2+--NH 4+--Na+--SO 4 2—NO 3—Cl—H 2 O aerosols. Atmos. Chem. Phys. 7, 4639–4659 (2007). Kim, Y. P., Seinfeld, J. H. & Saxena, P. Atmospheric gas-aerosol equilibrium I. Thermodynamic model. Aerosol Science and Technology 19, 157–181 (1993). Clegg, S. L., Brimblecombe, P. & Wexler, A. S. Thermodynamic model of the system H+- NH4+- Na+- SO42– NO3– Cl– H2O at 298.15 K. J. Phys. Chem. A 102, 2155–2171 (1998). Haslett, S. L. et al. Nighttime NO emissions strongly suppress chlorine and nitrate radical formation during the winter in Delhi. Atmos. Chem. Phys. 23, 9023–9036 (2023). Allen, A. G., Harrison, R. M. & Erisman, J.-W. Field measurements of the dissociation of ammonium nitrate and ammonium chloride aerosols. Atmospheric Environment ( 1967) 23, 1591–1599 (1989). Clegg, S. L. & Pitzer, K. S. Thermodynamics of multicomponent, miscible, ionic solutions: generalized equations for symmetrical electrolytes. J. Phys. Chem. 96, 3513–3520 (1992). Hu, Y.-F. & Guo, T.-M. Thermodynamics of electrolytes in aqueous systems containing both ionic and nonionic solutes. Application of the Pitzer–Simonson–Clegg equations to activity coefficients and solubilities of 1: 1 electrolytes in four electrolyte–non-electrolyte–H 2 O ternary systems at 298.15 K. Physical Chemistry Chemical Physics 1, 3303–3308 (1999). Lach, A. et al. Thermal and volumetric properties of complex aqueous electrolyte solutions using the Pitzer formalism–The PhreeSCALE code. Comput. Geosci. 92, 58–69 (2016). Zaveri, R. A., Easter, R. C. & Peters, L. K. A computationally efficient multicomponent equilibrium solver for aerosols (MESA). Journal of Geophysical Research: Atmospheres 110, (2005). Wexler, A. S. & Clegg, S. L. Atmospheric aerosol models for systems including the ions H+, NH4+, Na+, SO42-, NO3-, Cl-, Br-, and H2O. Journal of Geophysical Research: Atmospheres 107, ACH–14 (2002). Bai, H., Lu, C. & Ling, Y. M. A theoretical study on the evaporation of dry ammonium chloride and ammonium nitrate aerosols. Atmos. Environ. 29, 313–321 (1995). Sharma, G. et al. Gridded Emissions of CO, NOx, SO2, CO2, NH3, HCl, CH4, PM2.5, PM10, BC, and NMVOC from Open Municipal Waste Burning in India. Environ. Sci. Technol. 53, 4765–4774 (2019). Glauser, W. Delhi’s dumps are “public health time bombs”. Can. Med. Assoc. J. 185, E319–E320 (2013). Angmo, S. & Shah, S. Impact of Okhla, Bhalswa and Ghazipur Municipal Waste Dumpsites (Landfill) on Groundwater Quality in Delhi. Current World Environment 16, 210–220 (2021). Nagpure, A. S., Ramaswami, A. & Russell, A. Characterizing the spatial and temporal patterns of open burning of municipal solid waste (MSW) in Indian cities. Environ. Sci. Technol. 49, 12904–12912 (2015). Grell, G. A. et al. Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 39, 6957–6975 (2005). Emmons, L. K. et al. Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4). Geosci. Model Dev. 3, 43–67 (2010). Monforti Ferrario, F. et al. EDGAR v6.1 Global Air Pollutant Emissions (2022). Wiedinmyer, C. et al. The Fire INventory from NCAR (FINN): A high resolution global model to estimate the emissions from open burning. Geosci. Model Dev. 4, 625–641 (2011). Guenther, A. B. et al. The model of emissions of gases and aerosols from nature version 2.1 (MEGAN2.1): An extended and updated framework for modeling biogenic emissions. Geosci. Model Dev. 5, 1471–1492 (2012). Additional Declarations No competing interests reported. Supplementary Files ChlorideSupplementarynpj2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 23 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 16 Mar, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9138509","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611706820,"identity":"686d4040-03b5-432c-bc38-e47d278a53dc","order_by":0,"name":"Rakesh Maity","email":"","orcid":"","institution":"Indian Institute of Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Rakesh","middleName":"","lastName":"Maity","suffix":""},{"id":611706825,"identity":"06f46ffb-d82f-4360-8b18-039db7197943","order_by":1,"name":"Vikram Singh","email":"","orcid":"","institution":"Indian Institute of Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Vikram","middleName":"","lastName":"Singh","suffix":""},{"id":611706826,"identity":"2ddd092b-8ca4-41e4-9f2e-9ee4fe000c53","order_by":2,"name":"Dilip Ganguly","email":"","orcid":"","institution":"Indian Institute of Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Dilip","middleName":"","lastName":"Ganguly","suffix":""},{"id":611706829,"identity":"72d409e3-ab4c-4349-b930-6d6105b2ecb3","order_by":3,"name":"Mayank Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACCQiVIAck2BgYDID8AyABA8JajGFaJIjWktgA1gISOEDAYZLTDj+T+LkjLX1te/OzBwwFFnV8B5gffmAouINTi7R0mplk75mc3G1njpkbgBwmeYDNWILB4BlOLXLSCWYSvG0Vudtu5LBJgLQYHGAwA/rlMB4t6d8k/7ZVpJvdfwPTwv4NrxZp6Rwzad62nASzGzwwLTz4bZGcnVNsLduWZrjtTJqZRIKBhOTMwzzFQAZuLRK30zfefNuWLG92HBh0H/7U8fMdb9/44cMf3FqAgEUCzkwAEcwwBm7A/AG//CgYBaNgFIx4AACClk06ZQAaZAAAAABJRU5ErkJggg==","orcid":"","institution":"Indian Institute of Technology Delhi","correspondingAuthor":true,"prefix":"","firstName":"Mayank","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2026-03-16 13:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9138509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9138509/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105389255,"identity":"15a5b627-b768-4c13-920b-391ad0815542","added_by":"auto","created_at":"2026-03-25 13:04:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticulate Chloride concentrations Box and Whiskers plot of chloride observed by the instrument placed at IIT Delhi and base model (WRF-Chem) estimated chloride (observations (blue) vs model (red)), along with observed Chloride variation with observed meteorological variables (Temperature and Relative Humidity (RH)) for three seasons (Post-monsoon (5 November – 30 November 2020), Winter (5 January – 5 February 2021), Summer (30 April – 11 May 2021)). (a) \u003c/strong\u003eParticulate chloride\u003cstrong\u003e \u003c/strong\u003eBox and Whiskers plot for post-monsoon, \u003cstrong\u003e(b) \u003c/strong\u003eParticulate chloride\u003cstrong\u003e \u003c/strong\u003eBox and Whiskers plot for winter, \u003cstrong\u003e(c) \u003c/strong\u003eParticulate chloride\u003cstrong\u003e \u003c/strong\u003eBox and Whiskers plot for Summer, \u003cstrong\u003e(d) \u003c/strong\u003eVariation of observed chloride (black line) with RH (circles) and Temperature (color bar) for post-monsoon, \u003cstrong\u003e(e) \u003c/strong\u003eVariation of observed chloride (black line) with RH (circles) and Temperature (color bar) for winter, \u003cstrong\u003e(f) \u003c/strong\u003eVariation of observed chloride (black line) with RH (circles) and Temperature (color bar) for summer.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9138509/v1/903098274ee7a66f8f1cb74c.png"},{"id":105565665,"identity":"783f8ccf-2d11-4e9b-88d0-4dd4044da529","added_by":"auto","created_at":"2026-03-27 12:53:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57610,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChloride gas-to-solid equilibrium constant from the base model (MOSAIC) and sensitivity test of different equilibrium constants (K\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eeq\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e) from 10:00 to 18:00 with observed temperature, relative humidity (RH), particulate chloride in post-monsoon, along with the base model and the model with different equilibrium constant estimated particulate chloride and HCl. (a) \u003c/strong\u003eVariation of gas-to-solid equilibrium constant from MOSAIC thermodynamic model (green) and sensitivity test of equilibrium constant by different factors (purple – 3, navy – 6, red – 10) with particulate chloride (black), temperature (blue), RH (color bar) (During 10:00 to 18:00 Hr RH remains below 40%, indicating possibility of gas-to-solid partitioning), \u003cstrong\u003e(b) \u003c/strong\u003eModel-predicted particulate chloride using different equilibrium constant parameterizations compared with observations, \u003cstrong\u003e(c) \u003c/strong\u003eZoomed comparison of observed particulate chloride (black) with model predictions from the base MOSAIC configuration (green solid line with circles) and the revised equilibrium constant (K\u003csub\u003eeq\u003c/sub\u003e/10) simulation (orange-red solid line with circles). Corresponding model-predicted HCl is shown for the base model (green dashed line with squares) and the revised equilibrium constant (K\u003csub\u003eeq\u003c/sub\u003e/10) simulation (orange-red dashed line with squares).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9138509/v1/0a354c694493d9f12347ff21.png"},{"id":105565655,"identity":"9f574b6c-5b27-4eac-9c10-6bebffd19375","added_by":"auto","created_at":"2026-03-27 12:53:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFinal model estimated particulate chloride (red) and HCl (blue) diurnal across seasons in Delhi. (a) \u003c/strong\u003eParticulate chloride and HCl diurnal in post-monsoon; \u003cstrong\u003e(b) \u003c/strong\u003eParticulate chloride and HCl diurnal in winter; \u003cstrong\u003e(c) \u003c/strong\u003eParticulate chloride and HCl diurnal in summer.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9138509/v1/b4e33303a693ea71c3360b7e.png"},{"id":105389250,"identity":"f9383d46-6392-4991-af74-d0357ffe09d6","added_by":"auto","created_at":"2026-03-25 13:04:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73615,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHCl and particulate chloride spatial distribution across the northern Indian cities (name of the major cities are marked in Supplementary Fig. 9a). a\u003c/strong\u003e,\u003cstrong\u003eb\u003c/strong\u003e,\u003cstrong\u003ec\u003c/strong\u003e, represents the HCl concentrations in μg m\u003csup\u003e-3\u003c/sup\u003e across post-monsoon, winter and summer respectively. \u003cstrong\u003ed\u003c/strong\u003e,\u003cstrong\u003ee\u003c/strong\u003e,\u003cstrong\u003ef\u003c/strong\u003e, represents the particulate chloride concentrations in μg m\u003csup\u003e-3\u003c/sup\u003e across post-monsoon, winter and summer respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9138509/v1/2f072f47ab0025ad69873838.png"},{"id":105565901,"identity":"f046a54e-9aae-4def-aab2-e4d8fe707ba8","added_by":"auto","created_at":"2026-03-27 12:54:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46183,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel (WRF-Chem) estimated particulate chloride source contribution (yellow – industrial, residential and open biomass burning; green – open waste burning; red – agricultural residue burning; blue - energy) in Delhi for post-monsoon and winter seasons. (a) \u0026amp; (c) \u003c/strong\u003eSource contributions in post-monsoon season (a) stacked plot, (c) pie-chart; \u003cstrong\u003e(b) \u0026amp; (d) \u003c/strong\u003eSource contributions in winter (b) stacked plot, (d) pie-chart; \u003cstrong\u003e(a) \u003c/strong\u003eDiurnal of only agricultural residue burning (inset).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9138509/v1/7ad2074df041d119fc4eaa40.png"},{"id":105389254,"identity":"71daf4c6-bafd-4f8d-ad53-d9337f61e656","added_by":"auto","created_at":"2026-03-25 13:04:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":53747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel (WRF-Chem) estimated particulate chloride source contribution (yellow – industrial, residential and open biomass burning; green – open waste burning; red – agricultural residue burning; blue - energy) in satellite cities near to biomass burning activity for post-monsoon season. (a) \u0026amp; (c) \u003c/strong\u003eSource contributions in Karnal (a) stacked plot, (b) pie-chart; \u003cstrong\u003e(b) \u0026amp; (d) \u003c/strong\u003eSource contributions in Meerut (b) stacked plot, (d) pie-chart.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9138509/v1/31d58512fd61fd9054efa6ed.png"},{"id":105570217,"identity":"8c106a1b-37f6-4a2c-9871-cec8ff97f554","added_by":"auto","created_at":"2026-03-27 13:15:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1729242,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9138509/v1/c4366e83-7578-4e8a-8eff-2cf78fd8668e.pdf"},{"id":105389256,"identity":"e5b84a16-16c5-464c-b60f-15950fd0a90f","added_by":"auto","created_at":"2026-03-25 13:04:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3418253,"visible":true,"origin":"","legend":"","description":"","filename":"ChlorideSupplementarynpj2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9138509/v1/a92e3926611a26138196072d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Anthropogenic emissions drive elevated HCl and particulate chloride in polluted urban air","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParticulate chloride (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e) is an important yet understudied constituent of fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e), exerting a disproportionate influence on aerosol hygroscopicity, acidity, visibility degradation, and heterogeneous chemistry in the troposphere\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Owing to its strong affinity for water, chloride enhances aerosol liquid water content, intensifying light scattering and often causing severe visibility degradation, frequently exceeding 50% under humid conditions. In addition, chlorine radicals derived from particulate chloride and its gas-phase precursor hydrogen chloride (HCl) are highly reactive toward a broad range of volatile organic compounds (VOCs), often exceeding hydroxyl radicals in reactivity, thereby accelerating oxidation pathways and promoting secondary organic aerosol (SOA) formation\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Despite these critical roles, chloride chemistry remains poorly constrained in atmospheric models, particularly over polluted continental environments.\u003c/p\u003e \u003cp\u003eIn marine and coastal regions, particulate chloride is dominated by sea-salt emissions. In contrast, a growing body of observational evidence demonstrates that anthropogenic activities dominate chloride loading in inland urban atmospheres. Major non-marine chlorine sources include open waste incineration, industrial processes, coal combustion, brick kilns, and agricultural residue burning\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Rapid urbanization, expanding waste streams, and increasing fossil-fuel consumption have amplified anthropogenic chlorine emissions across densely populated regions such as East Asia and South Asia. Consequently, understanding particulate chloride formation pathways, gas-particle partitioning behavior, and source contributions has become particularly important in highly polluted continental regions such as East China and the Indo-Gangetic Plain (IGP).\u003c/p\u003e \u003cp\u003eThe IGP is among the most polluted regions globally, experiencing persistently hazardous air quality throughout the year\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Millions of residents across this densely populated corridor are chronically exposed to PM\u003csub\u003e2.5\u003c/sub\u003e concentrations far exceeding health-based guidelines\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Fine particulate matter in this region not only drives the air quality crisis but also strongly modulates aerosol acidity and aerosol water content (AWC), with cascading implications for acid deposition, visibility reduction, heterogeneous chemistry, and regional radiative forcing\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Despite extensive monitoring efforts, the formation mechanisms and source contributions of particulate chloride across the IGP remain poorly constrained, particularly in urban environments such as Delhi, where observed chloride concentrations are consistently high.\u003c/p\u003e \u003cp\u003ePrevious studies have identified open waste burning, industrial activities, and agricultural residue burning as major chlorine sources over northern India\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, particulate chloride concentrations exhibit pronounced sensitivity to meteorological conditions, including temperature, boundary-layer dynamics, and wind patterns\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Source apportionment remains challenging because particulate chloride, predominantly present as semi-volatile ammonium chloride (NH\u003csub\u003e4\u003c/sub\u003eCl), is largely secondary in origin\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. As a result, receptor-based approaches often misclassify chloride sources or fail to distinguish between primary emissions and secondary formation. At the same time chemical transport model simulations that include only waste burning emissions substantially underestimate observed chloride concentrations\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Moreover, most modelling efforts have focused primarily on winter conditions (10\u0026ndash;15\u0026deg;C), when low temperatures favor NH\u003csub\u003e4\u003c/sub\u003eCl stability, while neglecting warmer seasons when thermodynamic partitioning becomes critical. The combined influence of NH\u003csub\u003e4\u003c/sub\u003eCl temperature sensitivity and additional anthropogenic HCl sources beyond trash burning therefore remains inadequately explored.\u003c/p\u003e \u003cp\u003eAgricultural residue burning, another important source of HCl and particulate chloride\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, produces episodic yet intense pollution events across the IGP\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. As particulate chloride forms almost entirely through secondary gas-particle partitioning, quantifying contributions from such sources requires thermodynamically consistent chemical transport modeling\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Observational studies further demonstrate that effective equilibrium constants governing NH\u003csub\u003e3\u003c/sub\u003e-HCl partitioning under polluted conditions, particularly at elevated temperatures and high aerosol number concentrations, can be an order of magnitude lower than theoretical estimates, leading to systematic underprediction of particulate chloride in models\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. These discrepancies highlight fundamental limitations in current thermodynamic parameterizations controlling NH\u003csub\u003e4\u003c/sub\u003eCl formation under polluted continental conditions.\u003c/p\u003e \u003cp\u003eTo address these gaps, we use a modified version of the WRF-Chem model that explicitly incorporates anthropogenic HCl emissions, which are absent in standard model configurations, to simulate particulate chloride over northern India. Simulations are conducted for three contrasting periods encompassing a wide range of meteorological and thermodynamic conditions: post-monsoon agricultural biomass burning (November 2020), peak winter (January 2021), and summer agricultural biomass burning (30 April to 11 May 2021). While the modified model reproduces observed chloride concentrations during post-monsoon and winter, it initially predicts negligible levels in summer. Incorporation of an empirically adjusted thermodynamic equilibrium substantially improves the model\u0026rsquo;s ability to reproduce daytime chloride concentrations during post-monsoon season and yields realistic particulate chloride levels even under warmer summer conditions. Source attribution reveals that local emissions dominate Delhi\u0026rsquo;s particulate chloride burden, with open waste burning contributing approximately 35% and 44% during post-monsoon and winter, respectively, while agricultural residue burning accounts for only\u0026thinsp;~\u0026thinsp;14%. The model predicted HCl concentrations during summer were exceptionally high over northern India, which ranks among the highest reported globally for urban environments. Collectively, these findings establish anthropogenic emissions, particularly local waste burning, as the dominant driver of chloride formation in urban atmospheres and underscore the need to reassess chloride thermodynamics in chemical transport models to accurately represent polluted continental environments.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eObserved Seasonal Characteristics of Particulate Chloride\u003c/h2\u003e \u003cp\u003eObserved particulate chloride concentrations over Delhi were consistently high during the post-monsoon and winter seasons, highlighting the importance of chloride in the urban aerosol burden. Mean chloride concentrations reached 8.72 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in November 2020 and 9.5 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in January 2021, accounting for approximately 6% of non-refractory PM\u003csub\u003e2.5\u003c/sub\u003e (NR-PM\u003csub\u003e2.5\u003c/sub\u003e) mass (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, b). During the post-monsoon season, extensive agricultural residue burning in neighboring states of Haryana, Uttar Pradesh, and Punjab resulted in widespread pollution episodes over the Indo-Gangetic Plain (IGP)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Biomass burning is recognized as a global source of particulate chloride, contributing\u0026thinsp;~\u0026thinsp;16% of global particulate chloride emissions, with a smaller contribution to gaseous HCl\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, the extent to which regional biomass burning controls chloride levels within densely populated urban environments such as Delhi remains uncertain.\u003c/p\u003e \u003cp\u003eMeteorological conditions during the post-monsoon season were characterized by daytime temperatures near 25\u0026deg;C and nighttime temperatures around 15\u0026deg;C, with relative humidity (RH) varying from ~\u0026thinsp;30% during the day to ~\u0026thinsp;70% at night (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Mean PM\u003csub\u003e2.5\u003c/sub\u003e concentrations reached 206 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, corresponding to the \u0026ldquo;very poor\u0026rdquo; air quality category defined by the Central Pollution Control Board (CPCB)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e (Supplementary Fig.\u0026nbsp;1), with episodic peaks exceeding 500 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (severe category). Elevated nighttime RH coinciding with biomass burning underscores the importance of thermodynamic and microphysical controls on particulate chloride formation during this season.\u003c/p\u003e \u003cp\u003eWintertime chloride concentrations were comparable to post-monsoon levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), reflecting thermodynamically favorable conditions for gas-to-particle partitioning of HCl under colder temperatures and abundant ambient NH\u003csub\u003e3\u003c/sub\u003e. January temperatures ranged from 10\u0026ndash;18\u0026deg;C (mean 13.6\u0026deg;C), with RH between 50% and 80% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Severe pollution persisted, with mean PM\u003csub\u003e2.5\u003c/sub\u003e concentrations of 282 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (Supplementary Fig.\u0026nbsp;1). These conditions frequently coincide with pronounced visibility reduction, driven by high particulate chloride concentrations and enhanced aerosol liquid water content (ALWC)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn contrast, summer conditions (late April to early May) were marked by substantially lower background particulate chloride. Between 30 April and 11 May 2021, the observed mean chloride concentration was 1.40 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), although transient peaks exceeding 10 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e were observed during episodic biomass-burning events. Mean temperatures reached 31.85\u0026deg;C, while RH averaged\u0026thinsp;~\u0026thinsp;36.46% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). High temperatures and low humidity suppressed NH\u003csub\u003e4\u003c/sub\u003eCl formation, resulting in reduced particulate chloride, even though short-lived enhancements associated with burning events were still detectable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRepresentation of Chloride in the Model and Seasonal Performance\u003c/h3\u003e\n\u003cp\u003eGas-to-particle partitioning of HCl to particulate chloride is governed by reversible thermodynamic equilibrium, in which gaseous NH\u003csub\u003e3\u003c/sub\u003e reacts with HCl to form NH\u003csub\u003e4\u003c/sub\u003eCl in the solid or aqueous phase (R1)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In continental environments, anthropogenic HCl emissions often dominate the regional chlorine budget\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, while particulate chloride can readily volatilize back to the gas phase under warm or dry conditions\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. As a result, temperature and RH exert first-order control on chloride partitioning.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{N}{\\text{H}}_{4}\\text{C}\\text{l}(\\text{s}/\\text{a}\\text{q})\\rightleftharpoons\\text{N}{\\text{H}}_{3}\\left(\\text{g}\\right)+\\text{H}\\text{C}\\text{l}\\left(\\text{g}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003eR1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{K}\\left(\\text{T}\\right)=\\text{K}\\left({\\text{T}}_{0}\\right)\\text{exp}\\left[\\alpha\\left(\\frac{{T}_{0}}{T}-1\\right)+\\beta\\left(1+{ln}\\left(\\frac{{T}_{0}}{T}\\right)-\\left(\\frac{{T}_{0}}{T}\\right)\\right)\\right]\\)\u003c/span\u003e \u003c/span\u003e (Equilibrium constant at any temperature (T)), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{0}=298.15K\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha=-71.00\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta=2.40\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe WRF-Chem simulations reproduced key meteorological parameters (Temperature and RH) with high fidelity. During the post-monsoon period, correlation coefficients (R\u003csup\u003e2\u003c/sup\u003e) for temperature and RH were 0.87 and 0.77, respectively, increasing to 0.91 and 0.70 in winter. Summer performance was similarly strong, with R\u003csup\u003e2\u003c/sup\u003e values of 0.92 for temperature and 0.87 for RH (Supplementary Fig.\u0026nbsp;2). Simulated PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003ex\u003c/sub\u003e concentrations also showed good agreement with observations (Supplementary Fig.\u0026nbsp;1), providing confidence in the modeled chemical and dynamical environment controlling chloride partitioning.\u003c/p\u003e \u003cp\u003eInclusion of anthropogenic chlorine emissions enabled the model to successfully reproduce observed particulate chloride during the post-monsoon and winter seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, b). During the post-monsoon season, the simulated mean chloride concentration (8.55 \u0026micro;g m\u003csup\u003e-\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e) closely matched observations (8.72 \u0026micro;g m\u003csup\u003e-\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e), with a mean bias of -0.24 \u0026micro;g m\u003csup\u003e-\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, normalized mean bias of -2.7%, and an index of agreement of 0.75. Time-series comparisons further demonstrate strong agreement (Supplementary Fig.\u0026nbsp;3). However, the diurnal cycle revealed systematic daytime underestimation between 11:00 and 18:00 local time, while the nighttime concentrations were captured well (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and Supplementary Fig.\u0026nbsp;4a). This behavior is consistent with temperature-driven suppression of NH\u003csub\u003e4\u003c/sub\u003eCl stability during the day.\u003c/p\u003e \u003cp\u003eModel performance during winter was similarly robust. The observed mean chloride concentration (9.5 \u0026micro;g m\u003csup\u003e-\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e) was closely reproduced by the model (9.24 \u0026micro;g m\u003csup\u003e-\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e), with a mean bias of -0.42 \u0026micro;g m\u003csup\u003e-\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and an index of agreement of 0.70 (Supplementary Fig.\u0026nbsp;4b). As in the post-monsoon season, minor daytime underestimation persisted, reflecting thermodynamic constraints on gas-to-particle conversion.\u003c/p\u003e \u003cp\u003eIn sharp contrast, summer simulations substantially underestimated observed particulate chloride. While observations averaged 1.40 \u0026micro;g m\u003csup\u003e-\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, simulated values were only 0.07 \u0026micro;g m\u003csup\u003e-\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and Supplementary Fig.\u0026nbsp;4c), despite accurate representation of RH (model: 35.59%; observed: 36.46%; Supplementary Fig.\u0026nbsp;2e, f). These results suggest that the equilibrium parameterization governing the thermodynamic partitioning of HCl to particulate chloride under warm and dry conditions may be inaccurately represented and warrants re-evaluation.\u003c/p\u003e\n\u003ch3\u003eInfluence of Meteorological Controls on Chloride Partitioning\u003c/h3\u003e\n\u003cp\u003eBoth observations and simulations demonstrate a pronounced dependency of particulate chloride on meteorological conditions, particularly temperature and RH. In ammonia-rich continental urban environments, NH\u003csub\u003e4\u003c/sub\u003eCl is the dominant particulate chloride species\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Both observations and model simulations consistently show higher particulate chloride concentrations at night when temperatures are lower and reduced concentrations during the day, when elevated temperatures favor volatilization to the gas phase.\u003c/p\u003e \u003cp\u003eDuring the post-monsoon season, modelled mean temperatures (~\u0026thinsp;21\u0026deg;C) exceed those in winter (~\u0026thinsp;15\u0026deg;C), leading to stronger daytime suppression of particulate chloride. The equilibrium constant governing gas-to-particle partitioning is primarily temperature-dependent, while RH determines whether chloride resides in the solid or aqueous phase\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Daytime particulate chloride concentrations declined to 4.33 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in observations and 1.68 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in the model, (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) consistent with reduced partitioning under warmer conditions. During summer, mean temperatures near ~\u0026thinsp;31.9\u0026deg;C further inhibited condensation, explaining its severe underestimation.\u003c/p\u003e \u003cp\u003eDaytime RH during winter (~\u0026thinsp;50%) exceeded that in post-monsoon (~\u0026thinsp;35%), while summer RH remained lowest overall. For mixed salts such as (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, NH\u003csub\u003e4\u003c/sub\u003eCl, NH\u003csub\u003e4\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e, Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, MgSO\u003csub\u003e4\u003c/sub\u003e, K\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, the mutual deliquescence relative humidity (MDRH) is ~\u0026thinsp;46%\u003csup\u003e27\u003c/sup\u003e favoring solid phase partitioning under summer and daytime post-monsoon conditions. The model reproduced nighttime chloride concentrations well in both post-monsoon and winter, with simulated nighttime means of 12.67 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 12.70 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e closely matching observations (11.22 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 11.59 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively). This consistency points to thermodynamic rather than source-related limitations.\u003c/p\u003e\n\u003ch3\u003eRole of the Equilibrium Constant in Cl Partitioning\u003c/h3\u003e\n\u003cp\u003eGas-to-particle partitioning of NH\u003csub\u003e4\u003c/sub\u003eCl from gaseous NH\u003csub\u003e3\u003c/sub\u003e and HCl can proceed via two thermodynamic pathways: (i) condensation into the solid phase and (ii) dissolution in the aqueous phase. In this study, aerosol chemistry and thermodynamics were represented using the MOSAIC (Model for Simulating Aerosol Interactions and Chemistry) scheme\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In MOSAIC, gas-to-solid partitioning is governed by a single equilibrium reaction (R1, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({K}_{N{H}_{4}Cl}^{gs}\\)\u003c/span\u003e\u003c/span\u003e = 8.43 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;17\u003c/sup\u003e atm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, α = -71, β\u0026thinsp;=\u0026thinsp;2.40)\u003csup\u003e26\u003c/sup\u003e, gas-to-liquid partitioning proceeds through a four-step sequence. First, HCl(g) dissolves into the aqueous phase as H\u003csup\u003e+\u003c/sup\u003e(aq) and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e(aq); second, NH\u003csub\u003e3\u003c/sub\u003e(g) dissolves to form NH\u003csub\u003e3\u003c/sub\u003e(aq); third, NH\u003csub\u003e3\u003c/sub\u003e(aq) hydrolyzes to generate NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e (aq) and OH\u003csup\u003e\u0026minus;\u003c/sup\u003e, and finally, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e(aq) combines with Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e(aq) to form NH\u003csub\u003e4\u003c/sub\u003eCl (aq). The equilibrium constants for these reactions are adopted from Kim et al. (1993)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and the AIM model\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Model diagnostics indicate strong seasonal shifts in chloride phase state as particulate chloride resides predominantly in the aqueous phase during winter (~\u0026thinsp;90%), transitions to solid-phase dominance during summer (~\u0026thinsp;89%) and exhibits mixed partitioning during the post-monsoon season (Supplementary Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eDespite this thermodynamic treatment, the base model substantially underestimates chloride during warm/dry conditions, particularly during daytime in the post-monsoon and throughout summer. For example, during daytime in November, simulated particulate chloride averaged 1.68 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e compared to an observed mean of 4.33 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, while summer simulations predicted near-zero chloride (~\u0026thinsp;0.07 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). The diurnal behavior of the gas-to-solid equilibrium constant and its implications for daytime partitioning are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea shows post-monsoon average of observed particulate chloride, temperature, RH, and the MOSAIC gas-to-solid equilibrium constant. To contextualize phase-state behavior, an MDRH of 40% is assumed for a mixed salts comprising (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, NH\u003csub\u003e4\u003c/sub\u003eCl, NH\u003csub\u003e4\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e, Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, MgSO\u003csub\u003e4\u003c/sub\u003e, and K\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, lower than the value used in ISORROPIA II\u003csup\u003e27\u003c/sup\u003e. Although MOSAIC dynamically computes aerosol liquid water rather than explicitly prescribing MDRH, the overall phase behavior remains qualitatively similar. Between 10:00 and 18:00, when RH remains below 40%, gas-to-solid partitioning is favored, and an overly large equilibrium constant raises the saturation requirement for NH\u003csub\u003e4\u003c/sub\u003eCl formation.\u003c/p\u003e \u003cp\u003eHowever, since total chloride (HCl\u0026thinsp;+\u0026thinsp;particulate chloride) emissions remain approximately constant, the concurrent daytime boundary-layer growth and enhanced mixing further dilute HCl and NH\u003csub\u003e3\u003c/sub\u003e, limiting partitioning into the particulate phase. Two explanations could, in principle, account for the reduced modeled particulate chloride during summer and post-monsoon daytime conditions: (i) the effective equilibrium constant is too high, or (ii) additional, unrepresented particulate chloride species contribute substantially. The second explanation is less consistent with the strong nighttime agreement, suggesting that thermodynamic partitioning is the dominant issue.\u003c/p\u003e \u003cp\u003eAlternative chemical pathways are unlikely to explain this systematic bias. Haslett et al.\u003csup\u003e30\u003c/sup\u003e reported that nitryl chloride (ClNO\u003csub\u003e2\u003c/sub\u003e) production in Delhi is strongly suppressed by elevated nighttime NO concentrations, which rapidly titrates NO\u003csub\u003e3\u003c/sub\u003e (NO\u0026thinsp;+\u0026thinsp;NO\u003csub\u003e3\u003c/sub\u003e \u0026rarr; 2 NO\u003csub\u003e2\u003c/sub\u003e), thereby limiting NO\u003csub\u003e3\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e formation. Because N\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e is the key precursor for heterogeneous ClNO\u003csub\u003e2\u003c/sub\u003e production (N\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e \u0026rarr; ClNO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), suppression of N\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e reduces ClNO\u003csub\u003e2\u003c/sub\u003e formation, constraining morning ClNO\u003csub\u003e2\u003c/sub\u003e photolysis and chloride radical production. Instead, multiple studies have attributed similar model underestimation of particulate chloride to equilibrium constants that are effectively too large relative to values inferred from observations\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Allen et al. (1989)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e also found that measured equilibrium constants for gas-to-solid NH\u003csub\u003e4\u003c/sub\u003eCl partitioning at higher temperatures are lower than theoretical estimates derived for pure NH\u003csub\u003e4\u003c/sub\u003eCl systems\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This discrepancy is physically plausible because atmospheric aerosols are multicomponent mixtures, in which non-ideal interactions introduce excess Gibbs free energy that lowers the effective equilibrium constant relative to the idealized pure-salt behavior\u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. While MOSAIC\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e treats multicomponent aqueous thermodynamics using the Multicomponent Equilibrium Solver for Aerosols (MESA)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e solver, it does not explicitly represent metastable aerosols, unlike ISSOROPIA\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and AIM\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In addition, extremely high particle number concentrations typical of Delhi (~\u0026thinsp;10\u003csup\u003e4\u003c/sup\u003e \u0026ndash; 10\u003csup\u003e5\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) (Supplementary Fig.\u0026nbsp;6) and the IGP across seasons can suppress evaporation and enhance condensation, effectively lowering the operational equilibrium constant\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Measurements reported in previous studies indicate that effective equilibrium constants under polluted and warm atmospheric conditions can differ by more than an order of magnitude from theoretical estimates \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the sensitivity of modeled chloride partitioning to NH\u003csub\u003e4\u003c/sub\u003eCl volatility, we performed an additional simulation wherein the gas-to-solid equilibrium parameterization was reduced by a factor of 3, 6 and 10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), resulting in substantially higher simulated particulate chloride concentrations during the day. Daytime particulate chloride increased progressively as the equilibrium constant was reduced, with the best agreement obtained when the parameterization was decreased by a factor of 10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, c), consistent with values inferred in previous studies. Daytime post-monsoon particulate chloride increased from 1.68 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e to 3.72 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The largest improvement occurred in summer, where the simulated concentrations rose from ~\u0026thinsp;0.07 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e to 1.71 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (Supplementary Fig.\u0026nbsp;7). These results suggest that the effective volatility of NH\u003csub\u003e4\u003c/sub\u003eCl in ambient multicomponent aerosols may be higher than represented in the default thermodynamic formulation. The simulation using the reduced equilibrium constant improved agreement with observations and reproduced diurnal peaks more realistically. While the reduced equilibrium constant improves agreement with observations, further laboratory and field studies are needed to better constrain NH\u003csub\u003e4\u003c/sub\u003eCl thermodynamics under complex atmospheric conditions. Nonetheless, even with the reduced equilibrium constant, simulated particulate chloride occasionally drops to zero, whereas observed values rarely fell below 0.1 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (above the detection limit of both instruments), suggesting that additional sources and/or unresolved heterogeneous processes may contribute to sustaining a non-zero chloride background and warrant further investigation.\u003c/p\u003e\n\u003ch3\u003eSpatial Distribution of HCl and Particulate Chloride\u003c/h3\u003e\n\u003cp\u003eAcross all seasons, particulate chloride in Delhi exhibits a pronounced diurnal cycle, with peak concentrations during the early morning hours and minima during the daytime (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, gaseous HCl (mean\u0026thinsp;~\u0026thinsp;0.54 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in post-monsoon and ~\u0026thinsp;0.42 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in winter) displays an inverse diurnal behavior during the post-monsoon and winter seasons, characterized by daytime maxima and nighttime minima (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSummer conditions differ markedly. Although summer exhibits the highest mean HCl concentrations (~\u0026thinsp;2.66 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), its diurnal pattern is less pronounced than in cooler seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), reflecting the combined influence of elevated temperatures and a much deeper boundary layer. Total chloride (particulate chloride\u0026thinsp;+\u0026thinsp;HCl \u0026times; 35.45/36.46) exhibits broadly similar diurnal behavior across all three seasons (Supplementary Fig.\u0026nbsp;8), indicating this diurnal of HCl is closely correlated with boundary layer height. Daytime dilution associated with enhanced boundary-layer heights suppresses near-surface HCl, while a distinct morning peak around 10:00 AM likely arises from fumigation, whereby pollutants stored aloft overnight are rapidly mixed down to the surface following boundary-layer growth after sunrise (Supplementary Fig.\u0026nbsp;4). Peak summer HCl concentrations reach\u0026thinsp;~\u0026thinsp;6.87 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, among the highest values reported globally, implying a substantial potential exposure burden.\u003c/p\u003e \u003cp\u003eAt the regional scale, simulated gaseous HCl concentrations across northern Indian cities within the model domain range from 0.1 to 5 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e in all seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b, c). Elevated HCl levels during summer are widespread, consistent with higher temperatures suppressing partitioning into the particulate phase. Monthly contours of modelled particulate chloride further illustrate the seasonal behavior (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, e, f). Seasonal mean particulate chloride concentrations across seven major cities (Supplementary Fig.\u0026nbsp;9) consistently identify Delhi as the dominant hotspot, reflecting strong local emissions and dense urban activity. In contrast, Karnal and Meerut show pronounced enhancements during post-monsoon and summer biomass-burning periods, highlighting the influence of regional agricultural residue burning. The remaining cities exhibit relatively similar particulate chloride levels between post-monsoon and winter, suggesting comparable meteorological conditions and emission influences during these seasons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSource Attribution of Particulate Chloride\u003c/h2\u003e \u003cp\u003eIdentifying the dominant sources of particulate chloride in Delhi is critical for understanding its formation pathways and for designing effective air quality management strategies. Source attribution in this study is based on the anthropogenic chlorine emissions inventory developed by Zhang et al. (2022),\u003csup\u003e5\u003c/sup\u003e which represents six major continental chlorine source sectors. For clarity and regional relevance, these sources are grouped into four broad categories: open waste burning, agricultural residue burning, energy production, and other sources, including industrial, residential, and open biomass burning activities\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModel-derived source contributions for Delhi during the post-monsoon and winter seasons are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, with panels (a) and (c) representing post-monsoon conditions and panels (b) and (d) corresponding to winter. Open waste burning emerges as the dominant contributor to particulate chloride in both seasons. These emissions are primarily local in origin. India generates an estimated 70\u0026ndash;170 Tg yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of municipal solid waste, and Delhi alone produces approximately 9500 tons of waste per day, a substantial fraction of which is openly dumped and burned at major landfill sites\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. As a result, open waste burning accounts for about 34% of Delhi\u0026rsquo;s particulate chloride during the post-monsoon season, increasing to 44% in winter, consistent with enhanced waste-burning activity during colder months\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The energy sector constitutes the second-largest contributor, responsible for roughly 30% of particulate chloride in both seasons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, the contribution of agricultural residue burning within Delhi is comparatively modest. Although crop residue burning emits both HCl and particulate chloride, with reported emission factors of 0.18 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 0.30 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e (0.26 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Pandey et al., unpublished data), its contribution to Delhi\u0026rsquo;s particulate chloride burden is limited to approximately 14% during the post-monsoon period. This finding contrasts with the common perception that agricultural burning is the dominant driver of all post-monsoon pollution in Delhi. Nevertheless, its regional impact is substantial. Cities located closer to active burning regions exhibit much stronger influences from this source (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). For example, agricultural residue burning accounts for 63% of particulate chloride in Karnal and 38% in Meerut (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), where mean chloride concentrations are comparable to those observed in Delhi. The diurnal profile of biomass-burning-derived particulate chloride in Delhi during the post-monsoon season (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, inset) shows relatively weak diurnal variability, with only a modest morning enhancement, consistent with its predominantly regional and advective nature (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eTo further quantify the relative importance of local versus regional sources, an additional sensitivity simulation was performed in which local emissions from Delhi were excluded. The result indicates that local sources account for 63.8% of the total particulate chloride during the post-monsoon season, underscoring the dominant role of local anthropogenic activities, particularly open waste burning, in driving chloride pollution within the city, relative to transboundary or regional contributions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Summary and Conclusions","content":"\u003cp\u003eThis work quantifies the processes and sources controlling particulate chloride and HCl in the polluted urban atmosphere of northern Indian cities using a combination of high-time-resolution observations and a modified WRF-Chem framework that explicitly represents anthropogenic HCl emissions and chloride-containing particles. Simulations spanning three contrasting seasons, post-monsoon, winter, and summer, demonstrate that chloride in this inland megacity is overwhelmingly governed by anthropogenic emissions rather than marine influence, and that particulate chloride is dominated by semi-volatile ammonium chloride whose phase partitioning is tightly regulated by temperature and relative humidity.\u003c/p\u003e \u003cp\u003eA key finding is that commonly used thermodynamic parameterizations systematically overpredict NH\u003csub\u003e4\u003c/sub\u003eCl volatility under warm and/or dry conditions, leading to persistent model underestimation of daytime chloride in post-monsoon and severe under prediction in summer. Reducing the effective NH\u003csub\u003e3\u003c/sub\u003e-HCl gas-particle equilibrium constant by one order of magnitude substantially improved model performance, enabling realistic reproduction of both the magnitude and diurnal variability of particulate chloride across seasons. The remaining low bias, especially the tendency for modeled chloride to intermittently collapse to near-zero, suggests that additional chloride formation pathways, non-ideal aerosol effects, or heterogeneous processes may help sustain a non-zero chloride background under highly polluted conditions and merit further investigation. Importantly, Delhi exhibits exceptionally high summer HCl, among the highest reported globally for urban environments, underscoring the potential for substantial population exposure.\u003c/p\u003e \u003cp\u003eSource attribution indicates that Delhi\u0026rsquo;s chloride burden is dominated by local anthropogenic activities. Open waste burning is the single largest contributor, accounting for 34% of particulate chloride in the post-monsoon and 44% in winter, followed by the energy sector (~\u0026thinsp;30%). In contrast, agricultural residue burning contributes only\u0026thinsp;~\u0026thinsp;14% within Delhi, despite exerting strong influence in nearby cities such as Karnal and Meerut. A sensitivity experiment excluding Delhi emissions shows that local sources explain 63.8% of post-monsoon particulate chloride, confirming that local controls outweigh regional transport for chloride within the city. Overall, these results call for (i) revised thermodynamic treatments of NH\u003csub\u003e4\u003c/sub\u003eCl in chemical transport models to represent real polluted aerosol mixtures and (ii) targeted mitigation of open waste burning and related local emissions as a priority pathway to reduce chloride-rich PM in Delhi and other rapidly urbanizing Asian regions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eObservational Dataset\u003c/h2\u003e \u003cp\u003eHigh-time-resolution measurements of speciated PM\u003csub\u003e2.5\u003c/sub\u003e, elemental composition, and trace metals were conducted at the Indian Institute of Technology (IIT) Delhi (28.54\u0026deg; N and 77.19\u0026deg; E). Instruments were installed on the rooftop of a three-story building at a sampling height of approximately 12 m above ground level in a temperature-controlled environment. This site represents one of the state-of-the-art facilities in the region providing continuous, co-located measurements of PM\u003csub\u003e2.5\u003c/sub\u003e chemical speciation and elemental composition.\u003c/p\u003e \u003cp\u003eElemental and metal concentrations were measured using an XACT 625i (Sailbri Cooper Inc., Tigard, OR, USA), which employes reel-to-reel filter tape sampling coupled with continuous, nondestructive energy-dispersive X-ray fluorescence (EDXRF) analysis. Non-refractory submicron aerosol composition was measured using a Quadrupole Aerosol Chemical Speciation Monitor (Q-ACSM, Aerodyne Research Inc., Billerica, MA, USA). The ACSM quantifies sulfate, nitrate, ammonium, chloride, and organic aerosol mass concentrations through a flash vaporization at 600\u0026deg;C followed by electron impact ionization mass spectrometry.\u003c/p\u003e \u003cp\u003eGas-phase pollutant measurements were obtained from monitoring stations operated by the Central Pollution Control Board (CPCB; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cpcb.nic.in/\u003c/span\u003e\u003cspan address=\"https://www.cpcb.nic.in/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Meteorological variables were derived from the National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration (NOAA), using data from the ground-based station at Indira Gandhi International Airport (IGIA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncei.noaa.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncei.noaa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNumerical Simulations\u003c/h2\u003e \u003cp\u003eNumerical simulations were conducted to examine particulate chloride formation under three contrasting pollution regimes over northern India, each characterized by distinct emission patterns and thermodynamic conditions. The selected periods include (i) November 2020, representing the post-monsoon agricultural biomass-burning period, (ii) January 2021, corresponding to the winter extreme pollution period marked by low temperatures, high humidity, stagnant boundary-layer conditions, and elevated emissions, and (iii) May 2021, coinciding with the summer biomass burning season. These periods were chosen to capture the wide range of meteorological and chemical environments under which particulate chloride exerts a strong influence on regional air quality.\u003c/p\u003e \u003cp\u003eThe exact simulation windows were 5\u0026ndash;30 November 2020, 5 January-5 February 2021, and 30 April-11 May 2021. In addition to baseline simulations, targeted sensitivity experiments were performed for each season to assess the influence of thermodynamic equilibrium parameterization governing the gas-to-particle partitioning of NH\u003csub\u003e4\u003c/sub\u003eCl. Furthermore, sectoral contribution analyses of anthropogenic HCl and particulate chloride emissions were also conducted for all the simulation periods, following the methodology described in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eModel Configuration\u003c/h2\u003e \u003cp\u003eAll simulations were performed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem version 4.2.2\u003csup\u003e42\u003c/sup\u003e). The model was configured with two one-way nested domains at horizontal resolutions of 27 km and 9 km, respectively, centered over Delhi (28.70\u0026deg; N, 77.10\u0026deg; E) (Supplementary Fig.\u0026nbsp;10). The outer domain encompasses central and northern India to capture large-scale transport and synoptic influences, while the inner domain resolves Delhi and adjoining northern Indian states, allowing explicit representation of local emissions, boundary-layer processes, and regional-scale interactions. Gas-phase chemistry was represented using the Model for Ozone and Related Chemical Tracers (MOZART)\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, which includes 157 gas-phase reactions, 85 chemical species, and 12 aerosol components. Aerosol microphysics and chemistry were simulated using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, which resolves size-segregated aerosol processes including nucleation, condensation, coagulation, and thermodynamic gas-particle partitioning. A complete description of the physical and chemical parameterizations used in the model is provided in Supplementary Text 1.\u003c/p\u003e \u003cp\u003eSince the standard WRF-Chem configurations do not explicitly include anthropogenic HCl emissions, these emissions were incorporated in the present study to better represent chloride formation in polluted continental environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEmission Inventories\u003c/h2\u003e \u003cp\u003eAnthropogenic emissions were obtained from the Emissions Database for Global Atmospheric Research version 6 (EDGARv6)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, provided at a spatial resolution of 0.1\u0026deg; \u0026times; 0.1\u0026deg; based on 2018 activity data. Biomass burning emissions were taken from the Fire INventory from NCAR (FINN)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, while biogenic emissions were simulated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN)\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEmissions of hydrogen chloride (HCl) and particulate chloride were implemented using the global gridded anthropogenic chlorine emission inventory developed by Zhang et al. (2022)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This inventory, the only comprehensive global dataset representing continental chlorine sources, provides annual emissions at 0.1\u0026deg; \u0026times; 0.1\u0026deg; resolution for the period 1960\u0026ndash;2014. Emissions corresponding to the year 2014 were applied in this study. The inventory includes six major source sectors: energy production, industrial processes, residential households, open waste burning, open biomass burning and agricultural activities, enabling detailed assessment of sector specific contributions to anthropogenic chlorine over Delhi and northern India.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interest Statement\u003c/h2\u003e \u003cp\u003eThe authors declare no financial and non-financial competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRM, MK, DG and VS designed the research. RM carried out the chemical transport model simulations, reaction equilibrium adjustments. Funding acquisition by MK and VS. RM executed data analysis and formal interpretation. RM, MK, DG and VS led the manuscript writing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge Douglas Worsnop for his valuable suggestions during the preparation of the manuscript. This work is supported by the IRD Grand Challenge Project grant at the Indian Institute of Technology Delhi (Grant No. 428 IITD/IRD/MI01810G), originally funded by the Ministry of Human Resource Development (MHRD), Government of India, for the establishment of a state-of-the-art observational site. The High-Performance Computing Center (HPC) at the Indian Institute of Technology Delhi provided the computational resources for this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eGas phase pollutants and PM 2.5 data are taken from Central Pollution Control Board ( [https://www.cpcb.nic.in/](https:/www.cpcb.nic.in) ). The meteorological data taken from National Centers for Environmental Information ( [https://www.ncei.noaa.gov/](https:/www.ncei.noaa.gov) ). All other data analyzed during the study are included in this article and in the supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGunthe, S. S. \u003cem\u003eet al.\u003c/em\u003e Enhanced aerosol particle growth sustained by high continental chlorine emission in India. \u003cem\u003eNat. Geosci.\u003c/em\u003e 14, 77\u0026ndash;84 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiu, X. \u003cem\u003eet al.\u003c/em\u003e Modeling the impact of heterogeneous reactions of chlorine on summertime nitrate formation in Beijing, China. \u003cem\u003eAtmos. Chem. Phys.\u003c/em\u003e 19, 6737\u0026ndash;6747 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi, M. 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B. \u003cem\u003eet al.\u003c/em\u003e The model of emissions of gases and aerosols from nature version 2.1 (MEGAN2.1): An extended and updated framework for modeling biogenic emissions. \u003cem\u003eGeosci. Model Dev.\u003c/em\u003e 5, 1471\u0026ndash;1492 (2012).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-clean-air","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Clean Air](https://www.nature.com/npjcleanair/)","snPcode":"44407","submissionUrl":"https://submission.springernature.com/new-submission/44407/3","title":"npj Clean Air","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"air pollution, chloride aerosol, atmospheric HCl, open waste burning, Indo-Gangetic plain","lastPublishedDoi":"10.21203/rs.3.rs-9138509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9138509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParticulate chloride (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e) and hydrogen chloride (HCl) strongly influence aerosol hygroscopicity, acidity, and visibility in polluted urban atmospheres, yet they remain poorly represented in chemical transport models. We quantify the role of anthropogenic emissions in driving elevated Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e and HCl over Delhi by implementing anthropogenic HCl and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e emissions in WRF-Chem and simulating post-monsoon, winter, summer seasons. We show that the long-standing underestimation of particulate chloride in previous modeling studies over Asian urban environments arises from inadequate representation of gas-particle partitioning under high aerosol number concentrations. Revising the thermodynamic equilibrium constant using observation-constrained partitioning improves model performance, enabling accurate simulation of the observed magnitude and seasonal variability of particulate chloride. Observed HCl concentrations over northern Indian cities are among the highest reported globally for urban environments. Local anthropogenic emissions of Delhi dominate chloride formation, with open waste burning contributing 34% and 44% during the post-monsoon and winter seasons, respectively. In contrast, regional agricultural residue burning contributes only\u0026thinsp;~\u0026thinsp;14% within Delhi, despite enhancing chloride levels in nearby urban centers. These results establish anthropogenic chlorine emissions as a dominant driver of urban aerosol chemistry and underscore the need to revise chloride representation in chemical transport models for polluted inland cities.\u003c/p\u003e \u003cp\u003eMain Text\u003c/p\u003e","manuscriptTitle":"Anthropogenic emissions drive elevated HCl and particulate chloride in polluted urban air","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 13:04:05","doi":"10.21203/rs.3.rs-9138509/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-07T08:37:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T16:17:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T05:47:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29513174799942069769408868866040521081","date":"2026-03-25T03:28:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109117932602853891551872795809338103066","date":"2026-03-24T01:47:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-24T00:33:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T09:32:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T08:53:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Clean Air","date":"2026-03-16T13:21:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-clean-air","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Clean Air](https://www.nature.com/npjcleanair/)","snPcode":"44407","submissionUrl":"https://submission.springernature.com/new-submission/44407/3","title":"npj Clean Air","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"28ba99fd-3d32-4051-b3ec-1d34fea13310","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65085101,"name":"Earth and environmental sciences/Climate sciences"},{"id":65085102,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2026-05-14T00:53:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 13:04:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9138509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9138509","identity":"rs-9138509","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}