Black carbon characterization and its association with meteorological phenomena using network data

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Devara, Vijay K. Soni This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6162410/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jul, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 10 You are reading this latest preprint version Abstract Studies related to impact of black carbon (BC) aerosols on weather phenomena like monsoon breaks, winter fog events, pre-monsoon heatwaves etc. are sparse in India. This study fills the gap of observational information of BC aerosols and their relationship with meteorological phenomenon. We examined the interaction between BC aerosols and precipitation during the monsoon's active-break cycle, a critical period for agriculture, water resources, and weather patterns. Data from stations in rural and urban areas provided contrasting seasonal and diurnal variation. The diurnal pattern is closely linked to anthropogenic activities and meteorological factors. The study examines significant diurnal and seasonal variation in relation to local and regional meteorological variation. BC concentrations show distinct bimodal diurnal patterns, with major peak in the evening, between 2000 h to 2300 h IST and secondary peak in the morning between 0700 h to 0900 h IST. Seasonal variations show the lowest BC levels during the monsoon due to efficient wet scavenging, while the highest levels occur during the post-monsoon, primarily from agricultural burning. Meteorological factors like temperature, humidity, rainfall, and wind speed significantly influence BC dynamics. Higher temperatures and lower humidity increase BC levels, while rainfall reduces them, and wind disperses BC aerosols, affecting their concentration and distribution. Analysis of pre-monsoon heatwaves, winter fog events, and monsoon conditions reveals the complex interplay between BC aerosols and weather patterns. Local meteorological factors such as temperature inversions and wind patterns significantly influence the BC impact on weather phenomena. This research enhances the understanding of BC pollution and its diverse effects on weather and climate, emphasizing the importance of integrating meteorological factors into air quality management and policymaking. It lays the groundwork for developing targeted strategies to mitigate BC's adverse effects on health and environment in India. Carbonaceous aerosols variability monsoon scavenging heatwave Fog Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Black Carbon (BC) aerosol is a significant climate influencer emitted into the atmosphere through incomplete combustion of biomass, fossil fuels, and biofuels. Particularly in heavily populated tropical regions like Asia, BC contributes significantly to global aerosol surface forcing. South East Asia is hotspot for BC aerosols pollution, in India especially the Indo-Gangetic plains. (Prabhu et al., 2020 ; Singh et al., 2021 ; Ramanathan and Carmichael, 2008 ; Gupta et al., 2017 , 2023 ). BC aerosols, having mostly anthropogenic sources, are well recognized to be one of the major light absorbing components and second strongest contributor to the Global Warming and Climate Change after carbon dioxide. BC near the surface causes surface warming (Ban-Weiss et al., 2012 ). Being mostly in the sub-micron range and chemically inert, BC has a long atmospheric lifetime from several days to weeks depending on the meteorological conditions and hence is susceptible for long-range transport. India faces significant challenges in balancing energy needs with environmental concerns. Household energy use, particularly biomass-based cooking fuels, and coal fired power generation plants contributes substantially to air pollution and health issues (Kopas et al., 2020 ; Roy and Acharya, 2023 ). Increase in BC aerosol concentration reduces evaporation in Indian Ocean due to dimming. Due to this the meridional sea surface temperature decreases which is associated with the weaker monsoonal circulation (Meehl et al., 2008 ). The global mean radiative forcing of BC aerosol formed due to fossil fuel and biofuel burning has increased from + 0.20Wm -2 to + 0.40Wm -2 (Myhre et al., 2013 ). The impact of BC aerosols on monsoon dynamics is particularly important for the Indian economy and growth, making it essential to study their effects. The influences of BC aerosols on climate and environment are more significant in regional scale than in global scale (Wang, 2004 ). Many studies are conducted for seasonal variation of BC for example Tiwari et al., 2013 (New Delhi), Chauhan et al., 2024 (Varanasi), Meena et al., 2021 (Mahabaleshwar and Pune) and many more. BC measurements across India display a distinct seasonal pattern in near-surface BC mass concentrations, with peak values during the winter and the lowest levels during the monsoon season (Kumar et al., 2023 ). This seasonality is attributed to regional meteorological dynamics and emissions from various anthropogenic and biomass burning sources. BC has been found to have a negligible impact on solar heating rates in the tropical tropopause layer (Gao et al., 2008 ). However, in urban environments with high BC emissions, it can significantly enhance atmospheric heating, particularly during haze events (Zhao et al., 2020 ). This effect is further amplified over megacities in summertime, where the combination of intensive solar radiation, secondary aerosol formation, and cloud reflection can lead to a considerable increase in the temperature inversion above the planetary boundary layer (Liu et al., 2019 ). In the Brahmaputra River Valley, wintertime BC aerosols have been linked to strong radiative heating, with potential regional climatic impacts (Chakrabarty et al., 2012 ). BC has been found to significantly impact the formation of fog. Badarinath et al. ( 2007 ) and Ding et al. ( 2019 ) both found that BC can induce fog formation, with Ding et al. ( 2019 ) specifically highlighting its role in advection-radiation. Ganguly et al., (2006) found that fog enhances the removal of aerosols from the atmosphere through wet deposition. The dome effect of BC, which suppresses the planetary boundary layer height and weakens vertical mixing, further exacerbates the impact of BC on fog (Wang et al., 2018 ). The size distribution and source of BC, particularly during winter haze episodes, also play a role in its impact on fog (Wu et al., 2017 ). A recent study conducted by Bharali et al., ( 2024 ) focused on fog in the Indo-Gangetic Plain (IGP) using satellite data and evaluates the performance WRF-Chem model discussing secondary aerosol formation during fog in Delhi and the effects of agricultural burning on air quality in Punjab. However, it did not include ground-based BC data, which is a key component of our study. The increased meridional tropospheric temperature gradient in the pre-monsoon months of March-April-May contributes to enhanced precipitation over India in those months (Meehl et al., 2008 ). The remote influence of South Asian BC aerosol on the East Asian summer monsoon can cause a reduction in rainfall in the Yangtze River valley and intensified rainfall in northern and southern China (Mahmood and Li, 2014 ). The radiative effects of BC aerosols on the Indian monsoon can result in increased rainfall in northern India but decreased rainfall in southern India (Soni et al., 2018 ). The direct effects of BC aerosols on the East Asian subtropical summer monsoon can lead to an advance in the onset time of the monsoon (Wang et al., 2016 ). During the southwest monsoon season, breaks in rainfall can occur, leading to dry spells over northern India during July and August (Ramanadham et al., 1973 ; Raghavan, 1973 ). Gadgil and Joseph ( 2003 ) identified interannual variations in all-India summer monsoon rainfall linked to the number of break and active days, while Rajeevan et al. ( 2010 ) established criteria for identifying these events based on rainfall anomalies. Aerosol loading over South Asia, including the Himalayan foothills, has significantly increased in recent decades, impacting the radiation budget and water cycle. Aerosols modulate precipitation and cloud properties, including cloud droplet number and reflectivity (Hazra et al., 2013 ). Observational studies suggest that absorbing aerosols are more prominent during monsoon break phases, followed by active monsoon periods (Ravi Kiran et al., 2009 ). Aerosols also modulate monsoonal characteristics through land-atmosphere interactions and cloud invigoration, affecting rainfall over the Indian summer monsoon region (Niyogi et al., 2007 ; Sarangi et al., 2017 ). Researchers have shown that BC aerosols and greenhouse gases have distinct effects on the Asian summer monsoon. Xie et al. ( 2020 ) found that BC enhances precipitation dynamically, whereas greenhouse gases do so thermodynamically. Lau and Kim ( 2017 ) further noted that greenhouse gases have a stronger positive rainfall impact, while aerosols have a more pronounced negative effect. Guo et al. ( 2013 ) highlighted a significant decrease in precipitation during the East Asian Summer Monsoon due to increased BC emissions. When BC particles are present in the atmosphere, they can absorb and retain heat, leading to an increase in temperature (Bond et al., 2013 ). Jones et al. ( 2011 ) suggest significant influence of BC on global temperatures, but its impact is small compared to that from greenhouse gas emissions. Heatwaves are extreme meteorological events characterized by prolonged periods of excessively high temperatures, posing significant risks to human health, ecosystems, and socioeconomic systems. In India, heatwaves are a recurrent phenomenon, particularly during the pre-monsoon and summer seasons, exerting substantial impacts on various aspects of life. Recent long-term studies have shown an increasing occurrence of heatwaves, with the central and northwest parts of India being more susceptible to prolonged heatwave episodes (Bhattacharya et al., 2023 ; Ratnam et al., 2016 ; Rohini et al., 2016 ; Sharma and Mujumdar, 2017 ). Dave et al., ( 2020 ) examined the correlation between absorbing aerosols and summertime maximum temperatures (Tmax) in northwest India using TOMS-OMI and IMD data but relied on satellite and reanalysis data without direct BC measurements. Similarly, Mondal et al., ( 2021 ) used IMD data and ECHAM6-HAM2 model simulations for high-temperature extremes but did not incorporate actual BC datasets. Despite existing studies on BC concentrations in India, a significant research gap remains in quantifying the relative contributions of regional meteorological dynamics and emissions to BC seasonality, particularly in understanding its diurnal and seasonal variability. Limited research has explored the impacts of BC aerosols on monsoon breaks, winter fog events, and pre-monsoon heatwaves in North India. The interaction between BC aerosols and precipitation during the active-break cycle of the monsoon is crucial, with significant implications for agriculture, water resources, and regional weather patterns. By contrasting rural and urban settings, this study highlights the spatial heterogeneity of BC dynamics and their influence on local weather phenomena. Unlike previous research, which often focuses on isolated aspects of BC impacts (e.g., heatwaves or fog events), this study takes a holistic approach by examining BC interactions across multiple critical weather events. This study is distinguished by its reliance on ground-based observational data, addressing the limitations of satellite-based and model-driven studies. By integrating long-term observational datasets with a detailed analysis of BC interactions during extreme weather events, it offers a novel and comprehensive perspective on BC’s role in shaping regional climate and atmospheric processes in India. 2. Materials and Methodology The BC data from North Indian stations of IMD BC network (Ramesh et al., 2025 ) installed at various geographical locations having different environmental conditions is used for this study for the period 2016–2020. The seven wavelength Aethalometer is used to measure equivalent BC concentration and biomass burning contribution. It works on light wavelength-dependence on absorption principle using suitable mass absorption cross-section values (Petzold et al., 2013). It uses seven wavelengths (370, 470, 520, 590, 660, 880 and 950 nm) that allow spectral analysis for different purposes, such as mineral dust detection and source apportionment (Drinovec et al., 2015). It measures elemental carbon (EC) or BC mass concentration (in µg m − 3 ). Traditionally, the equivalent BC concentration is calculated using the light attenuation at 880 nm along with an absorption cross-section value of 7.77 m 2 g − 1 because absorption due to other aerosols is negligible at this wavelength (Drinovec et al., 2015; Sandradewi et al., 2008 ; Yang et al., 2009 ). The instrument uses Teflon coated glass fiber filter tape and an impactor (PM 2.5 ) at inlet limiting the inlet particle size to 2.5 µm. Plus, the Aethalometer (Model AE-33) uses dual-spot technique, which enables near real time compensation for the spot loading effect. More details about the performance of the Aethalometer (AE-33) can be found in Drinovec et al. (2015) and recently by Sonbawne et al., (2021) and references therein. The North Indian stations used in this study (Table 1 ) are Ranichauri, Srinagar, Chandigarh, Varanasi, Jodhpur and New Delhi. The meteorological parameters used are of IMD observatory at the same location as the BC monitoring station. Table 1 IMD BC Network Stations and Characteristics over North India Station Latitude Longitude Altitude(m) Environment Ranichauri 30.25 78.08 1950 Background, High Altitude Srinagar 34.08 74.83 1655 Valley, High Altitude Chandigarh 30.73 76.88 347 Urban Varanasi 25.3 83.01 88 Urban Jodhpur 26.3 73.01 217 Desert New Delhi 28.58 77.2 212 Urban mega city Ranichauri stands at an altitude of 1950 meters above sea level, making it a high-altitude station. It represents a background environment, away from urban and industrial centers. The station's weather is influenced by its altitude, experiencing cooler temperatures and varied seasonal climates. Srinagar is positioned at an altitude of 1655 meters. Nestled in a valley surrounded by the Himalayan Mountains, it offers insights into BC emissions in a high-altitude valley setting. Its climate is characterized by cold winters and moderate summers, heavily influenced by its unique geography. Chandigarh is situated at an altitude of 347 meters. This urban station serves as a representative of the urban environment in the region. The city experiences a subtropical climate with hot summers and cold winters, marked by monsoons. Varanasi lies at an altitude of 88 meters. This urban station provides data on BC emissions in a densely populated urban area. It experiences a humid subtropical climate with significant variations in temperature throughout the year. Jodhpur stands at an altitude of 217 meters, offering insights into BC emissions in a desert environment. The station experiences an arid climate with scorching summers and mild winters. As the capital of India, New Delhi is located at 28.58° latitude and 77.2° longitude, at an altitude of 212 meters. It represents the complex urban dynamics of a megacity, characterized by high population density and various sources of BC emissions. The city experiences a humid subtropical climate with hot summers, monsoon season, and cool winters. In the present study, the mass concentration of BC, primarily associated with fossil fuel emissions, particularly vehicular exhaust, at a wavelength 880 nm (BC 880 nm ) in µg m − 3 is studied at multiple locations associated with varying environments, utilizing the IMD network (Fig. 1 ). The one-minute interval data of BC data are used to calculate the hourly means. It is further used to compute the daily, monthly, seasonal means and to examine changes during 2016–2020 over North India. Meteorological correlations (Pearson) for the stations shown in Fig. 1 are reported at 95% confidence interval. During the period of heatwave, there is a long period of extremely hot weather compared to what's usually expected for a place over several years. Different regions and meteorological agencies might use different standards to identify heatwaves. In this study, we defined a heatwave as a day with a temperature higher than 40°C and at least 4.5°C warmer than the average daily maximum temperature over the past 30 years. Heatwave conditions vary by location due to different meteorological and geographical factors. As a result, heatwaves do not occur at all stations at the same time. Therefore, the analysis in this study is shown for different dates at different locations, satisfying all the heatwave conditions discussed. We also examined how fog, mist, and haze in winter relate to the concentration of BC. The World Meteorological Organization (WMO) defines "fog" as a condition where microscopic droplets reduce horizontal visibility at the Earth's surface to less than 1 km. "Mist," on the other hand, refers to conditions where the droplets do not reduce visibility to less than 1 km, and is often considered synonymous with "light fog." The term "smog" (a combination of "smoke" and "fog") is commonly used to describe conditions where fog and heavy air pollution are present, often involving chemical reactions between the fog droplets and various pollutants. Visibility reduction depends on the structure of the fog, particularly the number density and size distribution of the droplets, which can vary greatly in both time and space. Air in fog usually feels damp or moist. When illuminated, individual fog droplets are often visible to the naked eye and exhibit turbulent movement. Fog forms a whitish veil that blankets the landscape, while mist typically appears as a thinner, greyish veil. When combined with dust or smoke, fog may develop a faint coloration. For our BC-Fog analysis, we utilized METAR (Meteorological Aerodrome Reports) data from the winter season of 2016. This timeframe was selected as it corresponds to a period when fog is typically prevalent. The METAR data of New Delhi (VIDP), Jodhpur (VIJO), Chandigarh (VICG) and Varanasi (VEBN) are used in this study. The METAR data provides detailed meteorological information recorded at aerodrome stations (half hourly or hourly), offering insights into various atmospheric parameters such as visibility, humidity and temperature conditions during foggy episodes. According to the WMO, haze is defined as a suspension of extremely small, dry particles in the air, invisible to the naked eye, but numerous enough to give the air an opalescent appearance. Smoke, on the other hand, is defined as a suspension of small particles in the air produced by combustion. We also investigated the impact of the monsoon season on BC levels, specifically examining the variations between active and break periods of the monsoon. The classification of active and break periods of the monsoon was based on periods outlined in studies by Doyle et al., ( 2021 ) and the end-of-season report by the Indian Meteorological Department (IMD, 2016 ). We wanted to see how different phases of the monsoon affect BC levels by examining changes in weather conditions during active and break periods. 3. Results and Discussion The climate of India is primarily influenced by the Indian monsoon and Westerlies, with distinct seasons: winter, pre-monsoon, summer monsoon, and post-monsoon. The pre-monsoon season in North India lasts from March to May and is characterized by a gradual rise in temperatures, increasing humidity, and the development of thunderstorms and dust storms. The winds generally blow from the northwest, bringing in dust and dry air from the deserts of Pakistan and western India. The heat and dryness can lead to the formation of thunderstorms, which can be quite severe, bringing strong winds, heavy rain, and lightning. The shift of wind patterns from westerly to easterly brings moisture from the Bay of Bengal, the interaction between the hot, dry air of the Indian desert with the moist air causing thunderstorms, dust storms and rainfall. The monsoon season (JJAS) of North India includes increased rainfall, higher humidity, strong winds and frequent thunderstorms. The monsoon winds, known as the southwest monsoon, bring moist air from the Arabian Sea and the Bay of Bengal to the region. Most of the monsoon rainfall is caused by the interaction between the low-pressure system over the Bay of Bengal and the high-pressure system over the Himalayas, which leads to the formation of a monsoon trough. The monsoon rainfall brings relief from the scorching heat of pre-monsoon season and plays a crucial role in the agricultural production of the region. The heavy rains during the Monsoon season in North India can also cause flooding, landslides, and water logging in some areas. During the post-monsoon season (ON) in North India, the monsoon winds and rainfall begin to subside, and the temperature starts to cool down, and the humidity levels also decrease. The clear sky post-monsoon season is also characterized by the formation of fog and mist during the early morning and late evening reducing visibility in the Northern plains. This occasional occurrence of Western Disturbances, which are extra-tropical storms bringing rain and snowfall to the Himalayan region. Overall Dry weather conditions prevail over the region. 3.1 Diurnal and seasonal variations in BC The seasonal diurnal variations at each station are shaped by a combination of local and regional factors, including urbanization, altitude, and proximity to pollution sources. The combined influence of weather and emission patterns in different seasons contributes to the observed variations. The morning peak occurs between 07:00 and 09:00 IST, driven primarily by increased vehicular emissions, biomass burning, and a shallow atmospheric boundary layer. Following this peak, BC concentrations gradually decrease due to convective development and enhanced atmospheric dispersion. The lowest concentration occurs around 14 to 16 hours IST due to enhanced convective mixing and dilution effects and then attain peak between 20 to 23 hours IST (Figure 2(a)). The highest concentration of BC is observed during the evening, which corresponds with times when human activities like traffic and industrial processes are most active. Bimodal peaks differ by 1 to 2 hours because of regional and local meteorological conditions and emission sources. Among the cities, New Delhi consistently records the highest BC levels, exceeding 16 µg m -3 , followed by Chandigarh and Varanasi, whereas Ranichauri, a high-altitude site, exhibits the lowest concentrations, remaining below 2 µg m -3 throughout the day. The background station Ranichauri shows minimum concentration and is not having any pattern for overall diurnal variation for the study period. The stations in the North India shows higher concentration than the peninsular stations (Beegum et al., 2009; Kumar et al., 2023). The diurnal variation of BC concentrations of different seasons is depicted in Figure 2(b). The aerosol-meteorology interaction significantly impacts the surface concentration of BC aerosols. Diurnal variation of BC concentration is sensitive to temperature, RH, Wind and boundary layer (Chauhan et al., 2024; Kumar et al., 2023; Tiwari et al., 2013). In the winter season, BC levels typically begin low in the early morning, steadily rise throughout the day, and peak twice, once in the morning and again in the evening (with the latter peak being higher). This trend is mainly due to increased combustion activities for heating purposes during colder periods (Devara et al., 2024). During the pre-monsoon season, BC concentrations follow a similar diurnal pattern, but with slightly lower overall levels. This season serves as a transitional period between the winter and monsoon, marked by varying emissions and atmospheric conditions. The decrease in BC levels can be attributed to reduced heating demands and the expansion of the atmospheric boundary layer. During the monsoon season, BC concentrations show the lowest levels in the diurnal variation pattern throughout the year. This decline is primarily due to wet scavenging and deposition processes associated with increased rainfall and stronger winds. In the post-monsoon season, BC concentrations exhibit the highest levels, primarily due to emissions from stubble burning activities in North India (Beig et al., 2020; Paliwal et al., 2016). This increase underscores the significant impact of agricultural practices on BC levels during this period. Monthly diurnal variations are depicted in supplementary plots ( S1 & S2 ). The seasonal mean variation in black carbon (BC) concentrations across the study area reveals distinct patterns, with significant differences observed between locations and seasons (Figure 3, Table 2). BC concentrations, measured at 370 nm (BC370) and 880 nm (BC880), exhibit a consistent trend of peaking during the post-monsoon season and reaching their lowest levels during the monsoon. For instance, at Ranichauri, BC370 peaks at 3.07 µg m -3 during the post-monsoon season, while dropping to 1.42 µg m -3 during the monsoon. Similarly, BC880 reaches 2.05 µg m -3 in the post-monsoon and falls to 1.07 µg m -3 during the monsoon. This trend is mirrored in urban locations such as Srinagar, Chandigarh, and New Delhi, where post-monsoon BC370 concentrations reach 25.09 µg m -3 , 22.45 µg m -3 , and 29.55 µg m -3 , respectively, but decline sharply to 9.57 µg m -3 , 7.60 µg m -3 , and 6.02 µg m -3 during the monsoon. The post-monsoon peak is attributed to widespread agricultural residue burning, particularly in the Indo-Gangetic Plain (IGP), coupled with stable atmospheric conditions and lower temperatures that trap pollutants near the surface. In contrast, the monsoon season sees a significant reduction in BC levels due to efficient wet scavenging by rainfall, increased wind speeds, and enhanced vertical mixing, which disperse aerosols and reduce near-surface concentrations. Varanasi exhibits a unique pattern, with BC concentrations peaking during the pre-monsoon season (BC370: 16.0 µg m -3 ; BC880: 11.11 µg m -3 ) rather than the post-monsoon. This anomaly can be linked to the region’s dry and hot pre-monsoon conditions, which favor the accumulation of BC aerosols. Additionally, local emissions from vehicular and industrial activities, combined with frequent dust storms, contribute to elevated BC levels during this period. In contrast, rural areas like Ranichauri and semi-arid regions like Jodhpur generally show lower BC concentrations compared to urban centers, reflecting differences in emission sources and meteorological influences. For example, Jodhpur’s BC370 levels during the post-monsoon (9.27 µg m -3 ) are significantly lower than those in New Delhi (29.55 µg m -3 ), highlighting the impact of urbanization and anthropogenic activities on BC pollution. The post-monsoon season emerges as a critical period for BC pollution, driven by agricultural burning (Parali burning) and stable atmospheric conditions, while the monsoon season acts as a natural cleansing phase, significantly reducing BC levels. Table 2 : Seasonal Mean Variation of BC880 and BC370 with Standard Deviation for 2016-2020 in North India. City Parameter/ Season Monsoon Post-Monsoon Pre-monsoon Winter Chandigarh BC370 (µg m -3 ) 7.60 ±4.99 22.45 ±15.70 9.48 ±6.63 18.85 ±13.87 BC880 (µg m -3 ) 6.73 ±4.25 16.39 ±11.62 7.69 ±5.31 14.24 ±10.4 Jodhpur BC370 (µg m -3 ) 2.22 ±1.98 9.27 ±8.10 3.80 ±4.01 9.79 ±10 BC880 (µg m -3 ) 1.76 ±1.62 6.87 ±6.22 2.83 ±2.96 7.13 ±7.38 New Delhi BC370 (µg m -3 ) 6.02 ±4.56 29.55 ±21.52 11.23 ±10.06 26.05 ±19.52 BC880 (µg m -3 ) 5.03 ±3.92 19.13 ±14.36 9.05 ±8.15 19.16 ±14.46 Ranichauri BC370 (µg m -3 ) 1.42 ±1.54 3.07 ±1.95 3.56 ±4.12 3.22 ±2.82 BC880 (µg m -3 ) 1.07 ±1.04 2.05 ±1.22 2.00 ±1.67 1.84 ±1.58 Srinagar BC370 (µg m -3 ) 9.57 ±6.48 25.09 ±16.85 8.54 ±7.18 22.06 ±18.48 BC880 (µg m -3 ) 8.11 ±5.48 17.14 ±10.37 6.65 ±5.95 13.32 ±10.39 Varanasi BC370 (µg m -3 ) 5.90 ±4.78 14.64 ±12.32 16.52 ±13.23 18.55 ±15.89 BC880 (µg m -3 ) 4.09 ±3.13 8.70 ±6.90 11.11 ±8.32 10.21 ±8.11 3.2 Correlation of meteorological parameters with BC Concentrations The analysis of black carbon (BC) concentrations across multiple monitoring stations in North India reveals significant correlations with various meteorological parameters (Pearson correlation at 95% confidence level), which play a crucial role in shaping BC dynamics. These correlations vary across stations due to differences in local climates, emission sources, and environmental conditions as a shown in Table 3. Table 3 : Correlation analysis between various meteorological parameters and BC concentrations (p<0.05). City Parameter Tavg RH RF WS BC880 Chandigarh Tavg 1.00 RH -0.49 1.00 RF 0.42 0.43 1.00 WS 0.26 -0.58 -0.08 1.00 BC880 -0.57 0.24 -0.45 -0.46 1.00 Jodhpur Tavg 1.00 RH 0.01 1.00 RF 0.35 0.68 1.00 WS 0.59 -0.06 0.26 1.00 BC880 -0.74 -0.21 -0.43 -0.51 1.00 New Delhi Tavg 1.00 RH -0.56 1.00 RF 0.46 0.30 1.00 WS 0.54 -0.46 0.24 1.00 BC880 -0.73 0.20 -0.57 -0.63 1.00 Ranichauri Tavg 1.00 RH 0.49 1.00 RF 0.25 0.49 1.00 WS 0.27 -0.22 -0.13 1.00 BC880 -0.13 -0.67 -0.45 0.33 1.00 Srinagar Tavg 1.00 RH -0.64 1.00 RF -0.06 0.29 1.00 WS 0.41 -0.38 0.40 1.00 BC880 -0.48 0.30 -0.47 -0.74 1.00 Varanasi Tavg 1.00 RH -0.45 1.00 RF 0.35 0.41 1.00 WS 0.67 -0.45 0.29 1.00 BC880 -0.44 -0.33 -0.66 -0.25 1.00 A consistent negative correlation was observed between ambient temperature (Tavg) and BC880 concentrations across all cities, ranging from -0.44 (Varanasi) to -0.78 (Jodhpur) (p < 0.05), with the exception of the background station Ranichauri (-0.13). Warmer temperatures are associated with lower BC concentrations due to enhanced atmospheric mixing, increased boundary layer height, and greater dispersion of pollutants. For instance, in urban areas like New Delhi and Chandigarh, higher temperatures during the pre-monsoon season promote vertical mixing, diluting BC concentrations near the surface. This trend is less pronounced in Ranichauri, a rural site, where local emissions are lower, and temperature variations have a smaller impact on BC dynamics. The relationship between RH and BC concentrations is complex and varies across humidity ranges and locations. Most cities show negative correlations across different relative humidity categories, implying that as relative humidity increases, the BC tend to decrease. Cities in arid or semi-arid regions like Jodhpur show varying responses compared to more humid cities. At lower RH levels (0–60%), BC concentrations show mixed correlations, with positive values in some cities (e.g., Chandigarh: 0.10; Jodhpur: 0.21) and negative values in others (e.g., Ranichauri: -0.14; Varanasi: -0.30). This variability suggests that BC aerosols may undergo hygroscopic growth at moderate RH levels, increasing their size and mass concentration. However, at higher RH levels (>80%), the correlations become predominantly negative (e.g., Ranichauri: -0.44; Srinagar: -0.27), indicating efficient wet scavenging of BC particles through below-cloud and in-cloud processes. The freshly emitted uncoated BC aerosols are hydrophobic and insoluble in water. However, they become hygroscopic as they age by acquiring coating of secondary inorganic and organic aerosol compounds via coagulation with other particles and thereby act as cloud condensation nuclei (Motos et al., 2019; Tritscher et al., 2011; Weingartner et al., 2003). As BC aerosols can act as CCN and hence the below-cloud and in-cloud wet scavenging processes are the primary removal mechanism for atmospheric BC aerosols (Jacobson, 2012), making the relationship between RH and BC concentration important for understanding BC's atmospheric lifetime and transport. The correlation between daily mean values relative humidity (RH) categories and BC variable for various cities is shown in Table 4. The relative humidity is divided into three categories: 0-60%, 60-80%, and >80%. Table 4 : Correlation analysis between BC concentrations and relative humidity (RH in %) using daily data from 2016 to 2020. City RH (0-60) RH (60-80) RH (>80) Chandigarh 0.10 0.04 -0.07 Jodhpur 0.21 -0.18 -0.11 New Delhi -0.05 0.07 -0.07 Ranichauri -0.14 -0.20 -0.44 Srinagar 0.12 0.05 -0.27 Varanasi -0.30 -0.01 0.07 Rainfall exhibits a strong negative correlation with BC880 concentrations across all cities, ranging from -0.43 (Jodhpur) to -0.66 (Varanasi). Higher rainfall effectively scavenges BC aerosols from the atmosphere through wet deposition, leading to lower concentrations. For example, during the monsoon season, frequent and intense rainfall in Varanasi and Ranichauri results in a significant reduction in BC levels. This relationship underscores the importance of precipitation as a primary removal mechanism for BC aerosols. A negative correlation was observed between wind speed and BC880 concentrations across most cities, ranging from -0.25 (Varanasi) to -0.74 (Srinagar), with the exception of the background station Ranichauri, which showed a positive correlation (0.33). Higher wind speeds generally facilitate the dispersion and transport of pollutants away from the monitoring site, leading to lower BC880 concentrations. However, at Ranichauri, the positive correlation suggests that winds may advect BC from nearby sources, increasing local concentrations under specific wind patterns. Figure 4 illustrates the distribution of BC concentrations based on wind direction, highlighting the influence of regional transport on local BC levels. Wind direction plays a critical role in determining pollutant transport and concentration. Cities located downwind of major pollution sources, such as New Delhi and Chandigarh, experience higher BC levels (11.7 µg/m³ and 10.3 µg/m³, respectively), as prevailing winds carry pollutants from neighboring regions. In contrast, Ranichauri and Jodhpur, with fewer local pollution sources and better dispersion conditions, exhibit lower BC levels (1.4 µg/m³ and 4.1 µg/m³). Calm wind conditions (near-zero wind speeds) further exacerbate pollutant accumulation, creating localized hotspots. For instance, Jodhpur and Ranichauri, with higher percentages of calm conditions (46.3% and 39.8%, respectively), show increased BC buildup due to reduced dispersion. In contrast, Varanasi, with fewer calm conditions (18.2%), benefits from better pollutant dispersion, despite its lower BC concentrations, which are also influenced by its geographical location and lower population density. Wind roses reveal that winds from the west and northwest are associated with higher BC concentrations in many locations, likely indicating the presence of pollution sources in these directions. Seasonal wind patterns, such as monsoon winds, also play a significant role in shaping BC variability over multi-year datasets. During winter, lower wind speeds and increased emissions from heating and biomass burning contribute to higher BC levels, particularly in urban centers like New Delhi. In Srinagar, moderate BC concentrations (mean: 10 µg/m³) are exacerbated by the city’s valley location and low wind speeds, which trap pollutants. Similarly, in New Delhi, prevailing winds from the northwest transport pollutants from Haryana and Punjab, further elevating BC concentrations due to local traffic and agricultural burning activities. 3.3 BC impact on heat wave New Delhi, the capital city of India, experiences severe heat waves during the summer months as described in the methodology section, posing significant challenges to public health and infrastructure. BC is a key component of air pollution and has been implicated in exacerbating heat wave conditions. Figure 5 illustrates the variations in black carbon (BC) concentrations during a two-day heatwave episode in New Delhi (May 31 and June 1, 2019). The analysis reveals that BC concentrations do not significantly amplify the intensity of the heatwave during this period. Instead, BC levels exhibit a negative correlation (-0.36) with temperature, suggesting that higher temperatures lead to lower BC concentrations. This reduction is likely due to enhanced atmospheric mixing and increased boundary layer height during heatwave conditions, which promote the dispersion of pollutants. In contrast, BC concentrations show a positive correlation (0.28) with relative humidity, indicating that cooler, more humid periods are associated with higher BC levels. This trend may result from reduced atmospheric mixing and the dominance of local emission sources under such conditions. The findings suggest that during heatwaves, the relationship between BC concentrations and meteorological parameters is complex. Higher temperatures suppress BC concentrations by enhancing pollutant dispersion, while cooler, more humid conditions favor BC accumulation due to stagnant atmospheric conditions. However, the limited duration of the heatwave episode (two days) restricts the generalizability of these results. Longer-term studies are needed to better understand the interplay between BC aerosols and extreme weather events like heatwaves. Figure 6 illustrates the variations in black carbon (BC) concentrations during a five-day heatwave episode in Jodhpur (April 13 to April 17, 2017). The analysis reveals that BC concentrations were relatively high on the first day, ranging between 15–20 µg/m 3 , but remained comparatively low during the rest of the heatwave period. BC levels exhibited a weak positive correlation (0.12) with temperature, suggesting a slight increase in BC concentrations with rising temperatures in the desert environment. In contrast, a weak negative correlation (-0.15) was observed between BC and relative humidity, indicating that higher BC concentrations were associated with slightly lower humidity levels. These results suggest that, in Jodhpur, increasing temperatures may enhance BC levels, while higher humidity tends to reduce them. The weak positive correlation between BC and temperature during the heatwave may be attributed to localized emission sources, such as dust resuspension and biomass burning, which persist even under high-temperature conditions. The weak negative correlation with humidity likely reflects the influence of drier conditions, which favor the accumulation of BC aerosols by reducing wet scavenging. However, the overall low BC concentrations and weak correlations suggest that BC did not play a dominant role in amplifying the heatwave intensity in Jodhpur. These findings highlight regional differences in BC dynamics during heatwave events. Unlike in North India, where higher temperatures often suppress BC concentrations through enhanced atmospheric mixing, Jodhpur’s desert environment exhibits a more complex interplay between BC, temperature, and humidity. This underscores the importance of considering regional variability when assessing the impact of BC aerosols on extreme weather events. Figure 7 illustrates the variations in black carbon (BC) concentrations during a six-day heatwave episode in Varanasi (June 17 to June 22, 2018). The analysis reveals a moderate negative correlation (-0.43) between BC concentrations and temperature, indicating that BC levels decrease as temperatures rise. In contrast, a moderate positive correlation (0.43) is observed between BC and relative humidity, suggesting that higher humidity levels are associated with increased BC concentrations. These trends align with patterns observed in other humid subtropical environments, where heatwave episodes often end with a rise in humidity. In contrast, arid regions tend to exhibit the opposite behavior, with BC concentrations showing different dynamics under dry conditions. The negative correlation between BC and temperature during the heatwave suggests that high temperatures promote the dispersion or breakdown of BC aerosols, likely due to enhanced atmospheric mixing and increased boundary layer height. On the other hand, the moderate positive correlation with humidity indicates that BC levels tend to rise during more humid conditions. This may result from reduced atmospheric mixing or the activation of local emission sources, such as biomass burning or vehicular emissions, which are more prevalent during humid periods. Our findings suggest that BC concentrations in Varanasi are influenced by a complex interplay of temperature and humidity during heatwave events. While high temperatures reduce BC levels through enhanced dispersion, higher humidity appears to favor BC accumulation, possibly due to stagnant atmospheric conditions or increased local emissions. 3.4 BC impact on Fog The influence of BC on fog dynamics is a subject of immense interest due to its potential impact on atmospheric processes. BC, primarily emitted from combustion sources, can alter the microphysical and radiative properties of fog. The presence of fog enhances the removal of aerosols from the atmosphere through wet deposition (Ganguly et al., 2006). BC in the atmosphere can significantly influence fog formation, persistence, and dissipation. For instance, fog can considerably reduce solar heating, delaying the onset of convective mixing or reducing surface convection and boundary layer depth, thereby limiting ventilation. This phenomenon could explain the observed prolonged morning peak in BC concentrations. Similarly, the impact of delayed or reduced solar heating during intense foggy or hazy days has been observed on temperature and RH (Safai et al., 2008). BC particles can also act as cloud condensation nuclei (CCN), affecting the droplet size distribution within fog. By serving as nucleation sites for water droplets, BC may contribute to the formation of smaller and more numerous droplets. This alteration in droplet characteristics can influence the optical properties of fog, affecting visibility and radiative transfer. These interactions highlight the complex role of BC in modulating fog dynamics and its broader implications for atmospheric processes. Figure 8(a) illustrates the impact of BC during fog, mist, haze, etc. from 16 to 31 January 2016 in New Delhi. In foggy conditions, visibility is significantly reduced as compared to shallow fog, yet BC concentrations remain at comparable magnitudes. Our analysis reveals a negative correlation between relative humidity (RH) and BC concentration during fog (-0.31), indicating an inverse relationship. Conversely, in shallow fog, this correlation is less prominent (-0.19) (Figure 8(b)). As visibility is inversely proportional to humidity, and temperature affects visibility due to increased ABL, we observe that with increasing visibility, the correlation between temperature and BC concentration also increases. Specifically, during fog, the Temperature-BC correlation is -0.31, while for shallow fog it is -0.19, and for mist it is 0.59. These findings suggest that RH play a role in reducing BC concentrations. In haze conditions, the correlations align closely with the results presented in Table 3. During smoke events, the RH-BC concentration exhibits a positive correlation, suggesting an increase in BC concentrations for obvious reasons. The absorption of solar radiation by BC can lead to localized heating in the atmosphere. This heating may result in enhanced turbulence and vertical motion, impacting the development and dissipation of fog layers. This also reveals that the BC can affect the vertical distribution and horizontal extent of fog. In fog and haze, high relative humidity and low temperatures are common, but BC concentration does not exhibit a strong positive correlation. This suggests that other factors, such as aerosol size or composition, might be more significant in influencing these phenomena. Conversely, in mist and shallow fog, temperature plays a more crucial role, especially in mist, where a strong positive correlation with BC concentration is observed. This indicates that temperature is a key driver in the presence of BC under these conditions. For smoke, a slight positive correlation between BC concentration and relative humidity suggests that smoke may persist longer in humid environments, while temperature appears to have minimal impact. Figure 9(a) illustrates the impact of BC during fog, mist, haze from January 16th to January 31 st , 2016, in Chandigarh. In Chandigarh, when it's foggy, we found that humidity and BC have a negative relationship, with a correlation of -0.41. During misty conditions, this relationship is weaker, with a correlation of 0.14, and during haze, it strengthens to 0.51 (Figure 9(b)). Looking at temperature and BC, during fog, there's a positive correlation of 0.25. But during mist, this correlation becomes negative at -0.42, and during haze, it's even more negative at -0.56. This means that when it's foggy and warmer, BC levels tend to be higher, similar to what we observed in New Delhi, but it's different from normal weather conditions. In foggy conditions, higher concentrations of BC are associated with lower relative humidity, suggesting that fog tends to disperse or change as BC levels rise. During haze, an increase in BC is linked to higher relative humidity and lower temperatures, implying that dense haze likely creates cooler, more humid conditions. In misty conditions, a rise in BC concentrations primarily leads to a decrease in temperature, with only a slight increase in relative humidity, indicating that mist responds differently to BC accumulation compared to fog and haze. Figure 10(a) illustrates the impact of BC during fog, mist, and haze from January 16th to January 31st, 2016, in Varanasi. In Varanasi, the relationship between black carbon (BC) concentrations and meteorological conditions varies significantly during fog, mist, and haze events. During foggy conditions, BC and relative humidity exhibit a negative correlation (-0.39), indicating that higher humidity levels are associated with lower BC concentrations. This is likely due to the efficient removal of BC aerosols through wet deposition during fog events. In contrast, during misty conditions, the correlation between BC and humidity is weaker and statistically non-significant (0.06), suggesting that mist has a minimal impact on BC dynamics. However, during haze events, the correlation strengthens to 0.32, indicating that higher BC concentrations are associated with higher humidity levels under hazy conditions (see Figure 10(b)). Varanasi’s meteorological conditions during fog and mist events differ from those in New Delhi and Chandigarh. The city experiences higher humidity levels and weaker temperature inversions, which dampen the localized heating effect of BC and reduce its correlation with temperature. Additionally, the boundary layer in Varanasi during fog and mist events is often shallower and more stable, limiting vertical mixing and reducing the influence of BC on near-surface temperatures. The city’s location in the Indo-Gangetic Plain, coupled with its proximity to the Ganges River, creates unique microclimatic conditions. The frequent formation of fog and mist, along with riverine influences, further dilutes the temperature-BC relationship. When examining the relationship between temperature and BC, the correlations are statistically non-significant during fog (0.01) and mist (-0.08). However, during haze, a weak negative correlation (-0.21) is observed, suggesting that warmer temperatures during haze events are associated with slightly lower BC concentrations. These results indicate that foggy conditions facilitate BC removal through wet deposition, while hazy conditions promote BC accumulation under stable, high-humidity environments. This trend is consistent with observations in New Delhi and Chandigarh but is less pronounced in the Gangetic Plains due to regional differences in meteorological and emission characteristics. 3.5 BC concentration and active/break monsoon conditions The BC and Rainfall for Monsoon in 2016 is shown in Figure 11(a), where active spell is characterized by frequent and widespread rainfall while break spell is characterized by sporadic or absence of rainfall. During the active monsoon spell, BC concentrations low compared to the break monsoon period due to wet scavenging. The lower the rainfall during monsoon, the higher the concentration. The BC concentration declines sharply or remains low whenever the daily rainfall is 15mm or more. These findings suggest that the active monsoon phase generally leads to a slight reduction in BC concentrations across most regions, although there may be some variability depending on local factors. The correlation between temperature and BC concentrations is weakly positive during both active and break monsoon spells (Figure 11(b)). This suggests that temperature alone does not strongly regulate BC levels, possibly due to the complex interplay of emissions, atmospheric transport, and removal mechanisms such as wet deposition. During the break monsoon spell, reduced cloud cover and increased solar radiation may slightly enhance local convective mixing, but its impact on BC concentrations remains minimal. The correlation between RH and BC concentrations exhibits contrasting behavior (Figure 11(b)). During the active monsoon spell, a positive correlation of 0.26 indicates that higher RH levels correspond to increased BC concentrations. This could be due to the trapping of BC within the lower boundary layer under high moisture conditions, which limits vertical dispersion. Additionally, increased hygroscopic growth of aerosols may contribute to the retention of BC particles in humid environments. During the break monsoon spell, a negative correlation of -0.36 suggests that higher RH levels correspond to lower BC concentrations. This is likely due to reduced emissions from wet surfaces and enhanced removal of BC through hygroscopic growth, which accelerates wet deposition. The lower boundary layer height during humid conditions in the break monsoon may also facilitate more efficient BC removal. The correlation between rainfall and BC concentrations varies significantly between active and break monsoon phases. During the active monsoon spell, the correlation is statistically non-significant (-0.06), indicating that heavy and continuous rainfall does not substantially affect BC levels. This could be attributed to a balance between wet deposition and sustained emissions from anthropogenic sources. The continuous presence of convective activity may keep BC particles in suspension, preventing a significant decline in concentrations. However, during the break monsoon spell, the correlation strengthens to -0.43, indicating a more pronounced reduction in BC levels with rainfall. Since BC concentrations are typically higher during this period (Figure 11(a)) due to reduced atmospheric cleansing, any rainfall event exerts a stronger scavenging effect, leading to a noticeable drop in BC levels. This suggests that in the absence of sustained monsoonal precipitation, dry atmospheric conditions allow BC to accumulate, making sporadic rain showers during the break monsoon more effective in cleansing the atmosphere. 4. Conclusions This research provides comprehensive insights into the dynamics of BC pollution across North India, addressing key aspects of methodology, results, and implications. Methodological approaches, including Aethalometer measurements and meteorological correlations, facilitated the examination of BC variations at different temporal and spatial scales. The study elucidates distinct seasonal and diurnal patterns in BC concentrations influenced by factors such as urbanization, altitude, and meteorological conditions. The monsoon season exhibits the lowest BC levels due to wet scavenging, contrasting with elevated concentrations during post-monsoon periods attributed to agricultural activities. The mean BC mass concentration shows a negative correlation with ambient air temperature, rainfall, and wind speed, emphasizing the interplay between atmospheric conditions and pollutant levels. The BC concentration shows a negative correlation with RH at Ranichauri, Srinagar, Jodhpur (at higher humidity levels), and Varanasi (at lower humidity levels), and a positive correlation at Chandigarh, New Delhi (at lower to moderate humidity levels), and Jodhpur (at lower humidity levels). This suggests that the relationship between BC concentrations and relative humidity varies by city and humidity range, with BC generally decreasing as humidity increases in most locations. Warmer temperatures and higher wind speeds generally reduce BC concentrations through enhanced dispersion, while higher RH and rainfall promote BC removal via wet scavenging. Case studies on heatwaves, fog, and monsoon conditions offer valuable insights into BC's role in exacerbating weather phenomena, highlighting complex relationships influenced by local climate factors. The research identifies correlations between BC concentrations and temperature, humidity, and critical role of rainfall in regulating BC concentrations during different weather events, contributing to a deeper understanding of BC's impact on atmospheric processes. This study underscores the importance of considering meteorological factors in formulating effective air quality management strategies for mitigating BC pollution in North India, ultimately contributing to efforts aimed at improving environmental health and sustainability in the region. Declarations Acknowledgements We express our sincere gratitude to the Hon’ble Secretary, Ministry of Earth Sciences (MoES); Hon’ble Director General of Meteorology (DGM), India Meteorological Department (IMD), Government of India (GoI); Hon’ble Founder President; Hon’ble Chancellor; Hon’ble Vice Chancellor and Hon’ble Pro Vice Chancellor, Amity University Haryana (AUH), Gurugram, India, for their continued motivation and support. The authors are thankful to the personnel involved in the maintenance of the BC Network, IMD, India. Thanks, are also due to the Organizers of the National Symposium on Tropical Meteorology (TROPMET 2022) held in Bhopal, India, where the preliminary results of this study have been presented, discussed critically, and improved. The authors express their sincere gratitude to the Anonymous Reviewers for their insightful comments which improved the scientific content of the original manuscript. Author Contribution Vivek Kumar: Conceptualization, Methodology, Data curation, Software, Formal analysis, Investigation, Software, Validation, Visualization, Writing – original draft. Panuganti C.S. Devara: Supervision, Writing – review and editing, Visualization. Vijay K. Soni: Resources, Supervision, Writing – review and editing. Authors’ email address Vivek Kumar: [email protected] Panuganti C.S. Devara: [email protected] Vijay K. Soni: [email protected] Data availability Some data is provided in the form of plots in Supplementary Data files (S1 & S2). Complete data sets can be obtained from the Corresponding Author against a proper request. Ethical approval All authors have read, understood, and have complied as applicable with the statement on “Ethical Responsibilities of Authors” as found in the Instructions for Authors and are aware that with minor exceptions, no changes can be made to authorship once the paper is submitted. Conflict of interest The authors declare no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. In other words, there is no funding available for the work reported in this paper. References Badarinath, K.V.S., Latha, K.M., Chand, T.R.K., Reddy, R.R., Gopal, K.R., Reddy, L.S.S., Narasimhulu, K., Kumar, K.R., 2007. 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Monthly and spatially resolved black carbon emission inventory of India: uncertainty analysis. Atmos. Chem. Phys. 16, 12457–12476. https://doi.org/10.5194/acp-16-12457-2016 Petzold, A., Ogren, J.A., Fiebig, M., Laj, P., Li, S.-M., Baltensperger, U., Holzer-Popp, T., Kinne, S., Pappalardo, G., Sugimoto, N., Wehrli, C., Wiedensohler, A., Zhang, X.-Y., 2013. Recommendations for reporting "black carbon" measurements. Atmos. Chem. Phys. 13, 8365–8379. https://doi.org/10.5194/acp-13-8365-2013 Prabhu, V., Soni, A., Madhwal, S., Gupta, A., Sundriyal, S., Shridhar, V., Sreekanth, V., Mahapatra, P.S., 2020. Black carbon and biomass burning associated high pollution episodes observed at Doon valley in the foothills of the Himalayas. Atmospheric Research 243, 105001. https://doi.org/10.1016/j.atmosres.2020.105001 Raghavan, K., 1973. Break-Monsoon Over India. Mon. Wea. Rev. 101, 33–43. https://doi.org/10.1175/1520-0493(1973)1012.3.CO;2 Rajeevan, M., Gadgil, S., Bhate, J., 2010. Active and break spells of the Indian summer monsoon. J Earth Syst Sci 119, 229–247. https://doi.org/10.1007/s12040-010-0019-4 Ramanadham, R., Visweswara Rao, P., Patnaik, J.K., 1973. Break in the Indian summer monsoon. PAGEOPH 104, 635–647. https://doi.org/10.1007/BF00875908 Ramanathan, V., Carmichael, G., 2008. Global and regional climate changes due to black carbon. Nature Geosci 1, 221–227. https://doi.org/10.1038/ngeo156 Ramesh, K.J., Jena, C., Kumar, A., Kumar, V., Soni, V.K., 2025. Advances in Environmental Meteorology and Air Quality Early Warning. MAUSAM 76, 185–214. https://doi.org/10.54302/mausam.v76i1.6941 Ratnam, J.V., Behera, S.K., Ratna, S.B., Rajeevan, M., Yamagata, T., 2016. Anatomy of Indian heatwaves. Sci Rep 6, 24395. https://doi.org/10.1038/srep24395 Ravi Kiran, V., Rajeevan, M., Vijaya Bhaskara Rao, S., Prabhakara Rao, N., 2009. Analysis of variations of cloud and aerosol properties associated with active and break spells of Indian summer monsoon using MODIS data. Geophysical Research Letters 36, 2008GL037135. https://doi.org/10.1029/2008GL037135 Rohini, P., Rajeevan, M., Srivastava, A.K., 2016. On the Variability and Increasing Trends of Heat Waves over India. Sci Rep 6, 26153. https://doi.org/10.1038/srep26153 Roy, A., Acharya, P., 2023. Energy inequality and air pollution nexus in India. Science of The Total Environment 876, 162805. https://doi.org/10.1016/j.scitotenv.2023.162805 Safai, P.D., Kewat, S., Pandithurai, G., Praveen, P.S., Ali, K., Tiwari, S., Rao, P.S.P., Budhawant, K.B., Saha, S.K. and Devara, P.C.S., 2008: Aerosol characteristics during winter fog at Agra, North India, Journal of Atmospheric Chemistry, 61, 101-118. Sandradewi, J., Prévôt, A.S.H., Szidat, S., Perron, N., Alfarra, M.R., Lanz, V.A., Weingartner, E., Baltensperger, U., 2008. Using Aerosol Light Absorption Measurements for the Quantitative Determination of Wood Burning and Traffic Emission Contributions to Particulate Matter. Environ. Sci. Technol. 42, 3316–3323. https://doi.org/10.1021/es702253m Sarangi, C., Tripathi, S.N., Kanawade, V.P., Koren, I., Pai, D.S., 2017. Investigation of the aerosol–cloud–rainfall association over the Indian summer monsoon region. Atmos. Chem. Phys. 17, 5185–5204. https://doi.org/10.5194/acp-17-5185-2017 Sharma, S., Mujumdar, P., 2017. Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India. Sci Rep 7, 15582. https://doi.org/10.1038/s41598-017-15896-3 Singh, N., Mhawish, A., Banerjee, T., Ghosh, S., Singh, R.S., Mall, R.K., 2021. Association of aerosols, trace gases and black carbon with mortality in an urban pollution hotspot over central Indo-Gangetic Plain. Atmospheric Environment 246, 118088. https://doi.org/10.1016/j.atmosenv.2020.118088 Sonbawne, S.M., 2021. Multisite characterization of concurrent black carbon and biomass burning around COVID-19 lockdown period. Urban Climate 14. Soni, P., Tripathi, S.N., Srivastava, R., 2018. Radiative effects of black carbon aerosols on Indian monsoon: a study using WRF-Chem model. Theor Appl Climatol 132, 115–134. https://doi.org/10.1007/s00704-017-2057-1 Tiwari, S., Srivastava, A.K., Bisht, D.S., Parmita, P., Srivastava, M.K., Attri, S.D., 2013. Diurnal and seasonal variations of black carbon and PM2.5 over New Delhi, India: Influence of meteorology. Atmospheric Research 125–126, 50–62. https://doi.org/10.1016/j.atmosres.2013.01.011 Tritscher, T., Jurányi, Z., Martin, M., Chirico, R., Gysel, M., Heringa, M.F., DeCarlo, P.F., Sierau, B., Prévôt, A.S.H., Weingartner, E., Baltensperger, U., 2011. Changes of hygroscopicity and morphology during ageing of diesel soot. Environ. Res. Lett. 6, 034026. https://doi.org/10.1088/1748-9326/6/3/034026 Wang, C., 2004. A modeling study on the climate impacts of black carbon aerosols. J. Geophys. Res. 109, 2003JD004084. https://doi.org/10.1029/2003JD004084 Wang, D., Zhu, B., Jiang, Z., Yang, X., Zhu, T., 2016. The impact of the direct effects of sulfate and black carbon aerosols on the subseasonal march of the East Asian subtropical summer monsoon. JGR Atmospheres 121, 2610–2625. https://doi.org/10.1002/2015JD024574 Wang, Z., Huang, X., Ding, A., 2018. Dome effect of black carbon and its key influencing factors: a one-dimensional modelling study. Atmos. Chem. Phys. 18, 2821–2834. https://doi.org/10.5194/acp-18-2821-2018 Weingartner, E., Saathoff, H., Schnaiter, M., Streit, N., Bitnar, B., Baltensperger, U., 2003. Absorption of light by soot particles: determination of the absorption coefficient by means of aethalometers. Journal of Aerosol Science 34, 1445–1463. https://doi.org/10.1016/S0021-8502(03)00359-8 Wu, Y., Wang, X., Tao, J., Huang, R., Tian, P., Cao, J., Zhang, L., Ho, K.-F., Han, Z., Zhang, R., 2017. Size distribution and source of black carbon aerosol in urban Beijing during winter haze episodes. Atmos. Chem. Phys. 17, 7965–7975. https://doi.org/10.5194/acp-17-7965-2017 Xie, X., Myhre, G., Liu, X., Li, X., Shi, Z., Wang, H., Kirkevåg, A., Lamarque, J.-F., Shindell, D., Takemura, T., Liu, Y., 2020. Distinct responses of Asian summer monsoon to black carbon aerosols and greenhouse gases (preprint). Aerosols/Atmospheric Modelling/Troposphere/Physics (physical properties and processes). https://doi.org/10.5194/acp-2020-483 Yang, M., Howell, S.G., Zhuang, J., Huebert, B.J., 2009. Attribution of aerosol light absorption to black carbon, brown carbon, and dust in China – interpretations of atmospheric measurements during EAST-AIRE. Atmos. Chem. Phys. 16. Zhao, D., Liu, D., Yu, C., Tian, P., Hu, D., Zhou, W., Ding, S., Hu, K., Sun, Z., Huang, M., Huang, Y., Yang, Y., Wang, F., Sheng, J., Liu, Q., Kong, S., Li, X., He, H., Ding, D., 2020. Vertical evolution of black carbon characteristics and heating rate during a haze event in Beijing winter. Science of The Total Environment 709, 136251. https://doi.org/10.1016/j.scitotenv.2019.136251 Additional Declarations No competing interests reported. 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Soni","email":"","orcid":"","institution":"India Meteorological Department (IMD), New Delhi","correspondingAuthor":false,"prefix":"","firstName":"Vijay","middleName":"K.","lastName":"Soni","suffix":""}],"badges":[],"createdAt":"2025-03-05 12:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6162410/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6162410/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-025-14216-x","type":"published","date":"2025-07-03T15:58:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79576945,"identity":"54d65c66-b8e4-4545-97f4-efc4e9f593eb","added_by":"auto","created_at":"2025-03-31 11:23:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":202646,"visible":true,"origin":"","legend":"\u003cp\u003eMap depicting the experimental stations used in the study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/7780432b1bc7ac1c22309d3a.png"},{"id":79576906,"identity":"c4bcd96e-1e2b-44e4-ae70-a73f8a895aa6","added_by":"auto","created_at":"2025-03-31 11:23:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":277575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eDiurnal and (b) seasonal diurnal variation of BC concentration over North India (2016-20).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/6aa18807cdae4b2482633bb6.png"},{"id":79578099,"identity":"4fb26930-0ceb-468a-bcad-17093281ab7d","added_by":"auto","created_at":"2025-03-31 11:31:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117226,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal mean variation of BC370 and BC880 in North India for 2016-2020.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/484a43d5e6048dfa41c5a3a9.png"},{"id":79576934,"identity":"5f5c82bc-5b21-40a0-8c52-7314a6719f56","added_by":"auto","created_at":"2025-03-31 11:23:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":357305,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of Local Wind Direction to BC Concentration in North India (2016-2020).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/8c1d52af8b95275f32906c33.png"},{"id":79576964,"identity":"a98b12b9-b82f-4f10-99e1-ebee7c3d720f","added_by":"auto","created_at":"2025-03-31 11:23:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":349327,"visible":true,"origin":"","legend":"\u003cp\u003eNew Delhi heatwave in 2019 (May 31 and June 01)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/7065992edbd9a87eaa9cf9eb.png"},{"id":79576924,"identity":"2364b436-2286-431e-8573-8502407e9232","added_by":"auto","created_at":"2025-03-31 11:23:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":383439,"visible":true,"origin":"","legend":"\u003cp\u003eJodhpur heatwave in 2017(April 13 to April 17)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/8f463cf994a939a129d8f7c1.png"},{"id":79576913,"identity":"6db466fe-3930-4e69-92a7-45c1a8de6e06","added_by":"auto","created_at":"2025-03-31 11:23:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":338844,"visible":true,"origin":"","legend":"\u003cp\u003eVaranasi heat wave in 2018 (June 17 to June 22)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/1edf0a7083ee96af25eb578f.png"},{"id":79576922,"identity":"f2abc6d8-18bd-40b2-a5ab-b5b4606d53b5","added_by":"auto","created_at":"2025-03-31 11:23:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":323133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e BC impact during fog, mist, haze etc. and \u003cstrong\u003e(b)\u003c/strong\u003e correlation with BC during 16 Jan to 31 Jan 2016 at New Delhi\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/a8d8f2cee64dbaa234694424.png"},{"id":79576952,"identity":"5e8d064c-0a8b-414b-afb1-f35b538cbf02","added_by":"auto","created_at":"2025-03-31 11:23:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":394640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e BC impact during fog, mist and haze and \u003cstrong\u003e(b)\u003c/strong\u003e correlation with BC during 16 Jan to 31 Jan 2016 at Chandigarh\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/6247d36b35e89568fe53b43a.png"},{"id":79576933,"identity":"2980b3c6-39ab-4b05-b06b-9ca2734eca63","added_by":"auto","created_at":"2025-03-31 11:23:32","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":355516,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e BC impact during fog, mist and haze and \u003cstrong\u003e(b)\u003c/strong\u003e correlation with BC during 16 Jan to 31 Jan 2016 at Varanasi\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/71294b41d9f3029e58dcabf4.png"},{"id":79576959,"identity":"3c351508-5622-4652-ad7f-56a148de59b0","added_by":"auto","created_at":"2025-03-31 11:23:33","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":266431,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e BC concentration and rainfall variation and \u003cstrong\u003e(b)\u003c/strong\u003ecorrelation with BC during active and break monsoon in 2016 at New Delhi\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/267abdbdf3676c71f73e9bea.png"},{"id":86180048,"identity":"6d46ccf5-7eed-4001-a275-bdc15ff68b3c","added_by":"auto","created_at":"2025-07-07 16:21:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3791809,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/c476d2d5-4142-4b09-93df-34b83d6646c5.pdf"},{"id":79576902,"identity":"0bdf0145-c46f-443d-a314-99fa27d82e5d","added_by":"auto","created_at":"2025-03-31 11:23:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":542010,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6162410/v1/82f86d87629a3bfacd563f3c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Black carbon characterization and its association with meteorological phenomena using network data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBlack Carbon (BC) aerosol is a significant climate influencer emitted into the atmosphere through incomplete combustion of biomass, fossil fuels, and biofuels. Particularly in heavily populated tropical regions like Asia, BC contributes significantly to global aerosol surface forcing. South East Asia is hotspot for BC aerosols pollution, in India especially the Indo-Gangetic plains. (Prabhu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ramanathan and Carmichael, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). BC aerosols, having mostly anthropogenic sources, are well recognized to be one of the major light absorbing components and second strongest contributor to the Global Warming and Climate Change after carbon dioxide. BC near the surface causes surface warming (Ban-Weiss et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Being mostly in the sub-micron range and chemically inert, BC has a long atmospheric lifetime from several days to weeks depending on the meteorological conditions and hence is susceptible for long-range transport. India faces significant challenges in balancing energy needs with environmental concerns. Household energy use, particularly biomass-based cooking fuels, and coal fired power generation plants contributes substantially to air pollution and health issues (Kopas et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Roy and Acharya, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Increase in BC aerosol concentration reduces evaporation in Indian Ocean due to dimming. Due to this the meridional sea surface temperature decreases which is associated with the weaker monsoonal circulation (Meehl et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The global mean radiative forcing of BC aerosol formed due to fossil fuel and biofuel burning has increased from +\u0026thinsp;0.20Wm\u003csup\u003e-2\u003c/sup\u003e to +\u0026thinsp;0.40Wm\u003csup\u003e-2\u003c/sup\u003e (Myhre et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The impact of BC aerosols on monsoon dynamics is particularly important for the Indian economy and growth, making it essential to study their effects.\u003c/p\u003e \u003cp\u003eThe influences of BC aerosols on climate and environment are more significant in regional scale than in global scale (Wang, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Many studies are conducted for seasonal variation of BC for example Tiwari et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e (New Delhi), Chauhan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e (Varanasi), Meena et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e (Mahabaleshwar and Pune) and many more. BC measurements across India display a distinct seasonal pattern in near-surface BC mass concentrations, with peak values during the winter and the lowest levels during the monsoon season (Kumar et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This seasonality is attributed to regional meteorological dynamics and emissions from various anthropogenic and biomass burning sources.\u003c/p\u003e \u003cp\u003eBC has been found to have a negligible impact on solar heating rates in the tropical tropopause layer (Gao et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, in urban environments with high BC emissions, it can significantly enhance atmospheric heating, particularly during haze events (Zhao et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This effect is further amplified over megacities in summertime, where the combination of intensive solar radiation, secondary aerosol formation, and cloud reflection can lead to a considerable increase in the temperature inversion above the planetary boundary layer (Liu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the Brahmaputra River Valley, wintertime BC aerosols have been linked to strong radiative heating, with potential regional climatic impacts (Chakrabarty et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBC has been found to significantly impact the formation of fog. Badarinath et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Ding et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) both found that BC can induce fog formation, with Ding et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) specifically highlighting its role in advection-radiation. Ganguly et al., (2006) found that fog enhances the removal of aerosols from the atmosphere through wet deposition. The dome effect of BC, which suppresses the planetary boundary layer height and weakens vertical mixing, further exacerbates the impact of BC on fog (Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The size distribution and source of BC, particularly during winter haze episodes, also play a role in its impact on fog (Wu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A recent study conducted by Bharali et al., (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) focused on fog in the Indo-Gangetic Plain (IGP) using satellite data and evaluates the performance WRF-Chem model discussing secondary aerosol formation during fog in Delhi and the effects of agricultural burning on air quality in Punjab. However, it did not include ground-based BC data, which is a key component of our study.\u003c/p\u003e \u003cp\u003eThe increased meridional tropospheric temperature gradient in the pre-monsoon months of March-April-May contributes to enhanced precipitation over India in those months (Meehl et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The remote influence of South Asian BC aerosol on the East Asian summer monsoon can cause a reduction in rainfall in the Yangtze River valley and intensified rainfall in northern and southern China (Mahmood and Li, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The radiative effects of BC aerosols on the Indian monsoon can result in increased rainfall in northern India but decreased rainfall in southern India (Soni et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The direct effects of BC aerosols on the East Asian subtropical summer monsoon can lead to an advance in the onset time of the monsoon (Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). During the southwest monsoon season, breaks in rainfall can occur, leading to dry spells over northern India during July and August (Ramanadham et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Raghavan, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). Gadgil and Joseph (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) identified interannual variations in all-India summer monsoon rainfall linked to the number of break and active days, while Rajeevan et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) established criteria for identifying these events based on rainfall anomalies.\u003c/p\u003e \u003cp\u003eAerosol loading over South Asia, including the Himalayan foothills, has significantly increased in recent decades, impacting the radiation budget and water cycle. Aerosols modulate precipitation and cloud properties, including cloud droplet number and reflectivity (Hazra et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Observational studies suggest that absorbing aerosols are more prominent during monsoon break phases, followed by active monsoon periods (Ravi Kiran et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Aerosols also modulate monsoonal characteristics through land-atmosphere interactions and cloud invigoration, affecting rainfall over the Indian summer monsoon region (Niyogi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sarangi et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearchers have shown that BC aerosols and greenhouse gases have distinct effects on the Asian summer monsoon. Xie et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that BC enhances precipitation dynamically, whereas greenhouse gases do so thermodynamically. Lau and Kim (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) further noted that greenhouse gases have a stronger positive rainfall impact, while aerosols have a more pronounced negative effect. Guo et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) highlighted a significant decrease in precipitation during the East Asian Summer Monsoon due to increased BC emissions. When BC particles are present in the atmosphere, they can absorb and retain heat, leading to an increase in temperature (Bond et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Jones et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) suggest significant influence of BC on global temperatures, but its impact is small compared to that from greenhouse gas emissions. Heatwaves are extreme meteorological events characterized by prolonged periods of excessively high temperatures, posing significant risks to human health, ecosystems, and socioeconomic systems. In India, heatwaves are a recurrent phenomenon, particularly during the pre-monsoon and summer seasons, exerting substantial impacts on various aspects of life. Recent long-term studies have shown an increasing occurrence of heatwaves, with the central and northwest parts of India being more susceptible to prolonged heatwave episodes (Bhattacharya et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ratnam et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rohini et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sharma and Mujumdar, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Dave et al., (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) examined the correlation between absorbing aerosols and summertime maximum temperatures (Tmax) in northwest India using TOMS-OMI and IMD data but relied on satellite and reanalysis data without direct BC measurements. Similarly, Mondal et al., (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used IMD data and ECHAM6-HAM2 model simulations for high-temperature extremes but did not incorporate actual BC datasets.\u003c/p\u003e \u003cp\u003eDespite existing studies on BC concentrations in India, a significant research gap remains in quantifying the relative contributions of regional meteorological dynamics and emissions to BC seasonality, particularly in understanding its diurnal and seasonal variability. Limited research has explored the impacts of BC aerosols on monsoon breaks, winter fog events, and pre-monsoon heatwaves in North India. The interaction between BC aerosols and precipitation during the active-break cycle of the monsoon is crucial, with significant implications for agriculture, water resources, and regional weather patterns. By contrasting rural and urban settings, this study highlights the spatial heterogeneity of BC dynamics and their influence on local weather phenomena. Unlike previous research, which often focuses on isolated aspects of BC impacts (e.g., heatwaves or fog events), this study takes a holistic approach by examining BC interactions across multiple critical weather events. This study is distinguished by its reliance on ground-based observational data, addressing the limitations of satellite-based and model-driven studies. By integrating long-term observational datasets with a detailed analysis of BC interactions during extreme weather events, it offers a novel and comprehensive perspective on BC\u0026rsquo;s role in shaping regional climate and atmospheric processes in India.\u003c/p\u003e"},{"header":"2. Materials and Methodology","content":"\u003cp\u003eThe BC data from North Indian stations of IMD BC network (Ramesh et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) installed at various geographical locations having different environmental conditions is used for this study for the period 2016\u0026ndash;2020. The seven wavelength Aethalometer is used to measure equivalent BC concentration and biomass burning contribution. It works on light wavelength-dependence on absorption principle using suitable mass absorption cross-section values (Petzold et al., 2013). It uses seven wavelengths (370, 470, 520, 590, 660, 880 and 950 nm) that allow spectral analysis for different purposes, such as mineral dust detection and source apportionment (Drinovec et al., 2015). It measures elemental carbon (EC) or BC mass concentration (in \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Traditionally, the equivalent BC concentration is calculated using the light attenuation at 880 nm along with an absorption cross-section value of 7.77 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e because absorption due to other aerosols is negligible at this wavelength (Drinovec et al., 2015; Sandradewi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The instrument uses Teflon coated glass fiber filter tape and an impactor (PM\u003csub\u003e2.5\u003c/sub\u003e) at inlet limiting the inlet particle size to 2.5 \u0026micro;m. Plus, the Aethalometer (Model AE-33) uses dual-spot technique, which enables near real time compensation for the spot loading effect. More details about the performance of the Aethalometer (AE-33) can be found in Drinovec et al. (2015) and recently by Sonbawne et al., (2021) and references therein. The North Indian stations used in this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) are Ranichauri, Srinagar, Chandigarh, Varanasi, Jodhpur and New Delhi. The meteorological parameters used are of IMD observatory at the same location as the BC monitoring station.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIMD BC Network Stations and Characteristics over North India\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAltitude(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRanichauri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBackground, High Altitude\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSrinagar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eValley, High Altitude\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChandigarh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaranasi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJodhpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDesert\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew Delhi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban mega city\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRanichauri stands at an altitude of 1950 meters above sea level, making it a high-altitude station. It represents a background environment, away from urban and industrial centers. The station's weather is influenced by its altitude, experiencing cooler temperatures and varied seasonal climates. Srinagar is positioned at an altitude of 1655 meters. Nestled in a valley surrounded by the Himalayan Mountains, it offers insights into BC emissions in a high-altitude valley setting. Its climate is characterized by cold winters and moderate summers, heavily influenced by its unique geography. Chandigarh is situated at an altitude of 347 meters. This urban station serves as a representative of the urban environment in the region. The city experiences a subtropical climate with hot summers and cold winters, marked by monsoons. Varanasi lies at an altitude of 88 meters. This urban station provides data on BC emissions in a densely populated urban area. It experiences a humid subtropical climate with significant variations in temperature throughout the year. Jodhpur stands at an altitude of 217 meters, offering insights into BC emissions in a desert environment. The station experiences an arid climate with scorching summers and mild winters. As the capital of India, New Delhi is located at 28.58\u0026deg; latitude and 77.2\u0026deg; longitude, at an altitude of 212 meters. It represents the complex urban dynamics of a megacity, characterized by high population density and various sources of BC emissions. The city experiences a humid subtropical climate with hot summers, monsoon season, and cool winters. In the present study, the mass concentration of BC, primarily associated with fossil fuel emissions, particularly vehicular exhaust, at a wavelength 880 nm (BC\u003csub\u003e880 nm\u003c/sub\u003e) in \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e is studied at multiple locations associated with varying environments, utilizing the IMD network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe one-minute interval data of BC data are used to calculate the hourly means. It is further used to compute the daily, monthly, seasonal means and to examine changes during 2016\u0026ndash;2020 over North India. Meteorological correlations (Pearson) for the stations shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e are reported at 95% confidence interval.\u003c/p\u003e \u003cp\u003eDuring the period of heatwave, there is a long period of extremely hot weather compared to what's usually expected for a place over several years. Different regions and meteorological agencies might use different standards to identify heatwaves. In this study, we defined a heatwave as a day with a temperature higher than 40\u0026deg;C and at least 4.5\u0026deg;C warmer than the average daily maximum temperature over the past 30 years. Heatwave conditions vary by location due to different meteorological and geographical factors. As a result, heatwaves do not occur at all stations at the same time. Therefore, the analysis in this study is shown for different dates at different locations, satisfying all the heatwave conditions discussed.\u003c/p\u003e \u003cp\u003eWe also examined how fog, mist, and haze in winter relate to the concentration of BC. The World Meteorological Organization (WMO) defines \"fog\" as a condition where microscopic droplets reduce horizontal visibility at the Earth's surface to less than 1 km. \"Mist,\" on the other hand, refers to conditions where the droplets do not reduce visibility to less than 1 km, and is often considered synonymous with \"light fog.\" The term \"smog\" (a combination of \"smoke\" and \"fog\") is commonly used to describe conditions where fog and heavy air pollution are present, often involving chemical reactions between the fog droplets and various pollutants. Visibility reduction depends on the structure of the fog, particularly the number density and size distribution of the droplets, which can vary greatly in both time and space. Air in fog usually feels damp or moist. When illuminated, individual fog droplets are often visible to the naked eye and exhibit turbulent movement. Fog forms a whitish veil that blankets the landscape, while mist typically appears as a thinner, greyish veil. When combined with dust or smoke, fog may develop a faint coloration. For our BC-Fog analysis, we utilized METAR (Meteorological Aerodrome Reports) data from the winter season of 2016. This timeframe was selected as it corresponds to a period when fog is typically prevalent. The METAR data of New Delhi (VIDP), Jodhpur (VIJO), Chandigarh (VICG) and Varanasi (VEBN) are used in this study. The METAR data provides detailed meteorological information recorded at aerodrome stations (half hourly or hourly), offering insights into various atmospheric parameters such as visibility, humidity and temperature conditions during foggy episodes.\u003c/p\u003e \u003cp\u003eAccording to the WMO, haze is defined as a suspension of extremely small, dry particles in the air, invisible to the naked eye, but numerous enough to give the air an opalescent appearance. Smoke, on the other hand, is defined as a suspension of small particles in the air produced by combustion.\u003c/p\u003e \u003cp\u003eWe also investigated the impact of the monsoon season on BC levels, specifically examining the variations between active and break periods of the monsoon. The classification of active and break periods of the monsoon was based on periods outlined in studies by Doyle et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and the end-of-season report by the Indian Meteorological Department (IMD, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We wanted to see how different phases of the monsoon affect BC levels by examining changes in weather conditions during active and break periods.\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eThe climate of India is primarily influenced by the Indian monsoon and Westerlies, with distinct seasons: winter, pre-monsoon, summer monsoon, and post-monsoon. The pre-monsoon season in North India lasts from March to May and is characterized by a gradual rise in temperatures, increasing humidity, and the development of thunderstorms and dust storms. The winds generally blow from the northwest, bringing in dust and dry air from the deserts of Pakistan and western India. The heat and dryness can lead to the formation of thunderstorms, which can be quite severe, bringing strong winds, heavy rain, and lightning. The shift of wind patterns from westerly to easterly brings moisture from the Bay of Bengal, the interaction between the hot, dry air of the Indian desert with the moist air causing thunderstorms, dust storms and rainfall.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe monsoon season (JJAS) of North India includes increased rainfall, higher humidity, strong winds and frequent thunderstorms. The monsoon winds, known as the southwest monsoon, bring moist air from the Arabian Sea and the Bay of Bengal to the region. \u0026nbsp;Most of the monsoon rainfall is caused by the interaction between the low-pressure system over the Bay of Bengal and the high-pressure system over the Himalayas, which leads to the formation of a monsoon trough. The monsoon rainfall brings relief from the scorching heat of pre-monsoon season and plays a crucial role in the agricultural production of the region. \u0026nbsp;The heavy rains during the Monsoon season in North India can also cause flooding, landslides, and water logging in some areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the post-monsoon season (ON) in North India, the monsoon winds and rainfall begin to subside, and the temperature starts to cool down, and the humidity levels also decrease. The clear sky post-monsoon season is also characterized by the formation of fog and mist during the early morning and late evening reducing\u0026nbsp;visibility in the Northern plains. This occasional occurrence of Western Disturbances, which are extra-tropical storms bringing rain and snowfall to the Himalayan region. Overall\u0026nbsp;Dry weather conditions prevail over the region.\u003c/p\u003e\n\u003ch2\u003e3.1 Diurnal and seasonal variations in BC\u003c/h2\u003e\n\u003cp\u003eThe seasonal diurnal variations at each station are shaped by a combination of local and regional factors, including urbanization, altitude, and proximity to pollution sources. The combined influence of weather and emission patterns in different seasons contributes to the observed variations. The morning peak occurs between 07:00 and 09:00 IST, driven primarily by increased vehicular emissions, biomass burning, and a shallow atmospheric boundary layer. Following this peak, BC concentrations gradually decrease due to convective development and enhanced atmospheric dispersion. The lowest concentration occurs around 14 to 16 hours IST due to enhanced convective mixing and dilution effects and then attain peak between 20 to 23 hours IST (Figure 2(a)). The highest concentration of BC is observed during the evening, which corresponds with times when human activities like traffic and industrial processes are most active. Bimodal peaks differ by 1 to 2 hours because of regional and local meteorological conditions and emission sources. Among the cities, New Delhi consistently records the highest BC levels, exceeding 16 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e, followed by Chandigarh and Varanasi, whereas Ranichauri, a high-altitude site, exhibits the lowest concentrations, remaining below 2 \u0026micro;g \u0026nbsp; m\u003csup\u003e-3\u003c/sup\u003e throughout the day. \u0026nbsp;The background station Ranichauri shows minimum concentration and is not having any pattern for overall diurnal variation for the study period. The stations in the North India shows higher concentration than the peninsular stations (Beegum et al., 2009; Kumar et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe diurnal variation of BC concentrations of different seasons is depicted in Figure 2(b). The aerosol-meteorology interaction significantly impacts the surface concentration of BC aerosols. Diurnal variation of BC concentration is sensitive to temperature, RH, Wind and boundary layer (Chauhan et al., 2024; Kumar et al., 2023; Tiwari et al., 2013). In the winter season, BC levels typically begin low in the early morning, steadily rise throughout the day, and peak twice, once in the morning and again in the evening (with the latter peak being higher). This trend is mainly due to increased combustion activities for heating purposes during colder periods (Devara et al., 2024). During the pre-monsoon season, BC concentrations follow a similar diurnal pattern, but with slightly lower overall levels. This season serves as a transitional period between the winter and monsoon, marked by varying emissions and atmospheric conditions. The decrease in BC levels can be attributed to reduced heating demands and the expansion of the atmospheric boundary layer. During the monsoon season, BC concentrations show the lowest levels in the diurnal variation pattern throughout the year. This decline is primarily due to wet scavenging and deposition processes associated with increased rainfall and stronger winds. In the post-monsoon season, BC concentrations exhibit the highest levels, primarily due to emissions from stubble burning activities in North India (Beig et al., 2020; Paliwal et al., 2016). This increase underscores the significant impact of agricultural practices on BC levels during this period. Monthly diurnal variations are depicted in supplementary plots (\u003cstrong\u003eS1\u003c/strong\u003e\u0026amp;\u003cstrong\u003eS2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe seasonal mean variation in black carbon (BC) concentrations across the study area reveals distinct patterns, with significant differences observed between locations and seasons (Figure 3, Table 2). BC concentrations, measured at 370 nm (BC370) and 880 nm (BC880), exhibit a consistent trend of peaking during the post-monsoon season and reaching their lowest levels during the monsoon. For instance, at Ranichauri, BC370 peaks at 3.07 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e during the post-monsoon season, while dropping to 1.42 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e during the monsoon. Similarly, BC880 reaches 2.05 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e in the post-monsoon and falls to 1.07 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e during the monsoon. This trend is mirrored in urban locations such as Srinagar, Chandigarh, and New Delhi, where post-monsoon BC370 concentrations reach 25.09 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e, 22.45 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e, and 29.55 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e, respectively, but decline sharply to 9.57 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e, 7.60 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e, and 6.02 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e during the monsoon. The post-monsoon peak is attributed to widespread agricultural residue burning, particularly in the Indo-Gangetic Plain (IGP), coupled with stable atmospheric conditions and lower temperatures that trap pollutants near the surface. In contrast, the monsoon season sees a significant reduction in BC levels due to efficient wet scavenging by rainfall, increased wind speeds, and enhanced vertical mixing, which disperse aerosols and reduce near-surface concentrations. Varanasi exhibits a unique pattern, with BC concentrations peaking during the pre-monsoon season (BC370: 16.0 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e; BC880: 11.11 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e) rather than the post-monsoon. This anomaly can be linked to the region\u0026rsquo;s dry and hot pre-monsoon conditions, which favor the accumulation of BC aerosols. Additionally, local emissions from vehicular and industrial activities, combined with frequent dust storms, contribute to elevated BC levels during this period. In contrast, rural areas like Ranichauri and semi-arid regions like Jodhpur generally show lower BC concentrations compared to urban centers, reflecting differences in emission sources and meteorological influences. For example, Jodhpur\u0026rsquo;s BC370 levels during the post-monsoon (9.27 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e) are significantly lower than those in New Delhi (29.55 \u0026micro;g \u0026nbsp;m\u003csup\u003e-3\u003c/sup\u003e), highlighting the impact of urbanization and anthropogenic activities on BC pollution. The post-monsoon season emerges as a critical period for BC pollution, driven by agricultural burning (Parali burning) and stable atmospheric conditions, while the monsoon season acts as a natural cleansing phase, significantly reducing BC levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Seasonal Mean Variation of BC880 and BC370 with Standard Deviation for 2016-2020 in North India.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"650\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eParameter/ Season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMonsoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePost-Monsoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-monsoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eChandigarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBC370 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.60 \u0026plusmn;4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.45 \u0026plusmn;15.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.48 \u0026plusmn;6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.85 \u0026plusmn;13.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBC880 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.73 \u0026plusmn;4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.39 \u0026plusmn;11.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.69 \u0026plusmn;5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.24 \u0026plusmn;10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eJodhpur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBC370 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.22 \u0026plusmn;1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.27 \u0026plusmn;8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.80 \u0026plusmn;4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.79 \u0026plusmn;10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBC880 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.76 \u0026plusmn;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.87 \u0026plusmn;6.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.83 \u0026plusmn;2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.13 \u0026plusmn;7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eNew Delhi\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBC370 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.02 \u0026plusmn;4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.55 \u0026plusmn;21.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.23 \u0026plusmn;10.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.05 \u0026plusmn;19.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBC880 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.03 \u0026plusmn;3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.13 \u0026plusmn;14.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.05 \u0026plusmn;8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.16 \u0026plusmn;14.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eRanichauri\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBC370 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.42 \u0026plusmn;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.07 \u0026plusmn;1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.56 \u0026plusmn;4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.22 \u0026plusmn;2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBC880 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.07 \u0026plusmn;1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.05 \u0026plusmn;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.00 \u0026plusmn;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.84 \u0026plusmn;1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSrinagar\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBC370 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.57 \u0026plusmn;6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.09 \u0026plusmn;16.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.54 \u0026plusmn;7.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.06 \u0026plusmn;18.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBC880 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.11 \u0026plusmn;5.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.14 \u0026plusmn;10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.65 \u0026plusmn;5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.32 \u0026plusmn;10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eVaranasi\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBC370 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.90 \u0026plusmn;4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.64 \u0026plusmn;12.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.52 \u0026plusmn;13.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.55 \u0026plusmn;15.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBC880 (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.09 \u0026plusmn;3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.70 \u0026plusmn;6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.11 \u0026plusmn;8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.21 \u0026plusmn;8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.2 Correlation of meteorological parameters with BC Concentrations\u003c/h2\u003e\n\u003cp\u003eThe analysis of black carbon (BC) concentrations across multiple monitoring stations in North India reveals significant correlations with various meteorological parameters (Pearson correlation at 95% confidence level), which play a crucial role in shaping BC dynamics. These correlations vary across stations due to differences in local climates, emission sources, and environmental conditions as a shown in Table 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Correlation analysis between various meteorological parameters and BC concentrations (p\u0026lt;0.05).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eTavg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eBC880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 118px;\"\u003e\n \u003cp\u003eChandigarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eTavg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBC880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 118px;\"\u003e\n \u003cp\u003eJodhpur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eTavg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBC880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 118px;\"\u003e\n \u003cp\u003eNew Delhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eTavg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBC880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRanichauri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eTavg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBC880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 118px;\"\u003e\n \u003cp\u003eSrinagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eTavg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBC880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 118px;\"\u003e\n \u003cp\u003eVaranasi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eTavg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBC880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA consistent negative correlation was observed between ambient temperature (Tavg) and BC880 concentrations across all cities, ranging from -0.44 (Varanasi) to -0.78 (Jodhpur) (p \u0026lt; 0.05), with the exception of the background station Ranichauri (-0.13). Warmer temperatures are associated with lower BC concentrations due to enhanced atmospheric mixing, increased boundary layer height, and greater dispersion of pollutants. For instance, in urban areas like New Delhi and Chandigarh, higher temperatures during the pre-monsoon season promote vertical mixing, diluting BC concentrations near the surface. This trend is less pronounced in Ranichauri, a rural site, where local emissions are lower, and temperature variations have a smaller impact on BC dynamics.\u003c/p\u003e\n\u003cp\u003eThe relationship between RH and BC concentrations is complex and varies across humidity ranges and locations. Most cities show negative correlations across different relative humidity categories, implying that as relative humidity increases, the BC tend to decrease. Cities in arid or semi-arid regions like Jodhpur show varying responses compared to more humid cities. At lower RH levels (0\u0026ndash;60%), BC concentrations show mixed correlations, with positive values in some cities (e.g., Chandigarh: 0.10; Jodhpur: 0.21) and negative values in others (e.g., Ranichauri: -0.14; Varanasi: -0.30). This variability suggests that BC aerosols may undergo hygroscopic growth at moderate RH levels, increasing their size and mass concentration. However, at higher RH levels (\u0026gt;80%), the correlations become predominantly negative (e.g., Ranichauri: -0.44; Srinagar: -0.27), indicating efficient wet scavenging of BC particles through below-cloud and in-cloud processes. The freshly emitted uncoated BC aerosols are hydrophobic and insoluble in water. However, they become hygroscopic as they age by acquiring coating of secondary inorganic and organic aerosol compounds via coagulation with other particles and thereby act as cloud condensation nuclei (Motos et al., 2019; Tritscher et al., 2011; Weingartner et al., 2003). As BC aerosols can act as CCN and hence the below-cloud and in-cloud wet scavenging processes are the primary removal mechanism for atmospheric BC aerosols (Jacobson, 2012), making the relationship between RH and BC concentration important for understanding BC\u0026apos;s atmospheric lifetime and transport. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe correlation between daily mean values relative humidity (RH) categories and BC variable for various cities is shown in Table 4. The relative humidity is divided into three categories: 0-60%, 60-80%, and \u0026gt;80%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e: Correlation analysis between BC concentrations and relative humidity (RH in %) using daily data from 2016 to 2020.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 242px;\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003eRH (0-60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003eRH (60-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003eRH (\u0026gt;80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 242px;\"\u003e\n \u003cp\u003eChandigarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 242px;\"\u003e\n \u003cp\u003eJodhpur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 242px;\"\u003e\n \u003cp\u003eNew Delhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 242px;\"\u003e\n \u003cp\u003eRanichauri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 242px;\"\u003e\n \u003cp\u003eSrinagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 242px;\"\u003e\n \u003cp\u003eVaranasi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRainfall exhibits a strong negative correlation with BC880 concentrations across all cities, ranging from -0.43 (Jodhpur) to -0.66 (Varanasi). Higher rainfall effectively scavenges BC aerosols from the atmosphere through wet deposition, leading to lower concentrations. For example, during the monsoon season, frequent and intense rainfall in Varanasi and Ranichauri results in a significant reduction in BC levels. This relationship underscores the importance of precipitation as a primary removal mechanism for BC aerosols.\u003c/p\u003e\n\u003cp\u003eA negative correlation was observed between wind speed and BC880 concentrations across most cities, ranging from -0.25 (Varanasi) to -0.74 (Srinagar), with the exception of the background station Ranichauri, which showed a positive correlation (0.33). Higher wind speeds generally facilitate the dispersion and transport of pollutants away from the monitoring site, leading to lower BC880 concentrations. However, at Ranichauri, the positive correlation suggests that winds may advect BC from nearby sources, increasing local concentrations under specific wind patterns. Figure 4 illustrates the distribution of BC concentrations based on wind direction, highlighting the influence of regional transport on local BC levels.\u003c/p\u003e\n\u003cp\u003eWind direction plays a critical role in determining pollutant transport and concentration. Cities located downwind of major pollution sources, such as New Delhi and Chandigarh, experience higher BC levels (11.7 \u0026micro;g/m\u0026sup3; and 10.3 \u0026micro;g/m\u0026sup3;, respectively), as prevailing winds carry pollutants from neighboring regions. In contrast, Ranichauri and Jodhpur, with fewer local pollution sources and better dispersion conditions, exhibit lower BC levels (1.4 \u0026micro;g/m\u0026sup3; and 4.1 \u0026micro;g/m\u0026sup3;). Calm wind conditions (near-zero wind speeds) further exacerbate pollutant accumulation, creating localized hotspots. For instance, Jodhpur and Ranichauri, with higher percentages of calm conditions (46.3% and 39.8%, respectively), show increased BC buildup due to reduced dispersion. In contrast, Varanasi, with fewer calm conditions (18.2%), benefits from better pollutant dispersion, despite its lower BC concentrations, which are also influenced by its geographical location and lower population density.\u003c/p\u003e\n\u003cp\u003eWind roses reveal that winds from the west and northwest are associated with higher BC concentrations in many locations, likely indicating the presence of pollution sources in these directions. Seasonal wind patterns, such as monsoon winds, also play a significant role in shaping BC variability over multi-year datasets. During winter, lower wind speeds and increased emissions from heating and biomass burning contribute to higher BC levels, particularly in urban centers like New Delhi. In Srinagar, moderate BC concentrations (mean: 10 \u0026micro;g/m\u0026sup3;) are exacerbated by the city\u0026rsquo;s valley location and low wind speeds, which trap pollutants. Similarly, in New Delhi, prevailing winds from the northwest transport pollutants from Haryana and Punjab, further elevating BC concentrations due to local traffic and agricultural burning activities.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;3.3 BC impact on heat wave\u003c/h2\u003e\n\u003cp\u003eNew Delhi, the capital city of India, experiences severe heat waves during the summer months as described in the methodology section, posing significant challenges to public health and infrastructure. BC is a key component of air pollution and has been implicated in exacerbating heat wave conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 5\u0026nbsp;illustrates the variations in black carbon (BC) concentrations during a two-day heatwave episode in New Delhi (May 31 and June 1, 2019). The analysis reveals that BC concentrations do not significantly amplify the intensity of the heatwave during this period. Instead, BC levels exhibit a negative correlation (-0.36) with temperature, suggesting that higher temperatures lead to lower BC concentrations. This reduction is likely due to enhanced atmospheric mixing and increased boundary layer height during heatwave conditions, which promote the dispersion of pollutants. In contrast, BC concentrations show a positive correlation (0.28) with relative humidity, indicating that cooler, more humid periods are associated with higher BC levels. This trend may result from reduced atmospheric mixing and the dominance of local emission sources under such conditions.\u003c/p\u003e\n\u003cp\u003eThe findings suggest that during heatwaves, the relationship between BC concentrations and meteorological parameters is complex. Higher temperatures suppress BC concentrations by enhancing pollutant dispersion, while cooler, more humid conditions favor BC accumulation due to stagnant atmospheric conditions. However, the limited duration of the heatwave episode (two days) restricts the generalizability of these results. Longer-term studies are needed to better understand the interplay between BC aerosols and extreme weather events like heatwaves.\u003c/p\u003e\n\u003cp\u003eFigure 6\u0026nbsp;illustrates the variations in black carbon (BC) concentrations during a five-day heatwave episode in Jodhpur (April 13 to April 17, 2017). The analysis reveals that BC concentrations were relatively high on the first day, ranging between 15\u0026ndash;20 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, but remained comparatively low during the rest of the heatwave period. BC levels exhibited a weak positive correlation (0.12) with temperature, suggesting a slight increase in BC concentrations with rising temperatures in the desert environment. In contrast, a weak negative correlation (-0.15) was observed between BC and relative humidity, indicating that higher BC concentrations were associated with slightly lower humidity levels. These results suggest that, in Jodhpur, increasing temperatures may enhance BC levels, while higher humidity tends to reduce them.\u003c/p\u003e\n\u003cp\u003eThe weak positive correlation between BC and temperature during the heatwave may be attributed to localized emission sources, such as dust resuspension and biomass burning, which persist even under high-temperature conditions. The weak negative correlation with humidity likely reflects the influence of drier conditions, which favor the accumulation of BC aerosols by reducing wet scavenging. However, the overall low BC concentrations and weak correlations suggest that BC did not play a dominant role in amplifying the heatwave intensity in Jodhpur. These findings highlight regional differences in BC dynamics during heatwave events. Unlike in North India, where higher temperatures often suppress BC concentrations through enhanced atmospheric mixing, Jodhpur\u0026rsquo;s desert environment exhibits a more complex interplay between BC, temperature, and humidity. This underscores the importance of considering regional variability when assessing the impact of BC aerosols on extreme weather events.\u003c/p\u003e\n\u003cp\u003eFigure 7 illustrates the variations in black carbon (BC) concentrations during a six-day heatwave episode in Varanasi (June 17 to June 22, 2018). The analysis reveals a moderate negative correlation (-0.43) between BC concentrations and temperature, indicating that BC levels decrease as temperatures rise. In contrast, a moderate positive correlation (0.43) is observed between BC and relative humidity, suggesting that higher humidity levels are associated with increased BC concentrations. These trends align with patterns observed in other humid subtropical environments, where heatwave episodes often end with a rise in humidity. In contrast, arid regions tend to exhibit the opposite behavior, with BC concentrations showing different dynamics under dry conditions.\u003c/p\u003e\n\u003cp\u003eThe negative correlation between BC and temperature during the heatwave suggests that high temperatures promote the dispersion or breakdown of BC aerosols, likely due to enhanced atmospheric mixing and increased boundary layer height. On the other hand, the moderate positive correlation with humidity indicates that BC levels tend to rise during more humid conditions. This may result from reduced atmospheric mixing or the activation of local emission sources, such as biomass burning or vehicular emissions, which are more prevalent during humid periods. Our findings suggest that BC concentrations in Varanasi are influenced by a complex interplay of temperature and humidity during heatwave events. While high temperatures reduce BC levels through enhanced dispersion, higher humidity appears to favor BC accumulation, possibly due to stagnant atmospheric conditions or increased local emissions.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.4 BC impact on Fog\u003c/h2\u003e\n\u003cp\u003eThe influence of BC on fog dynamics is a subject of immense interest due to its potential impact on atmospheric processes. BC, primarily emitted from combustion sources, can alter the microphysical and radiative properties of fog. The presence of fog enhances the removal of aerosols from the atmosphere through wet deposition (Ganguly et al., 2006). BC in the atmosphere can significantly influence fog formation, persistence, and dissipation. For instance, fog can considerably reduce solar heating, delaying the onset of convective mixing or reducing surface convection and boundary layer depth, thereby limiting ventilation. This phenomenon could explain the observed prolonged morning peak in BC concentrations. Similarly, the impact of delayed or reduced solar heating during intense foggy or hazy days has been observed on temperature and RH (Safai et al., 2008). BC particles can also act as cloud condensation nuclei (CCN), affecting the droplet size distribution within fog. By serving as nucleation sites for water droplets, BC may contribute to the formation of smaller and more numerous droplets. This alteration in droplet characteristics can influence the optical properties of fog, affecting visibility and radiative transfer. These interactions highlight the complex role of BC in modulating fog dynamics and its broader implications for atmospheric processes.\u003c/p\u003e\n\u003cp\u003eFigure 8(a) illustrates the impact of BC during fog, mist, haze, etc. from 16 to 31 January 2016 in New Delhi. \u0026nbsp;In foggy conditions, visibility is significantly reduced as compared to shallow fog, yet BC concentrations remain at comparable magnitudes. Our analysis reveals a negative correlation between relative humidity (RH) and BC concentration during fog (-0.31), indicating an inverse relationship. Conversely, in shallow fog, this correlation is less prominent (-0.19) (Figure 8(b)). As visibility is inversely proportional to humidity, and temperature affects visibility due to increased ABL, we observe that with increasing visibility, the correlation between temperature and BC concentration also increases. Specifically, during fog, the Temperature-BC correlation is -0.31, while for shallow fog it is -0.19, and for mist it is 0.59. These findings suggest that RH play a role in reducing BC concentrations.\u003c/p\u003e\n\u003cp\u003eIn haze conditions, the correlations align closely with the results presented in Table 3. During smoke events, the RH-BC concentration exhibits a positive correlation, suggesting an increase in BC concentrations for obvious reasons. The absorption of solar radiation by BC can lead to localized heating in the atmosphere. This heating may result in enhanced turbulence and vertical motion, impacting the development and dissipation of fog layers. This also reveals that the BC can affect the vertical distribution and horizontal extent of fog.\u003c/p\u003e\n\u003cp\u003eIn fog and haze, high relative humidity and low temperatures are common, but BC concentration does not exhibit a strong positive correlation. This suggests that other factors, such as aerosol size or composition, might be more significant in influencing these phenomena. Conversely, in mist and shallow fog, temperature plays a more crucial role, especially in mist, where a strong positive correlation with BC concentration is observed. This indicates that temperature is a key driver in the presence of BC under these conditions. For smoke, a slight positive correlation between BC concentration and relative humidity suggests that smoke may persist longer in humid environments, while temperature appears to have minimal impact.\u003c/p\u003e\n\u003cp\u003eFigure 9(a)\u0026nbsp;illustrates the impact of BC during fog, mist, haze from January 16th to January 31\u003csup\u003est\u003c/sup\u003e, 2016, in Chandigarh. In Chandigarh, when it\u0026apos;s foggy, we found that humidity and BC have a negative relationship, with a correlation of -0.41. During misty conditions, this relationship is weaker, with a correlation of 0.14, and during haze, it strengthens to 0.51 (Figure 9(b)). \u0026nbsp;Looking at temperature and BC, during fog, there\u0026apos;s a positive correlation of 0.25. But during mist, this correlation becomes negative at -0.42, and during haze, it\u0026apos;s even more negative at -0.56. This means that when it\u0026apos;s foggy and warmer, BC levels tend to be higher, similar to what we observed in New Delhi, but it\u0026apos;s different from normal weather conditions.\u003c/p\u003e\n\u003cp\u003eIn foggy conditions, higher concentrations of BC are associated with lower relative humidity, suggesting that fog tends to disperse or change as BC levels rise. During haze, an increase in BC is linked to higher relative humidity and lower temperatures, implying that dense haze likely creates cooler, more humid conditions. In misty conditions, a rise in BC concentrations primarily leads to a decrease in temperature, with only a slight increase in relative humidity, indicating that mist responds differently to BC accumulation compared to fog and haze.\u003c/p\u003e\n\u003cp\u003eFigure 10(a)\u0026nbsp;illustrates the impact of BC during fog, mist, and haze from January 16th to January 31st, 2016, in Varanasi.\u003c/p\u003e\n\u003cp\u003eIn Varanasi, the relationship between black carbon (BC) concentrations and meteorological conditions varies significantly during fog, mist, and haze events. During foggy conditions, BC and relative humidity exhibit a negative correlation (-0.39), indicating that higher humidity levels are associated with lower BC concentrations. This is likely due to the efficient removal of BC aerosols through wet deposition during fog events. In contrast, during misty conditions, the correlation between BC and humidity is weaker and statistically non-significant (0.06), suggesting that mist has a minimal impact on BC dynamics. However, during haze events, the correlation strengthens to 0.32, indicating that higher BC concentrations are associated with higher humidity levels under hazy conditions (see Figure 10(b)).\u003c/p\u003e\n\u003cp\u003eVaranasi\u0026rsquo;s meteorological conditions during fog and mist events differ from those in New Delhi and Chandigarh. The city experiences higher humidity levels and weaker temperature inversions, which dampen the localized heating effect of BC and reduce its correlation with temperature. Additionally, the boundary layer in Varanasi during fog and mist events is often shallower and more stable, limiting vertical mixing and reducing the influence of BC on near-surface temperatures. The city\u0026rsquo;s location in the Indo-Gangetic Plain, coupled with its proximity to the Ganges River, creates unique microclimatic conditions. The frequent formation of fog and mist, along with riverine influences, further dilutes the temperature-BC relationship. When examining the relationship between temperature and BC, the correlations are statistically non-significant during fog (0.01) and mist (-0.08). However, during haze, a weak negative correlation (-0.21) is observed, suggesting that warmer temperatures during haze events are associated with slightly lower BC concentrations. These results indicate that foggy conditions facilitate BC removal through wet deposition, while hazy conditions promote BC accumulation under stable, high-humidity environments. This trend is consistent with observations in New Delhi and Chandigarh but is less pronounced in the Gangetic Plains due to regional differences in meteorological and emission characteristics.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.5 BC concentration and active/break monsoon conditions\u003c/h2\u003e\n\u003cp\u003eThe BC and Rainfall for Monsoon in 2016 is shown in Figure 11(a), where active spell is characterized by frequent and widespread rainfall while break spell is characterized by sporadic or absence of rainfall. During the active monsoon spell, BC concentrations low compared to the break monsoon period due to wet scavenging. The lower the rainfall during monsoon, the higher the concentration. The BC concentration declines sharply or remains low whenever the daily rainfall is 15mm or more. These findings suggest that the active monsoon phase generally leads to a slight reduction in BC concentrations across most regions, although there may be some variability depending on local factors.\u003c/p\u003e\n\u003cp\u003eThe correlation between temperature and BC concentrations is weakly positive during both active and break monsoon spells (Figure 11(b)). This suggests that temperature alone does not strongly regulate BC levels, possibly due to the complex interplay of emissions, atmospheric transport, and removal mechanisms such as wet deposition. During the break monsoon spell, reduced cloud cover and increased solar radiation may slightly enhance local convective mixing, but its impact on BC concentrations remains minimal.\u003c/p\u003e\n\u003cp\u003eThe correlation between RH and BC concentrations exhibits contrasting behavior (Figure 11(b)). During the active monsoon spell, a positive correlation of 0.26 indicates that higher RH levels correspond to increased BC concentrations. This could be due to the trapping of BC within the lower boundary layer under high moisture conditions, which limits vertical dispersion. Additionally, increased hygroscopic growth of aerosols may contribute to the retention of BC particles in humid environments. During the break monsoon spell, a negative correlation of -0.36 suggests that higher RH levels correspond to lower BC concentrations. This is likely due to reduced emissions from wet surfaces and enhanced removal of BC through hygroscopic growth, which accelerates wet deposition. The lower boundary layer height during humid conditions in the break monsoon may also facilitate more efficient BC removal. The correlation between rainfall and BC concentrations varies significantly between active and break monsoon phases. During the active monsoon spell, the correlation is statistically non-significant (-0.06), indicating that heavy and continuous rainfall does not substantially affect BC levels. This could be attributed to a balance between wet deposition and sustained emissions from anthropogenic sources. The continuous presence of convective activity may keep BC particles in suspension, preventing a significant decline in concentrations.\u003c/p\u003e\n\u003cp\u003eHowever, during the break monsoon spell, the correlation strengthens to -0.43, indicating a more pronounced reduction in BC levels with rainfall. Since BC concentrations are typically higher during this period (Figure 11(a)) due to reduced atmospheric cleansing, any rainfall event exerts a stronger scavenging effect, leading to a noticeable drop in BC levels. This suggests that in the absence of sustained monsoonal precipitation, dry atmospheric conditions allow BC to accumulate, making sporadic rain showers during the break monsoon more effective in cleansing the atmosphere.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis research provides comprehensive insights into the dynamics of BC pollution across North India, addressing key aspects of methodology, results, and implications. Methodological approaches, including Aethalometer measurements and meteorological correlations, facilitated the examination of BC variations at different temporal and spatial scales. The study elucidates distinct seasonal and diurnal patterns in BC concentrations influenced by factors such as urbanization, altitude, and meteorological conditions. The monsoon season exhibits the lowest BC levels due to wet scavenging, contrasting with elevated concentrations during post-monsoon periods attributed to agricultural activities. The mean BC mass concentration shows a negative correlation with ambient air temperature, rainfall, and wind speed, emphasizing the interplay between atmospheric conditions and pollutant levels. The BC concentration shows a negative correlation with RH at Ranichauri, Srinagar, Jodhpur (at higher humidity levels), and Varanasi (at lower humidity levels), and a positive correlation at Chandigarh, New Delhi (at lower to moderate humidity levels), and Jodhpur (at lower humidity levels). This suggests that the relationship between BC concentrations and relative humidity varies by city and humidity range, with BC generally decreasing as humidity increases in most locations. Warmer temperatures and higher wind speeds generally reduce BC concentrations through enhanced dispersion, while higher RH and rainfall promote BC removal via wet scavenging. Case studies on heatwaves, fog, and monsoon conditions offer valuable insights into BC's role in exacerbating weather phenomena, highlighting complex relationships influenced by local climate factors. The research identifies correlations between BC concentrations and temperature, humidity, and critical role of rainfall in regulating BC concentrations during different weather events, contributing to a deeper understanding of BC's impact on atmospheric processes. This study underscores the importance of considering meteorological factors in formulating effective air quality management strategies for mitigating BC pollution in North India, ultimately contributing to efforts aimed at improving environmental health and sustainability in the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe express our sincere gratitude to the Hon\u0026rsquo;ble Secretary, Ministry of Earth Sciences (MoES); Hon\u0026rsquo;ble Director General of Meteorology (DGM), India Meteorological Department (IMD), Government of India (GoI); Hon\u0026rsquo;ble Founder President; Hon\u0026rsquo;ble Chancellor; Hon\u0026rsquo;ble Vice Chancellor and Hon\u0026rsquo;ble Pro Vice Chancellor, Amity University Haryana (AUH), Gurugram, India, for their continued motivation and support. The authors are thankful to the personnel involved in the maintenance of the BC Network, IMD, India. Thanks, are also due to the Organizers of the National Symposium on Tropical Meteorology (TROPMET 2022) held in Bhopal, India, where the preliminary results of this study have been presented, discussed critically, and improved. The authors express their sincere gratitude to the Anonymous Reviewers for their insightful comments which improved the scientific content of the original manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eVivek Kumar:\u0026nbsp;Conceptualization, Methodology, Data curation, Software, Formal analysis, Investigation, Software, Validation, Visualization, Writing \u0026ndash; original draft. \u0026nbsp;Panuganti C.S. Devara:\u0026nbsp;Supervision, Writing \u0026ndash; review and editing, Visualization. \u0026nbsp; Vijay K. Soni:\u0026nbsp;\u0026nbsp;Resources, Supervision, Writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; email address \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eVivek Kumar:\u0026nbsp;[email protected]\u003c/p\u003e\n\u003cp\u003ePanuganti C.S. Devara: [email protected]\u003c/p\u003e\n\u003cp\u003eVijay K. Soni: [email protected]\u003c/p\u003e\n\u003ch2\u003eData availability\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSome data is provided in the form of plots in Supplementary Data files (S1 \u0026amp; S2). \u0026nbsp; Complete data sets can be obtained from the Corresponding Author against a proper request.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;All authors have read, understood, and have complied as applicable with the statement on \u0026ldquo;Ethical Responsibilities of Authors\u0026rdquo; as found in the Instructions for Authors and are aware that with minor exceptions, no changes can be made to authorship once the paper is submitted.\u003c/p\u003e\n\u003ch2\u003eConflict of interest \u0026nbsp;\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. In other words, there is no funding available for the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBadarinath, K.V.S., Latha, K.M., Chand, T.R.K., Reddy, R.R., Gopal, K.R., Reddy, L.S.S., Narasimhulu, K., Kumar, K.R., 2007. Black carbon aerosols and gaseous pollutants in an urban area in North India during a fog period. Atmospheric Research 85, 209\u0026ndash;216. https://doi.org/10.1016/j.atmosres.2006.12.007\u003c/li\u003e\n \u003cli\u003eBan-Weiss, G.A., Cao, L., Bala, G., Caldeira, K., 2012. Dependence of climate forcing and response on the altitude of black carbon aerosols. Clim Dyn 38, 897\u0026ndash;911. https://doi.org/10.1007/s00382-011-1052-y\u003c/li\u003e\n \u003cli\u003eBeegum, S.N., Moorthy, K.K., Babu, S.S., Satheesh, S.K., Vinoj, V., Badarinath, K.V.S., Safai, P.D., Devara, P.C.S., Singh, S., Vinod, Dumka, U.C., Pant, P., 2009. 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Science of The Total Environment 709, 136251. https://doi.org/10.1016/j.scitotenv.2019.136251\u003c/li\u003e\n\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":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Carbonaceous aerosols, variability, monsoon, scavenging, heatwave, Fog","lastPublishedDoi":"10.21203/rs.3.rs-6162410/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6162410/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStudies related to impact of black carbon (BC) aerosols on weather phenomena like monsoon breaks, winter fog events, pre-monsoon heatwaves etc. are sparse in India. This study fills the gap of observational information of BC aerosols and their relationship with meteorological phenomenon. We examined the interaction between BC aerosols and precipitation during the monsoon's active-break cycle, a critical period for agriculture, water resources, and weather patterns. Data from stations in rural and urban areas provided contrasting seasonal and diurnal variation. The diurnal pattern is closely linked to anthropogenic activities and meteorological factors. The study examines significant diurnal and seasonal variation in relation to local and regional meteorological variation. BC concentrations show distinct bimodal diurnal patterns, with major peak in the evening, between 2000 h to 2300 h IST and secondary peak in the morning between 0700 h to 0900 h IST. Seasonal variations show the lowest BC levels during the monsoon due to efficient wet scavenging, while the highest levels occur during the post-monsoon, primarily from agricultural burning. Meteorological factors like temperature, humidity, rainfall, and wind speed significantly influence BC dynamics. Higher temperatures and lower humidity increase BC levels, while rainfall reduces them, and wind disperses BC aerosols, affecting their concentration and distribution. Analysis of pre-monsoon heatwaves, winter fog events, and monsoon conditions reveals the complex interplay between BC aerosols and weather patterns. Local meteorological factors such as temperature inversions and wind patterns significantly influence the BC impact on weather phenomena. This research enhances the understanding of BC pollution and its diverse effects on weather and climate, emphasizing the importance of integrating meteorological factors into air quality management and policymaking. It lays the groundwork for developing targeted strategies to mitigate BC's adverse effects on health and environment in India.\u003c/p\u003e","manuscriptTitle":"Black carbon characterization and its association with meteorological phenomena using network data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 11:23:12","doi":"10.21203/rs.3.rs-6162410/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-25T12:59:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-25T07:47:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-14T10:57:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205689230547277424597444932502070786851","date":"2025-03-24T08:37:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28353627828059313791925683272847412865","date":"2025-03-24T05:55:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99802568997632557476057003394105938046","date":"2025-03-22T04:15:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-22T03:00:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-18T15:08:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T15:06:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-03-05T12:04:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"df53caef-f17a-4c1d-83b1-f8973f52a349","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:13:19+00:00","versionOfRecord":{"articleIdentity":"rs-6162410","link":"https://doi.org/10.1007/s10661-025-14216-x","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2025-07-03 15:58:31","publishedOnDateReadable":"July 3rd, 2025"},"versionCreatedAt":"2025-03-31 11:23:12","video":"","vorDoi":"10.1007/s10661-025-14216-x","vorDoiUrl":"https://doi.org/10.1007/s10661-025-14216-x","workflowStages":[]},"version":"v1","identity":"rs-6162410","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6162410","identity":"rs-6162410","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}